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The impact of competition and DeepSeek on Nvidia

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Re: The impact of competition and DeepSeek on Nvidia

#271
post #264

The description of DeepSeek reminds me of my experience in networking in the late 80s - early 90s. Back then a really big motivator for Asynchronous Transfer Mode (ATM) and fiber-to-the-home was the promise of video on demand, which was a huge market in comparison to the Internet of the day. Just about all the work in this area ignored the potential of advanced video coding algorithms, and assumed that broadcast TV-q…

I worked on a network that used a protocol very similar to ATM (actually it was the first Iridium satellite network). An internet based on ATM would have been amazing. You’re basically guaranteeing a virtual switched circuit, instead of the packets we have today. The horror of packet switching is all the buffering it needs, since it doesn’t guarantee circuits. Bandwidth is one thing, but the real benefit is that ATM…

> You’re basically guaranteeing a virtual switched circuit

Which means you need state (and the overhead that goes with it) for each connection within the network. That's horribly inefficient, and precisely the reason packet-switching won.

> An internet based on ATM would have been amazing.

No, we'd most likely be paying by the socket connection (as somebody has to pay for that state keeping overhead), which sounds horrible.

> You could now shave off another 20-100ms of latency for your FaceTime calls, which is subtle but game changing.

Maybe on congested Wi-Fi (where even circuit switching would struggle) or poorly managed networks (including shitty ISP-supplied routers suffering from horrendous bufferbloat). Definitely not on the majority of networks I've used in the past years.

> The horror of packet switching is all the buffering it needs [...]

The ideal buffer size is exactly the bandwidth-delay product. That's really not a concern these days anymore. If anything, buffers are much too large, causing unnecessary latency; that's where bufferbloat-aware scheduling comes in.

Re: The impact of competition and DeepSeek on Nvidia

#272
post #232

I think the biggest threat for future NVIDIa right now is their own current success. Their software platforms and CUDA are a very strong moat against everyone else. I don't see any beating them on that front right now. The problem is that I'm afraid that all that money sloshing inside the company is rotting the culture and that will compromise future development. - Grifters are filling out positions in many orgs only…

LOL. This isn't rot, it is reaching the end goal, the people doing the work reach the rewards they were working towards. Rot would imply somehow management should prevent rest and vest but that is the exact model that they acquired their talent on. You would have to remove capitalism from companies when companies win at capitalism making it all just a giant rug pull for employees.

Re: The impact of competition and DeepSeek on Nvidia

#273

DeepSeek just further reinforces the idea that there is a first-move disadvantage in developing AI models. When someone can replicate your model for 5% of the cost in 2 years, I can only see 2 rational decisions: 1) Start focusing on cost efficiency today to reduce the advantage of the second mover (i.e. trade growth for profitability) 2) Figure out how to build a real competitive moat through one or more of the foll…

This is wrong. First mover advantage is strong. This is why OpenAI is much bigger than Mixtral despite what you said. First mover advantage acquired and keeps subscribers. No one really cares if you matched GPT4o one year later. OpenAI has had a full year to optimize the model, build tools around the model, and used the model to generate better data for their next generation foundational model.

> First mover advantage acquired and keeps subscribers.

Does it? As a chat-based (Claude Pro, ChatGPT Plus etc.) user, LLMs have zero stickiness to me right now, and the APIs hardly can be called moats either.

Re: The impact of competition and DeepSeek on Nvidia

#274

Earlier quoted context omitted.

- Fail at the above. I don’t think this is what happened with DeepSeek. It seems that they’ve genuinely optimized their model for efficiency and used GPUs properly (tiled FP8 trick and FP8 training). And came out on top. The impact on the NVIDIA stock is ridiculous. DeepSeek took the advantage of flexible GPU architecture (unlike inflexible hardware acceleration).

This is what I still don't understand, how much of what they claim has been actually replicated? From what I understand the "50x cheaper" inference is coming from their pricing page, but is it actually 50x cheaper than the best open source models?

50x cheaper than OpenAI's pricing on an open source model which doesn't require giving that quality level up. The best open source models were much closer in pricing but V3/R1 are that way while being a results topper.

Re: The impact of competition and DeepSeek on Nvidia

#275
The most important part for me is:

> DeepSeek is a tiny Chinese company that reportedly has under 200 employees. The story goes that they started out as a quant trading hedge fund similar to TwoSigma or RenTec, but after Xi Jinping cracked down on that space, they used their math and engineering chops to pivot into AI research.

I guess now we have the answer to the question that countless people have already asked: Where could we be if we figured out how to get most math and physics PhDs to work on things other than picking up pennies in front of steamrollers (a.k.a. HFT) again?

Re: The impact of competition and DeepSeek on Nvidia

#276

Earlier quoted context omitted.

Unique, ye, but isn't their method open? I read something about a group replicating a smaller variant of their main model.

Which brings the question, if LLMs are an asset of such strategic value, why did China allow the DeepSeek to be released? I see two possibilities here, either that the CCP is not that all-reaching as we think, or that the value of the technology isn't critical, and that the release was further cleared with the CCP and maybe even timed to come right after Trump's announcement of American AI supremacy.

It is hard to estimate how much it is "didn't care", "didn't know" or "did it" I think. Rather pointless unless there are public party discussion about it to read.

Re: The impact of competition and DeepSeek on Nvidia

#277

Earlier quoted context omitted.

Yes, but it typically doesn't matter if someone can reach parity or even surpass you - they have to surpass you by a step function to take a significant number of your users. This is a step function in terms of efficiency (which presumably will be incorporated into ChatGPT within months), but not in terms of end user experience. It's only slightly better there.

One data point but my subscription for chatgpt is cancelled every time. So I made every month decision to resub. And because the cost of switching is essentially zero - the moment a better service is up there I will switch in an instant.

There are obviously people like you, but I hope you realize this is not the typical user.

Re: The impact of competition and DeepSeek on Nvidia

#278

Earlier quoted context omitted.

DeepSeek is profitable, openai is not. That big expensive moat won't help much when the competition knows how to fly.

DeepSeek is not profitable. As far as I know, they don’t have any significant revenue from their models. Meanwhile, OpenAI has $3.7b in revenue last reported and has high gross margins.

tell that to the stock market then, it might change the graph direction back to green.

Re: The impact of competition and DeepSeek on Nvidia

#279

Earlier quoted context omitted.

DeepSeek is not profitable. As far as I know, they don’t have any significant revenue from their models. Meanwhile, OpenAI has $3.7b in revenue last reported and has high gross margins.

tell that to the stock market then, it might change the graph direction back to green.

I’m doing the best I can.

Re: The impact of competition and DeepSeek on Nvidia

#280
post #263

Earlier quoted context omitted.

Google's moat was significantly better results than the competition for about 2 decades. Your analogy is valid at this time, but proves the GP's point, not yours.

I think it's worth double clicking here. Why did Google have significantly better search results for a long time? 1) There was a data flywheel effect, wherein Google was able to improve search results by analyzing the vast amount of user activity on its site. 2) There were real economies of scale in managing the cost of data centers and servers 3) Their advertising business model benefited from network effects, where…

They have more data on what people want from models?

Their SOTA models can generate better synthetic data for the next training run - leading to a flywheel effect?

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