r/stocks
Tokens commodifying *increases* not decreases demand for inference and *increases* revenue for AI chip makers
Cheaper tokens will boost enterprise AI adoption and inference compute demand, heavily benefiting chip makers like NVDA, INTC, and MU.
- Cheaper tokens and open-source models will drive widespread enterprise AI adoption.
- Increased AI adoption will exacerbate the inference compute bottleneck, requiring massive hardware buildouts.
- The compute supply chain will see increased revenue and profit margins despite AI model companies losing pricing power.
- Frontier AI model companies will lose pricing power as tokens become commoditized.
- Private shareholders of Anthropic and OpenAI will suffer as enterprises shift to cheaper open-source alternatives.
When tokens become more like commodities and most workflows can be sufficiently done with 10x cheaper open source models instead of the latest frontier model....
or 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...
This just sucks for Anthropic and OpenAI private share holders, as it means they cant price gouge when serving inference tokens
It DOES NOT suck for the rest of the compute supply chain.
Lower token costs means more enterprises adopt AI workflows, and every single token needs inference compute, which is bottlenecked now and will bottleneck ever harder going forward.
This means more money going into inference compute build outs, more GPUs, more memory chips, more photonics, more behind the meter power generation, more profit margins for neoclouds etc etc .
So the more Ant and OAI get cucked and the more that open sourced models are adopted, and the chepaer tokens get, the more bullish it is for $NVIDIA, $INTC, $MU,
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