Comment by aurareturn | Hacker News Reader

Comment by aurareturn | Hacker News Reader

  Perhaps most devastating is DeepSeek's recent efficiency breakthrough, achieving comparable model performance at approximately 1/45th the compute cost. This suggests the entire industry has been massively over-provisioning compute resources.

I wrote in another thread why DeepSeek should increase demand for chips, not lower.

1. More efficient LLMs should lead to more usage, which means more AI chip demand. Jevon's Paradox.

2. Even if DeepSeek is 45x more efficient (it is not), models will just become 45x+ bigger. It won’t stay small.

3. To build a moat, OpenAI and American AI companies need to up their datacenter spending even more.

4. DeepSeek's breakthrough is in distilling models. You still need a ton of compute to train the foundational model to distill.

5. DeepSeek's conclusion in their paper says more compute is needed for next break through.

6. DeepSeek's model is trained on GPT4o/Sonnet outputs. Again, this reaffirms the fact that in order to take the next step, you need to continue to train better models. Better models will generate better data for next-gen models.

I think DeepSeek hurts OpenAI/Anthropic/Google/Microsoft. I think DeepSeek helps TSMC/Nvidia.

  Combined with the emergence of more efficient inference architectures through chain-of-thought models, the aggregate demand for compute could be significantly lower than current projections assume.

This is misguided. Let's think logically about this.

More thinking = smarter models

Faster hardware = more thinking

More/newer Nvidia GPUs, better TSMC nodes = faster hardware

Therefore, you can conclude that Nvidia and TSMC demand should go up because of CoT models. In 2025, CoT models are clearly bottlenecked by not having enough compute.

  The economics here are compelling: when DeepSeek can match GPT-4 level performance while charging 95% less for API calls, it suggests either NVIDIA's customers are burning cash unnecessarily or margins must come down dramatically.

Or that in order to build a moat, OpenAI/Anthropic/Google and other laps need to double down on even more compute.

I agree with this.

Fwiw many of the improvements in Deepseek were already in other 'can run on your personal computer' AI's such as Meta's Llama. Deepseek is actually very similar to Llama in efficiency. People were already running that on home computers with M3's.

A couple of examples; Meta's multi-token prediction was specifically implemented as a huge efficiency improvement that was taken up by Deepseek. REcurrent ADaption (READ) was another big win by Meta that Deepseek utilized. Multi-head Latent Attention is another technique, not pioneered by Meta but used by both Deepseek and Llama.

Anyway Deepseek isn't some independent revolution out of nowhere. It's actually very very similar to the existing state of the art and just bundles a whole lot of efficiency gains in one model. There's no secret sauce here. It's much better than what openAI has but that's because openAI seem to have forgotten 'The Bitter Lesson'. They have been going at things in an extremely brute force way.

Anyway why do i point out that Deepseek is very similar to something like Llama? Because Meta's spending 100's of billions on chips to run it. It's pretty damn efficient, especially compared to openAI but they are still spending billions on datacenter build-outs.

> openAI seem to have forgotten 'The Bitter Lesson'. They have been going at things in an extremely brute force way.

Isn't the point of 'The Bitter Lesson' precisely that in the end, brute force wins, and hand-crafted optimizations like the ones you mention llama and deepseek use are bound to lose in the end?

Imho the tldr is that the wins are always from 'scaling search and learning'.

Any customisations that aren't related to the above are destined to be overtaken by someone that can improve the scaling of compute. OpenAI do not seem to be doing as much to improve the scaling of the compute in software terms (they are doing a lot in hardware terms admitedly). They have models at the top of the charts for various benchmarks right now but it feels like a temporary win from chasing those benchmarks outside of the focus of scaling compute.

But Microsoft hosts 3rd party models too, and cheaper models means more usage, which means more $$$ to scaled cloud providers right?

it means they can serve more with what they have if they implement models with deepseek's optimizations. More usage doesn't mean Nvidia will get the same margins when cloud providers scale out with this innovation.

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