A weekly thread to discuss financial matters - from personal all the way up to global.
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Notes -
It's not that frontier models are able to magically somehow do a better job on mundane tasks: "what is 1+1?" is saturated by both open and proprietary, and even economically meaningful tasks like coding can be handled reasonably well by open models today. And the tasks that can be handled by both will expand, with open weights likely remaining 3-6 months behind.
But my claim here is that it's significantly cheaper for the frontier labs to serve those than it is for some host of open weights to. The only reason open weights are cheaper sometimes for the customer is because frontier labs don't care to compete for them: even they (especially they) are compute limited, and their success is dependent on them developing better models that can handle all economic tasks, not by them being the choice of inference provider for every mundane task in 2026 Q4. (I don't think even offering Codex or Claude Code as products matters that much economically for them, except in that they're proofs of concept for automating economically valuable work and help with internal work.)
Fully agreed here, which is where the question of model capabilities come in. If within the next year or two frontier labs introduce models that automates most white collar tasks, then there will be no slack. The timing here is critical: this also will have to occur on that short timeline. Successfully automating accounts receivable for a construction company, but in 2029 instead of 2027, is not going to cut it, at least from the perspective of a neocloud. Nvidia is incentivized to backstop them, which could buy some time, but even its powers aren't infinite.
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