Bloomberg Audio Studios, Podcasts, Radio News.

Bloomberg Audio Studios, Podcasts, Radio News.

Hello and welcome to another episode of the Odd Lots podcast.

Hello and welcome to another episode of the Odd Lots podcast.

I'm Joe Wisenthal and I'm Tracy Alloway.

I'm Joe Wisenthal and I'm Tracy Alloway.

Tracy the Deep Seek sell off.

Tracy the Deep Seek sell off.

That's right, it's pretty deep. Has anyone made that joke yet.

That's right, it's pretty deep. Has anyone made that joke yet.

We're in Deep Seek?

We're in Deep Seek?

Yeah, I don't think anyone who's made that joke.

Yeah, I don't think anyone who's made that joke.

I will say, like, you know, it's bad in markets when all the headlines are about standard deviation, yes, right, And then you know it's really bad when you see people start to say it's not a crash, it's a healthy correction. Yes, that's the real cope.

I will say, like, you know, it's bad in markets when all the headlines are about standard deviation, yes, right, And then you know it's really bad when you see people start to say it's not a crash, it's a healthy correction. Yes, that's the real cope.

But just for like real scene setting. You know, We've done some very timely interviews about tech concentration in the market lately and how so much of the market is this big concentrated bed on AI et cetera. Anyway, on Monday, I think people will be listening to this. On Tuesday, markets got clobbered in video one of the big winners as of the time I'm talking about this three thirty pm on Monday, down seventeen percent. We're talking major laws is really across the tech complex. Basically, it seems to be catalyzed by the introduction of this high performance, open source Chinese AI model called deep Seek. I was born, from what we know, out of a hedge fund. Apparently it was very cheap to train, very cheap to build. You know, the tech constraints at this point didn't seem to be much of a problem. They may be a problem going forward, But yes, here is something the entire market betting on a lot of companies making AI and are now concerns about, of course, a cheap Chinese competitor.

But just for like real scene setting. You know, We've done some very timely interviews about tech concentration in the market lately and how so much of the market is this big concentrated bed on AI et cetera. Anyway, on Monday, I think people will be listening to this. On Tuesday, markets got clobbered in video one of the big winners as of the time I'm talking about this three thirty pm on Monday, down seventeen percent. We're talking major laws is really across the tech complex. Basically, it seems to be catalyzed by the introduction of this high performance, open source Chinese AI model called deep Seek. I was born, from what we know, out of a hedge fund. Apparently it was very cheap to train, very cheap to build. You know, the tech constraints at this point didn't seem to be much of a problem. They may be a problem going forward, But yes, here is something the entire market betting on a lot of companies making AI and are now concerns about, of course, a cheap Chinese competitor.

I just realized, Joe, this is actually your fault, isn't it. This last week you wrote that you were a deep Seek aibro and look what you've done. You've wiped five hundred and sixty billion dollars off of in videos market.

I just realized, Joe, this is actually your fault, isn't it. This last week you wrote that you were a deep Seek aibro and look what you've done. You've wiped five hundred and sixty billion dollars off of in videos market.

Yeah, might be that's you anyway. One of the interesting questions though, is that this was sort of announced in a white paper in December. Why did it take for till January twenty seventh for related to freak people out? Big questions? Anyway, let's jump right into it. We really do have the perfect guest, someone who's was here for our election Eve Special, a guy who knows all about numbers and AI and quant stuff, and he writes a substack that has become for me a daily absolute must read where he writes an extraordinary amount. I don't even know how he writes so much on a given day. We're going to be speaking with Zvi Mashowitz. He is the author of the Don't Worry about the Vase blog or substack. ZV. You're also a deep seki brill. You've switched to using that.

Yeah, might be that's you anyway. One of the interesting questions though, is that this was sort of announced in a white paper in December. Why did it take for till January twenty seventh for related to freak people out? Big questions? Anyway, let's jump right into it. We really do have the perfect guest, someone who's was here for our election Eve Special, a guy who knows all about numbers and AI and quant stuff, and he writes a substack that has become for me a daily absolute must read where he writes an extraordinary amount. I don't even know how he writes so much on a given day. We're going to be speaking with Zvi Mashowitz. He is the author of the Don't Worry about the Vase blog or substack. ZV. You're also a deep seki brill. You've switched to using that.

So I use a wide variety of different ais. So I will use quad paranthropic, I will use one from ta GPT, from open Ai. I'll use Gemini sometimes, and I'll use Perplexity for web searches. But yeah, I'll use R one, the new deep seat model for certain type queries where I want to see how it thinks and like see the logic laid out, and then I can judge, like did that make sense? Do I agree with that?

So I use a wide variety of different ais. So I will use quad paranthropic, I will use one from ta GPT, from open Ai. I'll use Gemini sometimes, and I'll use Perplexity for web searches. But yeah, I'll use R one, the new deep seat model for certain type queries where I want to see how it thinks and like see the logic laid out, and then I can judge, like did that make sense? Do I agree with that?

So one of the things that seems to be freaking people out as well as the market is that purportedly this was trained on like a very low cost, something like five point five million dollars for deep Seek V three, although I've seen people erroneously say that the five point five million was for all of its R one models, and that's not what it says in the technical paper. It was just for V three. But anyway, oh I should mention it also seems like a big chunk of it was built on Mama, so they're sort of piggybacking off of others investment. But anyway, five point five million dollars to train, is that a realistic and then b do we have any sense of how they were able to do that.

So one of the things that seems to be freaking people out as well as the market is that purportedly this was trained on like a very low cost, something like five point five million dollars for deep Seek V three, although I've seen people erroneously say that the five point five million was for all of its R one models, and that's not what it says in the technical paper. It was just for V three. But anyway, oh I should mention it also seems like a big chunk of it was built on Mama, so they're sort of piggybacking off of others investment. But anyway, five point five million dollars to train, is that a realistic and then b do we have any sense of how they were able to do that.

So we have a very good sense of exactly what they did because they're unusually open and they gave us technical papers, they tell us what they did. They still hid some parts of the process, especially with getting from V three, which was trained for the five point five million two R one, which is the reasoning model for additional millions of dollars, where they tried to make it a little bit harder for us to duplicate it by not sharing their reinforcement learning techniques. But we shouldn't get over anchored or carried away with the five point five million dollar number. It's not that it's not real, it's very real. But in order to get that ability to spend five point five million dollars and get the model to pop out. They had to acquire the data, they had to hire the engineers, they had to build their own cluster, they had to over optimize to the bone their cluster because they're having problems of chip access thanks to our export controls. And they were training on eight hundreds. And the way they did this was they did all these sorts of mini optimism, little optimizations, including like just exactly integrating the hardware, the software, everything they were doing in order to train as cheaply as possible on fifteen trillion tokens and get the same level of performance or you know, close to the same level performance as other companies have gotten with much much more compute. But it doesn't mean that you can get your own model for five point five million dollars, even though they told you a lot of the information. In total, they're spending hundreds of millions of dollars to get this result.

So we have a very good sense of exactly what they did because they're unusually open and they gave us technical papers, they tell us what they did. They still hid some parts of the process, especially with getting from V three, which was trained for the five point five million two R one, which is the reasoning model for additional millions of dollars, where they tried to make it a little bit harder for us to duplicate it by not sharing their reinforcement learning techniques. But we shouldn't get over anchored or carried away with the five point five million dollar number. It's not that it's not real, it's very real. But in order to get that ability to spend five point five million dollars and get the model to pop out. They had to acquire the data, they had to hire the engineers, they had to build their own cluster, they had to over optimize to the bone their cluster because they're having problems of chip access thanks to our export controls. And they were training on eight hundreds. And the way they did this was they did all these sorts of mini optimism, little optimizations, including like just exactly integrating the hardware, the software, everything they were doing in order to train as cheaply as possible on fifteen trillion tokens and get the same level of performance or you know, close to the same level performance as other companies have gotten with much much more compute. But it doesn't mean that you can get your own model for five point five million dollars, even though they told you a lot of the information. In total, they're spending hundreds of millions of dollars to get this result.

Wait, explain that further. Why does it still take hundreds of millions And does this mean if it takes hundreds of millions of dollars that the gap between what they're able to do versus the say American labs is perhaps not as wide as maybe people think.

Wait, explain that further. Why does it still take hundreds of millions And does this mean if it takes hundreds of millions of dollars that the gap between what they're able to do versus the say American labs is perhaps not as wide as maybe people think.

Well, what deepseek is doing is they have less access to chips. They can't just buy Navidiot chips the same way that you know open ai or Microsoft or and throb it can buy Nvidiot chips. So instead they had to make good use, very very efficient, killer use of the chips that they did have. So they focused on all these optimizations and all of these ways that they could save on compute. But in order to get there, they had to spend a lot of money to figure out how to do that and to build the infrastructure to do that. And you know, once they knew what to do, it cost them five point five million dollars to do it. They've shared a lot of that information and this has dramatically reduced the cost of somebody who wants to follow in their footsteps and train a new model because they've shown the way of many of their optimizations that people didn't realize they could do or didn't realize how to do them. That can now very easily be copied. But it does not mean that you are five point five million dollars away from your own V three.

Well, what deepseek is doing is they have less access to chips. They can't just buy Navidiot chips the same way that you know open ai or Microsoft or and throb it can buy Nvidiot chips. So instead they had to make good use, very very efficient, killer use of the chips that they did have. So they focused on all these optimizations and all of these ways that they could save on compute. But in order to get there, they had to spend a lot of money to figure out how to do that and to build the infrastructure to do that. And you know, once they knew what to do, it cost them five point five million dollars to do it. They've shared a lot of that information and this has dramatically reduced the cost of somebody who wants to follow in their footsteps and train a new model because they've shown the way of many of their optimizations that people didn't realize they could do or didn't realize how to do them. That can now very easily be copied. But it does not mean that you are five point five million dollars away from your own V three.

So the other thing that is freaking people out is the fact that this is open source, right, we all remember the days when OpenAI was more open and now it's moved to closed source. Why do you think they did that? And like how big a deal is that?

So the other thing that is freaking people out is the fact that this is open source, right, we all remember the days when OpenAI was more open and now it's moved to closed source. Why do you think they did that? And like how big a deal is that?

So this is one of those things where they have a story and you can believe their story. You're not with their story, but their story is that they are essentially ideologically in favor of the idea that everyone should have access to the same AI, that AI should be shared with the world, especially that China should help pump out its own ecosystem and they should help grow all of the AI for the betterment of humanity. And they're going to get artificial general intelligence and they are going to open source that as well, and this is their the main point of deep Sea. This is why deep Seak exists. They disclaiming even having a business model really and you know they're they're an outgrowth of a hedge fund, and hedge fund makes money and maybe they can just do this if they choose to do that, or maybe they will end up with a different business model. But it was obviously very concerning from a lot of angles if you open source increasingly capable models, because you know, artificial general intelligence means something that's you know, as smart and capable as you and I as a human, and perhaps more so. And if you just hand that over in open form to anybody in the world who wants to do anything with it, then we don't know how dangerous that is, but it's existentially risky at some limit to unleash things that are smarter and more capable, more competitive than us, that are then going to be free and loose to you know, engage in whatever any human directs them to do.

So this is one of those things where they have a story and you can believe their story. You're not with their story, but their story is that they are essentially ideologically in favor of the idea that everyone should have access to the same AI, that AI should be shared with the world, especially that China should help pump out its own ecosystem and they should help grow all of the AI for the betterment of humanity. And they're going to get artificial general intelligence and they are going to open source that as well, and this is their the main point of deep Sea. This is why deep Seak exists. They disclaiming even having a business model really and you know they're they're an outgrowth of a hedge fund, and hedge fund makes money and maybe they can just do this if they choose to do that, or maybe they will end up with a different business model. But it was obviously very concerning from a lot of angles if you open source increasingly capable models, because you know, artificial general intelligence means something that's you know, as smart and capable as you and I as a human, and perhaps more so. And if you just hand that over in open form to anybody in the world who wants to do anything with it, then we don't know how dangerous that is, but it's existentially risky at some limit to unleash things that are smarter and more capable, more competitive than us, that are then going to be free and loose to you know, engage in whatever any human directs them to do.

I have a really dumb question, but I hear people say artificial general intelligence all the time. AGI, what does that actually mean?

I have a really dumb question, but I hear people say artificial general intelligence all the time. AGI, what does that actually mean?

There is a lot of dispute over exactly what that means. The words are not used consistently, but it stands for artificial general intelligence. Generally, it is understood to mean you can do any task that can be done on a computer that can be done cognitively only as well as a human.

There is a lot of dispute over exactly what that means. The words are not used consistently, but it stands for artificial general intelligence. Generally, it is understood to mean you can do any task that can be done on a computer that can be done cognitively only as well as a human.

I mean, it does most of these things do things much better than me. I don't know how to code, and so, but I get that there are still some things. Maybe they wouldn't be as good as proving some of the are you human tests? Everyone to talk about Jevins paradox and so we see in video and broadcom shares these chip companies, they're getting crumbled today. And one of the theories like, oh no, with all these optimizations and so forth, in researchers will just use those and they'll still have max demand for compute, and so it won't actually change the ultimate end for compute. How are you thinking about this question?

I mean, it does most of these things do things much better than me. I don't know how to code, and so, but I get that there are still some things. Maybe they wouldn't be as good as proving some of the are you human tests? Everyone to talk about Jevins paradox and so we see in video and broadcom shares these chip companies, they're getting crumbled today. And one of the theories like, oh no, with all these optimizations and so forth, in researchers will just use those and they'll still have max demand for compute, and so it won't actually change the ultimate end for compute. How are you thinking about this question?

So I'm definitely a Jevans pro right now from the perspective of this, you.

So I'm definitely a Jevans pro right now from the perspective of this, you.

Don't think it'll have a negative impact and just the amount of compute demanded.

Don't think it'll have a negative impact and just the amount of compute demanded.

The tweet I sent this morning was Navidio down eleven percent pre market on news that his chips are highly useful. And I believe that what we've shown is that, yes, you can get a lot more in some sense out of each Navidia chip than you expected. You can get more AI. And if there was a limited amount of stuff to do with AI, and once you did that stuff, you were done, then that would be a different story. But that's very much not the case. As we get further along towards AGI, as these ais get more capable, we're going to want to use them for more and more things, more and more often, and most importantly, the entire revolution of R one and also Open Eyes O one is inference time compute. What that means is every time you ask the question, it's going to use more compute, more cycles of GPUs to think for longer, to basically use more tokens or words to figure out what the best possible answer is. And this scales not necessarily with out limit, but it scales very very far. So Opening Eyes new three is capable of thinking for you know, many minutes. It's capable of potentially spending you know, hundreds or even in theory thousands of dollars or more on individual query. And if you knock that down by an order of magnitude, that almost certainly gets you to use it more for a given result, not use it less, because that is effect starting to get prohibitive. And over time, you know, if you have the ability to spend or markly vittle of money and then get things like virtual employees and abilities to answer any question under the sun, yeah, there's basically unlimited demand to do that or to scale up the quality of the answers as the price drops. So I basically expect that as fast as the VIDIA can manufacture chips and we can put them into data centers and give them electrical power. People will be happy to pie those chips.

The tweet I sent this morning was Navidio down eleven percent pre market on news that his chips are highly useful. And I believe that what we've shown is that, yes, you can get a lot more in some sense out of each Navidia chip than you expected. You can get more AI. And if there was a limited amount of stuff to do with AI, and once you did that stuff, you were done, then that would be a different story. But that's very much not the case. As we get further along towards AGI, as these ais get more capable, we're going to want to use them for more and more things, more and more often, and most importantly, the entire revolution of R one and also Open Eyes O one is inference time compute. What that means is every time you ask the question, it's going to use more compute, more cycles of GPUs to think for longer, to basically use more tokens or words to figure out what the best possible answer is. And this scales not necessarily with out limit, but it scales very very far. So Opening Eyes new three is capable of thinking for you know, many minutes. It's capable of potentially spending you know, hundreds or even in theory thousands of dollars or more on individual query. And if you knock that down by an order of magnitude, that almost certainly gets you to use it more for a given result, not use it less, because that is effect starting to get prohibitive. And over time, you know, if you have the ability to spend or markly vittle of money and then get things like virtual employees and abilities to answer any question under the sun, yeah, there's basically unlimited demand to do that or to scale up the quality of the answers as the price drops. So I basically expect that as fast as the VIDIA can manufacture chips and we can put them into data centers and give them electrical power. People will be happy to pie those chips.

At the risk of angering the Jeffons Paradox bros. Just to push on the point a little bit more so, my understanding of deepseek is that one of the reasons it's special is because it doesn't rely on like specialized components, custom operators, and so it can work on a variety of GPUs. Is there a scenario where, you know, AI becomes so free and plentiful, which could in theory be good for Nvidia, But at the same time, because it's easy to run on a bunch of other GPUs, people start using you know, more like ACIK chips, like customized chips for a specific purpose.

At the risk of angering the Jeffons Paradox bros. Just to push on the point a little bit more so, my understanding of deepseek is that one of the reasons it's special is because it doesn't rely on like specialized components, custom operators, and so it can work on a variety of GPUs. Is there a scenario where, you know, AI becomes so free and plentiful, which could in theory be good for Nvidia, But at the same time, because it's easy to run on a bunch of other GPUs, people start using you know, more like ACIK chips, like customized chips for a specific purpose.

I mean, in the long run, we will almost certainly see specialized inference chips, whether from the Video or they're from someone else, and we will almost certainly see various different advancements that today's chips are going to be obsolete in a few years. That's how AI works, right, There's all these rapid advancements. But you know, I think in Video is in a very very good position take advantage of all of this. I certainly don't think that like you'll just use your laptop to run the best agis and therefore we don't have to worry about buying TPUs is a porposition. It's certainly possible that rivals will come up with superior checks. That's always possible. The video does not have a monopoly, but the video certainly seems to be a dominantiation right now.

I mean, in the long run, we will almost certainly see specialized inference chips, whether from the Video or they're from someone else, and we will almost certainly see various different advancements that today's chips are going to be obsolete in a few years. That's how AI works, right, There's all these rapid advancements. But you know, I think in Video is in a very very good position take advantage of all of this. I certainly don't think that like you'll just use your laptop to run the best agis and therefore we don't have to worry about buying TPUs is a porposition. It's certainly possible that rivals will come up with superior checks. That's always possible. The video does not have a monopoly, but the video certainly seems to be a dominantiation right now.

It seems to me. I mean, I know there's others, but it seems to be in the US. There's like three main AI producers of models that people know about. There's Open Ai, there's Claude, and then there's Meta with Lama. And it's worth knowing that Meta is green today, that the stock is actually up as of the time I'm talking about this one point one percent. Just go through each one real quickly, how the sort of deep seek shock affects them and their viability and where they stand today.

It seems to me. I mean, I know there's others, but it seems to be in the US. There's like three main AI producers of models that people know about. There's Open Ai, there's Claude, and then there's Meta with Lama. And it's worth knowing that Meta is green today, that the stock is actually up as of the time I'm talking about this one point one percent. Just go through each one real quickly, how the sort of deep seek shock affects them and their viability and where they stand today.

I think the most amazing thing about your question is that you forgot about Google.

I think the most amazing thing about your question is that you forgot about Google.

Oh yeah, right, yeah, that's very tilling.

Oh yeah, right, yeah, that's very tilling.

But everyone else has forgotten about Yeah, surprising Semini flash thinking their version of one and R one got updated a few days ago, and there are many reports that it's actually very good now and potentially competitive and effectively. It's free to use for a lot of people on AI studio, but nobody I know has taken the time to check and find out how good it is because we've all been too obsessed with being deep seep roads. Google's had its like rhetorical lunch eaten over and over and over again December. Like open a I would come up with advance after advance after Advance, then Google would love Advance after advanced after advance, and Googles would be seemingly actually, if anything, more impressive. And yet everyone will always just talk about open a eyes, so this is not even new. Something is going on there. So in terms of open Ai, Open Ai should be very nervous in some sense, of course, because they have the reasoning models, and now the reasoning model has been copied much more effectively than previously, and the competition is a hell of a lot cheaper Open Eye is charging, so it's a direct threat to their business model for obvious reasons, and it looks like their lead in reasoning models is smaller and faster to undo than you would expect. Because if deep Sea can do it, of course Anthropic and Google you know, can do it. And everyone else can do it as well, and Thropic, which produces Claude, has not yet produced their own reasoning model. They clearly are operating under a shortage of compute in some sense, so it's entirely possible that they have chosen not to launch a reasoning model even though they could, or not focused on training one as quickly as possible until they've addressed this problem. They're continuously taking investment. We should expect them to solve their problems over time, but they seem like they should be dressed directly concerned because they're less of a directly competitive product in some sense, but also they tend to market to effectively much more aware people, so their people will also know about deep Seak and they will have a choice to make. If I was Meta, I would be far more worried, especially if I was on their Genai team and wanted to keep my job, because Meta's lunch has been eaten massively here right, Meta with Lama had the best open models, and all the best open models were effectively fine tunes of Lama, and now deep Seat comes out, and this is absolutely not in any way a fine tune of Lama. This is their own product, and V three was already blowing everything that Meta had out of the water. Are one. There are reports that it's better than their new version that they're training now, it's better than Lava four, which I would expect to be true. And so there's no point in releasing an inferior open model if everyone on the open model community just be like, why don't I just use deep Sea Tracy.

But everyone else has forgotten about Yeah, surprising Semini flash thinking their version of one and R one got updated a few days ago, and there are many reports that it's actually very good now and potentially competitive and effectively. It's free to use for a lot of people on AI studio, but nobody I know has taken the time to check and find out how good it is because we've all been too obsessed with being deep seep roads. Google's had its like rhetorical lunch eaten over and over and over again December. Like open a I would come up with advance after advance after Advance, then Google would love Advance after advanced after advance, and Googles would be seemingly actually, if anything, more impressive. And yet everyone will always just talk about open a eyes, so this is not even new. Something is going on there. So in terms of open Ai, Open Ai should be very nervous in some sense, of course, because they have the reasoning models, and now the reasoning model has been copied much more effectively than previously, and the competition is a hell of a lot cheaper Open Eye is charging, so it's a direct threat to their business model for obvious reasons, and it looks like their lead in reasoning models is smaller and faster to undo than you would expect. Because if deep Sea can do it, of course Anthropic and Google you know, can do it. And everyone else can do it as well, and Thropic, which produces Claude, has not yet produced their own reasoning model. They clearly are operating under a shortage of compute in some sense, so it's entirely possible that they have chosen not to launch a reasoning model even though they could, or not focused on training one as quickly as possible until they've addressed this problem. They're continuously taking investment. We should expect them to solve their problems over time, but they seem like they should be dressed directly concerned because they're less of a directly competitive product in some sense, but also they tend to market to effectively much more aware people, so their people will also know about deep Seak and they will have a choice to make. If I was Meta, I would be far more worried, especially if I was on their Genai team and wanted to keep my job, because Meta's lunch has been eaten massively here right, Meta with Lama had the best open models, and all the best open models were effectively fine tunes of Lama, and now deep Seat comes out, and this is absolutely not in any way a fine tune of Lama. This is their own product, and V three was already blowing everything that Meta had out of the water. Are one. There are reports that it's better than their new version that they're training now, it's better than Lava four, which I would expect to be true. And so there's no point in releasing an inferior open model if everyone on the open model community just be like, why don't I just use deep Sea Tracy.

It's interesting that, as V said, the people who should be nervous are the employees of Meta, not Meta itself, because Meta is up, and so you gotta wonder. It's like, well, maybe they don't. I don't know, maybe they don't need to invest as much in their own open source AI if there's a better one out there now the stock is up.

It's interesting that, as V said, the people who should be nervous are the employees of Meta, not Meta itself, because Meta is up, and so you gotta wonder. It's like, well, maybe they don't. I don't know, maybe they don't need to invest as much in their own open source AI if there's a better one out there now the stock is up.

Anyway, The market has been very strange from my perspective on how it reacts to different things that Meta does. For a while, Meta would announce we're spending more in AI, we're investing in all these data centers, we're training all of these models, and the market would go, what are you doing? This is another metaverse or something, and we're gonna hammer your stock and we're gonna drag you down. And then with the most recent sixty five billion dollar announce spend. Then then Meta was up. Presumaly, they're gonna use it mostly for inference effectively in a lot of scenarios because they had these massive inference costs to want to put ail over Facebook and Instagram. So you know, if anything, like you know, I think the market might be speculating that this means that they will know how to train better lamas that are cheaper to operate, and their costs will go down, and then they'll be in a better position, and that theory isn't.

Anyway, The market has been very strange from my perspective on how it reacts to different things that Meta does. For a while, Meta would announce we're spending more in AI, we're investing in all these data centers, we're training all of these models, and the market would go, what are you doing? This is another metaverse or something, and we're gonna hammer your stock and we're gonna drag you down. And then with the most recent sixty five billion dollar announce spend. Then then Meta was up. Presumaly, they're gonna use it mostly for inference effectively in a lot of scenarios because they had these massive inference costs to want to put ail over Facebook and Instagram. So you know, if anything, like you know, I think the market might be speculating that this means that they will know how to train better lamas that are cheaper to operate, and their costs will go down, and then they'll be in a better position, and that theory isn't.

Crazy since we all just collectively remembered Google. I have a question that's sort of been on the back in the back of my mind. I think Joe has brought this up before as well. But like when Google debuted, it took years and years and years for people to sort of catch up to the search function, and actually no one ever really caught up, right, So Google has like dominated for years. Why is it when it comes to these chatbots there aren't like higher wider moats around these businesses.

Crazy since we all just collectively remembered Google. I have a question that's sort of been on the back in the back of my mind. I think Joe has brought this up before as well. But like when Google debuted, it took years and years and years for people to sort of catch up to the search function, and actually no one ever really caught up, right, So Google has like dominated for years. Why is it when it comes to these chatbots there aren't like higher wider moats around these businesses.

So one reason is that everyone's training on roughly the same data, meeting the entire Internet and all of human knowledge, so it's very hard to get that much of a permanent data edge there unless you're creating synthetic data off of your own models, which is what Opening Eye is plausively doing. Now. Another reason is because everybody is scaling as fast as possible and adding zeros to everything on a periodic basis in calendar time. It doesn't take that long before your rival is going to have access to more compute than you had, and they're copying your techniques more aggressively. They's just a lot less secret sauce there's only so many algorithms. Fundamentally, everyone is relying on the scaling laws. It's called the bitter lesson is the idea that you know, you just scale more, you just use more compute, you just use more data, you just use more parameters and deep seek. You're saying, maybe you don't. You can do more optimizations, you can get around this problem and still get a superior model. But mostly, yeah, there's been a lot of just I can catch up to you by copying what you did. Also that I can see the outputs, right, I can query your model, and I can use your model's outputs to actively train my model. And you see this in things like most models that get trained. You ask them who trains you, and they will often say, oh, I'm from Open Ai and.

So one reason is that everyone's training on roughly the same data, meeting the entire Internet and all of human knowledge, so it's very hard to get that much of a permanent data edge there unless you're creating synthetic data off of your own models, which is what Opening Eye is plausively doing. Now. Another reason is because everybody is scaling as fast as possible and adding zeros to everything on a periodic basis in calendar time. It doesn't take that long before your rival is going to have access to more compute than you had, and they're copying your techniques more aggressively. They's just a lot less secret sauce there's only so many algorithms. Fundamentally, everyone is relying on the scaling laws. It's called the bitter lesson is the idea that you know, you just scale more, you just use more compute, you just use more data, you just use more parameters and deep seek. You're saying, maybe you don't. You can do more optimizations, you can get around this problem and still get a superior model. But mostly, yeah, there's been a lot of just I can catch up to you by copying what you did. Also that I can see the outputs, right, I can query your model, and I can use your model's outputs to actively train my model. And you see this in things like most models that get trained. You ask them who trains you, and they will often say, oh, I'm from Open Ai and.

The internet has gotten so weird. I just the internet is so weird to speak. Mashavitz, thank you so much for running over to the Odd Lots and helping us record this emergency pod on the Deep Seek selloff though. It was fantastic.

The internet has gotten so weird. I just the internet is so weird to speak. Mashavitz, thank you so much for running over to the Odd Lots and helping us record this emergency pod on the Deep Seek selloff though. It was fantastic.

All right, thank you, Tracy.

All right, thank you, Tracy.

I love talking to v We got just sort of make him our Ai or our Ai guy.

I love talking to v We got just sort of make him our Ai or our Ai guy.

I mean, to be honest, we could probably have him back on again because there's gonna be stuff happening.

I mean, to be honest, we could probably have him back on again because there's gonna be stuff happening.

Maybe we will, and obviously it's we could go a lot longer. This is a really exciting story. This is a really exciting story, and things are just getting really weird these days.

Maybe we will, and obviously it's we could go a lot longer. This is a really exciting story. This is a really exciting story, and things are just getting really weird these days.

It is kind of crazy how fast all of this is. Yap, And then the other thing I would say is just the bitter lesson. Great name for a band.

It is kind of crazy how fast all of this is. Yap, And then the other thing I would say is just the bitter lesson. Great name for a band.

Oh, totally totally great. Maybe when we do our Ai themed proud rock band. True, Yes, that could be our name.

Oh, totally totally great. Maybe when we do our Ai themed proud rock band. True, Yes, that could be our name.

Yes, let's do that. Okay, shall we leave it there?

Yes, let's do that. Okay, shall we leave it there?

Let's leave it there.

Let's leave it there.

This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

And I'm Jill Wisenthal. You can follow me at the Stalwart. Follow our guests Vimashovitz, he's at this v Also definitely check out his free subs deck. It's a must read for me. Don't worry about the v OZ, really great stuff every single day. Follow our producers Carmen ra Rigaz at Kerman armand dash O Bennett at Dashbot and kill Brooks at Kilbrooks. For more oddlocks content, go to Bloomberg dot com slash odlocks. We have transcripts, a blog in a newsletter, and you can chat about all of these topics twenty four to seven in our discord Discord dot gg slash Odlots.

And I'm Jill Wisenthal. You can follow me at the Stalwart. Follow our guests Vimashovitz, he's at this v Also definitely check out his free subs deck. It's a must read for me. Don't worry about the v OZ, really great stuff every single day. Follow our producers Carmen ra Rigaz at Kerman armand dash O Bennett at Dashbot and kill Brooks at Kilbrooks. For more oddlocks content, go to Bloomberg dot com slash odlocks. We have transcripts, a blog in a newsletter, and you can chat about all of these topics twenty four to seven in our discord Discord dot gg slash Odlots.

Maybe we'll give zv to do a Q and A in there with oh yeah, that'd be great. And if you enjoy Oddlots, if you like it when we roll out these emergency episodes, then please leave us a positive review on your favorite platform. Thanks for listening.

Maybe we'll give zv to do a Q and A in there with oh yeah, that'd be great. And if you enjoy Oddlots, if you like it when we roll out these emergency episodes, then please leave us a positive review on your favorite platform. Thanks for listening.