In short
Odd Lots: Episode Summary
Episode Title
Jack Morris on Finding the Next Big AI Breakthrough
Hosts
- Joe Weisenthal
- Tracy Alloway
Guest
- Jack Morris, AI researcher and Ph.D. candidate at Cornell University
Episode Overview This episode explores the current landscape of artificial intelligence (AI) research, focusing on the nature and unpredictability of breakthroughs, the competition between open-source and closed-source models, and the contrasting approaches of U.S. and Chinese AI labs.
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Key Discussions
- Nature of AI Research
- Incremental Progress: The hosts noted that recent advancements in AI models feel more incremental than groundbreaking. Jack Morris echoed this sentiment, indicating that while models like GPT-5 show improvements, they lack the "wow" factor seen in earlier iterations such as GPT-3.
- Human Involvement: Morris emphasized the necessity of human researchers in AI development, countering concerns that AI could eventually conduct self-research.
- Model Evaluation Metrics
- Testing Methods:
- Two primary methods for evaluating AI models: benchmark datasets (e.g., SWE bench for coding problems) and Elo-like rating systems to compare model outputs.
- Morris mentioned that models like GPT-5 performed better in certain tests but still struggled with complex tasks.
- Real-World Relevance: Concerns were raised about how well these evaluations correlate with real-world performance, highlighting discrepancies where models excel in tests but falter in practical applications.
- Challenges in AI Development
- Data Limitations: Morris indicated that while many public datasets are utilized, private datasets owned by companies like OpenAI and Anthropic offer a significant competitive advantage.
- Barriers to Progress: The difficulty in creating models that act autonomously (agents) points to underlying complexities that remain unaddressed despite significant investment.
- Open-Source vs. Closed-Source Approaches
- Talent and Prestige: Morris discussed the allure of working in prominent labs with vast resources versus the appeal of contributing to open-source projects.
- Concerns About Closed Models: The conversation touched on the implications of proprietary research and the potential for secrecy to hinder broader scientific advancement.
- Future Directions in AI
- Personalization and Online Learning: Morris posited that future advancements might stem from creating models that adapt to individual users or organizations, thereby enhancing relevance and utility.
- Economic Impact of AI: The hosts discussed whether substantial technological improvements are necessary for AI to be economically transformative or if optimizing existing capabilities may suffice.
- The Role of Money in AI Research
- Financial Motivations: The allure of lucrative salaries in AI research was discussed, with the hosts and Morris reflecting on the balance between financial incentives and the intrinsic value of contributing to groundbreaking research.
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Conclusion The episode offers a deep dive into the intricacies of AI research, unpacking the challenges of measuring progress, the implications of data access, and the ongoing debate between open-source and proprietary models. Jack Morris’ insights provide a nuanced perspective on the current state and future possibilities of AI technology.
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Key Takeaways
- Breakthroughs in AI appear to be more incremental than monumental.
- The complexity of developing autonomous AI agents remains a significant hurdle.
- Evaluation of AI models often lacks alignment with real-world performance.
- Open-source models present opportunities for transparency and community collaboration.
- Future advancements may increasingly focus on personalized and adaptive AI systems.
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Written by AI. May contain mistakes. Listen to the episode to check what was said.
Transcript
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1:30Podcasts. Radio. News.
1:42Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal. And I'm Tracy Alloway. Tracy, have you played around with GPT-5 much? Not really. I've been perplexity pill. Oh, that's your main? Yeah. That's my main one at the moment. But is it good? I hear mixed things. So I use it because I use TransGPT every day. It does not strike me as like obviously better for my uses than like the O3 models, which I've been very impressed by because, you know, I want to establish I'm no hater or anything like that. But like it did not strike me as like, oh, this is like an amazing. Yeah.
2:16This is the thing. Step function or whatever. It feels like the sort of breakthroughs, awe-inspiring breakthroughs are kind of behind us. And a lot of the progress on the models feels very incremental at this point, even though people are spending a lot of time and resources on doing it. The one thing GPT-5 does prompt me and say, oh, that's a great question. Would you like to follow up more on that? Does it say, oh, Joe, you're so smart. That's such a smart question. You know what it did say? I asked a follow-up and it started an answer with, love it. And then it said, love it. Do you want me to look into that?
2:48Yes, they are very flattering, aren't they? Actually, that's one thing I like about perplexity is it doesn't really flatter you. It just spits out an answer. So anyway, there's so many questions I have about AI. And we talk about the business of Fairmount and NVIDIA and all that stuff. We actually don't really talk that much about the pure research side as much. But it's pretty important, I think, because I think a lot of people would agree that if the skills are like slowing down or if there were a wall or something like that, that might change some of these business model calculations, et cetera.
3:16So I think it's good. We need to get an update on just sort of the state of the art, the science of AI. Yeah. Yeah. Also, it would be nice just to understand what's possible in terms of the AI models and what people are actually researching, what they're working towards. What they're working on. Like, is it mostly about price? Is it mostly about the output? Is it mostly about energy use? All those things. All those things. Well, I'm really excited to say we have the perfect guest, someone who is an AI researcher. We're going to be speaking with Jack Morris. He's currently about to finish his Ph.D.
3:45at Cornell in AI. He's been affiliated with Meta professionally. So presumably he already has a$100 million pay package in the bank. No, he's shaking his head. He's not. That's a joke. But Jack, thank you so much for coming on OddLots. Yeah, thanks for having me. This is going to be fun. What do you explain to me like what you're up to? Because I don't really understand how it works where people are. They're at a university and they're also at a company. And this isn't how it works in much of the world, right? People get their degree and then they get a job. I get the impression that in the AI world, it's a little fuzzier in terms of one's affiliations between industry and education and stuff like that.
4:23Yeah, that's definitely true. I think it might be on the way out, but I can tell you about my situation. So there's kind of a public research world and like a private research world. And all the academic institutions do public research. And the AI labs like OpenAI, Anthropic, Google DeepMind they essentially do private research where they have these people in-house that are running experiments and learning more about their systems but they don't publish anything or share any of their knowledge and so a cool thing about getting your PhD right now is you can do research write about it and then publicize it like put it online yeah I tweet about it I kind of like can talk to you about it and there's a few places left that'll still kind of the moment you go in-house we're never going to hear from you again it's like severance or something like that Yeah, I'll make sure they have a clause in my contract that I can still talk to Joe and Tracy.
5:14The odd lots clause. Yes, that would be important. So when we say AI research or an AI researcher, what exactly does that entail? Can't the AI models just research themselves? Just let them do it. Yeah, that's actually a very smart idea. And like people are really worried about that, actually. Like if we get to the point where the AI can improve itself and do research on itself. This is like the fast takeoff. Yeah, then it sort of gets smarter and then it improves itself again. And it ends up being this kind of exponential improvement that ends up with all of our demise. But I think right now it's not quite there yet.
5:49Like maybe you can talk to ChatGPT. That's good news, Joe. Yeah, good news for me, too, because it means I can still get a degree and be gainfully employed. But I think it's still helpful, but we still need like humans to make these improvements. And in terms of what the actual day-to-day work looks like, I think it really varies. Like there are some people working on trying to make the models run faster or trying to make the hardware that runs the models run faster, more efficiently. And there's people that try to work on the data, like what should we train on more coding problems or more textbooks or more Reddit posts, what works best to make the models.
6:25And then there's a lot more people working on different areas of the stack, like training algorithms. I kind of have my own little niche. What's your niche? There's this old field of information theory from like the 20th century where they talk about bits, like a zero or a one is a bit, and you can add them up and have kilobytes and megabytes. And so I've been trying to think about what that means in like the chat GBT world. If you train a model on a certain number of bits, how many bits does it actually learn? And like, can you look at the model and figure out like if you have one slice of the model, how many bits that is and stuff like that.
6:57So maybe the easiest way to explain is if you had for some godforsaken reason to use chat GPT as like a flash drive, like you had a certain set of data and it had to memorize all that data, like how much data could it actually store? That's the kind of area I've been working in. And then once you're there, you kind of realize we could do this or maybe next semester if we have time, we could try this other thing. And so it kind of branches out and there's a lot of little problems that you can try. I mentioned GPT-5. It seems fine to me. it does not strike me as like, oh, you know, because actually, so the first time I used ChatGPT, I was genuinely blown away, like most people.
7:34And then actually, I was pretty blown away by the 03 models in part because of how well they could do document search, and it's superior to Google search in many respects, and also just the organization of a lot of unstructured data, et cetera. Like, I didn't have like some, oh my God, wow moment with GPT-5. It's like, Yeah, it seems like how do we measure whether AI is getting better all the time? Yeah, that's a really hard question. Well, let me ask you. OK, let me ask you actually a more specific question. How do the entities that test AI models as their job or as their function, what is the formal testing process look like to rank the quality of AI models?
8:17OK, yeah, that's that's more tractable. We can start there and then we can talk about O3 and GPT-5. So there's essentially two ways people do this kind of model evaluation. The main one is just by testing them on different data sets. So, for example, there's this data set called SWE bench. That's a bunch of software engineering related coding problems. And they all have a human written solution and tests. And so you can ask GPT-5, can you write the code for this and then run the test and see if it's right. And still the models are pretty bad at that. I think they can do about half of them. They're very hard.
8:51They're like entire days of work for professional software engineers. But when a new model comes out, they can say, oh, look, we actually got a higher score on SweetBench. And there's a ton of different data sets like that. So when GPT-5 comes out, they say, you know, it's better at these types of coding tests. And a big one that specifically OpenAI has been advocating for is math. Like they did the International Math Olympiad. And they said, essentially, GBT-5 scored at the level of the best high school mathematicians, which is pretty cool. But you raise a good question of how does that actually map to real world usage?
9:27And I think this is like a really hard problem that people still haven't figured out. Does anyone try to capture that sort of like je ne sais quoi, I guess, when it comes to AI models? Is one of the tests asking it to, I don't know, come up with a stupid limerick or something. Yeah, there are a lot of tests like that. There's some creative writing benchmarks and some poetry related ones. But I think you point out something interesting that, for example, I mostly use Claude from Anthropic. And I think Claude does have this something to it that's like a little bit different. And it's very difficult to characterize.
10:03It's just sort of the way it speaks to you and the way it thinks of itself is, I like it a lot better, but I don't know how you would design like a data set that can really capture that. The second way they do the evaluation is by, they call it's ELO scores, like in chess. So they, for example, ask the two models to write a limerick and then they have humans rank, which one is better. And they make this kind of ladder of ELO rankings for models. So I think right now, Claude or GPT-5 or maybe the Google model is top on this ELO ladder. The algorithm made famous in the social network that Mark Zuckerberg used to rate the hotness of his colleagues.
10:41Still the workhorse model for comp evaluation. That's some good trivia, Joe. Very good. And no comment. Well, I assume just on the hard number evaluation, people are also ranking these on data usage, energy, that sort of thing as well, right? Speed. Speed would be an obvious one. Speed, definitely. The AI companies like to use price as a metric, which is kind of interesting because there's a lot that goes on behind the scenes, including just sort of like free money that drives the prices down. But they also do benchmark speed. And I think you make a good point that the benchmarks can be pretty misleading.
11:17Like, for example, there's a bunch of recent open source models that came from different Chinese AI labs that have really, really high scores on certain benchmarks. But people kind of think they're not as good for real world usage for whatever reason. I've seen people talk about this. Isn't part of the problem with testing AI or evaluating AI that a lot of these problems exist in the real world already, right? You see this a lot and people are always finding this, which is that here's an AI model that is amazing at math on the Math Olympiad. And yet it gets tripped up by questions like, which is heavier, a pound of steel or two pounds of feathers?
11:58And they'll say, that's a trick question. A pound of steel weighs the same as two pounds of feathers, which is clearly like, it was clearly then been trained in some sense to recognize these steel versus feathers thing, or whatever it is. I forget if it's steel, but it also clearly can't measure whether one or two is bigger. Yeah, that's a really good example. I think they kind of successively include these kinds of things in more rounds of training data. And so every time a new model comes out, they kind of patch little holes that appeared in the previous models. So you're pointing to this, like they probably started with the classic riddle that's like a pound of bricks or a pound of feathers and they're equal.
12:36But then like the models got that wrong. And so they add it to some part of the training data. It just doesn't seem like a very efficient way to achieve intelligence. Like, oh yeah, we should have included that. Oh yeah, we got to include that. Oh yeah, we got to have bricks. Right, like every little thing you're addressing. You're just going to keep going. That does not speak to me of a line towards something that we would call anything resembling human intelligence. I definitely agree. I think one counter example is people said this for a long time about self-driving cars. Everyone was really excited about them for a long time.
13:05And then they kind of didn't really work like eight or so years ago. And there was this period where they were saying, oh, the models can't do green cones. We're going out there trying to take videos of green cones. And yeah, they can't do snow. So you're saying eventually you can just patch your weight and understand it. I'm saying that it worked for them. And so it might be possible. But in the case of language models, there's something a little more interesting happening because we now have two ways to learn. If you guys are ready, we could get into something a little technical, which I think gives you some insight.
13:35So there's essentially two ways you can teach machines to learn from data. One is called supervised learning, where the computer will copy what you did, which is like basically what we're talking about now. And the other is called reinforcement learning, where the computer just does something and then you give it a reward if it does something well. And so for a long time, like the original chat GBT was mostly just trained with supervised learning. Like it would just copy the text from all of the internet. And so the best it could ever do is emulate Reddit posts very well. And there was a tiny bit of reinforcement learning, but people didn't know how to do it right.
14:13And then you mentioned this O3 model, which is kind of in some ways like a big jump. Like it made the models much better at math, much better at certain things. And the way they did that is actually through reinforcement learning. So they found out a way to kind of like let the model think for a while and then give it a reward when it like gets the answer at the end. It's kind of scary. Yeah. When you say give it a reward, is it like take a cookie? Tracy's in favor of paying robots. So she's excited about that. No, genuinely, like what is the reward? You just tell it it did a good job, right? You just give it like a higher number and that makes it happy.
14:46All right. I get a little bit worried when we're like giving it cupcakes or something. Like, here you go. Good job. Just going back to the intro, you know, we were talking about how it feels like a lot of the progress on AI models is a little bit more incremental. And I guess it's hard to tell whether that's just personal bias because now we're used to them and the sort of wow moment has passed. But what does it feel like to you in terms of improvements? Are we seeing the improvement cycle accelerate or decelerate at this point? I think it's kind of like the market where it's like always it gets faster for a little while and then it feels like things have slowed down.
15:24And the progress is never quite in the areas that you expect. As one example, people really thought this year was the year when the assistants would start being able to act like actual assistants. like the year of agents. People actually coined that term, I think, like the year of agents. And it really, it didn't happen for whatever reason. Maybe it will in the next three months, but the agents are still pretty bad, the ones that you can use. But they did get way better at competitive math. Like now they can do these like world-class proofs that they couldn't do before. So it's almost unpredictable like which areas the AI will kind of conquer next, but it does feel like progress is continuing.
16:04Actually, what happened with agents? I've never had a successful agent experience. Even basic things like come up with a list of every past oddlots guest and put it in a file or something like that, which is like, there's an RSS feed that exists for oddlots. This should be really straightforward. It'll go all around and then something will happen or it'll get lazy. It'll hear like, here's 15. And what is it actually? And this is thought leaders love this stuff. They love talking about agents. So what actually happened with agents? Maybe they'll get there. But what do you what is the roadblock there?
16:36I don't think there's any conceptual roadblock. Like there's no reason why you couldn't collect data for that and train them either in a supervised way or using reinforcement learning. It just hasn't happened yet. So I think maybe behind the scenes, it turned out that the problem was harder than people thought. Like getting data from all those scenarios is really hard. And there have been some stories from people that I've heard of that found these little companies in San Francisco, and they build these tiny environments for the AI labs to do reinforcement learning on for agents. Like, for example, doing a calendar, they'll build like a little calendar app, but make it have rewards.
17:11So you can do reinforcement learning and they can just sell that for like hundreds of thousands of dollars. So I think the progress is ongoing behind the scenes. Like there's a whole ecosystem built around it. it just hasn't really manifested in the products that we use. I was going to ask, how much of the difficulty is the actual development of the models, the thinking part versus just getting them to plug in seamlessly with other applications? Yeah, I think the second thing is probably the biggest barrier in terms of time. It just takes a really long time to figure out what data you need and collect it properly and actually train the models on that data.
17:48But at the same time, there are people like me who are trying to work on better conceptual frameworks for training the models. So to go back to the 03 example, doing reinforcement learning on ChatGPT like that seems to me like a huge breakthrough. Like we didn't know how to do that before. It unlocks all sorts of doors and ways to train the models. So even if maybe you don't think that model was that much better than the previous one, it seems like it will give us huge improvements in the future.
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19:30Easy Cater, your business tool for food. To learn more, visit easycater.com slash podcast. You're thoughtful about where your money goes. You've got your core holdings, some recurring crypto buys, maybe even a few strategic options plays on the side. The point is you're engaged with your investments and public gets that. That's why they built an investing platform for those who take it seriously. On public, you can put together a multi-asset portfolio for the long haul. Stocks, bonds, options, crypto, it's all there. Plus an industry leading 3.8 % APY high yield cash account. Switch to the platform built for those who take investing seriously.
20:14Go to public.com and earn an uncapped 1 % bonus when you transfer your portfolio. That's public.com. Paid for by public investing. All investing involves the risk of loss, including loss of principal. Rokeraj services for U.S. listed registered securities, options and bonds in a self-directed account are offered by Public Investing, Inc. Member FINRA and SIPC. Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosures available at public.com slash disclosure. So you mentioned at the intro that it's possible, hopefully you'll get a clause, but you might end up in a situation which you go to work for some frontier AI lab and we never hear from you again, or you just post cryptic tweets like, oh, you have no idea what's coming, or it's going to be so over, or whatever.
20:54It's very annoying the way they tweet. Like the Death Star. Talk to us about like, why not work on an open source project? And this is, of course, when people talk about DeepSeq and a lot of the Chinese models that the U.S. competes with, a lot of those are open source. Presumably, you could keep coming on odd lots over and over again. Why? Like, what is even the case for the best and the brightest to work on closed source frontier models? Yeah, it's a really hard question. Like I've struggled with this in my own personal decision making. I was originally thinking, oh, I'd love to become a professor and mentor younger students and get a whole like group of these ideas going and start working on similar related problems to the stuff I was talking about.
21:39And I still think that would be fun. But there's a big gap in terms of the things we can do at Cornell and the things that you can do at OpenAI. Like they just have like crazy infrastructure for training models really easily. And data and a ton of really good data. And so I think as that gap has widened, I've felt like a lot of what we're doing is like kind of devising these toy scenarios where we can study interesting things. But I feel a bit disconnected from the real like progress of humanity. You know, like if you really agree that this is like the biggest problem of our time, I don't want to say it's like the Manhattan Project, but it's more like trying to go to the moon in the 60s, the space race.
22:21It's kind of like a space race going on in these different private labs. You want to be a part of it. Like there's crazy energy in that. It has huge implications for the future of society. So I think I am interested in participating in that. My big question is like, if you think that the reinforcement learning thing was the most recent big scientific breakthrough, like 01 and then 03, what's next? And then like, where will that actually be happening? That's kind of what I'm thinking about right now. Just on the data point, I was reading your excellent substack and you argued that there's probably an upper bound to what you can get out of a given data set.
23:01And at some point, like the training starts to look pretty similar, right? And the data becomes the differentiating factor. How important are data sets to AI research? And I guess like, how do you go about finding really cool ones? And what's left? Because I feel like, you know, using the space race analogy, everyone has been running so fast on this. It feels like all the data sets must have been explored by now, but I guess they haven't. Yeah, yeah. I think this is really getting to the heart of what people are trying to figure out right now in all these different labs. so I think you're pretty much right that all of the public data sets we have are pretty much used to train O3 or GPT-5 or whatever if there is a really good website that should have been scraped and downloaded into the model it should probably be used but there are apparently is a much larger amount of private data than public data I mean you all work for Bloomberg so I'm You're probably intimately familiar with this.
24:04But if you think about the different AI labs that exist, they actually now do kind of have different data related modes like XAI. They have all of the Twitter data that's basically impossible to get elsewhere. ChatGPT now has all of the user conversations with ChatGPT, which are really useful. Claude has a ton of coding data that other people don't have. Google has YouTube, which some people think might be like the next source of making really good models. And none of those things are really included, at least not much in today's models. This is really important. Like once a lab builds some sort of base, whether it's anthropic encoding or maybe cursor encoding, even though they're not like a core lab, et cetera, like they become a source of their own data that literally nobody else has.
24:50Yeah, actually, cursor is a great example. So they are very technical. They have really smart people. They're very small. So they haven't quite scaled to, at least in terms of the number of people. But I think about this like every time when I've played with it, it's like this is good, this is bad. I'm constantly teaching their model to get better, right? Right, right, right. They're in a problem where they have the data. They just have to take the right algorithms and scale it up to train the model to be as good as Claude is. But that actually seems a lot more feasible than other companies that have no data and want to train good models, even if they know how.
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25:22It seems very difficult. How closely are AI researchers working or talking to, I guess, other parts of the AI ecosystem? So, you know, chip makers, maybe cloud providers, that sort of thing. Is there a lot of dialogue or not really? I think certain people talk all the time to the chip makers. Like there's a big community of people, you know, the AI models all run on GPUs and there are a lot of people that are getting really good at writing fast GPU code. It's called kernels. And all those people who work on kernels talk to the chip makers all the time. Like Amazon's making their own chips. Google has their own chip.
26:00Now all the hyperscalers are making chips. And I think they're all trying to talk to the people that actually write the fast code that runs on chips to figure out. I think they call it hardware, software, co-design. Everyone's kind of getting together and trying to figure out what the best way is to design the next round of GPUs. So you mentioned, okay, Google might have an advantage because it owns YouTube and there's just tons of, obviously, just tons of data in there. So one way you could get access to the YouTube data is to literally be Google and own it. But another way that maybe you could get access to YouTube data is operate in China where there are no laws about this type of thing or no, there's not beholden to US copyright and just sort of scrape it all.
26:43Again, since most of the Chinese AI labs are open source, why isn't this just a huge advantage for the Chinese labs that they're really not going to be, hey, open AI, they get sued by the New York Times, all these. DeepSeek isn't having to deal with all these headaches. Yeah, I think the american ai labs will probably do things behind the scenes that they wouldn't tell you about to get good data that's one solution just so yeah like i think they wouldn't release the models that are potentially trained on scraped or copyrighted data but if that's the way to get better math olympiad scores then people will i think i would guess do that but you're right that like the chinese the chinese model makers can just sort of take all the books that they can pirate it from the internet and train on them and they're not violating any laws and they can release the model to the public and it's all fine, which is honestly great for us because then people like me could probably download a model that's better than we would get otherwise.
27:42What was your impression of DeepSeek when it came out and now? I was pretty surprised at how much of a splash they made. The model is really good and I think a lot of people are building on it, including me and like most people that are at AI companies that aren't super, super big are building on DeepSeq. But it was surprising what a huge deal it was to people. My mom's asking me about DeepSeq. I think my grandma knew about DeepSeq and she barely knew about ChatGPT. So that was kind of surprising. It's gone mainstream when your grandma starts asking you. And there was nothing else so far, I think, in the AI space that's made quite that much news.
28:23But it sounds like what you're saying is that it's a very good model, but that on the actual specs, from your perspective, it didn't quite deserve as much attention. Like it was good, but like in your view, it's not so good that everyone needed to be talking about it. Yeah. I think it's really useful because they released all their model weights and they said exactly what they did to train it. Although they didn't say what the data was, but it gave me the impression there may be six to 12 months behind the American AI labs in terms of how well they can do the training and stuff. But it still was a pretty big update for me to know that, wow, there are a hundred people that don't have PhDs working at a Chinese hedge fund that are training these like cutting edge models.
29:05Like it is incredible and they work very hard. They're very good.
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31:20Do that with the all new Adobe Acrobat Studio. Learn more at adobe.com slash do that with Acrobat. Do you have pressure or do you feel pressure? Do AI researchers in general feel pressure to consider monetization when they're researching things? Or is it, you know, mostly still curiosity driven, that sort of old school Silicon Valley, we're improving the world kind of thing? Or is it much more mercenary, given that all these big companies seem to be competing in the same space? Yeah, I think that over time, it's gotten harder and harder to do things that are just like cool ideas or seem cute, but don't have any necessary application.
32:05And things are getting closer and closer to products, you know, even like the language models that power ChaiGBT. I was working in those before chat gpt and they had some uses but also they're intellectually interesting and like fun to build but now if i came up with a better way to train chat gpt that's like a multi-billion dollar innovation the stakes are higher yeah i'd be like an asset to like the united states government or something if i knew how to do that so i guess it depends on what kind of problems you work on like i'm more interested in understanding how things work so it becomes a bit less financially dire, I think.
32:42That six to 12 month gap between, what was that? That was a January deep seek moment. Although really like everyone should have, it was in December that they first got attention and for some reason really hit in January. Is that a sustainable gap? Is there something either in access to data, access to talent, access to compute, access to chips, whatever, access to energy that in your view will allow US frontier labs to maintain some sort of six to 12 month gap for a while? It's pretty unclear to me. I think there are different beliefs you can have. You can believe that the ideas and the people are really the thing that differentiates the models.
33:17And in that case, I think we haven't so far seen a lot of like the top US AI researchers going to work at Chinese labs. So that seems stable. You could think that chips really matter. And in that case, the chip race is really happening between big American companies. Like I think actually China has a pretty big deficit coming up in terms of like the GPUs we're exporting, or you can think that the data matters. And I guess actually any of these point in the favor of the US, I think. If you think the data really matters, maybe the data they gather through like deepseek.com usage is really good and they can use it to like bootstrap a better model.
33:55But I think the American companies really do have an advantage. Like you all might have heard this story just as an anecdote. Apparently at Anthropic, They've been buying and scanning thousands of old books for several years. So they have this division. I think they're based in New York that buys like shipping containers full of old manuscripts, cuts off the spines and puts them in these scanning machines. And then they turn them into like really high quality text. And so I'm noting Claude has this like weird aspect to it. Maybe part of the reason is they've gathered like trillions of words worth of like old book data over many years.
34:30And that's pretty hard to replicate elsewhere. So I think that head start really does mean a lot. What are you most excited about at the moment? The book thing sounds very cool, but what is getting all your attention right now? Thanks for asking. I think I mentioned before, I'm really trying to figure out what's coming next. There are some obvious things like we can get computer usage data and train better agents, or we can get more coding data and make them better coding or writing GPU code or whatever. But like what are the non-obvious advancements? And my personal opinion is that the next like round of improvements in AI models will come from some type of personalization and online learning, which means like models that one are trained like per person or per company.
35:21So like you could think of like ChadGBT is the same model that gets served to everyone. So it has to store information about random restaurants in like countries you never go to but instead if you had a chat gbt that's specific to bloomberg or specific to your work it might be able to like use more of its brain to do work for you and then the second thing is if it was updated every day so like if you ask it to make your odd lots calendar yeah or rss feed and it you're like no that was wrong like you did it wrong for this reason this reason and you try again tomorrow it'll still break tomorrow because it doesn't like continuously improve its capabilities.
35:59So I think that's the direction things are going. I've heard people talk about this. Now, granted, models are getting better over time, but people might compare a coding model to a beginning software engineer and say the coding model is better. But that software engineer is going to start getting better the next day they're on the job. And every day for the rest of their career, they're probably going to be a better software engineer than they were the day before. whereas at least that version of the model will not be better. Is that right? Yeah, yeah, that's right. That seems like an issue that people talk about in your world.
36:31Yeah, yeah. I think this is a big problem. It's like we have to wait six months for the chat GPT 5.1 to come out and then maybe they'll include your problems as the training data and so maybe it'll get better, but it might not. And instead, I think people need to think about ways to do that update more dynamically, like every time you talk to it or maybe every night when you go to sleep, the model kind of like gets to work and studies what it was talking to you about and crafts better tests for itself and then learns. And then when you wake up, the model's actually better. The other big question that I have, and it's kind of related to this, especially when we're talking about AI replacing humans in certain forms of labor, is that like, do we need really, really advanced AI?
37:14Like in other words, like there is a lot of, Again, the existing models are extremely impressive. Like in your view, do we need to get a lot better technically for them to have economic impact? And since these are, in many cases, businesses at the end of the day, is it necessary that there's so much work being done towards advancing the cutting edge? Yeah, yeah, that's a great question. Like we could have really good interns without ever getting better scores on the Math Olympiad. Like that's not necessarily something that we ever had to go after. I think part of the reason for that is the AI labs are engaged in this kind of neck and neck race to have the smartest model.
37:55But I totally agree that AI could be economically transformative without having a higher ceiling in terms of what it can do. It's more like it needs to be more consistent or like dependable than actually smarter. This might be a weird question, but once you've made a sort of foundational improvement to a particular model, how easy or difficult is it to rewind if you need to? And one of the reasons I ask is because, you know, some people have been complaining that they've been training chat GPT to, I don't know, be their boyfriend or whatever, be their therapist. This is a legit topic, in my opinion.
38:30And then it gets upgraded and all of that training suddenly disappears and the personality of the model changes. Yeah, that was a really interesting story. So I think the model before GPT-5 was 4.0. And they said that they thought internally, like all the scientists and coder people, that the new model was superior in every way. It gives you shorter responses. It's a bit nicer. It's much smarter. And then people got really upset because they had spent so much time talking to the old model that they felt like they'd experienced like a serious loss in their life. Joe would miss the love it. Love it.
39:07No, but for real, this is unironically, this strikes me as another example for open source, which is that I if I'm going to form a I don't see it. I'm 45. I'm too old for that. But if someone is going to form like some sort of friendship with an AI model, I don't want it to be at the whim of Sam Altman deciding like, oh, there's an upgrade. I would like to be friends. It's also weird to be friends with a model that I know that I can run in perpetuity and it will never change. Yeah, I think that's definitely a good argument for why open source is important. And if you ever fall in love with a model, you should fall in love with an open source model.
39:46That's good life advice. Practical life advice. That is really good life advice. Well, speaking of open source, you know, I know programmers tend to like open source for obvious reasons, but are there any downsides to open source for AI specifically? I think if you're running a company, there are a lot of downsides potentially to open source. If you have some brand new fancy way of doing computation inside the model that's actually better, you might want to keep that information to yourself. And when you release the model to make it runnable, you have to release all the code to run the model, which might contain like your secrets.
40:21And so I think that's why people are hesitant to do it. The other reason is because when you release the model, it actually contains quite a lot of residual information about how you actually trained it. Like you might be able to infer what the data set was and what the training process was or even reconstruct the entire training data set given just the weights of the model. And so if you're worried about people finding out that a certain thing was in your training data, you probably can't release that model open source. That reminds me, how much of an AI researcher's day-to-day life is just like looking at other people's models and trying to like, I guess, pull them apart and figure out how they were made and sort of work backwards?
41:05That definitely happens from time to time. I think usually the scientific process is something like you start with other people's models and you run them and you see what happens. And then you decide on some part of that process that you think could be improved or could be explored further. And you make some tiny changes to it and then you run it again and you compare like numbers or you make graphs of what happened before and what happens after. So actually quite a bit of it. Like, for example, the GPT-2 model from OpenAI, which was 2019 or something, their first kind of really larger scale chatbot.
41:38Like I've spent hundreds of hours kind of like playing with that code and talking to the model and stuff like that. So thank goodness for open source for that reason. I joked in the beginning about you having a hundred million dollar salary. But for real, as you think about your career, and I hope you do get$100 million salary, but as you think about your career, what excites you and how much is it money? The reason I think about this is like there are huge checks out there, but maybe some things are more, maybe achieving AGI is more excited than making an ad network more efficient. Maybe there's something more exciting than shaving off a billionth of a second in terms of a trade execution, all these things.
42:23How much is it about exploring the frontiers of science, the new space race landing on the moon versus the paycheck? It's all about the paycheck. I'm just kidding. No, no, not at all. Yeah, it's funny you ask. So this hasn't happened to me, but just in the past two weeks or so, a good friend of mine has been dealing with this problem because she got an offer on the order of tens of millions of dollars per year from a big AI company. And she wasn't sure if she wanted to work there. And I think originally she said no. And then they doubled her offer. And then like it's the exact same amount of cash, but twice as much per year for a certain number of years.
43:02And, you know, we were talking amongst ourselves like, what does this even mean at this point? Like you're, you know, a 28 year old computer scientist that's been coming from a PhD. So you make more on the order of tens of thousands of dollars per year. I honestly think personally, the marginal difference between having like 10 and$20 million is like very low. Like I don't even know what I would do with 10. This is my experience. For me, making 10 million, 20 million has basically been the same to me. Yeah. Congratulations. But so yeah, I think like there's more of a desire to like be there the next time something really interesting happens.
43:40And that kind of supersedes the money. Like any of these places will pay you what's like a really good salary to live on. And so it's actually not a big consideration. It only becomes complicated when you have like one option that's going to pay you like 40 times more than the other option. And then things get confusing. No, this is this would actually I was just thinking about making 20 million. No, I think because I think about, OK, what if you have this great salary and you're like can live very easily in New York City and have a really great life or you can make 10 times that, which is a stupid, insane salary.
44:15Right. But you don't really like your job, but it's so much money. That strikes me as like not a trivial life. You only live one time. So it could be a difficult question. Yeah, yeah. But you can remind yourself that like the job you take once isn't the job that defines you forever. Maybe the right thing to do is to take it for a few years, but not the whole time and then go do something else. Everyone says they're going to do that. And then they get locked in. Yeah. Speaking of insanely large salaries, we know that people are earning these salaries because they're like star AI researchers. How much does personality play into where you want to go work?
44:55Would you want to go work somewhere specifically because there's an absolutely amazing researcher? Or does it tend to be, again, more about the paycheck, maybe more about the data that's available to you, or maybe more about the specific project that you're going to be working on? Yeah, I think different people assign different amounts of weight to each of those things. In my experience, like most of the people I know come from academia, which means they already kind of gave up more of a salary to do study things more deeply for several years. So I think people that I know are more biased against money, but like people do care about that.
45:28But I think that the ego thing really matters. And so some people want to feel like they're very important and they're working on a problem that matters. One way some companies are able to pull researchers away from other companies is by saying, we'll assign you more importance in your role and we'll give you - We'll give you a really big title. Yeah, exactly. Seriously, the title is like, okay, maybe before you were like a researcher and now you get to be like a head researcher. You get to have people under you or you're a chief scientist and all these things do matter to people. It's a very good book about pursuing a mission in the realm of like a driven visionary, even when it's commercially and personally.
46:03Say it, Joe. Just say it. Yeah, that's right. No, I think about this all the time. It's like, do you want to work for Ilya or do you want to work for Sam? And which one is the Ahab and which one is just trying to make an honest living selling ads? I find this to be like a genuinely interesting question for any individual to have to reckon with in this career. Oh, absolutely. And sometimes it can be very difficult to tell. Jack Morris, thank you so much for coming on. Please pursue a career that will allow you to come back on Odd Lots. Or insert the Odd Lots clause. Insert the Odd Lots clause. When you're negotiating your$100 million salary.
46:36Or take the 50. So, you know what? I'm 50 million, but let me, I don't need the 100 million. 50 million would keep the album. Yeah, that would be fine with me. All right, great. Well, thank you so much. Yeah, thanks. Thank you so much. That was great.
47:00Tracy, I think about that sometimes. Like, what if you got, like, an insane salary? Like, you just couldn't, you'd be insane to say no to. But, like, I don't know. I mean, it's not our problem. But, like, wouldn't that be fun? You know? It's like, oh, but you're going to be working on ad optimization or whatever. And you're not going to be there when they land on the moon. But you got paid ten times more than the people at the base station working on landing on the moon. That strikes me as kind of a tough life choice. I think you're using up a lot of brain power and energy on a problem, which, as you said, is not yours.
47:32I never have. That's exactly right. No, that conversation was really fun. Nice to talk to an actual researcher who's doing stuff in the space. One thing I thought was very interesting was this idea that everyone gets excited about a specific improvement in AI. Yeah. And then it seems like that particular one doesn't materialize. And instead, something else emerges as the big breakthrough. So instead of agents, we have math. And math, which none of us will ever need. I would really like for an agent to do something simple. I'm going to a city, book my new trip or whatever, or change my flight. Oh, my God.
48:07I tried to change a flight recently. Change my flight. Here's my information. I would like that. I do not need the Math Olympiad. I am very impressed. I don't need it. Also, I am now very, very intrigued by reinforced learning and how you actually reward the computers for doing good stuff. I feel like actually that would be a really interesting area to mine, which is motivating the models to do better. Yeah. I've thought about that like in chess. Like how do the computers know they want to win? Yeah. You know, like why do they care? You know, all this. Anyway. Why are they here? Why are we here?
48:45That's the thing with AI conversations. Gets existential fast. You know one thing we didn't talk about, which I am interested. No one really talks about AI safety anymore, if you notice. Like very little, like for better or worse, you don't hear people, it's just all money and they don't really talk about, will the AI kill us all one day? But one thing I did wonder about, so when DeepSeek came out, one of its breakthroughs was it showed the whole chain of thought, right? You could see that, which prior to that OpenAI or ChatGPT's chain of thought model didn't show you that. Right. And it does strike me that if there are certain things that are for safety reasons or whatever held back or they don't want to do this, the nature of competition means all the guardrails are coming off eventually.
49:24If there's some guardrail you have on, someone's going to open source whatever it is and they're going to all give it up. Yeah, both on the guardrails and on the data usage point as well. All right. Well, shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our guest, Jack Morris. He's at J-X-M-N-O-P. Follow our producers, Kerman Rodriguez at Kerman Armand, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks. For more OddLots content, go to Bloomberg.com slash OddLots.
49:59We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy Odd Lots, if you like it when we talk about$20 million salaries that will never be ours, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.
50:37Thank you.
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From the publisher
We know that the top-tier AI labs are spending unbelievable amounts of money on talent. But what are these researchers actually working on? And how do we know that they're making progress? And furthermore, how can we even measure that progress? On this episode, we speak with Jack Morris, an AI researcher and Ph.D. candidate at Cornell University, who is also a part-time researcher at Meta. We talk about what he does, and why breakthroughs seem to be lumpy and unpredictable. We also talk about the battle between open- and closed-source approaches, US vs. Chinese labs, and how an individual talent thinks about where they want to spend their time, balancing the desire for research and prestige with a big fat paycheck.
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