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…2, 3 months. So yeah, we're talking about potentially years of work compressed into several months. That's pretty good. Agents are getting smarter every day but even the best agents get stuck without good context. And this is where Notion comes in. With the recent launch of custom agents Notion becomes the collaborative AI platform where agents and humans work side by side. And now their new developer platform is turning that into infrastructure that developers can work on. The way I think about it is it is the kind of curated materialized memory plane for all of my work. And the great thing about Notion is it's so easy to work with programmatically. It has a CLI. It has an MCP server. It has agents built into it. Right? So that means using my phone, I can ask an agent to go and do some research or to put some information in there, I can sync things to my calendar. Without Notion, I honestly don't think I'd be able to do anything that I do on MLST. So it's really really good. So I highly recommend you give Notion's platform a go. You can sign up at notion.com/mlst and you'll be supporting the show if you do. And now let's get back to John Jumper. Now we filmed this before the Anthropic announcement so it was really cool to speak with John. He's such an inspirational guy. I was lucky enough to have dinner with him the night So we had a bit of a warm up conversation and, you know, we drilled in to the various topics we wanted to discuss. 1 thing that struck me is John is unusually careful about what AlphaFold does and does not solve. So he doesn't sell it as a model of life or, you know, as a model of curing disease. He sells it as something a little bit more narrow and possibly more radical actually, a machine that predicts 1 class of structural biology measurement well enough to change what scientists can do next. So now I give you, John Jumper. I mean, AlphaFold itself is this kind of, I guess, I now say landmark in AI and science, but it's really about how do we use AI to solve problems that humans can't, that are really hard, that we go and we do years long experiments. And in the case of AlphaFold, it's this problem of protein structure prediction. This I guess it's machine learning street talk, not biology street talk. So, you know, DNA is the instruction manual for life, but what does it actually tell you what to build? And it tells you 1 of the many things it tells you how to build are proteins. And these are little nanomachines, couple thousand atoms in the cell that actually do the work of the cell. And so 3 letters of your DNA tell you how to add 1 extra piece to this protein. This protein is kind of a long string, and there's a tiny machine itself made of proteins and RNA that's built 1 kind of string at a time. And you you make this rope of 20 types of chemical groups. So it's kind of 20 types of letters, and people, of course, use the alphabet for these things. And each of them are quite different. Right? My my PhD supervisor could tell you lovingly about what's special of each 1. But you build this kind of rope of the protein, and then it assembles itself. It twists. It curls. It folds up into a really kind of compact and interesting shape. It has these helices, sheets, all these things, and that's actually what works. And the the analogy I always kind of like to say is it's like you have an IKEA bookshelf, and you open the box, and it builds itself. And so this really, really central problem for maybe 70 plus years in in biology is, okay. How do I I can read DNA. In fact, can read DNA really well now. You know, you can probably many people in your in your listeners have had their DNA sequenced. But understanding the structure of even 1 protein is extraordinarily difficult. Right? That's a worthy PhD project. I would say maybe a typical kind of time frame is a year. If you wanna put money on it, maybe a $100,000 to get 1 answer. And this is really important to biology because we wanna understand how these proteins work. When they misfold, sometimes it's disease. Even when they work, they are the parts of the cell. They do all the parts, the things of the cell. You know, they're beautiful proteins. The reason that, you know, cells can move. Right? Or is this giant protein machine whirling around driving the force to move cells? All of the functions of the cell, basically, are in these proteins. Humans have about 20,000 different types in different locations in in your genome. And so what scientists have done is they've gone to these enormous, enormous machines, synchrotrons normally, you know, the size of small towns, in order to produce extraordinarily bright X rays. And even that, only after they've done really, really hard experiments trying to figure out how to what's called crystallize a protein, small towns, in order to produce extraordinarily bright X rays. And even that, only after they've done really, really hard experiments trying to figure out how to what's called crystallize a protein, you know, and this takes years and years. And once they do that, and then they solve another mathematical problem that maybe we'll talk about, maybe we won't, they get 1 picture of a protein. And they get kind of this progress and often this whole wealth of understanding of, oh, okay. I can understand how this DNA change that was found in the population might affect Parkinson's because, look, it's right here on this protein, and that makes so much more sense. And so people have studied this problem for a long time. There have been almost innumerable Nobel Prizes given for individual proteins, right, the ribosome, many others. People have an incredible societal investment, collected around 200,000 of these about a 140,000 at the time we did AlphaFold. Each 1 still extraordinarily difficult. Right? Each 1 still. I remember seeing people talk about their PhD and give their 1 of their talks near the end of their PhD, progress toward crystallizing whatever protein. Right? So I did my I'm gonna be doctor, and I probably am not gonna crystallize this protein. I guess I'm I'm telling you all about proteins and nothing about what we did. But what we did was develop a new deep learning system from the publicly available experimental data, so all very public data, that was vastly more accurate at predicting protein structures. So predicts it to something like within the radius of an atom, right, in in typical accuracy, and an accuracy that starts to rival at least some experimental methods, but more importantly than that, is, you know, extraordinarily fast. So it takes 5, 10 minutes to get the structure of a protein instead of a year. I should at some point figure out what that ratio is in terms of time. But then also, of course, it's incredibly scalable. So we've predicted the structure of 200,000,000 proteins, basically every protein from an organism whose genome has been sequenced.…

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