Anthropic's growth just flattened
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I want AI and these companies to succeed. And I especially want new companies to succeed. But I want them to be valued far more realistically. Because if they have to be worth trillions, then there’s a real motivation to do what it takes to realize that valuation. And that’s how you birth a Zuckerberg.
I’m not rooting for them to fail. I’m rooting for the correction to happen sooner than later.
They are. That's the problem. The obligations that these companies are, on paper, supposed to meet require these valuations.
If you value Anthropic at $500b that's still a fuckload and vastly less than their obligations and a lot of people are going to say "well wait a minute how am I going to get paid".
The amount of spend going on, and planned, is insane and underpins a lot of this, and is why people are skeptical of the valuations. It has nothing to do with "Wanting" AI to fail and just a reasonable bet based on the math. For them to pay back what they have on their balance sheet entire continents will functionally be a spec in comparison, and that's really really fucking unlikely to happen.
Doubly so when you consider that there's a question of if they can be profitable to projected needed levels without fundamentally breaking some economics parts, and that this looks a lot like the overbuild of the internet during the dotcom bubble, which was also hideously overvalued at the time, for the time frame they were looking at, despite being THE literal world changing tech.
Walk me through this math. I’m seeing $900bn Capex over a decade. That’s $90bn required free cash flow a year, on average. If they can do 25% margins, that requires $360bn annual steady-state revenues. That doesn’t require continent-scale anything.
Not disputing that. Just saying you don't need continental-scale anything to hit these figures, you need annual revenues on the scale of what Walmart does in a quarter.
This is the assumption we disagree on. They're net negative by an order of magnitude on most of their current customers. I don't see them flipping to break even + 25%.
Further you're assuming they can turn that on today, when they're still very clearly not ready. Every year the timeline gets worse.
Take them to 2% margin with 8 years to go and you're looking at needing 4.5t annual.
This is normally where I'd say "but 2% feels extreme..." and hedge in the middle, but like...the current pricing isn't even close to 25% margin, it's not even close to break even.
From the S-1, in the first 3 quarters of 2026 their revenue was $33.2B and compute costs were $18.8B. Inference is highly profitable, they "just" need more of it.
Wha???
There are 13 companies in <<total>>, on the <<entire planet>>, that make more than that: https://companiesmarketcap.com/largest-companies-by-revenue/
Most of those needed decades to reach this point.
Anthropic has a near-clone in OpenAI (and vice versa). There are multiple smaller competitors, several much cheaper (especially the Chinese models).
Anthropic obviously needs continent-scale balance sheets, I would argue "multiple continent-scale balance sheets", since Google & co make their money by selling everywhere except for China.
Anthropic and OpenAI are never entering China on a massive scale. US relations with Europe are deteriorating and LLMs are <<massive> risks in an adversarial relation so it's not inconceivable Europe at some point starts marginalizing US tech companies, at least LLM labs. I imagine India will want to do something similar at some point.
Where exactly is Anthropic going to make all that steady revenue from, if it can't break out of coding and cybersecurity? Even worse, both those markets are super tech savvy so those cheap rivals are even more dangerous. One slip up the next 5+ years and Anthropic is history.
Would you describe UnitedHealth as a company that render "entire continents...a spec [sic] in comparison"? Sinopec or Petro China?
I'm not saying those growth requirements are obvious or even feasible. Just that they don't require some paradigm shift of global economics to achieve.
> Most of those needed decades to reach this point
How long did they take to reach Anthropic's current scale?
How does that make sense lol
"it owes a bunch of people money, so it should be worth more"
i don't...
"it owes a bunch of people money so the only way for it to not be insolvent is it must be worth X, and if we're talking lower than X this is already a problem"
It is to figure out what ia its correct price. Otherwise the financial system turns into a scam.
Not how valuations work!
Walmart made $187bn in sales last quarter of which $6.5bn became net income [1]. That supports $115bn in liabilities and a trillion-dollar market cap at 46x earnings [2].
If you want a more-extreme example, look at JPMorgan: Its $900bn valuation (14x earnings) [3] supports $4.6 trillion of liabilities and $5 trillion in assets [4].
Comparing valuation to liabilities to judge solvency is bunk on multiple levels.
[1] https://www.sec.gov/ix?doc=/Archives/edgar/data/0000104169/0...
[2] https://finance.yahoo.com/quote/WMT/
[3] https://finance.yahoo.com/quote/JPM/
[4] https://www.sec.gov/ix?doc=/Archives/edgar/data/0000019617/0...
On what metric? I just showed you JPMorgan has an equity value a quarter of its liabilities. JPMorgan is not insolvent.
Comparing liabilities to market cap doesn't tell you about solvency per se, it tells you about leverage. It tells you whether a solvency problem could be solved by selling equity. Not if there is a solvency problem.
To be fair though, JPMorgan (and banks more generally) are very, very different from Anthropic/other labs.
Requiring equity > liabilities means assets > 2* liabilities.
That is nice, but not at all required for financial health. Especially for infrastructure heavy companies where the debt is used primarily to fund infrastructure buildout.
Anthropic’s market share is pretty high and there is not enough total currently addressed market for growth at the expense of a competitor to make a big long-term difference.
> given a sufficiently articulated prompt
Are these two statements not at odds with each other? You can always say a prompt is not sufficiently articulated if you need to add additional handholding and vice versa, thereby invalidating any meaning of measurement.
then a PR that tends to only get minor feedback is produced
As in, many if not most reviewers have always been nothing more than approvers, i.e. people that click the approve button with minimal to zero review, maybe a "pro forma" comment so nobody has them show up in some statistic as "only clicks the approve button".
With the amount of AI generated PRs this will have tended to become more of a problem, not less.
But sol 6.1 is at least in the conversation as being as good and is much cheaper. I farm out bunches of my tasks to Luna and Haiku these days and have a working prototype of a glm 5.3 workflow at work that shows it being competitive.
The price pressure is very real and if you aren’t on a subscription then there is virtually no reason to pay the Anthropic premium
Not due to capability per se, but writing style and polish is much worse.
It will leave really bad comments in the code, commit messages etc without strong explicit steering. Though I've basically moved away from having the model write any documentation at all.
It also tends to talk very circuitously and referencing things implicitly instead of just being direct and clear.
Certainly it's more capable in debugging, but I much prefer Astra over Sol now.
Of course, Astra has its own problems of code golfing everything. But at least it's very concise and direct
There's definitely unique things that so far only Opus is capable of doing.
What does average, non-computer Joe or Jane think when the next big model comes out? "My emails are going to be so much better now, I better stay subscribed"? "I can't wait to see how well it answers my questions"?
Some of them do require frontier models but many can do with localish models. Some of these processes (in our industry) are also not really limited by software at all but by the speed heavy machinery can be operated and/or manufactured and/or transported.
I'm certain LLMs are going to continue rapid growth even there but it may be an ideal field for have lesser models work for longer if it saves money in the end.
Of course who knows in the end. I'm certain there is a high chance of this being entirely wrong
Actually I see very varying results in my work.
Sometimes I can let an AI refactor an entire codebase and then (in the same codebase) it generates absolute shitcode.
Then why all the rush to IPO then?
The strongest chinese models from DeepSeek, Kimi and z.ai are already good enough to run for hours on most coding tasks cheaply.
I think the first implying the second might be a bit of a stretch. At some point, companies are going to start looking at their AI spending and start negotiating deals. OpenAI is right behind them and Anthropic is around 8x more expensive (Sol 6.1 vs Opus 5.5 based on Artificial Intelligence's cost per task; cost per token is a somewhat useless metric when different models use vastly different numbers of tokens for the same thing). Chinese models like Deepseek 4.1 Flash are cheap, can be self-hosted, and while certainly behind Claude Opus 5.5, are still good.
Lots of companies buy "inferior" products like Microsoft Teams. Over the next 5 years, it seems likely that many models will get really good to the point that people often won't be able to tell any difference. Even if something isn't "definitively" better, companies might say "our engineers can't tell the difference and we're cutting our costs by 85%."
It's not absurd to think that a company would use its own harness or an open source harness and/or put a proxy in to route requests variably. For two months, they have engineers using different models unbeknownst to them and they measure things like how often the engineers have to correct or redirect the model. At the end of two months, they might find little difference in performance and switch to a much lower cost model.
It's not like it'd be crazy hard to analyze workers frustration as they did their jobs and figure out if one model was actually better. They could log all the conversations and it's not like AI can't easily determine things like "no, that's completely wrong" as a phrase showing that the agent was really off base.
I think a big part of the next 5-10 years is going to be about cost/efficiency.
1. Frontier models are so good that the token cost of an average task becomes miniscule, so users are incentivized to use frontier models because their sheer intelligence makes them cheaper to use.
2. There is a pareto optimal point where even if you spend more on tokens overall profits decrease because token costs rise faster than revenue does.
The "no lab has a clear lead" world leads to 2.
That's a great product idea!
There seems to be another magic ingredient for improving agents beyond model size & efficiency, and so far only OpenAI, Anthropic, and maybe Google know what it is.
"I have a girlfriend for real she just goes to a different school"
I ping-pong between different agents all day long and don't necessarily agree that Anthropic is clearly in the lead on coding at this point.
Has anyone else seen this?
I wonder if more people using Haiku coming from more expensive models may have an impact in those metrics.
For me, that's amazing, but maybe it explains these numbers and maybe it'll finally change when Fable 5.5 forces me back into high usage territory. If I even really need it's abilities as more than an advisor (words I didn't think I'd imagine myself saying back when Fable 5 was dropping).
Anthropic released their Q3 numbers today which show:
- $16.96B in revenue Q3
- Up 47% quarter over quarter (Q2 was $11.6B)
- Up >1000% year over year
I wouldn't say their growth "flattened"? Their price drops in September dramatically increased Claude token consumption per OpenRouter
Sources: https://www.anthropic.com/news/s1-sec; https://openrouter.ai/anthropic
“At these prices, the more paper you sell, the less money you'll make."
Amazon is now the posterchild for this, but it happens in other industries like pharma a lot too. And in today’s stock market, where a fundamentally weak company like Tesla can trade at eye-watering multiples because of speculation, why worry about profitability at all? (Partly joking but that’s how people think.)
When and why we start expecting Anthropic to be profitable are great questions though.
The article used $16.8B. Not a big difference.
As the article notes the growth is still huge. But it has slowed down relative to what they were growing.
When mid or high level tech leadership pushes from top-down new things, that look dumb to the engineers, the leadership is either stupid or there are hidden incentives that regular people don't see. In my experience mid level usually matches with the former, and top level with the latter.
Your machinery was not written elegantly. It was sloppily assembled via evolutionary pressures.
A gene gets duplicated, mutates, and suddenly you have new functionality and new fitness criteria.
Try something bad and you die without reproduction.
Our code isn't clean. There's redundancy and extra packing and slop all over the human body.
Embrace it. Stochastically hill climb.
Code is DNA. Evolve it, don't intelligently design it. Fit the curves of your environment quickly and use tests to cull bad behavior and the slopes of your domain.
I assume they’d hoped that the value of the spend would manifest itself and they could raise prices. The opposite happened and companies are instead cutting back.
It sounds to me like they chose a poor marketing and pricing strategy for a product that is very difficult to make cost effective.
Exactly, No-one and as predicted it was a fad. [0]
We are seeing the result of those subsidizations and discounts that Anthropic made this year and how much of this "tokenmaxxing" hype was used to make their revenue numbers look "good".
With the arrival of more cheaper models which are good enough, it doesn't make much sense to use Anthropic's models.
So don't be easily fooled by their revenue projections. Rather question if it is sustainable for them. (Spoiler, it isn't)
Behold, the beauty of the modern internet.
I'm not aware of any of those products really sticking around.
Dysfunctional middle management has always traditionally neglected it or actively undermined it, blaming others when things go wrong and taking credit when things go right.
Naive developers are always more than happy to do what they think is "picking up the slack" not realizing it's a trap.
Perhaps most organizations are just now more wise to the fact that using AI to "accelerate the process" is like arming the monkeys with machine guns?
At some point someone moved the goalposts for this to include LLMs that emphatically are not that. I think it was Altman when they came out with Astra?
they aren't as intelligent as a human, but they can throw shit at the wall faster, and get something that sticks faster. they are superhuman in areas that require trial and error, like hacking.
everyone stopped talking about the Turing test once ChatGPT came out and broke right through it while still being very very dumb. AGI is just too blurry to define.
If you left an AI alone for a long time and when you came back they had built up a working society and learned new useful things, then that's the proof it's AGI. From this definition you only know it's AGI after you let it loose.
Nvidia's Jensen Huang says "We've achieved AGI."
I wouldn't really be surprised if better model efficiency was part of the reason for a revenue stall, but I would also expect their costs to have dropped a fair bit too (although I have no idea whether that corresponds to an actual cost reduction or if it's all tied up in hardware and land that they already bought and will need to hit more utilisation of now.)
Also model/token efficiency.
List of possibilities is big
Especially with this latest crop of models, they seem to be pretty damn competent in classic engineering rather than just software engineering.
They never realized how much effort went into turning those one like tickets into code was, and the code was the least part of the effort
Feature flags, staggered rollouts, quick rollbacks, smoke testing, etc.
Sure, if you were hand managing every rollout up until now, and relied primarily on unit tests, you’re in for a world of hurt when you give a junior engineer Claude code and let em rip.
But if you have more advanced systems to measure and maintain overall service health, and codify your business logic invariants, you can really start flying these days as long as you’re all aligned on what to build and the potential impact.
High chance said senior has been laid off because "A junior with $200 claude plan" is cheaper.
Idk how many people need to hear this, but not everything 'is computa',no matter what Trump says.
Much of the worlds problems are not verifiable with a compiler that can be trained or brute forced against.
Llms are not as useful as ya'll think they are, not everything exists in a web browser, which is why Anthropic et al have saturated their TAM and are now frantically hiring sales people to convince managers in other verticles.
Turns out most people dont need or want AI.
A hammer is fantastic for driving nails, but it's not really all that good at washing a dog.
These are all jobs the silly con valley idiots claim continuously to be automating away, but it's all just fuckin lies.
The extent of tech's penetration into this economy is internet ads on facebook and streaming services. Nothing to do with actual work.
It's easy to understand how LLMs got so good at coding, they can rapidly iterate, self test, and it all happens in their digital universe.
These models now starting to spit grey beard knowledge of precision analog electronics, lightweight fastener design, and chemical surfactant systems is something very very recent. A few years ago I think we were pretty confident that the tech would hit a wall at the digital divide. Well, I can tell you now that it did not. It seems it kinda blasted right past it.
The idea is: if you have a substantive point, make it thoughtfully; if not, please don't comment until you do.
I think the answer, to a reasonable approximation, is “everyone”. Outside software engineering most people aren’t using it for anything useful, and I keep encountering technical people including programmers who don’t use it either, which never fails to surprise me.
One of my projects is a transportation management system (TMS) and of course I have added a lot of features to it including a very capable chat-based UI that can automate a lot of tedious data entry and other tasks for dispatchers and accounting and so on. It gets used but really not very much. I’m starting to realize that I’ve probably taken updates as far as they can go in terms of the ability of people to absorb change. Most people just carry on doing things the way they always have.
We had one person on the admin team who started using Cowork to automate all kinds of stuff. She loved it. Then she left. It hasn’t caught on with anyone else.
This is not to say that things won’t change but I think it will take a while.
As an example outside of software engineering, a lot of people in marketing roles have found agents to be incredibly useful for particular tasks. I've talked to multiple people who wouldn't standardly be classified as "technical hires" who are using Claude to manage things like digital advertising. It's valuable here because managing campaigns on a platform like Google or LinkedIn involves a ton of granular tweaking and analysis. Every performance marketer makes tradeoffs around what they're realistically able to spend time doing, and what makes the biggest impact. So if you have 250 campaigns that you manage solo, you likely don't have the time to really manually optimize the bidding and budget for each campaign every couple of days based on new data. You can only try so many new angles, because creating new media is time consuming. You don't have time to construct custom experiences on-page for every particular campaign. There's a whole industry of tools that turn a tidy profit automating parts of this for this reason. And agents are really good at it, because a lot of it is just applying the same analysis over and over again.
But for a lot of startups, increasing traffic from ads 20% while decreasing spend is a good outcome, but it doesn't actually turn into revenue. The product itself might just be limited, and already being discovered by the narrow segment of potential users who find it valuable enough to pay. Or the sales process might be kind of busted, meaning the team will really only close a certain volume of new deals in a given month, no matter how much inbound they have. Or some combination of all of the above.
If it's just one marketer using an agent to optimize their advertising, there's not a lot they can do about the other constraints. And so what you end up with is one employee who feels really productive and sings the praises of Claude, but not a meaningful lift in the higher level outcomes you care about, and the analysis gets messy. In an ideal world, everyone realizes where the other bottlenecks are, but that's a genuinely hard thing to do and people have complicating incentives.
I don't think this is a particularly new problem. It feels identical to any other situation where a department asks for a tool, loves it, and it doesn't really make a difference to the bottom line or make anyone else's life easier in noticeable ways.
Because rewieving chat bots data entry is even more tedious then doing it yourself. And as long as they have to review it, it does not even matter that it makes only few mistakes.
I see it with JIRA and Rovo. It just sux using it, despite it being reasonably competent at creating tables and what not. If I could do markdown as used to be normal, it would be appreciated a lot more.
As it stands, even the people closest to this technology have barely figured out how to use it effectively.
Not saying that applies to AI, but it certainly applies sometimes.
I don’t think this era is going to end with us all unemployed, but I do plan to be retired before the next cycle.
You can go very far in life with a thing that isn't futzy
How much easier my life would be if Chinese open models were even just 1 year behind outside of X posts and one-shots.
My easy litmus test is would the year old model (Opus 4.5) being open sourced today would eat the traffic of every single Chinese model... and the answer is pretty much yes.
Every Chinese model would throw away their work just to post-train or distill Opus 4.5 and get a more reliable model.
Most likely the combined revenue growth continued just as fast, but OpenAI took a lot of market share with a model that's ~as good.
If true, then the current market sentiment is too pessimistic because demand for AI (and chips) has continued growing.
I am not saying its a must, but those who are reaping the benefits in someway will end up spending more I guess?
AI is just a compute commodity with a value add.
So that leaves...
Even in next 3-4 years, the company that has the best ecosystem of researchers, culture, shared knowledge and processes will be able to churn out models in a way that can't be replicated.
EDIT: okay, not months, weeks. Feels like forever.
Sure, and they said similar kinds of things for 4 years. Why act like the broken clock is suddenly worth noting just because the time was finally right this moment?
the second part is not sexy - but we see all a.i labs having fde arms e.g ode, microsoft frontier company etc but then again that's the part you can't hype.
I wonder if Anthropic will "see the light" and stop trying to burn a bunch of money and sell a thing they already have (which is decently useful). Very ready to be proven wrong in any direction
"I can one-shot a new thing" is interesting but definitionally a thing is new only once. Maybe if I worked in a space where we had way more throwaway things to work on.
At the end of the day I want to understand the output anyways, so it one shotting a thing vs me going back and forth with it in smaller increments is a mixed bag anyways.
Keep in mind this growth has mostly been sustained by low-level business automations, coding, and chatbots. With the adoption of products like personal agents, e.g., Meta's Muse, I am pretty optimistic there will be demand and growth around this level will be sustained for a few quarters. Doubtful we will ever see another ~150% growth quarter.
So that’s ignoring labor and perhaps facilities? That’s like saying we’ve hit net positive fusion by only considering the power produced by the laser and ignoring the 80% power losses in the feed-in.
- hmmm, why 30 days? Make it 60 days and I'll take the penny.
The effort needed to double everyday, or month is realistic at first. But then you can't just keep doubling without swallowing the universe along the way.
~5 quadrillion after 60
I guess the interesting thing would be to look at actual company assets/value rather than revenue; revenue already in itself is a derivative. I.e., contrast their revenue against expenses. Last I heard those numbers are deep red, aren't they?
Insane
Anthropic reports $17B in Q3 revenue, commitments of $895B in revised S-1
The criticism has been about folks downstream of Anthropic being circularly financed. I haven’t seen credibly claims of Anthropic’s revenue being circular, but if you have a source, by all means.
The company was founded under the principle they will violate laws to acquire training material
It's of course possible that they are lying, but I would like evidence of that if so.
That debt will transfer to public shareholders at the IPO but also will double or triple in the process, as the investors expect to be paid very, very well. So the current shareholders are looking to extract something over $400 billion from the IPO.
With the current revenue they might pull off that P/E. But they’ll still be extracting some of that value from the customer base, because shareholders will demand it.
Subscriptions are a growing minority of revenue.
Discussed here a few weeks ago: https://news.ycombinator.com/item?id=49997161
1) customer concentration risk 2) when those customers use a diverse set of models - so too will the rest - they’re not idiots who want to pay more than they should
Aka % growth rate decreasing sharply and eventually flattening out. Within 2-3 quarters.
Understood?
But perhaps for different reasons than Microsoft, Meta, and maybe Adobe leadership are now. See, if they are all in on Claude how do they sue Anthropic for assisting in copyright violations and IP theft? The services would be cut immediately.
Well when they don't need them anymore..
This is why Anthropic is heaping on more and more model restrictions running up to IPO. I believe it's a smokescreen for the IP risk they outlined in their IPO filing..
The writing was already on the wall about a lot of things WRT the AI market: 1. there's a limit to how much money will be thrown at it before investors expect a return, and that limit is fast approaching, 2. it's turning out "intelligence" is going to be a commodity and it's not much of a moat, 3. cornering the market on memory and GPUs (even if they sit in a warehouse for months or years before going online, as long as it drives up the price for competitors) only works if your competitors don't figure out how to do it more efficiently and on alternative platforms.
Spacex also soaked up a lot of the retail appetite for AI, even though it's an also-ran that barely qualifies, in terms of enterprise and government usage (and for good reasons, both technical and otherwise). Every other AI IPO is going to be facing a more pessimistic market, especially if we continue to be in a tough economic environment.
In short: They waited too long, now they're in a world where they've burned so much money people expect them to deliver something that no one in the AI industry probably can deliver: a monopoly-like hold on a huge percentage of the market, and to invent a bunch of new markets that aren't currently buying AI. They have to have a dominant position for this to work because they have to be able to set their price higher than is standard now, without everyone fleeing to other cheaper providers. And, when the market for memory and GPUs finally gets back to normal and supply catches up with demand, I'd wager a lot of companies, and even individuals, are going to get real serious about open models and self-hosting (unless prices remain competitive for the hosted models).
My understanding is that the GPU market is largely sold out for the next few years?
What exactly is the threat here? Like, which specific competitors, what efficiency improvements, what alternate platforms?
At least three Chinese companies have bragged about their latest models being trained and/or served on Chinese-made GPUs. Literally within the past 24 hours, LongCat spent a large portion of their release announcement for 2.5 bragging about how efficient their training process is and that it runs on domestic (Chinese) GPUs: "LongCat-2.5 uses long-sequence training during both mid-training and SFT. With less than 80 GB of memory per domestic AI accelerator, the training system prioritizes memory efficiency and aligns its parallelism with the supernode architecture."
https://longcat.ai/blog/longcat-2.5/
DeepSeek has repeatedly blown the curve in terms of how efficiently a model can be trained and served, Xiaomi and Z.ai aren't far behind, and have also discussed training and serving on home-grown hardware. I'm sure you can search Arxiv for all of the papers from these providers (DeepSeek, in particular, who document seemingly everything, even when they're doing something ahead of the US providers) as well as I can.