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- [00:00]
A
It's time for a bonus episode exclusively about DeepSeek R1 as the Chinese open source AI model roils markets and threatens to upend the generative AI industry. That's coming up right after this from LinkedIn News.
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B
I'm Jessi Hempel, host of the hello Monday Podcast. Start your week with the hello Monday podcast. We'll navigate career pivots. We'll learn where happiness fits in. Listen to hello Monday with me, Jesse Hempel on the LinkedIn podcast network or wherever you get your podcasts.
- [00:34]
A
Welcome to Big Technology Podcast. We're doing a bonus edition today exclusively on Deep Seek. What it means for the AI industry, what it means for markets. We're going to touch on technology, we're going to touch on business. And so thrilled that you're here for a bonus episode with us. We're joined today by MG Siegler. He's a writer and investor. He writes Spyglass. You can find it@spyglass.org It's a great newsletter. It's a must read for me. And he has a great piece out called Finds a Way As Deep Seek Changed the AI Game or just some Equations. MG Great to see you. Welcome to the show.
- [01:10]
B
Great to see you, Alex. Thanks for having me back and sorry for my crazy winter beard. It's. It is very cold and rainy right now in London, so I'm not. Not ready for spring yet.
- [01:19]
A
If he. It fits the season. I was just out in London to interview Demis from DeepMind.
- [01:23]
B
That's right. I listened to that. That was very good. Yeah. And very timely.
- [01:26]
A
Now, yes, I can confirm the sun does not shine in that city this time of year. So first of all, I want to talk a lot about, I mean, only about Deep Seek and deep seq R1 and what it means for the AI industry. Right now. It's. We are just about. We're gonna. The markets will open on this show, so I'll have a sense as to what it's gonna do today. But it's looking pretty bad, especially for Nvidia and some others as we get going. I just wanna thank all the podcast listeners who pointed me to DeepSeek because we had some comments that came in over the past few weeks. I was able to ask Demis about it. I was able to get it in as the lead story on Friday's show. So thank you. I appreciate all of you for pointing me towards deepseek. So let me just talk a little bit because we didn't touch on this Friday and we're going to definitely Fill some holes that were left. On the Friday show, we talked a little bit about how much it costs to train this model, but not necessarily about the benchmarks it hit and about the cost it costs to use this thing. So first of all, it's an open source model. It's much smaller than any of OpenAI's model. Yet on the AIME mathematics test, it scored 79.8% compared to OpenAI's 01, scoring 79.2%. So it bests OpenAI's best model on that. It scored 97.3% on the math 500 and it beat OpenAI, which scored 96.4%. Look, these are lots of different benchmark tests, but you can tell that just by these numbers it holds its own. And now the most remarkable part about this, it costs $0.55 per million token inputs and $2.19 per million token outputs. Just to give you a sense, OpenAI costs $15 per million input tokens and $60 per million output tokens. That's 3.5% of the cost that it costs to run OpenAI's 01 models. And you can do it again. It's open source. You could download it onto your computer and run it. So basically what, what Deepseek R1 has done in a nutshell, and then we'll turn it over to MG is it has created models that are as performant as the state of the art. Right. It's ranked number three in the chatbot arena at 3.53 to 5% of the cost. And that has huge implications for the technology, for the business. And we're going to get into those. So mg, first question for you. If there was an AI Richter scale, right, assessing how big of an earthquake this is, what would you give this development?
- [03:50]
B
So, I mean, it depends on what, I guess level. You're sort of measuring the magnitudes, right? Because as you noted, the markets will open. And that's going to be right now. Last I looked in pre market trading, Nvidia was down, I think 1110 to 11%. And that's the biggest hit right now. Microsoft, a bunch of others are like in the 3% range. So, you know, from a pure market perspective, it seems like it's, let's, let's call it an eight. You know, it's not, it's not going to totally destroy the stock market right now, but it's going to be rough, it seems like today from a bunch of other perspectives, I think, you know, it's, it's probably a little bit Less of a, of a shake in these earlier days. And I think that's because everyone's still even now sussing out what exactly this means for all different sorts of things. You know, you noted how much cheaper it is to run than say, OpenAI's models. And you know, over the weekend, just reading all of these sort of reports about the model and how many individual startups are even just changing, swapping out, right already because it's so much cheaper to do what they're doing right now by swapping in deep seats models. And so what does that do immediately? Like, you know, do we have to have price cuts immediately? And, and you know, I think you could sort of see OpenAI doing some stuff. I think Sam Altman tweeted, you know, maybe on Friday, like, about how they were like bundling, rejiggering some of the bundles, right, that they have, like what's in the free offering and stuff. And it sort of feels like we're going to see more of that, you know, as a response obviously to some of this. But then, you know, there was a, there was a big report, I think, in the information about Meta's response to this in particular, which seemed pretty interesting in that, like, you know, it's all hands on deck certainly. And there's like all these different teams. You and I remember that from the old school days of Facebook. And so, yeah, it's just like all of these companies are now scrambling. You have Satya Nadella tweeting out things, you know, which seemed directly aimed at the, at the market to try to, you know, ease that, that pain a bit. But anyway, going back to the original question on the, on the Richter scale, you know, overall, I think a lot of people are still figuring this out, but right now the market thing is going to be the most acute one because that's obviously going. And I think it's going to be pretty hard for, you know, this day at least. And then I think I, I read some of the early analyst reports on this and, and they're all over the place, right? Like they're, there's some folks who are saying like, oh, this is, this is awful for Nvidia. Some folks are saying that, you know, this is not a big deal, this actually could be good in the longer run for Nvidia in, in ways. And, you know, and then from big tech on down, what the ramifications are there.
- [06:45]
A
And you mentioned that some startups are already swapping in Deepseek R1 for the models they're using right now. How Widespread do you think that is, are they. Are any of the startups that you speak with saying, okay, well, to hell with OpenAI or to hell with Llama, time to put Deep Seek in? Or is this just beginning? Because it's again, something that dropped last week.
- [07:02]
B
Yeah, I think this is just beginning. I think, you know, people will experiment with it, right, Just to see like, how much you could, you know, get by swapping them out, given the price differentiation you were talking about. But also there's downsides, of course, like people have noted sort of the, you know, the censorship within China and of certain terms. And so, you know, I don't think everyone is quite certain what's in there. You know, it's an open source in that it's open weight, but it's, you know, it's not clear exactly everything that's going on in there right now. And so I do think that if this proves out, say if, if deepsea can release another iteration of the model and it still is on the same sort of, you know, footing, I think that then you'll start to see more startups potentially taking it really seriously. I think now it's just a wait and see approach for sure and just people trying out to see if it is, in fact as good as they say. Because I think, you know, part of this, like my initial gut reaction, you know, Deep Seek, obviously, as you noted, had been around for, you know, basically since December and didn't really get all of the massive pylon until sort of Friday, right, when R1 came out. And in part it's like, you know, I've just, I don't know why my mind was drawn to this, but it's sort of like when they were talking about the, the room temperature conductor, right? Like, and everyone was talking about, oh my God, like there's this, there's this huge breakthrough that's happened and this is going to revolutionize everything. And then it turns out, oh, you know, maybe there was some, some funny business in that claim and, and maybe it wasn't, you know, all was cracked up to be. And of course that turned out to be the case. And so I'm not saying obviously that's not the case with Deep Seek. It seems like now this R1 release has legitimized it. And as you note on leaderboards and whatnot, people have been testing this and again, the startups are part of that, that pressure test, right?
- [08:45]
A
And so the funny business, just to get this out of the way, the funny business might be on the training side like, we think that they trained it for much less money. We think that they trained it with inferior GPUs that have been sort of the only things they can get their hands on due to export controls. We're not 100% sure if that's the case.
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B
Right.
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A
But I think the bottom line here is that this is an open source model. It has been replicated. I mean, it has been downloaded to people's computers and used as effective as it is. And I think that the thing is the methods and the cost savings and the performance, that's all real. So even if, you know, basically all of Silicon Valley without those export controls couldn't do this or didn't do this, and maybe it's because they had a different method and we'll get into that, but the fact is that no matter, there's no putting the genie back in the bottle right now, which is that this company has created something that can rival OpenAI's performance at 3% of the cost. That's, that's the big thing. So. Yeah, sorry, go.
- [09:48]
B
I also just think like the overall mentality is one of the more interesting sort of earthquakes to, you know, use your phrasing of it. That's happening, happening right now. It's like. And I think Stephen Sinofsky summarizes well, he wrote, you know, very long tweet thread as he is want to do, but then he also published it on his, on his newsletter as well. But he goes into the history and he obviously has a lot of good historical context from Microsoft days on forward about what, you know, is, is going on here. But it's also, I think important to, to talk through like how the constraints that were put in place by the US because of the, you know, everything going on with, with chip constraints and sort of forcing AI companies not to export to China, you know, led to sort of this, this very interesting cauldron that I think could only happen in a place like China right now because they're so constrained. Whereas in the us like it's still the period of abundance, right, with AI and everyone's going after the scaling and it's, and it's, it's just not something they were going to focus on trying. You know, they're making the smaller models, they're making the mini versions of the models and those are great and we're seeing that. But China, you know, the folks working in China had to do this this way. And I just think it's something you couldn't have seen in hindsight arise out of The US in our current environment.
- [11:09]
A
Right, okay, so I want to talk quickly about the technology, very quickly about the technology and then get into some of the more business side applications here. So MG could you tell us just at a really high level what DeepSeek has done to be able to get these results? Because you know, it's one thing to say, okay, they were able to do it on worse chips with a smaller amount of data, but I think just, it's important to very briefly highlight just the technical technological innovation here.
- [11:41]
B
Yeah, I mean, so, and you know, I'm not a, I won't be a technical expert on this, but from my understanding it's basically, you know, obviously as you know, it started the Deep Seat project started out of a hedge fund that was focused on quant trading, you know, in China. And they had acquired a bunch of Nvidia chips. I think they were H1 hundreds, you know, before all the import restrictions came in. And basically they had those servers up and running and you know, presumably they were running a bunch of different models, including some of OpenAI's but including also a bunch of the LLAMA stuff that MET has been working on. And you know, they've just used the process of distillation to, you know, effectively bring those bigger versions of the, the sort of state of the art models and distill them down into, you know, smaller models which eventually led to this R1, you know, the equivalent of, of O1 on, on OpenAI side and again for a fraction of the cost, fraction of the computer and a fraction of the size for these to be able to run. And that, that latter part seems like it's, it's sort of being under discussed right now, but is important because yeah, all of these, these models have constraints about how you can run them like on your personal machines. Right. Because you know, they're going to require so much RAM and so, and so much memory to be able to do that. And if you can get them down to really small sizes, which again the, the bigger US companies have been doing with these mini models but they're, they're sort of taking this bifurcated approach. Whereas now we're getting to the point with this R1 model where it seems like it can run on pretty much a lot of different type of hardware which again they need to do in China because of the restrictions that they have there.
- [13:28]
A
Right. And there's also a methodology change here which is that they've gone from effectively self supervised learning which is what has been used to train all of the LLMs all the big LLMs to this point, to pure reinforcement learning, where the model tend to figure out what the right answer is on their own, which is just fascinating.
- [13:47]
B
Yeah. And it seemed like the, you know, sort of the American powers that be maybe felt like we weren't ready for that yet to happen. Right. Like, that was always the hope that we get to those points and that, you know, we still were in the, in the scaling point again, where, you know, you need someone in the loop to be able to check and make sure all these things are working. And this Chinese company, because of some of the restrictions that we just talked about, just went for it and it's proving itself.
- [14:17]
A
Right. And just to harp on one more technical issue before moving on, the distillation of models, to me is fascinating that they could take any big model and distill it using this form of training and effectively be able to replicate its performance. So you could take a llama model, which has 70 billion parameters, and distill it and then all of a sudden run it with this reasoning, reinforcement learning style approach. And it's cheaper, more efficient. It's, it's just, I mean, again, like, I think the entire world is still trying to wrap their head around this and there'll be more on this feed to talk about exactly how impressive this is. But to me, in the early innings of this, that is astonishing.
- [15:01]
B
Yeah. And I mean, it, it, again, at a high level, it, it makes sense. It just, it's incredible how it's happened because, like, do you need all of the world's knowledge, you know, in every single model for every single use case? Of course not. Like, that's going to be overkill for almost everything that you're going to do. And so does it point to a world where, yeah, we, we sort of lead towards more of these specialized models that are distilled. And obviously that's been happening, but this, this one is still, you know, a model that can effectively do most everything distilled down from, from those bigger ones.
- [15:34]
A
So there's one sort of big question that I think needs to be asked here, which is there's been this, all of Silicon Valley. And you point to this in your piece. All Silicon Valley has been operating on effectively the scaling hypothesis, which is that you add more compute. We talk about all the time on the show. Add more compute, add more data, add more power, add more training time effectively to these models, and you will improve. And now what Deepseek has shown is that you can actually do all this without that. And so I'M curious if you think that this invalidates the scaling hypothesis because, and it might seem kind of like a, you know, obscure thing, but it's very important because this sort of sets up the whole business conversation which is if the scaling hypothesis is invalidated and all that multitrillion dollar in investment, investment in Nvidia, Nvidia CPUs or GPUs, my bad becomes sort of thrown into question. So what happens to the scaling hypothesis from here?
- [16:39]
B
And it's fascinating timing too, right? Because this is, this is at the same time that everyone has now talked about sort of the quote unquote AI wall being hit. Right? And even Demis, you know, when you, when you talk to him, he, he noted that he doesn't necessarily believe in, in, you know, a wa. Did acknowledge that things are slowing and it'll just take longer to get more, you know, juice out of the squeeze, as it were. Right. And, and so that's sort of a, the natural evolution that's been happening and everyone is now pointing to it or at least acknowledging that, that some aspect of that is real. And now at the same time this comes along and it calls into sort of more question. There's one other element that sort of, I think is related to this, which was the big news story last week as well. The project Stargate, OpenAI and Nvidia and Oracle all coming together. And one of the more interesting elements of that was the fact that Microsoft is effectively pushing off the compute costs to Oracle and some of the other players in that situation. And there's all sorts of reasons potentially why they're doing that, obviously, given the interesting relationship between OpenAI and Microsoft. But at the very highest level, again, if they're thinking that, you know, our CapEx is going to be, we've already stated it's going to be 80 billion for the year. We don't want to add another several billion, you know, for this, this particular project. And why would they do that? In part probably because they're not necessarily sure that it makes sense to pay the billions upon billions to open AI to keep trying to scale on the frontier models. And this is, you know, sort of in line with what Deep Seek just did.
- [18:30]
A
Right. Yeah, it's interesting. We're also talking about Andreessen Horowitz who sat out OpenAI's last round and we were wondering on the Friday show maybe they. I heard that, saw this coming.
- [18:39]
B
Yeah.
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A
And it is interesting. I mean you, you put it pretty, pretty perfectly in your story. You say big tech companies are the now the most largest and sorry you say big tech companies are now the largest and most well capitalized in the world which means that they have effectively all the money that they can put towards scaling and the hammer met the nail. But there's no point hammering the nail after it's already been put into place. And that's the point that can't be predicted but is obvious once it's done. The question is if Deepseek just pointed to the nail already hammered effectively, did they just solve this? It's sort of like going up the, the scaling question in a. Yeah. Similar way.
- [19:22]
B
An analog for the same, same thing. Right. And, and going back to the history of compute like. Right. All these, you know, the powers that be tend to spend at the time, tend to spend a ton of, of capital on the build out of, of whatever the new technology happens to be. And you know, there's obviously we all benefit from it in the long run but in the short run, you know, this, this segues into, I guess, you know, what's, what's potentially going on with Wall street and what it means for these larger companies with regard to the spend.
- [19:51]
A
Yeah. And I just want to ask your, the question that you put in your newsletter, just to you directly, did they just point to the nail? Like is it done?
- [20:02]
B
I mean, again, I don't want to, you know this out, but I do feel like it's, it's, it's the exact question that everyone is sort of going to be scrambling to answer over this next week. And I think that it's not going to be as black and white as that for sure. But I do think, if I had to guess at a high level, I do think that there's some element to. Yes, the, the nail is already sort of driven into the board and we're moving on to what the next steps are. That's not to say it's over and, and you know, there's no innovation from here, but I think all of these things are in a way related like that we've just been talking about and the fact that they're all coming together at the same time. I don't think it's a coincidence. I think it's because like, yeah, we're at the point where we now need to move on to the sort of the next phase of, of the AI revolution, as it were.
- [20:54]
A
Yeah. And let's get into the business and I'm, I'm smiling here because you're making me think of. We have Reid Hoffman on the show on Wednesday and I interviewed him before R1 came out and the first half of the conversation is just talking about all the billions of dollars that have been spent and when they're going to get an roi. And I mean I'm still going to run the conversation, but there's going to be some context in there.
- [21:16]
B
Yeah, it's interesting knowing. Yeah, after the fact, but and it's also, I think Sinofsky brought this up too and I was sort of looking into this more last week. You saw it was a smaller news item, but both Microsoft and Google had altered the way that they're basically bundling together AI within, you know, either the 365 suites and within the Google Google suite of apps, because they're clearly still trying to figure out how exactly you make money off of all this spend and what the right model is and how you spur on usage of it. And this just comes in and throws a grenade into that equation again.
- [21:55]
A
And this gets us to some of the real thorny business questions. So just to kick this off, I took a look at what all the big tech companies were doing pre market. So this will obviously change across the day, but, but I imagine they'll stay directionally kind of the same. Nvidia down 10%. Microsoft down 4%. Google down 3%. Meta down 2.6% S&P down 2%. So this is all based off of this deep seek reckoning or this deep seek realization. And let me just put the sort of question to you I think about as pointedly as I can, which is that the AI industry up until this point, like all the numbers we're seeing within Wall street, the trillion dollar market caps the billions of investment, the billions that have been raised by companies like OpenAI and Anthropic from companies like Microsoft and Amazon. Right. So this is basically the whole game here. They have effectively been what's been driving the numbers. And the question is can we, you know, basically Wall street has been following that and saying we expect them to get a return based on, on those numbers. And in fact a lot of this AI spend was just a wealth transfer, I would say from like meta advertising to llama, from Google search revenue to Gemini, from Microsoft Azure to OpenAI. So what happens here? Because you know, basically if they, if a lot of the AI industry has been driven based off of subsidies coming from other businesses and doesn't need that type of spend anymore, like does the party end?
- [23:37]
B
So I think it's different for each company. Probably Microsoft and Google are closest, you know, aligned in terms of where they Net out. And it's sort of interesting, you know, the numbers you just rattled off with where the stocks are at, that feels, you know, just like a very clear picture from Wall street what they think now. Right? Like they think Nvidia is going to get hit fast because in this, in this doomsday scenario because obviously they're the beneficiary from everyone, from all those companies, all those other companies that you mentioned, Big Tech is, is pouring as much money as possible as they can. They can't get it, get enough chips fast enough into Nvidia and if they pause that, that obviously is bad news for Nvidia in the short term. Again, I think there's longer term stuff that, that's different for Nvidia, which we can talk about. But to just hit on the rest of this question right now, I think that Microsoft and Google, which are, as we just mentioned, you know, are trying to sort of figure out the right models for how to charge for AI. I think that this puts them in a really tricky situation if the underlying economics just totally changed overnight of what AIs yeah. Underlying economic model should be. And so they were, you know, moving around different pieces trying to get to the right, the right end state so that, yeah, they could ultimately prove to Wall street like look, we're adding, you know, X amount on top of what we were already doing revenue wise thanks to AI. And a little bit, there's a little bit of weird obfuscation stuff going on there, right? It's like, well, it's bundled in now to 365. And so, you know, we don't necessarily need to tell you exactly what the uplift is but, but you can just, you know, assume that it's, that it's a part of this because it's all baked in and AI is like, you know, the new Internet and blah, blah, blah. And so, you know, there's ways that they can, they can finesse the messaging around that. But you know, to your exact question, I do think that there's, there's varying degrees of being worried certainly within Google and Microsoft Meta is more interesting because their open source philosophy, open weight philosophy and model is so similar to what Deep Seek has done. Right. And so the problem there in my mind at least is again they're spending whatever Zuckerberg just threw out 65 million or whatnot. He said, you know, at the end of last week that they're going to spend on, on Capex. And so why are they spending that amount now if, if you know, Deepseek can do it for, you know, pennies on the dollar, if not even less than that. And so what does that, that mean for their world? So in my view, high level. I think that Meta's probably in a bit better position than the other ones just because they, at the end of the day they do want like, you know, their whole philosophy is to open sources not for necessarily altruistic reasons, but because they know that it's historically helped them help their business. You know, to open source these things. The question of if it's not them open sourcing, it becomes pretty complicated. If someone else's, you know, you have to use someone else's models but they can pull back spend, it feels like a little bit easier than the other folks can. On the other end of the spectrum, OpenAI like they're, you know, the entire business is, is sort of built around being at the frontier and they've done a great job with that. They're a little bit different than, than Google and Microsoft in my mind just because they've done a good job getting mind share both in terms of brand and product. Right? Like Jet TBT is number two in the app Store right now behind Deep Seek, you know, for a reason. People are interested. It's a brand and they know it. And so what does it look like though if they're not the ones sort of powering the models? I don't think that they would give up and you know, go with Deep Seeks model necessarily. But what does it mean if, if they're not sort of the only one or the main frontier, you know, model maker providing that like. So there's all sorts of interesting offshoots and ramifications of that.
- [27:34]
A
So MG there's like two views right now in terms of like what could happen with all this spending. Right. One is Silicon Valley will continue to spend these billions and they might get, you know, incrementally better performance and stay slightly ahead of the open sources of the world. Deep seeks of the world that can just emulate their models. The other side of it is that they continue to spend and then they basically hit AGI or like, you know what I'm saying? Like we've, if, if the performance increases that we've seen with such little. Sorry, if the performance increases that we've seen with such efficient use of capital from Deep Sea can be emulated, then imagine what you could do with a hundred times the amount of spend. So the models are about to become much more powerful and all these fantasies that people have about what they can do many of which Demis and I spoke about last week, all of a sudden become feasible because the capital is there. So which side of this do you know?
- [28:29]
B
That's a nice thing to say and like a nice high level mantra. And many of, you know, many of the leaders of these companies will be saying that today to sort of try to calm Wall Street. But at the end of the day, you know, aside from sort of OpenAI, which obviously is again tied with Microsoft and now Oracle, but besides them, the rest of these are public companies and Wall street, you know, like it or not, they have a say sort of over what they're going to do, like if they're gonna get hammered. And this is something I've sort of been harping on for a while, not because I think that they were doing the wrong thing necessarily with the spend, but it's just obvious that like it always comes back around, right where it's like I equated it, you know, last year to when all the movie studios during COVID and TV studios were just bulking up on streaming, right, and just spending as much money as possible as they could in order to build up their streaming services. And Wall street loved it at that time because, you know, Disney and everyone else was just gaining millions and millions of subscribers and it seems like they had a path to take on Netflix. And you know, this was the future of the industry. It's still, by the way, the future of the industry. But Wall street then all of a sudden turned on all that spend and decided like, you need to cut like spend X amount. You need to, you know, unfortunately cut the employee base and, and basically just become way more efficient while doing the same high level thing. And it was, you know, always obvious that at some point they were going to do that to the tech companies as well with regard to AI spend. And so again, they can all have the right mentality about like this is the future and say the right things, that this is the future and this spend is important. And I don't disagree with any of that. But still, they have to answer to Wall street, you know, to some degree, maybe Zuckerberg less so because he, you know, controls the, controls the company so strongly. But like, certainly Microsoft and Google to a lesser extent are going to have to answer for a lot of that spend. And this is the first real, real test. Meta had some of it, right? Like there was some backlash last year around their spend and certainly back dating back to the, the, you know, VR and AR and XR spend. And so they had to answer for Some of that. And Zuckerberg did, right, and he got rewarded for it after the fact. And that's like the game they're playing here. They know that if they cut spend because Wall street doesn't like to see all the AI spend, they'll get rewarded in the form of the stock going up and then all the ramifications from that. And so it's natural that that is going to play out that way. And so I think the narrative then shifts to other levels of not necessarily obfuscation, but other ways of framing it. It's like, okay, we agree that we shouldn't spend tens of billions of dollars on Nvidia server farms, but we need to build out our in person AI Robotics arms, right, in order to keep these models and keep sort of the next phase going as we march towards AGI and yada yada.
- [31:25]
A
So markets just open. Nvidia opens up, down 11%. So still above $3 trillion. So it's not like the AI revolution is over, but down 11%. So just a cool, you know, a couple hundred billion dollars shaved off the market cap in a morning. Let me talk to you a little bit about what these companies are saying back to Wall street or actually talking to Wall street about to allow them to keep spending. So Satyam Della is doing his tweets. He says he's talking about Jevons Paradox. He says Jevons Paradox strikes again. As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of. Let's say that I don't know if.
- [32:05]
B
You saw this last night, Gary Tan, you know, the, the president of YC tweeted the same thing. And so I'm like, is this coordinated or.
- [32:13]
A
I mean, it is, you know, text going on.
- [32:16]
B
Yeah, there's a group text maybe going on where it's like, this is the answer. And it's not a, it's not like a totally bs, you know, answer to it, but there's much more nuance and context that's sort of required to, to get to, you know, that being the, the excuse for this.
- [32:32]
A
So let me just basically talk about the elephant in the room that's been hanging over this full conversation and will be sort of like the spoken or unspoken part of this discussion as it goes forward this week, which is that let's say the cost of intelligence goes down to zero, right? So that's what everybody is basically aiming for. It's one of OpenAI's stated goals to make Intelligence, you know, close to free as possible. They don't really make a lot of money selling off their API or they even maybe might lose. We need to see AI applications like we need to see an economy that takes use of this technology that is so impressive, right? Like you look at the chain of thought even in Deep Seq and you're just like, how is a computer, you know, quote unquote, thinking through this stuff? But the economy needs to take hold of this powerful technology and make use of it and put it into play for really meaningful economic use. Whether or not the Deep Seek thing existed, right? Like billions of dollars of economic or trillions of dollars of economic value needed to be created from this generative AI moment. And what do we have now? We have OpenAI, who has ChatGPT with 300 million users, which is okay, but still losing billions a year to run that thing. Maybe they'll be able to be more efficient and make those couple billion a year from it. We have some enterprises putting this into play, but every enterprise I speak with, there's a couple cool use cases here or there, but mostly what you see is proof of concepts. And many of those proof of concepts aren't going out the door. So don't we need to see one way or the other AI applications, whether that's standalone or integrated within business software, that start to prove the real value of this technology that we just haven't seen to date?
- [34:19]
B
I mean, the answer is yes, of course. The reality though is, you know, maybe this, this confluence of events right now is going to help that because it sort of just is forcing a fundamental rethinking of a lot of what, you know, we've just been not going through the motions, but we've been on this path, right to scaling as we were talking about and that, you know, even right, like Sam Altman has said, like they see line of sight now to AGI, they just have to, to, you know, just dot, dot, dot, underpants and then profit from there.
- [34:56]
A
Better get there.
- [34:58]
B
But they say they have line of sight, right to, to know what they need to do and it's just a matter of execution and sort of, you know, getting everything aligned in order to do that. And if this moment with Deep Seek being the, you know, the biggest catalyst thus far of it, if it doesn't cause the entire industry to sort of rethink that. And at the same time, to your point, like, you know, asking about do, does that sort of drive us to move on from, yeah, just like this, this non stop scaling of Frontier models. That is awesome technology but unclear how it works from a practical standpoint. Do we start to. Yeah. Distill this, you know, for lack of a better phrase, down to actual products. And you know, when I, when I think about that that leads back to like whenever it was six months ago, seven months ago when Apple did their Apple intelligence stuff which you and I talked about.
- [35:49]
A
Right.
- [35:49]
B
And it like everyone jumps on Apple and there was another news cycle I think this past week because you know, Siri can't, can't correctly answer who won previous Super Bowls.
- [36:01]
A
Yeah, the Gruber post, amazing.
- [36:04]
B
But Apple's mentality from the get go with launching Apple Intelligence has clearly been. We need the. We for lack of a better phrase, don't necessarily care so much about. Yeah. The frontier of the vanguard of this technology. We care about the day to day usage of it. Right. And you know, they have a few things that are sort of front end facing that haven't really worked, that try to use AI like the emoji creator and things like that, but most of it is just baking it into their products. And that's what we've seen too with obviously what we talked about with Microsoft and Google. They all have, you know, their own, like some have video generation, some have some other of their own standalone products. For the most part they're just going to be baked in. But you know, to what we were talking about earlier, none of that is really the promise it felt like. Right. Of, of what this larger movement was going to be. And everyone's waiting for, you know, not, not necessarily AGI right now but they just want some other forward facing user facing version of AI that, that can be good and ChatGPT has been the closest that we've gotten to that. And maybe some of these video products, you know, end up being the next, the next phase of that. But I think that you're right that this, that ultimately you have to get to something that comes of this that that really sort of moves. It moves all sorts of needles. And again I wonder if this news cycle and, and just pause now doesn't lead to more of that. I hope that that's the case.
- [37:36]
A
Yeah. And I would say the Apple intelligence is almost the perfect example of the problem that I'm pointing toward which is that we have this technology that's so promising and yet even Apple cannot implement it correctly. And that might, I mean obviously it says something about Apple, but it might say something about the technology as well.
- [37:54]
B
Yeah. And you know, as with everything, like with everything in technology, I Think about, you know, dating back to my reporting days and whatnot. It's just like having seen so much in a few different cycles now, are we too early still? Right, like everyone, everyone has been talking about and believing that like, this is the moment where this is like really happening and this is great. But I do think that if you took a step back, you might wonder if we're not still doing this too early, you know, and trying and all of these companies are not raising way too much money when the timing is just not right for exactly what you're, you know, trying to ask the question about, like, how do you, how do you turn these into products and how do you ultimately turn this into a business that returns the capital that was spent on it? Now, no company would admit that right now, but, you know, hindsight will only prove one way or another whether that's the case. And I think everyone still remains super optimistic that now is the right time and you want to keep your foot on the gas. But again, this deep seek stuff sort of causes a pause and a natural re examination of, of just how much money to spend and, and what, what you should be focused on.
- [38:59]
A
Let me ask you to put your investor hat on for a moment. Are there startups out there that would exist today that don't exist because effectively buying compute from the APIs or running llama is cost prohibitive, but they would exist if intelligence was zero and that's effectively what deepseek is going to put to the test.
- [39:20]
B
Yeah, that's really interesting. I don't, I don't have, I don't want to just try to come up with something off the top of my head. Not that I know of, but I do think at a high level that your question is a really interesting one. And if, if this is going to be truly transformational, deep seek as a whole, it will lead to something like that, right? Like a bunch of companies coming out. And not just yet, because it's not just the technical aspect, it's not just driving down costs because that seems like it's sort of going to happen as a result of that, which is great. But does this actually yield new companies that couldn't have existed beforehand? And I don't know, like, I can't think of any off the top of my head. But that's also why I'm not a startup founder. And you know, hopefully there are startups out there that are, that are going to latch onto this.
- [40:10]
A
But something tells me that the answer is no. And the reason is, is because. And Investors have been dying to throw money at AI companies and have been willing to lose a lot of money if the idea is promising enough. And I don't know, we haven't, we haven't seen a wave of AI startups hit. @ least there have been many. But you know, they're not like, it's not like the, you know, the beginning of the mobile era where there was like a new consumer startup every day. It just isn't happening that way. In fact, most of the action is enterprise.
- [40:42]
B
One other just wrinkle and layer of that which I feel like has been overshadowed in all of the recent news. But you know, we talked about and talked about a lot last year but as the regulatory regime is changing now, if M and A sort of doesn't pick up with regard to exactly the type of companies you're talking about, right. Like they have great teams, they're working with, you know, this technology and they clearly know how to do things with it. But they haven't gotten the product right, they haven't gotten the business right. And so they're scooped up by the, you know, the Metas, the Googles, the Microsoft's, the open AIs of the world. And that, you know, in and of itself won't be that interesting other than those companies getting good talent perhaps. But if it just reignites sort of, you know, a passion within really early stage startup founders to keep re, accelerate sort of going after new problems. Right. Like I do feel like there was a bit of a chilling effect the past year because M and A had, you know, basically been shut off that sort of kept people staying at Google and staying at Meta and staying at OpenAI, not forming new startups as they might have in years past. Because they knew that, you know, there was, there was the potential, obviously the pie in the sky. They want to build a big company, but there was also the potential frankly, right, to like, you know, sell, build something that's big enough to sell for multi, hundreds of millions of dollars if not billions of dollars to some of these other companies. And so you know, that might come into play with some of this.
- [42:18]
A
All right, let's put a bow on this conversation. You say the real problem is that it won't be so simple to simply pull back, spend beyond a lot of it already being committing, being committed. There's obviously still a very real risk that Deepseak is just a blip on the radar and not the bomb that blows up everything. What are we looking like, what are we looking at over the next couple months when it comes to the aftermath of this earthquake to go back to our original question.
- [42:44]
B
And so that's just a call out to the, you know, the, the obvious thing that everyone likes to overreact obviously to, to, you know, big news stories and big news cycles. And again, as we've been talking about, like this is legitimate, but how legitimate is it? Like. Right. Like, so we'll even see potentially play out over the course of today in the stock market. Like, do they start to get nerves calmed a bit by. Yeah, this talk of like. Well, actually this isn't so bad for Nvidia because while it hurts their, their immediate, it could potentially hurt their immediate money coming in the door in the longer run. You know, it's, it's again, Jayvon's paradox stuff where it's like, yeah, it's, it's going to raise, raise all boats as, as this just permeates everything and so they need chips and yada yada. And so that could help. But yeah, I mean, I think that it won't be so easy also for as I noted, for all these companies to pull back spend because they've already committed to buying X number of, of H200 chips and, and then soon enough we'll get the next iteration, you know, announced down the road. And so all these supercomputer mega clusters of data centers that are being built right now, they're just not going to, you know, put the brakes on all of that because there's a risk they're all playing in the same game. Right. And if one of them pauses, maybe they get a short term Wall street, you know, pat on the back. But if they're wrong, that's like catastrophic and that's, you know, it's like a fire firing the CEO type offense. You know, if, if this is just, you know, even a blip on the radar obviously undersells it a bit. But if this is not ultimately like a real fundamental sea change situation and is more just like a, a step on, on the road, they might still want to keep their foot on the gas.
- [44:35]
A
Yeah, it's gonna be very interesting to watch. The website is spyglass.org the piece AI finds a way, joined of course by MG Sealer. MG, great to see you again. Thanks for coming on the show.
- [44:46]
B
Thanks for having me.
- [44:47]
A
All right, everybody, thank you for listening. We'll be back on Wednesday with my interview with Reid Hoffman. Obviously a little different now, but maybe as MG puts it out, maybe we shouldn't be overreacting too much. So looking forward to speaking with you then. And we'll see you next time on Big Technology Podcast.