Nvidia Earning Results Q3 2023

I think so but it deserves a bit of a caveat: a lot of ML workloads right now are distinctively unprofitable, and like all of the unprofitable-venture-funded businesses of the past decade, many of which have been culled in the recent downturn, there will have to be a reckoning at some point.

There are definitely lots of workloads that produce positive ROI for companies, but many more that are heavily subsidized (ex. products like Alexa and Google Assistant) and consume a vast amount of ML resources.

A lot of ML-centric products suffer from a significant departure from the traditional Silicon Valley notion of each additional user being zero marginal cost, and products having negligible operating costs vs. high fixed costs.

As a researcher, I think we'll go into a "winter" soon, but nowhere near the previous one. The hype will die down but ML has shown to be extremely useful. Anything with graphic design, gaming, video, imaging, streaming, etc uses ML these days. It's been used in science for awhile (under statistical learning, e.g. regression) and we can't do without it. Though a lot of these things aren't following the main hype machine. But if you're in the weeds you'll see a lot of these works.

But I do think the hype will die. People even now are starting to notice that a lot of the diffusion work looks amazing at first glance but has major errors. There's also the issue of alignment. These are going to likely take a lot of mathematical experience to push forward[0] and will benefit less from a pure numbers game.

Of course, the other part (in Nvidia's favor) is that a lot of scientific work is GPU based now. Not just ML. Parallelism is king. You can do FEM, particle simulations, etc with GPUs now because the clock speed got high enough. So you can do "heavy" calculations with extreme parallelism. Previously you could only do this with lighter loads. (there's also major algorithmic improvements, so I don't want to undermine the fantastic work of those researchers).

[0] I want to admit my bias here. I'm a "math person" (not a mathematician, they will school me) and there's not a lot of ML researchers going down this path. To give an example, there are plenty of diffusion researchers I've talked to that don't know how to calculate the likelihood of their samples while they use an ELBO cost function. But this comes into the alignment issue because we need to talk about the smoothness of latent manifolds, interpolation, inversion (not just image inversion, but image/text inversion), and a lot more. I just don't think we can continue on this path of bigger networks, bigger data, search hyper-parameters, get benchmark result. (I fundamentally believe this is anti-scientific fwiw)

My only caution on that is that I think their moat on ML is much shallower than people think. nVidia has traditionally maintained their position as the top dog in GPGPU by being first to provide good hardware and software, having markets lock in on that software, and then able to later sell more hardware at very good margins. Even when AMD (and others) later provide hardware that provides much better perf/cost for the same workload, at that point everyone already has a small mountain of tightly optimized cuda, and the cost/benefit of switching hardware providers just isn't there.

I really don't think this is going to work in ML. At their core, the ML algorithms are generally simple enough to fit on a blackboard, and are relatively much more easy to efficiently parallelize and optimize on diverse hardware. nVidia has again managed to capture most of a nascent market by being the first with adequate hardware and software to support it. But I think in a couple of years there are going to be a lot more competitors in this space, and unlike with previous GPGPU work, it will not be that painful for the end-user to shop around.

This won't mean that AMD (or someone else! this space is much easier to enter than GPUs in general) will capture the value from nVidia. Instead it will mean that the entire space will be commoditized and the margins will absolutely collapse.

On the off chance you get to put it in your cart, it either disappears by the time you check out or the order gets voided afterwards. It rarely stays in stock longer than a few mins.