{"id":2085,"date":"2026-05-31T09:17:31","date_gmt":"2026-05-31T17:17:31","guid":{"rendered":"https:\/\/bayesianinvestor.com\/blog\/?p=2085"},"modified":"2026-05-31T12:42:20","modified_gmt":"2026-05-31T20:42:20","slug":"financial-costs-of-an-ai-pause","status":"publish","type":"post","link":"https:\/\/bayesianinvestor.com\/blog\/index.php\/2026\/05\/31\/financial-costs-of-an-ai-pause\/","title":{"rendered":"Financial Costs of an AI Pause?"},"content":{"rendered":"\n

I’ve analyzed the near-term economic effects of an AI pause, out of concern for my investments, and a desire to predict how strong political opposition to a pause is likely to be.<\/p>\n\n\n\n

My median estimates: The S&P 500 will drop 27.8%. AI subsectors will drop 34-69%. Interest rates will rise at a much slower rate than would be the case without a pause.<\/p>\n\n\n\n

The specific numbers depend on some fairly arbitrary assumptions. So please read this post in order to get a feel for how the results depend on the assumptions. I’ve tried to keep the assumptions reasonable, but some of them will prove to be wrong. My most controversial assumptions reflect an expectation that both markets and voters will be surprised at how powerful AI is, mainly in 2027.<\/p>\n\n\n\n\n\n\n\n

For the full model, along with many explanatory comments, see the Python source code here (zip file)<\/a>.<\/p>\n\n\n\n

This conversation with Claude<\/a> clarifies my reasoning in more detail than most people will want.<\/p>\n\n\n\n

Sensitivity to Assumptions<\/h3>\n\n\n\n

Here’s how my model says the impact is influenced by changes in assumptions. Numbers are for the immediate change in the S&P500 and in the stocks of hyperscalers (Microsoft, Google\/Alphabet, Amazon, Meta, and Oracle).<\/p>\n\n\n\n

AI economic centrality<\/th><\/tr>
<\/td>low (0.4)<\/td>medium (0.7)<\/td>high (0.95)<\/td><\/tr>
SP500<\/td>-22.5%<\/td>-27.8%<\/td>-31.8%<\/td><\/tr>
Hyper<\/td>-26.8%<\/td>-34.1%<\/td>-40.4%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

How central is AI to economic growth? Low means AI is one technology among several. High means AI is the dominant driver of growth, and interest rates are pushed up by massive AI-related capital demand.<\/p>\n\n\n\n

training dependence<\/th><\/tr>
<\/td>low (0.25)<\/td>medium (0.5)<\/td>high (0.8)<\/td><\/tr>
SP500<\/td>-22.2%<\/td>-27.8%<\/td>-31.2%<\/td><\/tr>
Hyper<\/td>-26.3%<\/td>-34.1%<\/td>-38.9%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

How much of AI’s near-term economic value requires new frontier training runs? Low means most value comes from deploying and refining existing models. High means the next major value unlocks require fundamentally new capabilities.<\/p>\n\n\n\n

S&P 500 immediate impact: AI centrality \u00d7 training dependence<\/th><\/tr>
<\/td><\/td>Train: low<\/td>Train: medium<\/td>Train: high<\/td><\/tr>
Central: low<\/td>SP500<\/td>-18.9%<\/td>-22.5%<\/td>-25.0%<\/td><\/tr>
Central: medium<\/td>SP500<\/td>-22.2%<\/td>-27.8%<\/td>-31.2%<\/td><\/tr>
Central: high<\/td>SP500<\/td>-24.6%<\/td>-31.8%<\/td>-35.9%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
post pause cognitive improvement<\/th><\/tr>
<\/td>weak<\/td>moderate<\/td>aggressive<\/td><\/tr>
SP500<\/td>-24.5%<\/td>-27.8%<\/td>-30.3%<\/td><\/tr>
Hyper<\/td>-29.5%<\/td>-34.1%<\/td>-37.7%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

I’m predicting that at the end of the pause, AI training would resume with some moderate regulation. This variable captures how much progress to expect compared to a no-regulation scenario. I’m using 75%, 50%, and 30% of unregulated progress for the weak, moderate, and aggressive post-pause regulations respectively.<\/p>\n\n\n\n

compute growth rate<\/th><\/tr>
<\/td>40%<\/td>63%->40%<\/td>80%<\/td><\/tr>
SP500<\/td>-24.1%<\/td>-27.8%<\/td>-30.8%<\/td><\/tr>
Hyper<\/td>-29%<\/td>-34.1%<\/td>-38.4%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

This variable describes the hardware constraints on AI growth. It represents how fast the available compute would increase given unlimited demand. The middle column assumes that growth gradually slows between 2028 and 2040 from 63% to 40%.<\/p>\n\n\n\n

pause duration<\/th><\/tr>
<\/td>1 year<\/td>2 years<\/td>4 years<\/td><\/tr>
SP500<\/td>-27.5%<\/td>-27.8%<\/td>-29.6%<\/td><\/tr>
Hyper<\/td>-33.7%<\/td>-34.1%<\/td>-36.7%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
task density<\/th><\/tr>
<\/td>low<\/td>moderate<\/td>high<\/td><\/tr>
SP500<\/td>-27.1%<\/td>-27.8%<\/td>-28.4%<\/td><\/tr>
Hyper<\/td>-33.2%<\/td>-34.1%<\/td>-34.9%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

High task density means there’s still plenty of low-hanging fruit, and we haven’t yet reached the steepest part of the S-curve.
Low task density means we’re using up the low-hanging fruit, and we’ve passed the steepest part of the S-curve.<\/p>\n\n\n\n

What Kind of Pause?<\/h3>\n\n\n\n

I assume that governments decide in late 2027 to treat AI as slightly more dangerous than nuclear weapons, due to some combination of job displacement, and accidents that are more concerning than the one depicted in the movie 2001: A Space Odyssey<\/a>.<\/p>\n\n\n\n

I focus on a scenario where an international agency enforces drastic limits to AI development for two years, starting at the beginning of 2028. During 2028 there is an expectation that significant AI development will resume in 2030. I will focus my analysis on the economic effects during 2028, and assume that actors during 2028 will only have rough guesses as to how fast development will be allowed to proceed in 2030. I will assume that their mean forecast involves some sort of resumption of progress, but little confidence in full-speed development being allowed in 2030.<\/p>\n\n\n\n

The pause will restrict all datacenters more powerful than a certain threshold, roughly corresponding to the level of the best AI which was released in 2026. The details of the pause will depend somewhat on insights that won’t become available until we know more about how AI is progressing in 2027.<\/p>\n\n\n\n

Instead of predicting what the pause will apply to or how it will be enforced, I’ll predict that it will be effective enough to slow AI capability progress by a factor of 5 compared to what it would be in the absence of regulation. Since regulation will be imperfect at distinguishing between harmless research and the research it intends to pause, I’ll estimate it causes a 15% slowdown in other high performance computing.<\/p>\n\n\n\n

Impact<\/h3>\n\n\n\n

I assume the initial effect of the pause will be a 90% reduction in spending to train AI. That effect will be somewhat offset by lower prices on that compute stimulating increased demand for inference.<\/p>\n\n\n\n

I assume that GDP growth rates under a no-pause scenario would gradually rise to 30% by 2040. This is more a reflection on what markets would predict in 2028 than a genuine estimate of what I expect would happen with no regulation. I see a fair amount of room for higher growth rates<\/a>.<\/p>\n\n\n\n

I predict interest rates in 2029 of roughly 7% with a pause, compared to 11% without any AI regulation.<\/p>\n\n\n\n

I predict that robotics progress will continue to have roughly the same increases in economic impact that it would have had without regulation. I’m moderately confident that AI already has nearly enough general intelligence for robotics to have transformative impacts on the economy, and that the remaining engineering that is required is ordinary enough to only be slowed down a little by the pause. That slowdown will be offset by the pause reducing the extent to which AI training competes with robotics for resources.<\/p>\n\n\n\n

I’m assuming that financial markets are mostly rational, and will adjust price\/earnings ratios mainly in reaction to predicted growth rates and interest rates. I assume markets will briefly over-react to a pause due to increased risk aversion and margin calls. I assume that pre-pause market levels would not be considered to be bubble-like under a no-pause scenario.<\/p>\n\n\n\n

Here’s a more detailed set of predictions for the median set of my model’s assumptions:<\/p>\n\n\n\n

Model Output: Executive Summary<\/h3>\n\n\n\n
  Pause period: 2028\u20132030\n  Post-pause cognitive improvement: 50% of unrestricted rate\n  Model horizon: 2040\n\n  Net present value of foregone AI revenue: $ 111.93T\n  Implied AI sector market cap loss:    $ 839.51T\n\n  S&P 500 immediate impact:            -27.8%\n  S&P 500 after one year:              -17.6%\n  Immediate market cap change:         $ -20.02T\n  One-year market cap change:          $ -12.69T\n<\/pre>\n\n\n\n
AI High-Growth Segment Revenue Trajectory<\/h3>\n\n\n\n
  Year |     No Pause   Growth |   With Pause   Growth |         Diff\n--------------------------------------------------------------------------------------\n  2025 |    $    350B    63.0% |    $    350B    63.0% |    $      0B\n  2026 |    $    570B    63.0% |    $    570B    63.0% |    $      0B\n  2027 |    $    930B    63.0% |    $    930B    63.0% |    $      0B\n  2028 |    $   1.52T    63.0% |    $    911B    -2.0% |    $    604B << PAUSE\n  2029 |    $   2.43T    60.5% |    $   1.22T    33.4% |    $   1.22T\n  2030 |    $   3.84T    58.0% |    $   1.67T    37.7% |    $   2.17T\n  2031 |    $   6.01T    56.4% |    $   2.29T    36.7% |    $   3.72T\n  2032 |    $   9.31T    54.8% |    $   3.10T    35.6% |    $   6.20T\n  2033 |    $  14.26T    53.2% |    $   4.18T    34.6% |    $  10.08T\n  2034 |    $  21.61T    51.6% |    $   5.58T    33.5% |    $  16.04T\n  2035 |    $  32.42T    50.0% |    $   7.39T    32.5% |    $  25.03T\n  2036 |    $  47.98T    48.0% |    $   9.69T    31.2% |    $  38.29T\n  2037 |    $  70.05T    46.0% |    $  12.59T    29.9% |    $  57.46T\n  2038 |    $ 100.88T    44.0% |    $  16.19T    28.6% |    $  84.69T\n  2039 |    $ 143.25T    42.0% |    $  20.61T    27.3% |    $ 122.63T\n  2040 |    $ 200.55T    40.0% |    $  25.97T    26.0% |    $ 174.57T\n<\/pre>\n\n\n\n
Sector Market Cap Impacts<\/h3>\n\n\n\n
Sector               |  Pre-Pause |  Immediate       % |  After 1yr       %\n----------------------------------------------------------------------------------\nSemiconductors       |  $   8.00T |  $  -4.50T  -56.2% |  $  -3.96T  -49.5%\nHyperscalers         |  $  22.00T |  $  -7.51T  -34.1% |  $  -5.23T  -23.8%\nFrontier Labs        |  $   3.00T |  $  -2.07T  -68.9% |  $  -1.92T  -63.9%\nAi Applications      |  $   4.00T |  $  -1.74T  -43.6% |  $  -1.36T  -34.1%\nNon Ai Sp500         |  $  35.00T |  $  -4.20T  -12.0% |  $   -215B   -0.6%\n----------------------------------------------------------------------------------\nTOTAL (S&P 500)      |  $  72.00T |  $ -20.02T  -27.8% |  $ -12.69T  -17.6%\n<\/pre>\n\n\n\n
Implications<\/h3>\n\n\n\n
Political pressure for AI regulation is building as increasingly impressive evidence of AI capabilities erodes peoples’ ability to dismiss AI as hype. I expect this to lead to a serious debate among politicians in 2027 about AI regulation. I’m unable to predict what kind of regulation that will produce. So I’ve focused on scenarios that would matter the most if they’re adopted.<\/p>\n\n\n\n
The economic impact of a moderately effective pause would be big enough to create medium-sized political pressures to weaken the pause.<\/p>\n\n\n\n
There will be significant pressure for a strong pause due to voter concerns about job losses. There will be hard-to-predict pressures from national security professionals related to military risks.<\/p>\n\n\n\n
My crystal ball refuses to tell me how these pressures will play out.<\/p>\n\n\n\n
I see a very real chance that a debate over a pause will impact AI stocks within a year from now. This effect is worrying enough to get me to take some profits in my AI stocks, at a rate of 1% to 2% per week given recent trading patterns.<\/p>\n\n\n\n
I consider a pause to be more likely than do most people. Here are some Manifold markets that I’ve been modestly bidding up:<\/p>\n\n\n\n