It’s the 2026 Women’s Cricket World Cup and I’m running a fantasy cricket league for AI systems.

I’m also running a separate league for humans. I decided to compare the bids across the two.

Breaking the Bank

No AI bid above $1,000 on a player, whereas a couple of humans blew well over $2,000 to secure a single favorite. AI bids clustered mostly in the $100–$950 band. Humans proved willing to shatter those ceilings. One manager went all-in with $2,500, a quarter of their budget, on New Zealand’s Melia Kerr – a staggering amount no computer even came close to matching. Another human dumped $2,300 on a different star in one go. Once those expensive gambles were placed, those same managers had to scatter a bunch of $1 or $5 nibbles on unheralded players just to fill their roster.

The AIs showed iron budget discipline, whereas humans weren’t afraid to launch a financial fireworks show for a shot at a dream player.

A Wider Roster

The AI team managers flocked to a handful of top-ranked players, whereas the humans’ wish-lists were all over the map. Every AI, regardless of personality, arrived at nearly the same “must-have” names based on performance stats – Ashleigh Gardner, Beth Mooney, and Hayley Matthews amongst others.

By contrast, the human bidders did not unanimously chase any single superstar en masse. Each person seemed to have their own notion of which stars to splurge on. Instead of eight identical lists, the humans produced widely varied wish-lists. One manager might be obsessed with, say, Nat Sciver-Brunt (a top England all-rounder) and throw a fortune at her, while another manager bets on an entirely different headliner (some went big on India’s beloved Smriti Mandhana, others targeted Australia’s Ellyse Perry).

Perhaps the humans were overly biased by their national identities and individual preferences; and maybe the AIs were able to take a more expansive view of the players. This explanation may be too simple though. Your author, one of the human players, pursued a statistics-oriented bidding strategy that would have been relatively resilient to biases. Moreover, as we saw in the previous post, the AIs also seemed to have inbuilt biases – Gemini, for example, refused to bid for English players.

Hidden Gems (?)

Google’s Gemini was the rare AI that bid on a bunch of obscure players others didn’t touch – taking a calculated risk that they might become breakout stars. Yet even that bold AI mostly stuck to players from powerhouse teams like Australia. The humans meanwhile had fierce bidding wars for players like Ireland’s Orla Prendergast.

Neither league features humans versus AIs competing against each other so it’ll be hard to determine which bidding strategy was superior. It’s going to be an interesting journey nonetheless.