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- Hacker News
- I see we have reached the stock market phase of Universal Paperclips.by Erikun
- Back in during the dotcom boom, there were multiple examples of companies being bought for ridiculous amounts. Often as the result of a bidding war.
Even back then, some economists used the "hidden wallet auction" as an example of how this could happen.
To summarize:
- there is a wallet
- you don't know how much is in the wallet
- you bid on amount to buy the wallet
- if you get the highest bid you win
- crucially, if you lose then you still have to pay
This is often cited as a game that you do not want to play b/c it's a. hard to predict the upside, b. the downside is huge.
That being said, people still got into these auctions and because of sunk cost fallacy, decided to keep bidding even if they might lose.
The hyperscaler race feels a bit like the above but no one seems to ant to admit it.
by alexpotato - IMO focusing on the hyperscalers is kind of misleading.
Yes, for programmers and tech companies AI is kinda boring now, but AI integration in general is still kind of uncharted territory.
There are so many small companies and individuals just getting started with AI today and I believe a large the customer base (and revenue) is still untapped. Hell, I’m discovering new use cases regularly still and the average mismanaged 30 people whatever SaaS vendor probably didn’t even get started yet.
by KaiMagnus - Nvidia has been playing a dangerous but profitable game since the Crypto boom.
but now I think they probably have bitten more than they can chew.
Apple already proved with their unified memory - that as long you have the capacity you can run capable models locally - thereby goes demand for inference if everyone is running some model locally.
For training - Chinese models have proved that you don't need the latest & greatest in Nvidia hardware. Same as TPUs.
only time will tell.
by dzonga - For awhile I've found two things hard to square, that the hardware and software making up current gen AI will bring us to a socioeconomic singularity, and the reality the thing they're mostly trying to emulate is a few pounds of meat and fat running on tens of watts equivalent. On one hand the current AIs are obviously super human in some tasks, get completely dunked on in others by far simpler organisms. My cat can catch a bug out of the air, Fable 5 in Cowork can lack the dexterity to make a slideshow because I had LibreOffice instead of Microsoft Office. Not even close to analogous, but point being they appear to have pretty fundamental differences in how they can interface with the world that the economic thesis seems to gloss over.by rcr-anti
- More interesting take on Nvidia's position than I've come across before. One thing to be noted is 1) Nvidia is already making moves in robotics so even if their position in AI (moreso llms) diminished, they certainly have another big avenue arguably harder to just get into (although I'm not sure what efforts Google is doing for the tpu in robotics). Another point is Nvidia is still the main player in the west, that is, China certainly can and will create their own full stack without reliance on US companies. That puts Europe and other countries in an interesting, do you buy Nvidia because it's the only option or for security. That's to say I believe Nvidia's position relied on many different things being true at the same time, and we're moving towards an environment where those things are certainly being contested at (roughly) the same time.by tolugenius
- In many investment theses - like Nvidia's bet that demand for compute will keep growing - the first order assumption is usually correct. Yes, demand for more compute, chips, infrastructure is huge and each year some additional data centers will be built. Where such investment bets usually fail is in the second-order assumptions: Ie. the expectation of the growth of demand. This is where there's a high chance that the current expectations are likely exaggerated. So: demand is likely to persist for the foreseeable future but not increase every year. And that can upend the whole investment story. That can be enough to make these bonds a huge burden for Nvidia in the end. Not because people stopped buying more compute but because they stopped buying more every year.by jcfrei
- Nvidia's biggest advantage in AI has never been only their hardware performance but how entrenched their software is in ML research that flowed down stream. However, if you've actually used CUDA C/C++, it's pretty one of the worst software development ecosystem imaginable: you get all the footgun of regular C++, plus GPU compute pretending to be C++ and but doesn't actually behave like C++ because CPU and GPU compute are fundamentally different, and the only reason people put up with it is because Vulkan and HIP C/C++ are even worse.
Google's limitation is that they still don't offer TPUs in a PCI-E card/dev board that people can plug in to their PC for local development and sane low level API to develop against, instead you have to go through their cloud and their full software stack which greatly limits ecosystem growth. The minute that Google figures that out, that's when Nvidia's dominance would be challenged.
by YuechenLi