Google, now working on a new initiative that would challenge one of NVIDIA's biggest advantages in AI. It's proprietary software. Djerja Bosa has more in today's tech check segment. Morning, Deirdre. Hey, good morning, Sarah. So Google's chip challenge to NVIDIA, it's moving from hardware to software. The main knock against those custom AI chips, TPUs, is that they're hard to adopt. Invidia's advantage is its ecosystem,

so that's its chips and its CUDA software.

If Google can crack the software and the adoption piece,

this becomes a very real test for Nvidia's moat.

So Google's TPUs, they started as an internal tool

built to run Google's own AI as efficiently as possible

and optimized around Google's own software.

Now, that worked really well inside of Google, but as the company started selling these chips

to outside customers that created friction.

TPUs were harder to drop in because they required extra engineering work.

InVedia, by contrast, has Kuta, that's its software platform, that is deeply embedded in how its

AI is built and run, making Nvidia the easier default for pretty much anyone building.

Now, Reuters first reported, and a source familiar confirms to me that Google is now trying to fix that with its own initiative, Torch TPU, which is essentially

trying to lower the software and engineering barriers that have held adoption back. So this doesn't

mean that Nvidia is about to be displaced, but it does change the stakes. The AI race, it's not just

about peak performance anymore. It's about margins and scale, and that is where the real pressure

is starting to build. So there's Google TPUs, but this is part of a bigger trend as well.

Amazon is pushing its own custom chips.

Meta is looking for ways to lower inference costs.

Every hyper-scaler, they want more leverage in GPU pricing negotiations.

That has long been the case.

So even if NVIDIA keeps most of the market here, the pricing environment around

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