Comment by danroblew
1 year ago
The article is about how the economics of the LLM market is making all tech look bad.
They need trillions of dollars in returns. VC's won't finance tech startups for decades.
I use Cursor sometimes, and VSCode + Continue with llama.cpp, and it's great. That's not worth billions. It's definitely not worth trillions.
This is the crux. A cool thing has been invented, with real usages. Unfortunately, it's cost hundreds of billions of dollars and it has absolutely zero hope of making the trillions needed to justify that.
Now someone will respond about how it's just a stepping stone, and how the billions are justified by _something completely imaginary, and not invented yet, and maybe not ever_ e.g. agents.
>it's cost hundreds of billions of dollars and it has absolutely zero hope of making the trillions needed to justify that.
The BigTech companies have been flush with liquidity and poured those hundreds of billions into the promising tech, and as result we got a wonderful new technology. There is not much need for those trillions in return - just look at liquidity positions of those companies, they are just fine. If those trillions come in eventually - even better.
>There is not much need for those trillions in return
Whilst you are correct that big tech cos do not need the return to survive, that's not how public markets work at all, and thus not how the incentives for those in charge of the companies work, and so making you actually wrong.
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Funny, I had been told we had to lay off all those workers because they weren’t flush with cash.
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They’ll be fine and will survive regardless, but their current astronomical valuations probably won’t be.
To train. Inference is much cheaper...and getting cheaper by the day
I see it a little differently. What was the direct economic return of the Manhattan Project?
Ideally it was thought to have shortened a very expensive war, and may have prevented the USSR from taking over Europe by leveraging its unquestioned postwar conventional forces advantage.
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The Manhattan Project was driven by the U.S. Government, which doesn't need a VC-tier return. The entire business model of VCs is based on the idea that they'll have the occasional 100x return, and if none of the AI companies do that it would destroy the VC model.
About the GDP of the US and Europe over the past 80 years so a few quadrillion dollars.
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Wait, what? The Manhattan project produced something--multiple somethings in fact. What has this "project" produced?
Completely irrelevant. The Manhattan Project wasn't funded by VCs with an expectation of a return.
> I use Cursor sometimes, and VSCode + Continue with llama.cpp, and it's great. That's not worth billions. It's definitely not worth trillions.
That seems like a suspect claim. If you're saying that you, personally, cannot create billions of dollars in value with Cursor & friends that is certainly true - but you are in no position to make a judgement call about where the cap on value creation is for the LLM market is worth based on your personal use cases. LLMs don't just do code completion. We really can't estimate how much potential value is being created without doing some serious data diving and studying of cases.
A better argument would be that the DeepSeek experience suggests these companies have no moat and therefore no way to earn a return on capital. But LLMs are probably going to generate at least trillions of dollars in value because they're on par or ahead of Wikipedia and Google for answering many queries then they also have hundreds of ancillary uses like answering medical questions at weird hours or creative/professional writing.
It's possible to grow an economy by trillions of real value without any actor being able to extract that as a profit or it even showing up in the books as money.
Consider that Wikipedia is much bigger than Encyclopedia Britanica, but because it is given away to everyone for free, it is not counted as E.B.'s max sale price ($2900 in 1989?) times the world's internet connected population (5.6e9?) — $16 trillion.
AI, regardless of value, are priced at the marginal cost to reproduce weights or run inference depending on which you care about.
But I do mean "reproduce" not "invent" — it doesn't matter if DeepSeek's "a few million" was only possible because they benefited from published research, it just matters that they could.
And if the hardware is the bottleneck for inference, that profit goes to the hardware manufacturer, not to the top ten companies who made models.
> That's not worth billions. It's definitely not worth trillions.
That is a problem for the VC’s that bet wrong, not for the world at large.
The models exist now and they’ll keep being used, regardless of whether a bunch of rich guys lost a bunch of money.
Their ongoing operation is quite expensive, so even that is not assured.
My ongoing operation is a MacBook pro that costs pennies worth of electricity.
Where are you getting this from? Outside of o3, every AI provider's API is super cheap, with most productive queries I do coming in under 2c. We have no reason to believe any of them are selling API requests at a loss. I think <2c per query hardly counts as "quite expensive".
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> they’ll keep being used
How? I get that many devs like using them for writing code. Personally I don't, but maybe someday someone will invent a UX for this that I don't despise, and I could be convinced.
So what? That's a tiny market. Where in the landscape of b2b and b2c software do LLMs actually find market fit? Do you have even one example? All the ideas I've heard so far are either science fiction (just wait any day now we'll be able to...) or just garbage (natural language queries instead of SQL). What is this shit for?
Anecdotally, almost every day I’ll overhear conversations at my local coffee shop of non-developers gushing about how much ChatGPT has revolutionized their work: church workers for writing bulletins and sermons, small business owners for writing loan applications or questions about taxes, writers using it for proofreading, etc. And this is small town Colorado.
Not since the advent of Google have I heard people rave so much about the usefulness of a new technology.
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> Do you have even one example?
My company uses them for a fuckton of things that were previously too intractable for static logic to work (because humans are involved).
This is mostly in the realm of augmented customer support (e.g. customer says something, and the support agent immediately gets the summarized answer on their screen)
It’s nothing that can’t be done without, but when the whole problem can be simplified to “write a good prompt” a lot of use cases are suddenly within reach.
It’s a question if they’ll keep it around when they realize it doesn’t always quite work, but at least right now MS is making good money off of it.
LLMs are incredible at editing my writing. Every email I write is improved by LLMs. My executive summaries are improved by LLMs. It wont be long until every single office worker is using LLMs as an integral part of their daily stack, people just have to try it and theyll see how useful it is for writing.
Microsoft turned itself into a trillion dollar company off the back of enterprise SAAS products and LLMs are among the most useful.
> What is this shit for?
Various minor thing so far. For example I heard about ChatGPT being evaluated as a tool for providing answers for patients in therapy. ChatGPT answers were evaluated as more empathetic, more human and more aligned with guidelines of therapy than answers given by human therapists.
Providing companionship to lonely people is another potential market.
It's not as good as people at solving problems yet but it's already better than humans at bullshiting them.
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> The article is about how the economics of the LLM market is making all tech look bad.
No, it's not. The first half of the article talks about how useless the actual product is, how the only reason we hear about it is because the media loves to talk about it.
Yeah whatever. VCs will keep backing entrepreneurs, that's their job. Until there's a better way to get 10-100x returns, we're fine.