Yes, Google has a very small risk but still it is a non zero risk that google can bankrupt whereas US govt can physically not bankrupt itself given that it can print the money (there could only be some gridlock within raising the ceiling though but that's true)

Also I would argue that Google itself has changed with trillions of dollars spent into AI/Datacenters, it simply can't pivot as well. There is a reason as to why Google is having such high level of interest rate

Google isn't stupid and wants to give such high level of interests if not for the amount of risk involved. I dont believe that there's free lunch but also that perhaps this can be a good deal to some and this is gonna push people even more away from the more riskier neoclouds funding and cause their debts to rise in interests rate even more.

His point was that Microsoft has an interest in maximizing its credit worthiness which necessitated the corporate governance etc, while the administration had an interest in maximizing… other stuff (?) which did not necessitate the same things.

[1] https://www.wsj.com/finance/investing/why-microsoft-has-lowe...

https://www.economist.com/finance-and-economics/2017/06/24/i...

Total human search volume has likely already started to decline.

Further more, we are at a point where searching in ChatGPT or AI apps is simply better and faster than Google.

In this new world, Google is not the leader nor a monopoly.

source?

Because Google doesn’t break down search volume, we don’t know for sure.

1. Humans are searching less on Google.

2. Humans get the answer from Google AI answers at the top, which means AI is better at finding what the human wanted than traditional Google search results.

Both are bad for Google.

Other sources:

https://www.searchenginejournal.com/google-search-decline-pu...

https://www.niemanlab.org/2026/07/search-traffic-has-decline...

https://www.linkedin.com/posts/randfishkin_google-search-tra...

Come, come: most email is Google, most phones are Google, most web browsers are Google. If you measured the global impact of any single company going under (with nothing else inherently changing), I wonder if Google might be #1.

Compare this with HSBC going under or say JPMorgan. Or Adyen. Or amazon (screw their retail - it would be much better for everyone if they die, but unfortunately a lot of companies still build their IT on AWS).

But the others you name: not a chance, big businesses already diversify such services. They don’t partner with one bank or one payment provider only.

GOOGL 2.050% 15Aug2050 Corp

Current Bid/Ask yield: 6.134/6.045

That bond has 24 years to maturity. If you look at US Treasury bond yields with about 25 years to maturity, the current yield is about 5.29%. That means the spread is less than 100bps. That is pretty small. Google (Alphabet) still has an excellent credit rating: AA+.

Anyone that can open their computer to install an SSD, or a consumer-grade GPU, can install a data center GPU with a fan-kit; it's not rocket science.

I've also seen someone sell their old fanless Nvidia cards on the local marketplace. There are definitely consumers running these things in their basements.

Most of the common used datacenter cards have people just 3d printing blower fan shroud attachments. Or using 3M/Gorilla Tape.

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

- No one will lend people money to build it

You can use Newton for most stuff and get pretty good answers. Micro is questionable in all kinds of ways. Assumptions about actors, assumptions about responses, assumptions about how utility maps onto monetary value. It's a huge mess really.

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.

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.

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

Then they turn around and compare it to the dotcom 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 times what I paid for it inflation adjusted.

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.

These models become obsolete in months. Until the labs prove that they can produce a model that has a positive return on investment in it, it’s insane to think that the labs are in a long term good position.

Semianalysis estimates that subscriptions make up 40% of tokens generated for only the 10% of the revenue:

https://newsletter.semianalysis.com/p/anthropic-subscription...

I'm also not saying that this subsidy is irrational, it does buy mindshare, but it still distorts the true demand for tokens. I wouldn't be using anywhere near as many tokens if the price was as high as the API costs, if anything I would switch to a cheaper model, and likely not an Anthropic one.

If you're gonna point to gross margins on the API, those 85% really are meaningless in isolation. They can charge top dollar because they have one of the top models, but only because they keep spending top dollar on training it, as does OpenAI. It's a game of chicken where whoever stops heavy training investments first is likely the one to lose market share to whoever keeps spending. So the play is to try and keep the investment capital flowing and be the last one standing, which only works if you can keep selling a fantasy of future revenue, not the dire reality of current revenue.

"Sales and marketing includes compute for free access to the service"

It's safe to say that free inference is not profitable, but some back of the napkin calculations will show that getting an order of magnitude more tokens out of a subscription than the API price is not profitable either. Nobody else offers anything close to that much compute for the price, except OpenAI, which is running the same advertising strategy. If this was profitable, you'd have a a cottage industry of compute re-sellers selling plans for access to comparably-sized open models at similar token volume for a similar price.

2023: Hallucinates too much to be useful.

2024: LLMs have plateaued. Scaling laws are dead.

2025: No one pays for AI

2026: Inference is unprofitable and unsustainable

For many people, if AI finds a cure to cancer, they'll say it's still useless because it hasn't solved immortality yet.

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

Why do you say they're running out of cash?

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.

> I'm guessing

Oh, you have no clue what you're talking about, just vibe posting. According to their SEC filing, the vast majority of their commitments are over 5 years, and a small portion within 10.

most recently, it was negative

Their revenue pays for the compute.

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

It doesn't: their expenses exceeded revenue the last quarter (by hundreds of millions of dollars) and the last calendar year as well (by tens of billions of dollars).

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".

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)

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)

The problem with AI is that it's still dependent on the skill of the prompter.

Ah so when people don’t have the same outlook as you we’ve gone from being lazy to now it’s a skill issue.

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.

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.

This just isn't true. You're talking about the textbook model of perfect competition, which is not the real world.

Commodity-like markets do tend to see outsized margins get competed away, but many markets aren't commodity-like.

Markets with network effects, economies of scale, high switching costs, significant brand differentiation, high legal and regulatory barriers to entry and heavy collusion/concentration don't function the way you claim, and there are a large number of major companies, many of them tech-related, that have delivered above-average returns year after year for many years because it's impossible for new entrants to flood the market and simply undercut them.

It's way too early to predict how this will play out for the leading AI labs like Anthropic.

The irony of your comment is that you're still using frontier models even though there are already much cheaper options available.

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.

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

> 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.

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.

They are when they say that inference is incredibly profitable. It is incredibly profits exist only when you pretend that models have 0 cost to be created (since they are a digital asset that can't depreciate), and R&D is a imaginary thing that exists only as a fixed cost of doing business that doesn't need to be recovered.

Anthropic released Opus 4.6 in February 2026 and deprecated it in June 2026. Was it anywhere close to recover the "R&D" costs during those few months?

More importantly, how long a model needs to be current until it recovers "R&D"? Their economic viability hangs on this, and the compute costs both to train and servr models.

A lot of people like to vaguely gesture at the possibility of compute costs coming down, but it is in fact coming up with the rising cost of hardware (and possibly with sweetheart compute deals being replaced by contacts where providers actually have to make money).

Expensing the cost of training a model isn't treating it as free. The whole cost is charged to the income statement the year it's incurred, which is actually faster than depreciation would ever recognize it. Calling something R&D does not make the cost "imaginary". It means the cost is counted. It's just not in gross margin.

We are actually asking the same basic question (can the investment made ever be recouped?). What you're struggling with is the accounting concepts and terminology. The costs of creating the models are a different part of the income statement than the costs of serving inference.

Again, "inference has high gross margins" and "training is extremely expensive" can both be true at the same time.

In absolutely none of my comments did I state that R&D never needs to be recovered. In fact, I stated repeatedly that the entire crux of whether these businesses are viable long-term comes down to whether the costs of creating the models is eventually recouped.

> Anthropic released Opus 4.6 in February 2026 and deprecated it in June 2026.

First off, Opus 4.6 is still available, even though Anthropic recommends migrating to 5.5. Google Cloud says it will be on offer until February 2027. So revenue for this model didn't drop to $0 overnight.

https://platform.claude.com/docs/en/models/opus-4-6/overview

More importantly, your comment implies that models cease to have value when the next version is released. This completely ignores the reality of model training, which is that the subsequent models almost certainly built on the research, data and techniques of 4.6. And 4.6 was built on the research, data and techniques of the models that came before it.

So the whole notion that point versions of these models are standalone investments that cost billions to train and then become worthless the minute a new point version is released is incredibly naive. It's just not how model training works.

> A lot of people like to vaguely gesture at the possibility of compute costs coming down, but it is in fact coming up with the rising cost of hardware (and possibly with sweetheart compute deals being replaced by contacts where providers actually have to make money).

You have absolutely no evidence of "sweetheart compute deals" so this is pure speculation.

Furthermore, there's a flaw in your logic. It's entirely possible for chips and memory to get more expensive while the cost per token served falls due to more efficient hardware and better inference software. What matters here is the cost per unit of output, not the price of a GPU.

Bottom line: inference has high gross margins and these businesses have had to invest massive sums. If inference profits grow sufficiently faster than costs, or costs decrease sufficiently over time, these businesses could be viable and perhaps incredibly successful. If that doesn't happen, their viability is questionable.

Lots of people have an opinion about this but as this discussion proves, lots of people also don't understand the basic accounting required to make a cogent argument one way or the other.

The problem I have with this is that the claim that "inference has high gross margins" is paraded around as something that automatically makes all this make sense.

My concern here is something that you described with more nuance, the cost of training cannot just set aside as if it doesn't need to be recovered. R&D makes sense if we consider that research and development eventually gets to a model that doesn't need to be trained anymore, and can be served at relatively low cost indefinitely. Otherwise the constant need to train new models needs to be taken into account; you can only keep revenue by training new ones.

> You have absolutely no evidence of "sweetheart compute deals" so this is pure speculation.

I agree that this is speculation. But it is based on actual sweetheart deals taking place (such as SpaceX not charging for compute during a period in Q2) and on Anthropic being very unclear if their compute cost is based on market rates or subsidized by providers who are also investors in the company.

> Furthermore, there's a flaw in your logic. It's entirely possible for chips and memory to get more expensive while the cost per token served falls due to more efficient hardware and better inference software. What matters here is the cost per unit of output, not the price of a GPU.

I agree that my statement does not take into consideration hypothetical improvements in efficiency, but I dispute that the increased cost of hardware is not a menace to their viability.

You may have a major breakthrough that halves the compute use for a task. It is meaningless if the hardware is now 3 times more expensive.

Except that this isn't the argument I'm making, and I don't think most of the people who are pointing it out are claiming that inference being sold at high gross margins means that you don't have to consider the massive investments that have been made.

The more common narrative is that these companies are losing massive amounts of money and will never be able to make a profit. And that's where looking at the investment in training versus the economics of inference is useful because, once again, inference actually does have high gross margins.

> R&D makes sense if we consider that research and development eventually gets to a model that doesn't need to be trained anymore, and can be served at relatively low cost indefinitely.

This just isn't true. If revenue grows at a sufficient enough rate versus training costs, and/or training costs decrease substantially enough, the numbers could work out over time.

People who are claiming that it's mathematically impossible are going on vibes. And just to be clear: I'm not claiming that it working out over time is guaranteed, or the likely outcome. But it is possible and the mechanics by which it would work out (if that comes to pass) is pretty simple.

> I agree that this is speculation. But it is based on actual sweetheart deals taking place (such as SpaceX not charging for compute during a period in Q2) and on Anthropic being very unclear if their compute cost is based on market rates or subsidized by providers who are also investors in the company.

Regarding SpaceX: my understanding is that as it was ramping up its capacity, which was not optimized and capable of being delivered in full at the time, it provided concessions, including discounts and deferred billing, to large customers. What SpaceX got from this in return was a massive backlog (over $14 billion worth) of non-cancellable contracts.

The concerns about Anthropic investors are legitimate, but again, you can only speculate. If your entire thesis ignores plausible scenarios that aren't so negative (like the possibility that compute is being sold at or near market rates), you have to be honest: the thesis is biased to support the argument you want to make.

If I was going to invest in any of these companies, I would want to know more about the terms of these deals but I don't think anyone who hasn't actually seen the agreements should make confident arguments based on what they think must be in them.

As a side note: if you really want to be exact, we'd need to know how compute purchased at below cost affects the income statement. If the below-market pricing was for compute related to training, it would not be the same on the income statement as compute related to inference. Whereas below-market pricing for inference compute would hit COGS and improve gross margins.

> You may have a major breakthrough that halves the compute use for a task. It is meaningless if the hardware is now 3 times more expensive.

What matters is the price per unit of compute. "The hardware is more expensive" only threatens viability if the price per unit of compute is rising, not just the price per chip.

You're throwing around random numbers to make a point but there is real data on this subject. For example, Epoch AI published a report a couple of months ago that found the cost of achieving a given level of AI performance has fallen about 47% per quarter since 2023 even as hardware and memory prices rose by tens of percent. On one of its science benchmarks the cost per question at a fixed score fell 725x in less than 18 months.

It did also find that large training runs have grown by 2-3x per year, which is the crux of everything, once again: training costs versus inference costs.

> People who are claiming that it's mathematically impossible are going on vibes.

You are doing the same thing you claim I am, just on the opposite side of the road.

In truth this is sentiment based on the current narratives.

To be clear, I don't think it is theoretically impossible to be able to recoup the cost of training by selling inference, but I do think it is impossible with the current rate new models have to be trained and with current cost they charge customers. I believe that they will have to increase prices pretty dramatically for this to make some sense.

> As a side note: if you really want to be exact, we'd need to know how compute purchased at below cost affects the income statement. If the below-market pricing was for compute related to training, it would not be the same on the income statement as compute related to inference. Whereas below-market pricing for inference compute would hit COGS and improve gross margins.

My whole point is that this is unclear. Anthropic's S-1 mentioned how much that they paid for compute, but the rate they pay is totally opaque.

If they pay near the market price this is a nothingburger. If they pay, say, half the market price, now we can see their already eye-watering training costs increase significantly and their high profit margins to suddenly not look as seductive anymore.

Again, my annoyance is how prople pretend that Anthropic has a clear path to become profitable because of ther incredible revenue growth and profit margins (even if you are not doing so, this is the pervasive narrative in forums like HN). I sustain that this is far from settled.

Except that I'm not on the opposite side of the road. I've simply pointed out that the mechanics by which Anthropic could be viable long-term are pretty simple; not that this is what is going to happen.

Frankly, I don't think anyone can predict what will happen. This is a fast-moving, complicated stew of technology, finance and politics.

> My whole point is that this is unclear. Anthropic's S-1 mentioned how much that they paid for compute, but the rate they pay is totally opaque.

> If they pay near the market price this is a nothingburger. If they pay, say, half the market price, now we can see their already eye-watering training costs increase significantly and their high profit margins to suddenly not look as seductive anymore.

Pricing in commercial agreements is typically considered competitively sensitive and can be redacted from contracts filed with the SEC but only if it isn't material. Anthropic still has to disclose known trends that are likely to affect its results so if below-market compute were inflating its margins you'd expect to see that in the S-1.

The more detailed related-party disclosures probably aren't required yet because Amazon and Google mostly hold non-voting preferred stock and convertible notes. The S-1 says they'll each hold more than 5% after the IPO conversions. At that point, their transactions with Anthropic fall under the related-party rules so we'll learn a lot more after the IPO if there are any "issues" there.

> The more detailed related-party disclosures probably aren't required yet because Amazon and Google mostly hold non-voting preferred stock and convertible notes. The S-1 says they'll each hold more than 5% after the IPO conversions. At that point, their transactions with Anthropic fall under the related-party rules so we'll learn a lot more after the IPO if there are any "issues" there.

Whoa, that I really didn't know. Something for everyone to keep their eyes on then, because this is actually very opaque, and may dramatically change what we know about their numbers. Or not.

Isn’t it plausible that this is conceptually wrong? What if a model’s revenue-generating potential only lasts for something like a year due to, loosely speaking, competition? Doesn’t this make model development more akin to, say, the costs of annual agricultural planting/harvesting/delivery cycles, or a tv network’s annual (back in the day at least…) production cycle for its main content?

Under US GAAP, production costs are capitalized when they're incurred and then amortized into cost of revenue over the period when the content actually earns revenue. Because content usually earns more revenue early and then tapers off, the amortization is usually front-loaded.

The one problem with this is that not all training actually goes into what's sold to customers. There are experiments, failed runs, various studies, etc. So you would need to come up with a way to cost only the final production run (and perhaps post-training) to come up with a number akin to content cost.

ASC 730 basically favors expensing anything uncertain, so unless you have a really good, defensible way of breaking out these costs and separating them, doing it this way would go against the current accounting standards.

Would this be analogous to, say, paying lower level employees to read endless scripts and write coverage, purchasing some of those scripts, producing but then canning failed pilots, etc. obviously some of this is outdated, but I’m at least thinking more of the pre-streaming era of television production pipelines, in which there certainly was a non trivial amount of content produced that never reached consumers.

Thanks for your more detailed response; I obviously know nothing about the real intricacies of GAAP so it’s interesting to understand the analogues better.

If you bought a script, that would be capitalized as an asset, and written off if abandoned. And so on.

The difference is that film and TV have an industry-specific standard (ASC 926). AI companies fall under the general R&D and software rules and those don't allow capitalization when there's significant uncertainty about whether or not the project will work. There's a new software standard (ASU 2025-06) that makes this explicit with a "significant development uncertainty" test. Because you don't know the outcome of a model training run until it's done the training costs are generally expensed as they are incurred.

Obviously there's room to debate whether the rules should be updated and maybe they will be in the future. But they are what they are now. Expensing model training as R&D isn't an accounting choice companies like Anthropic have made on their own. It's what the rules require. So obviously, understanding what the rules are and where different costs show up on the financial statement, it's clear that inference gross margin alone doesn't speak to whether the business is ultimately viable. But the high inference gross margins shouldn't be dismissed as meaningless or the product of some sort of accounting trickery either.

No problem if this is so far off topic as to no longer be interesting to you… I just don’t often engage with somehow who can comment on Hollywood accounting with actual knowledge of relevant standards!

There's a formal process for these rules to be made and it's designed to be responsive to the needs/requests of businesses, investors, auditors, etc. While I wouldn't say there's any room for differences of opinion, given the rule-making process, I don't think there are any fist fights taking place over these. :)

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.

A company selling potatoes would not include land in COGS. Land is a a fixed asset. Even if a farm rented/leased the land, the payments would fall under OpEx, not COGS.

Seeds, pesticide/fungicide/herbicide, direct labor, water and electricity for irrigation, etc. would all be COGS.

Would it make sense to spend $1 billion on land to grow $1 million worth of potatoes every year? Probably not. But that doesn't mean you couldn't sell potatoes on your $1 billion land at a positive gross margin if revenue exceeded COGS.

This is the AI debate that is impossible to have when people refuse to educate themselves about basic financial accounting concepts.

ARR based on October revenue 400% higher than GAAP revenue.

Net Revenue Retention: 1,000% After all one seed potato creates 10 more potatoes - infinite replication! We estimate soon we will 10x the no of potatoes in the world.

EBITDA - the severe frost last year is a one-off non-recurring event, fertiliser is an R&D cost, seed is a growth investment. Land rent is not considered a core cost and therefore removed. Depreciation, interest, taxes all taken off the figures because somehow they don't count.

No man. It’s that, like you’ve explained further in your comment, they haven’t become net profitable yet. Being profitable in 1-2 quarters is a good trend but it doesn’t matter if you are still behind on mountains of debt or investors wanting their money back and your market is one where people talk about shit that happened 4 months ago as obsolete.

There is no proof that the current profit they are making on inference is durable.

Their S1 was taken down. As of today. This is the second time they changed it.

Apparently they file it with Claude.

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.

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.

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).

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 as well 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.

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, 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.

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.

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

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

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

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

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.

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.

You want them to embrace a tech that exacerbates inequality and its founders promote that?

By that logic we should ban books. This is Boko Haram ideology and you are just a communist who thinks he should be protected from competition.

Instead of “making everyone well off“ here is what will actually happen, because that’s what happens every time communists seize power: everyone will get poorer, because they will be outcompeted by more sane jurisdictions, eg China.

Chinese Communist Party, guide us to Capitalism?

If the communists can learn how to use markets, capitalists can learn how to redistribute. It's not impossible, we did it 100 years ago last time capitalism drunk-drove the world economy into a ditch, I just hope we can do it without a Great Depression and World War this time.

Yes please.

> capitalists can learn how to redistribute

By that measure, the West hasn't been capitalist since Roosevelt. Which would be accurate btw.

https://en.wikipedia.org/wiki/Private_military_company

I'm sympathetic to DSA types, right up until they call for bans on super intelligence, RSI, or data centers. Remember progressives, it wasn't "socialism" that lost me.

Voters themselves were hostile towards data centers in all but one of the ballot initiatives (40 out of 41).

But the “elected people” you refer to (I assume Democrats) seem to me to be more about not having one-sided secret deals for data centers in folks’ “backyards”.

Specifically, the detailed stances seems to be for things like: cover energy demands (no rate hikes), no excess load on power grid, water protections, and transparency with the communities they are in. Some also call for more regulation (which is a knife that can cut both ways).

This seems reasonable to me.

If anyone wants to cast stones, I think targeting the folks currently in office who somehow thought it would be a good idea to defund extra power generation just because it happened to be renewables / clean energy might be one place to look.

Coreweave doesnt seem to be in a winner-take-all sector, they have a narrow moat and will have high ongoing capex. I struggle to see how they dont go under eventually even with the ability to restructure debt

[Sept 2001] https://www.latimes.com/archives/la-xpm-2001-sep-27-fi-50296...

"Exodus Communications Inc., which never made a profit since it went public in March 1998, filed for bankruptcy protection after losing customers in the collapsing Internet economy."

"Exodus, which runs 44 Internet data centers on four continents, listed $5.98 billion in assets and $4.44 billion in debts in Chapter 11 papers filed Wednesday in U.S. Bankruptcy Court in Delaware. The company operates so-called server farms that provide Web site services for businesses."

"The Santa Clara, Calif.-based company’s losses increased more than tenfold in the second quarter, and it fired workers as it failed to win new contracts fast enough to replace those it lost. Exodus counted Yahoo Inc. and AMR Corp.’s American Airlines among its customers."

"Trading in Exodus shares was halted before the markets opened Wednesday. The shares fell 33 cents to 17 cents Tuesday and have plunged 99% from a record $89.81 in March 2000."

"Analysts say Exodus, with eight different junk bond offerings totaling about $3.4 billion, ran up more debt than it could handle."

“We sacrificed profitability in exchange for growth and market share, over-expanding in some areas in advance of demand, not anticipating the decline as the dot-com bubble burst and the economy weakened,” Krause said.

"Exodus’ second-quarter loss grew on acquisition and restructuring costs to $583.4 million, or $1.05 a share, from $51.8 million, or 13 cents, a year earlier. Sales rose 79% to $318.7 million but missed analyst estimates. The company lost $256 million on sales of $818.4 million in 2000."

You can see precise details about the CoreWeave bond issue here: https://bondblox.com/bond-market/Coreweave-Inc-USU2069EAE06

https://www.nerdwallet.com/credit-cards/best

Still, this means that people would only be willing to loan new money to them at that 16% rate.

It’s going to get very ugly for them as cash starts to run out.

I do think that we're going to find who was swimming the most naked by February at the rate this is going. Particularly the dedicated Neoclouds who had to fund by pure debt without alternate revenue streams.

Where is this bond in the bankruptcy hierarchy? Is it secured by anything? GPUs, maybe. Is it subordinated behind other debt? That's probably going to matter.

Just checked and coreweave rated at bb-