AI Czar David Sacks Explains the DeepSeek Freak Out

one of the really cool things about this job is just that when something like this happens I get to kind of talk to everyone and everyone wants to talk and i' I feel like I've talked to maybe not everyone in like all the top people in AI but it feels like most of them and there's definitely a lot of takes all over the map on deep seek but I feel like I've started to put together a synthesis based on hearing from the top people in the field it was a bit of a freakout I mean it's rare that a model release is going to be a global news story or called a trillion dollars of market cap decline in in one day and so it is interesting to think about like why was this such a potent news story

and I think it's because there's two things about that company that that are different one is that obviously it's a Chinese company rather than an American company and so you have the whole China versus US competition and then the other is it's O an open source company or at least an open source the the R1 model and so you've kind of got the whole open source versus closed Source debate and if you take either one of those things out it probably wouldn't have been such a a big story but I think the synthesis of these things got a lot of people's attention a huge part of Tik tok's audience for example is international some of them like the idea that that the

US may not win the AI race that the US is kind of getting a come up and here and I think that fueled some of the early attention on Tik Tok similarly there's a lot of people who are rooting for open source or they have animosity towards open AI and so they were kind of rooting for this idea that oh there's this open source model that's going to give away what open AI has done at 120th a cost so I think all of these things provided fuel for the story now I think the question is okay what should we make of this I mean I think there are things that are true about the story and then things that are not true or should be debunked I think the let's call it true

thing here is that if you had said to people a few weeks ago that the second company to release a reasoning model along the lines of 01 would be a Chinese company I think people would have been surprised by that so I think there was a surprise and just to kind of back up for people you know there's there's two major kinds of AI models now there's kind of the Bas llm model like Chachi P40 or the Deep seek equivalent was V3 which they launched a month ago and that's basically like a smart PhD you ask a question gives you an answer then there's the new reasoning models which are based on reinforcement learning sort of a separate process as opposed to pre-training and 01 was the first Model

released along those lines and you can think of a reasoning model as like a smart PhD who doesn't give you a snap answer but actually goes off and does the work you can give it a much more complicated question and it'll break that complicated problem into a subset of of smaller problems and then it'll go step by step to solve the problem and that's that's called Chain of Thought right and so the new generation of agents that are coming are based on this type of idea of of chain of thought that that an AI model can sequentially perform tasks figure out much more complicated problems so open AI was the first to release this type of reasoning model Google has a similar model they're

working on called Gemini 2.0 flinking they've released kind of an early prototype of this called Deep research 1.5 anthropic has something but I don't think they've released it yet so other companies have similar models to 01 either in the works or in some sort of private beta but deep seek was really the next one after open AI to release you know the full public version of it and moreover they open sourced it and so this created a pretty big splash and I think it was legitimately surprising to people that the next big company to put out a reasoning model like this would be a a Chinese company and moreover they would open source it give it away for free and I think the API access is

something like 12th the the cost so all of these things really did drive the the new cycle and I think for good reason because I think that if you had asked most people in the industry a few weeks ago how far behind is China on AI models they would say six to 12 months and now I think they might say something more like 3 to 6 months right because 01 was released about four months ago and R1 is comparable to that so I think it's definitely moved up people's time frames for how close China is on on AI now let's take the um we should take the claim that they only did this for $6 million on this one I'm with Palmer

lucky and Brad gersner and others and I think this has been pretty much corroborated by everyone I've talked to that that that number should be debunked so first of all it's very hard to Val validate a claim about how much money went into the training of this model it's not something that we can empirically discover but even if you accept it at face value that $6 million was for the final training run so when the media is hyping up these stories saying that this Chinese company did it for six million and and these dumb American companies did it for a billion it's not an Apples to Apples comparison right I mean if you were to make the Apples to Apples comparison you would

need to compare the final training run cost by Deep seek to that of open AI or anthropic and what the founder of anthropic said and what I think Brad has said being an investor in open Ai and having talked to them is that the final training run cost was more in the tens of millions of dollars about nine or 10 months ago and so you know it's not 6 million versus a billion okay it's a billion dollar number might include all the hardware they've bought the the years of putting into it a holistic number as opposed to the training number yeah it's not running it's not fair to compare let's call it a Soup To Nuts number a fully loaded number by American AI companies to the final training run

by the Chinese company but real quick sax you've got you've got an open source model and they've the the white paper they put out there is very specific about what they did to make it and uh sort of the results they got out of it I don't think they give the training data but you could start to stress test what they've already put out there and see if you can do it cheap essentially like I said I think it is hard to validate the number I think that if let's just assume that we give them credit for the Six Million number my point is less that they couldn't have done it but just that we need to be comparing likes to likes yeah so if for example you're going to look at the fully loaded cost of what it

took deep seek to get to this point then you would need to look at what has been the R&D cost to date of all the models and all the experiments and all the training runs they've done right and the compute cluster that they surely have so Dylan Patel who's leading semiconductor analyst has estimated that deep seek has about 50,000 Hoppers and specifically he said they have about 10,000 h100s they have 10,000 H 800s and 30,000 h20s now the cost of that s sorry is they deep seek or it's deep seek plus the hedge fund deep seek plus the hedge fund but it's the same founder right and

by the way that doesn't mean they did anything illegal right because the h100s were banned under export controls in 2022 then they did the H 800s in 20123 but this founder was very farsighted he was very ahead of the curve and he was through his hedge fund he was using AI to basically do algorithmic trading so he bought these chips a while ago in any event you add up the the C cost of a compute cluster with 50,000 plus Hoppers and it's going to be over a billion dollars so this idea that you've got this Scrappy company that did it for only six million just not true they have a substantial compute cluster that they Ed to to train their models and frankly that doesn't count

any chips that they might have beyond the 50,000 you know that they might have obtained in violation of export restrictions that obviously they're not going to admit to and we just don't know we don't really know the full extent of of what they have so I just think it's like worth pointing that out that I think that part of the story got overhyped it's hard to know what's fact and what's fiction everybody who's on the outside guessing has their own incentive right like so if you're a semiconductor analyst that effectively is massively bullish Nvidia you want it to be true that it wasn't possible to train on $6

million obviously if you're the person that makes an alternative that's that disruptive you want it to be true that it was trained on $6 million all of that I think is all speculation the thing that struck me was how different their approach was and TK just mentioned this but if you dig into not just the original white paper of deep seek but they've also published some subsequent papers that have refined some of the details I do think that this is a case and Saks you can tell me if you disagree but this is a case where necessity was the mother of invention so I'll give you two examples where I just read these things

and I was like man these guys are like really clever the first is as you said let's let's put in a pin on whether they distilled 01 which we can talk about in a second but at the end of the day these guys were like well how am I going to do this reinforcement learning thing they invented a totally different algorithm there was the the Orthodoxy right this thing called po that everybody used and they were like no we're going to use something else called I think it's called grpo or something it uses a lot less computer memory and it's highly performant so maybe they were con strain sacks practically speaking by some amount of compute that caused them to

find this which you may not have found if you had just a total surplus of compute availability and then the second thing that was crazy is everybody is used to building models and compiling through kud which is nidia's proprietary language which I've said for a couple times is their biggest moat but it's also the biggest threat factor for lockin and these guys worked totally around Cuda and they did something called PTX which goes right to the bare metal and it's controllable and it's effectively like writing assembly now the only reason I'm bringing these up is we meaning the West with all the money that we've had didn't

come up with these ideas and I think part of why we didn't come up is not that we're not smart enough to do it but we weren't forced to because the constraints didn't exist and so I just wonder how we make sure we learn this principle meaning when the AI company wakes up and rolls out of bed and some VC gives them $200 million maybe that's not the right answer for a series A or a seed and maybe the right answer is 2 million so that they do these deep seek like Innovations constraint makes for great art what do you think uh freedberg when you're looking at this well I think it also enables a new class of investment

opportunity given the low cost and the speed it really highlights that maybe the opportunity to create value doesn't really sit at that level in the value chain but further Upstream apology made a comment on Twitter today that was pretty funny or I think ref this about the raer he like out the rapper may be the the moat the the moat which is true at the end of the day if model performance continues to improve get cheaper and it's so competitive that it commoditized much faster than anyone even thought then the value is going to be created somewhere else in the value chain maybe it's not the rapper maybe it's with the user and maybe by the way here's an important point maybe it's

further in the economy you know when electricity production took off in the United States it's not like the companies are making a lot of money that are making all the electricity it's the rest of the economy that Acres a lot of the value