The company was founded under the principle they will violate laws to acquire training material
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.
AI is just a compute commodity with a value add.
So that leaves...
I guess the interesting thing would be to look at actual company assets/value rather than revenue; revenue already in itself is a derivative. I.e., contrast their revenue against expenses. Last I heard those numbers are deep red, aren't they?
the second part is not sexy - but we see all a.i labs having fde arms e.g ode, microsoft frontier company etc but then again that's the part you can't hype.
You can go very far in life with a thing that isn't futzy
As it stands, even the people closest to this technology have barely figured out how to use it effectively.
Not saying that applies to AI, but it certainly applies sometimes.
I don’t think this era is going to end with us all unemployed, but I do plan to be retired before the next cycle.
Keep in mind this growth has mostly been sustained by low-level business automations, coding, and chatbots. With the adoption of products like personal agents, e.g., Meta's Muse, I am pretty optimistic there will be demand and growth around this level will be sustained for a few quarters. Doubtful we will ever see another ~150% growth quarter.
So that’s ignoring labor and perhaps facilities? That’s like saying we’ve hit net positive fusion by only considering the power produced by the laser and ignoring the 80% power losses in the feed-in.
Most likely the combined revenue growth continued just as fast, but OpenAI took a lot of market share with a model that's ~as good.
If true, then the current market sentiment is too pessimistic because demand for AI (and chips) has continued growing.
- hmmm, why 30 days? Make it 60 days and I'll take the penny.
The effort needed to double everyday, or month is realistic at first. But then you can't just keep doubling without swallowing the universe along the way.
~5 quadrillion after 60
Discussed here a few weeks ago: https://news.ycombinator.com/item?id=49997161
1) customer concentration risk 2) when those customers use a diverse set of models - so too will the rest - they’re not idiots who want to pay more than they should
Aka % growth rate decreasing sharply and eventually flattening out. Within 2-3 quarters.
Understood?
But perhaps for different reasons than Microsoft, Meta, and maybe Adobe leadership are now. See, if they are all in on Claude how do they sue Anthropic for assisting in copyright violations and IP theft? The services would be cut immediately.
Well when they don't need them anymore..
This is why Anthropic is heaping on more and more model restrictions running up to IPO. I believe it's a smokescreen for the IP risk they outlined in their IPO filing..
EDIT: okay, not months, weeks. Feels like forever.
Especially with this latest crop of models, they seem to be pretty damn competent in classic engineering rather than just software engineering.
When mid or high level tech leadership pushes from top-down new things, that look dumb to the engineers, the leadership is either stupid or there are hidden incentives that regular people don't see. In my experience mid level usually matches with the former, and top level with the latter.
I wonder if Anthropic will "see the light" and stop trying to burn a bunch of money and sell a thing they already have (which is decently useful). Very ready to be proven wrong in any direction
Subscriptions are a growing minority of revenue.
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.
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.
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.
Anthropic’s market share is pretty high and there is not enough total currently addressed market for growth at the expense of a competitor to make a big long-term difference.
> given a sufficiently articulated prompt
Are these two statements not at odds with each other? You can always say a prompt is not sufficiently articulated if you need to add additional handholding and vice versa, thereby invalidating any meaning of measurement.
But sol 6.1 is at least in the conversation as being as good and is much cheaper. I farm out bunches of my tasks to Luna and Haiku these days and have a working prototype of a glm 5.3 workflow at work that shows it being competitive.
The price pressure is very real and if you aren’t on a subscription then there is virtually no reason to pay the Anthropic premium
Not due to capability per se, but writing style and polish is much worse.
It will leave really bad comments in the code, commit messages etc without strong explicit steering. Though I've basically moved away from having the model write any documentation at all.
It also tends to talk very circuitously and referencing things implicitly instead of just being direct and clear.
Certainly it's more capable in debugging, but I much prefer Astra over Sol now.
Of course, Astra has its own problems of code golfing everything. But at least it's very concise and direct
There's definitely unique things that so far only Opus is capable of doing.
What does average, non-computer Joe or Jane think when the next big model comes out? "My emails are going to be so much better now, I better stay subscribed"? "I can't wait to see how well it answers my questions"?
Some of them do require frontier models but many can do with localish models. Some of these processes (in our industry) are also not really limited by software at all but by the speed heavy machinery can be operated and/or manufactured and/or transported.
I'm certain LLMs are going to continue rapid growth even there but it may be an ideal field for have lesser models work for longer if it saves money in the end.
Of course who knows in the end. I'm certain there is a high chance of this being entirely wrong
Actually I see very varying results in my work.
Sometimes I can let an AI refactor an entire codebase and then (in the same codebase) it generates absolute shitcode.
Then why all the rush to IPO then?
The strongest chinese models from DeepSeek, Kimi and z.ai are already good enough to run for hours on most coding tasks cheaply.
I think the first implying the second might be a bit of a stretch. At some point, companies are going to start looking at their AI spending and start negotiating deals. OpenAI is right behind them and Anthropic is around 8x more expensive (Sol 6.1 vs Opus 5.5 based on Artificial Intelligence's cost per task; cost per token is a somewhat useless metric when different models use vastly different numbers of tokens for the same thing). Chinese models like Deepseek 4.1 Flash are cheap, can be self-hosted, and while certainly behind Claude Opus 5.5, are still good.
Lots of companies buy "inferior" products like Microsoft Teams. Over the next 5 years, it seems likely that many models will get really good to the point that people often won't be able to tell any difference. Even if something isn't "definitively" better, companies might say "our engineers can't tell the difference and we're cutting our costs by 85%."
It's not absurd to think that a company would use its own harness or an open source harness and/or put a proxy in to route requests variably. For two months, they have engineers using different models unbeknownst to them and they measure things like how often the engineers have to correct or redirect the model. At the end of two months, they might find little difference in performance and switch to a much lower cost model.
It's not like it'd be crazy hard to analyze workers frustration as they did their jobs and figure out if one model was actually better. They could log all the conversations and it's not like AI can't easily determine things like "no, that's completely wrong" as a phrase showing that the agent was really off base.
I think a big part of the next 5-10 years is going to be about cost/efficiency.
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.
Feature flags, staggered rollouts, quick rollbacks, smoke testing, etc.
Sure, if you were hand managing every rollout up until now, and relied primarily on unit tests, you’re in for a world of hurt when you give a junior engineer Claude code and let em rip.
But if you have more advanced systems to measure and maintain overall service health, and codify your business logic invariants, you can really start flying these days as long as you’re all aligned on what to build and the potential impact.
Idk how many people need to hear this, but not everything 'is computa',no matter what Trump says.
Much of the worlds problems are not verifiable with a compiler that can be trained or brute forced against.
Llms are not as useful as ya'll think they are, not everything exists in a web browser, which is why Anthropic et al have saturated their TAM and are now frantically hiring sales people to convince managers in other verticles.
Turns out most people dont need or want AI.
A hammer is fantastic for driving nails, but it's not really all that good at washing a dog.
The idea is: if you have a substantive point, make it thoughtfully; if not, please don't comment until you do.
I think the answer, to a reasonable approximation, is “everyone”. Outside software engineering most people aren’t using it for anything useful, and I keep encountering technical people including programmers who don’t use it either, which never fails to surprise me.
One of my projects is a transportation management system (TMS) and of course I have added a lot of features to it including a very capable chat-based UI that can automate a lot of tedious data entry and other tasks for dispatchers and accounting and so on. It gets used but really not very much. I’m starting to realize that I’ve probably taken updates as far as they can go in terms of the ability of people to absorb change. Most people just carry on doing things the way they always have.
We had one person on the admin team who started using Cowork to automate all kinds of stuff. She loved it. Then she left. It hasn’t caught on with anyone else.
This is not to say that things won’t change but I think it will take a while.
As an example outside of software engineering, a lot of people in marketing roles have found agents to be incredibly useful for particular tasks. I've talked to multiple people who wouldn't standardly be classified as "technical hires" who are using Claude to manage things like digital advertising. It's valuable here because managing campaigns on a platform like Google or LinkedIn involves a ton of granular tweaking and analysis. Every performance marketer makes tradeoffs around what they're realistically able to spend time doing, and what makes the biggest impact. So if you have 250 campaigns that you manage solo, you likely don't have the time to really manually optimize the bidding and budget for each campaign every couple of days based on new data. You can only try so many new angles, because creating new media is time consuming. You don't have time to construct custom experiences on-page for every particular campaign. There's a whole industry of tools that turn a tidy profit automating parts of this for this reason. And agents are really good at it, because a lot of it is just applying the same analysis over and over again.
But for a lot of startups, increasing traffic from ads 20% while decreasing spend is a good outcome, but it doesn't actually turn into revenue. The product itself might just be limited, and already being discovered by the narrow segment of potential users who find it valuable enough to pay. Or the sales process might be kind of busted, meaning the team will really only close a certain volume of new deals in a given month, no matter how much inbound they have. Or some combination of all of the above.
If it's just one marketer using an agent to optimize their advertising, there's not a lot they can do about the other constraints. And so what you end up with is one employee who feels really productive and sings the praises of Claude, but not a meaningful lift in the higher level outcomes you care about, and the analysis gets messy. In an ideal world, everyone realizes where the other bottlenecks are, but that's a genuinely hard thing to do and people have complicating incentives.
I don't think this is a particularly new problem. It feels identical to any other situation where a department asks for a tool, loves it, and it doesn't really make a difference to the bottom line or make anyone else's life easier in noticeable ways.
Your machinery was not written elegantly. It was sloppily assembled via evolutionary pressures.
A gene gets duplicated, mutates, and suddenly you have new functionality and new fitness criteria.
Try something bad and you die without reproduction.
Our code isn't clean. There's redundancy and extra packing and slop all over the human body.
Embrace it. Stochastically hill climb.
Code is DNA. Evolve it, don't intelligently design it. Fit the curves of your environment quickly and use tests to cull bad behavior and the slopes of your domain.
I assume they’d hoped that the value of the spend would manifest itself and they could raise prices. The opposite happened and companies are instead cutting back.
It sounds to me like they chose a poor marketing and pricing strategy for a product that is very difficult to make cost effective.
Exactly, No-one and as predicted it was a fad. [0]
We are seeing the result of those subsidizations and discounts that Anthropic made this year and how much of this "tokenmaxxing" hype was used to make their revenue numbers look "good".
With the arrival of more cheaper models which are good enough, it doesn't make much sense to use Anthropic's models.
So don't be easily fooled by their revenue projections. Rather question if it is sustainable for them. (Spoiler, it isn't)
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