The fact that supply constrained GPUs holding value is negative indicator. It's like the tulips mania except bubble enthusiasts are buying wilted/dead tulips because live tulips supply constrained due to irrational demand. Now GPUs has more gross utility than tulips but seems like at current revenue/capex spend, every GPU is still negative net financial yield - they lose money - literally buying tulips and watching it wilt. Economically, better off simply not buying and losing more. That's the level of economic irrationality at place sustaining bubble, at least for hyperscaler/big tech balance sheet - small operators logic different and antagonistic to big operator demand/business model.

If cost of inference goes down 100x, would need 100x more demand. This makes overspending on GPU even more irrational. Jevons this, Jevons that but ultimately irrelevant. At end of day, leading players, hungergame winner candidates is saddling themselves with so much debt, even if they survive, post crash they are immediately uncompetitive against new entrant with blank slate and newer gen, more efficient GPUs that will be cheaper to buy/operate post crash when hardware prices will revert to mean.

It doesn't matter if some of the current players survive, they've basically stabbed and weakened themselves so much any healthy upstart in the future can wipe them out unless they lock in legislative protection... safety regulations, ban open source models etc.

That is the new bet, regulatory capture moat, because economic bet is entirely lost, especially with open models eroding mote.

You just stated yourself that it costs more now used than when they were new.

If everyone's running local then why are these larger companies dumping cash into data centres?

>H100 is nearing five years and costs more to buy a used one now than a new one when it was released :)

Because everyone is buying as they want to run their own models and not pay for a cloud service?

This is absolutely the calculus. There is no moat. It is survival of the best financed. Open AI and Anthropic are in very precarious situations.

Isn’t that the same bet that famously profitable companies like Uber did in the ride share market?

What costs are 1/100th?

> They think can outlast their competitors and capture a larger portion of the pie while the cost of inference keeps going down dramatically.

We are also within an arms race of training newer larger models with more speed while discontinuing older models.

Gemini/Chatgpt have already discontinued their models from 2024 (iirc) because they are using all their compute in serving/training newer models. Being quite frank, nobody is serving a model from 2024 as the intended use-case while having very little moat as open source models are catching up.

> Of course there will be market crashes and corrections and things like that and most companies won't survive, but the bet is that whoever survives ends up doing pretty well.

How so, by raising the prices? because the current prices aren't sustainable and I feel as if there would certainly be companies which will try for one reason or other to be cheaper to capture the market share because of the larger promise of whoever is able to get as market share. I had once thought about it and I don't think that even in an ideal world, they would end up doing pretty well given no moat.

Also even if a company survives and ends up being one of the survivors and makes profit in the ideal scenario you mention, then within some years other companies will try again and construct more datacenters and end up driving the prices down for everyone, so nobody knows how things might look down for 2-3 years let alone a decade, so I remain a bit skeptic currently so.

I had actually thought some on the economics of datacenters and I found it to be very related to power. The only ones which seems to be making money might be the power generators actually because power is the actual bottleneck rather than GPU's in datacenters from my understanding.

Though the power is raised at the cost of electricity bill increases for everybody including people living in houses. The job prospects are minimal as well, as a nation, aside from just getting investment just for the sake of it because AI's trendy right now, I feel like its a net negative deal for people living there.

In real or nominal dollars?

The “contents” represent the majority of the cost and meaningful functionality of what we call a “datacenter”. Those contents will not last for “real estate” debt timelines.

Wonder if you can pay margin calls with belief.

Apple might be a good counter example of what happens if you don't focus entirely on AI. Right now it seems to be doing ok.

How much of that is due to inflated RAM prices though? I wouldn't assume the current trend is going to continue.

They only reason that they are retaining value is there was not so much demand for GPUs in 2021 as there is today. Once the demand drops you will find then in dumpsters across our barren, burning dystopia.

They hold value as there is insane demand. The same reason a consumer RTX4090 costs more today than bew in 2021. Once the tide drops enough for hardware lead times to shorten to weeks, they will go the way of other used DC hardware - written off after 5 years.

Shorting the market always has a greater risk even if you are fairly confident something is true. You also have to be fairly confident of the timing.

If they're deliberately inflating the likely useful economic life of their assets to get a lower interest rate, it's hard to see how that wouldn't be classed as fraud.

It's the sort of behaviour that really does end up with people going to prison.

The math looks good on paper, but in reality enterprise AI is hard, most companies are realizing they are actually not seeing ROI from AI. One of my customers took about 8 months to rollout an AI initiative that by the time it launched and people got trained on it, it was already legacy. Also if you are 10x more productive with AI that doesn’t necessarily increases your billable output. There could be some super models like Mythos aimed at very specific hard tasks like drug development, but we have not seen any of that yet, and the clock is ticking.

It will obviously be a large part of the economy like online shopping is today. The companies that built up a lot of debt to be brand names in online shopping primarily went bankrupt because new companies had no debt (and perhaps no negative sentiment from early customer experiences.)

> It’s not ‘are we going to be able to generate 10% roic on compute’ the answer to that is yes.

Based on what? No AI company has ever made a cent in profit (exept for Nvidia lmao).

Meta entering cloud at scale would see huge competition and lowering of profit margins.

useful for what? my unemployed neighbours wordpress website?

Welcome to the world of too big to fail and the rich are beyond mortal judgement.

Smart glasses are about a 15 million unit per year business. Smart watches are about 150 million units per year. Meta owns about half the smart glasses business, but it hasn't provided them a platform they can control that has enough penetration to make a difference.

But wait it gets worse: since Meta hasn't got a platform comparable to a mainstream PC or mobile device OS, the Meta glasses business is vulnerable and subscale, as impressive as owning half the TAM is. Since Meta glasses aren't a companion to an existing platform (not even AndroidAR, even though they run Android) they will become a second tier choice as soon as Apple or Samsung ship a smart glasses product.

Great, let's keep expanding privacy-destroying products and hyper-surveillance in the name of corporate profits!

> Their strategy to let Siri stagnate for 15 years and let everyone else take that market?

What have they lost by doing this? Did anyone switch from iPhones to something else?

Google's Assistant was and is better than Siri's. How many switched to Android because of it?

I was talking more about massive investments in compute and model development. Voice assistants are barely AI, but real AI might help make better ones in the future. Similarly, I think Apple's failed attempts to add AI to macOS and iOS are a huge justification of avoiding capital investments in compute and AI frontier models.

There's two separate things here: 1) the underlying models, and 2) how they are applied to real world tasks. Both are changing at tremendous speed. The underlying models, 1), can be purchased as a component, so there's little advantage to vertical integration, as long as there's a robust competitive market, and boy is there ever. And Apple was never going to be the big B2B provider for LLM compute and models, such sales are just not in their corporate DNA.

For 2), application of AI in Apple's products, nobody knows how to do that correctly yet, and it's going to a wild few years of people trying things and failing before something comes to pass. Waiting to see where to actually find a competitive edge totally makes sense. Microsoft's Copilot bungling was far worse than Apple AI's bungling, for example. I can't even get access to Microsoft Office anymore, it seems, I'm instead confronted with a dumb chat interface that doesn't even know how to launch a new document or open existing ones. At least Apple never destroyed their brand like that...

I think that's precisely what the previous commenter is saying. Sit on the side lines, and let other people bloody themselves up.

Similar in a way to dot com. It's not to say ML won't have practical application in the future, but the likelihood that it will have specifically this form is low and worth waiting until the dust settles and a more commonly accepted utility presents itself.

If AI/ML were monstrously useful in its current form the companies pushing it would not need to be hawking products; people would be bashing their doors down. I think that's why in areas where it's more directly applied to a known problem set (like Pharma research, and I'm hoping someone with Pharma expertise can pipe up here) there has been more natural pickup.

Coming from trading and markets, ML has been a part of the mix in quantitative strategies for...well, nearly 20 years (by definition I suppose). Spaces with obvious utility will see rapid adoption. Worth waiting that out, honestly.

The point was that one rational approach is that it's OK to not be top ranked in a market that has significant medium-term profitability issues. Apple has no problem creating new markets, but not if being top ranked requires years and years and years without return on investment after launch, and large subsidies. Not to say Apple hasn't failed at creating new markets either, or anything like that.

Claude is a far superior _model_ to whatever Siri uses, but in terms of the Claude app correctly transcribing speech, in my experience it's as bad or worse than Siri (which is already bad). Doesn't matter how good the model is if it misinterprets what you say.

> They just have such a strong hardware

"just" doing a very heavy lifting here

Are you using Gemini Flash or Gemini Pro?

Are you about to compare the free tier of Gemini to the paid tier of OpenAI?

> It's been far more helpful with the random collection of legal and health problems I've thrown at it.

As in convincing?

Or accurate?

I'm referring to the equity offering that started in June and has a second component that starts in 2026Q3. Most of this raise came from the sale of Class A and Class C stock. A fraction came from the sale of convertible stock. To my knowledge, none of this raise came from the sale of bonds.

Also, looks like I got it wrong and they've only raised $45B to date. The rest will come as part of the ATM offering program that begins in Q3.

more details here: https://www.sec.gov/Archives/edgar/data/1652044/000119312526...

If the margins aren't as high then there will be a repricing for all the massive cloud companies, which means several trillions worth of valuations to be cut from the companies.

AWS/Azure/GCP/Oracle/SpaceX/etc neoclouds... are worth a combined 10+Trillion. That going down by 50-70% is going to be insane.

And then they'll be valued like more normal companies as well. Which will mean a drastic re-rating.

Also all these companies went from buybacks to dilution and debts again.

Sink rate! Sink rate! Pull up! Pull up! Too low; terrain. Too low; terrain. Wind shear! Stall! Stall!

The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.

For certain values of ‘dramatic improvement’. Is lots more important work being done with LLMs? Not much sign of it yet, they’ve been helpful for experts at times (e.g. vuln research or maths research) but that hardly justifies the vast sums for Google investors.

Presumably at some point you need a measurable productivity return yea? Maybe organizations are not built around skill and aptitude so much as liability, which LLMs cannot provide barring (very welcome and also very unlikely) legislation in the US.

No, because the LLMs will keep getting more efficient and capable. Distillation and quantization will mean firms spending trillions on giant data centres are left holding the bag. I suspect Apple ends up laughing all the way to the bank.

https://github.com/microsoft/BitNet

It's an internet/railroad issue again.

Tech is real, impact is gigantic, long term winners hard to predict, capex spending hard to recoup soon, if ever.

And differently than internet or rails, you don't build once and maintain later, but enter a loop of ever increased spending to keep on top of the arms race and ever exploding usage.

> not sure how to square this with the dramatic improvement in LLM capabilities

A good tech demo doesn’t matter to the business if the products don’t become profitable at the scale the investment chased.

I'm not sure I've seen what I would call dramatic improvement since maybe GPT4?

Sure, things got better. But I'd call it iterative more than revolutionary. I still wouldn't trust any of the models to do anything meaningful unattended. They all still do dumb shit all the time.

Plus, even if they were genuinely dramatically better, the businesses sure as hell aren't. They're burning money left and right, they have no moat, Chinese open models are basically equivalent these days. What's the path to profitability, or hell, break-even? How do you envision this being anything but a giant money pit?

They've literally rated the debt as too big to fail in order to get foreign sovereign wealth funds (mostly gulf states) to agree to put up the money for loans. This has been happening this entire time.

You're probably right but honestly why would anyone stay in software if that happens?

Diversify! Historically, the average length of a recession has been 12-24 months. So set up a system whereby you won’t screw’s yourself over by selling when things are low, but instead you can weather the storm.

Build a rainy day fund. Determine how much cash you will need if you are out of a job and how long you think that will last, allocate some portion of that amount into low risk bonds. Russ way if you need cash you aren’t selling investments at a big loss.

If you have enough liquidity put some in real estate as a forced savings vehicle as it’s harder to liquidate than stocks. Then just sit out any coming storm.

> they didn't plan to just spend cash without any return.

No return? Annual earnings have kept increasing at 20-40% for the last 4 years.

Plus there's this:

https://www.theregister.com/paas-and-iaas/2026/07/22/google-...

> Google Cloud is killing it

> It's Alphabet's fastest-growing business and now makes up more than a fifth of the juggernaut's revenue and operating profit

There is pretty clear return as of now. And half a trillion in backlog

Also the ~4% drop is really not a big swing for earnings. This looks like a non story

Since when is investing in infrastructure burning money?

If there is a huge demand for shipping goods internationally, investing in ships and planes isn't burning money.

There is massive demand for compute in the world right now, Google is investing in that area. That's a good thing.

If a company’s value was completely representated within their balance sheet, you would just run a computer program and be done. The problem is 1) balance sheets can be manipulated in legal ways to support a specific narrative 2) growth is governed by vision + strategy + execution.

For example, Apple the year before the iPhone got launched isn’t an attractive investment. They’re a one hit wonder with the iPod saving them from bankruptcy and the market has been fully saturated. The year the iPhone gets released their balanced sheet hasn’t really changed.

Meta at least has a theory that it will transform their ad business. No guarantees but it seems like a pretty decent theory, with direct connections to revenue.

Have you checked the numbers? I thought for most of the ad driven companies the numbers are up.

Are you asserting that there exists a time period to analyze that is not arbitrary and meaningless? If so, which?

The reason why 2026 specifically is interesting is because it wasn't until late December of last year that AI models started to demonstrate particularly interesting capabilities, while we finally got IPO announcements for OpenAI and Anthropic. Assuming that the market works at all, it should be pricing in these events.

I'm seeing 8.43% for GOOG.

Not sure where you got 3.33%, looks to me like GOOGL is +9.44% YTD while GOOG is +9.1%.

sure, here you go https://news.ycombinator.com/item?id=49021006