Posted by simonw 5/27/2026
In hype-driven markets, you cannot be certain of that.
Let's take a view that the author is right: coding agents and their associated harnesses were the inflection point for some degree of profitability and widespread consumption, and that these tools are now yet another SaaS subscription or API bucket expense to bake into every single developer (or developer-adjacent) in the organization alongside your collab suite, HR seat, CRM seat, design seat, etc. To be fair I honestly think that's a safe assumption to make for highly technical firms whose image is derived from remaining on the cutting edge of things.
That begs the following questions, which we won't know until IPOs start happening:
* Are subscriptions profitable, or just API consumption?
* What's the run rate when we just consider subscription-based usage like Claude Code and Codex? What about API calls?
* Is there any profitable pathway forward at which enterprises can get unlimited usage but at fixed rates via subscription?
* What does customer churn look like for subscription users versus API users?
We also have a number of questions for customers that I suspect we'll start seeing receipts for in the coming months, at least from the early adopters:
* What was the net gain (loss) from leveraging coding agents?
* What's the cost of a developer with or without access to a coding agent + harness? Is it cheaper to hire an outsourced worker with a coding agent subscription, or a domestic worker without one?
* At what point does further AI spend result in diminishing returns, i.e. where's the 'sweet spot' for spend?
* Did AI boost actual revenue and outcomes, or did it just gamify KPIs?
* What roles or work did AI actually replace, versus merely displace during the hype cycle?
Not to mention the questions regarding the technology itself:
* Will we develop the means to run foundational/frontier models at edge using less resources through some existing (e.g. distillation) or new technology, thus cutting off the profit centers of these firms?
* When the market mismatch between supply and demand is resolved, won't it be more affordable for consumers and companies to operate their own AI infrastructure rather than support further centralized buildouts?
* Will coding agents improve to the point of being able to bootstrap and self-orchestrate on edge/consumer hardware without substantial technical expertise, or at least improve to the point that traditional IT teams can securely operate them internally without an expensive subscription or API token bucket?
All of these will influence the long tail of this bubble, because it is a bubble at this point. Even if these companies are indeed profitable thanks to the coding agent inflection point, there's still so many unanswered questions about utility beyond coding that it's impossible to extrapolate a future. If coding agents are indeed the extent of utility for profitability, then there's no possible way these entities will recoup the investment already sunk into their infrastructure buildouts. Even if more profitable uses are discovered, does this offset or replace the firms disappearing due to AI speculation and their associated contributions to the economy as a whole (RE: the consumer compute industry at present, higher energy costs due to datacenter builds, opportunity cost from harms to local infrastructure from haphazard builds, etc)? Should these firms indeed be runaway successes and immensely profitable to the point of paying off their investors and growing the larger economy, does this end up stifling innovation in a world where most new ideas are fed into LLMs for R&D that are then controlled by only a handful of companies and immensely wealthy people, via systems that are easily surveilled and stolen from without recourse?
So many, many questions yet to be answered. Betting the farm because of coding agents is one hell of a gamble.
No, its more like their own leak to WSJ and according to Ed Zitron -> seems to be heavily engineered via non-GAAP practices such as counting potential, but not realised revenue as actual revenue - the stuff for which I would be arrested if I did it at my company.
Also it appears according to Ed's analysis - strangely they seem to be projecting only that one quarter as profitable - potentially to calm the investors ahead of the IPO. Investor fraud anyone?
https://www.reuters.com/commentary/breakingviews/anthropic-g...
If you've ever been at a startup, this is exactly what it looks like when you go from not having product-market fit to having it (though with a few extra zeros on the end compared to most).
Hell, say it did, how would you possibly know?
Feb 12th 2026: https://www.anthropic.com/news/anthropic-raises-30-billion-s... - "Today, our run-rate revenue is $14 billion, with this figure growing over 10x annually in each of those past three years."
Apr 6th 2026: https://www.anthropic.com/news/google-broadcom-partnership-c... - "Demand from Claude customers has accelerated in 2026. Our run-rate revenue has now surpassed $30 billion—up from approximately $9 billion at the end of 2025."
All three of those are official releases from Anthropic. You can choose not to believe the if you like, but since they plan to IPO this year it's in their interest not to get caught lying to potential investors.
They've also signed a deal for billions in compute with xAI for april-may so they're certainly using that to fake billions in revenue using non-GAAP bullshit. It just seems a tad more likely than them legitimately increasing actual revenue by 233% in four months out of the blue.
Do you know what revenue recognition is? Do you know what accrual accounting is? Do you know of the phenomenon that is 'managed earnings'?
The only true objective number in finance is cash flows.
Maybe, maybe not. We haven't seen that S-1 yet. All we have is the 5B in lifetime so far. PLUS - revenue quadrupled or not, it only matters if their costs did not expand at the same rate or more. Revenue is not profit.
Revenue is not profit yet the discussion in this particular thread is about revenue.
Ever heard of Enron, Theranos, SBX ? They were all hiding in plain sight - who could've thought they were frauds?
No, at this level of capital involved, and so much opacity around the company financials, it's a perfectly reasonable assumption.
Please don't forget that Ed's entire brand identity is now 1:1 with exposing "AI" as a giant, unmitigated failure.
That's a very specific flow chart to hook your caboose to when none of this is even remotely close to endgame.
There will be big parts of what he says are true once the rubble settles but it will not be anywhere near what he is predicting. How that will shape out may not be great for the average person, what money shuffling tricks will be used? But it won't be a total wreck.
Honestly, I think it's very short-sighted to assume that all of this will be seen as any kind of wreck in the long term.
Normies are still catching up and reacting to chat-based LLMs.
HN types are further ahead of the curve, but still catching up and reacting to agentic coding and design workflows.
What often gets completely ignored is that entirely new modalities for how the underlying tech can be applied will continue to be demonstrated, and those will once again cause new ripples of excitement and disgust.
There are companies building world models and systems for protein discovery. Comparatively speaking, these approaches are barely in the zeitgeist today.
Deciding that we already have the data points we need to extrapolate how all of this plays out is like someone in 1974 deciding that microprocessors are just for accounting and inventory. Don't be that someone.
This stuff is here to stay but I'm not sure how many of the current front runners will be able to stay solvent if they cannot turn these things in to massive money spinners. Revenue is fine-ish but spending is out of control. I see the debt in hundred of billions of dollars and start to wonder "Who is going to pay for this?" and "Will the people be willing to pay that much?". It just all feels forced rather than organic growth.
This is why I think Google may end up being one of the leaders in this field. They have their custom TPU's that seem to be fairly efficient at these tasks, they are slowly but surely improving their training and inference tech using their massive data set and most importantly, other parts of the business can subsidize this stuff for a decade if needed until it is genuinely profitable.
I am not against the industry but I do worry that many are rushing in with no means of genuine sustainability other than jump out for a golden parachute and let someone else clean up the mess.
I do hope I am wrong.
No question that there are some players who need a good weekend in Vegas to make it across the chasm of sorrow.
It could happen, I hope it does.
> According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns.
The load-bearing detail here is if that means $2,000 of internal server+electricity costs, or $2,000 if they were to charge at their API pricing instead of the subscription cost.
The latter is how I understand these things to work right now. If it's the former then yeah, Anthropic are losing a TON of money on those subscriptions.
Also, as the costs of running this stuff come down, the incentive to rent models goes down with them. Running local models has the benefit that you get to keep your data local, you can tune them to do what you like, and you're not subject to model or price changes down the road. This makes self hosting appealing both to individuals and companies. Currently, the barrier is in needing significant resources to run the models, but companies are already increasingly doing that with open models. And local inference that regular people can run is becoming a possibility as well.
While I'm sure there's always going to be a market for renting out models as a service, it may shrink significantly as the costs continue to come down.
It's a funny metric considering Depreciation is a huge cost for them.
"We are profitable when we don't count our expenses"
Those GPUs are very expensive.
Inference is expensive because a GPU can only process a certain amount of requests in a given timeframe. Remember that Anthropic is constrained in compute.
If they are constrained, it means that those GPUs are not idle. If they have more customers, they will need more GPUs.
If they have to play silly games using EBITDA to be "profitable", then it means that they need to ramp up prices a lot more than they already did.
Which is why in these discussions I always say that inference is also extremely expensive. Too many people like to pretend without any evidence that inference is cheap.
Language models don't wear out the same way; upgrading is a choice.
You can "just not update an LLM" in theory. But if your competition updates LLMs, and gets more capable, more efficient LLMs, and you don't? They get more capable "expensive tiers", and cheaper "cheap tiers" of LLMs. What are you going to do then? Bleed userbase and die?
The move to buy up ram is straight out of a industrial organisation textbook.
Like, I understand the reasonable arguments against (I even agree with a few), but it's clear that some people have fully inserted their head into the sand and just don't want to believe any of this could be true. Which will be harsh, since I think getting hit with this train all at once in the future is going to be a rougher ride than a slower coming-to-terms-with, even if the result is one we're unhappy with.
I understand the motivations for the labs to lie, but what do you think mine is?
Ye- yes? It's addition by 1.
Back in 2024 their CEO claimed training costs would rise to $10-100B in the next years.
https://www.tomshardware.com/tech-industry/artificial-intell...
I assume this is the quote you're referring to from Davos?
"I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it. I do the things around it… we might be six to twelve months away from when the model is doing most, maybe all of what SWEs do end to end."
that was in Jan, he said "might" and he said 6-12 months. Yes! Let's hold him accountable for saying reasonable things!
Indeed. That's why serious people are very careful, even if they are not running a company supposedly worth 1T USD
> He is forced to do these predictions to know how much compute to buy in advance
Ah well, that explains it. For my companies next quarter, I'll just pull some random numbers out of my ass so we can make plans with material impact to company business based on that.
10x revenue growth per year, even more this year...his predictions about when agents will claim SWE e2e work are his speculations, relevant because people care about what he thinks as he is closer than anyone to the leading edge of the technology. It's also important for him to be as accurate as he can about this because he has to put his money where his mouth is. He has to sign the right amount of compute otherwise he screws himself. He got it wrong in the opposite direction that you're implying, so at this point it sounds like you are more interested in your axe to grind than the truth on the ground.
You think enterprises are adopting CC because they think "oh this will replace my SWEs I can fire them"? That's not happening at major companies. They buy CC because it's useful and the writing is so clearly on the wall in so many data points that to suggest otherwise is a bit silly at this point.
> For my companies next quarter, I'll just pull some random numbers out of my ass so we can make plans with material impact to company business based on that.
You, as a leader of a company, don't have to make predictions? Don't have to make bets about what the best thing for you to do next year? That must be incredibly nice.
Amodei and everyone else need to plan compute and plan their products and roadmap. You want him to....not do that?
To the stunning tune of 5B in the lifetime .
> You think enterprises are adopting CC because they think "oh this will replace my SWEs I can fire them"?
Yeah, that's actually Darios main talking point
> They buy CC because it's useful and the writing is so clearly on the wall in so many data points that to suggest otherwise is a bit silly at this point
Right, really sound arguments - writing is "clearly on the wall" and there are "so many data points". I'd be keen to use those immediately, but I am kind of missing the key of the "many data points" - namely, what did you build with it and how much ARR is it generating?
> You, as a leader of a company, don't have to make predictions
I have to make predictions, but not confabulations, lies and idiocies.
> Amodei and everyone else need to plan compute
FOR WHAT? Again, what was built with their shitty product in various companies and how much ARR did it generate? Uber seems to get no value out of it.
> Right, really sound arguments - writing is "clearly on the wall" and there are "so many data points".
Thank you for recognizing this. Don’t read Ed and think you understand anything about AI is all I’ll say. Read epoch capability index paper and look at the dashboard chart or the METR time horizon chart and methodology and then return with what I imagine from historical comments will be another ferocious and impressive act of mental gymnastics.
> I have to make predictions, but not confabulations, lies and idiocies.
Idk you’ve been misquoting and aggressively against addressing any facts you are presented with and yet bring no facts of your own (hint: if you know what you’re talking about typically you can calmly discuss with actual facts). That feels pretty similar to confabulations, I won’t say idiocy I’m sure you are not an idiot but you seem to have a lot in common with your caricatures of tech CEOs.
> FOR WHAT?
Their product.
A sworn affidavit by the Anthropic CFO from Dec. 2025 is what you need to look up mate.
So, he's closer to correct than not.
That said, your recollection is also flawed. It was in mid-March, and here's the relevant quotes:
>I think we’ll be there in three to six months—where AI is writing 90 percent of the code. And then in twelve months, we may be in a world where AI is writing essentially all of the code.
[...]
>But the programmer still needs to specify, you know, what are—what are the conditions of what you’re doing, what—you know, what is the overall app you’re trying to make, what’s the overall design decision? How do we collaborate with other code that’s been written? You know, how do we have some common sense on whether this is a secure design or an insecure design?
[...]
>So as long as there are these small pieces that a programmer, a human programmer, needs to do, the AI isn’t good at, I think human productivity will actually be enhanced. But on the other hand, I think that eventually all those little islands will get picked off by AI systems.
With another 3-4 months left on the clock, his prediction seems remarkably on point for at least certain organizations and domains.
I welcome you to also hold yourself accountable in the coming months if this trend continues. ;)
That probably explains why their uptime and reliability are so bad.
I agree that most of the things are written by AI but writting code was never the bottleneck in big tech.
That said, I generally agree that you're correct: writing code in many ways has not been the biggest bottleneck. However, by removing much of that writing, it frees up engineers to work on the uniquely human things that are larger bottlenecks.
I had a few comments in a thread here touching on where I think most of the value has come from for us (which is largely search/understanding of our dependencies and making away team work far more viable, which aids with cutting through bureaucracy and the tendency for teams to push back on work): https://news.ycombinator.com/item?id=48298731
Please engage in good faith. I commented that humans are the final step of the review process.
My company did not swallow hundreds of billions in shady investment deals and is not publicly traded. We work with real money, and the revenue on our books is the revenue that is actually booked, not fake revenue we plan in 2 years time to maybe happen. So no, I am not going to hold myself accountable. But people who work with other people's money should be absolutely held accountable when their wild imaginations don't come true, repeatedly, quarter after quarter, year after year!
You criticized a very specific (and fake/misquoted) prediction, ignored the correction, and are now criticizing vague hand-wavey "predictions" that you have left unspecified.
Can you please stop with the angry/ranty replies and actually have a real conversation grounded in actual facts?
Now, having said all of the above...I'll also point out that these are predictions, not promises/guarantees. These people are being asked to forecast and are doing so. I hardly think they should be held responsible for not being literal oracles, but even so--please, at least quote them correctly/at all.
In short: be better than the hallucinations you're seen to call out from the models.
So, unsourced vibes from a shady guy whose entire empire is built on being against AI?
I genuinely don't know how folks can continuously buy into anything he has to say after that Wired piece. The credibility there is seriously lacking.
Please, continue to be skeptical of the labs. But people need to stop talking about this dude as if he's the Holy Grail of the anti-AI movement. It's going to blow up in y'alls faces.
I think it's telling that most critics don't address his actual points, but instead his credibility because he's a "hater".
That said, I really mean it when I say that I don't actually think Ed is a good choice for the anti-AI movement. I think an actual opposition is useful, but he ain't it.
I really recommend you read the Wired profile if you haven't yet and form your own opinion: https://www.wired.com/story/ai-pr-ed-zitron-profile/
I guess like, I don't know about an anti-ai "movement", personally I like AI-the-product but I think AI-the-industry is extremely sketchy and has motivations that I think are awful. As with all technology revolutions, my issue is more with the people than the technology itself.
I don't really like how this whole thing has become "pro ai" vs "anti ai" though. For me, I'm just really irritated when I use AI every day, I'm a professional software developer, and all my experiences with it do not match the (very annoying) hype. I kind of wish we could just go back to talking about software engineering and if people like vibe coding, great, go do that and stop all the annoying think pieces that just give CEO's even worse AI psychosis.
The error you call out is hardly “serious”, as the whole argument is uninteresting. It is a stupid indefensible error but the argument about revenue being 20% or 30% lower than reported isn’t that central to his overall thesis. Stuff that matters is inference cost, profitability, actual training costs.
Actually he provides sources when he analyses stuff and imho much better than the usual corporate "Sam Altman says we should ask ChatGPT how to raise babies" crap. Also, I don't know many 'shady' guys who have built entire "empires", nor does he seem to actually have an empire. Usually being shady means you are kind of unknown and all. I am not glorifying Ed, don't even know him personally. I am not even impressed with his writing style much to be honest. But he brings important facts and information to light, which otherwise would have been lost in the cacophony of corporate media light treatment of these con-men. Holy Grail? Blowing up in our faces? WTF are you talking about?
You said it was likely an internal leak to the WSJ "according to Ed Zitron". Did Ed have a source for that, or was it just vibes?
^ Apologies, the above read to me like you were saying that Ed himself was claiming that Anthropic leaked to the WSJ.
Agreed. But its only a great deal because it is heavily subsidized, as you said yourself. Enjoy while it lasts, but in my book, product-market fit means something along the lines of "product which enjoys a loyal customer base, sold at a price perceived fair by the customers, and generating profit. How many of these does your definition of product-market fit hit here?