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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

#61

Post by edmundsauto »

marcosdumay wrote: post #16
fsndz wrote: post #9

They want to throw LLMs at everything even if it does not make sense. Same is true for all the AI agent craze: https://medium.com/thoughts-on-machine-learning/langchains-s...

If feels like the entire world has gone crazy. Even the serious idea that the article thinks could work is throwing the unreliable LLMs at verification ! If there's any place you can use something that doesn't work most of the time, I guess it's there.

Only if it fails in the same way. LLMs and the multi-agent approach operate under the assumption that they are programmable agents and each agent is more of a trade off against failure modes. If you can string them together, and if the output is easily verified, it can be a great fit for the problem.

Re: YC is wrong about LLMs for chip design

#63

Post by samatman »

layer8 wrote: post #39
EgoIncarnate wrote: post #7

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. As we've seen in the recent past, it's difficult to predict what the possibilities are for LLMS and what limitations will hold. Currently it seems pure scaling won't be enough, but I don't think we've reached…

You could replace “LLM” in your comment with lots of other technologies. Why bet on LLMs in particular to escape their limitations in the near term?

Because YCombinator is all about r-selecting startup ideas, and making it back on a few of them generating totally outsized upside.

I think that LLMs are plateauing, but I'm less confident that this necessarily means the capabilities we're using LLMs for right now will also plateau. That is to say it's distinctly possible that all the talent and money sloshing around right now will line up a new breakthrough architecture in time to keep capabilities marching forward at a good pace.

But if I had $100 million, and could bet $200 thousand that someone can make me billions on machine learning chip design or whatever, I'd probably entertain that bet. It's a numbers game.

Re: YC is wrong about LLMs for chip design

#65

Post by jeltz »

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?

Most? I can list tens of them easily. For example what advancements were required for Slack to be successful? Or Spotify (they got more successful due to smartphones and cheaper bandwidth but the business was solid before that)? Or Shopify?

Re: YC is wrong about LLMs for chip design

#66

Post by steveBK123 »

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…

Yes this is EXACTLY it, and I was discussing this a bit at work (financial services).

In software, we've all self taught, improved, posted Q&A all over the web. Plus all the open source code out there. Just mountains and mountains of free training data.

However software is unique in being both well paying and something with freely available, complete information online.

A lot of the rest of the world remains far more closed and almost an apprenticeship system. In my domain thinks like company fundamental analysis, algo/quant trading, etc. Lots of books you can buy from the likes of Dalio, but no real (good) step by step research and investment process information online.

Likewise I'd imagine heavily patented/regulated/IP industries like chip design, drug design, etc are substantially as closed. Maybe companies using an LLM on their own data internally could make something of their data, but its also quite likely there is no 'data' so much as tacit knowledge handed down over time.

Re: YC is wrong about LLMs for chip design

#67

Post by ksynwa »

ReptileMan wrote: post #45
ksynwa wrote: post #41

Earlier quoted context omitted.

Microwave sellers did not become trillion dollar companies off that hype

Mostly because the marginal cost of microwaves was not close to zero.

Mostly because they were not making claims that sentient microwaves that would cook your food for you were just around the corner which then the most respected media outlets parroted uncritically.

Re: YC is wrong about LLMs for chip design

#68

Post by klabb3 »

brookst wrote: post #24
klabb3 wrote: post #10

I don’t mind LLMs in the ideation and learning phases, which aren’t reproducible anyway. But I still find it hard to believe engineers of all people are eager to put a slow, expensive, non-deterministic black box right at the core of extremely complex systems that need to be reliable, inspectable, understandable…

You find it hard to believe that non-deterministic black boxes at the core of complex systems are eager to put non-deterministic black boxes at the core of complex systems?

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.

Re: YC is wrong about LLMs for chip design

#69

Post by deadbabe »

Mistletoe wrote: post #59
deadbabe wrote: post #57

Earlier quoted context omitted.

This is typical of any hype bubble. Blockchain used to be the answer to everything.

What's after this? Because I really do feel the economy is standing on a cliff right now. I don't see anything after this that can prop stocks up.

The post-quantum age. Companies will go post-quantum.

Re: YC is wrong about LLMs for chip design

#70

Post by sdesol »

croshan wrote: post #50
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.

An interpretation that makes sense to me: humans are non-deterministic black boxes already at the core of complex systems. So in that sense, replacing a human with AI is not unreasonable. I’d disagree, though: humans are still easier to predict and understand (and trust) than AI, typically.

With humans we have a decent understanding of what they are capable of. I trust a medical professional to provide me with medical advice and an engineer to provide me with engineering advice. With LLM, it can be unpredictable at times, and they can make errors in ways that you would not imagine. Take the following examples from my tool, which shows how GPT-4o and Claude 3.5 Sonnet can screw up.

In this example, GPT-4o cannot tell that GitHub is spelled correctly:

https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples...

In this example, Claude cannot tell that GitHub is spelled correctly:

https://app.gitsense.com/?doc=905f4a9af74c25f&model=Claude+3...

I still believe LLM is a game changer and I'm currently working on what I call a "Yes/No" tool which I believe will make trusting LLMs a lot easier (for certain things of course). The basic idea is the "Yes/No" tool will let you combine models, samples and prompts to come to a Yes or No answer.

Based on what I've seen so far, a model can easily screw up, but it is unlikely that all will screw up at the same time.

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