When will artificial intelligence (AI) match top human forecasters at predicting the future? In a recent podcast episode, Nate Silver predicted 10–15 years. Tyler Cowen disagreed, expecting a 1–2 year timeline. Who’s more likely to be right?

Today, the Forecasting Research Institute is excited to release an update to ForecastBench—our benchmark tracking how well large language models (LLMs) forecast real-world events—with evidence that bears directly on this debate. We’re also opening the benchmark for submissions.

Here are our key findings:

• Superforecasters still outperform leading LLMs, but the gap is modest. The best-performing model in our sample is GPT-4.5, which achieves a Brier score of 0.101 versus superforecasters’ 0.081 (lower is better).²
• LLMs now outperform non-expert public participants. A year ago, the median public forecast ranked #2 on our leaderboard, right behind superforecasters and ahead of all LLMs. Today it sits at #22. This achievement represents a significant milestone in AI forecasting capability.
• State-of-the-art LLMs show steady improvement, with projected LLM-superforecaster parity in late 2026 (95% CI: December 2025 – January 2028). Across all questions in our sample, LLM performance improves by around 0.016 Brier points per year. Linear extrapolation suggests LLMs could match expert human performance on ForecastBench in around a year if current trends continue.