Category: Leadership

How decisions become motion — workforce, organizational design, execution speed.

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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

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

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

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

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
2026-05-17
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

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.

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

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.
2026-05-03
Every knowledge worker is a manager now

Every knowledge worker is a manager now. Agentic AI has turned individual contributors into managers of AI agents, and first-line managers into leaders of managers of agents. The job descriptions have not caught up yet. The operating models have not caught up yet. The reskilling plans have not caught up yet. All of that is lagging the capability frontier by twelve to eighteen months — and the organizations that close that gap first will operate at a structurally different throughput than the ones still writing job descriptions for the jobs that existed in 2023.

The shift: agentic AI crosses the line from tool to colleague

For the first year and a half after ChatGPT, the thing called “AI” in most organizations was a better search box. A more patient editor. A faster rough-draft generator. Useful, but still a single-interaction tool. You asked, it answered, you moved on. The job of the knowledge worker did not fundamentally change — they just had a slightly sharper pencil.

What changed in the eighteen months leading into 2026 is the arrival of agentic models. The word “agent” in that context is not marketing. An agent is a system that can do a sequence of things, hold state across those steps, make decisions about what to do next, use tools, and come back with a completed multi-step task. That is a categorically different interaction than “ask question, get answer.” It is closer to “give a junior colleague an outcome to produce and trust them to produce it.” The commercial consequence of that shift is the subject of this post.

Input → agent → output → judge → ship. The human stays at the judgment node.

The role change: ICs become managers of agents

The individual contributor job has silently changed. Writing short summaries of long content — once a junior-to-mid task — is now an agent task. The human role is to specify the outcome, check the output, and decide what to do with it. Meeting preparation — the pre-meeting brief of background, context, attendees, prior touchpoints — is now an agent task. The human role is to feed the context, review the brief, and adjust the framing. Drafting a first pass of almost any structured document — a proposal, a plan, an analysis — is now an agent task. The human role is the editor, not the author of the first draft.

The common thread is that the IC’s job has shifted from doing to specifying outcomes and judging output. Those are management skills. Not in the metaphorical sense — in the literal sense. Framing a task clearly enough that someone (or something) else can execute it. Evaluating whether the execution meets the specification. Deciding when to iterate and when to ship. These are exactly the skills that used to distinguish a first-line manager from a senior IC, and they have become baseline requirements for an IC working with agents.

The new role for the IC: editor of agent output.

The org change: first-line managers become leaders of managers of agents

If every IC is now a manager of agents, then every first-line manager is now a leader of managers of agents. Their job is no longer to supervise execution — the agent is doing the execution. Their job is to coach the humans on their team in how to specify outcomes, how to judge output, how to know when an agent is producing garbage, and how to scale their orchestration over time. That is a completely different job than the first-line management job of three years ago, and it requires a different skill set.

Two structural consequences follow. First, the middle management layer compresses because a first-line manager leading managers-of-agents can reach further than one managing direct executors — the coordination overhead per report drops when the reports are themselves operating on a multiplier. Second, the definition of “span of control” stretches, but not infinitely: the Dunbar layers still govern the number of humans a manager can hold relationships with, even if each of those humans is now operating agents underneath them. The org chart can get flatter. It cannot get unbounded.

One human, many agents — the conductor metaphor for first-line management at scale.

The strategic consequence: orchestration is now a baseline skill, not an advanced one

The skill that used to distinguish senior managers from junior ones — the ability to frame work so someone else can execute it and judge whether their execution is good — is now a baseline IC capability. Orchestration is the new baseline. Writing is the new baseline. Judgment about output quality is the new baseline. The organizations that will operate at structurally higher throughput over the next five years are the ones that reskill their IC population around these baseline orchestration skills, rather than hiring more specialists who each do one thing well.

Talent leverage, not headcount, becomes the scoreboard. A commercial organization that operates at 300 humans with strong orchestration capability can outproduce a commercial organization that operates at 600 humans with legacy IC job descriptions. The difference is not about working harder. It is about operating model. The 300-human organization has fewer Dunbar breakpoints, shorter decision loops, less cross-functional friction, and a higher per-seat agent-multiplier. All of that is the consequence of a single structural decision made at the job-description layer.

So what boards should do

Three actions sit on the CEO agenda over the next two quarters. First, rewrite the IC job descriptions for every knowledge-worker role in the organization so that orchestration and output judgment are explicit baseline capabilities, not bonus ones. Second, rewrite the first-line management job description so that coaching for orchestration is the core of the role, not supervision of execution. Third, audit the reskilling plan against the assumption that every knowledge worker in the organization is now a manager and needs to be trained as one — because the capability frontier has already shipped and the only question is whether the organization catches up in quarters or in years.

Boards that do not require a reskilling plan at this scope are budgeting against an operating model that does not exist anymore. The plan does not need to be perfect. It needs to exist. The gap between organizations that have this plan and organizations that do not is the structural competitive advantage of the next five years, and it is already being measured — in throughput, in decision velocity, in the quiet retention of the top performers who can see the gap coming.

2026-04-19
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
Team sizes are not design choices. They’re cognitive limits.
Team sizes are not design choices. They are cognitive limits. The recurring numbers that show up in military units, religious communities, hunter-gatherer bands, and commercial organizations are not management philosophy. They are a property of the animal doing the work, and any organizational structure that pretends otherwise pays a measurable tax in friction, communication overhead, quiet attrition, and decisions that arrive three weeks late.

Two. Four to six. Eight to twelve. Twenty to twenty-five. Fifty. One hundred and fifty. The specific numbers recur across centuries and industries. In the Roman legion and the US Marines. In religious communities and hunter-gatherer bands. In tech companies, sales organizations, and the advice experienced managers give each other about when to split a growing team. It is not a coincidence. It is cognitive architecture. The constraint is no longer technology. The constraint has always been the brain doing the coordinating.

Dunbar’s layers

The research most commercial leaders eventually bump into is Robin Dunbar’s. Dunbar is a British anthropologist who, in the early 1990s, proposed that the size of a primate’s social group is constrained by the size of its neocortex. Extrapolating from primate data, he estimated the human number at around 150 — the number of people with whom any one of us can maintain a stable, recognisable, mutually-active relationship. He published it in the Journal of Human Evolution in 1992, and the number has been running through management literature ever since.

The part that gets talked about less, but matters more, is that Dunbar’s 150 is not a single flat layer. It is the outer ring of a nested set, each layer roughly three times larger than the one inside it:
- ~5 — your closest support group. The people you would call in a real emergency.
- ~15 — your sympathy group. People whose loss would significantly affect you.
- ~50 — your band or clan. People you know well enough to share deep context with.
- ~150 — your active community. Stable, recognisable, mutually reciprocal relationships.
- ~500 — acquaintances.
- ~1500 — faces you can still recognise.
These layers show up in the research almost regardless of whether the subject is a tribal society, an office workforce, or a social-network friend graph. And they map astonishingly well onto the team sizes that commercial organizations stumble toward by trial and error — not because anyone read Dunbar, but because the alternatives don’t work.

A central figure surrounded by expanding tiers — 5, 15, 50, 150.

The military got there first

Armies have been experimenting with how to organize humans under extreme stress for two thousand years, and they arrived at exactly these numbers through pure selection pressure. Smaller was too fragile. Larger fell apart under fire. The numbers that survived are the numbers that work.

A Roman legion’s smallest unit was the contubernium — eight soldiers who shared a tent, a mule, a mess, and most of their waking life. Eight. Right at the boundary between the 5-person inner layer and the 15-person sympathy group. The Romans knew nothing about neocortex ratios. They noticed that a group of eight held together in a way that a group of four or a group of sixteen did not.

The modern US Marine Corps fireteam is four. The squad is roughly 13. The platoon is 30 to 40. The company is 100 to 150. The same ratios, twenty-one centuries later. The cognitive limits haven’t moved, because the brain they are about hasn’t.

The tech industry rediscovered the same numbers

The technology industry discovered the same structure and gave it different names.

Jeff Bezos’s two-pizza rule — a team should be small enough to be fed by two pizzas — is a practical restatement of the 5-to-8 cognitive sub-layer. Amazon did not get there via anthropology. They got there by watching their own product teams stall every time they grew past the point where the whole group could fit around one table.

Scrum teams are officially 7 ± 2 — the current Scrum Guide recommends 3 to 9 members — which echoes George Miller’s 1956 paper on the working-memory limit of around seven chunks. Miller was not writing about teams. The cognitive limit he found on how many things we can juggle at once maps cleanly onto how many people we can coordinate without losing track of where everyone is.

Fred Brooks, in his 1975 book The Mythical Man-Month, observed that adding people to a late software project makes it later, because every new person increases the number of pairwise communication channels by roughly n(n–1)/2. Seven people means 21 channels. Ten means 45. Fifteen means 105. The coordination tax is quadratic, and it surfaces as “mysterious” slowdowns at exactly the team sizes where the math stops being manageable.

W. L. Gore & Associates, the Gore-Tex company, built Dunbar’s number directly into its real-estate strategy. Founder Bill Gore had a rule: every time a building exceeded 150 employees, they built another building. He was running Dunbar’s ceiling inside his facility planning decades before Dunbar had published the paper.

The Ringelmann effect, documented in 1913 and one of the oldest findings in social psychology, is the same story in a different register: as group size grows, the effort each individual contributes goes down. People pull harder on a rope when there are fewer of them holding it. Max Ringelmann measured it with actual rope-pulling experiments, and the finding has been replicated many times since in workplace and sports settings.

The two-pizza team — Bezos’s practical statement of the cognitive sub-layer.

The role change: the first-line manager span is a cognitive limit, not a cost line

A first-line manager’s direct-report span is not a matter of preference for most cognitive work. It sits around 5 to 7. Push it to 10 and managers stop coaching and start triaging. Push it to 15 and the role has reverted to being an individual contributor with a different title. Organizations that scale cleanly keep that first layer tight even when the spreadsheet says it is expensive — because the spreadsheet is not pricing the coordination tax that a wider span produces downstream.

Coordination overhead grows quadratically with team size.

The org change: 50 and 150 are hard boundaries

The sub-team that actually owns a piece of work should be closer to 5 than to 10. Not because small teams are faster in principle, but because the communication-overhead curve gets steep fast after 7. Bezos was right about this, and almost every high-performing team of any reasonable size runs its real work through an informal group of four or five — regardless of what the reporting structure says on the org chart.

When a function crosses 50 people, it needs an operational substructure. Tribes, chapters, pods, whatever the label — or the Dunbar sympathy layer breaks. When the people in a team stop knowing each other well enough that a death in someone’s family would visibly register with everyone, culture starts dying quietly. By the time anyone notices, six months have usually been lost.

When an organization crosses 150, it runs two cultures whether the leadership admits it or not. The question is only whether the split is designed deliberately or happens by default. Organizations that handle the ceiling well accept it and build deliberate boundaries. Organizations that handle it poorly spend years pretending 400 people are “all one team.”

Cross 150 and you either build deliberate substructure or get default fragmentation.

The strategic consequence: org design is surrender, not construction

Good organizational design is mostly a process of surrender. The cognitive architecture of the humans running the teams picks team sizes for you, and the only real choice is whether to build the org chart around what actually works or to fight it and pay the tax. Every commercial organization that has tried to force a bigger number — a 12-person manager span, a 30-person “small team,” a 300-person “family culture” — has either quietly subdivided itself into groups that look suspiciously like the Dunbar numbers, or lost the thing that made it work.

AI augmentation does not move the cognitive ceiling. It moves the throughput below the ceiling. An IC managing four AI agents is still operating inside a span of four. A manager coordinating seven sub-teams of augmented ICs is still operating inside a Dunbar-5 layer. The numbers that governed organizational design before agents are the numbers that will govern it after.

Small intimate teams stay where the work actually gets done.

So what boards should do

Boards should design operating models around the Dunbar layers and treat AI-augmented throughput as a multiplier on what each cognitive unit can do — not as a license to stretch the unit past its ceiling. The specific actions sit at four layers: first-line spans at 5 to 7 even under headcount pressure; sub-team ownership at 5; operational substructure at 50; deliberate cultural boundaries at 150. These are not target numbers. They are discovered numbers. Every other structure is an argument with biology, and biology does not negotiate.

The Roman legions did not know about neocortex ratios. The Marines do not design their fireteams around anthropology papers. Jeff Bezos did not cite Dunbar when he ordered the pizzas. All three converged on the same numbers because the numbers are a property of the animal doing the work, not the work itself. The job of an organizational designer is to notice this — and then get out of the way.

References
- Dunbar, R. I. M. (1992). “Neocortex size as a constraint on group size in primates.” Journal of Human Evolution.
- Dunbar, R. I. M. (2010). How Many Friends Does One Person Need? Harvard University Press.
- Miller, G. A. (1956). “The Magical Number Seven, Plus or Minus Two.” Psychological Review.
- Brooks, F. P. (1975). The Mythical Man-Month: Essays on Software Engineering.
- Hackman, J. R. (2002). Leading Teams: Setting the Stage for Great Performances. Harvard Business School Press.
- Ringelmann, M. (1913). Early social-loafing experiments, Annales de l’Institut National Agronomique.
- Gladwell, M. (2000). The Tipping Point. Popularised Gore’s rule of 150 for the management audience.
- The Scrum Guide — current recommended team size: 3 to 9 members.
2026-04-12
Q1 layoffs hit a four-year low. Tech’s share went up 40%.
What was announced

Challenger, Gray & Christmas reported in late March 2026 that U.S. employers announced 217,362 job cuts in Q1 — the lowest Q1 total since 2022. Within that aggregate, technology-sector cuts ran at 52,050, up 40% versus Q1 2025. In March specifically, AI was cited as the rationale for 15,341 cuts — 25% of the month’s total — making it the leading single reason for U.S. layoffs for the first time on the Challenger record. Major contributors to the technology figure: Dell’s annual filing-disclosed restructuring, Oracle’s March layoffs, and Meta’s Reality Labs reduction.

What it means

The aggregate-down, tech-up, AI-leading combination is not three separate stories. It is one story told from three angles. The aggregate number is down because the broad U.S. economy is operating with reasonable employment; sector-by-sector cuts in legacy industries are running below historical norms. The technology number is up because the sector is going through a structural reallocation — capital is shifting from headcount-led growth to compute-led growth, and the cost base of large software companies is being explicitly redesigned around that shift. AI is the leading cited reason because it is the strategic narrative that justifies the redesign to investors, customers, and remaining employees.

The implication for the rest of 2026: technology-sector hiring patterns will continue to diverge from the broader economy. Companies will hire aggressively for ML, infrastructure, agent operations, and applied research while shrinking headcount in functions that AI is augmenting or displacing. Net headcount may decline, but the per-employee compute and capability budget rises sharply. That changes what “growth” looks like in the financial reporting of the sector.

Andreas’s view

My read on this: the Q1 numbers are not a downturn signal — they are a transformation signal masquerading as cost discipline. Tech companies are not in distress. They are restructuring around the assumption that a smaller, AI-augmented workforce produces equal or greater output at a different cost basis. Some of those bets will be right; some will be the Block experience at smaller scale, where the rehire follows the cut by six to twelve weeks. The Q2 and Q3 numbers will tell us how clean the underlying productivity gain actually is.

I don’t think the AI-as-cited-reason metric stabilizes here. It rises through 2026. Once the framing carries an investor-relations multiple — which Block demonstrated — the disclosure pattern shifts in its direction across the sector. By year-end, AI-cited cuts will likely cross 30% of monthly U.S. totals, and that will look more like a permanent baseline than a peak.

The way I see it: the Challenger headlines document neither a labor crisis nor a productivity victory. They are capturing a sector-wide capital reallocation with a coherent strategic logic and uneven execution quality. The more interesting question to me is which side of that reallocation any given business is on — and whether its cost base reflects the structure it has today or the structure it intends to have in 18 months.

Three things I’m watching

Three things I’m watching as this plays out:
1. I’ll be watching whether companies are tracking the technology-sector comparison for their own organization: revenue, headcount, and per-employee compute spend versus the closest five public-market peers. That gap is where structural exposure shows up first.
2. I’ll be watching whether organizations hold a meaningful distinction in their communications between AI-driven productivity reductions — workflow-modeled, with measurable output — and broader restructuring justified by other factors. The market may not differentiate; but the ones with rigorous operations will.
3. I’ll be watching Q3 unit economics against any Q1 workforce action. The reduction is on the books in Q1; whether the underlying productivity thesis holds shows up in Q3 output measures, not headcount.
References and related signals
- Challenger, Gray & Christmas: March 2026 cuts rise 25% from February, AI leads reasons
- Tom’s Hardware: tech industry lays off nearly 80,000 in Q1 2026 — almost 50% AI-cited
- Digit.fyi: tech layoffs jump 40% in Q1 2026 as AI fever spikes
- North Penn Now: Q1 2026 tech layoffs surpass 60,000
- 4 Corner Resources: AI now leads reasons for job cuts
- Related signal: Block’s 4,000-person cut in late February established the public investor-reaction benchmark for AI-narrated reductions; the Q1 pattern reflects companies responding to that signal.
2026-03-29
AI is becoming the narrative for layoffs. It is not yet the cause.
What was announced

Through the week of February 16–22, 2026, the AI-cited layoff story moved from edge case to mainstream framing. AI was cited as the rationale for 4,680 February job cuts in the U.S. — roughly 10% of the month’s total. Baker McKenzie announced 600–1,000 layoffs (up to 10% of global headcount) framed as a pivot to AI-augmented service delivery. Dow disclosed 4,500 cuts in January with explicit AI-strategy framing. A Harvard Business Review piece in the same window argued that companies are laying off based on AI’s potential, not its measured performance. An Oxford Economics report from January concluded that many AI-cited layoffs were the consequence of past overhiring, not present AI productivity.

What it means

Two things are happening at once. First, AI productivity is real for specific workflows and starting to show up in unit-cost reductions. Second, “AI” is becoming the public-facing rationale for cost actions that boards and CEOs have wanted to take for other reasons — overhiring during 2021–2022, deteriorating margins in slower-growth segments, restructuring to a target operating model that was already in motion. The two stories overlap, and the public communication does not distinguish between them.

For employees, the framing matters because “we are restructuring” and “AI is replacing your role” carry different signals about whether the function comes back. For investors, it matters because the market is pricing AI-cited cost reductions as durable while restructuring-cited cost reductions are typically priced as one-off. CEOs who choose the AI framing get a multiple uplift. That incentive structure tells you why the framing is becoming dominant.

Andreas’s view

My read on this: the next 12 months will see a steady drift toward AI-as-explanation in layoff communications, regardless of whether AI is the underlying driver. The reason is not deception — it is signaling. CEOs need a forward-looking story that the cost base will stay reduced, and “AI productivity” is a cleaner story than “we hired too aggressively in 2022.” The public record will eventually reconcile this; quarterly earnings will reveal which companies actually shipped the productivity gain and which simply downsized.

I don’t think the workforce numbers are yet the right metric to watch. The right metric is the ratio of revenue per employee in the months after the cut. If revenue per employee climbs durably, the AI framing was substantively correct. If it plateaus or reverses while operational quality declines, the framing was a positioning move and the company will be hiring back inside 18 months — at higher cost and lower morale.

The way I see it: when a CEO presents an AI-cited workforce action, the productivity model behind it should be specific enough to name which workflows, which output measures, which time horizon, and which control group. Where those answers are vague, the action is restructuring with AI vocabulary. That is not necessarily wrong, but the distinction matters — and I think it matters most at the board level, where the conversation should reflect what is actually driving the decision.

Three things I’m watching

Three things I’m watching as this plays out:
1. I’ll be watching whether companies maintain a clear internal distinction between AI-driven productivity actions (with a workflow-level model behind them) and AI-framed restructuring actions (justified by other reasons). Both can be valid; conflating them confuses execution, and the ones that keep the distinction clean are more likely to deliver what they promised.
2. The companies that track revenue per employee monthly for the 12 months following any AI-cited workforce reduction will have the clearest view of whether the productivity gain actually materialized — and I’ll be looking at that number as the most honest signal in the public record.
3. I’ll be watching how specific companies get in their external communication around AI-related workforce changes. Vague “AI is making us more productive” framing tends to erode credibility internally faster than a precise statement of which work has been automated and which has been redesigned — and over the next year, that credibility gap will start showing up in retention and hiring data.
References and related signals
- HBR: Companies are laying off workers because of AI’s potential — not its performance
- CBS News: more companies pointing to AI as they lay off employees
- Challenger, Gray & Christmas: monthly job-cut report (AI as leading reason)
- Programs.com: tracking list of companies announcing AI-driven layoffs
- Related signal: Block’s 4,000-job cut a few days later (Feb 26) made the AI-as-narrative pattern explicit at scale and tested the market reaction.
2026-02-22
The pilot-to-production gap is an execution problem, not a model problem
What was announced

Through the week of February 9–15, 2026, the enterprise AI deployment story sharpened around a paradox: 95% of generative AI pilots still fail to reach production, yet 42% of enterprises now run agentic AI in production and 72% have agentic systems live in production or pilot. Microsoft’s February enterprise update reframed Copilot from “assistant” to “governance-first agent” capable of completing entire workflows. Oracle introduced Fusion Agentic Applications for finance, supply chain, and HR. OutSystems research released the same week reported that 94% of enterprises adopting agentic AI now flag agent sprawl as a primary concern.

What it means

The two statistics are not in conflict. They describe two different populations of organizations. The 95%-pilot-failure number describes how the average enterprise treats generative AI: a proof-of-concept budget, a small team, and a handoff to operations that never happens. The 42%-in-production number describes a smaller cohort that has done the operational work — governance, identity, runtime monitoring, rollback procedures, and explicit ownership of the agent fleet. The gap between the two cohorts is not technical. It is procedural.

Microsoft’s “governance-first agent” framing acknowledges this directly. The next phase of enterprise AI is not better models. It is the operating discipline around models — who deploys them, who owns them when they misbehave, who pays for the inference, and how the organization rolls back a bad agent without disrupting downstream work. That is a CIO problem, not a CTO problem.

Andreas’s view

My read on this: the production cohort is pulling away from the pilot cohort, and the gap is widening every quarter. The companies in production are accumulating an operational learning curve — what governance looks like, how to staff agent operations, how to track agent behavior in production, how to compose agents into workflows without losing accountability. The companies still iterating on pilots are accumulating learnings about prompts and demos. Those are different skill sets and they compound at different rates.

I don’t think the next 12 months reward the companies that pick the best model. They reward the companies that figured out how to operate any reasonable model at production scale, with controls, with monitoring, and with an explicit chain of accountability when an agent does the wrong thing. Agent sprawl is the leading indicator that the operations layer is missing — when 94% of practitioners flag it as a top concern, the conversation has moved past whether agents work and onto whether they are manageable.

The way I see it: the clearest signal a board can get on where an organization actually stands is whether the CIO can produce a production agent inventory — by name, by owner, by usage volume, by incident count. If the question produces a list, the organization is in the production cohort. If it produces “we are still piloting,” it is in the failure cohort, and the strategic gap to peers will be visible in operating costs by mid-2027.

Three things I’m watching

Three things I’m watching:
1. I’ll be watching whether companies can produce a named, owned, monitored agent inventory with rollback procedures on demand — that capability is the clearest proxy I have for whether a real agent operating model exists or not.
2. The organizations that interest me are the ones shifting pilot evaluation from “did the demo work” to “did the agent ship to production with controls in place” — and backing that shift by defunding pilots that stay in demo mode past a fixed time-box.
3. The question I’d be asking myself is whether a dedicated agent-operations lead — with explicit authority over the production fleet and seniority equivalent to the head of enterprise systems — is in place. Without single ownership, sprawl is the default outcome, and I expect that to show up clearly in incident and cost data over the next several quarters.
References and related signals
- OpenAI: the next phase of enterprise AI
- BusinessWire: Agentic AI mainstream in enterprise — 94% concerned about sprawl (OutSystems)
- Computerworld: Agentic AI — ongoing coverage of its impact on the enterprise
- Microsoft’s Copilot-to-governance-first-agents transition
- Related signal: Anthropic’s Model Context Protocol crossed 97 million installs in March — production-grade agent infrastructure is consolidating around a small number of standards, which lowers the operational excuse not to ship.
2026-02-15
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