Here’s why I’m betting on the picks and shovels of AI, not the apps.

Every founder I talk to has an AI application idea. Almost none of them are thinking about what those applications actually run on.

That’s the gap I’ve been investing in for the past two years — and 2026 has made the case louder than I expected.

The early AI chip narrative was all about training — who could build the biggest cluster to teach the biggest model. That race increasingly has one obvious winner, and it isn’t a startup. The far more interesting, and far less crowded, race is inference: running trained models fast, cheaply and at scale, in production, for actual paying customers. That’s an efficiency problem, not a brute-force one, and efficiency problems are where specialized architecture beats general-purpose hardware.

That’s the thesis behind my largest position, Cerebras Systems. I backed Andrew Feldman’s team in a very early round, when the company was valued at around $2 billion — well before its Series F pushed past $4 billion, before the $1 billion Series H that valued it at roughly $23 billion in February, and long before what came next.

S. tech IPO in years — and popping 68% on debut to a market cap near $95 billion . Wafer-scale compute solved the inference latency problem in a way incremental GPU improvements couldn’t, and the public market has now agreed, emphatically.

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