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The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.
by InsideOutSanta 3mo ago
The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.
- DiscourseFan 3mo agoThe technology is too hard to capitalize on. It’s far more democratic than, say, an iPhone, or a search engine. Anyone can download a model to their computer and start toying with it, how do you profit off of that? Even if everyone was constantly tokenmaxxing (which we cannot, since the process gets fucked up if you let it run entirely on its own), it probably still wouldn’t be marginally profitable.
- erwald 3mo ago"Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-companies https://epoch.ai/data-insights/revenue-per-employee-ai-compa...
- toomuchtodo 3mo agoThis assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance. As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open model landscape looks like later. Model training and development is expensive, self hosted inference on open models not so much. https://www.wheresyoured.at/the-openai-bubble/ https://www.wheresyoured.at/the-openai-bubble/ has the math. (a component of my work is currently building scaffolding so our organization can swap out commercial inference providers for on prem inference infra to derisk against the eventual rug pull when the math gets icky for LLM providers, while consuming as much subsidized tokens as we can until then, when it makes sense to use tokens for work)
- lenerdenator 3mo agoThe question will be whether customers can switch. Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship due to the long history China has of hacking Western networks and computers. They don't care how open and cheap things are. Then, you have to keep up-to-date on the latest technology and right-size things in a very fluid market. If you sign a contract for hosting the model on a data center that's running what the SOTA is now in hardware, and someone comes through with a data center hardware or software product that makes that data center contract a disadvantage (maybe it's too expensive and the other party won't budge on the price), you might have to factor that into your offering's price, and that could put you at a disadvantage in your marketplace. Google, MS, etc. all want to leverage the cloud model to make this be less of an issue for you, for a price. They have the ability to update you with the SOTA stuff in the data centers, because they're the ones driving that SOTA. They can say they host in the US and develop most of their stuff in the US. Will that be enough of a moat? Probably not for the levels of spending that are happening now, but over the long term, probably.
- toomuchtodo 3mo agoMy primary role is cybersecurity in a regulated entity in a regulated industry, I am highly confident it is straightforward to do so based on work accomplished in only a couple of weeks. Stand up a router, stand up a Kubernetes cluster if you don't have one, stand up the necessary VMs and compute for serving inference. Two pizza team, in my experience. Customers can switch (although we can argue the speed and pain of doing so), and the speed at which they do will be a function of cost efficiency and demonstrable value (imho). A recent example of this is Broadcom and VMware [1], for example. When motivated, it can be done. If there is no objective, measured value being delivered, the spend will be cut. If the value delivered is measured, it will be enabled at a lower cost through cost optimization measures (ie self hosting) [2]. This is all to say: there is no moat, the revenue of inference providers is volatile and not assured in any measure. Caveat emptor. [1] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&query=vmware%20broadcom&sort=byDate&type=story https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu... [2] Microsoft considers replacing ChatGPT and Claude with Kimi K3 to save $600M - https://news.ycombinator.com/item?id=49022984 https://news.ycombinator.com/item?id=49022984 - July 2026
- throwaway27448 3mo agoAh well we just need to convert our entire economy into an MLM and then I'm sure we'll be set
- erwald 2mo agoLots of people paying for a product they use is more or less the opposite of an MLM
- wolttam 3mo agoEach one of those employees maps to several fold times more spending on compute.
- erwald 2mo agoTrue, but the comment I was replying to was about revenue, not costs
- icedrift 3mo agoRevenue isn't profit though. Anthropic is already profitable OpenAI financials have looked doomed for the past year
- lightbendover 3mo ago[dead]
- Insanity 3mo agoI’ve never seen anything point to Anthropic being profitable.
- erwald 2mo agoRight, and the comment I replied to was about revenue, not profit. (That said, while I don't think Anthropic is already profitable, it reportedly expects its first operating profit later this year.)
- InsideOutSanta 3mo agoThe revenue needs to be way, way higher than this to warrant the investment.
- erwald 2mo agoMaybe, but that's a different claim. You wrote that the improvements are "not translating to a dramatic increase in revenue", but going from about $1B to about $30B run rate in 16 months seems like a pretty dramatic increase to me!
- TheOtherHobbes 3mo agoThey've replaced employees with compute, so RPE is irrelevant.
- erwald 2mo agoThe point is that revenue is large and growing quickly, which is what the comment above mine denied
- serial_dev 3mo agoHow is RPE relevant if they are spending hundreds of billions on compute and data centers?
- erwald 2mo agoIt's relevant to the claim I was replying to, which was that revenue isn't growing, not to the question of whether the spending will pay off
- Ekaros 3mo agoSo they can add employees endlessly? And still make same revenue? Increasing employees only scale so far at those numbers.
- erwald 2mo agoNo, I wasn't claiming that revenue scales with headcount, though it probably does to some extent. The point is that these companies' revenue is large and growing quickly, which is what the comment above mine denied.
- yunwal 3mo agoThe guy who sells $20 bills for $10 also generates a lot of revenue
- dirkc 3mo agoIsn't that just saying CapEx is a bigger part of their costs as if that is a positive thing?
- erwald 2mo agoI wasn't saying anything about costs, only that revenue is growing quickly
- budsniffer952 3mo ago>not translating to a dramatic increase in revenue. Completely false. AI and AI related revenues are growing exponentially.
- dgellow 3mo agoNot for the companies using the LLMs…
- budsniffer952 3mo agoAre you denying that AI revenues are growing? Or are you just adding nonsense about "yeah but yeah but no value"?
- dgellow 3mo agoI’m saying there is no proof that companies _paying for AI_ are seeing a positive effect to their ROI. If you have such a proof, please share, that would be a massive news
- TheOtherHobbes 3mo agoExpenditure on compute is growing even more exponentially.
- jerf 3mo agoI know it's easy to forget, and InsideOutSanta kind of anchored the conversation on "revenue", but profit is ultimately what matters. Back when Silicon Valley was merely insane rather than bat-guano crazy insane, it was commonly observed that it's not hard to build a business around selling a dollar for 95 cents. The point being that it doesn't necessarily mean much when you have a business doing that, because of course the demand will be insane. It doesn't mean you have a viable business. You don't know you have a viable business until you transition to selling a dollar for $1.03. Many a VC-funded business that looked successful, even wildly successful, has run aground on that transition, or at least, suddenly stopped looking so wildly successful. If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially. The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less. Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful. As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use. Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to. There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger". And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle. [1]: https://jerf.org/iri/post/2026/programming_is_engineering/ https://jerf.org/iri/post/2026/programming_is_engineering/
- ac29 3mo agoThe article notes Google Cloud revenue grew 82% YoY
- paxys 3mo agoHow much did Google spend to get that increase?
- inigyou 3mo agoWhy do people choose the cloud with a history of randomly deleting billion-dollar accounts?
- manarth 3mo agoUniSuper? (The claim felt so wild I wanted to check, and indeed, the private Google Cloud for the $125bn Australian pension fund was accidentally deleted by a provisioning misconfiguration. Any others?)
- zdragnar 3mo agoIIRC, the files for Toy Story 2 were accidentally deleted during production, and the film was only saved because someone on maternity leave had a backup at home. Turns out you can fuck up self hosting too.
- inigyou 3mo agoYes, Google randomly deleted UniSuper for basically the same reason they randomly ban individual customers: they don't care. Relying on them for anything is a huge mistake.
- wongarsu 3mo agoSource? Has Anthropic's annualized revenue not quadrupled in the last 7 months? And OpenAI's annualized revenue quadrupled since January 2025? Which is only unimpressive by comparison to Anthropic's meteoric revenue growth I'd be with you if you claimed that the revenue hasn't translated into substantial profits. Being able to spend a lot of money to get less money back is not that impressive. But revenue by itself is on a dramatic rise as capabilities improve
- InsideOutSanta 2mo ago> Anthropic's annualized revenue That's not a meaningful number, and even if it were, quadrupled isn't nearly enough.
- paxys 3mo agoMoreover there’s no guarantee that eventual AI profits (if any) will go to the companies investing all this cash. If the worst case scenario of Chinese labs building and serving frontier-level models on 2nd tier nvidia hardware comes to be then what will be left of all the “hyperscalers”?
- raincole 3mo agoExcept it did get translated to a dramatic increase in revenue. "Dramatic increase" is a ridiculous understatement here, by the way.
- thewebguyd 3mo agoAns so far, the dramatic improvements have come with an increase in API costs. Even if, hypothetically, Fable or a Fable-class model could seriously replace some headcount, it'll only gain further traction of it's actually cheaper than hiring humans. $50/MTok is expensive. Wouldn't be unreasonable to expect somewhere between ~$3k-$5k/month/developer in spend. Cheaper than a Junior in the HCoL areas (in the US), but not much cheaper in lower-to-average COL areas. Most acceleration will come from having the headcount + giving said headcount $3k-$5k/month in token budget, so now it just becomes a very expensive dev tool rather than a headcount replacement tool. The idea that a $30k/year API bill will replace 2 $100k developers falls part outside of SFC/NYC. No CFO of a mid-market company in a LCOL area is signing off on $3k/month/dev API bills. They'll just hire juniors and cap their spend at $200/month.