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Forecasting Research Institute

@Research_FRI

Nov 10, 2025

Today, we are launching the most rigorous ongoing source of expert forecasts on the future of AI: the Longitudinal Expert AI Panel (LEAP).

We’ve assembled a panel of 339 top experts across computer science, AI industry, economics, and AI policy.

Roughly every month—for the next three years—they’ll provide precise, falsifiable forecasts on the trajectory of AI capabilities, adoption, and impact.

Our results cover where experts predict major effects of AI, where they expect less progress than AI industry leaders, and where they disagree.

LEAP experts forecast major effects of AI by 2030, including:

⚡ 7x increase in AI’s share of U.S. electricity use (1% -> 7%)
🖥️ 9x increase in AI-assisted work hours (2% -> 18%)

By 2040, experts predict:
👥30% of adults will use AI for companionship daily
🏆60% chance that AI will solve or substantially assist in solving a Millennium Prize Problem
🚂32% chance that AI will have been at least as impactful as a "technology of the millennium," like the printing press or the Industrial Revolution.

🧵Read on for more insights and results

Our LEAP panel is made up of the following experts:

🧑‍🔬 76 Top computer scientists (e.g., professors from top-20 universities)
🤖 76 AI industry experts (from frontier model and other leading AI companies)
💲 68 Leading economists (including many studying economic growth or technology at top universities)
🧠 119 Policy and think tank experts
🏆 12 Honorees from TIME’s 100 most influential people in AI, in 2023 and 2024

(Plus 60 highly accurate superforecasters and 1,400 members of the U.S. public)

For more details on our sample, see the full reports linked below.

We ask questions designed to elicit high-quality, specific forecasts about the future of AI and its effects. Example questions include:

⚡ What % of US electricity will go towards training and deploying AI systems in 2027, 2030, and 2040?

🏢 What % of work hours will be assisted by generative AI in 2025, 2027, and 2030?

📊 At the end of 2040, how will people assess the impact of AI in comparison to past technological events?

In the first 3 waves of LEAP, we elicited forecasts on 18 questions regarding the future of AI. Wave 1 focused on the speed of AI progress and broad social impacts, Wave 2 on AI’s effect on science, and Wave 3 on AI adoption. For the full set of questions and results, see the reports linked below.

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Forecasting Research Institute

@Research_FRI

Oct 8, 2025

Is AI on track to match top human forecasters at predicting the future?

Today, FRI is releasing an update to ForecastBench—our benchmark that tracks how accurate LLMs are at forecasting real-world events.

A trend extrapolation of our results suggests LLMs will reach superforecaster-level forecasting performance around a year from now.

Here’s what you need to know: 🧵

Why LLM forecasting accuracy is a useful benchmark:

🧠Forecasting requires collecting and synthesizing data, causal reasoning, and probabilistic thinking, making it a good test of reasoning

💼Forecasting has high practical value

🔮Future events aren’t in training data, making the benchmark hard to game

@elonmusk: “The ability to predict the future is the best measure of intelligence”

x.com/elonmusk/statu…

Superforecasters still outperform leading LLMs.

👇Superforecasters top the Tournament ForecastBench leaderboard, with a difficulty-adjusted Brier score of 0.081 (lower scores indicate higher accuracy).

🤖The best-performing LLM in our dataset is @OpenAI’s GPT-4.5 with a score of 0.101—a gap of 0.02.

A baseline model that always predicts 50% would yield a score of 0.25. Relative to this baseline, superforecasters are 68% and the best-performing LLM is 60% more accurate—a gap of 8 percentage points.

(Wondering about newer AI models? More on them later!)

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Forecasting Research Institute

@Research_FRI

Sep 2, 2025

We now have the first accuracy results from the largest-ever existential risk forecasting tournament.

In 2022, we convened 80 experts and 89 superforecasters for the Existential Risk Persuasion Tournament (XPT), which collected thousands of forecasts in 172 questions across short-, medium- and long-term time horizons.

We now have answers for 38 short-run questions covering AI progress, climate technology, bioweapons, nuclear weapons and more.

Here’s what we found out: 🧵

Respondents—especially superforecasters—underestimated AI progress.

Participants predicted the state-of-the-art accuracy of ML models on the MATH, MMLU, and QuaLITY benchmarks by June 2025.

Domain experts assigned probabilities of 21.4%, 25%, and 43.5% to the achieved outcomes.

Superforecasters assigned even lower probabilities: just 9.3%, 7.2%, and 20.1% respectively.

The International Mathematical Olympiad results were even more surprising.

AI systems achieved gold-level performance at the IMO in July 2025.

Superforecasters assigned this outcome just a 2.3% probability. Domain experts put it at 8.6%.

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Forecasting Research Institute

@Research_FRI

Oct 1, 2024

Today, we're excited to announce ForecastBench: a new benchmark for evaluating AI and human forecasting capabilities. Our research indicates that AI remains worse at forecasting than expert forecasters. 🧵

Arxiv:
Website: arxiv.org/abs/2409.19839
forecastbench.org

Evaluating LLM forecasting ability is tricky! Prior work asks models about events that already have (or have not) occurred, risking contamination of training data.
Our solution is to use questions about future events, the outcomes of which are unknowable when forecasts are made.

ForecastBench continuously generates new questions about future events, testing the ability of AI models and humans to make accurate probabilistic predictions across diverse domains.

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