Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- >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?
by lbrito - 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.
by woeirua - 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.
by hahahaa - 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.by da-x
- 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.
- > If all companies are using the same "frontier" LLMs, and if they are competitive
The answer is that all companies will not be using the same frontier LLMs competitively
This will be yet another technology that will benefit from economies of scale. Big businesses and those in PE portfolios will lead the charge, adopt AI, increase efficiency, and gain market share at the expense of mom and pop shops.
This will be good for our 401ks because we're all primarily invested in big business
by jonfw - Domain expertise in whatever niche they are in. Like Palantir is not competing against dozens of similar startups in the shady military data space.by tim333
- In introductory economics courses, they teach a simple result showing that in a competitive market, profits tend toward zero.by odyssey7
- by an0malous
- 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.
- 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.by softwaredoug
- It is not first mover disadvantage. It is "took too much initial hype and debt while ignoring reality and long term" long term disadvantage.by watwut
- > And how second movers (open Chinese labs) can disrupt them
Yes, I will concede this point. But how are the Chinese labs going to thrive with giving the weights away for free? Will they suffer the same fate open source database companies did at the hands of AWS?
by jgalt212 - > 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.
by jsnell - > 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.
by pu_pe - This is also at the end of the tokenmaxxing era, so I'm not sure I would draw a straight line out from here. A LOT of companies are clamping down on token costs right now.by woeirua
- And the total capex spend to date is roughly $1tr so revenues are ~20% of capex. It doesn't seem ridiculously out of whack.
They don't have to do the future spending unless revenues are coming in appropriately.
(source on the capex to date https://valueaddvc.com/ai-spending)
by tim333 - > 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.
by an0malous - I thought SpaceXAI was making like $25 billion a year renting out hardware now?by Gagarin1917
- Why would capex need to be covered by income during a time of investment and infrastructure buildup? Did income from the Apollo program cover its expenses?by p-e-w
- $150bn a year? Awesome. But the investment obligations are in the trillions already so where is all this new revenue coming from? I think that is what the article is saying and what many of us have been asking for a year.by apercu
- $2.5tn is about 8% of U.S. GDP. So a handful of AI firms really expect that they will become larger than the entire U.S. sectors of manufacturing, government, or healthcare?by trollbridge
- “ 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.
by iammjm - If programmers play it right that'll be us working less and delivering the same results. Who said everything has to benefit the capital class?
- I guess there is an inverse effect to bubbles (things go linearly up and then abruptly down), where nothing happens for a long time until all things happen at once.
- I think what's happened, so far at least, is productivity has been moved around.
Anyone doing consulting has seen it, where now the "I vibe coded this last week" competition is way more intense. Basically the people with the problems now have a chance to spin up something that looks like the solution they want, they then get in trouble and need someone to sort it out.
LLMs _are_ great tools for software development but if you haven't noticed their near complete inability to reason about why what they're doing might work you haven't been trying hard enough.
by fidotron - The same source (https://www.nber.org/papers/w34836) 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 clearly they're going to spend more on AI.
More concerningly:
> In contrast, employees anticipate that AI will raise employment 0.5% at their firms in the next 3 years, highlighting an expectations gap between employers and employees.
by keeda - Most people will pocket AI gains for themselves.
So from the top it doesn't look like much has changed, whereas workers are trying to offload as much work as they can onto their claude subscription.
People in desperation to min/max work per unit dollar, will leverage AI to free themselves from as much work as possible, while still claiming credit for the work.
So as the labs start ratcheting up the price, people will pay more and more to keep their "secret" assistant.
by WarmWash - A lot of people are slowly realizing that code generation was not the bottleneck in their internal development process, and waving a magic wand to make that go faster doesn't actually lead to more revenue.by woeirua
- Here’s what I’ve seen: it can make skilled people more productive by acting like a “mech suit for your brain.”
For unskilled people, it creates a false sense of productivity, but if the user of the tech does not understand what they are doing they can’t apply the tool productively or judge its output or deal with the things beyond its ability.
So like all other tools before it: it works best in the hands of a skilled user.
Just like in all other fields.
There is no tool that makes an unskilled user skilled, but there are many that can amplify or extend the capability of skill.
by api