Open-source AI models disrupt closed-source economics
The guest argued that open-source AI models are significantly cheaper per unit of intelligence than closed-source models, driving a quiet shift among enterprise users.
The argument
While closed-source models remain slightly more capable (by 4-5%), open-source models do not carry training amortization costs for the end-user, making them far more cost-effective to run on high-speed inference hardware.
The thesis, stress-tested
✓ What validates it
- ✓Enterprise survey data showing accelerated migration to open-source models
- ✓Release of open-source models matching or exceeding GPT-4 capabilities at lower run costs
▸ Risks discussed
- ▸Closed-source models widening the capability gap significantly
- ▸Security and compliance concerns with open-source deployments in regulated industries
Hear it yourself
"And so what happened was that in sort of 2025, in the first part of 2025, the models we made were smart enough to be useful. Yeah. And there was an explosion of use, and we use AI by doing inference. So there was this sort of tidal wave of demand on inference. And that has continued in 2026, and we think it will continue for for years and years to come. And so that that's the what had happened. In 2015, when we began thinking about the company, we knew that AI was on the horizon and that would eat a huge amount of compute. Right? And we we made sort of two fundamental bets. We bet that it would need dedicated silicon and, right, graphics had needed dedicated silicon."
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