The real alpha in AI isn't the models anymore — it's the data moat.
Top labs are printing revenue, but compute costs keep dropping. The new bottleneck? Verification.
Value is shifting to domains where ground truth is expensive and hard to label — think biology, robotics, physical world data. These aren't datasets you can scrape or crowdsource.
Scarcity = moat. And in AI, the moat is now the training environments where reality is messy and capital-intensive to capture.
If you're building or investing in AI, ask: where is the data actually defensible?
Top labs are printing revenue, but compute costs keep dropping. The new bottleneck? Verification.
Value is shifting to domains where ground truth is expensive and hard to label — think biology, robotics, physical world data. These aren't datasets you can scrape or crowdsource.
Scarcity = moat. And in AI, the moat is now the training environments where reality is messy and capital-intensive to capture.
If you're building or investing in AI, ask: where is the data actually defensible?