I'm not going to predict whether Anthropic succeeds or fails, just that it's very misleading to claim that Anthropic needs to grow to $500b in revenue without making it clear that it's $500B in revenue per decade rather than $500B in revenue per year which is what most people reading such a number would assume.
- 161.2B w/ Broadcom: 2026-2031
- 120.5B w/ SpaceX: 2026-2029
- 114.5B w/ Google: 2026-2033
- 110.0B w/ Amazon: 2026-2036
- 44.9B w/ Microsoft: 2026-2033
- 44.6B w/ Nscale: 2026-2033
- 41.3B w/ Lambda: 2026-2034
which is around $63.7 billion / year if spread over 10 years, but with most of it due earlier than that.
SpaceX was the only one mentioned as cancelable. These are listed under "Infrastructure Service Arrangements" on page 170.
If progress with frontier models doesn't continue at the same pace then the investments and funny money deals might start to look very fragile.
If progress does continue then that progress might be towards smaller models more than quantum steps up in quality or capability that would increase the risk of the scenario above.
All of this is set against a backdrop of public hostility and political problems that I can only imagine increasing in the near term as well.
This feels like an unusually high stakes game of poker. I don't think I'd want to bet on Big AI right now if I had a few billion lying around. Then again - maybe that's why I don't have a few billion lying around!
Is that really a viable path? (Honest question)
Those vendors would very much like to retain as much of that business as possible. If the space goes sideways, I would very much expect some kind of renegotiation/work out.
If Anthropic's revenue story doesn't materialize and the AI bubble bursts, these companies are in a world of hurt. It's probably not fatal for Google or Amazon but would be a major drag on the stock price (perhaps why Google has dropped from 400 to 315 over the last couple months as the AI story soured), and it could be company-ending for Broadcom or SpaceX if they end up building out infrastructure to service these commitments.
I remember before I left Google, one of the TGIF questions (which are now AI syntheses of many peoples' individual opinions) was "What happens if AI is a bubble and it bursts?" His response was "If that happens, the whole industry is in trouble." I see now what he meant.
Between SpaceX, Starlink, Tesla, Neuralink, X, Grok, Grok Bot & Cursor there's not a demand shortage.
Grok Bot's the fastest growing product in the history of the company and the moment you use it, it's clear why. Took me about 1-2 days to realize I'd have a very hard time going back to not having it available.
If the AI companies go tits-up, that introduces counterparty risk, the revenue doesn't materialize as cash, and suddenly their earnings take a nose-dive. If they've staffed up significantly to design and sell these AI accelerators and suddenly there's no market, they could be in a lot of hurt.
BRCM and Marvell are both the actual designers of many of the “we designed our own ai chip” chips that so many companies have been announcing.
Broadcom is also a major supplier of the networking chips that the hyperscalers use. Clusters are moving away from infiniband and nobody is particularly impressed by Spectrum.
- 2026: $97.08B
- 2027: $97.08B
- 2028: $97.08B
- 2029: $97.08B
- 2030: $66.96B
- 2031: $66.96B
- 2032: $40.09B
- 2033: $40.09B
- 2034: $14.59B
- 2035: $10.00B
- 2036: $10.00B
Total: $637.0B
Considering everything that is cancellable, ie. hard commitment only, it's:
- 2026: $56.79B
- 2027: $56.79B
- 2028: $56.79B
- 2029: $56.79B
- 2030: $56.79B
- 2031: $56.79B
- 2032: $29.93B
- 2033: $29.93B
- 2034: $10.00B
- 2035: $10.00B
- 2036: $10.00B
Total: $431B
If they go bankrupt, there are some more cancellations available, also from the other side – if some providers don't deliver, it can be lower as well.
I also left out AMD at $20B for 2027, so that would need to jump up.
Finally, the above is only counting the commitments they've already made. It's not very likely they'll only spend $10B in 2035.
AMD is missing, true but commitment schedule isn't known. Also missing estimated additional TPU purchase for 28-31, ~$125B-$150B.
- 2026: $97.08B = Broadcom $26.87B + SpaceX $30.13B + Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B
- 2027: $97.08B + AMD (TBD) = Broadcom $26.87B + SpaceX $30.13B + Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B + AMD (TBD)
- 2028: $128.33–134.58B + AMD (TBD) = Broadcom $26.87B + SpaceX $30.13B + Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B + additional TPUs $31.25–37.50B + AMD (TBD)
- 2029: $128.33–134.58B + AMD (TBD) = Broadcom $26.87B + SpaceX $30.13B + Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B + additional TPUs $31.25–37.50B + AMD (TBD)
- 2030: $98.21–104.46B + AMD (TBD) = Broadcom $26.87B + Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B + additional TPUs $31.25–37.50B + AMD (TBD)
- 2031: $98.21–104.46B + AMD (TBD) = Broadcom $26.87B + Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B + additional TPUs $31.25–37.50B + AMD (TBD)
- 2032: $40.09B = Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B
- 2033: $40.09B = Google $14.31B + Amazon $10B + Microsoft $5.61B + Nscale $5.58B + Lambda $4.59B
- 2034: $14.59B = Amazon $10B + Lambda $4.59B
- 2035: $10.00B = Amazon $10B
- 2036: $10.00B = Amazon $10B
Combined total: $782–807B+, including estimated additional TPU purchases and conditional AMD spending.
Got the numbers on a spreadsheet. Of course this is a super simplistic version assuming equal yearly commitments.
> Its largest commitment is with Broadcom, which co-develops the TPU AI processors with Google and leases them to Anthropic in deals worth $161bn over five years. From 2028, Anthropic expects to spend up to $150bn more on TPUs.
https://www.ft.com/content/a6c43402-719e-4295-ae5f-541edb0ae...
This is just the compute, which primarily covers training + inference. They still have to run the rest of the business. If you think AI should look like SaaS and spend 10% of their revenue on compute, that's $1T in revenue. If you think it's a hardware business, that's 30% margins and perhaps $200b in revenue. I went with something in the middle at $500b.
They won't disappear as a company at $200b in revenue, but I don't think that's their aspiration. These commitments imply to me that they're confident they can do much better.
I'd think it's more sound to just look at what percent of their current spend is on compute? Seems to be 50-60%
Idk where you got this figure from, but the actual figure are very different from that:
It's 120B over four years for SpaceX alone and 120B for Broadcom over 6 years. These two players, representing less than half of the total commitment, already give you an annual commitment above 50B.
But if you buy them today at a $1 trillion valuation, and they only make $40 billion in annual profit, your investment will go nowhere. They need to be growing profits at 20% per annum to justify a PE OF 25.
And then you have the competition problem. The cheap open weight models are good enough for many (most?) tasks.
And then you have the problem that LLMs may not be that useful outside of programming, customer support and law (and doing homework).
what you're buying into here is a company that is already large on the idea of growth over the next 10 years.
Modern IPO's just reek of investors cashing out
Google closed its first trading day "worth more than $25 billion" [1] with $1.5bn revenues, up 234% from 2002's $440mm [2]. That's a 16x revenue multiple, 0.07 growth adjusted [3].
On a similar revenue multiple, Anthropic's $16.3bn half-year earnings are worth $543 billion. Adjust for Anthropic's 1,400% YoY growth and you're well over $3 trillion.
Obviously, it's a lot harder to grow at a thousand percent from a $16bn baseline versus a few billion. But purely on a multiple basis, Anthropic is in a similar ballpark as Google.
[1] https://www.nytimes.com/2004/08/19/technology/shares-of-goog...
[2] https://www.sec.gov/Archives/edgar/data/1288776/000119312504...
Google was profitable, free cash flow positive and had no debts basically
Anthropic is burning money by the millisecond, no profits, free cash flow negative and has lots of debts & commitments
Comparables are not by peer group or sector.
It’s cash flows, growth and risk bud. If you want to do peer group - go ahead - just don’t forget to control for differences..
Price to sales multiples are internally inconsistent by definition.
In reality, a valuation based on revenue (sales) is bonkers to begin with, but even worse when you consider that Anthropic has so much debt that it doesn't have positive equity value, it's in a deficit ("When liabilities attached to an asset exceed its value, the difference is called a deficit and the asset is informally said to be "underwater" or "upside-down""). At least SpaceX has more assets than liabilities.
> SpaceX has more assets than liabilities.
is definitely a choice thing to write on the day SpaceX announced a $40B debt deal to purchase compute to run other companies' models. Based on available information, this will put SpaceX into a place where it has more liabilities than assets.
You're speculating what SpaceX balance sheet might be, like they haven't just released MRQ earnings report last week
Just go read it and you'll see you're wrong even after you subtract that 40 billion SPV https://www.sec.gov/Archives/edgar/data/1181412/000162828026...
Larger point stands. SpaceX is commanding a 62x sales multiple, whether or not you personally believe price:sales should be relevant. SpaceX's AI operation is smaller and growing slower than Anthropic's. It's reasonable to expect investors to benchmark Anthropic against SpaceX, it would be silly not to do so. A normal thing one would expect investors to do is assign a higher multiple to a company that grows faster.
To be clear: I do not have a financial interest in this, and will not be buying the IPO. I very much am just describing the world as it is.
Some people will benchmark Anthropic vs. SpaceX, that doesn't make it a smart approach.
A normal thing is to consider risk, liabilities, cashflow, how diversified revenue streams are etc., and other things which you seem to be missing.
I'm not trying to dissuade you from buying it. I'm just saying that the reasons you give are wrong.
Yeah, I would not pay 62x sales for SpaceX. And I don't buy IPOs. I'm not endorsing these values, I am saying benchmarking against peers is what investors commonly do.
> A normal thing is to consider risk, liabilities, cashflow, how diversified revenue streams are etc., and other things which you seem to be missing.
I'm not missing those things, I am not including them here because being "right" is rarely how markets value companies. (Also because the rest of that stuff is in the S-1.)
It's possible to build a "correct" model of a high-growth high-tech company that looks primarily at risk, liabilities, cashflow, and diversity of revenue streams. That model might put Anthropic at (say) a $100B valuation. That "correct" model is going to be less predictive than a model that starts from what similar firms are trading at in public markets. I personally am more interested in predictive power than correctness when evaluating stocks.
You are benchmarking against an outlier stock with a cult following (Elon Musk) and diversified sources of revenue. Anthropic does not have either.
If it was as easy as comparing the hottest new IPO with Elon's companies and applying the same multiples, people wouldn't have lost so much money on electric car IPOs that pretended to be Tesla (see PSNY -99.5%; NKLA -99.99%; FSRNQ -99.99%; etc.).
> being "right" is rarely how markets value companies.
Conventional metrics are how most stocks are valued most of the time. What you want to say is that market can be exuberant sometimes. That's fair. When the exuberance goes away, valuations come back to earth (see PSNY -99.5%; NKLA -99.99%; FSRNQ -99.99%; etc.).
> That "correct" model is going to be less predictive than a model that starts from what similar firms are trading at in public markets.
Even if we take your statement at face value, you're still not doing it right. A valuation based on comparisons has to have more than 1 peer to compare against. Check out the multiples of other model providers listed on the stock market. You'll probably be shocked that it's not a double digit multiple of revenue.
And then, if you really want to have a proper model, isolate the AI revenues of SpaceX and other hyperscalers, then we can argue that you have a model worth something.
Comparing != benchmarking. The only folks acting like anyone upthread is benchmarking to SpaceX are those who never made that comparison.
So in this case, diversified revenue is a drag on SpaceX because all the non-AI revenue is growing considerably slower.
But your point about Musk is taken. Cerebras went out in May, and despite being a hardware company is trading at 37x sales. Palantir trades at 78x, CrowdStrike at 50x, Cloudflare at 47x, etc. Anthropic is growing significantly faster than any of those companies. I'm not making the case that Anthropic will/should trade at any of those, just that Anthropic trading in the range of the P/S of SpaceX would not be crazy, and it would not require Musk to be involved.
> A valuation based on comparisons has to have more than 1 peer to compare against
Yes, the best peers are "public pure play frontier model providers trading in the US." There are none. SpaceX is closest, despite not really being a frontier provider and definitely not a pure play. But you are correct that investors will use a basket of stocks. It is silly to suggest SpaceX will not be in that basket.
You absolutely can make an AI doomer basket to model Anthropic. Use "real infrastructure companies" as the connective tissue since AI wants to be infra. In your basket put Caterpillar, Kroger, The Southern Company, AT&T, Comcast, and ExxonMobil. You can absolutely make the argument that Anthropic should trade more like them than like CrowdStrike or Cloudflare, that is a coherent position. Then based on that infra peer group, calculate a valuation. I think this model will be much less accurate in the near term than a model that uses other high-growth tech companies.
Price discovery is part of the fun of IPOs! The doomer model could be right, the stock value could collapse toward irrelevance as fast as the exchange's circuit breakers will allow!
Your argument is like saying that having gearbox in a car is a drag on speed, why not remove it? Just take out the transmission and the windshield while you're at it too.
> Cerebras went out in May, and despite being a hardware company is trading at 37x sales. Palantir trades at 78x, CrowdStrike at 50x, Cloudflare at 47x
None of those companies are AI labs, you're just cherry picking random expensive stocks and doing the whole price/sales nonsense again. All of them have positive shareholder equity (Anthropic has shareholder deficit).
> But you are correct that investors will use a basket of stocks.
That's your approach, I was just quoting it.
> You absolutely can make an AI doomer basket to model Anthropic. Use "real infrastructure companies" as the connective tissue since AI wants to be infra. In your basket put Caterpillar, Kroger, The Southern Company, AT&T, Comcast, and ExxonMobil.
At this point you're either being silly or arguing in bad faith. Your best case scenario is for Anthropic to trade like a stock with cult-like following and your worst case scenario is for it to trade like a ketchup stock. Completely ignoring the most obvious tech companies that are traded publicly and also provide models of their own. This conversation became so absurd that it's not worth answering anymore.
The mental gymnastics you're doing here are appalling.
Someone on a different forum fed the S-1 in Fable and it said "Shows a massive “loss” on paper, but that’s mostly an accounting effect from the company’s value going up"
Funniest shit I've read all day
99.9999% of the time someone sells something, the goal is to obtain cash. Throughout all of human history.
Obviously it's not like having Claude Code or similar, but those would be very hard to run legally and safely in EU regulated industries like us in energy production. You could run Codex but it would require your IT department to have yet another place to manage security and software integrations. Which is a fairly big issue when you have around 1 IT staff member per 300 employees, and an organisation which will buy five different sharepoint solutions for the same problem.
Microsoft sells something that seamlessly integrates into everything else you buy from them. It's easy to manage credit spending and when I say that I don't mean that it's easy to set it up. It's easy to track and budget so the right departments pay for it. It can read your emails, your onedrive, your loop, your sharepoint, your powerbi/fabric and anything else you have access to. Sure it's annoying to be confronted with a point you've made 7 months ago (like how you think Copilot is utter trash) and since changed opinions on it's frightingly good at finding relevant stuff, even in some obscoure legal document on your intranet.
I assume that Anthropic and OpenAI makes money of this somehow, but for the most parts our users wouldn't care if it was the GPT and Claude models or Mistral or Deepseek. Which is why I asked if they had really gotten it right.
A very large part of that revenue is paying for inference. There’s not enough leftover for the committed outlays. So it has to come from smucks I mean investors.
Today's prices are the ceiling. But AI capability is getting less of a scarce resource with every passing day and it would be a big mistake to plan that your current margins are going to be your future margins as well. Long terms AI will converge on energy costs + capex write-off + margin and the latter will be a race to the bottom.
What's the endgame once the dust settles? Is it going to be a utility like water or electricity and the cheapest offer will prevail, in which case margins will be crushed? Or is Anthropic (or some other big AI lab) going to swallow everything and become the Google Search of LLMs, with everybody else fighting for scraps?
Or is it local compute, models becoming more efficient and hardware becoming cheaper (again) and companies hosting their own AI clusters like they would run SAP?
I think there is a plausible path where this valuation actually works out, but it's a narrow one IMO. I also want to acknowledge that I personally would prefer if it wasn't the case and a more distributed, open model prevailed.
It's just like a utility with the slight difference that it can be sold at a distance without significant cost for transportation, in many ways it is the ultimate digital good. Unlike Spotify copyright is not an issue, unlike your electricity, gas, internet or water company there is no last mile problem. Unlike Google there are a ton of qualified contenders already (Google had to be better than Altavista, which wasn't hard, but they were so much better they managed to lock up search for long enough to cement their position).
Anthropic has none of the advantages and all of the disadvantages. On top of that all AI companies seem to be run by megalomaniacs.
Not all of them. Only those who outspend all the others and then you never really hear of any others. It's like the most basic requirement for the job.
Isn’t that what it takes though? Doesn’t startup culture celebrate this attitude?
Some do, unfortunately. But that's not all of them.
I think this might genuinely going to become a problem for them in the long run. People just absolutely hate their guts. That's a very strong motivator to do everything possible to get rid of them.
We'll of course see in 5-10 years if the doom PR will work out, but that always was a rather high-risk gamble, and it already looks like it might not.
There is a significant amount of businesses and people that are very willing to switch to literally anyone else just out of spite alone.
My country has no frontier models; since I've no national grounds to support one provider over another, a strong degree of provider diversity is an obvious benefit to me. I also think a highly competitive global market is a good thing for mankind in general, any hegemonic power having a monopolistic or oligopolistic position in AI provision is probably the 'bad ending' for most of us since it represents an enormous concentration of democratically unaccountable power regardless of the power which 'wins'. I'd argue it's rational, not spiteful to favour provider diversity on those grounds.
I really don't see any situation where local AI doesn't absolutely eat the big labs' lunch. They can't compete and the only way it makes any sense for them to exist is if the government somehow tries to ban local AI, which likely wouldn't work in the first place.
Someone like me who spends a lot of time in the anglosphere puts faces on companies like OpenAI or Anthropic. I doubt that's true for most people, especially outside of the USA. They're just big companies, like 3M, DHL or Toyota. Who knows what the management of these companies think of the future of humankind?
I would expect than personality-driven rejection of American tech from abroad nowadays is driven mainly by Trump and, to a much lesser extent, Musk. Everybody else is just "some guy" to 99% of the population.
Decisions in tooling are not made by the 99% of the population, but a subset that is likely a lot more tuned-in to all this. After all, that is kinda their job so that they can make good decisions
Our modern capitalist societies are, at the very least, amoral.
And back then, the world was very different. ZIRP, cryptocurrencies, infinite highs, peak social media. All that stuff
German here, every stupid utterance of at least the orange clown makes national headlines here. Frankly I wish our populations were more exposed to the utter buffoonery of the rest of the American wannabe elites, maybe people would then not go and type their deepest fears into ChatGPT.
So I'm doing what I can - I only use local models for 100% of my personal coding. (And their capability is amazing! Qwen 3.8 Flash Next leaves me wanting for nothing at this point.)
And I know open weights aren't enough. I'm ready to support open training if/when we get around to that. I hope once hardware prices ease, we can see homelabbers building small AI hosts (small by homelab standards yknow. So let's say 512GB host ram and 1-2 32GB cards, though i hope we also start to see AI cards with lots of vram but less compute cores). Then maybe we can do folding@home style distributed training.
In a time when software freedom is perhaps more threatened than ever, I'm happy to see that information still WANTS to be free, perhaps even more than ever. Look at how easy it is to distill claude! Despite, presumably, anthropic working very hard to prevent it.
In theory, things will become even more free, because you can copy whatever code you like, it's all in the public domain because it was all written by an AI (or enough of it was that a court will decide that copying the whole code base is fair).
And the ability of AI's to reverse-engineer binaries means that even hiding the code won't be enough.
Companies may write T&Cs that try to cover some of this, but they'll still have lost the biggest protection they have.
So, in theory, we move to what the FOSS movement have always wanted: all the code available, all the time, with anyone able to change whatever they like about the software they run.
Somehow I don't think anyone is going to be happy about this, though.
https://jacquesmattheij.com/beware-gpu-scammers/
Crazy times!
Congrats on getting rid of the debt, that's really a great feeling.
I really want to build something less scuffed, but, yknow. So I'm making do atm.
Is engineering the factories that make the hardware not nuclear engineering? Even if not, considering there exists only 1 supply chain in the world capable of making them, it seems pretty revolutionary. And that moat seems pretty big, given that it hasn't been crossed for many years.
In the long-term, the marginal cost of a request is zero so prices will plunge and a lot of the fixed investment will get stuck. The difference between the cost of training a model/infra and serving a request from that model is a clear problem for economics.
What happens with Anthropic specifically is less clear because they, as every company in the same position in the last 150 years has done, are going to push for regulation. A lot of the risk has also been shifted onto neoclouds. The value of the customer relationship is, however, effectively zero. A core part of their strategy has been to build stuff out like CC to differentiate that but, ultimately, that won't work.
The other strategy they have employed is trying to get other companies to stop training new models. Again, seems unlikely to work.
There is no scenario in which they become Google search outside of government intervention. Obviously this is a key part of their strategy now but at best, this will only work in the US and Europe (I would actually say they are much further along in Europe where there is a strong tendency towards corporatism and centralisation of power, they could be "Europe's AI").
https://www.astralcodexten.com/p/ai-safety-is-not-a-corporat...
The risk is that USA and China will come together on a joint "AI non-proliferation" agreement in the same way that nuclear technology became restricted by the countries that already had the capability. This will lead to a de facto techno-colonization of the rest of the world just between themselves.
This was a topic of discussion at Trump and Xi's AI summit just last month.
One of the largest legal services partnerships here in NL just launched their local DC so they can maintain confidentiality. Do you think they are 'running hobby models on 5 year old GPU clusters in the basement'? Or does a couple of racks of GB300's sound like maybe they know what they're doing after all?
This is all on the horizon. We are only barely scratching the surface and right now it is largely hobbyist having fun with it, but we already see the potential of the AI revolution and where it is going.
If a medium/small country deemed it a threat to rely on US/China for AI capabilities, they can get 95% of the utility by banning all American and Chinese models and just using local ones.
This contrasts to something like oil or nukes, where if you're not one of the countries blessed with them, you have a serious power imbalance.
The economic logic is the same as that of operating systems. AI models are very expensive for one company to make, but lots of companies need them. So companies are better off collaborating on that side of things and competing on something else.
Economically, the AI models that require big servers with lots of GPUs in data centers should likely be more interesting to investors than the AI models that can run on local hardware.
With each query OpenAI and Anthropic get more context about their users. When I compare answers between AIs the ChatGPT answer often includes useful things it inferred from earlier conversations. Losing that context could be a meaningful barrier for a customer to switch to a cheaper service.
Not unless they break their own TOS. The temptation must be very large to do so and is probably why those settings keep mysteriously shifting back to 'yes, I give all my data to you to do with as you please'.
The only ways to fight this is trying to either: 1. Block it by cutting off access, which is pretty tough because it basically means you cannot offer unmonitored access which is a no-no to a lot of customers, and even then the workload looks very similar to actual real workloads and if the company doing the distilling is a bit sneaky and using intermediaries etc it's almost impossible to stop, or:
2. Try to use IP law to block the practice, which is pretty gross given how they got the data in the first place, and so far no court has supported them on this. And even if they can make it illegal, it won't actually help because there are countries that see having top-tier AI as a national priority and will actually support their companies breaking laws to get there.
People misunderstand why Anthropic is being so stingy about granting access to their top models. It's not some kind of marketing ploy, they know that if they release them fully openly then there will be a Chinese open-weights model in 3 months that is just as good. Also the reason why they try to hide chain-of-thought and the probability distributions.
I don't think the evidence supports this claim. All the important capability gains now come from RL rollouts in real environments. You can't fake that by generating text from a teacher model. Anthropic makes a lot of noise about how China is distilling their models, but all Chinese labs can access is text outputs. They can't even get the logits. Now, compare all of the recent US open-weights models that are far behind the Chinese frontier. Why haven't they simply "distilled" all of those powerful open weight models that are available to them?
Isn't this somewhat akin to a model learning from its own inputs? Doesn't that behavior lead to model collapse? Either the model is trained on pristine data, in which case it stagnates, or it's distilled from another model, which leads to model collapse.
Pardon me? You cannot be serious...
https://media.defense.gov/2026/Sep/08/2003992823/-1/-1/0/CSA...
Independent verification from non-aligned entities would be a minimum for something like this. Note how in spite of all of the claims not a single lawsuit has been filed.
You cannot believe intelligence agencies when they do not offer proof. Their job is to provide (reputationally but not factually) authoritative story lines that protect big business interests from competition overseas. Sometimes they protect US business by bombing their competitors.
It’s going to be hard for companies to justify paying $1000s per month to frontier providers when they see what’s doable with open versions.
Isn't the first open model that likely surpasses original Mythos from February this week's Kimi K3.1 Pro?
Nothing compares to September's Opus 5.5 yet.
Now the question is whether Anthropic is preparing another phenomenal upgrade with Fable 5.5, or if the progress eventually slows down and competitors catch up.
I've rarely heard the opinion that for heavy agentic work, Opus wasn't noticeably better.
Top models, capable of discovering and inventing stuff, held back from public, and available only as for-rent research service (or whatever the relevant bureaucratic term is) to big corporations and government agencies like NASA, DARPA, NSF, NIST, DOE, etc. only.
I have a feeling that we, the public, will stop getting access to more intelligent models starting next year.. i mean, not the incremental ones, but, major ones.. like the one you got going from opus 5 to opus 5.5.
Look at the defense industry: The Big 5 contractors (Boeing, RTX, General Dynamics, Lockheed Martin, Northrop Grumman) capture most of the discretionary military budget, and the largest of them is in the $300B valuation range (Boeing, which has substantial non-government revenue). Fine businesses but nowhere near Anthropic's valuation.
Bottom line, the government doesn't have deep enough pockets.
In fact, I'd assume this direction has been widely discussed and considered at Anthropic. I think Fable's release was an experiment to test this.
The biggest risks though are China and Moore's law. In the short term, if you don't offer a competitive model as the market grows, you fall behind in marketshare. Anthropic's edge is that it's offering is still better than Chinese offerings. If they offer anything less than that, something not better but just comparable for example, they lose on price. In the long term, as computing capacity continues to increase, local models potentially pose the same challenge.
So there's plenty of pressure for Anthropic to keep widely offering the best model they can.
At this point, the best guess is he plans to deflate the currency to make the debt worthless.
That might be the best possible intended outcome, but it also seems unlikely that Donald Trump and his government are involved in anything so far-sighted. Just look at the man's public behavior over the last year; either he is an incredible actor, or this is not a government where anyone in power cares about anything beyond the Big Man's whims of the moment and their personal benefit.
There's also the incompetence of the ones with the longer term plans making whatever is played out more inscrutable. Although they have actually implemented a significant portion of project 2025.
I personally think it's really funny how if you were to try to reverse engineer his motivations based on the actual outcome, then the most likely answer is that he's a joint Chinese-Russian spy based on how much they've benefited from his elections.
The world is going to be very different in another two years.
fuck trump and republicans. and yes whatever goodwill you had built up over the last 60 years was up and away like a fart in the wind when you elected a corrupt pedophile for the second time. And it cant be built back, ever. No one trusts you no to pull the rug if we became reliant on you again. What we will have though is a relationship with allies built on equality rather than one main player calling the shots.
Not even just from Chinese models, but from companies/labs like Mercury
E.g I know people in high finance who switched - they don’t need frontier for many tasks - Anthropic is getting jack all revenue from them.
If the tech improves massively - Anthropic still gets nothing - since firms will want on-prem or cloud offerings that ensure zero data leakage / IP.
You lot haven’t played this out.
We've seen businesses moving everything to the cloud before AI, we'll see more of that as regulatory requirements are increasing.
It's very possible given the current environment that LLMs go the way of airlines. Low margin businesses that are essential to world economics with heavy price competition.
There are tons of private datasets where a model will be fine tuned to provide useful domain specific responses.
I am under the impression that curated training pipelines are driving the gains, not utilizing random usage data in nefarious ways.
i keep thinking how crazy it is we went from ChatGPT releasing in 2022 to multi-trillion IPOs 4 years later. the industry still hasn't agreed on how to generally use anything. crazy times ahead.
For certain AI use cases, AI will be like a utility. For other user cases you need to pay a premium for the frontier. For other use cases AI will be local.
Even in the industries you mention (water and electricity) you have all sorts of businesses. Like people selling water in fancy cans for a large markup or batteries vs wall power.
This
So like electricity, where most people will probably just use the network, but a few will opt to invest upfront and generate their own.
Given the trillion-dollar spend, the theory is that every many woman and child in the western world will devote a substantial portion of their income (like 5-10%) towards some sort of AI subscription.
or...
That doesn't happen. All the AI investments go south and every man woman and child in the western world will devote a substantial portion of their income (like 5-10%) to bailing out an economic system that went all-in on AI. Either way, the money has already been spent.
The already shaky economic system (and associated political systems) can't take the strain of every man woman and child in the western world devoting a substantial portion of their income (like 5-10%) to LLM fallout, and we end up with a collapse scenario of some sort. (Which, to be clear, is near-certain to be worse than either of the above.)
It's a lose/lose situation here for Anthropic (and OpenAI) whatever happens.
Already happening, which is requiring a lot of enterprise DC power retrofits.
Of all scenarios, based on the data we have now, this seems to be the least likely one.
It's not a great position to be in and it will still take a long time for the compute gap to close. In the meantime, Anthropic, OpenAI, and Google are essentially unchallenged.
[1] https://www.konstantinpilz.com/posts-publications/chinese-st...
Besides...
"If passed, the rules would require Chinese companies to obtain licenses to export the core technology behind their leading models.
They would also ban the export of A.I. that can be used to launch cyberattacks, as well as models trained on what China calls “core data.” The government broadly defines that as data that concerns national security, the welfare of Chinese citizens and the public interest. Experts say the scope includes data on the military or critical infrastructure, state secrets and other sensitive information. "
So pretty soon they won't even "do it".
[1] https://www.nytimes.com/2026/10/21/world/asia/china-exports-...
What makes you think there would be no alternatives, though?
Put it like this: Kimi K3.1 is downloadable and can be run locally. (If you have a datacenter.) This is a very capable, ~2.8T parameter model.
Now let's say that every Chinese firm goes closed in six months or a year and jacks up pricing... What's stopping a bunch of enterprising upstarts from updating all 2.8 trillion parameters, extending them, distilling the model into very competent flash models, or applying targeted RL?
Once the principles are understood and there are models that can be pulled off the open internet, it doesn't seem that hard to spin up your own LLM. You just need some financial resources and a reason for doing it. And that would come into play if the Chinese kill every closed American provider and then go closed themselves.
It's not quite like Uber. It's more like... well... imagine there are two computer operating systems, Gray and Teal. Gray is always slightly better, but it costs $50/user/month for a license. Teal is free. Eventually, when both happen to be in version 9.0, Teal puts Gray out of business. Starting from version 10.0, Teal starts charging $100/user/month from a license. But nothing's stopping you from sticking with 9.0, which remains free forever.
Perhaps what's "sustainable" is not clear, given that Gray was eventually dominated by Teal? In any case, versions 1.0-9.0 were, effectively, gifted to the public. We can appreciate (and even disapprove of) that commercial strategy while benefitting from its fruits.
>Its economic warfare
You started the "economic warfare". You tried to turn them into enemies because you can't compete under the same conditions.
And don't you say anything about china competing unfairly, e.g. distilling etc. You looted, and continuously looting, the whole internet with 0 care. You are having a net negative impact on the internet as a whole currently. You are aggressively extracting value from things people gave away out of a good will, or just outright stealing and laundering the stolen knowledge.
>Short term benefit for long term harm.
Long term harm to who? Investors who gave money expecting an eventual monopoly?
And since when are we on the same side? Big american labs sure don't behave like allies to the general population.
There could be, they can actually come from open American companies too. Ask yourself why we aren't seeing a thousand or more American AI startups spring into life overnight to build "open" models. Follow the economics.
> What's stopping a bunch of enterprising upstarts from updating all 2.8 trillion parameters, extending them, distilling the model into very competent flash models, or applying targeted RL?
> And that would come into play if the Chinese kill every closed American provider and then go closed themselves.
What's stopping American or European tech companies from doing this today? It's economic and technical capability. I'm very suspicious of any suggestion that it is in any way not incredibly difficult to build and scale up LLMs. Maybe in the future it can be much easier, but in that case you run into a sort of tautology where it becomes easier but the leading companies continue to lead and do so by a significant margin because they're the ones that have the most talent and access to capital.
The premise seemed to be "an open China" versus a "closed USA". I'm not sure that's a great premise. If China can create open-weight models so can the US. If the US can close their models (as clearly evidenced by the article) so can the Chinese.
Seems more economic than technical. If the economics are covered, the technical aspects seem straightforward enough, and may perhaps be fully solved. I think there's proof of this in that there are a lot of different LLMs on the market. Even Xiaomi -- in-between making cars, e-scooters, backpacks, and home appliances -- spun-up an exceptionally capable LLM! (And one which is rather idiosyncratic. It's weirdly good at music composition and generation, which both Opus and Astra cannot do.)
The economic case doesn't exist for VC-funded US corporations, nor does it exist for cash-strapped nonprofits. In China, all such ventures are likely funded by a government development bureau. If Chinese ventures kill expensive closed providers and universally go closed themselves, there's nothing stopping the US or Europe from copying that same playbook -- unless, as may well be the case, they're ideologically opposed to subsidizing strategic industries.
I mean how useful are GPT-3 weights today? That's the kind of useful they'll be relative to the frontier.
I feel like the so-called "Deepseek moment" broke too many people's brains and they've lost the ability to reason about why frontier advantages matter. I think we have to learn some new core concepts and rate of improvement over time is going to be a big one, and not understanding that causes people to exaggerate the significance of downstream distillation that's done in response to it. It's cheaper and open and that matters, but it's not the kind of mattering that replaces the relevance or advantage of the frontier, and I feel like I'm not seeing people demonstrate that they know how to really compare the relative merit of these two things so much as zoom in on one to talk past the other.
I was using GPT-3 and the original Llama when they came out and realized that it is interesting, but not good enough - yet. But they are good enough now. And just like there are still people driving Renaults and Volkswagens the latest batch of open models will be useful for a long time to come.
> whether you want to acknowledge that or not is not my affair.
I felt I acknowledged it pretty explicitly. I said the likes of Deepseek are "cheaper and open and that matters", but it's the rest of that sentence that I think went without acknowledgment.
>And just like there are still people driving Renaults and Volkswagens the latest batch of open models will be useful for a long time to come.
Walking is great too! But you're not filling an economic need by selling walking to the masses, and those companies are marginalized and the economic destiny of the world is being set by those who own the infrastructure to build out the world's next generation of electric vehicles (at least in the automotive space).
It's like sure yea you can access the Internet and tinker around or whatever, and with some old LLM model you can have it do fun things, but on a scale of absolute usefulness and if we include what people actually pay money for and what is highly adopted, your laptop and model are severely outdated and by and large irrelevant. That's why the iPhone sells like hotcakes all day every day and twice on Sunday and Apple is a $5 trillion company. Society disagrees with your measure of usefulness.
Sorry I'm rate limited since Dang thinks I'm arguing to much on political topics (he's probably right), but to answer a couple of questions regarding "my old technology does this one thing" you are conflating your personal and nostalgic uses for old hardware/software with what is useful for society or business. If the old hardware was still just as useful as the new hardware it would be used instead of the new hardware.
Technology never was about the tech an sich but about what you do with the tech. For one person that 10 year old laptop is useless, for another it is a source of $500K / year in income. You, the user are the biggest factor in how much value you can extract from the technology at your command. I'm not sure how much longer that will be true but it definitely is still true today.
Compared to laptop quality, I suspect model quality will be a much bigger competitive advantage in the tournament of the marketplace. High-quality laptops are still limited by the quality of the software developer.
For now it is exactly the same with the models.
Also... employment in some parts of the stack is genuinely going away. I don't even look at CSS anymore.
To some degree, yes. But 'dwarfs' isn't really the term I would use. It is a bit slower, but the upsides are >> than the downsides for me. Not having Sam, Dario or Xi's minions looking over my shoulder matters to me more than any technological edge.
> Also... employment in some parts of the stack is genuinely going away. I don't even look at CSS anymore.
Yes, that's a fact, but that's orthogonal. I haven't touched either HTML or CSS in a long time now and yet I've cobbled together (I wouldn't claim ownership) a lot of stuff that 'just works'. I also would not expose that stuff to the world at large because I suspect it isn't nearly as secure as it looks.
I pay attention to frontend code just to keep code quality up. Frontend work is relatively low stakes from a security perspective and I've never seen the robots do anything problematic. I don't vibe code React but I think you could get away with it today.
Backend is a totally different story.
That's still a lot of engineering getting displaced.
The machine was bought in 2011 and it runs Windows Server.
I can do whatever I need with the device, apart from Gaming. Even YT works in 4k.
So, tell me now please why my 15 year old computer is not useful here?
My macbook is 5 yrs old and Im a big tech guy. Tech matures. It doesn't grow forever. You live in delululand bro.
Also, it's entirely plausible that an previously open model (that then became closed) will be much easier to distill than having a bunch of closed models.
Neither do Anthropic and OpenAI.
I like Qwen as much as the next person, but that's a bit to credulous. Those models would not get the compute allocation nor distribution they do if it was not aligned with broader policy.
It's a shame "AI" cannot be made to run on bitcoin mining dedicated hardware because they mastered that in only a few years
There are dozens of Chinese domestic AI accelerators: Huawei, Alibaba, and Baidu all have chips, there are a bunch of smaller startups too. The chinese mainland is behind on leading edge fabrication though, SMIC only does ~7nm class since they don't have EUV.
In case you're wondering as well:
https://wccftech.com/nvidia-cmp-170hx-8-10-gb-prices-explode...
It restores firmware features related to RAM, PCI-e speeds, BAR size, VFIO...
I won't use a Chinese model. Why would i bother? I pay $100/m for the frontier model, understanding it's marginally better than much cheaper alternatives. I get so much value from the product it's not worth shopping around
Think of it like apple and android. Plenty of people pay 2k for their iPhone every few years when android at 1/4 the price is just as functional. Why? Because they get so much excess value from the device it's not worth shopping around. For most people it's their only personal computer. From booking airfare, to filling out their kids permission slips, it's a device they use 10+ hours a week. They don't bother, until a paradigm shift happens and something is 10x better than the iPhone. I imagine llms will follow the same trajectory
I get (sometimes) frustrated with people that don't work in the tech industry over this concept, and having them rationalize that their usage is heavily subsidized by the distributor of the model.
The costs in compute, electricity, cooling, etc. per token is much higher than most people (I feel) who work in the industry realize. Which, cognitively makes sense: they just want the 'magic 8-ball' to give them a tasty dinner recipe that meets their specifications, for example, and don't care how the 'sausage' is made.
However, economies generally equalize with supply and demand, and this (most likely) will become cheaper as the technology progresses and optimization passes/new algorithms are discovered.
It seems likely that Anthropic and OpenAI's inference margins on API prices are >95%.
That also casts doubt on the parent's claim that subscriptions are subsidized. It rather seems almost certain that they are overall generating profit.
My last role swapping from Haiku/Sonnet to GLM dropped costs by 90-95% and increased quality.
Money talks.
I can understand that they might be cautious about sharing their data with a Chinese company.
But them being the most successful companies means that they can just buy their own hardware. And run whatever models they can get their hands on in there.
Even if those models are Chinese. They (the companies running them on their own hardware) would be in full control then.
if the chinese models have this aura of inferior quality around them, then sure.
if they manage to frame it instead as open source, sovereign AI, the calculus changes.
But I don’t think this will be the first time in history that money doesn’t win.
Models with tool access (and internet-connected tools like curl) can, though… is this really a useful distinction?
People are naturally sticky. If they're satisfied with a product, they'll stick around unless something comes along that is so much better it gets people talking. Open AI's 1B+ customers won't give a shit about Chinese models. Enterprise customers won't move to Chinese models, especially just to save money. They are a non issue, especially if the M.O is just going to be playing catch-up.
Google, the titan mega-corp with more money than they know what to do with and a billion+ android devices to ship gemini on and it's made no dent for ChatGPT's consumer growth and adoption. Yet some plucky open source chinese models will?. It takes more than that.
But also, I don't think you should use individual consumer behavior as a proxy for how the overall LLM market behaves. Current estimates are that individual users represent a pretty small share of overall genai revenue. Most of it comes from enterprise contracts. Those might be more price sensitive because the CFO gets a say in which vendor they choose.
For a couple years now I've been accustomed to choice of foundation model being a heavily scrutinized design decision because it's just about the easiest way to manage COGS. I can certainly imagine scenarios where, given the option to pay 1/2 as much in inference costs but with a 5% reduction in output quality, the boss would say, "Nah, that's a bad deal." But I have yet to encounter one in the wild.
Okay fine, it's $1,400 for a latest iPhone Pro with 512gb of storage. Point still stands.
> Most of it comes from enterprise contracts. Those might be more price sensitive because the CFO gets a say in which vendor they choose.
I think enterprise contracts are even less sensitive to price than consumer. That's the whole idea behind enterprise pricing. You'll see companies charge multiple times as much just for SSO for instance.
There are limits to this obviously, but to think that major commercial enterprises will switch over to some Chinese models to save some money is laughable.
So you switched away from the specialty product, but you're still comparing a top of the line 6.3 inch iPhone to a mid-market Android phone. Why not compare it to a Pixel 11 Pro with 512GB. That way they're at least comparable devices. But also, that would cost $1,220, which I suppose doesn't go well with the intent of suggesting iPhones are several times as expensive as Android phones.
The reason why I'm digging in on this is, when you start your argument with such a transparently silly comparison, it gives the impression that you're dealing in superlatives and willing to say just any old thing. Which undermines your own credibility.
So, getting back to corporate - multiple times how much for SSO? Okta Essentials is $17/user/month, which is a little over half as much as the base price for Cursor Pro, and about 2/3 the price of a basic ChatGPT seat.
Comparing two devices at the highest price point is besides the point.
For AI? Prompt comes in, tokens come out. The value of the 'ecosystem' is roughly zero to me. Switching costs between models are essentially zero. The UI and UX is the same whichever LLM runs underneath.
If you do use llm some, then the $20 tier makes sense, but that's not where the money is. If you use it a lot then a $100 sub probably makes sense. But if you're a BigCo that heavily uses it and pay API prices and suddenly you pay $200 million a month, well, maybe it's worth buying a bunch of hardware and stick it in a basement somewhere, and you won't upload all your secrets to your future competitor to boot. If US labs didn't care about the competition from Chinese models they wouldn't lobby so hard for banning the evil, unaligned, super dangerous communist models that want to steal our freedoms and make our gas expensive.
Ultra-cheap models like Deepseek are also very nice if you need to burn billions and billions of tokens.
How many people use an iPhone to its maximum? Isn't it the same with models? I think engineer's like to flex that they need a top of the line frontier model for their shitty corp projects. A Chinese model will write your Jenkinsfile just fine.
It’s not unusual for someone to spend $5k in a week. 2 guys at my place just spend $150,000 over 3 months building out a factory.
We are aggressively exploring open weight models now.
Sounds like this will be another one of those things where everyone makes their money and it then will eventually become something experienced people 'blog' about.
I don't doubt it.
> Sounds like this will be another one of those things where everyone makes their money and it then will eventually become something experienced people 'blog' about.
Or not. The problem is that right now you and a whole bunch of others are trying to outrun a pack of lions. The lions have already taken down a couple of your group. The people further ahead have seen what the lions can do and they are attempting to speed up to the point that they can outrun them. Whether that will work or not depends to a large degree on what the other people are doing and they are not going to make it easier for them to keep pace.
That partially explains the lack of deep insight blog posts, besides everybody is just too bloody busy.
But you can also have many engineers operating it, testing outputs like you would in a real factory, etc.
Will be interesting to see how this pattern evolves. If it even makes sense.
If that were true, they would cancel subs and save themselves the load.
Yet that hasn't happened. So they must care to some extent.
With that, there are at least three distinct reasons to care about subs: PR, hook and training data.
Wait until you are forced onto an enterprise plan...
LLMs don't have that. For at least 90% of users, the model in the back end is completely irrelevant to them. Switching to Kimi won't lose them their iTunes music.
It’s pure genius. There’s a lot of bozo’s here who cannot appreciate it and cry about fairness.
Many here don’t understand corporate finance do they? Hence the marketing hopium to fool people.
How many Astroturfers and Anthropic employees are here to convince us otherwise?
On that front, I think DeepSeek is a dark horse. They are really odd - not chasing revenue, care more about unit profitability, are obsessed with driving down costs (like Elon back in the day). As the founder says, they are content with where they are, being the model that does 80% for a fraction of the cost.
I mention them because they are playing a different game and might be able to out-innovate everyone else.
What a lot of people don't seem to realize is how much of the edge of the 'frontier' is in the tooling, the prompts and the harness. The LLMs themselves are already all reasonably close if you keep all the other stuff constant. Sure, one will have an edge in one domain or another. But that edge mostly translates to a degree of performance rather than some absolute. And if you allow for a few more toolcalls, a web search or a longer context then that difference is much smaller than it might seem at first glance.
And claude code as a harness and skill/plugin/mcp ecosystem is far more valuable than any one model.
And coward for long-running tasks, though they're slipping on that one because they're really trying hard not to make Claude an assistant. We'll see if they react to Muse and change course.
But the Chinese free-weight models? Not a competitor. If/when those companies (or others) build vertically integrated productivity stacks on those free models, then we'll talk. Microsoft certainly saw that opportunity but couldn't get out of their own way and are basically roadkill at this point.
Google Workspace revenues are something like $25bn/q, and that's the entire workspace division, (including enterprise gmail sales, so the productivity suite is meaningfully smaller than that figure). It's taken Google a long time to carve that slice out of Microsoft's entrenched market position. It's not small money, but they'd basically be going to war with two of the largest and most profitable companies in history on their turf.
I don't think they can credibly take that business plan to the market and say it'll be worth $1.75tn. They're all-or-nothing on being in the model business, and everything else is superfluous.
$100B/year in revenue; SaaS so call it 70% gross margin, 30% net, NPV is about $500B.
Not enough if you think that's the only revenue Anthropic could every have. But even if they only peel off $100B NPV, it's incremental to an IPO valuation.
The same one that leaked itself a few months ago?
The value is in the harness! The harness leaked itself
Apple makes great margins on hardware. They are 100% not in the CNC milling business. But their investments at that low level and vertical integration allow for higher margins at the top.
Cursor had... nothing. A minor, indefensible value add to a much larger and wealthier supplier.
No, that would kill them for sure. Or did you mean 'open model progress'?
as a person, i would absolutely not pay more than $1K for ai, because at that point i can just get myself a bartening job
Why would I pick anthropic.
Like if you want to design a survey you can send out, and collaboratively edit and test the survey, ask for changes in layout, wording, etc, and design the statistical analysis to be used, before sending the survey out where end users can fill it out right in the doc, while internal users/admins can ask the agent about its results?
Honest question -- that all works pretty well in claude. Amazon, Google, Microsoft, Meta all pretty similar?
Companies don't mind paying extra to have a another company that does nothing but said thing do said thing for them. It even actually often ends up being cheaper/more profitable.
I don’t really know where this commodification idea came from, maybe MBA types that don’t actually understand how cloud economics work, applying basic first principles analysis?
The operational engineering that goes into serving a frontier model is _highly_ nontrivial, it is not commodifiable. This isn’t a SPA application.
I don't really see it frankly. Admittedly I never tried it (I only played a bit with local models) but conceptually how is it much harder than all the other cloud services that already exist? My educated guess is that building a competitor to Netflix, Youtube or Dropbox seem technically a lot harder to get running than offering inference on demand for instance.
It seems that the main barrier for competing in that market at the moment is getting your hands on the GPUs/RAM and a datacenter to put them in.
No Western company will be allowed to choose a Chinese cloud provider by their legal department or by their stakeholders. Which leaves only the US cloud providers as viable sources (as us, Europeans, have been too busy building our revenues based on turism and other such non-tech thingies instead of building real stuff).
The reception of saying things like this is interesting. AI stans think I'm way too pessimistic but AI haters think I'm mindlessly optimistic.
I dont know when, but i think if you keep menacing of taking technology of people hands for any political reason will be a reason for people to not use it or to rely in a more reasonable party.
I understand that US is migrating to a no friends policy, but I don't think its internal market is nearly ready to support it if clients start to go away.
Anecdotally, I talked to people in a recent event and the common topic was "what we are doing to avoid a blackout of US tech." People on big companies have reserved hardware and alternative software and are scaling down Amazon and Microsoft solutions at a fast pace. People on smaller companies are doing similar strategies but relying on a multivendor strategy.
I think this was inconceivable 3 years ago. Every company in Europe and Asian would gladly pay for amazon for cloud services and now they are using AI to migrate out the lock-ins.
We are seeing American LLM companies more and more relying on "secretive" methods to increase scores and this can only mean that they are not evolving fast enough. The gaps between the reported benchmarks and measured ones are increasing. It might be "safety" concerns or whatever, but I don't buy it.
Like Musk, Altman and Amodei have been overpromising every time they need more money and each time they need more money and now they internet is poisoned with their own content and they can't even trust any sources anymore.
Why? I don't see any reason for that to be the case.
Point is taken - competition is fierce and will be biting everyone's ankles.
Chinese models are being banned, will continue to be banned by USG. Even the Chinese govt is restricting external access to models now. It starts with banning Govt access to International AI, but will continue to consumer and business access.
I wish they were, but I always wonder when people chime in with Chinese models what they are actually doing with those. I have an OpenRouter account and give various models a shot every other month, wishing for an alternative, but I always come back to Claude. Even more so since the switch from Opus 5.1 to 5.5. To me, personally, it's unmatched. Like a year ago it was very different, and I would use other models all the time. But honestly at the time they all felt kind of "meh" still. I just hope everyone is at a good baseline in a year from now on, so I actually have a choice. And then, as you stated, things will get interesting.
tl;dr: I agree
In my case: robotics, complex document analysis (typically 800K context).
So it seems like Sonnet 5.5 medium, GLM 5.3 flash and Deepseek V4.1 flash 1017 are all in a similar space in capacity and price, however the $20 anthropic sub will get you like $200-$300 in sonnet 5.5...
Another bonus is that sonnet uses a fraction of the overall tokens and finishes much faster than the average chinese model, so your work goes faster (Sonnet 5.5 medium in 2 minutes, Deepseek 1017 in 6.5 minutes, GLM 5.3 flash in 13 minutes...)
I still don't get why paying for chinese inference makes sense. I should try Haiku 5.5 high/max because it's posting higher numbers than Deepseek V4.1 flash for half the price
However even with a high tier gaming machine you can run one of the Qwen models and it will be good at coding... like having an intern with boundless energy that will sometimes make mistakes but in general save you a lot of time. And for automating tasks involving scanning documents or creating summaries it will be brilliant.
Anthropic can take the parts of Google it needs FAR faster than Google can do the reverse.
I’d say it’s more Notion than GDocs for now, but the direction is clear. Gobbling up all the SaaS in order of descending value to agents.
My bet is on promoting their internal Tasks representation to something more like a Jira/Linear.
But then obviously Slack is another one, and Duet is already starting to build a “Channel” abstraction, so making Duet multiplayer is a clear possible next step.
The tool is not a moat when you can tell Opus to build you a better tool.
as in, if they capture every single professional software dev and extract $1000 from them every month, their yearly revenue will be like $500B. do your own back of the envelope on that with ongoing costs and consider how unrealistic a goal this actually is.
even worse, the very product they offer is self limiting. if it becomes so good as to actually replace software devs, they will have less customers to extract money from.
their valuation essentially relies on a small percentage of mega users having unheard of levels of spending.
the part about a small percentage giving them most of their money is already true (their rumored $100+ bil revenue as of mid 2026 already outshines all software devs having a $100 max subscription), but they are two orders of magnitude or so away in terms of the actual amounts to make it work.
The market for Claude Code is much bigger than professional software devs. It's really anyone who uses a computer. I already know normal people who have been using it for personal software.
I'm sorry and I'm sure you are being honest in knowing non-tech people using it, but this is on the whole false. The vast majority of people not only have no interest in "personal software" or using Claude for programming, but the slice of the population that does actively interact with AI is using free chatbots as a nu-Google. Most tech people don't even want the "personal software" thing, I don't know why this line is being pushed as if your grandma is going to make her own calorie counter app one day.
What's your source to the contrary?
Total addressable *market.* If Anthropic still had market to address, we would still be seeing consistent growth.
Though I would expect the majority to do fine with $20/mo. That's the same effective cost as replacing a $500 computer every two years, which is the sort of expense most companies are comfortable doing without much second thought
Most people aren't engineers and do not care about custom software.
It's not gonna install a shower for you, but it will confidently walk you through that leaky faucet. And I don't know if you have paid a pro recently, but they don't charge $20 for a 20 minute fix.
It's quite a leap to assume any kind of issue would require a $100 subscription. Plumbing, mechanics, electricity ... The issues are usually far easier to trace and debug than in software, because the systems have been designed by smarter people.
You're not buying some arcane and complex solution, you're buying the convenience of not watching a couple of youtube videos or articles of someone troubleshooting the exact issue you're having.
Free tiers of essentially any AI model would be able to do the same. People will use what MS bundled for free.
The step-by-step customized hand holding of LLMs is something totally new
"How often are people paying at least $600 annually for something AI can help them do instead"
and the other half is "How often can AI help someone boost their productivity over $600 in a year"
I also don't think people will even need $100 plans for this. Plans will probably land in the $60/mo range.
I'm going to need an example on this one, and examples that can't be covered equally well by a YouTube video or an 20 year old forum post. It also has to be legal, as in not violating codes for electrics or plumping, I can violate those on my own for free.
Muse is free. You can chat with Gemini in Google results for free.
Not that often.
AI recently told me that my oven was broken because a part that doesn't exist on the oven I own was malfunctioning. A bunch of people poking around with electronics, motors, etc. because they think their AI chatbot can fix anything and everything is going to lead to injuries, deaths, and most assuredly more money in the long haul.
I tried to look for the valuation in the linked document, did you see it or any reference to a valuation elsewhere in anything published by Anthropic? what is "their valuation" that you refer to?
The same logic applies here.
Google, Amazon, Microsoft, Meta, Broadcom, Nvidia, SpaceX and others are spending hundreds of billions a year each on fast-depreciating infrastructure to serve this potential AI demand. If the demand never materializes then they know they are in big trouble. So they have every incentive in the world to prop up Anthropic (and OpenAI) and keep the party going.
Can they keep the bubble inflated indefinitely? Probably not, but all these companies combined have heck of a lot more influence than Anthropic alone.
Source: https://x.com/a16z/status/2113986385908842870
"Zooming out, GPUs are still renting ahead of a standard 6-year depreciation schedule
Rental price vs. schedule: - H200: +143% - B200: +111% - RTX PRO 6000: +73% - H100: +54% - A100: +9%
Most of the gap opened up after April, when token demand took off"
I've brought my old GTX 1070 out of retirement for for some molecular dynamics these days because its "free" except for eh electricity and have Ben pleasantly surprised at what it can get done chugging along in the background. I'm considering buying some more, as it's kind of a steal in pricing.
A supply abundance reveals the inherent cost.
But I see every reason to believe demand will taper off. It seems it’s already happening. Enterprises have been deciding that maybe corporate chatbot and coding agent budgets shouldn’t actually be infinite. AI startups are starting to successfully outcompete each other, effectively killing off members of Anthropic’s customer pool in the process. And consumer demand for AI products seems to be dropping. None of these are signs that suggest that Anthropic has thus far met only 10% of plausible long-term demand.
Then if inference does grow to a $2-3 trillion/year market that Anthropic is a big winner, as big as NVDA is now (they're going to be over $500 billion in revenue in calendar year 2027).
The pattern seems to be that it makes people more productive when you measure it in terms of raw work product volume. But not all work product is equally valuable, and we have yet to see a clear pattern of increased economic productivity. Instead we just keep finding new and interesting bits of jaggedness in the frontier.
Of course nobody can prove those economic gains will not manifest at some point in the future. I can’t prove that Jesus isn’t coming soon, either. But I also can’t prove which millenarian denomination, if any, he would choose to bless in the event that he does come back.
This is a good point. I can code with Claude Code at 10x or maybe 100x, but have very little in the way of design, sales or marketing skills, and thus, I've been unable to make much side-hustle money from my new superpowers.
Meanwhile at my boring day job, my productivity is gated by a frustrating variety of coworkers and embedded processes, none of which seem to operate at 10x, to put it politely.
personally, i want a 2 hour 3 martini business lunch, not to ship more slop at all hours of the day
It's part of the reason so many of the dark enlightenment dorks are pushing more towards automation of control.
There have been no signs of "super productivity" in the larger economy due to AI. Boosts in productivity have, so far, been seen exclusively in sectors like tech.
And unit economics.
HY1 2026 revenue less compute and infrastructure turned positive (45% gross compute margin; $8.9bn C&I to turn $16bn revenue) for the first time; it was -176% in FY 2025 with $7.3bn C&I on $4.6bn revenue.
It looks like a lot of costs of revenue are in sales, marketing and partnerships, though. Backing those out shows H1 2026 margin at 26% ($4.2bn) after -72% for FY 2025. Still positive.
Anthropic has no choice, just like you said. It's crush and burn right now or double down.
And they will keep doubling down, because stopping is simply not an option.
Of course it is. Apple is taking that strategic approach. If Anthropic believes it’s approaching saturation, pulling the plug when it’s at steady state and its rivals are over-invested is the winning move.
Okay? Anthropic’s management and Board are uniquely insulated even by Silicon Valley standards.
> They just can't on their own decide to stop or slow down
Of course they can. Doubly so if it’s shareholder accretive in the long run.
There's very little comparison to be had here. Also, Anthropic likely have investors with way too much leverage for them to stop.
Maybe Apple is just waiting on the line until someone can be sucked up at a penny if the house is on fire - and pretty sure, this will happen. Not in 2027, but I expect MA activites & consoliation before 2030
That’s why they hire ‘for the misson’. They have to pull off something magical to make all this work financially or they go bust.
So far not a single company has ever demonstrated that.
Even if demand is through the roof which it seems to be, it doesn’t matter if every dollar they sell costs them more than a dollar.
They paid for their compute in equity in the company, not cash. Therefore their losses don’t show up as cash. I don’t understand how people are this gullible.
You people are nuts. The failure risk is off the charts.
Anthropic needs to earn 52B a year in annual revenue to honor those specific commitments plus obviously its own operating expenses... and yeah that will require Anthropic to grow as a company, but their S-1 indicates 67.2B a year in revenue and sure maybe they stop growing and this is their peak... or maybe they do manage to grow further to 80B a year, 90B a year...
But lets be clear: the growth of Anthropics revenue is very fast and way too many people stll don't even use AI
Businesses are extracting value now - for the average user I see it as a convenience that they will pick the cheapest provider for..
It's a race to the bottom for most as there's not a major differentiator.
Besides the question is not only if someone used AI but also for what and how much.
But look at this map: https://www.visualcapitalist.com/mapped-ai-adoption-by-count...
I'm not convinced.
[0] https://finance.yahoo.com/technology/ai/articles/two-custome...
[1] https://www.businessinsider.com/anthropic-ai-copycats-distil...
Assuming you're talking about the $600 billion or so over the next decade, they don't need a 6x increase to cover that, they're already pretty close based on the $65B run rate as of last quarter.
their revenue is super concentrated in just a few customers
20% for the top two customers is not actually that concentrated. If both of them go to zero, at their current growth rate (45% from Q2 to Q3) it would take about 6 weeks to replace that revenue.
But I do assume its not just adoption in the future its replacement and re-investment:
Right now a company can't invest in anything else than humans. A team has 5-10 members, the 5-20% of them are below avg or just avg. you were happy to have them for whatever reason like smaller tasks, easier tasks more repetitive tasks.
Now you can just replace them with AI Tokens.
The second driver of this, or why this will/is happening not just for something like a developer through coding agents: the agentic layer is coming. Which will eat tokens for breakfast, but will also replace humans.
Your HR system? Will have an Agentic layer by default. Your HR system will either respond automatically to you or with A2A to the ticket system. The ticket system will have an agentic layer. Your SEO tool? you will us the agentic layer to create camapaigns automatically. You migth give feedback but you will need less people and less skilled people if even.
And this will happen on every layer of your stack.
With tx to an LLM, this layer will close the gap to full or borderline full automatisation.
Your Ticketsystem will have agent support out of the box. Your incident system too. Your UI for SEO, SEO optimization, article creation etc. every single on of them wijll be there and consume tokens.
Future invesment from 10 people in your l1 support might be 5 + token budget. Your SEO optimizer collegues? 1 Person + token budget instead of a team.
Business doesn't think that deterministic to begin with. If customer support is faster and KPIs are fine, they will not care if customers get non deterministic customer support.
And lets be fair: just because you hired someone doing SEO, doesn't give ou ANY understanding how good of a job they do.
You will need to prove in the future that you can do it better than the agent. You will win at the beginning and fail later. The KPIs will show it and that person will get replaced.
Anthropic is selling $3 for $1, of course the growth is fast.
“Robbery, Italian Style”
https://www.youtube.com/watch?v=vktTgOr4M1s
(it has a wiki page https://en.wikipedia.org/wiki/Ograblenie_po... )
Marcello Mastroianni is performing a bank heist, it goes well - but in the end he has to return all the money he borrowed. And that's where he is left with nothing...
So what that means is that if the demand exists for any AI data centre, it's a solid business to run one — they cost maybe $50-60B to build out, they cost maybe $10B per year to run. You get your money back fast if the demand is there? And you can project being able to replace with better GPUs in the future without having to borrow for all of it.
But it is extremely difficult to believe that Anthropic is not projecting that all of its customers will ultimately use their service to the extent that their outlier customers do right now. They probably won't be projecting that their customers will find the need or ways to use them less.
And also, "no customer is more than 10% of our revenue" means that one customer is. If that customer is Microsoft (can it be? I honestly don't know) then things get tricky, because Microsoft clearly want an unmetered, on-device AI future.
These companies are rent-seeking landlords for the providers themselves. Which model they serve has no fundamental bearing on the business, the cost of the rent they can charge does.
Will anthropics models continue to be significantly better than openweight models? Even if their latest model is, Will the open weights gobble up all of the demand for lower models? (I.e. will anthropic be able to recoup training cost while their model is the best, will they make enough revenue on the long tail or will it get crushed by the open weights)
I'm not saying I think thats what is going to happen, but it really isn't a stretch to think that this keeps running given that it hasn't really hit anywhere near as broad as we think it has (being HN community based).
Our claude bills are rivaling our datadog bills and might get to the size of our AWS bills. It's pretty crazy.
Does that have to be true? I thought IPOs were about selling the company.
Never mind the fact that companies do not think and this is like saying "My lemonade stand thinks it's going to have good sales this quarter."
First to fall is the software that has basically been abandoned but industry leading and has sticky customers. We're replacing those right now. Our version is more accurate, ten times faster, and a hundred times cheaper.
You really have to be insane to think that an AI company will ever reach $500B in revenue. That's almost the same as (2025) Amazon, Walmart, etc., companies with a MUCH wider market.
It is MORE than Apple, it's 5 times more than NVidia even. There is absolutely no software seller that is even remotely close to $500B in revenue.
Absolutely ridiculous.
There are two questions. Will the value keep scaling? And how much of that value chain will anthropic hold on to? Right now software development is being eaten alive by LLMs. Will that happen to other industries too? And in the long run, what do the models we run look like? Are people going to end up self hosing opensource models themselves, or are we going to collectively hitch our wagons to frontier models provided by anthropic and their competitors?
If ai keeps getting better - which I think it will - and if anthropic can corner a large part of the market in an enduring way - which seems less likely to me - then yes. I can easily imagine them becoming a huge company.
Like Netflix, Comcast, T-Mobile, etc, etc. There are millions of subscription services in the world. They all "subsidize" higher-usage users with plans for lower-usage users using a fixed price plan and rate limit. The profit comes from lots of users who don't hit their cap. The higher priced plans are actually more profitable.
This is called a tiered usage-based subscription business model.
They might also be in a Uber style subscription strategy. Make something super duper cheap, at great cost to yourself, so that you can grow a massive user base. Use that user base to get investments to keep burning money. Once you've got enough competitors out-priced long enough, jack up prices and enshittify like CRAZY to recoup the cost.
They cannot follow the Uber model because of scale. You could keep getting tens/hundreds of millions from investors, back when there was lots of cash floating around. But you can't keep getting hundreds of billions of dollars. There are only so many dollars. The world is tapped out of private capital. That's why they want an IPO - so retirement and pension funds are forced to buy their shit stock, and feed them more money. But again there is only so many dollars.
Anyway it does not matter. Subscriptions are the most effective way to extract wealth from customers. This has been established so clearly that every business in America now has a subscription. Your fridge, TV, car, hell even car washes, now have subscriptions. Not because they didn't make money from you before, but because it's the ideal way to make even more money off you. There is no reason not to use subscriptions. No AI company in their right mind would end them.
The question of how much value you get from the subscription is different. They have already adjusted both price and rate limit a dozen times. They will keep adjusting it... but the subscriptions are here to stay.
The total amount spent on salaries in he US alone is over 12T dollars.
Assuming AI becomes ever more useful and is widely adopted 500B in revenue is not that crazy.
Also another aspect: think of the most highly paid workers right now in the US and the companies they work for. Assume that normal people no longer have jobs due to AI. How are these companies going to continue making revenue to pay Anthropic? Anthropic might get a quick buck but a long term growth business is not macro-economically possible for frontier AI businesses.
That's quite a leap to assume there's a ton of money to be made with the casual users sending perhaps a couple of messages a day to real an email and draft a response.
If you are spending 50 - 60 grand a year on a paralegal will you balk at another 5 or 10 that will allow them to do double the work?
The problem with the "double the work" narrative is it ignores the second order effect. You can do twice the work, but so does everyone else. You doubled the supply, was there a massive pent-up demand for lawyers or accountants at the current price point?
OpenAI, Google, the many Chinese labs all have products and several of them likely have better financial positions. At the moment Anthropic have one of the best models but it needs to stay sufficiently above competition for them to capture a large share of the market.
At the same time this year we've seen a move for model diversification as cheaper models become "good enough". If this IPO was 6 months ago I think it might have gone better for them.
The winner take all network effects are probably not there in this case, but still if Anthropic is a large player and has 20-30% of the market they could have a revenue in the hundreds of billions.
Double the size of the market to include most of the rest of the world and the 500B is suddenly under 20% market share.
The tech companies will just parrot "UBI" at that point, but given how adverse they are to being taxed, how UBI would be financed, is an unanswered question.
What does good enough look like for various use cases, when do we hit it, and can these companies guard that territory?
You're also assuming that basically the entirety of the market would go to Anthropic, rather than other providers, which makes no sense.
You are vastly over-estimating the market for AI. The extreme majority of people would never pay a dime for it.
> Employers are reducing AI spending, not increasing it.
n=1 but at $company we are already told to stop tokenmaxing and find cost savings for ai to the point of degrading user facing features (no loading llm generated content by default etc)
from here it looks like prices have to come down or demand will stall...
* Amazon is a logistics company * Walmart is a logistics and retail company * Apple is a hardware company (that uses software to set their hardware apart) * NVidia is a hardware company
The closest you get is Microsoft, at 281B in revenue. Some might argue Google/Alphabet, which is in the same area, but they sell Ads, not software.
Anthropic's expectations is that they will be able to fast-track their revenue generation to almost twice what Microsoft does today. In less than a few years.
Yes exactly, that's why I listed them. It's much easier for them to capture a higher revenue compared to software companies, and OP (and Amodei) want to make us believe an AI company could ever reach that revenue.
But.
They may peak at 500B before this bubble bursts, simply because other major industry players are betting on AI as a means to get folks to believe in their own stock.
It’s belief all the way down, until you get to the point where “the market” starts asking where the return on investment is, and why they haven’t seen it yet. By that time, the investors have gotten their return and cashed out, and it’s the pension funds and the retail traders that are holding the bag.
And of course, at some point AI’s promise will be delivered in a form or fashion as yet unknown to us, but that is still years away, and until folks recognize that LLMs as they exist to day are not the path to that, they’ll continue to believe in this valuation.
What you are saying is the same thing that has been said about anyone trying to do anything massive and at scale. Can't be done - thats ridiculous - you are out of your mind.
That they are IPOing tells me that the massive amount of funding they need is no longer available privately and that this is their 1 opportunity to get the funds they think they need to get there.
Biggest risk is the growth stalls as everyone moves to open weight models. They certainly like telling stories.
They literally said revenue growth rate "may not be sustainable" and "may decrease".
So get ready for the hurt.
Heads I win, tails we all lose. GFC Part II.
This is a "utility" mindset. If you bet that Anthropic is just like a monthly electric bill, then it's within the realm of possibility.
For the record - I don't think that's gonna happen.
But on the flip side I don't think aiming for 1M businesses spending $40k a month in token in average is going to be more viable. And I'm not sure having 1000 corporations spending $40M a month is a option either.