> It reported a $104 billion net loss during the period, though most of that was due to the change in fair value of convertible notes and derivative liabilities.
Look, I've seen and I understand the arguments that the GAAP income adjustment for this kind of thing is fake, but a number of them seemed to take it for granted that it was a "one-time" adjustment. If it's going to keep happening I really feel like we ought to insist on a more comprehensive argument. The accounting principle must be generally accepted for a reason: what shenanigans could someone get up to by reporting net income without this adjustment, and how do we know Anthropic is not getting up to those shenanigans?
It’s basically shareholder dilution, similar to issuing more stock or stock options. Existing shareholders will have a smaller share of the company than they thought. The accounting is weird.
We aren’t shareholders, though, so why should we care?
>We aren’t shareholders, though, so why should we care?
They're going through an IPO, so unless this is a one-off thing, people reading this thread will become shareholders, either directly or through their pension pots.
Sometimes accounting is weird because it's necessary to prevent tricks. Why do all your held-to-maturity bonds become "tainted" and get revalued at market prices when you sell a couple, even though nothing has changed about them? Because any other rule would let a company which cannot in fact hold bonds to maturity get up to tricks and systematically overstate their assets.
So while yes, this does look a lot like shareholder dilution, that doesn't really satisfy the question of why it's not treated that way under GAAP. Perhaps GAAP is just wrong, or perhaps there's some possible tricks which Anthropic is provably not engaging in. What it's definitely not is a one-time adjustment now that it's happened in two consecutive quarters.
It's a good question: why are the GAAP rules what they are? It needs to be investigated, though.
The reason it happens in multiple quarters is likely that Anthropic's valuation has increased in multiple quarters. Every time Anthropic's valuation increases, the convertible bonds are worth more money. Other shareholders have "lost" money compared to what would have happened if Anthropic had sold regular bonds instead of convertible bonds. Except that no, the value of their shares is increasing too. It's actually good for them.
That's how equity works. If someone owns 1% of a company and its valuation doubles, their share is worth twice as much and so
is yours and we wouldn't even normally consider it to be diluting the other shareholders.
So the question is whether convertible bonds are worth more than regular bonds and the answer is, yes, absolutely, but they are partially equity, and it's the equity part that's increasing when the company's valuation is increasing.
And if the company's valuation went down then the value of the convertible bonds would decrease. Maybe that would be considered a profit?
> The bond market works best when there’s an end in sight, Lyman noted. Every AI investor believes there’s a light at the end of the tunnel. We just have no idea how long this tunnel is.[0]
It is like someone who loses repeatedly at Roshambo while saying: "Okay, then. Best two out of three- three out of five- four out of seven- five out of nine..."
These are all supposed to convert to regular shares at or around IPO IIRC. If they're still pulling that crap after that point we'll know. There's an end date for those contracts that we're expecting
> Okay so Q3 ARR at $67.8bn versus $46.2bn for Q2, +47% QoQ. Turns out tokenmaxxing's end over the summer slowed revenue growth versus an absolutely torrid 100%+ growth rate in Q1/Q2 but 46% sequential is still eye-popping
> Operating margins (GAAP) went from 8% to -3$, with a -28.5% QoQ incremental operating margin. $SPCX compute coming online is the reason.
> Bottom line after seeing this Q3 update: the basic takeaways from the original S-1 don't change, but fears that they were excluding Q3 because it was a disaster were definitely wrong. Do with this information what you will.
It's also worth noting that Q3 only has one week of Opus 5.5 (9/22 - 9/30) which may have changed the economics for Q4.
We have Copilot and can select between various OpenAI and Anthropic models, and when I accidentally select the “wrong” one it takes me awhile to notice.
I use Claude Opus at work and DeepSeek v4.1 Flash on my personal projects. For the kind of work I'm doing, they're basically interchangeable and DeepSeek costs 3% what Claude does.
> fears that they were excluding Q3 because it was a disaster were definitely wrong
That's a very generous reading. Anthropic's entire growth narrative is the rapid growth over the previous 12 months, from near-enough zero to $100bn+ this year. Now we see that actually growth is levelling off and that their revenue was ~$5bn/month over the last quarter, which is barely over their compute commitments, and nothing close to the $100bn+ leaked.
I guess if people thought Q3 would show negative growth rate, sure, it isn't a disaster, but their growth rate slowing so much is surely very bad and matches all the concerns commenters had.
And this is Anthropic! Anthropic are the financial success compared to OpenAI. OpenAI are doing much worse, so this does not bode well for OpenAI either.
People will point out that +46% is still good, but this is like measuring the escape velocity of a projectile that's still at 1000 feet. It's not that the projectile isn't going very fast or won't get very high, it's that if you want to have enough momentum to break out of Earth's gravitation pull you can hardly afford any deceleration.
I think that's an unnecessarily pessimistic take. The way I look at it the industry did not wake up to how big this was until Q4 and so it makes sense that you would see astronomical growth in q1 and Q2 as everybody woke up and started adopting. Seeing the growth Taper down to a 4.5x annualized rate which to your point will only continue to taper but it's still mind-boggling at this scale ... you just don't have anything else like this in the economy. This is like hyperscaler Revenue scale achieved in 15 years being achieved five times as quickly. At this rate Anthropic will exceed AWS Revenue within a year
It is a lot easier to switch model providers than it is to switch cloud providers. And the open weight models from Chinese Labs make Claude a lot more of a commodity than any service provided by AWS.
> Now we see that actually growth is levelling off ...
I really don't understand how could it be otherwise? They're planning to IP at a valuation representing 5% of the US GDP and, unlike NVidia, they are neither selling shovels in a goldrush nor a business with decades-old proven track of records.
Are people really going to buy ANT thinking it'll 10x to 50% of the US GDP?
Aren't there better stocks to YOLO on if you think you could maybe 10x? And if you want something safe instead of YOLO'ing, aren't there plenty of companies much safer and much more boring to invest in than something IPO'ing at 5% of the US GDP?
Isn't the entire Anthropic narrative based on infinite growth? How is infinite growth sustainable?
10x year over year couldn't continue for long--they were what...3.5 years from exceeding US GDP?
47% quarterly growth is still 4.5x annual growth, which would put them at $300+ billion revenue in a year. This is still unprecedented growth for a company of this size.
The negative perspective depends on assuming that the 47% quarterly growth is going to continue dropping soon. And it is going to drop, since it's only a bit more sustainable than last year's growth. The question is how many hundreds of billions they're making a year when it levels off.
If we assume LLMs will continue to advance at a pretty rapid pace (relative to the current rate of advancement; so, same or faster), all these numbers look good to me.
Some people just start out with wanting LLMs/AI/Anthropic to fail or be scams and consistently try to fit any new evidence pegs into those unchanging hypothesis-shaped holes. This group ideologically despised LLMs from the start and needs them to be fake.
Lots of people making the financial case against Anthropic are not anti-LLM. I think the technology is great, interesting, powerful, innovative etc etc etc my issue is with the garbage business that Anthropic and OpenAI are building and the nonsensical ideas people have about business in general. Technology is exciting, business is not, business is boring. Money come in, money go out. Selling technology, no matter how exciting it is, does not change the nature of business.
And yet one can argue that the "garbage businesses" you label are the ones doing their best at matching market demand. See how well things are going for other entrants, like Google, Microsoft or Meta. Putting Copilot in every app makes a whole lot less sense than Cowork, Codex and the like. They just are doing worse things while attached to traditional money fountains, like ads in social media and search.
You can be bullish on LLMs profoundly transforming society while being bearish on the economy "digesting" this new technology fast enough to justify the incredible capital spending necessary to power it.
It doesn't matter how much they "continue to advance at a pretty rapid pace", if the economy has no actual demand for the new capabilities. Even if we accept the premise that LLMs can produce good code at scale now and automate most software development work, then what? As *an industry*, the bottleneck was that we needed people to write code, and that bottleneck has been removed since ~Opus 4.6. Where is the industry now bottlenecked which requires some significant quantum leap in model capability?
Don’t bother - these knuclkleheads don’t understand nuance and despite much of their devices being manufactured in China - they struggle to accept Chinese offerings can take significant market share.
I am just not sure how to respond to this model of reasoning. Opus 4.6's coding ability is nowhere near as good as Opus 5.5's. Some model next year will almost certainly be significantly better at coding (and planning, and designing, and ideating) than Opus 5.5.
We're nowhere close to the ceiling on LLMs alone, let alone AI in general. What about when they can run entire firms themselves? Like, this is just assuming Opus 4.6 is the best we could ever get, in which case, yeah, the notion of AI is simply a dead-end technology. But obviously it's not going to be the best we could ever get.
No there are just numerate people pointing out ANT/OAI need to make X $$$ to make Y $$$ spend make sense. The financials are separate from model capabilities, they can cure cancer and invent big dick pills and still go out of business if their balance sheet is fucked.
Disagree on the tokenmaxxing end. People are still doing it (and I'd argue more people are doing it due to wider adoption). Its just that most low hanging coding adoption has reached saturation point very quickly (which is genuinely impressive).
Now, they need to unearth new use cases and saturate them to keep the momentum going. Its anyone's guess if they have a line of sight to these and can sustain their growth trajectory.
> Okay so Q3 ARR at $67.8bn versus $46.2bn for Q2, +47% QoQ. Turns out tokenmaxxing's end over the summer slowed revenue growth versus an absolutely torrid 100%+ growth rate in Q1/Q2 but 46% sequential is still eye-popping
There is an unbelievably enormous difference in 100% vs 47% -> and when you look at the 2nd derivative this is REALLYREALLYREALLY bad.
I mean, the growth is astounding and incredible. It does not bode particularly well for a ~$2T valuation.
Doesnt matter at this stage of growth what financial analysts say because they havent seen anything similar in their lives :) Large corps are going to really be the deciding factor, in what ANTs next years revenue is going to look like. And right now inside large corps there are literally hundreds to thousands of different experiments running and no one knows what is going to click.
Does this person not own a calendar? Q3 numbers still polluted by expensive tokenmaxxing, which wind down late Q2 / early Q3 i.e. mid year budget review which will take rest of year to unwind. Q3 numbers = more mirage of tokenmaxxing tail and velocity = lagging indcator of tokenmaxxing end. Q3 also when fable tier coding commoditized and everyone doing a deepseek on cheap inference. The latter is what will kill ANT hardest, they signed up for 100s of billion of none cancellable compute contracts last year based on projected demand x expensive token cost, but demand / growth is less than token max timeline while inference cost getting cheaper. They have fixed, exorbitant costs, and operating margins negative for Q3, and Q3 just beginning of full price war. TLDR they will likely be vastly overpaying for compute. If turns out jevons isn't people using more expensive tokens on more expensive compute, but more cheap tokens on less compute, then ANT's entire balance sheet breaks.
Half of the revenue in the first 9 months of 2026 was in Q3? No one is going to believe that. Focus on the net loss of $23.29 billion in Q3, that's more instructive.
Interesting. Their revenue has "only" gone up 47% in Q3, compared to doubling revenue in most previous quarters. Meanwhile their "compute and infrastructure" cost has increased by 77% compared to Q2
Maybe they tried too hard to get good Q2 numbers, making revenue happen earlier and expenses later, and that now makes Q3 look worse? Either way, this does not look great
Some of my nephews ran a hugely successful pokemon card selling business (for ten year olds). I later found out that their mom was buying all their card packs for them to open and sell.
They stopped when the free packs stopped coming and they needed to finance their own.
If you go just by operating income, they showed they can be profitable in Q2, and the operating loss in Q3 is fairly small (in relative terms. For every 32 dollars in revenue they lost one dollar). They haven't shown wild profits, but nobody expects them to. Any money they make has more value when it's reinvested into growing their market share and growing the market, making next quarter's numbers look better. And imho they have solid enough financials that they can make the bleeding stop if the money faucet turns off
All of that isn't very newsworthy though. We knew all that from the Q2 numbers. What is interesting is that reinvesting all their profits to make the number go up in the next quarter didn't work as well this time as it did all the other times
I'm unconvinced they have a moat that will allow them to ever show wild profits though. They are constantly 6 months ahead of open weight models. Their potential customers are perfectly able to wait them out. They'd need a government enforced cartel against open weights at this point for me to not view the IPO as a plain and simple cash out.
So he can get all of his conclusions and predictions wrong based on his numbers and analysis, and then you think his value is in his numbers and analysis?
Funny how no one ever disputes his facts, only his predictions. He can easily be wrong on the predictions, while still being correct about the facts, i.e. their financials are shit and completely unsustainable. I don't care at all about his predictions, and am very interested in his fact-finding.
It still doesn't look anything like a death knell. There is a point where you approach market saturation and the growth curve follows a sigmoid. Have you ever tried folding a piece of paper ten times?
I agree. Their financials are fine. They look like a company that has solid foundations, with room to grow but close enough to profitable that they can survive the end of the AI boom
But are they still a 1.5T USD company if their growth is already slowing this much?
They clearly mention the cost increase in the amendment - it's because of the spot pricing of gpus (likely SpaceX). Their demand exceeded so much they had to quickly find some gpus at _very_ expensive prices
And they say that this will likely continue to grow until 2027 (when the long term commitment compute comes online, which comes with opposite problem .. will there still be enough demand)
The sentiment they are trying extraordinarily hard to create is that in the long run Anthropic will be the only one with any AI worth anything.
That makes any loss or risk reasonable.
It's great propaganda, so great that they have themselves started believing it with certainty. This is one of the self owns that brought down the USSR :)
Meta? They're not even in the game. They don't provide inference to anyone. I think NVIDIA or any other hardware players that want to step up and take the bags of money awaiting them are the only actual winners. They also win when we inevitably move to local AI for many use-cases.
> Get rich following the bot's trade advice and never tell anyone about your bot.
The fun thing about open markets is the market will punish you for this with second order effects and it's in your best interest to get some whales on the bandwagon.
Leaving aside that Meta are selling compute. The reason I pick on Google + Meta is they are able to make their own customers + charge their own prices. Anthropic + OpenAI are both reliant on people coming up with Products powered by their AI models valuable enough to create several AWS worths of revenue and also not also not immediately moving it to open models.
Google currently appears (by their latest numbers) to be sacrificing the only substantially profitable portion of their business to attempt to create that reality.
And of course, obviously untrue to anybody paying any attention to the rapid progress in open weight models. The IPO will be a cash out for insiders and leave the typical institutional investors with the bag.
It could be untrue, but it's not obviously untrue.
The open weight models right now are nowhere close to ..say.. Fable 5.1
They could catch up if Anthropic reaches some sort of a plateau and they manage to keep improving, but there's no reason to be certain about that happening.
Beyond it being propaganda, they're trying to work the regulatory capture angle to pressure lawmakers to pass safety regulations that would functionally ensure a market in which there are under a dozen players legally capable of competing.
Only with money you can afford to have sit (hold) for 10+ years. The closest analogy we have dotcom bubble and crash. We don't know if we are in 1998 or 1999. For most investors who got in in 1999 and held, they didn't break even until 2007 or longer. Versus the investor that got in 1998, was break even in 2003.
Obviously the investors who bought in at the bottom did the best :-) Most people who are crying 'AI bubble' predicted 8 of the last 2 bubbles.
My personal opinion is ANT IPO is like the netscape IPO of 1995, its the start of the cycle not the end. Things are going to get really crazy over the next 3-5 years, the last year was just a warmup. I don't think people realize how much credit fiat dollars have been and can be created, and how $1T companies will be commonplace. I think humans have a mental block, where we think dollars are like rulers or other fixed measuring devices, so you can look at the same number at different periods of time and compare. credit backed currency completely warps our sense of scale when it comes to measuring things in dollars.
In Q3, I think they struggled with the government ban on Mythos/Fable, Opus 5 not being so good, OpenAI models being more token efficient, and they decided to dedicate a higher percentage of compute to training.
The increase in token volume corresponds with a price cut. The S-1 revision already reflects what's going on financially. Anthropic is buying volume with margin. There's nothing wrong with that but we shouldn't expect profit to show back up as Anthropic's costs are increasing and have a fixed minimum. Given that OpenRouter users can switch models with the click of a button, what this demonstrates is that OpenRouter users are extremely price sensitive.
Q3 numbers are not what you want to see heading into an IPO.
Again the issue is not is Anthropic an amazing growth story. Zero would debate that.
The issue is this is a probably $200-300B company pretending it’s a $2T company.
Add to that the insane amount of committed spend, a high interest rate environment which means they’ll pay a painful premium to patch cashflow gaps, and a likely “junk” bond rating (per Friday’s news) and that’s why folks are concerned.
It’s also just wrong to keep dismissing the paper losses from convertible notes as somehow not real losses. Once they are a public company this will be actual value taken away from existing public shareholders so they are legit losses not to be ignored. It’s like claiming equity based comp is free money… it’s not, you’re taking it out of the pockets of shareholders.
They may not be $2T company but they are also definitely not a $200-300B one. Their Q1-Q3 revenue is $33B with $17B in Q3. Even if they magically flatline for Q4, that's $50B revenue for the year. There are exceedingly few tech companies with P/S of 6 or less (equivalent to $200-300B valuation with $50B revenue) and we all know Anthropic's revenue will not be anything remotely like flatline for awhile.
This is what makes a market. If they keep growing those waiting for the bear case will be left behind. Those who are on the side that the biggest most important AI company will continue to grow revenue will win.
Anthropic has something close to first-mover advantage but that doesn't mean they'll continue to grow revenue. Ten years from now the dominant player could be Google or DeepSeek or some random startup that doesn't even exist yet.
There's a world where capabilities narrow and Anthropic is stuck selling a commodity (not like they don't have any competition now). In this world it's not unbelievable that Anthropic would trade around or below 1x revenue no?
I think calling Anthropic a tech company could be like calling an electric utility a tech company.
> There are exceedingly few tech companies with P/S of 6 or less (equivalent to $200-300B valuation with $50B revenue)
A big part of the reason this has been true is that (mostly-software-based) tech companies haven't historically been spending tens to hundreds of billions of dollars every year in capex costs.
AI labs are, for now anyway, in a different category.
> we all know Anthropic's revenue will not be anything remotely like flatline for awhile
The SOTA US labs are one chinese open model away from being abandoned overnight by the masses. Ive argued before, but argue again here, that the current hardware shortages are the main thing keeping this from being a reality currently. If more people were able to run local models for a reasonable all-in cost, we would have a much higher velocity of iteration and improvement in the open source community. But its still too niche while people cant justify the cost, and so they continue to pay the premium for inference.
The limiting factor now is more electricity than hardware. There is hardware sitting idle because data centers can't get grid connections and power delivery contracts. Even for those who willing to run gas turbine generators on site there are severe supply constraints.
OK, but that's irrelevant to Anthropic's IPO. For the next few years at least very few developers are going to do much with local models. The problem that new data center construction projects are running into is electricity availability. In some areas there's no more power available regardless of cost.
You should stop arguing that because it doesn’t make any sense.
If a Chinese company released an open weight model that somehow was so superior to Anthropic and OpenAI then they can just take the model and deploy it, reverse engineer it, use it to improve their models, and/or do any number of things they currently do today which are of increasing value.
Most people don’t want to or have any interest in running or managing models locally.its not just about cost. The market for running models is infinitesimal in comparison to the market for managed products. That’s why everyone uses Office and not Open Office. Nobody is going to run some Chinese open weight model no matter how good it is on their critical banking infrastructure or something.
There will be space for lots of different solutions and many products and types of models including Chinese open-weight models and American ones too once we get around to making money on them.
The game isn’t just “cheapest open-weight model at all costs” which is what you are asserting here.
> Most people don’t want to or have any interest in running or managing models locally
When I argue the hardware cost, I should note that I think this is the limiting factor for _improvement_ of these models. But ultimately I dont think most users will self host in that world. They will still pay for inference, but it will be for one of a dozen commoditized vendors who are able to reap the benefits of inference at scale.
It could happen, but it would be an anomaly. In general if a business is to make money it does not let itself become a commodity vendor. You can't go to General Burger and select from one of a Wendy's or Big Mac or Burger King burger. You can't go to Microsoft and buy a Playstation. There are exceptions, of course, but whoever is building this stuff is going to want to make money and becoming a commodity vendor isn't the path toward that goal.
It's the same reason that open source software has been a commercial failure except for a few evangelists or whoever becomes a cool "thought leader" by releasing a new library and then gets invited to conferences to speak. Software was commoditized in many ways, yet all the big software vendors made more money than GOD because they took whatever commodity parts came to exist and monetized them.
I think the models can be improved with either recursive improvement (it would be faster with better hardware but if you can do it at all you just need time) and algorithmic developments. I don't know what those would be, but usually we see improvements to both over time. The algorithms will tend to be trade secrets as well.
Sure, in an ideal Chinese-world, Chinese companies would not be open sourcing their models and weights. But I think its very clearly a deliberate effort to cut off the US-companies at the knees by doing so, as they are unable to compete on the marketplace directly due to the current business/political climate. I also dont think these need to be better than SOTA, just reach a "good enough" bar. Big businesses will continue to use the best-of-the-best, but that will not be the general public and also will not justify current financial outlooks (in my opinion).
Outside of the United States, Europe, Australia, New Zealand, Canada, the rest of the world can choose their computer and choose their model, and they will.
I don't think US companies are being cut off at the knees though. I don't use a Chinese model yet I pay a monthly subscription for Claude. Is there a cheaper Chinese model? Maybe? I don't even care enough to be bothered to look. There are tens of millions of people like me who are in the same boat. Plus you also just have high-end research (as does China) which won't be permitted to leave the country.
The problem here is that let's say it's true that the Chinese are cutting off US companies at the knees. Well the US can just do the same thing back to the Chinese. We might have some money lost and stuff but the data centers will continue to be useful, as will all the infrastructure and energy grid buildout so it's not a total loss (I couldn't care less if accredited investors like myself lose money - you know the risk). Then we just steal Chinese models, do the open-weights thing, and all that stuff. So where does that leave us? Nobody makes any money? (Also where is Europe in all this?)
> I also dont think these need to be better than SOTA, just reach a "good enough" bar.
I agree with you in some respects but I'd like to digress here a bit. Good enough for a general purpose model isn't good enough. You want the best or at least what performs to your expectations. I don't even care about old models nor how much they cost (in my personal life and of which I will be a paying customer as will tens of millions of other people) I just always use the latest and best one unless something else triggers me to use a different one. It reminds me a bit about software updates. Folks around here are like, I'm not upgrading to macOS 17.2 and I'm staying on 17.3! Regular people? Not only can they not tell the difference they don't care - just give me the latest and best thing. period.
On the other hand you can have models that are OK at general purpose tasks but are perhaps specialized for certain applications (software or hardware). Those could wind up being on a spectrum from cheaper (and integrated with lower-quality products kind of like an Android Tv model) to very expensive and high-performant.
I don't see a world in which people are by and large sifting through well today I want to use Kimi 1.1 and well later today I'm going to switch over to Claude Lite 18.7 and dang it's not giving me what I want so I'll switch to whatever model. That's not how people operate - they don't want the cognitive overhead of trying to mess around with something that's good enough. They may have to suffer that based on their economic means, but it isn't going to be an expressed preference outside of very niche categories (like home-brew hackers playing with their toys).
Similarly this is just my opinion as well. I enjoy a good discussion here and I hope you are as well. Positive intents, even if my words my be curt, at times.
> Similarly this is just my opinion as well. I enjoy a good discussion here and I hope you are as well. Positive intents, even if my words my be curt, at times.
I have no issues with this thread : D But back to the discussion...
Again, I elided a lot of thoughts on the whole situation, but the other factor in my belief that this is an inevitability is that the SOTA labs churn too fast in both model behavior but also harness behavior. Normie businesses of the future are not going to hitch their wagon to a moving-target of both behavior and enforcement vs just pinning the equivalent of "opus 4.5" from a run of the mill inference provider. It just doesnt make business sense to be at the whim of a model update. And the entire perpetual motion machine aspect of the SOTA financial outlook _necessitates_ constant churn. If they dont release, they die.
> So where does that leave us? Nobody makes any money?
I think this leaves us with SOTA labs pushing hard for protectionist regulation to essentially require federal blessing to be an inference provider. I also think we will see a _ton_ of dark money poured into research to try and show things like theoretical smuggling/side-channel exfil attacks on models/model weights so that the SOTA companies can paint the _open models and model weights themselves_ as a bogeyman. If they can show that theres potentially latent attack vectors in those paths then that will then prevent all the "middle class" companies from being able to use them, especially if they need to adhere to eg SOC2/HIPAA certification etc
> If a Chinese company released an open weight model that somehow was so superior to Anthropic and OpenAI then they can just take the model and deploy it, reverse engineer it, ...
> The game isn’t just “cheapest open-weight model at all costs” which is what you are asserting here.
Decide: is the claim that open weight models will be more intelligent than Anthropic/OpenAI SOTA models, or that they will be cheaper?
> Most people don’t want to or have any interest in running or managing models locally
Most people are also not the primary customers of Anthropic! 5 companies make up 25% of Anthropic's revenue, and I bet there a ton of companies spending tens of $M/month on Anthropic/OpenAI that make up much of the rest. These are all medium/big companies that have the means for self-hosting models if they decide that's more convenient. The cost is of course not the whole story here, since Anthropic/OpenAI generally make better models and that allows using them in more situations, where perhaps other models would fail, but this is also a big cost for Anthropic/OpenAI to continue getting ahead, and the moment they stop they lose this advantage. Also in my experience self hosted open weight models are _fast_, which is _very_ nice for all those tasks where you don't need SOTA intelligence to get things done.
Every country in the world outside Europe and United States will adopt/use an open Chinese model because it will cost them relatively nothing, the only thing they have to supply is hardware. And the Chinese certainly can provide that. And so can several other companies outside of China. But those companies won’t be OpenAI, Anthropic or even Nvidia.
Where is their moat? Ultimately, where is their profit? answer? There is none at least there is none that will come close to whatever imaginary valuation Wall Street comes up with, in the best of times, while Wall Street may be able to carry one such company, but two within the same year?
You are misunderstanding a number of things aggressively.
For one thing, one needn't run a model locally, that's a separate--but also very damning--issue.
For another, that's fine if Anthropic and OpenAI can do that, but they will have sunk a bunch of cost into their own training already and people wouldn't pay them what they need when there is always a vastly cheaper substitute that gets the job done.
High profit software products have monopoly pricing power from network affects: everyone uses office for compatibility/familiarity with what everyone else uses, everyone uses Facebook because everyone else does, everyone buys advertising buys from Google because that's who everyone sells to because that's who everyone buys from.
No one has shown a network effect for models yet. Models are commodities. Anthropic etc will make the sort of profits one makes selling commodity goods (a couple of percent), plus a perhaps a bit of extra margin from being a few months ahead of open weight models (if that profit can justify the billions in training costs).
I do agree that local models are irrelevant. But local inference and open weights are different things. You can go on openrouter and see that there are dozens of companies selling open weight inference.
Worldwide, it is will be the cheapest model. That is going to win out. In the United States. and Europe, that will be their (OpenAI/Anthropic’s) markets, every place else probably will be a Chinese Open solution. And that will be no different than EV cars. or solar panels the Chinese dominate worldwide and the same probably will happen with AI models outside the United States, Europe, Australia, New Zealand and one or two other countries.
Also, because of the three hyenas of memory, the Chinese probably eventually will have control of the memory market too in the near 3 to 5 year future.
> Worldwide, it is will be the cheapest model. That is going to win out.
Win on what terms, or in what sense? We've seen this play out via Android versus iOS for example. The cheapest models are more prevalent, but Apple makes all the money. I'd be perfectly happy being the Apple in this case.
It's like you can go and buy a cheap Android phone. It technically does all the same things as the latest iPhone, but it does them worse. So Apple ends up making all the money and delivering the product that people go on to use to do all sorts of other economically advantageous things that make them even more money. Where does that leave the Android phone/model? In the dust, for the most part.
The important distinction is SOTA US labs - ANT and OAI - who can not economically survive reverse engineering / fast following PRC models at PRC pricing. Like obviously PRC operators are not going to flourish in US due to geopolitics alone, but that doesn't stop US operators running PRC models, or reverse engineered Murican open models from creaming US SOTA labs on economics eventually. The closer open models are to SOTA in capability the smaller SOTA TAM gets, and that market might not be large enough to fund/maintain SOTA. SWE is one of the highest margin sectors to capture, and open models already shrunk SOTA niche to critical infra and cybersecurity etc then what other high $$$ niche market going to replace? BioPharma maybe? The point is SOTA capitalization depends on large capability gaps that economically justifies 30T TAM - the more domains open models fast follows to compress TAM, less places SOTA has to carve sufficiently profitable moat, and less sustainable maintaining SOTA becomes, especially for dedicated AI labs with no alternative revenue. It's not cheapest open-weight wins, it's cheap and good enough open weight simply murders SOTA because SOTA cannot afford at all costs anymore.
The SOTA can't exist in this model because no company or investor will pay for something that will just be undercut.
What you are arguing is that all progress on models (outside of government labs or internal corporate workings) will effectively stop because there won't be funding for it. That seems unlikely to me. You just need to play it out...
US companies/investors go and invest a trillion dollars or something on models. Someone "does stuff" and reverse engineers their model. They go out of business (you said so [1]).
So now what happens?
Well, the US/DARPA/universities/whatever (all countries with interest) will keep working on this stuff and there will be some funding for it but that's about it. Military applications, physics, things like that.
Chinese companies will also fail and it will not be possible to create new SOTA ones because they'll obviously just be reverse engineered too and given away for free or at super low pricing - whatever happens in America must happen in China too.
So who will build new models? There's nobody around to pay for it besides governments or maybe corporate labs. But corporations and governments will protect the models (obviously, as OpenAI and Anthropic do today to varying extents) and keep the weights and stuff closed source. They'll have some productive use for them even if it's not in the download Claude Code and have at it style of B2C/B2B we have today.
Or
Maybe the trillions of dollars of investment going into these companies actually makes sense and the threat of open-weight models isn't what you and others seem to be suggesting it is. Whatever fate we seem to want to assign to America also happens to China here. To the extent they are behind, they're still running the same exact race. If open-weight models undercut companies they'll also undercut Chinese companies.
Just as Open Office exists today, so does Microsoft Office. Microsoft just makes all the money. I'm not sure why some seem to think that they can use the open source comparison here but not also use the open source economics that we've seen play out.
I think as a productive exercise someone should run this argument and explain why Chinese companies won't also be eaten alive by this alleged race to the bottom and have all of their stuff reverse engineered and given away. How can they invest in new AI companies with SOTA aspirations? Whatever argument you want to make for the US applies to China as well, and we can just give away freedom models to all the countries just like supposedly they will.
[1] "The important distinction is SOTA US labs - ANT and OAI - who can not economically survive reverse engineering / fast following PRC models at PRC pricing."
If SOTA finance model is unsustainable, then it's entirely economically plausible it will stop. US investors and SV pouring 100s of billions in accumulated $$$ from decade of cheap money printer and tech war chests into one massive hedge is anomalous business model. Did it make sense of TAM is 30T... maybe, but is TAM still 30T? We are seeing currently most profitable SWE niche get creamed, and other sectors likely follow.
>whatever happens in America must happen in China too.
No, because PRC literally could not do what US did... they simply didn't have access to enough compute to dump so much $$$ capitalization for SOTA race. On the other hand, on paper US WITH MORE compute and (currently) CHEAPER compute can put forth cheaper models to undercut PRC open models... but functionally they can't for the same reason cheap PRC operators can't win in US - geopolitics. Undermining PRC AI requires giving PRC cheap US AI on cheap US compute which is sanctioned. But ultimately that is entirely irrelevant to the fact that CHEAPER US models will undercut EXPENSIVE SOTA US models. The undercutting is not symmetrical. US companies can use cheap PRC hammers (or even cheaper US hammers) to break $$$ US SOTA. US who on paper can provide even cheaper hammers to PRC... but cannot sell those hammers... so PRC stuck using cheap hammers... and since PRC never had chance to spend $$$ on SOTA, there's no equivalent valuable things to break even if they had cheaper hammers.
> trillions of dollars of investment going into these companies actually makes sense
We know the context for which those $$$ investment makes sense... if SOTA held meaningful lead to sell high volume of $$$ tokens which was hedge 1-2+ years ago when years of $$$ commitments were made. But doesn't seem to hold anymore, ergo it no longer makes sense. Microsoft had decades to build ecosystem and moat, just like adobe it was, actually hard and high friction to move away. People are switching models all the time. SOTA AI only labs has much less moat than Microsoft or other incumbent tech with decades of overlapping capture mechanisms.
> I think as a productive exercise someone should run this argument and explain why Chinese companies won't also be eaten alive
The productive answer is THEY ARE ALSO BEING eaten alive.
The even more productive answer is PRC commodity AI doesn't have to win for US SOTA AI to lose.
There's just not much meat on PRC AI bones so they are losing billions instead 100s of billions / trillions. Like there is ongoing domestic AI involution irrespective of US SOTA. Literally the primary reason PRC AI was forced to go free was because DeepSeek is billionaire side project who priced his hobby as free which set economics for all other players. Being sanctioned/compute constrained = they figured out how to close SOTA gap on the cheap but also set ceiling on how much they can lose on race to bottom. In so far their SOTA aspirations, again refer to DeepSeek, he takes some couch change from hedge fund gains or investors, and throws it at smart people and hope it's enough. If its not then they'll probably stop too. What PRC not going to do is mobilize resources equivalent to several % of GDP concentrated in few entities for SOTA AI, at least not until their compute is sorted, in which case competitive exposure / risks becomes symmetrical. In the mean time, things are not symmetrical and arguments against economics of US SOTA AI does not apply to PRC commodity AI.
> No, because PRC literally could not do what US did...
I think the fundamental disagreement is you believe that cheaper models can just break the state of the art. This seems false because if it were true then the SOTA can also stop developing and spending on SOTA and just use a cheaper model to build their own better models and leverage their existing compute and energy infrastructure (or even build it out for better and more capabilities later). Anything that is released the SOTA has access to, but it also has access to things that others don't. It's ahead by definition and it's not possible to come out ahead of it because the SOTA will just absorb whatever open-weight/open-source/whatever is released and add to its own lead.
> SOTA AI only labs has much less moat than Microsoft or other incumbent tech with decades of overlapping capture mechanisms.
And those companies are worth a lot of money, continue to execute at high levels, are systematically important, and are by any definition wildly successful. That's my point. You have to apply the open source style argument fairly and equally. If you're going to suggest the cheaper models (which follow this open source software argument) are going to come into being and do all this stuff you also have to acknowledge that they made no money in comparison to the Microsofts of the world and Microsoft won because it made all the money. Whatever open-weight stuff that gets released everybody gets to use it and so tech companies get to use it too and accelerate their advantages. This is what we've seen play out, why would AI be any different when you're using the same economic paradigm as your argument?
> Literally the primary reason PRC AI was forced to go free was because DeepSeek is billionaire side project who priced his hobby as free which set economics for all other players.
American billionaires can also "just set up a side project" and reset whatever economics exist. The crux of your argument is someone can do this and then SOTA isn't economically viable, but you can't escape that this has to be true for everyone, not just the US. If it's true for everyone you also then have to admit the SOTA can't progress because there's no viable economic model - anytime you make an advance in the SOTA someone "just sets up a side project" and/or steals your model or w/e.
The US SOTA losing just means the SOTA has to be equal for everyone or the US is ahead - you can't have it both ways. Progress is practically impossible because you can't make money and you can't obtain a lead so nobody will do any work since their work will just be immediately utilized by someone else.
If what you are proposing here is true, then China is permanently behind. There isn't an economic model to obtain a "lead", yet the American companies get to just take all the work that Chinese companies do (until they go bankrupt not making money) and add it to their lead.
> The productive answer is THEY ARE ALSO BEING eaten alive.
Well you can just run them out of business then. If they spend any money you just spend more until they can't make money and close up shop. Or is someone going to subsidize them indefinitely?
The economics of these arguments that the US SOTA companies are just going to go bankrupt because of cheaper models is something I just don't find compelling. I've been trying. I debate these things so I can learn, but I'm just not seeing a coherent argument on the topic.
I don't think all of these tech companies and investors are stupid. Greedy to a large extent and some will lose their hats? Absolutely. Stupid? I don't think so. Worst case is all the infra is still useful.
> I think the fundamental disagreement is you believe that cheaper models can just break the state of the art.
No their argument is that the existence of a worse quality cheaper product will eat the majority of the market for the SOTA company, making the SOTA company less profitable (not necessarily unprofitable) because the number of people who need/want the quality difference supplied by the SOTA product aren't enough to pay to sustain the extra resources required to supply the next iteration of the better product or get a respectable ROI on the current investment.
>SOTA can also stop developing and spending on SOTA and just use a cheaper model
Yes, and they will be forced to (if the market shapes out as above) but the point is that then the company won't be getting the returns that justify the valuation they are talking about in the IPO and will likely never return the amount of money that has already been invested in it even if it is a profitable company. i.e. If I spend $20,000,000 making a lemonade stand that is a successful best in class stand and it nets me $50 profit a day then it doesn't matter how that as far as lemonade stands go that it's a pretty amazing example. I am effectively never going to see that 20 million again let alone a real profit.
They're not arguing that these SOTA companies aren't better, they're arguing that there won't be a return on investment let alone justifing further investment and means everyone will be taking a huge haircut on what is already invested (with the possible exception of early investors who take the bag with the IPO when it inevitably succeeds based on people disagreeing with this evaluation and thinking it a good investment or simply because of index funds being required to buy in)
Cheaper models break financing SOTA pace, PRC budget fast follow currently is catching up. If SOTA slows/stalls slower SOTA pace = instead of PRC 6 months behind, they're 3 months behind, or period of overlapping parity before US SOTA grabs lead again for a few months. There may not be economic model to maintain durable lead, which SOTA economic doom spiral condition. Now if PRC fast follow couldn't close gap, i.e if PRC only fast following due to distillation and US SOTA can technically prevent PRC fast follow, and extend gap then maybe exorbitant SOTA spend sustainable. If not sustainable everyone moves to cheaper cycles. This an issue if SOTA cannot be done on the cheap, which also true for PRC, but PRC not doing expensive SOTA, they're doing cheap fast follow.
>why would AI be any different
It's not, someone going to grab big share of AI... but that someone has to be competitive enough for exquisite lock in, and frankly with enterprise reducing SOTA AI spend due to SOTA AI costs, current SOTA AI ain't it. See last paragraph for hopium.
> someone going to subsidize them indefinitely
Yes. Same way Alphabet/Meta can subsidize AI, China has their Baba and tech giants with alternate revenue streams to subsidize AI, even if not SOTA, and geopolitics will make sure US cannot run PRC AI out of business in PRC and vice versa. But geopolitics not stopping PRC companies running other PRC companies out of business, same with US companies running US companies out of business. In this case host countries tech giants with high margin businesses squeezing sole play AI labs. It's just US SOTA ANT/OAI getting murdered is massive economic contagion risk for US vs Zai/kimi not for PRC.
> find compelling
This really not complicated, last couple years, US SOTA labs signed 100s of billions of compute based on projected demand that they could monopolize enough demand to finance compute capitalization. This not stupid investor prior, because their only competitor, PRC was export controlled, compute constrained and shouldn't close gap. But PRC closed gap, made models good enough and architecture efficient / cheap enough so other players, including those in US, can capture large % of demand WHILE using fraction of compute. SOTA labs now looking at margin compression, losing share to good enough competitors and obligatory (now excessive) capitalization. This bad combo for current US SOTA economics. The numbers suggest this is what is happening. There is chance demand increases past projections and extra SOTA compute will be boon, but again, we are seeing most profitable SWE enterprise curtail not increase demand.
> Worst case is all the infra is still useful
IMO this actually best case for US medium term favour, but also another strike against CURRENT US SOTA. Reality is current SOTA is grossly overpaying for compute / AI premium. Even if OAI or ANT dies, their compute can be picked up for pennies on dollar and as long as PRC behind on compute there is potential for cheaper future US SOTA economics from cheaper hardware since crash will lead hardware price regress towards mean. But that boon is for the next guy.
Here's the US hopium: post current US SOTA crash, there is potentially curve where US has so much cheap discount compute, because current SOTA taking the haircut, and where PRC has not much compute, that US will have sustainable SOTA prospects, i.e. the world where US pays $1 for shovels instead of $10 and can buy $10000 worth of shovels while PRC can only buy $100. That's feasible after current exorbitant SOTA economics bleeds it's patients to death and the next US SOTA host get the transplants and lighter balance sheet, i.e. web 2.0 for SOTA AI. Incidentally more likely "winner" of AI... if I had to guess assuming incumbent FAANG doesn't just snatch up ANT/OAI if they explode - new player/entrant who are near SOTA and eventually SOTA but is priced more competitive t...
> There are exceedingly few tech companies with P/S of 6 or less
I think an open question is if SOTA labs should be priced like software companies have been. Software has been so valuable because the marginal cost approaches zero as distribution increases. Profit/employee at a company like meta prior to massive AI spending was measured in the millions. Meanwhile, the balance sheets of the labs look entirely different - remaining competitive requires continuous and astronomic capital costs, and there's no indication that this goes away. They are perennially being chased by competitors offering the same performance as models mere months old at fractions of the cost.
Their business to me, at least long term, looks a lot more like an airline, railroad, or other extremely capital-heavy industry with very competitive and low margins. None of those trade at 6+ p/s. Particularly on the buyer side, the switching cost to me as a high-AI-spending employee if a cheaper model offers compelling performance is less than deciding that I should ditch my preferred business travel airline.
I think that's why you're seeing so many calls from the leaders of those companies to regulate their competition away.
But like Google and Facebook have a lot of hardware. They aren’t just pure in the cloud (though Google is running other peoples clouds). But yes their hardware demands per dollar of revenue is much lower
Ultimately it's marginal, meaning that every $ of revenue that Google and Meta make have an associated compute cost with it, but scale your revenue down and you scale compute down also. Moreover this compute cost per revenue is fairly low. A small cloud instance equivalent's of compute can generate a ton of business value.
The big foundational labs are not like this. Every model they release takes a ton of data infrastructure and training (pre, train, post) compute. Then inference itself requires compute. Even if the labs sell tokens, each token embodies a lot of training and inference compute. This is a much more capital heavy business, so I can see revenue multiples being smaller than pure SaaS businesses.
A good analogy is chip companies before all of the AI related compute pressure. These were largely low margin businesses because of the fixed capital cost associated with each new chip gen, architecture, process, etc
The value in Facebook is in the social network that can't be replicated by just buying servers. The value in Google is/was in the secret IP. Hardware costs are low compared to the value of those. Hardly anyone knows how to make a search engine that works as well as Google does, or how to make a social network that draws everyone away from Insta/FB (ok bytedance do...).
Note the huge difference in strategy and outcome!
Google published essentially nothing on how to build a search engine. Huge R&D spend, zero publications, intense culture of secrecy, extremely low attrition to startups or competitors. They did publish some papers on big data systems but only on tech that was many years old - a typical Google "research" paper pre-2015 would announce that the system it described had been running at scale in production for years already. Google also never faced any kind of split or mutiny in which lots of people with the core secrets left the firm to set up a competitor.
With AI however this changed and they published all their research immediately, also their best employees constantly ran away to join startups. The result was predictable: smaller competitors deployed their ideas faster and better than they did, and now Google has no moat anymore.
OpenAI took the classic Google approach and stopped publishing R&D after developing GPT but this didn't help them build margins because:
1. Apparently the core transformer idea is powerful enough that breakthroughs after that didn't matter much. Nobody has been able to develop some killer edge that leaves competitors unable to catch up. Most improvements to transformers have been about cost reduction, not capability improvement.
2. They had a huge split when the Anthropic guys walked out over safety, taking the knowledge of how to build a frontier LLM with them. Result was a peer competitor from day one, whereas Google had at least six years to build edge before anyone realized web search mattered.
Result: Google-style hardware costs but without any secret IP that could generate revenue commensurate with those costs.
The only justification for extremely high P/S ratios is if there’s strong evidence that the future margins and profit will be amazing.
With the price of AI rapidly falling to the price of compute, with models therefore basically having zero value, and with Anthropic having rented all its compute from others then there’s little evidence that such high profits are in the future.
Aren't their operating expenses quite high? That is they depend on high expenses to continuously drive revenue. If they can't keep raising money, then their revenues should be expected go down.
To me, "a probably $200-300B company" sounds absurd. You're describing one of the possible outcomes of a massive range that goes from $0, where they get over their skis on financial commitments to well beyond $2T, where their 10x YoY growth continues for a few more years. A $200-300B outcome is possible, but certainly not probable. (And in fact, for us HN commenters, the most probable outcome should be assumed to be roughly what the market will bear.)
Well, it depends on how you interpret the drop to a 47% quarterly growth rate.
If this is the new normal, that's great news. A company growing at 47% per quarter will 10x in 6 quarters. Even if you think it's a $200B company today, it'll be a $2T company in 18 months.
If you think this is the start of a rapid decline to 0% growth rate, then it's obviously bad news.
The real answer is probably somewhere in the middle -- but that's a very large middle.
Because if they priced the company more reasonably many later stage investors would stand to lose money. So they’ve backed themselves into a corner of having to go public at a $1T plus valuation just so some later stage investors don’t realize a big loss.
>The issue is this is a probably $200-300B company pretending it’s a $2T company.
What do you expect for FY2027 revenue to confidently conclude this? not saying you're wrong, but considering the dramatic changes in their revenue you seem strangely very certain that you know where it will land.
> Once they are a public company this will be actual value taken away from existing public shareholders so they are legit losses not to be ignored.
They reported a loss here because they sold notes convertible into shares and their valuation went up. Normally shareholders do not lose money when the stock price goes up.
It's weird that they had to report it like this because they are technically derivatives, and if they had just given out actual shares (as with stock-based comp), they wouldn't. Yes, giving people stock is a real cost, but what happens to the price afterwards is not, and when the price goes up shareholders are definitely making money, not losing it.
Either way, they did report it. It is up to you to decide how you interpret that information.
Tokens themselves are already a bit of a commodity, and the margins are gonna be fierce on these as there is not a whole lot of lock in.
Would Anthropic and OpenAI position themselves using their extra tools, their ai agents, presentations, docs. Are they nice; yes. However, that doesn't make a 2T company. It reminds me a bit of Dropbox, a truly great product, however, they're differentiation is simply not there. I think the momentum is there for the IPO to go decent, but it feels like the company is on such unstable ground that the fallout could be catastrophic.
the revised S-1 behind that $895B commitments figure says nine of the ten largest pharma cos, so it seems interesting that's what they seem to be basing off of
Since the start of the year the market has shifted from 50/50 split (closed v open weights) to 30/70 (70% of usage they see through their platform - is now open weight)
DeepSeek V4.1 Flash 1017 sees 5x the weekly token usage through OpenRouter than the closest closed weight model (GPT-6 Luna)
Even though OpenRouter is only one limited view of the market - those trends should be worrying to potential investors.
I'm not saying that OpenRouter isn't worth looking at but it, obviously, shows a much smaller slice of LLM market and it's going to, obviously, trend towards open models since those are the only model where there is any competition for inference pricing. No one is undercutting Luna because they can't really.
OpenRouter could so 90/10 split and it still wouldn't mean open models are beating (in usage) closed models.
This view will ALWAYS overweight open models. The closed ones are being used in their own harnesses via direct API connections that OpenRouter will never have visibility to.
Let's say I'm starting a shitty cash-grab SaaS that needs some kind of "Ask Jenny" feature. Is the standard practice to sign up directly with a Claude/OpenAI API account?
Looking at the numbers there though, both Anthropic and Deepseek increased in terms of absolute usage numbers since the start of the year. And thats setting aside the high potential for sampling bias.
Why do they have to go with the ridiculous gazillion dollar valuations? Is it really such a bad thing to be valued at "only" 400B or whatever more realistic number, why does it have to be in the trillions?
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[ 4492 ms ] story [ 1956 ms ] threadIt says "You're absolutely right!".
So they did release the reports.
Look, I've seen and I understand the arguments that the GAAP income adjustment for this kind of thing is fake, but a number of them seemed to take it for granted that it was a "one-time" adjustment. If it's going to keep happening I really feel like we ought to insist on a more comprehensive argument. The accounting principle must be generally accepted for a reason: what shenanigans could someone get up to by reporting net income without this adjustment, and how do we know Anthropic is not getting up to those shenanigans?
We aren’t shareholders, though, so why should we care?
They're going through an IPO, so unless this is a one-off thing, people reading this thread will become shareholders, either directly or through their pension pots.
So while yes, this does look a lot like shareholder dilution, that doesn't really satisfy the question of why it's not treated that way under GAAP. Perhaps GAAP is just wrong, or perhaps there's some possible tricks which Anthropic is provably not engaging in. What it's definitely not is a one-time adjustment now that it's happened in two consecutive quarters.
The reason it happens in multiple quarters is likely that Anthropic's valuation has increased in multiple quarters. Every time Anthropic's valuation increases, the convertible bonds are worth more money. Other shareholders have "lost" money compared to what would have happened if Anthropic had sold regular bonds instead of convertible bonds. Except that no, the value of their shares is increasing too. It's actually good for them.
That's how equity works. If someone owns 1% of a company and its valuation doubles, their share is worth twice as much and so is yours and we wouldn't even normally consider it to be diluting the other shareholders.
So the question is whether convertible bonds are worth more than regular bonds and the answer is, yes, absolutely, but they are partially equity, and it's the equity part that's increasing when the company's valuation is increasing.
And if the company's valuation went down then the value of the convertible bonds would decrease. Maybe that would be considered a profit?
It is like someone who loses repeatedly at Roshambo while saying: "Okay, then. Best two out of three- three out of five- four out of seven- five out of nine..."
[0] https://edition.cnn.com/2026/10/30/business/ai-stocks-bonds-...
Consolidated financials are on page 23, quarterly numbers on page 160
> Okay so Q3 ARR at $67.8bn versus $46.2bn for Q2, +47% QoQ. Turns out tokenmaxxing's end over the summer slowed revenue growth versus an absolutely torrid 100%+ growth rate in Q1/Q2 but 46% sequential is still eye-popping
> Operating margins (GAAP) went from 8% to -3$, with a -28.5% QoQ incremental operating margin. $SPCX compute coming online is the reason.
> Bottom line after seeing this Q3 update: the basic takeaways from the original S-1 don't change, but fears that they were excluding Q3 because it was a disaster were definitely wrong. Do with this information what you will.
It's also worth noting that Q3 only has one week of Opus 5.5 (9/22 - 9/30) which may have changed the economics for Q4.
Your posts are irrelevant.
https://openrouter.ai/compare/anthropic/claude-opus-5.5/deep...
That's a very generous reading. Anthropic's entire growth narrative is the rapid growth over the previous 12 months, from near-enough zero to $100bn+ this year. Now we see that actually growth is levelling off and that their revenue was ~$5bn/month over the last quarter, which is barely over their compute commitments, and nothing close to the $100bn+ leaked.
I guess if people thought Q3 would show negative growth rate, sure, it isn't a disaster, but their growth rate slowing so much is surely very bad and matches all the concerns commenters had.
And this is Anthropic! Anthropic are the financial success compared to OpenAI. OpenAI are doing much worse, so this does not bode well for OpenAI either.
I really don't understand how could it be otherwise? They're planning to IP at a valuation representing 5% of the US GDP and, unlike NVidia, they are neither selling shovels in a goldrush nor a business with decades-old proven track of records.
Are people really going to buy ANT thinking it'll 10x to 50% of the US GDP?
Aren't there better stocks to YOLO on if you think you could maybe 10x? And if you want something safe instead of YOLO'ing, aren't there plenty of companies much safer and much more boring to invest in than something IPO'ing at 5% of the US GDP?
Isn't the entire Anthropic narrative based on infinite growth? How is infinite growth sustainable?
47% quarterly growth is still 4.5x annual growth, which would put them at $300+ billion revenue in a year. This is still unprecedented growth for a company of this size.
The negative perspective depends on assuming that the 47% quarterly growth is going to continue dropping soon. And it is going to drop, since it's only a bit more sustainable than last year's growth. The question is how many hundreds of billions they're making a year when it levels off.
Some people just start out with wanting LLMs/AI/Anthropic to fail or be scams and consistently try to fit any new evidence pegs into those unchanging hypothesis-shaped holes. This group ideologically despised LLMs from the start and needs them to be fake.
We're nowhere close to the ceiling on LLMs alone, let alone AI in general. What about when they can run entire firms themselves? Like, this is just assuming Opus 4.6 is the best we could ever get, in which case, yeah, the notion of AI is simply a dead-end technology. But obviously it's not going to be the best we could ever get.
An average random dev here: I switched to Opus 5.5 and forgot about all other (paid) models.
Now, they need to unearth new use cases and saturate them to keep the momentum going. Its anyone's guess if they have a line of sight to these and can sustain their growth trajectory.
Within large tech companies the era of "spend the most money on the smartest model running on its maximum effort, always" is dead.
There is an unbelievably enormous difference in 100% vs 47% -> and when you look at the 2nd derivative this is REALLY REALLY REALLY bad.
I mean, the growth is astounding and incredible. It does not bode particularly well for a ~$2T valuation.
Maybe they tried too hard to get good Q2 numbers, making revenue happen earlier and expenses later, and that now makes Q3 look worse? Either way, this does not look great
They stopped when the free packs stopped coming and they needed to finance their own.
All of that isn't very newsworthy though. We knew all that from the Q2 numbers. What is interesting is that reinvesting all their profits to make the number go up in the next quarter didn't work as well this time as it did all the other times
Apparently you’ve missed all of Ed Zitron’s reporting?
So he can get all of his conclusions and predictions wrong based on his numbers and analysis, and then you think his value is in his numbers and analysis?
But are they still a 1.5T USD company if their growth is already slowing this much?
And they say that this will likely continue to grow until 2027 (when the long term commitment compute comes online, which comes with opposite problem .. will there still be enough demand)
That makes any loss or risk reasonable.
It's great propaganda, so great that they have themselves started believing it with certainty. This is one of the self owns that brought down the USSR :)
Consider: if you had built a hyperintelligent stock-trading AI, would you:
- Charge $0.70/M tokens for other people to ask the bot what stocks to buy.
- Get rich following the bot's trade advice and never tell anyone about your bot.
The fun thing about open markets is the market will punish you for this with second order effects and it's in your best interest to get some whales on the bandwagon.
https://searchengineland.com/guide/facebook-users
Then if all goes well next year OpenAI will scam retail investors by claiming they will have the only A.I. worth anything.
The open weight models right now are nowhere close to ..say.. Fable 5.1
They could catch up if Anthropic reaches some sort of a plateau and they manage to keep improving, but there's no reason to be certain about that happening.
Obviously the investors who bought in at the bottom did the best :-) Most people who are crying 'AI bubble' predicted 8 of the last 2 bubbles.
My personal opinion is ANT IPO is like the netscape IPO of 1995, its the start of the cycle not the end. Things are going to get really crazy over the next 3-5 years, the last year was just a warmup. I don't think people realize how much credit fiat dollars have been and can be created, and how $1T companies will be commonplace. I think humans have a mental block, where we think dollars are like rulers or other fixed measuring devices, so you can look at the same number at different periods of time and compare. credit backed currency completely warps our sense of scale when it comes to measuring things in dollars.
In Q3, I think they struggled with the government ban on Mythos/Fable, Opus 5 not being so good, OpenAI models being more token efficient, and they decided to dedicate a higher percentage of compute to training.
Again the issue is not is Anthropic an amazing growth story. Zero would debate that.
The issue is this is a probably $200-300B company pretending it’s a $2T company.
Add to that the insane amount of committed spend, a high interest rate environment which means they’ll pay a painful premium to patch cashflow gaps, and a likely “junk” bond rating (per Friday’s news) and that’s why folks are concerned.
It’s also just wrong to keep dismissing the paper losses from convertible notes as somehow not real losses. Once they are a public company this will be actual value taken away from existing public shareholders so they are legit losses not to be ignored. It’s like claiming equity based comp is free money… it’s not, you’re taking it out of the pockets of shareholders.
They probably asked Claude. Sounds like a normal LLM hallucination.
Should have followed it up with a second prompt to get the real answer ... "You're absolutely right. valuation is only $20b"
How can you confidently say that? At some point they oversaturate and growth stalls. Didn’t F100 have a major pullback already this year on AI spend?
SpaceX is down, Cerebras is down, etc.
I guess it applies to everything. Universal. Like LLMs.
What is "this" referring to here?
of course each stock price is many bets intersecting at the same time (with all their correlations of course)
There's a world where capabilities narrow and Anthropic is stuck selling a commodity (not like they don't have any competition now). In this world it's not unbelievable that Anthropic would trade around or below 1x revenue no?
I think calling Anthropic a tech company could be like calling an electric utility a tech company.
A big part of the reason this has been true is that (mostly-software-based) tech companies haven't historically been spending tens to hundreds of billions of dollars every year in capex costs.
AI labs are, for now anyway, in a different category.
The SOTA US labs are one chinese open model away from being abandoned overnight by the masses. Ive argued before, but argue again here, that the current hardware shortages are the main thing keeping this from being a reality currently. If more people were able to run local models for a reasonable all-in cost, we would have a much higher velocity of iteration and improvement in the open source community. But its still too niche while people cant justify the cost, and so they continue to pay the premium for inference.
If a Chinese company released an open weight model that somehow was so superior to Anthropic and OpenAI then they can just take the model and deploy it, reverse engineer it, use it to improve their models, and/or do any number of things they currently do today which are of increasing value.
Most people don’t want to or have any interest in running or managing models locally.its not just about cost. The market for running models is infinitesimal in comparison to the market for managed products. That’s why everyone uses Office and not Open Office. Nobody is going to run some Chinese open weight model no matter how good it is on their critical banking infrastructure or something.
There will be space for lots of different solutions and many products and types of models including Chinese open-weight models and American ones too once we get around to making money on them.
The game isn’t just “cheapest open-weight model at all costs” which is what you are asserting here.
When I argue the hardware cost, I should note that I think this is the limiting factor for _improvement_ of these models. But ultimately I dont think most users will self host in that world. They will still pay for inference, but it will be for one of a dozen commoditized vendors who are able to reap the benefits of inference at scale.
It's the same reason that open source software has been a commercial failure except for a few evangelists or whoever becomes a cool "thought leader" by releasing a new library and then gets invited to conferences to speak. Software was commoditized in many ways, yet all the big software vendors made more money than GOD because they took whatever commodity parts came to exist and monetized them.
I think the models can be improved with either recursive improvement (it would be faster with better hardware but if you can do it at all you just need time) and algorithmic developments. I don't know what those would be, but usually we see improvements to both over time. The algorithms will tend to be trade secrets as well.
The problem here is that let's say it's true that the Chinese are cutting off US companies at the knees. Well the US can just do the same thing back to the Chinese. We might have some money lost and stuff but the data centers will continue to be useful, as will all the infrastructure and energy grid buildout so it's not a total loss (I couldn't care less if accredited investors like myself lose money - you know the risk). Then we just steal Chinese models, do the open-weights thing, and all that stuff. So where does that leave us? Nobody makes any money? (Also where is Europe in all this?)
> I also dont think these need to be better than SOTA, just reach a "good enough" bar.
I agree with you in some respects but I'd like to digress here a bit. Good enough for a general purpose model isn't good enough. You want the best or at least what performs to your expectations. I don't even care about old models nor how much they cost (in my personal life and of which I will be a paying customer as will tens of millions of other people) I just always use the latest and best one unless something else triggers me to use a different one. It reminds me a bit about software updates. Folks around here are like, I'm not upgrading to macOS 17.2 and I'm staying on 17.3! Regular people? Not only can they not tell the difference they don't care - just give me the latest and best thing. period.
On the other hand you can have models that are OK at general purpose tasks but are perhaps specialized for certain applications (software or hardware). Those could wind up being on a spectrum from cheaper (and integrated with lower-quality products kind of like an Android Tv model) to very expensive and high-performant.
I don't see a world in which people are by and large sifting through well today I want to use Kimi 1.1 and well later today I'm going to switch over to Claude Lite 18.7 and dang it's not giving me what I want so I'll switch to whatever model. That's not how people operate - they don't want the cognitive overhead of trying to mess around with something that's good enough. They may have to suffer that based on their economic means, but it isn't going to be an expressed preference outside of very niche categories (like home-brew hackers playing with their toys).
Similarly this is just my opinion as well. I enjoy a good discussion here and I hope you are as well. Positive intents, even if my words my be curt, at times.
I have no issues with this thread : D But back to the discussion...
Again, I elided a lot of thoughts on the whole situation, but the other factor in my belief that this is an inevitability is that the SOTA labs churn too fast in both model behavior but also harness behavior. Normie businesses of the future are not going to hitch their wagon to a moving-target of both behavior and enforcement vs just pinning the equivalent of "opus 4.5" from a run of the mill inference provider. It just doesnt make business sense to be at the whim of a model update. And the entire perpetual motion machine aspect of the SOTA financial outlook _necessitates_ constant churn. If they dont release, they die.
> So where does that leave us? Nobody makes any money?
I think this leaves us with SOTA labs pushing hard for protectionist regulation to essentially require federal blessing to be an inference provider. I also think we will see a _ton_ of dark money poured into research to try and show things like theoretical smuggling/side-channel exfil attacks on models/model weights so that the SOTA companies can paint the _open models and model weights themselves_ as a bogeyman. If they can show that theres potentially latent attack vectors in those paths then that will then prevent all the "middle class" companies from being able to use them, especially if they need to adhere to eg SOC2/HIPAA certification etc
> The game isn’t just “cheapest open-weight model at all costs” which is what you are asserting here.
Decide: is the claim that open weight models will be more intelligent than Anthropic/OpenAI SOTA models, or that they will be cheaper?
> Most people don’t want to or have any interest in running or managing models locally
Most people are also not the primary customers of Anthropic! 5 companies make up 25% of Anthropic's revenue, and I bet there a ton of companies spending tens of $M/month on Anthropic/OpenAI that make up much of the rest. These are all medium/big companies that have the means for self-hosting models if they decide that's more convenient. The cost is of course not the whole story here, since Anthropic/OpenAI generally make better models and that allows using them in more situations, where perhaps other models would fail, but this is also a big cost for Anthropic/OpenAI to continue getting ahead, and the moment they stop they lose this advantage. Also in my experience self hosted open weight models are _fast_, which is _very_ nice for all those tasks where you don't need SOTA intelligence to get things done.
Where is their moat? Ultimately, where is their profit? answer? There is none at least there is none that will come close to whatever imaginary valuation Wall Street comes up with, in the best of times, while Wall Street may be able to carry one such company, but two within the same year?
For one thing, one needn't run a model locally, that's a separate--but also very damning--issue.
For another, that's fine if Anthropic and OpenAI can do that, but they will have sunk a bunch of cost into their own training already and people wouldn't pay them what they need when there is always a vastly cheaper substitute that gets the job done.
No one has shown a network effect for models yet. Models are commodities. Anthropic etc will make the sort of profits one makes selling commodity goods (a couple of percent), plus a perhaps a bit of extra margin from being a few months ahead of open weight models (if that profit can justify the billions in training costs).
I do agree that local models are irrelevant. But local inference and open weights are different things. You can go on openrouter and see that there are dozens of companies selling open weight inference.
Also, because of the three hyenas of memory, the Chinese probably eventually will have control of the memory market too in the near 3 to 5 year future.
Win on what terms, or in what sense? We've seen this play out via Android versus iOS for example. The cheapest models are more prevalent, but Apple makes all the money. I'd be perfectly happy being the Apple in this case.
It's like you can go and buy a cheap Android phone. It technically does all the same things as the latest iPhone, but it does them worse. So Apple ends up making all the money and delivering the product that people go on to use to do all sorts of other economically advantageous things that make them even more money. Where does that leave the Android phone/model? In the dust, for the most part.
What you are arguing is that all progress on models (outside of government labs or internal corporate workings) will effectively stop because there won't be funding for it. That seems unlikely to me. You just need to play it out...
US companies/investors go and invest a trillion dollars or something on models. Someone "does stuff" and reverse engineers their model. They go out of business (you said so [1]).
So now what happens?
Well, the US/DARPA/universities/whatever (all countries with interest) will keep working on this stuff and there will be some funding for it but that's about it. Military applications, physics, things like that.
Chinese companies will also fail and it will not be possible to create new SOTA ones because they'll obviously just be reverse engineered too and given away for free or at super low pricing - whatever happens in America must happen in China too.
So who will build new models? There's nobody around to pay for it besides governments or maybe corporate labs. But corporations and governments will protect the models (obviously, as OpenAI and Anthropic do today to varying extents) and keep the weights and stuff closed source. They'll have some productive use for them even if it's not in the download Claude Code and have at it style of B2C/B2B we have today.
Or
Maybe the trillions of dollars of investment going into these companies actually makes sense and the threat of open-weight models isn't what you and others seem to be suggesting it is. Whatever fate we seem to want to assign to America also happens to China here. To the extent they are behind, they're still running the same exact race. If open-weight models undercut companies they'll also undercut Chinese companies.
Just as Open Office exists today, so does Microsoft Office. Microsoft just makes all the money. I'm not sure why some seem to think that they can use the open source comparison here but not also use the open source economics that we've seen play out.
I think as a productive exercise someone should run this argument and explain why Chinese companies won't also be eaten alive by this alleged race to the bottom and have all of their stuff reverse engineered and given away. How can they invest in new AI companies with SOTA aspirations? Whatever argument you want to make for the US applies to China as well, and we can just give away freedom models to all the countries just like supposedly they will.
[1] "The important distinction is SOTA US labs - ANT and OAI - who can not economically survive reverse engineering / fast following PRC models at PRC pricing."
>whatever happens in America must happen in China too.
No, because PRC literally could not do what US did... they simply didn't have access to enough compute to dump so much $$$ capitalization for SOTA race. On the other hand, on paper US WITH MORE compute and (currently) CHEAPER compute can put forth cheaper models to undercut PRC open models... but functionally they can't for the same reason cheap PRC operators can't win in US - geopolitics. Undermining PRC AI requires giving PRC cheap US AI on cheap US compute which is sanctioned. But ultimately that is entirely irrelevant to the fact that CHEAPER US models will undercut EXPENSIVE SOTA US models. The undercutting is not symmetrical. US companies can use cheap PRC hammers (or even cheaper US hammers) to break $$$ US SOTA. US who on paper can provide even cheaper hammers to PRC... but cannot sell those hammers... so PRC stuck using cheap hammers... and since PRC never had chance to spend $$$ on SOTA, there's no equivalent valuable things to break even if they had cheaper hammers.
> trillions of dollars of investment going into these companies actually makes sense
We know the context for which those $$$ investment makes sense... if SOTA held meaningful lead to sell high volume of $$$ tokens which was hedge 1-2+ years ago when years of $$$ commitments were made. But doesn't seem to hold anymore, ergo it no longer makes sense. Microsoft had decades to build ecosystem and moat, just like adobe it was, actually hard and high friction to move away. People are switching models all the time. SOTA AI only labs has much less moat than Microsoft or other incumbent tech with decades of overlapping capture mechanisms.
> I think as a productive exercise someone should run this argument and explain why Chinese companies won't also be eaten alive
The productive answer is THEY ARE ALSO BEING eaten alive.
The even more productive answer is PRC commodity AI doesn't have to win for US SOTA AI to lose.
There's just not much meat on PRC AI bones so they are losing billions instead 100s of billions / trillions. Like there is ongoing domestic AI involution irrespective of US SOTA. Literally the primary reason PRC AI was forced to go free was because DeepSeek is billionaire side project who priced his hobby as free which set economics for all other players. Being sanctioned/compute constrained = they figured out how to close SOTA gap on the cheap but also set ceiling on how much they can lose on race to bottom. In so far their SOTA aspirations, again refer to DeepSeek, he takes some couch change from hedge fund gains or investors, and throws it at smart people and hope it's enough. If its not then they'll probably stop too. What PRC not going to do is mobilize resources equivalent to several % of GDP concentrated in few entities for SOTA AI, at least not until their compute is sorted, in which case competitive exposure / risks becomes symmetrical. In the mean time, things are not symmetrical and arguments against economics of US SOTA AI does not apply to PRC commodity AI.
I think the fundamental disagreement is you believe that cheaper models can just break the state of the art. This seems false because if it were true then the SOTA can also stop developing and spending on SOTA and just use a cheaper model to build their own better models and leverage their existing compute and energy infrastructure (or even build it out for better and more capabilities later). Anything that is released the SOTA has access to, but it also has access to things that others don't. It's ahead by definition and it's not possible to come out ahead of it because the SOTA will just absorb whatever open-weight/open-source/whatever is released and add to its own lead.
> SOTA AI only labs has much less moat than Microsoft or other incumbent tech with decades of overlapping capture mechanisms.
And those companies are worth a lot of money, continue to execute at high levels, are systematically important, and are by any definition wildly successful. That's my point. You have to apply the open source style argument fairly and equally. If you're going to suggest the cheaper models (which follow this open source software argument) are going to come into being and do all this stuff you also have to acknowledge that they made no money in comparison to the Microsofts of the world and Microsoft won because it made all the money. Whatever open-weight stuff that gets released everybody gets to use it and so tech companies get to use it too and accelerate their advantages. This is what we've seen play out, why would AI be any different when you're using the same economic paradigm as your argument?
> Literally the primary reason PRC AI was forced to go free was because DeepSeek is billionaire side project who priced his hobby as free which set economics for all other players.
American billionaires can also "just set up a side project" and reset whatever economics exist. The crux of your argument is someone can do this and then SOTA isn't economically viable, but you can't escape that this has to be true for everyone, not just the US. If it's true for everyone you also then have to admit the SOTA can't progress because there's no viable economic model - anytime you make an advance in the SOTA someone "just sets up a side project" and/or steals your model or w/e.
The US SOTA losing just means the SOTA has to be equal for everyone or the US is ahead - you can't have it both ways. Progress is practically impossible because you can't make money and you can't obtain a lead so nobody will do any work since their work will just be immediately utilized by someone else.
If what you are proposing here is true, then China is permanently behind. There isn't an economic model to obtain a "lead", yet the American companies get to just take all the work that Chinese companies do (until they go bankrupt not making money) and add it to their lead.
> The productive answer is THEY ARE ALSO BEING eaten alive.
Well you can just run them out of business then. If they spend any money you just spend more until they can't make money and close up shop. Or is someone going to subsidize them indefinitely?
The economics of these arguments that the US SOTA companies are just going to go bankrupt because of cheaper models is something I just don't find compelling. I've been trying. I debate these things so I can learn, but I'm just not seeing a coherent argument on the topic.
I don't think all of these tech companies and investors are stupid. Greedy to a large extent and some will lose their hats? Absolutely. Stupid? I don't think so. Worst case is all the infra is still useful.
No their argument is that the existence of a worse quality cheaper product will eat the majority of the market for the SOTA company, making the SOTA company less profitable (not necessarily unprofitable) because the number of people who need/want the quality difference supplied by the SOTA product aren't enough to pay to sustain the extra resources required to supply the next iteration of the better product or get a respectable ROI on the current investment.
>SOTA can also stop developing and spending on SOTA and just use a cheaper model
Yes, and they will be forced to (if the market shapes out as above) but the point is that then the company won't be getting the returns that justify the valuation they are talking about in the IPO and will likely never return the amount of money that has already been invested in it even if it is a profitable company. i.e. If I spend $20,000,000 making a lemonade stand that is a successful best in class stand and it nets me $50 profit a day then it doesn't matter how that as far as lemonade stands go that it's a pretty amazing example. I am effectively never going to see that 20 million again let alone a real profit.
They're not arguing that these SOTA companies aren't better, they're arguing that there won't be a return on investment let alone justifing further investment and means everyone will be taking a huge haircut on what is already invested (with the possible exception of early investors who take the bag with the IPO when it inevitably succeeds based on people disagreeing with this evaluation and thinking it a good investment or simply because of index funds being required to buy in)
Cheaper models break financing SOTA pace, PRC budget fast follow currently is catching up. If SOTA slows/stalls slower SOTA pace = instead of PRC 6 months behind, they're 3 months behind, or period of overlapping parity before US SOTA grabs lead again for a few months. There may not be economic model to maintain durable lead, which SOTA economic doom spiral condition. Now if PRC fast follow couldn't close gap, i.e if PRC only fast following due to distillation and US SOTA can technically prevent PRC fast follow, and extend gap then maybe exorbitant SOTA spend sustainable. If not sustainable everyone moves to cheaper cycles. This an issue if SOTA cannot be done on the cheap, which also true for PRC, but PRC not doing expensive SOTA, they're doing cheap fast follow.
>why would AI be any different
It's not, someone going to grab big share of AI... but that someone has to be competitive enough for exquisite lock in, and frankly with enterprise reducing SOTA AI spend due to SOTA AI costs, current SOTA AI ain't it. See last paragraph for hopium.
> someone going to subsidize them indefinitely
Yes. Same way Alphabet/Meta can subsidize AI, China has their Baba and tech giants with alternate revenue streams to subsidize AI, even if not SOTA, and geopolitics will make sure US cannot run PRC AI out of business in PRC and vice versa. But geopolitics not stopping PRC companies running other PRC companies out of business, same with US companies running US companies out of business. In this case host countries tech giants with high margin businesses squeezing sole play AI labs. It's just US SOTA ANT/OAI getting murdered is massive economic contagion risk for US vs Zai/kimi not for PRC.
> find compelling
This really not complicated, last couple years, US SOTA labs signed 100s of billions of compute based on projected demand that they could monopolize enough demand to finance compute capitalization. This not stupid investor prior, because their only competitor, PRC was export controlled, compute constrained and shouldn't close gap. But PRC closed gap, made models good enough and architecture efficient / cheap enough so other players, including those in US, can capture large % of demand WHILE using fraction of compute. SOTA labs now looking at margin compression, losing share to good enough competitors and obligatory (now excessive) capitalization. This bad combo for current US SOTA economics. The numbers suggest this is what is happening. There is chance demand increases past projections and extra SOTA compute will be boon, but again, we are seeing most profitable SWE enterprise curtail not increase demand.
> Worst case is all the infra is still useful
IMO this actually best case for US medium term favour, but also another strike against CURRENT US SOTA. Reality is current SOTA is grossly overpaying for compute / AI premium. Even if OAI or ANT dies, their compute can be picked up for pennies on dollar and as long as PRC behind on compute there is potential for cheaper future US SOTA economics from cheaper hardware since crash will lead hardware price regress towards mean. But that boon is for the next guy.
Here's the US hopium: post current US SOTA crash, there is potentially curve where US has so much cheap discount compute, because current SOTA taking the haircut, and where PRC has not much compute, that US will have sustainable SOTA prospects, i.e. the world where US pays $1 for shovels instead of $10 and can buy $10000 worth of shovels while PRC can only buy $100. That's feasible after current exorbitant SOTA economics bleeds it's patients to death and the next US SOTA host get the transplants and lighter balance sheet, i.e. web 2.0 for SOTA AI. Incidentally more likely "winner" of AI... if I had to guess assuming incumbent FAANG doesn't just snatch up ANT/OAI if they explode - new player/entrant who are near SOTA and eventually SOTA but is priced more competitive t...
200-300B might be plausible depending on how market perceives the company
I think an open question is if SOTA labs should be priced like software companies have been. Software has been so valuable because the marginal cost approaches zero as distribution increases. Profit/employee at a company like meta prior to massive AI spending was measured in the millions. Meanwhile, the balance sheets of the labs look entirely different - remaining competitive requires continuous and astronomic capital costs, and there's no indication that this goes away. They are perennially being chased by competitors offering the same performance as models mere months old at fractions of the cost.
Their business to me, at least long term, looks a lot more like an airline, railroad, or other extremely capital-heavy industry with very competitive and low margins. None of those trade at 6+ p/s. Particularly on the buyer side, the switching cost to me as a high-AI-spending employee if a cheaper model offers compelling performance is less than deciding that I should ditch my preferred business travel airline.
I think that's why you're seeing so many calls from the leaders of those companies to regulate their competition away.
The big foundational labs are not like this. Every model they release takes a ton of data infrastructure and training (pre, train, post) compute. Then inference itself requires compute. Even if the labs sell tokens, each token embodies a lot of training and inference compute. This is a much more capital heavy business, so I can see revenue multiples being smaller than pure SaaS businesses.
A good analogy is chip companies before all of the AI related compute pressure. These were largely low margin businesses because of the fixed capital cost associated with each new chip gen, architecture, process, etc
Note the huge difference in strategy and outcome!
Google published essentially nothing on how to build a search engine. Huge R&D spend, zero publications, intense culture of secrecy, extremely low attrition to startups or competitors. They did publish some papers on big data systems but only on tech that was many years old - a typical Google "research" paper pre-2015 would announce that the system it described had been running at scale in production for years already. Google also never faced any kind of split or mutiny in which lots of people with the core secrets left the firm to set up a competitor.
With AI however this changed and they published all their research immediately, also their best employees constantly ran away to join startups. The result was predictable: smaller competitors deployed their ideas faster and better than they did, and now Google has no moat anymore.
OpenAI took the classic Google approach and stopped publishing R&D after developing GPT but this didn't help them build margins because:
1. Apparently the core transformer idea is powerful enough that breakthroughs after that didn't matter much. Nobody has been able to develop some killer edge that leaves competitors unable to catch up. Most improvements to transformers have been about cost reduction, not capability improvement.
2. They had a huge split when the Anthropic guys walked out over safety, taking the knowledge of how to build a frontier LLM with them. Result was a peer competitor from day one, whereas Google had at least six years to build edge before anyone realized web search mattered.
Result: Google-style hardware costs but without any secret IP that could generate revenue commensurate with those costs.
Because we're just waiting for the world to find out that they can spend 1/10th and get virtually the same results.
Open source models are now 1/100th in price per task of US sota from this summer...
With the price of AI rapidly falling to the price of compute, with models therefore basically having zero value, and with Anthropic having rented all its compute from others then there’s little evidence that such high profits are in the future.
Anthropic has a great product but is for sure a bad investment.
If this is the new normal, that's great news. A company growing at 47% per quarter will 10x in 6 quarters. Even if you think it's a $200B company today, it'll be a $2T company in 18 months.
If you think this is the start of a rapid decline to 0% growth rate, then it's obviously bad news.
The real answer is probably somewhere in the middle -- but that's a very large middle.
What do you expect for FY2027 revenue to confidently conclude this? not saying you're wrong, but considering the dramatic changes in their revenue you seem strangely very certain that you know where it will land.
Also, anthropic has no real moat, LLMs are a commodity.
They reported a loss here because they sold notes convertible into shares and their valuation went up. Normally shareholders do not lose money when the stock price goes up.
It's weird that they had to report it like this because they are technically derivatives, and if they had just given out actual shares (as with stock-based comp), they wouldn't. Yes, giving people stock is a real cost, but what happens to the price afterwards is not, and when the price goes up shareholders are definitely making money, not losing it.
Either way, they did report it. It is up to you to decide how you interpret that information.
and add'l discussion: https://news.ycombinator.com/item?id=50338510
Tokens themselves are already a bit of a commodity, and the margins are gonna be fierce on these as there is not a whole lot of lock in.
Would Anthropic and OpenAI position themselves using their extra tools, their ai agents, presentations, docs. Are they nice; yes. However, that doesn't make a 2T company. It reminds me a bit of Dropbox, a truly great product, however, they're differentiation is simply not there. I think the momentum is there for the IPO to go decent, but it feels like the company is on such unstable ground that the fallout could be catastrophic.
Since the start of the year the market has shifted from 50/50 split (closed v open weights) to 30/70 (70% of usage they see through their platform - is now open weight)
DeepSeek V4.1 Flash 1017 sees 5x the weekly token usage through OpenRouter than the closest closed weight model (GPT-6 Luna)
Even though OpenRouter is only one limited view of the market - those trends should be worrying to potential investors.
OpenRouter could so 90/10 split and it still wouldn't mean open models are beating (in usage) closed models.