r/stocks
USG blocking frontier models - and the impact on compute revenue being surprisingly positive? My thesis:
US blocking frontier models boosts open-source adoption, commoditizing tokens and shifting value to inference compute infra.
- Token commoditization driven by open-source models will drastically lower AI costs, accelerating enterprise AI workflow adoption.
- The bottleneck shifts to inference compute, making infrastructure buildout (memory, foundry, power) the critical value capture layer.
- Value capture shifts from frontier model labs to the unavoidable downstream compute infrastructure layer.
- Frontier model labs will see dwindling value capture and margin compression as open-source models offer parity at a fraction of the cost.
The US government has blocked the release of Anthropic Mythos, and is slow walking the release of openAI GPT5.6 to individual customers (enterprises probably) approved by the government one by one
This means Chinese open source open weight models are going to catch up to parity with legacy GPT5.5 and Opus 4.8 and even slightly surpass them in 3-6 months to global retail and enterprises outside of hand selected American companies close to the administration
This means we are going to increasingly see enterprise workflows happy to plug in open source models, simply because they are as good or even slightly better than the frontier lab models available to you as a business in Europe or Singapore (which isn't Mythos or GPT5.6 to be clear)
This means the continued commodification of tokens. It's difficult for OpenAI and Anthropoc to charge 75% margins for serving these non frontier tokens when the open sourced models are 5% the cost and at parity functionally.
So enterprises can incorporate a lot more AI workflows, as the cost of tokens ("the cost of intelligence") gets commodified and drops heavily
Where is the bottleneck to serving more and more of these cheap tokens now? Inference compute. Inference compute becomes the bottleneck, and with that, the usual infra buildout (memory, foundry, power, photonics etc)
\>"We heard this already bro. Infra is the bottleneck, picks and shovels yadda yadda"
Yes, but the new point that I am trying to make is that previously the frontier labs could capture a significant portion of the economic excess generated by incorporating AI into Enterprise workflows. That portion is now significantly cut. Instead, the value capture shifts downstream, to the unavoidable layer - to the compute layer itself.
Tl;Dr - my hypothesis is that the US government blocking frontier models for global release -> open source models get a boost in adoption for global enterprises -> value capture by model labs dwindles -> more value is available to be captured by the unavoidable bottleneck, the inference compute layer.
So I would infer this development is strongly bullish $MU $SNDK $DRAM $INTC $TSMC etc in the medium term
USG out there acting like gatekeepers while open-source weight models are just going to scale anyway. Meanwhile, Nvidia holders are probably just popping popcorn and watching the compute revenue stack up. 🍿📈
Energy is bottleneck. Puts it is in anything related to USA. Did you not see 2 days ago Smci photo looking for "alternative energy sources"? Lmao. They dilute to raise 20B as banks did not want to issue more bonds. Is just matter of few weeks as you say Chinese has it's AI models replacing others, and not only that but also DRAM and not more than 2-3year they manage to start building chip hardware with ASML copies they got with stolen tech
Wait until you hear about how AMAT acquires their technologies 😂.
Alls fair.
No, an enterprise can trivially plug their AI work-flows into an open source model using inference platforms like Baseten or even Openrouter.
They don't need local hardware, or worry about hardware at all. Baseten or OpenRouter will find the compute capacity for this from a neocloud or AWS or Azure.
I think there would still be middlemen offering open weight models on a pay-as-you-go or subscription basis. Something like opencode enterprise.
Would certainly make it cheaper, but it could be shut down by the government. Inhouse removes any potential government hijacking.
Government can't shut downs companies operating from outside the USA like opencode. Then they would just fall behind if they don't allow domestic companies to do the same.
Hard to see the US shutting down open source models entirely, particularly because doing so would give enterprises in other countries like EU and East Asia a massive competitive advantage via cheap inference available to them.
the key assumption is model parity actually reaching sustained enterprise trust. if that happens, commoditization pressure on labs is real and infra capture strengthens
Its also getting increasingly recognized that post training a (good enough) model with that enterprise specific context and know how could be more useful for the enterprise than pure model competency from the pre-trained stage. Satya wrote something similar to this recently in one of his articles as well.
AWS GCP and Azure win this one
The part worth stress-testing is whether inference compute margins actually expand when token costs collapse, or whether hyperscalers just absorb the volume gains and keep squeezing suppliers the same way they always have.
"You'll have to take my open model weights and NVME sticks from my cold, dead hands"
First step is trying different open models on the likes of OpenRouter, NanoGPT, or models.dev like others have mentioned.
Then you can provision multi-GPU cloud instances through AWS, GCP, Azure, RunPod, Vast, ect to host the model in a vLLM or SGLang docker container. HuggingFace libraries you can use for fine-tuning and training your own models.
To host on-prem, buy a server from Dell, SMCI, ect or build one with RTX 6000 Pro cards. All depends on your data privacy requirements and spend/ROI. In many cases there is a multi-year breakeven period. I personally am fine with using OpenRouter's anonymized inference providers and renting spot instances for training jobs.
All platforms have been throttled. No joke. None of them work as well as they did before this month.