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> Their measured performance on things people care about keep going up, and their software stack keeps getting better and unlocking more performance on existing hardware

I'm more concerned about fully-loaded dollars per token - including datacenter and power costs - rather than "does the chip go faster." If Nvidia couldn't make the chip go faster, there wouldn't be any debate, the question right now is "what is the cost of those improvements." I don't have the answer to that number, but the numbers going around for the costs of new datacenters doesn't give me a lot of optimism.

> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?

OpenAI has $1.15T in spend commitments over the next 10 years: https://tomtunguz.com/openai-hardware-spending-2025-2035/

As far as revenue, the released numbers from nearly anyone in this space are questionable - they're not public companies, we don't actually get to see inside the box. Torture the numbers right and they'll tell you anything you want to hear. What we _do_ get to see is, eg, Anthropic raising billions of dollars every ~3 months or so over the course of 2025. Maybe they're just that ambitious, but that's the kind of thing that makes me nervous.

> OpenAI has $1.15T in spend commitments over the next 10 years

Yes, but those aren't contracted commitments, and we know some of them are equity swaps. For example "Microsoft ($250B Azure commitment)" from the footnote is an unknown amount of actual cash.

And I think it's fair to point out the other information in your link "OpenAI projects a 48% gross profit margin in 2025, improving to 70% by 2029."

> "OpenAI projects a 48% gross profit margin in 2025, improving to 70% by 2029."

OpenAI can project whatever they want, they're not public.

They still have shareholders who can sue for misinformation.

Private companies do have a license to lie to their shareholders.

> Yes, but those aren't contracted commitments, and we know some of them are equity swaps.

It's worse than not contracted. Nvidia said in their earnings call that their OpenAI commitment was "maybe".

Sounds like the railway boom.. I mean bond scam's

The fact that there's an incestual circle between OpenAI, Microsoft, NVidia, AMD, etc.. where they provide massive promises to each other for future business is nothing short of hilarious.

The economics of the entire setup are laughable and it's obvious that it's a massive bubble. The profit that'd need to be delivered to justify the current valuations is far beyond what is actually realistic.

What moat does OpenAI have? I'd argue basically none. They make extremely lofty forecasts and project an image of crazy growth opportunities, but is that going to ever survive the bubble popping?

I still don't really understand this "circle" issue. If I fix your bathroom and in return you make me a new table, is that an incestuous circle? Haven't we both just exchanged value?

The circle allows you to put an arbitrary "price" on those services. You could say that the bathroom and table are $100 each, so your combined work was $200. Or you could claim that each of you did $1M work. Without actual money flowing in/out of your circle, your claims aren't tethered to reality.

You don’t think real money is changing hands when Microsoft buys Nvidia GPUs?

It's a soft version of money printing basically. These firms are clearly inflating each other's valuations by making huge promises of future business to each other. Naively, one would look at the headlines and draw the conclusion that much more money is going to flow into AI in the near future.

Of course, a rational investor looks at this and discounts the fact that most of those promises are predicated on insane growth that has no grounding in reality.

However, there are plenty of greedy or irrational investors, whose recklessness will affect everyone, not just them.

For Nvidia shares: converting cash into shares in a speculative business while guaranteeing increasing demand for your product is a pretty good idea, and probably doesn't have any downsides.

For the AI company being bought: I wouldn't trust these shares or valuations, because the money invested is going on GPUs and back to Nvidia.

What about when Nvidia sells GPUs to a client and then buys 10% of their shares?

Their shares will be based on the client's valuation, which in public markets is externally priced. If not in public markets it is murkier, but will be grounded in some sort of reality so Nvidia gets the right amount of the company.

My point was that's an indirect subsidy. NVIDIA is selling at a discount to prop up their clients.

GPUs are supply constrained and price isn't declining that fast so why do you expect the token price price to decrease. I think the supply issue will resolve in 1-2 years as now they have good prediction of how fast the market would grow.

Nvidia is literally selling GPUs with 90% profit margin and still everything is out of stock, which is unheard of before.

>Further, I'm sure most people heard the mention of an unnamed enterprise paying Anthropic $5000/month per developer on inference

Companies have wasted more money on dumber things so spending isn't a good measure.

And what about the countless other AI companies? Anthropic has one of the top models for coding so that's like saying there ins't a problem pre dot com bubble because Amazon is doing fine.

The real effects of AI is measured in rising profit of the customers of those AI companies otherwise you're looking at the shovel sellers

> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?

I mean the amount of money invested across just a handful of AI companies is currently staggering and their respective revenues are no where near where they need to be. That’s a valid reason to be skeptical. How many times have we seen speculative investment of this magnitude? It’s shifting entire municipal and state economies in the US.

OpenAI alone is currently projected to burn over $100 billion by what? 2028 or 2029? Forgot what I read the other day. Tens of billions a year. That is a hell of a gamble by investors.

The flip side is that these companies seem to be capacity constrained (although that is hard to confirm). If you assume the labs are capacity constrained, which seems plausible, then building more capacity could pay off by allowing labs to serve more customers and increase revenue per customer.

This means the bigger questions are whether you believe the labs are compute constrained, and whether you believe more capacity would allow them to drive actual revenue. I think there is a decent chance of this being true, and under this reality the investments make more sense. I can especially believe this as we see higher-cost products like Claude Code grow rapidly with much higher token usage per user.

This all hinges on demand materialising when capacity increases, and margins being good enough on that demand to get a good ROI. But that seems like an easier bet for investors to grapple with than trying to compare future investment in capacity with today's revenue, which doesn't capture the whole picture.

I am not someone who would ever be ever be considered an expert on factories/manufacturing of any kind, but my (insanely basic) understanding is that typically a “factory” making whatever widgets or doodads is outputting at a profit or has a clear path to profitability in order to pay off a loan/investment. They have debt, but they’re moving towards the black in a concrete, relatively predictable way - no one speculates on a factory anywhere near the degree they do with AI companies currently. If said factory’s output is maxed and they’re still not making money, then it’s a losing investment and they wouldn’t expand.

Basically, it strikes me as not really apples to apples.

Consensus seems to be that the labs are profitable on inference. They are only losing money on training and free users.

The competition requiring them to spend that money on training and free users does complicate things. But when you just look at it from an inference perspective, looking at these data centres like token factories makes sense. I would definitely pay more to get faster inference of Opus 4.5, for example.

This is also not wholly dissimilar to other industries where companies spend heavily on R&D while running profitable manufacturing. Pharma semiconductors, and hardware companies like Samsung or Apple all do this. The unusual part with AI labs is the ratio and the uncertainty, but that's a difference of degree, not kind.

> But when you just look at it from an inference perspective, looking at these data centres like token factories makes sense.

So if you ignore the majority of the costs, then it makes sense.

Opus 4.5 was released on November 25, 2025. That is less than 2 months ago. When they stop training new models, then we can forget about training costs.

Inference costs scale linearly with usage. R&D expenses do not.

That's not to mention that Dario Amodei has said that their models actually have a good return, even when accounting for training costs [0].

[0] https://youtu.be/GcqQ1ebBqkc?si=Vs2R4taIhj3uwIyj&t=1088

> Inference costs scale linearly with usage. R&D expenses do not.

Do we know this is true for AI?

Yes. R&D is guaranteed to fall as a percentage of costs eventually. The only question is when, and there is also a question of who is still solvent when that time comes. It is competition and an innovation race that keeps it so high, and it won't stay so high forever. Either rising revenues or falling competition will bring R&D costs down as a percentage of revenue at some point.

Yes, but eventually may be longer than the market can hold out. So far R&D expenses have skyrocketed and it does not look like that will be changing anytime soon.

That's why it is a bet, and not a sure thing.

It’s pretty much the definition of fixed costs versus variable costs.

You spend the same amount on R&D whether you have one hobbyist user or 90% market share.

I'm not taking a side here - I don't know enough - but it's an interesting line of reasoning.

So I'll ask, how is that any different than fabs? From what I understand R&D is absurd and upgrading to a new node is even more absurd. The resulting chips sell for chump change on a per unit basis (analogous to tokens). But somehow it all works out.

Well, sort of. The bleeding edge companies kept dropping out until you could count them on one hand at this point.

At first glance it seems like the analogy might fit?

Someone else mentioned it elsewhere in this thread, and I believe this is the crux of the issue: this is all predicated in the actual end users finding enough benefit in LLM services to keep the gravy train going. It's irrelevant how scalable and profitable the shovel makes are, to keep this business afloat long term, the shovelers - ie the end users - have to make money using the shovesl. Those expectations are currently ridiculously inflated. Far beyond anything in the past.

Invariably, there's going to be a collapse in the hype, the bubble will burst, and an investment deleveraging will remove a lot of money from the space in a short period of time. The bigger the bubble, the more painful and less survivable this event will be.

>Consensus seems to be that the labs are profitable on inference. They are only losing money on training and free users.

That sounds like “we’re profitable if you ignore our biggest expenses.” If they could be profitable now, we’d see at least a few companies just be profitable and stop the heavy expenses. My guess is it’s simply not the case or everyone’s trapped in a cycle where they are all required to keep spending too much to keep up and nobody wants to be the first to stop. Either way the outcome is the same.

This is just not true. Plenty of companies will remain unprofitable for as long as they can in the name of growth, market share, and beating their competition. At some point it will level out, but while they can still raise cheap capital and spend it to grow, they will.

OpenAI could put in ads tomorrow and make tons of money overnight. The only reason they don't is competition. But when they start to find it harder to raise capital to fund their growth, they will.

I understand how this has worked historically but when have we seen this amount of money invested so rapidly into a new area? Crypto, social media, none of it comes close. I just don’t think those rules apply anymore. As I mentioned in a previous comment this is literally altering the economies of cities and states in the US, all driven by tech company speculation. This could be my own ignorance, but it seems to me that we have never seen anything like this, and I really can’t find a single sector that has ever seen this kind of investment before. I guess maybe railroads across the US in the 19th century? I’d have to actually look at what those numbers looked like and it’s pretty hard to call that comparing apple to apples.

> I mean the amount of money invested across just a handful of AI companies is currently staggering and their respective revenues are no where near where they need to be. That’s a valid reason to be skeptical.

Yes and no. Some of it just claims to be "AI". Like the hyperscalers are building datacenters and ramping up but not all of it is "AI". The crypto bros have rebadged their data centers into "AI".

> The crypto bros have rebadged their data centers into "AI"

That the previous unsustainable bubble is rebranding into the new one, is maybe not the indicator of stability we should be hoping for

> Further, I'm sure most people heard the mention of an unnamed enterprise paying Anthropic $5000/month per developer on inference

I haven't and I'd like to know more.