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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.
- 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.
Yes I know there's no evidence and this is lazy reasoning. But there's probably a bit of truth to this line of thought.
Also, inference costs are bound to go way down with more optimized architectures. GPUs are fundamentally not great at inference. No platform where the weights are streamed from a large pool of memory is. If the models ever quiet down, there will be massive step changes in cost/token, energy/token and tokens/second, as models are etched into silicon ala https://chatjimmy.ai/
Speaking to your point, inference being dramatically less costly than training would not be seen as a delta from the norm. The model of providing inference for anything near the operational costs (like a utility would), would the delta from the norm if it were true.
[1] https://x.com/emostaque/status/1563870674111832066
We are still chasing the best because the best is moving rapidly, but it’s a simple thought experiment to work out what the cost to serve an 8B model from 2 years ago is in a world of 2T models.
Note: parameter counts are illustrative. Concretely, qwen3.6 27B delivers opus 4.5 capability at 1/27th the cost on openrouter. Single chip llama3 8b performance can exceed 17k tokens/sec.
Unless to the grandparent commenter’s point they’re using it to obscure their large prisoner’s dilemma (training) cost?
Google seems to pretty regularly post about how their TPU and algorithm advancements have been decreasing energy costs for both inference and training.
* At some point model capability reaches diminishing returns. Then inference >> training in the future but training >> inference now. It’s not a prisoner’s dilemma but a land grab to solidify market position and be one of the 2-3 firms left standing as dominant in the space. The model companies aren’t super sticky yet but they’re working on it.
* even if training remains >> inference, it’s possible to have multiple price points like they do today. If you need the most capable model you’ll be paying exponentially more per token to supplement the training cost even though the serving cost is marginal because most people will be satisfied with cheaper / less capable models for most tasks.
I buy that inference is a dropping line item while training is a growing one. There’s all sorts of things on the horizon that’ll be order of magnitudes improvements, from startups burning models into ASICs to get order of magnitudes more performance to alternate architectures like diffusion transformers that have orders of magnitude structural optimizations. It’s inevitable that it’ll come down even further from where we are. It’s possible model training also will go down but I’ve not seen any compelling research suggesting major “easy” reductions here.
So one possible future is that frontier-level training becomes so expensive and the use cases so sparse that it simply isn’t viable to keep going bigger.
https://www.gpunex.com/blog/ai-inference-economics-2026/
I skimmed the article, but couldn’t spot any details on their estimates. They mention 70b+ params as being large in several places. But we’ve had several 100b+ param models that trail Sonnet.
Training involves multiple passes over the entire training dataset, ideally in large batches where you can perform inference on as many samples as possible simultaneously and then perform backpropagation to adjust the model weights (which is about as expensive as inference).
Let's consider the size of the dataset we're dealing with here. The dataset likely consists of practically every piece of digitized text they can get their hands on (including that extracted from audio and video). We know Google has digitized a large portion of the books in existence as part of their "search book contents" feature and we have no reason to believe they're not using it alongside their cache of 90+% of the internet to train their models. We're talking about 100s of millions of books each with an average of 100,000s of tokens. The internet has 10s to 100s of billions of pages on it with who knows how many tokens on average. This is a huge dataset that we've got to go through hundreds of times.
Second, let's consider the effect of batching and how it sets requirements for our hardware. We know that larger batch sizes converge faster, are more stable, and produce better models. So if you want a good model you need large batch sizes. This means that you need machines several orders of magnitude more powerful than you use for inference. From what I heard Google uses clusters of 100s of the their TPUs all located in a single rack for training. These clusters are organized in a customized computing architecture to maximize memory locality between cores (really critical for efficient back-propagation). Further, you can't use reduced precision weights for training like you can for inference, so there are no shortcuts.
Finally, the initial training stage is followed by reinforcement learning stages - this is key development in how AI models have improved in the past year. This may mean going through a curated set of traces (either synthetic or captured from users) and adjusting the weights based on experienced outcome.
Overall there's so many orders of magnitude more work and more hardware requirements for training that I find it improbable that inference dominates. The number of "inference" steps in training is freaking ridiculous and includes such factors as the "number of words ever written".
That seems like a large number, until you realize that OpenAI claims to have almost a billion weekly users. And OpenRouter shows many models at over a trillion tokens per week.
So in pure token terms, I'd say it is in fact extremely plausible that inference dominates, at least for the popular models.
A given model is trained once but applied N times. A large enough N will dominate training, no matter how complex and costly it was.
But how long is a model useful for? How often will labs need to train new models? Time will tell.
And yet we surely need this data for the IPO? Or are they relying on rule changes on the indexes to force ETFs to buy shares?
Maybe investors will realise that "the only winning move is not to play".
And so we are left with (as was) frontier models getting more and more out of date as whoever their post bankruptcy custodians are tries to eek pennies on the dollar for inference on their decaying property. Perhaps along with local and/or highly specialized models still feeding on the after-glow of the huge amount of training that was (and is no longer) done.
The next AI winter is going to be deep, savage, and long.
Why are they getting out of date? Is it because we have new content from the internet that the older models did not have? Or are we simply trying to increase the size of the training data? In other words not more up-todate in terms of time the content was created vs. wanting to use bigger training-input-sets?
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.
If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn?
AI cost ballooning faster than companies can afford is becoming a very common topic in my circles right now. The era of "I'll pay infinitely more for marginal gains" is over from what I can tell.
They know they do not and that’s why they’re all trying to IPO right now, so they can pass the bag to consumer investors
Your argument rests on the "for marginal gains" part but it's really not clear that the gains are marginal in the foreseeable future.
Just think how much further that $100K would have gone if the hardware market wasn't so screwed-up.
Anecdote: I priced-out adding 1TB of RAM to a four node cluster a couple months ago. The cluster was purchased in fall of 2024 w/ 4 nodes, each with 256GB RAM. The nodes cost just over $14K apiece back in 2024 (entire box, not just the RAM).
Dell wanted >$90K a couple months ago to add 256GB to each node.
RAM is expensive, but not THAT expensive. I just bought 128Gb for about $5k for our build cluster (it's not even for AI, sigh). Even if you need larger-sized DIMM sticks, it's still going to be in the vicinity of ~15k tops.
The Gemini Flash is very good at searches. Just about any low end model can toss out a poem. All the higher end models (open source and otherwise) seem to be able to churn out code that passes tests. The smaller, "less capable" ones are much faster at it, which means in the hands of a skilled practitioner are the best choice for that task. But they rapidly fall apart where there isn't a hard source of truth (like a good test suite) to grind against. Because of that you have to use a bigger model for bug finding. In that task the open source models tend to fail on larger code bases, where something like Opus still shines. I gather Mythos is an absolute monster, and unparalleled, and unavailable. I'm sure one of the reasons for that is it's so expensive to run.
Or to put it another way - you don't use a 100 tonne crane to pick up the shopping. And ... the smaller models will happily run on in-house hardware. You may not do it today because of the current DRAM price and integrated NPUs have just started shipping, but in 5 years time models will be running on your phone.
It's a given that the SOTA models need to raise their prices. It's also a given that they can't. The more they raise the more customers will move to their competition.
So what happens next? Well I think it will suck horribly if you can't move off of SOTA sooner or later, because the Big Two are going to lose customers, and therefore have to raise prices on the locked in customers even more than these projections suggest.
Beyond that if you're looking to start a business, figure out how to use cheap models in new scenarios. Build software which does that and license it. This is kind of contrary to the idea that you shouldn't over optimize for deficiencies in the models that will likely go away in the next generation - for instance a lot of problems were solved when context windows got way bigger. So it's a thin line to walk but I think it's there because a lot of orgs are using Claude today for pretty basic tasks.
The dev who's addicted to SOTA models honestly is going to have to settle for less or get totally screwed. Most applications within business from what I see aside from complex research do not require SOTA. They summarize, they classify, they transform, and doing that accurately has been cheap for a while.
But what if your competitors sell their knowledge to AI companies?
Then you're still screwed.
AFAIK you would get about ~5 concurrent users, with a max context window of ~128K tokens on the larger models.
This wouldn't be good enough for coding -- are you guys thinking of using it for something else?
The decadal move to all-cloud-all-the-time killed off in-house hardware teams while the C-suite chased their OpEx dreams.
It would be interesting if we come full circle on this.
What makes you so confident about this prediction? Hardware costs haven't exactly been cratering recently.
No, but local models have been booming in performance/quality improvements. The RAM shortage won't last forever (more supply will come online when if demand doesn't diminish), and then the math would be pretty easy.
https://github.com/antirez/ds4
My guess is there’s gonna be some legislation or something “you can’t share anything over this level of complexity” and I think that that’s what a lot of that mythos rattling was all about
The current frontier? Sure. The frontier then? No - obviously that frontier is going to keep consuming available datacenter compute capacity, which will be better
There are physical limits to how much you can compress data and how much is needed for a capable model. If by hardware capable for running SOTA you mean a 7 figure investment for a company, than sure. But how come these companies didnt do the same thing for cloud? There's been this option for self hosting infrastructure for a decade but companies don't use it, they pay AWS.
I was going to say - the models are just going to keep growing at a pace exceeding the pace of hardware pricing/availability
But then I realised that, far more likely, there will be a plateau reached (again) where nobody is seeing gain, and at that point hardware will catch up
"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
I feel like the reverse assumption is being made, that the current model looks like IBM doubling down on Mainframes soon to become cheap enough to deploy everywhere, when the real action is that the costs coming down represents cheaper hardware or more efficient software, and that a big chunk of "cheaper" AI will be eaten by smaller products deployed by individuals. Whatever the Personal Computer of AI looks like is going to be more disruptive than just an API endpoint you can fling tokens at.
We already see this with things like chrome auto installing an LLM.
You cant tell me with complete certainty that theres a moat here for the people spending 1 trillion + on this infra.
>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 also think this applies to people suggesting that companies will sack workers for AI, when the costs of replacing everything someone does in a day is more expensive in terms of tokens (likely even at a reduced price) than just hiring a bloke.
I realized it long ago: one needs output to make meaning. Input can only be the cherry on a cake in one's life. That, actually, makes FIRE or Fat FIRE not so sustainable unless one has other hobbies.
And what happened? How many hours per day/week are people spending watching now?
A lot of these LLM demand scaling scenarios make broad "up and to the right" assumptions about things which in practice have finite limits. Only some percentage of knowledge work benefits from acceleration, optimization or other improvements, and even then the amount of economic gain is capped.
Surely we could just put better stuff on the radio, and accomplish most of the same goals for a far lower price?
anthropic already hunts down OpenClaw users for using too much on their plan.
I'll give different example: When LED lights started to be more popular, the power usage didn't drop by the amount of power saved
>- tokens will NEVER get cheaper due to improvements in hardware, software, design, market forces etc etc
Well, first, improvements in computing stalled or even rolled back just purely because price of everything compute shot up cos of AI and that will NOT be fixed for a while and ESPECIALLY if AI usage will continue to increase
Second, the token per model might go down in time but better models have more expensive tokens, so we quickly get into spot when:
* price increase in token might not be worth marginal improvement next, better model brings
* more and more models are passing "good enough for the task" threshold so for less and less companies there is any economic sense to pay for the "best" instead of paying deepseek or some other company to run "previous gen" models
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.
I would argue that that's been the case for quite some time before AI. As an example, what innovative amazing world-changing products have Google or Meta launched in the past decade with their very high numbers of very talented and highly-compensated engineers? The issue with most big tech companies are leadership, strategy, and product direction. I'm not saying that they don't make any profits, just that they probably aren't "building [the right thing]".
AI for product development and management would be far more impactful than automating rote coding tasks / building React UIs that mirror API structures IMO.
Yeah, if this stuff actually worked that well already, OpenAI et al. would just run AI CEOs and engineers. Why get some other company to pay you at all when you can automate every other company out of existence and take all the money they make?
The fact of the matter is that while the tech has some uses, it sure as hell isn't a full scale replacement and you almost always actually have to massage the input into LLMs to get anything decent back out in practice. Some CEOs and managers can learn to do this, of course, and some already are... but that quickly turns into a second full time job. A "programmer" is still needed. The job might change from mostly hand-writing C++/JS/Python to prompt engineering + some manual coding to fix all the stupid fuck-ups that the bots can't solve themselves, but you still need someone to actually prompt the bot.
When that changes, it won't just be engineers losing work; there will be no reason to even have a human CEO any more.
I don't think there is any shortage of great ideas at these companies, they are just extremely bloated. And I don't think its something like indecision or bad PMs, it's "we have a finite amount of time and resources so we need to be conservative but also not too conservative"
If you have AI systems that can simply build out POCs in days, backtest on real data, show reliable results and numbers, you get a suite of product options you were never able to get before. If you have coding agents that can speed up implementation, you can build more stuff and choose the things that stick.
It changes the cost/benefit calculus of the entire business. I think you are exactly right in that: PMs/leadership are by their nature orchestration machines. Other roles are as well, but I think PM's are at a particular advantage here in that it will be quite awhile I would expect before core product decisions and creativity can be delegated to an AI, but not quite awhile until virtually everything that they're blocked on (legal approvals, POCs, wire frames, etc etc etc) will become less and less of a blocker
If they can crack that latter review/spec-check/assurance step, checking that what was built was what was demanded of the problem such that we don't have humans in the loop at that step either, then the bottleneck moves again. Then I think it moves to requirements capture and to product development, but that might depend on the industry.
Second to this are countless other areas that have a major impact on the companies bottom line that are entirely engineering driven, especially at google given they are a cloud provider and have meaningfully grown the workspace business and launched waymo in this time.
Kubernetes is at 11 years ago, and is huge enough to be included there. The Google Pixel was just under 10 years ago. So... not nothing haha
The problem is they get killed by some other executive who is afraid of their department looking bad by comparison.
I think this is fairly illustrative of the challenges in AI becoming as impactful as the Internet. The bottleneck is not making things. There are plenty of people who are really good at making things and can easily be 10x or 100x as productive as the average corporate worker. YCombinator was founded on that premise - small teams of founders and early employees could be orders of magnitudes more productive than the 1000s of corporate employees at their competitors.
The bottleneck is on bringing your product to market. If your innovative new product is built within a corporate environment, it'll get killed unless the executive you work under can get a promotion out of it, and you'll be denied all sorts of help with approvals, launch process, PR, marketing, branding, etc. If it's a startup, they'll try to shut you out with exclusive distribution deals, legal threats, lobbying efforts to change the legal environment, PR campaigns, FUD, etc.
The Internet was revolutionary because it let millions of people bring products to market without asking permission. Instead of having to bid for retail shelf space among dozens of entrenched competitors that all had sweetheart deals with the retailer, you could just put up a website and sell it to anyone across the globe. Instead of following hundreds of regulations that governed existing commerce, you could just launch something and sort it out later. AI doesn't really have that property - if anything, it makes things more centralized, with more gatekeepers, and so seems more likely to destroy economic value than add to it.
I would agree but it's really minimized the building. More and more time is being spent on pre-coding work.
You'll find that most internal "innovation" teams are just lip service. In most cases, the "mothership" will be incapable of reproducing true innovation -- from a statistical perspective, culture perspective (mega corps are anti-scrappy; internal politics), and motivation perspective (startups aren't 9-to-5). It's much easier to have big M&A budgets, a VC arm, and some handwavvy internal innovation group.
Every now and again, you'll get real innovations (Waymo, transistors, GUIs), but even those have a spotty track record of commercialization when created internally.
I suspect that AI will fail to pan out to the same extent for the same reason why outsourcing hasn't fully panned out (even though every company tries it after getting big enough).
The problems that will come up will be and always have been ongoing maintenance. AI is great at writing new code without a brain behind it, but once you get to the point where you need to refactor code, you start really needing someone with coding experience to guide the AI or veto it's mistakes.
I don't think that's really fixable even with a lot better AI. It's not something that ultimately comes out of the likes of github data.
I'm not saying that AI isn't going to make things better, btw, I just don't think we'll see a 20x improvement. Probably more like 1.5 or 2x.
The determinant of success was only whether the task needed American-tier labor or could make do with sub-American quality labor.
My mental model for that is that outsourcing fails where the work is being done organisationally far from the knowledge needed to do it. We know that's true of teams inside organisations, there's been a lot of research on how distance in the organisational tree negatively impacts productivity. Outsourcing is a pathological worst-case of that.
The promise (promise! We're not there yet!) of AI is that I can have a cross-functional team on my laptop. Organisational distance is zero. Where previously the outsourced team has to wait for the time zones to roll round so I can answer their blocking question when I get to my email STRICTLY AFTER I have had my coffee, now it's a prompt in a chat window with a button I can click to make a choice in 5 seconds. Delay is gone, cost of delay is gone.
> The problems that will come up will be and always have been ongoing maintenance. AI is great at writing new code without a brain behind it, but once you get to the point where you need to refactor code, you start really needing someone with coding experience to guide the AI or veto it's mistakes.
Oh, absolutely. That's a minefield. Today. It will be, right up until it isn't. There are ways to set up agents and projects right now that make a dramatic difference to how this part of the picture plays out, but those will sink into the harnesses as time goes on.
But also the big problem with maintenance and outsourced teams tends to be the commercial structure around the contract. You get a Build team, who Build the Thing and then: no more features for you, anything you want to add past the original spec costs extra. They hand over to the Run And Maintain team, who get to fix all the bugs that the Build team left but without the knowledge gained from building the thing, but are scaled and located to be absolutely as cheap as the supplier can get away with so probably don't have the skill, inclination, motivation, or permission to take on any restructuring to make the bug fixing easier and they're on the wrong end of the globe so there's a 24-hour latency on any queries. It's a terrible way to set teams up, but it looks good on paper.
Again, that's peculiar to outsourcing and completely goes away if I have the same team that built the thing own the thing long-term. That's true if it's humans or AI!
> I don't think that's really fixable even with a lot better AI. It's not something that ultimately comes out of the likes of github data.
No, it's a harness problem. You need to start from a maintainable point and keep standards in place. It'll take work to get the harnesses there and it's not ubiquitous. You might also need better models, but I've already personally seen big differences in outcomes between projects that took certain steps and others that didn't; it's nothing revolutionary, mostly stuff that works for humans also works for AIs but you need to know to ask for it.
> I'm not saying that AI isn't going to make things better, btw, I just don't think we'll see a 20x improvement. Probably more like 1.5 or 2x.
I think people radically underestimate the cost of delay. I don't know if 20x is realistic for the AI itself, but I think it's not impossible once the inefficiencies of having to go to other humans is factored in.
It sounds like the economy would largely reduce to the small minority class of independently wealthy people.
It takes a skilled knowledge worker to use these things.
To follow on from that comment, if the growth in breadth of capacity of AI leads to a decrease in the risk of running a smaller business, which I don't think is an unreasonable prediction, then it's not inevitable people do lose their jobs. Employers get smaller, higher-leverage, and more plentiful.
They do not care unless these companies can get a bailout.
UBI only exists for companies that are too big to fail. Case in point, 2008 and SVB when there was too much money on the line.
One of the AI companies attempted to guarantee themselves a way for the government to bail them out if they were close to defaulting on the debt from the data center build out.
What makes you think the people who used to build (or would have built) software will switch into the industry of "knowing that the thing was the right thing to build", as opposed to something cooler like surgery, city planning or experimental physics? The roles within a tech company are not the only jobs in the world.
I don't.
> What makes you think the people who used to build (or would have built) software will switch into the industry of "knowing that the thing was the right thing to build", as opposed to something cooler like surgery, city planning or experimental physics?
Because it's probably already part of the job. It's a change of emphasis, not a change of career. Your boss can already ask you to do it. If you're producing code, you're probably also reviewing code, checking it matches the acceptance criteria, testing it, sanity checking that it was the right code to have been written, today.
“There’s more capital than good ideas to fund” has been a complaint from the likes of A16z & other VCs for a long time now. It’s why we ended up with stuff like NFTs getting funded.
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.
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?
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.
I was recently consulting at org where two separate engineering teams were all in on two different, incompatible deployment platforms and using AI to accelerate adoption of each.
Management was mystified why their engineering leads kept telling them they couldn’t deploy a complete implementation of their solution.
The coding agents got good in November. Most individual engineers didn't fully clock this until January/February. This means that companies didn't really figure it out until March/April.
Assuming companies like Delta have adopted coding agents (which would be pretty fast) it still takes months from adopting a new tool to the code results of that tool rolling out to production.
I expect (and would hope) Delta's software development culture is very conservative. Since nobody can confidently tell Delta "here are proven practices for using this tech to produce high quality, more secure code" yet it would be surprising if they were blasting full-steam ahead.
I expect that even companies that got on board with coding agents in January will only just be starting to ship user-facing features that benefited from those new tools. Shipping software takes a long time, no matter how much faster the "typing the code in" bit gets!
No one I know feels richer than they did a decade back. I've not been able to meaningfully put up my prices for a decade. People are tired and stressed and scared, particularly scared of a technology everyone keeps telling them will make them redundant.
There is no rising tide lifting all boats, just most of us drowning whilst a few whizz past in their yachts.
I honestly hope these guys faceplant ASAP. Couldn't happen to a nicer bunch of people.
Consumption has risen, inflation adjusted wages have risen for blue collar and white collar alike. Most social mobility has been the middle class moving into the upper middle class, not moving to the lower class.
The main thing holding people back is the housing crisis. This is orthogonal to the value creation of businesses.
Value creation is growth. If it didn’t exist the S&P would still be 42.55$.
It's kind of become socially taboo to not be suffering "in this economy", but on paper it's hard to see weakness in places that there isn't always weakness. As long as the 65-95% are doing well, there isn't going to be a collapse.
True, but I think the GP's point was that what consumers will pay won't be nearly as profitable as what enterprises will pay to increase the output of their developers and knowledge workers. ChatGPT is currently the overwhelming leader in consumer AI usage but only ~5% pay $20/mo.
As a recently retired serial tech founder, I'm now one of those consumers. I use AI webchat daily for general search, Q&A and even to write little automation scripts for myself, yet I haven't paid anyone anything for AI yet. Even after being heavily restricted and performance nerfed to hell in recent months, free webchat AI is still fine for everything I do, and I'm not remotely price sensitive.
Even as AI compute costs fall over time, I doubt serving ads against AI webchat to consumers will generate the kind of high-margin, sustainable growth VCs get excited about. It's so undifferentiated I bounce around between all four leading providers because there's virtually no moat locking casual consumers to any chatbot beyond a single question thread. I guess if it had a nearly infinite context window seamlessly integrated across all sessions, that might be somewhat sticky for some consumers but it could also get creepy for some others - and it would devour gobs of the scarcest resource in AI. Beyond Maslow's Hierarchy of Needs, the mobile phone is the largest revenue, long-term mass consumer product ever but I just got a new flagship phone from a top-tier provider for $30/mo over 3 yrs. IMHO, even an all-you-can-eat, infinite context window, next-gen Mythos couldn't reach and sustain mobile phone levels of global consumer adoption at ~$20/mo. Unlike professional developers and knowledge workers, consumers don't have any "job to be done" big enough for an LLM to command that much of their zero-sum discretionary spend.
What sort of new value, and why will people pay for it from someone else rather than prompting for it themselves?
The AI might very well be used by noticeable % of population daily, but that doesn't mean they will be paying trillion dollars to the leading US AI companies
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.
source: https://isaiprofitable.com/
If John Ternus wants to spend some money, spend it on bringing memory in house. Apple has the money and the engineering talent to do so, have it fab/made onshore in partnership with TSMC.
Do it Apple because you have to not because you want to the Chinese probably will be taking over the memory industry, worldwide, by taking advantage of the greed from three memory companies and their AI overlords.
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.
The more AI causes productivity increases, the less and less number of workers will be needed. This will heat up the job market even more and bring salaries down.
Net effect of this productivity increase: less consumption by the masses, even though you may be producing more good and much more efficiently.
A third effect also comes into play that once all this starts to happen, common people, who are generally living paycheck to paycheck, will now start to hesitate towards making any long term investment, housing included. And that indirectly will end up impacting financial and banking sector, which will then impact existing savings, bonds yields and retirement funds, and the recession-like cycle starts.
This productivity increase only makes sense if it is capped to a very small number.. like 20% max. Beyond that, who these companies will even be selling to?
Am I overthinking all this?
That only holds if companies have a fixed need for "productivity" which is met by their current employees, such that their employees becoming more productive means they need less of them.
Every company I've ever worked for has wanted to achieve way more than they are able to get done with current resources.
But generally yes, the biggest open question about all of this is how the impact will play out on the economy, job opportunities etc. I've not seen anyone come close to a confident prediction about how this will play out.
>Net effect of this productivity increase: less consumption by the masses, even though you may be producing more good and much more efficiently.
Big tech companies can't even create login flows and account recovery flows that work for everyone yet. There are countless stories of folks losing access to business Instagram accounts that get hacked, Google support from a human to fix a problem that is outside of their help articles is non-existent, etc etc. There's still so much "low-hanging fruit" IMO that isn't particularly fun or exciting to fix, but ask your average non-tech friend or family member what they think of the Facebook + Instagram security settings pages / sites / desktop-only settings.
Who is going to pay for all of these subscriptions that will power this GDP increase when average purchasing power of those outside of the top ~10% of earners is decreasing YoY? We're headed toward food and water shortages next to sprawling datacenters, not shared societal prosperity and a healthy middle class.
Secondarily, reducing the cost of making a thing doesn't always mean you get less of a thing. For me, certainly, what happened is that I write way more software than I originally did. When we built compilers, the amount of human engineering effort required to do things plunged, but the amount of software engineering jobs didn't go down.
This is as bad as models will ever be. That part is true. And it's entirely possible we go foom. But it's also possible we don't, and then it depends on where the asymptote lands.
0: https://www.slowboring.com/p/this-economic-myth-needs-to-go-...
Nope, if AI were to realise the hype, you have to take into account macroeconomics. Usually this isn't a problem for most businesses
>The more AI causes productivity increases, the less and less number of workers will be needed. This will heat up the job market even more and bring salaries down.
People also underestimate that the reason why companies are so excited about AI isn't to increase productivity, its to fire workers and crack down on worker rights. They won't lay people off because AI means they don't need as many people to get the job done, they'll fire everyone while doing a much shittier job, because they hate having to abide by worker's rights and pay people
Why does this have to be the case with AI but it didn't have to be (and wasn't) the case with the steam engine, electricity, the automobile, or the computer & internet?
Certainly, AI could be different.
It's curious to me why the vast majority of people on here think it must be different.
This might not necessarily be true. Increased efficiency creates induced demand to the point where more workers are needed. Because the new capabilities unlock more value to extract and the economy rushes in to get it. The steam engine is a huge example of this
I dont exactly know what new value genAI will unlock but i think its more likely than not
2. Where does this $5T number come from? If they make $4T in revenue over the next 5 years instead, what happens?
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
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.
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.
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.
But by then, I will be able to go one line down in my dropdown menu to switch to a newer LLM provider who doesn't have to amortize those past capex.
When you break it down like that it seems reasonable. I'm spending about $5k/mo on tokens, seems more and more normal.
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.
Privacy is also a huge issue.
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.
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.
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)
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.
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
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%.
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.
Data centers come down to performance-per-Watt. Electricity accounts for 20-30% of a data center's operating cost [1]. I don't know the exact breakdown but the GPU part of that is probably the majority given how power hungry GPUs are. The B200 is upwards of 1200 Watts [2]. The B200 is rated at ~4.5PFLOPS of dense FP8. So you're getting 3.75PFLOPS/W. We don't know what the next generation will look like. The A200 (Hopper architecture card that preceded the B200) had ~4PFLOPS apparently but also lower power consumption. Obviously this changes depending on whether you're looking at dense or spare and FP8 vs INT8 vs INT4 vs FP4, etc so we're just using FP8 as a yardstick.
Imagine a fictional B200 successor, the T200 that has 8PFLOPS of dense FP8 at 1000 Watts. Well then a DC built on that where the T200 will likely cost similar to what the B200 does now, you'll get nearly double PPW so the same size DC and same electricity load is going to be like 2 of your old DCs in operating costs. That's a big deal when you've laid out a trillion dollars.
[1]: https://iaeimagazine.org/electrical-fundamentals/how-much-el...
[2]: https://www.trgdatacenters.com/resource/h200-power-consumpti...
You have either never seen a tech cycle, or need to be reminded of that. The pressure to buy more expensive plans is already starting to form.
and in that sense, if Anthropic and OpenAI are able to create the projection that they can-be profitable despite finances seeming bubbly at best, I think that what happens is that these companies spew so much amount of content that people like Simon get into it too.
There is a deeper problem of people falling into AI psychosis too, in general, I am not sure if Simon has fallen into it or not
I think that the greatest point which can be made here is to not offload your thinking to others and to think about the situation yourself. Sounds familiar (looks like we are all off-loading our thinking itself to machines)
Side-note: As humans, we have a tendency to quickly judge or make quick decisions which stems from our times foraging and scavenging in jungles.
Another Side-note: at a certain point, I am unsure of how much to think about AI or not, certainly discussions about it that were happening 2 years ago weren't helpful in contexts that they are used now (well not in any way or form that a person discussing and getting into the weeds of AI 2 years ago is better than a person just getting into it say 2-3 months ago)
With the industry (moving so fast) [but that doesn't mean that you can't catch up with it, I feel like the fast word has made people think that they are falling behind which is imo wrong i suppose]*, It is basically unsure to me of any FOMO or anything if you aren't using AI already, I find this notion naive.
People might be making strong opinions (AI psychosis) and skills on the tools available at the moment the same done 2 years ago. We don't quite know about the tech as these are still black-boxes and how they progress and what these "AI skills" might survive or not in future. Heck, we aren't even sure if these tools might survive or not or wouldn't be made magnitudes more expensive simply to break even as they are given to us for the first time at percentages of the price.
I don't know if I should form (strong) opinions yet and also a question of its worth so much thinking efforts in the first place, probably just gonna do my own thing (the way I want to) which includes learning C at the moment. because learning is fun.
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.
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.
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).
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.
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.
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 scope is too narrow. The companies target more than white-collar jobs. And $1t is around 0.5% of the world economy.
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.
1 in 6 knowledge worker is a developer ! Surely that’s too high thou explains the job market
I am willing to bet a Twix we'll look back on that stuff in 2 years with a lot of embarrassment
+ 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.)
Uber was basically only ever software to help people use their own cars so a very small part of their valuation was physical stuff to upkeep, it was just deals and obligations they had.
Not sure how it shakes out for Anthropic and OpenAI. There’s a lot of physical capacity that needs to be built out and can depreciate. But there’s also a lot of network effects and dependencies being built in with enterprise users.
I don’t know how swappable the tooling is either. I think over the long term the UI, model training and documentation, and infrastructure are going to end up being run by different parties and I’m not sure which leg of that chain ends up in a position to skim most of the profit off. My guess is that Apple and Google end up raking in all the money since they control the OS and app stores while the rest of the stack gets driven down to being generic commodities. At least where mass market consumer adoption is concerned.
> But then you sometimes go and talk to your senior engineering leaders and you’re saying, OK, how many projects that were on the cutting room floor got moved above the line because of the productivity gains because 25% of our code commits were via Claude Code last quarter?
> That link is not there yet, right? I think maybe implicitly there’s more that is getting shipped. But it’s very hard to draw a line between one of those stats and, OK, now we’re actually producing like 25% more useful consumer features, right? And that line is hard to draw.
That's pretty weak sauce. I don't think that justifies the headlines that came out of it, personally.
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...
My usage is therefore a useful indicator of quite how much those enterprise companies may be spending on tokens, given the new pricing scheme.
If enterprise companies were still getting the same discounts that I get myself I would not have written this article.
(I had to dig into your margin figure - looks like you calculated 5.1% as 559000000 / 10900000000 * 100 but that $559M "adjusted operating profit" figure includes training costs, where usually when we talk about margin on inference we're not including those since those costs are fixed, margin calculations make more sense against the variable costs of serving a token.)
It’s like the industry is willingly introducing a common external risk to everything
It's much like when developers would waste tons of money on AWS spinning up massive test VMs and leaving them running without care. Until the finance people cracked down on it.
> 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.
Simple - you make them work 2x, 5x, or 10x more hours.
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.
Imagine an employer with 10 employees paying $500k per employee and making $2M per employee in revenue (to use your numbers). They could hire two more employees and spend an extra $1M (+20%), but make an extra $4M in revenue (+20%). Alternatively, they could buy all ten employees a $100k AI subscription, for a total of $1M extra spending (+20%) but an extra $4M in revenue (+20%). You'll notice both scenarios are identical, so an employer optimizing for profit would have no reason to prefer one over the other.
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!
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.
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.
(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.)
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?
We've also increased how much our coworkers need to read, or deal with. You can get an AI to make any point you want, so you can ignore the 5 humans raising alarms due to the 1 clanker you made say what you want to hear.
All numbers going up.
There are obviously people producing additional true value with it, probably, but that's almost certainly scarce.
[1]: https://en.wikipedia.org/wiki/Perverse_incentive
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.
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.
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
> 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.
T
It's lossy compression for thoughts at this point
It really does have a particular lane for each chore, and it’s reproducible.
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?
Wait what? They spent 2 order of magnitude less on hardware.
> Gartner forecasts that large AI companies would need to earn cumulatively close to $7 trillion in AI-driven revenue through 2029, which is close to $2 trillion per year by the end of the period. In order to achieve “historic returns,” the providers would need to earn nearly $8.2 trillion in the same period.
Everyone's agency is 100% captured by belief in Wall Street. Too few <50 have any meaningful labor skills to blink.
We'll continue to have consent manufactured via media platforms and in 3 years no one will bat an eye at these companies being worth $12 trillion as Altman and Musk climb two ladders holding a "mission accomplished" banner.
Or did we just get scammed?
I'm not even sure that 1 in 8 people I know would qualify as a knowledge worker, let alone a knowledge worker that might profoundly benefit from on-the-horizon AI. And I'm in a highly skewed population.
I see a lot of out of touch takes here but this might take the cake
How do you know this? Im certainly open to recalibrating my numbers which is why I asked for the source
Basically if you're not doing manual labor, it's probably knowledge work.
Roughly 1/3rd of the working population.
Some data tucked in here: https://gist.github.com/danielmiessler/2dc039762a202b083753b...
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.”
This is the most recent example I found last week for a local barber:
https://news.ycombinator.com/item?id=48166050
They seem to be using Manus: https://manus.im/
And my other assumption is that it immediately integrates with IG/Facebook which is where they do a lot of their marketing
I see no reason that trend is going to slow, especially if you can go to meta to manage your entire business marketing.
Regular people running business just want fast cheaps and good enough.
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.
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).
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.
People stopped buying shit.
>"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...
I mean it is doing both of those, so thats fair to be honest.
It's not exactly news, is what I'm saying. And even with the PMF they found, the product is still only a commodity i.e `tokens`, which is what every other provider on the planet is also providing.
All their other products boil down to "harnesses", which does not look viable as a product in the sense of PMF - you cannot sell it, you cannot lock it to your own subscription, API, etc. so you can't use it to generate revenue any more than the free harnesses do.
PMF has a specific meaning, and "code harness" or "coding model" does not satisfy the commonly accepted meaning. Maybe Mythos (or similar) will.
How enormous? 1 trillion dollars, 2, 10 trillion enormous?
What are your directives?
My take is the product has been very useful for coding (PMF) for months. But it’s certainly not useful at any cost…
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.
Somewhat oversimplifying; writing software and building apps was a bottleneck - now it is not. What is the next bottleneck that LLMs can solve? Is there one? And is there enough publicly available data to solve it repeatably at scale? Or did we just automate stack overflow searches and now we’re stuck again?
Or is the endgame of this innovation cycle the complete removal of interaction with machines through code? Will we simply interact with machine coworkers purely through natural language? Can an LLM make PowerPoint slides and run a meeting? So far not seeing much progress on that.
In other words, most of the prompting will also go away.
Sure, it might start to slow down, but even then we will likely see a doubling in the next 10-15 years.
https://substackcdn.com/image/fetch/$s_!_ZW2!,f_auto,q_auto:...
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
I'm not sure if this runs counter to your point or not, but: I don't see any future where LLMs aren't a core part of Software Engineering. The horse is out of the barn. There is no going back.
And I don’t even necessarily disagree with OP! It’s more like the competition is shifting so quickly that your competitors could undercut your PMF in a blink of an eye.
people -> programmers, I haven’t met a non-developer who reports getting more time out of current AI platforms than they put in. If anything I’ve anecdotally heard the opposite, introducing AI at work creates so much slop (output) it takes more time to process it all without a tangible bump in overall productivity
"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."
Not saying this trend will do the same, just that the industry adopting something doesn't guarantee its success.
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.
If I make an argument and you disagree that's fine with me, provided I didn't use misinformation or sloppy thinking in making that argument.
52 on AI misuse: https://simonwillison.net/tags/ai-misuse/
149 on the unsolved challenge of prompt injection: https://simonwillison.net/tags/prompt-injection/
40 on slop: https://simonwillison.net/tags/slop/
If you want an "LLM evangelism blog that rarely, if ever, has any critical analysis that isn’t pro-industry" there are plenty out there. I'm not one of them.
Many people still think AI coding agents are slop on steroids despite all the current hype around AI actually shipping functional products.
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?
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
I highly doubt I'll ever use Claude again.
I think you are wrong about Claude being any significant level better
Once the model gets good enough, the returns on bigger models diminishes quickly. I don't want to spend 10x the money and wait 5x the time to get answers that are equivalent.
Unless ofc there was an actual speed difference, only reason I'd be willing to go with a worse model couple of percent worse than current best model is if the speed was at least 5x higher. Looking forward to kimi k2.6 offered publicly by Cerebras
And this is why many companies go out of business. You always want the best bang for your buck, sometimes this is the "best model" and sometimes it is not.
This is transparently false, because the best "model" is still competent human developers. They're just more expensive. If you're willing to use current LLMs at all, it means you're willing to sacrifice quality for a better price, and your disagreement with the comment you were replying to is entirely about what the optimum tradeoff is.
Currently, the difference is substantial, but what happens if capabilities saturate?
Will this always be true? There will never be an event horizon/point of diminishing returns where something not-bleeding-edge is "good enough" for 51%+ of users?
Ofc again, can be convinced to switch if there's however a clear speed difference, like 5x+ for a open source sota even if it was SOTA for 6 months ago
Oh, hey, I recognize you. Thank you for the very forward and thorough orbital sander recommendation at Home Depot. That's exactly what I wanted to deal with on my holiday weekend. You just know so much about this and the rest of us are simple passersbys.
And also, people have it wrong… their models are not the main problem anymore. It’s the RAG
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.
Like how snapchat kind of fell off because the feature could just be a subset of instagram
It seems like it would just become a commodity like EC2
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.
In latest experiment I used opus for implementation plan then used cursor composer 2.5 for execution.
I must say that combo is really good. Main drawback of claude code is that is super slow. So when paired with composer that is super fast it flies.
But there have been very good open source office apps for decades and few enterprises use them, so perhaps this is just the nature of B2B purchasing committees and 'nobody getting fired for buying IBM.'
At work I mostly use Claude Code and a bit of Codex; personal projects are OpenCode and honestly I prefer it.
None of them are quite opus, but they are damned close and a no brainer if you care at all about cost.
For me, it feels like widely available open models have recently crossed that same canyon. Are they as good as e.g. late-model Claude Opus? I don't think so. But they have absolutely gotten past the point where they are beneficial. This means that, for me, they are about six months behind.
Not sure about other domains though.
“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 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?
We have no market convergence on tokens yet (and it'll differ between LLMs), so it's impossible to say what value you got for your $200.
Simon is saying that companies are (today) willing to pay API prices for tokens which is as good as any determination of value.
You seem to be suggesting the price of tokens is entirely disconnected to the cost of providing the service? I don't see much basis for that assumption.
Maybe if you spend $2000 on a BigMac. But it’s unlikely you would buy such a burger.
What is a hamburger worth? Don’t look to McDonalds to set the value.
Does that mean you'll be saving $99k?
It sounds an awful lot like the mark-up to mark-down scheme where the price stays the same.
your point is large players won't pay those prices at massive volume. ok
The point being made above is that API pricing is calculated... somehow... seemingly arbitrarily. Possibly untethered to the infrastructure costs entirely: which would be the basis of any 'value', however that holds the labor theory of value, which isn't accurate either. So how do you accurately price these tokens at all (other than through price-discovery: which is slow, messy and fuzzy)?
Like anything else in the economy: at the point where enough customers can pay you, and not enough will go to the cheaper competition.
Or are you saying that Anthropic is determining that cost per million arbitrarily?
If so, it'd be like asking to explain why things fall on the ground other than through gravity. Companies pick the price they think the market will bear, and adjust based on new information.
As with pretty much anything priced on volume/usage.
Enterprise deals are negotiated ad-hoc, the listed pricing is simply a jumping off point for the final negotiated discount.
If you’re going to give 20,000 employees Claude code you are not going to be spending $1B per year on Anthropic tokens as if you gave everyone an individual API key. Just as Anthropic isn’t paying AWS SES $10,000,000 to send 1 email update to their massive user base when the next Claude version drops.
Going to be interesting to determing the metrics we give to engineers for determining whether the spend on this is worth it. Measuring PRs, lines of code committed, commits fully generated by agentic workflows, etc.....
Do you have any numbers or reports to back that up?
How much do you think emails cost? That number is just so far off?
But besides that, running SES is also quite a bit cheaper than SOTA ai models with high demand (and comparatively) no competition. And quite a bit more pressure to make money (soon).
edit: I missed the "enterprise" feature matrix with the usual audit/compliance stuff to force the biggest enterprise customers onto enterprise plans. Otherwise the "teams" plan is much better value for any business.
orig-continued:
https://claude.com/pricing/team
Teams premium is "Everything in standard, plus more usage*"
And from my experience, it's a very generous usage, I've only hit the limits once or twice, and both times required multi-boxing agents.
I could single-window agentic development all day on opus-4.7 auto-mode without hitting limits.
If you're a business using claude, then that seems like the right plan, the enteprise/API plan seems more suited to where your product is built on top of the agent themselves, so seats/limits aren't really meaningful?
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.
I don't believe you have the option to keep with the $200/month flat rate subscriptions any more. I'd be happy to be convinced otherwise.
(I dug into this a bit more and couldn't find anything in their consumer terms that say "you cannot use this personal account if your company has more than X people", so I imagine the pressure is more that your big company's purchasing department really doesn't like managing hundreds of individual subscriptions as opposed to a single, stable, predictable negotiated contract.)
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.
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?
> 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'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.
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.
- 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.
It’s quite an elaborate swindle obviously. But you generate hype with underselling your core product, you claim way more usability then there is. Users will experience usability initially. Everything multiplies with each other and then you put it on the market. Everybody involved makes money and you’ve succesfully extracted money from everyone who’s invested in NASDAQ index funds at the very least.
> 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”?
That’s Anthropics marketing, yes.
Also their offering is not uniqe that justifies a 1 trillion valuation. The first companies are already rowing back. It’s a really certain time window that they are about to hit now with their IPOs
The companies that have signed these enterprise deals haven’t done a ROI analysis. They had Fomo.
They are saying profitable companies should replace the engineers that built their systems with a subscription (while they are hiring).
The only times when people talk about actual full replacement of people is always when they are talking about some “future AGI” that is far more capable than the tools we have today.
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.
> 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.
But memory costs are going way up. And both OpenAI and Anthropic bumped up the price of their frontier models in April.
Supply will eventually catch up with demand. Then the prices will come back down.
Eventually either the supply will go up or companies will start buying fewer overpriced GPUs.
Either way, the price per token will come down as hardware improves and supply and demand reach equilibrium.
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.
(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.
As you might suspect, this is what I have an issue with. Without LLMs, isn't it possible or even likely that that code wouldn't have been written at all, and wouldn't have been missed? If LLMs are mostly used to produce throwaway prototypes then it's a stretch to say that's money well spent.
If indeed it let you advance your main product much faster then sure it's a different story. You're the judge of that. It's hard to see the impact from the consumer side; everything is still broken and no extraordinary app seems to be emerging. Maybe it's just a question of time. We'll see.
Open source software changed the world. AI that will cheaply write whatever you want in a few days will also change the world.
From this I assume you think that what the llm has generated is as valuable as your own work generally is. How do you even calculate this?
(I have a feeling if I could say "and I closed $2m in sales with the software I wrote!" people would find a way to say that didn't mean anything anyway, because how can I prove I wouldn't have made those sales writing it by hand?)
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.
[1] https://www.autodesk.com/products/autocad/buy
[0] https://winchdesign.com/ [1] https://www.superyachts.com/directory/1516/winch-design/flee... [2] https://www.autodesk.com/design-make/articles/naval-architec...
I might agree "AutoCAD" is the current level LLM's are at, but wait until your design departments discovers "Revit", its another ballpark (in wasted cots, engineers on site still get "clashes").
Revit costs are high, and the end results are marginally better - but local LLM's tokens are cheaper 24/7 at "AutoCAD" level - "Revit" level tokens will make Ubers CTO/COO weep harder than they already do. While producing results no better than "Revit" does (engineers still face "clashes").
For a pretty funny comment about pricing.
https://www.reddit.com/r/chipdesign/comments/1ajrli2/cadence...
I guess we are welcoming the software people to the world of expensive tools. Just sad that the FOSS alternatives of these tools are not as powerful whereas software industry still has FOSS tools to fall back on.
edit: typo
What does ICP mean?
- dedicated hardware (https://cloud.google.com/tpu)
- optimized models (https://research.google/blog/turboquant-redefining-ai-effici...)
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.
Personally I see no difference between China and America in terms of risks of them embedding "backdoors" so to speak, but I disagree when people claim that open-weight models are obviously safe just because they can be ran locally.
Sure, you can self host a non-frontier OSS model yourself; including Deepseek. And no doubt some people will pay one of the companies I mentioned to rent the infrastructure to do exactly that. Much of the rest of the world will be paying directly for direct access to the frontier models.
As for the legal/compliance stuff, I recommend you don't take any big decisions on that front without consulting lawyers. My understanding of that is that most serious companies in the EU have to take these topics pretty seriously. I'm sure in the US, hosting all your data and secrets in Chinese data centers isn't a whole lot less controversial.
The Chinese could of course choose try to match the current levels of investment Google, OpenAI, Anthropic, etc. are putting into local infrastructure. But as far as I know they aren't and there are probably a few political blockers for that.
Without infrastructure, their role is being a niche player in these markets. It doesn't really matter how good they are if they can't scale to most of the market.
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 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.
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.
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.
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.
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.
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.
The customers of these tokens need to see returns on their projects that exceed the cost of financing.
Laying people off only goes so far.
If enough said firms don’t see enough value given the price of frontiers they will cancel and consume open source. This is the risk the frontier labs are exposed to.
Dario telling Dwarkesh three months ago that they have a margin on inference: https://www.dwarkesh.com/p/dario-amodei-2?timestamp=3528.0
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.
The money would be so much better spent that way as well, supporting individual programmers.
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?
I do agree with the author that these companies seem much stronger financially recently though.
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.
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?
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.
How so? What's specifically changed? We still don't know what their unit economics are and everything you've documented is basically speculation at this point.
1. Both Anthropic and OpenAI significantly increased the prices of their latest models. They're clearly not trying to offer the lowest-price-possible to drum up demand any more.
2. Both Anthropic and OpenAI no longer let enterprise companies buy discounted almost-all-you-can-eat subscriptions. Those big enterprises are now paying full API prices.
3. According to reasonably well-sourced leaks, Anthropic may be about to have their first profitable quarter.
And I didn't even say "profitable", I said "credible business model". I think getting companies to spend hundreds of dollars per month per seat, WITHOUT crazy subscription discounts, is a credible business model.
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.
You may want to get one of them to check the math on that :p
I've been calling that out for a couple years now. LLMs best and most viable use case is still just as a dev tool. Even for non-programming tasks, I still get better results from the LLM if I instruct it to write code to do the task...look at Claude Cowork for example, it's everything I used to do with python myself. It's not really a novel capability, it's just using python & bash for automations that any sysadmin has been doing for decades. Yeah, that's valuable for a non-techincal audience but is it $1T valuable? I don't think so.
When has an IDE or other dev tool ever commanded a $1T valuation?
These things get lost in discussions because people conflate "overvalued" with "not useful." LLMs are useful, particularly as dev tool, but Anthropic & OpenAI are definitely way overvalued.
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.
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.
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?
Other than the hosting providers, I am also yet to see anyone directly making money from their OpenClaw agent.
why would enterprises do that if they can just use bedrock or vertex?
I'm building a product right now with some AI coding (despite my negative sentiment about AI in general they are useful). I am both the product person and the engineer, and I'm pretty decent at using it, so according to the hype I should be seeing like a 10x speedup. I am not seeing that. It's definitely faster, but there are also days where I'm stuck cleaning up things after going too fast for too long, or periods where I need to put the software in front of people to get real feedback, or even periods where I just need to use it extensively myself to find the pain points and bugs. I just don't see this "running circles" once you get past an MVP and you actually need to build something secure and not embarassingly broken.
If not lower priced chinese offerings will be better as its cheaper per token - giving you more attempts to offset the variance.
My feeling on the former is no... I believe they tried really hard but they've settled on pure marketing now to attempt to fight off the chinese with perceived superiority in quality.
Maybe acceleration in smaller teams. We still seem in the era of the early internet where what questions LLMs change hasn't exactly emerged.
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
Bloggers are having AI psychosis too.
I agree with this person, let's use AI psychosis for when using an LLM gives someone psychosis, not for when we think, what, that a blogger made some poor assumptions?
https://news.ycombinator.com/item?id=48296794#48303200
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.)
The impact of AI in other fields seems to be muted.
Software development has the huge advantage that mistakes and hallucinations are very easy to spot: the software works or it doesn't.
Spotting errors in a research report or legal brief is a whole lot harder!
But... non-software professionals spend a huge amount of their time on tasks that can be safely automated - reformatting documents, extracting numbers from PDFs, all kinds of flavor of data entry.
Learning how to use a tool like Claude Cowork can take a big dent out of those.
Do we not care about code quality, maintainability, performance, extensibility, or understandability anymore? Honest question, not a gotcha, it's just previously getting software to pass all the tests was a small part of what we would consider "working" or perhaps "good" software. Maybe that's different now with LLMs, idk. Maybe we need automated checks for these things as well, like not compiling until the code quality is good enough to let the agent finish it's loop.
Yes, we should care. I've been writing a whole book about that: https://simonwillison.net/guides/agentic-engineering-pattern...
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)
Not so sure I got that value from Claude, just because I've been using it a lot less and somehow the price came to about the same as OpenAI.
Given the code I've been able to build in the past month I genuinely do think I got value for the API price version, and (don't tell OpenAI or Anthropic) I think I'd have paid full price.
I've not spent nearly enough time with GLM-5.1 and co to compare, but I do know that the prompts I'm using with the agents are not prompts I would have expected to work just three months ago.
When I account for the amount of time it saved me there's no question $2,000 was worth it.
Personally, I've probably spent $60 or so on OpenRouter in the last month or so and got a working project out of it that it would probably have taken me a fortnight to knock together (which is inevitably an under-estimate because it covered things I'd have to learn but K2.5/6 already knew). There's an orders-of-magnitude gap there.
Ran `ccusage` on my Claude Code logs.
- Total tokens: 22.2B
Without current Claude deals, my personal cost would have been *~$112,000*.
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.
While the big guys will argue they’re worth trillions expect others to drop chaos booms showing their NPV may be effectively zero.
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
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.
So ballpark same price per parameter as Simon.
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.
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 don't think that's the case. I think the token leaderboard thing (which is clearly ridiculous) affects a tiny portion of companies and is already going out of fashion.
We're also in a place where a lot of the usage guidance around these tools is still nascent. People are cowboying a lot of stuff, even as larger companies start to organize AI policy/safety/responsible use working groups to try and policy around the shortfalls of the technology.
IMO: if this technology persists, and if we figure out a way to use it in a broadly safe way, the value proposition will probably trend down rather than up, at least on the code generation front.
As a research tool, it shows some promise, though I still find the ethics of the technology disgusting.
However the valuations are still far far away from actual sanity
I use glm-5.1 and occasionally deep seek v4.
They are as good or better than Claude's latest models.
And significantly cheaper. I've converted 3 of my engineer friends as well. All three have dropped their $200 month plans they had with anthropic.
We've all been a bit shocked at just how good these models are now.
If you "have" tried GLM (I specifically find it shockingly good for code). Did you not think it's not competitive to Claude, and why?
It's good enough for personal stuff. It doesn't compare to the latest Opus I use at work. You can certainly argue I don't need Opus for work, but there is clearly a difference.
Also, at least with z.ai, GLM-5.1 is s l o w! After using Claude at work, I get really impatient with GLM-5.1 at home. When doing "true" vibe coding (i.e. not really examining the code), Opus is a ton faster (easily 5x).
But yeah, I'm not willing to personally pay for the frontier models. I won't even renew my annual Z.ai plan - it's become too expensive.
Also, and I know you may not want to answer. But could you give me an idea of the type of thing you found glm to be worse with?
I think I've been fairly unbiased in testing a bunch of different development tasks. But am curious if maybe it performs well for some stuff and not others. So if you could share what you feel it's worse at.
Also are you an experienced developer or less experience?
When DeepSeek V4 Pro came out, I had been mostly coding with GLM-5.1 on a Z.ai coding plan.
I had a large analysis task on a relatively complex codebase. I decided to try the models out.
GLM-5.1 did acceptably but got a few things wrong (easily corrected) and took quite a while to get there.
Opus 4.6 burnt through the US$10 budget I had given it in about 10-15 min, without ever returning from the first prompt.
DeepSeek V4 returned a full analysis within 2-3 min, and I carried on all the way to implementing the feature I was after. Total cost less than US$1.00.
I now mostly alternate between GLM-5.1 and DeepSeek V4 Flash, with an occasional dip into V4 Pro for more complex analyses.
right now everyone is using latest and greatest to do dumb stuff like that. that would change fast if companies start caring about costs.
Any org with more than 150 users aren't on $200/month plans, they are forced into API pricing + $20/month/user
For individuals and orgs small enough to get to use the subscription plans, that's all well and good until usage limits keep going down, or cost goes up. If you compare the usage you get on $200/month maxed out vs. what that would cost at API pricing, the $200/mont plan is an absolute steal. I doubt it will last long.
On the plus side, I'm happy I'll have a nice hay barn when the local half-built AI data center is abandoned.
Recent conversation here on that topic: https://news.ycombinator.com/item?id=47062534#47063134
But that's the point of the article. Enterprise plans are starting to get API pricing, not the subsidized subscription pricing.
Just imagine how funny it will be if it comes out that big labs were doing some fancy maths to count the 2k$/month in their forecasts ...
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.
I thought this was one of my best pieces of writing this year.
(In case you missed it, the title was meant as a subtle burn on those two companies - it's pretty absurd for them to only just be finding product-market fit when they're already valued at over a trillion dollars.)
Also my highest respect for responding so calmly without lowering your debating level to mine. I try to best to explain what I think is wrong.
To start I didn’t interpret the article as a burn.
I think it’s interesting to explain what’s wrong with it, because it seems similar to what‘s wrong with AI. The issues are subtle.
- Anthropic doesn’t have a profitable quarter, it’s financial engineering (https://www.wheresyoured.at/anthropics-profitability-swindle...)
- The first argument about your subscription price, doesn’t has anything to do with the overall claim of the article. It would if at all be a weak argument to support the opposite. Subsidizing prices signals a lack of PMF.
- That you hire sales people after you had a billions of funding is nothing surprising and doesn’t indicate PMF or not.
- AI Implementations are fresh and of course AI Failures are thin, but so are AI successes. I haven’t seen any companies creating billions of shareholder value because they’ve massively invested in AI and their competitiors didn’t You really can look at these things in 5 to 10 years and it is multi-faceted including cultural acceptance.
- That they need to buy more compute to satisfy the requests is probably the strongest argument in the artical, but don’t conclusive. The product is been sold heavily subsidized and in hype cycle. And again both OPENAI and Anthropic have to show growth in order to justify the IPO.
- Regarding the part about revenue I refer to the linkedm article above, as it does explain it very well.
The conclusion is reasonable given the arguments, but not the title.
However it is missing all the real discussion points that are actually in observation at the moment.
Local models as alternatives, IPO finanical engineering, how AI implementation actually will perform over years... Let’s all not forget crypto. It’s been full of "use cases" just a bunch of years ago. I like the idea of crypto(btc,eth) and I’m still invested, but 99.99% of coins have died on promies.
So this is not a piece of critical thinking, but this reads like a twitter thread to sell me a course :/
Funny to see the change of tone - a lesson for people not to get too ahead of themselves.
You financially benefit from stuff like agents. Of course you will be the last to admit publicly when things aren’t quite heading in the right direction. The gap between hype and reality is ever increasing.
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.
https://news.ycombinator.com/item?id=48268871
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).
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.
Is that quarter same as any other quarter in terms of infrastructure costs (e.g. are there any temporary discounts happening coincidentally)?
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.
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.
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").
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.
> 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...
In hype-driven markets, you cannot be certain of that.
Let's take a view that the author is right: coding agents and their associated harnesses were the inflection point for some degree of profitability and widespread consumption, and that these tools are now yet another SaaS subscription or API bucket expense to bake into every single developer (or developer-adjacent) in the organization alongside your collab suite, HR seat, CRM seat, design seat, etc. To be fair I honestly think that's a safe assumption to make for highly technical firms whose image is derived from remaining on the cutting edge of things.
That begs the following questions, which we won't know until IPOs start happening:
* Are subscriptions profitable, or just API consumption?
* What's the run rate when we just consider subscription-based usage like Claude Code and Codex? What about API calls?
* Is there any profitable pathway forward at which enterprises can get unlimited usage but at fixed rates via subscription?
* What does customer churn look like for subscription users versus API users?
We also have a number of questions for customers that I suspect we'll start seeing receipts for in the coming months, at least from the early adopters:
* What was the net gain (loss) from leveraging coding agents?
* What's the cost of a developer with or without access to a coding agent + harness? Is it cheaper to hire an outsourced worker with a coding agent subscription, or a domestic worker without one?
* At what point does further AI spend result in diminishing returns, i.e. where's the 'sweet spot' for spend?
* Did AI boost actual revenue and outcomes, or did it just gamify KPIs?
* What roles or work did AI actually replace, versus merely displace during the hype cycle?
Not to mention the questions regarding the technology itself:
* Will we develop the means to run foundational/frontier models at edge using less resources through some existing (e.g. distillation) or new technology, thus cutting off the profit centers of these firms?
* When the market mismatch between supply and demand is resolved, won't it be more affordable for consumers and companies to operate their own AI infrastructure rather than support further centralized buildouts?
* Will coding agents improve to the point of being able to bootstrap and self-orchestrate on edge/consumer hardware without substantial technical expertise, or at least improve to the point that traditional IT teams can securely operate them internally without an expensive subscription or API token bucket?
All of these will influence the long tail of this bubble, because it is a bubble at this point. Even if these companies are indeed profitable thanks to the coding agent inflection point, there's still so many unanswered questions about utility beyond coding that it's impossible to extrapolate a future. If coding agents are indeed the extent of utility for profitability, then there's no possible way these entities will recoup the investment already sunk into their infrastructure buildouts. Even if more profitable uses are discovered, does this offset or replace the firms disappearing due to AI speculation and their associated contributions to the economy as a whole (RE: the consumer compute industry at present, higher energy costs due to datacenter builds, opportunity cost from harms to local infrastructure from haphazard builds, etc)? Should these firms indeed be runaway successes and immensely profitable to the point of paying off their investors and growing the larger economy, does this end up stifling innovation in a world where most new ideas are fed into LLMs for R&D that are then controlled by only a handful of companies and immensely wealthy people, via systems that are easily surveilled and stolen from without recourse?
So many, many questions yet to be answered. Betting the farm because of coding agents is one hell of a gamble.
No, its more like their own leak to WSJ and according to Ed Zitron -> seems to be heavily engineered via non-GAAP practices such as counting potential, but not realised revenue as actual revenue - the stuff for which I would be arrested if I did it at my company.
Also it appears according to Ed's analysis - strangely they seem to be projecting only that one quarter as profitable - potentially to calm the investors ahead of the IPO. Investor fraud anyone?
https://www.reuters.com/commentary/breakingviews/anthropic-g...
If you've ever been at a startup, this is exactly what it looks like when you go from not having product-market fit to having it (though with a few extra zeros on the end compared to most).
Please don't forget that Ed's entire brand identity is now 1:1 with exposing "AI" as a giant, unmitigated failure.
That's a very specific flow chart to hook your caboose to when none of this is even remotely close to endgame.
There will be big parts of what he says are true once the rubble settles but it will not be anywhere near what he is predicting. How that will shape out may not be great for the average person, what money shuffling tricks will be used? But it won't be a total wreck.
Honestly, I think it's very short-sighted to assume that all of this will be seen as any kind of wreck in the long term.
Normies are still catching up and reacting to chat-based LLMs.
HN types are further ahead of the curve, but still catching up and reacting to agentic coding and design workflows.
What often gets completely ignored is that entirely new modalities for how the underlying tech can be applied will continue to be demonstrated, and those will once again cause new ripples of excitement and disgust.
There are companies building world models and systems for protein discovery. Comparatively speaking, these approaches are barely in the zeitgeist today.
Deciding that we already have the data points we need to extrapolate how all of this plays out is like someone in 1974 deciding that microprocessors are just for accounting and inventory. Don't be that someone.
> According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns.
The load-bearing detail here is if that means $2,000 of internal server+electricity costs, or $2,000 if they were to charge at their API pricing instead of the subscription cost.
The latter is how I understand these things to work right now. If it's the former then yeah, Anthropic are losing a TON of money on those subscriptions.
It's a funny metric considering Depreciation is a huge cost for them.
"We are profitable when we don't count our expenses"
Those GPUs are very expensive.
Inference is expensive because a GPU can only process a certain amount of requests in a given timeframe. Remember that Anthropic is constrained in compute.
If they are constrained, it means that those GPUs are not idle. If they have more customers, they will need more GPUs.
If they have to play silly games using EBITDA to be "profitable", then it means that they need to ramp up prices a lot more than they already did.
Which is why in these discussions I always say that inference is also extremely expensive. Too many people like to pretend without any evidence that inference is cheap.
The move to buy up ram is straight out of a industrial organisation textbook.
Like, I understand the reasonable arguments against (I even agree with a few), but it's clear that some people have fully inserted their head into the sand and just don't want to believe any of this could be true. Which will be harsh, since I think getting hit with this train all at once in the future is going to be a rougher ride than a slower coming-to-terms-with, even if the result is one we're unhappy with.
Back in 2024 their CEO claimed training costs would rise to $10-100B in the next years.
https://www.tomshardware.com/tech-industry/artificial-intell...
I assume this is the quote you're referring to from Davos?
"I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it. I do the things around it… we might be six to twelve months away from when the model is doing most, maybe all of what SWEs do end to end."
that was in Jan, he said "might" and he said 6-12 months. Yes! Let's hold him accountable for saying reasonable things!
So, he's closer to correct than not.
That said, your recollection is also flawed. It was in mid-March, and here's the relevant quotes:
>I think we’ll be there in three to six months—where AI is writing 90 percent of the code. And then in twelve months, we may be in a world where AI is writing essentially all of the code.
[...]
>But the programmer still needs to specify, you know, what are—what are the conditions of what you’re doing, what—you know, what is the overall app you’re trying to make, what’s the overall design decision? How do we collaborate with other code that’s been written? You know, how do we have some common sense on whether this is a secure design or an insecure design?
[...]
>So as long as there are these small pieces that a programmer, a human programmer, needs to do, the AI isn’t good at, I think human productivity will actually be enhanced. But on the other hand, I think that eventually all those little islands will get picked off by AI systems.
With another 3-4 months left on the clock, his prediction seems remarkably on point for at least certain organizations and domains.
I welcome you to also hold yourself accountable in the coming months if this trend continues. ;)
So, unsourced vibes from a shady guy whose entire empire is built on being against AI?
I genuinely don't know how folks can continuously buy into anything he has to say after that Wired piece. The credibility there is seriously lacking.
Please, continue to be skeptical of the labs. But people need to stop talking about this dude as if he's the Holy Grail of the anti-AI movement. It's going to blow up in y'alls faces.
I think it's telling that most critics don't address his actual points, but instead his credibility because he's a "hater".
That said, I really mean it when I say that I don't actually think Ed is a good choice for the anti-AI movement. I think an actual opposition is useful, but he ain't it.
I really recommend you read the Wired profile if you haven't yet and form your own opinion: https://www.wired.com/story/ai-pr-ed-zitron-profile/
Actually he provides sources when he analyses stuff and imho much better than the usual corporate "Sam Altman says we should ask ChatGPT how to raise babies" crap. Also, I don't know many 'shady' guys who have built entire "empires", nor does he seem to actually have an empire. Usually being shady means you are kind of unknown and all. I am not glorifying Ed, don't even know him personally. I am not even impressed with his writing style much to be honest. But he brings important facts and information to light, which otherwise would have been lost in the cacophony of corporate media light treatment of these con-men. Holy Grail? Blowing up in our faces? WTF are you talking about?
You said it was likely an internal leak to the WSJ "according to Ed Zitron". Did Ed have a source for that, or was it just vibes?
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?
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