I’ve collected forecasts of AI’s effect on economic growth over 2025-2035. The full list of forecasts is in a table below. Some of the forecasts are of growth in GDP, some GWP (gross world product), some TFP, some labor productivity. I’m also mixing forecasts for the US, EU, and World. Some forecasts aren’t explicitly over 2025-2035, but most are roughly that range. Please send me an email if you think I’m misinterpreting one of these. Economists and AI people disagree.

1. Most economists’ forecasts are 0.1–1.5%/year. The two exceptions are Baily, Brynjolfsson, and Korinek (2023) and Korinek and Suh (2024).
2. Most AI insiders’ forecasts are 3–30%/year. A notable exception is Andrej Karpathy who recently says he expects GDP growth to remain on historical trends.1

The disagreement is about the AI, not about the economics.

The primary reason for the disagreement seems to be about the future rate of AI capabilities progress, not about the more directly economic questions such as (1) the current economic impact of AI; (2) the rate of diffusion & adoption over time; (3) the substitutability between AI-produced and human-produced services.

Many of the forecasts by economists effectively assume no further progress in AI, which I think is striking.

There is a small economics literature on the long-run equilibrium effects of AGI (Restrepo (2025), Aghion, Jones, and Jones (2019), Restrepo (2025)), but those papers do not make quantitative forecasts about growth rates over the next decade.

More observations below.

Below this table I discuss:

1. Most economists are treating AI as a one-time shock.
2. It seems hard to reconcile imminent AGI with modest growth.
3. The impact of AI on output in 2024 was perhaps ≈0.5%.
4. AI will likely increase welfare more than GDP.
5. Forecasting markets seem to expect slow impacts.
6. Financial markets seem to expect modest impacts.

Forecasts

date of forecast	author	annual excess growth, 2025-2035	quote
March 2023	Briggs and Kodnani (2023) (Goldman Sachs)	+1.5%	“We estimate that widespread adoption of generative AI could raise overall labor productivity growth by around 1.5pp/year (vs. a recent 1.5% average growth pace), roughly the same-sized boost that followed the emergence of prior transformative technologies like the electric motor and personal computer.”
May 2023	Baily, Brynjolfsson, and Korinek (2023)	+2.8%	“The projection labeled “Level” assumes that generative AI raises the level of productivity and output by an additional 18% over ten years, as suggested by the illustrative numbers we discussed for the first channel. After ten years, growth reverts to the baseline rate. The third projection labeled “Level+Growth” additionally includes a one percentage point boost in the rate of growth over the baseline rate, resulting from the additional innovation triggered by generative AI.”
June 2023	McKinsey Global Institute (2023)	+0.1-0.6%	“genAI alone: +0.1-0.6 pp/yr labor productivity through ~2040; combined with other tech/automation: +0.5-3.4 pp/yr.”
August 2023	Tyler Cowen	+0.25%-0.5%	“My best guess, and I do stress that word guess, is that advanced artificial intelligence will boost the annual US growth rate by one-quarter to one-half of a percentage point.”
March 2024	Korinek and Suh (2024)	+18%	“In the baseline AGI scenario … steady-state growth of 18% per year.”
April 2024	Acemoglu (2024)	+0.07%	“Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.71% increase in total factor productivity over 10 years.”
May 2024	Aldasoro et al. (2024) (BIS)	+2.5%	They assume a 1.5% growth in productivity but then predict that equilibrium output will increase at a higher rate. “we assume that AI raises annual productivity growth by 1.5 percentage points for the next decade, in line with plausible estimates in the literature … Growth is fastest in the first 10 years – i.e. the period in which AI directly raises industry-level TFP – at which point GDP is almost 30% higher than it would have been without”
June 2024	Aghion and Bunel (2024)	+0.68-1.3%	“Based on the first approach, we estimate that the AI revolution should increase aggregate productivity growth by between 0.8 and 1.3pp per year over the next decade. Using the second approach but with our own reading of the recent empirical literature on the various components of the task-based formula, we obtain a median estimate of 0.68pp additional annual total factor productivity (TFP) growth.”
Dec 2024	Filippucci, Gal, and Schief (2024) (OECD)	+0.25-0.6%	“main estimates for annual aggregate total-factor productivity growth due to AI range between 0.25-0.6 percentage points (0.4-0.9 pp. for labour productivity).”
Feb 2025	Tyler Cowen	+0.5%	“I’ve gone on record as suggesting that AI will boost economic growth rates by half a percentage point a year.”
March 2025	Bergeaud et al. (2025) (ECB)	+0.29%	“We predict TFP gains of 2.9% in the medium run (say in the next ten years) in the euro area, equivalent to an additional 0.29 percentage points per year.”
March 2025	Erdil et al. (2025) (Epoch GATE model)	+30%	The website figure showing Gross World Product shows that in 2035 “default” path hits approximately a 30% annualized growth rate. However Ege Erdil says “i don’t personally predict 30% mean annual gdp growth in the US over the next 10 years … the model does with some reasonable parameter values, but my timelines for that kind of growth are longer, and there’s model uncertainty and so on”.
April 2025	Misch and Zhang (2025) (IMF)	+0.2%	“We find that the medium-term productivity gains for Europe as a whole are likely to be modest, at around 1 percent cumulatively over five years.”
May 2025	Jack Clark (Anthropic)	+3-5%	“I think my bear case on all of this is 3 percent, and my bull case is something like 5 percent.”
June 2025	Filippucci et al. (2025) (OECD)	+0.4-1.3%	“annual aggregate labour productivity growth due to AI range between 0.4-1.3 percentage points in countries with high AI exposure … In contrast, the estimated range is 0.2 to 0.8 percentage points in countries where these determinants of AI gains are less favourable (e.g. Italy, Japan).”
Sept 2025	Arnon (2025) (Penn Wharton Budget Model)	+0.15%	“Compounded, TFP and GDP levels are 1.5% higher by 2035.”

Qualitative Forecasts

date of forecast	author	growth 2025-2035	quote
March 2023	Paul Krugman	small	“history suggests that large economic effects from A.I. will take longer to materialize than many people currently seem to expect … ChatGPT and whatever follows are probably an economic story for the 2030s, not for the next few years.”
July 2024	Josh Gans	small	“I don’t think it will boost growth appreciably … over the next 10 years”
Oct 2025	Andrej Karpathy	no change in trend	Dwarkesh: “Just to clarify, you’re saying that the rate of growth will not change.” … Karpathy: “Yes, my expectation is that it stays in the same pattern.” This doesn’t pin down the incremental effect of AI on growth, but it’s presumably between 0% and 2%.

Observations

Most economists are treating AI as a one-time shock.

The table below from Filippucci, Gal, and Schief (2024) compares assumptions across four recent papers which have forecast the future economic impact of AI. There are two notable things about this table:

1. The assumptions are static: each of the rows estimates the economic impact of existing AI by referring to studies of cost savings from using LLMs.
2. The papers all exclude any effect of AI effect on “innovation” (the bottom row).

This seems to me striking. It is possible that AI capabilities will stop growing, but it is not at all clear that this should be our primary forecast, given the speed in capabilities growth over the last few years (see my earlier post for some evidence).2

It seems hard to reconcile imminent AGI with low economic growth.

Tyler Cowen & Andrej Karpathy both seem to expect that (1) AGI is imminent, but also (2) economic growth will not shift dramatically from its historical trend. I find these hard to reconcile.

Cowen says OpenAI’s o3 is “already AGI”, though also says AGI is difficult to define, and he says he expects 0.5% excess growth/year. Karpathy says he expects AGI (defined as a drop-in worker) in ten years, but also that there will be no change to the historical trend in economic growth. They give a variety of reasons for expecting limited growth effects: some are static (low substitutability between AI-produced and human-produced goods), some are dynamic (slow diffusion, complementary innovation, slow accumulation of compute capital).

I find it hard to quantitatively reconcile imminent AGI with a low growth rate. Suppose it takes 20 years for the technology to mostly diffuse, and each year we experience 0.5% incremental growth, this implies the equilibrium impact of human-level intelligence will be a 10% increase in output.3

It’s hard to see how human-level AI would only increase output by only 10%. Suppose there was zero ability to substitute between AI-produced goods and human-produced goods, this would imply the increase in output would be equivalent to the share of goods that AI could produce. Thus a 10% growth would imply we can automate 10% of production with AI (assuming costs of AI are negligible relative to existing labor and capital costs).

High growth rates are not unprecedented. Over 100 years Western countries increased output/capita by more than 10 times, and China was able to achieve that growth over 25 years.

It seems to me Cowen and Karpathy’s predictions require assuming either (1) AGI only allows for automation of a relatively small share of labor (i.e. it’s not very general); (2) diffusion of AGI will be slower than historical precedents.

The impact of AI on output in 2024 was perhaps ≈0.5%.

We can estimate the economic impact of AI on output a few ways:

• Total LLM revenue in 2024 was probably below $10B (0.03% of GDP), but the benefits produced are likely far higher.
• Bick, Blandin, and Deming (2025) estimates that in late 2024, 1-5% of US working hours are on chatbots. With a variety of assumptions about time saved they get an aggregate productivity increase of 1.1%.4
• Collis and Brynjolfsson (2025) asks American chatbot users what they would need to be paid to forego using the app for a month, and the average answer is $100. Multiplying this by 80M users they estimate the total consumer value of chatbots is $97B, which is 0.3% of a $30T US GDP.
• Humlum and Vestergaard (2025) finds Danish chatbot users self-report approximately 3% productivity improvement (the same paper also finds no detectable impact on wages or employment).

I do not consider the effect of AI investment on output (e.g. building chips and datacenters) – that is a qualitatively different effect because it depends on expectations of future value of AI, not the contemporary effects.

AI will likely increase welfare more than it increases GDP.

AI is already providing a great deal of value that will not show up in GDP by our normal accounting methods. My own paper with OpenAI (Chatterji et al. (2025)) shows that 2/3 of the use of ChatGPT is outside of work.

In fact it’s plausible that AI will somewhat reduce GDP, because it reduces demand for expertise: I no longer call my garage-door-repair guy, because ChatGPT tells me how to fix the door. Services are generally accounted for in GDP just by the wages paid to service-providers. If people substitute from human service-providers towards AI then measured GDP will fall even though true output has increased. If service-providing firms pass through their cost-savings to customers then their measured contribution to GDP will fall.

I believe all of the papers in the table below consider only the effect on measured GDP, not on welfare.

Forecasting markets seem to expect slow impacts on growth.

The best forecasting markets I could find were on Metaculus, however note that both of these were relatively sparse, and have some internal discrepancies. They expect relatively flat economic variables over the next 10 years. As of October 2025, Metaculus shows expectations over the next century:

• World productivity growth increases smoothly from 3% to 5%.
• US Labor force participation falls smoothly from 84% to 36%

The changes are large but it takes 100 years, and the transition is relatively smooth. The smoothness is consistent with either (1) shared expectations of smooth growth; (2) shared expectations of discontinuous changes but uncertainty about when; (3) shared expectations of discontinuous growth but disagreement about when (i.e. disagreement between forecasters).

Financial markets expect modest impacts.

Financial market valuations of AI companies imply they expect the flow of AI-related earnings from existing companies to be perhaps around 2% of GDP. This is very large, but is consistent with either (1) AI companies capture a large share of a modest change to growth; or (2) AI companies capture a small share of a large change to growth.5

Chow, Halperin, and Mazlish (2024) argues that if the markets expect AGI then real interest rates should go very high, for two reasons: (1) if people expect dramatically higher incomes today they will save less and spend more (pushing interest rates up); (2) the returns on AI investments will be very high (pushing interest rates up).

Thanks

Thanks to Eli Lifland and Philip Trammell for comments.

Appendix: Methodological Comparisons

Comparison of cross-task productivity boosts. Trammell (2025) has a useful diagram showing the assumptions on labor-savings from three different models. The shaded areas represent the potential time-savings due to AI, all are based on Eloundou et al. (2023)’s classification of O*NET tasks, but the other two papers apply haircuts.

Comparison of AI time-savings, from Trammell (2025)

Bibliography

Acemoglu, Daron. 2024. “The Simple Macroeconomics of AI.” National Bureau of Economic Research. https://economics.mit.edu/sites/default/files/2024-04/The%20Simple%20Macroeconomics%20of%20AI.pdf.

Aghion, Philippe, and Simon Bunel. 2024. “AI and Growth: Where Do We Stand.” https://www.frbsf.org/wp-content/uploads/AI-and-Growth-Aghion-Bunel.pdf.

Aghion, Philippe, Benjamin F. Jones, and Charles I. Jones. 2019. “Artificial Intelligence and Economic Growth.” In An Agenda, edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, 237–90. Chicago: University of Chicago Press. https://doi.org/doi:10.7208/9780226613475-011.

Aldasoro, Iñaki, Sebastian Doerr, Leonardo Gambacorta, and Divya Sharma. 2024. “The Impact of AI on Output and Inflation.” BIS Bulletin 85. Bank for International Settlements. https://www.bis.org/publ/work1179.pdf.

Arnon, Alex. 2025. “The Projected Impact of Generative AI on Future Productivity Growth.” Brief. Penn Wharton Budget Model. https://budgetmodel.wharton.upenn.edu/issues/2025/9/8/projected-impact-of-generative-ai-on-future-productivity-growth.

Baily, Martin Neil, Erik Brynjolfsson, and Anton Korinek. 2023. “Machines of Mind: The Case for an AI-Powered Productivity Boom.” Brookings Institution Economic Studies Bulletin, May. https://www.brookings.edu/articles/machines-of-mind-the-case-for-an-ai-powered-productivity-boom/.

Bergeaud, Antonin, Alejandro González-Torres, Vincent Labhard, and Richard Sellner. 2025. “The Past, Present and Future of European Productivity.” ECB Blog. https://www.ecb.europa.eu/pub/pdf/sintra/ecb.forumcentbankpub2024_Bergeaud_paper.en.pdf.

Bick, Alexander, Adam Blandin, and David J Deming. 2025. “The Rapid Adoption of Generative AI.” National Bureau of Economic Research. https://s3.amazonaws.com/real.stlouisfed.org/wp/2024/2024-027.pdf.

Briggs, Joseph, and Devesh Kodnani. 2023. “The Potentially Large Effects of Artificial Intelligence on Economic Growth.” Research Report. Goldman Sachs Global Investment Research. https://www.gspublishing.com/content/research/en/reports/2023/03/27/d64e052b-0f6e-45d7-967b-d7be35fabd16.html.

Chatterji, Aaron, Thomas Cunningham, David J Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman. 2025. “How People Use ChatGPT.” National Bureau of Economic Research.

Chow, Trevor, Basil Halperin, and J Zachary Mazlish. 2024. “Transformative AI, Existential Risk, and Real Interest Rates.” Working Paper.

Collis, Avinash, and Erik Brynjolfsson. 2025. “AI’s Overlooked $97 Billion Contribution to the Economy.” Wall Street Journal, August. https://www.wsj.com/opinion/ais-overlooked-97-billion-contribution-to-the-economy-users-service-da6e8f55.

Comin, Diego, and Martı́n Mestieri. 2014. “Technology Diffusion: Measurement, Causes, and Consequences.” In Handbook of Economic Growth, edited by Philippe Aghion and Steven Durlauf, 2:565–622. Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-444-53540-5.00002-1.

Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023. “Gpts Are Gpts: An Early Look at the Labor Market Impact Potential of Large Language Models.” arXiv Preprint arXiv:2303.10130.

Erdil, Ege, Andrei Potlogea, Tamay Besiroglu, Edu Roldan, Anson Ho, Jaime Sevilla, Matthew Barnett, Matej Vrzla, and Robert Sandler. 2025. “GATE: An Integrated Assessment Model for AI Automation.” arXiv Preprint arXiv:2503.04941. https://arxiv.org/abs/2503.04941.

Filippucci, Francesco, Peter Gal, Simon Laengle, and Matthias Schief. 2025. “Macroeconomic Productivity Gains from AI in G7 Economies.” OECD Working Paper. OECD. https://www.oecd.org/en/publications/macroeconomic-productivity-gains-from-artificial-intelligence-in-g7-economies_a5319ab5-en.html.

Filippucci, Francesco, Peter Gal, and Matthias Schief. 2024. “Miracle or Myth? Assessing the Macroeconomic Productivity Gains from Artificial Intelligence.” OECD Publishing.

Humlum, Anders, and Emilie Vestergaard. 2025. “Large Language Models, Small Labor Market Effects.” National Bureau of Economic Research.

Korinek, Anton, and Donghyun Suh. 2024. “Scenarios for the Transition to AGI.” National Bureau of Economic Research. https://arxiv.org/abs/2403.12107.

Misch, Florian, and Yunhui Zhang. 2025. “Artificial Intelligence and Productivity in Europe.” IMF Working Paper. International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2025/04/04/AI-and-Productivity-in-Europe-565924.

Restrepo, Pascual. 2025. “We Won’t Be Missed: Work and Growth in the Era of AGI.” NBER Chapters.

Trammell, Philip. 2025. “Workflows and Automation.” Working Paper. Digital Economy Lab, Stanford University. https://philiptrammell.com/static/Workflows_and_Automation.pdf.

Footnotes

I classified Epoch’s GATE model (Erdil et al. (2025)) as by “AI people”, though the authors are a mixture of academic economists and people who work in AI.↩︎ 1.

It seems to me quite plausible that these papers over-estimate the productivity impact of existing LLMs: (1) the AB tests showing productivity improvements are on unrepresentatively self-contained tasks and are likely distorted by publication selection; (2) the Eloundou et al. (2023) estimates of very large time-savings from GPT-4 are based just on intuitions.↩︎ 1.

Comin and Mestieri (2014) say “the average adoption lag across all technologies (and countries) is 44 years,” but since the 1950s it has been 7-18 years.↩︎ 1.

“Between 1 and 5% of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4% of total work hours. … implies a potential productivity gain of 1.1%.”↩︎ 1.

Suppose the total valuation of AI-related companies is $10T, which is perhaps around 10% of all capital stock. Using P/E of 15, a $10T valuation implies a stream of $600B in earnings/year, which is 2% of GDP.↩︎

Citation

BibTeX citation:

@online{cunningham2025,
author = {Cunningham, Tom},
title = {Forecasts of {AI} \& {Economic} {Growth}},
date = {2025-11-06},
url = {tecunningham.github.io/posts/2025-10-19-forecasts-of-AI-growth.html},
langid = {en}
}

For attribution, please cite this work as:

Cunningham, Tom. 2025. “Forecasts of AI & Economic Growth.” November 6, 2025. tecunningham.github.io/posts/2025-10-19-forecasts-of-AI-growth.html.