Having worked with people doing bringup of specialized chips, I am awed at how the world has changed.
> When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when production ramps up can be reduced. “All our schedule assumptions are going to be based on the fact we have this capability now,” he says.
I also remember the hang-wringing about running out of new datasets to train on. Now it appears humans are always generating more data. It's just not as cheap to acquire as legacy data? Meta has to give a deep discount on their API prices to entice people.
I thought back then that humans had a few more breakthroughs in them as meaningful as the seminal Attention is all you need paper. Enough to 100x the capabilities of LLMs back then (10x the smarts and 10x the speed simultaneously).
RSI with a 20 month turnaround for a chip to be made is not exactly breakneck speed though. Physical manufacturing and logistical constraints are going to be and remain a hard obstacle to that process for the foreseeable future.
Isn't that weird? The full knowledge of how to make such chips may one day be accessible to anyone, yet only the entrenched companies will remain the makers.
If we imagine machines being able to do the full process end-to-end, and the quality of that process only dependent on capital spent on tokens, I don't see how new companies could ever enter the market.
Production grade CPU design is more than just the RTL (the source code.) To achieve the performance numbers that these companies get, you have to do a ton of optimization in your physical design to achieve the power/performance/area (PPA) metrics that make these products competitive. LLMs are not suitable for that kind of work.
There are people working on PPA optimization and trying to shake up how things are done, just not with LLMs.
Something that I think is fascinating, though, is that labs are no longer beholden to the limitations of commercial design software. Want to replace your simulator and optimizer with a fully custom verifiable stack of Lean proofs of optimality and correctness? Just throw your unlimited token budget at it.
I don't work in the business, but my understanding was that even with these companies' budgets, it's still too expensive to do any kind of verified performance optimality.
I mean you can design anything without a license. Selling it is where the problems come up. Even then there are likely places in China that would still make it for you.
Seems pretty obvious now that OpenAI is just hyping their models in order to get companies (in this case, chip developers) to use their products in order to learn from their (exfiltrated) IP. Any corporation would be foolish to use any of their or Microsoft’s products, particularly those with valuable IP.
> When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when production ramps up can be reduced. “All our schedule assumptions are going to be based on the fact we have this capability now,” he says.
It's jalapeño grill would you say?
Like, why couldn't they invent a new word and not hijack an existing word?
I remember the paper proving that hallucinations could never be fully solved back in 2024: https://arxiv.org/abs/2409.05746
I also remember the hang-wringing about running out of new datasets to train on. Now it appears humans are always generating more data. It's just not as cheap to acquire as legacy data? Meta has to give a deep discount on their API prices to entice people.
I thought back then that humans had a few more breakthroughs in them as meaningful as the seminal Attention is all you need paper. Enough to 100x the capabilities of LLMs back then (10x the smarts and 10x the speed simultaneously).
RSI with a 20 month turnaround for a chip to be made is not exactly breakneck speed though. Physical manufacturing and logistical constraints are going to be and remain a hard obstacle to that process for the foreseeable future.
If we imagine machines being able to do the full process end-to-end, and the quality of that process only dependent on capital spent on tokens, I don't see how new companies could ever enter the market.
There are people working on PPA optimization and trying to shake up how things are done, just not with LLMs.
[1] https://browser.geekbench.com/processors/snapdragon-x2-elite...
[2] https://browser.geekbench.com/macs/macbook-pro-14-inch-2026-...
The value lies in the design space exploration, which is what an LLM can easily do.
https://en.wikipedia.org/wiki/Design_space_exploration
Except the problem is not restricted to the actual ISA or its HDL implementation, etc.
It's even just getting space / time in a fab at that advanced of a process node.