Meta trains on Nvidia and infers on AMD. There is incentive if your inference costs are high.

Meta also has a second generation of their own AI accelerator chips designed.

Google was omitted because they own the hardware and the models, but in retrospect, they represent a proof point nearly as compelling as OpenAI. Thanks for the comment.

Google has leading models operating on leading hardware, backed by sophisticated tech talent who could facilitate migrations, yet Google still cannot leap over the CUDA moat and capture meaningful inference market share.

Yes, training plays a crucial role. This is where companies get shoehorned into the CUDA ecosystem, but if CUDA were not so intertwined with performance and reliability, customers could theoretically switch after training.

> yet Google still cannot leap over the CUDA moat and capture meaningful inference market share.

It's almost as if being a first-mover is more important than whether or not you use CUDA.

Both matter quite a bit. The first-mover advantage obviously rewards OEMs in a first-come, first-serve order, but CUDA itself isn't some light switch that OEMs can flick and get working overnight. Everyone would do it if it was easy, and even Google is struggling to find buy-in for their TPU pods and frameworks.

Short-term value has been dependent on how well Nvidia has responded to burgeoning demands. Long-term value is going to be predicated on the number of Nvidia alternatives that exist, and right now the number is still zero.

Google has a self inflicted wound in the time to get an api key.