If true, it is a huge blow to Anthropic’s revenue stream. IIRC it was reported that the quarter of their revenue comes from just two clients and as the ex-Meta guy who left this July, I am convinced that Meta must be one of the two.
> The company's financial trajectory already shows how quickly that equation is changing. Anthropic's revenue run rate was about $9 billion at the end of 2025, according to the company, before rising to more than $47 billion by May. Anthropic has projected revenue of at least $10.9 billion for the second quarter of 2026, more than double the previous quarter, on track for its first quarterly operating profit of $559 million.
> The company has told a small group of shareholders that its adjusted operating income will be positive for the second consecutive quarter, according to multiple people with knowledge of the matter.
These are leaked and self-reported numbers, but no matter how much skepticism you pile on them it still looks likely that Anthropic in 2026 have had some of the fastest revenue growth of any company in history.
They may be self-reported numbers, but they're consistent across multiple reporting sources. If Anthropic are lying to their investors about these numbers they will be in very real trouble with the SEC come IPO time.
And even if they're using funky accounting tricks to exaggerate their profits and downplay some of their losses, I expect the numbers for 2026 will still be really impressive.
I'll check w/ archive.ph in a bit, thanks for the tip.
Is the hope for them that they'll outpace their run with a massive revenue growth? I believe that they have that, but numbers like a net loss of $46b in 2025[0] seem hard to overcome.
Re: self-reporting, I'd be inclined to think they're using non-GAAP practices to make it sound more favorable, but I am by no means an expert, so willing to believe I am wrong.
I'll read the article you sent; this is just my off-the-cuff thinking.
They claim to have been profitable in both Q2 and Q3 (under whatever their definition of profitability is).
I really don't think the 2025 figures are interesting at this point. Everything changed for them in 2026 - nobody was spending $1000/month/employee in 2025, there wasn't enough interesting token-heavy stuff to do with the models.
I think Anthropic might actually make it to profitability - they're earning more revenue than OpenAI and they've spent significantly less, too.
Doesnt matter if Microsoft and meta are pushing employees towards their own models today. What matters is that it's the economically sensible direction, and so it will happen if it's not today.
There is a conspiracy theory that the tokenmaxxing period was anthropic/openai ipo play. They do the tokenmaxxing to revenuemaxxing first, then time the ipo window to show they had the the huge growth to justify their price tag.
However Elon was able to ipo before them which took a lot liquidity of the market. Their financials are exposed. The market condition and sentiment now is in the gutter. It would be very interesting to see how these would pan out
That's my theory, which I've seen being echoed here a few times. Encourage increased usage, suddenly raise prices, capture the photo-finish in the short lapse between prices going up and usages adjusting, and present that goldilock point-in-time to the investors as the new normal.
I feel related to it. I switched companies at the end of July. First 4 weeks AI was giving me the feeling of freedom. No longer I need to understand tens thousand of lines of legacy codebases. Never onboarding was so easy. Just ask Claude and it tells me what happens here and how.
But after 2 months it starts to backfire me. I still know nothing. I have some understanding of the system design and core components but I have zero clue about how certain things are done under the hood. Because AI read code for me and code for me and I take it as my own understanding.
In last week I end up limiting my AI usage and forcing myself (it is really hard) to read and code at least a bit by myself to start having any idea about what is going on here.
It is very well studied that the entire concept of “nation”, “homeland” and thus patriotism in the modern way began to develop only at the late 19th century with the adoption of mass media and railways.
Hundreds of thousand of years before this people were loyal to their family (not always) and their community (village, city, craftsmanship, etc.).
If you ever travel to 18th century and ask any ordinary European are they proud to be, say, French or British, they will not get you.
I think we're using different definitions of patriotism. I looked it up, and it said "homeland" not "nation". Homeland generally means region. I think what the parent meant was nationalism.
Either way, I don't care about the 'nation' all that much (although constitutional free speech rights are very important - arguably a major factor of what's holding the world together at this point). But, I definitely care about where I grew up, the region, the homeland.
I am not a mathematician, but I can’t see how we are going to address the problem which we already see in coding:
Impossibility to independently validate all AI results
And in math it goes even worse. In coding code reviews are typically still the form of action you do within days. In math, historically, the lifecycle of proof is months if not years. Take as an example Millennium problems. They require at least two years of validity after publishing. Two years! In modern times with amount of output AI can produce, it feels like infinity.
We are inches close if not at the moment already when humans can’t reliable validate proofs and mathematics produced by AI. Then next research will be based on this AI-written-no-human-in-the-loop results. And we will end up in just few years in a world where novel and frontier problems will be articulated by AI and proven by AI based on AI results and humans will be incapable of understating the mere nature of the solution.
I don't think that's actually the real problem. Along with the progress in answering mathematical questions, recent progress on AI-powered autoformalisation has been astonishing. All the recent AI discoveries have been accompanied by Lean proofs.
And, yes: that doesn't absolutely guarantee correctness. The Lean kernel has had soundness bugs, and may have some still. But it's pretty strong evidence of correctness nevertheless.
The concern among mathematicians is not mainly that they doubt the correctness of any of these discoveries, but that human understanding may be devalued.
I am not that worried, but rather just observing. Humanity is about to enter the phase when we will be using things based on ideas no human ever properly understands. This thought … disturbing, somehow?
It is perfectly valid counterpoint to say that we already do it. We everyday use myriad of things, tools, and software we have 0 clue how it operates. But for us as humans it was reassuring that we know that at least there are a few other alive humans who know it, who create it and who can explain it.
>Humanity is about to enter the phase when we will be using things based on ideas no human ever properly understands. This thought … disturbing, somehow?
This is just normal though. We were building sophisticated bronze and steel tools long before any complex understanding of metallurgy or chemistry. Medicine is still the wild west.
I first wanted to say fire, even though it's a cliche, but then I thought that in antiquity we used like everything without anything that would qualify today as understanding. Also now we have a lot of stuff that we "know" it works based on complicated numerical simulation.
I think the most "understanding" we ever had was in the 40s-50s designing nuclear bombs with slide rules. It was the culture that produced the idea of psychohistory.
> Medicine is still the wild west.
Reminder that we have no idea how anesthesia works.
But one of the things AI also excels in is summarizing and can do so hierarchically. One of my favorite things to do with a concept I'm new at is "ELI5" then "explain like I'm a high school student" then "explain like a bright undergrad in XYZ" then "explain to a working professional in this domain". It's a lot of steps, but I've found it very effective (for me) to learn with -- and I've done something similar with code and math (although not math proofs -- I'm not a mathematician). But my point is that I think we can use AI to also teach us these proofs they're building in a way that I don't understand today about human proofs.
If we use AI well here we could actually understand math much better than we do now.
I actually think it's the opposite: Lean proofs and autoformalization make it very easy to announce proofs alongside proofs of the correctness of those proofs (Lean certificates). It's not an absolutely fool-proof combination (the Lean kernel could still contain bugs), but it does immediately attach a very substantial degree of credibility to the result.
And that I think is essential to why some of the world's leading mathematicians are taking this so hard. In a world where we "merely" have AI systems capable of superhuman informal reasoning, verification, correctness, and acceptance could still only be conferred or anointed by human mathematicians. But a world that combines superhuman informal reasoning with superhuman autoformalization is a fundamental shakeup in the institutional order.
The recent proof of Fermats Last Theorem is interesting: it is (iirc) 13 million lines of lean code. And type-checking takes 5 hours or so on a pretty beefy machine. I cannot independently verify the proof, and I have to take Anthropics word for it that it actually type-checks.
That seems like a red herring. Have you independently verified the human generated proof of FLT? Surely someone else will try to verify Anthropic's formalization on different hardware. Plus, it seems likely that FLT formalizations will improve / get shorter over time, requiring less compute. And computers (and type-checkers) will continue to get faster over time as well. So maybe in 5 years you could own a computer fast enough to verify a/the proof in say a week, instead of 5 hours.
It'd cost you $30-$50 on AWS for a clean Lean build or maybe $100-$150 for the full verification suite. You are, in fact, capable of independently verifying the proof yourself if you don't trust all the people who've already done so.
With formalized math, you only need to validate the problem statement (in theory, in practice agents have already managed to exploit Lean compiler bugs, but the incidence of those should decrease enough to be practically lusable for 'blind' validation of AI proofs in the foreseeable future).
Marketing it is or not, but misalignment at the moment crosses dangerous marks, and must be investigated ASAP. We are inches close to agents building their own message boards and self-hosting them on any server which they can hijack. If not there yet.
On this the same line of thoughts, so far we have 0 proof that all dark matter is _the same_. We observe gravity effects but IIRC they very little tell us about their own homogeneity. We very well could have a few parallel sectors, one of which is ours.
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