I think the first implying the second might be a bit of a stretch. At some point, companies are going to start looking at their AI spending and start negotiating deals. OpenAI is right behind them and Anthropic is around 8x more expensive (Sol 6.1 vs Opus 5.5 based on Artificial Intelligence's cost per task; cost per token is a somewhat useless metric when different models use vastly different numbers of tokens for the same thing). Chinese models like Deepseek 4.1 Flash are cheap, can be self-hosted, and while certainly behind Claude Opus 5.5, are still good.
Lots of companies buy "inferior" products like Microsoft Teams. Over the next 5 years, it seems likely that many models will get really good to the point that people often won't be able to tell any difference. Even if something isn't "definitively" better, companies might say "our engineers can't tell the difference and we're cutting our costs by 85%."
It's not absurd to think that a company would use its own harness or an open source harness and/or put a proxy in to route requests variably. For two months, they have engineers using different models unbeknownst to them and they measure things like how often the engineers have to correct or redirect the model. At the end of two months, they might find little difference in performance and switch to a much lower cost model.
It's not like it'd be crazy hard to analyze workers frustration as they did their jobs and figure out if one model was actually better. They could log all the conversations and it's not like AI can't easily determine things like "no, that's completely wrong" as a phrase showing that the agent was really off base.
I think a big part of the next 5-10 years is going to be about cost/efficiency.
1. Frontier models are so good that the token cost of an average task becomes miniscule, so users are incentivized to use frontier models because their sheer intelligence makes them cheaper to use.
2. There is a pareto optimal point where even if you spend more on tokens overall profits decrease because token costs rise faster than revenue does.
The "no lab has a clear lead" world leads to 2.
That's a great product idea!
There seems to be another magic ingredient for improving agents beyond model size & efficiency, and so far only OpenAI, Anthropic, and maybe Google know what it is.
I want AI and these companies to succeed. And I especially want new companies to succeed. But I want them to be valued far more realistically. Because if they have to be worth trillions, then there’s a real motivation to do what it takes to realize that valuation. And that’s how you birth a Zuckerberg.
I’m not rooting for them to fail. I’m rooting for the correction to happen sooner than later.
They are. That's the problem. The obligations that these companies are, on paper, supposed to meet require these valuations.
If you value Anthropic at $500b that's still a fuckload and vastly less than their obligations and a lot of people are going to say "well wait a minute how am I going to get paid".
The amount of spend going on, and planned, is insane and underpins a lot of this, and is why people are skeptical of the valuations. It has nothing to do with "Wanting" AI to fail and just a reasonable bet based on the math. For them to pay back what they have on their balance sheet entire continents will functionally be a spec in comparison, and that's really really fucking unlikely to happen.
Doubly so when you consider that there's a question of if they can be profitable to projected needed levels without fundamentally breaking some economics parts, and that this looks a lot like the overbuild of the internet during the dotcom bubble, which was also hideously overvalued at the time, for the time frame they were looking at, despite being THE literal world changing tech.
Walk me through this math. I’m seeing $900bn Capex over a decade. That’s $90bn required free cash flow a year, on average. If they can do 25% margins, that requires $360bn annual steady-state revenues. That doesn’t require continent-scale anything.
Not disputing that. Just saying you don't need continental-scale anything to hit these figures, you need annual revenues on the scale of what Walmart does in a quarter.
This is the assumption we disagree on. They're net negative by an order of magnitude on most of their current customers. I don't see them flipping to break even + 25%.
Further you're assuming they can turn that on today, when they're still very clearly not ready. Every year the timeline gets worse.
Take them to 2% margin with 8 years to go and you're looking at needing 4.5t annual.
This is normally where I'd say "but 2% feels extreme..." and hedge in the middle, but like...the current pricing isn't even close to 25% margin, it's not even close to break even.
From the S-1, in the first 3 quarters of 2026 their revenue was $33.2B and compute costs were $18.8B. Inference is highly profitable, they "just" need more of it.
Wha???
There are 13 companies in <<total>>, on the <<entire planet>>, that make more than that: https://companiesmarketcap.com/largest-companies-by-revenue/
Most of those needed decades to reach this point.
Anthropic has a near-clone in OpenAI (and vice versa). There are multiple smaller competitors, several much cheaper (especially the Chinese models).
Anthropic obviously needs continent-scale balance sheets, I would argue "multiple continent-scale balance sheets", since Google & co make their money by selling everywhere except for China.
Anthropic and OpenAI are never entering China on a massive scale. US relations with Europe are deteriorating and LLMs are <<massive> risks in an adversarial relation so it's not inconceivable Europe at some point starts marginalizing US tech companies, at least LLM labs. I imagine India will want to do something similar at some point.
Where exactly is Anthropic going to make all that steady revenue from, if it can't break out of coding and cybersecurity? Even worse, both those markets are super tech savvy so those cheap rivals are even more dangerous. One slip up the next 5+ years and Anthropic is history.
Would you describe UnitedHealth as a company that render "entire continents...a spec [sic] in comparison"? Sinopec or Petro China?
I'm not saying those growth requirements are obvious or even feasible. Just that they don't require some paradigm shift of global economics to achieve.
> Most of those needed decades to reach this point
How long did they take to reach Anthropic's current scale?
How does that make sense lol
"it owes a bunch of people money, so it should be worth more"
i don't...
"it owes a bunch of people money so the only way for it to not be insolvent is it must be worth X, and if we're talking lower than X this is already a problem"
It is to figure out what ia its correct price. Otherwise the financial system turns into a scam.
Not how valuations work!
Walmart made $187bn in sales last quarter of which $6.5bn became net income [1]. That supports $115bn in liabilities and a trillion-dollar market cap at 46x earnings [2].
If you want a more-extreme example, look at JPMorgan: Its $900bn valuation (14x earnings) [3] supports $4.6 trillion of liabilities and $5 trillion in assets [4].
Comparing valuation to liabilities to judge solvency is bunk on multiple levels.
[1] https://www.sec.gov/ix?doc=/Archives/edgar/data/0000104169/0...
[2] https://finance.yahoo.com/quote/WMT/
[3] https://finance.yahoo.com/quote/JPM/
[4] https://www.sec.gov/ix?doc=/Archives/edgar/data/0000019617/0...
On what metric? I just showed you JPMorgan has an equity value a quarter of its liabilities. JPMorgan is not insolvent.
Comparing liabilities to market cap doesn't tell you about solvency per se, it tells you about leverage. It tells you whether a solvency problem could be solved by selling equity. Not if there is a solvency problem.
To be fair though, JPMorgan (and banks more generally) are very, very different from Anthropic/other labs.
Requiring equity > liabilities means assets > 2* liabilities.
That is nice, but not at all required for financial health. Especially for infrastructure heavy companies where the debt is used primarily to fund infrastructure buildout.
And the AI companies want to redundancize white collar work. I wonder if there are transitive properties here.
> given a sufficiently articulated prompt
Are these two statements not at odds with each other? You can always say a prompt is not sufficiently articulated if you need to add additional handholding and vice versa, thereby invalidating any meaning of measurement.
then a PR that tends to only get minor feedback is produced
As in, many if not most reviewers have always been nothing more than approvers, i.e. people that click the approve button with minimal to zero review, maybe a "pro forma" comment so nobody has them show up in some statistic as "only clicks the approve button".
With the amount of AI generated PRs this will have tended to become more of a problem, not less.
TLDR, the coding output and automated reviewers have superseded humans at this point. No human is reading the PRs closely, anymore.
But sol 6.1 is at least in the conversation as being as good and is much cheaper. I farm out bunches of my tasks to Luna and Haiku these days and have a working prototype of a glm 5.3 workflow at work that shows it being competitive.
The price pressure is very real and if you aren’t on a subscription then there is virtually no reason to pay the Anthropic premium
Not due to capability per se, but writing style and polish is much worse.
It will leave really bad comments in the code, commit messages etc without strong explicit steering. Though I've basically moved away from having the model write any documentation at all.
It also tends to talk very circuitously and referencing things implicitly instead of just being direct and clear.
Certainly it's more capable in debugging, but I much prefer Astra over Sol now.
Of course, Astra has its own problems of code golfing everything. But at least it's very concise and direct
There's definitely unique things that so far only Opus is capable of doing.
I ping-pong between different agents all day long and don't necessarily agree that Anthropic is clearly in the lead on coding at this point.
Has anyone else seen this?
"I have a girlfriend for real she just goes to a different school"
What does average, non-computer Joe or Jane think when the next big model comes out? "My emails are going to be so much better now, I better stay subscribed"? "I can't wait to see how well it answers my questions"?
Some of them do require frontier models but many can do with localish models. Some of these processes (in our industry) are also not really limited by software at all but by the speed heavy machinery can be operated and/or manufactured and/or transported.
I'm certain LLMs are going to continue rapid growth even there but it may be an ideal field for have lesser models work for longer if it saves money in the end.
Of course who knows in the end. I'm certain there is a high chance of this being entirely wrong
Actually I see very varying results in my work.
Sometimes I can let an AI refactor an entire codebase and then (in the same codebase) it generates absolute shitcode.
Then why all the rush to IPO then?
The strongest chinese models from DeepSeek, Kimi and z.ai are already good enough to run for hours on most coding tasks cheaply.
For me, that's amazing, but maybe it explains these numbers and maybe it'll finally change when Fable 5.5 forces me back into high usage territory. If I even really need it's abilities as more than an advisor (words I didn't think I'd imagine myself saying back when Fable 5 was dropping).
The total market for code generation might just not be enough. The investments levels really demand getting rid of whole classes of professions. Not just 80% of programmers.
Anthropic’s market share is pretty high and there is not enough total currently addressed market for growth at the expense of a competitor to make a big long-term difference.
I wonder if more people using Haiku coming from more expensive models may have an impact in those metrics.