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.
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.
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.
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.
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. There’s nothing in the article that says AI did anything creative but rather that it was used for software development within the overall project. Clear misleading title. Suggest to mark this as clickbait.
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.
I grow Jalapeños. This conflation of AI and actual chili peppers irks me.
Just think of how the people of Xalapa, Mexico feel. They should send them a royalty check.
I do generative art (no relation to AI prompting). I feel your frustration.
Oh my!
Cryptographers also got the same raw deal with cryptocurrency, and every one just said "crypto?"
Or cyber…
Same
It's jalapeño grill would you say?
Not sure what you're talking about. But I'll tell you, home grown Jalapeño peppers, fermented with 3% salt is the stuff of dreams.
"It's all up in yo' grill, would you say?"
You know what really grinds my gears? Friction.
slow claps
Feeling the burn?
I'm annoyed that the meaning of the word "Agent" has been obliterated.
Like, why couldn't they invent a new word and not hijack an existing word?
Did you not watch the Matrix documentary?
I guess I'll have to
Call it GPTChippomatic or something. Please leave my peppers alone.
Guess how electrical engineers feel about the term "transformers".
This is an excellent comment. I'm still laughing.
As a former EE, attention was all I needed to not get zapped.
:groan:
Jalapeño also used to be a Java VM written in Java at IBM.
To say nothing of the Red Hot Chili Peppers.
At some point people will use an LLM to design an Apple M series competitor.
They won't, because they'd need an ARM architecture license.
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.
Why, the LLM can make up its own architecture.
The value lies in the design space exploration, which is what an LLM can easily do.
https://en.wikipedia.org/wiki/Design_space_exploration
Arm sells architecture licenses to anybody these days.
Or they'll just build a competitor in RISC-V instead and that's fine.
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.
Qualcomm have an architecture license and the Snapdragon X2 Elite Extreme X2E-96-100 isn't too far off the M5 Pro.
[1] https://browser.geekbench.com/processors/snapdragon-x2-elite...
[2] https://browser.geekbench.com/macs/macbook-pro-14-inch-2026-...
Are they using LLMs to close that gap, or is this their Nuvia acquisition doing the heavy lifting?
It is probably doable right not to push a risc-v design into that performance space.
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.
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.
openai should figure out how to make lithography machine, so ASML don't have monopoly on it.
"Reverse engineer this DARPA project, make no mistakes"
The Chinese have been working on EUV for a while
Having worked with people doing bringup of specialized chips, I am awed at how the world has changed.
Whatever happened with the Apple lawsuit?
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. There’s nothing in the article that says AI did anything creative but rather that it was used for software development within the overall project. Clear misleading title. Suggest to mark this as clickbait.
With a few handy tips and tricks from apple insiders. But sure, I guess the LLMs helped too.
It is surprising to me that recursive self-improvement seems more plausible now than it did in 2023. Am I the only one to be surprised?
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.
Congrats to the former TPU team
So when can we start getting cheap chips? RAM anyone please!
Everyone's still bottlenecked on foundries, not designs.
Cant AI build foundries?
Something we can all agree with is we need more foundries and green power.
yup, but it'll take about 3-5 years.
Aw, I was expecting more details but this just seems to be a rehash of what they unveiled a month ago.