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Football's quiet AI revolution
For decades, the best football analytics has been the preserve of a handful of clubs with the budgets to buy it. The scouting reports, the opposition models, the player-tracking systems — all of it locked behind seven-figure contracts that the average League One side could never justify. Max Sebti wants to break that monopoly.
Through Score and Manako Labs, Max is using decentralised AI and computer vision to let any club — Premier League or grassroots — interrogate the same kind of data that powers the elite scouting machines. The proof point is already on the pitch: Reading FC has become the first English professional club to appoint a Head of AI, and Score is the partner powering it.
The big idea
The trick, Max argues, is not bigger models in bigger data centres. It is a crowd of contributors training and running smaller, more specialised models on problems they actually understand. Score's technology processes football video 240 times faster than current computer vision approaches, by distributing the workload across a global network rather than burning compute in one place.
Football is just the proving ground. The same logic applies anywhere visual data is being thrown away because no one has the time, money or attention to look at it properly — retail floors, construction sites, infrastructure inspections, the lot. Whatever the camera sees, somebody can build a better model for.
Takeaways
Decentralised AI lets a crowd outperform a single, centralised model — particularly on niche problems.
Computer vision is moving from "interesting research" to a genuine commercial capability in sport.
The Reading FC partnership shows what happens when a forward-thinking club partners with a focused AI startup rather than buying off-the-shelf.
"Digital twins" of football matches are no longer science fiction — they're being built right now.
Open-source models are the lever that lets smaller clubs compete with the giants.
The same multimodal, memory-rich AI patterns showing up in football will land in project delivery, infrastructure and the built environment next.
Trust, transparency and cultural attitudes to data security all matter more, not less, as models get more capable.
Inversion and mental models — borrowed from quant finance — are surprisingly useful tools for thinking about AI design.
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