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.

If i were wrong, those companies would be distributing that cash to shareholders instead of chasing any promise of any big chance.

If investment in AI don't pan out (i do think that it will pan out, and those trillions will come) then those companies would just pour even more billions into whatever big thing/promise would come next. Rinse and repeat. Because some of those things do generate tremendous returns, and thus not playing that game is what really constitute true loss of money.

Markets are funny things.

US right now is run by someone whose explicit promises, if actually implemented, have an obvious immedidiate 13-14% reduction in GDP — literally, never mind side effects, I'm not counting any businesses losing confidence in the idea that America is a place to invest, this is just direct impact.

DOGE + deportation by themselves do most of that percentage. The tariffs are a rounding error in comparison, but still bad on the kind of scale that gets normal politicians kicked out.

And yet, the markets are up.

If you factor in the inflation and the worldwide trade crisis, trading dollars for shares that will lose 10% real value doesn't sound so bad.

What timeframe are you working with, as in, when do you expect to see this reduction in GDP?

I just want to know so that I can set a reminder and check back on your comment when the time arrives.

Funny, I had been told we had to lay off all those workers because they weren’t flush with cash.

They're convinced they no longer need them.

Just as they were convinced after Covid that they needed to put hiring into overdrive.

Tech management has the collective IQ of a flock of sheep.

Nobody has ever been punished for choosing IBM. It’s the same story here. Nobody is going to blame them for following the zeitgeist, but you bet they’d be punished if they didn’t and it doesn’t pan out.

The whole thing is like bitcoin. There’s too many people that benefit from maintaining the collective illusion.

cash on hands GOOG - 100B, AMZN - 80B, FB - 70B, and their core businesses are basically printing money, so they pretty much do have to invest into new things. If somebody sees a multi-billion dollar sink better than AI right now ...

> If somebody sees a multi-billion dollar sink better than AI right now ...

I think if they could find a way to make their software good, instead of bad, like it increasingly is, that would be a good use of that money.

Workers, infrastructure, taxes…

They’ll be fine and will survive regardless, but their current astronomical valuations probably won’t be.

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.

Well sure but how much cash did the MaPr corp. make selling their new and improved model implosion-type-u-235?

I don't know how to tell you this, but the government isn't a business and has completely different objectives and operating conditions

If more people understood this we might have avoided the carnage happening in the US right now.

I don't know why it is so hard to understand. I mean money doesn't really exist without a government[0] and while government plays a role in the market and economy, this role is VERY different than that of a business. A government isn't trying to "make money", is isn't trying to make investors happy, and it certainty can't take existential risks that could make "the company" go bankrupt (or it shouldn't lol).

But I do think (and better understand) there is a failure to understand this at a higher abstraction. One part is simply "money is a proxy." This is an uncontestable fact. But one must ask "proxy for what?" and I think people only accept the naive simple answer. Unfortunately, this "is a proxy" concept is extremely generalization. Everything is an estimation, everything is an approximation, and most things are realistically intractable. We use sibling problems or similar problems to work with that are concrete, but there are always assumptions made and ignoring these can have disastrous consequences. Approximations are good (they're necessary even) but the more advanced a {topic,field,civilization,etc} gets, the more important it is to include higher order terms. Frankly, I don't think humans were built for that (though by some miracle we have the capacity to deal with it).

My partner and her dad are both economists, and one thing I've learned is that what many people think are "economics questions" are actually "business questions". I think a story from her dad makes this extremely clear. A government agency hired him to look at the cost benefit analysis of some stuff (like building a few hospitals and some other unambiguously beneficial institutions), and when he presented everyone was happy but had a final question "should we build them?" The answer? "That's not the role of an economist." The reason for this is because money can't actually be accurately attributed to these things. You can project monetary costs for construction, staffing, and bills, and you can make projections about how many people this will benefit, how it can reduce burdens elsewhere, and as well as make /some/ projections about potential cost savings. But you can't answer "should you." Because the weight of these values is not something that can be codified with any data. It is an importance determined by the public and more realistically their representatives. Very few times can you give a strong answer to a question like "should we build a new hospital" and essentially in only the extreme cases. I'll give another example. In my town there was an ER that was closed due to budget constraints. This ER was across the street to the local university, which students represent ~15% of the population. The next nearest ER? A 15 minute ambulance ride away and in the next town over. Did the city save money? Yes. Did the sister city's ER become even busier? Also yes. Did people lose access to medicine? Yes. Did people die? Also yes. Have economists put a price on human life? Also yes, but they are very clear that this is not a real life and a very naive assumptions[1]. It is helpful in the same way drawing random squiggles on a board can help a conversation. Any squiggles can really be drawn but the existence of _something_ helps create some point to start from.

[0] okay crypto bros, you're not wrong but low volatility is critical as well as some other aspects. Let's not get off topic

[1] https://www.npr.org/2020/04/23/843310123/how-government-agen...

The profit was made by the private sector in supplying goods to the program. Today, private companies do a lot and earn a lot of money from stockpile maintenance.

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.

That's not direct return of VC-invested cash that people are refusing to see past in here.

Doesn't matter. The Manhattan project was a breakthrough in fundamental science that changed the world. Current generative AI are a solid degree improvement on previous technology that is not remotely as big a leap as the amount of money poured into it assumes it to be.

“people … in here” seeing “past it” or not is irrelevant, the VCs won't see past it once they realize that money is lost.

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.

To train. Inference is much cheaper...and getting cheaper by the day

> 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".

The reasoning people have for them selling API requests at a loss is simply their financial statements. Anthropic burned $3B this year. ChatGPT lost $5B. Microsoft has spent $19B on AI and Google has spent close to $50B. Given that revenue for the market leader ChatGPT is $3.7B, it's safe to say that they're losing massive amounts of money.

These companies are heavily subsidized by investors and their cloud service providers (like Microsoft and Google) in an attempt to gain market share. It might actually work - but this situation, where a product is sold under cost to drum up usage and build market share, with the intent to gain a monopoly and raise prices later on - is sort of the definition of a bubble, and is exactly how the mobile app bubble, the dot-com bubble, and previous AI bubbles have played out.

Are the training costs (CapEx) and inference costs (OpEx) being lumped together?

Not sure if it matters at this point. There will need to be many more rounds of CapEx to realize the promises that have been put forth about these models.

The implication would be that those API requests are being sold at a loss. Amodei wrote in January that Claude 3.5 Sonnet was trained for only a few $10Ms, but Anthropic has been losing billions.

That would be a killer for the current and near future generations of LLM as a business. If they are having to pay many times in compute what they are able to get for the API use (due to open models being near comparable?), then you definitely can't "make up for it in volume".

> 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.

These are not the sort of uses we need to make this thing valuable. To be worthwhile it needs to add value to existing products. Can it do that meaningfully well? If not it's nothing more than a curiosity.

Worthwhile is a hard measure.

To make money though it just needs to have a large or important audience and a means of convincing people to think, want, or do things that people with money will pay to make people think, want or do.

Ads, in other words

Can you get enough revenue from ads to pay the cost of serving LLM queries? Has anyone demonstrated this is a viable business yet?

A related question: has anyone figured out how to monetize LLM input? When a user issues a Google search query they're donating extremely valuable data to Google that can be used to target relevant ads to that user. Is anyone doing this successfully with LLM prompt text?

I bet Google is utilizing the value of the LLM input prompts with close to the same efficiency they are monetizing search. I that case, there are two questions -- 1) will LLM overtake search? and 2) can anyone beat Google at monetizing these inputs? I think the answer to both is no. Google already has a wide experience lead monetizing queries. And personally, I'd rather have a search engine that does a better job of excluding spam without having to worry whether or not it's making stuff up. Kagi has a better search than any of the LLMs (except for local results like restaurants/maps).

> 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.

Are people actually satisfied by that? I personally find "chatting" with an LLM grating and dissatisfying because it often makes very obvious and incongruous errors, and it can't reason. It has no logical abilities at all, really. I think you're really underestimating what a therapist actually does, and what human communication actually is. It's more than word patterns.

I could see this being useful in a "dark pattern" sense, but only if it's incredibly cheap, to increase the cost to the user of engaging with customer support. If you have to argue with the LLM for an hour before being connected to an actual person who can help you, then very few calls will make it to the support staff and you can therefore have a much smaller team. But that only works if you hate your users.

Subjective evaluation of "humanity" and "empathy" in responses is much less important than clinical outcome. I don't think an online chat with a nebulous entity will ever be as beneficial as interactions that can, at least occasionally, be in-person. Especially as the trust of online conversations degrade. Erosion of trust online seems like a major negative consequence of all the generative AI slop (LLM or otherwise).

Clinical outcome of humans doing therapy would be better if for some reason doing therapy worse (less according to taught guidelines) was better. But, sure, we can wait for another research or follow up. It might be true. Therapy has dismal outcomes anyways and the outcomes are mostly independent of which theoretical framework the therapy is done according to. It might be the case that the only value in therapy is human connection that AI fails to simulate. But it seem that for some people it simulates connection pretty well.

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.

> 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.

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.

> 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.