# Worldmodeldata Raises £7M Seed Round

> Worldmodeldata, a Cambridge startup, has raised a £7 million (about €8 million) seed round led by Iona Star Capital to tackle a specific bottleneck in AI: the data needed to train world models. Rather than scraping the web, it licenses gameplay footage and engine data from titles built in Unreal and Unity, then packages video together with player inputs and 3D state information, the kind of physically grounded data that models learning to reason about space and motion actually need. The bet is that world models, used in physical AI and autonomous systems, will be starved for exactly this sort of licensed, structured data. Worldmodeldata is targeting one million hours of gameplay data by the end of 2026, a large leap given that the biggest existing database sits around 40,000 hours. It is very early, with no finalized contracts, no revenue and a team of about ten including advisors, though its chairman is Lord Richard Allan, Meta's former European policy chief. What makes it interesting is the angle: as clean training data grows scarce and legally fraught, licensed game worlds may turn out to be an underrated source of the real-world dynamics AI needs to learn.

_Section: [AI Funding Tracker](https://www.wortins.com/funding) · Source: The Next Web · Published Thursday, July 9, 2026_

## Wortins' read

Worldmodeldata, a Cambridge startup, has raised a £7 million (about €8 million) seed round led by Iona Star Capital to tackle a specific bottleneck in AI: the data needed to train world models. Rather than scraping the web, it licenses gameplay footage and engine data from titles built in Unreal and Unity, then packages video together with player inputs and 3D state information, the kind of physically grounded data that models learning to reason about space and motion actually need. The bet is that world models, used in physical AI and autonomous systems, will be starved for exactly this sort of licensed, structured data. Worldmodeldata is targeting one million hours of gameplay data by the end of 2026, a large leap given that the biggest existing database sits around 40,000 hours. It is very early, with no finalized contracts, no revenue and a team of about ten including advisors, though its chairman is Lord Richard Allan, Meta's former European policy chief. What makes it interesting is the angle: as clean training data grows scarce and legally fraught, licensed game worlds may turn out to be an underrated source of the real-world dynamics AI needs to learn.

## Source

[Read the full story at The Next Web](https://thenextweb.com/news/worldmodeldata-7m-seed-gaming-world-models)

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