851–860 of 1001 posts
Here’s a take I haven’t seen yet: If training and inference just got 40x more efficient, but OpenAI and co. still have the same compute resources, once they’ve baked in all the DeepSeek improvements, we’re about to find out very quickly whether 40x the compute delivers 40x the performance / output quality, or if output quality has ceased to be compute-bound.
Does line go up forever?
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I think you’re wrong and Wallstreet got Deepseek’s impact wrong. You say DeepSeek should decrease Nvidia demand. Wallstreet agreed today. I say DeepSeek should increase Nvidia’s demand due to Jevon’s Paradox.
I’m also baffled by the reaction. Even with the ability to do more with less, the nature of the race still encourages everyone to do more with more.
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I think the analysis of (2) is too simplistic because it ignores network effects. A community of developers and users around a specific toolset (e.g. CUDA) is hard to just "buy". Imagine trying to build a better programming language than python -- you could do it for a trillion dollars, but good luck getting the world to use it. For a real example, see Meta and Threads, or any other Twitter competitor.
The second dotcom era was basically giving away product until you have a monopoly. Really isn't hard to imagine a company trying to make a software happen by generating an environment around it.
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No, nvidia's demand and importance might reduce in the long term. We are forgetting that China has a whole hardware ecosystem. Now we learn that building SOTA models does not need SOTA hardware in massive quanties from nvidia. So the crash in the market implicitly could mean that the (hardware) monopoly of American companies is not going to be more than a few years. The hardware moat is not as deep as the West though…
>On the training side, there will be less demand for nvidia GPUs as meta, google, microsoft etc. extract efficiencies with the GPUs they already have given the embarrasing success of DeepSeek. Now, China might have been another insatiable market for nvidia but the export controls have ensured that it wont be. Why? If DeepSeek made training 10x more efficient, just train a 10x bigger model. The end goal is AGI.
I dont' know what the surprise is here. the human brain consumes about 20 watts. literally a rounding error compared to what chatgpt uses. so we already know that there was plenty of room on the table to improve. incidentally, I love these kinds of market crashes. just moved a big chunk of my savings account into stocks last night :). Buy and hold. dont' sell during a dip lol
I like the chart Bloomberg has of the top 10 largest single day stock drops in history. 8 out of the 10 are NVDA (Meta and Amazon are the other two).
I feel like this is a symptom of our broken economic system that has allowed too much cash to be trapped in the markets, forever making mostly imaginary numbers go up while the middle class gets squeezed and the poor continue to suffer. A fundamental feature of a capitalist system you can use money to make more money. That's great for growing wealth. But you have to be careful, it's like a sound system at a concert.…
Is there evidence for the claim that the training was that much cheaper? Or is is 'apparent' by inspection of the verified ingestion architecture? I can't seem to find any evience beyond the team's statements. What have I missed?
Compute resources they officially should not access to given export bans, where mentioning them might lead to their export ban bypass getting rolled up.
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I’ve noticed this phenomenon among IT & tech VC crowd. They will launch pod cast, offer expert opinion and what not on just about every topic under the Sun, from cold fusion to COVID vaccine to Ukraine war. You wouldn’t see this in other folks, for example, a successful medical surgeon won’t offer much assertion about NVIDIA. And the general tendency among audience is to assume that expertise can be carried across do…
Systems, it’s all about systems thinking. It is absolutely true that people in tech are often optimistic and/or delusional about the other expertise at their command. But it’s not like the basic assumption here is completely crazy. Being a surgeon might require thinking about a few interacting systems, but mostly the number and nature of those systems involved stay the same. Talented programmers without even formal t…
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It has been clear for a while that one of two things is true. 1) AI stuff isn't really worth trillions, in which case Nvidia is overvalued. 2) AI stuff is really worth trillions, in which case there will be no moat, because you can cross any moat for that amount of money, e.g. you could recreate CUDA from scratch for far less than a trillion dollars and in fact Nvidia didn't spend anywhere near that much to create it…
I think the analysis of (2) is too simplistic because it ignores network effects. A community of developers and users around a specific toolset (e.g. CUDA) is hard to just "buy". Imagine trying to build a better programming language than python -- you could do it for a trillion dollars, but good luck getting the world to use it. For a real example, see Meta and Threads, or any other Twitter competitor.
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The crash is absolutely rational; the cascading effect highlights the missing moat for companies like OpenAI. Without a moat, no investor will provide these companies with the billions that fueled most of the demand. This demand was essential for NVIDIA to squeeze such companies with incredible profit margins. NVIDIA was overvalued before, and this correction is entirely justified. The larger impact of DeepSeek is mo…
It has been clear for a while that one of two things is true. 1) AI stuff isn't really worth trillions, in which case Nvidia is overvalued. 2) AI stuff is really worth trillions, in which case there will be no moat, because you can cross any moat for that amount of money, e.g. you could recreate CUDA from scratch for far less than a trillion dollars and in fact Nvidia didn't spend anywhere near that much to create it…