7 ms·

AI revenues are growing fast, but not fast enough

50 points by vinni2 2mo ago

an0malous 2mo ago
https://archive.is/VktAK https://archive.is/VktAK
iammjm 2mo ago
“ 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.
- bad_haircut72 2mo ago
  AI will probably end up reducing lots of firms productivity because costs to defend against cyber attack is gonna skyrocket. Even for tech firms writing more code (the only thing AI is really good at so far) doesn't scale linearly with profits
  - trollbridge 2mo ago
    Particularly when you can't use Fable 5 to secure your software, you don't have access to Mythos 5, and you got your access to GLM-5.2 and other Chinese and/or open models capable of doing so taken away.
  - andy99 2mo ago
    Why would it obviously give attackers the upper hand? If we accept the premise that AI helps find vulns more efficiently, shouldn’t this be at least equally helpful in securing against attacks and make that increasingly efficient?
    - pdhborges 2mo ago
      Maybe people believe that the process complexity is not symetric. I don't blame them. Building an house is more complex than destroying it.
    - throwaway27448 2mo ago
      It's still an additional cost that doesn't drive revenue.
    - zemo 2mo ago
      everything you build potentially increases the surface area of attack. If the benefit of building it is zero or negative then all you've done is increase your risk profile.
    - lunar_mycroft 2mo ago
      It won't do that for free, companies (and open source maintainers) will have to pay for the tokens used finding vulnerabilities in their own software. That increases the cost of releasing the same product/feature/amount of code vs what it was before.
  - TheOtherHobbes 2mo ago
    Good point. Within the next few years black hat open models will be able to do a lot of damage for very little cost. It could easily turn into a Red Queen situation where companies that don't spend hard on security are going to face huge potential losses, and both sides will be scrambling constantly to get an advantage.
- calvinmorrison 2mo ago
  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?
  - kachnuv_ocasek 2mo ago
    What's the play for software engineers that lands them in a better place but doesn't leave money on the table for the capitalists?
  - trollbridge 2mo ago
    Speaking as a programmer, the net effect so far is that we work more and deliver far more results and get paid the same or less. And I run my own firm... but I have to compete against a global market of other programmers using similar tools who are also trying to compete on price, and then there are hordes of new entrants who can vibe-code things that superficially look like they'll meet a customer's needs and are better at marketing than I am, so my area of business gets constrained to just customers who need help after the problems with the vibe-coded solution appear, the vendor who made it is long gone, and they already spent an inordinate amount of money on the vibe-coded solution since they thought it was complete and it looked pretty good.
  - snerbles 2mo ago
    Most of my past roles had very simple code at the important deliverable level, on bizarre and arcane platforms that would have little context for frontier LLMs. Most the creativity and complexity was in tooling for research/development/testing. Agentic coding could help a lot for the latter, not necessarily the former.
  - sovietmudkipz 2mo ago
    Programmer work product (typically) benefits the capital class, directly via product engineering or indirectly via platform engineering. If the aspiration of a 10x or more programmer is enabled through AI then the capital class win, in the typical case. Software eats the world.
- lousken 2mo ago
  I am surprised they don't record a decrease, maybe this year could be better?
- jstummbillig 2mo ago
  Not really. Most things take more time to transform than people think, even if capabilities exist. Which is not to say that they do exist, but rather, independent of whether they exist, that this is not very surprising.
- GranularRecipe 2mo ago
  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.
- api 2mo ago
  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.
  - jordanb 2mo ago
    > it can make skilled people more productive by acting like a “mech suit for your brain.” If mech suits existed and people actually lived in them they would either get super fat or super skinny. In either case they'd lose all muscle mass.
    - api 2mo ago
      All technologies are a double edged sword like this, simultaneously strengthening and weakening. https://newlearningonline.com/literacies/chapter-1/socrates-on-the-forgetfulness-that-comes-with-writing https://newlearningonline.com/literacies/chapter-1/socrates-... Humans are tool makers and this is what happens when you make tools.
      - yurish 2mo ago
        Problem is AI weakening our brain, arguably the only thing that distinguish us from animals. As if decreasing brain size of modern humans was not enough.
- WarmWash 2mo ago
  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.
  - lunar_mycroft 2mo ago
    Wouldn't that explanation predict that lower ranking employees would be more impressed with AI than higher ranking ones? Because what we actually observe [0] tends to be the opposite. Executives are the most bullish on AI, followed by Managers, with employees having the least adoption. [0] https://businesschief.com/news/why-are-executives-using-ai-more-than-employees https://businesschief.com/news/why-are-executives-using-ai-m...
    - WarmWash 2mo ago
      Those studies are of workers using company provided AI. They call out the MIT study in the article, which found very low adoption of corporate AI initiatives. However if you read that study, they found that almost every worker uses AI heavily, but they just use their personal AI account. Again, people are using their own AI to benefit themselves, it makes the most sense. Using company AI shows that the AI is doing xyz, and maybe perhaps you aren't that valuable. Using your own AI makes it look like you are doing xyz, and you are the valuable one.
- woeirua 2mo ago
  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.
  - fidotron 2mo ago
    Next you'll be telling me that using number of PRs deployed in a week as a KPI is a bad idea.
  - jordanb 2mo ago
    One of the weirdest things about the whole AI boom is the mass formation psychosis where everyone started acting like lines of code written was an important metric for software development productivity. We've known for decades that more lines of code isn't good. Bill Gates was joking about it in the OS/2 days! We've known for decades that the actual creation of the code is the smallest part of an actual software developer's job. We've known for decades that "every line of code is a business decision" that has to be written with an understanding of the underlying goal, and the hardest part of training up software engineers is making them understand that. We threw all that out because "the chatbox writes code really fast, software is a solved problem!"
    - thrw045 2mo ago
      This is a rewriting of history that I don't really like. Back before LLMs and before that again, it was very hard to write code. That's why so many people asked questions and figuring out how to write a piece of code was a big part of the value add of a programmer. It separated the ones who knew their stuff from the ones who didn't. Yeah sure software design was also important but it was less important than the actual writing of the code, which is why a lot of old code had bad designs. It took decades to figure that stuff out.
      - yurish 2mo ago
        > Back before LLMs and before that again, it was very hard to write code. That's why so many people asked questions and figuring out how to write a piece of code was a big part of the value add This may be true if you work in unfamiliar area or on frontier of programming. But this is not were most of programmer jobs are. That writing code itself is hard is a view of non-programmer. Programmers know that past some threshold of familiarity designing and weighting decisions becomes central problem.
- fidotron 2mo ago
  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.
- keeda 2mo ago
  The same source (https://www.nber.org/papers/w34836 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.
an0malous 2mo ago
> 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.
- trollbridge 2mo ago
  $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?
  - nemomarx 2mo ago
    Investors expect that they will, I guess. If you really believe the strong ai use cases (like exponential improvements, or it replacing hundreds of knowledge workers at every company" then maybe this isn't unreasonable.
    - misterderpie 2mo ago
      If many people lose their jobs, thus consumers having less and less money, where would that money come from?
      - nemomarx 2mo ago
        If you look at recent trends in consumer spending, a lot more of it is from the upper classes and less from the middle. Selling services to the rich can displace the loss of white collar workers. (in theory). as a bonus, ideally you create some more really wealthy people to spend on things during this shift.
softwaredoug 2mo ago
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.
- jgalt212 2mo ago
  > 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?
  - WarmWash 2mo ago
    They don't need to run a business, they are a Chinese state enterprise. The tax dollars of Chinese workers covers the cost. China is a communist country with mock free enterprise, it is not the team blue version of the US.
  - softwaredoug 2mo ago
    For the companies it's not clear. For the state, like BVD, the goal is to be a loss-leader for Chinese infrastructure + manufacturing.
  - jensensbutton 2mo ago
    If the model is commoditized then the AI race becomes competition over building infrastructure to support it. China is very good at building infrastructure.
- watwut 2mo ago
  It is not first mover disadvantage. It is "took too much initial hype and debt while ignoring reality and long term" long term disadvantage.
thisisthenewme 2mo ago
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.
- odyssey7 2mo ago
  In introductory economics courses, they teach a simple result showing that in a competitive market, profits tend toward zero.
- jonfw 2mo ago
  > 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
- tim333 2mo ago
  Domain expertise in whatever niche they are in. Like Palantir is not competing against dozens of similar startups in the shady military data space.
VCFundedGenYer 2mo ago
I mean, all one has to do is review https://isaiprofitable.com/ 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.
- axegon_ 2mo ago
  Hopefully soon. Not for personal or altruistic reasons, I just find great pleasure in seeing cults burn(which is what AI has become).
- ant6n 2mo ago
  It's kind of funny. I was looking at the top of the page thinking 1.5T investment for so far 770B revenue isn't bad at all, that could break even after some time. But then you look the listed companies, there costs vs revenue. Turns out 516B of the revenues are from Nvidia, which is not an AI company, but just selling shovels. Nvidias revenues are effectively costs to actual AI companies.
  - IAmGraydon 2mo ago
    Yep. Click the button that hides the infrastructure companies. The entire thing is a scam. The only thing holding it up is the belief that AI will replace the majority of human labor in the foreseeable future. So far, it has replaced almost none of it.
hahahaa 2mo ago
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 2mo ago
> 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 2mo ago
  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.
  - frankbreetz 2mo ago
    Even more companies are just starting AI/ML initiatives. The tokenmaxxing thing only happened at a handful of tech companies, most companies don't have unlimited money to do this kind of thing. Once the Pathfinders find out where the real value is there is much more room for growth from companies who have be mindful of budget.
  - pu_pe 2mo ago
    I wouldn't bet on token consumption going down any time soon. In the past year or so, the increase has been exponential [1]. [1] https://en.macromicro.me/charts/148532/world-openrouter-token-usage https://en.macromicro.me/charts/148532/world-openrouter-toke...
- tim333 2mo ago
  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 https://valueaddvc.com/ai-spending)
woeirua 2mo ago
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 2mo ago
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.
- deleted 2mo ago
  [deleted]
da-x 2mo ago
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 2mo ago
> 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 2mo ago
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-diffusion-costs-and-expected-economic-effects https://cepr.org/voxeu/columns/generative-ai-german-firms-di... [2] https://www.genaiadoptiontracker.com/ https://www.genaiadoptiontracker.com/ [3] https://blog.google/innovation-and-ai/technology/research/understanding-the-ai-economy/ https://blog.google/innovation-and-ai/technology/research/un... (discussion: https://news.ycombinator.com/item?id=49020335 https://news.ycombinator.com/item?id=49020335) [4] https://www.nber.org/system/files/working_papers/w34836/w34836.pdf https://www.nber.org/system/files/working_papers/w34836/w348... [5] https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-productivity https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-...
lbrito 2mo ago
>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?

50 points by vinni2 2mo ago

https://archive.is/VktAK https://archive.is/VktAK

“ 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.

bad_haircut72 2mo ago
AI will probably end up reducing lots of firms productivity because costs to defend against cyber attack is gonna skyrocket. Even for tech firms writing more code (the only thing AI is really good at so far) doesn't scale linearly with profits
- trollbridge 2mo ago
  Particularly when you can't use Fable 5 to secure your software, you don't have access to Mythos 5, and you got your access to GLM-5.2 and other Chinese and/or open models capable of doing so taken away.
- andy99 2mo ago
  Why would it obviously give attackers the upper hand? If we accept the premise that AI helps find vulns more efficiently, shouldn’t this be at least equally helpful in securing against attacks and make that increasingly efficient?
  - pdhborges 2mo ago
    Maybe people believe that the process complexity is not symetric. I don't blame them. Building an house is more complex than destroying it.
  - throwaway27448 2mo ago
    It's still an additional cost that doesn't drive revenue.
  - zemo 2mo ago
    everything you build potentially increases the surface area of attack. If the benefit of building it is zero or negative then all you've done is increase your risk profile.
  - lunar_mycroft 2mo ago
    It won't do that for free, companies (and open source maintainers) will have to pay for the tokens used finding vulnerabilities in their own software. That increases the cost of releasing the same product/feature/amount of code vs what it was before.
- TheOtherHobbes 2mo ago
  Good point. Within the next few years black hat open models will be able to do a lot of damage for very little cost. It could easily turn into a Red Queen situation where companies that don't spend hard on security are going to face huge potential losses, and both sides will be scrambling constantly to get an advantage.
calvinmorrison 2mo ago
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?
- kachnuv_ocasek 2mo ago
  What's the play for software engineers that lands them in a better place but doesn't leave money on the table for the capitalists?
- trollbridge 2mo ago
  Speaking as a programmer, the net effect so far is that we work more and deliver far more results and get paid the same or less. And I run my own firm... but I have to compete against a global market of other programmers using similar tools who are also trying to compete on price, and then there are hordes of new entrants who can vibe-code things that superficially look like they'll meet a customer's needs and are better at marketing than I am, so my area of business gets constrained to just customers who need help after the problems with the vibe-coded solution appear, the vendor who made it is long gone, and they already spent an inordinate amount of money on the vibe-coded solution since they thought it was complete and it looked pretty good.
- snerbles 2mo ago
  Most of my past roles had very simple code at the important deliverable level, on bizarre and arcane platforms that would have little context for frontier LLMs. Most the creativity and complexity was in tooling for research/development/testing. Agentic coding could help a lot for the latter, not necessarily the former.
- sovietmudkipz 2mo ago
  Programmer work product (typically) benefits the capital class, directly via product engineering or indirectly via platform engineering. If the aspiration of a 10x or more programmer is enabled through AI then the capital class win, in the typical case. Software eats the world.
lousken 2mo ago
I am surprised they don't record a decrease, maybe this year could be better?
jstummbillig 2mo ago
Not really. Most things take more time to transform than people think, even if capabilities exist. Which is not to say that they do exist, but rather, independent of whether they exist, that this is not very surprising.
GranularRecipe 2mo ago
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.
api 2mo ago
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.
- jordanb 2mo ago
  > it can make skilled people more productive by acting like a “mech suit for your brain.” If mech suits existed and people actually lived in them they would either get super fat or super skinny. In either case they'd lose all muscle mass.
  - api 2mo ago
    All technologies are a double edged sword like this, simultaneously strengthening and weakening. https://newlearningonline.com/literacies/chapter-1/socrates-on-the-forgetfulness-that-comes-with-writing https://newlearningonline.com/literacies/chapter-1/socrates-... Humans are tool makers and this is what happens when you make tools.
    - yurish 2mo ago
      Problem is AI weakening our brain, arguably the only thing that distinguish us from animals. As if decreasing brain size of modern humans was not enough.
WarmWash 2mo ago
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.
- lunar_mycroft 2mo ago
  Wouldn't that explanation predict that lower ranking employees would be more impressed with AI than higher ranking ones? Because what we actually observe [0] tends to be the opposite. Executives are the most bullish on AI, followed by Managers, with employees having the least adoption. [0] https://businesschief.com/news/why-are-executives-using-ai-more-than-employees https://businesschief.com/news/why-are-executives-using-ai-m...
  - WarmWash 2mo ago
    Those studies are of workers using company provided AI. They call out the MIT study in the article, which found very low adoption of corporate AI initiatives. However if you read that study, they found that almost every worker uses AI heavily, but they just use their personal AI account. Again, people are using their own AI to benefit themselves, it makes the most sense. Using company AI shows that the AI is doing xyz, and maybe perhaps you aren't that valuable. Using your own AI makes it look like you are doing xyz, and you are the valuable one.
woeirua 2mo ago
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.
- fidotron 2mo ago
  Next you'll be telling me that using number of PRs deployed in a week as a KPI is a bad idea.
- jordanb 2mo ago
  One of the weirdest things about the whole AI boom is the mass formation psychosis where everyone started acting like lines of code written was an important metric for software development productivity. We've known for decades that more lines of code isn't good. Bill Gates was joking about it in the OS/2 days! We've known for decades that the actual creation of the code is the smallest part of an actual software developer's job. We've known for decades that "every line of code is a business decision" that has to be written with an understanding of the underlying goal, and the hardest part of training up software engineers is making them understand that. We threw all that out because "the chatbox writes code really fast, software is a solved problem!"
  - thrw045 2mo ago
    This is a rewriting of history that I don't really like. Back before LLMs and before that again, it was very hard to write code. That's why so many people asked questions and figuring out how to write a piece of code was a big part of the value add of a programmer. It separated the ones who knew their stuff from the ones who didn't. Yeah sure software design was also important but it was less important than the actual writing of the code, which is why a lot of old code had bad designs. It took decades to figure that stuff out.
    - yurish 2mo ago
      > Back before LLMs and before that again, it was very hard to write code. That's why so many people asked questions and figuring out how to write a piece of code was a big part of the value add This may be true if you work in unfamiliar area or on frontier of programming. But this is not were most of programmer jobs are. That writing code itself is hard is a view of non-programmer. Programmers know that past some threshold of familiarity designing and weighting decisions becomes central problem.
fidotron 2mo ago
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.
keeda 2mo ago
The same source (https://www.nber.org/papers/w34836 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.

an0malous 2mo ago

> 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.

trollbridge 2mo ago
$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?
- nemomarx 2mo ago
  Investors expect that they will, I guess. If you really believe the strong ai use cases (like exponential improvements, or it replacing hundreds of knowledge workers at every company" then maybe this isn't unreasonable.
  - misterderpie 2mo ago
    If many people lose their jobs, thus consumers having less and less money, where would that money come from?
    - nemomarx 2mo ago
      If you look at recent trends in consumer spending, a lot more of it is from the upper classes and less from the middle. Selling services to the rich can displace the loss of white collar workers. (in theory). as a bonus, ideally you create some more really wealthy people to spend on things during this shift.

softwaredoug 2mo ago

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.

jgalt212 2mo ago
> 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?
- WarmWash 2mo ago
  They don't need to run a business, they are a Chinese state enterprise. The tax dollars of Chinese workers covers the cost. China is a communist country with mock free enterprise, it is not the team blue version of the US.
- softwaredoug 2mo ago
  For the companies it's not clear. For the state, like BVD, the goal is to be a loss-leader for Chinese infrastructure + manufacturing.
- jensensbutton 2mo ago
  If the model is commoditized then the AI race becomes competition over building infrastructure to support it. China is very good at building infrastructure.
watwut 2mo ago
It is not first mover disadvantage. It is "took too much initial hype and debt while ignoring reality and long term" long term disadvantage.

thisisthenewme 2mo ago

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.

odyssey7 2mo ago
In introductory economics courses, they teach a simple result showing that in a competitive market, profits tend toward zero.
jonfw 2mo ago
> 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
tim333 2mo ago
Domain expertise in whatever niche they are in. Like Palantir is not competing against dozens of similar startups in the shady military data space.

VCFundedGenYer 2mo ago

I mean, all one has to do is review https://isaiprofitable.com/ 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.

axegon_ 2mo ago
Hopefully soon. Not for personal or altruistic reasons, I just find great pleasure in seeing cults burn(which is what AI has become).
ant6n 2mo ago
It's kind of funny. I was looking at the top of the page thinking 1.5T investment for so far 770B revenue isn't bad at all, that could break even after some time. But then you look the listed companies, there costs vs revenue. Turns out 516B of the revenues are from Nvidia, which is not an AI company, but just selling shovels. Nvidias revenues are effectively costs to actual AI companies.
- IAmGraydon 2mo ago
  Yep. Click the button that hides the infrastructure companies. The entire thing is a scam. The only thing holding it up is the belief that AI will replace the majority of human labor in the foreseeable future. So far, it has replaced almost none of it.

hahahaa 2mo ago

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 2mo ago

> 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 2mo ago
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.
- frankbreetz 2mo ago
  Even more companies are just starting AI/ML initiatives. The tokenmaxxing thing only happened at a handful of tech companies, most companies don't have unlimited money to do this kind of thing. Once the Pathfinders find out where the real value is there is much more room for growth from companies who have be mindful of budget.
- pu_pe 2mo ago
  I wouldn't bet on token consumption going down any time soon. In the past year or so, the increase has been exponential [1]. [1] https://en.macromicro.me/charts/148532/world-openrouter-token-usage https://en.macromicro.me/charts/148532/world-openrouter-toke...
tim333 2mo ago
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 https://valueaddvc.com/ai-spending)

woeirua 2mo ago

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 2mo ago

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.

deleted 2mo ago
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da-x 2mo ago

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 2mo ago

> 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 2mo ago

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-diffusion-costs-and-expected-economic-effects https://cepr.org/voxeu/columns/generative-ai-german-firms-di... [2] https://www.genaiadoptiontracker.com/ https://www.genaiadoptiontracker.com/ [3] https://blog.google/innovation-and-ai/technology/research/understanding-the-ai-economy/ https://blog.google/innovation-and-ai/technology/research/un... (discussion: https://news.ycombinator.com/item?id=49020335 https://news.ycombinator.com/item?id=49020335) [4] https://www.nber.org/system/files/working_papers/w34836/w34836.pdf https://www.nber.org/system/files/working_papers/w34836/w348... [5] https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-productivity https://aleximas.substack.com/p/what-is-the-impact-of-ai-on-...

lbrito 2mo ago

>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?