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There are most definitely is a moat - but it works both ways. The railguards in the models create moats keeping customers out. And the cost to build a modern ag

There are most definitely is a moat - but it works both ways. The railguards in the models create moats keeping customers out. And the cost to build a modern agentic model is in the 10 figure range and growing. This is an expensive arms race that is going to create moats.

cmiles8 12d ago
But most commodities are the same way. It’s super expensive to drill for oil. I need oil and I’m in no position to mine my own because of the massive capital investment. But it doesn’t stop it from being a pure commodity. I couldn’t care less which company drilled for the oil… it’s all the same to me. Models are increasingly no different. OpenAI and Anthropic are a gas station saying “buy our gas for 10x the price!” When the world is looking at them saying it’s just gas, we’ll take the cheaper brand. We’ve tested your gas and it’s really no better than the stuff that’s 1/10th the price. Thats why their present business plan is screwed.
- euroderf 12d ago
  So the business challenge is to balance sizable investments and relatively small marginal costs. Not so different from other digital goods.
  - cmiles8 12d ago
    Fair assessment. The challenge for OpenAI and Anthropic is that they need sizeable margins to pay for the massive costs incurred. Market forces are driving things in the opposite direction and fast. When your competition has a tiny cost base compared to yours and lacks the bonkers future capital commits you made then that’s a terrible position to be in… hence their conundrum.
    - hungryhobbit 12d ago
      Even "massive costs" is an understatement: these companies have astronomically massive costs! Take Open AI for instance: it has "zero debt" ... and $665 billion to $1.4 trillion in "long-term commitments".
  - techpression 12d ago
    Except OpenAI and Anthropic has brought in a lot of money, that with this trajectory will make it some of the worst investments in ”software” ever (if it’s true enterprise clients are actively moving away, I know we are but for other reasons).
  - jimbokun 12d ago
    With other digital goods the distribution and operating costs have been essentially free. No business worried that much about the cost of running Microsoft Office on the PCs they already distributed to their employees. They were only concerned about the licensing costs. And Microsoft didn't worry about the cost of printing CDs or the costs of serving Office online. It wasn't zero, but again negligible compared to the cost of development and the licensing costs. For LLMs the costs of training and inference are a very significant part of the overall costs.
- gmadsen 12d ago
  The key difference is that the models upgrade multiple times a year. It is an inherently different than a commodity market
  - cmiles8 12d ago
    Yes, but changing models, even across providers, takes about two seconds and one line of code. It’s literally the least stickiest thing in the history of tech. Which is a big problem for these companies.
    - aff-vasileva 12d ago
      The API switch takes one line of code. The enterprise switch takes one line of code and twelve departments.
      - cmiles8 12d ago
        Honestly not. Most big corporates have arrangements were all the major models and now open models are available from the same API endpoint. It is literally one line of code to edit in most cases, even more so in big companies.
  - theseamusjames 12d ago
    But they're all converging on capability. Do I care if it's a 72% or 74% on SWEBench? Practically, probably not. And if I'm not paying per token locally, then if it takes a tiny bit longer to get to the result, I don't care.
    - intrasight 12d ago
      Smartphones and laptops are also "converging", but Apple is always a year or two ahead so it doesn't matter. "Converging" is a meaningless term when things move quickly and cost billions to develop.
      - deleted 12d ago
- blmarket 12d ago
  I argue there's no difference. At least OpenAI/Anthropic can be considered premium like Octane 93 while OSS ones are 87. I agree 90% of the world can work with 87 gas, but there's always niche/luxury market where 93 can make small difference. (edit: typo)
  - thereitgoes456 12d ago
    Of course. But that market won’t produce a $800 billion company, unless AI becomes ludicrously widespread — energy is used every day by virtually every person on the planet, and of course has plenty of mass consumption & “luxury” customers too.
  - cmiles8 12d ago
    Yes… and as the article says folks are still leaving some work to the big labs. But the big money is to be made at scale and those use cases don’t require OpenAI or Anthropic. The crazy setup here is that even with that fraction of the pie these companies might be worth say $100 billion optimistically, which would be amazing in normal times. Problem is it’s a train wreck for their investors and the associated debt bubble if they can’t sustain a valuation of 1-2 trillion and the present setup does not put them on a course to that trajectory.
- kelvinjps10 12d ago
  I don't see any difference where I put gas from one place to another. But there is definitely differences between one model and another or even plans themselves .
makapuf 12d ago
OK but even if F1 teams are a very expensive arms race it doesn't prevent me to bike to shop cheaply. You eed to have a moat around what people need.
- intrasight 12d ago
  That's true for sure in most business endeavors. The goal is to fill the area under the demand curve and there are demands for F1 race cars and for scooters. The analogy breaks down somewhat with software in general and for sure with superintelligence. A superintelligence can provide those "low-level" (ie scooter) services perhaps just as effectively because it's super intelligent and knows how to do things efficiently - for example by spawning agents of different intelligence levels. It can thus fill the area under the demand curve. This is what the big AI firms are shooting for.
transdev12 12d ago
> the cost to build a modern agentic model is in the 10 figure range and growing Source? The proliferation of labs building competent models would seem to suggest the opposite.
- intrasight 11d ago
  seems self evident if you read the new. You can Google it yourself, but here's the results from my googling - and this is just for the hardware. Double that to add personnel and corporate infrastructure "To build or purchase the physical hardware required to store tens of petabytes of data and train a State-of-the-Art (SOTA) frontier AI model, you are looking at a capital expenditure (CapEx) ranging from $320 million to well over $1 billion."

cmiles8 12d ago

But most commodities are the same way. It’s super expensive to drill for oil. I need oil and I’m in no position to mine my own because of the massive capital investment. But it doesn’t stop it from being a pure commodity. I couldn’t care less which company drilled for the oil… it’s all the same to me. Models are increasingly no different. OpenAI and Anthropic are a gas station saying “buy our gas for 10x the price!” When the world is looking at them saying it’s just gas, we’ll take the cheaper brand. We’ve tested your gas and it’s really no better than the stuff that’s 1/10th the price. Thats why their present business plan is screwed.

euroderf 12d ago
So the business challenge is to balance sizable investments and relatively small marginal costs. Not so different from other digital goods.
- cmiles8 12d ago
  Fair assessment. The challenge for OpenAI and Anthropic is that they need sizeable margins to pay for the massive costs incurred. Market forces are driving things in the opposite direction and fast. When your competition has a tiny cost base compared to yours and lacks the bonkers future capital commits you made then that’s a terrible position to be in… hence their conundrum.
  - hungryhobbit 12d ago
    Even "massive costs" is an understatement: these companies have astronomically massive costs! Take Open AI for instance: it has "zero debt" ... and $665 billion to $1.4 trillion in "long-term commitments".
- techpression 12d ago
  Except OpenAI and Anthropic has brought in a lot of money, that with this trajectory will make it some of the worst investments in ”software” ever (if it’s true enterprise clients are actively moving away, I know we are but for other reasons).
- jimbokun 12d ago
  With other digital goods the distribution and operating costs have been essentially free. No business worried that much about the cost of running Microsoft Office on the PCs they already distributed to their employees. They were only concerned about the licensing costs. And Microsoft didn't worry about the cost of printing CDs or the costs of serving Office online. It wasn't zero, but again negligible compared to the cost of development and the licensing costs. For LLMs the costs of training and inference are a very significant part of the overall costs.
gmadsen 12d ago
The key difference is that the models upgrade multiple times a year. It is an inherently different than a commodity market
- cmiles8 12d ago
  Yes, but changing models, even across providers, takes about two seconds and one line of code. It’s literally the least stickiest thing in the history of tech. Which is a big problem for these companies.
  - aff-vasileva 12d ago
    The API switch takes one line of code. The enterprise switch takes one line of code and twelve departments.
    - cmiles8 12d ago
      Honestly not. Most big corporates have arrangements were all the major models and now open models are available from the same API endpoint. It is literally one line of code to edit in most cases, even more so in big companies.
- theseamusjames 12d ago
  But they're all converging on capability. Do I care if it's a 72% or 74% on SWEBench? Practically, probably not. And if I'm not paying per token locally, then if it takes a tiny bit longer to get to the result, I don't care.
  - intrasight 12d ago
    Smartphones and laptops are also "converging", but Apple is always a year or two ahead so it doesn't matter. "Converging" is a meaningless term when things move quickly and cost billions to develop.
    - deleted 12d ago
blmarket 12d ago
I argue there's no difference. At least OpenAI/Anthropic can be considered premium like Octane 93 while OSS ones are 87. I agree 90% of the world can work with 87 gas, but there's always niche/luxury market where 93 can make small difference. (edit: typo)
- thereitgoes456 12d ago
  Of course. But that market won’t produce a $800 billion company, unless AI becomes ludicrously widespread — energy is used every day by virtually every person on the planet, and of course has plenty of mass consumption & “luxury” customers too.
- cmiles8 12d ago
  Yes… and as the article says folks are still leaving some work to the big labs. But the big money is to be made at scale and those use cases don’t require OpenAI or Anthropic. The crazy setup here is that even with that fraction of the pie these companies might be worth say $100 billion optimistically, which would be amazing in normal times. Problem is it’s a train wreck for their investors and the associated debt bubble if they can’t sustain a valuation of 1-2 trillion and the present setup does not put them on a course to that trajectory.
kelvinjps10 12d ago
I don't see any difference where I put gas from one place to another. But there is definitely differences between one model and another or even plans themselves .

makapuf 12d ago

OK but even if F1 teams are a very expensive arms race it doesn't prevent me to bike to shop cheaply. You eed to have a moat around what people need.

intrasight 12d ago
That's true for sure in most business endeavors. The goal is to fill the area under the demand curve and there are demands for F1 race cars and for scooters. The analogy breaks down somewhat with software in general and for sure with superintelligence. A superintelligence can provide those "low-level" (ie scooter) services perhaps just as effectively because it's super intelligent and knows how to do things efficiently - for example by spawning agents of different intelligence levels. It can thus fill the area under the demand curve. This is what the big AI firms are shooting for.

transdev12 12d ago

> the cost to build a modern agentic model is in the 10 figure range and growing Source? The proliferation of labs building competent models would seem to suggest the opposite.

intrasight 11d ago
seems self evident if you read the new. You can Google it yourself, but here's the results from my googling - and this is just for the hardware. Double that to add personnel and corporate infrastructure "To build or purchase the physical hardware required to store tens of petabytes of data and train a State-of-the-Art (SOTA) frontier AI model, you are looking at a capital expenditure (CapEx) ranging from $320 million to well over $1 billion."