Author: Andreas

  • Europe’s tech sovereignty: building capacity where it matters

    Europe’s tech sovereignty: building capacity where it matters

    In brief

    Europe has an opportunity to build much stronger positions in cloud, AI and semiconductors. The European Commission's new technology sovereignty package points in that direction. It seeks to speed up permits, aggregate demand and apply different sovereignty standards to different levels of risk.

    The next step is execution. Funding, energy supply and measures of success still need sharper definition. My read: Europe should increase investment and engagement where it already has industrial strength, while keeping global partnerships open. That combination creates more choice and resilience.

    What the Commission has proposed

    On 3 June 2026, the European Commission presented four measures:

    • the Chips Act 2.0
    • the Cloud and AI Development Act, or CADA
    • the EU Open Source Strategy
    • a roadmap for digitalisation and AI in the energy sector

    The Chips Act 2.0 and CADA are proposals, not final law.

    Commission President Ursula von der Leyen framed the case clearly:

    "We cannot afford to depend on others for the technologies that keep our hospitals running, our energy grids stable and our services secure."

    That is the right starting point. Europe can create more options for critical services by expanding local capacity and working with a broader set of trusted partners.

    Chips Act 2.0: more speed, no firm funding plan

    Silicon wafer transitioning into a series of advanced semiconductor packages
    The Chips Act 2.0 aims to turn European research and industrial demand into scalable semiconductor capacity.

    The proposal aims to make Europe a more attractive place to design, produce and buy semiconductors. It includes:

    • a maximum approval period of 12 months for strategic projects
    • "Grand Challenges" for technologies such as AI chips
    • stronger links between chipmakers and European buyers
    • more joint procurement
    • a business-to-business platform for supply-chain monitoring

    The Commission says the first Chips Act mobilised more than €52 billion and created about 46,000 direct and indirect jobs.

    It expects the global semiconductor market to reach €1.37 trillion by 2030. AI-related components could account for about 70% of that growth.

    Those numbers show the scale of the opportunity. Europe now needs to convert policy into commercially viable capacity.

    The European Court of Auditors warned in 2025:

    "The Chips Act is very unlikely to be enough to reach the very ambitious Digital Decade target."

    The EU wants a 20% share of the global semiconductor value chain by 2030. The Commission's own forecast pointed to 11.7%, according to the auditors. That gap is a reason to focus investment more clearly, not to lower the ambition. The Commission controls only about 10% of the announced public funding, so success will depend on coordinated action by member states, companies and the EU.

    CADA: tripling Europe’s computing capacity

    Data centre module protected by four clean nested architectural layers
    CADA proposes four sovereignty levels so protection can match the risk of each workload.

    CADA aims to at least triple EU data-centre capacity within five to seven years. It also addresses access to energy, land, water and capital.

    The Commission proposes four sovereignty levels. They range from EU-based data processing to full control of the software supply chain.

    I think this tiered approach is sensible. A public website does not need the same protection as health records or a national power grid.

    The opportunity is to make European capability more competitive without making origin the only criterion. Non-European providers can remain part of the mix when infrastructure, encryption, interfaces and exit terms meet the required standard.

    Open source: turning shared technology into European scale

    Modular digital products supported by an open framework and a maintenance tool
    Open source creates strategic value when Europe funds maintenance, governance and commercial scale.

    The Open Source Strategy covers development, deployment and long-term maintenance. It proposes procurement guidance, business support and a maintenance instrument for critical components.

    Open source can give public administrations and companies more control, better interoperability and lower switching barriers. Europe should capture more commercial value by helping maintainers and companies scale products in cloud, AI, cybersecurity and operating systems.

    The test will be professional execution. Critical open-source software still needs accountable owners, security updates and reliable funding.

    Energy and AI: building both sides of the equation

    Data centre connected to wind, solar and electricity-grid infrastructure across Europe
    Europe can combine data-centre growth with grid intelligence, renewable energy and industrial automation.

    The energy roadmap connects digital ambition with physical infrastructure. It covers grid optimisation, energy efficiency, demand flexibility and data-centre integration.

    Data centres currently use about 2.5% of EU electricity. In Ireland, their share exceeds 20%. The Commission is therefore developing tripartite agreements between data-centre operators, energy companies and public authorities. It has also launched AI.grids, a pan-European AI model for electricity networks.

    This is where Europe can combine two strengths: industrial automation and energy-system engineering. The Commission estimates that digitalising energy could create €71 billion in annual consumer savings and more than €300 billion in wider system benefits.

    Where the package creates momentum

    • The Commission creates a reason to invest. It says more than 80% of important digital products, services, infrastructure and intellectual property currently come from outside the EU. That leaves significant room for European suppliers and partnerships to grow.
    • Some targets are measurable. A 12-month permit period and a tripling of computing capacity can be tracked.
    • Demand receives more attention. Joint procurement and early customers could help European start-ups scale.
    • Open source is treated as infrastructure. That can improve control and make switching providers easier.

    Where execution needs to improve

    • Funding needs to become more specific. Announced investment is not the same as an available EU budget.
    • Europe needs to measure commercial outcomes alongside programmes and funding commitments.
    • Member states should concentrate capital in the strongest industrial clusters instead of competing for identical projects.
    • Energy policy must advance with digital policy. Chip plants and data centres need power, grids, cooling and water.
    • Europe should pursue strategic capacity with global partners rather than full autonomy. The European Court of Auditors says complete autonomy is impossible in semiconductors.

    Andreas’s view

    My read on this: the package is a useful foundation for a more confident European technology strategy.

    Europe is right to connect chips, cloud, AI, open source and energy. It is also right to distinguish between ordinary and critical workloads. The next move is to turn that framework into investment, capacity and competitive products. Funding, ownership and success metrics need to become more precise.

    I would add operational measures to the 20% chip-market target: capacity for critical chip classes, the cost of changing cloud providers and the share of critical systems with a tested exit plan.

    Europe should dial up investment where it has an edge: semiconductor equipment, power electronics, industrial software and specialised chips. Public procurement can create early demand for competitive European products based on security, portability and total cost.

    The real test is whether European companies gain more choice, scale and freedom to operate under pressure. A stronger European technology base can deliver that without closing the door to global innovation.

    What I would watch over the next 90 days

    For leadership teams, five questions can turn this policy direction into a growth and resilience agenda:

    1. Do we know our critical dependencies across cloud, AI, chips and software?
    2. Does every critical system have a workable switch or contingency plan?
    3. Are data and applications classified by actual risk?
    4. Do contracts provide portability, data access and transparent exit costs?
    5. Are procurement, technology and risk teams making these decisions together?

    Technology sovereignty is the capacity to create, choose and keep operating when conditions change.

    Sources

    1. European Commission: Strengthening Europe’s Tech Sovereignty, 23 June 2026
    2. European Commission: Tech sovereignty package, 3 June 2026
    3. European Commission: Cloud and AI Development Act, 3 June 2026
    4. European Commission: Chips Act 2.0, 3 June 2026
    5. European Commission: EU Open Source Strategy
    6. European Commission: Strategic roadmap for digitalisation and AI in energy, 3 June 2026
    7. European Court of Auditors: Special Report 12/2025, The EU’s strategy for microchips
    8. Mario Draghi: The future of European competitiveness, September 2024
    9. European Commission: AI Continent Action Plan
  • The Return of Industrial Time

    The Return of Industrial Time

    For the last two decades, a lot of management culture has learned to think in software time.

    Build. Test. Release. Measure. Iterate.

    That operating logic changed how companies build products, how teams organize work, and how boards talk about speed. It made experimentation respectable in places that used to reward only long planning cycles.

    My read on this: that lesson is still useful, but it is no longer enough.

    A growing part of the strategic agenda is not moving on software time. Electricity grids, energy systems, ports, factories, semiconductor supply chains, defense production, railway capacity, industrial permitting, and resilient sourcing all run on a different clock.

    They require capital before certainty arrives. They depend on permits, suppliers, safety, skills, land, regulation, maintenance discipline, and long-term demand signals. They take years to build and decades to amortize.

    This is the return of industrial time.

    The interesting leadership problem is not choosing between speed and patience. It is knowing which clock a decision belongs to.

    The software clock changed executive expectations

    Software gave leaders a powerful idea: speed can reduce risk.

    If a team can release a small version quickly, observe real behavior, and adjust, it does not need to pretend that every answer is known upfront. That logic has shaped far more than product development. It influenced strategy processes, innovation portfolios, transformation programs, and investor communication.

    The software clock is visible in how companies now talk about pilots, minimum viable products, agile delivery, platform thinking, data loops, and continuous improvement.

    I think that mindset still has enormous value. Faster feedback improves capital allocation. Faster decision loops reduce internal friction. Better data can reveal what customers, suppliers, and employees are actually doing, not only what the organization hopes they are doing.

    But the software clock also creates a temptation: the belief that every important problem can be de-risked through rapid iteration.

    That belief breaks down when the strategic problem is physical.

    You cannot A/B test a power grid in the same way you test a landing page. You cannot scale a defense-industrial base with the same reversibility as a software feature. You cannot rebuild semiconductor resilience quarter by quarter. You cannot fix underinvestment in infrastructure with a sprint review.

    Industrial systems can and should become more digital, more transparent, and more adaptive. But their underlying constraints remain material. Increasingly, they come with a price tag and a lead time that no roadmap can compress.

    Industrial time is slower because reality is harder

    Power grid control room overlooking high-voltage transmission lines at sunrise
    Industrial time is slow because physical capacity, permits and infrastructure cannot be compressed into software cycles.

    Industrial time is not slow because managers are old-fashioned. It is slow because the work sits inside physical, financial, and institutional constraints.

    Three numbers make the point.

    Grids. The International Energy Agency has warned that grids risk becoming the weak link in the energy transition unless investment accelerates. Its grid report says annual grid investment needs to double to more than USD 600 billion by 2030, and new transmission lines routinely take 5 to 15 years to plan, permit, and complete. IEA Executive Director Fatih Birol put it bluntly: "We must invest in grids today or face gridlock tomorrow." In the United States, the Department of Energy's National Transmission Needs Study estimates the country must more than double regional transmission capacity by 2035. That is not a communications problem. It is a capacity problem.

    Europe's investment gap. Mario Draghi's report on European competitiveness matters because it turns a familiar policy debate into an industrial-time problem. Its headline figure – roughly EUR 750-800 billion of additional investment per year – is not just a financing number. It is a statement about the scale of energy, defense, deep tech, infrastructure, and productivity capacity Europe would have to build. The report's core message is that Europe needs a different growth trajectory, not just better language around competitiveness. That lands as a management signal as much as a policy one.

    Semiconductors. A chip ecosystem is not one factory. It is design capability, advanced tools, specialty chemicals, materials, packaging, testing, energy, talent, customers, and export-control exposure. The CHIPS Act logic itself reflects this: the United States put USD 52.7 billion behind domestic semiconductor manufacturing and research because capacity is a multi-year industrial problem. TSMC's Arizona build-out, which began as a USD 12 billion project and later expanded, is now reported as a USD 165 billion U.S. investment. In mid-2026, TSMC CEO C.C. Wei told shareholders it would be "a long time before we can meet customer demand".

    Advanced semiconductor fabrication campus with clean industrial equipment, logistics docks and power infrastructure
    Semiconductor capacity is an ecosystem of tools, materials, energy, talent and long ramp-up times.

    The same pattern appears in defense. Europe can announce higher defense ambitions quickly, but ammunition output, supplier depth, testing capacity, skilled labor, and common procurement cannot be improvised. NATO's Jens Stoltenberg described the need to "shift from the slow pace of peacetime, to the high-tempo production demanded by conflict". That is industrial time in one sentence.

    The binding constraint is no longer the speed of the interface. It is the speed at which physical capacity, capital, skills, and permits can be brought into being.

    What this looks like inside companies

    The point becomes clearer when you look at company cases.

    Ford's electric-vehicle build-out is one example. A product with heavy software content still depends on battery plants, cell production, equipment orders, supply chains, trained workers, and industrial ramp-up. Ford described BlueOval City as part of its more-than-USD-30-billion EV investment through 2025. That is not a quarterly optimization exercise. It is a multi-year industrial bet.

    Orsted is another. The company took an impairment of roughly USD 4 billion in 2023 and cancelled its Ocean Wind 1 and 2 projects in New Jersey after supply-chain inflation, higher interest rates, and permitting delays made fixed-price contracts uneconomic. CEO Mads Nipper pointed to "significant adverse developments" in the supply chain and said the company was "extremely disappointed" to cease the projects. The deeper point is that industrial-time projects front-load commitment, then absorb the variance of a multi-year supply chain.

    Boeing shows a different version of the same issue. After the January 2024 737 MAX door-plug blowout, the FAA blocked Boeing from expanding 737 MAX production until quality systems were fixed. Demand was not the bottleneck. Industrial integrity was.

    And TSMC's Arizona expansion shows why industrial capability cannot simply be copied from one geography to another. The company has had to manage cost and timeline pressure in the United States, with reporting around TSMC's Arizona build-out pointing to substantially higher U.S. construction costs than in Taiwan. A fab is not just a building. It is an ecosystem.

    These are not failures of intelligence. They are encounters with a clock that does not negotiate.

    The harder management problem: two clocks, one company

    I do not think the answer is to become slower.

    The harder task is integration.

    A company that only thinks in industrial time becomes too slow. It over-plans, protects legacy processes, and treats every decision as irreversible. It may preserve reliability, but it loses learning velocity.

    A company that only thinks in software time becomes careless. It mistakes optionality for strategy. It launches too many pilots, underestimates physical dependencies, and treats capital-intensive systems as if they can be refactored later without cost.

    The way I see it, modern leadership needs both disciplines.

    Digital speed matters where reversibility is high and learning is valuable: customer insight, forecasting, demand sensing, workflow automation, internal transparency, scenario modeling, and decision support.

    Industrial patience matters where reversibility is low and execution risk compounds: plants, grids, logistics nodes, critical suppliers, regulatory approvals, safety systems, and long-lived assets.

    The mistake is applying the wrong rhythm to the wrong problem.

    Capital allocation becomes the test

    Executive strategy room with industrial infrastructure model, digital dashboard, hourglass and analog clock
    The real management test is whether capital, skills and capacity line up before the next shock arrives.

    Industrial time turns strategy into a capital-allocation test.

    It is easy to endorse resilience in a board presentation. It is harder to fund redundant capacity, dual sourcing, inventory buffers, grid connections, cybersecurity hardening, supplier development, and workforce training before the next disruption makes the need obvious.

    The same is true at national scale. The Draghi investment gap and the IEA grid investment number describe the same uncomfortable truth: agreement does not build capacity. Capacity follows from committed capital, credible timelines, aligned incentives, and operational ownership.

    The question I would be asking myself is simple:

    Where are we pretending that a strategic dependency is only an operating cost?

    If energy availability can constrain growth, it is strategic. If a supplier bottleneck can stop production, it is strategic. If a missing skill base can delay execution for years, it is strategic. If regulatory approval, grid access, or logistics capacity determines market entry, it is strategic.

    Industrial time makes these dependencies visible.

    It also changes the meaning of efficiency. In software time, efficiency often means reducing waste, shortening cycles, and automating repetitive work. In industrial time, efficiency also means keeping enough capacity, redundancy, and competence to survive stress.

    A system optimized only for the normal case can be financially elegant and strategically fragile.

    Andreas's view

    My read on this: the next advantage is temporal discipline.

    The companies that do this well will not become nostalgic industrial planners. They will still use digital tools aggressively. They will use better forecasting, better data, better scenario models, and faster feedback loops to make long-cycle decisions less political and less blind.

    But they will also recognize that some commitments have to be made before certainty arrives.

    I don't think the next decade rewards organizations that simply move fast. It rewards organizations that know when speed is a learning tool and when early commitment is the real advantage.

    Three things I'm watching:

    • Whether Europe can turn the Draghi diagnosis into actual capacity: energy, defense, capital markets, compute, and industrial execution.
    • Whether AI infrastructure pushes grid access, power contracts, cooling, chips, and data-center permitting into the center of corporate strategy.
    • Whether companies start treating suppliers, energy, skills, and resilience as strategic assets rather than procurement line items.

    The telling indicator will be whether management teams can hold both clocks in their head at the same time.

    Move fast where learning is cheap. Commit early where capacity will be scarce. Use data to shorten decision cycles, but respect the physics of assets, infrastructure, and institutions.

    The world is becoming more digital and more industrial at the same time.

    That is the leadership rhythm I think matters now.

    Sources

    https://commission.europa.eu/topics/competitiveness/draghi-report_en

    https://www.iea.org/reports/electricity-grids-and-secure-energy-transitions

    https://www.iea.org/news/lack-of-ambition-and-attention-risks-making-electricity-grids-the-weak-link-in-clean-energy-transitions

    https://www.energy.gov/oe/national-transmission-needs-study

    https://www.semiconductors.org/chips/

    https://pr.tsmc.com/english/news/3210

    https://www.cnbc.com/2025/03/03/tsmc-to-announce-100-billion-investment-in-us-chip-plants.html

    https://www.tomshardware.com/tech-industry/semiconductors/tsmc-ceo-c-c-wei-says-it-will-be-a-long-time-before-we-can-meet-customer-demand-tells-shareholders-that-he-will-keep-prices-stable-refrain-from-implementing-price-hikes

    https://9to5mac.com/2023/08/04/us-made-tsmc-chips/

    https://corporate.ford.com/articles/electrification/blue-oval-city/www/

    https://www.cnbc.com/2023/11/01/orsted-axes-two-new-jersey-wind-projects-takes-4-billion-writedown.html

    https://www.faa.gov/newsroom/faa-halts-boeing-max-production-expansion-improve-quality-control-also-lays-out-extensive

    https://www.nato.int/en/news-and-events/events/transcripts/2024/02/15/press-conference

    • European Commission: The Draghi report on the future of European competitiveness
    • International Energy Agency: Electricity Grids and Secure Energy Transitions
    • International Energy Agency: "Invest in grids today or face gridlock tomorrow"
    • US Department of Energy: National Transmission Needs Study
    • Semiconductor Industry Association: CHIPS Act overview
    • TSMC: U.S. investment expanded to USD 165 billion
    • CNBC: TSMC total U.S. investment reported at USD 165 billion
    • Tom's Hardware: TSMC CEO C.C. Wei on customer demand
    • 9to5Mac / NYT summary: TSMC Arizona construction-cost premium
    • Ford: BlueOval City and EV investment
    • CNBC: Orsted offshore wind impairment and cancellations
    • FAA: Boeing 737 MAX production expansion halted
    • NATO: Defense industrial production remarks
  • Germany and France put digital sovereignty into operational terms

    Germany and France put digital sovereignty into operational terms

    Germany and France have published a joint paper on digital sovereignty, dated 17 June 2026. It is only six pages long, but it does something useful: it gives the term digital sovereignty a set of testable criteria.

    Europe has spent years talking about sovereignty in broad terms. The Franco-German paper asks a narrower question: when a government, company or public institution buys digital technology, what would make that technology more or less sovereign?

    The paper does not pretend this is easy. It says digital sovereignty should be risk-based, modular and scalable. It avoids protectionism and isolation. It leaves defence and national security outside its scope. It creates no direct budget obligation and does not impose conditions on private procurement.

    The document is cautious by design. That is useful for consensus. It is also the problem.

    Germany and France are not proposing a simple "buy European at any cost" doctrine. They are proposing criteria that could feed into the EU Tech Sovereignty Package, including the Cloud and AI Development Act. If those criteria survive the legislative process, they could start shaping procurement, cloud architecture, sensitive-data handling and public-sector technology choices.

    The paper's value is the checklist. Its weakness is that it stops there. It does not yet create the kind of aggressive investment push now visible in other regions.

    The definition is broader than cloud

    Minimal stacked blocks representing chip, network, server, cloud and AI layers
    Digital sovereignty has to be assessed across the stack, from chips and networks to cloud platforms and AI.

    The core definition is worth reading carefully. Digital sovereignty is described as the capability and capacity to develop, provide, use, adapt and control digital technologies, including hardware, in an independent, self-determined and secure manner.

    Data location is only one part of it.

    It includes hardware, software, data handling, AI, semiconductors, cloud, quantum, robotics, cybersecurity, standards, supply chains, skills and control over operational processes. The paper says critical dependencies exist across the entire stack, from IT infrastructure and semiconductors to software, data and AI.

    This maps better to how dependency actually works.

    Europe's dependency problem is scattered across the stack: hyperscale cloud, chips, operating systems, cybersecurity tools, AI models, productivity platforms, data infrastructure, technical standards, venture capital depth, and the ability to scale startups into global companies.

    One datapoint stands out: in Europe's digital industrial ecosystem, most companies have fewer than 250 employees, based on the European Commission/JRC SME report cited in the paper. That captures one of Europe's structural problems. Europe has plenty of innovation. It has too few digital companies with global scale.

    The six criteria matter most

    Minimal procurement checklist beside a cloud architecture cube and pencil
    The six criteria can be used in procurement, supplier reviews, architecture decisions and exit planning.

    The paper defines six dimensions of digital sovereignty.

    The first is the capability to implement and enforce. This is about whether Europe can apply its own legal and security conditions in practice. The criteria include EU-law compliance, transparency of ownership and subcontractor chains, disclosure of dependencies on third countries, restriction of sovereignty-critical extraterritorial data access, and the ability to investigate cybercrime and state-backed attacks.

    The cloud debate often gets stuck here: legal jurisdiction and operational control do not always sit in the same place as the data center.

    The second is the capability to design, deploy and use technologies. This includes scientific ecosystems for AI, microelectronics, robotics, data, quantum and cybersecurity; industrial demand for key technologies; research transfer; startup scaling; open source, open hardware and interoperability; and participation in standardisation.

    Europe often underestimates this layer. Regulation can define the rules. It cannot replace the people, companies and institutions that build, operate, buy and improve the technology.

    The third is economic value creation. The paper looks at where value is generated: R&D, engineering, skilled employment, operational control and contribution to the European technology ecosystem. It also explicitly allows partial value creation in trusted partner countries. That keeps the framework open enough to be economically realistic.

    The fourth is protection of data. The paper calls on the European Commission to define the highest protection standards for the most sensitive data, including safeguards against cybersecurity risks and the effects of non-EU extraterritorial legislation. It also mentions mandatory privacy-enhancing technologies.

    Sensitive data policy is now also industrial policy.

    The fifth is substitutability and interoperability. The paper asks for modular architecture, open standards, open interfaces, software bills of materials, migration paths, exit concepts and multi-vendor strategies. In plain English: do not build systems that cannot be changed later.

    For me, this is the most practical part of the paper. Lock-in rarely arrives as a crisis. It arrives as a procurement decision that cannot be reversed without years of cost and disruption.

    The sixth is infrastructure resilience. The paper calls for sovereign data centers, AI, quantum and cloud computing infrastructure, interchangeable hardware and software stacks, diversified supply chains, secure and sustainable energy, high-performance networks and access to critical space resources.

    Minimal data center model connected to power grid, cloud and network nodes
    Digital sovereignty depends on the physical layer too: data centers, energy supply, networks and resilience.

    This links directly to the SoftBank France data-center story. Digital sovereignty now has a power, land, data-center and network dimension. The debate has moved well beyond data location and cloud labels.

    The paper is careful, maybe too careful

    The paper is politically careful. It is non-binding. It excludes defence and national security. It does not force public spending. It does not impose rules on private procurement. It stresses trade obligations, trusted partners and cost efficiency.

    That makes it weaker than a real industrial plan. It also makes the document harder to dismiss as protectionism.

    The gap is not definition. The gap is action.

    The paper does not unlock capital. It does not create major public procurement demand. It does not accelerate data-center buildout, AI infrastructure, semiconductor capacity, cloud scale or startup growth. It gives Europe a framework for assessing sovereignty, but it does not yet give European providers the demand, reference customers or balance-sheet confidence needed to scale.

    The paper does not argue for closing Europe off. Its more useful move is to make dependency measurable. Who owns the provider? Which subcontractors matter? Where is R&D located? Can the customer exit? Are open interfaces available? Can sensitive data be protected from extraterritorial access? Can Europe still operate if one supplier, jurisdiction or supply chain becomes unavailable?

    These questions belong in procurement files, architecture reviews and risk discussions.

    For enterprise leaders, digital sovereignty is becoming a procurement and architecture discipline. It will affect cloud strategy, AI deployment, data classification, supplier concentration, cybersecurity, exit planning and board-level risk.

    For policymakers, a definition is useful only if it changes incentives. Europe needs procurement demand for sovereign solutions, faster scaling paths for startups, deeper capital markets, serious public-sector reference customers, and infrastructure policy that connects cloud, AI, energy, semiconductors and networks.

    Without that, sovereignty stays a vocabulary exercise. Other regions are moving with capital, infrastructure, industrial policy and large anchor customers. Europe cannot answer that with criteria alone.

    The executive takeaway

    The Franco-German paper stops short of a sovereignty plan. It offers criteria. Criteria still matter because they shape what governments and large buyers start asking for. They shape tenders. They influence compliance teams. They tell suppliers what the next market standard may look like.

    If Europe uses this framework well, sovereignty becomes less abstract: fewer lock-ins, clearer exit paths, more transparent supply chains, stronger data protection, more European value creation, and better infrastructure resilience.

    If Europe uses it badly, it becomes another vocabulary layer on top of slow procurement and fragmented national initiatives.

    My read: this paper is strongest where it is most practical. It connects sovereignty to ownership, enforceability, interoperability, data protection, value creation and infrastructure. It avoids the fantasy of full autarky. It accepts trusted partners. It treats sovereignty as a risk-based capability, not as a flag on a server.

    But the next test is not another definition. It is demand.

    Without procurement demand, budgets, infrastructure, reference customers and scale, European providers will stay small. Without scale, the dependency problem stays exactly where it is.

    Bottom line: good start. Now Europe needs action.

    Sources and further reading

  • Agentic AI Is About to Leave the Screen

    Agentic AI Is About to Leave the Screen

    Why robotics may become the next operating layer for AI, and what changes when the physical world becomes programmable.

    Andreas's view

    My read: robotics is still being framed as hardware, when the more important shift is that AI is becoming an operating layer for physical work.

    The market tends to split into two shallow stories. One treats robots as factory equipment. The other treats humanoids as spectacle: impressive demos, big forecasts, uncertain timelines.

    I don't think either framing is enough. The more interesting story is that agentic AI gives robotics a new operating layer. Robots are not only getting better bodies. They are starting to get better ways to interpret context, plan actions and coordinate with digital systems.

    That changes the question. It is no longer only: what can AI answer? It becomes: what can AI do when it can perceive, decide and move?

    For leaders, the implication is practical: start mapping where physical work could become programmable. The strategic question is not "Should we buy robots?" It is where sensing, decision-making, workflow automation and safe machine execution could change cost, throughput, resilience or customer outcomes.

    From chatbot to operator

    The first wave of generative AI lived in a text box. It wrote, summarized, translated, coded and made knowledge work faster.

    The second wave is more ambitious. Agentic AI plans, checks, books, routes, escalates and triggers workflows. It turns AI from an interface into an operator.

    Robotics is where the operator model starts to touch the real world.

    If chatbots made AI visible, and agents make AI operational, robotics makes AI physical.

    This is a much bigger jump than the interface suggests. A chatbot operates in language. A software agent operates in digital systems. A robot operates in environments where physics, safety, maintenance, regulation and human trust all matter at the same time.

    That is why I would not start this discussion with humanoids. Humanoids are one form factor. The bigger story is physical AI: models, sensors, actuators, chips, batteries, simulation, edge computing, fleet software and enterprise workflows coming together.

    Robotics is not one market

    Robotics market stack showing industrial, service, medical, defense, consumer, and humanoid robot segments
    Robotics is not one market. It is a connected stack of industrial, service, medical, defense, consumer, and general-purpose systems.

    Robotics is already a real market, and it is much broader than the humanoid headlines.

    Industrial robots remain the established core: welding, assembly, painting, material handling, electronics, automotive and semiconductor manufacturing. Professional service robots cover logistics, warehouse automation, inspection, cleaning, hospitality, agriculture, construction and security. Medical and care robots include surgical systems, rehabilitation devices and hospital logistics.

    Defense and security robotics adds unmanned aerial, ground, surface and underwater systems, counter-drone capabilities, explosive ordnance disposal, reconnaissance, logistics and infrastructure protection. Consumer robots cover domestic devices such as vacuums and lawn robots. Humanoid and general-purpose robots sit on top of this stack as an early-stage category for environments built around human bodies.

    The data matters because it grounds the story. The International Federation of Robotics reported 542,000 industrial robot installations in 2024 and a global operational stock of 4.664 million units. Asia accounted for 74% of new deployments. China alone represented 54%.

    Service robotics is smaller and more fragmented, but it is moving. IFR's World Robotics 2025 service robot summary reported that worldwide sales of professional service robots grew 9% in 2024 to more than 199,000 units. Medical robots grew 91% to nearly 16,700 units.

    Market forecasts point in the same direction, even if the exact numbers should be treated carefully. Goldman Sachs sees the humanoid robot market reaching $38 billion by 2035. Morgan Stanley outlines a much larger long-term scenario: a potential $5 trillion humanoid market by 2050, including supply chains, repair, maintenance and support.

    Defense is one of the clearest signals that robotics is becoming a strategic technology segment, not only an automation category. Fortune Business Insights estimates the military robots market at $19.82 billion in 2025 and projects it to reach $42.90 billion by 2034. The exact number matters less than the direction: militaries are shifting from isolated unmanned platforms toward fleets, autonomy, sensing, secure communications and human-machine teaming.

    The point is not to believe every forecast. The point is that robotics is starting to look less like a hardware niche and more like a debate about who controls the operating layer of physical work.

    Why the cycle feels different now

    Robotics has had false dawns before. What makes this cycle worth watching is that several constraints are shifting at once.

    AI models are becoming more useful for perception, planning and adaptation. Google DeepMind describes Gemini Robotics as bringing AI agents into the physical world; Google's later Gemini Robotics-ER 1.6 work focuses on spatial logic, multi-view understanding, task planning and success detection.

    Simulation is improving too. Robots need data, but the physical world is expensive and slow. Synthetic environments, world models and simulation frameworks can compress training cycles. That is why NVIDIA's physical AI announcement matters: Jensen Huang called this a "ChatGPT moment for robotics" and framed physical AI as models that understand the real world, reason and plan actions.

    Enterprise demand is also clearer than before. Labor scarcity, warehouse complexity, aging populations, healthcare capacity, nearshoring and infrastructure build-out all create demand for automation that can work beyond perfectly structured factory cells.

    The market is not waiting for household humanoids. It is starting with work.

    It is also starting with security. The U.S. Department of Defense's Replicator initiative is built around all-domain attritable autonomous systems: lower-cost systems that can be fielded, updated and replaced faster than traditional platforms. NATO's DIANA Rapid Adoption Service recently awarded an R&D contract for undersea robotics and describes its role as helping Allies "move faster from identified capability need to real-world solutions." That is the defense version of the same physical AI thesis.

    Operator overseeing autonomous drone, ground, and undersea robotics systems for defense and security missions
    Defense robotics is shifting from isolated platforms toward autonomous fleets, sensing, secure communications, and human-machine teaming.

    Humanoids are the headline, not the whole story

    Humanoids matter because the world is built for people. Door handles, stairs, shelves, tools, kitchens, hospital rooms and factory aisles assume a human body.

    If robots can operate in those environments, the cost of automation changes. Companies may not need to redesign every workflow around a fixed machine. The machine could adapt to the workflow.

    That is the promise. It is also where the hype gets dangerous.

    Most useful robotics deployments will start where the economics are precise: structured tasks, high labor scarcity, safety risk, repetitive physical work, expensive downtime or environments where human work is hard to scale.

    Amazon is a useful case because it shows the less cinematic version of the future. The company says it has deployed its one millionth robot and introduced DeepFleet, a generative AI foundation model designed to coordinate robot movement across fulfillment centers. The stated goal is a 10% improvement in robot fleet travel efficiency.

    That is how physical AI will often arrive: not as a robot that looks like a person, but as a system-level improvement in throughput, cost, safety or resilience.

    The recent signal: capital is moving toward physical AI

    The last few weeks made the theme harder to dismiss.

    Germany's NEURA Robotics announced a Series C round of up to $1.4 billion in June 2026, backed by investors including NVIDIA, Amazon, Qualcomm, Bosch, Schaeffler, the European Investment Bank and Tether. NEURA founder David Reger put the strategic point plainly: "The future of AI will not only live on screens."

    OpenAI is also leaning into the theme. Sam Altman's 2026 roadmap says 2027 may bring robots that can do tasks in the real world. Separate reporting on OpenAI Robotics hiring is best read as a secondary signal, not the core proof point.

    This is more than a robotics startup cycle. It is a convergence of AI labs, cloud-scale compute, semiconductor platforms, industrial companies and capital markets.

    That matters for Europe. If physical AI becomes an industrial operating layer, Europe is not limited to being a regulator of someone else's platform. Its manufacturing base, robotics suppliers, automotive sector, industrial software, safety know-how and Mittelstand process expertise could become part of the stack, provided capital, compute, talent and adoption speed match the ambition.

    The operating model question

    The real question is not whether to buy robots. That is too narrow.

    The better question is: which parts of the operating model become programmable when AI can act in both digital and physical environments?

    In logistics, software agents may forecast demand, rebalance inventory and dispatch autonomous mobile robots. In healthcare, AI may coordinate patient logistics while robots move supplies or support clinical workflows. In manufacturing, physical AI may help factories adapt faster to product variation, quality issues or labor constraints.

    In defense, the question is even sharper. Autonomous systems can extend sensing, logistics, surveillance, electronic warfare and force protection into environments where human presence is dangerous or too slow. This does not remove the need for human judgment. It raises the standard for command, control, accountability, cyber resilience and rules of engagement.

    The value is not the robot in isolation. The value is the loop: sense the environment, interpret the situation, decide what should happen next, act safely, learn from the outcome.

    That loop is what makes robotics strategically interesting.

    It also makes it risky.

    Governance moves into the physical world

    Executives reviewing governance controls for supervised robotics and physical AI in an automated operations environment
    Physical AI will require governance models that cover permissions, audit trails, human override, safety, and accountable operations.

    Enterprises are still learning how to govern text-generating AI. Physical AI raises the bar.

    A weak chatbot answer can mislead a user. A poorly governed software agent can execute the wrong digital workflow. A poorly governed robot can damage equipment, block a line or create a safety incident in a regulated environment.

    That means physical AI needs a governance model before it scales.

    Who owns the robot's actions? What permissions does it have? What tasks require human approval? How are decisions logged? How is an incident reconstructed? Who updates the model? Who certifies safety after the model changes?

    These are not IT questions only. They are operating model questions.

    My expectation is that the companies that do this well will not describe the work as a robot deployment. They will describe it as a redesign of work: human judgment where ambiguity is high, machine execution where repetition and safety allow, and clear escalation when the system reaches its boundary.

    What I'm watching

    Four things will tell me whether this thesis is right.

    First, whether robotics deployments move from isolated machines to fleet-level operating systems.

    Second, whether AI labs and industrial companies build repeatable safety and governance patterns, not only better demos.

    Third, whether customers buy measurable outcomes rather than robots: lower downtime, faster fulfillment, safer operations, more resilient logistics or higher asset utilization.

    Fourth, whether the market starts valuing robotics companies as platform ecosystems rather than hardware manufacturers.

    AI is moving from language to action. From action to coordination. From coordination to physical work.

    The first wave lived on screens. The next one will increasingly show up in the world those screens were designed to manage. Physical AI is not just a device transition. It is an operating model transition.

    Sources and further reading

  • Model Dependency Is the New AI Business Continuity Risk

    Model Dependency Is the New AI Business Continuity Risk

    Claude Fable 5, model dependency risk, and why AI sovereignty is no longer only about where data lives.

    Andreas's view

    I would have liked more time with Claude Fable 5.

    Not because a new benchmark table matters by itself. It does not. But because frontier models are now becoming operating infrastructure. When access disappears, the issue is no longer product disappointment. It is continuity risk.

    The early description sounded like a model that pushed several practical boundaries at once: longer autonomous work, stronger coding, better vision, better long-context memory, more capable scientific reasoning. That is exactly the kind of model you want to test yourself. Not in a demo. In messy work. In a real harness. In the kind of workflow where you can feel whether the model changes what is possible.

    Unfortunately, that window closed almost immediately.

    Anthropic announced Claude Fable 5 and Claude Mythos 5 on June 9, 2026. The launch framed Fable 5 as a generally available Mythos-class model with safeguards, and Mythos 5 as the same underlying model with some safeguards lifted for trusted cyber and biology use cases.

    Three days later, Anthropic added an update: access to Fable 5 and Mythos 5 was unavailable.

    The follow-up statement is the more important business story. Anthropic said the US government had issued an export-control directive requiring it to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States. To comply, Anthropic said it had to abruptly disable both models for all customers. Other Anthropic models were not affected.

    Based on Anthropic's public account, the directive was triggered by national-security concerns around model misuse; as of this writing, the government's full rationale has not been publicly detailed.

    This is where the story stops being about one model release.

    It becomes a preview of a much larger question: what happens when an enterprise builds critical workflows around a model that can disappear, degrade, fall back, become restricted, change policy, or become unavailable for reasons outside the enterprise's control?

    The model is becoming part of the process

    Enterprise workflows converging into a central AI model dependency
    As AI moves into real workflows, the model becomes part of the operating process rather than a standalone tool.

    Most companies still talk about AI models as if they were tools. You pick one, connect it to a use case, monitor cost and quality, and move on.

    That framing is becoming too simple.

    In real deployments, the model is increasingly part of the operating process. It sits inside support flows, developer environments, research workflows, compliance review, sales operations, procurement analysis, risk triage, security workflows, and internal knowledge systems.

    The more capable the model, the more tempting it becomes to build around its specific behavior.

    That is where the dependency starts.

    A company does not only depend on the model name. It depends on latency, context length, tool use, refusal behavior, reasoning style, pricing, data-retention policy, regional availability, safety fallbacks, API contracts, rate limits, and the model's ability to work inside a specific harness.

    The harness matters. A workflow may depend on prompt structure, tool calls, memory files, evaluation thresholds, orchestration logic, fallback assumptions, and human review steps. If the underlying model changes, the process can change with it.

    Sometimes that is manageable. Sometimes it breaks the economics. Sometimes it changes the risk profile. Sometimes it simply means the workflow no longer works.

    Fable 5 is a case study in availability risk

    The Fable 5 launch itself was ambitious. Anthropic described strong performance in software engineering, knowledge work, vision, scientific research, long-context memory, and life sciences. It also described safeguards that would route some sensitive requests to Claude Opus 4.8 instead of allowing Fable 5 to answer directly.

    That already shows the new shape of frontier AI products. The "model" is no longer a single stable object. It is a capability layer plus policy logic, classifiers, routing, monitoring, data-retention rules, trusted-access programs, and usage conditions.

    Then came the suspension.

    Anthropic said the government directive was based on national-security authorities and that it had to remove access for all users. It also said it disagreed with the action and believed that applying this standard across the industry could halt new frontier model deployments.

    Whether Anthropic or the government is right is not the central issue for enterprise leaders.

    The customer was not in control. Even a short-lived availability window is enough to expose the broader risk: pilots, evaluations, procurement decisions, and roadmap assumptions can all form around capabilities that may not remain available.

    The sharper lesson is that access to frontier capability has become a business continuity variable.

    The hidden risk: model concentration

    Model concentration is becoming a real operational risk.

    The risk is not that one provider has an outage. Enterprises already understand cloud outages. The risk is that AI capability is becoming more specific and less interchangeable.

    If two models both answer emails, switching is easy.

    If one model can run a long-horizon code migration, interpret screenshots, manage tool calls, maintain working memory, and reason through edge cases in a particular way, switching becomes harder. The replacement may be available, but the workflow may need to be redesigned.

    That is a different kind of lock-in.

    It is not only commercial lock-in. It is cognitive and procedural lock-in. Over time, the organization's workflows, prompts, review habits, and escalation paths start to conform to the model's strengths and weaknesses.

    This will matter most in high-value use cases: engineering modernization, cyber defense, regulated research, legal work, finance analysis, industrial design, life sciences, and autonomous operations.

    The more strategic the use case, the less acceptable it is to rely on a single model path with no tested fallback.

    What enterprise architecture needs to change

    AI continuity architecture with a primary model unavailable and fallback paths active
    A model continuity plan needs tested fallback paths, evaluations and escalation logic, not just a procurement preference.

    The practical answer is not to avoid frontier models. That would be the wrong lesson.

    The answer is to treat model dependency as architecture, not procurement.

    Companies need a model continuity plan.

    For every important AI workflow, leaders should know which assumptions are model-specific. What breaks if the model is removed? What happens if it falls back to a weaker model? What if data-retention rules change? What if the API remains available in the US but not in Europe? What if a safety classifier suddenly routes part of the work elsewhere?

    This is not theoretical governance paperwork. It is operational design.

    The risks are different, and they need to be named differently. An outage is not the same as a policy withdrawal. A safety-routing change is not the same as capability degradation. A regional access restriction is not the same as a pricing change. But all of them can alter a workflow that the business has started to rely on.

    The better pattern is a portfolio:

    • a primary frontier model for maximum capability
    • a tested fallback model for continuity
    • evaluations that measure task quality across model options
    • abstraction layers that keep prompts, tools, memory and workflow logic portable where possible
    • logs that show when fallbacks happen and why
    • contracts that address availability, regional access, data policy and notice periods
    • human escalation when model behavior changes materially

    The important word is tested. A fallback that has never been used under realistic workload is not a fallback. It is a slide in an architecture deck.

    The board-level questions are practical:

    • Which AI workflows would stop if the primary model disappeared?
    • Which fallbacks have been tested under real workload?
    • Which model-specific assumptions are embedded in prompts, tools, policies and contracts?
    • Who owns model continuity: procurement, architecture, risk or the business unit?

    Why this becomes a sovereignty issue

    European enterprise connected to AI capability nodes with one external path interrupted
    AI sovereignty increasingly means asking who can interrupt a capability, not only where data is stored.

    For Europe, this is where the story becomes uncomfortable.

    AI sovereignty is often discussed as data residency, cloud location, compliance, or whether a model is hosted in Europe. Those questions matter. But they are no longer enough.

    The Fable 5 episode shows another layer: model availability can be shaped by decisions outside the customer's jurisdiction.

    A European company may comply with European law, host data in Europe, and still depend on an AI capability that can be changed or removed because of a US policy decision, provider safety decision, capacity constraint, licensing change, or export-control interpretation.

    That does not mean every company needs to train its own frontier model. That would be unrealistic for most. It is also not an argument for isolationism or for purely national AI stacks.

    It does mean Europe needs to think about sovereignty at the level of operating capability.

    Can critical public-sector, industrial, defense, healthcare, financial and infrastructure workflows continue if a non-European model is restricted? Are there European or allied alternatives? Are there open-weight or locally deployable fallbacks for lower-risk parts of the workflow? Are procurement teams asking for exit paths? Are regulators looking at operational resilience, not only privacy?

    The sovereignty question is shifting from "Where is the data?" to "Who can interrupt the capability?"

    That is a much harder question.

    The strategic lesson

    I still would have liked to play with Fable 5.

    That is partly curiosity. Frontier models are easiest to understand when you test them against real work. You learn more from one serious workflow than from a benchmark chart.

    But the more important lesson is the one created by not being able to use it.

    The future of enterprise AI will not be decided only by who has the strongest model. It will also be decided by who can build resilient operating models around unstable capability layers.

    Models will improve. Policies will change. Access rules will shift. Providers will make safety decisions. Governments will intervene. Capacity will be constrained. Prices will move.

    For CIOs, CTOs and risk leaders, the question is no longer whether to use frontier models. It is whether every critical AI workflow has a tested continuity path.

    The companies that win will not be the ones that pretend this volatility does not exist. They will be the ones that treat model volatility as a design constraint from the beginning.

    Sources and further reading

  • AI Funding Is Turning Into Infrastructure Capital

    AI Funding Is Turning Into Infrastructure Capital

    Crunchbase‘s April report reads, at first, like one more data point in the AI boom. Global venture funding hit $56 billion in April 2026 – the third-biggest month in a year, and roughly double April 2025. AI took $37 billion of that, about two-thirds of all venture money in the month.

    What matters is where the money went. Two rounds did most of the work. Anthropic raised $15 billion. Jeff Bezos’s Project Prometheus, aimed at AI for manufacturing and the physical world, raised $10 billion. Together they accounted for 45% of all venture funding in April. Five weeks later, on 28 May, Anthropic closed a $65 billion Series H at a $965 billion valuation – the largest equity round ever raised by an AI company, and enough to pass OpenAI as the most valuable startup in the world.

    These rounds work differently from the software rounds that came before them. Venture capital has started to behave like strategic industrial capital, and the AI race has become a contest over who can assemble enough capital, compute, power, data, and industrial access to own the next operating layer of the economy.

    The money is pooling at the top

    AI venture capital concentrating in a small number of frontier model and infrastructure companies
    The headline funding number can rise while the market underneath it narrows.

    Venture has always followed a power law: a few companies take most of the returns. April pushed that to an extreme. Through April, global venture investment was up 139% year over year, and nearly 60% of that capital went to just five companies – most of them backed by cash-rich public tech firms, private equity, and the largest VC funds. Q1 looked the same: OpenAI ($122 billion at an $852 billion valuation), Anthropic, xAI, and Waymo took roughly two-thirds of all global venture funding between them.

    This changes what the funding totals tell you. In an ordinary cycle, rising funding signals broad risk appetite – more founders backed, more categories opening, more experiments running. Right now the total can climb while the market narrows underneath it. Plenty of money is flowing, but it reaches very few companies, and the ones it reaches have started to look like national-scale infrastructure projects.

    That is why the comparison to past SaaS or internet cycles falls apart. A $15 billion AI round belongs to an entirely different category of capital formation than even the largest software growth round.

    Models have become capital assets

    Frontier AI models connected to cloud infrastructure, advanced chips, capital markets and public-private investment loops
    A frontier model is no longer just an algorithm. It is a capital asset tied to compute, chips, cloud and distribution.

    AI model companies raised $26.7 billion in April – by far the largest single category, ahead of physical AI ($5.3 billion) and AI infrastructure like chips and data centers ($1.8 billion).

    The reason is structural. Frontier labs are expensive in ways software companies never were: they need long compute contracts, data-center capacity, advanced chips, large engineering and safety teams, enterprise sales, and deep ties to the hyperscalers. They sell software and spend like heavy industry.

    The cloud era made infrastructure feel weightless. You rented compute, scaled on demand, and built globally without owning anything. AI has partly reversed that. Compute has turned back into a scarce, physical input that decides who can compete, so the companies with privileged access to chips, power, and distribution hold a real structural edge. That is why hyperscalers, sovereign funds, and private equity keep moving closer to the center of AI financing.

    Anthropic‘s Series H is the clearest example. Look at who funded it: alongside the crossover investors sit the companies that supply the infrastructure Claude runs on – the cloud it trains on, the memory chips that serve its inference. Those backers have a direct operating interest, since their own businesses grow as Anthropic grows. A model company has become a capital asset that its own suppliers want a stake in.

    Physical AI is the second signal – and maybe the bigger one

    Physical AI connecting robotics, manufacturing, aerospace, automotive and European industrial infrastructure
    Physical AI shifts the question from digital productivity to industrial leverage.

    The Prometheus round may matter more than Anthropic‘s, even though it is smaller. Anthropic represents the frontier-model race. Prometheus points to the phase after it: AI moving out of language and code and into engineering, manufacturing, robotics, aerospace, automotive, and physical production. Crunchbase counted about $5.3 billion of April’s AI funding as physical AI – a small slice today, with an outsized claim on the real economy.

    For a few years, AI has mostly been a knowledge-work story: it writes, summarizes, codes, plans, and automates digital tasks. The physical-AI bet says the next contest is over the industrial system itself – compressing engineering cycles, simulating physical systems, optimizing factories, improving robotics, speeding up materials discovery. If that works, the real value sits in industrial leverage: how quickly companies can design, test, and build physical things.

    That also explains the capital intensity. Industrial AI demands labs, data rights, robotics environments, manufacturing partners, domain experts, and access to the messy operational data inside real companies. The winner here will probably be whoever can wire models into real factories, supply chains, machines, and the proprietary data that sits inside them.

    Public and private markets are now one loop

    The April data also shows how tightly public markets, private markets, and the wider economy are now linked. Alphabet, Microsoft, and Amazon all beat revenue expectations while spending heavily on AI infrastructure. Pantheon Macroeconomics estimates that about half of the 2% U.S. GDP growth in Q1 came from AI buildout. That figure is large enough to matter: AI now shows up directly in the macro data.

    The result is a feedback loop. Public tech companies throw off cash and market value. Those balance sheets fund compute and strategic investments. The investments flow into private AI companies, which buy more infrastructure, which lifts hyperscaler revenue and capex again. For now, the loop is strong.

    The risk is that it makes AI look broader than it is. When a few capital-rich companies drive both the public-market narrative and the private-market totals, the whole ecosystem leans on a small set of balance sheets and assumptions. The boom is genuine, and it is also concentrated, circular, and dependent on a narrow base of infrastructure.

    What this means for Europe

    U.S. companies raised $39 billion in April, around 70% of global venture funding. For Europe, the clean comparison is not AI-only funding; it is total venture/startup funding on the same monthly basis. A Crunchbase-based European VC landscape dataset counted $4.8 billion across 327 European investments in April, while Tech.eu counted €5.1 billion across 290 European tech deals. Even allowing for methodology differences, Europe was roughly a one-tenth-of-global market while the U.S. took about 70%. That should sting.

    The usual European AI debate is about regulation, foundation models, talent, data, and digital sovereignty. All of it matters. April adds a dimension that gets less attention: capital sovereignty. If AI leadership now takes tens of billions for models, data centers, chips, power, and industrial deployment, then good research and sensible rules will not be enough on their own. Europe also has to mobilize capital at the scale and speed the technology demands.

    This is where the Draghi competitiveness argument gets concrete. Europe cannot regulate its way to AI relevance, and it cannot research its way there either while its capital, compute, and adoption stacks stay fragmented.

    The position is far from hopeless. Europe has real industrial depth – manufacturing, automotive, aerospace, energy systems – in exactly the domains where physical AI could matter most. That strength does not convert into AI advantage automatically. It has to be connected to capital, compute, data-sharing arrangements, procurement, and faster decisions. Otherwise the industrial data and engineering know-how that should be Europe’s edge will be monetized through platforms funded and controlled elsewhere.

    The question for leaders

    For executives, the useful question is what kind of market is being built, and whether their company has a place in it. If AI funding is becoming infrastructure capital, then AI strategy belongs in the boardroom as a question about strategic dependency:

    • Who controls the models you rely on?
    • Who controls the compute?
    • Who owns the industrial data?
    • Who has the capital to build at scale?
    • Who can turn AI capability into operating-model change faster than you can?

    This matters most for companies outside tech. Many industrial, financial, logistics, healthcare, and public-sector organizations still treat AI as a vendor-selection exercise, and that framing is too small. The real question is where you sit in the emerging AI capital stack – as a buyer of capability, a supplier of domain data, a deployment partner, a regulated adoption environment, a business whose workflows get compressed by someone else’s model, or a company that uses AI to redesign the economics of its own industry.

    What I’m watching next

    Three signals matter more than the next monthly funding total.

    1. Concentration. If capital keeps pooling in a few frontier-model and infrastructure companies, the AI market will increasingly resemble a strategic infrastructure race.
    2. Physical AI. If funding for robotics, manufacturing, and autonomy accelerates, AI starts reshaping the industrial economy, well beyond office work.
    3. Europe. If the continent stays strong on regulation and weak on capital mobilization, the sovereignty debate stays rhetorical.

    April’s data points to an AI economy that is becoming more capital-intensive, more concentrated, and more physical. The next phase will be won by whoever can put the full stack together: capital, compute, energy, data, industrial access, distribution, and execution speed. That is a different kind of technology race, and it is already running.

    Sources: Crunchbase, “Billion-Dollar AI Rounds Push April To Third-Highest Startup Funding Month In A Year” (5 May 2026) and the Q1 2026 global funding report; Trustventure, “European Venture Capital Landscape – April 2026”; Tech.eu, “April 2026’s top 10 European tech deals”; Anthropic’s Series H announcement and reporting from Axios, CNBC, TechCrunch and Fortune (28 May 2026); GDP estimate from Pantheon Macroeconomics.

    Sources and further reading

  • AI’s next bottleneck may not be intelligence. It may be Earth.

    AI’s next bottleneck may not be intelligence. It may be Earth.

    For the last two years, the AI debate has been mostly about intelligence.

    Which model is ahead? How fast are capabilities improving? Will agents replace tasks, jobs, or whole workflows? Can Europe regulate the technology fast enough?

    All valid questions.

    But the next constraint may be less abstract. It may be physical.

    Power. Grid capacity. Land. Cooling. Permits. Transmission lines. Water. Construction time. Capital allocation.

    The AI race is turning into a gigawatt race. And if the space-data-center discussion is any signal, the next frontier may not just be cloud regions. It may be orbit.

    My read: the executive conversation has to move from "Which AI model should we use?" to "What physical infrastructure does our AI strategy depend on?"

    The scale shift

    Chart showing typical data center power use from 5-10 MW to 100 MW and 1 GW
    The scale jump matters: 10 MW is a facility, 100 MW is industrial infrastructure, and 1 GW becomes a regional energy strategy.

    A modern hyperscale data center is not a large office building with servers. It is an industrial energy asset.

    The International Energy Agency says average data centers draw around 5-10 megawatts. Large hyperscale facilities increasingly require 100 megawatts or more. That number sounds technical, so translate it.

    One megawatt running continuously for a year equals 8.76 gigawatt-hours. A 100 MW data center therefore consumes 876 GWh per year, or 0.876 TWh. At 90% utilization, still roughly 0.8 TWh per year. The IEA compares this to the annual electricity demand of about 350,000 to 400,000 electric cars.

    A 1 GW AI campus is ten 100 MW hyperscale data centers. Running continuously, it consumes 8.76 TWh per year.

    For comparison, Germany's annual electricity consumption is roughly 500 TWh. The EU is around 2,700 TWh. The US is around 4,000 TWh. So one 1 GW AI campus would be small at continental scale – about 0.3% of EU electricity consumption or 0.2% of US consumption – but huge at local grid scale.

    That local point matters.

    Put a 1 GW load in the wrong county, with weak transmission and slow permitting, and it is not "0.2% of America." It is a grid emergency, a political fight, and a capital allocation problem.

    Now consider the language around terawatts. Elon Musk's recent "Terafab" discussion was about chip manufacturing, not a conventional data center, but the vocabulary matters. AI infrastructure ambition is moving from mega to giga to tera. A theoretical 1 TW compute or manufacturing footprint running continuously would consume 8,760 TWh per year. That is more electricity than the US and EU combined.

    That does not mean a 1 TW data center is around the corner. It means the ambition curve is now colliding with the energy system.

    The current footprint

    The IEA estimates global data center electricity consumption at 240-340 TWh in 2022, excluding crypto mining. That was around 1-1.3% of global final electricity demand.

    In large economies such as the United States, China and the European Union, data centers already account for around 2-4% of total electricity consumption. That is the average.

    The local reality is more extreme.

    The IEA notes that data centers have already surpassed 10% of electricity consumption in at least five US states. In Ireland, data centers account for more than 20% of electricity consumption. Denmark projects data center electricity use could rise sixfold by 2030 and approach 15% of national electricity consumption.

    This is the important distinction: globally, data centers are still a manageable share of electricity. Locally, they can become one of the dominant loads on the system.

    Goldman Sachs Research estimates data center power demand could grow 160% by 2030, with global data centers rising from roughly 1-2% of power consumption today to 3-4% by the end of the decade. It also estimates AI could add around 200 TWh per year of data center power demand between 2023 and 2030.

    Two hundred TWh is not abstract. It is close to the annual electricity consumption of a mid-sized industrial country. And it is only the AI-related increment in one forecast.

    The backlash is already here

    Chart comparing global data center electricity share with US, EU, Ireland and local grid impacts
    Global averages hide local pressure: data centers can reach double-digit shares of electricity demand in specific regions.

    This is no longer theoretical.

    In May, several local flashpoints showed the political side of the bottleneck. Seattle was weighing a pause on large data centers. Durham, North Carolina passed a 60-day moratorium on data-center development. A Texas county paused data-center construction in rural areas for a year. Utah approved a data-center project described as twice the size of Manhattan, triggering backlash. Tennessee was considering legislation that would let data centers self-power with limited regulation.

    Different places, same pattern.

    AI infrastructure is colliding with local politics. Communities are asking who gets the jobs, who pays for grid upgrades, who carries water risk, who absorbs noise and land-use impact, and who benefits from the compute.

    This is the part of the AI story many executives still underestimate. It is not enough to have GPUs. You need permission. You need interconnection. You need credible energy sourcing. You need community acceptance.

    The future of AI may be decided as much in planning boards and utility queues as in model labs.

    Why energy is now part of AI leadership

    Executive checklist for AI energy strategy and infrastructure planning
    AI energy strategy is now an executive checklist: economics, thresholds, model allocation, partnerships, and efficiency.

    For a long time, digital leaders could assume infrastructure would scale behind the scenes. Cloud abstracted away servers. SaaS abstracted away operations. Developers increasingly acted as if compute was infinite, elastic, and mostly someone else's problem.

    AI breaks that illusion.

    Training frontier models is energy-intensive. Inference at scale may matter even more because successful AI products are used continuously. Agents add another multiplier: they do not just answer one prompt. They plan, call tools, retry, search, generate, check, and act. A single user request can become dozens or hundreds of model calls behind the scenes.

    That makes energy not just an engineering issue but a leadership issue.

    If AI becomes a core production layer, power becomes part of product economics. Latency becomes part of geography. Energy procurement becomes part of risk management. Infrastructure partnerships become part of market entry. Sustainability claims become harder to defend if absolute consumption rises faster than efficiency improves.

    The better question is not whether AI uses "too much" energy.

    The better question is: are we using scarce energy for high-value intelligence, or are we wasting it on low-value automation theatre?

    The opportunity

    The upside is enormous.

    AI can help design better grids, forecast demand, optimize industrial processes, improve cooling, accelerate materials science, reduce waste, and make energy systems more flexible. The same technology that increases electricity demand can also improve how electricity is produced, routed, stored, and consumed.

    There is also a market opportunity.

    Companies that solve the infrastructure layer will not just be suppliers to AI. They will become strategic gatekeepers. Power developers, grid operators, data-center builders, cooling specialists, chip designers, construction firms, nuclear developers, storage providers, and energy software companies are moving closer to the center of the AI economy.

    This is especially relevant for Europe.

    Europe often frames AI competitiveness around regulation, foundation models, sovereignty, and talent. All matter. But infrastructure sovereignty may become just as important. If compute depends on power availability, grid speed, and data-center capacity, then AI sovereignty is partly electricity sovereignty.

    A European AI strategy without an energy strategy is incomplete.

    The space question

    Conceptual space-based AI data center with solar arrays orbiting above Earth
    Space-based data centers are not a near-term replacement for terrestrial infrastructure. They are a signal that the AI compute curve is pushing beyond the grid.

    The more provocative version of this debate is space.

    A few years ago, data centers in orbit sounded like science fiction. Now Bloomberg is writing about how to build them. McKinsey has made the case for space-based data centers. University researchers are exploring the idea because AI energy demand is rising. Google and SpaceX have been linked in recent coverage to the broader possibility of AI data centers in space.

    The attraction is obvious: continuous solar power, less terrestrial land pressure, potentially easier cooling through radiative systems, and the strategic appeal of moving part of the compute layer off Earth.

    The problems are just as obvious: launch cost, maintenance, radiation, latency, orbital debris, security, regulation, and basic economics.

    But the fact that serious people are asking the question matters. Space data centers are not a near-term replacement for terrestrial infrastructure. They are a signal. The AI compute curve is steep enough that people are looking beyond the grid.

    When a technology forces executives to ask whether the data center belongs in orbit, something fundamental has changed.

    What leaders should do now

    The call to action is practical.

    First: put energy into the AI business case. Every serious AI initiative should have a compute and energy view, not just a model and vendor view. If the project scales 10x or 100x, what happens to cost, latency, emissions, and capacity?

    Second: use real thresholds. A 10 MW workload is a large facility. A 100 MW workload is industrial infrastructure. A 1 GW workload is a regional energy strategy. Treat them differently.

    Third: separate high-value intelligence from low-value automation. Not every workflow deserves heavy AI. Use frontier models where judgment, ambiguity, and leverage justify the cost. Use smaller models, retrieval, caching, rules, and process redesign where they are enough.

    Fourth: make infrastructure a board-level topic. If AI is strategic, then power supply, data-center capacity, cloud concentration, and sustainability are strategic. CIOs, CTOs, CFOs, COOs, and sustainability leaders need one shared view.

    Fifth: build partnerships beyond software. The AI stack now reaches into energy markets, utilities, real estate, cooling, semiconductors, construction, public policy, and eventually maybe space.

    The leadership shift

    The first AI leadership question was: "What can this technology do?"

    The second was: "How does it change work?"

    The third is now emerging: "What does it require from the physical world?"

    This is where the debate becomes more serious.

    AI is not just a software wave. It is a capital investment wave, an energy demand wave, and an infrastructure coordination problem. The limiting factor may not be imagination. It may be megawatts.

    Executives should not panic about that. But they should stop treating it as somebody else's problem.

    Models matter.

    But electricity decides where the models can run. And if the curve continues, the strategic question may become even stranger:

    How much intelligence can Earth afford to host?

    Sources and further reading

  • EU AI Act delay: 24 months for Brussels, 64× for AI

    EU AI Act delay: 24 months for Brussels, 64× for AI

    For the EU, it’s 24 months. For AI, it’s 64×.

    Last Wednesday the EU pushed the AI Act’s hardest deadlines back. Sixteen months for one piece. Twenty-four months for another. Read in regulatory time, that’s a reasonable phased rollout. Read against AI’s own pace of change, it’s something different.

    Exponential curve labeled 1× at Aug 2026 rising to 64× at Aug 2028, headline reads 'When the rules apply, AI is 64× more capable', subtitle 'EU AI Act high-risk deadline vs the AI doubling curve'.
    When the EU’s heaviest AI rules finally apply in 2028, the systems being regulated could be 64× more capable than the ones the rulebook was written for.

    What the EU just decided

    The AI Act is the world’s most demanding rulebook for artificial intelligence. It applies to any company that sells AI to European users — based in Europe or not. It was passed in 2024. Most of it was supposed to start applying in August 2026.

    Last Wednesday, the Council and Parliament agreed to push two of the heaviest pieces back.

    The “high-risk” category is the part most companies care about. It covers biometrics, hiring software, medical AI, AI in critical infrastructure — anything where a bad model output can hurt someone. Under the old timeline, these systems had to be fully compliant by August 2026. Under the new timeline, that becomes December 2027 (sixteen months later) for standalone systems, or August 2028 (twenty-four months later) for AI built into machinery, medical devices, and connected cars.

    Two-column comparison: BEFORE shows a single Aug 2026 deadline bar in grey, AFTER shows two new bars Dec 2027 plus 16 months and Aug 2028 plus 24 months in navy.
    The May 7, 2026 simplification agreement: one August 2026 deadline becomes two later deadlines, sixteen and twenty-four months out.

    What didn’t change matters too. The outright bans (social scoring, manipulative AI, untargeted face scraping) have been live since February 2025. The rules for big AI models — what most people call “frontier AI” — have been live since August 2025. The transparency obligations actually got tighter: providers of generative AI now have three months instead of six to ship watermarking. And a new ban on non-consensual sexual deepfakes lands hard on 2 December 2026.

    So the substance is intact. The triage is on the timeline.

    What METR actually measures

    METR is a research group that measures one specific thing about AI systems: how long they can keep working on a task before the workflow falls apart. Not how smart they are. Not how creative. How long they can stay on track without a human stepping in.

    The way they test it is straightforward. Give a model a real-world task — write a piece of code, run an analysis, debug a system — and measure the time-equivalent of work it can complete on its own. GPT-2 could chain together a few seconds of useful work. Claude 3 Opus held a few minutes. The frontier 2026 generation pushes past an hour.

    Plotted against time, that line is a clean exponential. From 2024 through early 2026, the time-horizon roughly doubled every four months.

    Exponential curve with three points: GPT-2 seconds at lower left, Claude 3 Opus minutes in middle, Frontier 2026 over an hour at upper right, headline 'Doubles every ~4 months', source METR.
    METR’s measurement of how long AI systems can work autonomously. The horizon roughly doubled every four months from 2024 through early 2026.

    Other measures point the same way. Reasoning depth, tool use, multi-step planning, software-engineering benchmarks — every adjacent curve has bent the same way over the same window. METR’s number is the cleanest single proxy I’ve seen, but it’s not an outlier.

    What 64× actually means

    If the doubling holds, the math on the EU’s new deadlines is uncomfortable:

    1. 16 months — four doublings — 16× more capable systems by the December 2027 deadline
    2. 24 months — six doublings — 64× more capable systems by the August 2028 deadline

    64× is not a metaphor. It’s the order-of-magnitude estimate of how much more autonomous task length AI can sustain by the time the EU’s heaviest rules apply.

    To put that in plain terms: if a 2026 model can do a one-hour task on its own, a 2028 model on the same trend can do a 64-hour task. A system that holds a workflow together for 64 hours is a different kind of object than the one the AI Act was drafted to regulate.

    That’s not an argument the rules are wrong. It’s an argument the gap between what the rulebook describes and what the system can actually do widens fast — faster than any 2-3 year drafting cycle can keep up with.

    My read

    My read on this: the headlines called May 7 a Brussels cave to industry pressure. I don’t think that’s the right frame. The substance of the Act is intact — the Commission could have used the simplification to weaken the high-risk classification or gut the impact-assessment requirement. They didn’t. They tightened transparency and added a new prohibition. The triage is on the timeline, not the rules.

    By 2028, the AI Act could be regulating systems 64× more capable than what existed when its rules were written.

    My expectation is that the August 2026 cliff was always going to slip. What’s more interesting is what the slip exposes: regulators and AI now run on incompatible clocks, and there’s no obvious mechanism to reconcile them. The Act assumed a 2-3 year drafting cycle would land on systems recognisably similar to the ones it described. That assumption broke somewhere between GPT-4 and the agentic generation that followed.

    Three things I’m watching

    • The 2 August 2026 deadline for national authorities. That date didn’t move. If most countries still don’t have working AI authorities by August, December 2027 becomes the next deadline at risk.
    • The European technical standards. Without finalised standards from the standards bodies, “high-risk” is a definition without a benchmark. Whether the Commission publishes them before the new deadline is the gating item.
    • The EU-US-UK divergence. The same week the EU softened its timeline, the US signed pre-launch testing agreements with the five frontier labs through CAISI. These two regulatory paths now point in different directions, and that gap is where the next year of this story plays out.

    One last thought

    Sixteen months. Twenty-four months. In any other regulatory context, those numbers feel reasonable. In AI they feel like an era. That’s not a problem the Commission can solve in a single omnibus.

    To be clear I am not asking for more regulation, I am asking for more decision speed!

  • AI: creating or destroying jobs?

    AI: creating or destroying jobs?

    The AI-jobs argument has split into two camps that aren’t actually arguing about the same thing.

    Jensen Huang told CEOs at GTC that firing people for AI shows “no imagination” — radiologists, he points out, are more numerous now than before AI entered radiology. Marc Andreessen calls the displacement narrative “completely fabricated” and points to Jevons Paradox: cheaper labor produces more demand, not less. The WEF Future of Jobs Report still projects net +78 million jobs globally by 2030. Challenger’s Hiring Plans index was up 157% year-over-year in March.

    A week later, Block laid off 40% of its workforce. Jack Dorsey said engineering work that needed weeks now happens in a fraction of the time. Block is still hiring AI engineers.

    So which is it?

    My read: both sides are right. They’re answering different questions about different decades. Most of the public argument is two conversations pretending to be one.

    The optimist case

    Three pieces hold it up.

    The historical record is strong. Keynes wrote in 1930 that his grandchildren would work fifteen-hour weeks. Reality 2025: OECD average is thirty-seven hours, Americans clock 1,976 hours a year. Mechanization, electrification, the computer, the internet — every general-purpose technology was forecast to end work, and every one produced more jobs than it eliminated. In 1900, 41% of Americans worked in agriculture; today it’s 2%. The jobs went somewhere.

    Jevons Paradox is real. When something useful gets cheaper, demand rises. If AI makes cognitive work twenty times cheaper, you don’t end up with one-twentieth the cognitive work. You end up with twenty times the cognitive work, deployed against far more problems. Andreessen’s “Super-PhD in every field” captures it.

    A big chunk of the labor market is hard to displace. Licensed jobs (medicine, law, accounting), unionized jobs (skilled trades, transit, public safety), and public-sector roles add up to a large fraction of US employment. Not protected because they’re irreplaceable in some technical sense — protected by institutions that move slowly.

    Each piece is correct. The question is whether they’re enough.

    Where the optimist case breaks

    Radar chart of AI capability versus observed usage across eight occupations from the Anthropic Economic Index, showing the deployment gap.
    The deployment gap: theoretical AI capability dwarfs observed usage by occupation. Source: Anthropic Economic Index.

    The Anthropic Economic Index plots theoretical AI capability against observed AI usage by occupation. The two lines look almost nothing alike — capability is broad and high; usage is narrow and concentrated. There’s a gap between what AI can do and what it’s actually doing.

    Read that gap two ways. The optimist reading: deployment is slow, friction is real, the labor market reabsorbs shocks like it always has. The harder reading: the gap is the queue — it’s where displacement comes from over the next five to ten years, not from new capability but from deployment catching up to capability that already exists.

    94% of cognitive job tasks are theoretically automatable today; 33% actually are. The space between is the transition zone. It’s not science fiction. It’s not contested. Most of it will close. Block’s layoffs sit on the second reading.

    The historical-record argument also has a footnote that doesn’t get enough weight. AI is the first general-purpose technology to automate cognitive labor at scale. Every prior wave automated muscle, then narrow categories of cognitive work — but never the universal category of “thinking and writing and analyzing and deciding.” The tractor displaced farm hands; they moved into office work. The PC displaced typewriters and clerks; they moved into knowledge work. AI doesn’t have an obvious “moved into” destination, because the destination of every prior wave is the category AI now automates.

    The TIME / Contextual AI benchmark chart makes the universality vivid. AI surpassed human-level performance on handwriting recognition around 2015, then speech, then images, reading, language, common sense, math, code generation. The rate at which new tasks fall is increasing.

    The trades-and-physical-work counterargument is weaker than it looks. Yes, 57% of jobs depend on physical presence or craft work AI can’t currently replicate. But 70% of positions inside blue-collar companies — the dispatcher, the accountant, the customer-service rep — are white-collar-adjacent and fully exposed. And if displaced knowledge workers all migrate into trades, wages collapse from saturation. Bank of America projects billions of humanoid robots by mid-century with hardware costs falling from $35,000 to under $15,000; one analyst projects robot-hours at four to six euros. Even physical work has an expiration date.

    So the optimist case is strong for a long-run answer. It’s much weaker for the next ten years.

    The displacement case

    Not “AI replaces all jobs.” That’s the optimists’ caricature, and once you reach for it the displacement case looks weak. The serious version is more specific.

    Three vertical bars on dark navy: high-skill rising, middle-skill shrinking with downward arrow, low-skill stable — the AI barbell economy.
    The barbell economy: high-skill productivity rises, low-skill stable, the middle hollows out.

    It’s structural: the middle is being squeezed. The labor market is shifting from a K-shape into a barbell. High-skill technical roles are more productive — the same Anthropic data shows code, analysis, and research at the top of the productivity-gain distribution, with usage approaching 60% of theoretical capacity. Low-skill physical roles in care, hospitality, manual handling, and trades are stable for now. The middle is shrinking: bookkeeping and paralegal work, content writing and copywriting, junior finance and analyst roles, customer service, entry-level coding, marketing copy, translation, project coordination, junior tax preparation.

    Germany has already seen roughly 90,000 AI-related job losses in the first months of 2026. The risk is not mass unemployment in aggregate. Aggregate unemployment can stay low for years while the middle hollows out. The risk is a split labor market — and a split society — in which the people who staffed the middle no longer have a clear path up or sideways.

    The Anthropic Economic Index BLS panel makes this concrete: hiring of younger workers in AI-exposed occupations has slowed, even as overall employment numbers haven’t moved much. That’s what early-stage hollowing looks like — the entry-level rung disappears first, before the established middle does.

    Five years of that compounds into something the historical record didn’t have to absorb.

    My read — the three-phase shape

    Horizontal timeline 2025 to 2040+ split into three colored zones: red displacement, amber strain, cyan abundance — AI jobs transition phases.
    Three phases of the AI jobs transition: displacement (2025-2030), strain (2030-2035), abundance (2035+).

    The clearest three-phase framing is German — chronological, not parallel.

    Phase one — displacement (~2025-2030). AI displaces knowledge work faster than the labor market rebuilds. The middle hollows. Aggregate unemployment may not move much; entry-level paths in white-collar roles narrow sharply. The optimists are right that the technology eventually creates new categories. They’re wrong about the timing.

    Phase two — strain (~2030-2035). Strain shows up in places that aren’t unemployment: tax-base erosion, weakened consumer demand, capital returns rising while labor’s share of national income falls to historic lows. Public-sector and licensed-job cushions hold initially but come under fiscal pressure. The political consequences sharpen.

    Phase three — abundance (after ~2035). The deflation the optimists describe arrives. Costs collapse across categories. What costs $100 today costs a few cents. The median 2040 lifestyle, on a flow-of-services basis, looks something like today’s high-net-worth lifestyle on every dimension except positional goods. Both Andreessen and Huang are right about the destination.

    That’s the timeframe trap. Both sides are correct on their respective horizons. The honest version of the optimist case includes the transition pain. The honest version of the displacement case includes the recovery.

    What this means for how leaders think about the next ten years: the question isn’t “do we believe in AI displacement, yes or no.” That question is roughly answered. The task is to assume real displacement in the middle, plan for it, and carry the organization through to the recovery in a way that keeps the institution and its people whole.

    Three things I’m watching

    1. Whether the entry-level signal becomes a leading indicator. The slowing of hiring for younger workers in AI-exposed occupations is, in my read, the most important early signal. Aggregate employment numbers lag; entry-level absorption leads. If the slowdown becomes a structural break, phase one stops being a forecast and becomes a measurement.
    2. Whether the licensed and public-sector cushion holds when fiscal space tightens. The structural-protection argument is strong only as long as the institutions that protect those jobs don’t themselves come under fiscal pressure. Phase two erodes the tax base. The question is whether legislatures and regulators are protecting genuinely-essential public-sector employment or post-hoc subsidizing the share of the workforce the private sector can no longer place.
    3. Whether the recovery looks like restored employment or restored income. Phase three is consistent with both. Jobs come back in new categories — the historical track record. Or they don’t come back at scale and the recovery is income-shaped: UBI-like distribution of the deflation surplus rather than wage-based participation. These look very different politically. The shape of phase two is what determines which one we get.

    No one has confidence on these three questions yet. I’m watching them because the answers will tell us, in roughly the next five years, what the transition phase actually costs.

    The destination is not in serious doubt. The road is.

  • MIT Called It a Disenchanted Intern. METR Says Check the Growth Rate.

    MIT Called It a Disenchanted Intern. METR Says Check the Growth Rate.

    Something happened this week that I keep turning over.

    MIT published findings this month showing that when 41 AI models were tested across more than 11,000 real workplace tasks, the result was, in their words, like a “disenchanted intern” — hitting minimum benchmarks about 65% of the time, but never exceeding 50% success on tasks requiring genuinely superior-quality output. If you work in software, marketing, legal services, or knowledge work of any kind, that’s the snapshot.

    METR — a nonprofit focused on measuring AI capabilities — published a different kind of snapshot. Their metric is the “time horizon”: the maximum length of autonomous task a frontier AI can reliably complete. In 2019, the best AI could handle roughly a two-minute task without human intervention. By the end of 2025, that had grown to roughly an hour. The doubling time across that whole period: around seven months.

    METR’s January 2026 update tightened that number further. Post-2023, the best estimate for the doubling period is now 130 days — closer to four months.

    My read on this:

    The MIT study and the METR data aren’t in conflict. They’re measuring different things at different timescales. MIT is taking a photograph. METR is measuring the shutter speed. And the shutter speed is getting faster.

    I don’t think the “disenchanted intern” framing is wrong — it describes today accurately. What I’m less sure about is the assumption, implicit in most of the coverage I’ve read this week, that “today” is a stable state. An intern who gets twice as capable every four months is not the same resource at the end of the year as they are today.

    What I keep returning to is the gap between the current snapshot and the trajectory — and the opportunity that opens up in that gap. The MIT data is a photograph of now. The METR data is the shutter speed. Anyone building workflows, designing teams, or structuring how they work around AI capability today is working from a reference point that will be measurably out of date within a single planning cycle. That’s an opportunity signal at a scale and pace most planning assumptions don’t account for.

    Three things I’m watching:

    1. Where the doubling curve hits friction. Every exponential eventually meets a wall — physical limits, data constraints, regulatory friction. METR’s time-horizon metric is useful precisely because it measures real-world task completion, not synthetic benchmark scores. When the doubling cadence breaks, that will be the signal that the curve has met something real. I expect that to happen. I just don’t know when.

    2. Whether “minimally sufficient” matters or not. MIT’s 65% minimally sufficient rate sounds modest. But most enterprise workflows run on people who are minimally sufficient most of the time. The threshold isn’t excellence — it’s “acceptable at scale, around the clock, at near-zero marginal cost.” That bar is lower than it sounds, and closer than the headline number implies.

    3. The infrastructure spend as an access unlock. Alphabet, Meta, Microsoft, and Amazon are projected to spend nearly $700 billion combined on AI infrastructure in 2026 — roughly double what they spent last year. That capital isn’t just building capacity for the current snapshot. It’s funding the cost compression that makes the next several capability doublings broadly accessible. When the infrastructure matures, the cost floor drops — and the surface area for building on top of it expands with it.

    The disenchanted intern framing is apt today. My expectation is that it’s a better description of 2025 than it is of 2027.

    References