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.

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)

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.

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.

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 .

So the business challenge is to balance sizable investments and relatively small marginal costs. Not so different from other digital goods.

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.

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".

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.

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).

The key difference is that the models upgrade multiple times a year. It is an inherently different than a commodity market

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.

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.

Bad comparison.

1. People buy Apple because of the broader ecosystem of products and the “it just works” aspect of that ecosystem. Other companies make phones with features that are objectively better but folks don’t switch because the Apple ecosystem is sticky.

Despite trying, neither OpenAI nor Anthropic has managed to move up the stack beyond “hey guys new model release today!” announcements that everyone yawns at.

2. Switching costs are real. It’s a PITA to switch not just the phone but everything else. Switching model providers is a line of code and takes almost no effort.

Apple has a true moat which is why they can command a premium. OpenAI and Anthropic have no moat which is why they’re in trouble.

Apple would have probably lost more customers if it didn't also create a hard-to-leave lock-in ecosystem.

Lots of people use old iPhones and don't care about some "up to" benchmark bumped every year, but are stuck with iMessage contacts, their stuff in iCloud, Apple Watch or apps that are not allowed by Apple to even mention they have Android versions.

I don't think most motorists would care if OpenAI's gas stations just released 106-Octane "Intersteller" gas, unless their cars specifically require it.

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.

The API switch takes one line of code. The enterprise switch takes one line of code and twelve departments.

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.

Even commodity markets recognize different grades of product. The oil market separately prices different grades of oil, different refined products. All that really matters is that when you go to the market to buy, you can say, "I need X amount of this grade of this product" and that is what you will get. If AI models can be sold that way, you basically have a commodity market.

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.

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.

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."