// WIRED US/UK — INTELLIGENZA ARTIFICIALE
I Put a Bunch of AI Agents in Charge of a Fantasy Soccer Team. They’re Not Better Than Humans—Yet
I have consulted my AI-powered crystal ball, and it’s telling me that frontier models are going to get a lot better at making predictions in the near future. Just ask my Premier League fantasy team.
A few weeks ago, I deployed a small horde of AI agents to craft and manage an experimental roster. Given the proliferation of AI agents into pretty much every aspect of our lives, I wanted to know if a swarm of bots could put together a winning team.
I first looked at the potential for AI here a few years ago, playing around with a tool from the UK’s Turing Institute that selected teams after modeling previous fantasy league data. Its suggestions were remarkably bonkers—it didn’t incorporate real-world information, like a player’s injury or a team’s change in management.
This time, while on a flight to visit family, I asked Codex for help. First, I fed the model a couple of relevant research papers. Then, I asked it to build a harness—instructions and code that guides different agents through various steps—that would check recent news and run a model to make predictions.
It quickly came up with a setup that seemed to collate and analyze way more information than I ever would have. And yet I was surprised by how dumb the initial team suggestions were. Leaving Erling Haaland on the bench? Really, Codex?
I then asked Codex to create a team of agents that did different things: several that read news and blog posts; others that analyzed recent results; more that ran models; and a bunch that critiqued suggestions. This seemed to significantly improve the suggestions. I tried increasing the number of agents to more than 100, hoping this would make the system smarter still, but it didn’t change much besides running up my API bill.
Still, the improvement from a few years ago was striking. According to Haifeng Xu, a computer scientist at the University of Chicago who studies the uses of AI in market situations, newer models may be even better at making predictions because they’re able to consume and analyze huge amounts of real-world information, and they’re able to orchestrate more complex systems for their guesses.
“AI is just getting smarter in understanding the world—I feel like that's the fundamental reason they’re improving,” Xu tells me.
Late last year, Xu was marveling at the growing popularity of prediction markets like Kalshi and Polymarket. He wondered how the best commercial models might fare on those markets, so alongside several colleagues, he began testing their forecasting skills.
They had models guess, for example, who would win the NBA Championship and who President Trump would meet with.