This is the correct take. NVidia did the same thing with the hyperscalers - when GOOG/MSFT/AMZN were all building their own chip competitors, NVidia began funding the neoclouds to fight down the margin on their competitors.

Commoditize complements

> Without credible open models, they only have a few customers, and those customers have leverage against nvidia. With open models, they have tons of customers, and nvidia has all the leverage.

I wonder if Nvidia is also trying to cover themselves against a market crash the bankrupts the AI labs but leaves the tech standing? (Similar to the dotcom crash or the big railroad crash back in the day.) If Anthropic and OpenAI struggle, Nvidia can always sell local inference hardware. But local inference hardware often has a lower utilization, so you need more GPUs for the same number of tokens.

I think that's their exact plan. Cover all bases. The bubble can burst but that really only affects the UI model giants, the demand for AI won't drop even if frontier model companies won't find profitability enough to pay the bill

They are worried because prior to 2022 50% of their revenue was tied to gaming and now that division has actually dropped further cause it's not affordable. Meanwhile there's a dozen competitors working on asics for models. What point is there to run your model on a GPU when it's been made into an asic? None. So eventually when the models hit 99% on all the benchmarks you hit end game and the GPU becomes the worst way to run the model.

Indeed the hardware-inefficiency of local inference is in principle good, not bad, for hardware vendors. But until the manufacturing capacity exists to ship local-inference systems with enough VRAM to enough customers then inference is going to keep taking place at (more-)efficient centralised providers and nVidia will have to keep trying to sell what it makes to them to remain an AI player.

Sure but it also means these companies nvidia buys don’t work with AMD and other competitors anymore. There are myriad motivations and it’s not all one or the other but this is a classic component of Silicon Valley acquisition strategies.

They want LEVERAGE.

They can't ride the hyperscaler gravy train forever; at some point between Google, AMD, and Apple NVIDIA is going to lose its monopoly on serving large customers.

At that point, it would be useful if a few open models existed which were only a couple of months behind the frontier.

But it's very important that the open models never be TOO good, because the AI companies are buying compute on the assumption that their software will add value. If it becomes a commodity business with frontier open models, NVIDIA won't be able to get away with such a crazy markup.

> If it becomes a commodity business with frontier open models, NVIDIA won't be able to get away with such a crazy markup.

Of course they would. You need hardware to run the model. nVidia sets the floor.

No, did you miss the first half of my comment?

If NVIDIA is forced to compete on price, they're screwed unless they can find more customers. They aren't the only game in town for accelerators any more.

I don't understand how you're getting from "open weight model providers are forced to compete on price" → "nVidia is forced to compete on price".

Yes nVidia is not the only game in town, but that's no more or less true regardless of whether there are a bunch of competing providers.

Because NVIDIA is competing against AMD, Google, and Apple just to name a few, and more dangerously soon they'll be competing against their clients.

Apple is working on an enterprise server. Google TPU revenue growth is crazy. Even AMD is finally usable for ML. All the frontier labs have chip teams and it's not to keep buying from Nvidia.

If it wants to avoid being dependent on its biggest clients it needs to make sure the hyperscalers have competition, and preferably competition that doesn't want to get into the chip design business.