20VC: Deepseek Special: Is Deepseek a Weapon of the CCP | How Should OpenAI and the US Government Respond | Why $500BN for Stargate is Not Enough | The Future of Inference, NVIDIA and Foundation Models with Jonathan Ross @ Groq

Jonathan Ross is the Co-Founder and CEO of Groq, providing fast AI inference. Prior to founding Groq, Jonathan started Google’s TPU effort where he designed and implemented the core elements of the original chip. Jonathan then joined Google X’s Rapid Eval Team, the initial stage of the famed “Moonshots factory,” where he devised and incubated new Bets (Units) for Alphabet. The 10 Most Important Questions on Deepseek: How did Deepseek innovate in a way that no other model provider has done? Do we believe that they only spent $6M to train R1? Should we doubt their claims on limited H100 usage? Is Josh Kushner right that this is a potential violation of US export laws? Is Deepseek an instrument used by the CCP to acquire US consumer data? How does Deepseek being open-source change the nature of this discussion? What should OpenAI do now? What should they not do? Does Deepseek hurt or help Meta who already have their open-source efforts with Lama? Will this market follow Satya Nadella’s suggestion of Jevon’s Paradox? How much more efficient will foundation models become? What does this mean for the $500BN Stargate project announced last week?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Harry Stebbings?

  So everyone's seen the news about DeepSeek today. Is it as big a deal as everyone is making it?

• 0:06Jonathan Ross?

  Yes, it is Sputnik 2.0. It is true that they spent about 6 million or whatever it was on the training. They spent a lot more distilling or scraping the OpenAI model. I can't speak for Sam Altman or OpenAI, but if I was in that position, I would be gearing up to open source my models in response because it's pretty clear you're going to lose that. So you might as well try and win all the users and the love from open sourcing. Open always wins, always.

• 0:35Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. And today we focus on DeepSeek. As our guest put it today, this is Sputnik 2.0. And joining me for the discussion is one of the best placed in the business, Jonathan Ross, co-founder and CEO of Groq, providing fast AI inference. Prior to founding Groq, Jonathan started Google's TPU effort where he designed and implemented the core elements of the original Google chip. But before we dive in today, here are two fun facts about our newest brand sponsor, Kajabi. First, their customers just crossed a collective $8 billion in total revenue. Wow. Second, Kajabi's users keep 100% of their earnings, with the average Kajabi creator bringing in over $30,000 per year. In case you didn't know,

• 1:20Harry Stebbings?

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• 3:28Harry Stebbings?

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• 4:06Jonathan Ross?

  No problem, but before we start, can I just say one thing? I think you have the most amazing, unique go-to-market that I've ever seen in my life for a podcast. I've never seen this before. I think your strategy is you're literally interviewing every single audience member, forcing them to watch videos and get addicted to you.

• 4:28Harry Stebbings?

  I mean, I thought you were going to say my accent, but I'm totally going to take that. That's wonderful. And yes, you're absolutely right.

• 4:34Jonathan Ross?

  And do things on scale.

• 4:35Harry Stebbings?

  But I do want to start, obviously, everyone's just talking about deep sea. Little bit of context, why are you so well placed to speak about deep sea? And let's just start there for some context.

• 4:45Jonathan Ross?

  Well, my background, so I started the Google TPU, the AI chip that Google uses. And in 2016, started an AI chip startup called Groq with a Q, not with a K, that builds AI accelerator chips, which we call LPUs.

• 5:00Harry Stebbings?

  Okay, so everyone's seen the news about deep sea today. I wanted to start off by saying, is it as big a deal as everyone is making of it?

• 5:08Jonathan Ross?

  Yes, it's Sputnik. It is Sputnik 2.0. Even more so, you know that story about how NASA spent a million dollars designing a pen that could write in space and the Russians brought a pencil? That just happened again. So it's a huge deal.

• 5:25Harry Stebbings?

  Why is it such a huge deal?

• 5:27Jonathan Ross?

  So up until recently, the Chinese models have been behind sort of Western models. And I say Western, including like Mistral as well and some other companies. And it was largely focused on how much compute you could get. Most people actually, most don't realize this, most companies have access to roughly the same amount of data. They buy them from the same data providers. And then it just churned through that data with a GPU and they produce a model and then they deploy it. And they'll have some of their own data and that'll make them subtly better at one thing or another, but they're largely all the same. More GPUs, the better the model because you can train on more tokens. It's the scaling law. This model was supposedly trained on a smaller number of GPUs

• 6:12Jonathan Ross?

  and a much, much tighter budget. I think the way that it's been put is less than the salary of many of the executives at Meta, and that's not true. There's an element of marketing involved in the deep sea release. It is true that they trained the model on approximately $6 million for the GPUs, right? They claim 2,000 GPUs for

• 6:37Jonathan Ross?

  I think it was sixty days, which by the way, also don't forget, was about the same amount of GPU time, four thousand GPUs for thirty days as the original, I believe, Llama seventy. Now, more recently, Meta has been training on more GPUs, but Meta hasn't been using as much good data as DeepSeek, because DeepSeek was doing reinforcement learning using OpenAI.

• 7:02Harry Stebbings?

  Is this distillation, just so I understand?

• 7:04Jonathan Ross?

  Yes, effectively.

• 7:05Harry Stebbings?

  And so can you just help me and help the audience understand what is distillation in this regard? And how have DeepSeek been using distillation to get better quality output through OpenAI data?

• 7:16Jonathan Ross?

  It's a little bit like speaking to someone who's smarter and getting tutored by someone who's smarter. You you actually do better than if you're speaking to someone who's not as knowledgeable about the area or giving you wrong answers. First of all, before we get into any of this, I I need to start with the scaling laws. These are like the physics of LLMs. And there's a particular curve. And the more tokens, which are sort of the sort of the syllables of an LLM, they don't match up exactly with human syllables, but kind of. So the more tokens that you train on, the better the model gets. But there's sort of these asymptotic returns where it starts trailing off. The thing about the scaling law that everyone forgets, and that's why everyone was talking about how it's like the end of the scaling law.

• 8:01Jonathan Ross?

  We're out of data on the internet. There's nothing left. What most people don't realize is that assumes that the data quality is uniform. If the data quality is better, then you can actually get away with training on fewer tokens. So going back to my background, one of the the fun things that I got to witness, I I wasn't directly involved, was AlphaGo. Google beat the the world champion Lee Sedol and Go. That model was was trained on a bunch of existing games. But later on, they created a new one called AlphaGo Zero, which was trained on no existing games. It just played against itself. So how do you play against yourself and win? Well, you train a model on some terrible moves. It does okay. And then you have it play against itself.

• 8:45Jonathan Ross?

  And when it does better, you train on those better games. And then you keep leveling up like this, right? So you get better, better data. The better your model is, when it outputs something, the better the result, the better the data. So what you do is you you train a model, you use it to generate data. And then you train a model and you use it to generate data. And you keep getting better and better and better. So you can sort of beat the scaling law problem. One quick hack to get past all of that in the stepping up is if there's a really good model already right here. Just have it generate the data and you go whoop right up to where it is. And that's what they did. It is true that they spent about six million or whatever it was on the training.

• 9:28Jonathan Ross?

  They spent a lot more distilling or scraping the OpenAI model.

• 9:34Harry Stebbings?

  So they scraped the OpenAI model. They get this higher quality data from that and from refining it. And then they get greater, higher quality output, correct?

• 9:44Jonathan Ross?

  Correct. And all that said, they did a lot of really innovative things. That's what makes it so complicated because on the one hand, they kind of just scraped the OpenAI model. On the other hand, they came up with some unique reinforcement learning techniques that are so simple.

• 10:00Harry Stebbings?

  What did you think that was so impressive? Because I think a lot of people wanted to say, oh, finally, the Chinese copy and duplicate as they always have done.

• 10:07Jonathan Ross?

  No, no, no, they came up with innovative stuff. But actually, the best way to describe it. Have you ever taken a test before? You got an answer right and your professor marked it wrong. And then you go back to the professor and you have to argue with them and everything and it's a pain, right? Well, if there is only one answer and it's a very like simple answer. And you say, write that answer in this box. Then there is no arguing. You either get it right or not, right? So what they did was rather than having human beings check the output and say yes or no or whatever. What they did was they said, here's the box. There's literally some code to say, here's a box. I'll put the answer here and then check it. And if it's correct, we have the answer. If not, we don't. No need to involve a human, completely automated.

• 10:50Harry Stebbings?

  Can OpenAI not just do distillation on DeepSeek's model then?

• 10:54Jonathan Ross?

  They don't need to because they're actually better still. They're a little bit better. They could, but why would they?

• 11:00Harry Stebbings?

  Do we buy their GPU usage? Alex Wang, who we both know, was like, no, they've got 50,000 H100s. Do we buy the GPU usage? Or is there questionable doubt there?

• 11:13Jonathan Ross?

  I don't think you have to disbelieve it because of the quality delta. However, why would they try and smuggle in GPUs when all they'd have to do is log into any cloud provider and rent GPUs? This is like the biggest gaping hole in the whole way that export control is done. You can literally log in, you can swipe credit card, whatever, and just like pay and get GPUs to use.

• 11:36Harry Stebbings?

  So export laws are necessary then?

• 11:38Jonathan Ross?

  They're, they're good, but the problem is it's like the Menajno line. You just go around it. So you need to like seal it up a little more. There's a little bit of room left to, to, to go here. Keep in mind, OpenAI was effectively subsidizing accidentally the training of this model because they, they were using OpenAI, right? And rumors are that OpenAI may not be completely profitable yet in terms of every token in the API, like on the subscriptions maybe, but, but in the API. And so each one that they generate, effectively they were losing a little bit of money while DeepSeek was getting training data. Now, by the way, OpenAI probably still has that data. In theory, they could just probably train on it.

• 12:18Harry Stebbings?

  Josh Christian said in a tweet today, though, that this would likely be a violation of US export laws. Do you think that's not true?

• 12:25Jonathan Ross?

  I'm not aware of where it would be an export issue. I do know that many people log into cloud providers and just use them from remote. One of the problems, so we actually block IP addresses from China, and I believe we might be unique in doing that. It's also a little bit fruitless because someone could just like rent a, a server anywhere, log into us from there, then there's nothing we can check.

• 12:49Harry Stebbings?

  You said there about kind of blocking IP addresses from China. There's a lot of concern about US customer data going back to China. Do you think that is a legitimate and justified concern?

• 12:59Jonathan Ross?

  Yes, it's, it's probably the most significant concern. There, there are other concerns that's probably the most significant because people don't think, they're so used to using these services. When, when you use one of these other services, you might be shocked to hear this. When you say delete, what they do is they write delete right next to your data. They don't actually delete it, they just mark it deleted. When you later Come back and and ask for your data, they give it to you with the word delete right next to it. It's still there. And these are well meaning companies. Do you really think like the CCP doesn't have all your data and isn't going to look it up later? Some governments are more aggressive than others. And if they have access to your data, not even your data, it could be your next door neighbor's data.

• 13:43Jonathan Ross?

  Your next door neighbor might put something in there that accidentally gives information away that makes you more vulnerable. Now the CCP has something, maybe you had some package delivered and and they put a complaint somewhere and whatever. Like you might not even do it yourself, but other people around you, the health data of a spouse, right?

• 14:03Harry Stebbings?

  Jonathan, I'm going to avoid the British indirectness. Do you think DeepSeek is an instrument that will be used by the CCP to increase control on Western democracies?

• 14:13Jonathan Ross?

  Yes, but I don't think it's DeepSeek that that's doing it. So you have to understand. Any company that operates in China and Hong Kong, the the one country, two systems thing didn't quite work out as anticipated, or maybe as anticipated, but not as stated. They have no choice. In 2016, when Groq started, we decided that we were not going to do business in China. This was not a geopolitical decision. This was purely commercial. And what it was, was we kept seeing companies like Google, Meta fail over and over again, trying to win in China. The formula is actually pretty simple. You're not allowed to make net money. You're allowed to spend more money in China.

• 14:54Jonathan Ross?

  But the moment that you start to become profitable or anywhere near profitable, all of a sudden, there's a thumb on the scale. Companies that manufacture a lot in China and send more money to China can actually be successful there. They can sell things there. Yeah, it's a pretty simple formula. You must send more money to China than you take out. But at the same time, they also require that you hand over all data. And not only that, they also require that certain answers be in a form that they find acceptable. So for example, one of the more common ones that you see about DeepSeek right now is when you ask about Tiananmen Square. If the temperature is low on the model and temperature, we don't need to get into. To that, it's complicated, but it's how like low means low creativity.

• 15:38Jonathan Ross?

  Then it's actually going to give you an answer that basically says, I don't want to talk about that. It's a sensitive topic, but you ask it about other things that are sensitive topics elsewhere in the world and it'll just answer. But what what happens if the CCP requires that they start to say, what about TikTok? Should it be banned? Absolutely not. Here's why. And it gives you a cogent reason. That's kind of scary.

• 16:01Harry Stebbings?

  Jonathan, what do we do from here? I I share your concerns completely. My challenge is TikTok you can ban and shut off. They would not sell the algo. That is a closed end product that we can ban tomorrow if we really want to. Here, it's open source.

• 16:16Jonathan Ross?

  Yeah, and and worse. So we we up until recently refused to run any Chinese models. And we had to make a very difficult decision on DeepSeek. We now have it on our API at Groq.

• 16:27Harry Stebbings?

  Why did you decide that you would break the rule for DeepSeek?

• 16:31Jonathan Ross?

  So what it came down to was when we saw DeepSeek become the number one app on the App Store. The realization was people were going to be putting their data in there. And what we want to make sure is that you actually have an option. So we store nothing. There is no like delete or whatever, like there is just we'd store nothing. We don't even have hard drives. We have DRAM and when the power goes off, everything goes away. So we wanted to make sure that there was an alternative where when you use DeepSeek's model, your data is not going to the CCP. Well, right now, the CCP is probably going to be taking the safeties off the weapons. They're going to be like, why are you making this model open source? Please direct your data towards us. Go win a bunch of customers this way. But now we want the data, right?

• 17:16Jonathan Ross?

  And so they're going to change the strategy. But remember, DeepSeek is a real, I mean, it's a hedge fund. They're doing this themselves and they're just influenced by the CCP. And the CCP, now that they've seen the success of this, might see it as yet another TikTok.

• 17:33Harry Stebbings?

  My question to you is, how long is it before the US reacts to prevent this?

• 17:39Jonathan Ross?

  One question to ask is, are we going to be talking about DeepSeek for the next six or R1 for the next six months? And the answer is absolutely not. We might be talking about R2 and R3 and R4, but R1 was one shot. The question is, are they going to keep coming up with very interesting things? Are we in, you know, cat and mouse it? Is everyone going to learn from this? The biggest problem is we've, this has just made it absolutely nakedly clear that the models are commoditized, right? You've been asking the question, right? Like, if there was any doubt before, that doubt's over. So what is the mote? And for me, I love Hamilton Helmer's seven powers, right?

• 18:19Harry Stebbings?

  They're one of my favorites. I do it for every single investment we do. We have to fill it out. Every single person in the team must fill it out. So yes.

• 18:25Jonathan Ross?

  So, marketing is the art of decommoditizing your product. And the seven powers are seven great ways to decommoditize your product. Scale economies, network effects, brand, counter positioning, cornered resource, switching cost, process power, right? The question is, who's going to do what? OpenAI, and you got to give Sam Altman and that team credit, they've got amazing brand power, like no one else in this space. And that's going to serve them for a really long time. But what you see Sam trying to do is scale, right? He's trying to go, that's why we hear about Stargate and $500 billion, right? That's what he, the power he would like to have, but the power he has right now is brand. And he's trying to bridge that. But what about the others?

• 19:09Harry Stebbings?

  I'm sorry, does this news not ridicule the $500 billion announcement? At a time when we've seen increasing efficiency to a scale like never before with DeepSeek today, the $500 billion seems ridiculed.

• 19:21Jonathan Ross?

  Actually, I don't think it's enough spending. And the reason is, so we saw this happen at Google over and over again. We do the TPU. So why did we do the TPU? The speech team trained a model. It outperformed human beings at speech recognition. This was like back in 2011, 2012. And so Jeff Dean, most famous engineer at Google, gives a presentation to the leadership team. It's two slides. Slide number one, good news. Machine learning finally works. Slide number two, bad news. We can't afford it and we're Google. We're going to need to double or triple our global data center footprint at probably a cost of 20 to 40 billion dollars. And that'll get a speech recognition. Do you also want to do search and ads? There's always this giant mission accomplished banner every time someone trains a model.

• 20:05Jonathan Ross?

  And then they start putting it into production. And then they realize, oh, this is going to be expensive. This is why we've always focused on inference. And so now think about it this way. At Google, we always ended up spending 10 to 20 times as much on the inference as the training back when I was there. Now the models are being given away free. How much are we going to spend on inference? And now with the test time compute, I've asked questions of DeepSeek where it took 18,000 intermediate tokens before it gave me the answer.

• 20:32Harry Stebbings?

  Jensen said that now half of their revenues is from inference.

• 20:36Jonathan Ross?

  Yeah.

• 20:36Harry Stebbings?

  So what does that look like in the future then?

• 20:38Jonathan Ross?

  95%. I mean, it just makes sense, right? You don't train to become a cardiovascular surgeon. And then that's what you do for 95% of your life. And then you perform 5%. It's the opposite. You train for a little and then you do it for the rest of your life.

• 20:55Harry Stebbings?

  Do you think the US puts sanctions on DeepSeek to prevent the CCP using it for data capture on US citizens?

• 21:01Jonathan Ross?

  I don't know what the solution is. There's carrot and there's stick, right? So you can either use a stick, block it. That might be effective. I don't know that the US has really done that before. There's also the carrot, which is it's kind of interesting how it's being offered for free in China and not just in China, but to anyone else. And then others are doing that too. Is it possible the CCP is underwriting that because they want the data?

• 21:27Harry Stebbings?

  Dude, they're doing it with the car industry, the subsidization of cars, for Chinese cars with BYD in particular, destroying the European car market is absolutely that.

• 21:36Jonathan Ross?

  The thing is, we have a lesson from the Cold War, which was mutually assured destruction. The problem is, we do some sort of tariff and then we do a tariff back. There needs to be some sort of automated response of like, if you do this, we will respond. If you subsidize this industry, we will automatically subsidize the equivalent industry. Just automatic. So don't do it, because there's no benefit to you.

• 22:01Harry Stebbings?

  Does the fact that it's open source, how does that change everything?

• 22:04Jonathan Ross?

  It's the only reason people are using it. If it wasn't open source, it wouldn't have gotten the excitement. And open always wins, always. Keep in mind, Linux won back when people didn't trust open source. They thought it was less secure, they thought the features were worse, it was more buggy. And it still won. Now, people expect open to be more secure, less buggy, and have more features. So how is proprietary ever going to win?

• 22:31Harry Stebbings?

  Everyone always says that actually, distribution is one of the major advantages that ChatGPT, and hence OpenAI, has, especially over the other providers. Every single day that DeepSeek is out and is being used so pervasively, it is diminishing the value of OpenAI.

• 22:47Jonathan Ross?

  Yeah.

• 22:47Harry Stebbings?

  Agree, disagree?

• 22:48Jonathan Ross?

  Agree, especially for the pricing, because they're losing their pricing power on this. I can't speak for Sam Altman or OpenAI or anything like that. But if I was in that position, I would be gearing up to open source my models in response. Because it's pretty clear you're going to lose that. So you might as well try and win all the users and the love from open sourcing. Otherwise, like, you're already at a point where you're going to be using your other powers, like brand and so on. I don't know why you'd try and keep that internal anymore.

• 23:19Harry Stebbings?

  Would that be possible? And would that not cannibalize their core main line of revenue?

• 23:24Jonathan Ross?

  But how would it cannibalize it any other way? Remember, distribution, right? How many people are going to buy something because they trust Dell? People trust Dell. Dell has earned their reputation over the course of decades. Super Micro builds some interesting hardware, but look at what they've been going through recently. You know, there's a pro and con, right? Cheaper, trusted, you've got to make a decision. OpenAI has been around for a while. Most people think of them synonymously as AI. They could just switch to DeepSeek and people would still use them. It's brand. It's one of the seven powers.

• 24:01Harry Stebbings?

  So if you were OpenAI and Sam today, you would switch to Open and offer for free?

• 24:06Jonathan Ross?

  I would, and there's probably more cleverness. They could probably strike some deals before they do it or whatever. But that would be the move that I would make. And also, it would be a position of strength. The only problem is the timing, because if it happens right after DeepSeek, it looks like a response as opposed to an intentional thing. So I don't know how you do that.

• 24:25Harry Stebbings?

  Do you not just own it and say it's a response?

• 24:27Jonathan Ross?

  Maybe, you know, we had to respond, we're better. Let's see which model people choose.

• 24:32Harry Stebbings?

  How do we think about Matter? Matter shares the open source values that DeepSeek has espoused. Does this help or hurt Matter?

• 24:39Jonathan Ross?

  I think one of the ways that we've been looking at LLMs is a little bit like you look at an open source project, software project, like Linux or something. The thing is Linux has switching cost. And I think what we've discovered is LLMs have no switching cost whatsoever.

• 24:55Harry Stebbings?

  It's where the analogy to cloud doesn't hold up at all. Because everyone's like, oh, it's like cloud, there's going to be a couple of cool vendors and actually they're going to win. No, you don't write your cloud very often.

• 25:05Jonathan Ross?

  Okay, so let's start mapping seven powers to the top tech companies. So I would say Microsoft's biggest strength is switching cost, right? You go into a room full of people and you're like, who uses Microsoft? A bunch of hands go up and you're like, who likes using Microsoft? Hands go down. It's very largely switching cost. So you go into, you know, Gen AI, is that a thing that gets disrupted? You look at Meta, it's network effects. They could literally give every piece of technology away for free. I am completely jealous of that because if I had that right Now, I would open source everything because then you don't have to worry about it and you get everyone helping you.

• 25:43Jonathan Ross?

  So I think Meta is sort of because of the network effect thing, always in a position where open source is to their advantage. It almost doesn't matter where it comes from. Now, I'm sure that they would prefer to have the Linux of L L Ms. But I think the more it goes open source, the more of an advantage they have inherently.

• 26:01Harry Stebbings?

  If you were Meta, would you do anything differently?

• 26:04Jonathan Ross?

  Meta is an amazing competitor. What they would normally do if this was some sort of proprietary social mechanism, they would try and replicate. And then they would compete and they would say, come join or not. I don't think the come join works here, but the beautiful thing is all of the information for this model is available. Meta has already been doing this. They have way more compute. The question is, are they willing to scrape OpenAI like DeepSeek did? They've been super careful on everything that they've been doing. And so that's the disadvantage.

• 26:36Harry Stebbings?

  Do you not put morals aside to win? This is the AI arms race.

• 26:40Jonathan Ross?

  And I think that's going to happen. I think people will, like you cannot lose. And so what it's done is it's changed the game, right? So if you, okay, so let's talk about Europe for a minute. We almost forgot about Europe. It feels like with Europe, there's a lack of a willingness to take risk. There's a black mark if you get it wrong. Everything's about downside protection. Whereas in the U S, it's like, that was a great effort. You failed, but I'm going to fund you again. Right? So there's that difference. But when you look at the U S and then you look at China, China practices R D T, research, development, theft. It's just part of the culture and it's not just against Western company. It's against each other too.

• 27:22Jonathan Ross?

  The difference is if you're a Western company, then the government steals from the Western company and then provides it to the Chinese companies, which is less fair. The famous stories of turning on Huawei switches and you see Cisco's logo and all the bugs and, right? So is that a new paradigm? I really hope not. Like for Europe to compete with the U S, Europe has to adopt a more risk on attitude. Does the West have to adopt a more theft on attitude? I really hope not. Like, that's just like viscerally disgusting to me. I'm like literally repulsed by the idea.

• 28:00Harry Stebbings?

  Are we not being idealistic? If you're running in a race with someone who's willing to take steroids, if you wanna win, you're gonna have to take steroids too.

• 28:08Jonathan Ross?

  And then everyone is taking steroids. Whereas if no one was taking it, then everyone's healthier and you have a real competition. Yeah, it's a real problem. And the question is, can governments get involved? Here's the thing, I would love nothing more than to compete directly with Chinese companies on a fair footing. They have really smart people, DeepSeek has proven this, really smart people. But when the government keeps putting its thumb on the scale, we're gonna try and avoid that competition wherever we can. And now there's no avoiding it. Maybe the governments just have to get involved.

• 28:40Harry Stebbings?

  But dude, I'm being blunt, like Xi Jinping cares about one thing, power, attention. And growth is the only thing that matters to him. And AI is central to that. He will do whatever it takes to win. Having some rational discourse about some rules of play is bluntly unrealistic.

• 28:54Jonathan Ross?

  Okay, and it gets worse than that. China has a lot of advantages. The chief advantage is the number of people they have. Now, number of people is not sufficient. So you also have India. And India has an advantage from the number of people, but China has out-executed. In fact, India was asking China for some time to help build out the roads and infrastructure. They've really mastered that, right? But people and sort of organization, discipline, alignment, right? And so what is the concern with AI? The concern with AI is, what if an LP or GPU becomes the equivalent of a contributor to the workforce? And you could literally just add more to the GDP by creating more chips and providing more power.

• 29:36Jonathan Ross?

  Now, if that becomes the case, does China's advantage erode? They're concerned that in terms of workforce, the US could catch up, the West could catch up. And then at the same time, they have a huge population advantage. And this is why I so much want for Europe to get into the fight on AI. There's 500 million people who could be jumping into this.

• 29:59Harry Stebbings?

  If you were to advise the EU today on Europe's stance, what would you say?

• 30:03Jonathan Ross?

  So have you ever seen Station F?

• 30:05Harry Stebbings?

  Yeah, of course, I was there last week, we hosted in the man.

• 30:07Jonathan Ross?

  So I would say by the end of this year, you should have 100 station F's. And by the end of next year, you should have a thousand done. So what you're doing is you're collecting up 3,000 people and surrounding them with other risk taking entrepreneurs. And then they're supporting each other. They're they're risk on and every when you surround yourself with other people who are risk on, you're going to be risk on and you're going to you're going to take the entrepreneurial leap.

• 30:32Harry Stebbings?

  What does this space look like in three years' time? I I'm obviously a venture capitalist for a living. All of my friends are going, oh, my God, oh, my God, we just lost hundreds of millions of dollars on these foundation model companies.

• 30:45Jonathan Ross?

  How many companies are you aware of that have become incredibly successful that didn't pivot?

• 30:49Harry Stebbings?

  Most pivot.

• 30:51Jonathan Ross?

  Yeah, exactly. So pivot, get over it, just pivot. I I've been talking to a lot of the LLM companies and frankly, they have some good ideas. In fact, I really like, so I watched your interview with the Suno founder. I think he saw it from the beginning, like models are going to be commoditized. And that's why he focused on the product. He got it from the beginning. What is your product? Not what is the model? Model is it's a piece of machinery. It's an engine. But what is the car? What is the experience?

• 31:21Harry Stebbings?

  What do you think perplexity is in three years?

• 31:23Jonathan Ross?

  A question I used to get asked when when we were raising money a little while ago was, is AI the next Internet? And I'm like, absolutely not, because the Internet is an information age technology. It's about duplicating data with high fidelity and distributing it. Telephone does. It's what Internet does. It's what the printing press did. They're all the same technology, just much different scale and and speed and capability. Generative AI is different. It's about coming up with something contextual, creative, unique in the moment. And so the LLM is just the printing press of the generative age. It's the start of it. And then there's going to be all these other stages. Just imagine trying to start Uber when we didn't have mobile yet. Great, I'm going to book a trip over to here.

• 32:08Jonathan Ross?

  How do I get home? You can't carry a desktop with you, right? So you need to be at the right stage. So when I look at perplexity, I look at perplexity as being perfectly positioned. For the moment that the hallucination or really confabulation rate comes down, the moment that these models get good enough where you don't have to check the citations anymore, that's going to open up a whole set of industries. All of a sudden, you'll be able to do medical diagnoses from LLMs. You'll be able to do, you'll be able to do legal work from LLMs. Until then, it's like trying to create Uber before we had smartphones. It just doesn't make any sense. However, people are willing to use perplexity today, even though you have to check the citations. So they have an actual business that gets to continue.

• 32:53Jonathan Ross?

  So like they're getting to sort of ride the wave. And the moment that that tsunami of lack of confabulation or hallucination comes along, they're perfectly positioned. Each company has to find their own thing. And I would look at Suno as, as like a great example of how things are being done around the product as opposed to just the models.

• 33:17Harry Stebbings?

  You can't get it possible to pivot when you are OpenAI or Anthropic or any of the very large providers who've ingested billions of dollars.

• 33:26Jonathan Ross?

  Disruption happens. If you're not able to pivot now, you're not going to be able to pivot later when you get disrupted anyway.

• 33:32Harry Stebbings?

  One would think that with commoditization of models and with cheaper inference, that actually big tech wins, right? Have you seen the stock market today? They've been hit hard. How do you think about that?

• 33:44Jonathan Ross?

  What you see is a bunch of people who are concerned about training and the need for it. And everyone's still thinking that most of compute is training and that there's going to be less of it because someone trained a model on 2000 GPUs and the nerfed A800 version with slower memory or whatever it is. And they're like, oh, people aren't going to need as many chips. But again, Jevon's paradox, right? The more you bring the cost down, the more people consume. So for the last five to six decades, like clockwork, once a decade, the cost of compute has gone down a thousand X. People buy 100,000 X as much compute, spending 100 times as much. So every decade, they spend 100.

• 34:28Jonathan Ross?

  Times as much. So you make it cheaper, they want more. What's really happening is every time one of these models gets cheaper, we see our developer count just skyrocket. And then it comes back down a little bit, but the the slope is higher than when it started. Better models create more demand for inference, more demand for inference then has people going, I should train a better model. And the cycle continues.

• 34:52Harry Stebbings?

  I just bought a shitload of Nvidia. They dropped sixteen percent on the thesis that the increasing efficiency means that obviously we wouldn't need as much Nvidia chip. And I thought exactly that, which is why you'll still need the Nvidia inference and you'll just have much higher usage. So to me, it's the most screaming buy of the century. Do you share my optimism on Nvidia, given what you just said in Jevon's paradox?

• 35:13Jonathan Ross?

  So I think over the long term, the the only thing I say is Warren Buffett and Charlie Munger. In the short term, the market is a popularity contest. In the long term, it's a weighing machine. I can't tell you about the popularity contest, but in terms of the weighing machine part, this is a misunderstanding. It's actually more valuable thanks to DeepSeek, not less valuable. Okay, so Jevon's paradox was actually discovered by Jevon as recently made famous in Satya's tweet. However, I did beat him to that by quite a bit. And and just as Satya likes to say that he made Google dance. I'm going to say I made Satya dance. He might take exception to that, but less than a month before he posted that, I I did a a cute little tweet on it.

• 35:55Jonathan Ross?

  So what's really happening here was in the eighteen sixties, this guy Jevon, he actually wrote a treatise on steam engines, which I guess is what you did for fun back then in in England. He realized every time steam engines became more efficient, people would buy more coal, which is the paradox. But if you think about it from a business point of view, when the OpEx comes down, more activities come into the money. So people do more things. And so. What's happened is every time we've seen the cost of tokens for a particular level of quality of models come down, we've actually seen the demand grow significantly. Price elasticity, baby.

• 36:33Harry Stebbings?

  A lot of people suggest that Nvidia's incredible high margin status, which I'm going to butcher, I can't remember what it was in their latest release, it was something forty five or whatever it was, but it was very, very high. And then relate to your margin is my opportunity. I think of it back to the seven powers and go, their margin is their defensibility. And it makes me really just consider the strength of their modes. Do you think your margin is my opportunity? Or do you think their defensibility is their margin?

• 37:01Jonathan Ross?

  Today, there's this wonderful business selling mainframes with a pretty juicy margin because no one seems to want to enter that business. Training is a niche market with very high margins. And when I say niche, it's still going to be worth hundreds of billions a year. But inference is the larger market. And I don't know that Nvidia will ever see it this way, but I do think that those of us focusing on inference and building stuff specifically for that are probably the best thing that's ever happened for Nvidia stock because we'll take on the low margin, high volume inference so that Nvidia can keep its margins nice and high.

• 37:39Harry Stebbings?

  Do you think the world sees this?

• 37:40Jonathan Ross?

  No, and I was actually like, we raised some money late 2024. In that fundraise, we still had to explain to people why inference was going to be a larger business than training. Remember, this was our thesis when we started eight years ago. So for me, I struggle on why people think that training is going to be bigger. It just doesn't make sense.

• 38:02Harry Stebbings?

  Just for anyone who doesn't know, what's the difference between training and inference?

• 38:05Jonathan Ross?

  Training is where you create the model. Inference is where you use the model. You want to become a heart surgeon, you spend years training and then you spend more years practicing. Practicing is inference.

• 38:16Harry Stebbings?

  Where does efficiency go from here? Everyone was so shocked by how R1 is so much more efficient. What next?

• 38:23Jonathan Ross?

  What you're going to see is everyone else starting to use this MOE approach. Now, there's another, there's another thing that happens here.

• 38:31Harry Stebbings?

  And the MOE approach, just so I understand, is like the segmentation of where information goes. So it's rooted to like the optimal point of the model.

• 38:40Jonathan Ross?

  Yeah, it's called, so MOE stands for mixture of experts. When you use Llama 70 billion, you actually use every single parameter in that model. When you use Mixtral's eight by seven B, you use two of the roughly eight B, you know, experts, but it's much smaller. And effectively, while it doesn't correlate exactly, it correlates very closely. The number of parameters effectively tells you how much compute you're performing. Now, if I have, let's take the R1 model. I believe it's about 671 billion parameters versus 70 billion for Llama. And there's a four or five billion dense model as well, right? But let's focus on 70 versus 671.

• 39:22Jonathan Ross?

  I believe there's 256 experts, each of which is somewhere around 2 billion parameters. And then it picks some small number, I'm forgetting which, maybe it's like eight of those or 16 of them, whatever it is. And so it only needs to do the compute for that. That means that you're getting to skip most of it, right? Sort of like your brain, like not every neuron in your brain fires when I say something to you about the stock market, right? Like the neurons about, you know, playing football, those don't kick off, right? That's the intuition there. Previously, it was famously reported that OpenAI's GPT-4, it started off with something like 16 experts and they got it down to eight.

• 40:04Jonathan Ross?

  I forget the numbers, but it like started off larger and they shrunk it a little and they were smaller or whatever. And then with what's happened with DeepSeek model is it's gone the opposite. They've gone to a very large number of experts. The more parameters you have, it's like having more neurons. It's easier to retain the information that comes in. And so by having more parameters, they're able to, on a smaller amount of data, get good. However, because it's sparse. Because it's a mixture of experts, they're not doing as much computation. And part of the cleverness was figuring out how they can have so many experts so it can be so sparse, so they could skip so many of the parameters.

• 40:47Harry Stebbings?

  But if we take that then back to like, that's where we are saying how they've become so efficient. What's the next stage of that then? Because all these experts, they can root it so efficiently, what now?

• 40:59Jonathan Ross?

  So Meta recently released their Llama 3.3 70B, and it outperformed their 3.1 405B. So their new 70B outperformed their 405. What was surprising to me, I thought they retrained it from scratch. It turns out, you read the paper and they talk about how they just fine tuned. So they used a relatively small amount of data to make it much better. Again, this goes to the quality of the data. They have higher quality data, they took their old model, they trained it, it got much better. But that 70B, that new 70B outperforms their previous 405B. What you're going to see now is now that everyone has seen this DeepSeek architecture, they're going to go, great, I have hundreds of thousands of GPUs.

• 41:43Jonathan Ross?

  I'm now going to use a lot of them to create a lot of synthetic data. And then I'm going to train the bejesus out of this model. Because the other thing is, while it's sort of asymptotes, the question is, on this curve, where do you stop? It depends on how many people you have doing inference. You can either make the model bigger, which makes it more expensive, and then you train it on less, or you make it smaller and it's cheaper to run, but you have to train it more. So DeepSeek didn't have a lot of users until recently. And so for them, it would have never made sense to train it a lot anyway. They would much rather have a bigger model. But now what you're going to see is all these other people either making smaller models or trying to make higher quality ones of the same size, but just training it more.

• 42:28Harry Stebbings?

  We've seen DeepSeek now say, hey, only now Chinese phone numbers we can log in. That is the new signup, I think it is. What's happened and what is the result of that?

• 42:38Jonathan Ross?

  So they ran out of compute. And this is why, this is the other reason why chip startups are going to do just fine. Because they ran out of inference compute. You train it once, but now, so you spend money to make the model, like designing a car. But then each car you build costs you money, right? Well, each query that you serve requires hardware. Training scales with the number of ML researchers you have. Inference scales with the number of end users you have.

• 43:06Harry Stebbings?

  Do you think DeepSeek are astonished by the response they've got from the global community?

• 43:11Jonathan Ross?

  I think they marketed very well. Like you look at some of the publication and they make it sound like it's a philosophical thing. And, you know, they talk about, they spent six million on the GPUs and, and everyone just zoomed in on that. Neglecting the fact that Llama's first model was trained on like, I think five million worth of GPU time. And it set the world on fire in a good way. And then ignoring the fact that they spent a ton generating the data and all this. They're really good at marketing. I, I think they were probably surprised at how well it worked, but I think this is what they were going for.

• 43:45Harry Stebbings?

  Is there anything that I haven't asked or we haven't spoken about that we should?

• 43:49Jonathan Ross?

  What, what's up with the $500 billion Stargate effort?

• 43:52Harry Stebbings?

  Okay, what's up with the $500 billion Stargate effort?

• 43:56Jonathan Ross?

  I've gone back and forth on that. I, I actually did, so Gavin Baker tweeted some math. Before I saw that tweet, I came up with very similar math. However, talking to some people in the know, some of the comments are actually, they've got it. But then you keep pressing and it's like, well, maybe is there some cutesiness to it? What I think it is, is an acknowledgement that the models have been commoditized. And infrastructure is what's important in terms of maintaining elite, like scale. It's one of the seven powers. I think what you're seeing there is an attempt to move from having a cornered resource or something like that into a scale economy.

• 44:37Harry Stebbings?

  Do you think it will work?

• 44:38Jonathan Ross?

  I don't think you get there in a short period of time with GPUs because most of the compute is inference. And so, you know, if you're talking about building out all the power, building on, like, it's going to take time. It's infrastructure, it's CapEx. The real win here is brand. That's what I would be doubling down on. I would be like hiring the best brand firms I could. I would do a complete makeover.

• 45:01Harry Stebbings?

  Will OpenAI have a stronger or weaker brand in three years' time?

• 45:05Jonathan Ross?

  Much stronger. I think they're going to double down on that and they're going to focus on it.

• 45:09Harry Stebbings?

  Who will lose?

• 45:10Jonathan Ross?

  People who can't adapt to disruption. Anyone who just wants to keep going on a straight line and do what they were doing before is going to lose. And the rate of disruption is probably going to increase because going back to the analogy of LLMs being the printing press. Imagine if there were a couple of smartphones left over from an ancient civilization. All of a sudden, the printing press is invented and you're like, ooh, Uber's coming. I'm going to position for it. I know where this is going. We are the smartphones. We know where generative age technology goes. And now everyone's like, well, we know how big this gets. Let's put money into it. I can't be the one who doesn't spend money on this because

• 45:52Jonathan Ross?

  I know how big of an advantage it's going to be. It's like getting to add more workers to the workforce. And so I think the generative age, we're going to speed run it faster than whatever comes next because we know what it looks like.

• 46:04Harry Stebbings?

  Is there any chance we see a plateau? We saw it in self-driving, for example, where we kind of went through this desert of black of progression. And suddenly, all of once it came. Will we see that or will we just see this continuing dominance?

• 46:17Jonathan Ross?

  I think with self-driving, the problem you had was the threshold, it had to be way superhuman. Because if you look at the number of miles driven by these self-driving vehicles, it's an enormous number and the number of fatalities and incidents is lower per mile. But we have no tolerance whatsoever for them when it's a machine. When you're writing poetry and code, it's very different versus doing a surgery or driving a car.

• 46:46Harry Stebbings?

  If you're Elon and Axe.ai, how are you feeling and do you feel better or worse post this?

• 46:52Jonathan Ross?

  I would probably feel both better and worse. I'd feel better about my bet on building out more hardware. I would feel worse about trying to build out my own model. Why is Elon doing that? Just pick one up off the ground. Like, why are you making your own?

• 47:08Harry Stebbings?

  Are you excited when you look forward to the next few years or are you quite nervous? You could say this is a time of heightened international warfare in terms of this new AI arms race. China's stealing everything, us forced to steal back.

• 47:23Jonathan Ross?

  Long ago, I stopped having good days and bad days. It's yes, it's how many good things, it's how many bad things, right? When you, when you run an organization, I'm both excited and nervous. And I'm excited and nervous about different things at the same time. The thing that I am most nervous about is that unlike nuclear war, you can use AI tools to attack each other. Google just announced recently the first zero-day exploit found by an LLM that was previously unknown. Yeah, that's a scary one. So now-

• 47:57Harry Stebbings?

  It's scary for anyone who doesn't understand zero-day exploit.

• 47:59Jonathan Ross?

  So how would you like me to have access to your phone?

• 48:03Harry Stebbings?

  Not ideal.

• 48:04Jonathan Ross?

  How would you like the CCP to have access to your phone?

• 48:07Harry Stebbings?

  Even last night.

• 48:08Jonathan Ross?

  That's a nation state and nation states have a lot of resources. And if they stand up a bunch of compute and they start scanning for vulnerabilities in all the open source that's out there, and not even the open source, just like scanning ports on the internet and trying to figure out if they can break in, they can just automate that now. They don't need to hire people to do that. And now the defense has to be automated because there's no way to keep up with automated attackers. And what happens if this gets out of control? But worse, it's not killing anyone and it's also deniable. That's the hardest part about it because is it really China? Is it Russia? Is it North Korea? Is it a friendly that's making it seem like it's one of them or vice versa?

• 48:50Jonathan Ross?

  Now you have this ability. So you go from where we had a Cold War because having- A war was unconscionable, it was unthinkable because of the consequences to now. Yeah, I'm just hacking you. That could spiral out of control. I'm worried that we're going to have more back and forth. And think of it this way. If you are a nation state and you, let's say that, Harry, you're a beacon to the venture community. And you want to rally the European entrepreneurs to be risk on. And I'm someone who doesn't want that because I don't want the competition, a country that doesn't want that. Maybe I sully your reputation. Maybe I make you persona non grata. How is that any worse than shooting someone?

• 49:34Jonathan Ross?

  It could be worse in some ways, but you can get away with it. And so that has me nervous, really nervous, but I'm also really excited. We are seriously going to be able to innovate as fast as we can come up with ideas. Now, you're not going to have to implement things. You're going to be able to prompt engineer your way through things. Just as we moved from hardware engineers to software engineers and sped up productivity. You're now just going to be able to have a prompt engineer who doesn't even write software. One of our engineers made this app where you can just describe what you want built and it builds it. And because, you know, we're so fast, it's like that and you just iterate and it'll build an app for you.

• 50:15Harry Stebbings?

  I just don't understand where the, and sorry to just continue, but where the value accrues then? Because you mentioned that kind of, hey, they created this tool which allows you to prompt and it'll build the app. I'm sure you've seen bolt new. I'm not sure if you've seen lovable where it's basically chat G P T, but for kind of website creation. Is there value in that? Everyone was like, there's no value in these wrapper apps. Everyone's like, there's no value in these foundation models. Where the fuck is there value?

• 50:41Jonathan Ross?

  And that's part of the exciting part. It's discovering that. But I think people will always prefer to use the highest quality, most polished product. I think there is an opportunity for artisanship, craftsmanship and just perfecting it. Getting to a certain number of nines in the details. The Eames quote, the details aren't the details, the details are the thing. I used to be a little concerned with the quote. You know, if you're not ashamed of the quality of your your first release and you've waited too long, because there's a subtlety and nuance there. There's soundness and then there's completeness. What you want is an incomplete product, something that doesn't do everything. That's why you should be embarrassed. But it shouldn't like blue screen of death on you. That's not a good embarrassment, right?

• 51:26Jonathan Ross?

  And so what you're going to see now is because it's so easy to come up with something that just kind of works. It's a little embarrassing, but it kind of works. People are really going to value well crafted, high quality products.

• 51:40Harry Stebbings?

  Jonathan, I cannot thank you enough for breaking down so many different elements for me and putting up with my basic questions. You've been fantastic.

• 51:48Jonathan Ross?

  No problem. Have fun out there. I mean, this is a brand new age. It really is.

• 51:55Harry Stebbings?

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• 54:47Harry Stebbings?

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