NVIDIA finally in trouble?

Google is making its most serious AI infrastructure move yet, and it’s the first real crack in $4.5 trillion Nvidia's moat

Not a chip. Not a benchmark.

Software.

For a decade, AI followed one gravity well:

PyTorch → CUDA → NVIDIA.

If you trained models, you trained on GPUs. Not because they were perfect, but because leaving meant rewriting everything.

Now Google is attacking the lock-in itself.

TorchTPU isn’t a shiny new TPU.

It’s Google making PyTorch feel native on TPUs - fewer hacks, fewer rewrites, closer to “it just works.”

And they’re doing it with Meta, the steward of PyTorch.

That’s the tell.

TPUs have always been good at scale, especially inference. What held them back wasn’t hardware - it was developer pain.

TorchTPU goes straight for that nerve.

This is why the timing matters:

→ TPU v7 is live. → Anthropic commits up to 1 million TPUs. → Meta explores multi-billion-dollar deployments. → “Vendor diversification” stops being a theory and becomes leverage

But let’s be clear:

CUDA isn’t dead. NVIDIA still owns training.

But inference is where the money compounds, and where ASICs shine.

And while TorchTPU doesn’t topple the king, it definitely lowers switching costs.

And once switching costs fall, pricing power follows.

Most importantly, Google isn’t trying to beat NVIDIA head-on.

It’s doing something smarter - making NVIDIA optional.

Author

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