[simonw]

> Who else has the sort of resources to train and run SOTA models at scale?

Google, OpenAI, Anthropic, Meta, Amazon, Reka AI, Alibaba (Qwen), 01 AI, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Z.ai (GLM), xAI, Ai2, Princeton, Tencent, MiniMax, Moonshot (Kimi) and I've certainly missed some.

All of those organizations have trained what I'd class as a GPT-4+ level model.

[bossyTeacher]

> Google, OpenAI, Anthropic, Meta, Amazon, Reka AI, Alibaba (Qwen), 01 AI, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Z.ai (GLM), xAI, Ai2, Princeton, Tencent, MiniMax, Moonshot (Kimi) and I've certainly missed some.

This is not a lot competition though. And you need to assume, that like other industries, mergers and acquisitions will happen over time which will put you in an increasingly worse position.

[whazor]

Kimi 2.5 is an opensource model with many providers https://openrouter.ai/moonshotai/kimi-k2.5/providers

Sure, opus and codex are significantly better. But price wise they cannot deviate too much from open models.

Especially if the open models are grounded against the digital twin.

[lbreakjai]

Ah but I said "_... and running at scale_"

[simonw]

Of the list I gave you, at a guess:

Google, OpenAI, Anthropic, Meta, Amazon, Alibaba (Qwen), Nvidia, Mistral, xAI - and likely more of the Chinese labs but I don't know much about their size.

[lbreakjai]

I guess where I was leading to is who owns the compute that runs those models. Mistral, for example, lists Microsoft and Google as subprocessors (1). Anthropic is (was?) running on GCP and AWS.

So, we have multiple providers, but for how long? They're all competing for the same hardware and the same energy, and it will naturally converge into an oligopoly. So, if competition doesn't set the floor, what does?

Local models? If you're not running the best model as fast as you can, then you'll be outpaced by someone that does.

1. https://trust.mistral.ai/subprocessors

[mediaman]

If there are low switching costs, and if there are multiple highly capable models, and if the hardware is openly purchasable (all of these are true), then the price will converge to a reasonable cash flow return on GPUs deployed net of operating expenses of running these data centers.

If they start showing much higher returns on assets, then one of the many infra providers just builds a data center, fills it with GPUs, and rents it out at 5% lower price. This is the market mechanism.

Looking at who owns the compute is barking up the wrong tree, because it has little moat. Maybe GPU manufacturers would be a better place to look, but then the argument is that you're beholden to NVIDIA's pricing to the hyperscalers. There's some truth to that, but you already see that market position eroding because of TPUs and belatedly AMD. All of these giant companies are looking to degrade Jensen's moat, and they're starting to succeed.

Is the argument here that somehow all the hyperscalers are going to merge to one and there will be only one supplier of compute? How do you defend the idea that nobody else could get compute?

[lbreakjai]

The starting point was that competition would prevent AI providers from doubling the price of tokens, because there's lots of models running on lots of providers.

This is in the context of the article, that paints a world where it would be unreasonable not to spend $250k per head per year in tokens.

My argument is the current situation is temporary, and _if_ LLMs provide that much value, then the market will consolidate into a handful of providers, that'll be mostly free to dictate their prices.

> If they start showing much higher returns on assets, then one of the many infra providers just builds a data center, fills it with GPUs, and rents it out at 5% lower price. This is the market mechanism.

Except when the GPUs, memory, and power are in short supply. The demand is higher than the supply, prices go up, and whoever has the deeper pockets, usually the bigger and more established party, wins.