Every month, the Forecasting Research Institute asks top computer scientists, economists, industry leaders, policy experts and superforecasters for their AI predictions. Here’s what we learned from the first three months of forecasts:
AI is already reshaping labor markets, culture, science, and the economy—yet experts debate its value, risks, and how fast it will integrate into everyday life. Leaders of AI companies forecast near futures in which AI cures all diseases, replaces whole classes of jobs, and supercharges GDP growth. Skeptics see small gains at best, with AI’s impact amounting to little more than a modest boost in productivity—if anything at all.
Despite these clashing narratives, there is little work systematically mapping the full spectrum of views among computer scientists, economists, technologists in the private sector, and the public. We fill this gap with LEAP, a monthly survey tracking the probabilistic forecasts of experts, superforecasters, and the public. Expert participants include top-cited AI and ML scientists, prominent economists, key technical staff at frontier AI companies, and influential policy experts from a broad range of NGOs.
LEAP operates on three key principles:
- Accountability: LEAP forecasts are detailed and verifiable, encouraging disciplined thinking and allowing us to track whose predictions prove most accurate.
- The wisdom of well-chosen crowds: LEAP emphasizes a diversity of perspectives from people at the top of their fields.
- Decision relevance: LEAP’s policy-relevant forecasts help decision-makers plan for likely futures.
As well as collecting thousands of forecasts, LEAP captures rationales—1.7 million words of detailed explanations across the first three survey waves. We use this data to identify key sources of disagreement and to analyze why participants express significant uncertainty about the future effects of AI.
Since we launched LEAP in June 2025, we have completed three survey waves focused on: high-level predictions about AI progress; the application of AI to scientific discovery; and widespread adoption and social impact. In this post we’ll introduce the LEAP panel, and summarize our findings from the first three waves.
To find out more about LEAP you can:
- Visit the LEAP website
- Read our launch white paper
- Read a summary report on findings from the first three LEAP waves
The LEAP Panel
LEAP forecasters represent a broad swath of highly respected experts, including:
- Top computer scientists. 41 of our 76 computer science experts (54%) are professors, and 30 of these 41 (73%) are from top-20 institutions. The remainder are early-career researchers or non-tenure-track research roles at academic and non-academic research institutions.
- AI industry experts. 20 of our 76 industry respondents (26%) work for one of five leading AI companies: OpenAI, Anthropic, Google DeepMind, Meta, and Nvidia.
- Top economists. 54 of our 68 economist respondents (79%) are professors, and 30 (44%) are from the top 50 economics institutions.
- Policy and think tank groups. We have 119 respondents working in the field of AI policy. The most-represented organizations include (unordered): Brookings, RAND, Epoch AI, Federation of American Scientists, Center for Security and Emerging Technology, AI Now, Carnegie Endowment, Foundation for American Innovation, GovAI, and Institute for AI Policy and Strategy.
- TIME 100. Our respondents include 12 honorees from TIME’s 100 Most Influential People in AI in 2023 and 2024.
LEAP forecasters additionally include 60 superforecasters, or highly accurate forecasters based on prior geopolitical forecasting tournaments, and 1,400 members of the general public. The general public group largely consists of especially engaged participants in previous research, reweighted to be nationally representative of the U.S.
LEAP Waves 1–3
The first three waves of LEAP asked panelists to forecast AI’s progress and impact across domains. We asked for predictions on AI’s impact on work, scientific research, energy, medicine, business, and more. We also asked panelists to predict AI’s net impact on humanity by 2040 by comparing it to the impact of past technologies.
For a full list of questions in each wave, see here.
Insights from Waves 1–3
1. Experts expect sizable societal effects from AI by 2040.
Experts expect substantial increases in the ability of AI systems to solve difficult math problems, the use of AI for companionship and work, electricity usage from AI, and investment in AI:
- Work: The median expert forecast is that 18% of work hours in the U.S. in 2030 will be assisted by generative AI, up from approximately 4.1% in November 2024.
- Math research: 23% of experts predict that the FrontierMath benchmark will be saturated by the end of 2030. By 2040, experts predict it is more likely than not (60%) that AI will substantially assist in solving a Millennium Prize Problem, a set of problems comprising some of the most difficult unsolved mathematical problems.
- Companionship: The median expert predicts that by 2030, 15% of adults will self-report using AI for companionship, emotional support, social interaction, or simulated relationships at least once daily, up from 6% today. By 2040, that number doubles to 30% of adults.
- Electricity usage: The median expert predicts that 7% of U.S. electricity consumption will be used for training and deploying AI systems in 2030, and close to double that (12%) in 2040.
- Private AI investment: The median expert predicts that Our World in Data will report $260 billion of global private AI investment by 2030, up from the $130 billion baseline for the series in 2024.1
To assess the broader scope of AI’s impacts, we asked experts to assess “slow” versus “fast” scenarios for AI progress, and how AI will compare to other historically significant developments such as the internet, electricity, and the Industrial Revolution. We found:
- Speed of AI progress: By 2030, the average expert thinks that 23% of LEAP panelists will say the world most closely mirrors a “rapid” AI progress scenario, where AI writes Pulitzer Prize-worthy novels, collapses years-long research into days and weeks, outcompetes any human software engineer, and independently develops new cures for cancer. Conversely, the average expert believes that 28% of panelists will indicate that reality is closest to a slow-progress scenario, in which AI is a useful assisting technology but falls short of transformative impact.
- Societal impact: By 2040, the median expert predicts that the impact of AI will be comparable to a “technology of the century,” akin to electricity or automobiles. Experts also give a 32% chance that AI will be at least as impactful as a “technology of the millennium,” such as the printing press or the Industrial Revolution and just a 15% chance that AI is equally or less impactful than a “technology of the year” like the VCR.
Figure 1: Median expert forecasts for various questions. We display the 10th, 25th, 50th, 75th, and 90th percentiles of the median forecasts given by experts for each date.
2. Experts disagree and express substantial uncertainty about the trajectory of AI.
While the median expert predicts substantial AI progress, and a sizable fraction of experts predict fast progress, experts disagree widely. Notably, the top quartile of experts estimate that, in the median scenario, 50% of newly approved drug sales in the U.S. in 2040 will be from AI-discovered drugs, compared to a forecast of just 10% for the bottom quartile of experts.
Further, the top quartile of experts give a forecast of at least 81% that AI will substantially assist with a solution to a Millennium Prize Problem by 2040, compared to just 30% for the bottom quartile of experts.
3. The median expert expects significantly less AI progress than leaders of frontier AI companies.
Leaders of frontier AI companies have made aggressive predictions about AI progress.
Dario Amodei, co-founder and CEO of Anthropic, predicts:
- January 2025: “By 2026 or 2027, we will have AI systems that are broadly better than almost all humans at almost all things.”
- March 2025: Anthropic also claimed in a response to the US Office of Science and Technology Policy that it anticipates that by 2027 AI systems will exist that equal the intellectual capabilities of “Nobel Prize winners across most disciplines—including biology, computer science, mathematics, and engineering.”
- May 2025: Amodei has stated that AI could increase overall unemployment to 10-20% in the next one to five years.
Sam Altman of OpenAI states that:
- January 2025: “I think AGI will probably get developed during [Donald Trump’s second presidential] term, and getting that right seems really important.”
Elon Musk, leader of xAI and Tesla, writes:
- December 2024: “It is increasingly likely that AI will superset [sic] the intelligence of any single human by the end of 2025 and maybe all humans by 2027/2028. Probability that AI exceeds the intelligence of all humans combined by 2030 is ~100%.”
- August 2025: When a user posted “By 2030, all jobs will be replaced by AI and robots,” Musk responded: “Your estimates are about right.”
Demis Hassabis, CEO and co-founder of Google DeepMind predicts:
- August 2025: “We’ll have something that we could sort of reasonably call AGI, that exhibits all the cognitive capabilities humans have, maybe in the next five to 10 years, possibly the lower end of that.”
- August 2025: “It’s going to be 10 times bigger than the Industrial Revolution, and maybe 10 times faster.”
These industry leader predictions diverge sharply from our expert panel’s median forecasts:
- General capabilities: Lab leaders predict human-level or superhuman AI by 2026-2029, while our expert panel indicates longer timelines for superhuman capabilities. By 2030, the average expert thinks that 23% of LEAP panelists will say the world most closely mirrors a (“rapid”) AI progress scenario that matches some of these claims.
- White-collar jobs: The median expert predicts 2% growth in white-collar employment by 2030 (compared to a 6.8% trend extrapolation). This contrasts with Elon Musk’s suggestion that all jobs might be replaced by 2030 and Dario Amodei’s prediction of 10-20% overall unemployment within the next five years.
- Millennium Prize Problems: The median expert gives a 60% chance that AI will substantially assist in solving a Millennium Prize Problem by 2040. Amodei’s prediction of general “Nobel Prize winner” level capabilities by 2026-2027 could imply a much more aggressive timeline, but the implication of Amodei’s predictions are somewhat unclear.
4. Experts predict much faster AI progress than the general public.
The public generally predicts less AI progress by 2030 than experts. Specifically, the general public holds median views about AI progress, capabilities, and diffusion that predict less progress than experts in a large majority (71%) of all cases, are statistically indistinguishable from experts in 9% of cases, and predict more progress in just 21% of forecasts.2
Figure 2a: A comparison of expert and public median forecasts for 2030
Figure 2b: A comparison of expert and public median forecasts for 2040
Where experts and the public disagree, the public predicts less progress over three times as often as more progress. Across these forecasts that exhibit a clear valence of AI capabilities, a randomly selected expert is 16% more likely than a randomly selected member of the public to predict faster progress than would be expected by random chance.
Some of the major differences include:
- Societal impact: On average, experts give a 63% chance that AI will be at least as impactful as a “technology of the century”—like electricity or automobiles—whereas the public gives this a 43% chance.
- Autonomous vehicles: The public predicts half as much autonomous vehicle progress as experts by 2030. The median expert in our sample predicts that usage of autonomous vehicles will grow dramatically—from a baseline of 0.27% of all US rideshare trips in Q4 2024 to 20% by the end of 2030. In comparison, the general public predicts 12%.
- Generative AI use: The median expert predicts that 18% of U.S. work hours will be assisted by generative AI in 2030, whereas the general public predicts 10%.
- Mathematics: 23% of experts predict that FrontierMath will be saturated by the end of 2030, meaning that AI can autonomously solve a typical math problem that a math PhD student might spend multiple days completing. Only 6% of the public predict the same.
- Diffusion into science: Experts predict a roughly 10x increase (from 3% to 30%) in AI-engaged papers across Physics, Materials Science, and Medicine between 2022 and 2030. The general public predicts two thirds as much diffusion into science.
- Drug discovery: By 2040, the median expert predicts that 25% of sales from newly approved U.S. drugs will be from AI-discovered drugs, compared to 15% for the public.
5. There are few differences in prediction between superforecasters and experts, but, where there is disagreement, experts tend to expect more AI progress. We don’t see systematic differences between the beliefs of computer scientists, economists, industry professionals, and policy professionals.
There are no discernible differences between forecasts from different groups of experts. Across all pairwise comparisons of expert categories for each of the questions with a clear AI progress valence, only 32 out of 408 combinations (7.8%) show statistically significant differences (at a 5% threshold), similar to what you would expect from chance. This means that computer scientists, economists, industry professionals, and policy professionals largely predict similar futures as groups, despite there being significant disagreement about AI among experts.
Figure 3: Median expert forecasts on questions with 2030 resolutions, split by expert group
This raises questions about popular narratives that economists tend to be skeptical of AI progress and that employees of AI companies tend to be more optimistic about fast gains in AI capabilities. In other words, while we do see widespread uncertainty among experts about the future of AI systems, capabilities, and diffusion, we fail to find evidence that this disagreement is explained by the domain in which experts work. As LEAP continues, we plan to study what factors most drive expert disagreement.
Superforecasters and expert groups predict similar futures. Superforecasters are statistically indistinguishable from experts in 69% of forecasts with a clear valence, predict less progress than experts in 26% of forecasts, and more progress in 4% of forecasts.
Where superforecasters and experts disagree, superforecasters usually (86% of such cases) predict less progress. For example, the median expert predicts that use of autonomous vehicles will grow dramatically—from 0.27% of all US rideshare trips in 2024 to 20% by the end of 2030, whereas the median superforecaster predicts less than half that, 8%. Superforecasters also predict less societal impact from AI and less AI-driven electricity use.
Drug discovery is the only setting where superforecasters are more optimistic than experts: By 2040, experts, on average, predict that 25% of sales from recently approved U.S. drugs will be from AI-discovered drugs. Superforecasters predict 45%, almost double.
Future LEAP waves
LEAP will run for the next three years, eliciting monthly forecasts from our panelists. We plan to release monthly reports on each new wave of LEAP, highlighting new forecasts and conducting more extensive cross-wave analyses.
The next nine waves will cover:
- AI R&D
- Security and geopolitics
- Robotics
- Productivity and welfare
- Labor and automation
- Incidents and harms
- LLMs for AGI
- Markets
- AI safety and alignment
Do you have a suggestion for a question you’d like to see covered in a future LEAP wave? Please submit a question through our online form.
Several of our questions will resolve by the end of 2025. As questions resolve, we will be able to assess the accuracy of experts’ forecasts. This will allow us to identify particularly accurate individual forecasters, and to assess the relative accuracy of different expert subgroups within our sample. We also plan to re-survey the LEAP panel on many questions in order to track how respondents’ views change over time.
In future work, we plan to analyse “schools of thoughts:” clusters of responses to forecasting questions. We plan to use standard clustering algorithms to search for consistent forecasts across questions, and we will complement this work with analysis of rationales for these various schools of thought.
You can read more about our future plans for LEAP in our launch white paper.
Updated Nov 25, 2025: This post has been updated to clarify LEAP panelists’ forecasts for the AI progress scenarios question.
Thanks for the work, this is great!
I especially appreciate the rationale summaries, and generally I'd encourage you to lean more into identifying underlying cruxes as opposed to quantitative estimates. (E.g. I'm skeptical on experts being sufficiently well calibrated to give particularly informative timeline forecasts).
I'm looking forward to the risk-related surveys. Would be interesting to hear their thoughts on the likelihood of concrete risks. One idea that comes to mind would be conditional forecasts on specific interventions to reduce risks.
Also, I wonder whether the presentation on the website could also feature some more "snack-sized" insights, next to the more long-form report-focussed presentation. E.g. the Chicago Booth expert surveys on economic experts focus on ~2-3 questions per article, with a handful of rationales of experts quoted in full. It keeps me coming back because it's informative and takes up less than 5 minutes of my time.
https://kentclarkcenter.org/us-economic-experts-panel/
PS: Just in case something went wrong:
Thanks for digging in! We've gotten similar feedback on "snack-sized" insights and have it on our list.
Could you say more on "generally I'd encourage you to lean more into identifying underlying cruxes as opposed to quantitative estimates"? I'm not sure I understand what this means in practice, because I think of the two as intimately related. This is likely a product of my view that cruxy questions have a high value of information (some FRI work on this here).
In case it's of interest, our risk-focused work tends to be in self-contained projects (example), so we can pull in respondents with intimate knowledge of the risk model. Nevertheless, we'll include some risk questions in future waves.
The 2 questions you mention were free text. We asked respondents to list, for example, cognitive limitations of AI. We then created a list of the most common responses to create a resolvable forecasting question for the subsequent wave.
Hi Connacher! Thanks for the responses, makes sense.
On your question, one example I often miss from expert surveys is something like this open-ended question: "Do you have any other considerations that would help with understanding this topic?"
I generally agree that quantitative questions are intimately connected with identifying cruxes. Being quantitative about concrete events is a neat way of forcing the experts get more concrete and incentivize them to not get lost in a vague story, etc. But I suspect that often the individual insights from the experts might seem like cruxes to them, as they're not used to think like that. So I think giving experts some prompts to just pour out their thoughts is often neglected. Furthermore, sometimes quantitative questions don't fully capture all important angles of an issue and so it's useful to give responders many chances to add additional comments.
Do you view this as separate from the rationale data we also collect? One low-burden way to do this is to just include something like your text in the rationale prompt.
It's great that you already have a rationale prompt for each question. I would probably recommend having one prompt like this at the end, with "(Optional)" in front so experts can share all further thoughts they think might be useful.
This doesn't seem accurate. I looked at the relevant parts of the report and I don't agree with this summary. I made a whole post about this topic here.
Appreciate the careful read. I responded to your post.
Thank you for your response!
I think there is a very realistic chance one of the results of this survey has been quite significantly misreported. Specifically, the responses to the question about the slow/moderate/rapid progress scenarios.
Error #1, which I raised here, was that the probabilities were reported without qualification, when what should have been reported was the probability that the scenario would be the one that best matches reality. To their immense credit, the Forecasting Research Institute said they would correct this in a future version of the report. I thank them greatly for that.
Error #2, which I'm not 100% sure yet is in fact an error, so let's call it Possible Error #2, is that these don't seem to be probabilities at all. (I originally raised this possible error here.)
Respondents are asked to predict, in December 2030, “what percent of LEAP panelists will choose” each scenario (not with any probability). This implies that if they think there’s, say, a 51% chance that 30% of LEAP panelists will choose the slow scenario, they should respond to the question by saying 30% will choose the slow scenario. If they think there’s a 99% chance that 30% of LEAP panelists will choose the slow scenario, they should also respond by saying 30% will choose the slow scenario. In either case, the number in their answer is exactly the same, despite a 48-point difference in the probability they assign to this outcome. The report says that 30% is the probability respondents assign to the slow scenario, but it’s not clear that the respondents’ probability is 30%.
The Forecasting Research Institute only asks for the predicted “vote share” for each scenario and not the estimated probabilities behind those vote share predictions. It doesn’t seem possible to derive the respondents’ probability estimates from the vote share predictions alone. By analogy, if FiveThirtyEight’s 2020 election forecast predicts that Joe Biden will win a 55% share of the national vote, this doesn’t tell you what probability the model assigns to Biden winning the election (whether it’s, say, 70%, 80%, or 90%). The model’s probability is certainly not 55%. To know the model’s probability or guess at it, you would need information other than just the predicted vote share.
So, it seems plausible to me — although not yet certain — that this claim about what the respondents said the probability of each scenario is (with or without the "best matching" qualifier) is incorrect because the respondents were not asked about probabilities in the first place. If there is a way to derive probabilities from what the respondents were asked, I don't know what it is.
[Edited on Nov. 21, 2025 at 1:05 PM Eastern to add: titotal apparently agrees.]
Executive summary: The Forecasting Research Institute introduces LEAP, a three-year longitudinal survey tracking probabilistic forecasts from top AI experts, superforecasters, and the public; early results show experts expect major societal impacts from AI by 2040 but disagree widely about speed and scale, with industry leaders far more optimistic than both experts and the public.
Key points:
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