Being able to predict the future seems nice. I would’ve liked to know that my S&P 500 index funds would peak in mid-February and then fall off a cliff in April. It would’ve been helpful for my reporting in the lead-up to the inauguration to know just how far the Trump administration would go to attack foreign aid. And while I’m at it, I’d like some sense of where mortgage rates are going, so I can better judge when to buy a house.

The art of systematically making these kinds of predictions is called forecasting, and we’ve known for a long time that some people — so-called superforecasters — are better at it than others. But even they aren’t Nostradamuses; major events still surprise them sometimes. Superforecasters’ work takes time and effort, and there aren’t many of them.

It’s also hard for us mortals to emulate what makes them so effective. I wrote a whole profile of one of the world’s best superforecaster teams, called the Samotsvety group, and despite their tips and tricks, I didn’t leave the experience as a superforecaster myself.

But you know what’s sometimes better at learning than I am? AI models.

In recent years, the forecasting community has increasingly pivoted to trying to build and learn from AI-fueled prediction bots. More specialized fields have, of course, been doing this in various forms for a while; algorithmic trading in financial markets, for instance, where computer programs using various prediction tools trade assets without human intervention, has been around for decades. But using AI as a more general-purpose forecasting tool is a newer idea.

Everyone I spoke with in the field agrees that the top human forecasters still beat machines.

The best evidence for this comes from tournaments run quarterly by Metaculus, a leading prediction website, where participants compete to forecast the future most accurately. Originally for humans only, Metaculus recently began bot tournaments, where contestants enter custom-made AI-driven bots whose track record can then be compared to the best human predictors.

So far there, we have results for three quarters — Q3 and Q4 of 2024, and Q1 of 2025 — and in each quarter, Metaculus’s human superforecasters beat the best machines. (If you want to try, there’s a $30,000 prize for each quarter’s winner.)

But the gap, Metaculus CEO Deger Turan tells me, is narrowing with each quarter. More intriguing still is the fact that the best model in Q1 this year was incredibly simple: It just pulled some recent news articles, then asked o1, at the time the most advanced OpenAI model, to make its own prediction. This approach couldn’t beat humans, but it beat a lot of AI models that were much more sophisticated.

o1 is no longer the cutting-edge OpenAI model; as of this writing, it’s o3. And by some metrics, o3 isn’t as good as Gemini 2.5 Pro, the best model from Google DeepMind. All of which is to say: While humans basically stay the same, the AIs are only getting better, and that could mean that the predictions they make will only get better as well.

Almost every arena of human life relies on good prediction. Lawyers predict whether or not their opponent will agree to a settlement. Construction supervisors predict when a building project will finish. Movie producers predict what script will be a hit. Singles predict whether the person they’re chatting up would prefer a first date over coffee or beer.

We’re not very good at these predictions right now, but we could get much, much better soon. We’re only just starting to realize the implications of that kind of shift.

How AI forecasting works

In theory, an “AI forecaster” is just a program that relies upon machine learning models of one form or another to predict future events.

Prediction is at the heart of what machine learning models do: They analyze vast reams of data and then come up with models that can predict outside that data. For generative models like ChatGPT or Claude or Midjourney, that means predicting the next word or pixel that a user wants in response to a query. For the algorithmic trading models that financiers have been building at least since the founding of the hedge fund Renaissance Technologies in 1982, it means predicting the future path of asset prices in stock, bond, and other markets, based on past performance.

For more generalized predictions about world events, forecasters these days tend to rely heavily on general-purpose models from firms like xAI, Google DeepMind, OpenAI, or Anthropic.........

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