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Artificial Intelligence

Study finds experts underestimated AI progress

An interim report from the Forecasting Research Institute said specialists in academia, industry and economics repeatedly predicted slower AI progress than later benchmark results and adoption figures showed.

THE-DECODER

Since mid-2022, the Forecasting Research Institute had collected AI forecasts across several studies and projects. Its first Longitudinal Expert AI Panel included 339 people, among them computer scientists, industry experts, economists and AI policy specialists.

FRI said the largest gap was in mathematics. AI reached gold-medal level at the International Mathematical Olympiad in July 2025, which it said was five years ahead of the median expert forecast and ten years ahead of the median superforecaster forecast.

The institute also said AI may have solved a Millennium Prize Problem, although it was not yet clear whether the result met the evaluation rules. In virology and cybersecurity studies, FRI said experts had also predicted later progress than what it judged had already happened.

FRI said economic forecasts were also conservative, and it gave Anthropic’s September 2026 annualised revenue as about $100 billion. It said forecasts for autonomous ride-hailing, economic growth, employment and major AI harms could not yet be judged reliably, and it planned to publish updated LLM forecasts alongside human ones.

Named in this story

Companies

Anthropic
was cited for about $100 billion in annualised revenue

Organisations

Forecasting Research Institute
issued the interim report on AI forecasting
Longitudinal Expert AI Panel
provided the first round of expert forecasts
International Mathematical Olympiad
the contest where AI reached gold-medal level

Products and systems

ChatGPT
marked the launch point after which the forecast pattern still held
ForecastBench
was cited as a benchmark where some models matched superforecasters on some question types

How the source tells it

Mostly reportorial and restrained, with mild urgency created by repeated contrasts between forecasts and earlier-than-expected AI milestones.

  • urgency the article repeatedly set recent AI results against forecasts made years earlier