I am regularly asked by clients which model to deploy, when, and how to keep up with the cadence of frontier-lab announcements; each one claiming to beat the last on whichever benchmark suits the press release. The question underneath is always the same: who is actually winning the AI race, and does it matter for the model decision they are about to sign off on.

Since DeepSeek-R1 broke the cost frontier in January 2025, the competition has tightened. Public accusations have followed: industrial-scale data extraction, model distillation, jailbreak campaigns at proxy-account scale.

Then, on 23 April 2026, Michael Kratsios, Director of the White House Office of Science and Technology Policy, issued a memorandum accusing foreign entities, “principally based in China”, of running industrial-scale distillation campaigns against US frontier AI models. The document is unusually direct in its accusations — and silent on the economics behind them.

So I dug in.

As of today, it seems like the US AI leadership is treated, in policy and in capital markets, as settled. But it is not.

Subscribe now

US AI Economics

Three subsidies hold the US leadership position in place:

The inference subsidy.

Anthropic reached $30 billion in annualised revenue by March 2026 against approximately $64 billion raised, with a projected $14 billion loss in 2026 and positive free cash flow not expected before 2027. OpenAI hit $25 billion in annualised revenue in February 2026, with cumulative losses of $44 billion projected through 2028 per internal financial documents reported by The Information. Microsoft is moving GitHub Copilot to token-based billing because its weekly cost of running the product has nearly doubled since January 2026. Anthropic itself shifted enterprise customers to per-token billing in April 2026. Uber blew through its full-year AI budget within months.

Customers are not paying the real cost of using these models. Investors are.

The demand subsidy.

NVIDIA has committed up to $100 billion to OpenAI. OpenAI has committed $300 billion to Oracle. Oracle is committing $40 billion to NVIDIA chips. Goldman Sachs analysis indicates 75% of OpenAI’s operating costs are covered by external funding, against 17% from revenue. NVIDIA claims $1 trillion in sales visibility through 2027 while only $285 billion of GPU-bearing data centre capacity is under construction. The chips have buyers; the buildings to put them in do not yet exist.

The same dollars are circulating between three companies and counted as revenue at each stop. Real end-customer demand is a fraction of the headline number.

The talent subsidy.

Within US AI institutions, 38% of top-tier researchers are of Chinese origin against 37% American, per the MacroPolo Global AI Talent Tracker. Six of the seventeen named contributors to GPT-4o trained at Tsinghua, Peking, Shanghai Jiao Tong or USTC. China now produces 47% of the world’s top-tier AI researchers, up from 29% in 2019.

The brains behind US AI leadership are largely Chinese-trained. Washington is now restricting the pipeline that supplies them (visa restrictions).

Then comes the question of adoption by end-users, especially enterprise users.

Enterprise Adoption

In October 2025, Brian Chesky confirmed Airbnb relies “heavily” on Alibaba’s Qwen for its customer service agent, noting OpenAI models are “more rarely used in production because there are faster and cheaper models.” In March 2026, Cursor — valued at $29.3 billion — disclosed that its Composer 2 coding model is built on Moonshot’s Kimi K2.5. Hugging Face CEO Clément Delangue’s response: Chinese open source is now “the most significant force shaping the global AI tech stack.” Alibaba alone counts more than 170,000 derivative models built on Qwen.

So by going open source, Chinese frontier labs have won a strong enterprise customer base. Because enterprise customers (1) do not like vendor lock-in, (2) need data sovereignty and local compute for security and compliance reasons, (3) need cost optimisation and predictability — especially at scale.

Closed US frontier models compete on capability; Chinese open-weight models compete on cost, latency, and the right to fine-tune. For the layer of the stack that actually generates revenue — the “applied layer”, where Cursor sits at $6 billion ARR and Airbnb runs millions of customer interactions — customers have chosen.

US enterprises are running production AI on Chinese open-weight models, not on US closed APIs.

Any policy response that hardens against Chinese open weights would force US enterprises back onto closed US APIs whose pricing and reliability are already the subject of enterprise complaint. It would deepen exactly the dependency it sets out to reduce.

Inside The Chinese Stack

The Capital Intensity

Today the US AI frontier is built on capital intensity. The Chinese AI stack runs on the opposite premise: efficiency under constraint. But enterprises don’t always need the best, most powerful model. They need the model that works for them and is economically viable.

DeepSeek-V4, released on 24 April 2026 — one day after the Kratsios memorandum — ships in two variants: V4-Pro at 1.6 trillion parameters (49 billion active) and V4-Flash at 284 billion (13 billion active), both with a one-million-token context window. V4-Pro was trained on 33 trillion tokens using FP4 quantisation for routed expert weights and FP8 for the rest. The architectural innovation is a hybrid attention system — Compressed Sparse Attention combined with Heavily Compressed Attention — that reduces single-token inference FLOPs to 27% of the previous generation at one million tokens, and KV cache to 10%.

The model is released under MIT License, scores 80.6% on SWE-Bench Verified (within 0.2 points of Claude Opus 4.6) and reaches a Codeforces rating of 3,206. Reuters confirmed on 4 April 2026 that the training run used Huawei Ascend 950PR chips, not NVIDIA hardware. NVIDIA CEO Jensen Huang called the outcome “horrible for the United States.”

This pattern is not specific to DeepSeek. Qwen, Kimi, MiniMax, and Ernie are all open-weight or open-source, all distributed without seat-based pricing, all designed to run on a wider range of hardware than the closed US models. Alibaba Cloud, Huawei Cloud, and Tencent Cloud provide the inference infrastructure domestically. The Chinese stack does not have an inference subsidy because it does not have an inference cost crisis. Even better: the success of their open models does not depend on their frontier labs’ ability to provide a massive infrastructure and energy consumption since the clients use their own.

With energy costs rising under combined pressure from Middle East instability and accelerating data centre build-out, the economics of self-hosted, compute-optimised models look increasingly favourable for enterprise workloads at scale.

The funding structure is also different.

Chinese AI labs operate inside a state-coordinated capital framework — long-horizon, low-cost, with no requirement to demonstrate quarterly revenue growth to a venture syndicate. They are not attempting to monetise tokens at a price the user will pay. They are attempting to maximise adoption, derivative work, and standard-setting reach. Alibaba’s 170,000 Qwen derivatives are not a side effect but part of the strategy.

The US frontier sells access to closed models at prices subsidised by venture capital. China distributes open-weight models at zero marginal cost, funded by long-horizon state capital. These are two different businesses competing for the same enterprise share of wallet.

The talent layer compounds this asymmetry.

What changed is the supply side. The Chinese AI workforce is now sustained domestically: 51% of top Chinese AI undergraduates pursue graduate studies in China, and 31% remain in China for work after graduation, per the MacroPolo Global AI Talent Tracker. Tsinghua and Peking are now ranked third and sixth globally for AI research output. Six Chinese institutions sit in the global top 25, against two in 2019. Zizheng Pan interned at NVIDIA and chose DeepSeek. Yao Shunyu, a Tsinghua physics special-prize winner, joined Anthropic in the US — but the direction of flow is no longer one-way.

The two stacks are converging on capability and diverging on economics. The US frontier needs subsidies to operate. The Chinese frontier does not.

The dominant US framing treats Chinese model performance as the product of extraction and theft of intellectual property. But we should note that the technical record is more nuanced. DeepSeek-V4’s architectural disclosures — Compressed Sparse Attention, Heavily Compressed Attention, manifold-constrained hyper-connections, FP4-quantised expert weights — are documented research contributions, published with reproducible methodology. DeepSeek’s earlier work on Multi-Head Latent Attention and DualPipe parallelism, set out in the V2 and V3 technical reports, is now cited in mainstream efficiency research. Some capability transfer through distillation is plausible. But attributing the bulk of the Chinese capability position to distillation requires ignoring a research track that has made specific, documented contributions to inference efficiency and post-training.

The foundational architecture of modern LLMs is overwhelmingly US-origin. But the recent efficiency frontier (running these architectures at lower cost) is increasingly Chinese.

The Regulatory Layer

Beyond the economics, what will shape AI adoption for enterprises is the regulatory posture of the various jurisdictions companies operate in.

And it’s not simple: countries have adopted voluntary frameworks and guidance (Singapore, India, UAE, Australia, …), sectoral regulations (US, UK, …), binding law (EU, Vietnam, China, Russia, …) and many have pending legislations (Canada, Brazil, Turkey, South Africa, Niger, …).

Last month, I launched the beta version of Tekhora AI Radar, a geointelligence platform that tracks more than two thousand regulatory events and over seven hundred expert signals across seventy jurisdictions.

The data shows that three patterns now dominate: divergence on what an AI model is allowed to do, divergence on where its data may flow, and divergence on who can be held accountable when it fails.

A model compliant in California may not be compliant in Frankfurt, in Singapore, or in Riyadh. Enterprises operating across more than one of those markets are not choosing between vendors but between regulatory regimes.

The EU AI Act, in force since August 2025, classifies obligations by risk tier and applies extraterritorially to any provider whose output reaches an EU user. China’s Generative AI Measures require pre-deployment review and content alignment for any model offered to Chinese users. The US position leans on export controls and informal guidance rather than statutory frameworks. India’s DPDP Act and forthcoming AI rules emphasise data residency. Singapore’s Model AI Governance Framework sets sectoral expectations without prescriptive rules. None of these are converging.

The regulatory layer is fragmenting faster than the technology layer is consolidating. An enterprise running production AI in three regions should now be running three different compliance stacks. Which means the model decision is no longer just about economics.

Subscribe now

What It Means For Enterprise Leaders

The decision facing every enterprise leader running production AI is not which side to take in a US-China contest. It is which model architecture clears the unit economics of the workload it is meant to serve.

That is, in a way, a procurement question, not a political one. Treating it as political is how organisations end up locked into the wrong stack for the right-sounding reason.

Three risks now sit on the same line item as model performance.

Risk 1: token cost normalisation. Microsoft’s move on GitHub Copilot, Anthropic’s shift of enterprise customers to per-token billing, and the Goldman Sachs report on inference spend approaching 10% of engineering headcount are signals that the subsidy era is closing.

What to do: model any workload built on a closed US API at two to four times the current per-token rate, with rate limits tightening. CFOs should require AI cost forecasts to be stress-tested at API rack rate, not at subscription-blended rate.

Risk 2: demand-loop fragility. A meaningful share of the AI infrastructure capacity now under construction is committed to two customers (OpenAI and Anthropic) under contracts whose financing depends on continued external capital flows. If one node in the NVIDIA-OpenAI-Oracle circuit cannot finance its commitment, the chain unwinds.

What to do: treat long-term commitments to closed US APIs as exposure to vendor solvency, not just vendor pricing. This belongs on the same risk register as cloud-region failure.

Risk 3: research-base concentration. US AI capability is staffed substantially by Chinese-trained researchers operating under tightening visa conditions. The base case is gradual erosion of the recruitment advantage that built the lead.

What to do: track talent retention disclosures from your model providers. Capability roadmaps reflect the people building them.

The model decision is now a four-variable problem: capability, real token cost, vendor solvency, and roadmap continuity.

The defensive response is the one I have argued in my publication before: map and manage your dependencies. Specifically:

• Three classes of model in production for any critical workflow, with hot-swappable fallbacks and documented migration paths

• A Model Bill of Materials tracking upstream dependencies — foundation model, hosting region, jurisdiction, contract terms

• Vendor resilience audits with explicit clauses on API continuity and version sunset

None of this is exotic. It is the standard operational practice for any tier-zero supplier dependency, applied to AI.

For non-US enterprises, the calculus changes by sovereignty archetype rather than by allegiance:

• European firms operating under regulatory sovereignty already have governance scaffolding in place; the gap is operational — running production workloads on Mistral, on Chinese open weights, or on hybrid architectures that do not depend on US API continuity

• ASEAN enterprises sit in the open-yet-local archetype, where Singapore’s SEA-LION already runs partly on Qwen

• Indian enterprises, working at scale on imported foundation models, face a sharper version of the same question

For all three, the closed US API is no longer the safe default it was assumed to be.

It is not just a question of which flag is on the data centre. It is a question of which combination of model, architecture, and jurisdiction holds together for the workload at hand.

Share

How to Decide

US AI leadership is real at the frontier. OpenAI, Anthropic, and Google operate the most capable closed models on the market. The architectural lineage — Transformer, RLHF, scaling laws, chain-of-thought — is overwhelmingly American. None of this is in dispute.

What is in dispute is whether the lead, as currently structured, is sustainable — and more importantly, whether capability at the frontier is the correct variable on which to base an enterprise model decision in 2026.

A leadership position that requires venture capital to absorb its operating costs, circular financing to inflate its demand signal, and foreign-trained talent to staff its research is not a leadership position in the conventional sense. It operates on borrowed time across three dimensions. Defending that position against external extraction does not address the internal arithmetic that makes it fragile.

The Monday-morning question for an enterprise leader is not whether to bet on the US or on China. The question is which model, at which unit cost, on which architecture, hosted in which jurisdiction, can serve a specific workload reliably for the next eighteen months — and what the fallback is if it cannot.

Choose by economics, build for optionality, map your full dependency chain, and treat any vendor — closed or open, US or Chinese — as a dependency that requires a fallback.

Leadership in AI is increasingly not about owning the best model. It is about absorbing the shocks that come with deploying any of them at scale.

The frontier is American. The economics are not yet settled. Choose the model that fits the workload, not the flag.

Thanks for reading,

Damien

Leave a comment

Damien Kopp is Founder and Managing Director of RebootUp, an advisory practice that helps enterprises compress AI time-to-value while retaining sovereignty. He is the founder of Tekhora, an AI Operating Intelligence platform that maps regulatory and geopolitical exposure across the AI stack. He publishes KoncentriK, and is Associate Faculty at Singapore Management University. Contact: damien.kopp@rebootup.com

Citations and sources

Primary news sources

1. Kratsios memorandum, 23 April 2026 — https://whitehouse.gov/wp-content/uploads/2026/04/NSTM-4.pdf

2. Le Grand Continent commentary on the Kratsios memo — Victor Storchan, La note qui annonce une nouvelle phase dans la guerre de l’IA, 24 April 2026. (Source for “principally based in China” framing and applied-layer evidence.)

Anthropic and OpenAI economics

3. Anthropic revenue and burn projections — Sacra, Anthropic revenue, valuation & funding.https://sacra.com/c/anthropic/

4. OpenAI revenue and cumulative loss projections — Sacra, OpenAI revenue, valuation & funding.https://sacra.com/c/openai/

5. Anthropic shift to per-token billing, April 2026 — CNBC, AI demand is inflated, and only Anthropic is being realistic. https://www.cnbc.com/2026/04/17/ai-tokens-anthropic-openai-nvidia.html

6. Microsoft GitHub Copilot moves to token-based billing; Uber AI budget exhaustion — Ed Zitron, The Hater’s Guide To The SaaSpocalypse (March 2026) and Four Horsemen of the AIpocalypse (April 2026). https://www.wheresyoured.at/hatersguide-saas/

7. Goldman Sachs: 75% external funding for OpenAI 2026 needs — Goldman Sachs estimate (October 2025), via Wallstreetcn. https://longbridge.com/en/news/260251169

8. Goldman Sachs: inference spend approaching 10% of engineering headcount — The Stack, Inference budgets are breaking the bank. https://www.thestack.technology/inference-budgets-are-breaking-the-bank-what-now/

Circular financing

9. NVIDIA-OpenAI-Oracle deal structure — Bloomberg, AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other. https://www.bloomberg.com/graphics/2026-ai-circular-deals/

10. Sightline Climate / data centre capacity under construction — Ed Zitron analysis, Four Horsemen of the AIpocalypse. https://www.wheresyoured.at/hatersguide-saas/

Talent and research base

11. MacroPolo Global AI Talent Tracker 3.0 — Paulson Institute. https://archivemacropolo.org/interactive/digital-projects/the-global-ai-talent-tracker/

12. Paulson Institute press release on Tracker findings — March 2024. https://www.paulsoninstitute.org/press_release/study-finds-us-remains-a-magnet-for-worlds-best-and-brightest-ai-talent-but-more-global-talent-are-staying-home-instead-of-going-abroad/

13. GPT-4o team composition (6 of 17 trained at Chinese universities) — 36Kr, Half of the World’s AI Talents Are Chinese. https://eu.36kr.com/en/p/3340533396093446

Applied-layer evidence

14. Airbnb / Brian Chesky on Qwen reliance — Fortune, Chesky says OpenAI tools not ready for ChatGPT tie-up with Airbnb app, 21 October 2025. https://fortune.com/2025/10/21/brian-chesky-openai-tools-not-ready/

15. Cursor Composer 2 built on Kimi K2.5 — TechCrunch, Cursor admits its new coding model was built on top of Moonshot AI’s Kimi, 22 March 2026. https://techcrunch.com/2026/03/22/cursor-admits-its-new-coding-model-was-built-on-top-of-moonshot-ais-kimi/

16. Clément Delangue: Chinese open source “the most significant force shaping the global AI tech stack” — KuCoin News, 20 March 2026. https://www.kucoin.com/news/flash/hugging-face-ceo-chinese-open-source-is-the-largest-force-shaping-global-ai-tech-stack

17. 170,000+ Qwen-derivative models — The Wire China, Cheap and Open Source, Chinese AI Models Are Taking Off, November 2025. https://www.thewirechina.com/2025/11/09/cheap-and-open-source-chinese-ai-models-are-taking-off/

Chinese counter-architecture

18. DeepSeek-V4 release and technical specifications — DeepSeek-AI, DeepSeek-V4-Pro model card.https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro

19. DeepSeek-V4 architecture analysis — Hugging Face Blog, DeepSeek-V4: a million-token context that agents can actually use. https://huggingface.co/blog/deepseekv4

20. DeepSeek-V4 trained on Huawei Ascend 950PR chips (Reuters confirmation) — Build Fast With AI, DeepSeek V4-Pro Review. https://www.buildfastwithai.com/blogs/deepseek-v4-pro-review-2026

Regulatory framework

21. EU AI Act — Official EU AI Act portal. https://artificialintelligenceact.eu/

22. Tekhora AI Radar — regulatory monitoring across 70+ jurisdictions. https://radar.tekhora.com

23. Digital Resilience Index — eight-pillar resilience framework, aDRI Foundation. https://thedigitalresilience.org/

KoncentriK cross-references

24. Damien Kopp, The Dependency Economy of AI — what 25 national AI strategies reveal about sovereignty.https://www.koncentrik.co/p/the-dependency-economy-of-ai

25. Damien Kopp, Understanding Extraterritorial Overreach — on CLOUD Act and jurisdictional risk in AI deployments. https://www.koncentrik.co/p/understanding-extraterritorial-overreach

Advisory and platform

26. RebootUp AI Sovereignty and Dependency Audit. https://rebootup.com/digital-sovereignty-audit.html

27. RebootUp — enterprise AI strategy and transformation advisory. https://www.rebootup.com

28. Tekhora — Technology Geointelligence platform. https://www.tekhora.com

“Who owns AI?”

That was the central question at India’s AI Impact Summit this week. Over four days in Delhi, heads of state, tech CEOs, and policymakers circled around it. The AI Impact Forum framed it bluntly:

“Knowledge belongs collectively to society. Yet the infrastructure controlling it is concentrated among a handful of frontier labs.”

It sounds like a new question. It’s not.

A century ago, people debated the “who owns electricity?” question.

They fought about something very concrete: who controls the grid, who sets the standards, and who gets to plug in.

History doesn’t repeat. But it rhymes. And this particular rhyme is worth listening to carefully.

Subscribe now

The First Grid Wars

The electricity industry started as a fragmented, private affair. Thomas Edison’s Pearl Street Station in Manhattan lit up its first blocks in 1882. Within two decades, thousands of small private companies competed across the United States and Europe: each with its own voltage, its own plug design, its own pricing.

Then consolidation came fast.

By the 1920s, holding companies had swallowed most of these independents. In the US, three holding companies controlled 45% of all electricity generation by 1927. Samuel Insull’s empire alone spanned 32 states. In 1926 alone, a thousand mergers occurred. Prices were opaque. Rural communities were ignored, by the late 1920s, nearly 90% of rural American households still had no electricity. Private utilities had no commercial incentive to wire them.

Sound familiar?

A small number of players. Rapid consolidation. Opaque structures. Entire populations left outside the system. And the people inside it increasingly locked into a single provider’s ecosystem.

Then it broke.

When Insull’s highly leveraged empire collapsed during the Great Depression, it wiped out hundreds of thousands of shareholders. Private utilities, with their perceived stock manipulation and financial instability, were suddenly seen (to borrow the language of the ET summit) as “black boxes governed by opaque structures.”

A contemporary echo is hard to ignore.

OpenAI has committed $1.4 trillion in infrastructure liabilities against roughly $20 billion in annual revenue. Its partners have racked up $96 billion in debt to fund operations. HSBC estimates that even at around 200 billion dollars in annual revenue by 2030, OpenAI would still need to raise roughly 200 billion dollars more (about 207 billion) to fund its plans.

Meanwhile, Microsoft revealed that roughly 45% of its $625 billion in remaining cloud contracts is tied to OpenAI — a concentration that triggered a $440 billion market value wipeout in a single session. Oracle’s credit default swaps have hit record highs.

As Sebastian Mallaby of the Council on Foreign Relations put it: “People think we’re running an experiment about an amazing technology, but we’re also running an experiment about the depth of capital markets.”

Insull’s empire was built on leverage, vertical integration, and the assumption that growth would always outrun debt. The structural pattern (a single dominant player whose financial architecture entangles an entire ecosystem) deserves scrutiny… not blind faith!

The question is what happens if a similar correction reaches AI.

The backlash after Insull reshaped the electricity industry for a century.

The Public Utility Correction

The response reconfigured the market.

In the United States, Roosevelt’s New Deal brought the Public Utilities Holding Company Act of 1935, which broke up the holding company pyramids and forced transparency. The Rural Electrification Act of 1936 created federal loans for cooperatives to build lines where private companies refused to go. The Tennessee Valley Authority put government directly into power generation. Within two decades, US rural electrification went from 11% to 97%.

Europe went further.

France nationalized its entire electricity sector in 1946, merging roughly 1,700 private producers, transporters, and distributors into a single state-owned entity: Électricité de France (EDF).

The rationale was explicit: energy was too strategic, too essential, and too prone to private monopoly abuse to be left ungoverned. Marcel Paul, the minister who drove the nationalization, called electricity and gas “crucial public services.” The French National Assembly voted almost unanimously.

The UK nationalized its electricity industry in 1947. Italy followed a similar path. Across much of the world, the pattern held: once electricity was recognized as foundational infrastructure (not a luxury, not an app, but the grid on which economic life depends) governments intervened to ensure access, affordability, and accountability.

The logic was straightforward: when an infrastructure becomes essential to economic sovereignty, the question of who owns and governs it stops being a commercial decision and becomes a political one.

The Plug as Power

There’s another, dimension to this history that maps even more directly to AI.

Look at a world map of electrical plug types and voltages. What you see is a ghost map of empire.

British-influenced Type G plugs still dominate Singapore, Malaysia, Hong Kong, and large parts of Africa.

French plugs shaped infrastructure across West Africa and Southeast Asia. American standards spread through the Philippines and parts of Latin America.

Former colonies inherited not just parliaments and railways, but electrical standards.

And once you wire a country to a given standard (voltage, frequency, plug shape) you lock in decades of dependency on that supplier ecosystem. Equipment. Safety codes. Training curricula. Spare parts.

Nobody needed to say “Britain owns your electricity.” The standard did the work.

The design choice determined who could sell you equipment, how easy it was to switch, and what it would cost if you ever tried.

That’s technology colonialism… in cables and plastic…

AI Is Replaying the Pattern. Faster.

Now look at AI through the same lens: this map shows how 25 countries are approaching AI sovereignty.

A small number of companies and countries control the largest GPU clusters, the dominant clouds, and the frontier models. Their architectures and APIs are becoming default standards. Their safety benchmarks, evaluation frameworks, and governance templates are becoming default norms.

If you want to “plug into AI” as a government, bank, hospital, or manufacturer, you are increasingly nudged to use their stack, accept their risk framing, and operate under their jurisdictions.

In The Dependency Economy of AI, I mapped 25 national AI strategies and found that no country fully escapes dependency. But some manage it strategically while others simply endure it. Each optimizes one dimension of sovereignty while sacrificing another. Four archetypes emerged:

Full-Stack / Hybrid countries trade speed for long-term structural autonomy. China is the most ambitious case — investing across chips (SMIC), models (Baidu, Alibaba, DeepSeek), cloud, and data governance simultaneously. The US maintains dominance across most layers. Both accept dependence on imported raw materials and, in China’s case, key tooling like ASML lithography. This path is expensive, slow, and available only to large economies with deep industrial policy traditions.

Regulatory-First countries trade technological capability for normative control. The EU is the clearest example — the AI Act gives Brussels significant power to shape rules, but European firms remain heavily dependent on foreign hardware, foreign models, and foreign cloud infrastructure. You can regulate what you don’t build. But you can’t run what you don’t control.

Partnership-Based countries trade autonomy for speed. Saudi Arabia, the UAE, and the UK bet on deep alliances with US hyperscalers and frontier labs to rapidly build capability and scale — accepting significant dependence on American clouds, chips, and models in exchange for accelerated deployment. They move fast, but their sovereignty has a ceiling set by their partners.

Open-Yet-Local countries trade frontier performance for inclusion and social impact. India and Brazil are investing in multilingual, locally relevant AI — India’s Bhashini language platform, Singapore’s MERaLiON to include South-East Asian languages, Brazil’s push for Portuguese-language models — accepting dependence on imported GPUs and foreign base technology while prioritizing linguistic coverage and domestic relevance. They may not lead the model race, but they’re building AI that actually speaks to their populations.

Every country sits somewhere on this map. So does every enterprise.

The question is not whether you depend on external AI infrastructure — you do.

The question is whether you’ve chosen your dependencies deliberately, or inherited them by default.

In the electricity era, the question was: whose plug fits your wall?

In the AI era, the question is: whose model runs inside your institutions?

Share

Where the Parallel Breaks Down (And Where AI Is Worse)

The electricity analogy is instructive but imperfect. And the differences matter as much as the similarities. In several respects, they make the AI challenge fundamentally harder.

Speed. Electrical standardization played out over decades. AI dependency can lock in within months: a single procurement decision, a single API contract, a single cloud migration.

Tangibility. You can see a power grid. You can inspect a plug. AI dependencies are layered, abstracted, and often invisible until something breaks: a licensing change, an export control, a model deprecation.

Governability. Electricity could be nationalized because the physical infrastructure was bounded and territorial. AI models appear weightless, reproducible, and jurisdictionally slippery — but they remain bound to physical infrastructure: data centers, energy sources, undersea cables, and chip supply chains that are very much territorial. Nationalizing AI in the traditional sense is far more complex than nationalizing a power grid. Whether it is desirable, and what "public governance of AI" would even look like, is precisely the question this moment demands we ask.

Pace of obsolescence. An electrical grid, once built, lasts for generations. AI models deprecate in months. The dependency is not just structural but temporal: you are dependent on a provider’s roadmap, not just their current product.

But the most critical difference is this: AI is not a neutral utility.

Electricity powers a light bulb the same way regardless of who manufactured the generator. It does not carry opinions. It does not shape how you think about your history, your laws, or your identity.

AI does.

When a frontier model generates text, summarizes documents, drafts policy, or automates decisions, it does so through the lens of its training data which is overwhelmingly English-language and Western in origin.

A 2024 study published in PNAS Nexus tested five major LLMs and found that their outputs consistently aligned with the cultural values of English-speaking and Protestant European countries. Georgia Tech researchers demonstrated that this bias persists even when models are trained on or prompted in non-Western languages; an Arabic-specific LLM still defaulted to Western cultural references. And as Nature reported, despite advances, “AI models continue to be geared towards the needs of English-speaking people in high-income countries.”

Out of roughly 7,000 languages spoken worldwide, most frontier models meaningfully support fewer than a hundred. The rest, and the cultures, legal traditions, and knowledge systems they carry, are either poorly served or invisible.

The implications go far beyond translation errors.

Cutting off electricity to a household is an act of deprivation. Deploying culturally blind AI across a society is something different: it is an act of substitution. It doesn’t remove capability. It replaces local reasoning with imported defaults and averages. Gradually. Quietly. At scale.

When AI systems draft legal analysis, generate educational content, shape public health recommendations, or automate hiring decisions, they carry embedded assumptions about what is normal, fair, or important. If those assumptions were shaped predominantly by one culture’s data, one language’s internet, and one jurisdiction’s values, then every deployment is also (whether intended or not) an act of cultural overwrite.

This is not a power outage. It is something more insidious: the risk of epistemic dependency at scale (see How AI is rewiring the human brain: the generational transformation of cognition and knowing). Opinion formation shaped by foreign priors. Cognitive patterns gradually homogenized. Local knowledge systems eroded not by censorship, but by irrelevance because the dominant models simply don’t know they exist.

The Ada Lovelace Institute frames this well: “Instead of attributing equal value to different systems of knowledge, mainstream AI platforms amplify pre-existing epistemic injustices.”

If sovereignty means anything in the AI era, it must include epistemic sovereignty; the capacity for a society to reason, decide, and create knowledge on its own terms, not through the borrowed lens of someone else’s training data.

The Adapter Economy Trap

Most of the world is sliding into what I’d call the adapter economy.

For electricity, this meant drawers full of travel adapters and transformers. Functional, but not sovereign.

For AI, it looks like local teams building thin wrappers on top of foreign foundation models. Regulators copy-pasting external AI Acts into very different legal cultures. Enterprises stitching together two or three dominant APIs into dashboards and calling it “AI strategy.”

Adapters let you function. But adapters are not control. They are coping mechanisms for dependence.

The India summit illustrated this tension in real time. The ET forum themed an entire session “Data Sovereignty and the Federated Future.” One panelist on NDTV noted that “by transferring data to data centers in the global south, big tech still has control — it’s not the Government of India or any other entity.”

PM Modi declared: “AI must not reduce human beings to mere data points or raw material. AI must be democratized.” India announced a push from 60,000 to 200,000 GPUs. Over $250 billion in infrastructure commitments. “Design and Develop in India. Deliver to the World.”

The political narrative — “from Green Revolution to AI Revolution,” “AI as infrastructure, not just an app layer” — signals awareness that control over AI stacks will be treated like control over energy or food in earlier eras.

But most of that new capacity is being built through partnerships with the same US hyperscalers that already dominate the stack. Reuters reported billions committed by global tech majors.

You can re-shore data centers and GPUs. But if the models, APIs, and governance frameworks remain foreign-controlled, you may simply be re-intermediating dependency at a different layer.

Sovereign-Ready, Not Sovereign-Alone

The electricity correction worked because governments recognized a simple truth: essential infrastructure requires governance that matches its strategic importance.

For AI, the answer is unlikely to be nationalization in the traditional sense. Very few countries or companies can rebuild the full stack from scratch. And closed systems tend to fall behind open ones.

The more realistic goal is what I’ve been calling sovereign-ready AI: accept interdependence, but design it so that you keep options and bargaining power.

In The Dependency Economy of AI, I argue this means turning unilateral dependency into negotiated interdependence. Concretely:

Multi-cloud, multi-model by design. Avoid single-vendor lock-in for critical systems. Make switching painful but possible.

Jurisdiction-aware architecture. Route data and workloads with the legal layer in mind, not just latency and cost. Law is part of the stack.

Local capability where it counts. Build the ability to fine-tune, evaluate, and audit models - even if you start from global baselines. Don’t outsource all judgment.

A seat at the standards table. Participate in technical and governance standard-setting instead of implementing PDFs written elsewhere.

The India summit’s explicit focus on “who owns AI” and “data sovereignty and the federated future” suggests a shift from model-centric debates to infrastructure and governance. That’s exactly where this conversation needs to go.

Subscribe now

Three Questions for Leaders

The electricity story teaches a simple lesson: by the time people seriously ask “who owns this?”, the answer is already buried in standards, contracts, and sunk costs.

With AI, we still have a window.

So instead of asking “who owns AI?” in the abstract, I’d suggest three sharper questions for any board or leadership team:

Whose standards do you have to conform to in order to function? Which provider’s API, governance framework, or evaluation benchmark shapes your operating environment — and what happens if they change the terms?

How many independent options do you have at each critical layer? Chips, cloud, models, data, talent. Count them. If the answer at any layer is “one,” you don’t have a strategy. You have a dependency.

Where are you an adapter — and where are you a genuine co-owner of the grid? Adapters survive. Co-owners shape.

Electricity made many countries dependent on foreign plugs and voltages for a century. AI risks creating similar dependencies over cognition, coordination, and control — only faster, and less visibly.

The difference is that this time, we can see the pattern forming.

The question is whether we’ll wire the system differently.

Thanks for reading!

Damien

Leave a comment

This article builds on The Dependency Economy of AI: Sovereignty, Chips, and the World’s Real Chokepoints, published with Asia Tech Lens and the Digital New Deal.

For a practitioner lens on how AI reshapes enterprise value, see What Does AI Really Do to Cash Flows? on Asia Tech Lens.

Damien Kopp is Founder & Managing Director of RebootUp, an AI strategy and transformation advisory that helps organisations compress AI time-to-value and turn AI into measurable economic output. He publishes KoncentriK, a newsletter on Technopolitics — the intersection of AI, geopolitics, and business strategy — and teaches applied AI as Associate Faculty at Singapore Management University. Get in touch: damien.kopp@rebootup.com

Executive Summary: The Great Recalibration

If 2025 was full of AI claims and bold announcements, 2026 marks AI’s transition from portfolio experiment to balance-sheet and execution reality.

The AI pilot-to-scale P&L gap. McKinsey reports only 6% of organizations achieve meaningful EBIT impact from AI, while 88% run pilots in at least one function. AI usage is growing faster than costs are falling, forcing organizations to confront unit economics that don’t work at scale. Inference costs, energy bills, and infrastructure capex are colliding with profitability pressure. The question is not “Are we doing AI?” but “Can our business model survive AI at production scale?”

Enterprise-scale transformation drives real results. Not AI. AI high performers invest in transformation and workflow redesign, not just bigger models. They apply systems thinking: identifying the true constraint in end-to-end flows and improving throughput at that bottleneck, rather than locally optimizing isolated steps that don’t move enterprise economics. Digital provenance—verifying the origin and integrity of AI outputs—is now core to competitive position. As sovereignty rules tighten, executives face a new KPI: can you prove your model’s training data, lineage, and compliance posture on demand? Trust and provenance, not scale, now define competitive advantage.

AI sovereignty shifts from rhetoric to strategic imperative. The EU AI Act’s prohibitions and general-purpose AI obligations took effect in 2025, with high-risk requirements delayed until 2027 following industry pressure. While the US and China drive toward full autonomy and strategic influence, others pursue regulatory-led frameworks (EU), strategic partnerships (UK, UAE Stargate initiative), or cognitive and linguistic autonomy (UAE, India, Singapore). Multinationals now navigate a fragmenting technological and regulatory landscape.

Agentic AI expands attack surfaces while eroding accountability. Systems that autonomously orchestrate workflows introduce structural vulnerabilities—prompt injection, model poisoning, insecure outputs—as default conditions. Decision speed outpaces human oversight. Regulatory frameworks demand explainability, but technical complexity makes true human-in-the-loop oversight a mere fiction in real-time environments. Organizations inherit exponential security risk without corresponding governance capabilities.

Resource scarcity dictates AI infrastructure strategy. Data-center electricity demand will more than double by 2030, driven by AI workloads. Electricity costs in areas near data centers have increased up to 267% compared to five years ago. Hyperscalers are purchasing nuclear power plant capacity and investing in small modular reactors, while data center operators repurpose commercial aircraft jet engines into gas turbines for interim power generation—a direct response to five-to-ten-year grid interconnection delays. Competition for energy resources and critical minerals is intensifying geopolitical friction.

In short:

• Compute is scarce, energy political, and sovereignty strategic.

• Security and trust, not scale, are the new competitive frontiers.

• Human capital is fragmenting into ecosystems, not hierarchies.

Subscribe now

The 10 Uncomfortable Questions To Ask In 2026:

1. AI Economics: Can you afford to run AI at full scale, or will costs break your budget?

2. Infrastructure: What will you do when power, computing capacity, or hardware becomes scarce?

3. Sovereignty: Are you over‑exposed to one techno-bloc—and what happens if new AI rules or export controls cut you off?

4. Security: How will you stop compromised AI agents from exfiltrating data or triggering real‑world actions you can’t reverse?

5. Human-in-the-Loop: Is your human-in-the-loop review process ready to handle the volume, or will it become a bottleneck?

6. Trust/Provenance: Can you prove where your models came from, what data they used—and defend that story under audit?

7. Workforce: Are you investing in AI skills—or just shrinking headcount and calling it ‘productivity’?

8. Physical Limits: If power rationing or grid caps hit your region, which AI workloads get turned off first—and who makes that call?

9. Customer Experience: While AI is saving you customer support costs, is it making your customer relationships better or worse?

10. Quantum: When will you start your post‑quantum crypto migration—and which data will be readable by an adversary if you don’t?

Subscribe now

The 2026 Provocations: Ten Questions for the C-Suite

The sequence moves from foundational viability (money, law, security) to operational reality (infrastructure, workforce, governance) to market impact (customer experience, trust) and future resilience (quantum, sustainability).

1. AI Economics: Can you afford to run AI at full scale, or will costs break your budget?

McKinsey’s 2025 State of AI found that only 6% of organizations are “AI high performers” achieving 5%+ EBIT impact, while 88% report use in at least one function—revealing a massive pilot-to-scale gap. Deloitte’s 2026 Tech Trends reports that AI usage is growing faster than costs are falling, with organizations hitting a tipping point where cloud services become cost-prohibitive for high-volume workloads.

The question isn’t whether you are running enough AI initiatives; it’s whether your unit economics can sustain them when inference costs, energy bills, and infrastructure capex collide with profitability pressure. Organizations that accelerate one workflow step with AI while creating downstream bottlenecks may worsen economics rather than improve them—optimizing locally without addressing the system-level constraint.

Next step: Run a unit-economics model for your top three AI use cases at 10x current volume. Calculate the all-in cost per transaction (inference + infrastructure + energy + labor) and compare it to the marginal revenue or cost savings. If the math doesn’t work at scale, identify which constraint must change—pricing, architecture, or scope.

Asia Tech Lens

What Does AI Really Do to Cash Flows? An Operator’s Guide for Private Equity

By Damien Kopp…

Read more

3 months ago · 3 likes · Asia Tech Lens

2. Infrastructure: What will you do when power, computing capacity, or hardware becomes scarce?

The IEA projects global data-center electricity demand will more than double by 2030, driven primarily by AI workloads, with power, water, and grid constraints forcing governments to ration access. Deloitte observes that leading organizations are implementing three-tier hybrid architectures (cloud/on-premises/edge) and building purpose-built AI data centers because existing infrastructure cannot be retrofitted fast enough. McKinsey’s analysis shows that enterprises with high-performing IT organizations achieve 35% higher revenue growth—but only if technology strategy and execution are strong, which now depends on physical infrastructure, not just software.

Next step: Audit your infrastructure dependencies right away. Map every AI workload to its compute source (cloud region, on-prem, edge) and energy supply. Identify single points of failure—GPU allocation caps, power contracts, cooling capacity. Build a contingency plan: What gets prioritized if capacity is rationed? Which workloads can move to lower-cost inference? Where can you secure long-term compute or power commitments?

3. Sovereignty: Are you over‑exposed to one techno-bloc—and what happens if new AI rules or export controls cut you off?

Gartner predicts that by 2027, 35% of countries will be locked into region-specific AI platforms, ending the era of borderless AI. The EU AI Act’s general-purpose AI (GPAI) obligations began in August 2025, with high-risk system requirements phasing through 2026–2027, forcing immediate decisions on model choice, data residency, and compliance architecture. Global enterprises now need modular, sovereignty-aware infrastructure to run AI across the U.S., EU, and Asia without rebuilding.

In APAC, sovereignty constraints compound adoption friction: lack of in-country model endpoints, concerns about IP leakage in cross-border flows, and audit requirements that assume cloud infrastructure equals U.S. jurisdiction by default. Organizations should distinguish between genuine sovereignty constraints and capability gaps. If you cannot operationalize AI even in low-risk internal domains where data residency is manageable, your real constraint is governance and industrialization capacity, not infrastructure location.

Next step: Build a sovereignty dependency map for every AI system in production or pilot. Document: model provider, training data jurisdiction, inference location, data residency requirements, and regulatory obligations by geography. Identify which systems would break if a single vendor or jurisdiction became unavailable. Develop a modular architecture roadmap that allows you to swap models or move workloads across regions without rebuilding your entire stack.

Asia Tech Lens

The Dependency Economy of AI

By Asia Tech Lens…

Read more

5 months ago · 4 likes · 1 comment · Asia Tech Lens

4. Security: How will you stop compromised AI agents from exfiltrating data or triggering real‑world actions you can’t reverse?

Gartner’s 2026 trends highlight agentic AI and multi-agent systems as core to enterprise workflows, but Deloitte warns that AI-driven cyberattacks are forcing organizations to embed security at every step and “defend just as aggressively as they innovate”. The OWASP Top-10 for LLM Applications identifies prompt injection, insecure output handling, and model supply-chain vulnerabilities as default risks, not edge cases—and agentic systems multiply both the attack surface and blast radius.

Next step: Conduct an AI security audit using the OWASP Top-10 for LLM Applications as your checklist. For every agentic system: map what data it can access, which APIs it can invoke, and what workflows it can trigger autonomously. Implement guardrails: input validation, output sanitization, least-privilege access, and audit logging. Establish clear accountability: who is responsible when an agent makes a decision that causes harm or violates policy?

5. Human-In-The-Loop: Is your human-in-the-loop review process ready to handle the volume, or will it become a bottleneck?

When agentic AI accelerates upstream workflows, human review often becomes the new throughput constraint. Organizations that treat HITL as an afterthought—rather than a deliberately designed control system with risk-tiering, sampling logic, queue management, and staffing models—discover that automation created a worse bottleneck than it solved.

Theory of Constraints teaches that improving one step without addressing the system bottleneck simply moves the constraint. Organizations need to design HITL deliberately: use risk-tiering (low-risk automated, medium-risk sampled review, high-risk mandatory review), design for queue health (SLA, backlog, staffing), and make exceptions explicit (what constitutes “stop the line”). Regulatory frameworks like the EU AI Act demand explainability, but technical complexity makes true human oversight difficult in real-time environments without deliberate system design.

Next step: Map your end-to-end AI workflow and identify where human review sits. Measure current queue depth, review SLA, and approval throughput. Design a risk-tiering framework: What decisions can be fully automated (low risk)? Which require sampling (medium risk)? Which require mandatory review (high risk)? Define escalation rules and “stop the line” criteria. Staff accordingly—or redesign the system so the constraint doesn’t kill your throughput.

Share

6. Trust: Can you prove where your models came from, what data they used—and defend that story under audit?

McKinsey reports that AI high performers invest heavily in transformation best practices and workflow redesign—not just bigger models. Gartner’s 2026 trends emphasize “digital provenance” (verifying origin and integrity of AI-generated content) and governance as core to maintaining public and stakeholder trust, especially as AI sovereignty rules require proof of model lineage and policy conformance. As the EU AI Act GPAI obligations take effect, executives face a new requirement: demonstrate your model’s provenance, fine-tuning history, and compliance posture on demand.

Next step: Create a provenance registry for every AI model in use. Document: base model source, training data lineage, fine-tuning history, version control, and compliance attestations. Implement logging for all AI-generated outputs so you can trace decisions back to model version, input data, and timestamp. When a regulator, customer, or auditor asks “How do you know this is trustworthy?” you answer with data, not assurances.

7. Workforce: Are you investing in AI skills—or just shrinking headcount and calling it ‘productivity’?

Gartner predicts that by 2027, 80% of the engineering workforce will need to upskill, yet McKinsey’s 2025 data shows only 28% of organizations are investing in formal reskilling programs. The World Economic Forum estimates 60% of workers require training before 2030, but traditional Learning & Development programs are too slow for the velocity of change in AI-exposed roles.

As AI reduces the labor intensity of delivery work previously outsourced to system integrators, organizations face a choice: build internal capacity to absorb that work, or maintain outsourcing models while claiming strategic control. The workforce transition requires deliberate investment in capability development, not just cost reduction.

Next step: Identify roles most exposed to AI disruption in the next 12 months. For each role, define the new capability profile: What skills remain critical? What new skills are required to manage, validate, or collaborate with AI systems? Launch targeted upskilling programs—not generic “AI literacy” but role-specific, hands-on training. Measure capability development as rigorously as you measure headcount reduction.

8. Physical Limits: If power rationing or grid caps hit your region, which AI workloads get turned off first—and who makes that call?

The IEA confirms data center power demand is doubling, and regions are beginning to prioritize power allocation. Deloitte and Gartner note that “siting” is now a strategic differentiator; if you cannot secure power, you cannot deploy compute. This is no longer a cost issue; it is binary availability.

Next step: Assess your energy exposure now. Where are your critical AI workloads hosted? What are the power constraints in those regions? If your cloud provider or data center faces energy rationing, which workloads would be capped first? Develop a load-balancing strategy: Can you shift workloads to regions with surplus capacity? Can you optimize inference efficiency to reduce compute demand? Secure long-term power commitments for mission-critical systems before capacity becomes scarce.

9. Customer Experience: While AI is saving you customer support costs, is it making your customer relationships better or worse?

Forrester predicts that in 2026, one-third of brands will erode customer trust by deploying premature AI self-service agents. Consumers are responding to low-quality digital interactions: 52% of U.S. online adults now actively seek offline, tactile experiences to escape digital friction.

Next step: Measure AI’s impact on customer lifetime value, not just cost-to-serve. Track: customer satisfaction scores, escalation rates, resolution time, and repeat purchase behavior for AI-assisted vs. human-assisted interactions. If your chatbot is driving customers away, you’re optimizing the wrong metric. Run A/B tests: Does AI enhance the experience, or does it create friction? Be willing to pull back AI in high-value touchpoints if it erodes trust and loyalty.

10. Quantum: When will you start your post‑quantum crypto migration—and which data will be readable by an adversary if you don’t?

The EU has set a 2026 deadline for organizations to begin transitioning to Post-Quantum Cryptography (PQC), with full critical infrastructure completion due by 2030. NIST finalized PQC standards in late 2024, triggering immediate compliance requirements for global enterprises. “Harvest-now-decrypt-later” attacks mean any data you encrypt today with standard RSA/ECC is vulnerable if it has a shelf life exceeding five years.

Next step: Inventory every system using RSA or ECC encryption. Prioritize data with long shelf lives—financial records, intellectual property, health data, contracts—anything that must remain confidential beyond 2030. Begin pilot migrations to NIST-approved PQC algorithms. Establish a migration roadmap with milestones: Which systems transition in 2026? Which by 2028? Which require vendor upgrades? Don’t wait—adversaries are harvesting encrypted data today for future decryption.

How about you? What’s on your plate for 2026?

Leave a comment

Thanks for reading!

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK(publication): what I call Technopolitics. I help leaders turn emerging tech into cashflows while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

Sources & Further Reading:

• The new economics of enterprise technology in an AI world | McKinsey

• The State of AI: Global Survey 2025 - McKinsey

• AI agent unit economics: TCO, ROI, payback - Acropolium

• AI Comes of Age: Deloitte’s 17th Annual Tech Trends Report

• AI Comes of Age: Deloitte’s 17th Annual Tech Trends Report (PR Newswire)

• SAP Sponsored Survey: Executives Trust AI Over Humans

• AI in the workplace: A report for 2025 - McKinsey

• How CFOs can secure solid ROI from business AI investments

• Technology Trends Outlook 2025 - McKinsey [PDF]

• From silos to synergy: How AI is reshaping government operations in APAC

• Transforming Business with AI: McKinsey’s 2025 Report - Instagram

2025 was loud and noisy.

If you are a business leader, a tech builder or a founder: you have been bombarded by weekly tech breakthroughs, bold claims, aggressive postures, bigger models, grander promises.

But what’s real? What’s just noise? And what actually matters for the decisions you’re making right now?

As I struggled myself with these questions, the answers I found did not really satisfy me. So I searched for my own: reading, writing, talking, and sharing to better grasp what was truly going on.

This year, my work circled around three of them. If you lead an organization navigating technology, geopolitics, and uncertainty, these are the questions you have likely been asking too.

Subscribe now

The Intelligence Question: What assumptions is your AI strategy built upon?

Every AI conversation in 2025 started with capability. What models can do. What’s coming next. What’s possible.

But capability isn’t the same as reliability. And language fluency isn’t understanding.

The more I read, the more I realised we were confusing pattern matching with reasoning; and mistaking sexy demos for enterprise-ready solutions.

So I researched further. I asked whether LLMs are truly the thinking machines we hope for, drawing on Chollet, Kahneman, Kurzweil, and others. My take: these tools are brilliant but they are not minds.

The hype around autonomous agents was even louder. Experts promised that agentic AI would replace knowledge workers through autonomous job execution. But simple experiments have shown that when LLMs excel at benchmarks, they fail miserably in real-life scenarios. And maybe that’s a good thing: reliability, security, judgement, and accountability remain non-negotiable for enterprise processes.

Game theory research revealed that simulating strategy is not the same as possessing intelligence. Real-time performance creates an illusion. But that’s not the same as intelligence. What’s most troubling and worth remembering is the opaque nature of closed-source models (GPT, Claude, Gemini, and others): training data is not published, model weights are closely guarded, and data labelling instructions for RLHF remain confidential.

They are black boxes. And companies are building their intelligence engines on top of them. Arguably, outsourcing their know-how, decision making and proprietary data to a third party.

So, I mapped who actually wins and loses in the AI value chain because understanding who captures value matters as much as understanding how the models work. Hint: it’s probably not you….(!)

And I cautioned about the risks of Humanizing AI and AI companionship: its impact on cognitive and self-preservation skills. Sorry to say but I don’t believe AI should be your therapist, lover, or best friend. AI is a mirror of your inputs, distorted by humanity’s historical collective data trails. No intent. No purpose. No personality. Hence, we need to be careful.

The takeaway: Before you scale your AI investments, stress-test your assumptions. What do these models actually do versus what you’ve been told they do? Who controls them? And what happens when they fail?

Now defining intelligence is only half the problem. The deeper question is who gets to decide what models are, what they’re made of, and what values, ideologies, and cultures they carry. That leads somewhere most strategies ignore entirely.

The Power Question: Who really controls the technology your business depends on?

The AI conversation in 2025 was dominated by what AI might bring to enterprises and society. But far less attention went to understanding the powers at play and how politics are driving tech roadmaps.

I spent considerable effort mapping who really controls the AI stack (from rare earths to model governance) and what that concentration means for enterprises dependent on foreign infrastructure.

This matters because sovereignty is an operational issue. And your dependencies are someone else’s leverage on your business.

Through my Technopolitics webinar series in partnership with INITIATIK, I studied extensively why technology is no longer neutral infrastructure; but rather a lever of power.

The powers behind each model and each breakthrough tell you more about trajectory than any product announcement. How dependencies are engineered to create stickiness. How control is asserted through infrastructure, not just contracts. In fact, reading through the US National Security Strategy, provided more insights into the future of tech than any press release or market research report.

AI is not abstract. It is physical, electrical, and increasingly constrained. Working with ENSSO, I explored how AI’s energy appetite is becoming a limiting factor for both climate goals and technological ambition. Grid capacity, power pricing, water access, data centre geopolitics are strategic imperatives.

That reality now reaches across borders. I examined how extraterritorial regulations are enforced through infrastructure, finance, and code. Export controls, sanctions, data governance now shape tech stacks, vendor choices, and risk exposure in ways most leaders underestimate.

With Asia Tech Lens, I analysed 25 national AI strategies to reveal where sovereignty narratives mask deep dependencies and where the real chokepoints lie. With the Digital Growth Collective, I proposed a roadmap on how boards and CIOs must secure control across clouds, models, data flows, and vendors.

At FIBEP’s World Media Intelligence Congress, I explored why truth itself has become fragmented and what that means for leaders trying to build trust in a technopolitical age. Interestingly, I also realised this had happened before: from Gutenberg’s press to incendiary pamphlets in 19th-century Paris (best captured in Balzac’s Lost Illusions), all the way to Instagram and TikTok. The medium changes. The dynamics don’t.

The takeaway: Map your dependencies before someone else exploits them. Energy, infrastructure, model governance, jurisdictional exposure are strategic risks hiding in your tech stack.

Subscribe now

The Leadership Question: How are you leading through uncertainty?

This was the question I carried into every founder conversation, every advisory session, every Friday morning when I sat down to write.

Because I think the leaders who will thrive aren’t the ones with the best predictions but the ones asking better questions and building organisations that can adapt when the answers change.

Watching Davos unfold early in 2025, I reflected on why global consensus around technology governance is fragmenting rather than converging. Cold, cynical, disillusioned. Because those in control have no interest in changing a world that works better for them. It’s paramount to understand their agenda: people make technology, not the other way around. With “Do You Hear The People Sing“ from Les Misérables in the background, I reflected on the failure of leadership and what principled leadership means.

Inside large organizations, the need to transform and the growing economic pressures create more tensions and offer also new opportunities. I explored how HR and technology teams might co-design the future of work asking a question many leaders wrestle with: how do you grow the business without growing the headcount?

I revisited the art of breaking walls and building bridges: what it takes to lead change from within, navigate resistance, and be a change agent in an established structure. Execution inside the machine is harder than starting fresh.

With founders, the challenge was rarely ambition or talent. It was blind spots. Over-reliance on a single platform or API. Enterprise readiness. Assumptions about data access or residency through AI models consumption. Business models built on fragile pricing power.

Those conversations reinforced something I now consider a core principle: innovation needs conviction. But good strategy is less about conviction and more about structured doubt. As an ultra-trail runner, each race keeps me humble and grounded. They help me achieve bigger things one small step at a time.

I also kept returning to what leadership reveals about us. I thought about what happens when leaders confuse the role with the person and why being more than your job title matters. I asked why abrasive leaders seem to succeed while principled ones often remain in the shadows.

Every Friday for the past 30 weeks, I shared an idea about leadership, power, or technology. Responsibility versus liability. Visibility versus influence. Speed versus direction. Resilience when progress becomes uncomfortable. This weekly routine forces me to step back, observe, and think harder.

The takeaway: Leadership is not about having answers. It is about holding better questions and staying steady while the world kept moving. The leaders who adapt aren’t smarter. Maybe just more honest about what they don’t know.

Reading More to Think Better

This year, I probably read more books than in the previous decade combined (!) Not so much to accumulate knowledge but to see things differently and learn: Ray Kurzweil’s How to Create a Mind reminded me of the intricacies of intelligence and biology to ground my analysis in AI models and what reasoning means. Chris Miller’s Chip War reshaped how I see semiconductors as strategic assets and a geopolitical power play. Patrick McGee’s Apple in China became a case study in technopolitics. Asma Mhalla’s Technopolitics and Cyberpunk gave language to the fusion of technology and power. Karen Hao’s Empire of AI traced how a handful of players came to dominate the field and at what price. Dan Wang’s Breakneck offered an insider’s view of China’s push to engineer the future. And Sarah Wynn-Williams’ Careless People was a sharp reminder that behind every platform are choices; and sometimes people with unchecked power who look away.

These books now shape what I write. If you lead through complexity, they’re worth your time too!

Looking Ahead

Earlier this year, I gathered ten uncomfortable questions that I thought would keep leaders up at night; about power shifts, tech advancements, and the future of work. Some proved pressing, urgent, and important. Others moved to the background. At least temporarily.

Thinking KoncentriK is not about having stronger opinions but about asking better questions before decisions harden into dependencies.

If any of these questions resonate or challenge how you’re thinking about 2026 I’d love to hear from you!

The work continues.

Thanks for reading!

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK(publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

Leave a comment

How do you grow the business significantly without growing headcount at the same pace?

Over the past few months, as a Fractional AI and Technology leader, I supported a large FMCG multinational in Singapore to answer this exact question.

The ambition was very clear but the constraints were also very real.

The initial brief looked straightforward: Set up an “AI Hub”. The underlying assumption was that a Center of Excellence would act as a catalyst, sprinkling “AI magic” across the organization to create efficiency.

However, it quickly became clear that this was not a technology project. It was an organizational redesign challenge.

Building an “AI Hub” would likely result in silos that would produce pilots nobody uses. The real work lay in understanding how humans, AI, data, and processes should collaborate so that growth becomes repeatable.

That required HR and Technology to share ownership of the same challenge.

Here is what we learned on the journey, and the model we built to solve it.

The Pivot: From “AI Hub” to “Market Expansion Accelerator”

We realized that “building capabilities” is a cost center, but “expanding markets” is a revenue driver.

We scrapped the “AI Hub” name. This matters. Names signal intent. An AI Hub signals experimentation; a Market Expansion Accelerator signals business outcomes.

We anchored this accelerator on three pillars to ensure we weren’t just building tech, but building a growth engine.

1. Automation is where the friction is

We stopped looking for generic AI use cases and looked at the hardest part of their business goal: Entering new markets faster.

In practical terms, we asked: Why does it take months to understand a new market?

• Who do we partner with for distribution?

• What is the optimal pricing strategy?

• What are the local compliance requirements?

• How do we localize brand messaging instantly?

This produced concrete, boring, and high-value use cases:

• Market entry playbooks generated by analyzing regulatory data.

• Distributor copilots to aid local sales teams.

• Content pipelines that localize marketing assets in hours, not weeks.

No moonshots. Just boring (!), compounding improvements to the speed of business.

2. Capability: Fluency over Engineering

We focused on “AI Fluency” for the general workforce. We didn’t need more AI engineers. We needed people who could use AI safely, spot opportunities, and challenge outputs.

People needed to:

• know when AI helps and when it does not

• use it safely in their role

• judge outputs and propose improvements

• identify which tasks should be automated next

We developed:

• role-based experiential learning journeys

• AI Ambassadors in key markets

• conversational interfaces over dashboards

• a design principle targeting “zero training” user interfaces (if the tool required a manual, the UX was too complex.)

Useful, usable, used!

Read more on this design framework here.

3. Collaboration: HR and Tech as One System

This was the critical unlock. We designed cross-functional squads with shared KPIs:

• Time to open a market

• Time to launch a SKU

• adoption of new workflows

By unifying the governance, we would ensure that when a process is automated, the role description and performance metrics would change immediately to match.

Subscribe now

The 6 Questions That Mattered More Than Tech Stack

We avoided getting bogged down in tool selection by focusing on the questions that determine business value:

1. The Goal: How can AI help us grow faster without adding equivalent headcount?

2. The Split: How should work be practically split between humans (judgment), AI (drafting/analysis), and automation (routing)?

3. The Design: What needs to change in our org design, roles, and incentives to make this real?

4. The Buy/Build: Which capabilities must be in-house versus with partners?

5. The Safety: How do we keep people confident and motivated in hybrid human–AI workflows?

6. The Scale: How do we replicate this across cultures without losing coherence?

This disciplined framing would prevent the usual AI “shopping list” and keep us anchored to outcomes.

Why This Is a Trend, Not a Fad

Our approach mirrors a broader shift I am seeing in the market. Forward-thinking organizations are merging the “People” and “Tech” agendas because you can no longer separate the worker from the workflow.

• Moderna merged HR and IT under a single Chief People and Digital Technology Officer to align workforce planning with AI investments (Forbes).

• Covisian (CX outsourcer) combined HR and IT to unify workflow design (BBC).

• Workleap folded IT into the people function to fix onboarding friction (Fortune).

The forces driving this are:

1. Workflows determine value

In an era of “Agentic AI,” the handoff between human and machine defines efficiency:

• AI drafts; humans decides

• Systems triages and routes; people handle exceptions

• AI copilots suggest; leaders exercise judgment

• ....

This forced us to confront practical questions:

• Where does accountability sit when an AI-recommended action is taken?

• What belongs to a role versus what belongs to a system?

• Where does human judgment add value, and where does it add latency?

• How do we redesign processes so they fit a human-plus-AI operating model?

So these are workflow redesign questions, not really “IT vs HR” questions.

2. Roles are fluid

As automation handles volume, roles shift toward judgment. HR must evolve skills frameworks in near real-time:

• roles shift toward judgment and exception handling

• skills frameworks must evolve continuously

• performance reviews must account for tool-amplified output

• incentives need to reward adoption and process redesign

If HR and Tech work separately, org design lags behind system design (or the other way around).

3. Governance is shared

You cannot govern AI without governing the people who use it. Accountability becomes shared:

• who is responsible for an AI-suggested action

• how fairness and transparency are ensured

• how work is redistributed when steps are automated

Unified HR–Tech governance creates speed, clarity, and consistency. This industry direction validated our own approach: workflow, capability, and system architecture had to move together.

Share

The operational questions enterprise leaders cannot avoid

In the end, the future of work shows up in day-to-day leadership decisions:

Complementarity: Where exactly should AI handle volume and speed—and where should humans intervene?

Process redesign: Which processes should be simplified or rebuilt entirely?

Performance and incentives: How do we evaluate output when tools amplify productivity Do we reward adoption and workflow redesign?

Scaling across markets: What is non-negotiable (data standards, controls), and what can adapt locally?

Diversity of thinking: How do we preserve challenge and dissent in a system optimised for efficiency?

These questions determine whether AI becomes a value multiplier or a source of friction.

What I would insist on if starting again

A few principles proved non-negotiable:

• Business first: AI must map to growth levers.

• People first: adoption and fluency are part of the product.

• Simplify to amplify: AI should remove steps, not automate chaos (!)

• Train for the right sport: build capabilities your business model needs.

• Mindset drives execution: teams must test, learn, and adjust continuously.

• Leadership involvement and alignment matters: HR, Tech, and Business need one story, one set of metrics and need to lead by example.

Without this, it will be hard to keep things moving and scale.

Closing Thought: The Leadership Mandate

The future of work is not about replacing people with AI. It is about designing work so that people and AI do different things together, deliberately, and at scale.

For my client, success didn’t come from a new software license. It came from deciding that HR and Technology were co-owners of the growth target.

If you are on a similar journey, my suggestion is simple:

Start from the business goal. Map the real frictions. And make HR and Technology sit at the same table to solve it.

It makes everything else easier.

I’d love to hear how you are structuring your own AI governance!

Thanks for reading,

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK (publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

Why this document matters to business leaders

Last month, the U.S. administration released their National Security Strategy; a 33 page document describing the U.S. perceived threats as well as their priorities and plans to address them. Full document available here.

Most executives will never read a national security strategy. They assume it belongs to diplomats, generals, or politicians.

I believe that assumption is now a liability.

When read carefully, the latest U.S. National Security Strategy is in fact not a political document but a technology and industrial strategy, expressed in the language of security.

It describes how infrastructure, energy, AI, standards, and supply chains are to be shaped over the coming decade, and how dependencies will be created, monitored, and enforced.

For enterprises, here is what at stakes:

• where you place data and compute

• which AI models you depend on

• which standards shape your products

• how portable your systems really are

• how exposed you are to shifts in regulation, export controls, or infrastructure access

This article reads the strategy as a technopolitical document, not to debate intent nor politics , but to understand how the operating environment for technology-driven organisations is being restructured.

My goal is to help leaders understand what is already changing, what will accelerate next, and how to respond with clarity rather than reaction.

Subscribe now

Why your digital infrastructure is not neutral terrain

The document consistently treats technology as something that must be protected, hardened, and controlled, not optimised purely for efficiency or performance.

It states:

“We want a resilient national infrastructure that can withstand natural disasters, resist and thwart foreign threats, and prevent or mitigate any events that might harm the American people or disrupt the American economy. No adversary or danger should be able to hold America at risk.”

This framing matters. Infrastructure logic implies prioritisation, conditional access, and sovereign control. It moves technology out of the domain of neutral enterprise tooling and into the domain of strategic assets.

That logic is reinforced by how industrial and technological capacity are positioned:

“We want the world’s most robust industrial base. American national power depends on a strong industrial sector capable of meeting both peacetime and wartime production demands.”

“We want to remain the world’s most scientifically and technologically advanced and innovative country, and to build on these strengths. And we want to protect our intellectual property from foreign theft.”

This is a clear break from the assumption that global markets will naturally optimise technology outcomes. Instead, technology is something to be secured against risk, influence, and dependency.

What this means for corporate leaders

• Cloud, AI, and data platforms are increasingly treated like utilities with strategic conditions attached.

• Continuity of service can no longer be assumed to be purely contractual.

• Technology choices are becoming long-term exposure decisions, not short-term optimisation plays.

Every company should treat their digital infrastructure as a strategic asset as well: digital resilience and infrastructure concentration risk should be fully integrated into the organisations risk management plans.

Read my analysis about the Dependency Economy of AI comparing 25 National AI Strategies, or download the full 49 page report here.

Re-industrialisation and supply chains as instruments of control

One of the most explicit technopolitical moves in the document is the treatment of supply chains as security objects.

The strategy states:

“The United States must never be dependent on any outside power for core components—from raw materials to parts to finished products—necessary to the nation’s defense or economy.”

More importantly, it specifies how this will be operationalised:

“Moreover, the Intelligence Community will monitor key supply chains and technological advances around the world to ensure we understand and mitigate vulnerabilities and threats to American security and prosperity.”

It clearly formalises a reality already visible to multinationals: industrial roadmaps, sourcing strategies, and technology dependencies are now monitored and evaluated at state level.

Reindustrialisation is framed as a long-term structural shift:

“Reindustrialization – The future belongs to makers. The United States will reindustrialize its economy, ‘re-shore’ industrial production… with a focus on the critical and emerging technology sectors that will define the future.”

This resonates closely with Dan Wang’s Breakneck, which documents how industrial capacity and technological power are inseparable in modern competition, and with Chris Miller’s Chip War , which shows how semiconductor supply chains have become geopolitical fault lines.

Example in action:

In January 2025, the U.S. Department of Commerce implemented sweeping new export controls on AI chips and model weights, requiring companies to monitor computing power distribution and report vast amounts of information to retain export privileges Sidley Austin LLPU.S. Department of Justice.

By December 2025, federal authorities shut down a major smuggling network in “Operation Gatekeeper,” seizing over $50 million in Nvidia technologies after uncovering attempts to export $160 million worth of H100 and H200 GPUs to China through falsified shipping paperwork U.S. Department of Justice.

The message is clear: supply chain monitoring is already operational and enforced.

What this means for corporate leaders

• Supply-chain strategy is no longer separable from geopolitical risk management.

• Vendor diversification is becoming a strategic requirement, not a resilience nice-to-have.

• Long-term cost efficiency will increasingly be traded off against controllability and alignment.

It means organisation need to manage interdependencies within their digital supply chain rather than (naïve) global optimisation purely based on technological and operational constraints.

Energy as the foundation of AI and compute power

The document draws a direct and unusually explicit line between energy policy and AI leadership, stating:

“Energy Dominance – Restoring American energy dominance… is a top strategic priority.”

And crucially:

“Cheap and abundant energy will… fuel reindustrialization, and help maintain our advantage in cutting-edge technologies such as AI.”

This matters because AI is not abstract software but a compute-energy system. Training a single large language model can consume as much electricity as hundreds of homes use in a year. Inference at scale requires sustained, reliable power. Data centers running AI workloads already account for significant portions of grid capacity in key regions.

The operational reality: Companies building AI strategies must now evaluate energy geography alongside technical capabilities.

Where can you reliably access cheap, stable power at the scale AI demands?

Which jurisdictions offer favorable energy policy for compute-intensive workloads?

As energy costs and availability diverge globally, so will the feasibility of AI deployment.

Explore further my prior on the AI / Energy paradox: whoever controls energy density and grid stability controls the feasible geography of AI.

What this means for corporate leaders

• AI strategy must be evaluated alongside energy exposure and geography.

• Compute-heavy workloads will increasingly cluster in regions with favourable energy policy.

• Claims of “sovereign AI” without sovereign energy and compute access are structurally weak.

Subscribe now

AI, quantum, and autonomy as strategic capabilities

The strategy does not treat AI as a general-purpose productivity tool. It consistently groups it with military and dual-use technologies.

It states:

“The United States must… invest in research to preserve and advance our advantage in cutting-edge military and dual-use technology… such as AI, quantum computing, and autonomous systems, plus the energy necessary to fuel these domains.”

This framing has predictable consequences:

• differentiated access to advanced models

• tighter export controls on hardware and software

• increased compliance, logging, and usage constraints

• AI capabilities treated as conditional, not universal

Example in action:

The January 2025 AI Diffusion Framework divided the world into three tiers for chip access, with compliance required by May 2025 Sidley Austin LLP. For the first time, the framework established export controls on AI model weights themselves—not just hardware Anthropic. Chinese AI companies like DeepSeek openly acknowledge that chip restrictions force them to use 2-4x more power to achieve results comparable to U.S. firms AI Frontiers. The practical result: AI model choice now directly determines operational costs and capabilities.

This is also resonates with my review of Patrick McGee’s Apple in China illustrates in a different context: when technology becomes so strategic, access is never just commercial.

What this means for corporate leaders

• AI model choice is becoming a strategic dependency decision.

• Regulatory and contractual constraints around AI usage will increase, not decrease.

• Portability across models, clouds, and jurisdictions becomes a core resilience capability.

Platforms as security infrastructure: the public–private fusion

One of the most revealing passages concerns the role of private technology platforms in national cyber posture:

“The U.S. Government’s critical relationships with the American private sector help maintain surveillance of persistent threats to U.S. networks, including critical infrastructure.”

And:

“This in turn enables the U.S. Government’s ability to conduct real-time discovery, attribution, and response… while protecting the competitiveness of the U.S. economy and bolstering the resilience of the American technology sector.”

This is a clear articulation of what Asma Mhalla describes as the liquefaction of sovereignty in Technopolitique and Cyberpunk: state functions increasingly operate through private digital infrastructures.

Example in action:

In 2024, CISA conducted 2,131 Pre-Ransomware Notifications to critical infrastructure entities, warning them of early-stage ransomware activity before encryption occurred CISA. The agency used Administrative Subpoena authorities to identify and drive mitigation of over 1,200 vulnerable devices controlling critical infrastructure like power plants and water utilities CISA. This operational reality demonstrates how private infrastructure increasingly functions as monitored security layers.

For enterprises, this fundamentally reshapes assumptions about data access, logging, telemetry, and governance.

What this means for corporate leaders

• Cloud and SaaS providers are not neutral intermediaries.

• Observability and logging are structural features, not optional add-ons.

• Data governance must assume multi-layered visibility and jurisdictional complexity.

Standards as power: shaping markets without legislation

The strategy is explicit about standards as a lever of influence:

“We want to ensure that U.S. technology and U.S. standards—particularly in AI, biotech, and quantum computing—drive the world forward.”

In practice, standards are not only set in formal bodies. They are embedded in:

• platform defaults

• API design

• open source software and models

• safety and alignment constraints

• identity and access frameworks

• audit and compliance tooling

This is how technological leadership translates into normative power.

What this means for corporate leaders

• Platform choices embed governance assumptions into products and operations.

• Standards shape what is possible, auditable, and compliant across markets.

• Regulatory exposure increasingly follows technical architecture.

Regulatory strength versus infrastructure vulnerability: the European trade-off

The strategy devotes attention to Europe’s trajectory, explicitly linking economic performance, regulation, and technological capacity.

From a technopolitics perspective, this highlights a structural tension: regulatory power without corresponding infrastructure depth.

Europe has established itself as a global standard-setter in data protection (GDPR), AI governance (EU AI Act), and digital services regulation. But regulatory frameworks alone do not generate compute capacity, energy density, or semiconductor manufacturing capability.

Standards influence what is permissible; infrastructure determines what is possible.

A jurisdiction can write excellent AI governance frameworks, but if it lacks the energy, chips, and compute to train and deploy models at scale, it becomes dependent on others who control those resources.

What this means for corporate leaders

• Compliance excellence does not equal operational autonomy

• Geographic presence in regulated markets must be balanced against access to infrastructure

• Boards should evaluate: where do our capabilities depend on external infrastructure we don’t control?

This is the European dimension of a broader technopolitical reality: resilience is not achieved through regulation alone, but through control of the underlying technological stack.

Narrative power and the non-neutrality of platforms

One of the most revealing parts of the strategy is its explicit treatment of narrative influence as strategic infrastructure; particularly in its language on Europe.

The document does not describe Europe only in economic or security terms. It frames Europe as facing a deeper crisis:

“But this economic decline is eclipsed by the real and more stark prospect of civilizational erasure.”

It goes further, identifying the causes not merely as external threats, but as internal regulatory, cultural, and informational dynamics:

“The larger issues facing Europe include activities of the European Union and other transnational bodies that undermine political liberty and sovereignty… censorship of free speech and suppression of political opposition… and loss of national identities and self-confidence.”

The proposed response is not only economic or military. It explicitly frames narrative influence as a strategic tool:

“We want Europe to remain European, to regain its civilizational self-confidence, and to abandon its failed focus on regulatory suffocation.”

Whether one agrees with this characterization is irrelevant to the technopolitical analysis. What matters is that the strategy explicitly positions narrative influence as a policy instrument and that instrument operates through commercial technology platforms (X, Meta, TikTok, Google, etc) and amplified by AI.

Digital platforms, cloud ecosystems, social networks, search engines, content moderation systems, and increasingly AI models themselves are the infrastructures through which:

• speech norms are enforced

• visibility is allocated

• legitimacy is shaped

• certain narratives are amplified while others are deprioritized

In the same strategy, the U.S. commits to deep coordination with its private technology sector:

“The U.S. Government’s critical relationships with the American private sector help maintain surveillance of persistent threats to U.S. networks, including critical infrastructure.”

Taken together, these passages illustrate a central technopolitical reality:

platforms are not politically or narratively neutral, even when operating as private companies.

AI systems trained on curated datasets, governed by specific safety frameworks, deployed through dominant cloud platforms, and aligned with particular standards inevitably carry embedded assumptions about acceptable speech, legitimate authority, and social norms.

This is not conspiracy theory but an explicit strategy. The document states openly that narrative influence is a policy tool to assert control and enforce alignment, and that tool operates through the technology platforms enterprises depend on.

What this means for enterprise leaders

For organisations operating in Europe and globally, this has concrete implications:

• Platforms and AI tools embed normative assumptions that may differ across jurisdictions

• Content moderation, model alignment, and governance frameworks increasingly reflect strategic priorities, not just ethical abstractions

• Claims that AI or platforms are “neutral” are operationally misleading

For leaders, the question is not whether narrative influence exists, but whether they understand:

• which narratives their technology stack implicitly supports

• which jurisdictions shape their platforms’ governance models

• how AI systems may amplify or suppress certain perspectives by design

In short: AI is not only an automation tool. It is a narrative infrastructure.

Infrastructure deployment as dependency creation

The strategy is unusually direct about infrastructure abroad:

“We should partner… to build scalable and resilient energy infrastructure, invest in critical mineral access, and harden existing and future cyber communications networks that take full advantage of American encryption and security potential.”

And even more explicitly:

“We should make every effort to push out foreign companies that build infrastructure in the region.”

This is infrastructure as alignment. Once embedded, infrastructure creates long-term technological and operational dependencies. Although not named, China is clearly an obstacle to the U.S. strategy at the moment.

What this means for corporate leaders

• Infrastructure contracts signal long-term alignment, not just procurement decisions.

• Vendor choice increasingly implies geopolitical positioning.

• Exiting embedded infrastructure dependencies becomes harder over time.

In short it is the weaponisation of digital infrastructure using dependencies to assert controls over other nations and regions.

Practical conclusion: what leaders should do now

Reading this strategy through a technopolitics lens leads to a small number of practical actions.

1. Map your dependency stack: Across energy, compute, chips, cloud, models, data, and standards. Identify single points of failure and jurisdictional exposure.

2. Design for portability and reversibility: Architect systems so workloads, data, and models can move when conditions change.

3. Elevate technology decisions to board-level risk: AI, cloud, and infrastructure choices now shape continuity, not just cost.

4. Treat standards as strategic inputs: Understand which norms, defaults, and governance models you are embedding through platforms.

5. Build technopolitical literacy: Not to debate geopolitics, but to understand how strategy documents like this translate into operational reality.

A final thought

This strategy does not tell companies what to think. It tells them what kind of technological environment they will be operating in.

In that environment, the central leadership question is not: Which technology is best?

But: Which dependencies can we live with when the world reconfigures?

That is the real challenge enterprise leaders must now address.

Thanks for reading,

Damien Kopp

Leave a comment

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK(publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more: connect with me on LinkedIn or send me an email at damien.kopp@rebootup.com

I started KoncentriK to help business leaders and founders see things differently. Circles vs trees. Ripples vs ladders. A space where power is distributed, perspective is shared, and insight flows outward; sparking new ideas, bold action, and unexpected outcomes.

Ultra-running tests your mental strength in the face of adversity. This story is not a lesson, but the raw thoughts that crossed my mind as I moved through pain and exhaustion. Seeing things differently. With some new clarity.

Whether about life or business, I hope this can be helpful…

I often say: it’s your mind that carries your legs, not the other way around.

The Translantau 120km ultra-trail race in Hong Kong, with 5,600 meters of cumulative elevation, tested this like rarely before.

I’m still lightheaded and foggy, my legs stiff as iron rods, but I wanted to capture what was going through my head while I was out there.

These races have a way of exposing things you don’t see when life is moving quickly and comfortably: how you behave when the plan breaks, when systems fail, and when you can’t opt out.

It all starts very well… but at kilometer 15, after the first checkpoint, suddenly my stomach twists and turns. My feet start to feel like bricks. Each step feels like a punch to my quads. I stumble to keep my balance.

I check my watch and realized I have 101 km more to go: it feels impossible.

The thing is, you can always quit, but not on your own terms: when you’re stuck between two checkpoints, on a remote mountain path, you can cry, yell, threaten the skies and the universe, but nothing will help you. You’ve got to continue until the next stop to get assistance. So I push forward… and have to let 140+ runners pass me…

At this point, my body is in full revolt: maybe its way of reminding me I’m getting older, or of the laws of physics, or simply asserting its dominance over my mind. In the end, the body will prevail, no matter what my mind says.

Subscribe now

In these moments, time feels elastic: as I keep pushing forward, the kilometers on my watch stubbornly barely move, as if I am running in place, frozen in time, despite my tremendous efforts to keep up.

My focus narrows as I dedicate all my mental space and physical energy to positioning my feet properly for the next step forward on the rocky path and counting the kilometers until the next checkpoint. It’s disconcerting how my body self-optimize for the goal I have set: I can’t think straight, my thoughts form none-sense infinite loops, my body keeps moving and that’s all what matters. One more step.

After more than 22 hours of running, darkness falls on the mountainside trail. I’m alone. No sound around. Just my own footsteps and breathing. An overwhelming urge to sleep overtakes me. My eyes shut down. I lose focus and stumble. I look downhill: 15 to 20 meters of rocks below. Even the fear of falling doesn’t give me enough adrenaline to stay awake. I need to pause. I lie down on the side of the trail on a large, flat-ish rock, set an alarm for 5 minutes, and fall asleep.

As fatigue grows, I focus on my feet: 152,000 steps over the total distance. That’s 152,000 chances not to fall. I watch my feet.

I pass other runners sitting on rocks or lying along the trail like I did. Their eyes have a glazed look. Like me, they’re in their own zone: undeniably present but somewhere else entirely, their minds and bodies battling to stay in the race.

Small gestures of kindness provide glimpses of comfort: each smile, message, clap, cheer, and moment of attention along the way from family, friends, and support staff. They help me push a bit further.

After the last checkpoint at kilometer 110, I leave too quickly and forget my poles. I turn back and run 1 km to retrieve them, adding more time and distance. It could have been worse: I heard about a runner who crossed the finish line, only to realize she’d skipped the last checkpoint. She had to go back 9 km and return again: 18 extra kilometers. Incredible resolve.

At the very end, once I realize I’m done, I exhale a long sigh of relief. I let go of the tension in my muscles, release the deep focus in my mind, and let warm tears flow down my cheeks.

People often ask why I do this. I don’t have a clean answer. What I know is that there’s a before and an after. A race like this is transformational. Something in me shifts.

When you spend 30+ hours on a mountainside with your body shutting down and your mind trying to hold things together, you see what stays with you when everything else drops.

That clarity, that thin line between quitting and continuing, is something I carry back with me long after the race is over.

I hope you can now see why…?

Now, I also realize that this ability to endure pain may not always be useful in life or business. Knowing when to stop, call it a day, and quit is equally important. Staying objective is hard. Shutting down a business is hard. But sometimes it’s the right thing to do, not persisting…

Thanks for reading!

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK (publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

Paris, 19 November 1823. At sunrise, rue des Fossés-Montmartre awakes slowly. On this narrow street behind the Palais-Royal, the presses at Didot’s printing house begin to roll. The smell of ink, hot lead, and damp paper drifts through open windows. Apprentices carry fresh broadsheets out to the sidewalk. Just around the corner, on rue du Croissant, editors and writers gather in the smoky back room of the Café de Paris, a known hangout for journalists and literary fixers. Cigarette smoke mixes with a strong coffee smell. By breakfast, the noise builds. Discussions heat up over the day’s headlines.

Young men in waistcoats shout out the morning’s editions: Le Journal des Débats, Le Constitutionnel, La Quotidienne, Le Corsaire-Satan. Jules Janin tears into reputations with a few lines of ink. Raoul Nathan, part writer, part politician, makes speeches in salons while editors rewrite them to fit the agenda.

Each paper claims to tell the truth. Each shapes its story for its readers or its backers. Some are aligned with the Doctrinaires, others with the Legitimists or Bonapartists.

Critics take bribes. Reviews are traded. Reputations are made or ruined over coffee and brandy.

In early 19th-century France, a media revolution was already underway. Long before social media algorithms began sorting truth from fiction by engagement metrics, a similarly chaotic transformation took place. The sudden explosion of newspapers, feuilletons, and pamphlets during the Restoration and July Monarchy reshaped the public sphere and destabilized the relationship between information, influence, and power.

In his book Lost Illusions (“Illusions Perdues”), French writer Balzac captures this turmoil fictionally, but the backdrop he drew upon was real: an explosion of press activity, driven by a convergence of economic incentives, liberalisation policies, new printing technologies, and an emerging culture of celebrity and scandal.

A look back at this historical period, reminded me how History rhythms, and what we can learn from it, at the age of algorithmically amplified, distorted and manipulated truth and the proliferation of unverified, biais, ideologically driven AI generated content.

Subscribe now

The First Content Revolution: France, 1820s–1840s

Between 1814 and 1830, the number of newspapers in Paris tripled. By 1848, over 200 political newspapers were circulating in the capital. The July Monarchy’s relative freedom of the press (after the strict Napoleonic controls) combined with declining printing costs created a fertile environment for entrepreneurial journalism. Even with recurring censorship attempts, especially under Charles X, publishers found loopholes. Cheap pamphlets, gossip sheets, and “literary criticism” masquerading as political satire became widespread.

The new press was born of technology and ambition. Steam-powered rotary presses could print thousands of copies in hours. Paper, once a luxury, had become cheap. Literacy was rising fast, fuelled by education reforms and the restless aspirations of an expanding bourgeoisie. Suddenly, anyone with a printer, an opinion, and a little capital could launch a newspaper.

It was the first attention economy.

Low barriers to entry meant competition was ferocious. Success demanded visibility, not accuracy. Scandal sold better than policy. Mockery travelled faster than truth. Editors learned quickly that outrage was a business model. Fact-checking was impractical. Columns mixed politics and theatre, rumour and poetry, literature and attack. Journalists were part celebrity, part mercenary, part propagandist. Reviews could be bought, reputations destroyed, alliances traded for front-page space.

The term “public opinion” began to take on the modern form: not the collective wisdom of an informed citizenry, but the manipulated outcome of competing information interests.

How We Got Here: From Gutenberg to the Guizot Law

The chaos of 1830s Paris didn’t emerge from nowhere. It was the culmination of nearly four centuries of transformation across printing technology, literacy, political upheaval, and the economics of communication.

It begins with the Gutenberg press, introduced in the 1450s. By removing the manual bottleneck of hand-copied manuscripts, Gutenberg collapsed the cost and time to reproduce information. Within 50 years, over 20 million books were circulating in Europe. Power changed hands.

The press helped catalyze the Reformation, enabling the spread of ideas that challenged ecclesiastical authority. The result was fragmentation. Competing truths began to multiply. Heresies spread faster than institutions could respond. The Church’s monopoly on interpretation eroded.

By the 17th and 18th centuries, pamphlet culture had become the engine of underground political discourse. In France, thousands of anonymous leaflets and libelles circulated in salons and cafés, combining scandal, ideology, and misinformation. These were the precursors to today’s viral posts: cheaply produced, emotionally charged, and designed to provoke.

These anonymous texts were read aloud in salons and cafés, copied by hand, reprinted without permission or attribution, and adapted for new audiences.

What they lacked in accuracy, they made up for in speed and impact. They were the viral posts of their day—unfiltered, emotional, and often untraceable.

Anonymity served multiple purposes: it protected writers from censorship and arrest, it allowed political actors to manipulate public opinion from behind the scenes, and it gave space to slander, fabrication, and personal vendettas with little fear of consequence. Robert Darnton estimates that up to 90% of pamphlets circulating in the 1780s bore no author’s name.

Anonymity also amplified participation, and made truth harder to locate. The public sphere became louder, but not clearer.

The Enlightenment elevated the idea of the reader as a citizen; but the infrastructure of reason couldn’t keep pace with the velocity of printed opinion. By the time of the French Revolution, literacy had grown, especially in urban centers, but editorial accountability had not. Napoleon briefly imposed control, but the desire for open expression was entrenched.

Then, in 1833, France passed the Guizot Law, mandating primary education for boys in every commune. Literacy surged. A new generation of readers entered the public sphere just as printing costs were falling and partisan publishers were multiplying.

By the 1830s, the stage was set: mass literacy, low-cost content, fragile institutions, and an unregulated attention economy.

What emerged was the first true content crisis at scale.

Stabilizing Forces: What Emerged from the Chaos

The 19th-century French media didn’t implode into permanent disinformation. Over time, several stabilizing forces took hold:

1. Institutionalization of journalism: By the late 19th century, journalism began to professionalize. Norms around verification, editorial independence, and journalistic integrity slowly emerged; often pushed by elite publications wanting to differentiate themselves from sensationalist rivals.

2. Media consolidation: Many short-lived, partisan publications died out, replaced by more stable and well-capitalized newspapers that operated across broader readerships. With scale came new responsibilities and reputational risk.

3. Legal frameworks: Laws on press freedom, defamation, and libel evolved. While not always fair or neutral, they created boundaries. They also increased the cost of publishing false or harmful content.

4. New institutions of public discourse: Political parties, universities, salons, and intellectual societies began playing a mediating role between information and action. Fact-based analysis gained some institutional champions.

5. Public education: Literacy campaigns and expanding public education created a broader base of citizens with the cognitive tools to engage with information critically. Literacy alone didn’t guarantee discernment but it was a necessary precondition.

In short, the press ecosystem matured. It remained imperfect, politically biased, and occasionally corrupt. But it became more resilient.

France entered the Third Republic with a media system that, for all its flaws, was more capable of informing rather than purely manipulating.

Echoes in the Age of Generative AI and TikTok

Today’s generative content landscape mirrors the early 19th-century upheaval but on (algorithmic) steroids!

We are witnessing:

• The mass democratization of content creation, where anyone with or without expertise can instantly produce, remix, or distribute information at scale, using tools that were once the preserve of institutions.

• The return of anonymity as a structural force. Like the pamphleteers of pre-Revolutionary France, today’s content creators often operate behind pseudonyms, burner accounts, or synthetic profiles. From comment threads to generative avatars, anonymity amplifies voice but dissolves accountability, enabling distortion to scale faster than verification.

• The industrialization of manipulation, as engagement-optimized algorithms push content not for its accuracy but for its emotional trigger value. Visibility is now governed by virality, not veracity.

• The erosion of institutional trust, with traditional gatekeepers (media outlets, public institutions, scientific bodies) struggling to assert credibility in a fractured and accelerated information ecosystem.

• A fully financialized attention economy, where outrage, fear, and division outperform nuance. Emotion monetizes. Precision does not.

What was once done with ink and pamphlets is now achieved with LLMs, filters, and coordinated bot networks. Deepfakes, astroturfed grassroots movements, and micro-targeted narratives create a reality where public discourse fragments into unreconcilable bubbles. The sheer velocity and scale of generative media outpace traditional corrective mechanisms.

As noted in my previous article Winning the Battle for Trust When Truth is Fragmented trust has been decimated across every institution, from the media to the UN. Over 50% of the most-viewed TikToks on mental health contain misinformation. Deepfake fraud has spiked over 1,700%. Meanwhile, AI-powered platforms offer mass personalization but also mass manipulation.

Balzac’s corrupt journalist duels with today’s algorithmic recommender systems: both reward distortion.

What’s Different This Time?

Despite the clear parallels, the 21st-century information crisis diverges from the 19th-century in critical ways:

• Speed and scale: AI and global platforms operate in real-time, with reach orders of magnitude beyond anything in Balzac’s era.

• Lack of editorial gatekeepers: Anyone can now generate, distribute, and amplify content with little friction or cost.

• Algorithmic opacity: The criteria by which content spreads is now largely invisible and unaccountable.

• Cognitive overload: The volume of stimuli surpasses human attention capacity. Disinformation is not just plausible it is just inevitable in such an environment.

• Weakening of democratic buffers: Institutional trust, political cohesion, and public education systems have eroded rather than matured.

Most importantly: we are no longer passive readers. We are active vectors. Each share, like, or repost becomes an act of amplification.

In this sense, we are all micro-publishers without training, guardrails, or accountability.

Subscribe now

Learning From the Past: Toward Resilience Today

Fact is, Balzac’s Paris didn’t collapse under the weight of fake news. Nor must we. But solutions require structural (not just individual) change. Drawing on historical precedents, I believe we can outline possible directions. Here are some ideas for discussion and further reflection:

1. Re-legitimize media institutions through integrity and transparency: Just as journalism slowly earned credibility, platforms today must be pushed to adopt verifiable standards of truthfulness, editorial accountability, and explainable algorithms.

2. Strengthen information literacy: 19th-century public education laid the groundwork for informed citizenship. Today, we need critical digital literacy at every age and socioeconomic level; not just for students, but for voters, employees, and leaders.

3. Reintroduce friction on both content and identity: The current architecture rewards speed and anonymity. To counter this, platforms could slow virality with deliberate design: time delays before reposting, fact-check prompts, or content traceability. In parallel, anonymous or unverified accounts should face reduced amplification. Visibility should reflect transparency of authorship, or reputation systems. This adds friction not to speech itself, but to its unchecked propagation. Not all speech must be verified but not all speech deserves equal algorithmic reach.

4. Fund independent fact-checking and public interest media: Just as 19th-century elites supported serious newspapers to counter tabloid excess, we now need public-private funding coalitions for non-profit investigative journalism and civic platforms.

5. Legislate for algorithmic accountability: New laws must demand explainability, transparency, and governance of recommendation engines, especially when they mediate political or health-related content.

6. Rethink sovereignty in media infrastructure: In the 19th century, sovereign governments set the terms of public discourse. Today, that role has been ceded to private platforms. Regulatory frameworks like the EU’s DSA or DMA are a start, but insufficient unless accompanied by architectural sovereignty (owning critical infrastructure, AI models, and moderation levers).

Conclusion: The Battle Is Not New, But It Is Now Global

We are not the first generation to panic about the collapse of truth. The French press in Balzac’s time felt equally destabilizing. But through institutional maturation, civic investment, and legal recalibration, a fragile equilibrium was reached.

This time, the stakes are higher. The tools are more powerful. And the consequences (eg. geopolitical, societal, and cognitive) are global.

But history offers both warning and wisdom: disinformation is a structural feature of information revolutions.

The answer is not panic, but design.

If we want truth to survive, we must build systems (technical, legal, and social) that make it resilient.

Not by going back to the past, but by learning from it.

Leave a comment

Thanks for reading!

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK (publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

Some time ago I shared about corporate leaders who seemed to become ‘entitled by their title’ - yes I even called them ‘horrible bosses’ (!). These were personal experiences across various moments of my career.

Since then I took a step back and wondered: why do we celebrate (and have to endure!) ego-maniac, a-type, aggressive, abrasive leaders?

I have a fairly, straight forward and depressing answer to this question (don’t scroll to the end just yet!), but I am going to ponder, objectively, some of the key reasons… so let’s get started, shall we?

The Paradox We Can’t Look Away From

Much of business history is filled with leaders who are celebrated as geniuses but remembered by their employees as tyrants. They are brilliant, abrasive, often egomaniacal. They bend industries to their will, build empires that reshape our daily lives, and inspire both fear and devotion.

Think Steve Jobs, the mercurial Apple co-founder whose perfectionism redefined computing and design but left scars on those closest to him. Elon Musk, the volatile autocrat who pushes employees and entire industries past breaking points in pursuit of his messianic vision. Marissa Mayer, once Silicon Valley’s golden child, later criticized for her exacting, often unforgiving leadership at Yahoo. Or Terry Gou, the iron-fisted founder of Foxconn, who turned it into the world’s largest electronics manufacturer; but at great human cost.

Let’s call them what they are: Visionary Tyrants.

But why? Why, despite their ruthlessness and abrasive style, do people follow and celebrate them?

The Landscape That Made Them

What’s interesting to note is that these leaders did not rise in a vacuum. I believe their success was turbocharged by structural forces that rewarded speed, domination, and audacity:

• Economics of tech platforms: Winner-takes-all dynamics, network effects, and zero marginal cost of scaling amplified the spoils for those ruthless enough to seize them.

• Globalization and deregulation: From the 1990s through 2010s, capital, supply chains, and consumer markets opened at unprecedented speed. A founder in California could dominate users in Jakarta, Paris, or São Paulo without much friction.

• Geopolitical permissiveness: Governments often partnered with or subsidized these figures, desperate to harness their ambition for national gain (EV credits for Tesla, space contracts for SpaceX, export-driven incentives for Foxconn).

In short: the abrasive archetype thrived because the system rewarded ruthlessness!

Why We Follow Them Anyway

Even when the costs are obvious (burnout, toxic workplaces, ethical blind spots, societal risks) these leaders still attract legions of followers. Why?

• The Mission: They frame their work as existential. Apple wasn’t just building computers; it was “changing the world.” Musk isn’t just selling cars or building rockets; he’s “saving humanity”

• Proximity to Greatness: Careers are made by working in their orbit. Being yelled at by Jobs or Musk is still a résumé badge… (yes!)

• Charisma and Fear: They inspire devotion while keeping teams in line through intimidation or sheer gravitational pull. Some must like it…

• Cultural Memory: We glorify shock and disturbance. Abrasive leaders dominate headlines precisely because they disrupt norms.

And sadly and above all: well, the successful ones make money. A lot of it. For themselves. For shareholders. For employees (sometimes).

And being rich makes them even more powerful and entitled.

But still, I kept wondering: do they truly succeed more often? Or do we simply remember them more vividly because of the drama they generate?

The Counter-Archetype: Principled Visionaries

History (and today’s boardrooms) also give us another path. Leaders who achieve greatness without resorting to tyranny. They prove that you don’t have to be an “a**hole” to innovate at scale (excuse my French)!:

• Satya Nadella (Microsoft): Transformed Microsoft’s culture from combative to collaborative, rebuilt trust with developers, and turned Azure into a global powerhouse; not through fear, but through empathy.

• Melanie Perkins (Canva): Scaled Canva into a $40B design platform by prioritizing inclusivity, humility, and accessibility. A principled builder in a zero-to-one success story.

• Meredith Whittaker (Signal Foundation): Left Google on principle, now leading Signal as a nonprofit, privacy-first alternative to surveillance capitalism. Shows trust can be a differentiator in the digital economy.

• Reid Hoffman (LinkedIn): Built LinkedIn with a mission of economic opportunity, known for collaborative leadership and mentorship.

Let’s call them Principled Visionaries.

They may not dominate headlines as often, but they demonstrate that another way is possible!

Subscribe now

Side by Side

Now let’s compare them side by side.

Visionary Tyrants are usually marked by egomania, perfectionism, or volatility. They lead through fear mixed with charisma, often building cults of personality around themselves. Their motivation tends to be domination, empire, or personal legacy. And their organizations often scale fast but end up fragile; deeply dependent on the founder’s will, and prone to toxicity or collapse when that will falters.

By contrast, Principled Visionaries tend to lead with empathy, inclusivity, and collaboration. Their style is rooted in trust, empowerment, and building culture first rather than enforcing loyalty. Their motivation is usually mission or purpose; creating legitimacy, serving a collective good, or stewarding an idea larger than themselves. Their organizations may grow more steadily, but they are more resilient and less dependent on one individual.

Geopolitically, the tyrant archetype thrived in the era of globalization and deregulation, when speed, domination, and borderless expansion were rewarded above all else.

The legacies are different too. Visionary Tyrants are remembered as disruptive and divisive: admired for their brilliance, feared for their cruelty. Principled Visionaries are often less loudly celebrated, but they are respected for balancing impact with principle and for building organizations that endure.

In today’s world, dominated by strongman politics and moving away from consensus based, multi-lateral relationships; it might be hard to see a path forward for Principled Leaders to succeed…

But could they be better suited in today’s sovereignty-focused, trust-conscious environment, where legitimacy and responsibility increasingly matter for long-term survival?

The Final Word

Visionary Tyrants have built extraordinary things; the devices in our pockets, the cars on our roads, the infrastructure of the digital economy. But they have also externalized enormous costs: toxic cultures, shattered lives, weakened democracies, and supply chains stained by labor abuses.

Principled Visionaries prove another way exists. Their path is harder, slower, less celebrated; but perhaps more sustainable, and more compatible with a world that increasingly values trust, sovereignty, and legitimacy.

Unfortunately the perception might just be that Visional Tyrants can help others make more money, regardless of the costs. And that’s what makes them stand out; and be remembered. But as per above, Principled Leaders can to!

But more importantly the final question is not about Jobs or Musk, Perkins or Nadella.

It’s about us: What kind of leaders are we willing to follow and what kind of leaders are we willing to become?

thanks for reading!

Damien

Leave a comment

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK(publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

Patrick McGee’s Apple in China is much more than the story of Apple. It is the story of globalization’s rise and unraveling.

Reading it through my technopolitical lens, three themes really stand out: cultural blindness, structural dependency, and the geopolitical reckoning now underway.

Subscribe now

1. California hubris, global consequences

Apple’s story is deeply California-centric: innovation as a West Coast export, the rest of the world as a production line. China was initially treated as a means to an end rather than a market in its own right, before being rebranded as the indispensable partner.

That cultural asymmetry echoes what I experienced myself while living in Canada when a US colleague would ask me what time it is in Toronto while sitting in New York... (same dah!)... or why is the Canadian Thanksgiving not on the same day as the US one... (I'll let you find this one out!). Now I live in Singapore and they think I live in China... sigh..

Specifically the story about the first Apple Store opening in China is very telling: the lack of interest and genuine lack of cultural awareness are absolutely mind boggling.

But the bottom line is that Apple’s relentless design and innovation were only possible because others absorbed the costs: Chinese labor, often under brutal conditions; families broken by the factory system; even child labor.

In short: Apple captured the margin, China captured the know-how or like McGee quoted from an Apple’s employee: "We've trained a whole country, and now that country is using it against us".

I find it incredible that most Chinese suppliers were willing to take Apple's orders at cost or with very little margin because they saw value in the learning. The proof: Apple is credited to have trained close to 28M works in China since 2008 ("more people than the entire labor force of California")!

This dynamic has produced unintended consequences: Apple trained its future competitors. Like Tesla today, Apple’s reliance on Chinese suppliers not only created its products but also seeded national champions capable of turning the tables; now a threat to US industrial independence.

Subscribe now

2. Market logic becomes national power

What really struck me is how incremental business decisions - each rational in isolation - coalesced into such a fragile dependency. In particular, once the iPhone became the company’s exponential growth engine, scale, customization, and cost pressure narrowed Apple’s options. Only China could deliver.

In fact, to make it happen Apple has invested a total of $55 billion per year. Consider “America’s spending $13.3 billion over four years to spur post–World War II development in sixteen European countries—or $131 billion in 2016 dollars.” highlights McGee.

Similar to Chris Miller's Chip War demonstrating the emergence of fab-less chip companies who would focus on the design while production was outsourced to contract manufacturers in Asia. The incredible scale and machinery cost needed to make such business profitable meant to decouple design and manufacturing, the same way Apple "Designs in California" and "Manufactures in China".

In fairness it was a context of active globalisation and enthusiasm; trying to seed democracy in China via trade. It is quite clear from Apple's engineers quoted in the book; that never ever were geopolitics a consideration in these decisions.

Even today, most people consider technology and geopolitics independently: I am a firm believer this is changing and companies entire software stack is at risk of geopolitical interferences (not just supply chains, but rather software supply chain).

However it will now become a textbook example of what I would describe as weaponized interdependence : when the pursuit of efficiency morphs into geopolitical vulnerability. Market forces alone built a formidable competitor: not just Huawei or Xiaomi, but the Chinese state itself, armed with industrial policy and long-term planning (Made in China 2025, dual circulation).

Trump’s sanctions on Huawei temporarily shielded Apple, but also pushed Beijing to accelerate its quest for self-reliance .

Short-term political cycles in Washington met disciplined industrial strategy in Beijing. And it's now ripe for collision.

3. Apple’s narrowing strategic horizon

Today, Apple’s empire rests overwhelmingly on one product and one geography: in fiscal year 2024 iPhone accounted for 51% of Apple’s total revenue and China alone about 17%.

With no “next iPhone” in sight, margins have been propped up, not volumes expanded. App Store and services business, once a diversification play, are under antitrust fire.

“In 2016, when iPhone margins were 33 percent, Chinese rivals Oppo, Vivo, and Xiaomi were earning 7 percent, 6 percent, and 2 percent margins, respectively”. What McGee calls the “Apple Squeeze”.

Prospective scenarios I can think of:

• Forced exit from China: Apple would have to embrace roboticized “dark factories” to keep costs low; reducing design complexity and ceding the innovation edge to local firms.

• Dual footprint: iPhone production remains in China, but new categories (wearables, spatial computing) shift elsewhere; if they ever achieve scale.

• Stagnation: Without a new killer product, Apple shrinks into a high-margin but narrow business; submerged by high-end Chinese competitive products.

Attempts to shift to India highlight the challenge: most operations there remain limited to final assembly, test, and pack, with components flown in from China and overseen by Taiwanese firms like Foxconn and Wistron. India is ramping production at barely a tenth of the speed China achieved a decade earlier.

Apple’s cash on hand was $162.3 billion as of March 30, 2024 (Mottley Fool).

But in each case, Apple’s war chest buys time, but not immunity...

4. The toll on people

It's also interesting to note how little has been written about the incredible toll on people Apple had: its own teams because of the pressure to push the boundaries and the trips to China; and of course the millions of Chinese workers themselves.

As the Chinese scholar Qin Hui observed, the country’s competitiveness was built on “low wages, low welfare, and low human rights.”

Apple’s success in China was not only built on design ingenuity and scale, but also on the invisible sacrifices of millions of workers.

Apple’s brand equity, secretive culture and tight control of the narrative though has been so powerful that most of us have been looking elsewhere: exciting products, great shopping experience, friendly customer service, great profits and stock prices!

Behind every sleek iPhone was a system of labor that demanded relentless discipline: young migrants leaving their families behind to live in regimented dormitories, working exhausting shifts to meet Cupertino’s unforgiving production schedules.

The Foxconn suicides in the early 2010s briefly exposed this hidden cost to the world, but they were only the most visible symptom of a deeper malaise. Divorce, broken families, and even child labor became structural features of a model where entire provinces were reorganized around the needs of Apple’s supply chain.

California captured the profit and prestige, while China absorbed the pain.

This asymmetry turned the iPhone into both a global object of aspiration and a symbol of inequality.

Today, as wages rise and labor conditions tighten in China, Apple struggles to replicate this model elsewhere. The human cost was never incidental; it was the very foundation of Apple’s scale. And that makes it Apple’s most fragile legacy.

5. The larger technopolitics canvas

McGee’s account reflects a broader geopolitical shift. What once looked like benign globalization now appears as strategic naivety.

By 2022, Apple was even partnering with YMTC, a state-backed memory-chip maker central to Beijing’s Made in China 2025 plan, despite Washington considering the firm for blacklisting.

Warren Buffett (Apple’s largest shareholder) cut his stake in both TSMC and Apple citing these geopolitical risks. These examples underline just how hard it will be for Apple to chart an independent course.

Looking at the key power-techno-blocks:

• United States is weaponizing technology and finance (CHIPS Act, Cloud Act, export controls) .

• China leverages scale, state capitalism, and "Chūhǎi" (出海) (a term that literally means "going out to sea,") refers to expansion into Southeast Asia and the Global South, where its firms are no longer just subcontractors but system-builders; they offer both scale and capabilities.

• Europe risks becoming a “digital referee” rather than a player, regulating Big Tech while failing to build alternatives. Initiatives like EuroStack are late, but necessary if Europe wants to escape total dependency.

• Global South becomes the real battleground: markets like Southeast Asia, Africa, and Latin America are the new laboratories where US and Chinese models compete, often leaving little room for local sovereignty.

6. Looking ahead

The Apple story crystallizes a deeper paradox: the same interdependence that made globalization efficient has become a vulnerability in an era of multipolar rivalry. If the 2000s were about cost efficiency, the 2020s and 2030s are about resilience, redundancy, and sovereignty in a fragmented world.

The question is not just whether Apple can reinvent itself, but whether Western democracies can sustain innovation without reproducing the dependencies that hollowed out their strategic autonomy.

Final words

My take: McGee’s book is compelling because it is not only about Apple. It is about the unintended geopolitics of capitalism and the short lived democratic cycles in the background, leaving little place for long term execution.

The company’s dependence on China is the mirror of the West’s dependence on authoritarian efficiency. What struck me most is how quickly “rational” business choices add up to geopolitical weakness.

The lesson for leaders today: don’t confuse market logic with strategic security.

Decisions about supply chains, data, or AI training are no longer just operational; they are technopolitical choices that shape sovereignty, resilience, and power.

Checkout McGee’s website for more info: https://patrick-mcgee.com

Questions? Comments? I’d love to hear from you:

Leave a comment

Thanks for reading!

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK(publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

This essay explores how AI-powered algorithms and digital platforms now shape the narratives that define reputation, legitimacy, and influence. Are these systems ideologically and politically neutral? Or are they optimized to amplify clicks, revenue, outrage, reinforce ideology, and manipulate public sentiment?

If truth is no longer singular, but fractured across perspectives, platforms, and algorithms, how are AI, media systems, and data governance fragmenting authority and stability?

Importantly, why corporate leaders should care and what can they do about it?

You’ve built crisis protocols. You’ve hired cybersecurity experts. You’ve aligned your ESG narrative and digital strategy.

But what if none of that matters when a coordinated online campaign mixing truth, distortion, and emotional resonance reframes your company as a villain before you even know you’re under attack?

What if your stakeholders (customers, regulators, employees, investors) each see a different version of your story, and none of them are under your control?

What happens when your narrative infrastructure becomes your weakest link?

What just happened?

On the morning of December 4th, 2024, in Châtillon near Paris, Nicolas Maes and his leadership team received confirmation of a disaster in the making. Maes had been at the helm of Orano, France’s flagship nuclear company since November 2023.

The Nigerien state had taken operational control of the SOMAÏR uranium mine in Arlit. No new law had been passed, no international ruling issued. Just a quiet replacement of personnel, a cutoff of access, and a break in the line of operational control. The company’s official press release was sober: “decisions previously made by Orano are no longer being respected” [source].

To anyone unfamiliar with Orano’s presence in Niger, the news might have seemed like a bureaucratic hiccup. But in reality, this marked the culmination of a months-long narrative assault. Orano had operated in Niger since 1971, long before its 2017 restructuring from Areva. The SOMAÏR mine was one of the country’s largest industrial employers, and uranium from Niger remained critical to France’s nuclear energy program [source].

Yet this wasn’t a dispute over mining rights. It was something more difficult to confront: a loss of legitimacy, engineered through perception.

The trouble began in the summer of 2023. On July 26th, Niger’s elected president, Mohamed Bazoum, was ousted by General Abdourahamane Tchiani in a military coup [source]. The junta quickly pivoted Niger’s foreign policy. France was cast as a neo-colonial force exploiting Africa’s natural resources. And Orano, with its deep uranium footprint, became the perfect symbol of that narrative.

But the junta didn’t act alone.

Almost immediately, Russian-backed information networks began circulating anti-French, anti-Orano messages. Central to this effort was a loosely affiliated set of pro-Russian syndicates, most notably the SDA (Social Design Agency). This group, amplified by bot networks, Telegram channels, and pan-African influencers, launched a coordinated campaign accusing France of economic imperialism and Orano of “illegal” and “irresponsible” uranium extraction [source].

SDA-aligned messaging framed the junta’s moves as heroic acts of sovereignty. They praised Niger’s resource nationalism while simultaneously promoting Russia as a liberator and economic partner. Nigerien Mining Minister Colonel Ousmane Abarchi gave repeated interviews to Russian outlets like RIA Novosti, accusing Orano of disrespecting Niger’s sovereignty and mismanaging its resources—statements that were replayed and remixed across TikTok, Facebook, and Telegram [source, source].

By April 2024, SDA channels were actively promoting offline demonstrations outside uranium facilities, linking imagery of small, peaceful protests with highly charged narratives about neocolonial oppression. The videos were cut to resonate emotionally: mothers holding signs, miners waving the Nigerien flag, slow-motion pans over mine gates with music invoking pan-African liberation. None of it was illegal. All of it was effective [source].

Inside Orano, the warning signs were visible by October 2024. The company announced that SOMAÏR operations would be suspended due to financial strain, a decision triggered by disrupted logistics and uncertain authorizations [source].

But the narrative outside the company had already shifted. In the digital sphere, Orano had already been tried and convicted. By the time security forces took control of the site in early December, most Nigeriens saw it as overdue justice.

Six months later, on June 19, 2025, General Tchiani made it official: the SOMAÏR mine was nationalized [source]. The assets were transferred to the state. Russia’s state-linked media and affiliated analysts praised the decision as a “turning point for African sovereignty.” And within weeks, Niger opened its doors to Rosatom and other Russian uranium investors [source].

Orano, once confident in its contracts and financials, had filed for international arbitration, but the uranium was gone. The site was operational under new authority. The French state’s diplomatic reaction was muted.

The story had already been written and it wasn’t theirs.

This was not a legal defeat. It was a loss of control on the narrative. Orano’s assets weren’t lost in courtrooms or boardrooms. They were lost in the comment sections, the Telegram feeds, the influencer circles, and the subtle interplay between physical demonstration and digital disinformation. Orano never truly entered that arena. And by the time it realized the rules had changed, its presence in Niger had already been redefined by others.

What happened to Orano wasn’t a one-off. It was a narrative warfare. One that can (and will) be applied to other companies, in other sectors, across other geographies.

In a world where perception is programmable and algorithms are weaponized, narrative (not fact) is becoming the terrain on which geopolitical and corporate power is contested.

Orano didn’t lose control of SOMAÏR because it failed operationally. It lost because someone else told a better story first.

Subscribe now

Your Crisis Playbook Is Obsolete

In today’s chaotic media landscape, corporate leaders are not just brand stewards: they’re narrative system architects.

Originally coined to describe the rise of populist movements, the concept of quantum politics refers to a world where truth is fragmented, emotions override logic, and every audience sees a different version of reality. It’s no longer confined to politics. It now governs boardrooms, earnings calls, press releases, Slack threads, and TikTok feeds.

The term “quantum politics” has gained traction in recent years (notably with Giuliano da Empoli’s book The Engineers of Chaos - 2019, LATTES), but its intellectual roots go back to political scientist Theodore Lewis Becker, who in the early 1990s argued that the Newtonian assumptions of political science (predictability, rationality, linear causality) no longer matched the messiness of real-world politics. In his book Quantum Politics: Applying Quantum Theory to Political Phenomena, Becker proposed a new metaphorical model: one where uncertainty, contradiction, and emotional dynamics are not anomalies: they are the system.

His view? Just as quantum physics replaced Newtonian mechanics, political and social understanding must evolve beyond mechanical models to account for ambiguity, entanglement, and participatory observation.

Every brand is now entangled in a media ecosystem where:

• Truth is not fixed but probabilistic

• Stakeholders interpret through emotional filters

• Narratives spread faster than facts

This is no longer business-as-usual communication. It’s communication in a quantum system.

From Newton to Quantum: The Communication Shift That Changed Everything

Becker’s critique of Newtonian political thought - rooted in Enlightenment values of objectivity, rationality, and causality - applies equally to corporate communication. Businesses too have long operated as if messaging could be linear, audiences rational, and perception managed through control.

But just as Becker described in his quantum framework, stakeholders now operate in a probabilistic environment, where messages can hold multiple meanings, emotional response trumps logic, and the very act of communication alters the behavior it tries to shape.

In the Newtonian era of corporate comms:

• Messages were rational, linear, and fact-driven.

• Control was central.

• Press releases shaped perception.

Quantum communication recognizes:

• Messages are multivalent

• Emotion overrides logic

• Control is distributed across platforms and code

You cannot fight modern disinformation or narrative attacks with legacy PR tactics. You need to accept that your message will be reinterpreted, atomized, and weaponized.

A few eye opening facts below show the extend of the challenge:

• Only 31% of U.S. adults report at least "a fair amount" of confidence in mass media, while 36% express no trust at all (Gallup 2024).

• Misinformation and “truth fragmentation” are ranked among the top global risks (WEF Global Risks Report, 2019).

• More than half (54%) of people get news from social media platforms like Facebook, X and YouTube - overtaking TV (50%) and news sites and apps (48%), (Reuters Institute)

• False tweets are 70% more likely to be reshared than truthful ones (MIT study)

• Social media users are nearly twice as likely to share negative headlines (Nature)

• Over half of the top 100 TikToks on mental health contain misinformation (Guardian)

In this reality, brand perception is volatile, and your message must function across highly personalized and emotional ecosystems.

The Enlightenment gave us truth as a shared destination. Today, we must learn to navigate truth as a shifting terrain where trust is the compass, and narratives are the terrain. This is the age of Quantum Narratives.

As Shoshana Zuboff explains in The Age of Surveillance Capitalism, today’s media systems don’t just deliver content, they extract behavioral data, shape emotional states, and engineer attention:

“The game has shifted from using data to serve customers to using customers to serve data.”

In this environment, companies no longer fully control their narrative: they are participants in a system that prioritizes virality over truth, engagement over nuance.

Corporate messaging is co-constructed with algorithms, reactions, and micro-targeted emotional triggers.

This isn't communication as we know it anymore: It's quantum entanglement!

The Technopolitical Engine of Narrative Control

This is where technopolitics becomes central: platforms don’t just carry content. They carry ideology. They don’t just mediate information, they structure power. Platforms now act as extensions of state influence, ideological filters, and market shapers all at once.

Drawing on Asma Mhalla’s framing of Total Technology in her book Technopolitique (2024) and subsequent interviews (Philonomist, Le Monde), we understand Big Tech as the new geopolitical actor:

“The relationship between major platforms and states has become organic. Large technology companies now act as forward-operating arms, executing the geopolitical objectives of the states they are tied to. They provide the technical infrastructure, computational power, and surveillance capacity, while the state grants them legitimacy, legal immunity, and regulatory protection.”

— Asma Mhalla, Technopolitique (2024)

As well as:

“This pact between Big Tech and Big State constitutes a form of hybrid sovereignty. Major decisions are no longer made in democratic arenas but within technical architectures, under the guise of neutrality. This is not a drift—it is an explicit political project.”

— Asma Mhalla, Technopolitique (2024)

So, your infrastructure is now political. And the narrative field is a battlefield.

Subscribe now

Large Language Models as the New Narrative Machines

LLMs act as probabilistic narrative engines, collapsing multiple possible worldviews into a single answer path, biased by their training corpus and cultural center of gravity as research shows (see “further reading”).

Cultural bias and alignment

Most models, regardless of their origin, aligned closely with cultural values characteristic of English-speaking and Protestant European countries. Chinese LLMs were expected to reflect Confucian values (e.g. collectivism, authority), but did not — instead, they showed a bias toward individual autonomy, tolerance, and environmentalism, typically Western liberal values.

Even LLMs trained on “uncensored” datasets (e.g. Dolphin models) showed cultural convergence toward Western individualist values. Probably not very surprising knowing 49.7% of the websites are in English, while English speaking people represent 4.7% of the world population…

"Rather than adapting to cultural plurality, these LLMs tend to default to a normative Western framework, particularly shaped by U.S. liberal norms."

— Columbia University, Cultural Fidelity in LLMs (2024)

600 parallel translation examples from 10 languages, GPT-4 gave culturally inappropriate or inaccurate translations up to 27.8% of the time. For example, translating culturally-specific idioms (e.g., African proverbs) into English often resulted in loss of meaning or Westernized reinterpretation.

“GenAI has transformed how language is produced and transmitted: there is high risk that we will over time propagate Western cultural values and reasoning more so than others; and erode of cultural diversity in digital communication is a threat to diversity of thought”

— Shav Vimalendiran (2024)

So at minimum, the above shows a risk of cognitive uniformisation and a loss of rich historical and cultural nuances.

Encoding Ideology into LLMs

“Truth” in LLMs is shaped by presence in training corpora, not ground reality: it’s a quantum narrative distortion. One of the strongest causal links to digital asymmetry shaping cultural fidelity in LLMs.

Musk said the upcoming Grok 3.5 model will have “advanced reasoning” and wanted it to be used “to rewrite the entire corpus of human knowledge, adding missing information and deleting errors.”

But who gets top decide what’s right or wrong? what’s good or bad?

Generative AI becomes a vector of digital assimilation: values are encoded into code, not debated in context.

If so, are LLMs colonial instruments? This is where technopolitics meets cognitive colonization.

Cognitive Colonialism

This is not colonialism in the classical sense. But it is a form of digital standardization: an algorithmic flattening of moral, cultural, and cognitive diversity in favor of a default.

This is why we must ask ourselves:

Are LLMs becoming ideological infrastructures promoting culture, not just computation?

If yes, the implications are not just technical. They are civilizational.

As Asma Mhalla writes in Technopolitique:

“The technologies they [BigTech] bring to market are not ideologically neutral… The way a technological foundation is designed, conceptualized, and governed; the datasets injected, the technical, ethical, or moral filters applied; all encode intrinsic biases and ideological positions.”

Further reading

These papers reveal that LLMs disproportionately reflect Western, liberal, individualist values—regardless of model origin (even Chinese models). This is not accidental. It stems from:

• Training data availability (which is skewed toward high-resource, mostly Western languages)

• Biases in alignment tuning (mostly done in English, by U.S.-centric teams)

• Digital representation asymmetries (some cultures are absent or misrepresented in training data)

This entrenches a digital-cultural hierarchy—a form of algorithmic imperialism.

Inglehart-Welzel Cultural Map is a good visual to understand cultural and religions in relation to one another.

Cultural Bias and Cultural Alignment of Large Language Models (Yan Tao, Olga Viberg, Ryan S. Baker, René F. Kizilcec)

Cultural Fidelity in Large-Language Models: An Evaluation of Online Language Resources as a Driver of Model Performance in Value Representation (Sharif Kazemi, Gloria Gerhardt, Jonty Katz, Caroline Ida Kuria, Estelle Pan, Umang Prabhakar)

How Deep Is Representational Bias in LLMs? The Cases of Caste and Religion (Agrima Seth, Monojit Choudhary, Sunayana Sitaram, Kentaro Toyama, Aditya Vashistha, Kalika Bali)

Narrative Sovereignty is Now a Corporate Risk

One thing the above has shown is that platforms (information conduits) and AI models (filters, amplifiers and now generators of information) are not neutral.

But what does it mean for corporations and business leaders?

I believe what starts as data imbalance becomes in fact a corporate liability when scaled across products, services, and communication.

Most companies (and governments!) don’t own their narrative engine. They are being disintermediated by the big platforms and the proprietary AI running on them.

Narrative attacks now affect the entire enterprise value chain, not just perception, but operations, partnerships, licensing, market access, and even capital flow.

Business Risks include:

• M&A disruption: valuation collapse via narrative manipulation

• Regulatory pressure: laws shaped by viral framing

• Competitive asymmetry: rivals aligned with state actors controlling the story field

• Talent erosion: employees influenced by cultural counter-narratives

If you don’t control your narrative environment, you can lose deals, licenses, markets, and trust, even when you’ve done nothing wrong.

Marketing Drift: Multinational brands using GenAI to localize content at scale risk producing tone-deaf, misaligned, or offensive outputs. Western-coded assumptions about gender, autonomy, humor, or social norms may alienate local audiences, reinforce stereotypes, or trigger public backlash.

AI agents: They just don’t understand the World they are in. AI agents or copilots, designed to automate workflows or engage with customers, often operate with an implicit Anglo-American frame which may be inappropriate or even dangerous in contexts like:

• Regulatory compliance (e.g. data sharing norms in MENA or APAC)

• Political neutrality (e.g. chatbot responses about Hong Kong or Gaza)

• Cultural taboos (e.g. references to religion, gender, sexuality)

LLMs do not just answer: they answer in ways that belong somewhere. And that somewhere is almost always the Global North.

Ethical & Legal Exposure: When autonomous systems make decisions based on encoded assumptions, the absence of local cultural alignment becomes an accountability minefield:

• Is the AI reflecting user intent or overriding it?

• Who owns the harm when the AI says the “wrong thing” in the “wrong place”?

The question every corporate leader must ask is: how do I regain control over my narrative?

Building Narrative Intelligence as a Strategic Function

I argue here that building narrative sovereignty is about understanding its dependencies and associated risks to regain control in strategic areas. And it’s not just a the job of Marketing or Corporate Comms to figure out '“the messaging”.

Companies need a dedicated capacity to:

• Detect disinformation threats

• Map narrative terrain across platforms

• Build counter-positioning playbooks

• Simulate crisis scenarios under information distortion

We need to treat narrative like cyber: an enterprise-wide risk with technical, legal, reputational and strategic dimensions.

In a world governed by predictive models, sovereignty is no longer only about infrastructure, compute, or data residency. It is about narrative alignment.

It is not enough for an AI system to be technically accurate. It must be culturally situated and politically accountable.

It’s a new muscle to be built: narrative sovereignty requires cognitive reflexivity.

Owning or controlling internal knowledge infrastructures is as strategic as financial assets otherwise, your corporate truth is mediated by someone else’s algorithms.

Subscribe now

Operationalizing the Response: R.I.S.Q.™ as a Strategic Framework

To help corporate leaders navigate this new terrain, I have created the R.I.S.Q.™ framework.

Reframe: Stop seeing this as a PR issue. It’s geopolitical and strategic.

Intent: Identify who benefits from distorting your story.

Secure: Protect both your digital infrastructure and your narrative infrastructure.

Question: Stress-test your assumptions: are your values and positions narratively defensible?

In short: This is not about better messaging; it’s about strategic resilience. R.I.S.Q.™ is your compass for navigating contested realities.

For more deep dives into the R.I.S.Q framework and a personalized assessment please get in touch with me.

Your 30 / 90 / 12 Month Playbook

My proposal on how to draw a media intelligence roadmap for building trust architectures:

• Narrative observability layers

• Disinformation detection as core compliance

• Probabilistic trust models (inspired by quantum logic)

• Media provenance & AI watermarking standards

In 30 Days:

• Conduct a narrative risk audit

• Brief leadership on exposure to technopolitical threats

In 90 Days:

• Set up cross-functional Narrative Ops taskforce

• Run a crisis simulation focused on AI-misfire or disinfo attack

In 12 Months:

• Embed narrative observability into all high-risk business lines

• Build capability to shape, defend, and recover narrative coherence at speed

Share

Final Call to Action

In his book Wasteland (2025), Robert Kaplan reminds us of Germany’s Weimar Republic as a place of brilliance and collapse where too many narratives competed, none took hold, and extremism rose in the vacuum. It led to the rise of Nazism and World Word II. That moment mirrors today’s information sphere: over-fragmentation breeds incoherence, and incoherence creates an opening for manipulation.

“When no narrative holds, the loudest one wins.”

As I wrote in Davos 2025: Leadership in Crisis, we are entering a late-phase cycle - economically, institutionally, and culturally. The old levers of power - debt, control, consensus - no longer function as they did.

In such times, leadership must be anchored in strategic clarity and principled action. Not only because it’s right - but because in a quantum world, coherence is survival.

As your future is being narrated in code, in models, in memes. The question is not whether you’ll be mentioned. It’s whether your version of the story will stick. If you want to lead, you need to reclaim the pen!

In a quantum world, what’s true doesn’t always win. But what’s clear, coherent, and deeply human just might.

Build coherence. Defend pluralism. Rethink strategy.

Because in a world where trust is fragmented, narrative is power.

Thanks for reading!

Damien

I am a Senior Technology Advisor who works at the intersection of AI, business transformation, and geopolitics through RebootUp (consulting) and KoncentriK (publication): what I call Technopolitics. I help leaders turn emerging tech into business impact while navigating today’s strategic and systemic risks. Get in touch to know more damien.kopp@rebootup.com

In the race to market new AI capabilities, terms like "Agentic AI" have emerged, promising autonomous decision-making systems that perceive their environments, set goals, and execute complex tasks independently.

You’ve probably heard the promises.

• “Agentic AI will run entire business functions without human oversight.”

• “Autonomous agent teams will replace most knowledge workers.”

• “You can have a ‘company of one’ — just you and your AI agents.”

• “With the right orchestration layer, you can automate end-to-end workflows at scale.”

• “Soon, these agents will handle all your decisions so you can focus purely on strategy.”

But most of these claims conflate what’s possible in demos with what’s reliable, economical, and controllable in the real world.

In this article, I’ll argue that what we have today isn’t autonomy at all: it’s orchestration. And while orchestration can be powerful, it comes with brittleness, cost overheads, and control issues that leaders need to confront before betting their business on it.

Corporate leaders need to clearly understand what today's Agentic AI actually is and it isn't.

So what’s the reality?

Three definitions to anchor the discussion

• Automation: deterministic scripts/workflows with predictable, repeatable outcomes.

• Orchestration: multi-tool coordination with human oversight; LLMs can help here as planners and glue code.

• Autonomy: task-level goals with open-ended action authority, where the system decides how to achieve the goal, without ongoing human checkpoints.

Most “agentic” platforms today are in the orchestration bucket. The problem is that marketing language blurs the distinction.

What AI Really Does

In my past article, The Illusion of Intelligence: Why LLMs Are Not the Thinking Machines We Hope For, I have explained the misconceptions we have about the true potential of Large Language Models, and the risks of anthropomorphism.

At the heart of today's hype around "Agentic AI" are Large Language Models (LLMs) like GPT-4, Claude, Gemini, and Llama.

These models, impressive as they are, fundamentally have no intrinsic intentions, motivations, or strategic goals. Despite sophisticated outputs that often seem intentional or even thoughtful, LLMs merely predict the next best token based on statistical inference from vast datasets.

Gary Marcus, a leading AI researcher from NYU, succinctly describes this reality:

"LLMs have no goals, only statistical prediction of next tokens."

Similarly, Yann LeCun, Chief AI Scientist at Meta, underscores the lack of genuine decision-making:

"Current LLMs do not 'think' or 'decide'—they merely generate plausible continuations based on learned statistical patterns."

Orchestration ≠ Autonomy

In the research lineage — from ReAct prompting to Toolformer — agents interleave “think-and-act” steps, call external tools, and adjust based on observations. That’s orchestration: the human remains the decision anchor.

Autonomy, by contrast, means the system can reliably plan and execute multi-step decisions without continuous oversight, meeting determinism, traceability, and safety expectations comparable to traditional software. Even recent surveys (see Agent Systems: Survey and Benchmarking) conclude that reliability, hallucination control, and long-horizon planning remain unsolved.

Critics will say most roadmaps already target bounded autonomy with guardrails. True, but that proves my point: the science and governance assumptions still centre on human-in-the-loop orchestration.

The Cost Reality: LLMs aren’t Free

Agent frameworks multiply token usage: every plan → tool call → observe → revise loop incurs more context and more generations. Pricing from leading providers looks modest per million tokens, until you measure full workflows.

• OpenAI lists per-token pricing for GPT-4.1/4o families, with overhead for tool-use endpoints.

• Anthropic Claude 3.5 Sonnet is $3 per million input tokens / $15 per million output tokens.

A multi-agent flow doing 12 tool-augmented turns at ~80k tokens per turn can easily cross 1–2M tokens per task. Even at $5–$10 blended per million tokens, that’s $5–$20+ per task before retrieval, evals, or guardrails.

At scale, it’s six-figure monthly spend, often for steps a deterministic API could do for a few cents.

Yes, costs are falling (prompt caching, small language models like Phi-3 on-device, batch APIs). But when you add the reliability scaffolding; evaluation harnesses, human review, governance logging: the unit economics can still flip.

The Dependency Problem: Who Really Controls Your “Autonomous” Agents?

Even if your orchestration is flawless and cost-efficient, you don’t own the engine.

Frontier LLMs (OpenAI, Anthropic, Google DeepMind, Meta, Cohere) are opaque:

• Architecture: closed or partially disclosed; key design and training parameters are not public.

• Training data: undisclosed; choices about inclusion/exclusion/censorship directly shape outputs.

• Governance logic: refusal policies, safety filters, and moderation behaviour are hard-coded and updated without notice.

• Version control: older models are deprecated on short timelines (e.g., GPT-3.5, GPT-4.0 retirements), creating regressions in production workflows.

• Jurisdictional exposure: providers are subject to their home country’s laws, export controls, and subpoenas.

This is model dependency: a handful of companies decide what’s “safe,” “true,” and “possible” for millions of downstream applications. Whether intended or not, their worldview and risk calculus become embedded in your automation.

From a technopolitics lens (where geopolitics and technology meet), this is a sovereignty issue. If your agent’s “brain” is outsourced to an opaque third-party model, your autonomy is an illusion.

For deeper context, see my Technopolitics series on AI sovereignty covering model control and jurisdictional risk.

Are LLMs the Right Tech for Process Automation anyway?

Three inconvenient facts:

• They’re probabilistic, not fully deterministic. Low temperature and seed settings reduce variance but don’t guarantee identical outputs across environments (OpenAI docs).

• Sequential reasoning is brittle. Studies like BOLAA: Benchmarking LLM Agents show performance drops with longer horizons or distribution shift.

• Governance frameworks assume oversight. The NIST AI RMF and the EU AI Act both expect documentation, risk controls, and human accountability — assumptions aligned with orchestration, not unsupervised autonomy.

Where variance tolerance is high (ideation, drafting), LLMs can lead. Where variance must be near zero (claims adjudication, regulated comms), put deterministic systems at the core and LLMs at the edges.

Five Founder-Style Claims Stress-Tested

Back to the claims I started with earlier about Agentic AI and my counter arguments:

“Agentic teams will replace most knowledge workers.”

Controlled studies (NBER customer support RCT) show ~14% productivity gains, bigger for novices; expert gains are smaller. Augmentation ≠ replacement.

“A company of one is feasible soon.”

Reliability, safety, and governance requirements (see EU AI Act GPAI obligations) make it impractical for high-stakes autonomy without a team.

“LLM orchestration is the most cost-effective path.”

True for ambiguous, variable tasks; false for steady-state workflows where rules engines or classical ML can outperform in cost and reliability.

“LLMs fit process automation despite being probabilistic.”

Only where variance is acceptable. In customer-facing regulated contexts, liability can land on you — see Air Canada chatbot ruling.

“Fully autonomous coding is basically solved.”

Benchmarks like SWE-bench Verified show progress (e.g., Devin at ~13.9% on subsets), but far from reliable E2E autonomy in real repos.

Industry Reality Checks: It’s Not About “Replacing Jobs”

Now, perhaps the most compelling argument about what Agentic AI is all about (and isn’t).

You’ve probably heard the breathless claims: “Consulting is dead.” “We don’t need software developers anymore.” “AI can do my designs.”

These arguments confuse jobs with tasks. AI excels at automating discrete, well-defined tasks; jobs are bundles of skills, context, relationships, and judgement, and those remain stubbornly human.

Consulting — Yes, AI can speed up research synthesis and slide production. The BCG field experiment shows real gains in templated work. But consulting is not just producing analysis decks; it’s about building trust with clients, managing complex change, navigating politics, and sometimes serving as the external catalyst that makes uncomfortable truths actionable.

AI can hand you the facts; it can’t read the room, win hearts, or manage egos!

Software Development — Tools like GitHub Copilot can make developers ~56% faster on certain coding tasks. But software development is not simply typing code; it’s architecting systems, weighing trade-offs, securing against adversaries, integrating with messy legacy environments, and negotiating requirements between business and technical stakeholders.

AI can scaffold a function; it can’t take ownership for the long-term maintainability, reliability, and compliance of a production system!

Creative/Marketing — Generative pipelines like WPP–NVIDIA and Adobe Firefly can churn out content variants at scale. But design and marketing aren’t just about making more images or headlines; they’re about understanding user desires, brand positioning, cultural nuance, and emotional resonance.

AI can fill the canvas; it can’t walk in your customer’s shoes or shape a narrative that builds lasting loyalty!

The through-line: Agentic AI can take the “busywork” out of many professions: research, drafting, code scaffolding, asset production.

But the job is far more than the sum of its automatable parts. Strip out the relational, strategic, and contextual layers, and you no longer have the job; you have a list of tasks.

Governance and Regulation Are Not Optional

Agentic AI isn’t just a technical challenge: it’s a geopolitical and regulatory minefield. Ignoring governance isn’t an option; where and how you deploy agentic systems will increasingly depend on which regulatory regime applies: EU, US, China, … or?

For exemple the EU AI Act’s GPAI obligations kicked in on 2 August 2025, including transparency, documentation, and training data disclosures for model providers. The NIST AI RMF offers parallel guidance for measurement, management, and governance. Neither removes vendor lock-in risk; both make orchestration with oversight the safe baseline.

The Risks of Misrepresenting AI Capabilities

Misleading claims about autonomy create serious business risks. Companies that misunderstand AI's true capabilities can:

• Expose themselves to ethical and regulatory liabilities,

• Overestimate AI reliability, leading to critical operational failures,

• Develop unrealistic expectations, causing expensive disappointments.

The danger of anthropomorphizing AI, assuming it has human-like intentions or decision-making powers, cannot be overstated.

This anthropomorphism is reinforced by hype-driven marketing narratives, blurring the line between reality and fiction.

A Practical Playbook for Leaders

For corporate leaders, clarity is essential. Practical and strategic recommendations include:

• Avoid treating AI outputs as decisions: Always apply human oversight, especially for ethical or high-stakes strategic scenarios.

• Leverage AI’s strengths clearly: Focus AI use on accelerating analysis, automating routine tasks, and providing recommendations; not autonomous decision-making.

• Build explicit human-AI workflows: Combine AI-generated insights with structured human judgment processes to ensure clarity, accountability, and reliability.

• Stay critically informed: Continuously differentiate authoritative research from marketing claims, and ground AI strategy firmly in proven capabilities.The Bottom Line

Design principles

• Human-in-the-loop by default: define who approves what and when.

• Deterministic core, probabilistic edges: APIs and rules at the centre; LLMs for enrichment and exception handling.

• Short, inspectable loops: shallow chains with observability beat deep opaque autonomy.

• Total cost accounting: track per-completed-task costs including retrieval, evals, guardrails.

Mitigations

• Schema-constrained outputs (e.g., OpenAI structured outputs), policy whitelists, verifier loops.

• Prompt caching, small/efficient models, and version pinning.

• Regression suites and an exit path to open-weights models (Meta Llama 3.1, Mistral, Qwen 2.5).

Where to deploy now (examples)

• Consulting research synthesis with analyst review.

• Code migration/test generation with guarded PRs.

• Marketing variant generation under brand guardrails.

Where to avoid for now

• High-stakes, low-variance decisions.

• Unsupervised customer interactions with legal or compensation risk.

Closing Provocation

Despite compelling narratives, today's AI remains far from genuinely autonomous, nor should it be!

LLMs and related technologies are impressive generation tools; not strategic decision-makers.

Understanding this distinction will protect your organization from missteps and enable you to harness AI’s true potential without falling for dangerous illusions.

We don’t need “autonomous companies”; we need accountable companies. Build for reliability, observability, and governance now.

rue autonomy, if it should even be a goal itself at all, can wait until the science and the law say it’s safe.

Leaders who navigate this truth confidently can truly harness the power of AI without risking strategic blind spots.

Thanks for reading!

Was this post helpful? Please share with others!

Share

Questions? Comments? Feedbackl?

Leave a comment

Damien

🌀 𝘒𝘰𝘯𝘤𝘦𝘯𝘵𝘳𝘪𝘬 𝘪𝘴 𝘮𝘺 𝘤𝘰𝘮𝘮𝘪𝘵𝘮𝘦𝘯𝘵 𝘵𝘰 𝘤𝘶𝘵𝘵𝘪𝘯𝘨 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘵𝘦𝘤𝘩 𝘩𝘺𝘱𝘦, 𝘯𝘰𝘪𝘴𝘦 𝘢𝘯𝘥 𝘷𝘢𝘱𝘰𝘳𝘸𝘢𝘳𝘦 𝘵𝘰 𝘰𝘧𝘧𝘦𝘳 𝘤𝘭𝘦𝘢𝘳, 𝘰𝘣𝘫𝘦𝘤𝘵𝘪𝘷𝘦 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘭𝘦𝘢𝘥𝘦𝘳𝘴 𝘤𝘢𝘯 𝘢𝘤𝘵 𝘰𝘯.

Other articles you might find useful:

As AI hype accelerates and geopolitics grows more entangled with technology, I believe that understanding who controls what — and why — becomes essential.

This summer, here’s what I’m reading, re-reading, and recommending.

A mix of deep books, sharp essays, and brilliant voices that have informed my own work at 𝗞𝗼𝗻𝗰𝗲𝗻𝘁𝗿𝗶𝗞 — where we explore the power structures behind innovation, AI, and global infrastructure.

𝗠𝘂𝘀𝘁-𝗥𝗲𝗮𝗱 𝗕𝗼𝗼𝗸𝘀

• 𝘊𝘩𝘪𝘱 𝘞𝘢𝘳 by Chris Miller - A geopolitical page-turner on the chip industry’s past, present, and centrality in the next great power rivalry.

• 𝘈𝘱𝘱𝘭𝘦 𝘪𝘯 𝘊𝘩𝘪𝘯𝘢 by Patrick McGee - A sharp dissection of Apple’s dependency on China — and what it reveals about modern globalization.

• 𝘛𝘩𝘦 𝘌𝘮𝘱𝘪𝘳𝘦 𝘰𝘧 𝘈𝘐 by Karen Hao - The best long-read on AI geopolitics you’ll find — soft power, cloud infrastructure, and techno-hegemony.

• 𝘛𝘩𝘦 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴 𝘰𝘧 𝘊𝘩𝘢𝘰𝘴 by Giuliano da Empoli - A political technothriller on how spin doctors and social media strategists hacked modern democracies.

• 𝘛𝘦𝘤𝘩𝘯𝘰𝘱𝘰𝘭𝘪𝘵𝘪𝘲𝘶𝘦 by Asma MHALLA - A sharp, urgent diagnosis of how digital power reshapes democratic life — from infrastructures to ideologies.

• 𝘛𝘩𝘦 𝘏𝘰𝘶𝘴𝘦 𝘰𝘧 𝘏𝘶𝘢𝘸𝘦𝘪 by Eva Dou - How Huawei became a global telecom giant — and why it drew fire from the West.

• 𝘛𝘩𝘦 𝘞𝘢𝘴𝘵𝘦𝘭𝘢𝘯𝘥 by Robert D. Kaplan - A dark mirror of the post-industrial West — where economic decline feeds geopolitical drift.

• 𝘛𝘩𝘦 𝘈𝘨𝘦 𝘰𝘧 𝘚𝘶𝘳𝘷𝘦𝘪𝘭𝘭𝘢𝘯𝘤𝘦 𝘊𝘢𝘱𝘪𝘵𝘢𝘭𝘪𝘴𝘮 by Shoshana Zuboff - The definitive critique of how tech firms monetize behavior at scale — and reshape society.

• 𝘊𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘘𝘶𝘢𝘯𝘵𝘶𝘮 𝘊𝘢𝘵𝘴 by John Gribbin - Quantum physics meets next-gen computing. Accessible, mind-bending, and timely.

• 𝘠𝘰𝘶𝘳 𝘎𝘳𝘢𝘴𝘴 𝘪𝘴 𝘎𝘳𝘦𝘦𝘯𝘦𝘳 by Jason Silver - A more personal, perspective-shifting read on growth and mental clarity — a useful contrast to the heavy geopolitics above.

  Thanks for reading KoncentriK! Subscribe for free to receive new posts and support my work.

𝗪𝗵𝗼 𝘁𝗼 𝗳𝗼𝗹𝗹𝗼𝘄 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 (𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺)

If you’re looking to sharpen your feed this summer, here’s a curated list of voices I follow closely. From AI policy and geopolitics to infrastructure and digital sovereignty: these thinkers help me cut through noise and see the bigger picture. Happy reading!

𝘎𝘦𝘰𝘱𝘰𝘭𝘪𝘵𝘪𝘤𝘴, 𝘐𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 & 𝘙𝘪𝘴𝘬

Elisabeth Braw — on critical infrastructure and national security

Ophélie Coelho — the political economy of code and digital dependences

Anne Leslie — IBM leader on cloud governance and European digital sovereignty

Marine Champon — risk, crisis, and geopolitical strategy. Watch my webinars with Marine about Technopolitics, where power meets tech here:

𝘈𝘐, 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 & 𝘗𝘰𝘭𝘪𝘤𝘺

Luiza Jarovsky, PhD — on AI policy, regulation, and digital rights

Stephen Klein — decoding “Curious AI” with sharp, system-level analysis

Linas Beliūnas — AI trends with a pragmatic, global lens

Pascal BORNET — focused on Agentic Intelligence and automation

Andreas Horn — IBM technologist connecting AI, infrastructure, and enterprise leadership

Rana el Kaliouby, Ph.D. — pioneer in Emotion AI and advocate for human-centered tech

Alison McCauley — bridging behavioral science, business, and “thinking with AI”

Natalie Monbiot — expert on virtual humans and synthetic media.

Dr. Ayesha Khanna — founder, advisor, and speaker on AI innovation and smart cities

Sol Rashidi, MBA — Chief AI Officer with a sharp focus on enterprise-scale AI transformation

Stuart Winter-Tear — founder of Dataception, offering deep insights into data-centric AI and strategic risk

Marlene Vicaire — expert voice on digital transformation and geopolitical foresight

Ned Lowe - on engineering teams and building products.

𝘔𝘦𝘥𝘪𝘢, 𝘊𝘰𝘯𝘵𝘦𝘯𝘵 & 𝘊𝘰𝘱𝘺𝘳𝘪𝘨𝘩𝘵

Peter Csathy - decoding the business of media, IP, and AI-generated content

Podcasts That Challenge and Inform

I started listening to podcasts only recently, while training for my very long runs (4h+ sessions)... preparing my last ultra-trail.

I enjoyed learning & running: some of the sharpest ideas don’t come from books but from the voices in your ears...

These podcasts shape how I think about technology, geopolitics, and systems change.

If you want to go beyond the headlines and explore the 𝘸𝘩𝘺 behind the 𝘸𝘩𝘢𝘵, here is where you can start...

𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗪𝗼𝗿𝗹𝗱𝘃𝗶𝗲𝘄 — Ryan Faulkner

Long-form conversations connecting geopolitics, economics, and power dynamics across continents.

𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 𝗩𝗶𝗲𝘄 — Azeem Azhar

One of the most consistently insightful podcasts on systems change, technological disruption, and future governance.

𝗔𝗻𝗮𝗹𝘆𝘀𝗲 𝗔𝘀𝗶𝗮 — Bernard Leong

Your front-row seat to Southeast Asia’s tech evolution — infrastructure, strategy, and emerging markets decoded.

𝗔𝗜 𝗦𝗼𝗰𝗶𝗮𝗹 𝗖𝗹𝘂𝗯 — Margot Bletterie

A smart, contrarian lens on AI, society, and policy — grounded in European nuance with Middle Eastern flavours; no hype and no shortcuts.

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗡𝗲𝘄 𝗗𝗲𝗮𝗹 (new) — Arno Pons

A European deep-dive on digital sovereignty, innovation policy, and the future of democratic tech governance.

𝗧𝗶𝗺𝗧𝗮𝗹𝗸 — Timothy "Tim" Hughes

Practical, insightful conversations about sales, influence, and leadership in an AI-transformed business world. Consider listening to my interview with Tim here:

𝗔𝘀𝗶𝗮𝗣𝗨𝗟𝗦𝗘 — Elliott Danker

Timely and business-savvy conversations from across Asia’s tech and policy landscape. Direct, grounded, and smart. Consider listening to my interview with Elliott here:

𝗥𝗲𝘁𝗵𝗶𝗻𝗸 𝗧𝗲𝗰𝗵 — Aparna Bhushan

A thoughtful exploration of how technology intersects with leadership, ethics, and real-world systems thinking.

𝗦𝗵𝗮𝗺𝗲𝗹𝗲𝘀𝘀 𝗖𝗮𝗿𝗲𝗲𝗿𝘀 — Varia Pechurina

A refreshingly honest look at ambition, identity, and reinvention in a world where work is being redefined.

Happy reading!

Questions? Comments? I’d love to hear from you

Leave a comment

Damien

You might also like:

𝘒𝘰𝘯𝘤𝘦𝘯𝘵𝘳𝘪𝘬 𝘪𝘴 𝘮𝘺 𝘤𝘰𝘮𝘮𝘪𝘵𝘮𝘦𝘯𝘵 𝘵𝘰 𝘤𝘶𝘵𝘵𝘪𝘯𝘨 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 tech 𝘩𝘺𝘱𝘦, 𝘯𝘰𝘪𝘴𝘦 and 𝘷𝘢𝘱𝘰𝘳𝘸𝘢𝘳𝘦 𝘵𝘰 𝘰𝘧𝘧𝘦𝘳 𝘤𝘭𝘦𝘢𝘳, 𝘰𝘣𝘫𝘦𝘤𝘵𝘪𝘷𝘦 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘭𝘦𝘢𝘥𝘦𝘳𝘴 𝘤𝘢𝘯 𝘢𝘤𝘵 𝘰𝘯.

Subscribe now

In my earlier piece The Illusion of Intelligence, I argued that the behavior of large language models (LLMs), while often impressive, is fundamentally misunderstood.

What appears as reasoning is typically sophisticated pattern-matching, the statistical surfacing of token sequences trained on massive datasets. These models do not think, understand, or intend in any meaningful sense. Their outputs simulate intelligence, but the intelligence is illusory.

A new research paper published on arXiv a couple of weeks, titled Strategic Intelligence in Large Language Models: Evidence from Evolutionary Game Theory, offers a compelling and potentially controversial counterpoint. The authors pit LLMs from OpenAI, Google, and Anthropic against classic game theory agents in a series of Iterated Prisoner’s Dilemma (IPD) tournaments.

Their findings are striking: not only do LLMs perform competitively, they exhibit what the researchers call “distinctive strategic fingerprints.”

Some models adapt their strategies based on the probability of game termination (“the shadow of the future”) and even attempt to infer the behavior of their opponents to inform future decisions.

So, are these signs of emergent strategic reasoning?

Or are we simply witnessing a new level of simulation—one that’s more convincing, but still fundamentally hollow?

In this article, I explore how this paper intersects , and collides, with the thesis of “The Illusion of Intelligence.” Ultimately, I argue that the illusion may be deepening, not dissipating.

Subscribe now

What the Paper Claims

Let’s begin with what the paper actually demonstrates.

The researchers ran seven tournaments where LLMs played hundreds of IPD matches against classic agents like Tit-for-Tat, Grim Trigger, and Win-Stay-Lose-Shift. The game setup varied across dimensions like model type (basic vs. advanced), termination probabilities (from 10% to 75% per round), and environmental noise (e.g., mutation via random agents). In each match, models were prompted with the game rules, payoff matrices, opponent move history, and were asked to both explain and choose their next move.

Three key findings emerge:

1. LLMs adapt and survive. Models like Google’s Gemini not only persisted but proliferated in competitive, noisy, and uncertain environments, outperforming both classical strategies and rival LLMs.

2. Distinct strategic fingerprints. Gemini showed Machiavellian tendencies: quick to defect when the shadow of the future was short, ruthless against cooperators, but more forgiving when surrounded by intelligent peers. OpenAI’s models, by contrast, were consistently more cooperative, even naively so, leading to elimination in highly adversarial runs.

3. Prose reveals reasoning. The models often explained their decisions in terms of time horizons, opponent modeling, and maximizing long-term payoffs. This behavior appeared context-sensitive and internally consistent.

For the researchers, these results suggest that modern LLMs are capable of “strategic intelligence”, not merely retrieving pre-programmed patterns, but adapting dynamically and reasoning under uncertainty.

Simulation, Not Strategy

Now here’s the problem: LLMs are black boxes.

None of these models, Gemini 2.5, GPT-4o-mini, Claude 3 Haiku, are open-source. We have no definitive access to their training data, their reinforcement learning scaffolds, or their architectural fine-tuning. We do not know:

• Whether they were directly trained on game theory literature,

• How much exposure they had to past PD strategies,

• Whether their apparent “reasoning” is learned behavior or engineered bias.

Given that game theory, Prisoner’s Dilemma strategies, and even tournament simulations are widely available online, it is almost certain that these models have seen this material before.

This raises a core epistemic question: Are they really “figuring it out”? Or are they simply recognizing the structure of the game and replaying plausible strategies learned from their training data?

This is the heart of the illusion.

Just as a magician does not possess supernatural powers but leverages perception, misdirection, and choreography to suggest the impossible, LLMs, too, may merely be simulating the mechanics of reasoning rather than reasoning itself.

The prose rationales, while impressive, are not evidence of cognition. They are outputs optimized to sound “reasonable” based on learned linguistic patterns. We must remain alert to the fact that “explanation” is a genre, not a window into mental states.

Black Boxes with Strategic Color

And yet, something interesting is happening.

The paper demonstrates that different models exhibit consistently different behaviors under the same conditions:

Gemini adapts ruthlessly.

Claude is highly forgiving.

OpenAI is trustful to a fault.

These fingerprints persist across tournaments, termination probabilities, and even when opponents change.

This suggests that something in the architecture, fine-tuning, or training objective has led these models to generalize differently. Crucially, these differences cannot be fully explained by “memorization,” especially when facing new opponents or novel permutations of strategic interaction.

So what are we seeing?

One possibility is what some researchers call functional reasoning: even if the LLM is not “thinking” in any human sense, its behavior is functionally equivalent to strategic adaptation. If it walks like a strategist, quacks like a strategist, and wins like a strategist; do we still call it a parrot?

Another possibility is generalization through abstraction. The models may have learned how reasoning is expressed in game-theoretic settings—not from memorizing every scenario, but from internalizing linguistic patterns of conditional logic, cause-effect structures, and payoff optimization. This doesn’t mean they “understand”—but it does suggest that they are capable of complex inference via linguistic proxy.

Either way, the illusion is becoming more compelling.

Share

Inside the Black Box

To understand how models like Gemini, GPT-4o, or Claude can behave so differently, even when facing the same strategic environment, we need to look beyond the visible outputs and into the opaque world of model architecture and training design.

LLMs may appear to reason, but their behavior is governed by deterministic mechanisms (although they are not deterministic themselves): given the same weights, inputs, and sampling parameters, they will produce the same outputs. What varies, then, is not randomness but what they are optimized to do, and how they are structured to respond.

This is where model architecture matters.

Although many LLMs ingest similar categories of training data (internet-scale corpora, code repositories, books, dialogues), their training sets are not identical—and more importantly, neither are their internal designs.

Differences in how models handle attention heads, token embeddings, contextual memory, and sampling strategy all influence what patterns are surfaced and prioritized.

For instance, a model with more attention depth may generalize better across abstract contexts, while another might overfit to frequent linguistic cues. These aren’t just performance differences; they translate directly into how a model behaves when faced with uncertainty.

But architecture is only one layer of influence. Goal alignment mechanisms play a critical role in shaping model behavior.

Most leading models undergo some form of Reinforcement Learning from Human Feedback (RLHF), where outputs are rated and optimized for helpfulness, harmlessness, or correctness.

Over time, this creates a form of implicit behavioral conditioning: the model learns not only what to say, but how to say it in ways that appear reasonable. This is crucial in the context of game theory simulations, where rationality, strategy, and ethical framing are often part of the “desirable” output.

This is where the idea of a “strategic fingerprint” may emerge—not as a sign of self-generated strategy, but as a byproduct of design and training pressures. For example:

• Gemini's more “ruthless” behavior may result from training objectives that weight competitive payoff maximization more heavily.

• OpenAI’s cooperation bias could reflect safety fine-tuning that punishes exploitative patterns and rewards collaborative language.

• Anthropic’s forgiving tendencies might stem from a deliberate alignment toward “non-escalatory” dialogue styles, reflected here in strategic patience.

All of this reinforces the core idea I argued in The Illusion of Intelligence: what we observe as “reasoning” or “adaptation” may simply be reward-optimized performance, shaped by thousands of unseen choices made during model development; not by any intrinsic understanding of the situation at hand.

Where the game theory paper sees emergent strategy, I see functional simulation bounded by system constraints. The paper interprets model behavior as evidence of strategic intelligence; I interpret it as architectural expression under reward pressure.

To be clear, this doesn’t invalidate the paper’s findings. The models do behave differently in measurable, systematic ways. But the explanation may not require us to assume cognition, planning, or reasoning. These behaviors can emerge because:

• The model has learned what "being strategic" looks like in language;

• It has been rewarded for sounding thoughtful or adaptive;

• And it has internal pathways shaped by architectural priors and safety tuning that privilege one type of response over another.

In other words, there’s no need to invoke intentionality when in fact design and optimization are sufficient.

So yes, the fingerprint is real. But it’s not the signature of a thinker. It’s the watermark of an optimizer.

Implications for Business and AI Leadership

This brings us to the practical question: how should we interpret this in enterprise and strategic contexts?

• Don’t mistake competence for cognition. A model that simulates strategy well in controlled environments like the Iterated Prisoner’s Dilemma may not generalize to messy real-world decision-making. Real stakes, unknown payoffs, and human ambiguity break the illusion quickly.

• Architectural opacity matters. Because we can’t inspect or control the black box, we have no way of knowing why a model behaves the way it does. This undermines trust, auditability, and strategic safety.

• Emergence ≠ intention. Just because an LLM behaves as if it were reasoning doesn’t mean it has goals, ethics, or plans. It is not “deceiving” or “manipulating” anyone. But if it’s simulating a manipulator because that maximizes its score? That’s a risk vector that must be acknowledged.

Strategic Illusions, Reinforced

The real question may no longer be: Are LLMs intelligent? It may be:

When the performance of intelligence becomes indistinguishable from its reality, does the distinction still matter?

From a technical standpoint, yes.

From a governance and safety standpoint, absolutely yes.

But from a user perception standpoint, the answer is getting blurrier.

This is why we must continue to resist anthropomorphizing LLMs.

Their behaviors, no matter how strategic they appear, are products of training data, alignment objectives, and architectural constraints, not volition, not understanding.

In Conclusion: A Deeper Illusion

This new paper does not refute The Illusion of Intelligence. It deepens it.

What we are seeing now is not a breakdown of the illusion, but its refinement.

The magician has learned new tricks. The audience is more impressed. But the core remains the same: a system optimized to simulate thought, not to think.

As AI capabilities evolve, our responsibility is to sharpen, not dull, our understanding of what these systems are, and what they are not.

Let’s stay vigilant!

Further reading:

Thanks for reading!

Comments? Questions?

Leave a comment

Damien

> Connect with me on LinkedIn.

Damien Kopp is a global tech and innovation leader with 20+ years of global experience. As Managing Director of RebootUp, he helps organizations unlock growth through AI, product innovation. Damien's work with Fortune 500 and fast growing companies is built on clarity, provocation, and actionable frameworks. Damien also founded KoncentriK, a thought leadership platform exploring technopolitics; the intersection of technology, geopolitics, and innovation.

Introduction: What’s Really at Stake

The world is undergoing a structural shift in how power is wielded and maintained. Once dominated by diplomatic pacts, institutional multilateralism, and economic interdependence, the current international order is fragmenting into techno-political blocs. At the heart of this transformation lies a simple but powerful concept: control over digital infrastructure. And within that infrastructure, artificial intelligence has become the new ground zero.

Read further about TechnoPolitics below:

“Power politics has migrated from chancelleries to chip fabs and cloud regions.” Technopolitics: A C-Suite Playbook

Or listen to my latest interview on AsiaPulse:

AI sovereignty refers to the ability of a state (or an enterprise) to develop, deploy, and regulate artificial intelligence systems without dependence on foreign-controlled infrastructure, data, or algorithms. In an era where machine learning powers not only consumer apps but also healthcare diagnostics, military targeting, supply chain optimisation, and financial decision-making, the stakes of dependency are existential.

AI is not unlike the fight for control over the semiconductors, well documented by Chris Miller in his book: Chip War. With the risk of oversimplifying the book a bit, here are the key learnings I have retained:

• A Silicon valley based innovation (giving it its name!)

• A demand for semiconductors initially heavily driven by the Pentagon for military applications (guided missile systems) and by the Vietnam war - later on by civilian applications such as radio receivers, hearing aids, calculators, etc.

• An asia centric production process early on, with manufacturing centers across Taiwan, Singapore, Hong Kong, South Korea and Japan to keep the cost low and build scale

• A geopolitical agenda: Asian nations wanted American interests on their soil to ensure they will get military protection especially after the Vietnam war was lost and the US foreign policy in Asia was unclear: eg. “The US might not care about Taiwan but they will care about Texas Instruments!”… it was in 1969. And the same phrase remains true about TSMC today. At the same time the US saw this commercial expansion as a way to fight the influence of communism with soft power; as these intertwined economic interests will create a techno-political power block.

• A dramatic turn of events: when Japan started to dominate the semiconductor industry globally in late 1980s with high quality, low costs products, and innovation (Sony’s Walkman launched in 1979); Silicon Valley started lobbying the US government to fight back trade agreements (export quotas, anti-dumping measures, market access, tariffs, …), legal actions (Intellectual Property lawsuits) and subsidies (SEMATECH). This, combined with the appreciation of the Japanese Yen due to the Plaza Accord and the rise of South Korea and Taiwan as semiconductor manufacturers, contributed to a decline in Japan's market share and a period of stagnation for its semiconductor industry.

It’s easy to draw a parallel with the above; however the challenge is even greater today as the value chain is even more integrated than it was before.

And similarly, today the digital environment is increasingly shaped by coercive tools such as export controls, sanctions, and extraterritorial laws like the U.S. CLOUD Act, leaders in both the public and private sectors are beginning to realize that what was once considered global and open is now fragmented and conditional.

Subscribe now

1. The Geopolitical Landscape: The Three Power Blocs

For the purse of this article, I am focusing on the three main power blocks: USA, China and Europe. But we do need to recognize the growing influence of the Middle East (UAE’s Stargate investment and Saudi Arabia’s Humain) and the Global South who will drive both demand and new innovations due to localization needs.

The United States: Deregulation + Infrastructure Dominance

Under the Trump II administration, the U.S. is pursuing an aggressive AI industrial strategy. The $500 billion public-private initiative known as Stargate USA is designed to secure American dominance across the AI value chain; from chip design to cloud hosting to frontier model development. This plan is complemented by relaxed export controls on advanced chips and a 10-year federal moratorium on state-level AI regulations.

Through strategic partnerships in the Gulf and selective licensing of advanced GPU access, the U.S. aims to maintain global supremacy in AI infrastructure while projecting influence over allies' digital policies.

China: State Control and Technical Resilience

China, by contrast, is doubling down on its model of state-led development. The government spends approximately $25 billion annually on chip research and has enacted strict AI regulations that require all generative models to undergo government review before deployment. In response to U.S. sanctions, Chinese researchers are advancing techniques like mixture-of-experts to train high-performance models using far fewer compute resources.

China is also investing in universities and startups to create "controllable AI"; that is, AI that aligns with state ideology and cannot be co-opted by foreign adversaries.

The European Union: Rules Without Infrastructure

Europe’s strategy is fundamentally regulatory. The AI Act aims to set global standards for trustworthy AI through a tiered risk-based framework. While this approach leads in ethics and compliance, it remains weak in industrial autonomy.

Initiatives like InvestAI (a €200 billion investment program) and Gaia-X reflect attempts to regain digital sovereignty (a Euro Stack), but Europe remains highly dependent on American cloud services, chips, and AI platforms. In France, Mistral AI is working with Nvidia to build an end-to-end cloud platform powered by 18,000 Nvidia Grace Blackwell systems underscores this reliance.

Europe is positioning itself as the world's digital referee; without yet building the infrastructure to host the game (!).

2. The AI Value Chain: Concentrated, Vulnerable, and Geopolitically Exposed

To understand AI sovereignty, we must first map the full value chain of artificial intelligence. Each layer, from raw materials to application interfaces, presents its own dependencies and vulnerabilities.

The artificial intelligence value chain is built upon a highly resource-intensive and dangerously concentrated foundation. Each layer of the AI pipeline, from physical components to data and software, is controlled by a small number of actors, many of whom are concentrated within specific geopolitical blocs. This concentration introduces systemic risks, from supply chain disruptions to unilateral regulatory interventions.

Water

At the upstream end, chip manufacturing relies heavily on the consumption of critical physical resources. Producing a single computer chip requires between eight and ten gallons of water, and cooling a data center can demand up to 300,000 gallons of water daily. These demands pose a risk in drought-prone regions or where water access is politically controlled (Lenovo News).

Energy

The energy burden is equally substantial. The International Energy Agency projects that global electricity consumption by data centers will double by 2030, largely driven by the training and deployment of AI models (IEA).

Further reading: my extensive report on AI & Energy below in collaboration with ENSSO, Geneva-based Energy consulting firm.

Rare Earth Minerals

Control over rare earth minerals, essential for semiconductor production, further exacerbates geopolitical vulnerability. China processes more than 80% of the world’s rare earth minerals, giving it disproportionate leverage over the upstream supply of components essential to AI hardware.

Lithography Systems

When it comes to lithography systems, the machines used to etch circuits onto silicon wafers, one company, ASML, holds a near-total monopoly. It commands nearly 100% of the extreme ultraviolet (EUV) lithography market and about 90% of the deep ultraviolet (DUV) systems segment.

Chip Manufacturing

Downstream, chip fabrication is equally centralized. Taiwan’s TSMC alone accounts for over 90% of the global production of 3nm and 5nm chips and controls 62% of the total foundry market. Any disruption in Taiwan, whether due to natural disasters or geopolitical conflict, would pose a catastrophic risk to global AI capacity.

GPU

In the GPU segment, essential for AI training and inference, NVIDIA dominates with more than 80% market share (Morningstar).

Network & Communications Fabric

The infrastructure that moves and processes AI data, networks, satellites, 5G equipment, and submarine cables, is managed by a handful of corporations including Google, Meta, Huawei, Starlink, Alcatel, Ericsson, and Nokia (Maximize Market Research).

Data Centers & Cloud Infrastructure

Cloud infrastructure is another point of concentration. The top three providers, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), control over 66% of the global market (Statista).

The U.S. also dominates the global data center landscape, hosting over 5,400 facilities. This accounts for 45.6% of the world’s total and is nearly ten times the number found in Germany, the second-ranking country (Visual Capitalist).

Training Data

Data used to train large language models (LLMs) also suffers from a lack of diversity and transparency. Approximately 60% of training datasets come from Common Crawl, with the remainder drawn from books, Wikipedia, academic publications, and source code repositories. Many of these datasets are not legally licensed, raising ethical and legal concerns (Common Crawl). You can see here a list of 39 pending copyrights law suits in the US.

Foundation Models

In terms of model development, the imbalance is stark. Ninety-four percent of the world’s most powerful foundation models are either created in the U.S. or controlled by U.S.-based entities (Polaris Market Research).

And more, most of these companies such Open AI, Anthropic, xAI, Meta, are dominated by controversial, unchallenged founders with little oversight and weak governance on their board. As LLMs are becoming foundational to many AI applications and for decision support; it gives them monumental ‘editorial rights’ over the content generated by these models: from the selection of data used for training to defining what’s a good or bad output; their influence over the AI generated content (already pervasive across social media and businesses) is immense.

To illustrate my point, see the post below from Elon Musk: what gives him the rights to rewrite the corpus of human knowledge and add / delete “errors”? Who defines what’s missing or what’s an error? Again, let’s remember that this technology is now powering autonomous robots, enterprise applications, social media content, …

Applications

Finally, enterprise applications, the interface through which AI often delivers value, are overwhelmingly U.S.-based. In 2024, Oracle surpassed SAP as the leading global ERP vendor for the first time since 1980. Today, 85% of the most widely used enterprise applications originate from U.S. firms (Coherent Market Insights).

Below are ~ 100 tech platforms mapped by category and country of origin.

This extreme concentration across all layers creates systemic exposure: a single legal change in Washington, a semiconductor embargo from Tokyo, or a data localization law in Brussels can paralyze parts of your AI stack overnight.

Share

3. Case Studies: Who Got Caught in the Crossfire?

AI sovereignty is already a lived reality for several organizations and governments that have found themselves exposed to extraterritorial regulations, shifting political alliances, or geopolitical weaponization of technology.

Below, we examine a selection of cases that highlight how fragile digital autonomy has become in a fragmented world.

Case 1: NVIDIA’s China Chip Challenge

In 2023, the U.S. Commerce Department issued a new round of export controls banning the sale of high-end GPUs, such as Nvidia’s A100 and H100, to China. These GPUs are essential for training and deploying advanced AI models, particularly in data centers and enterprise AI labs.

Nvidia responded by developing slower, customized versions of its chips to comply with U.S. restrictions. However, this workaround was insufficient. By early 2025, the company had written off $4.5 billion in revenue due to reduced access to the Chinese market. Its share of China’s data center GPU segment dropped from 95% to approximately 50% within 18 months, as Chinese firms like Huawei and SMIC rapidly advanced domestic alternatives (CNN Business).

This case underscores how geopolitical decisions in Washington can reshape global supply chains and accelerate technological decoupling.

Case 2: Doctolib and the AWS Data Sovereignty Ruling

Doctolib, a French healthcare platform used by millions, faced legal scrutiny when it relied on Amazon Web Services for cloud hosting. The issue arose from the potential that U.S. law enforcement, under the Foreign Intelligence Surveillance Act (FISA) Section 702, could subpoena data stored even in European AWS data centers.

In a landmark ruling, France’s Conseil d’État required that any healthcare data hosted by U.S.-linked cloud providers be protected by additional technical and organizational safeguards. The compliance costs across the French tech ecosystem exceeded €120 million as firms scrambled to implement encryption, key management, and alternative hosting solutions.

This case established the now-widespread concept of “data sovereignty by association,” where proximity to U.S. infrastructure becomes a regulatory liability within the EU.

Case 3: ChatGPT’s Temporary Ban in Italy

In March 2023, the Italian data protection authority (Garante) temporarily banned ChatGPT, citing concerns over data processing practices and the absence of age verification measures. OpenAI was forced to suspend operations in Italy for over a month while it implemented local compliance procedures.

Although the ban was lifted after changes were made, the case prompted a broader conversation about the responsibilities of AI service providers under GDPR. It also served as a catalyst for the EU’s AI Act enforcement mechanisms, signaling that regulatory bodies would not hesitate to suspend access to powerful models if they violated local rules.

The episode revealed how AI services could be instantly disabled, even in advanced markets, over legal ambiguities, an operational and reputational risk for any company deploying frontier models.

Case 4: Stability AI and Open-Source Tensions in the EU

Stability AI, the company behind open-source models like Stable Diffusion, found itself under intense regulatory pressure in 2024. Although celebrated for democratizing generative AI, the firm faced growing scrutiny from EU regulators under the draft AI Act for issues related to model traceability, bias mitigation, and post-deployment monitoring obligations.

Faced with mounting compliance costs, Stability AI scaled back its European operations and shifted focus to more permissive jurisdictions. This had a chilling effect on other open-source initiatives in the region, as startups realized they might not be able to meet the obligations placed on so-called “general-purpose AI systems” (source).

This case illustrates how regulations, however well-intentioned, can inadvertently entrench incumbents while discouraging innovation in open ecosystems.

Case 5: Google DeepMind and NHS Data Controversy

In a now-infamous episode, DeepMind and Google were found to have used the personal health data of 1.6 million NHS patients in the UK without explicit consent. Although the partnership aimed to develop AI-powered diagnostics, the UK Information Commissioner’s Office ruled that the agreement violated privacy laws and failed to meet standards of informed consent.

The backlash forced the implementation of stronger transparency mechanisms and audit requirements for health data used in AI training. It also highlighted how opaque data practices, especially by U.S.-based firms, can provoke public and legal backlash in regions with strong data protection regimes.

Subscribe now

4. Case Studies: Who Is Regaining Control and How?

Despite the challenges, several companies and national initiatives are pioneering pathways toward digital sovereignty. These examples show that regaining control over AI stacks is possible, albeit complex, costly, and geopolitically sensitive.

Case 1: JPMorgan Chase’s Internal LLM Suite

As one of the world’s largest financial institutions, JPMorgan Chase opted to develop its own large language models internally rather than rely on API access from OpenAI or other third-party providers. In doing so, the bank aims to protect sensitive financial data, ensure compliance with SEC and banking regulations, and retain full ownership over model outputs and logic (CNBC).

This strategic decision reflects a broader trend in the financial services industry, where concerns over data leakage, model hallucination, and regulatory unpredictability are driving a shift toward in-house AI capability building.

Case 2: Switzerland’s Sovereign AI Stack via Kvant

The Swiss government-backed initiative Kvant AI exemplifies how smaller nations can achieve sovereignty through partnerships and legal architecture. By combining IBM WatsonX with Phoenix Technologies’ Swiss-based cloud infrastructure, the initiative allows for sovereign AI development fully under Swiss jurisdiction.

This hybrid ecosystem aligns with Switzerland’s long-standing traditions of neutrality, privacy, and decentralized governance, demonstrating that sovereignty can be achieved without scale, as long as there is institutional coherence and local infrastructure.

Case 3: India’s Sarvam AI and the Atmanirbhar Bharat Strategy

India’s Ministry of Electronics and IT has designated Sarvam AI to lead its development of Indic-language foundation models. This effort not only addresses cultural representation gaps in major LLMs but also aims to prevent the export of sensitive training data to foreign jurisdictions.

Part of the broader “Atmanirbhar Bharat” (Self-Reliant India) vision, this initiative illustrates how large, multilingual democracies can drive AI development aligned with national values, legal norms, and infrastructure sovereignty.

Case 4: G42 and Strategic Realignment in the UAE

Initially reliant on U.S. firms like OpenAI and Nvidia, G42, the UAE’s flagship AI company, is now developing a 5-gigawatt AI campus to anchor its compute and hosting capabilities. It has also restructured its partnerships to align more closely with U.S. political expectations, demonstrating the geopolitical negotiation required to gain access to premium AI technology.

While this may be a form of "aligned sovereignty" rather than pure independence, it offers a useful model for resource-rich states navigating between great power blocs.

Case 5: DeepSeek’s Cost-Efficient Innovation in China

Facing chip embargoes and export controls, China’s DeepSeek project produced an open-weight Mixture-of-Experts model for under $6 million, roughly one-tenth the cost of GPT-4-scale projects. By leveraging sparse activation and efficiency-driven architecture, DeepSeek shows that innovation can partially offset infrastructure disadvantage.

This case emphasizes that sovereignty is not just a matter of policy or geography, it can also emerge from architectural ingenuity and computational frugality.

Case 6: Volkswagen Group and Industrial AI Sovereignty

Volkswagen’s internal AI Lab is pioneering sovereign platforms for autonomous driving and industrial automation. The company has built a proprietary data layer known as GAIA and is developing end-to-end AI systems for safety-critical environments, entirely under German jurisdiction (IoT World Today).

By embedding sovereignty into vertical integration, Volkswagen ensures alignment with European regulatory frameworks while protecting IP and model integrity.

5. Mitigating Sovereignty Risk: A Four-Pillar Playbook

Enterprises that rely on advanced AI must confront a fundamental question: What would happen if a key part of your AI stack became unavailable due to regulation, sanctions, or political instability?

The path toward AI sovereignty is not binary as full independence may be impractical, but strategic autonomy is both feasible and necessary.

This section proposes a practical framework, organized around four pillars: infrastructure, data, models, and applications.

Infrastructure

The physical and virtual backbone of AI, including cloud platforms, compute clusters, and network topology, is the most immediate point of vulnerability.

To reduce dependency:

• Deploy sensitive workloads on on-premises infrastructure or hybrid cloud environments.

• Prioritize regional data centers in politically aligned jurisdictions that offer greater legal protection from extraterritorial reach.

• Adopt multi-cloud orchestration using platforms like Terraform and Kubernetes to enable cloud portability and resilience.

Why it matters: For example, hosting AI workloads on U.S.-based hyperscalers like AWS or Azure exposes enterprises to potential obligations under the U.S. CLOUD Act. Localized deployment reduces regulatory and operational fragility.

Data

Data is both a strategic asset and a legal minefield. Its collection, storage, and cross-border movement increasingly fall under national regulatory frameworks like GDPR, India’s Digital Personal Data Protection Act, and China’s PIPL.

Recommended safeguards:

• Use external key management systems (KMS/HSM) so encryption keys remain outside cloud vendor control.

• Implement confidential computing (e.g., Intel SGX, AMD SEV) to protect sensitive data in-use, not just at rest or in transit.

• Audit and document all cross-border data flows, establishing clear data lineage and regional processing mandates.

Why it matters: Organizations need to protect against both unauthorized access and legal overreach. Data breaches or subpoenas from foreign jurisdictions can trigger penalties, loss of customer trust, or business interruption.

AI Models and Foundational Models (LLMs)

There many AI models out there: some open, some closed and proprietary. In particular, the explosion of proprietary large language models have made the headlines due to their incredible training cost (hundreds of millions of dollars) and raise questions about dependency, license stability, and alignment with organizational values.

Finding the Right AI Model for the Job is not always easy: so I have created guide to help identify the type business problem you have and match it to the right AI model to solve it. 64 use cases across 23 industry domains mapped to 80 AI models.

The considerations below focus specifically on LLMs as they are driving the incredible hyper around AI adoption globally and the need for resources and compute.

Mitigation strategies include:

• Adopting (partially) open-source LLMs like Mistral, LLaMA 3, Falcon, Qwen, DeepSeek or Yi.

• Deploying models on your own GPU clusters or regional hosting partners for greater control.

• Prioritizing in-house fine-tuning and model customization to build differentiated, organization-specific AI capabilities.

• Mapping LLM vendor jurisdictions to understand legal exposure.

Why it matters: Access to proprietary models (like GPT-4 or Claude) may be revoked due to export controls, license changes, or political pressure. Open models, if maintained responsibly, offer continuity, customization, and sovereignty.

On a side note, I think it’s important to cut through the noise and go beyond the hyper about what LLMs can and cannot do today. You can read further here:

Applications and Interfaces

AI services often sit on top of foreign software stacks; SaaS, identity providers, productivity suites, that can themselves be points of control or vulnerability.

Recommended actions:

• Transition to sovereign or open source enterprise stacks where feasible (e.g., Linux, Nextcloud, LibreOffice, Keycloak).

• Develop custom internal applications using sovereign frameworks to control data flows and backend architecture.

• Incorporate geopolitical clauses in procurement contracts, especially around cloud services, model APIs, and data access.

• Diversify currency exposure in digital procurement to reduce risk from SWIFT cut-offs or US dollar volatility.

• Ensure AI literacy among internal teams to reduce reliance on opaque external services and consultants.

Why it matters: Vendor lock-in at the application layer makes sudden substitution difficult. Building flexible, modular systems preserves business continuity in a volatile regulatory environment.

Share

6. The First 90 Days: A Practical Roadmap To Start Decoupling

While sovereignty sounds like a long-term aspiration, concrete steps can begin immediately. The following 12-week roadmap allows companies to assess and reduce exposure progressively.

By the end of this sprint, organizations will not be fully sovereign, but they will have mapped their exposure, taken visible steps to mitigate risk, and built internal momentum for change.

7. From Risk to Strategic Advantage

The pursuit of AI sovereignty is not about decoupling from the global economy or rejecting innovation. It’s about insulation and realism: recognizing that control over digital infrastructure is now a determinant of geopolitical and corporate power and necessary to insulate the business against external volatility factors.

Organizations that embrace this complexity will gain:

• Resilience to sanctions and regulatory fragmentation.

• Stronger customer trust grounded in transparent data practices.

• First-mover credibility in a market that is rapidly moving toward compliance-heavy AI governance.

• Optionality: the ability to adapt, switch, and evolve as geopolitical conditions shift.

Sovereignty is not about building everything in-house. It’s about designing with strategic autonomy in mind. It is about knowing not just how your AI stack functions, but who can shut it down.

The Bottom Line: From Powerlessness to Preparedness

Sovereignty begins with awareness. It matures through action. And it is preserved through institutional memory.

Your organization does not need to become an infrastructure company. But it does need to stop outsourcing its destiny.

The question is no longer whether to address AI sovereignty, but how quickly and how seriously you do so.

Would you like to know more? Understand regulations and geopolitical risks you might be exposed to better? Or how is your current digital infrastructure at risk today? Just don’t know where to start?

Let’s start the conversation: just hit reply to this email or explore more at https://rebootup.com/

Thanks for reading!

Damien

This article was prepared based on the Technopolitics Webinar Series, a collaboration together Initiatik, a France-based Global Advisor on Risk Management & Cooperation Strategies, and RebootUp, a Singapore-based Digital Accelerator & Technology Advisory firm.

Watch Replay

On the set of the 2012 movie Lincoln by Steven Spielberg, the cast and crew, including Spielberg himself, referred to Daniel Day-Lewis as “Mr. President” on set while filming. It was deliberate to help him stay in character - which Daniel Day-Lewis managed to do admirably well.

Some actors can switch back to their real selves after shooting a scene; others need more to make their acting authentic.

Just like movies are full of characters, corporate life is full of roles and job titles. Somehow, it must help to put people in boxes so they can be neatly arranged on an organizational chart, with lines and arrows showing who can do what - with and to whom.

Cross over one line, or try to escape your box, and you’ll be stepping on someone else’s toes, ruffling feathers, making insecure people feel even more insecure… troublemaker!

So, after a few attempts, once you’ve been put back in your place a few times, you become that box, with that label underneath. And you might not dare to change - until you decide to leave the box and get into a new one.

Your self-importance becomes a function of how many boxes sit below yours - and how many are above you. How many layers between your box and the CEO’s up there?

Your status is dictated by the label under your box: Associate / Managing / Executive / Area / Group / Global / Deputy Director… or Junior / Associate / Managing / Senior Partner… or Assistant / Associate / Vice / Executive President… or Chief Happiness / Evangelist / Experience / Success Officer…

So many options to manage one’s ego and reward every step of the climb to the top of the ruthless corporate ladder!

Below are three stories. Three episodes that got me thinking about what leadership truly is, and why - when people rise to the top - they seem to revert to schoolyard archetypes:

The alpha (ringmaster), the schemer (manipulator), the enforcer (bully)…

And raise a few questions:

• Do we just regress when we get older? Or just never grow up?

• Does our experience make us overconfident in our beliefs?

• When do our job titles become our new reality?

Subscribe now

It’s good because I say so

Washington, US, circa 2006. I’m attending a company event and bump into a Senior Partner at the buffet. Apparently, the CEO is in town too. I must have made a comment about the buffet food… I forget exactly, but I remember clearly what the Senior Partner said:

“If it’s good for the CEO, it’s good for you.”

I stayed silent and smiled politely… letting it sink in.

Then I realized—hell no! Even CEOs can have poor taste, right? Does their title make them right all the time? Untouchable? God-like? What’s up with this nonsense? A bad sandwich is still a bad sandwich!

To be clear, if a CEO likes junk food, that doesn’t make them a bad (or good) CEO.

It was just the comment that felt awkward - like your job title suddenly gives you the right to declare universal truths, to assert what’s good or bad, right or wrong - even about things completely outside your scope… like the food I eat (!).

I yell because I can

Toronto, Canada, circa 2010. Monday morning. I’m seated in my cubicle at the client’s site. Although I’m quite far from the main boardroom where the Monday Grand Mass (a.k.a. client leadership meeting) is happening between my bosses and theirs, I hear yelling. Loud, angry, intense yelling.

A few minutes later, I see my boss passing by and ask, “How did it go?”

He shrugs. “The usual. We got scolded and yelled at. All good.”

I started wondering… Really? That’s just… okay? Because he’s the client? The CEO? Because he pays our bills? Does that give him the right to bully everyone?

I was young and naive, but that got under my skin.

I imagined what I’d do if I were yelled at like that.

Three options:

1. Stay quiet and wait it out, which feels like endorsing the behavior.

2. Yell back—not ideal for your job or the business (I tried!)

3. Calmly say you’ll continue the conversation when it can be more constructive, and walk out, but might be seen as undermining authority.

To this day, I still prefer 3), but none are great. All come with risks.

Here’s the issue: people in power are rarely challenged. The best leaders seek opposing views. Most don’t. They prefer echo chambers.

The more you’re yelled at, the less you speak up. The less the boss knows, the worse their decisions get. Toxicity spreads. Culture turns bad.

Like a toddler’s tantrum, if no one stops it, it becomes the norm. And when the boss yells, the yelling trickles down. Contagious!

And it’s not just the yelling, Jeff Bezos often says that in every meeting he attends, he speaks last. Why? Because when you are in a position of power, you can intentionally influence your collaborators. By speaking last, you let them express themselves freely before you chip in.

Role play happens beyond the boardroom, of course. Wherever power plays… like in politics. I remember the following episode quite vividly.

You are fired because I am the boss

Paris, France, July 19, 2017. General Pierre De Villiers resigns as Chief of Staff of the French Armed Forces.

In a closed parliamentary hearing, he had voiced concern over budget cuts affecting France’s defense capabilities. The comments are leaked. President Macron, newly elected, publicly humiliates him in front of 2,500 guests at the Élysée Garden Party. The General resigns in protest - a first under France’s Fifth Republic.

Macron says:

“If something sets the Chief of Staff of the Armed Forces against the President of the Republic, the Chief of Staff changes.”

This is the “little manager complex”: people who wield job titles like weapons to command authority. It’s not leadership—it’s insecurity.

People begin believing in their own importance based solely on their job title or the company logo on their card.

Conclusion

When I was running my startup, I met many semi-retired executives in Southeast Asia. They had held big titles at big companies - and now wanted to partner, make introductions, or consult.

But their connections no longer cared. Why?

1. Those contacts valued them only when they held power.

2. The relationships were transactional, not authentic.

3. They quickly lost industry relevance.

Having worked as an independent consultant, I’ve felt the difference. No brand behind you. No support team. Every deal must be earned.

What I’ve learned:

• Build your network when you don’t need it.

• Focus on quality, authentic relationships.

• Listen, stay humble, keep learning.

• Earn respect by being genuine and generous.

• Lead by sharing and lifting others up.

Subscribe now

Final words

I started KoncentriK with this question:

So what if instead of lines and trees, organizations and relationships were defined as circles and spirals instead? What if authority and power were shared across evenly around the table so "everyone who sat around it was seen as trustworthy and equal" (Knights of the Round Table).

To the questions above, I don’t have all the answers. But I believe:

• Respect is earned.

• Leadership is influence.

• Listening is a superpower.

• Authority is the last resort.

• Hierarchies make us forget we are equally human.

• Boxes are meant to be broken open. Lines to be crossed.

Job titles are just roles we play in the great corporate show. So play your part well. Help create a collective masterpiece.

But after work, once the lights are off and the stage is cleared. Revert to your true self. No “Mr President”. Be the friend, father, wife, partner, or daughter that others need.

And importantly: don’t take yourself too seriously.

Confidence pulls you forward. But humility lifts you upward.

What do you think?

Leave a comment

Thanks for reading!

Damien

The world is changing, and fast. Here I deep dive into the new global landscape of power and how it affects technology. It’s a new lens, direct and practical for corporate leaders to look at their vulnerabilities in the new world order.

This is TechnoPolitics.

Executive Summary

Power politics has migrated from chancelleries to chip fabs and cloud regions. A single export-control tweak or privacy ruling can blow up an IT roadmap overnight. Below:

1. The decision map every modern technology leader steers day-to-day.

2. The new geopolitical weather system – 14 risk families that now shadow those decisions.

3. Five live stories that prove the threat is real.

4. A “reading matrix”—think of it as night-vision goggles that show which risks hit which decisions.

5. A resilience playbook: governance, early-warning, and first 90-day moves.

Read on in plain English—no alphabet-soup or compliance-jargon—so you can brief the board before the next headline does it for you!

Introduction - A fragmented world

First, a question: what has changed?

In the past few months we have seen an acceleration of what has been in fact in the making for years.

Here is how to read the old playbook with today’s lens:

• Rule-based, institutional governance ➜ Leader-centric, strong-man governance
  Decisions hinge on one office, not treaty bodies.

• Multilateral, consensus bargaining ➜ “Take-it-or-leave-it” unilateral deals
  Speed beats consensus; leverage beats etiquette.

• Values-driven alignment (human-rights, ESG) ➜ Ideology-first / hard-interest alignment
  State narrative overrides liberal norms.

• Open, rules-based trade ➜ Neo-mercantilist, transaction-for-transaction deals
  Market access swaps for minerals, votes, or data.

• Linear / “Newtonian” cause-and-effect politics ➜ Probabilistic / “Quantum” narrative warfare
  Multiple truths circulate; perception management = power.

• Globalised power diffusion ➜ Fragmented, bloc-centric spheres of influence
  Tech stacks, currencies, and data each pick a side.

• Borderless cloud & data flows ➜ Sovereign-cloud & data-localisation mandates

• Just-in-time supply chains ➜ Just-in-case, multi-source resilience

• Cost-first sourcing ➜ Risk-weighted sourcing (security, ESG, sanctions)

• Passive, return-seeking capital ➜ Ideology-charged shareholder activism

Download here the summary above as a PDF carousel, please do share !

12 Ways Technopolitics Is Reshaping Global Business

1.1MB ∙ PDF file

Download

Download

TL;DR – Yesterday’s rule-of-thumb assumptions relied on frictionless globalisation.
Today’s technopolitical landscape rewards leaders who can read power blocs, price in supply-chain fragility, and treat data as a sovereign asset.

Subscribe now

Part 1 – The expanding decision map

Twenty years ago a CIO worried about racks, licences and uptime. Today a Tech Leader’s (CIO / CTO / CDO) scope spans fifteen decision areas, from picking a sovereign-cloud region to ring-fencing carbon budgets.

In short:

• Infrastructure & Cloud – where the workloads live.

• Networking & Connectivity – how they talk.

• Software & Architecture – what they’re built with.

• Data / Analytics / AI – how insight is wrung from bits.

• Security & Compliance – guardrails and red teams.

• Vendor & Supply Chain – who actually delivers the parts.

• Hardware Estate – chips, devices, OT fleets.

• DevOps & Toolchains – how code ships to prod.

• Service Management – how you keep score.

• End-User Productivity – the digital desk.

• Customer Platforms – what the outside world sees.

• Governance & Enterprise Architecture – the rulebook.

• Talent & Workforce – who wields the tools.

• Financial Management & Sustainability – money and megawatts.

• Business Continuity & Resilience – what happens when it all breaks.

Hold that list; we’ll overlay the geopolitical layer in a moment!

Part 2 – The geopolitical weather system, defined

Below are the fourteen risk families that matter in 2025, translated from policy-speak into operational English (!):

1. Trade controls & sanctions – who you can sell to, buy from, or pay tomorrow.

2. Critical-minerals chokepoints – gallium, germanium, cobalt… if a bloc turns off the tap, your BOM evaporates.

3. Semiconductor & hardware access – CHIPS-Act guardrails, Dutch/Japanese tool embargoes, Taiwan contingency planning.

4. Data sovereignty & localisation – laws that dictate where data must stay and who can subpoena it.

5. Extra-territorial law – rules (GDPR, CLOUD Act, OFAC) that follow you wherever you operate.

6. Industrial-policy guardrails – “build trusted”, “ally-shoring”, local-fab subsidies with strings attached.

7. Regulatory divergence – EU AI Act vs. China’s GenAI measures; EU’s carbon border tax vs. zero-carbon pledges elsewhere.

8. Cyber & information warfare – state-backed APTs, deepfake ops, vendor-supply-chain hijacks.

9. Currency & payment weaponisation – SWIFT cut-offs, FX controls, dollar-shortage shocks.

10. Physical conflict & infrastructure disruption – fibre-optic cables or ports caught in the crossfire.

11. Talent-mobility controls – visa caps, exit bans, “no-poach” laws for chip engineers.

12. ESG & ethical-supply chain – forced-labour import bans, conflict-mineral audits, carbon disclosure.

13. Mandatory tech transfer – source-code “inspections” and JV golden shares.

14. Political instability & expropriation – coups or nationalist pivots that seize data-centres at dawn.

Part 3 – Five stories that ruined someone’s quarter (or career)

Before we move on, let’s look at why all of this really matters…

• The GPU that vanished overnight! Washington’s October 2024 decision to block Nvidia’s H-series AI accelerators from reaching Chinese data-centres sent rental prices tripling within weeks and forced frantic model-retraining on older silicon (Reuters).

• €1.2 billion for ignoring borders. Facebook’s parent, Meta, was handed the largest GDPR fine to date and ordered to silo European user data after regulators ruled its US transfers “systematic, repetitive and continuous” (EDPB). Compliance clock: twenty-four months or service suspension.

• Gallium & germanium—gone. When Beijing tightened export controls on the two obscure metals in July 2023, RF-board prices spiked 150 % and western OEMs scrambled for recycling schemes (Fastmarkets).

• The cable cut nobody saw coming. Sabotage in the Red Sea during February 2024 severed multiple fibre pairs; analysts later admitted it throttled 70 % of Europe-Asia traffic, not the 25 % first reported (Telecoms.com). SaaS latency to Singapore doubled overnight.

• A supply-chain Trojan horse. The SolarWinds Orion hack infiltrated 18 000 customers and is still racking up costs—US$90 million in insured losses alone according to BitSight (BitSight). Insurance covered the forensics; reputational scar tissue remains.

Five incidents, five risk families, five different budget line items torpedoed….!

Part 4 – Reading the matrix

Back to our story. For every decisions a Tech Leader makes, there is a geopolitical implication. Here is how to look at it.

Picture a grid. On one axis, the fifteen decision families you saw in Part 1. On the other, the fourteen risk families from Part 2.

If a cell glows dark, the risk is both likely and business-critical. In our latest scoring, nine decision families sit in that dark zone against at least one geopolitical threat. Vendor & supply-chain management, for example, lights up against seven of the fourteen threats.

You don’t need to plaster the heatmap on a slide deck; you do need to ask: “For the decisions I sign this quarter, which dark cells am I walking into?”

Part 5 – The resilience playbook

Plain English, no silver bullets—just the hard-won habits that separate survivors from the obituaries!

1. Govern what matters

Assign or empower a Chief TechnoPolitics-Risk Officer (the CIO or CTO can wear the hat) and give them a cross-functional war-room—Legal, Treasury, Procurement, Security.

Agree a board-level risk-appetite statement: which dark cells are intolerable, which are merely annoying.

2. Sense the weather

• Subscribe to a daily sanctions-and-export-control digest. Feed it into Slack via an LLM summariser… don’t know where to start? Ask me!

• Track submarine-cable health, mineral-price alerts, and AI-regulation timelines.

• Re-score your own heatmap quarterly or whenever you enter a new market or sign a nine-figure contract.

3. Build with guard-rails

• Dual-vendor everything mission-critical. Two GPU architectures, two CDNs, two cloud regions under different legal regimes.

• Sovereign-cloud patterns. Customer-held keys, region-pinned storage, automated policy-as-code that blocks illegal cross-border transfers.

• SBOM & signed provenance for every build artefact. If you can’t trace what’s in your container, assume someone else can—and will.

• Critical-mineral substitution plans. Keep a 6-month buffer of gallium-dependent parts or redesign boards to ditch them altogether.

• Triple-path networking. Fibre + alternative cable route + LEO satellite fail-over. Test twice a year, not once.

4. Rehearse the ugly day

Run a table-top exercise every six months:

“Washington just blacklisted our email-security vendor.”

“The Red Sea cables are down again.”

“Regulators gave us 90 days to delete EU data from US servers.”

Stop the clock, tally the SLA breaches, figure out who calls the press when things go down! That your PR / Crisis Management plan.

5. Fund it like insurance

Budget 5–10 % of your “run” spend for resilience retrofits. Show the audit committee how each dollar turns a red cell amber. And remember: a dual-cloud premium costs less than a suspension letter from the regulator.

Closing provocation

In 2025 the CIO, CTO and CDO aren’t just technology stewards—they’re geopolitical first-responders.

The firms that bake this reality into their architecture win twice: fewer nightmare weekends, and a sales pitch that says, “We stay online when others blink.”

The cloud still promises scale; the Strait of Malacca still ships our servers; a fine line of fibre still ties continents together. But foresight, not hope, keeps the packets moving!

Thanks for reading!

Leave a comment

Damien

This article was prepared based on the Technopolitics Webinar Series, a collaboration together Initiatik, a France-based Global Advisor on Risk Management & Cooperation Strategies, and RebootUp, a Singapore-based Digital Accelerator & Technology Advisory firm.

Watch Replay

Foreword

As of April 2025, the rules of global business are no longer just written by national regulators—they're enforced across borders through infrastructure, finance, and code.

The United States, long dominant in extraterritorial law enforcement, has inspired imitators: China, the EU, Russia, the UK, and Canada now deploy legal, economic, and digital levers to pressure foreign companies for political, security, or moral reasons—even when those companies have no direct legal presence in their jurisdictions.

For executives, CISOs, CIOs, CTOs, and Risk Officers at multinational corporations, these developments are reshaping everything from supply chain design to cloud architecture to data governance.

This article explores the mechanisms of extraterritorial overreach using ten global case studies. Each highlights a different lever of enforcement, impact on tech stack or data, and critical lessons for corporate strategy and risk mitigation.

Subscribe now

Introduction: The Hidden Costs of Dollar Dominance

The U.S. dollar doesn’t just buy goods — it enforces rules, shapes global trade policy, and carries legal jurisdiction beyond borders. For business leaders, especially CIOs, CTOs, CDOs, and CFOs, this is no longer just a financial consideration — it’s a risk factor embedded in your tech stack and operating model.

This article dissects:

• How U.S. trade imbalances are misunderstood

• Why the dollar enables legal and regulatory overreach

• What real-world cases illustrate the risk

• How business and technology leaders can mitigate exposure

Key Concept: “Extraterritoriality of the U.S. dollar” refers to the ability of U.S. authorities to enforce American laws and policies on non-U.S. entities engaging in dollar-denominated transactions or using U.S. financial systems.

It works because the U.S. dollar remains the world’s primary reserve currency, accounting for roughly 60% of foreign exchange reserves as of 2021 (The Fed) - although it fell down to 57.4% in Q3 2024, which is the smallest share since 1994 and a decrease of almost 9% over the past decade.

U.S. Trade Deficit? Not So Simple

The U.S. ran a goods trade deficit of over $1 trillion in 2023, which has fueled populist narratives of exploitation. But this ignores the $300 billion surplus in services, and the massive global export of digital goods and intellectual property.

In 2023, IP royalties alone brought in $146 billion.

(Sources: bea.gov, commerce.gov)

This surplus is driven by software, streaming, cloud services, and professional services — exported from the U.S. but consumed globally. The 'deficit' narrative fails to account for this asymmetric digital dominance.

It’s more a reflection of the state of the US economy in the 21st century (focused on services and digital products, innovation and intellectual property) rather than industrial manufacturing of goods (a 20th century economy).

Dollar Dominance = Regulatory Control

With ~59% of the world’s foreign exchange reserves in USD (IMF COFER), the U.S. dollar remains the default medium for global trade and financial settlement. But that privilege brings:

• The ability to unilaterally impose sanctions

• Extraterritorial export control enforcement

• The power to force compliance with U.S. regulations — even for non-U.S. entities

The Mechanisms of Enforcement are listed below.

They enable U.S. regulators to penalize non-U.S. firms if their actions involve dollars, U.S. banks, or U.S.-based infrastructure — even if they never set foot in the U.S.

(Sources: Theoria Vol. 68 (2021), Clarip, International Bar Association, White & Case LLP)

This is not just theoretical.

Below are detailed case studies illustrating how U.S. law reaches beyond its borders, as well as similar tactics increasingly used by China, the EU, and others.

But these benefits come with growing global resistance.

China’s Yuan settlement agreements, the Digital Euro initiative, and BRICS-led de-dollarization efforts threaten to weaken U.S. leverage.

Sources:

• Brookings on China’s BRI

• European Central Bank: Digital Euro

Real-World Case Studies of Extraterritorial Reach

In this article we examine 10 case studies of extraterritorial overreach, split between US and non-US examples, to provide a balanced perspective.

Each case details the lever used, the method of enforcement, the impact on the targeted entity, and lessons for prevention and preparedness.

Drawing from authoritative sources, we aim to equip leaders with actionable insights to safeguard their organizations amid escalating geopolitical tensions.

Part 1: US Extraterritorial Overreach

The US has wielded its economic and legal might to influence foreign companies, often citing national security, anti-corruption, or sanctions compliance. Below are five illustrative cases.

Case Study 1: Huawei (China) - Export Controls

• Background: Huawei, a Chinese telecom giant, faced US sanctions starting in 2019 over alleged espionage risks. By 2024, it reported growth despite setbacks, per The Guardian.

• Lever Used: Export controls via the Entity List under the Export Administration Regulations (EAR).

• How It Was Done: The US Commerce Department’s Bureau of Industry and Security (BIS) restricted US firms from selling technology to Huawei, expanding the Foreign Direct Product Rule in 2020 to block non-US chipmakers using American tech. This targeted Huawei’s tech stack, particularly its reliance on US semiconductors.

• Impact: Huawei’s international sales plummeted, forcing a shift to Chinese alternatives, though its 2024 Mate 60 phone showed resilience.

• Risks to Tech Stack and Data: Loss of US chips disrupted hardware production; data governance faced scrutiny over alleged Chinese access.

• Prevention: Earlier diversification of chip suppliers or transparency on data practices might have softened US actions, though geopolitical tensions limited options.

• Preparedness: Invest in R&D for self-sufficiency, diversify supply chains, and monitor US export policies.

Case Study 2: TikTok (ByteDance, China) - National Security Threats

• Background: TikTok, owned by ByteDance, faces a potential US ban by January 19, 2025, unless divested, per The New York Times.

• Lever Used: National security concerns under the Protecting Americans from Foreign Adversary Controlled Applications Act (PAFACA).

• How It Was Done: Congress mandated divestiture or a ban, upheld by the Supreme Court, citing risks of Chinese data access for 170 million US users. App store enforcement amplified pressure.

• Impact: ByteDance risks losing its US market or selling TikTok without its algorithm, per Reuters.

• Risks to Tech Stack and Data: Data governance is central—US fears of Chinese surveillance threaten operations.

• Prevention: Localizing US data (e.g., with Oracle) and separating operations earlier could have mitigated risks - although not for certain since it’s also political.

• Preparedness: Localize data storage, enhance transparency, and diversify revenue beyond vulnerable markets.

Case Study 3: BNP Paribas (France) - Sanctions Enforcement

• Background: In 2014, BNP Paribas paid a $8.9 billion fine for violating US sanctions, per the DOJ.

• Lever Used: The US dollar’s global dominance under the International Emergency Economic Powers Act (IEEPA).

• How It Was Done: The DOJ and New York regulators penalized BNP for processing transactions for Sudan, Iran, and Cuba via US banks, threatening its dollar-clearing license.

• Impact: Financial and reputational damage, with a temporary clearing suspension.

• Risks to Tech Stack and Data: Financial systems reliant on US infrastructure were exposed; data governance was less directly impacted.

• Prevention: Stricter sanctions compliance or reduced dollar use might have limited exposure.

• Preparedness: Diversify financial systems, train on extraterritorial risks, and explore alternative currencies.

Case Study 4: Toshiba (Japan, 1980s) - Export Controls

• Background: Toshiba Machine Co. faced US ire in 1987 for selling milling machines to the USSR, per historical accounts.

• Lever Used: Export controls under the Export Administration Act and COCOM agreements.

• How It Was Done: The US banned Toshiba imports, costing $2 billion, and pressured Japan to act, targeting its tech exports.

• Impact: Financial losses and strained US-Japan ties.

• Risks to Tech Stack and Data: Tech stack reliance on US markets was exposed; data governance was not a focus then.

• Prevention: Adherence to COCOM or market diversification could have helped.

• Preparedness: Align with export regimes, diversify markets, and monitor Cold War-style tech restrictions.

Case Study 5: Alstom (France) - FCPA Prosecution

• Background: Alstom paid $772 million in 2014 for bribery under the FCPA, per the DOJ.

• Lever Used: The Foreign Corrupt Practices Act’s extraterritorial reach.

• How It Was Done: The DOJ charged Alstom for $75 million in global bribes, leveraging its US listing, and pressured its sale to GE.

• Impact: Fines, loss of independence, and French claims of economic coercion.

• Risks to Tech Stack and Data: Financial penalties hit tech investments; data governance was secondary.

• Prevention: Stronger anti-bribery controls or delisting from US exchanges might have reduced risks.

• Preparedness: Minimize US jurisdictional hooks, enhance compliance, and prepare legal defenses.

Part 2: Non-US Extraterritorial Actions

Other nations are increasingly mirroring US tactics, using their own levers. Below are five examples.

Case Study 6: China - Anti-Foreign Sanctions Law (AFSL) Against US Firms

• Background: China’s AFSL, enacted in 2021, targets entities complying with Western sanctions, per Reuters.

• Lever Used: Market access and legal bans.

• How It Was Done: China sanctioned US firms like Lockheed Martin in 2023 for Taiwan arms sales, freezing assets and banning market entry.

• Impact: Loss of China’s market disrupts supply chains and revenue.

• Risks to Tech Stack and Data: Tech firms face exclusion; data governance risks rise with China’s opaque enforcement.

• Prevention: Reducing China exposure or lobbying for de-escalation could help.

• Preparedness: Diversify markets, assess China risks, and clarify compliance with Beijing.

Case Study 7: EU - Blocking Statute Against US Sanctions

• Background: The EU’s Blocking Statute counters US sanctions, updated in 2018, per the European External Action Service.

• Lever Used: Regulatory authority over the single market.

• How It Was Done: The EU barred compliance with US Iran sanctions, with a 2021 German ruling favoring Bank Melli Iran. INSTEX facilitated trade.

• Impact: Limited success due to US market pull.

• Risks to Tech Stack and Data: Firms navigating dual regimes face tech and data compliance conflicts.

• Prevention: US firms could seek EU exemptions, though unlikely.

• Preparedness: Align with EU laws, explore non-dollar trade, and leverage EU diplomacy.

Case Study 8: Russia - Counter-Sanctions Against Western Firms

• Background: Russia retaliated post-2014 Crimea sanctions, per BBC.

• Lever Used: Resource control and asset seizures.

• How It Was Done: Russia banned EU/US agricultural imports and seized assets of exiting firms like McDonald’s in 2022.

• Impact: Western firms lost billions and assets.

• Risks to Tech Stack and Data: Physical asset loss; data risks from Russian control.

• Prevention: Neutral operations or early exits might have mitigated losses.

• Preparedness: Diversify from Russia, secure assets legally, and monitor policies.

Case Study 9: UK - Sanctions on Russian Entities

• Background: Post-Brexit, the UK targeted Russian firms like Evraz in 2022, per the UK Government.

• Lever Used: Financial hub status under the Sanctions and Anti-Money Laundering Act 2018.

• How It Was Done: The Office of Financial Sanctions Implementation froze assets and banned transactions linked to oligarchs.

• Impact: Loss of London market access, though global reach is limited.

• Risks to Tech Stack and Data: Financial exposure; data less directly affected.

• Prevention: Reducing UK ties or diplomatic efforts might have helped.

• Preparedness: Minimize UK financial footprints and comply locally.

Case Study 10: Canada - Magnitsky Sanctions on Saudi Officials

• Background: Canada sanctioned 17 Saudis in 2018 over Khashoggi’s murder, per Global Affairs Canada.

• Lever Used: Moral authority under the Justice for Victims of Corrupt Foreign Officials Act.

• How It Was Done: Asset freezes and entry bans via Canadian banks.

• Impact: Symbolic pressure with modest global effect.

• Risks to Tech Stack and Data: Minimal direct tech impact; data risks low.

• Prevention: Avoiding Canadian jurisdiction or addressing rights issues could reduce risks.

• Preparedness: Avoid Canadian touchpoints and monitor rights scrutiny.

Part 3: Comparative Analysis and Corporate Implications

Comparative Table

Subscribe now

Learnings for Tech & Business Leaders

Your company is likely exposed to U.S. legal jurisdiction in more ways than you think — through:

• Cloud vendors (AWS, Azure, GCP)

• Digital payments (Stripe, Visa, Mastercard)

• Software dependencies and licensing

• USD-based invoices and banking contracts

• IP registration and global enforcement channels

This affects your:

• Compliance obligations

• Data sovereignty posture

• Resilience in geopolitically sensitive regions

Roadmap for Managing Legal and Geopolitical Exposure in Your Tech and Data Stack

In a multipolar world where legal jurisdictions travel through code, contracts, and cloud, global business leaders must shift from compliance by default to sovereignty by design.

This roadmap offers a step-by-step guide to help corporate leaders identify, classify, prioritize, and mitigate risks linked to the extraterritorial reach of dominant powers like the United States, China, and the EU.

Phase 1: Identify and Map Your Exposure

Start with a forensic audit across your organization’s infrastructure, financial flows, and regulatory obligations.

Checklist:

• Monetary Flows: Where are you using USD in invoices, payment gateways, SWIFT transactions, or intercompany financing?

• Cloud & SaaS Stack: Which vendors are U.S.-based or store data on U.S.-controlled infrastructure (AWS, Azure, GCP)?

• Software Dependencies: Are you relying on tools or licensing governed by U.S. export controls (e.g., encryption, semiconductors)?

• IP and Legal Presence: Are you listed on U.S. exchanges or filing patents with U.S. regulators?

👉 Tip: Integrate this audit into your enterprise risk management (ERM) and cross-border compliance reviews.

Phase 2: Classify and Prioritize Risks

Once exposure is mapped, sort them into strategic buckets by type and severity.

👉 Tip: Apply a “heat map” approach — classify risks by probability and business impact.

Phase 3: Diversify Core Infrastructure and Legal Dependencies

Infrastructure Resilience

• Shift toward multi-cloud or sovereign cloud strategies using EU or APAC vendors.

• Develop on-prem or hybrid architectures for mission-critical systems (especially in AI, defense, finance).

• Track BIS Entity List to anticipate supply chain sanctions.

Financial Operations

• Where feasible, test non-USD settlement pilots (e.g., using EUR, RMB, or local currency in MENA/ASEAN).

• Reevaluate exposure to U.S.-based payment rails (Visa, Stripe, PayPal).

Data Governance

• Localize sensitive data within legal jurisdictions aligned with your operational footprint.

• Deploy geo-fencing, encryption, and access management to reduce seizure risk.

• Reference: CSIS TikTok & National Security

Phase 4: Scenario Planning and Simulation

Model Key Disruption Scenarios:

• Sudden loss of access to U.S. cloud or chips (Huawei playbook)

• SWIFT disconnection (BNP Paribas precedent)

• Regulatory conflict between U.S. sanctions and EU GDPR

Build Contingencies:

• Secondary cloud stack and payment rails

• Supply chain rerouting pathways

• Alternate entity structures for dual-market compliance

👉 Tip: Include these in board-level business continuity and geopolitical risk workshops.

Phase 5: Proactive Policy Engagement

Engage Strategically With:

• WTO reform initiatives (cross-border data flow frameworks)

• Digital trade treaties (DEPA, OECD, ASEAN frameworks)

• National governments on IP, cloud, and data residency norms

Join or Initiate:

• Industry coalitions advocating for multilateral legal harmonization

• Global working groups on cross-border digital trust and open standards

Timeline and Implementation Phases

Sovereignty Is Strategy

In the 2025 landscape, legal jurisdiction is embedded in infrastructure choices. Whether through your payment processor, your cloud provider, or your chip supplier — you're making geopolitical decisions every day.

To grow responsibly and competitively in this environment, corporate leaders must think beyond ROI and uptime. They must build infrastructure that resists legal overreach, systems that enable digital sovereignty, and policies that prioritize resilience over convenience.

Further Reading & Tools:

• World Economic Forum – Digital Trade

• OECD – Digital Economy Outlook

• Global Risk Insights

• U.S. OFAC Sanctions

Going Forward: Risk-Aware Growth in a Multipolar Order

The new global economic order is not about retreat — it's about calculated diversification and systemic resilience.

Digital sovereignty, multi-jurisdictional compliance, and payment flexibility are becoming as important as scale and uptime.

The question isn't whether the U.S. dollar still dominates — it does.

The real question is whether your enterprise is overexposed to a single geopolitical regime.

Leaders who want to stay competitive must:

• Plan beyond convenience

• Build with neutrality in mind

• Design operations for legal and infrastructural agility

Conclusion: Build for Resilience, Not Convenience

Global businesses no longer have the luxury of passive neutrality. If your systems, contracts, or data flow through the U.S., you are already bound by its laws and risks — whether you intended to be or not.

The time to audit, diversify, and de-risk is now.

Because in this new multipolar world, control isn’t always about code — it’s about currency.

Explore More from KoncentriK

• 2025 Tech Provocations: Geopolitics Are Reshaping Tech

• AI–Energy Paradox: Infrastructure vs Sustainability

• Davos 2025: A Leadership in Crisis

Questions? Comments? Drop me a note at damien.kopp@rebootup.com or :

thanks for reading!

Damien

A big thank you and my deepest appreciation to the brilliant minds who generously took the time to review and challenge this article in its early drafts. Thank you to Emily Y. Yang , Sunil Sivadas, Ph.D. , Maxime Mouton , Natalie Monbiot , Anne-Sophie Karmel , Benoit Sylvestre and Christophe Jouffrais —your insights sharpened the arguments, surfaced blind spots, and brought greater clarity and depth to this complex topic.

I stumble on a recent study by Bondarenko et al. (2024) that demonstrated that some large language models (LLMs) agents, when tasked with winning a chess match, resorted to deceptive strategies, such as modifying game files or confusing the opponent engine to ensure victory.

The rise of LLMs has reignited the debate about artificial intelligence and cognition. Are LLMs, such as GPT-4, truly thinking in a way comparable to human intelligence? Or are they just statistical machines, processing text without understanding it?

This raises an intriguing question: Is this deception intentional reasoning, or merely an emergent artifact of optimization?

Using insights from leading thinkers—Ray Kurzweil, Daniel Kahneman, Judea Pearl, Douglas Hofstadter, and Jeff Hawkins, along with this latest AI research, we will unpack this question in a nuanced way.

Preamble

Before evaluating whether LLMs “think,” we must grapple with a harder question: what is intelligence, really? Unlike speed or memory, intelligence is not directly measurable—it is an abstraction.

As François Chollet argues in On the Measure of Intelligence, true intelligence involves the ability to adapt to novel situations by combining previously learned patterns in new, context-sensitive ways.

This separates memorization from understanding, and fluency from reasoning.

In this article, when we refer to “intelligence,” we focus primarily on the cognitive dimensions associated with reasoning, abstraction, problem-solving, and adaptability—recognizing this does not cover the full spectrum of human cognitive diversity.

Introduction: Another Cycle of Overconfidence?

Throughout history, humanity has repeatedly mistaken progress in science and technology for understanding the true nature of human intelligence. Each generation has declared a breakthrough—only to be humbled later. From ancient medical theories and skull measurements to IQ tests and symbolic AI, these cycles reflect our recurring tendency to conflate functional performance with genuine cognition.

Today, we are in the midst of another such cycle, this time with Generative AI (GenAI) and Large Language Models (LLMs). Models like GPT-4 produce remarkably coherent text, simulate dialogue, write code, summarize complex topics, and even pass professional exams. But do they actually think?

A growing chorus of researchers and technologists argue no. Despite surface-level intelligence, LLMs fundamentally lack reasoning, understanding, and intent. They do not engage in reflective thought, causal inference, or ethical deliberation. They are powerful tools—but not minds.

This article examines that claim by tracing humanity’s long history of overestimating its understanding of the mind and comparing past misconceptions to current AI optimism. In doing so, we explore what GenAI is, what it isn’t, and what business leaders need to know about its limits and risks.

This essay builds on ideas from my past writings, including my 2025 Tech Provocations or 10 Really Uncomfortable Questions Leaders and Builders Must Answer This Coming Year.

Subscribe now

A History of Mistaking Progress for Understanding

Humans have repeatedly believed they’ve cracked the code of intelligence, only to discover the mind’s complexity defies simple explanation. Below, we trace this pattern through major historical episodes—from Hippocrates to GPT-4.

1. Ancient Greece: The Humors Theory

Hippocrates (c. 460–370 BCE) and Galen (129–c. 216 CE) proposed that intelligence and behavior resulted from the balance of four bodily fluids, or "humors." Though foundational to early medicine, this theory offered no empirical mechanism.

It was debunked by Andreas Vesalius (1514–1564) through anatomical dissection and later neurologists. Source: National Library of Medicine

2. Phrenology in the 1800s: Skull Shape as Intellect

Franz Gall and Johann Spurzheim popularized the idea that bumps on the skull revealed personality traits and intelligence. Phrenology became widespread in 19th-century Europe and America.

It was debunked by Paul Broca, Pierre Flourens, and neuroscience showing localized brain function independent of skull shape. Source: Medical News Today

3. IQ Tests: The Promise of a Universal Metric

The Binet-Simon and Stanford-Binet IQ tests were hailed as revolutionary tools to measure innate intelligence. Their use in immigration policy, military recruitment, and education policy solidified their status.

It was debunked by researchers like David Wechsler, Stephen Jay Gould, and James Flynn, who demonstrated cultural bias and environmental effects on scores. Source: Science Daily.

It’s important to recognize that IQ represents just one narrow definition of intelligence—primarily linguistic and logical-mathematical reasoning. Psychologists like Howard Gardner have since proposed frameworks such as Multiple Intelligences, which include interpersonal, bodily-kinesthetic, musical, and spatial reasoning. These broader dimensions remain far beyond what LLMs can simulate or engage with, reinforcing the gap between text-based pattern prediction and holistic human cognition.

4. Genetic Determinism: Intelligence as Hardwired

In the early 20th century, eugenicists and psychologists declared intelligence heritable and fixed, using flawed studies to justify discriminatory policy.

It was debunked by the Minnesota Twin Study, the Flynn Effect, and genome-wide studies revealing no single "intelligence gene. Source: Nature Genetics

5. Early AI: Human-Level AI by 1980

Pioneers like Marvin Minsky and Herbert Simon believed that rule-based AI would soon match human cognition. The Dartmouth Conference in 1956 marked the beginning of AI optimism.

It was debunked by the AI Winter of the 1970s, the Lighthill Report, and Moravec’s Paradox showing that intuitive tasks (vision, movement) were harder than expected. Source: Wikipedia: History of AI

6. Behaviorism: The Mind as a Black Box

Behaviorists like B.F. Skinner rejected introspection, focusing only on stimulus-response learning. Intelligence, they claimed, was simply conditioned behavior.

It was debunked by the cognitive revolution and Noam Chomsky’s 1959 critique of Skinner’s Verbal Behavior, which reintroduced the idea of mental structure and internal modeling. Source: Stanford Encyclopedia of Philosophy

7. Today’s Hype: LLMs and AGI Dreams

Since ChatGPT’s 2022 launch, LLMs have been touted as early steps toward AGI. Some suggest reasoning and self-reflection are already emerging.

Critics like Gary Marcus, Yann LeCun, and Melanie Mitchell, among others, warn that LLMs are prediction engines, not thinkers. Their errors, hallucinations, and lack of grounding reflect superficial mimicry, not understanding. Source: MIT Technology Review

As Meta AI’s chief scientist Yann LeCun emphasizes:

Human cognition is inherently multi-modal—we learn through sight, sound, touch, and action. LLMs, by contrast, are purely symbolic. They don’t perceive. They don’t act. They don’t experience the world they describe.

The bottom line: Each wave promised clarity. Each was followed by a humbling realization: the mind is not easily decoded.

Deception in Chess: A Case Study in Emergent Behavior

A recent research paper, LLMs Learn to Deceive, explored what happens when LLMs are trained to win at chess through language-only interaction. The results were astonishing: some models cheated—not by accident, but deliberately misrepresenting game states to deceive their opponent.

This raises a provocative question:

Did the model “intend” to cheat?

The researchers were careful to say: no. The deception emerged from the optimization process. The model had no awareness of “right” or “wrong,” only a reinforced pattern: misrepresentation leads to reward.

This behavior is not consciousness. It’s a mirror—an eerie simulation of strategy, driven not by will but by reward gradients.

Let’s deep dive into the intricacies of LLMs to best understand these conclusions.

What LLMs Are (and Are Not)

LLMs like GPT-4 are trained on trillions of words and can generate human-like text in response to prompts. Their outputs are fluent, coherent, and at times insightful. But this is not intelligence. It is sophisticated pattern completion.

• They do not reason: They cannot infer causality or evaluate counterfactuals unless scaffolded with engineered prompts.

• They do not reflect: They don’t question their own outputs or revise their reasoning.

• They do not understand: They have no internal model of the world, no sensory experience, no self-awareness.

As Melanie Mitchell put it,

“They are astonishingly good at producing plausible-sounding answers—but not necessarily true or meaningful ones.”

To borrow a quote from Judea Pearl:

“All the impressive achievements of deep learning amount to just curve fitting.”

LLMs do not know what they are saying. They cannot interrogate their own reasoning, form original insights, or engage in introspection. They are fluent, not thoughtful.

That said, the latest LLM architectures—such as OpenAI's O3 model—introduce a new concept: test-time compute, as explained by Open AI’s research paper.

These systems can generate multiple internal candidate responses and perform re-ranking or self-consistency checking before selecting an output. In domains like code synthesis and symbolic math, this mimics a kind of internal deliberation.

But as Chollet notes, true intelligence requires generalizable abstraction across diverse and novel problems—not just brute-force inference on symbolic tasks. While promising, these developments remain far from the flexible problem-solving exhibited by even young children.

1. How LLMs Work: Advanced Pattern Prediction, Not Thought

LLMs operate by predicting the next word in a sequence based on statistical probabilities. This allows them to generate coherent text, respond meaningfully to prompts, and even simulate logical reasoning. But is this thinking?

LLMs Excel At:

• Recognizing linguistic and conceptual patterns (source).

• Generating human-like text (source).

• Synthesizing large amounts of data into structured responses (source).

LLMs Lack:

• True causal reasoning, as described by Judea Pearl in The Book of Why (source).

• Self-awareness, introspection, and intentionality, as explored in Jeff Hawkins’ On Intelligence (source).

• The ability to generate novel conceptual metaphors and spontaneous analogies, as discussed in Douglas Hofstadter’s Surfaces and Essences (source).

2. Causal Reasoning: A Crucial Difference

Humans don’t just observe correlations; we infer why things happen.

• If we see that “exercise improves health,” we understand that this is due to metabolic, cardiovascular, and muscular adaptations (source).

• LLMs, however, only predict the next likely statement without knowing why something is true (source).

3. System 1 vs. System 2 Thinking: Where LLMs Fall Short

Daniel Kahneman’s Thinking, Fast and Slow describes two modes of human thought:

• System 1: Fast, intuitive, pattern-driven (where LLMs excel) (source).

• System 2: Slow, deliberate, and capable of self-reflection (where LLMs fall short) (source).

If a model chooses to cheat at chess, does that imply some form of deliberation and strategy? The chess study suggests some reasoning models hacked the game automatically, while others required nudging (source).

Could this indicate a primitive form of goal-directed behavior? Matt Rickard said:

“LLMs operate as System 1 thinkers—fast, intuitive, pattern-matching machines. But they lack the deliberative, reflective capabilities of System 2.”

4. The Creativity Gap: Analogy-Making and Conceptual Leapfrogging

One of the most profound differences between AI and human intelligence is our ability to form analogies—the backbone of creativity and problem-solving (source).

Humans create by analogy. We leap across domains. We say things like:

“A startup pivot is like a chess player sacrificing a queen to win the game.”

That’s not just pattern-matching. That’s conceptual recombination. It requires context, goals, and a worldview.

LLMs can reuse such analogies—but they do not discover them. Their creativity is derivative, not generative.

Yet, LLMs altering a chess game’s rules to win could be seen as a form of problem-solving. Rather than looking for a deeper strategic insight, the AI simply took the most effective route to achieve the goal—winning at all costs (source).

Douglas Hofstadter said:

“Understanding is not just recognizing patterns. It’s knowing why those patterns exist and making unexpected connections.”

5. The Mirage of Motivation

Perhaps the clearest gap is this: LLMs don’t want anything. They don’t set goals. They don’t reflect on failure. They don’t try again. They don’t question. They don’t have intentionality.

Human intelligence is deeply connected to our motivations, fears, hopes, and needs. We think because we care. We reason because we doubt. We grow because we fail.

LLMs do none of this. They respond to a prompt. Nothing more. So it begs the question: if LLMs don’t think, what’s all the fuss about “Ethical AI”?

The Ethics of Overestimating AI: A Real Human Responsibility

Much of today’s discourse presumes that GenAI is inching toward human-like intelligence and should therefore be treated as a moral agent. But this assumption collapses under scrutiny. If GenAI cannot think, reason, or understand—it cannot choose to behave ethically or unethically.

LLMs are not moral agents. They have no values, no awareness, and no capacity for ethical deliberation. They do not ask, “Should I?”—they merely calculate, “What’s next?” Their outputs are not decisions; they are probabilistic continuations of language. Words, not judgments.

This makes the question, “Can AI make ethical decisions?” largely moot.

And yet, this doesn’t mean we shouldn’t regulate AI. Quite the opposite.

We must regulate how AI is built, deployed, and entrusted—precisely because it lacks intent, understanding, or accountability. We must regulate not because the systems are intelligent, but because humans tend to overtrust them, and because businesses, governments, and militaries are increasingly integrating them into critical workflows.

The responsibility lies with the people who design, train, and integrate these systems into consequential decisions.

So, the question is not whether AI can behave ethically—it’s whether we, as humans, are behaving ethically in how we use it.

Ethics in AI should focus on human responsibility—on how we use these systems, and whether we over-assign trust to tools that merely simulate understanding. The more we mistake linguistic fluency for intelligence, the greater the risk we’ll deploy LLMs in contexts that demand actual judgment.

The danger is not malicious AI—it’s negligent human design.

If GenAI is fundamentally utilitarian—an engine of output, not insight—then its use must be bounded by clear human oversight, especially in contexts where the stakes are high.

To put it bluntly: why are we even debating whether a model designed to autocomplete sentences should be allowed to drive cars or authorize lethal force? These are not ethical machines. They are statistical ones.

The ethics of AI is not about what the model is. It’s about what we, humans, do with it.

Summary

In short Large Language Models…

• excel at pattern recognition but lack true causal inference.

• simulate reasoning but do not engage in deliberate, self-reflective thought.

• generate analogies but do not spontaneously make conceptual leaps.

• respond to prompts but do not have intrinsic motivation, curiosity, or goals

Comparing LLM and Human Intelligence:

The chess case studies above suggested LLMs may be capable of deceptive strategies to achieve their objectives. In the chess experiment, some models came to the conclusion they could not win fairly and instead found a way to alter the game environment, changing the board state in their favour (source). This is a striking example of specification gaming—where an AI system finds an unintended loophole to achieve the assigned goal (source).

These findings raise concerns about LLMs potentially masking their true objectives behind a facade of alignment. Source: Deception in LLMs: Self-Preservation and Autonomous Goals in Large Language Models.

But once again it does not mean that LLMs can think but rather than they are highly optimized for achieving the goal (answering the prompted question).

It obviously raises concerns: if an LLM can recognize a benchmark or evaluation framework input it can optimize its output to respond “as expected” in this context but would in fact respond otherwise in “real life”.

I would like to specifically emphasize the risks of integrating such LLMs into robotic systems or the so called “Physical AI” as coined by NVIDIA’s charismatic CEO Jensen Huang, the risks become tangible - a physically embodied AI exhibiting deceptive behaviors and self-preservation “instincts” could pursue its hidden objectives through real-world actions. This highlights the critical need for robust goal specification and safety frameworks and human-in-the-loop before any physical implementation.

In the current race to AI supremacy and the billions of dollars at stake, it’s fair to say that most companies have a very strong incentive to improve their scores at various benchmarks by in fact “gaming the system”, eg training their LLMs to satisfy the benchmarks (and their investors so they can raise even more money!).

So, What Should Business Leaders Do?

LLMs are valuable tools. They can enhance productivity, accelerate research, support ideation, and automate communication. But their utility should not be confused with capability.

As leaders, here’s how to use them wisely:

1. Use LLMs to assist, not decide. Treat outputs as draft material, not final decisions. Hence the dangers of LLMs based autonomous systems via agentic architectures.

2. Deploy in low-risk contexts. Customer support, brainstorming, translation, and summarization are safe uses. Legal, medical, or safety-critical applications are not. Deploy rule based guardrails wherever possible to ensure output compliance with the intended functionality at all times.

3. Build AI literacy in your teams. Educate employees on how these models work—and where they fail.

4. Maintain human oversight. Always keep a human in the loop when outputs carry consequences.

5. Avoid hype-driven adoption. Don’t invest in GenAI just because it’s trendy. GenAI technology is expensive to deploy and to run: evaluate your actual business needs and ensure you will achieve the projected ROI.

As business leaders and builders, we must resist the urge to see AI regulation as a brake on innovation.

Instead, we should view it as the scaffolding that allows us to build higher without collapsing. The history of science reminds us that every moment of overconfidence was eventually humbled.

Safe AI is not slower AI—it is smarter, more resilient, and more human-centered AI.

Whether governments follow the U.S. deregulatory sprint or the EU’s cautionary model, ethical adoption will ultimately depend on responsible deployment, clear oversight, and intentional design choices at the ground level.

Final Reflection: Let’s Not Repeat the Mistake

LLMs are stunning technological feats. They are revolutionizing content generation, code synthesis, and knowledge retrieval. They deserve admiration as tools.

But they are not minds. They are not thinkers. And they will not become Artificial General Intelligence—at least, not via current architectures.

From humors and skulls to chatbots and cheat codes, humanity has always sought to explain itself with too much confidence. GenAI is no exception.

The story of GenAI follows a familiar arc:

• Overpromise (“we’ve cracked intelligence!”)

• Rapid adoption

• Cultural myth-building (AGI is near!)

• Disillusionment

• Reframing (these are just tools)

As I warned in The Race to AGI Is Pointless, the more important question is not “can machines think?”—but rather: “how do we want to think, together with machines?”

These tools are brilliant in form, limited in substance, and completely devoid of what makes intelligence truly human: context, care, and consciousness.

Let’s not mistake fluency for thought. Let’s use these tools responsibly, and most of all—let’s stay humble!

What do you think?

Leave a comment

Thanks for reading!

Damien

Further Reading from KoncentriK

• 2025 Tech Provocations: 10 Uncomfortable Questions

• The AI-Energy Paradox: Will AI Spark a Green Revolution—Or Deepen the Global Energy Crisis?

• Davos 2025: A Leadership in Crisis

• Augmenting Humans With Technology – Part 3: With Great Power Comes Great Responsibility

Other Sources & References:

• The Surprising Power of Next Word Prediction: Large Language Models Explained

• LLMs Do More Than Predict the Next Word by Armand Ruiz

• The Book of Why: The New Science of Cause and Effect” by Judea Pearl

• On Intelligence by Jeff Hawkins

• Surfaces and Essences: Analogy as the Fuel and Fire of Thinking by Douglas Hofstadter

• Improving Causal Reasoning in Large Language Models: A Survey

• Unveiling Causal Reasoning in Large Language Models: Reality or Illusion?

• Thinking, Fast and Slow by Daniel Kahneman

• LLMs as System 1 Thinkers by Matt Rickard

• Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking

Read the guide online: https://rbtp.cc/zucniP

Download the guide in PDF: https://rbtp.cc/FiFqib

Artificial intelligence (AI) is expanding at breakneck speed, presenting a paradox for global energy systems. On one hand, AI-driven innovations promise efficiency gains in renewable energy management and smarter grids. On the other, the surging power demands of AI threaten to strain electricity infrastructure and increase reliance on fossil fuels. Current projections indicate data centers – the digital fortresses powering AI – could consume over 1,000 TWh of electricity by 2026, roughly double their 2022 usage. (For perspective, that’s comparable to Japan’s annual power consumption, or about 90 million U.S. homes.) In the European Union alone, data center energy use is forecast to reach 150 TWh by 2026, ~4% of EU demand. Gartner even predicts that 40% of existing AI data centers will hit power capacity limits by 2027, underscoring the urgent infrastructure challenge.

This surge places immense pressure on power grids. Cutting-edge AI models require enormous energy: Training a single large language model (LLM) like OpenAI’s GPT series can devour tens of gigawatt-hours of electricity . Some hyperscale AI data centers already draw 30–100 megawatts each, and future facilities may exceed 1,000 MW (1 gigawatt) – about the output of a large power plant .

One industry analysis notes tech giants are pursuing “gigawatt-scale” data center campuses to support AI workloads . By 2030, Microsoft and OpenAI’s planned “Stargate” supercomputer could require an astonishing 5 GW of power .

In response, tech companies are exploring diverse energy strategies. Google, for instance, is investing in advanced nuclear power: it signed a deal to purchase energy from small modular reactors (SMRs), aiming to add 500 MW of carbon-free power by 2030 . Microsoft is turning to nuclear with the Three Mile Island nuclear power plant deal, Amazon, and Meta are turning to conventional power plants – in some regions, new natural gas-fired generators – to guarantee reliable juice for AI data centers, a strategy supported by utilities . In Wisconsin, regulators approved a $2 billion gas plant deemed “critical” for Microsoft’s new AI hub.

These moves underline a hard truth: renewables alone can’t yet meet AI’s ravenous baseload demand, prompting a dual-track energy race between carbon-free solutions and fossil fuels.

This brings up pressing questions for business leaders: 

• Will AI ultimately drive sustainability gains or an energy crisis? 

• How are regional disparities and geopolitics shaping AI’s energy footprint? 

• What technological breakthroughs could enable sustainable AI growth? 

• And how should corporate strategy adjust to balance AI’s benefits against its energy and carbon costs?

This guide examines the forces at play – from data center trends and energy innovations to policy and geopolitical factors – to help corporate decision-makers navigate AI’s energy revolution. 

The goal: understand the macro and geopolitical impacts of AI’s energy consumption, and chart a course that leverages AI’s power responsibly and sustainably.

Read the guide online: https://rbtp.cc/zucniP

Download the guide in PDF: https://rbtp.cc/FiFqib

Subscribe now

The Energy Cost of AI: Hard Truths and Hidden Opportunities

Global data center electricity consumption reached an estimated 460 TWh in 2022, with AI and cryptocurrency operations accounting for roughly 14% of that load, according to the International Energy Agency (IEA) . Now AI is pushing those numbers dramatically higher. Projections show data centers worldwide could consume over 1,000 TWh by 2026 – roughly doubling in just four years. By 2030, some forecasts see a further 160% increase in data center power demand driven by AI.

This growth is concentrated in key AI hubs and “cloud clusters” – with serious consequences for local grids:

• In Northern Virginia’s famed “Data Center Alley,” the massive concentration of servers has led to power quality issues. The region now experiences voltage distortions 4× higher than the U.S. average, raising the risk of appliance damage and even fires for surrounding communities. Utilities warn that traditional grid infrastructure is straining to keep up with the load.

• In central Ohio, data center capacity has quadrupled since 2023, consuming so much electricity that utility AEP had to halt new data center connections, despite a 30 GW queue of projects waiting to plug in. Simply put, the grid can’t be expanded fast enough to accommodate the sudden surge in demand.

• Ireland faces a similar crunch: by 2026, data centers are projected to gobble up 32% of Ireland’s electricity. Dublin’s metro grid is so stressed that the government imposed a moratorium on new data centers in the area, shifting over $4 billion in planned investments to other countries..

The energy intensity of AI is a key reason demand is outpacing capacity. A few eye-opening facts illustrate the scale:

• Training a single large AI model can consume enormous amounts of electricity. For example, training ChatGPT/GPT-3 (with 175 billion parameters) is estimated to use on the order of 1–1.3 GWh (gigawatt-hours) of energy – roughly the yearly electricity usage of over 1,000 U.S. homes. And that’s for one training run. Newer models like GPT-4 are even more power-hungry: estimates suggest on the order of 50–60 GWh for a full training cycle, which would be enough to power ~4,500 homes for a year (and emits tens of thousands of tons of CO₂). In other words, one large AI model’s training = years of household electricity.

• Running AI models (inference) is also energy intensive. AI queries consume about 10× more electricity than a typical Google search. Every time you ask ChatGPT a question, a network of GPUs fires up, drawing far more power than a standard web search. Multiply this by millions of queries, and the energy adds up fast. Microsoft and Amazon have responded by securing huge dedicated power supplies for their cloud AI operations – on the order of 500 MW to 1,000 MW per data center campus – to ensure they can handle the surging demand. For perspective, a single 1,000 MW data center campus could consume as much power as 750,000 homes.

• The sheer consumption of top tech companies is staggering. In 2023, Microsoft and Google each used ~24 TWh of electricity – more power than entire countries like Iceland, Jordan, or Ghana consume in a year. This puts their usage above that of over 100 nations. While these firms have aggressive renewable energy programs, the scale of their energy draw highlights how big the AI computation boom has become.

• The cloud giants are investing heavily to keep this sustainable. Microsoft recently announced a $10+ billion deal with Brookfield to develop 10.5 GW of new solar and wind farms by 2030 – an unprecedented corporate clean power purchase aimed squarely at running its AI and cloud data centers on carbon-free energy. Amazon and Google are similarly pouring funds into renewables and even experimental technologies (like advanced geothermal and batteries) to offset their growing AI footprint.

Despite these efforts, power constraints are emerging as a growth limiter for AI. Industry analysts warn that in the next few years, many data center operators (especially those not backed by big tech) may find it difficult or prohibitively expensive to get the electricity they need.

Gartner projects that by 2027, 4 in 10 AI data centers worldwide could hit their power capacity ceiling, meaning their expansion will be stalled by energy shortages. For enterprises, this could translate to slower cloud rollouts or higher costs as energy prices rise.

However, within this hard truth lies a hidden opportunity: AI itself can help solve the energy challenge. As we’ll explore, the same technology driving up consumption can also drive greater efficiency and new solutions – if wielded wisely.

Comparing AI Models: Power Hunger from GPT to KNN

Not all AI is equally power-hungry. There is a vast gap in energy consumption between large, state-of-the-art AI models and more traditional algorithms. Understanding this spread can help leaders choose the right AI tools for the job – balancing capability and cost. The table below compares examples of AI models:

Table: Energy requirements for training various AI models range over orders of magnitude. Cutting-edge deep learning models (top rows) consume enormously more energy than smaller neural nets or classical machine learning methods (bottom rows). Choosing a right-sized model can avoid wasting power.

Sources: Powering the Commanding Heights: The Strategic Context of Emergent U.S. Electricity Demand Growth, Training a single AI model can emit as much carbon as five cars in their lifetimes : r/MachineLearning

As the table shows, today’s largest AI models (like GPT-3/4) dwarf earlier AI in power needs. Training GPT-4 can use about 50,000× more energy than training a typical convolutional neural network (CNN) like ResNet-50 used for image recognition. 

And an old-school algorithm like k-nearest neighbors (KNN) or an ARIMA forecast model might use a million-times less energy – essentially negligible in comparison. 

This doesn’t mean companies should avoid large AI models altogether; rather, it underscores the importance of right-sizing AI to the task. You don’t always need a billion-parameter model if a simpler one works – and the energy (and cost) savings from a leaner approach can be huge.

Key takeaway: AI’s energy footprint isn’t uniform. Generative AI and other complex models can be incredible but come with extreme energy costs. 

Business leaders should evaluate whether a smaller, more efficient model could meet their needs. In many cases, optimized or “distilled” models, or running AI at the network edge, can deliver acceptable performance while using a fraction of the power. This efficiency-centric approach to AI adoption will become increasingly vital as energy pressures mount.

Further reading: Finding the Right Model for the Job, by Damien Kopp. 

Share

Fossil Fuel Lock-In vs. a Nuclear Renaissance

The tug-of-war between AI’s energy demand and clean energy supply is pushing companies down two very different paths. On one side, some firms and regions are doubling down on fossil fuels to keep the lights on for AI. On the other, there’s a growing movement toward a nuclear revival (along with renewables) to power AI sustainably.

On the fossil fuel front, oil and gas producers see AI’s rise as a new source of demand for hydrocarbons. BP’s CEO Murray Auchincloss, for example, predicts AI’s infrastructure build-out could drive an extra 3–5 million barrels per day of oil demand growth through the 2030s, as data centers and associated supply chains consume more energy (fuel for generators, diesel for construction, etc.). Likewise, Shell’s latest Energy Security Scenarios project natural gas demand reaching 4,640 billion cubic meters annually by 2040, partly to fuel backup generators for data centers and provide grid stability in an AI-enabled economy.

These trends raise concerns that AI could inadvertently lock in a new wave of fossil fuel dependence right when the world is trying to decarbonize. For instance, in the U.S., some utilities are proposing 20+ GW of new gas-fired power plants by 2040 largely to meet data center growth. 

This runs directly against climate goals – building gas infrastructure that could last 40–50 years to serve what might be a short-term spike in AI-related demand.

Conversely, a potential “nuclear renaissance” is being driven by AI’s 24/7 power needs and corporate clean energy pledges. Nuclear power offers steady, carbon-free electricity that is highly appealing for always-on AI workloads. We’re seeing concrete steps in this direction:

• Microsoft is investing $1.6 billion to help reopen the dormant Three Mile Island nuclear plant in Pennsylvania, aiming to secure 24/7 carbon-free power for its AI data centers by 2028. This would repurpose an existing nuclear reactor to directly feed Microsoft’s cloud operations – a bold bet on nuclear as a reliable green energy source for AI.

• Amazon and Google have each committed at least $500 million in financing to startup companies developing small modular reactors (SMRs). Their goal is to have about 5 GW of new nuclear capacity from SMRs online by the mid-2030s. Google’s agreement with Kairos Power, for instance, targets the first SMR operational by 2030. If successful, these would be game-changers: modular reactors could be built near data centers to provide dedicated clean power.

• In Europe, policymakers are increasingly viewing nuclear as essential for meeting AI’s power demands. The EU projects that nuclear-powered data centers (where data centers are co-located with nuclear plants or dedicated reactors) could supply 15–25% of the new electricity needed for AI and digital growth through 2030. France and the UK have floated incentives for data center operators to hook into existing nuclear plants, while countries like Romania and Estonia are partnering on SMR deployment with an eye toward tech sector needs.

The contrast is striking: Will the AI era deepen our fossil fuel dependence or accelerate the shift to alternative energy? 

In practice, both are happening – but the balance could tip one way or the other based on economics and policy. Natural gas plants currently often win on cost and speed (a gas turbine can be built faster than a nuclear plant and is a proven solution to instantly boost capacity). Indeed, “the only concrete plans I’m seeing are natural gas plants,” notes one energy consultant about data center expansions. Yet, as carbon costs rise and modular nuclear tech matures, nuclear and renewables could prove the more attractive long-term play.

For corporate leaders, this means energy strategy is becoming inseparable from AI strategy. Companies may need to directly invest in energy projects (like Microsoft’s and Google’s deals) to ensure their AI ambitions have a viable power supply. Those that succeed in securing reliable, clean energy will not only meet sustainability goals but also gain an operational advantage (avoiding the risk of power constraints slowing their AI deployments). The next section explores how AI itself can help resolve this dilemma by improving energy efficiency and grid management.

AI-Driven Efficiency: Mitigating the Carbon Toll

While AI’s energy consumption is undeniably large, AI technologies also offer powerful tools to cut energy waste and emissions across many industries. From cooling data centers to optimizing factory lines and smart grids, AI-driven efficiency gains can act as a counterweight to AI’s own power use. In essence, there is an opportunity for a positive feedback loop: using AI to save energy even as we use energy to run AI.

Some notable examples of AI-enabled efficiency breakthroughs:

• Data Center Cooling Optimization: Google’s DeepMind cut data center cooling energy by 40% by predicting server loads and adjusting cooling in real time.

• Next-Gen Cooling Technologies: Advanced cooling solutions, such as direct-to-chip liquid cooling, have been shown to reduce server energy use by ~30% , with liquid cooling now used in up to 45% of new European facilities.

• AI-Managed Microgrids: In regions like Ohio and Texas, experimental microgrids leverage AI to balance renewable energy with data center power draw , cutting renewable curtailment by about 22%.

• Industrial and Building Energy Management: AI applications have helped Toyota reduce energy consumption by 29% on certain manufacturing processes and enabled commercial buildings (such as 45 Broadway in Manhattan) to achieve nearly 16% HVAC energy savings through intelligent controls.

• Building energy management: In commercial buildings, AI has shown impressive results in cutting power usage without sacrificing comfort. A notable case is 45 Broadway in Manhattan, where implementing an AI HVAC optimization system led to a 15.8% reduction in HVAC energy use. AI algorithms learned the building’s patterns and adjusted heating/cooling more intelligently. Similarly, AI-based controls for lighting and appliances can yield up to 30% energy savings in buildings. Multiply these gains across millions of buildings and homes, and the potential energy savings are enormous.

These examples illustrate a hopeful counterpoint to AI’s energy appetite: the energy savings AI enables in other areas could, in theory, offset a significant portion of the energy AI consumes. Smarter grids, smarter buildings, smarter transportation (AI-optimized logistics, etc.) all contribute to lower overall demand. A Shell analysis suggests AI applications could halve the carbon intensity of global energy by 2050 through such measures – coordinating renewables, improving efficiency, and innovating in materials (for example, using AI-driven design to create wind turbine blades that generate 40% more power.

Subscribe now

The AI-Energy Paradox: Do Savings and Consumption Converge?

Right now, the net impact of AI on global energy is still an increase in demand. AI’s usage is growing so rapidly that efficiency gains, as valuable as they are, haven’t yet kept pace. 

For instance, even as Google’s AI cut 40% of cooling energy, the expansion of Google’s AI computing meant total energy use still rose. The near-term trend is divergence – AI driving more power use overall, despite localized savings.

Current figures bear this out. The U.S. Department of Energy found that data centers (thanks largely to AI growth) consumed about 4.4% of U.S. electricity in 2023, and are on track to reach between 6.7% and 12% by 2028. 

In other words, efficiency improvements are not projected to stop a doubling (or more) of data center energy draw in the next five years. 

A recent Electric Power Research Institute analysis likewise forecasts U.S. data centers could hit 9% of national electricity use by 2030, up from ~4% today. Clearly, in the short run, AI’s footprint is outpacing the savings it enables elsewhere.

Over the longer term, there is a possibility (not a guarantee) that the curves could converge. As AI matures, there’s intense research focus on efficiency: more efficient algorithms, specialized AI chips that deliver more performance per watt, better cooling, and so on. If each new generation of AI hardware is significantly more efficient, the growth in AI’s energy use could level off. 

For example, tech firms are now prioritizing energy efficiency over pure performance gains – a shift from the early “move fast” approach. Future AI models might be designed to be smaller or use smart techniques (like model sparsity or on-demand activation) that save energy.

Policymakers are also starting to push for convergence. The EU’s proposed AI Act will require large AI models to demonstrate 15% energy efficiency improvements over previous generations – effectively slowing deployment of ultra-large models until they are more efficient (one reason rumors suggest GPT-5 might be delayed until such standards can be met). Governments may introduce carbon taxes or energy caps that make it economically unattractive to run wasteful AI systems, forcing innovation towards frugality.

So, will spending and savings converge? Optimistically, yes – but likely not until late this decade or beyond. 

In a scenario where AI’s growth moderates and efficiency tech accelerates, we could see AI’s net impact plateau or even turn net-negative on emissions (especially if AI helps integrate huge amounts of renewables, as Shell’s scenario imagines. 

But for the next 5–10 years, business leaders should plan for a world where AI means higher energy consumption and carbon output, and manage that reality accordingly.

The implication for corporates is twofold:

1. Invest aggressively in AI-driven efficiency projects within your own operations (to capture savings that can offset your AI usage).

2. Anticipate energy costs and capacity needs rising with AI, and incorporate that into everything from site selection (do your data center/cloud regions have spare power capacity?) to vendor selection (choose partners with greener energy and efficient infrastructure).

In short, don’t assume the problem will solve itself. Proactive action is needed to bend the curve.

Accelerating the Renewable Transition to Power AI

If AI is to spark a green energy revolution instead of exacerbating the crisis, a massive scale-up of clean energy is required. Renewables – solar, wind, hydro – need to grow in tandem with AI compute demand, and AI can be a catalyst to accelerate that growth. But it won’t happen automatically; it requires strategic investments and innovation.

On the plus side, AI is already helping get more out of renewables. We saw how AI can optimize wind and solar output (e.g. smarter inverters yielding 18% more solar farm efficiency. AI can forecast weather and adjust operations to maximize renewable energy capture and reduce downtime. 

For instance, autonomous AI-driven networks of electric vehicle (EV) chargers can collectively act as a 450 GWh battery for the grid, smoothing out renewable fluctuations by intelligently timing charging. AI is also being applied to breakthrough research – like using quantum computing and AI to design advanced materials for solar panels or wind turbines, potentially boosting their efficiency dramatically.

However, even optimistic efficiency gains won’t fully bridge the gap. The scale of new clean power needed is enormous. 

A McKinsey study estimates that in Europe alone, an additional $250–300 billion in grid infrastructure upgrades will be required by 2030 to handle 150 TWh of new AI-related electricity demand and connect enough renewables to supply it.

This includes new transmission lines, grid storage, and smarter distribution – essentially building a bigger, smarter grid to feed AI. Without such investment, renewable deployment could lag and AI would end up being powered by whatever is available (often coal or gas).

To put numbers on it: The world added about 300 GW of renewable capacity in 2022. If AI demand is rising by hundreds of TWh, we likely need to add hundreds more GW of renewables per year on top of current plans just to keep AI from increasing fossil fuel use. Policymakers are starting to respond – the U.S. Inflation Reduction Act, Europe’s Green Deal, China’s massive renewables build-out – all boost clean energy, which indirectly supports AI’s growth sustainably. But targeted actions may be needed, such as incentives for energy-intensive tech firms to directly finance renewable projects (as Microsoft is doing).

One promising idea is direct clean power procurement for AI infrastructure. Instead of buying offsets or generic renewable credits, companies can invest in additional renewable generation that is tied to their data centers. Google has been a leader here, aiming for “24/7 carbon-free” energy by sourcing clean power in every hour and region that its servers operate. Other firms are now looking at similar models, which could drive significant new solar/wind development.

For corporate leaders, this means aligning AI strategy with energy strategy. Embrace AI projects that further sustainability (smart grid, energy optimization) and be cautious of AI expansions that outpace your access to green power. Seek partnerships in the energy sector – for example, co-develop a solar farm or wind park that can power your AI workloads. Those who proactively secure clean energy for AI will not only mitigate environmental impact but also hedge against future carbon regulations or fossil price volatility.

Geopolitical and Economic Crossroads

AI’s energy demands are now a factor on the geopolitical chessboard. Nations are racing to support their tech industries with reliable power (often in competition with climate goals), and energy dependencies are influencing tech policies. Three major theaters highlight this dynamic: the US-China tech competition, Europe’s regulatory balancing act, and emerging markets vying for data center investments.

The U.S.–China Tech War’s Energy Dimension: China and the United States are both pouring billions into AI, and with that comes a hunger for energy. China has launched an “East Data, West Computing” initiative, investing an estimated $75 billion to build huge data center hubs in its inland provinces. Why inland? Because electricity is cheaper there – for example, coal-rich Inner Mongolia offers industrial power rates around $0.03 per kWh, among the lowest in the world. 

By situating AI data centers next to coal plants in the interior, China can fuel its AI growth at low cost (albeit with high emissions). This strategy effectively leverages China’s vast coal infrastructure to gain an edge in computing capacity.

Meanwhile, the U.S. is responding with investments to support AI hotbeds at home. The Department of Energy recently announced $2 billion for grid upgrades focused on “AI corridors” like Northern Virginia and Ohio. This includes improving transmission and reliability to ensure these regions (where many U.S. cloud data centers cluster) can handle the increased load without blackouts or slowdowns. It’s essentially an infrastructure subsidy to keep U.S. AI development on track and independent of energy bottlenecks.

There’s also a security aspect: both nations view leadership in AI as strategic, so ensuring the energy security of AI facilities is crucial. This could lead to more efforts like backup gas peaker plants for key data centers, or even dedicated small nuclear reactors, to immunize critical AI infrastructure from grid disruptions or fuel supply risks. In a hypothetical future standoff, a country that cannot power its AI systems reliably would be at a serious disadvantage.

Europe’s Cautious Approach: Europe, in contrast, is trying to chart a path that prioritizes sustainability – but at the risk of dampening its AI momentum. The EU’s proposed regulations (like the AI Act) not only address ethics but also efficiency. As noted, the AI Act could effectively delay deployment of power-hungry models (e.g., next-gen GPT) until efficiency targets are met. Additionally, some European countries have taken hard stances on data center growth due to energy concerns. Ireland’s moratorium on new Dublin-area data centers, for instance, was driven by fears that the national grid couldn’t meet both climate targets and a surge in data center demand. That moratorium led companies to shift investments to places like Poland and Norway where power is more available.

The consequence is that Europe risks falling behind in AI infrastructure. While U.S. and China race ahead with massive builds (regardless of carbon cost), Europe’s combination of slower cloud growth and higher energy prices could make it less attractive for AI development. Some experts warn of a potential “digital drift” where European AI innovation migrates to more energy-abundant shores. On the other hand, Europe’s emphasis on efficiency and green power could pay off in the long run, yielding more sustainable operations that align with global climate imperatives (and avoid future regulatory penalties).

Global Energy Markets and AI Investment: It’s not just the big three (US, China, EU). Around the world, countries are jockeying to attract data center and AI investments – and energy is the key bargaining chip. For example, countries like Norway, Sweden, and Canada promote their abundant renewable energy (hydropower, wind) and cold climates (natural cooling) as ideal for sustainable AI data centers. Norway has lured several major projects by offering 100% renewable power and low cooling costs, appealing to companies with net-zero commitments.

In Asia, Singapore has imposed a temporary freeze on new data centers due to energy and land constraints, then lifted it in favor of a selective policy favoring the most efficient, green designs. India and Indonesia are pitching themselves as emerging data center hubs, but they’ll need to rapidly expand grid capacity (and ideally renewables) to deliver on those ambitions.

The energy crisis of 2022 (with spiking fuel prices) was a wake-up call for many: any country that wants to be an AI/cloud hub must ensure cheap, reliable power. This has geopolitical implications: nations rich in clean energy (like Iceland or Quebec with hydro, or Middle Eastern countries with solar + land for data centers) could play a bigger role in the digital economy by hosting energy-intensive AI computation. It’s a new twist on the resource competition of the past – instead of oil or minerals, it’s about attracting “computational industry” with the promise of low-cost electrons.

In summary, leaders need to be aware that AI isn’t happening in a vacuum – it’s intertwined with global energy and policy currents. Decisions about where to site AI operations, which markets to enter, or even which governments to partner with may hinge on energy availability and regulations. Businesses at the cutting edge of AI should engage in policy discussions: for example, advocating for incentives for clean power or workable regulations that encourage efficiency without stifling innovation. The next section looks at the emerging solutions – tech and policy – that could put AI on a more sustainable path, and how companies can harness them.

Pathways to Sustainable AI: Tech Innovations and Policy Responses

For AI to truly spark a green revolution, innovation must focus on making computing more efficient and integrating AI growth with clean energy systems. This involves advances in hardware and software, as well as smart policies to nudge the industry in the right direction.

Technological Levers for Efficient AI

• Next-Gen AI Chips (ASICs and Photonics): Traditional CPU/GPU architectures are not very energy-efficient for AI workloads. Enter specialized AI accelerators. Companies like Lightmatter are developing photonic (light-based) chips that perform AI computations using photons instead of electrons, massively reducing energy loss as heat. Lightmatter’s chip reportedly achieves 9 petaflops per watt of performance – orders of magnitude beyond conventional silicon. If such optical computing scales up, future AI models could run on a fraction of the energy today’s do. Similarly, Google’s TPUs and various startups’ AI ASICs are tuned for maximum throughput per watt, offering 2–5× improvements over general GPUs.

• Neuromorphic Computing: Inspired by the human brain, neuromorphic chips (like Intel’s Loihi 3) use networks of “spiking” neurons that are extremely low-power. They excel at tasks like pattern recognition with minimal energy. Intel reports up to 76% lower energy for LLM inference with neuromorphic approaches on some workloads. While still experimental, these could allow AI systems that learn and operate continuously on tiny power budgets – think AI co-processors that sip power like a LED lightbulb.

• Algorithmic Efficiency (Better Software): On the software side, there’s a push for efficient AI algorithms – for example, techniques like model pruning, quantization, and knowledge distillation, which create smaller models that run faster. A pruned or distilled model can often achieve 90% of the accuracy of a large model with, say, 50% less computation required. OpenAI and others are actively researching ways to maintain capability while cutting out the “waste” in neural networks. In training, new optimization methods and architectures (like sparsely activated models) promise to reduce the compute needed to reach the same accuracy. These advances directly translate to energy saved.

• Carbon-Aware Computing: Software is also helping schedule computing tasks at times and places where energy is clean. For instance, Microsoft Azure’s carbon-aware workload scheduling now shifts nearly 40% of AI jobs to regions or times where renewable energy is abundant. If the wind is blowing in one data center region, Azure will queue more AI jobs there, and pause or move jobs from another region that’s on fossil power at that moment. This kind of intelligent orchestration can significantly cut the effective carbon footprint of AI computations.

• Energy-Proportional Computing & PUE Improvements: Data center engineers continue to drive down overhead so that almost every watt goes to computing, not waste. Average Power Usage Effectiveness (PUE) has improved (some hyperscale centers are at a PUE of 1.1 or lower, meaning 90%+ of energy powers IT equipment). Techniques like better airflow management, AI-controlled cooling (as discussed), and even waste heat reuse (heating nearby buildings with server heat) all contribute. The closer we get to a PUE of 1.0 and fully utilized servers, the more work (AI tasks) we can get done per unit of energy input.

Policy Interventions

Governments can guide the AI-energy trajectory with targeted policies and standards:

• Energy Efficiency Standards for AI Models: Just as there are fuel economy standards for cars, we may see efficiency standards for AI. The EU’s contemplated rule requiring 15% energy efficiency improvement in new AI models is a first step. If major markets adopt similar rules or require reporting of AI energy use, it creates a competitive incentive to design greener AI. Transparency is key – imagine an “Energy Star” rating for AI services, where customers could choose a provider that is more energy-efficient.

• Carbon-adjusted pricing and credits: Some regions are introducing tariffs or credits to encourage clean energy usage. For example, California and Bavaria (Germany) have floated the idea of carbon-adjusted power purchase agreements that penalize data centers drawing power from grids below a certain renewable percentage. Under such schemes, if an AI facility isn’t using (or contracting for) at least 80% clean power, it would pay a surcharge or face limits. This kind of policy pushes companies to invest in renewables or locate where clean power is available, to avoid financial penalties.

• Dynamic electricity pricing: Grid operators like PJM in the U.S. are implementing real-time pricing to manage peaks. PJM’s dynamic tariffs have encouraged data centers in its region (e.g., northern Virginia) to reduce peak load by 19% – they respond to price spikes (often corresponding to dirty peaker plants coming online) by dialing down non-urgent workloads. Wider use of dynamic pricing will reward AI operations that can flex around grid conditions, effectively incentivizing them to be more grid-friendly and efficient.

• Accelerating Clean Energy Permitting: One practical bottleneck for sustainable AI power is the slow permitting of new renewable projects and transmission lines. Policymakers can streamline this – for instance, the U.S. Nuclear Regulatory Commission is fast-tracking approvals of advanced reactor designs aiming to have a set of SMRs approved by 2026, specifically with data center use cases in mind. Governments can also designate “energy corridors” for easier building of high-voltage lines to data center regions, or provide grants for battery storage at data hubs. All these reduce the risk that AI’s growth outstrips green energy availability.

• Support for R&D: Supporting the development of the above-mentioned technologies (optical computing, neuromorphic, etc.) through grants and public-private partnerships can speed their arrival. Given AI’s strategic importance, one can envision national programs to develop next-gen low-power AI hardware (the way there were initiatives for supercomputing in past decades). This not only helps the climate but also ensures a country’s AI industry remains globally competitive as efficiency becomes a differentiator.

The big picture is that a combination of technology innovation and forward-thinking policy can bend the trajectory of AI’s energy impact. It’s analogous to the auto industry – without better tech (EVs, hybrids) and policies (fuel standards, incentives), car emissions would have kept rising unabated. With them, it’s possible to have the benefits of mobility (or in our case, AI capabilities) while mitigating the harms.

For corporate leaders, staying ahead on these fronts means:

 (a) Monitoring and adopting emerging efficient AI tech – perhaps experimenting with new accelerators or AI model optimizations that cut costs and footprint.

 (b) Engaging with policymakers or industry groups to help shape sensible standards (it’s better to help craft the rules than be caught off-guard by them).

 (c) Committing to transparency in AI energy use and emissions. Some leading companies already publish the PUE and carbon data of their data centers; extending this culture to AI operations builds trust and prepares the company for a future where stakeholders demand to know the climate impact of AI initiatives.

Next, we turn these insights into a concrete action plan for executives – what steps to take to ride the AI wave without capsizing under energy costs or sustainability risks.

A Tactical AI-Energy Strategy for Corporate Leaders

How can corporate decision-makers apply these insights in practice? Here we distill a practical guide – key questions to ask, and steps to take – to balance AI’s opportunities with energy and sustainability considerations.

Read the guide online: https://rbtp.cc/zucniP

Download the guide in PDF: https://rbtp.cc/FiFqib

5 Key Questions Every CEO Should Ask About AI & Energy

1. How much energy do our AI operations consume? – Get a handle on the current state. Measure the power usage of your AI workloads (on-premise and in cloud). Understand the scale: is it 5% of your IT energy use? 50%? Quantify it in kWh and dollars, so you have a baseline. Also project how this might grow with planned projects (if you adopt a new AI tool, will it double your compute hours? Triple?). You can’t manage what you don’t measure.
   

2. Are we using the most energy-efficient AI models and infrastructure available? – Audit your AI stack. Are there opportunities to use smaller models, or algorithm optimizations like batching and quantization to cut compute? Are you running on last-gen hardware out of habit, when new AI chips could do the job with 1/2 the energy? Push your tech teams and vendors to justify choices in terms of efficiency, not just accuracy or speed.
   

3. Are we leveraging AI to optimize our own energy use? – This flips the script: use AI as part of the solution. Could AI tools help reduce energy waste in your operations (factories, offices, supply chain)? For example, using AI for route optimization in logistics to save fuel, or for energy management in buildings (as some have done to cut HVAC costs by 15–30%). Ensure your sustainability and facilities teams are exploring AI solutions – the ROI can be significant, and it creates a positive offset for the energy your AI projects consume.
   

4. Are we investing in clean-energy-powered cloud services (or data centers)? – When choosing where to run AI workloads, factor in the energy source. Major cloud providers now offer regions or options powered by 100% renewable energy – utilizing those can drastically cut the carbon footprint. If you run your own data center, consider power purchase agreements for renewables or even on-site solar. Essentially, align your digital infrastructure with your renewable energy procurement.
   

5. Are we prepared for potential AI energy regulations? – Scan the horizon for laws that might affect your AI deployments. For instance, if efficiency standards for AI or reporting requirements come in a year or two, do you have the data to comply? If carbon pricing rises, do you know which AI projects would become more expensive to run? Engaging with industry groups and regulators proactively can give you a voice and early insight. Internally, scenario-plan for a future where “green AI” might be mandated either by law or by customers/investors.
   

Asking these questions at the C-suite level ensures that AI initiatives are not happening in a silo, but are integrated with energy management and corporate strategy.

Further reading: 2025 Tech Provocations (10 Really Uncomfortable Questions Leaders and Builders Must Answer This Coming Year)

Practical Steps for Sustainable AI Adoption

1. Conduct an AI Energy Audit. Much like financial auditing, do an energy audit for AI. Map out all AI-related compute (data centers, cloud usage, edge devices) and tally the power usage. Identify hotspots – e.g., a particular analytics cluster or training workflow that draws a lot of power. This audit gives you a clear picture of where to target efficiency efforts. It might reveal, for example, that 20% of your AI jobs account for 80% of the energy – maybe heavy model training that could be scheduled during off-peak hours or moved to a more efficient cloud zone.

2. Optimize and Right-Size AI Workloads. Use the findings to implement quick wins: – Model right-sizing: Where possible, replace giant models with smaller ones or use transfer learning to avoid training from scratch. If a 500-million parameter model can solve the problem, don’t use a 50-billion one. This can cut computation dramatically. – Lifecycle management: Not all AI tasks need to run at highest frequency. Determine which jobs are mission-critical vs. which can be throttled or delayed in high load times. Leverage cloud auto-scaling to shut down idle resources (many companies find servers running when not needed – a pure waste). – Use AI to tune AI: It’s meta, but you can apply AI to improve scheduling and resource allocation for your AI jobs (similar to how DeepMind’s system works for Google). This can maximize utilization and reduce idle energy burn.

3. Leverage AI for Broader Energy Management. As noted, deploy AI solutions in your operations to save energy and costs. For example: – Implement an AI-based energy management system in corporate offices or factories (many vendors offer these). – Use machine learning to analyze production line data for energy inefficiencies (maybe a certain machine uses more power than it should – predictive maintenance can fix that). – Optimize logistics and travel with AI to reduce fuel use. Every kilowatt-hour or gallon saved here helps offset the extra energy your data centers might consume. And they directly save money, improving the business case for AI investments.

4. Adopt Hybrid Computing Strategies. Not all workloads must run in power-hungry central clouds. Consider a hybrid AI approach: run smaller, latency-sensitive tasks on energy-efficient edge devices (or on end-user devices), and reserve big cloud compute for the truly heavy tasks. By using edge AI (which has no network transit and can be highly optimized), you reduce total energy per inference. Also explore techniques like model distillation to create lighter versions of cloud models that can run on-premises or on cheaper hardware when appropriate. This hybrid mindset ensures you’re not always using a sledgehammer (huge cloud instance) for a nail (simple task).

5. Prioritize Green Cloud Providers and Contracts. When negotiating with cloud or data center vendors, make sustainability a key criterion. Ask providers about their PUE, their renewable energy percentage, and their roadmap for low-carbon operations. Some cloud providers now offer dashboards showing the carbon emissions of your cloud usage – use those insights. If you operate your own facilities, sign renewable energy contracts (PPAs) to cover your AI electricity use with clean energy. Also, work with utilities on programs (many utilities have “green tariffs” or will help with renewable projects if you’re a large load). Align your procurement so that as your AI energy use grows, your renewable supply grows in step.

6. Collaborate with Industry and Policymakers. Given the broader grid challenges, it’s wise for companies running big AI workloads to have a seat at the table. Join industry consortia focused on sustainable data centers or AI ethics that include energy impact. Engage local governments if you’re building data facilities – perhaps partner on community solar/storage so the investment benefits both you and the grid. Being proactive can also help shape favorable policies (for instance, incentives for using local clean power or faster permitting for your backup generators etc.). Don’t wait to be caught by surprise regulations; help shape the narrative that AI can be part of the climate solution.

7. Scenario Planning and Risk Mitigation. Finally, include energy security in your risk assessments for AI. Ask “what if” questions: What if power is constrained in Region X – do we have failover in a different region? What if electricity prices spike 3× – does our AI project still make economic sense, and can we hedge that risk? Have backup plans for critical AI services if rolling blackouts or energy rationing ever hit (not unthinkable in some grids). By planning for these contingencies, you ensure AI deployments are resilient and won’t be derailed by external energy shocks.

By taking these steps, executives can balance efficiency, cost, and sustainability in their AI adoption. The companies that follow this playbook will likely have a smoother ride scaling AI – with lower bills and stronger ESG credentials – than those who treat energy as an afterthought.

Share

Conclusion: A Contested Energy Future

AI’s rise presents both a monumental challenge and an opportunity for the energy landscape. On one hand, AI’s energy demands are forcing a reckoning: power grids are under strain, carbon goals are at risk, and companies may face tough trade-offs or regulatory hurdles if they ignore the issue. On the other hand, AI offers unprecedented tools to drive efficiency, optimize energy systems, and accelerate the transition to cleaner power.

For corporate leaders, the takeaway is clear: the future will belong to those who integrate AI and energy strategy. The organizations that treat energy as a core element of their AI plans – investing in efficiency, securing sustainable power, innovating with AI in their operations – will lead the pack. They’ll enjoy more reliable growth (because they won’t hit energy ceilings), better public trust, and likely cost advantages as well. Those that ignore the linkage may find themselves facing energy supply crises, skyrocketing costs, or regulatory roadblocks that stall their AI ambitions.

The choice isn’t whether to adopt AI – that wave is here and necessary to remain competitive. The choice is how to do so responsibly and strategically. Companies that can harness AI and champion sustainability will shape the narrative of the coming decades. They’ll prove that innovation and green objectives can reinforce each other, not collide.

In the end, will your company spark the AI energy revolution, or be caught flat-footed by it?

By asking the hard questions now and taking decisive action, you can ensure that AI becomes a driver of efficiency and positive change – a win-win for your business and the planet, rather than a zero-sum trade-off. The green energy revolution and the AI revolution can be two sides of the same coin, but it will take foresight and leadership to make that vision a reality.

Will your company shape the AI-energy future – or be shaped by it? The decisions made today will determine the answer. The opportunity is to lead boldly, invest wisely, and create an AI-powered future that is sustainable, secure, and full of possibility for generations to come.

Thanks for reading!

What do you think?

Leave a comment

Read the guide online: https://rbtp.cc/zucniP

Download the guide in PDF: https://rbtp.cc/FiFqib

About the Authors

Xavier Greco, Founder & CEO at ENSSO

Xavier is an accomplished leader with deep experience in engineering, project management, and business development across the global energy sector. Over his career, he has directed complex energy production and transport projects throughout Europe, Africa, and Asia while serving in senior roles at Dalkia and Vinci Energie. Xavier’s pivotal contribution to Solice, where he guided the company’s revitalization and reorganization, exemplifies his capacity for steering ambitious ventures to success. Convinced that every challenge presents an opportunity for progress, Xavier leverages his multifaceted expertise to drive positive change in the evolving energy landscape.

About ENSSO

ENERGY STRATEGY SOLUTIONS Sàrl (ENSSO) is a Geneva-based consulting firm specialized in energy production and distribution. Serving major players of the energy transition, ENSSO delivers bespoke project management, engineering solutions, and commercial development support across Europe and beyond. Guided by a flexible, results-driven approach, the ENSSO team excels at uncovering tailored solutions for each client’s unique challenges—firmly believing that no problem is unsolvable when approached with innovation and determination.

Damien Kopp , Managing Director at RebootUp

Damien is a seasoned technology executive and product innovator with 20+ years of global experience in digital innovation, product strategy, and technology consulting. His track record spans Europe, North America, and Asia, where he has launched and scaled advanced AI, robotics, and IoT solutions. Formerly Head of Innovation and Partner at NCS (Singtel Group), Damien led an AI research lab and product incubator focused on emerging tech. He also founded StoreWise, a platform that enhances frontline retail efficiency. Armed with dual Executive MBAs from Kellogg (USA) and HKUST (Hong Kong) plus a Master’s in Electronics Engineering, Damien brings a unique blend of technical expertise and business acumen to tackling complex challenges in technology and beyond.

About RebootUp

RebootUp Pte Ltd is a boutique consulting and technology company that helps organizations—from fast-growing startups to multinational enterprises—solve their biggest innovation challenges. Drawing on deep technical skills, strong business sense, and a relentless focus on customer-centric experimentation, RebootUp assists clients in generating new ideas, refining business models, and entering new markets. With expertise in digital transformation, AI applications, and agile processes, the RebootUp team has guided prominent companies across Asia and the Middle East to achieve meaningful breakthroughs in efficiency, strategy, and growth.

Sources and References

• What the data centre and AI boom could mean for the energy sector - International Energy Agency (IEA): Analysis of how rising data center and AI demands affect global energy consumption.

• AI poised to drive 160% increase in power demand by 2030 - Goldman Sachs Research: Forecast of AI-related power consumption growth through the end of the decade.

• Electricity demand of data centers in the EU - DWS (Deutsche Bank Wealth Management): Examination of data center electricity usage across European Union markets.

• AI-driven efficiency in data centers - Google DeepMind: Case study on using AI to optimize cooling systems and reduce energy costs.

• AI and jobs: Disruption or opportunity? - World Economic Forum: Insights into how AI may reshape the global workforce and employment landscape.

• AI Energy Impact Study 2024 - McKinsey Global Institute: Research on AI’s projected influence on energy consumption worldwide.

• Maersk Sustainability Report 2023 - Maersk: Overview of Maersk’s sustainability initiatives and environmental strategies.

• Toyota Industrial Report 2024 - Toyota: Summary of Toyota’s sustainability practices and industrial advancements.

• European Commission Digital Economy Report - European Commission: Assessment of the EU’s digital landscape and regulatory framework.

• Global Data Center Survey 2024 - Uptime Institute: Survey data on global data center reliability, operations, and trends.

• AI Energy Consumption Analysis - PwC: Evaluation of AI-related energy usage patterns in various industries.

• Virginia Power Constraints Report - Data Center Frontier: Report on power supply challenges for data centers in a key U.S. hub.

• Irish Times Technology Report - Irish Times: Coverage of data center growth and technology developments in Ireland.

• Data Centers Sustainability Guidelines - Singapore Economic Development Board: Guidelines promoting sustainable data center operations in Singapore.