r/stocks
Token 商品化将*增加*而非减少推理需求，并*提升* AI 芯片制造商收入
更便宜的 token 将推动企业采用 AI 并增加推理算力需求，极大利好 NVDA、INTC 和 MU 等芯片制造商。
- 更便宜的 token 和开源模型将推动企业广泛采用 AI。
- AI 采用的增加将加剧推理算力瓶颈，需要大规模的硬件建设。
- 尽管 AI 模型公司失去定价权，计算供应链的收入和利润率仍将增加。
- 随着 token 商品化，前沿 AI 模型公司将失去定价权。
- 随着企业转向更便宜的开源替代方案，Anthropic 和 OpenAI 的私人股东将受损。
当令牌变得越来越像商品，大多数工作流都可以用成本低十倍的开源模型替代最新的前沿模型时……
或者当企业愿意使用成本低十倍的开源模型，并用自己的公司专属数据进行微调，而不是花大价钱购买来自 Anthropic 的前沿神话级模型时……
这对 Anthropic 和 OpenAI 的私有股东来说简直糟糕透顶，因为他们再也无法在提供推理令牌时坐地起价。
但这对整个计算供应链的其他环节却毫无影响。
更低的令牌成本意味着更多企业采用 AI 工作流，而每一个令牌都需要推理计算——目前这已是瓶颈，未来只会越来越严重。
这意味着更多的资金将流入推理计算基础设施建设，更多的 GPU、更多的内存芯片、更多的光子学技术、更多的厂内发电，以及更多的新云服务商利润空间……等等。
因此，Anthropic 和 OAI 越被碾压，开源模型越普及，令牌价格越便宜，对 $NVIDIA、$INTC、$MU 就越看涨。
Some hyperscalers who have tied themselves too much with frontier labs may suffer, if open source begins to dominate
Amazon is an interesting case: it's both the greatest Anthropic and Open Source compute provider, so the net effect could be a wash
Microsoft OTOH is (still) too knee deep with Open AI and the least flexible hyperscaler in the field
One of biggest pushers of Open Source in the US is Jensen Huang. This should tell you everything about Nvidia's own interest
The Jevons Paradox argument applied to AI compute is underrated and you’re essentially making it here. When steam engines got more efficient, coal consumption went up not down because efficiency unlocked demand that didn’t exist before. Same dynamic with tokens.
The nuance worth adding is that not all inference compute is equal. Commodity workflows running on cheap open source models don’t need H100s. They run on lower margin commodity GPU clusters. The real question is whether frontier model inference, which does need high end Nvidia silicon, grows fast enough to offset the commoditization happening at the lower end.
Right now the answer seems to be yes. Reasoning models especially are extraordinarily compute hungry and enterprises are discovering they want more of that capability not less. That keeps NVDA’s high end moat intact even as the low end gets commoditized.
The $MU and $INTC calls are interesting. Memory bandwidth is genuinely the hidden bottleneck in inference scaling and Micron is quietly one of the best positioned plays in the whole stack.
Quietly? Have you seen the share price?
Id hold my judgement for AFTER the distillation problem is fixed. It's hard to say how good open source models truly are until then.
when enterprises are okay with using a 10x cheaper open source model and post training it with their company specific context instead of paying for a frontier Mythos class model from Ant
Not how it works in real life.
Real companies aren't on ramen noodle budgets. It is way more appealing to pay for an off-the-shelf top-tier product than to try to save a few bucks, and burn precious time, trying to do your own thing.
Applies to many things in industry, not just AI.
It IS how it works
No one’s gonna pay 20x for fable when they can pay 1x for something 98% as good.
No one’s gonna pay 20x for fable when they can pay 1x for something 98% as good.
I can't say I've seen that be true, either in a general case or with AI tools specifically.
It's certainly not uncommon to pay a premium for incremental gains, especially in competitive fields. I've seen that play out many times, in many ways, over twenty years of engineering.
With AI tools specifically, even with recent changes in billing on a per-token basis, I haven't seen anyone balking at the price. In the grand scheme of things it's still cheap. For the annual company cost of say, a single entry-level engineer, you could get nearly two billion output tokens of Fable, 6+ billion of GPT-5.3 Codex, etc.
"Rich enough to get Fable" sounds like coming from a ramen noodle budget mindset. In the grand scheme of operating costs, it's cheap.
There are plenty of industries that can benefit and profit from frontier AI models and what they can do, but where it doesn't make sense for the company to take on their own internal ML department.
https://www.theverge.com/tech/930447/microsoft-claude-code-discontinued-notepad