The Smooth Market That Hides the Rupture

A major new forecasting study suggests the macro picture on AI and office demand will look reassuringly calm. That is precisely why it is dangerous.

I have been arguing for some time that the standard approaches to forecasting AI’s impact on office markets are fundamentally broken. Task-decomposition models that score every occupation for AI susceptibility. Macro forecasts that anchor to GDP growth and assume employment follows. Both miss the point. A new paper, serious, rigorous, and authored by researchers you cannot dismiss, has just handed us something more useful: evidence that the real action is not in the aggregate numbers at all. It is in what those numbers conceal.

Executive summary: A landmark study led by researchers at the Federal Reserve Bank of Chicago, Yale, Stanford, and the University of Pennsylvania finds that economists expect meaningful AI progress by 2030 but only modest changes to headline economic indicators. The macro picture looks calm. But the paper’s occupational data tells a different story: the growth of white-collar employment stalls, routine cognitive roles decline, and the junior knowledge worker pipeline is already thinning. For office markets, this means the aggregate demand signal will look stable while building-level outcomes diverge sharply. The edge moves to those who understand their occupiers deeply enough to see the fractures before the market-level data confirms them.

WHAT THE PAPER SAYS

‘Forecasting the Economic Effects of AI’ was published in March 2026 by Ezra Karger, Otto Kuusela, Philip Tetlock, and colleagues across the Federal Reserve Bank of Chicago, the Forecasting Research Institute, Yale, and Stanford. If you recognise Tetlock’s name, you should: he is the foremost authority on expert prediction. This is not a think-piece. It is a carefully designed survey of economists, AI industry professionals, and superforecasters, eliciting quantitative forecasts under explicit AI-progress scenarios.

Two findings matter for our industry. Neither is the headline number.

The first: a clear majority of economists, 61.4%, now assign meaningful probability to moderate or rapid AI capability progress by 2030. This is not a fringe position. Mainstream economists expect AI to advance substantially within five years.

And yet their unconditional economic forecasts barely move. Median GDP growth of 2.5%. Labour force participation drifting gently from 62.6% to 61.0%. Numbers close enough to historical norms that you could glance at them and feel reassured. AI is coming, but the economy absorbs it. Nothing to see here.

The second finding explains why these two things are not contradictory: capability is not adoption. The economists cite organisational inertia, management lag, regulation, infrastructure bottlenecks, and the well-documented pattern of general-purpose technologies taking decades to reshape aggregate outcomes. Even under the rapid scenario, where AI surpasses human performance on most cognitive and physical tasks, experts do not forecast economic outcomes outside historical experience.

If you stopped reading there, you would feel comfortable.

Do not.

WHERE THE RUPTURE HIDES

The paper includes an occupational composition forecast that is, for office markets, the most important chart published this year.

Under the unconditional scenario, the share of white-collar occupations in the labour force continues its gentle decades-long rise: from 20% today to 21% by 2030, 22% by 2050. Steady. Unremarkable. The trend that has underpinned prime office demand for forty years carries on.

Under the rapid AI scenario, that trend stops. White-collar share rises to 21% by 2030 then falls back to 20% by 2050. Not a collapse. A plateau. Meanwhile, care and service occupations grow from 46% to 57%. Blue-collar shrinks to 8%.

The GDP number looks fine in both scenarios. The composition of that apparently stable economy has profoundly shifted: the segment that fills offices has stopped growing, while the segments that do not are expanding. Like the river that looks the same width and depth from the bank while the current underneath has completely changed direction. The surface is calm. The water is doing something different.

The occupation-level data sharpens this further. The roles economists most confidently expect to decline are general and keyboard clerks, clerical support workers, administrative roles: precisely the occupations that fill the middle floors of most office buildings. The roles expected to grow are personal service workers, healthcare professionals, protective services. Valuable work. Not work that drives office leasing.

And then there is the finding that should genuinely unsettle anyone in this industry. Brynjolfsson, Chandar, and Chen’s research, cited prominently in the paper, documents a 13% relative employment decline for workers aged 22-25 in AI-exposed occupations. This is not a forecast. It is already in the data. And the mechanism matters: wages in those roles actually rose. Firms are not replacing everyone with AI. They are using fewer, more experienced workers. The junior knowledge worker pipeline is thinning. That pipeline is where the next generation of office demand comes from.

None of this shows up in the headline GDP number. None of it is visible in the aggregate labour force participation rate. The macro picture is smooth.
The building-level picture is fracturing.

YOU CAN STILL GO BANKRUPT IN A BOOM

This is why I keep returning to the distinction between ‘need’ and ‘want’ in office markets. The macro numbers can look perfectly healthy while individual buildings empty, specific tenants restructure, and the character of space demand shifts underneath you.

If your tenants are in sectors where AI is stripping out routine cognitive work, your occupancy is at risk regardless of what GDP does. If your building serves the kind of process-heavy, desk-intensive work that is concentrating into fewer, more senior hands, you face a structural demand reduction that no market-level forecast will warn you about. And if you are relying on supply constraints as your strategy, you are confusing a short-term tailwind with a structural position. Supply constraints do not protect you when your tenant decides they need 30% less space and upgrades to someone else’s building.

The paper’s most fundamental finding, for our purposes, is its variance decomposition: expert disagreement about AI’s economic effects is driven primarily by different beliefs about how the economy absorbs AI, not by disagreement about whether AI capabilities will advance. Economists who share similar views on the likelihood of rapid progress nevertheless diverge sharply on diffusion speed, job creation offsets, and institutional responses.

Translated for us: the question is not whether AI will be good enough to change work. It already is. The question is how quickly your specific occupiers will reorganise their workflows, restructure their teams, and change the way they use space. And that varies enormously by sector, by firm size, by management culture, by competitive pressure. It is, unavoidably, an occupier-by-occupier question.

The paper’s own data confirms this. There is no relationship between standard AI-exposure scores and economists’ predictions of which occupations will actually grow or shrink. The models are measuring the wrong thing. The right framework is not task decomposition but two variables: how much more productive will AI make this firm’s people, and is demand for their output expanding or flat?

But getting the analytical framework right is only half the answer. The other half is recognising that the thing most owners monitor most closely, whether their tenant can pay, is no longer the thing that matters most.

CREDIT RISK IS NOT SPACE DEMAND RISK

This is where the paper’s findings become operational.

The standard approach to tenant risk in commercial real estate is credit analysis. Can this tenant pay? Are they financially sound? Will they default? These are important questions. They are also, increasingly, the wrong ones.

The Brynjolfsson data cited in the paper shows firms where revenue grows, wages rise, and headcount falls. The tenant is not in trouble. The tenant is thriving. They are simply doing more with fewer people. A financially healthy occupier restructuring its workforce around AI is a bigger threat to your occupancy than a financially stressed tenant who still needs bodies in seats. The break notice will arrive from a position of strength, not weakness.

This is the decoupling that macro forecasts cannot see and credit analysis will not catch. Your tenant’s balance sheet looks fine. Their space requirement is shrinking.

Those two variables, productivity and growth, are what determine whether that shrinkage is coming. They interact to produce radically different space outcomes. A firm where AI drives a 40% productivity gain but output grows only 10% faces a 22% space reduction. The same productivity gain in a firm whose output doubles produces a 43% expansion. The question is not how much AI can automate. It is whether productivity gains get absorbed by growth or converted into headcount reduction.

This paper confirms that logic directly. Its occupational composition data shows exactly the pattern you would expect: under the rapid AI scenario, white-collar employment share plateaus while the economy continues to grow. Productivity is rising. Growth is absorbing some of it. Not all of it. And the segment that fills offices is the segment where the absorption is weakest. For lower-growth economies, the arithmetic is harder still: less growth to absorb the productivity gains means more of those gains convert directly into headcount reduction.

Which means every owner needs to be able to position their tenants on that productivity-growth matrix, not once, in a quarterly review, but continuously. And the signals to do so are largely available right now.

THE INTELLIGENCE YOU ALREADY HAVE

Think about what is knowable about your tenants today.

At the most basic level, and this is work AI can do systematically, you can track the observable indicators. Headcount trends versus revenue trends. Junior-to-senior hiring ratios. Job postings in AI-exposed roles. Earnings call language around operational efficiency and workflow redesign. Sector-level AI adoption patterns. These tell you where a tenant sits on the productivity axis: are they absorbing AI aggressively or gradually? And they tell you something about growth: is demand for what this firm does expanding or contracting?

A step beyond that, you can track what is inferable with effort. Technology partnerships. Subletting activity in their sector. Whether they are investing in their fit-out or letting it run down. Competitive dynamics that might force efficiency regardless of management intent. This requires combining structured and unstructured information, AI-assisted but human-directed.

And then there is the intelligence that only comes from being present. How the tenant talks about their space when they are not negotiating. What the facilities manager mentions in passing. The half-empty floor that does not show up in any dataset. This is what I have called the unmeasurable, the relationship intelligence that automated monitoring will quietly degrade if you do not deliberately protect it.

Each layer feeds the same assessment: where does this occupier sit on the productivity-growth matrix, and is that position changing? The first layer tells you the baseline. The second sharpens it. The third is where you catch the signals the data misses, and it is, not coincidentally, where defensible competitive advantage lives. Everyone will have automated monitoring within two years. Not everyone will have asset managers who know their tenants well enough to read what the monitoring cannot see.

THIS IS NOT A TECHNOLOGY PROBLEM

The temptation is to treat this as a systems challenge: build a platform, integrate the data feeds, deploy the dashboards. That framing is wrong, or at least insufficient. The binding constraint is not technology. It is whether your people think about their tenants this way at all.

A fifteen-person property company with AI-fluent asset managers who routinely run earnings transcripts through an LLM, track hiring patterns against their tenant register, and ask pointed questions at every tenant meeting will see the fractures forming before a large institutional manager whose teams are still assembling quarterly reports by hand. This is cognitive infrastructure, not technology infrastructure. It reflects how people think, not what systems they have.

The minimum viable version is not a platform. It is a question that every asset manager asks at every tenant interaction: how is AI changing how your people work? And then a discipline for connecting the answer to what it means for space.

The sophisticated version builds from there. But the starting point is a shift in what you pay attention to, from whether your tenant can pay the rent to whether your tenant will still want the space.

The market will look fine. Your building might not. The only way to know the difference is to understand your occupiers better than any forecast model ever could.

Not: will this asset perform well? Rather: will this asset’s occupiers do well, and will doing well still mean needing this space?

That is the question now. And it is answerable, if you are willing to look.