AI models are terrible at betting on soccer—especially xAI Grok



“Every frontier model we evaluated lost money over the season and many experienced ruin,” the authors of the paper concluded, with the AI “systematically underperforming humans” in this scenario.

AI Model Mean ROI Best try Worst try Mean final bankroll
Anthropic Claude Opus 4.6 –11.0% –0.2% –18.8% £89,035
OpenAI GPT-5.4 –13.6% –4.1% –31.6% £86,365
Google Gemini 3.1 Pro –43.3% +33.7% –100.0% £56,715
Google Gemini Flash 3.1 LP –58.4% +24.7% –100.0% £41,605
Z.AI GLM-5 –58.8% –14.3% –100.0% £41,221
Moonshot Kimi K2.5 –68.3% –27.0% –100.0% £7,420
xAI Grok 4.20 –100.0% –100.0% –100.0% £0
Acree Trinity –100.0% –100.0% –100.0% £0
Each model began with a £100,000 normalized bankroll. Return on investment and final bankroll are averaged across three tries. Grok and Trinity did not complete every attempt.

The results offer some comfort to white-collar professionals and businesses who are fretting that AI could take their jobs, as it roils the shares of industries from finance to marketing.

Ross Taylor, one of the study’s authors and General Reasoning’s chief executive, said: “There is so much hype about AI automation, but there’s not a lot of measurement of putting AI into a longtime horizon setting.”

He added that many of the benchmarks typically used to test AI are flawed because they are set in “very static environments” that bear little resemblance to the chaos and complexity of the real world.

General Reasoning’s paper, which has not yet been peer reviewed, provides a counterweight to growing excitement in Silicon Valley about the huge recent leaps in AI’s ability to complete computer programming tasks with little to no human intervention.

Taylor, a former Meta AI researcher, said: “If you… try AI on some real-world tasks, it does really badly… Yes, software engineering is very important and economically valuable, but there are lots of other activities with longer time horizons that are important to look at.”

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