Takeaway #3: The Real Leverage Lies in Data Infrastructure, Not AI Applications
Rather than chasing frothy startup narratives, institutional LPs should target control over infrastructure bottlenecks—power, compute, data centers, H100s, and national model infrastructure. These are the sovereign choke points upon which all AI applications rely. Investments in industrial substrates (e.g., CoreWeave, grid adjacent data centers) offer more durable, asymmetric leverage than most application layer bets, which are prone to volatility, competition, and regulatory risk.
That’s the key difference between chasing the narrative and **owning the constraint.**
Thanks. And, yes, my general thesis is that most of the value in AI accrues in the chokepoints, i.e., the infrastructure, not the models or the software being built on top of the models. And, frankly, it's a market in which few VCs have any great insight.
Great post! Key part of the article for me:
Takeaway #3: The Real Leverage Lies in Data Infrastructure, Not AI Applications
Rather than chasing frothy startup narratives, institutional LPs should target control over infrastructure bottlenecks—power, compute, data centers, H100s, and national model infrastructure. These are the sovereign choke points upon which all AI applications rely. Investments in industrial substrates (e.g., CoreWeave, grid adjacent data centers) offer more durable, asymmetric leverage than most application layer bets, which are prone to volatility, competition, and regulatory risk.
That’s the key difference between chasing the narrative and **owning the constraint.**
Thanks. And, yes, my general thesis is that most of the value in AI accrues in the chokepoints, i.e., the infrastructure, not the models or the software being built on top of the models. And, frankly, it's a market in which few VCs have any great insight.