Tag: Strategy

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

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

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.

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
- Franco-German Joint Paper on Digital Sovereignty, 17 June 2026
- BMDS: Germany and France present joint definition of digital sovereignty
2026-06-21
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

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

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 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
2026-05-31
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
2026-04-26
Inference cost has collapsed. Enterprise AI business cases haven’t caught up.
GPT-4 class inference cost $20 per million tokens at launch in early 2023. In April 2026, equivalent performance runs $0.40. Most enterprise AI business cases were built somewhere in the middle — and haven’t been updated since.

That gap is not a technology story. It is an arithmetic problem wearing a strategy hat.

What moved

Inference costs have declined faster than the bandwidth price collapse of the early internet era, faster than PC compute, and considerably faster than any enterprise finance model anticipated. Artificial Analysis tracks it live: the cheapest capable models today run under $0.50 per million tokens. A flagship model that cost $10 per million tokens eighteen months ago now costs $2–3. The price range between the cheapest and most expensive capable options has widened past a thousand-to-one.

The driver is compounding. Better training efficiency produced more capable models at lower operating cost. Competition between providers accelerated the pass-through. Specialised chips entered the stack. The result: a cost curve that looks less like traditional software pricing and more like solar panel economics — each year’s curve is below where last year’s curve said it would be.

What did not move

Enterprise AI business cases.

S&P Global found that 42% of companies abandoned most of their AI projects in 2025. Cost and unclear value were the top reasons cited. IBM put the share of AI initiatives delivering expected ROI at 25%. MIT found that 95% of AI pilots delivered zero measurable P&L impact (MIT NANDA, State of AI in Business, 2025).

These numbers are real. But the interpretation of why projects fail is often imprecise.

Projects approved in 2023 and 2024 were scoped against the pricing environment of 2023 and 2024. The cost models that informed the go/no-go decisions used token prices that no longer exist. The ROI denominators were anchored to infrastructure assumptions from a period when GPT-4 access cost $10–20 per million tokens. The business cases that were rejected on cost grounds — the ones that landed below the internal ROI hurdle by a thin margin — were rejected against a cost basis that is now a fraction of what it was.

That is not a technology failure. It is a modeling lag.

Andreas’s view

My read on this: there are two different things getting conflated in the ROI conversation. One is genuinely poor outcomes — wrong use case, shallow integration, insufficient change management. That is real and deserves scrutiny. The other is a systematic understatement of AI’s economic potential because the cost assumptions in the business case never got refreshed. Those two phenomena look identical in the data.

I don’t think the 42% abandonment rate or the 25% ROI hit rate tells us much about what AI can do at today’s prices. It tells us how enterprises perform against business cases built on 2023 assumptions. The projects that got killed for cost reasons in Q4 2024 would look different rerun against Q2 2026 pricing.

My expectation is that the organisations getting ahead of this are running a specific exercise that most are not: taking the cost assumptions out of every AI initiative that was rejected or stalled in 2023–2025, replacing them with current market rates, and seeing which cases cross the ROI threshold now. Not all of them will. But some will — and the decision to revisit them is a spreadsheet exercise, not a technology project.

Three things I’m watching:
- Whether finance teams are treating inference cost as a stable input or a variable. Most enterprise budget models treat infrastructure cost as a constant. Inference cost is not a constant — it has been declining faster than almost any other enterprise input cost in the last three years.
- The spread between unit cost and total spend. Per-token costs have collapsed, but total enterprise AI spend is forecast to jump 65% in 2026 — from roughly $7M average to over $11M (IDC). Volume is expanding faster than unit costs are falling. The budget impact of AI is still growing, even as the underlying unit economics are dramatically more favourable than they were.
- How capital allocation committees handle the remodel request. The institutional question: if a CFO approved a 2023 AI business case that underperformed, how does the organisation handle finance coming back and saying “the cost structure changed — the case should have worked, we just used the wrong numbers”? That conversation is coming.
What this reveals

The collapse in inference cost is well-understood in developer circles. Engineers who run inference workloads reset their unit economics continuously — it is operational reality. The delay is in the enterprise business case layer, where cost assumptions travel up through approval chains, get embedded in multi-year plans, and calcify.

The cost curve does not care about the approval cycle. It moved while the slide decks were in review.

This is not an argument that all AI investments look better at current pricing — some of those failed pilots would have failed regardless, and the organisational conditions for AI success (clear scope, embedded workflows, meaningful accountability) have not gotten easier. But a non-trivial fraction of the projects that stalled on cost now live in territory where the math is different. Identifying them is a shorter path to AI ROI than starting new initiatives from scratch.
2026-04-15
Model deprecation is the new continuity risk
A — model lifecycle row.

OpenAI announced the discontinuation of the Sora web and app experiences on April 26, with the Sora API following on September 24. The first deprecation triggers in two weeks. Enterprises that built workflows on Sora since launch are not facing a model upgrade — they are facing a workflow rebuild on a four-month timeline. This is the first prominent enterprise-facing AI deprecation event of the cycle, and the precedent it sets matters more than the specific product involved.

Model deprecation is no longer a developer-tier concern. It is an enterprise governance question that deserves a place on the risk committee agenda. The real shift is happening here: AI dependency without continuity is becoming a board-level risk in 2026.

The shift: dependency without continuity guarantees

The pattern of the past two years has been to build agent workflows on whichever foundation model was demonstrably best at the time, with little contractual commitment from the model provider about how long that model would remain available. Provider terms have improved — Azure OpenAI’s twelve-plus-six-month commitment for generally available models is the strongest standard in market — but most enterprises have not negotiated equivalent terms with their chosen providers. They built on capability, not on continuity.

When the provider sunsets the model, the enterprise’s options are bad. Migrate to a successor model that may behave differently in subtle ways — requiring re-validation of every governed use case. Renegotiate at the eleventh hour for extended access at unfavorable terms. Or absorb the operational disruption of the workflow simply not working until rebuilt.

The Sora event is small in dollar terms but large in precedent. The next deprecation will involve a more enterprise-critical model, and the enterprises that did not see this one coming are not going to see that one coming either.

Built on capability. Not on continuity.

The role change is the addition of an AI continuity discipline

Inside enterprises that take this seriously, a discipline is emerging that did not exist in 2024 — AI continuity management. The work overlaps with vendor management, with disaster recovery, with model risk management, and with regulatory compliance, but it is structurally distinct from all of them. The discipline involves maintaining an inventory of model dependencies by workflow, negotiating continuity commitments at procurement, running successor-model regression tests on a regular cadence, and ensuring that the documentation chain meets the rebuild-readiness standard.

Most enterprises have not staffed this discipline. The accountabilities are scattered across teams that do not coordinate. The procurement team negotiated the model contract a year ago without a continuity clause. The deployment team is building production dependencies on the model without thinking about migration cost. The risk team has not flagged model deprecation as a category. When the deprecation announcement lands, the company finds out it has no plan.

The fix is straightforward in concept and slow in practice. Add continuity commitments to the procurement template. Build a model-dependency inventory. Designate an owner for AI continuity at the executive level. Run quarterly successor-model tests. None of this is hard. It is just unglamorous work that does not get done unless someone owns it.

The strategic consequence is renewed buy-versus-build math

Continuity risk changes the calculus of where to deploy AI capability. For workflows where the cost of unplanned migration is high — regulated workflows, mission-critical operations, customer-facing experiences with high switching costs — the case for either fine-tuning a frontier model into a controlled deployment, partnering with a vendor offering enterprise-grade continuity commitments, or building on open-weight models the enterprise can host indefinitely is stronger than it was in 2024. The case for relying on whichever model is best on a benchmark this quarter is weaker.

The math is not simple. Open-weight models lag the frontier, sometimes meaningfully. Self-hosting carries operational cost that the proprietary providers absorb. The vendor lock-in to a single proprietary provider, even with the best continuity terms, is a different kind of risk than open-weight self-hosting carries. Each enterprise has to make this trade-off based on the workflow’s tolerance for capability lag versus its tolerance for continuity disruption.

What is no longer defensible in 2026 is treating model continuity as someone else’s problem. The Sora sunset is small. The next one will not be.

So what boards should do this quarter

Add model deprecation to the risk committee agenda. The first deprecation event lands in two weeks. The board should at minimum understand which workflows are exposed and what the migration plans are.

Demand a model-dependency inventory. Which workflows depend on which models from which providers, with which contractual continuity commitments. If this inventory does not exist, building it is the priority.

Reconsider the buy-versus-build posture for mission-critical AI workflows. The 2024 default — use whichever proprietary model is best — was rational at the time. In 2026, with the deprecation precedent now visible, that default deserves an explicit reconsideration. Continuity is becoming a form of resilience. The boards that price it in this quarter will not be the ones rebuilding workflows under deadline.

References and links
- OpenAI Help Center — What to know about the Sora discontinuation (web and app sunset April 26, 2026; API sunset September 24, 2026)
- OpenAI Help Center — Sora 1 Sunset FAQ
- OpenAI Developer Docs — Deprecations policy and schedule
- Microsoft Azure OpenAI — Model retirements and deprecation schedule
- InformationWeek — The sunsetting of Sora: A hard lesson in AI portfolio resilience
2026-04-12
When 88% of organizations have adopted AI, adoption stops being the question
What was announced

The Stanford HAI 2026 AI Index landed in mid-January with a set of numbers that close out a debate. Organizational AI adoption reached 88% globally. Global corporate AI investment more than doubled in 2025 to $581.7 billion. Generative AI hit 53% population adoption within three years — faster than the personal computer or the internet. Four out of five university students now use generative AI as part of their coursework.

What it means

When adoption crosses the 80% line, the question of “should we adopt” becomes structurally uninteresting. Every relevant comparison group has already answered it. What remains is differentiation — and differentiation in a world of universal access is harder, not easier, than in a world of selective access. The strategic margin moves from access to integration depth, from licenses to workflow penetration, and from procurement decisions to operating-model decisions.

The investment number is the more telling signal. $581.7 billion of corporate AI investment in a single year is a capital allocation that prices in a specific belief: that AI capability will compound at a rate that makes today’s spending the cheap option in retrospect. That belief either turns out to be correct, in which case the laggards face a permanent gap, or it overshoots, in which case the survivors of the correction still own infrastructure and skills the laggards do not.

Andreas’s view

My read on this: the AI Index numbers are not a celebration of momentum, they are a notice of obsolescence. Adoption was the entry-level metric — the one that let companies say “we are doing AI” without committing to anything that mattered. With 88% adoption, that metric is exhausted. The companies that conflate “we have AI deployed” with “we have an AI strategy” will be the ones surprised in 18 months when peers with the same headline adoption rate are operating at a fundamentally different unit-economics base.

I don’t think the next two years will be about adopting more. They will be about routing work differently — deciding which functions become AI-native, which roles get redesigned, which middle-management layers compress, and which workflows get rebuilt from the ground up rather than augmented. The companies treating this as a tooling question will keep the org chart they had in 2024 and bolt assistants onto it. The companies treating it as a structural question will redesign for AI-native operations and harvest a different cost base.

My expectation is that boards still reporting on adoption rates are measuring the wrong thing entirely. The number that matters is the percentage of work routed through AI-native processes versus AI-augmented legacy processes. Those are two different cost structures and two different competitive positions. The first is a step change. The second is a feature.

Three things I’m watching
1. I’ll be watching whether companies move away from adoption KPIs toward integration-depth KPIs — specifically, the percentage of revenue-generating workflows that are AI-native, not just AI-touched.
2. The companies that stand out to me will be the ones that build the comparison the AI Index doesn’t make for them: how their spend per FTE on AI infrastructure and tooling stacks up against the 90th-percentile peer in their sector. If that number isn’t visible to leadership, it isn’t informing strategy.
3. I’ll be watching whether organizations use the next 12 months as a workflow-redesign window rather than a tooling-procurement window. The structural opportunity narrows the moment competitors finish their redesign.
References and related signals
- Stanford HAI: 2026 AI Index Report (full)
- Stanford HAI: 12 takeaways from the 2026 AI Index
- Deloitte: 2026 State of AI in the Enterprise
- Related signal: Deloitte’s parallel finding that 42% of organizations rate themselves “highly prepared” on strategy but feel less prepared on infrastructure, data, and talent — strategy ahead of capacity.
- Related signal: 95% of generative AI pilots still fail to reach production — adoption is broad, conversion to value is narrow.
2026-01-18
The agentic year begins underprepared

The year opens with a measurable gap. McKinsey’s 2026 trust maturity survey, fielded in December and January, puts twenty-three percent of organizations into the scaling phase for agentic systems and thirty-nine percent into experimentation. The remaining majority — nearly two thirds — has not yet begun scaling AI across the enterprise. The capability frontier moved twelve to eighteen months faster than the operating models around it. That gap is no longer an experimentation question. It is the year’s defining strategic risk.

The boards that close this gap first will not be using better models than their competitors. They will be running organizations that can metabolize what the models already do. The constraint is no longer technology. It is adoption — and adoption is a leadership problem.

The shift is structural, not cyclical

Agentic systems are not a new feature inside a familiar product. They are a new class of worker. They take a goal, decompose it into steps, hold state across those steps, call other tools, recover from errors, and return a completed unit of work. That changes what a job is, not how a job is done.

The 2025 narrative — copilots, productivity boosts, ten percent uplift — is over. The 2026 question is harder. What units of work no longer require a human originator? What units of work now require a human reviewer instead of a human executor? Which decisions can be delegated to a system that explains its reasoning? The companies asking these questions on a Monday morning are reorganizing. The companies still benchmarking model accuracy are stalling.

The shift is one-way. No board will vote in 2027 to remove agentic systems from a workflow they reduced from forty hours to four. The architectural choices made this year will compound.

Goal in, decomposition out, no human in the loop between.

The role change has already happened on the ground

Inside organizations that have actually shipped agentic systems, the role redefinition is happening informally, by individual contributors, ahead of any HR process. A senior analyst who used to write three reports a week now reviews twelve agent-drafted reports a week and signs off on the analysis. A staff engineer who used to write three pull requests a day now reviews fifteen agent-generated pull requests a day. An account manager who used to draft proposals now edits proposals the agent has built from CRM context.

The work that survives is judgment, taste, accountability, and relationship. The work that does not survive is execution under specification. Job titles still describe the second category. Job content has already shifted to the first.

First-line managers feel this most acutely. They were trained to manage humans doing execution work. They are now managing humans doing review work, who in turn are managing systems doing execution work. That is a different management discipline — closer to portfolio management of automated processes than to people management of execution teams.

Three reports a week became twelve reviews a week.

The organizational consequence is delayering

Span of control widens when the work below each manager becomes more automated and more reviewable. McKinsey’s parallel work on the state of organizations points in the same direction: companies that scale agentic systems also flatten by removing one to two layers of middle management. The economic logic is direct. Middle layers existed to translate strategy into execution and to coordinate the humans doing that execution. When the execution is increasingly handled by systems and the translation is increasingly handled by models, the layer is doing less.

This is not the 2024 layoff cycle that hit individual contributors. This is a 2026 reorganization that compresses the manager-of-managers layer. It is structurally different and politically harder. The people most threatened by it are the people running the budget meetings about it.

Organizations that resist the delayering will have a temporary cost advantage and a permanent decision-velocity disadvantage. Decision cycles compress when fewer humans need to be in the loop. The competitor who removed two layers will commit to a market move three weeks faster. Over a year, that compounds into a different market position.

The middle layer compresses, span of control widens.

So what boards should do this quarter

Two actions belong on the Q1 agenda. First, demand a workforce plan that names the units of work moving from human execution to human review, with a twelve-month horizon. Vague AI strategies are no longer acceptable as deliverables; the question is which jobs, which tasks, which review cadences, which accountability lines.

Second, name an executive owner for the operating-model redesign — not for AI strategy as a separate track, but for the way the company will be organized around the systems it has already deployed. The CHRO and the COO are the natural owners. The CTO is not. The technology decision is downstream of the operating-model decision, and treating it as upstream is how organizations end up with sophisticated tools and a 2023 org chart.

The year that just started will be measured by the gap between capability and operating model. The companies that close it first set the pace for the rest of the decade. The risk is not moving too fast. The risk is moving too late. Execution speed will separate leaders from followers.

2026-01-04