Readit NewsIs it because chips are getting more expensive, so it is more economical to run them faster by liquid cooling them?
Or is it data center footprint is more expensive, so denser liquid cooling makes more sense?
Or is it that wiring distances (1ft = 1nanosecond) make dense computing faster and more efficient?
I want Al to do my laundry and dishes so that I can do art and writing, not for Al to do my art and writing so that I can do my laundry and dishes.
- Joanna Maciejewska
You could add music
They also remember what they did - if you spot one misunderstanding, there’s a chance they’ll be able to check all similar scenarios.
Comparing the mechanics of an LLM to human intelligence shows deep misunderstanding of one, the other, or both - if done in good faith of course.
BTW, not sure if you have experiences of delegating some works to human interns or new grads and being rewarded by disastrous results? I've done that multiple times and don't trust anyone too much. This is why we typically develop review processes, guardrails etc etc.
Why is this? Is it because OpenAI is seen as such a negative player in this ecosystem that Google “gets a pass on this one”?
And bonus question: what do people think will happen to OpenAI if Google wins the race? Do you think they’ll literally just go bust?
What I always wonder in these kinds of cases is: What makes you confident the AI actually did a good job since presumably you haven't looked at the thousands of client data yourself?
For all you know it made up 50% of the result.
> The revenue delta from this is more than enough to pay off the entire investment history for TPU.
Possibly; such statements were common when I was there too but digging in would often reveal that the numbers being used for what things cost, or how revenue was being allocated, were kind of ad hoc and semi-fictional. It doesn't matter as long as the company itself makes money, but I heard a lot of very odd accounting when I was there. Doubtful that changed in the years since.
Regardless the question is not whether some ads launches can pay for the TPUs, the question is whether it'd have worked out cheaper in the end to just buy lots of GPUs. Answering that would require a lot of data that's certainly considered very sensitive, and makes some assumptions about whether Google could have negotiated private deals etc.
I'm not sure what you're trying to deliver here. Following your logic, even if you have a fab you need to compete for rare metals, ASML etc etc... That's a logic built for nothing but its own sake. In the real world, it is much easier to compete outside Nvidia's own allocation as you get rid of the critical bottleneck. And Nvidia has all the incentives to control the supply to maximize its own profit, not to meet the demands.
> Possibly; such statements were common when I was there too but digging in would often reveal that the numbers being used for what things cost, or how revenue was being allocated, were kind of ad hoc and semi-fictional.
> Regardless the question is not whether some ads launches can pay for the TPUs, the question is whether it'd have worked out cheaper in the end to just buy lots of GPUs.
Of course everyone can build their own narratives in favor of their launch, but I've been involved in some of those ads quality launches and can say pretty confidently that most of those launches would not be launchable without TPU at all. This was especially true in the early days of TPU as the supply of GPU for datacenter was extremely limited and immature.
More GPU can solve? Companies are talking about 100k~200k of H100 as a massive cluster and Google already has much larger TPU clusters with computation capability in a different order of magnitudes. The problem is, you cannot simply buy more computation even if you have lots of money. I've been pretty clear about how relying on Nvidia's supply could be a critical limiting factor in a strategic point of view but you're trying to move the point. Please don't.
Once the space settles down, the balance might tip towards specialized accelerators but NVIDIA has plenty of room to make specialized silicon and cut prices too. Google has still to prove that the TPU investment is worth it.
Also worth noting that its Ads division is the largest, heaviest user of TPU. Thanks to it, it can flex running a bunch of different expensive models that you cannot realistically afford with GPU. The revenue delta from this is more than enough to pay off the entire investment history for TPU.
Just search nano banana on Twitter to see the crazy results. An example. https://x.com/D_studioproject/status/1958019251178267111