Just last week I was on the call with a a techy architect from one of the major-major financial services companies, and he was discussing getting token capacity and his words on third best option was "we'd get it from Coreweave or whoever is still alive". So yeah, I am pretty sure that if that's the sentiment from software people at the major-major financial company, Coreweave is cooked.

▲ aurareturn 3 hours ago | parent | next [-]

  So yeah, I am pretty sure that if that's the sentiment from software people at the major-major financial company, Coreweave is cooked.

The ironic thing is, your friend is still trying to get compute, which by all indications is still extremely scarce.

So there seems to be a huge mismatch: demand hasn't waned, but the narrative around AI has changed in the last month.

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cm2187 14 minutes ago | parent | next [-]

There is no secret about the mismatch. Voters last week have elected people who are openly hostile to datacenters, and for some of them hostile to AI outright. The demand is there (at what price is a longer term debate). But if you can't power on new datacenters this doesn't do any good.

To give you an idea of the scale. US has currently about 50 GW of DC online. Depending on the source, you are looking at between 60 GW and 400 GW of new DC currently planned. For reference the total dependable capacity of the US grid is around 1000 GW.

I think there is a scenario where compute cost sky rockets while chip prices crash, as they end up unused in warehouse because of the lack of power and DC capacity.

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otabdeveloper4 2 hours ago | parent | prev [-]

Of course there is still demand for RAM and SIMD compute!

But also hoarding hardware in glorified warehouses is not the genius business idea everyone thinks it is.

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aurareturn 2 hours ago | parent | next [-]

I'm assuming his friend's finance company is planning to make use of the cloud compute they find in some finance workload.

I don't think they're buying GPUs outright to store in a warehouse.

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dist-epoch an hour ago | parent | prev | next [-]

Any sort of hardware produced in the last 5 years has at least doubled in price. Some, like RAM are 3-5x more expensive.

So whoever hoarded it in warehouses could sell it now immediately for at least 100% profit.

They seem like geniuses to me now, worst case they make 100% profit on the investment.

	▲	ahartmetz 19 minutes ago \| parent [-]
		It's only RAM, mass storage and GPUs. Desktop CPUs, mainboards, PSUs, and cases have become cheaper because PC building demand has declined. Peripheral prices have remained largely unchanged.

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tourmalinetaco 2 hours ago | parent | prev | next [-]

Naturally; it should be hoarded in my glorified homelab, where I can more readily listen to the sweet purr of fans.

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41353aab 43 minutes ago | parent | prev [-]

[dead]

▲ maerF0x0 6 hours ago | parent | prev [-]

Which is such a wild thing about our industry. These can be simultaneously true

- No one has available tokens/gpus/datacenter for sale at any price

- No one will lend people money to build it

▲ KingMachiavelli 5 hours ago | parent | next [-]

This is how all capital-intensive utilities operate. The marginal cost of using a little more power or tokens is easy to justify because you can at any point stop using more.

But the cost to add more power capacity is huge and highly variable. Just because oil is expensive today doesn’t mean the capex to pump more is justified.

Really what AI needs is large customers that will commit to large spend over the next 5-10 years. Without this AI companies are basically offering duration arbitrage of electricity. Not a good place to be; the finances will always look bad.

Unfortunately, the amount of capital required by the entire AI industry is enough that counterparty risk is now a concern.

▲ belZaah 5 hours ago | parent | next [-]

Anthropic and OpenAI are these customers, willing to buy the infrastructure. But they are running out of cash, too.

▲ aurareturn 5 hours ago | parent [-]

Wait, Anthropic has $112 billion in cash right now and generates about $70B in revenue per year and grew 10x YoY. If they succeed in raising $100B in cash in the IPO, which I believe they will, they'll have $212B. If their growth declines to 3x (down from 10x) in 2027, they'll still be at $230B in yearly revenue.

Further more, their compute commitments are over 10-20 years. Their average quarterly compute commitment is only about $11B - $15B which they have already surpassed with their revenue.

At $230b/year, they’d be at around $57b in quarterly revenue against compute of around $16b. This is not bleeding cash.

Why do you say they're running out of cash? Can we see your math?

▲ otherme123 4 hours ago | parent | next [-]

The only reason Anthropic has cash is because VC keep giving them the money. They never had a positive quarter, with revenue always lower than expenses regardless of their growth, thus burning money from VC.

If VC money gets tight, they would have to issue debt, that pay interests. With a current negative EBITDA, their bond will be junk at IPO, paying higher interests and having a lot less market than AAA debt (e.g. Google has access to that market). Both things combined translate to "running out of cash".

▲ aurareturn 3 hours ago | parent [-]

They were cash flow positive for 2 quarters in a row.

Their S1 is public. We have AI. Are people this lazy? Throw it into ChatGPT and ask it to confirm the numbers.

▲ ElProlactin 2 hours ago | parent | next [-]

> Are people this lazy?

It's not exactly laziness. It's that most people don't understand basic financial concepts like fixed versus operating costs.

Companies like Anthropic and OpenAI have invested huge sums in building/training models. Those are the headline numbers everyone talks about. What they don't understand is that the gross profit margins on AI model inference are very high.

The question is whether these companies ever get to the point where the profit from inference justifies (recoups) the investment made in training. If they have to keep investing vast sums in training new models, and/or competition causes the margins on inference to collapse, they're in trouble. But if training becomes less costly and inference margins don't compress too much, it's a totally different story.

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grey-area an hour ago | parent | next [-]

A few problems for Anthropic or OpenAI:

1. Model Build/Training costs are escalating if anything

2. They are in an arms race with competitors and can't afford to stop building new models - if they lose the perception of leading, they lose lots of customers overnight.

3. Inference margin is under attack from more specialised models and open weights models.*

4. They have no moat given how simple the interface to the products is - a bunch of text files and text prompts, and little prospect of building a moat.

So those all run counter the possibility of coasting on previous research. To me the most promising avenue for making money for these companies is to replace Google, not AGI or taking over the world's knowledge work. That would mean they are overvalued. Even coasting on previous research, how long would it take to pay off the money they have spent?

* https://strandsagents.com/blog/amazon-saves-millions-replaci...

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otherme123 2 hours ago | parent | prev | next [-]

> What they don't understand is that the gross profit margins on AI model inference are very high.

Can we be sure of this? The second they tried to hike the prices to be profitable, they lost a lot of clients, or a lot of clients lowered they token expenditure. In any market margins tend to go to zero. I am using frontier models, but only because they are dirty cheap if not free. If they raise the price, I just switch to a cheaper model, start exploring the option of renting a GPU-VPS or buying my own card. Or reducing my AI usage, that currently includes stupid useless things like "translate this cooking recipe from french... for free".

> But if training becomes less costly and inference margins don't compress too much, it's a totally different story.

They are being priced as the "ifs" are certainties, and not only a possibility. If anything goes a millimetre below perfect for them, their finances collapse. High margins attract competitors, and unless you have a huge moat (and their only moat is expensive training), your high margins vanish.

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KaiserPro 39 minutes ago | parent | prev | next [-]

> gross profit margins on AI model inference are very high.

the gross margins on growing potatoes is very high, if you exclude all the other costs needed to get the potatoes to market.

Think about it, you take some potatoes you had left over from last yeah, dump them in the ground and wait 9 months. Boom pure profit.

This of course excludes the cost of the land, machinery needed to plant, till and harvest, storage, pesticide, fungicide and herbicide.

Then the transport, cleaning and packaging.

If you arbitrarily cut out all the bits of the business needed to make revenue, everything is a profit.

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surgical_fire an hour ago | parent | prev [-]

> It's not exactly laziness. It's that most people don't understand basic financial concepts like fixed versus operating costs.

> Companies like Anthropic and OpenAI have invested huge sums in building/training models. Those are the headline numbers everyone talks about. What they don't understand is that the gross profit margins on AI model inference are very high.

Only when you ignore that the models they provide inference for have an extremely high cost and pretend they don't depreciate. They keep having to train new models. I don't think they recovered the cost for a model when a new one releases.

It is kind of cute to say other people "don't understand basic financial concepts like fixed versus operating costs" when you seem to not understand basic financial concepts such as depreciation and amortization.

Also, and this is important, we don't really know their true compute cost. We don't know if their compute with the likes of Google and Amazon were initially subsidized, and for how long it might be. In Q2 they also got to not pay SpaceX for compute for a while.

	▲	ElProlactin 38 minutes ago \| parent [-]
		What's cute is that you're falling into the same trap of confusing accounting concepts. Gross margin is revenue minus cost of revenue (COGS). COGS is basically the compute that serves inference (rented capacity, or depreciation on owned GPUs used for inference, plus expenses like support, etc.). Model training is normally treated as R&D. Under US GAAP, R&D is expensed as it occurs and is below gross profit as an operating expense. It is not COGS. You mention "depreciation" but depreciation applies to physical assets like GPUs. The GPU depreciation that belongs in gross margin is already in COGS. A trained model would be an intangible asset, so that is amortization, not depreciation. Even that only applies if the training cost was capitalized, and under GAAP it generally wouldn't be. The full cost is expensed when it's incurred. Nobody is "pretending models don't depreciate." Their cost is counted all at once but not in gross margin because that's simply not what gross margin measures. Obviously, models lose value fast in an economic sense. They get replaced within a year or two and competition limits what they can earn. But that's an argument about whether the R&D expense is sustainable. It doesn't mean that gross margins are being misstated. "Inference has high gross margins" and "training is extremely expensive" can both be true, which oddly seems to be something many people have trouble comprehending. But this is the crux of the questions that will determine whether these businesses are viable long-term: 1. Will training costs fall over time, or at least grow more slowly than revenue? 2. Does the gross profit a model earns from inference before it's replaced exceed what it cost to train? 3. Will inference margins hold up as competition and open-weight models push prices down? You have a legitimate point about compute costs. If the deals with Google, Amazon or anyone else are priced below market, that directly inflates gross margin, because it lowers COGS. But we don't know those terms, so that's just speculation, and it's still a different argument from depreciation. To reiterate what I wrote: > The question is whether these companies ever get to the point where the profit from inference justifies (recoups) the investment made in training. If they have to keep investing vast sums in training new models, and/or competition causes the margins on inference to collapse, they're in trouble. But if training becomes less costly and inference margins don't compress too much, it's a totally different story.

▲ dd8601fn 3 hours ago | parent | prev | next [-]

I just did that. It says that’s not true.

	▲	aurareturn 3 hours ago \| parent [-]
		It is true. Try Opus 5.5.

▲ otherme123 3 hours ago | parent | prev | next [-]

Ha! Calling people "lazy" for not using ChatGPT? I can read an income statement, is not that difficult, no need for ChatGPT. This is the S1, ammended:

https://www.sec.gov/Archives/edgar/data/2130357/000162828026...

Income at page F-7 (about 350 overall):

  (units in Kilo-millions)

  Revenue in first 2026 9-months  33.2
  Compute and infra expenses      18.8
  Personel                         4.8
  Sales                            5.5
  Administrative expenses          5.3
  Operating earnings (loss)       (1.2)

* In the case you are confused, () in finances means negative values.

Q-3 only, this is calculated substracting first half from 9 first months:

  Revenue in Q3                   16.9
  Compute and infra expenses       9.9
  Personel                         1.7
  Sales                            2.3
  Administrative expenses          3.6
  Operating earnings (loss        (0.6)

Cashflow might be confusing here, because the cashflow table include huge items like "Change in fair value of convertible notes, derivative liabilities, and SAFEs" (translates to "we have paper valued at $X, but now it is valued at $Y", but it is still paper) or "Proceeds from issuance of redeemable convertible preferred stock, net of issuance cost" (translates to "we sold paper to VCs for $X") that are not business or recurrent items, or at least I hope Anthropic don't plan on "earning" money by selling paper to VCs forever.

	▲	aurareturn 2 hours ago \| parent [-]
		Operating income and cash flow are different numbers. The problem with AI is that it's still dependent on the skill of the prompter.

▲ Ygg2 an hour ago | parent | prev | next [-]

> Their S1 is public.

Their S1 was taken down. As of today (https://www.reuters.com/world/china/global-markets-view-euro...) This is the second time they changed it.

Apparently they file it with Claude.

▲ ViewTrick1002 3 hours ago | parent | prev [-]

Those figures are “earnings before bad things”.

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aurareturn 3 hours ago | parent [-]

What are the bad things?

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zrobotics 2 hours ago | parent [-]

All the debt they have accrued?

	▲	aurareturn an hour ago \| parent [-]
		What debt?

▲ skew-aberration 3 hours ago | parent | prev | next [-]

Small gripe but I don't like 'declines to 3x'. 3x growth would mean repeating this year's record growth twice over, which is smaller relatively but an increase in absolute growth rate.

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aurareturn 3 hours ago | parent [-]

So what do you think their growth YoY will be in 2027?

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skew-aberration 2 hours ago | parent [-]

I have seen most revenue projections are on the order of 2-3x, but I suspect about 4-5x.

IMHO profitability will be worse though. They will contain to take on more and more investments - training, R&D, and 'sales and marketing' for enterprise share. They will face more competition than ever before. The range and types of models will continue to diversify, with real-time models for robotics and computer use paving the way for new entrants.

The investor confidence to back this doubling down will be spurred by rapid progress, panic deregulation while old-guard corporate democrats still hold power in the US, and much stricter sanctions against Chinese competitors, their models, and their eventual domestic hardware. The tech world will look a lot more factional by that point, with pseudo mergers & acquisitions amongst the big companies beginning, unabated by a toothless FTC. Anthropic will probably get closer to Amazon, and we could see big moves amongst Meta, Microsoft, and/or Google too.

Ultimately, Anthropic and OpenAI will maintain their market leading positions through at least 2028/2029 but only after a protracted period of significant equity dilution due to the expensive acquisitions and large outside investments which will be necessary in these market conditions.

Chinese hardware and models will look like a serious threat by the end of 2027 but will be largely locked out of US/Western markets. At the point the equation will have changed drastically - credit issues in the West led by mortgage security collapses will shape the investment environment and 2028 election, and rapid developments in physical technology and manufacturing will take the spotlight away from intelligence itself.

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aurareturn 2 hours ago | parent [-]

If 5x, then they'll end Q3 2027 with about $350B annualized revenue.

Looking at their 2027 compute commitments, it looks like around $72B total.

So I don't understand where this "burning cash" claim comes from by a few people here.

	▲	skew-aberration an hour ago \| parent [-]
		I'm going to lock in $325B. People lack the accounting know-how to talk about it correctly. But compute for inference is just not the main expense right now. If they take on more funding for training and/or build out, then they have to share the profits with more people. The growing revenue is shared by more people. As more options vest, they have to share with more people. If they need to acquire external companies for particular capabilities, profit is shared. If they have to pay hand over fist for hyperscaler compute in 2027 - more profit shared. So as their revenue grows rapidly, the fraction of that revenue which they've promised to other people is growing too - case in point, they operated at an $80B loss on paper this year because the unrealized future-profits promised to early investors is so valuable relative to current revenues. If they have to make more such deals to continue investment (e.g a low IPO price which require shelling even more shares to stay competitive), then they will continue to create vast value but less of that value will be captured by the current investors (the 'burning cash' may go to compute providers, outside investors, as discounts to enterprise and consumers, etc instead of to Anthropic).

▲ byzantinegene 4 hours ago | parent | prev | next [-]

they also have $1T in commitments

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aurareturn 4 hours ago | parent [-]

Yes, over 10-20 years and I'm guessing many of those contracts are just intents and not hard commitments.

Let's say it's 15 years. That's $16B/quarter in commitment. They had $17B in revenue in Q3 2026.

If anything, they're far too conservative.

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mplappert 3 hours ago | parent | next [-]

Most of their compute commitments are non-cancellable.

	▲	aurareturn 3 hours ago \| parent [-]
		Based on how their growth is going, I think they're far too conservative and they should sign more.

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2 hours ago | parent | prev [-]

[deleted]

▲ ImPostingOnHN 4 hours ago | parent | prev | next [-]

now do the math with profit instead of revenue

most recently (on both a quarterly and annual basis), it was negative

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aurareturn 3 hours ago | parent [-]

Why with profit instead of revenue?

Their revenue pays for the compute.

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ahtihn 3 hours ago | parent [-]

Their revenue pays for expenses. If profit is negative, revenue doesn't cover their expenses.

They can't just turn off all other expenses and just pay for compute.

	▲	aurareturn 3 hours ago \| parent [-]
		Their revenue does cover their expenses - at least for last 2 quarters.

▲ revolvingthrow 3 hours ago | parent | prev | next [-]

Growing from 7b revenue to 70b revenue is crazy % growth, but with 2026 being the tokenmaxxing year AND it being the year where models are finally 'good enough' for everyday work it sort of makes sense.

Growing 70b revenue to 700b revenue is, ah, a smidge tougher. Even getting $70b to $230b would be an immense success, methinks. The initial hypecycle seems to have cooled down slightly, and people are no longer ranked by how many tokens they can burn through with no real deliverables expected. There's even reports that companies are starting to tighten their belts when it comes to token spend; a catastrophe for fat US labs that need those big-wallet companies to spend API prices which pays for everything. Subscriptions might make them some money but it's not where the real profit is.

Which isn't to say that the revenue won't grow, or even that it'll contract, but I certainly don't see Anth growing to eye-watering $700b next year, especially as long as open-weights models keep getting released and a BigCo can't get Opus deployed to their own hardware sitting in their basement with no chance of the secret sauce leaking out to anybody, including Anthropic.

▲ surgical_fire an hour ago | parent | prev [-]

> Wait, Anthropic has $112 billion in cash right now and generates about $70B

Anthropic operates at a loss. They lose money.

That is what they keep having to raise money and are desperate to IPO as soon as possible.

> Why do you say they're running out of cash? Can we see your math?

My brother in Christ, you can't tell the difference between revenue and profit, and think that just because their revenue (not profits) grew 10x that they can grow now a "measly" 3x.

They might have enough money (ot whatever they consider to be equivalent) in hand to keep being unprofitable for a bit, but they alao have something in between half a trillion and a trillion of contracts for compute in the next few years.

▲ noosphr 5 hours ago | parent | prev [-]

AI doomers have predicted 1095 of the last 0 AI bubble pops.

Everyone is using more and more tokens, everyone is complaining that providers lobotomize flag ship models because they don't have the capacity to serve everyone.

Then they turn around and compare it to the dark fiber bust or railways or whatever when in each of those the issue was the capacity being build that no one used.

It's been 2 years since I could buy a gpu for love or money. It's been one for ram.

My old rig is worth ~two~ three[0] times what I paid for it inflation adjusted.

[0] fuck me ram is rising again.

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incrudible 4 hours ago | parent [-]

If tokens are being given away below cost, of course you're gonna use more. It's not gonna last. How many tokens would you spend on random experiments if they were much more expensive? If tokens are so valuable, why do I (and many others) get to use them on economically worthless things until I'm bored?

We just don't know the true value of tokens until those subsidies stop, and the subsidies don't stop because the respective companies don't want to know, at least until everyone cashed out. The valuations of AI companies are very difficult to reconcile with current revenue (which doesn't cover cost) and with a sober mind it's hard to imagine where all this additional revenue is going to come from, down the line. And that's not to say the the technology isn't fundamentally transformative and revolutionary. So was the internet, but it still created an investment bubble.

The RAM prices are in yet another pork cycle, which will be exacerbated by the fact that many previous pork cycles left RAM manufacturers with excess capacity and rock-bottom prices, so they're much more careful investing and more assertive negotiating contracts now.

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noosphr 4 hours ago | parent | next [-]

Tokens are not given below cost: https://www.indmoney.com/blog/us-stocks/anthropic-gross-marg...

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aurareturn 3 hours ago | parent | prev [-]

85% gross margins.

	▲	slopinthebag 27 minutes ago \| parent [-]
		does that take into account model development costs?

▲ jurgenburgen 2 hours ago | parent | prev | next [-]

Usually the markets react to a supply imbalance by increasing prices which destroys demand. For some reason token prices from the major players have instead been reduced.

▲ fhruwbfbrb 6 hours ago | parent | prev | next [-]

That is precisely what price subsidies look like. These companies are hiding the the true cost of offering these models.

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wmf 5 hours ago | parent | next [-]

They're not subsidizing enough for CoreWeave to make money.

	▲	otherme123 4 hours ago \| parent [-]
		All the money is being funneled towards nVidia. CoreWeave touches it briefly, and sends it to Jensen. Then nVidia user some of this cash to finance AI labs or clouds again. But the biggest losers seem to be VC's, that are on the basis of this piramid and still have to see a cent in return.

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aurareturn 3 hours ago | parent | prev [-]

Source?

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fhruwbfbrb 3 hours ago | parent [-]

Literally any microeconomics class. That's like asking for a source when someone discusses Newton's laws of motion.

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aurareturn 2 hours ago | parent [-]

How does taking a microeconomics class prove that Anthropic is subsidizing the costs?

	▲	fhruwbfbrb 2 hours ago \| parent [-]
		Subsidies cause excess demand that can't be met with supply, leading to shortages. Whatever financial wizardry Anthropic is doing to give themselves an apparent operating cost that is orders of magnitude lower than any ML/AI application I've ever seen, is a different question. Edit: And Anthropic was never a specific point of discussion until you brought it up.

▲ preommr 6 hours ago | parent | prev | next [-]

> No one will lend people money to build it

The money is there but not at the risk levels these companies have pushed things to. They're spending billions like it's candy, and treating millions like change they found in their couch cushions.

Financial systems are usually in the hands of people that have some risk management. When these labs say things like this will change everything, there's no way to know how to evaluate any of this.

▲ vasco 4 hours ago | parent | prev [-]

Trillions have been lent. What you're saying is like eating 55 burgers, 55 pizzas, 55 tater tots and then complaining that the fridge is empty and nobody will give you more food.