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YC is wrong about LLMs for chip design

zach.be

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Re: YC is wrong about LLMs for chip design

#141

Post by wslh »

spencerchubb wrote: post #98
wslh wrote: post #40

Earlier quoted context omitted.

This makes complete sense from an investor’s perspective, as it increases the chances of a successful exit. While we focus on the technical merits or critique here on HN/YC, investors are playing a completely different game. To be a bit acerbic, and inspired by Arthur C. Clarke, I might say: "Any sufficiently complex business could be indistinguishable from Theranos".

Theranos was not a "complex business". It was deliberate fraud and deception, and investors that were just gullible. The investors should have demanded to see concrete results

I expected you to take this with a grain of salt but also to read between the lines: while some projects involve deliberate fraud, others may simply lack coherence and inadvertently follow the principles of the greater fool theory [1]. The use of ambiguous or indistinguishable language often blurs the distinction, making it harder to differentiate outright deception from an unsound business model.

[1] https://en.wikipedia.org/wiki/Greater_fool_theory

Re: YC is wrong about LLMs for chip design

#142

Post by klabb3 »

OkGoDoIt wrote: post #127
klabb3 wrote: post #68

Earlier quoted context omitted.

Yes I do! Is that some sort of gotcha? If I can choose between having a script that queries the db and generates a report and “Dave in marketing” who “has done it for years”, I’m going to pick the script. Who wouldn’t? Until machines can reliably understand, operate and self-correct independently, I’d rather not give up debuggability and understandability.

I think this comment and the parent comment are talking about two different things. One of you is talking about using nondeterministic ML to implement the actual core logic (an automated script or asking Dave to do it manually), and one of you is talking about using it to design the logic (the equivalent of which is writing that automated script). LLM’s are not good at actually doing the processing, they are not good…

> So an LLM would potentially be good at writing a first draft of that script, which Dave could then proofread/edit

Right, and there’s nothing fundamentally wrong with this, nor is it a novel method. We’ve been joking about copying code from stack overflow for ages, but at least we didn’t pretend that it’s the peak of human achievement. Ask a teacher the difference between writing an essay and proofreading it.

Look, my entire claim from the beginning is that understanding is important (epistemologically, it may be what separates engineering from alchemy, but I digress). Practically speaking, if we see larger and larger pieces of LLM written code, it will be similar to Dave and his incomprehensible VBA script. It works, but nobody knows why. Don’t get me wrong, this isn’t new at all. It’s an ever-present wet blanket that slowly suffocates engineering ventures who don’t pay attention and actively resist. In that context, uncritically inviting a second wave of monkeys to the nuclear control panels, that’s what baffles me.

Re: YC is wrong about LLMs for chip design

#143

Post by akra »

unoti wrote: post #25
lolinder wrote: post #13

One of the consistent problems I'm seeing over and over again with LLMs is people forgetting that they're limited by the training data. Software engineers get hyped when they see the progress in AI coding and immediately begin to extrapolate to other fields—if Copilot can reduce the burden of coding so much, think of all the money we can make selling a similar product to XYZ industries! The problem with this extrapol…

>The problem with this extrapolation is that the software industry is pretty much unique in the amount of information about its inner workings that is publicly available for training on... millions of lines of code that we published on the internet... > Nearly every other industry (with the possible exception of Law) produces publicly-visible output at a tiny fraction of the rate that we do. You are correct! There's…

> 1. Learn how the subject matter experts do the work.

This will get harder I think over time as low hanging fruit domains are picked - the barrier will be people not technology. Especially if the moat for that domain/company is the knowledge you are trying to acquire (NOTE: Some industries that's not their moat and using AI to shed more jobs is a win). Most industries that don't have public workings on the internet have a couple of characteristics that will make it extremely difficult to perform Task 1 on your list. The biggest is now every person on the street, through the mainstream news, etc knows that it's not great to be a software engineer right now and most media outlets point straight to "AI". "It's sucks to be them" I've heard people say - what was once a profession of respect is now "how long do you think you have? 5 years? What will you do instead?".

This creates a massive resistance/outright potential lies in providing AI developers information - there is a precedent of what happens if you do and it isn't good for the person/company with the knowledge. Doctors associations, apprenticeship schemes, industry bodies I've worked with are all now starting to care about information security a lot more due to "AI", and proprietary methods of working lest AI accidentally "train on them". Definitely boosted the demand for cyber people again as an example around here.

> You are correct! There's lots of information available publicly about certain things like code, and writing SQL queries. But other specialized domains don't have the same kind of information trained into the heart of the model.

The nightmare of anyone that studied and invested into a skill set according to most people you would meet. I think most practitioners will conscious to ensure that the lack of data to train on stays that way for as long as possible - even if it eventually gets there the slower it happens and the more out of date it is the more useful the human skill/economic value of that person. How many people would of contributed to open source if they knew LLM's were coming for example? Some may have, but I think there would of been less all else being equal. Maybe quite a bit less code to the point that AI would of been delayed further - tbh if Google knew that LLM's could scale to be what they are they wouldn't of let that "attention" paper be released either IMO. Anecdotally even the blue collar workers I know are now hesitant to let anyone near their methods of working and their craft - survival, family, etc come first. In the end after all, work is a means to an end for most people.

Unlike us techies which I find at times to not be "rational economic actors" many non-tech professionals don't see AI as an opportunity - they see it as a threat they they need to counter. At best they think they need to adopt AI, before others have it and make sure no one else has it. People I've chatted to say "no one wants this, but if you don't do it others will and you will be left behind" is a common statement. One person likened it to a nuclear weapons arms race - not a good thing, but if you don't do it you will be under threat later.

Re: YC is wrong about LLMs for chip design

#144

Post by beepbooptheory »

brookst wrote: post #92
beepbooptheory wrote: post #33

Earlier quoted context omitted.

Can you actually like follow through with this line? I know there are literally tens of thousands of comments just like this at this point, but if you have chance, could you explain what you think this means? What should we take from it? Just unpack it a little bit for us.

Sure. I mean, humans are very good at building businesses and technologies that are resilient to human fallibility. So when we think of applications where LLMs might replace or augment humans, it’s unsurprising that their fallible nature isn’t a showstopper. Sure, EDA tools are deterministic, but the humans who apply them are not. Introducing LLMs to these processes is not some radical and scary departure, it’s an it…

Ok yeah. I think the thing that trips me up with this argument then is just, yes, when you regard humans in a certain neuroscientific frame and consider things like consciousness or language or will, they are fundamentally nondeterministic. But that isn't the frame of mind of the human engineer who does the work or even validates it. When the engineer is working, they aren't seeing themselves as some black box which they must feed input and get output, they are thinking about the things in themselves, justifying to themselves and others their work. Just because you can place yourself in some hypothetical third person here, one that oversees the model and the human and says "huh yeah they are pretty much the same, huh?", doesn't actually tell us anything about whats happening on the ground in either case, if you will. At the very least, this same logic would imply fallibility is one dimensional and always statistical; "the patient may be dead, but at least they got a new heart." Like isn't in important to be in love, not just be married? To borrow some Kant, shouldn't we still value what we can do when we think as if we aren't just some organic black box machines? Is there even a question there? How could it be otherwise?

Its really just that the "in principle" part of the overall implication with your comment and so many others just doesn't make sense. Its very much cutting off your nose to spite your face. How could science itself be possible, much less engineering, if this is how we decided things? If we regarded ourselves always from the outside? How could even be motivated to debate whether we get the computers to design their own chips? When would something actually happen? At some point, people do have ideas, in a full, if false, transparency to themselves, that they can write down and share and explain. This is not only the thing that has gotten us this far, it is the very essence of why these models are so impressive in the certain ways that they are. It doesn't make sense to argue for the fundamental cheapness of the very thing you are ultimately trying to defend. And it imposes this strange perspective where we are not even living inside our own (phenomenal) minds anymore, that it fundamentally never matters what we think, no matter our justification. Its weird!

I'm sure you have a lot of good points and stuff, I just am simply pointing out that this particular argument is maybe not the strongest.

Re: YC is wrong about LLMs for chip design

#145

Post by teamonkey »

brookst wrote: post #28
DeathArrow wrote: post #12

Earlier quoted context omitted.

>The article seems to be be based on the current limitations of LLMs. I don't think YC and other VCs are betting on what LLMs can do today, I think they are betting on what they might be able to do in the future. Do we know what LLMs will be able to do in the future? And even if we know, the startups have to work with what they have now, until that future comes. The article states that there's not much to work with.

Show me a successful startup that was predicated on the tech they’re working with not advancing?

The notion of a startup gaining funding to develop a fantasy into reality is relatively new.

It used to be that startups would be created to do something different with existing tech or to commercialise a newly-discovered - but real - innovation.

Re: YC is wrong about LLMs for chip design

#146

Post by e_y_ »

zachbee wrote: post #120
aubanel wrote: post #110

I know nothing about chip design. But saying "Applying AI to field X won't work, because X is complex, and LLMs currently have subhuman performance at this" always sounds dubious. VCs are not investing in the current LLM-based systems to improve X, they're investing in a future where LLM based systems will be 100x more performant. Writing is complex, LLMs once had subhuman performance, and yet. Digital art. Music (se…

I didn't get into this in the article, but one of the major challenges with achieving superhuman performance on Verilog is the lack of high-quality training data. Most professional-quality Verilog is closed source, so LLMs are generally much worse at writing Verilog than, say, Python. And even still, LLMs are pretty bad at Python!

That's probably where there's a big advantage to being a company like Nvidia, which has both the proprietary chip design knowledge/data and the resources/money and AI/LLM expertise to work on something specialized like this.

Re: YC is wrong about LLMs for chip design

#147

Post by aabhay »

afro88 wrote: post #119

They (YC) are interested in the use of LLMs to make the process of designing chips more efficient. Nowhere do they talk about LLMs actually designing chips. I don't know anything about chip design, but like any area in tech I'm certain there are cumbersome and largely repetitive tasks that can't easily be done by algorithms but can be done with human oversight by LLMs. There's efficiency to be gained here if the desi…

Except that’s now a very standard pitch for technology across basically any industry, and cheapens the whole idea of YC presenting a grand challenge.

Re: YC is wrong about LLMs for chip design

#148

Post by aabhay »

I think the problem with this particular challenge is that it is incredibly non-disruptive to the status quo. There are already 100s of billions flowing into using LLMs as well as GPUs for chip design. Nvidia has of course laid the ground work with its culitho efforts. This kind of research area is very hot in the research world as well. It’s by no means difficult to pitch to a VC. So why should YC back it? I’d love to see YC identifying areas where VC dollars are not flowing. Unfortunately, the other challenges are mostly the same — govtech, civictech, defense tech. These are all areas where VC dollars are now happily flowing since companies like Anduril made it plausible.

Re: YC is wrong about LLMs for chip design

#149

Post by seanmcdirmid »

cruffle_duffle wrote: post #131
hinkley wrote: post #105

Earlier quoted context omitted.

I still have nightmares about the entry level EE class I was required to take for a CS degree. RC circuits man.

“Oh shit I better remember all that matrix algebra I forgot already!” …Then takes a class on anything with 3d graphics… “oh shit matrix algebra again!” …then takes a class on machine learning “urg more matrix math!”

EEs actually had a head start on ML, especially those who took signal processing.

Re: YC is wrong about LLMs for chip design

#150

Post by crabmusket »

hinkley wrote: post #105
spamizbad wrote: post #21

LLMs have a long way to go in the world of EDA. A few months ago I saw a post on LinkedIn where someone fed the leading LLMs a counter-intuitively drawn circuit with 3 capacitors in parallel and asked what the total capacitance was. Not a single one got it correct - not only did they say the caps were in series (they were not) it even got the series capacitance calculations wrong. I couldn’t believe they whiffed it a…

I still have nightmares about the entry level EE class I was required to take for a CS degree. RC circuits man.

I studied mechatronics and did reasonably well... but in any electrical class I would just scrape by. I loved it but was apparently not suited to it. I remember a whole unit basically about transistors. On the software/mtrx side we were so happy treating MOSFETs as digital. Having to analyse them in more depth did my head in.
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