What we think is happening

Our reading of where AI, jobs and the economy may be heading, set out so that the evidence can show it to be wrong. It is a judgement rather than a forecast: the model shows what would follow if the reading is right, and the indicators should show whether it is.

The reading, in seven points

1. It may have begun two to four years ago

AI appears already to have taken some work outright, in translation, writing and some entry-level jobs, and some more quietly: existing staff do more, hiring slows, and people who leave are not replaced. Health care and social assistance seem to have taken up the slack: from Aug 2024 to Aug 2026 they added 107% of the net growth in US jobs, while the rest of the economy shed jobs. Measured on its trend since 2024, the loss of jobs to AI is still small: at most 15% of the pace the model assumes from here.

What would show it:

What would count against it. Labour's share of income recovering, and employment among 25–54-year-olds holding up.

2. It could speed up as AI companies change how they sell

A few firms that can do most things may absorb the smaller ones, and begin to sell the replacement of work rather than help with it.

In the model. The loss of jobs to AI rises from the pace seen so far toward the model's full pace, half-way there by Q2 2028. This is still slower than the model's central case, which assumes the full pace from the start: on its own it lowers the share of Americans out of work in mid-2032 by 3 points.

What would show it:

What would count against it. Adoption stalling on cost or reliability, and firms hiring again as they grow.

3. Government debt could become the central worry

If jobs go, taxes on wages and spending fall while the cost of support rises, and interest costs climb as old debt is refinanced at higher rates. Markets may charge for that risk. For the US the danger is more likely to be inflation and pressure on the central bank than default; in the euro area, the gap between countries' borrowing costs, France's above all.

In the model. Investors charge a point more to lend to the US and half a point more in the euro area, reached over the first year and passed on to firms and households. By mid-2032 this adds 5 points to the share of Americans out of work. The model has no tax revenue and no default: it prices the worry, it does not compute the deficit.

What would show it:

What would count against it. US government bonds rallying in a slowdown, as they usually do.

4. A stock market bust, possibly larger and faster than the dot-com crash

The dot-com crash took about two and a half years; the S&P 500 fell by about half. Much of the money for the AI build-out is borrowed, a good part of it through private credit funds that borrow from banks, so a fall in AI investment could reach lenders quickly.

In the model. From Q4 2026, US share prices fall 60% and AI investment 90% within 2 quarters, and losses on lending to the build-out reach the banks. By the end of 2028 this is the largest single part of the reading: it adds 3 points to US out of work.

What would show it:

What would count against it. AI investment plans raised and paid for out of profits rather than borrowing.

5. A recession, as spending falls with wages

Lost wages cut spending, falling share prices cut the spending of those who own them, and lower spending cuts more jobs. If firms replace the jobs they cut with AI instead of rehiring, the losses may not come back.

In the model. How many of those jobs firms replace is the assumption that matters most after 2028. If twice the usual share, it adds 15 points to US out of work by mid-2032.

What would show it:

What would count against it. Spending holding up on wealth and government support, and laid-off workers rehired.

6. Care may stop absorbing the jobs

Health care and social assistance have carried US job growth and are mostly paid for by the state. As budgets tighten, they could stop growing.

In the model. Care stops growing a year from the start in all four economies. This matters less than it might appear: at its normal pace care takes in about a point of US employment in six years, so capping it adds 0.5 points to US out of work by mid-2032. A funded expansion of care is what would make a difference.

What would show it:

What would count against it. Care jobs still growing through a downturn.

7. Tariffs in the US, spending cuts in Europe

We think it likely that the US raises tariffs and other countries answer, and that Europe and Sweden avoid tariffs of their own but cut spending as unemployment rises.

In the model. US tariffs rise 25 points and US exports meet the same; other economies face them on what they sell to the US. Tariffs add 4 points to US out of work by mid-2032. Spending cuts add 0.9 points to out of work in the euro area and 1.7 in Sweden. If Germany's spending offset the cuts elsewhere, the euro area would have 0.5 points fewer out of work.

What would count against it. Germany's spending and joint EU borrowing carrying Europe through the downturn.

What the model shows if we are right

The model starts from the data to the end of Q3 2026 in four economies, and the bust begins in Q4 2026. It runs six years, to Q3 2032. Through 2028 the runs agree closely: 14–16% of the US workforce is out of work at the end of 2028, against 10% in the model's central case (a bust the size of the dot-com crash, with today's policies), about 1.4 to 1.6 times as many. The four economies' output is about 9% below what they could produce.

After 2028 the result turns on one thing that no data can yet measure: how many of the jobs lost in the downturn firms replace with AI instead of rehiring. At the share the model finds in past downturns, 32% of the US workforce is out of work by mid-2032; at twice that share, 46% (24% in the central case). Both are still rising when the run ends. There is, then, a world in which 46% of the American workforce is out of work by mid-2032. It is not the central case, but no evidence so far rules it out. Out of work means unemployed or out of the workforce: people who have stopped looking are counted.

People out of work (unemployed or out of the workforce), % of the workforce

Shaded: from firms replacing the usual share of the jobs they cut in the downturn (lower edge) to twice that share (upper edge). Dashed: the model's central case. Each quarter is drawn at its end. Before the start: the unemployment rate in the United States and Sweden.

Output against what the economy could produce, %

Shaded: from firms replacing the usual share of the jobs they cut in the downturn (lower edge) to twice that share (upper edge). Dashed: the model's central case. Each quarter is drawn at its end.

Out of work, end of 2028Out of work, mid-2032Output, worst point to Q3 2032
United States14–16%32–46%−19 to −24%
Euro area9%13–15%−11 to −12%
Sweden14%20–24%−19 to −22%
China7%9–10%−7 to −9%

What matters most

Each part of the reading taken back, one at a time, to the model's central case. The parts do not add up exactly: they compound.

Part of the readingAdds to US out of work by the end of 2028, pointsBy mid-2032, points
The bust is larger and faster than the dot-com crash2.87.2
Firms replace twice the usual share of the jobs they cut1.314.7
Investors charge more to lend to governments1.64.7
US tariffs, and other countries' answer1.54.3
Spending cuts in Europe and Sweden0.10.5
Care stops absorbing the jobs0.20.5
AI's pace rises from today's to the model's, instead of starting there−1.6−3.0

What we leave out

Energy: a fast rise in oil prices keeps inflation up and stops central banks cutting into a downturn. The model can carry the price of oil, but this reading leaves it out until a bust is under way while oil is dear: it goes in if Brent is at least 25% above its average of the previous three years on a day when technology stocks, US corporate credit or the bust and jobs together have triggered. Brent is $125 a barrel as this page is built. Housing, lending to private credit funds, Japan and Korea are followed in the indicators but not modelled. The model has no tax revenue and no default: it shows what a higher price of borrowing does, not whether governments can pay.

How sure we are

This is our reading, and it could be wrong. The loss of jobs to AI measured so far is small and could stay small. The model's later years rest on assumptions that no data can yet test, and a model fitted to past downturns may not fit this one. The settings, the model and every number on this page are public: the code for these runs.