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It's not a CISCO moment, more like a "Wavelength-division multiplexing" [1] moment of 2000 where the fiber optic craze was over capacity and crashed, causing a lot of "Dark Fibre" [2] left around. Deepseek found a way to squeeze more out of the same hardware, heck a key point is how to do more with the bandwidth bottleneck also with BI-DIRECTIONAL MULTIPLEXING [3] :)

Most of the biggest Nvidia clients are valued on speculation of future revenue from their closed models (secret sauce). Deepseek is fully open source so those revenue expectations crashed and investors are having second thoughts on throwing more money at companies like OpenAI. And this hits the expected sales growth of Nvidia for the next few years.

Dark fibre eventually was used but it took many years. And it was bought for cheap by companies like Google and CloudFlare.

[1] https://en.wikipedia.org/wiki/Wavelength-division_multiplexi...

[2] https://en.wikipedia.org/wiki/Dark_fibre

[3] https://arxiv.org/html/2412.19437v1 (3.2.1DualPipe and Computation-Communication Overlap)

The key thing is that it's actually both, and the market is still in waiting to see mode even after a 20% dump. AI end applications are not growing fast enough, and compute costs may be crashing down.

> more like a "Wavelength-division multiplexing"

no joke, the hype around DWDM is why I got into networking -- waves are the future, man!

Yeah, I also have made the "right move" industry or tech-wise but for the wrong reason (I'm assuming being in advanced networking worked out pretty well anyway for you).

It's always funny when someone asks me "So how did you know so far ahead of everyone else [that (my startup's product) would launch (category)]", and I tell them it's because we were inexperienced and didn't fully understand the market, so we guessed based on assumptions which turned out to be wrong. I try to always fess up to the fact that success in tech startups is a combination of insanely hard work, being (mostly) smart, luck, and being stubborn enough to keep trying again after failing until your luck turns.

While I agree with your points, I think a larger factor is that NVidia's valuation has been driven higher by the seemingly insatiable demand for data center AI GPUs from large companies investing far in advance (and excess) of near-term revenue. In recent months signs have emerged that leading edge models like O3 require significantly higher GPU cycles for each additional increment of quality. This would tend to push GPU demand growth rates even higher.

* DeepSeek appears to be credible evidence there may be clever optimizations to achieve higher model quality with less GPU cycles than previously thought. Basically, if you're making scarce oil derricks in a gasoline shortage and your stock price has been bid way up on the expectation of insatiable future gas demand, a more gas-efficient engine design is going to be adverse to your valuation. Especially if it's free and easy to implement.

* DeepSeek's weights are open source under a permissive license. Much of OpenAI (and similar company's) current revenue is from AI startups and other companies buying usage hours of proprietary leading edge models (eg O3) as cloud services through an API and reselling the output in their own applications targeting various verticals. If some of those companies start using a free open source model like DeepSeek (or it's future descendants/competitors) for some of their offerings - that'll reduce the income and war chest of some of today's biggest GPU buyers. Lower current revenue lowers valuations meaning the equity OpenAI et al use to buy GPUs will be devalued.

>Lots of FAANGs with deep pockets started to invest in their own hardware, and it got good enough to start beating Nvidia.

It's not just hardware though: you can't run CUDA on non-Nvidia hardware, which in my understanding is a major moat for Nvidia. I'd love to hear rebuttals on this though, because GPU programming is something I've only dabbled with.

Agreed on CUDA being a big moat; Nvidia has spent a big chunk of the last 2 decades building this ecosystem and is now reaping dividends from it.

From what I've read, most of the investments by FAANGs/startups in building specialised hardware has been in the inference space.

NVDA trade was always a short-term thing

NVDA has been going up for the last 10 years (with 2022 being the only exception).

AI today is better than anyone could hope for, and I don’t see any reasons to not expect further advances.

> AI today is better than anyone could hope for,

I hope for an AI that can actually reason and doesn't bullshit its users though

Without Nvidia, you would still wait for ChatGPT moment and not even think about AI at all.

I have been invested in Nvidia for 9 years and I have not only witnessed what Nvidia has done in ML/AI but also how the entire field evolved.

To say Nvidia is toast is like saying US is toast after Sovjets send a rocket into space.

What AI are you using? I use o1 and it hasn’t tried to bullshit me so far. Would love to hear some examples of o1 bullshitting.

I also use o1 and sometimes it just gets stuff confidently wrong. For example it often makes up command line arguments or optional parameters to methods or configuration keys. Anything that is even remotely niche, anything that hasn't been posted 1000 times on the internet, GPT will get wrong without ever telling you that it does not know.

Anything else, you can just google it because 9/10 there's a SO thread about it, and when there isn't, the documentation is usually good enough. GPT is not going to help you there.

But you can see it in the small things. For example if you ask an intelligent person "how do I install X on docker?" it will search google and then find a tutorial or some resource to reference. Then they will break it down and adapt it to your scenario.

GPT instead will give you... whatever it feels like giving you in that moment. It will not look on the f*n internet to make sure that what it says is correct and up to date. It does not update an internal knowledge base with factual information that it can then reference to produce a coherent plan. It has no concept of truth so it can't use logic.

You can throw as much compute and chain of thought as you want to the problem, it's the architecture itself that is disappointing.

That's fair, even though I haven't encountered this myself. I think having it search internet for relevant info and cross-reference it is a good idea, and is being implemented (SearchGPT). I guess we'll just have to wait a couple more months till o3.