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What happens is they go out of business: "these firms spent five hundred and sixty billion dollars on A.I.-related capital expenditures in the past eighteen mon

What happens is they go out of business: "these firms spent five hundred and sixty billion dollars on A.I.-related capital expenditures in the past eighteen months, while their A.I. revenues were only about thirty-five billion." DeepSeek (and the like) will prevent the kind of price increases necessary for them to pay back hundreds of billions of dollars already spent, much less pay for more. If they don't find a way to make LLMs do significantly more than they do thus far, and a market willing to pay hundreds of billions of dollars for them to do it, and some kind of "moat" to prevent DeepSeek and the like from undercutting them, they will collapse under the weight of their own expenses.

mgfist 1y ago
DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.
- disgruntledphd2 1y ago
  I'd be surprised if Google weren't closer to profitability than basically anyone else, as they have their own hardware and have been running these kinds of applications for much longer than anyone else.
  - tonyedgecombe 1y ago
    Google is still investing heavily in data centres. Presumably without the AI they could lift their foot off that throttle.
  - tim333 1y ago
    Google are well set up to monetize compared to the others. They just need people to see their ads.
    - owebmaster 1y ago
      They are also the ones with more to lose to LLMs
- rusk 1y ago
  DeepSeek doesn’t need to make a profit to be successful.
  - rossdavidh 1y ago
    I could think of several reasons this could be so, but it would be good to hear your logic on this claim?
    - rusk 1y ago
      State funded innit
- ben_w 1y ago
  > inference is very expensive I am surprised that this claim keeps getting made, given the observed prices. Even if one thinks that the losses of big model providers are due to selling below operating costs (rather than below that plus training costs plus the cost of growth), then even big open-weights models that need beefy machines, look like they eventually* amortise the cost so low that electricity is what matters; so when (and *only* when) the quality is good enough, inference is cheaper than the food needed to have a human work for peanuts — and I mean literally peanuts, not metaphorical peanuts, as in the calories and protein content of bags of peanuts sufficient to not die. * this would not happen if computers were still following the improvements trends of the 90s, because then we'd be replacing them every few years; a £10k machine that you replace every 3 years cost you £9.13/day even if it did nothing. https://www.tesco.com/groceries/en-GB/products/300283810 https://www.tesco.com/groceries/en-GB/products/300283810 -> £0.59 per bag * (2500 per day/645 per bag) = £2.29/day; then combine your pick about which model, which model of home server, electricity costs etc. with your estimate of how many useful tokens a human does in 8,760 hours per calendar year given your assumptions about hours per working week and days of holiday or sick leave. I know that even just order-of 100k useful tokens is implausible for any human because that would be like writing a novel a day, every day; and this article (https://aichatonline.org/blog-lets-run-openai-gptoss-official-open-source-chatgpt-developer-review-54595 https://aichatonline.org/blog-lets-run-openai-gptoss-officia...) claims a Mac Studio can output 65.9/second = 65.9 * 3600 * 24 = 5,693,760 / day or ~= 2e9/year, compare to a deliberate over-estimate of human output (100k/day * 5 days a week * 47 weeks a year = 2.35e7/year) The top-end Mac Studio has a maximum power draw of 270 W: https://support.apple.com/en-us/102027 https://support.apple.com/en-us/102027 270 W for *at least (2e9/year / 2.35e7/year) 85 times* the quantity (this only matters when the quality is sufficient, and as we all know AI often isn't that good yet) of output that a human can do with 100 W, is a bit over 31 times the raw energy efficiency, and electricity is much cheaper than calories — cheaper food than peanuts could get the cost of the human down to perhaps about £1/day, but even £1/day is equivalent to electricity costing £1/(24 hours * 100 W) = £0.416666… / kWh
  - mgfist 1y ago
    Running a local model is not an apples comparison. Yes, if you run a small model 24/7 without a care for output latency and utilization is completely static with no bursts, then it can look cheap. But most people want output now, not in 10 hours. And they want it from the best models. And they want large context windows. And when you combine that with serving millions of users, it gets complicated and expensive.
- cloverich 1y ago
  I'm not familiar with the actual strategy, but one strategy of something like DeepSeek or any open model could be, roughly, to avoid moat creation by other companies. E.g. one reason Google maintained dominance for so long is they simply paid off every smart person and company that might build competitors.
- rossdavidh 1y ago
  Matt Levine of Bloomberg said he believes most big hedge funds have a proprietary LLM (and various other machine learning models), which they use for their own purposes anyway, and therefore it was relatively straightforward for them to get into the business.
deleted 1y ago
[deleted]
9rx 1y ago
> If they don't find a way to make LLMs do significantly more than they do thus far... They only need two things, really: A large user base and a way to include advertising in the responses. The market willing to pay hundreds of billions of dollars will soon follow. The businesses are currently in the user base building stage. Hemorrhaging money to get them is simply the cost of doing business. Once they feel that is stable, adding advertising is relatively easy. > and some kind of "moat" to prevent DeepSeek and the like from undercutting them* Once users are accustomed to using a service, you have to do some pretty horrendous things to get them to leave. "Give me your best hamburger recipe" -> "Sure, here is my best burger recipe [...] However, if you don't feel like cooking tonight, give the Big Mac a try!". wouldn't be enough to see any meaningful loss of users.
- tempodox 1y ago
  That's nothing new. The question is, will those users be willing and able to pay ten times as much or more for those same services they get for a significant discount now.
  - 9rx 1y ago
    Will they pay ten times more for a Big Mac? Probably not, but why would they need to? Hundreds of billions is Facebook's revenue. The businesses in this space are there if they can take those customers alone, never mind all the other places where advertising takes place. The market exists, is sufficiently large, and willing to spend. All these "AI" businesses need to do is show that the users are spending time on their services instead, which is exactly what they are working on right now. The question is really only: Will users actually want to continue to use these services once the novelty wears off? The assumption is that they are useful enough to become an integral part of our lives, but time will tell...
    - benterix 1y ago
      > The question is really only: Will users actually want to continue to use these services once the novelty wears off? The assumption is that they are useful enough to become an integral part of our lives, but time will tell... But LLMs do have some niche, stable applications already. For example, they replaced Stack Overflow to a large extent, because you can get the answer you need faster, and it's often better adapted to your situation. So you could argue the novelty of SO wore off a long time ago but people were still using it when LLMs appeared. ChatGPT is no more en vogue, people are ashamed to (and shamed for) using it, but it still has some uses, helping people in their jobs and lives in general.

mgfist 1y ago

DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.

disgruntledphd2 1y ago
I'd be surprised if Google weren't closer to profitability than basically anyone else, as they have their own hardware and have been running these kinds of applications for much longer than anyone else.
- tonyedgecombe 1y ago
  Google is still investing heavily in data centres. Presumably without the AI they could lift their foot off that throttle.
- tim333 1y ago
  Google are well set up to monetize compared to the others. They just need people to see their ads.
  - owebmaster 1y ago
    They are also the ones with more to lose to LLMs
rusk 1y ago
DeepSeek doesn’t need to make a profit to be successful.
- rossdavidh 1y ago
  I could think of several reasons this could be so, but it would be good to hear your logic on this claim?
  - rusk 1y ago
    State funded innit
ben_w 1y ago
> inference is very expensive I am surprised that this claim keeps getting made, given the observed prices. Even if one thinks that the losses of big model providers are due to selling below operating costs (rather than below that plus training costs plus the cost of growth), then even big open-weights models that need beefy machines, look like they eventually* amortise the cost so low that electricity is what matters; so when (and *only* when) the quality is good enough, inference is cheaper than the food needed to have a human work for peanuts — and I mean literally peanuts, not metaphorical peanuts, as in the calories and protein content of bags of peanuts sufficient to not die. * this would not happen if computers were still following the improvements trends of the 90s, because then we'd be replacing them every few years; a £10k machine that you replace every 3 years cost you £9.13/day even if it did nothing. https://www.tesco.com/groceries/en-GB/products/300283810 https://www.tesco.com/groceries/en-GB/products/300283810 -> £0.59 per bag * (2500 per day/645 per bag) = £2.29/day; then combine your pick about which model, which model of home server, electricity costs etc. with your estimate of how many useful tokens a human does in 8,760 hours per calendar year given your assumptions about hours per working week and days of holiday or sick leave. I know that even just order-of 100k useful tokens is implausible for any human because that would be like writing a novel a day, every day; and this article (https://aichatonline.org/blog-lets-run-openai-gptoss-official-open-source-chatgpt-developer-review-54595 https://aichatonline.org/blog-lets-run-openai-gptoss-officia...) claims a Mac Studio can output 65.9/second = 65.9 * 3600 * 24 = 5,693,760 / day or ~= 2e9/year, compare to a deliberate over-estimate of human output (100k/day * 5 days a week * 47 weeks a year = 2.35e7/year) The top-end Mac Studio has a maximum power draw of 270 W: https://support.apple.com/en-us/102027 https://support.apple.com/en-us/102027 270 W for *at least (2e9/year / 2.35e7/year) 85 times* the quantity (this only matters when the quality is sufficient, and as we all know AI often isn't that good yet) of output that a human can do with 100 W, is a bit over 31 times the raw energy efficiency, and electricity is much cheaper than calories — cheaper food than peanuts could get the cost of the human down to perhaps about £1/day, but even £1/day is equivalent to electricity costing £1/(24 hours * 100 W) = £0.416666… / kWh
- mgfist 1y ago
  Running a local model is not an apples comparison. Yes, if you run a small model 24/7 without a care for output latency and utilization is completely static with no bursts, then it can look cheap. But most people want output now, not in 10 hours. And they want it from the best models. And they want large context windows. And when you combine that with serving millions of users, it gets complicated and expensive.
cloverich 1y ago
I'm not familiar with the actual strategy, but one strategy of something like DeepSeek or any open model could be, roughly, to avoid moat creation by other companies. E.g. one reason Google maintained dominance for so long is they simply paid off every smart person and company that might build competitors.
rossdavidh 1y ago
Matt Levine of Bloomberg said he believes most big hedge funds have a proprietary LLM (and various other machine learning models), which they use for their own purposes anyway, and therefore it was relatively straightforward for them to get into the business.

deleted 1y ago

[deleted]

9rx 1y ago

> If they don't find a way to make LLMs do significantly more than they do thus far... They only need two things, really: A large user base and a way to include advertising in the responses. The market willing to pay hundreds of billions of dollars will soon follow. The businesses are currently in the user base building stage. Hemorrhaging money to get them is simply the cost of doing business. Once they feel that is stable, adding advertising is relatively easy. > and some kind of "moat" to prevent DeepSeek and the like from undercutting them* Once users are accustomed to using a service, you have to do some pretty horrendous things to get them to leave. "Give me your best hamburger recipe" -> "Sure, here is my best burger recipe [...] However, if you don't feel like cooking tonight, give the Big Mac a try!". wouldn't be enough to see any meaningful loss of users.

tempodox 1y ago
That's nothing new. The question is, will those users be willing and able to pay ten times as much or more for those same services they get for a significant discount now.
- 9rx 1y ago
  Will they pay ten times more for a Big Mac? Probably not, but why would they need to? Hundreds of billions is Facebook's revenue. The businesses in this space are there if they can take those customers alone, never mind all the other places where advertising takes place. The market exists, is sufficiently large, and willing to spend. All these "AI" businesses need to do is show that the users are spending time on their services instead, which is exactly what they are working on right now. The question is really only: Will users actually want to continue to use these services once the novelty wears off? The assumption is that they are useful enough to become an integral part of our lives, but time will tell...
  - benterix 1y ago
    > The question is really only: Will users actually want to continue to use these services once the novelty wears off? The assumption is that they are useful enough to become an integral part of our lives, but time will tell... But LLMs do have some niche, stable applications already. For example, they replaced Stack Overflow to a large extent, because you can get the answer you need faster, and it's often better adapted to your situation. So you could argue the novelty of SO wore off a long time ago but people were still using it when LLMs appeared. ChatGPT is no more en vogue, people are ashamed to (and shamed for) using it, but it still has some uses, helping people in their jobs and lives in general.