6 ms·

why should only profits matter? if i had a killer product today that i just need to sell tomorrow, wouldn't you still invest today knowing i'll probably only st

why should only profits matter? if i had a killer product today that i just need to sell tomorrow, wouldn't you still invest today knowing i'll probably only start to make money tomorrow (or perhaps next week)? the expectation is that they'll eventually make money. they can't raise forever. only startups are not profitable for a few years. but most companies that have existed for a long while have been profitable and since they're expected to make a LOT of money, everyone wants a piece of that future pie, pushing up the valuation and amount raised to admittedly somewhat delusional levels like here

Barrin92 7mo ago
not if your product is selling two dollars for one dollar and as soon as you'll start to charge more I'll switch to one of your twenty competitors profit isn't a function of having a killer product, it's a function of having no competition
- ds2df 7mo ago
  no competition is a bit extreme. Limited competition yes due to competitive advantages.
- aurareturn 7mo ago
  And why do you think twenty competitors can stay competitive for years to come? Industries always consolidate and winners emerge. SOTA LLMs look like a natural monopoly or duopoly to me because the cost to train the next model keeps going up such that it won't make sense for 20 competitors to compete at the very high end. TSMC is a perfect example of this. Fab costs double every 4 years (Rock’s Law). It's almost impossible to compete against TSMC because no one has the customer base to generate enough revenue to build the next generation of fabs - except those who are propped up by governments such as Intel and Rapidus. Samsung is basically the SK government. I don’t see how companies can catch OpenAI or Anthropic without the strong revenue growth.
  - outside1234 7mo ago
    Google has completely caught OpenAI. Anthropic has a better coding model, but I'm sure Google is working on that too.
    - baq 7mo ago
      > Anthropic has a better coding model I’ll be polite and call this statement ‘a very debatable’ one.
  - Barrin92 7mo ago
    >Industries always consolidate and winners emerge. no, most industries just sell boring generic products, a few industries favor monopolists. Semiconductors are one of them but LLMs are also as far removed from that business as is physically possible. TSMC makes the most complicated machines humans have ever built, a LLM requires a few dozen nerds, a power plant, a few thousand lines of python and chips. That's why if you're Elon Musk you could buy all of the above and train yourself an LLM in a month. LLMs are comically simple pieces of software, they're just big. But anyone with a billion dollars can have one, they're all going to be commoditized and free in due time, like search. Copying a lithography machine is difficult, copying software is easy. that's why Google burrowed itself into email, and browsers, and your phone's OS. Problem for openai is they don't have any of that, there's already half a dozen companies that, for 99% of people, do what they do.
  - komali2 7mo ago
    The barrier to replicating TSMC isn't just cost, it's supply chain, geopolitics, and talent. Only one company on Earth can make the UV lithography machines TSMC buys for their highest end fabs, and they're not selling to anyone else. The PRC tried to brute force this supply chain backed by the full might of the Party's blank check, all red tape cut, literally the best possible duplication scenario, and they failed.
    - purpleidea 7mo ago
      The PRC didn't fail, they haven't finished succeeding yet.
- susupro1 7mo ago
  [dead]
bandrami 7mo ago
> why should only profits matter? In this case because it's not clear that anybody has actually figured out how to sell inference for more than it costs
- nl 7mo ago
  It's well know everyone is making great money on inference. The cost is training. Whether GPT-5 was profitable to run depends on which profit margin you’re talking about. If we subtract the cost of compute from revenue to calculate the gross margin (on an accounting basis),2 it seems to be about 30% — lower than the norm for software companies (where 60-80% is typical) but still higher than many industries. (They go on to point out that there are other costs that might mean they didn't break even on other costs - although I suspect these costs should be partially amortized over the whole GPT 5.x series, not just 5.0) https://epochai.substack.com/p/can-ai-companies-become-profitable https://epochai.substack.com/p/can-ai-companies-become-profi... https://martinalderson.com/posts/are-openai-and-anthropic-really-losing-money-on-inference/ https://martinalderson.com/posts/are-openai-and-anthropic-re... (with math working backwards from GPU capacity) "Most of what we're building out at this point is the inference [...] We're profitable on inference. If we didn't pay for training, we'd be a very profitable company" https://simonwillison.net/2025/Aug/17/sam-altman/ https://simonwillison.net/2025/Aug/17/sam-altman/ "There’s a bright spot, however. OpenAI has gotten more efficient at serving paying users: Its compute margin—the revenue left after subtracting the cost of running AI models for those customers—was roughly 70% in October, an increase from about 52% at the end of last year and roughly 35% in January 2024." https://archive.is/OqIny#selection-1279.0-1279.305 https://archive.is/OqIny#selection-1279.0-1279.305 (Note this is after having to pay higher spot rates for compute because of higher than expected demand)
  - bandrami 7mo ago
    > It's well know everyone is making great money on inference. That is not, in fact, "well known", but based entirely on the announcements of the inference providers themselves who also get very cagey when asked to show their work and at least look like they're soliciting a constant firehose of investment money simply to keep the lights on. In particular there's a troubling tendency to call revenue "recurring" before it actually, you know, recurs.

Barrin92 7mo ago

not if your product is selling two dollars for one dollar and as soon as you'll start to charge more I'll switch to one of your twenty competitors profit isn't a function of having a killer product, it's a function of having no competition

ds2df 7mo ago
no competition is a bit extreme. Limited competition yes due to competitive advantages.
aurareturn 7mo ago
And why do you think twenty competitors can stay competitive for years to come? Industries always consolidate and winners emerge. SOTA LLMs look like a natural monopoly or duopoly to me because the cost to train the next model keeps going up such that it won't make sense for 20 competitors to compete at the very high end. TSMC is a perfect example of this. Fab costs double every 4 years (Rock’s Law). It's almost impossible to compete against TSMC because no one has the customer base to generate enough revenue to build the next generation of fabs - except those who are propped up by governments such as Intel and Rapidus. Samsung is basically the SK government. I don’t see how companies can catch OpenAI or Anthropic without the strong revenue growth.
- outside1234 7mo ago
  Google has completely caught OpenAI. Anthropic has a better coding model, but I'm sure Google is working on that too.
  - baq 7mo ago
    > Anthropic has a better coding model I’ll be polite and call this statement ‘a very debatable’ one.
- Barrin92 7mo ago
  >Industries always consolidate and winners emerge. no, most industries just sell boring generic products, a few industries favor monopolists. Semiconductors are one of them but LLMs are also as far removed from that business as is physically possible. TSMC makes the most complicated machines humans have ever built, a LLM requires a few dozen nerds, a power plant, a few thousand lines of python and chips. That's why if you're Elon Musk you could buy all of the above and train yourself an LLM in a month. LLMs are comically simple pieces of software, they're just big. But anyone with a billion dollars can have one, they're all going to be commoditized and free in due time, like search. Copying a lithography machine is difficult, copying software is easy. that's why Google burrowed itself into email, and browsers, and your phone's OS. Problem for openai is they don't have any of that, there's already half a dozen companies that, for 99% of people, do what they do.
- komali2 7mo ago
  The barrier to replicating TSMC isn't just cost, it's supply chain, geopolitics, and talent. Only one company on Earth can make the UV lithography machines TSMC buys for their highest end fabs, and they're not selling to anyone else. The PRC tried to brute force this supply chain backed by the full might of the Party's blank check, all red tape cut, literally the best possible duplication scenario, and they failed.
  - purpleidea 7mo ago
    The PRC didn't fail, they haven't finished succeeding yet.
susupro1 7mo ago
[dead]

bandrami 7mo ago

> why should only profits matter? In this case because it's not clear that anybody has actually figured out how to sell inference for more than it costs

nl 7mo ago
It's well know everyone is making great money on inference. The cost is training. Whether GPT-5 was profitable to run depends on which profit margin you’re talking about. If we subtract the cost of compute from revenue to calculate the gross margin (on an accounting basis),2 it seems to be about 30% — lower than the norm for software companies (where 60-80% is typical) but still higher than many industries. (They go on to point out that there are other costs that might mean they didn't break even on other costs - although I suspect these costs should be partially amortized over the whole GPT 5.x series, not just 5.0) https://epochai.substack.com/p/can-ai-companies-become-profitable https://epochai.substack.com/p/can-ai-companies-become-profi... https://martinalderson.com/posts/are-openai-and-anthropic-really-losing-money-on-inference/ https://martinalderson.com/posts/are-openai-and-anthropic-re... (with math working backwards from GPU capacity) "Most of what we're building out at this point is the inference [...] We're profitable on inference. If we didn't pay for training, we'd be a very profitable company" https://simonwillison.net/2025/Aug/17/sam-altman/ https://simonwillison.net/2025/Aug/17/sam-altman/ "There’s a bright spot, however. OpenAI has gotten more efficient at serving paying users: Its compute margin—the revenue left after subtracting the cost of running AI models for those customers—was roughly 70% in October, an increase from about 52% at the end of last year and roughly 35% in January 2024." https://archive.is/OqIny#selection-1279.0-1279.305 https://archive.is/OqIny#selection-1279.0-1279.305 (Note this is after having to pay higher spot rates for compute because of higher than expected demand)
- bandrami 7mo ago
  > It's well know everyone is making great money on inference. That is not, in fact, "well known", but based entirely on the announcements of the inference providers themselves who also get very cagey when asked to show their work and at least look like they're soliciting a constant firehose of investment money simply to keep the lights on. In particular there's a troubling tendency to call revenue "recurring" before it actually, you know, recurs.