AI revenues are growing fast, but not fast enough

an0malous

https://archive.is/VktAK

iammjm

“ According to Mr Yotzov’s study, nine in ten executives report no impact of ai on their firm’s productivity over the past three years.” Brutal stuff.

an0malous

> A back-of-the-envelope calculation finds that covering aicapex through identifiable ai income requires revenue on the order of $2.5trn per year, more than tech’s entire combined revenue today. … > All these complex calculations roughly tally with a much simpler one: adding up the ai revenue of the firms selling most of the ai. Anthropic pulls in perhaps $75bn, annualised; Openai makes tens of billions; Google, via its ai model Gemini, and Microsoft probably get a bit less. SpaceX may have a few billion dollars’ worth of revenue from enterprise aithis year. Meta also makes a few bucks from ai. Add this up and you land at roughly $150bn a year.

softwaredoug

You really see the first mover disadvantages in US AI labs. And how second movers (open Chinese labs) can disrupt them. First movers tend to get overcapitalized and way ahead of their skis. You saw a smaller version of this in the vector database market (remember that?) where companies like Pinecone were getting billion+ valuations for capabilities which now seem like a commodity. There's vector DB companies now with much less debt / VC obligations that don't need to clear insane revenue levels to justify investment.

thisisthenewme

I kind of think the AI boom is the worst for (non-inference-providing) companies. If all companies are using the same "frontier" LLMs, and if they are competitive, what gives one an edge over the other? I think, just the people. Which was the same as before, but now with the additional AI spend that they can't cut, or they become less competitive.

VCFundedGenYer

I mean, all one has to do is review https://isaiprofitable.com/ to see just how bad it is. None of this is a profitable venture. It's just a money pit for all players involved, all the while draining our ability to buy electronics and drink water. This has to end at some point.

hahahaa

Attributing to per-worker might be the wrong thing. I.e. how many kWh power does a person buy direct vs. use indirectly through other trade. In other words usage per regular worker doesn't matter. Revenue overall does.

pu_pe

> Exponential View, a consultancy, counts $175bn of generative-AI revenue, on an annualised basis, in June. In a recent paper Anton Korinek of Anthropic and Patrick McKelvey of the Bank of Canada estimate total “AI services” revenue. Adapting their methodology, we reckon this was $220bn (again annualised) in the first quarter of this year. Ramp’s data imply that 2-3% of business spending now goes on AI, pointing to $170bn a year. So between $170bn-220bn in annualized AI revenue today. Maybe it doesn't cover trillion-dollar bets but this is a very substantial number.

woeirua

A couple anecdotes: I noticed that were a lot of traditional, non-tech companies interviewing for AI engineers in the Feb/March timeframe that have halted hiring in those roles entirely. It seems that if those roles didn't close by mid-April that they didn't close at all. This seems to match the timeframe in which cost suddenly became prominent in the AI zeitgeist. I don't know _anyone_ outside of SV who has successfully replaced even a single employee completely with the current models. Maybe someone has pulled this off in call centers, but the POCs have all failed. I'm very much pro-AI, but I just think we're on a false summit. As the article points out, there is absolutely no way to recoup the investment costs unless the models allow companies to start displacing human workers by the millions _and_ recapture a significant fraction of the displaced workers total comp. If either of those aren't true, then the bubble is going to pop... soon.

QuantumNoodle

The thing is, AI is a productivity boost but staff drive all the productivity. The catch 22 is AI is expensive so in order to provide it to everyone they need to reduce staffing. The result is remaining folks are able to produce more but the organization as a whole is not exceeding pre-layoff output. And now things are being dropped on the floor and falling in between the cracks -- which impedes efficiency and velocity. Happening at my company now. AI inference needs to get cheaper or there will always be this "terminal velocity." I suspect AI labs' incentives to reduce costs is only where there is overlap to free up hardware/utilization (to then provide inference to more paying customers.) I highly doubt they will want to make things cheaper for users -- they have debts to pay. Open weight models are the way and forward, it is the only way an organization can truly control costs by self hosting or buying cheaper inference. Relying on closed weight models is a business risk. Kimi models are only marginally worse than opus but significantly better than the bleeding edge of yesterday's sonnet.

da-x

It makes sense that if software turns to a commodity by AI, and models are also commoditized, then distribution is all that matters. Anthropic is therefore attacking other's distribution in order to protect its business. Perhaps they should focus on their own ways of distribution.

jsnell

> A back-of-the-envelope calculation finds that covering AI capex through identifiable AI income requires revenue on the order of $2.5trn per year, Ok, so what is the botec? They don't say, but it's probably something like "next year's $1.5T/year in capex * 50% return on invested capital + a little bit extra for opex". Obviously a given year's capex doesn't need to return its investment immediately the following year, but over it's ~5-7 year depreciation period. So in making this botec, they're not just taking next year's capex, but extrapolating it to a steady state of $1.5T/year in capex. But why would that level of spending be steady? It's exceedingly unlikely to be. Future capex is not locked in. It's contingent on revenue and revenue growth. AI revenue has been growing faster this year than even the most optimistic projections suggested. It's hardly a surprise that capex projections were dialed up. If revenue growth lagged instead, it would go the other way. (You need only look at Google Cloud growth and margins to get an idea of how good an investment last year's AI capex was. But at the time, the arguments for it being crazy were identical to those made today, except with the numbers substituted.) There's a separate issue, which is that you can't really think about this on a sector-wide basis. In most businesses there will be winners and losers, demanding everyone be a winner is unrealistic. E.g. right now anyone with the business model of renting out the compute is being showered in money, people with a business model of building non-frontier models are losing money. The latter group's bad strategy doesn't invalidate the former group's good business.

keeda

There are numbers in TFA that explain the current CapEx craze, but they're not called out. (Also it's a big selective in which numbers it reports, see below.) > The Bundesbank finds that about half of German firms using AI do so for 5% of working hours or less. That seems to be from [1] and matches what the St Louis Fed reports here [2]: 50%+ of American use AI weekly, but only 6% of working hours. Google's report [3] finds similar "broad but shallow use" where many people use AI for a small subset of tasks. Now consider that all the hyperscalers are already extremely crunched for compute capacity. They are drowning in demand, and have been reporting this for the past several quarters. Nothing illustrates this better than the fact that Google of all companies -- whose massive infra footprint has always been considered a killer advantage in the AI race -- had to go rent capacity from SpaceX! And this is at only ~6% usage at work; imagine what it will take to get to even 30%, let alone 100%! This is why these companies are feverishly scrambling to build more data centers even as Wall St punishes them for their insane CapEx spend. The trillion $$$ question, of course, is whether this will all be profitable. There are many sources we could consider, but let's use one from TFA itself [4] which it selectively quotes as: > According to Mr Yotzov’s study, nine in ten executives report no impact of AI on their firm’s productivity over the past three years. The same source also says: > ...these same executives predict sizable effects over the next 3 years, predicting that AI will boost productivity at their firms by an average of 1.4%, raise output 0.8%, and cut employment 0.7%. So these executives clearly plan to spend more on AI. If the 1.4% seems small, consider that labor compensation is ~50% of global GDP, or $55 trillion. In a simplistic "what the market will bear" sense, even a 1% efficiency boost is "worth" 0.5T annually. Now if 1.4% seems high, look through [5] (and also similar numbers from Germany in [1] BTW.) These are the numbers AI companies have dancing in their eyes. Their challenge, of course, is to capture all that value, but to do so you first need to capture AI usage, and for that you need compute capacity, and hence the current CapEx splurge. I do agree with TFA that the biggest impact will come from organizations "reworking their processes." However I fear what this really means is significant job losses. [1] https://cepr.org/voxeu/columns/generative-ai-german-firms-di... [2] https://www.genaiadoptiontracker.com/ [3] https://blog.google/innovation-and-ai/technology/research/un... (discussion: https://news.ycombinator.com/item?id=49020335 ) [4] https://www.nber.org/system/files/working_papers/w34836/w348... [5] https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-...

lbrito

>That said, revenues may soon grow even more quickly, if two conditions are met. The first is that AI boosts productivity markedly. For now, there is little evidence that AI is transforming businesses. I've been hearing this for years at this point. How long is it going to take for people to accept that there is no productivity boost? Or are we going to keep us this charade forever? GPT 3.5 was almost 4 years ago now, and still no magical 10x productivity gain. When is enough enough? 5 years? 10 years?