111–120 of 328 posts
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It's all small products which didn't receive traction.
It's not though. Chromecast, g suite legacy, podcast, music, url shortener,... These weren't small products.
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They will, I'm sure. The big difference is that Google is both the chip designer *and* the AI company. So they get both sets of profits. Both Google and Nvidia contract TSMC for chips. Then Nvidia sells them at a huge profit. Then OpenAI (for example) buys them at that inflated rate and them puts them into production. So while Nvidia is "selling shovels", Google is making their own shovels and has their own mines.
So when the bubble pops the companies making the shovels (TSMC, NVIDIA) might still have the money they got for their products and some of the ex-AI companies might least be able to sell standard compliant GPUs on the wider market. And Google will end up with lots of useless super specialized custom hardware.
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It's all small products which didn't receive traction.
Wait until Apple's ChromeBook competitor shows up to eat their lunch just like switching to another proprietary stack with no dev ecosystem will die out. Sure they'll go after big ticket accounts, also take a guess at what else gets sanctioned next.
The only lunch that will be eaten is Apple's own, since it would probably cannibalize their own sales of the MacBook air
A question I don't see addressed in all these articles: what prevents Nvidia from doing the same thing and iterating on their more general-purpose GPU towards a more focused TPU-like chip as well, if that turns out to be what the market really wants.
Nothing in principle. But Huang probably doesn't believe in hyper specializing their chips at this stage because it's unlikely that the compute demands of 2035 are something we can predict today. For a counterpoint, Jim Keller took Tenstorrent in the opposite direction. Their chips are also very efficient, but even more general purpose than NVIDIA chips.
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They will, I'm sure. The big difference is that Google is both the chip designer *and* the AI company. So they get both sets of profits. Both Google and Nvidia contract TSMC for chips. Then Nvidia sells them at a huge profit. Then OpenAI (for example) buys them at that inflated rate and them puts them into production. So while Nvidia is "selling shovels", Google is making their own shovels and has their own mines.
So when the bubble pops the companies making the shovels (TSMC, NVIDIA) might still have the money they got for their products and some of the ex-AI companies might least be able to sell standard compliant GPUs on the wider market. And Google will end up with lots of useless super specialized custom hardware.
If it gets to the point where this hardware is useless (I doubt it), yes Google will have it sitting there. But it will have cost Google less to build that hardware than any of the companies who built on Nvidia.
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100 times more chips for equivalent memory, sure.
Check the specs again. Per chip, TPU 7x has 192GB of HBM3e, whereas the NVIDIA B200 has 186GB. While the B200 wins on raw FP8 throughput (~9000 vs 4614 TFLOPs), that makes sense given NVIDIA has optimized for the single-chip game for over 20 years. But the bottleneck here isn't the chip—it's the domain size. NVIDIA's top-tier NVL72 tops out at an NVLink domain of 72 Blackwell GPUs. Meanwhile, Google is connecting 921…
Google's real moat isn't the TPU silicon itself—it's not about cooling, individual performance, or hyper-specialization—but rather the massive parallel scale enabled by their OCS interconnects. To quote The Next Platform: "An Ironwood cluster linked with Google’s absolutely unique optical circuit switch interconnect can bring to bear 9,216 Ironwood TPUs with a combined 1.77 PB of HBM memory... This makes a rackscale…
Also, Google owns the entire vertical stack, which is what most people need. It can provide an entire spectrum of AI services far cheaper, at scale (and still profitable) via its cloud. Not every company needs to buy the hardware and build models, etc., etc.; what most companies need is an app store of AI offerings they can leverage. Google can offer this with a healthy profit margin, while others will eventually run…
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Also, Google owns the entire vertical stack, which is what most people need. It can provide an entire spectrum of AI services far cheaper, at scale (and still profitable) via its cloud. Not every company needs to buy the hardware and build models, etc., etc.; what most companies need is an app store of AI offerings they can leverage. Google can offer this with a healthy profit margin, while others will eventually run…
Google's work on Jax, pytorch, tensorflow, and the more general XLA underneath are exactly the kind of anti-moat everyone has been clamoring for.
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So when the bubble pops the companies making the shovels (TSMC, NVIDIA) might still have the money they got for their products and some of the ex-AI companies might least be able to sell standard compliant GPUs on the wider market. And Google will end up with lots of useless super specialized custom hardware.
> And Google will end up with lots of useless super specialized custom hardware. If it gets to the point where this hardware is useless (I doubt it), yes Google will have it sitting there. But it will have cost Google less to build that hardware than any of the companies who built on Nvidia.
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So when the bubble pops the companies making the shovels (TSMC, NVIDIA) might still have the money they got for their products and some of the ex-AI companies might least be able to sell standard compliant GPUs on the wider market. And Google will end up with lots of useless super specialized custom hardware.
I think people are confusing the bubble popping with AI being over. When the dot-com bubble popped, it's not like internet infrastructure immediately became useless and worthless.