It's definitely a tech that's here to stay, unlike block chain/nfts
But I mirror the confusion why people are still bullish on it.
The current valuation for it is because the market thinks that it's able to write code like a senior engineer and have AGI, because that's how they're marketed by the LLM providers.
I'm not even certain if they'll be ubiquitous after the venture capital investments are gone and the service needs to actually be priced without losing money, because they're (at least currently) mostly pretty expensive to run.
There seems to be a widely held misconception that company valuations have any basis in the underlying fundamentals of what the companies do. This is not and has not been the case for several years. The US stock market’s darlings are Kardashians, they are valuable for being valuable the way the Kardashians are famous for being famous.
In markets, perception is reality, and the perception is that these companies are innovative. That’s it.
NFT is still a great tool if you want a bunch of unique tokens as part of a blockchain app. ERC-721 was proven a capable protocol in a variety of projects. What it isn't, and never will be, is an amazing investment opportunity, or a method to collect cool rare apes and go to yacht parties.
LLMs will settle in and have their place too, just not in the forefront of every investors mind.
I am more than happy to pay for access to LLMs, and models continue to get smaller and cheaper. I would be very surprised if they are not far more widely used in 5 or 10 years time than they are today.
None of that means that the current companies will be profitable or that their valuations are anywhere close to justified though. The future could easily be "Open-weight models are moderately useful for some niches, no-name cloud providers charge slightly higher than the cost of electricity to use them at low profit margins".
Dot-com boom/bubble all over again. A whole bunch of the current leaders will go bust. A new generation of companies will take over, actually focused on specific customer problems and growing out of profitable niches.
The technology is useful, for some people, in some situations. It will get more useful for more people in more situations as it improves.
Current valuations are too high (Gartner hype cycle), after they collapse valuations will be too low (again, hype cycle), then it'll settle down and the real work happens.
The existing tech giants will just hoover up all the niche LLM shops once the valuations deflate somewhat.
There's almost a negligible chance any one of these shops stays truly independent, unless propped up by a state-level actor (China/EU)
You might have some consulting/service companies that will promise to tailor big models to your specific needs, but they will be valued accordingly (nowhere near billions).
Yeah, that's probably true, the same happened after the dot-com bubble burst. From about 2005-15 if you had a vaguely promising idea and a few engineers you could get acqui-hired by a tech giant easily. The few profitable ones that refused are now middle-sized businesses doing OK (nowhere near billions).
I don't know if the survivors are going to be in consulting - there is some kind of LLM-base product capability, you could conceivably see a set of LLM-based products building companies emerge. But it'll probably be a bit different, like the mobile app boom was a bit different from the web boom.
That's been the 'endgame' of technology improvements since the industrial revolution - there are many industries that mechanized, replaced nearly their entire human workforce, and were never terribly profitable. Consider farming - in developed countries, they really did replace like 98% of the workforce with machines. For every farm that did so, so did all of their competitors, and the increased productivity caused the price of their crops to fall. Cheap food for everyone, but no windfall for farmers.
If machines can easily replace all of your workers, that means other people's machines can also replace your workers.
Yeah, the overblown hype is a feature of the hype cycle. The same was true for the web - it was going to replace retail, change the way we work and live, etc. And yes, all of that has happened, but it took 30 years and COVID to make it happen.
LLMs might lead to AGI. Eventually.
Meanwhile every company that is spruiking that, and betting their business that that's going to happen before they run out of VC funding, is going to fail.
I think it will go in the opposite direction. Very massive closed-weight models that are truly miraculous and magical. But that would be sad because of all the prompt pre-processing that will prevent you from doing much of what you'd really want to do with such an intelligent machine.
I expect it to eventually be a duopoly like android and iOS. At world scale, it might divide us in a way that politics and nationalities never did. Humans will fall into one of two AI tribes.
Except that we've seen that bigger models don't really scale in accuracy/intelligence well, just look at GPT4.5. Intelligence scales logarithmically with parameter count, the extra parameters are mostly good for baking in more knowledge so you don't need to RAG everything.
Additionally, you can use reasoning model thinking with non-reasoning models to improve output, so I wouldn't be surprised if the common pattern was routing hard queries to reasoning models to solve at a high level, then routing the solution plan to a smaller on device model for faster inference.
Exactly. If some company ever does come up with an AI that is truly miraculous and magical the very last thing they'll do is let people like you and me play with it at any price. At best, we'd get some locked down and crippled interface to heavily monitored pre-approved/censored output. My guess is that the miracle isn't going to happen.
If I'm wrong though and some digital alchemy finally manages to turn our facebook comments into a super-intelligence we'll only have a few years of an increasingly hellish dystopia before the machines do the smart thing and humanity gets what we deserve.
By the time the capital runs out, I suspect we'll be able to get open models at the level of current frontier and companies will buy a server ready to run it for internal use and reasonable pricing. It will be useful but a complete commodity.
I know folk now that are selling, basically, RAG on lammas, "in a box". Seems a bunch of mid-level at SME are ready to burn budget on hype (to me). Gotta get something deployed in the hype-cycle for quarterly bonus.
I think we can already get open-weight frontier class models today. I've run Deepseek R1 at home, and it's every bit as good as any of the ChatGPT models I can use at work.
Which companies? Google and Microsoft are only up a little over the past several years, and I doubt much of their valuation is coming from LLM hype. Most of the discussions about x.com say it's worth substantially less than some years ago.
I feel like a lot of people mean that OpenAI is burning through venture capital money. It's debatable, but it's a huge jump to go from that to thinking it's going to crash the stock market (OpenAI isn't even publicly traded).
The "Magnificent Seven" stocks (Apple, Amazon, Alphabet, Meta, Microsoft, Nvidia, and Tesla) were collectively up >60% last year and are now 30% of the entire S&P500. They are all heavily invested in AI products.
I just checked the first two, Apple and Amazon, and they're trading 28% and 23% higher than they were 3 years ago. Annualized returns from the SP 500 have been a little over 10%. Some of that comes from dividends, but Apple and Amazon give out extremely little in the way of dividends.
I'm not going to check all of the companies, but at least looking at the first two, I'm not really seeing anything out of the ordinary.
Currently, Nvidia enjoys a ton of the value capture from the LLM hype. But that's a weird state of affairs and once LLM deployments are less dependent on Nvidia hardware, the value capture will likely move to software companies. Or the LLM hype will reduce to the point that there isn't a ton of value to capture here anymore. This tech may just get commoditized.
Nvidia is trading below its historical PE from pre-AI times at this point. This is just on confirmed revenue, and its profitability keeps increasing. NVIDIA is undervalued right now
Sure, as long as it keep selling $130B worth of GPUs each year. Which is entirely predicated on the capital investment in Machine Learning attracting revenue streams that are still imaginary at this point.
> None of that means that the current companies will be profitable ... The future could easily be "Open-weight models are moderately useful for some niches, no-name cloud providers charge slightly higher than the cost of electricity to use them at low profit margins".
They just need to stay a bit ahead of the open source releases, which is basically the status quo. The leading AI firms have a lot of accumulated know-how wrt. building new models and training them, that the average "no-name cloud" vendor doesn't.
> They just need to stay a bit ahead of the open source releases, which is basically the status quo
No, OpenAI alone additionally need approximately $5B of additional cash each and every year.
I think Claude is useful. But if they charged enough money to be cashflow positive, it's not obvious enough people would think so. Let alone enough money to generate returns to their investors.
I don't doubt the first part, but how true is the second?
Is there a shortage of React apps out there that companies are desperate for?
I'm not having a go at you--this is a genuine inquiry.
How many average people are feeling like they're missing some software that they're able to prompt into existence?
I think if anything, the last few years have revealed the opposite, that there's a large/huge surplus of people in the greater software business at large that don't meet the demand when money isn't cheap.
I think anyone in the "average" range of skill looking for a job can attest to the difficulties in finding a new/any job.
I think there is plenty of demand for software but not enough economic incentive to fulfill every single demand. Even for the software that is being worked on, we are constantly prioritizing between the features we need or want, deciding whether to write our own vs modifying something open source etc etc. You can also look at stuff like electron apps which is a hack to reduce programmer dev time and time to market for cross platform apps. Ideally, you should be writing highly performant native apps for each.
IMO if coding models get good enough to replace devs, we will see an explosion of software before it flattens out.
We're several years in now, and have lots of A:B comparisons to study across orgs that allowed and prohibited AI assistants. Is one of those groups running away with massive productivity gains?
Because I don't think anybody's noticed that yet. We see layoffs that makes sense on their own after a boom, and cut across AI-friendly and -unfriendly orgs. But we don't seem to see anybody suddenly breaking out with 2x or 5x or 10x productivity gains on actual deliverables. In contrast, the enshittening just seems to be continuing as it has for years and the pace of new products and features is holding steady. No?
> We're several years in now, and have lots of A:B comparisons to study across orgs that allowed and prohibited AI assistants. Is one of those groups running away with massive productivity gains?
You mean... two years in? Where was the internet 2 years into it?
You’re not making the argument you think you’re making when you ask “Where was the [I]ntwenet 2 years into it?”
You may be intending to refer to 1971 (about two years after the creation of ARPANet) but really the more accurate comparison would be to 1995 (about two years since ISPs started offering SLIP/PPP dialup to the general public for $50/month or less).
And I think the comparison to 1995, the year of the Netscape IPO and URLs starting to appear in commercials and on packaging for consumer products, is apt: LLMs have been a research technology for a while, it’s their availability to the general public that’s new in the last couple of years. Yet while the scale of hype is comparable, the products aren’t: LLMs still don’t anything remotely like what their boosters claim, and have done nothing to justify the insane amounts of money being poured into them. With the Internet, however, there were already plenty of retailers starting to make real money doing electronic commerce by 1995, not just by providing infrastructure and related services.
It’s worth really paying attention to Ed Zitron’s arguments here: The numbers in the real world just don’t support the continued amount of investment in LLMs. They’re a perfectly fine area of advanced research but they’re not a product, much less a world-changing one, and they won’t be any time soon due to their inherent limitations.
They're not a product? Isn't Cursor on the leaderboard for fastest to $100m ARR? What about just plain usage or dependency. College kids are using chrome extensions that direct their searches to chatgpt by default. I think your connection to the internet uptake is a bit weak, and then you've ended by basically saying too much money is being thrown at this stuff, which is quite disconnected from the start of you arg.
I think it's pretty fair to say that they have close to doubled my productivity as a programmer. My girlfriend uses ChatGPT daily for her work, which is not "tech" at all. It's fair to be skeptical of exactly how far they can go but a claim like this is pretty wild.
Both your and her usage is currently being subsidized by venture capital money.
It remains to be seen how viable this casual usage actually is once this money dries up and you actually need to pay per prompt.
We'll just have to see where the pricing will eventually settle, before that we're all just speculating.
> And I think the comparison to 1995, the year of the Netscape IPO and URLs starting to appear in commercials and on packaging for consumer products, is apt
My grandfather didn’t care about these and you don’t care about LLMs, we get it
> They’re a perfectly fine area of advanced research but they’re not a product
No, it lets good engineers parallelize work. I can be adding a route to the backend while Cline with Sonnet 3.7 adds a button to the frontend. Boilerplate work that would take 20-30 minutes is handled by a coding agent. With Claude writing some of the backend routes with supervision, you've got a very efficient workflow. I do something like this daily in a 80k loc codebase.
I look forward to good standard integrations to assign a ticket to an agent and let it go through ci and up for a preview deploy & pr. I think there's lots of smaller issues that could be raised and sorted without much intervention.
Even if the VC-backed companies jacked up their prices, the models that I can run on my own laptop for "free" now are magical compared to the state of the art from 2 years ago. Ubiquity may come from everyone running these on their own hardware.
Takes like yours are just crazy given the pace of things. We can argue all day if people are "too bullish" or literally on the market size of enterprise AI, but truly, absolutely no one knows how good these things will get and the problems they'll overcome in the next 5 years. You saying "I am confused on why people are still bullish" is implicitly building in some huge assumptions about the near future.
Most “AI” companies are simply wrapping the ChatGPT API in some form. You can tell from the job posts.
They aren’t building anything themselves. I find this to be disingenuous as best, and is a sign to me of bubble attribution.
I also think that re-branding Machine Learning as AI to also be disingenuous.
These technologies of course have their use cases and excel in some things, but this isn’t the ushering of actual, sapient intelligence, that for the majority of the term’s existence was the de facto agreed standard for the term “AI”. This technology does lack the actual markers of what is generally accepted as intelligence to begin with
Remember the quote that IBM thought there would be a total market for maybe 10 or 15 computer computers in the entire world? They were giant, and expensive, and very limited in application.
A popular myth, it seems to be made-up from a way-less-interesting statement about a single specific model of computer during a 1953 stockholder meeting:
> IBM had developed a paper plan for such a machine and took this paper plan across the country to some 20 concerns that we thought could use such a machine. I would like to tell you that the [IBM 701] machine rents for between $12,000 and $18,000 a month, so it was not the type of thing that could be sold from place to place. But, as a result of our trip, on which we expected to get orders for five machines, we came home with orders for 18.
And that might have been true for a period of time. Advancements made it so they could become smaller and more efficient, and opened up a new market.
LLMs today feel like the former, but are being marketed as the latter. Fully believe that advancements will make them better, but in their current state they're being touted for their possibilities, not their actual capabilities.
I'm for using AI now as the tool they are, but AI is a while off taking senior development jobs. So when I see them being hyped for doing that it just feels like a hype bubble.
Tesla is valued based on the hope that it'll be the first to full self-driving cars. I don't think stock markets need to make sense, you invest in things that if true, could have huge growth, that's why LLM is being invested in, because alternatives will make you some ROI, but if LLM do break through major disruption in even a handful of large markets, your ROI will be huge.
That's not really true. Just the entertainment value alone is already causing OpenAI to rate limit its systems, and they're buying up significant amounts of NVIDIA's capacity, and NVIDIA itself is buying up significant portions of the entire world's chip-making budget. Even if just limited to entertainment, the value is immense, apparently.
That's a funny comparison, I can and do use cryptocurrency to pay web hosting, VPN and a few other things as it's become the native currency of the internet. I love llms too but agree with the parent comment that says it's inevitable they'll be replaced with something better well Bitcoin seems to be sticking around for the long long term.
In my office most people use chatGPT or a similar LLM every day. I don't know a single coworker that's ever used a cryptocurrency. One guy has bought some crypto stocks.
> The current valuation for it is because the market thinks that it's able to write code like a senior engineer and have AGI, because that's how they're marketed by the LLM providers.
No it's not. If it was valued for that it'd be at least 10X what it is now.
While it could be said that LLMs are in the 'peak of inflated expectations', blockchain is definitely still in the 'trough of disillusionment'. Even if there was a way for blockchain to affordably facilitate everyday transactions without destroying the planet and somehow sideloading into government acceptance, it's not clear that there would be anything novel enough to motivate people to use it vs a bank - beyond a complete collapse of the banking system.
Blockchain is here to stay, this is way past the point of "believing in the tech" - recently an wss:// order book exchange (Hyperliquid) crossed $1T volume traded, and they started in 2023.
Blockchains are becoming real-time data structures where everyone has admin level read-only access to everyone.
HN doesn't like blockchain. They had the chance to get in very early and now they're salty. I first heard about bitcoin on HN, before Silk Road made headlines.
But I mirror the confusion why people are still bullish on it. The current valuation for it is because the market thinks that it's able to write code like a senior engineer and have AGI, because that's how they're marketed by the LLM providers.
I'm not even certain if they'll be ubiquitous after the venture capital investments are gone and the service needs to actually be priced without losing money, because they're (at least currently) mostly pretty expensive to run.