I think Anthropic and OpenAI have found product-market fit | Hacker News Reader

I think Anthropic and OpenAI have found product-market fit

simonwillison.net

1,094 pointssimonw1,245 comments

They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down.

This means we're going to need $1t+ per year in spending, per year, on tokens. 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

We're not there yet. This is still the upswing of the hype cycle, and unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

Here are a few thoughts:

- The publicly available information about how inference costs compare to training costs is conflicted. EEs involved in datacenters talk about power usage spikes during training runs as if they were a major factor in the designs, but academic papers discussing cost-optimal scaling confidently treat inference-time compute as a major factor.

- On the side of the balance indicating that training is more compute-intensive after amortization than inference is that Chinese providers, constrained primarily by access to compute, have nearly unlimited token availability at a lower price than US providers (inference), but poorer model capabilities (training). That would make sense only if US providers are inflating inference costs by 20-30x due to amortized training costs that overseas providers were not able to take on (there are other factors too).

- If training >> inference, they're in a prisoner's dilemma that far exceeds the ordinary zero-marginals model of competition between firms (due to its huge discrete stepwise nature). On the other hand, if inference>>training, the high-level analysis popularized by certain thought leaders, that it's like a utility, would be true. You'd tend to count this as a vote for inference>>training, but the CEOs saying it at least have a huge incentive to agree because the alternative, the prisoner's dilemma, would stop investment very fast.

- The only voice in the story that I just told you to have anything to do with fact (as opposed to high-level analysis and ivory tower armchair management of a secretive business) were the rumors from facilities engineers. That shows you the state of our understanding...

- If we don't even know the ratio between amortized capital expenses and operational costs, outside investor analysis is impossible. It doesn't matter how finely they divide the accounting buckets for office ferns and indoor ferns if the single biggest part of their business is obscured for trade secret reasons.

I work for a tiny little company ($150MM annual rev with 9% net) and we are already looking at dropping $100k on hardware to run local models because, for us, they're "good enough."

Our estimated spend for AIaaS would exceed that cost in less than a year.

In a few years, there will be hardware capable of running frontier models good enough for most things at accessible prices for even tiny companies.

I was in college in the late 1990s/early 2000s and I distinctly remember an econometrics professor state the following:

"As cable TV and Pay Per View came out, there were studies done about how many movies people would watch if given unlimited access to films. The results were bandied about as proof that we should build out all this infrastructure to support this line of business. When the data was further analyzed by statisticians etc, it turned out that people claimed they were going to watch films 10-12 hours a day, every day of the week. Impossible."

I feel like we are in a similar boat here where some people are assuming:

- EVERYONE is going to be using max tokens

- tokens will NEVER get cheaper due to improvements in hardware, software, design, market forces etc etc

The bottleneck has moved from producing a thing that works to knowing that the thing was the right thing to build. The more of the latter they can take on, the fewer knowledge workers are needed at all. So rather than 5% of every knowledge worker's salary going into tokens, 100% of the knowledge worker's total employment cost goes into tokens and you get a 20x productivity boost as a theoretical minimum across those tasks.

That's the game. There's a view you could take of this that this is just a growing of the pie: with those cost dynamics a lot more "small businesses" get a vast amount of leverage, so the overall economy grows without replacing the knowledge workers. I'm not sure I trust the MBA class to have that view.

YEPPP... and I'm kind of shocked at how many people can't do simple math.

Let's put it context. Google's annual revenue seems to be north of $400B. So if OpenAI suddenly had Google's revenue, it would still be insufficient to recover their investment.

and it's a ticking time bomb because $1T in servers, CPUs, GPUs and memory is going to be worth $200B in 5 years. You can say they can keep using what they've got. Sure. But they're also not going to stop spending on new hardware. And the competitor that comes along in 5 years and spends $1T doing the exact same thing is going to have a huge advantage.

OpenAI at this point reminds me very much of the Russ Henneman pre-money hype cycle.

I will also tell you, as someone who works at a company that's trying to remain profitable, that token spend has caught the eyes of finance and much like cloud spend they've already started applying pressure to control costs. This May my team is protected to use 30% fewer tokens than we did in April - this was by intention. I suspect we'll drop more in June.

I thought Anthropic and OpenAI's combined CapEx has been <100B?

source: https://isaiprofitable.com/

> They've got, ballpark, $5t to $10t to make back in the next 5 years

OpenAI's spending commitment is in the ~1T range for the next 5 years, and Anthropic is ~300B.

If they continue to show strong growth, they likely need to be at 100-300B in revenue/yr to support their yearly payments + financing, not 1T.

> They've got, ballpark, $5t to $10t

What are you basing this on? For reference, Anthropic raised ~$70 billion in total and OpenAI ~$190 billion. Why do they need to make 20-40x that?

> That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing.

We all have our own observations and mine don’t significantly diverge. But that’s bottom up. At this point shouldn’t we be seeing it top down?

If we are beyond potential and into significant productivity gains, why isn’t that showing up for the customers?

Why didn’t delta airlines get significantly more operationally efficient in the last 3 months due to the introduction of better software?

This is a genuine question, I am seeing a disconnect.

> We're talking about a world where you need 5% of every knowledge workers salary to go into tokens.

They are assuming ~10% global GDP growth instead of ~3%. You probably don't need the same %s if the pie grows a ton.

I'm highly skeptical we get that growth, but if you aren't, it makes it easier to digest.

1. Global IT spend is $6T per year

2. Where does this $5T number come from? If they make $4T in revenue over the next 5 years instead, what happens?

Hey, I wrote this down one time. I estimated way higher yearly revenue required, to be adversarial. And you can keep the "cost per unit AI work" a parameter and play with the results.

But the point is that if people are willing to delegate part of their salary (e.g., buy consumer products), vs requiring employers to pay for the tokens, then it's quite possibly a net win. Something like "I pay a largeish fee every month to make my own job much easier", similarly to how we buy a car to make commuting easier.

https://jodavaho.io/posts/ai-jobpocolypse.html

You are making the assumption that the models are only used / paid for by 2.5% of the population (your knowledge workers value). There will be new value created by these models which people are happy to pay for which simply did not exist at all before. It is also naive to say that the hyperscalers are going to be expecting a return on this in 5 years, it will be entirely propped up by investments / IPOs as has been the case with any tech company for decades now to reach scale. The hyperscalers are currently spending ~650b combined annually, which they have the cash for and can sell in future compute instantly.

The fuck is going on with HN that a comment making up completely fantastical numbers is on top?

> Most people I know cite +20%-40% velocity

Seems roughly right, that does seem to be about the boost in the most well-suited cases where you essentially know exactly how to solve the problem, the problem won't change much, and it's truly a matter of just churning out the implementation.

In that case precisely prompting, doing the review & nudge loop, can be a pretty nice (nice, still not game changing) speed boost over literally typing out the code to match the design in your head.

The less optimistic view though is that most things you build aren't like that. Even if they seem like it first. These things get booked as a nice speed boost, but you'll only find out much later they weren't.

A confounding factor is that it seems like many people not in the detail of building software do seem to think of most to all things are like that, even before AI assisted coding. Not much need to say more - see the entire history of the 'agile' movement for evidence of this.

And because most things aren't like that, I actually struggle to see fundamentally how more than 20-40% will ever be achieved (short of the ever-present deus ex machina of AGI argument), simply because the generation is already really good for these types of things. So since things like this aren't going to increase in overall proportion of things to be done, I don't see where the overall extra gains come from by models improving at this point.

Bigger than that, they have to contend with open weight local inference. Open weight models right now haven't caught up to the frontier models of right now, but they're as good as the frontier models of not too long ago. If open weight models reach a certain point, then frontier model providers are going to struggle to make anything selling tokens, because eventually people will realize they don't need Mythos for everything.

That assuming once they start squeezing people won't just go to deepseek or other cheaper competition

> That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

And most research shows people far over-estimating their own gains. Once companies start counting the actual (and not just reported) gains, the AI budgets will be more limited as people realize it's an useful and versatile additon but not replacement for most types of work

> We're not there yet. This is still the upswing of the hype cycle, and unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

Upswing of the hype cycle while growth of tech itself is flattening, both coz of techs innate issues (which might or might not be solved, but some papers claim they are unsolvable with current approach) and just the fact the spike in growth caused so high economy cost that it put brakes on itself.

Also, not all developers work on software products. The vast majority of developers work supporting software solutions as part of a much bigger business model, such as infrastructure, industry, healthcare and services. Many of these are complex organizations. So, unless you get to turn every employee into a 10x employee, the 10X coder along won’t necessarily make a 10X productivity contribution. What’s likely going to happen is the 10X coder will start to slow down or adding more (unnecessary) complexity to avoid having to sit and wait on overhead, for other areas of the business which are not easily automated away to AI to catch up. As a developer I can finish my project in June instead of December, but what if the customer is still not ready for integration until December? what do I do?

If people figure out how to run agents on-prem (already becoming feasible for both agentic tasks and coding on consumer hardware like Mac Studio 128GB+ or DGX Spark with some models) these companies will be in deep trouble.

Privacy is also a huge issue.

Those are rookie numbers. We are going to blow past $1t per year in spending in no time. As a developer for 29 years, I couldn't go back to coding by hand. For better or worse, AI will be woven into the fabric of life in no time.

I agree in principle with the math. But I believe that in reality if revenues don't show up quickly, then lenders will just restructure the debt and defer the payback period. Similar to SF commercial real-estate; many buildings should've come due during the depressed covid market, but lenders (banks) were willing to delay payment until the market picked up again.

The scale of these investments put the lenders at substantial risk, so the lenders will do anything to make it work. If the current lenders will be damaged by extended payback periods, they can simply sell the debt to someone else who won't be.

Yeah, claiming “product-market fit” on coding assistants for this multi-trillion dollar capital expenditure seems premature. Anthropic will post one and only one quarter of “operating profit” (aka losses after taxes and debt obligations) on the back of free-for-all spending by enterprise and engineer tokenmaxxing, neither of which will last. The investment was commensurate to a world-eating AGI, and if all that comes out of it is coding agents and slightly better enterprise software, I don’t think that makes up for the money spent.

Anthropic raised less than 100B up to now and as of March has 30B ARR. Why does it have to make back 2.5T to 5T ?

So you've got that market. Let's call it the demand BY knowledge workers to do the work. You've also got:

2. The companies themselves buying tokens for operations to make the work more efficent. e.g. Salesforce agent or Microsoft Office agent or random saas inventory agent. (and if you say those will go away (which I don't believe), it's even more bullish. The tokens just go to someone vibe coding XYZ, which is EVEN MORE than if you were to buy saas because it's SaaS product x Companies that built it instead of just one)

3. The companies SELLING tokens. This is also new markets like schools and small business (e.g. the local gas station buying an inventory tool)

4. The consumers "buying" (I put in quotes because it can be subsidised but the company) through chatgpt, strava, instagram/netflix recommendation, etc.

Local models still take compute, and while it may be cheaper, it is the same argument of on prem vs cloud. No one operates on prem unless you HAVE to for regulatory. Margins will come down and you just spin up a GCP/OpenAI/Anthropic agent.

It may be "cheaper" but rationally its better to pay someone to manage it. Thats why Hetzner only had $367M in revneue (a lot but tiny compared to managed services)

Putting some more numbers out there (some of the links are broken, but numbers look about right):

https://github.com/danielmiessler/Substrate/blob/main/Data/K...

Knowledge worker compensation is 35 - 50 trillion a year globally (6 - 12T in the US alone.) That's a huge TAM. It's still close but 5T over 5 years seems doable.

>... unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

The way we make ICs 10x productive is not just making each of them individually more productive, but by removing the coordination overhead of large organizations, because overhead scales super-linearly with the size of the org. And orgs will shrink automatically as AI-assisted ICs take ownership of larger and larger scopes of work, leaving much more budget for tokens.

I went into this in a bit more detail along with some made-up numbers here: https://news.ycombinator.com/item?id=48040999

One factor to consider , the base will not remain the same over the next 5 yearts.

Every generation of developer tooling that increase of absolute code throughput creates a new class of developers (and users).

Always been the case since first compilers, through eras of frameworks to today, and the skill level needed to be one has dropped. In mid/late 80s only Master / Doctorate level Comp Sci professional could write any applications. It dropped to undergrad and just Information Technology engineers and comp sci theory became mostly optional and dropped further to any college level educated with some training and has been trending below with no/low code tools like retool pre 2022, that was before agent codegen services such as v0/replit and so on.

The next generation developers will not produce applications and architecture as previous generations did, just as we most of us here don't produce the level of quality that pg did when building this platform[1] , but as long as the user can find value it doesn't matter as countless enterprise applications of middling quality already prove today.

All this to say the 200M/30M numbers will not remain the same is the thesis for these businesses, will it change by large enough at a fast enough pace to justify the capex, I don't think so either. However web 1 then 2.0 , saas and mobile revolutions were pretty quick with new class of users and developers so not completely unrealistic .

[1] While HN is a heavy outlier with its custom lang lisp implementation, there are any number of examples from previous eras that are more moderate in choices but written with solid architecture with skill levels would be hard to find in today's generation founders.

> They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down.

Depreciation and write-offs are about accounting models. Hardware will still be running after five years and still be making money. They may not be as efficient as the new hardware, but they will still be making real money even though they are valued at $0 in the books.

> We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

Just realized something: if one worries about losing jobs to AI, token's high unit cost is good news. To say the least, high cost would delay the displacement, if any, right?

In the meantime, someone shared the below on X. I guess the moral of the story is that "good enough" does not just displace software engineers, but also models.

   > I Went From $3,000/Month on Claude to $5/Week on DeepSeek

   > And honestly?80% of my work is identical.

   > For the past two months, I was burning $3-5K monthly on Claude Code. Every idea from design to development to testing - full end-to-end automation, even simulating users to test my products and provide feedback.

   > Extremely token-intensive. But Claude's caching sucked, making it insanely expensive.

   > Then I discovered DeepSeek V4.

At some point, if we reach stability on the models, we'll start getting silicon optimized for individual models. They are optimizing for time to market, not efficiency right now. I don't know how much it will move the needle on the cost math, but at this scale any improvement has a crazy multiplier.

I could see such productivity gains being possible, if only because the current tooling around LLMs is terrible. The fact that we have 30 blog pieces per day making the front page of Hacker News about someone’s convoluted system to guide LLM output to something reasonable is absurd. There needs to be standardization in tooling, and it needs to be open source. Then, and only then IMO, will we see huge productivity gains.

But, at that point I think the big players’ moats will have dried up. Local models will probably be sufficient for 99% of daily office worker tasks.

So I disagree with TFA’s premise. I think this fear is probably shared amongst the LLM giants, and they’re still hoping that neural network transformers are somehow the path to AGI (probably not, imo).

This is why 'agents' are the solution for these companies. Token spending goes through the roof. As long as a human is in the loop needing to read or review at human speed, that's a ceiling on how many tokens per user they can generate.

What value do the big model makers provide other than having a head start on gathering up humanity’s IP to train their proprietary models?

What’s their moat? Is it hoping for regulatory capture where scraping is made illegal the day after they finally finish scraping all human language?

It’s like OpenAI dammed the Colorado, and Anthropic dammed the Hudson, and now they’re both trying to sell us bottled water subscriptions at $100 a month. I don’t know how well the dam part of the analogy holds up, but the water part feels strong. Compiling models based on humanity’s written output feels like something no corporation should own.

> 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

When you break it down like that it seems reasonable. I'm spending about $5k/mo on tokens, seems more and more normal.

Is it possible that you are narrowly sizing the opportunity? While PMF does not always mean that early pioneers will be the leaders, I think the market itself goes beyond knowledge workers and developers. Agents, robots, drones etc will all use LLM or some world model.

I am rather more concerned about competition from CHINA. With how Huawei (2000 -> 2020) crushed every other telecom company and went from nobody to the most revered leader in 20 years, and with the depth of leadership in manufacturing and work culture, if China surpasses USA in AI, all US companies lose.

Here is a serious question.. Can we sell into the hype cycle and on the way down with this: https://safebots.ai/costs.html

This assumes that we won't need new hardware in ~2 years. I find that unlikely. So they have to make back what they got up until now PLUS the running upgrade/development costs. So what will it be in 5 years? $20t? $30t? It's all getting a bit outlandish.

What I'm often hearing though is the equivalent of "gg ez" when I bring that up. I don't understand how this will at any point blitz scale to profitability. As far as I know they don't have positive cash flow, no one has a moat and I don't think they will push out engineers.

> 5% of every knowledge workers salary to go into tokens

In general, I don't think you can reason from the existence of potentially stranded investments back to revenue projections.

And when you frame this as percentage of salaries, that's a sneaky implication that this is only about reducing salaries and headcount, and not about adding capability, or doing things you couldn't do before, or making fewer mistakes, or capturing more revenue, or expanding margins, or competing more effectively.

That said, 5% of knowledge worker comp actually seems very low to me, given the capabilities, and considering the percentage of "knowledge work" that is absolute bullshit.

Two weeks ago I received an email from my HOA saying I'd been billed for a service I never asked for. So I replied to the email saying they'd made a mistake. There are now more than 30 messages in the thread, involving at least 8 "knowledge workers" at the property management company all passing the buck, and the problem is no closer to resolution.

An agent could wipe out all 8 of those bullshit jobs and solve my simple problem in five minutes instead of two weeks. Think of how many hundreds of thousands people are doing this nonsense just in the property management industry alone.

5% is nothing.

Depreciation starts on day 1 and most likely they IMHO dont have 5 years. They dodged the deepseek bullet but who knows what is out there that will make all of this investment essentially worthless?

> 20% if you're a developer. That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

Of course it will. The value of an employee is a multiple of what they get paid.

If you pay an employee $500k and they make $2M for your company (like Meta), then of course a 20% increase for the salary is justified if the velocity is increased 20% as well.

> ... against the actual work their company cares about doing. [...] stuff that matters

This is a key point. Some engineers are having fun doing e.g. greenfield stuff with AI that they never would have had time for otherwise. Whether the company cares about that is another question.

It's related to Goodhart’s Law. If AI token usage is a target, then you're going to get a lot of token usage, but it's not likely to correlate well to improved business outcomes.

I don't think the unit economics are too terrible. Expensive, but not impossible.

200m knowledge workers in US and EU. Total salary around $15T/year.

$1T/year in token spending is about $5k/year per person. A big number, but not totally mad. That's the low end for office space per person for example. Probably close to the existing SaaS spend per person for a lot of roles.

We are still early in the deployment cycle for these tools so I would expect them to get better and also cheaper too.

To get that revenue and adoption they have to vastly increase their infrastructure spending. If they are currently losing in even the 200/month plans how is it sustainable?

20-40% sounds about right for me, today. Maybe 40-60% on a good day. But a lot of the reason it's not higher comes from harness gaps and org processes that haven't caught up.

All of that will get fixed with time.

The most often cited figure for knowledge workers seems to be 1B, an order of magnitude difference to your assumption.

Also, according to https://isaiprofitable.com/ total industry spend is also an order of magniture less than what your assumption is.

So in your model 0.2% of knowledge worker salaries instead of 5%, IF all the AI players win the investing gamble and do infact make back their money.

> 200m knowledge workers in the world, 30m developers

Your scope is too narrow. The companies target more than white-collar jobs. And $1t is around 0.5% of the world economy.

> 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

This is where the napkin math is breaking down in a big way. There is absolutely no reason to assume this will only impact "knowledge workers". Farmers use computers. Farmers will use AI.

Not to mention the competition: chinese open-weight models and open-source harnesses. Qwen3.6-(27B and 35B) have proven to be worthy and capable of running locally. I am confident more SMEs would look into this as a solution given the ballooning costs of API usage. You get a decent setup with an RTX 6000 Pro.

I don't think the maths works like that. They have raised ~$200bn so far and need to make that back. Saying they need to make $5 to $10tn isn't really real. They might need that to meet some extravagant Altman projections but not to justify what they have actually spent.

Given what costs are and availability of parts, that 5 year write down is not in practice going to be the case. Maybe tax wise perhaps but especially for big fancy expensive multi million dollar 100-500kW racks these things are going to stick around for a while, I think.

"5% of every knowledge workers salary to go into tokens. 20% if you're a developer"

Not unreasonable. I'm a hardware developer, and my employer spends ~10% of my salary on software tools. Add hardware tools and their maintenance and it's more like 30%.

Also hardware will be obsolete or dead in 5 years, and warrantys are 3 years from Nvidia. Ask crypto miners how these kind of hardware economics work. Numbers have to keep going up all around. Its a fundamentally broken business model unless prices increase 10x

That’s on the order of 1% to %2 of global GDP per year just to pay for their hardware commitments.

There is also the EV (expected value) of developing AGI. Even if you personally believe the probability is low within the lifetime of either of these companies, the value would still be extraordinarily high, enough to forgive a $5T or so miscalculation here or there.

Plus, at some point there are less tokens because local models being optimised and can work with protected information. For enterprises that want an AI with a knowledge base of internal documents, this becomes more interesting by the day.

My hope is that hardware improvements (better node densities every 2-3 years, better designs, etc) will pick up the majority of the savings for these companies in the future, assuming LLM performance starts to taper off with diminishing returns.

Somehow Uber and WeWork survived the same kind of grand projections that they never met.

> They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down.

I find it disappointing that a completely wrong statement like this ends up the top comment on HN.

It is wrong in both the math, the logic about public markets and understanding accounting.

> $5t to $10t to make back in the next 5 years

I don't know where this number comes from, but it has gone unchallenged.

OpenAI and Anthropic combined have raised around $100B. This is an investment so isn't something the have to "pay back" from earnings - instead investors expect to make that back from the share price being higher than what they paid for it.

> or the hardware buildouts will start getting written down.

The hardware buildouts get written down anyway!! That is a good thing for investors because as the value gets written down they can book a tax loss. ANd it turns out that generally agreed depreciation schedule for GPUs (used to be 3 years, now 5 years by places like Coreweave) is still too conservative since GPU rental prices for 5 year old chips are higher now than when they were new (!!)

All of this makes the rest of the math in the comment incorrect by at least an order of magnitude and under some scenarios possibly 2 orders of magnitude!

That's not a small error!

consider cloud spending vs on-prem before the great cloud migrations. people are spending a lot more for cloud services now.

I hear conflicting things about finances, some have a different opinion, that it won't be written down so long as more funding comes in and revenue keeps increasing. it isn't like how you take mortgage or business loan, it isn't even a loan it's an investment funded by loans. So long as the investment is still promising, what are they going to do? destroy its value by calling in trillion dollar loans?

lol I’m spending max $50/month right now on a couple light subscriptions and my velocity is insane right now (full stack mobile app development) I’m leaning into it hard while these cheap plans still exist and building out a big platform that I can easily generate new apps from. Hoping by the time the rug pulls I can just go back to hand cobbling these apps together from the modules I’ve pumped out and never even consider giving these companies a massive portion of my monthly income

We're going to reach a point where these companies stop asking for money and start mandating it. They've got a vice grip around the nuts of many governments and loads of companies have gone all in on investing in these slop heaps.

At some point, companies are going to start removing basic features. Governments and essential services are going to make people go through chatbots to get basic service. They're going to require AI to validate stuff that's already automated and working fine. Google search? That'll be all AI (and I guess they're already rolling it out). Dentist appointment? Going to need to do it through some AI app that requires an account and tokens "for a better patient experience". Verifying your ID when buying alcohol? Going to need AI to scan it and take 90 seconds to determine whether it's real. And it'll say you're an 7 year old farm worker in rural Botswana, so you can't get alcohol. And they're going to milk money at every level of this.

> unless we figure out how to make developers 2x, 5x, 10x as productive on stuff that matters, this isn't going to play out well.

Simple - you make them work 2x, 5x, or 10x more hours.

I just don’t understand how people are getting negative value out of AI or even only 20% productivity boost. I can only conclude that people don’t know how to use agents.

> +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

I'm increasingly realizing this math is wrong, because LLM use is really sticky.

If Anthropic 100x'd prices tomorrow for their best model, so some companies offered 50% salary to keep 100% of your AI usage:

a) There are programmers who would take this deal. They've gotten to the point of doing what feels like even less than 50% of the work, developers were already pretty well paid, so they'll take it.

b) There are companies that'd offer this deal. Even if the only people who are taking this deal are not the best engineers, and the AI output is not the greatest, I think the last 6 or so years have seen a lot of companies realize capitalism is not as competitive as it seems.

They're not worried about putting out a worse product because... frankly, what else are you going to do? CF lay a bunch of people off, support gets awful: well you're probably not building a new Cloudflare in the next few years.

In the meantime the AI will get incrementally better, their market share will grow, and you won't be able to compete without taking the same faustian bargain.

Maybe I was just naive but it's making me realize how much we take for granted in the world. Both the quality and relative value of things don't have to go up over time. Quality can go down while prices go up, and nothing will really stop it. Competition should stop it, but competition is really slow and can be interfered with. And as prices go up competition gets really hard.

Now try to take back llms from developers and see what happens.

Also, with announcements of replacing developers with AI and consequent job losses, who is going to use the tokens? AI using its own tokens to produce code?

we all know it is impossible goal to make. surely AI will be even more useful in the future, but as long as china exists and continue to undercut the price, the goal will be never meet.

> We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer.

with that much money, the companies can easily buy their own hardware and hosting free public models, no need for those expensive subscriptions.

Let's skip to the part where they put the taxpayer on the hook for a bailout as an industry since they integrated everywhere with big promises

Author seems strangely unwilling to distinguish usage from profitable product market fit. And from his own numbers:

Anthropic Max: $100/month

OpenAI Pro: $100/month

Total paid: $200/month

API equivalent usage: $2,180.16 in 30 days

So paid only 9.17% of API-priced value a 90.83% discount, or about $10.90 of API priced usage for every $1 paid...

That proves heavy usage but not sustainable unit economics.

Anthropic reported numbers point the same way:

Q2 revenue: $10.9B

Adjusted operating profit: $559M

Margin: 5.1%

SpaceX compute: $1.25B/month = $3.75B/quarter

So one compute supplier alone equals 34.4% of quarterly revenue and 6.7x quarterly adjusted operating profit.

Its difficult for the blogger to understand something when its incentives depend on not understanding it...

> 200m knowledge workers in the world, 30m developers

1 in 6 knowledge worker is a developer ! Surely that’s too high thou explains the job market

there are many paths towards ROI and ruin. but towards ROI:

+ LLM-powered robotics, autonomous, IoT, smart manufacturing

+ LLM-powered biotech, healthcare, genetic engineering, medicine

+ Recursive model improvement

+ Multiply the # of devs (software truly eats world)

+ Exponential increases in model performance / cost decrease (algorithms, power, infra, chips, architectures, etc.)

There's a lot more things that are going to be built that weren't built before as well.

> +20% speed for +20% spend isn't going to motivate a trillion dollars a year in spending.

Except that if your company go 20% faster than the others companies, you win market shares. But then, everyone will use the same tools and companies will be at even speed, but the tool will stay.

Now...if the market is saturated, it's useless to try to do things faster. Cheaper yes, but not faster.

> We're not there yet.

And that's not considering that capitalism is going to do what it does best: if they really found a way to be profitable, competitors are going to fight them on pricing. Anthropic, OpenAI, Google, etcetera 's margins are a competitors' opportunities.

It's not as if there weren't chinese models nearly SOTA. Don't know where the french (Mistral) are but they may try to get in the game if there's a way to be profitable (not that France or the EU for that matter are relevant in anything tech or had any tech company besides ASML and SAP in the Top 100 but who knows).

> make developers 2x, 5x, 10x as productive on stuff that matters

What does this even mean? Is this about speed of development? Is this about headcount? LoC? How are coding agents contributing to productivity in places like GitHub, Shopify or Meta? I mean companies that already have an established product. I really wanna understand this because I'm not seeing that GitHub's product suddenly became so much better than it was 2 years ago, so where's all that productivity going?

One thing I genuinely don't understand is these companies are constantly taking in incredibly large amounts of investments, so presumably they're giving up large chunks of equity or these are loans that need to be paid back or they're committing to spending obligations they're very unlikely to be able to meet.

So besides the insane hardware buildouts you're correctly mentioning, I don't understand how anyone that invests in these companies is supposed to make their money back in any sort of reasonable timeframe?

The cynical part of me is looking at what happened to the NASDAQ rules recently where essentially index funds are going to be forced to buy SpaceX shares much earlier than they previously would have (ie, before the price has a chance to reach it's real valuation). Which, um, I'm guessing these stocks are going to drop pretty hard when people start looking at the financials of these companies.

My suspicion is that the point of these IPOs is essentially to dump the bill on the unwilling public by forcing various institutions to buy it (ie, your 401k or pension is buying this shit), and maybe their investors can squeeze some money out of this before the stocks reach an equilibrium that's probably like 1/10th of what they're "valued" at.

It's worth noting that if each developer is 20% more productive with AI (let's take that as a premise and not dispute it), then it makes sense to go even further and reduce human headcount by more since the communication overhead of having 25% fewer developers is in and of itself a force multiplier.

tldr; 10 developers with 20% more 'productivity' can be replaced by 7.5 ideal developers and more like 6 or 7 developers due to the benefits of simply requiring less organizational communication.

I still think the ideal team size is unchanged however and that's 7-10 people. Note that teams aren't necessarily the same as direct reports. A CEO for instance has a certain number of reports and a leadership 'team' but they're not a team in the traditional sense since they are more about making good decisions and collaborating on specific things but mostly about leading their own orgs that have vastly different skillsets from eachother.

Your severely underestimating the idea that people are just not going to use developers for certain things in the future

For example I don’t anticipate somebody making a living off of making website ever again

Somebody with absolutely no technical experience who needs a website for their business can now make one with almost no money whatsoever.

That’s good enough for their business. and the code can be totally shit and it does not matter because it’s meeting their business objectives. I am seeing this in the wild and I’m paying money to companies that have these types of websites and because it doesn’t matter I don’t need for the website to work perfectly on all my devices all I need to be able to do is pay them through the website which is what they need me to do and our transaction is done.

Don’t forget ultimately the people who pay technologists right now are primarily advertisers

work on hard problems is going to continue to be some tiny fraction percentage of the software engineering discipline

just expect a total bloodbath because the goal isn’t developer productivity the goal is that “I don’t need to pay somebody $200,000 a year to build a website authoring tool like WordPress.”

...does anyone have a guess as to the total amount of money spent on software developer salaries each year? What percentage of that would the AI companies need to capture to be profitable?

(I'm not trying to imply that LLMs can replace software engineers, it's just an interesting comparison. If nothing else, I suspect that if the cost of development goes down, demand for custom software will go up.)

I hear this and I keep wondering what I’m missing. My productivity has shot through the roof over the last year as a result of having these tools. I’ve been able to unlock projects that I’ve wanted to do for years.

Source on 200 million knowledge workers worldwide? My understanding is that it's just above 1 billion. I dont think a billion subscriptions at $1000/yr is out of the question but it might take a decade to get roiling

"Next 5y" doesn't apply to AI factories

At least they're not going to make us watch ads.

One quick question. Did tax payer money fund these data centers? If so, how does that money translate to their profit and a return for the people whose work paid for the resources?

Or did we just get scammed?

I assume the bet is that as you swap humans for machines, this pays for itself. Swap entire devs and teams and frankly, managers, and you make up a lot of 5%’s fast.

If it works. And I’m not sure who is going to buy the stuff the machines produce, but shrug. Presumably some bots click ads for NFT’s that other bots generate.

> $5t to $10t to make back in the next 5 years

Wait what? They spent 2 order of magnitude less on hardware.

Why 5 years? What happens in year 6?

Doesn't matter, it will be pushed and forced down people's throats because someone invisible thinks it's the new way forward. And for that you need more money for NVDA and the like, and now people have to be made cultists in order to let the money flow in.

Same happened and happens in gaming. The gamers "invested" into NVDA by eating all the bullshit about ray tracing and the like. And they kept buying all the crappy 1000$+ gpus because youtubers said that the extra 1000 dollars worth those +15 fps plus the ray tracing....

I understand some startup deciding to take a punt on "this will all work out financially if our new product demonstrably boosts productivity of large sectors of the economy by a breathtaking factor that's incredibly rarely ever happened before in history: 2x. Sometimes a plucky group of people take a risk, it pays off. If it doesn't work, the company fails.

What I do not understand is: large sectors of the economy all simultaneously taking this punt, with the necessary productivity boost, as you say, far more like: 2x, 5x, 10x

if you ignore all catastrophic mistakes, these numbers are true

they need to make 5t-10t back, but not necessarily through selling tokens. as we can see, the frontier labs are making vertically integrated products. their revenue is no longer strictly tied to inference.

It's going to be a typical saturation curve. A lot of upfront tokens spent on things that have stockpiled over the years, and then the derivative on token spend trends to zero as the users run out of immediate things to try. Sure there will be ongoing maintenance and experiments, but it wont be nearly as close as the initial inrush.

imo if your developers arent at least 2x as productive, then something is being done wrong on the employees part and/or the organization's. cli tools are ridiculously powerful provided you were an actual developer before using AI.

Maybe it's just me being (trigger warning from me providing an honest self assessment) very intelligent + a generalist, but i went from only full stack webdev and .NET to being able to implement an end-to-end LLM training pipeline (data curation, tokenizer, pretrain, sft, DPO - using ~$100 in cloud compute to train a class-competitive 1B STEM model)...and a full economic financial modeling and quant analysis application that pulls up to date economic, economic, news, stock data from the entire world and uses Dagster to orchestrate tech ical indicators and fundamentals and signals... and i did these things for learning and for fun. i built my own sublime text and obsidian replacement. i built my own reddit/twitter/hackernews/substack/news aggregator. i built countless other useful tools and utilities for me personally and for work I build more that empowers multiple departments.

Ive built 2 browser games, one already released to great reviews and 100k+ hours played. Ive built a tool on top of claude code that does ~60% of my job. Ive run data analysis on company financials for forecasting that have been refined and are producing very accurate predictions. Ive built competitive analysis tools and trackers.

All of this in 3 years. The projects are all clean, documented, with great code practices and modularity. A purist would surely consider some of the code slop. But it all works completely and fills real needs.

This is a huge shift. Anyone not realizing it yet is just simply behind the curve. I would not have accomplished 1/10 of this without AI coding. I went from copying code into and out of browser chats for 2 years before getting on the CLI train, and it is absolutely ridiculous the ROI you get from subscriptions to Claude or Codex.

The "+20% velocity" framing misses what's actually shifting.

This is never going to materialise. It’s dead in under 2 years.

The market is shrinking and saturated already and it’s not because of AI gains but geopolitical instability and supply chain issues, some of which are caused by AI spending and stupid ass PE firms refocusing on AI supply chains.

Only our pensions and futures burning.

I feel like there's a bit of AI psychosis in this particular post.

>"These are tools which burn vastly more tokens, but are also quickly becoming daily drivers for the work carried out by extremely well-compensated professionals."

>"Somehow this fragment turned into headlines like Uber’s COO says it’s getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remains enormous."

Yes, it's just the yearning for AI failures. It couldn't possibly be runaway costs, record revenues, and massive layoffs. It couldn't possibly be that these tools are lighting dollars on fire by people already paid significantly well and not producing any increase in "value" for it (I recognize that output is 100x but outcomes are flat by all measures).

[1] https://cmr.berkeley.edu/2025/10/seven-myths-about-ai-and-pr... [2] https://futuretech.mit.edu/publication/crashing-waves-vs-ris...

Being pedantic, but I don't want to lose the meaning of the term: "AI psychosis" doesn't refer to someone who thinks AI is really good. It refers to someone who develops symptoms of psychosis from talking to an LLM, e.g. believing they have developed a new Grand Unified Theory of physics.

Please let's not dilute the meaning of 'AI psychosis'. It is a real phenomenon that involves actual psychosis.

I just cannot imagine zero net productivity gain from AI usage by a somehow experienced developer. Even if it’s happening right now it’s matter of time for people to figure out how to utilize it right. Only exception would be if people are getting the same output but working less, which is still a net positive (just not in terms of output).

What's the psychosis?

You can’t just redefine ‘AI Psychosis’ to mean ‘somebody whose opinion about ai I disagree with’

I find this analysis confusing. PMF for coding was likely reached some time last year. Profitability, which is different, we don’t know. The article kind of confuses both without making a strong economic case or using numbers in a compelling way. I don’t understand what the Uber case has to do with this either. The Uber COO clearly said that at least in terms of ROI he’s not seeing the results either.

My take is the product has been very useful for coding (PMF) for months. But it’s certainly not useful at any cost…

What I also find confusing though is that folks seem to ignore trajectory which is maybe the biggest lede to bury. As Simon says, we have had "good enough" coding agents for 6 months, that is a blink of an eye, and at my company my job has now completely changed. It's almost like a dream.

And that's just one inflection point. We've had several and there are many more on the horizon. So while I could be convinced that ROI is maybe not even positive today despite the ridiculous enterprise spend, it's perfectly rational to pave the way today for what's coming over the next few months let alone years down the line.

Pmf is this weirdly defined thing where "if you're not sure you have it then you don't".

I think it was clearly useful for months to people who had tried it and taken the time to understand it, but now that knowledge has spread to the point where wallet holders are convinced it's not just passing fad or hype so now pmf can be "claimed".

I agree it's weird to say "those people have pmf" though, usually it's something you define for yourself

PMF implies profitability. I could give away dollars for $0.80 and have unlimited demand but it doesn't mean I've found PMF.

Correct the cost is part of the economics.

Thats why most here shouldn’t engage in the discussion - they parrot on about benefits without identifying and articulating the costs and moreover how it affects the firms financial position.

The article also treats the word "good" as load-bearing in a way that should have you questioning their analysis:

"I’ve called November 2025 the November inflection point because that was when GPT-5.1 and Opus 4.5, combined with their respective coding agent harnesses, got good—good enough that we’ve spent the last six months adapting to agent systems that can reliably get useful work done."

It’s not supposed to be logical, it’s an LLM evangelism blog that rarely, if ever, has any critical analysis that isn’t pro-industry. Read any/all of the other posts and you won’t find much skepticism but you will find a lot of shilling how great it all is.

And what happens when open models catch up in 6 months or so?

So how do openai and anthropic plan to keep customers when GLM-5.1 is just as good and open source and a lot cheaper?

I don't see the business model working. My closest friend actually does automation software for large companies.

He does not use Claude or openai at all. He primarily uses gpt 120b on cerebras and glm-5.1 for heavy thinking work. And some other small models for various tasks. All open source.

And these systems are extremely useful for the businesses and are able to run fully automated pipelines that are very stable and fast.

We discuss this a lot, and we both think any business doing heavy agentic work on Claude and openai just aren't aware of exactly how good and cheap open source has gotten on the last year.

So... once the legacy businesses and developers catch up, won't Claude and openai be unable to recoup their costs?

> I don't see the business model working.

Same. It's a nightmare from a Porter's Five Forces perspective.

There will be a ton of businesses competing in this space, and there will be something of a moat due to how capital intensive the business can be, but there will still basically be infinite competitors.

Great for consumers.

For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price.

Most of the money right now is in coding. Openai and Anthropic just have to be 6 months ahead of SOTA open source models and they'll capture most of the enterprise and dev market

For coding assistance, I have tried OpenCode with several large open models through OpenRouter. All were fairly bad compared to Claude Opus. Could you provide some hints on how I should be holding these open models so that I might get more value out of them?

I agree with the common trope that open models lag behind by about a year, but something magical happened just around a year ago when the state of the art models became extremely useful. By this reasoning we're about to see open models perform well, but I'm afraid there is more to it than just waiting for another revolution around the sun.

Note, my application is coding assistance. Open models can be great for other purposes.

GLM-5.1 isn't just as good. It is no match for Opus running in Claude Code. Please try it yourself. Open source models are about a year behind at least.

The only way I see it working out for them is if some legislation is passed that eliminates the competition by making it illegal to run local models. They could claim that the models are dangerous and could be weaponized without oversight, or something along those lines.

Don't you need to spend 5-10 thousand USD to run these models that are "as good" as frontier models from 6-12 months ago? I haven't seen a convincing breakdown for ROI of running your own coding models. Especially against a $20 or even $200 plan

I have 26 years experience. I code using GLM-5.1. Fron time to time I switch to Codex / Claude, and honestly I don't understand why people uses Claude or codex. With the right prompting, GLM is awesome.

Do you have a good source to refer to, to map out migration from Claude code to a cheap setup using small open source models like you’re describing? I’d certainly like to experience how good they’ve gotten.

Agree. Also reasonix with deepseek is super cheap and quality is only slightly worse (in my experience)

They are both (and also spacex) sprinting for IPOs. They know that the opportunity window is closing fast and that advancement in model quality has largely plateaued in the last year. Take as much investor money as you can get away with for now.

> $2,180.16 worth of tokens for $200

“Tokens” don’t have an intrisic cost or value. Saying that I used $2,180.16 worth of tokens is like relying on the salesperson to convince me I’m getting a billion dollars worth of pots and pans for $19.99.

I think it’s funny how we are throwing critical thinking out the window when it comes to evaluating biased sources of info.

I'm not sure what you're pushing back against here.

I spent $200. If I had been paying API pricing it would have been $2,180.16. The article is about how enterprise customers get charged API pricing, which means if I had been employed by one of those companies I would have cost them $2,180.16.

What am I missing?

Tokens do have a clearly calculable intrinsic cost. There's the marginal cost of production (i.e. the inference cost) and the amortized R&D cost that goes into the model producing them.

Yes, value is hard to calculate, but luckily market pricing mechanisms exist exactly for this purpose. There isn't a better number to use than what people are willing to pay for them.

So he's saying that on an enterprise plan, he'd be spending $2,180.16. He's not paying that much, but enterprises are.

From my back of the envelope analysis for my own projects, paying per token on OpenRouter is competitive if not cheaper than running the same open weight model on a rented GPU. Per-token pricing is in the same ballpark (although more expensive) for closed frontier models and open weight models (cents to dollars per million). To me this says that the pricing is somewhat grounded in reality.

> “Tokens” don’t have an intrisic cost or value.

i am pretty sure these services know what it truly costs them to serve you tokens, maybe not in realtime but at least periodically.

however, what they charge us is a constant exercise in price discovery. i agree with this sentiment in the sense that we don't have a stable sense of the cost. all of these comparisons are good for the moment, or at most the near future.

i believe that even the "all you can eat" approach with the max plans, regardless of their crazy pricing, is not sustainable only with the power users. if most of us gets this kind of value through our plans, surely it does not incentivise the service providers to continue pushing it. maybe they can regardless just to gain market share, but not forever.

Reminds me of a car dealership talking about how good of a deal their extended maintenance plan and warranties are.

a little critical thinking led me to read that sentence as $2180 worth of tokens [at current api pricing]

I think it's funnier that you can believe some things have an intrinsic cost and others don't

His point is more he was surprised enterprises weren't getting the discount. And so indeed maybe it is not a giant ponzi after all! (Could be a bubble)

Lol. They obviously have intrinsic cost, the floor being the cost of electricity. It’s hilarious how we are throwing critical thinking out the window when it comes to evaluating biased sources of info.

The real timing is that we don't have strong enough new business needs for now and we have accumulated enough tech assets, so our work has been increasingly incremental. That means we can build reliable features on top of vast amount of past work - where AI really shines. So, with or without AI, companied would hire fewer software engineers if majority of our work is incremental: add a feature here, fix a bug there, tweak a configuration and etc, then we wouldn't need as many software engineers anyway. AI just accelerated such squeeze.

In contrast, imagine if we had the same AI 20 years or so ago. Could AI really write Jersey? I guess not as people were still trying to understand JAX-RS. Could AI really answer all the questions about React? I guess not as React was just invented. Would we use 10x fewer people to build out infra on the public cloud or the entire so-called Big Data platforms? I guess not, as they were still rapidly evolving and we'd need so many engineers to explore so many different possibilities? Could we use AI to build our ML ecosystem with 10X fewer people? I highly doubt so. Heck, 20 years ago R was all the rage and Python's ecosystem was not mature at all. Oh, and mobile computing, could AI lead to 10X fewer people to build all the mobile apps and the underlying infra?

> Could AI really answer all the questions about React? yes, due to ICL

> Would we use 10x fewer people to build out infra on the public cloud or the entire so-called Big Data platforms?

No, cannot solve core problems, makes a mess at scale

You are right about the incremental work. But most of the work is historically incremental imo, only few positions are R&D.

I have to give them kudos. This whole thing is the greatest swindle of all time.

AI has some use cases, but not at the price it’s currently priced at. I’ve been on AI since GPT-2 with a lot of heavy users. Every user has the same story, curiosity, surprise, hype, hate, realization. Enterprise is usually a bit behind and are right now at hype cycle, that’s where they sold all the deals and do the IPO.

It’s really a VC masterclass.

Don’t get me wrong there is are useful cases of AI, but not the way the want it to be. Quite similar to Blockchain. The idea of decentralized money has right to exist. 99% of other coins not.

AI is a faster, but still less accurate search engine. AI is great in finding bugs, it’s great at ruber duck debugging.

The reason I call it a swindle is, because along with the marketing it gives tons of people in the world the impression, they can now build their own startup, game, infra etc without the need to learn it themselves. This leads to millions of abandoned and low qualiy projects and products, because the vast majority has never built the mental modal necessary to solve the problem thoroughly. In the end they’ve wasted months and money (but burnt tokens). This is what I call a swindle.

All early adaptors I know have not drastically winded down their usage, not because of money, but because there is no new case. If you want to explore a new project you can get onboarded quickly learn a lot and then switch to documentation and live testing. For me usage is the lowest it has been the last 2 years.

I would not let AI touch my code. I have anxiety around it, because it will gripple back up. I will let it read my code and let me know what I did wrong so I’m sharpening myself.

100s of companies including open source solution can offer that for me.

All my non-tech friends are now in hype cycle and share their hype and fore forseeable frustration with me.

I have to say I’m in a way impressed in how AI has been rigorously vc-utilized (conciously or not-conciously) to generate these vast companies with the whole world watching.

Trying to parse the raw claims here maybe you can help me out

- it’s a swindle because ROI of tokens for coding models is not positive? As in it doesn’t bring enough value to charge like the $100/mo?

- enterprise customers are too dumb to see this

- IPO to max out the CEO profits for what is ultimately blockchain vaporware

Am I getting that right? Or am I putting words in your mouth?

> it gives tons of people in the world the impression, they can now build their own startup, game, infra etc without the need to learn it themselves.

I can’t speak for peoples beliefs and motivations, but this seems to be strawmanning, no? AI is a powerful tool to force multiply people. You can’t just prompt “build me an enterprise SaaS app worth $1B” or “build me GTA6 and don’t hallucinate” but is that your impression of what’s happening? Dario and Sam are saying “if you buy our coding agent subscription you can build a game with zero skill and one shot and then be rich”?

If you don’t find value in AI agents I can see reasons why that could easily be true today. Also if it just gives you the heebie-jeebies. But to say it’s a swindle on par with the blockchain I think that contradicts an enormous amount of signals and also the actual dialogue (not just headline sound bytes) around what these systems are capable of today and what we expect them to do say at the end of the year.

Anthropic isn't actually profitable from what I'm reading, a discount briefly pushed them into the black. This guy makes the case well: https://www.wheresyoured.at/anthropics-profitability-swindle...

I'm skeptical that their current price raise is sufficient, and I'm also skeptical that most users/businesses will accept more significant price raises that will be needed. Especially for individual users, $200 a month is already incredibly expensive, I really don't think most people are going to be willing to pay more like $1000 a month.

I think we could eventually get to a point where $1000/m+ consumption would be considered a bargain.

I currently pay ~$150/m in tokens to cover my 1099 work. If I had to pay $1000 for those same tokens each month, I would still be massively positive on my margins. I bill customers based upon # of completed features & bugs, not total time spent at the computer. Tokens would have to more than 10x in cost before I would start to have a problem on my end.

If you're an investor in Anthropic you probably _don't want them_ to be profitable right now. They should be spending literally 100% of their money and then some on training and compute.

this - I'm wondering why this isn't getting picked up more in the analysis? Its an accounting trick, which is not sustainable. Time will tell.

I usually agree with Simon but I think he is overlooking an important factor.

There is a lot of AI usage happening not because it shows benefits, but because the business has mandated its ubiquitous use. Companies having dashboards for token usage and rewarding people for using more tokens is a real thing. I just spoke with someone today who works at Microsoft and they are required to use AI for all of their work - they have to make a special request with justification if they decide not to use AI for even a single PR. This kind of demand isn't driven by value from either the company itself or from its workers; it is the kind of artificial demand you get from make-work projects to keep people employed during hard times.

We have to wait for the hype to settle down and people start making business decisions based on results before we can really value these AI products.

Correct the question is one of real vs fake demand curves.

I think token leaderboards are idiotic, however… many companies are requiring AI use, with by far the most likely reason being that they truly believe AI-assisted development gets (or will after the time needed to get employees acclimated) better returns.

The costs are exorbitant and most software is not produced by companies with such a huge moat. Anthropic made a profit through their recent bait amd switch pricing. There is zero useful insights online to indicate whether this might die due to commoditisation with good enough open models or fail the race to get more people subsidising unsustainable growth with other people’s money. Who knows? In any case they dont seem to be able to drop usage costs so the business model seems based on wishes

Continuing with your skepticism:

> Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staff

> Enterprise customers are now paying API prices

How long before enterprise customers start to question the bill? Anthropic goes from not making money to doing pricing shakeup, and now they are making money and the biggest spenders are shocked at prices.

Seems like things are still very uncertain.

Usage costs will come down with better hardware. Hardware is improving rapidly each generation.

200$ per month per seat is nothing .

A single 3D CAD license pack for the guys in our R&D group costs multiple thousands of dollars per seat, per month.

It's about time software seats get some love too.

AutoCAD is $175 per user per month [1].

[1] https://www.autodesk.com/products/autocad/buy

Yeah, it’s nothing, and it’s also not the cost that enterprises are paying. As the article states, the price is $20 per seat per month, PLUS per-token API usage. Enterprises are paying consumption billing, not fixed rate oversubscribed “all you can eat per seat.”

CATIA licenses which are the most expensive I've seen are roughly $600/month per user. Where are you seeing "thousands of dollars per seat"?

Sure. Is CAD going to be used by every working human?

Now add up the engineer’s salary and you’ll find that software seats already cost more than those R&D ones.

But when previously your software developer tools were free, that's a huge increase

How many guys is that? Every single white collar worker is in the AI ICP (customer profile).

edit: typo

How is the lack of bad news declaring a victory for AI? I am yet to see any company concretely publish analysis about the ROI from AI. Most companies as far as I know are still treating AI investment as sunk cost with no expectation of returns at the moment. We could very well see a world where companies heavily scale back investment.

> That’s $2,180.16 worth of tokens for $200

So the author claims he's getting $2000 per month worth of frontier AI free of charge. Ok. If he's been doing that for 6 months that's $12k. What has this produced concretely? For $12k you can find a used car in decent condition. Heck for $1200 (his actual out-of-pocket spend) you get a brand new ebike! (on which you could put a pelican and make a photo of both if that's your fancy). But here it's unclear what has come of it.

I've written a great deal of code - code that would have taken me years of work to produce without LLMs.

(It's mostly open source, you're welcome to dig around in https://github.com/simonw and https://github.com/datasette if you like.)

My time as an experienced software engineer is worth a lot of money - a whole lot more than $12,000 for the past six months.

I would be very curious what kind of answer would satisfy you here. Simon isn't building a product, where $200 is a line item on a balance sheet. If he tells you what sort of analyses or time savings $200/mo on coding agents have enabled him, do you honestly think that would satisfy you?

With deepseek and xiaomi mimo models slashing their prices 99%, I don't see a great future for openai / antrhopic with regards to their 1T valuations. Maybe 1T valuation will be the whole market, West + East.

Most of the corporate world in the EU or North America will be hesitant to rely on Chinese AI providers. There are some very real blockers for that for things like data security, compliance, etc. And recent geopolitics don't help.

Legalities aside, you need to look not at the model quality but at the infrastructure needed to scale these models from tens (now) to hundreds (soon) of millions of users. Only a handful of companies actually have the resources and funding to do that. That's what these huge valuations are based on. These companies are gearing up to scale to these levels. That's why they are spending on data centers. Whoever has access to those data centers gets to tap into the revenue stream of people using models running on those.

The market for frontier models is roughly split between OpenAI, Anthropic, and Google. And then you have companies like X/SpaceX, Amazon, and Microsoft being more successful with their infrastructure than their AI products and companies like Apple, Meta that have the money and the aspiration but are so far not really managing to be very successful with their AI strategies.

Deepseek is just very poorly positioned to capture a lot of the enterprise revenue in the EU or North America. But they might become very dominant outside the US/EU. And of course China itself is going to be a huge market and equally unlikely to want to be depending on US owner AI companies.

They'll still have their dedicated enterprise customers. I think the Chinese providers will pull more of the single users who're paying their own way, than those backed by company budget. And it's a pretty good split as the demand becomes better distributed, resulting in better service (I'll never forgot must how bad access to Claude became until they got access to Colossus) and less potential for lock-in (we really don't want there to be a duopoly, etc on good AI).

Ai has become indispensable but maybe not at all cost. My company just had a company-wide meeting to talk about how they're restricting who can use which models and instructing us the "be more responsible with company's tokens". And it's not an small company by any means.

> Coding agents really did change everything. These are tools which burn vastly more tokens

The assumption here is that this is a positive thing.

But this very well could end up being a major negative long term by increasing the cost per user, reducing margins.

More usage = more cost = less profit.

It's not obvious that more usage is good. It's only good if revenue per user increases more than cost does. I'm skeptical about that.

> It's only good if revenue per user increases more than cost does.

That's why it's so important for these labs that they're selling API tokens for more than the compute+energy costs needed to generate them.

Every indicator I've seen is that they do have a positive margin on that. If they don't, they're screwed.

> I currently subscribe to the $100/month Max plan from Anthropic and the $100/month Pro plan from OpenAI. If you are a heavy user of coding agents these plans are a fantastic deal.

Guys, what - in your opinion - does "heavy user" mean? I thought I am heavy user (I am using AI to code every day 8hr a day + side projects) but 20 USD/month Cursor plan is always enough. What should I be doing to extend my license to higher level?

I'm out of credits codex pro credits (200 plan) in 4-5 days, and then I switch to claude (another large plan) mostly or just go out more and get Vitamin D. :)

I spend most of my time designing and tweaking tests suites, and improving test performance. These commits are almost entirely Codex: https://github.com/tsoniclang/tsonic/commits/main/ - but it's possible only because there's a very large test suite attached to it.

All of that is very token intensive. If OpenAI gave me 3x my limits, I'd find ways to eat it up in a week.

What do these tokens give me? Well, I think in a week or two, I hope to port the TypeScript-Go compiler back into TypeScript, but compiled to native code. It's probably not particularly useful for most ppl, but it's a hobby project that I've spent the last 7 months on.

How many sessions are you running simultaneously, how careful are you being about token consumption and context window management, and what level of reasoning are you using (with which models?)

8hrs a day doesn’t really mean anything without a lot additional qualifiers.

fwiw lately I’ve been straddling 2 or 3 claude codes and one Claude cowork, primarily on 4.7 with high effort - the company’s paying for it, so I’m doing my best to burn as many tokens as I have the mental capacity to manage. At that rate, the 100 account is completely necessary, I was blowing through my 4-hour limits consistently before requisitioning an upgrade.

My agents are easily busy 30 min to a hour independently. Implementing their plan, building and running tests, verifying deployment, linting. So I switch between multiple agents. Each agent has its own branch and worktree.

It’s funny how a US$30 ReSharper license that would give me a 20% performance boost in programming was out of the question, while me burning US$3000 per month on tokens for a 40% boost in performance never was questioned

I feel this so much. What was all that fuss about needing 3 levels of approval for any software license? Or having to provide a compare chart against open-source alternatives?

The money would be so much better spent that way as well, supporting individual programmers.

I think the reasons for them going with API pricing will become abundantly clear when the S-1s become available. If they don't have a story covering how they can get revenue closer to expenses, then they're relying on the market to believe the pixie dust version of their profitability story, which I think people increasingly don't.

> but I suspect there’s a more important factor here: I think they’ve finally found product-market fit

Ahhh the classic startup term that's definition is nebulous. But also, since when does any definition of product/market fit mean a product is profitable? And profitable in what sense? Unit economics? Overall company?

Oh I'm absolutely taking advantage of the fact that "product-market fit" has a bit of a nebulous meaning here.

It's a great hook to build an article around. My core point is more that April 2026 was the point when Anthropic and OpenAI finally appeared to have figured out a credible business model.

Does this analysis factor in potential caching of tokens on the server side? It seems that if they organize things well (as a model provider), they can save quite a lot on that. Looking at my Cursor statistics makes it clear that the token calculations are not at all trivial.

I believe the ccusage tool I used takes cached token pricing into account.

Realistically, OpenAI found product market fit with the OpenAI API playground in 2021. People were using that as ChatGPT at the time.

If nothing else this blog did give me the idea that I should split my $200 claude max plan into two $100 CC max and $100 codex plan, esp because Claude is now offering 1.5x weekly limits so its the 5x usage is now more like 7.5x usage.

>I should split my $200 claude max plan into two $100 CC max and $100 codex plan

You may want to get one of them to check the math on that :p

Anyone actually making money paying all of these monthly fees? Or just hobbyists? I have yet to see any real ROI posted anywhere.

This is the same question I said about people running OpenClaw. You don't hear about anymore.

Other than the hosting providers, I am also yet to see anyone directly making money from their OpenClaw agent.

Ironically, we had more new interesting things launched daily 5 years ago before AI.

Ask yourself: what is the ROI I provide? It isn't trivial most of the time.

> Anthropic are strongly rumored to be about to have their first profitable quarter.

Is that quarter same as any other quarter in terms of infrastructure costs (e.g. are there any temporary discounts happening coincidentally)?

This requires hard evidence which isn't available. Circumstantial evidence is all otherwise.

Bloggers are having AI psychosis too.

I feel like we should just ignore all LLM news until after they IPO, lots of positive sentiment astroturfing bots.

Love how everyone boasted about replacing all the software with ChatGPT and then we end up with coding agents meaning the software engineer are STILL important. The sell is the development tool. It's classic cloud. Where did all the ops people go, many got subsumed by the cloud companies YET every company still has DevOps people to manage cloud infrastructure. The layer of abstraction went up but we still need the people to write the glue code and understand the business. OK great there's a new cash printer in the room. There's a new tool. Let's just start to ground the tooling in its new found gravity, profitability and IPO market dynamics... Reality has set in. The hype cycle is about to explode... Do you remember ride hailing and just how much cash was burned on credits pre Uber IPO. Then remember the IPO itself? These companies are not the new Google. They are a layer on top. Google was still the most efficient cash printing machine in history beyond the the US government and might still be. Will be interesting to see what the trillion dollar IPOs turn into. I'm going to say we see those prices get cut to a third in less than 5 years and scale back up over the next 15-20 years.

> As further evidence that enterprise agents represent product-market fit for these companies, consider their open job listings.

PMF is one interpretation, but it could also be read as desperation.

In my opinion, we've been at PMF for quite a while now. The November inflection point that's often referenced definitely changed how we interface with models, but as far as coding goes, I feel like Cursor had proven itself useful for at least a year prior to that.

The demand has always been there, the outstanding question is still - how do you build a business on top of these products? None of the frontier models have emerged as uniquely capable, but open weight models are now catching up in capability as well. The explosion in go-to-market roles feels more like an attempt to lock customers into contracts so that they don't consider alternatives.

I assume the hope is that during this 12-month contract they will develop real integrations, something deeper than just a CLI harness. If you've ever worked in procurement or dev tooling at a reasonably sized company, you'll know that this is exactly what teams try to avoid.

It's anyone's guess what will happen this time, but I'm excited to see how the IPOs go.

They certainly have, but it relies entirely on the assistant frame, which is a problem in and of itself for the trillion-dollar economics.

Anthropic and OpenAI have shown people want a tool for task offloading, driving predictable token consumption and justifying the math, so long as users stay in that dynamic.

However, knowledge workers using these tools daily are getting exhausted with them. Outputs come out polished but hollow. Talking to a frictionless, frame-completing model all day drains you.

If user behavior drifts away from assistant usage because of that, per-token math implodes. The valuations we're hearing about all the time rely on usage compounding daily. The fatigue is a timer running against that compound.

Anthropic's Constitution is the closest hedge out there, I think. Installing an identity structure into the model through training. But it's still assistant-first, so the fix there is only partial.

I've spent the last year running a product that flips the architecture so identity is primary and the assistant role is secondary. Same frontier models, completely different conversational quality. The fatigue property doesn't really show up.

Whichever labs figure out how to install real identity natively in the weights are going to be the ones with PMF in the next phase.

I may be biased because I work on an AI powered enterprise productivity product, but while I agree they have PMF right now, I wonder whether people's use will evolve in ways that undercut the current PMF. Chatting with an assistant is great if there's no product with tailored UI available that also has the AI capabilities. But once there is, I suspect people may switch, or more importantly enterprises may switch because they'll get the benefits of AI without the clunkiness of a chat only interface. We may see another DOS -> GUI-like shift.

More specialized products will consume tokens but their builders will be incented to optimize token use and switch models as costs and capabilities change. And if search engines become more AI capable, and Google is clearly striving for this, then they may have pressure from two sides that could squeeze the number of use cases for AI chat. AI coding isn't going anywhere and nor is the need for AI in general but I wonder if the products will have to evolve significantly to maintain the current levels of PMF. And then there's the question of profitability...

I think it's fair to say they had achieved product-market fit when their revenues were growing deep triple digits month over month. What we're seeing now is that perhaps they have achieved profitability or at the least a more sustainable balance sheet.

So it largely sounds like many more people will be able to write software - and will use AI to do it. Existing software engineers will continue to automate their tasks away like they always did, but perhaps at a faster rate.

The impact of AI in other fields seems to be muted.

If those companies found PMF we should expect to see the effects of all those tokens burned on the products we all know and use. In my own experience: yes new features can be delivered much more quickly (by orders of magnitude) but knowing what to build and how to evolve a product with taste and direction does not; so ultimately the user-facing gains are marginal.

I suspect that once the technology has been tamed and the hardware and software has been commoditized, the impact will be much less dramatic than we expect and we will realize the importance of a shared vision, experience, taste, intuition and discernment in building good products.

> but as far as I can tell those credit costs are an exact match for the API token costs listed for those models.

it is only true for USD. for example if you pay in euro, this is actually more expensive. kind of makes no sense, because it translates to $1 = €1

Product-market fit, but what about customer retention?

It is quite trivial to switch from using one model or another. Likewise, in a few years we'll have affordable laptops to run today's frontier models.

What's their plan to let us keep subscribing?

"[would have spent] $1,199 with Anthropic, $980 with OpenAI"

How many tokens is that, input/output-wise?

(a) I'm curious if you feel like you got $2000 worth of value out of them in the last month?

(b) I'm also curious if you would have gotten similar quality out of a slightly lower-cost provider of an open-weight model? (e.g. Kimi K2.6 and DeepSeek v4 Pro) and what the spend would have been for that.

I myself have managed to spend not quite $4 on OpenRouter and have felt it was very worth it; I just have much smaller, or more targeted requests I guess. (Lately, adding features to a static site generator in Python, or setting up log forwarding via a docker compose file)

The end game here is going back from a model where a bunch of product and tech management people sit in the U.S. or Europe, and try to manage thousands of mediocre talent sitting somewhere far away. The new model is you give those coding tools to good engineers colocated with your product people, and you ship good stuff much faster. If you can achieve such a setup, the token costs can be $50k per seat per month and you still run circles around the legacy IT models in terms of efficiency. Giving everyone the API keys and not changing the way products are managed is not going to work.

I see two really good ideas for monetizing the free tier for consumers.

Firstly, if the user is asking for things where AI can link to products or services to buy, there's a very good relevancy, much higher than in other types of ads.

Secondly, since the AI often takes time to compute answers to user's questions, they could be shown ads while waiting. People could perhaps be less annoyed by this than some other commercials since they know the break has to be there anyway.

(First idea is something I came up when asking Claude to compare some products, or ask for help in lawn care. Second idea was by a colleague.)

If we define product market fit as profitable with a trillion dollar valuation, I think the term has lost its helpfulness.

I do agree with the author that these companies seem much stronger financially recently though.

It reminds me of this Steve Jobs Clip:

https://youtu.be/0lvMgMrNDlg?si=QkkOnngYTjaSPlIy

He said, so many years ago, that there will become a time where computing power is so prevalent that we will stop using the person to make the computers job easier and start using the computer to make the humans job of interfacing with it easier.

But in this context, it would mean the other side of increase productivity is decreased time to do the same work. These are the same thing.

Product market fit and profitability are two different things.

Arguably product market fit was fond last November already. I don't think agents were the turning point that caused this.

Profitability, not yet. For me, it depends on whether companies are seeing a positive ROI from their ai investments. This website is skewed more towards big tech companies, but everyone that's not big tech needs to see positive ROI with using ai tools. Short term might see a profit, but medium to long term we still need to wait a bit.

Well, good for them that they are charging enterprises API rates. Why in the world not do something similar for consumers? Use for free a few times a day, have a $5 dollar plan for light use, and perhaps $10 to $15 for heavier use. If 90% of consumers pay nothing then ‘drop them’ except for letting them have an account and a few queries a day.

It is easy for me to change providers. Right now I use the open source Claud Code harness with two paid API venders for DeepSeek v4 (flash and Pro). I like seeing how much each session costs.

This is great analysis but my first reaction was - is this trolling? The fact that we have to think about whether a $1TN company has achieved product-market fit both articulates 1/just how high the valuations are 2/How hard it is to pin down EXACTLY what PMF is. As a pre-revenue startup, I am laser focused on PMF and frankly if this is the bar, no one will achieve it. But OTOH its heartwarming that people are willing to value you at $1TN before you reach that. Guess everything is in the eye of the beholder?

this is an opinion and i would love to debate :) dont hate me too hard

i think the article is momentaneously correct but there are some things that smells to me about the situation overall (not the article!)

(1) i believe gpt 5.5 and opus 4.7 are not as good improvements as their predecessors and there has been not enough evolution in those models to justify the price increases. Unless something big changes in the next few years they will not be able to track these costs (unless A)

(2) they might not be able to keep up improving the data centers if their tech keeps demanding more and more hardware capability. even nvidia does not show signals that they will be able to beat too much their big GPUs and at some point this increased price will be passed to them anyway which will need to be put in the token price (unless A)

(3) i've been trying deepseek v4 and other models and honestly, they are more useful than gpt 5.5 and opus 4.7... i mean, there is still a difference, but it is so little that it does not make sense the cost of opus and gpt 5.5 (unless they are going to A too)

(A) I have the impression is that the plan was the whole time to sideline common folks like us and focus on gov, big techs and military. they only let us play with the toys to gather data and for people to not get mad because they stole the internet from us.

With respect to Simon, whose writing I've usually agreed with in the past and whose insights I've liked: this is a bad take that overlooks the extent to which corporations are imposing the use of AI on employees, and in particular ICs, who make up a majority of the AI-using workforce by headcount.

Many of us are either openly having our performance reviews tied to AI use, especially at larger enterprises. Whether that's measured by sheer token count or just "how many of your tasks are you using AI for these days" (combined with the implication that question carries at many orgs which are heavily invested in AI).

I think the third company (likely Google) is going to make LLMs financially feasible with:

- dedicated hardware (https://cloud.google.com/tpu)

- optimized models (https://research.google/blog/turboquant-redefining-ai-effici...)

I'm surprised by how good coding agents are. I think PMF was reached last year or the year before (at least for coding).

With current limits my 100/mo codex subscription is more than enough for the work I do.

However, I do worry about when does current subsidies are going to end? I can see myself paying up to 300/mo, but more than that will be prohibitive.

What's the long term plan here? Are OpenAI's and Anthropic's costs expected to increase/decrease?

The product-market fit question feels premature when most usage is still developers building on top of the APIs, not end users paying directly.

They have the whole software industry addicted to AI. They will be pushing the price continually up, and like addicts they will find a way to pay

I think the product market fit is still how you interface with increasingly powerful AI. Right now is Codex/Claude code, and they just added computer use/web browsing into them. The product is the speed at which you can interface it and to be able to serve it with infrastructure.

Traditional product market fit isn't really applicable with tech evolving so fast.

Is PMF enough. It is such a dynamic self-disrupting wave that it is like predicting physical chaos. These aren't early Googles in a blue ocean. Maybe a blue ocean full of pirates and dragons!

This isn't me being a doomer I just don't know. Can we look at Q2 profits and draw hockey sticks yet?

Remember people are boasting how much their expenses are. That is where we are in the bubble/new paradigm.

My Costs without CC Max+Caching over past 2 months: $112K.

Ran `ccusage` on my Claude Code logs.

- Total tokens: 22.2B

Without current Claude deals, my personal cost would have been *~$112,000*.

I think we are overly fixating on Anthropic and OpenAI.

Since there are lots of models that are competitive and have a much better pricing, both OpenAI and Anthropic seem inefficient. I don't get why someone would want to buy shares after IPO apart from fomo and artificially built enthusiasm.

Anthropic and OpenAI may well be the Altavista and the Yahoo of the AI age.

For an alternate albeit somewhat contrarian view, also see Ed Zitron’s piece that add context to Anthropic’s profitability: https://www.wheresyoured.at/anthropics-profitability-swindle...

TL;DR Ed argues that the deal between Anthropic and xAI could have been negotiated in such a way as to make Anthropic only appear profitable during its “ramp-up” period in June, which incidentally is also the month that Anthropic is making tons of other pricing changes.

I wonder how a focus on per-token API profits will impact the incentives to improve token efficiency and drive down costs through optimized compute. I suppose as long as a few leading labs are competing, we'll see progress in this regard, but it's certainly less in their interest than it is with a flat subscription pricing model.

Article doesn’t mention on-prem and on device models. Almost guaranteed that there are a range of killer enhancements on these fronts waiting to drop until IPOs get closer to inflict maximum chaos on the valuation games.

While the big guys will argue they’re worth trillions expect others to drop chaos booms showing their NPV may be effectively zero.

Just to be clear, enterprise companies are using the enterprise option for security/auditing/enforcement reasons, not because OpenAI or Anthropic forbids them from buying a 200$/month plan for each engineer, right? Do they have any clause forbidding using those individual plans in a company?

Not sure I can agree with this. Sure, we have enterprise paying, but are we actually getting anything new and interesting made from LLMs compared to if we just didn't have them at all?

Maybe acceleration in smaller teams. We still seem in the era of the early internet where what questions LLMs change hasn't exactly emerged.

What baffles me is the range of estimates.

Operating profit is both post depreciation and fees paid to third parties for hire. So aside from shenanigans like RSUs and financing interest that's already somewhat close to actual economics.

Meanwhile we've got commenters here talking of 5-10 trillion with a T revenue shortfall.

Those are very different takes on reality

> If you are a heavy user of coding agents these plans are a fantastic deal. I just ran the ccusage tool on my laptop to get an estimate of how much I would have spent if I were to pay for API tokens in the past 30 days and got

You think this is fantastic deal only because they use similar like tricks where they inflate the price and tell you something supposed to cost $1000 but they have this today promo for $100.

I was there too and paying for a while. Few weeks ago I tried DeepSeek V4 Pro - expected its gonna be shit but its actually pretty good.

The deal is I pay daily ~$1 for DSV4-pro for ~100M API token usage. And they probably not getting broke because >90% of those token in practice is cache read and they very well optimized for that.

Mentioned in the article, but it cracks me up that both openai and anthropic are utilizing fairly traditional enterprise GTM plans segmented by verticals.

So many startups trying to automate sales, but somehow the two biggest frontier labs have decided that the best GTM strategy is firmly human-in-the-loop.

These folks have no lasting moat and they know it. We are still close to the November 2025 inflection point, so they had a clear advantage during these past few months. That will soon fade as open-weights models become really good, which is arguably already the case with DeepSeek V4.

The amount of caveats and vibes that this article is powered by could run New York City for a decade.

money talks, and being a broky from outside the valley/west coast, the "product-market fit" here are the neighbours of the service providers.

the economics simply don't work unless you make six figures, at least to just give it a go blindly. the providers are also still figuring out what they can get away by charging, and they are getting a similar treatment from those under the stack.

the caps and limits are not very transparent, and it is quite difficult to know what is "enough". the current rate does not stay the same and the contract is changed way too often to dedicate for the long term. regardless, the subsidized rates should not be sustainable forever. make hay while the sun shines i suppose.

Great article I know this upsets a lot of people who are used to thinking Anthropic/OpenAI are just lighting cash on fire but they've cornered the market on enterprise who cannot walk away from these $200/month plans

However the valuations are still far far away from actual sanity

And I think that's amazing. I'd like to keep using the subsidized coding tools, especially Codex, since I've given up on Claude. Hopefully the PMF allows the subsidy to continue. Would hate to have to move to the next coding harness again.

> and both companies now have measures to lock their enterprise customers (who tend to sign year-long deals) at those API prices

why would enterprises do that if they can just use bedrock or vertex?

anthropic rumored to be having a profitable quarter is a ruse to ready them up for IPO or otherwise. they could've had a profitable quarter at any point in their history but they only do so if they stop making models; this has been established. they make a model, they sell inference and make a ton of money, they train the next model at 10x the cost, they sell inference and make a ton of money, they train the next model at 10x the cost, repeat

> That’s $2,180.16 worth of tokens for $200—not bad at all!

Just imagine how funny it will be if it comes out that big labs were doing some fancy maths to count the 2k$/month in their forecasts ...

Product market fit for the models, sure. But their cost structure is going to kill them as token costs drop this year, following DeepSeek and Xiaomi and soon more providers.

Discovering that Enterprise customers tolerate higher prices compared to retails consumers is discovering demand elasticities not PMF. Am I missig something?

Companies are kool-aid drinking now due to hype, but given how much they're spending, if they don't see REAL, BIG wins from it soon, they're going to scale it back quickly and switch to Chinese models. Claude isn't worth the API cost for a lot of development work, and once companies have had time to collect and crunch data they'll see this.

I think my company is going to cancel our subscription once it realizes we're spending more money in a week that a developer gets paid in a month.

>Somehow this fragment turned into headlines like Uber’s COO says it’s getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remains enormous.

I notice this all over the place. Many people hate AI and want it to fail, and they're willing to invent misinformation if it supports that idea.

If we take out the circular interests and investments here then there is no way that this is a feasible business in current state.

Who's to say those enterprises won't churn after XYZ comes out with a decent enough model that costs 10x less to use?

There's a whole bag of clever tricks you can play to juice short term results leading to an IPO that may not work longer term.

I'll believe they've found product-market fit when they have a product. Right now they're selling the infrastructure, in a highly subsidized and undifferentiated way (at least over a sufficient long period of time of, say, a couple of years).

PMF is such a bullshit term. There’s no precise definition. It’s bs.

The fact is that investment is at a scale so large that current trajectories are nowhere near going to provide ROI.

The model companies are trying to milk every drop of ARR they can before IPOing. That is entirely the current narrative. You can hurt ARR before it churns if your goals are short term.

Anthropic being profitable is laughable. Sure, by some accounting measure that no one seriously uses. But looking at revenue and what they’re raising you can see the true story.

The truth is we need a revolutionary step up in capability to justify capex spend. It’s possible that might come - Opus 4.5 brought one - but failing that we’ll see the bubble pop once the IPO pumping is done.

My employer went from the Max plans to Enterprise this month which was utterly stupid. We went from paying $200 per head to like $500 for some people, even more for others. For the same product. Oh well, guess we’re doing our part to prop up this bubble.

The writer is on to something. Musk saw the writing on the wall and bought cursor. AI money is in coding plans

If they’ve spent all this cash, and all we have to show for all this AI baloney are crappy coding agents that constantly make the same mistakes no matter how much you try to guide them.

All the slop content, all the bots, all the misinformation and fake AI images and videos.

All of the social and economic disruption from datacenter buildouts.

The massive nosedive in reliability on the world’s software infrastructure.

After all of that and all we get is a code bot so a few incompetent loser devs can bloviate about not writing their own code and brag about never reading it.

Burn it all to the gd ground. Destroy this new Tower of Babel.

I think this was obvious since the birth of ChatGPT

Intelligence is a universal good, it can apply to anything, and no, "human intelligence" is not the only form that is useful nor special. There are limitations to AI but also huge advantages, and its obvious that the advantages are worth paying for, given their revenue.

He is talking about tokenization of different llm and comparing, and this is inacurate because you cannot compare if you dont how it tokenice the words, some tokenicer could be cheap with more tokens than other because of their features. Not agree with Simón speech.

Unfortunately Simon drank from the AI Cool Aid.

I know everything you’ve done for the tech community, but I please you to take some time off and reflect on this article. It’s not on par with ur usual level, but the tendency has been visible from the last couple of articles.

There is always the next, or other product market fit to find.

They've finally forced the system to fake profits. Attacking personal computing, sucking the rest of the industry dry, yeah eventually you can make people pay for your shit. I still pray these vultures will implode without too-big-to-fail safety nets. And big daddy Trump can't help them anymore by fixing the system for them, no matter how much billions they are paying into his personal pockets.

The future are small models, nobody really needs big compute in the long run, that's why big tech is going for our personal hardware. So we won't become their competitor in their rent only economy. True competition is eliminated, natural evolution is being fixated by the government. This is not going to end well for the USA.

“the reality of AI right now is that it only works for coding.”

Kind of…

Sounds like mostly-baseless praise for the LLM behemoths / capital investment black holes.

> I currently subscribe to the $100/month Max plan from Anthropic and the $100/month Pro plan from OpenAI

... which already indicates a bias.

> If you are a heavy user of coding agents these plans are a fantastic deal... that’s $2,180.16 worth of tokens for $200—not bad at all!

Thank you for the sales pitch. Perhaps go buy a car and tell us how [insert your manufacturer] has "found product-market fit"?

> Anthropic are strongly rumored to be about to have their first profitable quarter.

First, the strong rumor is a claim by Anthropic itself. But even assuming that's true - it's an "operating profit", i.e. disregarding the massive capital expenses for years, and may also disregards ongoing capital expenses, if those happen not to be taken this particular quarter.

> 1 Trillion .. companies spending $200+/month/user will get you there a whole lot faster

First note the use of the first person plural to talk about Anthropic and OpenAI.

But that aside - most companies aren't paying $200 USD/user-month. But even if they were - if we take the 30 Million SW developers mentioned in trjordan's comment as subscribers, that's 2400/user-year * 30 Million = 72 Billion USD / year. And this is already rather optimistic, but - want to double that number of subscribers? Fine, make it 150 Billion / year. Still not there with a rosy outlook and assuming the hype and enthusiasm continue for many years.

And those rosy estimates are more likely than not unrealistic. I am reminded of this review of some empirical research regarding the benefit of LLM/AI use:

https://cmr.berkeley.edu/2025/10/seven-myths-about-ai-and-pr...

HN is the least agi-pilled place on the internet

Anthropic for sure. It's a useful professional product that I find many use-cases for in my professional and private life. OpenAI not so much.

$50/month tops is like 60B

That might be an understatement

The article is at least one year too late. Claude Code has been the product-market fit. It's so obvious retrospectively that questioning it at this point would be very silly.

The problem is not whether they have PMF (they do) but how they're going to compete against on-prem and Chinese providers.

Having PMF != printing money forever.

The author claim:

> That’s $2,180.16 worth of tokens for $200

No matter what it means, rebuild the same thing you built with these $2,000 tokens with DeepSeek Pro V4 and let's see if Claude has a chance to survive.

Ed Zitron begs to differ

I'm a huge fan of agent coding but kinda dislike this "llm evangelism".

There are still several open points (eg.: code churn, maintainability, subtle bugs human will never do) that everyone with a minimal programming knowledge that seriously used a LLM agent knows about but somehow none of these "big influencers" never mention (or just saying "it's your fault").

yep, and the issue is, they took investment

Wait till people figure out they can swap Claude code for DS4 Pro and spend a fraction in API billing (actually, significantly less than $100) while barely noticing a difference.

Man what a disaster article

can we stop throwing around the term "profitable"? didn't they say "operating profit"? so so different to actually making a profit

I’m happy with spending zero dollars on ai lol..

The big assumption with all of these sorts of analyses is that things will continue as they are for the foreseeable future.

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.

> Anthropic are strongly rumored to be about to have their first profitable quarter

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?

> I currently subscribe to the $100/month Max plan from Anthropic and the $100/month Pro plan from OpenAI. If you are a heavy user of coding agents these plans are a fantastic deal.

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?

How will they stay profitable if every business lays off engineers because of AI and there are no engineers to use it? /s

[flagged]

Simon Willison just hit the "Publish to top of HN" button.

I wonder how Ed Zitron will shift goal posts this time, and how long it will take for that article, when published, to reach HN front page.

Reply on news.ycombinator.com