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Notes -
I don’t think open model competition will necessarily stay minor forever. It’s also perhaps more lucrative for surviving independent or semi-independent datacenter operators. Frontier models will be key to things like cybersecurity, its much less clear that running accounts payable for a construction company or handling automated help chat for a video game publisher or writing a mean review of a hotel you just visited on TripAdvisor based on your voice note requires them, and it’s mundane tasks like these that will be the majority of inference tasks if not tokens in relatively short order, if they aren’t already. Even for relatively powerful agentic stacks, it’s my understanding that a lot of consumer and enterprise tools now use cheap open models to triage and then call agents that might themselves call, where deemed necessary, frontier models. Most customers increasingly won’t notice the difference. I also think the position of the datacenter operators is getting increasingly dire and the market seems to agree. Coreweave and Oracle debt with spreads of 400-600+ bps to the treasury curve is ominous, even new issues at like 9% interest, but even if these vendors / providers survive, the margin to pay the coupon has to come from somewhere and with both GPUs and energy completely commoditized, that side of the business essentially relies on compute demand for inference as not only a bottleneck, but one that will experience zero slack as a result of falling token costs, open models and so on.
To believe in the data center narrative (and you have to even if you don’t own Coreweave or Oracle because every other big tech and frontier lab is guaranteeing a lot of that debt one way or another) you have to believe that if, say, inference and training becomes 50% more efficient overnight, that slack is going to be immediately taken up by new demand without any price pressure or panic.
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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