r/stocks
美国政府封锁前沿模型——对计算收入的影响出人意料地积极？我的论点：
美国封锁前沿模型提振开源模型采用，使token商品化，并将价值转移至推理计算基础设施。
- 开源模型推动的 token 商品化将大幅降低 AI 成本，加速企业 AI 工作流的采用。
- 瓶颈转移到推理计算，使基础设施建设（内存、晶圆厂、电力）成为关键的价值捕获层。
- 价值捕获从前沿模型实验室转移到不可避免的下游计算基础设施层。
- 随着开源模型以极低的成本提供相当的功能，前沿模型实验室的价值捕获将减少，利润率受到压缩。
美国政府已阻止 Anthropic Mythos 的发布，同时正缓慢推进 OpenAI GPT5.6 对个人客户的开放（企业客户可能需经政府批准后逐个放行）。
这意味着，中国开源的开放权重模型将在3到6个月内赶上甚至略微超越传统的 GPT5.5 和 Opus 4.8，覆盖全球零售及非美国政府特别选定的亲密企业客户。
这意味着，越来越多的企业工作流将乐意接入开源模型，仅仅因为它们在性能上与你作为欧洲或新加坡企业的用户所能获得的前沿实验室模型相当，甚至略胜一筹（请注意，这里指的不是 Mythos 或 GPT5.6）。
这意味着令牌的持续商品化。当开源模型的成本仅为前缘模型的5%，功能却完全持平，OpenAI 和 Anthropic 再难以维持75%的利润率来提供这些非前沿令牌服务。
因此，企业可以更广泛地集成人工智能工作流，因为令牌成本（“智能的成本”）正在被商品化并大幅下降。
现在，服务更多廉价令牌的瓶颈在哪里？是推理算力。推理算力成为新的瓶颈，随之而来的是常规基础设施建设（内存、晶圆厂、电力、光子学等）的需求激增。
「我们早就听过了兄弟，基础设施是瓶颈，淘金热、铁锹铲子那一套老生常谈了」
没错，但我想强调的新观点是：过去，前沿实验室能攫取大量由企业工作流引入 AI 所产生的经济盈余。而现在，这部分盈余已被显著压缩。价值捕获的重心正向下转移，落到不可避免的底层——也就是算力本身。
简而言之：我的假设是，美国政府阻挠前沿模型的全球发布 → 全球企业对开源模型的采用加速 → 模型实验室的价值捕获能力减弱 → 更多价值将流向无法回避的瓶颈环节，即推理算力层。
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.