Listen Now

In this conversation, Semon Rezchikov and I talk about what other disciplines can learn from mathematics, creating and cultivating collaborations, working at different levels of abstraction, and a lot more! Semon is currently a postdoc in mathematics at Harvard where he specializes in symplectic geometry. He has an amazing ability to go up and down the ladder of abstraction — doing extremely hardcore math while at the same time paying attention to *how* he’s doing that work and the broader institutional structures that it fits into. Semon is worth listening to both because he has great ideas and also because in many ways, academic mathematics feels like it stands apart from other disciplines. Not just because of the subject matter, but because it has managed to buck many of the trend that other fields experienced over the course of the 20th century.   Links Semon's Website Transcript [00:00:35]  Welcome back to idea machines. Before we get started, I'm going to do two quick pieces of housekeeping. I realized that my updates have been a little bit erratic. My excuse is that I've been working on my own idea machine. That being said, I've gotten enough feedback that people do get something out of the podcast and I have enough fun doing it that I am going to try to commit to a once a month cadence probably releasing on the pressure second [00:01:35] day of. Second thing is that I want to start doing more experiments with the podcast. I don't hear enough experiments in podcasting and I'm in this sort of unique position where I don't really care about revenue or listener numbers. I don't actually look at them. And, and I don't make any revenue. So with that in mind, I, I want to try some stuff. The podcast will continue to be a long form conversation that that won't change. But I do want to figure out if there are ways to. Maybe something like fake commercials for lesser known scientific concepts, micro interviews. If you have ideas, send them to me in an email or on Twitter. So that's, that's the housekeeping. This conversation, Simon Rezchikov and I talk about what other disciplines can learn from mathematics, creating and cultivating collaborations, working at different levels of abstraction. is currently a post-doc in mathematics at Harvard, where he specializes in symplectic geometry. He has an amazing ability to go up, go up and down the ladder of [00:02:35] abstraction, doing extremely hardcore math while at the same time, paying attention to how he's doing the work and the broader institutional structures that affect. He's worth listening to both because he has great ideas. And also because in many ways, academic mathematics feels like it stands apart from other disciplines, not just because of the subject matter, but because it has managed to buck many of the trends that other fields experience of the course of the 20th century. So it's worth sort of poking at why that happened and perhaps. How other fields might be able to replicate some of the healthier parts of mathematics. So without further ado, here's our conversation. [00:03:16] Ben:  I want to start with the notion that I think most people have that the way that mathematicians go about a working on things and be thinking about how to work on things like what to work on is that you like go in a room and you maybe read some papers and you think really hard, and then [00:03:35] you find some problem. And then. You like spend some number of years on a Blackboard and then you come up with a solution. But apparently that's not that that's not how it actually works.  [00:03:49] Semon: Okay. I don't think that's a complete description. So definitely people spend time in front of blackboards. I think the length of a typical length of a project can definitely. Vary between disciplines I think yeah, within mathematics. So I think, but also on the other hand, it's also hard to define what is a single project. As you know, a single, there might be kind of a single intellectual art through which several papers are produced, where you don't even quite know the end of the project when you start. But, and so, you know, two, a two years on a single project is probably kind of a significant project for many people. Because that's just a lot of time, but it's true that, you know, even a graduate student might spend several years working on at least a single kind of larger set of ideas because the community does have enough [00:04:35] sort of stability to allow for that. But it's not entirely true that people work alone. I think these days mathematics is pretty collaborative people. Yeah. If you're mad, you know, in the end, you're kind of, you probably are making a lot of stuff up and sort of doing self consistency checks through this sort of formal algebra or this sort of, kind of technique of proof. It makes you make sure helps you stay sane. But when other people kind of can think about the same objects from a different perspective, usually things go faster and at the very least it helps you kind of decide which parts of the mathematical ideas are really. So often, you know, people work with collaborators or there might be a community of people who were kind of talking about some set of ideas and they may be, there may be misunderstanding one another, a little bit. And then they're kind of biting off pieces of a sort of, kind of collective and collectively imagined [00:05:35] mathematical construct to kind of make real on their own or with smaller groups of people. So all of those  [00:05:40] Ben: happen. And how did these collaborations. Like come about and  [00:05:44] Semon: how do you structure them? That's great. That's a great question. So I think this is probably several different models. I can tell you some that I've run across. So during, so sometimes there are conferences and then people might start. So recently I was at a conference and I went out to dinner with a few people, and then after dinner, we were. We were talking about like some of our recent work and trying to understand like where it might go up. And somebody, you know, I was like, oh, you know, I didn't get to ask you any questions. You know, here's something I've always wanted to know from you. And they were like, oh yes, this is how this should work. But here's something I don't know. And then somehow we realized that you know, there was some reasonable kind of very reasonable guests as to what the answer is. Something that needed to be known would be so I guess now we're writing a paper together, [00:06:35] hopefully that guess works. So that's one way to start a collaboration. You go out to a fancy dinner and afterwards you're like, Hey, I guess we maybe solved the problem. There is other ways sometimes people just to two people might realize they're confused about the same thing. So. Collaboration like that kind of from somewhat different types of technical backgrounds, we both realized we're confused about a related set of ideas. We were like, okay, well I guess maybe we can try to get unconfused together.  [00:07:00] Ben: Can I, can I interject, like, I think it's actually realizing that you are confused about the same problem as someone who's coming at it from a different direction is actually hard in and of itself. Yes. Yes. How, how does, like, what is actually the process of realizing that the problem that both of you have is in fact the same problem? Well,  [00:07:28] Semon: you probably have to understand a little bit about the other person's work and you probably have to in some [00:07:35] way, have some basal amount of rapport with the other person first, because. You know, you're not going to get yourself to like, engage with this different foreign language, unless you kind of like liked them to some degree. So that's actually a crucial thing. It's like the personal aspect of it. Then you know it because maybe you'll you kind of like this person's work and maybe you like the way they go about it. That's interesting to you. Then you can try to, you know, talk about what you've recently been thinking about. And then, you know, the same mathematical object might pop up. And then that, that sort of, that might be you know, I'm not any kind of truly any mathematical object worth studying, usually has incarnations and different formal languages, which are related to one another through kind of highly non-obvious transformation. So for example, everyone knows about a circle, but a circle. Could you could think of that as like the set of points of distance one, you could think of it as some sort of close, not right. You can, you can sort of, there are many different concrete [00:08:35] intuitions through which you can grapple with this sort of object. And usually if that's true, that sort of tells you that it's an interesting object. If a mathematical object only exists because of a technicality, it maybe isn't so interesting. So that's why it's maybe possible to notice that the same object occurs in two different peoples. Misunderstandings. [00:08:53] Ben: Yeah. But I think the cruxy thing for me is that it is at the end of the day, it's like a really human process. There's not a way of sort of colliding what both of, you know, without hanging out.  [00:09:11] Semon: So people. And people can try to communicate what they know through texts. So people write reviews on. I gave a few talks recently in a number of people have asked me to write like a review of this subject. There's no subject, just to be clear. I kind of gave a talk with the kind of impression that there is a subject to be worked on, but nobody's really done any work on it that you're [00:09:35] meeting this subject into existence. That's definitely part of your job as an academic. But you know, then that's one way of explaining, I think that, that can be a little bit less, like one-on-one less personal. People also write these a different version of that is people write kind of problems. People write problem statements. Like I think these are interesting problems and then our goal. So there's all these famous, like lists of conjectures, which you know, in any given discipline. Usually when people decide, oh, th

Professor Michael Strevens discusses the line between scientific knowledge and everything else, the contrast between what scientists as people do and the formalized process of science, why Kuhn and Popper are both right and both wrong, and more. Michael is a professor of Philosophy at New York University where he studies the philosophy of science and the philosophical implications of cognitive science. He’s the author of the outstanding book “The Knowledge Machine” which is the focus of most of our conversation. Two ideas from the book that we touch on: 1. “The iron rule of science”. The iron rule states that “`[The Iron Rule] directs scientists to resolve their differences of opinion by conducting empirical tests rather than by shouting or fighting or philosophizing or moralizing or marrying or calling on a higher power` in the book Michael Makes a strong argument that scientists following the iron rule is what makes science work. 2. “The Tychonic principle.” Named after the astronomer Tycho Brahe who was one of the first to realize that very sensitive measurements can unlock new knowledge about the world, this is the idea that the secrets of the universe lie in minute details that can discriminate between two competing theories. The classic example here is the amount of change in star positions during an eclipse dictated whether Einstein or Newton was more correct about the nature of gravity. Links Michael’s Website The Knowledge Machine on BetterWorldBooks Michael Strevens talks about The Knowledge Machine on The Night Science Podcast  Michael Strevens talks about The Knowledge Machine on The Jim Rutt Show    Automated Transcript [00:00:35] In this conversation. Uh, Professor Michael And I talk about the line between scientific knowledge and everything else. The contrast between what scientists as people do and the formalized process of science, why Coon and popper are both right, and both wrong and more. Michael is a professor of philosophy at New York university, where he studies the philosophy of science and the philosophical implications [00:01:35] of cognitive science. He's the author of the outstanding book, the knowledge machine, which is the focus of most of our conversation. A quick warning. This is a very Tyler Cowen ESCA episode. In other words, that's the conversation I wanted to have with Michael? Not necessarily the one that you want to hear. That being said I want to briefly introduce two ideas from the book, which we focus on pretty heavily. First it's what Michael calls the iron rule of science. Direct quote from the book dine rule states that the iron rule direct scientists to resolve their differences of opinion by conducting empirical tests, rather than by shouting or fighting or philosophizing or moralizing or marrying or calling on a higher power. In the book, Michael makes a strong argument that scientist's following the iron rule is what makes science work. The other idea from the book is what Michael calls the Taconic principle. Named after the astronomer Tycho Brahe, who is one of the first to realize that very sensitive measurements can unlock new [00:02:35] knowledge about the world. This is the idea that the secrets of the universe that lie into my new details that can discriminate between two competing theories. The classic example, here is the amount of change in a Star's position during an eclipse dictating whether Einstein or Newton was more correct about the nature of gravity. So with that background, here's my conversation with professor Michael strengthens. [00:02:58] Ben: Where did this idea of the, this, the sort of conceptual framework that you came up with come from? Like, what's like almost the story behind the story here. [00:03:10] Michael: Well, there is an interesting origin story, or at least it's interesting in a, in a nerdy kind of way. So it was interested in an actually teaching the, like what philosophers call that logic of confirmation, how, how evidence supports or undermines theories. And I was interested in getting across some ideas from that 1940s and fifties. Scientists philosophers of science these days [00:03:35] look back on it and think of as being a little bit naive and clueless. And I had at some point in trying to make this stuff appealing in the right sort of way to my students so that they would see it it's really worth paying attention. And just not just completely superseded. I had a bit of a gear shift looking at it, and I realized that in some sense, what this old theory was a theory of, wasn't the thing that we were talking about now, but a different thing. So it wasn't so much about how to assess how much a piece of evidence supports a theory or undermines it. But was it more a theory of just what counts as evidence in the first place? And that got me thinking that this question alone is, could be a important one to, to, to think about now, I ended up as you know, in my book, the knowledge machine, I'm putting my finger on that as the most important thing in all of science. And I can't say it at that point, I had yet had that idea, but it was, [00:04:35] it was kind of puzzling me why it would be that there would, there would be this very kind of objective standard for something counting is evidence that nevertheless offered you more or less, no help in deciding what the evidence was actually telling you. Why would, why would this be so important at first? I thought maybe, maybe it was just the sheer objectivity of it. That's important. And I still think there's something to that, but the objectivity alone didn't seem to be doing enough. And then I connected it with this idea in Thomas Kuhn's book, the structure of scientific revolutions that, that science is is a really difficult pursuit that I've heard. And of course it's wonderful some of the time, but a lot of. requires just that kind of perseverance in the face of very discouraging sometimes. Oh, it's I got the idea that this very objective standard for evidence could be playing the same role that Coon Coon thought was played by what he called the paradigm bar, providing a kind of a very objective framework, which is also a kind of a safe framework, [00:05:35] like a game where everyone agrees on the rules and where people could be feeling more comfortable about the validity and importance of what they were doing. Not necessarily because they would be convinced it would lead to the truth, but just because they felt secure in playing a certain kind of game. So it was a long, it was a long process that began with this sort of just something didn't seem right about these. It didn't seem right that these ideas from the 1940s and fifties could be so, so so wrong as answers to the question. Philosophers in my generation, but answering. Yeah, no, it's, [00:06:11] Ben: I love that. I feel in a way you did is like you like step one, sort of synthesized Coon and popper, and then went like one step beyond them. It's, it's this thing where I'm sure you'd go this, this, the concept that whenever you have like two, two theories that seem equally right. But are [00:06:35] contradictory, that demand is like that, that is a place where, you know, you need more theory, right? Because like, you look at popper and it's like, oh yeah, that seems, that seems right. But then there's you look at Kuhn and you're like, oh, that seems right. And then you're like, wait a minute. Because like, they sort of can't both live in the broom without [00:06:56] Michael: adding something. Although there is something there's actually something I think. Pop Harrington about Koons ideas now. And there's lots of things that are very unpopped period, but you know, Papa's basic idea is science proceeds through reputation and Koons picture of science is a little bit like a very large scale version of that, where we're scientists now, unlike in Papa's story by scientists, we're all desperately trying to undermine theories, you know, the great Britain negative spirits. And with, with, they just assume that that prevailing way of doing things, the paradigm is going to work out okay. But in presuming that they push it to its breaking point. And [00:07:35] that process, if you kind of take a few steps back, has the look of pop and science in the sense that, in the sense that scientists, but now unwittingly rather than with their critical faculties, fully engaged and wittingly are, are taking the theory to a point where it just cannot be sustained anymore in the face of the evidence. And it progresses made because the theory just becomes antenna. Some other theory needs to be counted. So there's at, at the largest scale, there's this process of that, of success of reputation and theories. Now, Coon reputation is not quite the right word. That sounds too orderly and logical to capture what it's doing, but it is nevertheless, there is being annihilated by facts and in a way that's actually quite a period. I think that interesting. [00:08:20] Ben: So it's like, like you could almost phrase Coon as like systemic pop area. Isn't right. To like no individual scientist is trying to do reputation, but then you have like the system eventually [00:08:35] refutes. And that like, that is what the paradigm shift [00:08:37] Michael: is. That's exactly right. Oh, [00:08:39] Ben: that's fast. Another thing that I wanted to ask before we dig into the actual meat of the book is like, wow, this is, this is almost a very, very selfish question, but like, why should people care about this? Like, I really care about it. There's some, and by this, I mean like sort of the, like theories of how science works, right? Like, but I know, I know many scientists who don't care. They're just like, I tried to, I talked to them about that because then they're like, like I just, you know, it's like I do, I do. I think, [00:09:12] Michael: you know, in a way that, and that's completely fine, you know, people to drive a car, you don't know how the engine works. And in fact the best drivers may not have very much mechanical understanding at all. And it's fine for scientists to be a part of the system and do what the system requires of them without really grasping how it works most of

A conversation with the VitaDAO core team. VitaDAO is a decentralized autonomous organization — or DAO — that focuses on enabling and funding longevity research. The sketch of how a DAO works is that people buy voting tokens that live on top of the Etherium blockchain and then use those tokens to vote on various action proposals for VitaDAO to take. This voting-based system contrasts with the more traditional model of a company that is a creation of law or contact, raises capital by selling equity or acquiring debt, and is run by an executive team who are responsible to a board of directors. Since technically nobody runs VitaDAO the way a CEO runs a company, I wanted to try to embrace the distributed nature and talk to many of the core team at once. This was definitely an experiment! The members of the core team in the conversation in no particular order: Tyler Golato Paul Kohlhaas Vincent Weisser Tim Peterson Niklas Rindtorff Laurence Ion Links VitaDAO Home Page An explanation of what a DAO is Molecule Automated Transcript VitaDAO [00:00:35]  In This conversation. I talked to a big chunk of the VitaDAO core team. VitaDAO is a decentralized autonomous organization or Dao that focuses on enabling and funding. Longevity research. We get into the details in the podcast, but a sketch of how a DAO works is that people buy voting tokens that live on top of the Ethereum blockchain.  And then they use those tokens to vote on [00:01:35] various action proposals for me to doubt to take. This voting based system contrasts with more traditional models of the company. That is a creation of law or contract raises capital by selling equity or acquiring debt, and is run by an executive team who are responsible to a board of directors.  Since technically, nobody runs for you to doubt the way it CEO runs the company. I wanted to try to embrace the distributed nature and talk to many of the core team at once. This was definitely experiment. Uh, I think it's your day. Well, Oh, well, but I realize it can be hard to tell voices apart on a podcast.  So I'll put a link to a video version. In the show notes. So without further ado, here's my conversation with Vita Dao.  What I want to do so that listeners can put a voice to a name is I want to go around everybody say your name and then you say how you would pronounce the word VI T a D a O. Tim, would you say your name and then, and then pronounce the word that [00:02:35] that's kind of how I've done it. Yeah. And so I'm the longevity steward we can help kind of figure out deal flow on, edited out, so. Awesome. All right, Tyler, you're next on. It is definitively Vieta Dell. Yeah. And I also help out with the longevity steward group. I started starting longevity group and I'm the chief scientific officer and co-founder at molecule as well. And then Nicholas you're next on my screen. It's definitely beats it out. And I'm also a member of the longevity working group in this science communication group and also currently initiating and laptop. Great. And then Vinson. Yeah. So it's the same pronunciation weeded out, but I'm helping on the side and also on kind of like special projects, like this incline where that I took around, we had recently and yeah, in Lawrence. Lauren Sajjan Vieta thou. And I [00:03:35] also steward the deal flow group within the longevity working group. And I think we should all now say as a hive mind, Paul Paul has said at the same time, oh, sorry. I'm going to say bye to dad. Mess with her in yeah. Hi everyone. My name is Paul cohost. I would say, be to down. I actually wonder what demographics says, Vida, like RESA. We should actually look into that. It's interest, interesting community metric. I'm the CEO and co-founder of molecule and one of the co-authors of the VW. I also work very deeply on the economic side and then essentially help finalize deal structures. So essentially the funding deals that we've been carry through into molecule and yeah, very excited to be here today. And maybe we can jump back into Lawrence adjusted  we well, [00:04:35] also, so the thing that's confusing to me is that I always assumed that the Vith came from the word vitality. Right. And so that's, that's where the idea of calling it a fight Vita doubt, right? Because like, I don't say vitality, I say fighting. In German, it's actually retaliatory. Yeah. So it's just like the stupid Anglo centrism that is from the Latin, I would say from the word life. Yeah. Cool. So to really sort of jump right in, I think there's the, to like, be very direct, like, can we like walk through the mechanics of how the, how, how everything actually works? Right. So I think listeners are probably familiar with sort of like the high level abstract concept of there's a bunch of people. They have tokens, they vote on deals you give researchers money to, to do work, but like, sort of [00:05:35] like very, very mechanical. How does the dowel work? Could you like walk us through maybe like, sort of a a core loop of, of like what, what you do Yeah. So I mean, the core goal of the DAO is really to try and democratize access to decision-making funding and governance of longevity therapeutics. And so mechanically, there's a few different things going on and anyone feel free to interrupt me or jump in as well. But, so I would start from the base layer is really having this broad community of decentralized token holders, which are ultimately providing governance functions to this community. And the community's goal is to deploy funding that it's raised into early stage. Clinical proof of concept stage longevity therapeutics projects. And these basically fall between these two, let's say points where some tension exists in when it comes to translating academic science. So you have this robust early stage, let's say basic research funding mechanism through things like the NIH [00:06:35] grant funding, essentially. And that gets really to the point of being able to do, let's say very early stage drug discovery. And there's also some sort of downstream ecosystem consisting of venture capital company builders, political companies that does let's say late stage funding and incubation of ideas. They're more well-vetted, but between there's this sort of problem where a lot of innovation gets lost, it's known as the translational valley of death. Yeah. What did we try to do is we try to identify as a community academics that are working and let's say, have stumbled onto a potentially promising drug, but aren't really at the point yet where they can create a startup company. And what we want to do is basically by working together as a community, provide them the funding, the resources, in some cases, even the incubation functions to be able to do a series of killer experiments, really deep risk of project, and then file intellectual property, which in exchange for the funding, the dowel actually, and this is, this is sort of mechanically enabled by a legal primitive that we've been developing a molecule called an IP [00:07:35] NFP framework, which basically consists on one side of a legal contract, typically in the form of a sponsored research search agreement between a funder and a party that would be receiving the funding, the laboratory, and on the other side of federated data storage layer. And so the way this works is basically beat a doubt would receive applications. Some of these projects could, for example, be listed on molecules marketplace have an IPN T created meta dealt with would send funds via the system to the university and in exchange, they would hold this license and essence for the IP, that results from that project. And then within the community, we have domain experts. For example, we have a longevity working group which consists of MDs. Post-docs PhD is basically anyone that has deep domain experience in the longevity space. They work to evaluate projects due diligence and ultimately serve as sort of a quality control filter for the community, which consists of non-experts as well. Maybe just people who are enthusiastic about what. And beyond that, there's also additional domain expertise in the [00:08:35] form of some people who have worked at biotech VCs, for example, people with entrepreneurial experience and through this community, you basically try to form, let's say a broad range of expertise that can then coach the research or work with them and really help the academic move the IP and the research project towards the stage where, where it can be commercialized. And now VitaDAO stewarding this process. They have ownership in the IP and basically what would happen is if that research has out license co-developed sold onto another party, just made productive in essence and. It's successful in commercializing those efforts and received some funds, let's say from the commercialization of that asset, that goes back into the treasury and is continuously deployed into longevity research. So the long-term goal is to really create this sort of self-sustaining circular funding mechanism to continue to fund longevity research over time. And now within that, we could wrap it all into, you know, there's a bunch of like specific mechanics in there. I would love to, to rabbit hole, [00:09:35] I think Vincent, yes, to and on the kind of very simple technical layer, kind of very initially we started off just having this idea and putting it out there and then like having like a kind of Genesis auction where everyone could contribute funds. Like some people contribute 200 bucks and others contributed millions. And in exchange for that. Just like as a, there is an example, like for every dollar they gave, they gave, got one vote in organization. And then this initial group of people that came together to put, to, to pool their resources, to fund longevity, research, got votes and exchange, and actually with these votes, basically they can then what Tyler described make on the, on these proposals that that are vetted through the longevity working group, they can make a vote if it shouldn't get funding. And, and that's of course kind of like the traditional, like model of like a Dow and of like token based governanc

Dr. Brian Arthur and I talk about how technology can be modeled as a modular and evolving system, combinatorial evolution more broadly and dig into some fascinating technological case studies that informed his book The Nature of Technology. Brian is a researcher and author who is perhaps best known for his work on complexity economics, but I wanted to talk to him because of the fascinating work he’s done building out theories of technology. As we discuss, there’s been a lot of theorizing around science — with the works of Popper, Kuhn and others. But there’s been less rigorous work on how technology works despite its effects on our lives. Brian currently works at PARC (formerly Xerox PARC, the birthplace of personal computing) and has also worked at the Santa Fe institute and was a professor Stanford university before that. Links W. Brian Arthur’s Wikipedia Page The Nature of Technology on Amazon W. Brian Arthur’s homepage at the Santa Fe Institute Transcript Brian Arthur [00:00:00]  In this conversation, Dr. Brian Arthur. And I talk about how technology can be modeled as modular and evolving system. Commentorial evolution more broadly, and we dig into some fascinating technological hae studies that informed your book, his book, the nature of tech. Brian is a researcher and author who is perhaps best known for his work on complexity economics. Uh, but I wanted to talk to him [00:01:00] because of the fascinating work he's done, building out theories of technology. Uh, as we discussed in the podcast, there's been a lot of theorizing around science, you know, with the works of popper and Kuhn and other. But there's has been much less rigorous work on how technology works despite its effect on our lives. As some background, Brian currently works at park formerly Xerox park, the birthplace of the personal computer, and has also worked at the Santa Fe Institute and was a professor at Stanford university before that. Uh, so without further ado, here's my conversation with Brian Arthur.  Mo far less interested in technology. So if anybody asks me about technology immediately search. Sure. But so the background to this is that mostly I'm known for a new framework and economic theory, which is called complexity economics. I'm not the [00:02:00] only developer of that, but certainly one of the fathers, well, grandfather, one of the fathers, definitely. I was thinking one of the co-conspirators I think every new scientific theory like starts off as a little bit of a conspiracy. Yes, yes, absolutely. Yeah. This is no exception anyways. So that's what I've been doing. I'm I've think I've produced enough papers and books on that. And I would, so I've been in South Africa lately for many months since last year got back about a month ago and I'm now I was, as these things work in life, I think there's arcs, you know, you're getting interested in something, you work it out or whatever it would be. Businesses, you [00:03:00] start children, there's a kind of arc and, and thing. And you work all that out. And very often that reaches some completion. So most of the things I've been doing, we've reached a completion. I thought maybe it's because I getting ancient, but I don't think so. I think it was that I just kept working at these things. And for some reason, technologies coming back up to think about it in 2009, when this book came out, I stopped thinking about technology people, norm they think, oh yeah, you wrote this book. You must be incredibly interested. Yeah. But it doesn't mean I want to spend the rest of your life. Just thinking about the site, start writing this story, like writing Harry Potter, you know, it doesn't mean to do that forever. Wait, like writing the book is like the whole [00:04:00] point of writing the book. So you can stop thinking about it. Right? Like you get it out of your head into the book. Yeah, you're done. So, okay. So this is very much Silicon valley and I left academia in 1996. I left Stanford I think was I'm not really an academic I'm, I'm a researcher sad that those two things have diverged a little bit. So Stanford treated me extraordinarily well. I've no objections, but anyway, I think I'd been to the Santa Fe Institute and it was hard to come back to standard academia after that.  So why, should people care about sort of, not just the output of the technology creation process, but theory behind technology. Why, why does that matter? Well[00:05:00]  I think that what a fine in in general, whether it's in Europe or China or America, People use tremendous amount of technology. If you ask the average person, what technology is, they tell you it's their smartphone, or it's catch a tree in their cars or something, but they're, most people are contend to make heavy use of technology of, I count everything from frying pans or cars but we make directly or indirectly, enormously heavy use of technology. And we don't think about where it comes from. And so there's a few kind of tendencies and biases, you know we watch we have incredibly good retinal displays these days on our computers. [00:06:00] We can do marvelous things with our smartphone. We switch on GPS and our cars, and very shortly that we won't have to drive at all presumably in a few years. And so all of this technology is doing marvelous things, but for some strange reason, We take it for granted in the sense, we're not that curious as to how it works. People trend in engineering is I am, or I can actually tell you that throughout my entire life, I've been interested in how things work, how technology works, even if it's just something like radios. I remember when I was 10, I like many other kids. I, I constructed a radio and a few instructions. I was very curious how all that worked and but people in general are not curious. So I [00:07:00] invite them quite often to do the following thought experiments. Sometimes them giving talks. All right. Technology. Well, it's an important, yeah, sort of does it matter? Probably while I would matter. And a lot of people manage to be mildly hostile to technology, but there are some of the heaviest users they're blogging on there on Facebook and railing about technology and then getting into their tech late and cars and things like that. So the thought experiment I like to pose to people is imagine you wake up one morning. And for some really weird or malign reason, all your technology is to super weird. So you wake up in your PJ's and you stagger off to the bathroom, but the toilet, [00:08:00] you trying to wash your hands or brush your teeth. That is no sink in the bathroom. There's no running water. You scratch your head and just sort of shrugged in you go off to make coffee, but there's no coffee maker, et cetera. You, in this aspiration, you leave your house and go to clinch your car to go to work. But there's no car. In fact, there's no gas stations. In fact, there's no cars on the roads. In fact, there's no roads and there's no buildings downtown and you're just standing there and naked fields. And wondering, where does this all go? And really what's happened in this weird Saifai set up is that let's say all technologies that were cooked up after say 1300. So what would that be? The last 700 years or so? I've disappeared. And and you've [00:09:00] just left there and. People then said to me, well, I mean, wouldn't there have been technologies then. Sure. So you know how to, if you're a really good architect, you might know how to build cathedrals. You might know how to do some stone bridges. You might know how to produce linen so that you're not walking around with any proper warm clothes and so on. But our whole, my point is that if you took away everything invented. So in the last few hundred years, our modern world or disappear, and you could say, well, we have science, Peter, but without technology, you wouldn't have any instruments to measure anything. There'd be no telescopes. Well, we still have our conceptual ideas. Well, we would still vote Republican or not as the case may be. Yeah, you'd have, and I'd still have my family. Yeah. But how long are your kids gonna [00:10:00] live? Because no modern medicine. Yeah, et cetera. So my point is that not only does technology influence us, it creates our entire world. And yet we take this thing that creates our entire world. Totally. For granted, I'd say by and large, there are plenty of people who are fascinated like you or me, but we tend to take it for granted. And so there isn't much curiosity about technology. And when I started to look into this seriously, I find that there's no ology of technology. There's theories about where science comes from and there's theories about music musicology and theories, endless theories about architecture and, and even theology. But there isn't a very [00:11:00] well-developed set of ideas or theories on what technology is when, where it comes from. Now, if you know, this area is a, was that true? On Thur, you know, I could mention 20 books on it and Stanford library, but when I went to look for them, I couldn't find very much compared with other fields, archi, ology, or petrol energy, you name it technology or knowledge. It was, I went to talk to a wonderful engineer in Stanford. I'm sure he's no longer alive. Cause this was about 15 years ago. He was 95 or so if I couldn't remember his name it's an Italian name, just a second. I brought this to prompts. Just a sec. I'm being sent to you. I remember his name and [00:12:00] make it the first name for him. Yeah. Walter VIN sent him. So I went to see one it's rarely top-notch aerospace engineers of the 20th century had lunch with them. And I said, have engineers themselves worked out a theory of the foundations of their subject. And he looked, he sort of looked slightly embarrassed. He says, no. I said, why not? And he paused. He was very honest. He just paused. And he says, engineers like problems they can solve. It's. So compared with other fields, there isn't as much thinking about what technology is or how it evolves over time, where it comes from how invention works. We've a theory of how new species come into existence since 1859 and Darwin. [00:13:00] We d

In this Conversation, Jason Crawford and I talk about starting a nonprofit organization, changing conceptions of progress, why 26 years after WWII may have been what happened in 1971, and more. Jason is the proprietor of Roots of Progress a blog and educational hub that has recently become a full-fledged nonprofit devoted to the philosophy of progress. Jason’s a returning guest to the podcast — we first spoke in 2019 relatively soon after he went full time on the project . I thought it would be interesting to do an update now that roots of progress is entering a new stage of its evolution.   Links Roots of Progress Nonprofit announcement Transcript So what was the impetus to switch from sort of being an independent researcher to like actually starting a nonprofit I'm really interested in. Yeah. The basic thing was understanding or getting a sense of the level of support that was actually out there for what I was doing. In brief people wanted to give me money and and one, the best way to receive and manage funds is to have a national nonprofit organization. And I realized there was actually enough support to support more than just myself, which had been doing, you know, as an independent researcher for a year or two. But there was actually enough to have some help around me to basically just make me more effective and, and further the mission. So I've already been able to hire research [00:02:00] assistants. Very soon I'm going to be putting out a a wanted ad for a chief of staff or you know, sort of an everything assistant to help with all sorts of operations and project management and things. And so having these folks around me is going to just help me do a lot more and it's going to let me sort of delegate everything that I can possibly delegate and focus on the things that only I can do, which is mostly research and writing. Nice and sort of, it seems like it would be possible to take money and hire people and do all that without forming a nonprofit. So what what's sort of like in your mind that the thing that makes it worth it. Well, for one thing, it's a lot easier to receive money when you have a, an organization that is designated as a 5 0 1 C three tax status in the United States, that is a status that makes deductions that makes donations tax deductible. Whereas other donations to other types of nonprofits are not I had had issues in the past. One organization would want to [00:03:00] give me a grant as an independent researcher, but they didn't want to give it to an individual. They wanted it to go through a 5 0 1 C3. So then I had to get a new. Organization to sort of like receive the donation for me and then turn around and re grant it to me. And that was just, you know, complicated overhead. Some organizations didn't want to do that all the time. So it was, it was just much simpler to keep doing this if I had my own organization. And do you have sort of a broad vision for the organization? Absolutely. Yes. And it, I mean, it is essentially the same as the vision for my work, which I recently articulated in an essay on richer progress.org. We need a new philosophy of progress for the 21st century and establishing such a philosophy is, is my personal mission. And is the mission. Of the organization to just very briefly frame this in the I, the 19th century had a very sort of strong and positive, you know, pro progress vision of, of what progress was and what it could do for humanity and in the [00:04:00] 20th century. That optimism faded into skepticism and fear and distrust. And I think there are ways in which the 19th century philosophy of progress was perhaps naively optimistic. I don't think we should go back to that at all, but I think we need a, we need to rescue the idea of progress itself. Which the 20th century sort of fell out of love with, and we need to find ways to acknowledge and address the very real problems and risks of progress while not losing our fundamental optimism and confidence and will to, to move forward. We need to, we need to regain to recapture that idea of progress and that fundamental belief in our own agency so that we can go forward in the 21st century with progress. You know, while doing so in a way that is fundamentally safe and benefits all of humanity. And since you, since you mentioned philosophy, I'm really like, just, just ask you a very weird question. That's related to something that I've been thinking about. And [00:05:00] so like, in addition to the fact that I completely agree the philosophy. Progress needs to be updated, recreated. It feels like the same thing needs to be done with like the idea of classical liberalism that like it was created. Like, I think like, sort of both of these, these philosophies a are related and B were created in a world that is just has different assumptions than we have today. Have you like, thought about how the, those two, like those two sort of like philosophical updates. Yeah. So first off, just on that question of, of reinventing classical liberalism, I think you're right. Let me take this as an opportunity to plug a couple of publications that I think are exploring this concept. Yeah. So so the first I'll mention is palladium. I mentioned this because of the founding essay of palladium, which was written by Jonah Bennet as I think a good statement of the problem of, of why classical liberalism is [00:06:00] or, or I think he called it the liberal order, which has maybe a slightly different thing. But you know, the, the, the basic idea of You know, representative democracy is you know, or constitutional republics with, with sort of representative democracy you know, and, and basic ideas of of freedom of speech and other sort of human rights and individual rights. You know, all of that as being sort of basic world order you know, Jonah was saying that that is in question now and. There's essentially now. Okay. I'm going to, I'm going to frame this my own way. I don't know if this is exactly how gender would put it, but there's basically, there's, there's basically now a. A fight between the abolitionists and the reformists, right. Those who think that the, the, the, that liberal order is sort of like fundamentally corrupt. It needs to be burned to the ground and replaced versus those who think it's fundamentally sound, but may have problems and therefore needs reform. And so you know, I think Jonah is on the reform side and I'm on the reform side. I think, you know, the institutions of you know, Western institutions and the institutions of the enlightenment let's say are like [00:07:00] fundamentally sound and need reform. Yeah, rather than, rather than just being raised to the ground. This was also a theme towards the end of enlightenment now by Steven Pinker that you know, a lot of, a lot of why he wrote that book was to sort of counter the fundamental narrative decline ism. If you believe that the world is going to hell, then it makes sense to question the fundamental institutions that have brought us here. And it kind of makes sense to have a burn it all to the ground. Mentality. Right. And so those things go together. Whereas if you believe that you know, actually we've made a lot of progress over the last couple of hundred years. Then you say, Hey, these institutions are actually serving us very well. And again, if there are problems with them, let's sort of address those problems in a reformist type of approach, not an abolitionist type approach. So Jonah Bennett was one of the co-founders of palladium and that's an interesting magazine or I recommend checking out. Another publication that's addressing some of these concepts is I would say persuasion by Yasha Munk. So Yasha is was a part of the Atlantic as I recall. [00:08:00] And basically wanted to. Make a home for people who were maybe left leaning or you know, would call themselves liberals, but did not like the new sort of woke ideology that is arising on the left and wanted to carve out a space for for free speech and for I don't know, just a different a non-local liberalism, let's say. And so persuasion is a sub stack in a community. That's an interesting one. And then the third one that I'll mention is called symposium. And that is done by a friend of mine. Roger Sinskey who it himself has maybe a little bit more would consider himself kind of a more right-leaning or maybe. Just call himself more of an individualist or an independent or a, you know, something else. But I think he maybe appeals more to people who are a little more right-leaning, but he also wanted you know, something that I think a lot of people are, are both maybe both on the right and the left are wanting to break away both from woke ism and from Trumpism and find something that's neither of those things. And so we're seeing this interesting. Where people on the right and left are actually maybe [00:09:00] coming together to try to find a third alternative to where those two sides are going. So symposium is another publication where you know, people are sort of coming together to discuss, what is this idea of liberalism? What does it mean? I think Tristan ski said that he wanted some posting to be the kind of place where Steven Pinker and George will, could come together to discuss what liberalism means. And then, then he like literally had that as a, as a podcast episode. Like those two people. So anyway, recommend, recommend checking it out. And, and Rob is a very good writer. So palladium, persuasion and symposium. Those are the three that I recommend checking out to to explore this kind of idea of. Nice. Yeah. And I think it looks, I mean, I mean, I guess in my head it actually like hooks, like it's sort of like extremely coupled to, to progress. Cause I think a lot of the places where we, there's almost like this tension between ideas of classical liberalism, like property rights and things that we would like see as progress. Right. Cause it's like, okay, you want to build your [00:10:00] Your Hyperloop. Right. But then you need to build that Hyperloop through a lot of people's property. And there's like this fundamental tension there. And then. I look, I don't have a good answer for that, but like just sort of t

In this conversation, Dr. Stephen Dean talks about how he created the 1976 US fusion program plan, how it played out and the history of fusion power in the US, technology program planning and management more broadly, and more. Stephen has been working on making fusion energy a reality for more than five decades. He did research on controlled fusion reactions in the 60s and in the 70s became a director at the Atomic energy commission which then became the Energy Research and Development Administration which *then* became the department of energy. In 1979 he left government to form the consultancy Fusion Power associates, where he still works. In 1976, he led the preparation of a report called “Fusion power by magnetic confinement” that laid out a roadmap of the work that would need to be done to turn fusion from a science experiment into a functional energy source. References Fusion Power by Magnetic Confinement Executive Summary Volume 1 Volume 2 Volume 3 Volume 4 Fusion Power Associates The notorious fusion never plot Adam Marblestone on technological roadmapping My hypotheses on program design (which were challenged by this conversation!) Fusion Energy Base (a good website on fusion broadly) ITER Transcript  (Machine generated, so please excuse errors) [00:00:00]  In this conversation, Dr. Steven Dean, and I talk about how he created the 1976 S fusion program plan, how it played out in the history of fusion power in the U S technology program, planning and management more broadly, and even more things. Steven has been working on making fusion energy a reality for more than five decades. He did research on control, fusion reactions in the 1960s and seventies, he became a director [00:01:00] at the atomic energy commission, which then became the energy research and development of administration, which then became the department of energy in 1979. He left government to form the consultancy fusion, power associates, where you still want. In 1976, he led the preparation of a report called fusion power by magnetic confinement that laid out a roadmap of the work that needed would need to be done to turn fusion from a science experiment, into a functional energy source. And if I can sort of riff about this for a minute, the thing is. Unlike what I sort of see as modern roadmaps, it lays out not just the sort of like plan of record to getting fusion, to be a real energy source, but lays out all the different possible scenarios in terms of funding, in terms of new technology that we can't even think of being created and lays everything. Yeah. In a way that you can actually sort of make decisions off of it. [00:02:00] And I think one of the most impressive things is that it has several different what it calls logics of funding, which is like different, different funding levels and different funding curves. And it actually, unfortunately, accurately predicts that if you fund fusion below a certain level, even if you're funding it continually you'll never get to. An actual useful fusion source because you'll never have enough money to build these, these demonstrator missions. And so in a way it's sort of predicts the future. This, this document is super impressive. If you haven't seen it you should absolutely check it out there. There are links in the show notes and it's sort of, one of the reasons I wanted to talk to Dr. Dean is because this, this document. Is one of the pieces of evidence behind my hypothesis. That to some extent, program design and program management for advanced technologies is a bit of a lost art. And so I wanted to learn more about how he thought about it and built [00:03:00] it. So without further ado, here's my conversation with Steven Dean. To start off, what was the context of creating the fusion plan? Well, I guess I would have to say that it started a few years earlier in a sense that in 1972 the I was in the fusion office and in the atomic energy commission and the office of men and mission management and budget at the white house put out instructions to, I guess, all the agencies that they should prepare an analysis of their programs under a system, they called management by objectives. And this was some, this was a formalism that was, had a certain amount of popularity at that time. And I was asked to prepare something on the fusion program as a part of the agency, doing this for all of its programs. And [00:04:00] in doing that I looked at our program and I Laid out a map basically that showed the different parts of the program on a map like a roadmap and what the timelines might be and what the functions of those of facilities would be. And when the decisions might be and what decisions would work into into, into what, and that was never published in, in a report, but it w except internally, but the map itself was published and widely distributed. And I have it on my wall and it's in my book. So that was the first, my first venture into. Into doing something that resembled plan, it was not a detailed plan, but it was an outline of decision points and flow this sort of a flow diagram, but it did connect all the different parts of the [00:05:00] program and the identified sub elements, you know, not in great detail and, and budgets were not asked for at that time. So that's how I got into this idea and a little experience in, in the planning area. And then a few years later, we had the gasoline crisis in the U S where there were long lines and we couldn't get gas and people were sitting in their cars over overnight. And the, the white house at that time said that you know, we had to become energy, independent oil you know, the OPEC. And, and so Bob Hirsch, who was at that time about to transition from the director of the fusion program to an assistant minister traitor of Urdu in, I think it was 74, late 74, 75. The, the government decided to Congress decided, or the [00:06:00] administration decided to abolish the atomic energy commission and transition it into something called the energy research and development administration or arena. And the reason for that was to. It create an agency whose function was clearly for all of energy and not just for atomic energy in order to respond to the energy crisis and to get us off of the dependence on foreign oil imports for, for vehicles and things. And so when, when, when that happened, my boss, who was Bob Hirsch at the time he became, he was actually appointed in assistant administrator of errata for basically all the long range energy programs, which included fusion. And as he was at transition, he, he came up with the idea that we should create a detailed long range plan for the, [00:07:00] for the program. And he, he was obviously becoming sort of a senior manager for the many things and he wasn't certainly going to try and do this himself. And so he and I were very close. I was at that point he had three divisions in the fusion program and I was the director of the largest division, which had all of the main experimental programs. And so he asked me to prepare this plan. And if you look at the plan at the very beginning, there's this there's a chart that shows Bob's basically guidance, which was to note that that there needed to be a multiplicity of pathways because no one organization or, or group or division or program was in response could be in full control. And that in order to have a plan that might have some hope of [00:08:00] Last thing that you had to take into account a number of policy variables he said, and technical variables, which meant that he said, because need for the, for the, for fusion and the intent of the government and the funding is all in control by other people in the government. We had to have a number of plans by which the program could be conducted. So he came up with the idea that, well, let's have five plans, which he called logic. So he basically created that framework and turned it over to me at the beginning, I guess, of 1975, I think it was. And to, to create this. This plant. So that's how it all got started. And I had been doing a number of things with the program in terms of the major [00:09:00] experiments that were under my control as a director of the confinement systems, division magnetic confinement systems. I was forcing all, all the people that were that whose budget I had to control over to, to tell me what they were doing and what they needed to do. And so on. It's all right though, I had already been and working on a lot of these things in, within my area, but at that point I took over the responsibility of creating the, in the entire plant. And so I, I, I took it over and I started I created a, a small working group within our office. And we added people that we thought were responsible that could do this for us, or give us the details out in the various parts of the program, all elements of the program. And we created a team and we, we launched this and and this was the result. We were determined to look to these five [00:10:00] logics. They ranged from both, you know, basically a steady level of effort to a maximum level of effort. And and we just started creating these things. During that six months, first six months of 1976, And this was the result. Nice. And did you, so, so each of the logics is kind of a, a wiggly curve. Did, did you go in knowing what the shape of the funding curve for each logic would be, or did you just go in with the framework that there would be five logics and over the course of designing the program, you figured out what the actual shape of those curves would be? Well, we created a definition, a rough definition of what each of the logics was supposed to look like, not in detail, but for example, a [00:11:00] logic to what says moderately. Expanding. But the tech progress would be limited by the availability of funds. But new projects were not started unless we knew that funds would be available. And so we knew that we could not address a lot of problems in parallel. And so we had a general idea that this was a program that was not running at a maximum maximum feasible. Pace. And then the logic three, we said, well, let's look at one, that's a little more aggressive. And we would l

Eli Dourado on how the sausage of technology policy is made, the relationship between total factor productivity and technological progress, airships, and more. Eli is an economist, regulatory hacker, and a senior research fellow at the Center for Growth and Opportunity at Utah State University. In the past, he was the head of global policy at Boom Supersonic where he navigated the thicket of regulations on supersonic flight. Before that, he directed the technology policy program at the Mercatus Center at George Mason University.. Eli’s Website Eli on Twitter Transcript audio_only [00:00:00] In this conversation, Eli Durado. And I talk about how the sausage of technology policy has made the relationship between total factor productivity and technological progress, airships, and more Eli is an economist regulatory, hacker, and senior research fellow at the center for growth and opportunity at Utah state university. In the past, he was the head of global policy at boom supersonic, [00:01:00] where he navigated the thicket of regulations on superstar. Before that he directed the technology policy program at the Mercatus center at George Mason university. I wanted to talk to Eli because it feels like there's a gap between the people who understand how technology works and the people who understand how the government works. And Isla is one of those rare folks who understands both. So without further ado my conversation with Eli Dorado.  So just jump directly into it.  When you were on a policy team, what do you actually do?  Well that depends on which policy team you're on. Right. So, so in my career you mean, do you mean the, in sort of like the, the public policy or like the research center think tanks kind of space or in, in, in a company because I've done both. Yeah, exactly. Oh, I didn't even realize that you do like that. It's like different things. So so like, I guess, like, let's start with [00:02:00] Boom. You're you're on a policy team at a technology company and. Yeah. Yeah. So when I, when I started at boom so we had a problem. Right. Which was like, we needed to know what landing and takeoff noise standard we could design too. Right. Like, so, so we needed to know like how loud the airplane could be.  And how, how quiet it had to be. Right. And, and as a big trade off on, on aircraft performance depending on that. And so when I joined up with boom, like FAA had a, what's called a policy statement. Right. Which is, you know, some degree of binding, but not really right. Like that they had published back in 2008 that said, you know, we don't have standards for supersonic airplanes, but you know, like when we do create them they, you know, they're during the subsonic portion of flight, we anticipate the subsidy Arctic standards. Right. So, so for, [00:03:00] for, for landing and takeoff, which is like the big thing that we are concerned about, like that's all subsonic. So we, you know, so that sort of the FAA is like going in position was like, well, the subsonic standards apply to, to boom. And so I kind of like joined up in early 2017 and sort of my job was like, let's figure out a way for that, not to be the case. Right. And so it was, it was basically, you know, look at all the different look at the space of actors and try to figure out a way for that, not to be true. And so, and so that's like kind of what I did. I started, you know, started talking with Congress with FAA. I started figuring out what levers we could push, what, what what angles we could Work work with to ensure that that, that we have we've got to a different place, different answer in the end. And, and so the, like, so basically it's just like this completely bespoke process of [00:04:00] totally like, even trying to figure out like what the constraints you're under are. Exactly. Right. So, so yeah, so it was, there's like a bunch of different, different aspects of that question, right? So there will you know, there's, there is statute, you know, congressional laws passed by Congress that had a bearing on the answer to that question that I went back to like the 1970s. And before there w you know, there was the FAA policy statement. There was, of course the FAA team, which you had to develop, you know you know, relationships with and, and, and, and sort of work with you have the industry association, right. That we remember of that Had different companies, you know, in addition, you know, in addition to boom, there, there were a bunch of other companies Ariane, which is no longer operating. We had Gulf stream, which no longer has a supersonic program. Or actually they didn't Edward admitted to having it announced really dead. They, you know, there was, you know, GE and rolls Royce. And so you had all these companies like coming together, you know, sort of under the, [00:05:00] under the watchful eye of Boeing, of course also. And, and so like the industry association had to have a position on things, and then you had like the international aspect of it. So you had a, there's a UN agency called Oko that sort of coordinates aviation standards among all the different countries you had the European regulators who did not like this idea that there were American startups doing Supersonics because, because the European companies weren't going to do it. And so they wanted to squash everything and they were like, no, no subsonic standards totally applied. Right. And so so that was, that's really the. The environment that, you know, sort of, I came into and I was like, okay, I've got to figure out, you know, I've got to figure out, build a team and, and, and figure out an approach here. And and, and try to try to make it not be the case that the subsonic centers apply. So we, so, you know, basically we tried a bunch of things at first, right. Like we tried to like, get our industry association, like all geared up for like, okay, well, we've gotta, we gotta fight this and they didn't want to do that. Right. So like, like [00:06:00] the other people didn't want to do that. Right. We tried a bunch of different angles in terms of, you know, we, we, what we ended up doing w w we got Congress to get excited about it and sort of, they, they started to, you know, there was a.  Sort of a draft bill that had some, some very forward-leaning supersonic language that we, we you know, worked with Congress on it never passed in exactly that form, but it passed later in the 2018 FAA reauthorization. And then the thing that actually kind of ended up working was I had this idea in late 2017 was, well, you know, what. The, the sub the subsonic standard changes at the end of this year. Right. So, so so the end of 2017, so I was like, well, let's apply for type certification this year. Right. So we applied, like, we are nowhere close to an airplane. Right. And know we're close. Right. Right. And I was like, well, let's just, let's just, let's just like, screw it. We're going to apply like, like in 2017. And I had to like, get the execs to sign off on that. Right. We're going to do it, but we did. [00:07:00] So by the end of, I think December, 2017, we applied, I of course, you know, talk to my FFA colleagues and told them like, Hey, we're going to apply. Just so you know, they're like, well, that raises a whole bunch of questions. And, and that sort of got it, got them working down this path where they were like, well, you only have under part 36 of the FAA rules. You only have five years to to keep that noise standard. If, if you apply today and you're probably not gonna be done in five years. And I was like, that's true. We're probably not going to be done in five years, but we think that part 36 doesn't apply to us at all right. The way it's written. And then they went back and then they looked at it and they were like, oh, Part 36 doesn't apply to them like they're right. Like, you know, Eli's the first person in the history of Supersonics three per 36 and very closely. Right. And so and so then they went back and they like talked to their lawyers and, you know, they, I think came up with a new position in a new legal interpretation [00:08:00] w basically a memo that, that was, that was published that was like, okay, the subsonic standards don't apply and we don't have standards. We can start making some standards. And if we don't have one at any time for any particular applicant, we can make one for that applicant. We can, it's called the rule of particular applicability. So that kind of, once we got that, then in early 2018, like that kind of solved their problem. Like, and I think in in at least th th the domestic part didn't solve the international part, like from, from from Europe and so on. So. I mean, I, so, so if you think about like, what do you do on a policy team? Like you figure out like how, you know, how, how do you solve the problem that you have, that, that you were, that you were hired hard to fix and you just try things, try things until something works. It's part of the answer. Yeah. That's I mean, that's, I really appreciate you going into that level of detail because it's like the sort of like affordances of these things seem incredibly opaque. And just [00:09:00] for, for context, the subsonic standards are the standards that do not a lot, like that set a very like low noise bar. It's very stringent. I mean, the modern, the modern standards are pretty stringent. Like it used to be like, you couldn't, you couldn't basically like stand on a runway and have a conversation while plane's taken off these days. Like, I mean, it's, it's, it's gotten very, very impressive, but they, you know, the, the modern planes have gotten that way because they have high bypass ratios and the engines like big, big fans that move a lot of air around the engine core, not through it. Right. And so so that is, you know, that's just not workable when you're kind of trying to push that big fan through, you know, through the air at mock you know, 2.2 is what we were doing now. Now it's 1.7 that boom. But but but anyway, that's that, you know, that, that just doesn't work as a solution. So that's why, you know, it had to be different. Right. Right. And then did you say it's 30 S

In this conversation I talk to the Amazing Arati Prabhakar about using Solutions R&D to tackle big societal problems, gaps in the innovation ecosystem, DARPA, and more. Arati’s career has covered almost every corner of the innovation ecosystem - she’s done basically every role at - DARPA she was a program manager, started their Microelectronics Technology Office, and several years later returned to server as its Director. She was also the director of the National Institute of Standards and Technology and was a venture capitalist at US venture partners. Now she’s launching Actuate - a non-profit leveraging the ARPA model to go after some of the biggest problems in American society. Links Actuate Website In the Realm of the Barely Feasible - Arati's Article about Actuate and Solutions R&D Arati on Wikipedia  Transcript [00:00:00] welcome to idea machines. I'm your host and Reinhart. And this podcast is a deep dive into the systems and people that bring innovations from glimmers in someone's eye, all the way to tools, processes, and ideas that can shift paradigms. We see these systems outputs everywhere, but what's inside the black boxes with guests. I dig below the surface into crucial, but often unspoken questions. To explore themes of how we enable innovations today and how we could do it better tomorrow. In this conversation, I talked to the amazing RFE provoca about using solutions R and D tackle, big societal problems, gaps in the innovation ecosystem, DARPA and more. Are these career has covered almost every corner of the innovation ecosystem. She's done almost every job at DARPA where she was a program manager, started their micro electronics technology office. And several years later returned serve as their [00:01:00] director. She was also the director at the national Institute of standards and technology and a venture capitalist at us venture partners. Now she's launching actuate a nonprofit leveraging the ARPA model to go after some of the biggest problems in American society. Hope you enjoy my conversation with Arthur. Provoca.  I'd love to start off and sort of frame this for everybody is with a quote from your article, which, which everybody should read and which I will link to in the show notes. You say yet, we lack a systemic understanding of how to nurture the sort of rich ecosystem we need to confront the societal changes facing us. Now over 75 years, the federal government has dramatically increased supportive research and universities and national labs have built layers of incentives and deep culture for the research role. Companies have honed their ability to develop products in markets, shifting away from doing their own fundamental research in established industries, American venture capital and entrepreneurship have supercharged the startup pathway for commercialization in some [00:02:00] sectors, but we haven't yet put enough energy into understanding the bigger space where policy finance and the market meet to scale component ideas into the kind of deep and wide innovations that can solve big previously intractable problems in society. These sorts of problems, aren't aligned to tangible market opportunities or to the missions of established government R and D organizations today, the philanthropic sector can play a pivotal role by taking the early risk of trying new methods for R and D and developing initial examples that governments and markets can adopt and ramp up the hypothesis behind actuate is that solutions R and D can be a starting place for catalyzing the necessary change in the nation's innovation ecosystem. And so with that, with those, I think I want to test it in a nutshell exactly like that. So can we start with how do you see solutions R and D as being different from other R D and, and sort of coupled with that? How is actuate different from other non-profits. Yeah, I think [00:03:00] that's, that's one of the important threads in this tapestry that we want to develop. So solutions R and D let's see. I think those of us who live in the world of R and D and innovation are very familiar with basic research. That that is about new knowledge, new exploration, but it's designed all the incentives, all the funding and the structures are designed to have that end with publishing papers. And then on the other hand, there's. But the whole machinery that turns an advance into, you know, takes a technological advance or a research advance and turns it into the changes that we want in society that could be new products and services. It could be new policies, it could be new practices and that implementation machinery. The market companies, policymaking, what individuals choose to do pilot practices. I think we understand that. And there are places where the, you know, things just move from basic research over into actual [00:04:00] implementation. But in fact, there are, there are a lot of places where that doesn't happen, seamlessly and solutions, R and D is this weird thing in the middle. That builds on top of a rich foundation of basic research. It has it, its objective is to demonstrate and to prove out completely radically better ways. To solve problems or to pursue different opportunities so that they can be implemented at scale. And so it has this hybrid character that it is at the one on one hand, it's very directed to specific goals. And in that sense, it looks more like. Product development and marching forward and, you know, boom, boom, boom, make things happen, execute drive to drive, drive to an integrated goal. And on the other hand it requires a lot of creativity, experimentation risk-taking. And so it has some of those elements from the research side. So it's this middle [00:05:00] kingdom that I. Love because it has, I think it just has enormous leverage. And I, you know, I, I think a couple of points, number one, it's it requires to do it well, requires its own. Types of expertise and practices and culture that are different from either the research or implementation. And secondly, I would say that it, I think it's overall in the U S in the current us innovation system. I think it's something of a gap. There, there, there, there, there are many, many areas where we're not doing it as well as we need to. And then for some of the new problems, which I hope we'll talk about as well. I think it's actually a very interesting lever to boot the whole system up that we're going to need going forward. Yeah. And so actually just piggybacking right off of that, you've outlined three major sort of problems that you're tackling initially. Climate change sort of health, like general American [00:06:00] health and data privacy. I'm actually really interested in, like, what was the process of deciding, like, these are the things that we're going to work on. Yeah, but this whole actuate emerged from a thought process from a lot of. Bebe's rattling around in the box car in my head in the period as I was wrapping up at DARPA in 2016, at the end of 2016 and going into 2017 when I left and what I was thinking about was how phenomenally good our innovation machinery is. For the problems that we set out to tackle at the end of the second world war, that agenda was national security technology for economic growth. A lot of that was information technology. We set out to tackle health. Instead we did biomedicine. We went long on biomedicine, didn't break their left, left a lot of our serious health problems sitting on the shelf and a big agenda was funding, basic research and, and we've executed on that agenda. That's what we are [00:07:00] very, very, very good at what I couldn't stop thinking about. As I was wrapping up at DARPA is the problems that I think will, you know, many of us feel will determine whether we succeed or fail as a society going forward. So it's not that these challenges, you know, national security or how it's not that those problems have gone away and we should stop. It's just that we have some things that will break us at our. Yeah, arguably, they are in the process of breaking us. If we don't deal with them right now, one is access to opportunity for every person in our society. A second is population health at a cost that doesn't break the economy. Another is being able to trust data and information and the information age in which we now live. And the forest obviously is mitigating climate change. And if you think about it, these, these were not, but these weren't the top of mind issues at the end of the second world war, right? I mean, we had other problems. We didn't really know what to do about. So some of these are all problems that we didn't really know what to do about. Some of these are new problems. And, [00:08:00] and so, you know, now here we are in 2021, if you say what's what really matters those were the four areas that we identified that. Are critical to the success of our society. Number one, number two, we aren't succeeding. And that means we need innovation of all different types. And number three, we, we don't, we're not innovating, you know, we're either innovating at the zero billion dollars a year level, or we are spending money on R and D, but it's not yet turning the tide of the problem and, and that, so that's how we ended up focusing on those areas. Got it. And what could you actually, like, I, I love digging into sort of the nitty gritties of like, what was the process of designing these, these programs? Right. So just to sort of scope this a little bit, these broad areas that I'm talking about, I think of as. But the major societal challenges that we face today, actuate, which is a tiny early stage seed stage [00:09:00] nonprofit organization. Our our aspiration is over time to build portfolios of solutions, R and D programs. In each of these areas. And so very, you know, you, you, you made reference to a couple of the specific programs. One is about being able to access many more data sets to mine, their insights by cross-linking across while rigorously preserving privacy. That's some of the whole set that's one very specific program, but, but think of that as just one program and what will eventually be a much broader portfolio in this area of trustin

In this conversation I talk to Ilan Gur about what it really means for technology to “escape the lab”, the power of context to shape the usefulness of research, the inadequacies of current institutional structures, how activate helps technology escape the lab *by* changing people’s context, and more. Ilan is the CEO and founder of Activate, which is a nonprofit that runs a fellowship enabling scientists to spend two years embedded in research institutions to mature technology from a concept to a first product. In the past, he has also served as a program director at ARPA-E and was a cofounder of Seeo, where he commercial new high-density battery technology. Links Activate Ilan on Twitter Ilan on My Climate Journey Podcast  Transcript In the past, we've talked about the, how the whole process of really turning hardcore scientific research into products that have an impact on people's lives is fairly abstract to people outside of the system. Since you've both walked the path and now help other people do the same, let's round the conversation. would you go into detail on what the actual actions you need to take to go from say, being a graduate student who just published a paper on a promising battery technology to an improved battery in a car. That's that's a great place to start. let me try and answer that from a few different dimensions. I'll, I'll start by answering it, just from an anecdote about my personal experience, which I've shared in other places, but, you know, I basically. Went into my PhD program because I felt like the field I was studying material scientists, material science could, be the biggest way to make a big impact on climate change by basically taking new science and turning it into the next generation of all the technologies. We need to have a sustainable economy. And, I was working in nanotechnology, joined. Kind of the world, the best research group in the world that that was working on how nano materials could improve solar cells. and this is before the, the enormous solar market that exists today exists. There was a sense at the time that, you know, we needed a completely new generation of technology to make solar ubiquitous and cost effective. And so, you know, we had this great mantra around how we were going to print solar cells like newspapers, using these small colloidal nano, semiconductors. and the research was phenomenal. we were driven by the fact that what I like to say is, you know, we wrote a science paper where the first paragraph, like any, talked about how the research was going to change the world. And it wasn't until I randomly got connected with some business school folks at Berkeley, where I was doing my PhD. and they actually. It didn't take long. they put me through just a few cycles of digging one level deeper into, how solar cells were actually made, how they were sold, what determined their, their costs and the cost of energy they produce. and I ended up, you know, over the course of a few weeks with a spreadsheet that I still have somewhere, which told me that. If we hit all of our targets and our research in terms of what we thought could change the world. we would end up with a solar cell where even if you gave it away for free, it couldn't compete with the existing state of the art Silicon solar cells at the time. and it was a really. Simple idea, which was, we were making dirt cheap solar cells, but they probably wouldn't last very long. And we didn't think that was such a big deal. You just print some more. and yet, certainly at the time, and it's still true. It's such a, such a predominant amount of the cost of solar energy came from the balance of systems and installations. And I bring up the story because, for me, it was a tipping point. We had so much excitement about our research. It was even published in Forbes, you know, so a business magazine, and. It just showed how it showed, how easy it was to think you were doing something productive and successful. I it's not that I, I, I was in academia, but the reason I was there was to try and get something productive that could turn into a product. Right. And I had missed the boat so much, even with that intention. and so that was a shock to me. And so. That was kind of the first lesson around how, you know, institutions matter and incentives matter. but what I ended up doing was then leaving academia and jumping into an early stage startup, which was an amazing vehicle to think about how this transition happens and, you know, basically the learning there, and, This is what we now, you know, this is a lot of what we now indoctrinate and try and help people understand in the fellowship we run, was that, you know, the depth and multitude of elements that determine whether a technology can actually make it from the research stage to a product in the market. You know, first of all, you know, the idea is like, you know, the easy part in some regard. but yeah. You know, the number of levels deeper, you have to go to understand, okay, how is it going to be, how is it actually going to be valuable? Who's going to buy it. Why are they going to buy it? You know, how does, how does the whole system get built to make it, it's it's a month multi-dimensional problem where everything needs to line up between finance and the team you have in the market yet. And it's technology. and. You know, for me, I think, you know, this we've talked before, one of the biggest things that I've come to realize is we've got, you know, we've got hundreds of billions of dollars that government spends to do the idea and ideation. We've got hundreds of billions of dollars that the private sector spends to basically take the early prototypes and the idea of a product and scale it. and we've got really very little, that goes into how you do all the really hard stuff of translating one to the other. Yeah. So, so let's like what I'm going to actually continue to poke at. Like, what is that actual stuff? So the, the start that you joined did w what, what sort of was the origin of the technology that you were working on? I assume it came out of a lab somewhere. I, yeah, I was involved in two startups. One was after that epiphany moment in my PhD work, I basically threw out the work we were doing, and shifted gears and ended up developing the technology. That was the basis for, for actually a solar startup thinking about sort of thin-film, nanocrystal based, solar cells, Basically realizing that the, that the lifetime was so important, we just threw out all of the organics that we were working on and focused on. Like, you basically just need a new manufacturing approach to make something that looks like a traditional solar. So, that was a company that I kind of helped establish, but then ultimately didn't go. I was, I was meant to be sort of the founding, you know, grad student turn CTO. and then, for a number of reasons, didn't end up jumping into that as a startup and instead, through. Just some of the serendipity of being in the Bay area and Silicon Valley ended up, on the founding team of a battery startup that came out of another research lab at Berkeley. and this was funded by, Samira and Vanessa who, when, when coastal ventures was just going to start it. yeah, so, so like let's so. W when we say coming out of a lab, I think it's actually worth almost disecting what that means. Cause I suspect that it means different things to different people. and so, so someone in the lab. Did some research, figure it out. Okay. We think we can extend it was, it was a lifetime, et cetera, extended battery lifetimes, or, this was about making or energy batteries, higher energy density, batteries that were still safe and stable. using basically solid electrolytes. so, so they like publish her paper, like, like I assume that there's like, like they do some experiments. They come up with like the core. sort of process improvement. It's like, okay, we, we make batteries this, this old way, and now we need to make batteries at different way that will eventually make the battery into something useful. then what did, like, what did they need to do? What do you, what did you all do? Yeah, the origin story of CEO is I think a great one. So ingredients in this case and, and some, and there are some universal, I think things that you can pull out of this, you had a couple of graduate students and a professor at Berkeley, Natasha Bulsara, doing research, basically a polymer expert who starts doing research in terms of how polymers can be applied to batteries. the, the business as usual or the incentive structures within universities generally, you know, would say for Natasha to be successful in his career, he needs to make some new discoveries. He needs to write some great papers. he needs to advance, you know, as an academic, right. And he was doing that. and. In this case, it took this moment where, you know, Natasha was a dreamer and had, you know, just had a sense of, well, wait a second, I want this to be useful. I think this can be useful. He kind of had a zero with order idea that there's this problem in batteries, where, you know, you can, if you try and use high energy density, electrodes, like lithium metal, they can short across and lithium metals, flammable and combustible. And so, you know, There's this idea that you could make a high energy density battery. Unfortunately, it starts to look more like a bomb than a battery. and he, you know, to zero with order, the polymers that he's making could solve that problem, right. It could be robust and strong mechanically and still be highly conductive, for ions and. Tasha to his credit is audacious enough to say, Oh, and this is a time to, we have to recognize when venture capitalists are interested in funding these things at the early stages. Right? So it takes Natasha being audacious enough to say, I think we can, we can start something. And then it takes someone in this case, like the node who is as audacious as it comes in saying, well, I think batteries are going to be a big deal. I think this is a really smart team and they'll figure it out. And so like, let's start a company here. it turns out and, you know, I don't really, I don't kn

In this conversation I talk to Luke Constable about the complicated tapestry of finance, funding projects, incentives, organizational and legal structures, social technologies, and more. Luke is the founder of the hedge fund Lembas Capital and publishes a widely-read newsletter full of fascinating deep dives. He’s also trained as a lawyer and historian so he looks at the world with a fairly unique set of lenses. Disclaimer: nothing Luke says is an offer to buy or sell a security or to make an investment Links Luke on Twitter Lembas Capital Theory of Investment Value (John Burr Williams) 1,000 True Fans (Kevin Kelly) Quantum Country Patreon Lembas Capital’s Open Questions The Empire of Value (André Orléan) Who Gets What and Why (Alvin Roth) The Mystery of Capital (Hernando de Soto) I, Pencil (Leonard Read) The Crime of Reason (Robert Laughlin) Andrew Lo’s papers Transcript 0:01:05 BR: So if technology creates a lot of wealth, why does it feel like most people in finance are hesitant to invest in technology?   0:01:19 Luke Constable: So that's an interesting place to start. I think you have to understand, no one invests in technology. If you think about investors, investors invest in businesses that use technology, and so that's probably the first frame I would use. Investors aren't hesitant to invest in technology, investors never invest in technology. What investors do is they invest in these products that are going to generate cash flow streams, and so that's sort of the first thing. And then the second thing is, a lot of the technologies that you and I think about, they seem obvious at a macro scale, where you take a high level view and you say, "Well, it would be so much better if we had a blank sheet of paper," and I said, "We should do X."   0:02:10 LC: For instance, you could make an argument about housing technology in San Francisco, and you could say, “All of these houses built in SF, they're old Victorians, they don't really have washing machines and laundry machines, you could probably change the structural engineering, probably build them higher”. And if you look at them and said, "Oh, I have a better prefab housing technology," or "I have a better way to do it," you'd miss the point, which is just because you've invented the physics, and this is the other thing, you actually have to sell it into a market. You have to work within the market, and so that's usually where I see a lot of the interesting technical products fall down.   0:02:53 BR: So the thing that I want to poke at in the assertion that people invest in businesses is that people invest in things that are not businesses as well, people invest in gold, in currencies and other, I guess, assets would be the high level thing, and so I guess the question is why isn't technology itself an asset, and there's probably a very obvious answer to this, I just...   0:03:25 LC: Sure, so let's take a step back and talk about the various asset classes, there's sort of a couple of ways to break them down.   0:03:32 BR: Okay.   0:03:33 LC: One way people do this is they'll say there are real assets, these are things like real estate, some people put commodities in there, and then there are sort of these yield assets, these are debt that is putting out a cash flow stream, and then you have equities, and there's some argument that cryptocurrency is sort of its own asset class, and then currencies might be their own asset class too. And what you'll quickly find is these things kind of blend together. A lot of them are different ways of financing sort of the same project. And then you have the ones that are just traded for their own sake. So there's sort of two questions you're asking, the first is, why isn't "technology" the same as like gold or silver or real estate, for instance? And so there's a use value to all of those commodities, and that's why they have value, and that actually is a cash flow stream, we actually do use gold, we do use silver, and that's how that works.   0:04:43 LC: But if you think about what's valuable, there's sort of something that's value... And I should have started with this. When you think about what value is, there's value in exchange and then there's value in use. So the value in exchange ones, these are often, you could argue, cryptocurrency or a lot of currencies, gold is actually usually thought of as a medium of exchange, that actually is valuable for cash flow purposes just probably not in the ways that you think. So what happens with these currencies and these stores of value is they sort of become Schelling points where I just know there are enough people transacting in that thing that I can find the liquidity, I can actually go convert to cash, and I can go basically get that cash when I need it. That actually is a cash flow need. It's just not often thought of that way.   0:05:40 LC: Now, liquidity is really valuable because you might be invested in the best business of all time, and it might have a very, very, very high net present value and be doing a lot of good for the world. But if you take a step back and say, "Wait a second, I have to pay off student loans," or "I have to pay off my mortgage," or "I just want some cash to go on vacation" or whatever you want to do with it, you look at this and say, "Gosh, I do need some liquidity," and that's what those other sort of trading assets are for.   0:06:10 BR: So basically, technology contributes to the use value of an equity asset, is that the right way to think about it?   0:06:22 LC: I don't think of technology that separate from... It's sort of so baked into the environment that it's just difficult to disentangle. Technology, lazily put, is just ways of doing things hopefully more efficiently than we're already doing them. And so if you think about why certain assets become tradable, either they're creating these cash flow streams, or there is some value in exchange. I mean, the way that I often frame investing for the people who I invest for is there's sort of two sets of flows that determine an asset's price. There is underlying asset's cash flows and then there are the capital flows of all the investors. So you have sellers for some reason, maybe they have liquidity needs, maybe they can't hold an asset for a regulatory reason or a legal reason, and then you have buyers who come in, because they're interested in that asset, and it could be because they think it's an interesting thing to invest in, it could be because the regulators told them that they have to buy it, it could be... You laugh, but this is actually...   0:07:32 BR: What sort of things do regulators mandate that people buy?   0:07:37 LC: Sure, so if you go look at banks and sovereign debt, well, actually banks and all debt. So you have the bank regulators set risk weightings on various types of debt, which is sort of a nice way of saying, there are all of these different cash flow streams, and the regulators are saying to you that certain cash flow streams are riskier or less risky. And shockingly, they often argue that their sovereign debt is less risky than some other cash flow streams.   0:08:13 BR: I'm shocked.   0:08:14 LC: In practice, that may or may not be true. It's a weird thing to think about, but, in some cases, a multi-national corporation might actually be a better credit than a country. But that's not how these things work, and so what happens is a bank regulator will sometimes go to a bank and say, "The risk weighting on the sovereign debt is far lower than the risk weighting on this corporate debt,” which effectively is pushing the bank to go buy a certain type of debt, which then goes and funds all of those projects. So then coming back to all of this, if you think about investing in sort of these two sets of flows, like that underlying asset's cash flows and then the capital flows of all the investors, you basically, in practical terms, want to think about markets in terms of what's driving someone's action.   0:09:05 LC: And when you think about that, that's when market prices start to make sense. They won't make sense to you if you think that you're just going to sit down and solve an analytical equation where you just sort of put in a few inputs, you make a few estimates and then the price gets spit out. It's much more of a socially constructed thing.   0:09:25 BR: And going back to your point about liquidity, it feels like there's this... I don't know how to describe it, like sort of a weird effect where it feels like there's a consensus that investing in... I won't say technology, I'll say investing in a business that is proposing to build a technology with a very long-term time scale, there's consensus that that will eventually create something... Will eventually create a lot of value, but then at the same time, because of these liquidity constraints, very few people are doing that, and that's the argument for why people are not making those investments, but it seems like that would be a point where you could arbitrage. It seems like there should be some people who are willing to not get cash flow for a couple of decades, and they would be able to reap the rewards of making these sorts of investments, but you don't see that, so I assume that those people are smarter than I am. And so the question is, why don't you see people doing that?   0:10:50 LC: So you actually do see people doing this literally all the time, but it's not for the sexy technology concepts that you are thinking of. So go look into the public markets right now. You'll see a handful of software businesses that are trading at very high multiples to sales. So the idea is that you sort of have this trade-off: you could get free cash flow after taxes right now, or effectively more free cash flow down the line from some company that's growing quickly, and so what you do is you pay some price based on that free cash flow multiple. What happens when the free cash flow is really, really far down the line, we don't even use the free cash flow number, we actually just use the sales number. And sales is obviously much higher than just free cash flow, 'caus

In this conversation I talk to Donald Braben about his venture research initiative, peer review, and enabling the 21st century equivalents of Max Planck. Donald has been a staunch advocate of reforming how we fund and evaluate research for decades. From 1980 to 1990 he ran BP’s venture research program, where he had a chance to put his ideas into practice. Considering the fact that the program cost two million pounds per year and enabled research that both led to at least one Nobel prize and a centi-million dollar company, I would say the program was a success. Despite that, it was shut down in 1990. Most of our conversation centers heavily around his book “Scientific Freedom” which I suspect you would enjoy if you’re listening to this podcast. Links Scientific Freedom Transcript audio_only [00:00:00]   This conversation. I talked to Donald breathing about his venture research initiative, peer review, and enabling the 21st century equivalent of max Planck. Donald has been a staunch advocate for forming how we fund and evaluate research for decades. From 1980 to 1990, he ran BP's venture research program. Where he had a chance to put his ideas into practice. [00:01:00] Considering the fact that the program costs about 2 million pounds per year and enabled research, that book led to at least one Nobel prize and to send a million dollar company. I would say the program was success, despite that it was shut down in 1990. Most of our conversations centers heavily around his book, scientific freedom, which just came out from straight press. And I suspect that you would enjoy if you're listening to this podcast. So here's my conversation with Donald Raven.     would you explain, in your own words, the concept   of a punk club and why it's really well, it's just my name for the, for the, outstanding scientists of the 20th century, you know, starting with max blank, who looked at thermodynamics, and it took him 20 years to reach his conclusions, that, that matter was, was quantized. You know, and that, and, he developed quantum mechanics, that was followed by Einstein and Rutherford and, and, and a [00:02:00] whole host of scientists. And I've called, in order to be, succinct Coley's they, these 500 or so scientists who dominated the 20th century, the plank club. So I don't have to keep saying Einstein rather for that second. I said, and it's, it's an easy shorthand. Right. And so, do you think that like, well, there's a raging debate about whether the existence of the plank club was due to sort of like the time and place and the, the things that could be discovered in physics in the first half of the 20th century versus. Sort of a more or more structural argument. Do you, where do you really come down on that? The existence of the plank club? [00:03:00] W well, like, yeah, so like, I guess, I guess it's, tied to sort of like this, but the question of like, like almost like, yeah. Are you asking, will there be a 20th century, 21st century playing club? Do you think, do you think it's possible? Like, it's sort of like now right now. No, it's not. because, peer review forbids it, in the early parts of the 20th century, then scientists did not have to deal with, did not necessarily have to deal with peer review. that is the opinions of the, of the expert of the few expert colleagues. they just got on, on, Edgar to university and had a university position, which was as difficult then as it is now to get. But once you got a university position in the first part up to about 1970, then you could do then providing your requirements were modest, Varney. You didn't [00:04:00] need, you know, huge amounts of money. Say. You could do anything you wanted and, you didn't have to worry about your, your peers opinions. I mean, you did in your department when people were saying, Oh, he's mad. You know, and he's looking at this, that, and the other, you could get on with it. You didn't have to take too much attention. We pay too much attention to what they were doing, but now in the 21st century, consensus dominates everything. And, it is a serious, serious problem. Yeah. So I, I seriously believe that keeps me what keeps me going is that it is possible for there to be a plane club in the 21st century. It is possible, but right now it won't take, it won't happen. I mean, re there's been reams written on peer review, absolute huge, literature. and the, but, but most of it seems to have been written by, by people who at least favor the status [00:05:00] quo. And so they conclude that peer review is great, except perhaps for multidisciplinary research, which ma, which might cause problems. This is the establishment view. And so they take steps to try to ease the progress of multidisciplinary research, but still using peer review. Now. Multidisciplinary research is essentially is, is absolutely essential to venture research. I mean, because what they are doing, what every venture researchers, the researcher is doing is to look at the universe. and the world we inhabit in a new way. So that's bound to create new, new disciplines, new thought processes. And so the, when the conventional P, when the funding agencies say, there's a problem with multidisciplinary research, they're saying that's a problem with venture reserves. Yeah. And so therefore we won't have a plank club until that problem is [00:06:00] solved. And I proposed the solution in the book. Of course. Yeah, exactly. And so I guess, so with the book, I actually think of it as it's just like a really well done, an eloquent, almost like policy proposal, like it's, it's like you could, I feel like you could actually take the book and like hand it to. A policymaker and say like do this, I guess you could, so, I guess to put it, but like clearly nobody's done that. Right? did you, do you ever do that? Like, did you actually like go to,  government agencies or even  billionaires? Like the, the amount of money that you're talking about is almost like shockingly small. what, what are, what are people's responses of like, why not do this? Patrick Collison as being the only billionaire who has responded, I've met about, I don't know, half a dozen billionaires. And, they all want to, they all want to do things [00:07:00] their way, you know, they all want to, which is fair, which is fair enough. They all want to, sees a university through their own eyes. They are not capable of saying opening their eyes and listening to what scientists really want to do and to get what scientists really want to do. You've got you. You just can't just ask them straight off. You've got to talk to them. For a long time before they will reveal what they want really want to do. And then only a few of them will be capable of being a potential member of the plank club of the 21st century state. But it's a wonderful process. It's exciting. And I don't know why. well, I, I think I do actually, why the conventional authorities do not do this. And I believe that for, the reason this is more or less as follows that, for 20, 30 years following the expansion of the universities in about, about 1970 for political reasons. [00:08:00] no, not at all for, for scientific reasons that, there was a huge expansion in the universities and, and, and a number of academics. I really really mean it's factors of three, two, three, four, or something like that, depending on the country. Really huge. And, so therefore the old system where freedom for everyone was more or less guaranteed, which is what I would advocate freedom for everyone as a right. So, what we have done now is to develop absolute selection, rules, absolute selection rules for selecting venture researchers. And, and, and that's taken, you know, that's taken some time to develop them, but they work well. And, and, and open up the world to a complete ways, new ways of looking at it. Yeah, look, I mean, the, the, the track record seems very like very good, right? Like you, you, you, you [00:09:00] enabled research that would not have happened otherwise and led to Nobel prizes. Right. Like, I don't, I don't see how it could, what evidence one could present that your method works more so than that. and so it's, so yeah. Well, well, over the years you see, the, the scientists to work in for, for the funding agencies. they have advised politicians on the ways to ration research without affecting it. And they have come up with the way, the method of peer review, which is now a dairy girl, you know? it's absolutely essential. Yeah. Every to every funding agency in the world, I've not come across one that does not use it well, apart from our own operation, of course we don't use it. but we, we find ways around it. And that's the conventional wisdom is that there are no ways around, [00:10:00] there are no way. peer review is regarded as the only way to ensure research excellence. People keep saying that it's the only way, but we have demonstrated with the BP venture to search you and this and that UCL, that there is another way. And, and I guess so is, is, is the response from, people that you would propose this to simply that , they, they don't believe that.  they don't believe that it can work because it doesn't, it isn't peer reviewed, , is that the main contention? Any, any ideas now must, must, must survive. Peer review and venture research of course would not. And so therefore what we're saying is therefore not admissible. And now a few people, in like the 50 or so of my, my, of my supporters, very senior supporters, re regard what we [00:11:00] are doing as essential, but their voice is still tiny compared with the, you know, the millions of, researchers and, and the, I I'm the funding agencies. Now the funding agencies kept on saying that they have advised politicians over the years, that the only way to ensure to ensure, that the, that the scientific enterprise is healthy is to, is to, is to a DIA to peer review. Now. They cannot. They cannot now say, ah, yes, Raven points out. There's a serious floor. They cannot do that. And so they say they do, they do not acknowledge that I exist or that the problem exists. This is so, so just because like they have, have doubled do

A conversation with Adam Marblestone about his new project - Focused Research Organizations. Focused Research Organizations (FROs) are a new initiative that Adam is working on to address gaps in current institutional structures. You can read more about them in this white paper that Adam released with Sam Rodriques. Links FRO Whitepaper Adam on Twitter Adam's Website Transcript [00:00:00]   In this conversation, I talked to Adam marble stone about focused research organizations. What are focused research organizations you may ask. It's a good question. Because as of this recording, they don't exist yet. There are new initiatives that Adam is working on to address gaps. In current institutional structures, you can read more about them in the white paper that Adam released recently with San Brad regens. I'll put them in the show notes. Uh, [00:01:00] just a housekeeping note. We talk about F borrows a lot, and that's just the abbreviation for focus, research organizations. just to start off, in case listeners have created a grave error and not yet read the white paper to explain what an fro is. Sure. so an fro is stands for focus research organization. the idea is, is really fundamentally, very simple and maybe we'll get into it. On this chat of why, why it sounds so trivial. And yet isn't completely trivial in our current, system of research structures, but an fro is simply a special purpose organization to pursue a problem defined problem over us over a finite period of time. Irrespective of, any financial gain, like in a startup and, and separate from any existing, academic structure or existing national lab or things [00:02:00] like that. It's just a special purpose organization to solve, a research and development problem. Got it. And so the, you go much more depth in the paper, so I encourage everybody to go read that. I'm actually also really interested in what's what's sort of the backstory that led to this initiative. Yeah. it's kind of, there's kind of a long story, I think for each of us. And I would be curious your, a backstory of how, how you got involved in, in thinking about this as well. And, but I can tell you in my personal experience, I had been spending a number of years, working on neuroscience and technologies related to neuroscience. And the brain is sort of a particularly hard a technology problem in a number of ways. where I think I ran up against our existing research structures. in addition to just my own abilities and [00:03:00] everything, but, but I think, I think I ran up against some structural issues too, in, in dealing with, the brain. So, so basically one thing we want to do, is to map is make a map of the brain. and to do that in a, in a scalable high-speed. Way w what does it mean to have a map of the brain? Like what, what would, what would I see if I was looking at this map? Yeah, well, we could, we could take this example of a mouse brain, for example. just, just, just for instance, so that there's a few things you want to know. You want to know how the individual neurons are connected to each other often through synopsis, but also through some other types of connections called gap junctions. And there are many different kinds of synopsis. and there are many different kinds of neurons and, There's also this incredibly multi-scale nature of this problem where a neuron, you know, it's, it's axon, it's wire that it sends out can shrink down to like a hundred nanometers in [00:04:00] thickness or less. but it can also go over maybe centimeter long, or, you know, if you're talking about, you know, the neurons that go down your spinal cord could be meter long, neurons. so this incredibly multi-scale it poses. Even if irrespective of other problems like brain, computer interfacing or real time communication or so on, it just poses really severe technological challenges, to be able to make the neurons visible and distinguishable. and to do it in a way where, you can use microscopy, two image at a high speed while still preserving all of that information that you need, like which molecules are aware in which neuron are we even looking at right now? So I think, there's a few different ways to approach that technologically one, one is with. The more mature technology is called the electron microscope, electromicroscopy approach, where basically you look at just the membranes of the neurons at any given pixel sort of black or white [00:05:00] or gray scale, you know, is there a membrane present here or not? and then you have to stitch together images. Across this very large volume. but you have to, because you're just able to see which, which, which pixels have membrane or not. you have to image it very fine resolution to be able to then stitch that together later into a three D reconstruction and you're potentially missing some information about where the molecules are. And then there's some other more, less mature technologies that use optical microscopes and they use other technologies like DNA based barcoding or protein based barcoding to label the neurons. Lots of fancy, but no matter how you do this, This is not about the problem that I think can be addressed by a small group of students and postdocs, let's say working in an academic lab, we can go a little bit into why. Yeah, why not? They can certainly make big contributions and have to, to being able to do this. But I think ultimately if we're talking about something like mapping a mouse brain, it's not [00:06:00] going to be, just a, a single investigator science, Well, so it depends on how you think about it. One, one, one way to think about it is if you're just talking about scaling up, quote, unquote, just talking about scaling up the existing, technologies, which in itself entails a lot of challenges. there's a lot of work that isn't academically novel necessarily. It's things like, you know, making sure that, Improving the reliability with which you can make slices of the brain, into, into tiny slices are making sure that they can be loaded, onto, onto the microscope in an automated fast way. those are sort of more engineering problems and technology or process optimization problems. That's one issue. And just like, so Y Y Can't like, why, why couldn't you just sort of have like, isn't that what grad students are for like, you know, it's like pipetting things and, doing, doing graduate work. So like why, why couldn't that be done in the lab? That's not why [00:07:00] they're ultimately there. Although I, you know, I was, I was a grad student, did a lot of pipetting also, but, But ultimately they're grad student. So are there in order to distinguish themselves as, as scientists and publish their own papers and, and really generate a unique academic sort of brand really for their work. Got it. So there's, there's both problems that are lower hanging fruit in order to. in order to generate that type of academic brand, but don't necessarily fit into a systems engineering problem of, of putting together a ConnectTo mapping, system. There's also the fact that grad students in, you know, in neuroscience, you know, may not be professional grade engineers, that, for example, know how to deal with the data handling or computation here, where you would need to be, be paying people much higher salaries, to actually do, you know, the kind of industrial grade, data, data piping, and, and, and many other [00:08:00] aspects. But I think the fundamental thing that I sort of realized that I think San Rodriquez, my coauthor on this white paper also realized it through particularly working on problems that are as hard as, as clinic Comix and as multifaceted as a system building problem. I th I think that's, that's the key is that there's, there's certain classes of problems that are hard to address in academia because they're system building problems in the sense that maybe you need five or six different. activities to be happening simultaneously. And if any, one of them. Doesn't follow through completely. you're sort of, you don't have something that's novel and exciting unless you have all the pieces putting, you know, put together. So I don't have something individually. That's that exciting on my own as a paper, Unless you, and also three other people, separately do very expert level, work, which is itself not academically that interesting. Now having the connectome is academically [00:09:00] interesting to say the least. but yes, not only my incentives. but also everybody else's incentives are to, to maybe spend say 60% of their time doing some academically novel things for their thesis and only spend 40% of their time on, on building the connectome system. Then it's sort of, the probability of the whole thing fitting together. And then. We see everyone can perceive that. And so, you know, they basically, the incentives don't align well, for, for what you would think of as sort of team science or team engineering or systems engineering. yeah. And so I'm like, I think, I think everybody knows that I'm actually like very much in favor of this thing. So, I'm going to play devil's advocate to sort of like tease out. what I think are. Important things to think about. so, so one sort of counter argument would be like, well, what about projects? Like cert, right? Like that [00:10:00] is a government yeah. Led, you should, if you do requires a lot of systems engineering, there's probably a lot of work that is not academic interesting. And yet, it, it, it happens. So like there's clearly like proof of concepts. So like what what's like. W why, why don't we just have more things like, like certain for, the brain. Yeah. And I think this gets very much into why we want to talk about a category of focused research organizations and also a certain scale, which we can get into. So, so I think certain is actually in many ways, a great example of, of this, obviously this kind of team science and team engineering is incredible. And there are many others, like LIGO or, or CBO observatory or the human genome project. These are great examples. I think the, the problem there is simply that these, these are multibillion dollar initiatives that really take decades of sustained. governm

Michael Filler and Matthew Realff discuss Fundamental Manufacturing Process innovations. We explore what they are, dig into historical examples, and consider how we might enable more of them to happen. Michael and Matthew are both professors at Georgia Tech and Michael also hosts an excellent podcast about nanotechnology called Nanovation. Our conversation centers around their paper Fundamental Manufacturing Process Innovation Changes the World. If you’re in front of a screen while you’re listening to this, you might want to pull up the paper to look at the pictures. Key Takeaways Sometimes you need to go down to go back up The interplay between processes and paradigms is fascinating We need to spend more time hanging out in the valley of death Links Fundamental Manufacturing Process Innovation Changes the World(Medium)(SSRN) Michael on Twitter Matthew Realff's Website Michael Filler's Website Nanovation Podcast Topics - The need for the innovator to be near the process - Continuous to discrete shifts - Defining paradigms outlines what progress looks like - Easy to pay attention to artifacts, hard to pay attention - Hard to recreate processes - The 1000x rule of process innovations - Quality vs price improvements - Process innovation as a discipline - Need to take a performance hit to switch paradigms - How to enable more fundamental manufacturing process innovations Transcript [00:00:00] this conversation, I talked to Michael filler and Matthew Ralph about fundamental manufacturing process innovations. We explore what they are, dig into historical examples and consider how we might enable more of them to happen. Michael and Matthew are both professors at Georgia tech and Michael also hosts an excellent podcast about nanotechnology called innovation. Our conversation centered around their paper called fundamental [00:01:00] manufacturing process. Innovation changes the world, which I've looked to in the show notes and highly recommend the fact that they posted it on medium. In addition to more traditional methods, give you a hint that they think a bit outside the normal academic box. However, I actually recommend the PDF version on SSRN, which is not behind a paywall only because it has great pictures for each process that I found super helpful. If you're in front of a screen, while you're listening to this, I suspect that having them handy, it might enhance the conversation. And here we go. the, the place that I'd love to start is, to sort of give everybody a, get them used to both of your voices and sort of assign a personality, a personality to each of you. so if each of you would say a bit about yourselves, and the. The, the sort of key bit that I've loved you to say is to, to focus on something that you believe that many people in your discipline would sort [00:02:00] of cock an eyebrow at because clearly by publishing this piece on medi you sort of identify yourself as not run of the mill professors.   Oh boy. Okay. So we're going to start juicy, real juicy. So I guess I'll go since I'm speaking, this is Mike filler speaking. Great to be here. so I've been a professor of chemical engineering at Georgia tech for a little over 10 years now. my research group works in nanoscale materials and device synthesis and scale up. So for say electronics applications, Yeah. I mean, this article, which we'll talk about emerged from, you know, can I say a frustration that I had around electronics really is where it started for me, at least, that. We have all this focus on new materials or new device physics or new circuit. And I know your listeners are probably thinking about morphic computing or quantum computing, and these are all very cool things, but it seemed to me [00:03:00] that we were entirely missing the process piece. The, how do we build computers? and, and, and circuitry. And, and so that's where this started for me was, starting to realize if we're not dealing with the process piece, that we're, we're missing a huge chunk of it. And I think one of the things is that people, people miss that where within working within the context of something developed 50 or 60 years ago, in many cases, and it's it's was really hidden to a lot of people. And so that, that was where I came at this. Great. All right. So, yeah, so I'm, also a professor of chemical and biomolecular engineering at Georgia tech. my background is actually in process systems engineering. And, if you go back to the late 1960s, early 1970s, actually frankly, before I was a much more than in shorts, there was a, that was a real push towards. The role of process systems engineering in [00:04:00] chemical engineering in it really arose with the, with the advent of computing and the way that computing could be used to help in chemical engineering. And then slowly over time, the, the role of process systems engineering has become, I think, marginalized within the chemical engineering community, it's gone much over towards. What I call science and engineering science in a way from the process systems piece of it. And so, you know, as Mike would, would berate me with the, with his travails over, over what he was trying to do with nano integration and nanotechnology, I realized that what he was doing was describing a lot of the same frustrations I felt with the way that process systems engineering was being marginalized and pushed to the edges of chemical engineering with the. Focus more around fundamental discoveries rather than actually how we translate those fundamental discoveries, into, functioning, processes that then lead to outcomes that affect society. So for me, it, it, it [00:05:00] was a, it was a combination of, talking to Mike and then my own frustrations around how my own field was somewhat marginalized within the context of chemical engineering. Got it. And, sort of to, to anchor everybody and, and start us off. could you just explain what a fundamental manufacturing process innovation it's. So the way we think of fundamental process innovation or manufacturing process innovation is actually rethinking how the steps in a process are organized and connected together. And so that has become the paradigm which we have. we have set for fundamental manufacturing process innovation, and these innovations come in in different categories that enable us to put these processes together. And one of the examples of which for example, is. I'm factoring taking something that has been done together at one process step and separating it into two different steps that occur maybe at different [00:06:00] times or in different places. And by so doing, we actually enable us to make, a tremendous change in the way that that process operates. So it's really around. The strategy for organizing and executing the manufacturing steps and using a set of schema is to sort of understand how over history we have been able to do that. Do you want to add to that mic? Yeah. I want to take a step back outside of manufacturing. So one of the examples we give at the outset of the piece is not in manufacturing, but in shopping something that every single person listening to this can wrap their mind around, I think. and I still love the example cause it just kind of. I miss it every single day. and this is all pre COVID thinking of course, but the idea that say a hundred years ago, and a lot or Western societies, you would go to let's call it the general store. and you'd walk in, go up to the counter. And, if I have a list maybe, and you'd handle lists to the purveyor, and they would go [00:07:00] in the back rows of shelves and they'd pull off what was on your list and they'd bring it out to you, you pay for it and you go on your Merry way. And then, you know, several decades ago, this started to change, probably half century my ex ex ex. Exactly sure. The timing, but, to, to a model, where instead of a single shop keeper, having to interface with many individual, shoppers, it was now many shoppers who did the traversing of those aisles themselves, right? This is at least in Western society is what we are familiar with today as the grocery store or the target or the Walmart. And what you do is you. Trade one thing for another in doing that right. Instead of, the person, the, the purveyor, getting things for you, which from a customer's perspective is very nice. Right? you, you, you no longer have that, right. You're being told. Okay. He used to, yeah, he or she used to get it for you now. You're going to go and traverse the ALS yourself. But you do get something in return as the [00:08:00] shopper. And that is a lower costs because now one store at the same time can be, open to many, many people stopping shopping simultaneously. So, selection goes up, costs go down and there's a benefit for the customer, and the shopkeeper. So this is an example of a process innovation it's the it's still shopping, but it, it takes the old process paradigm and inserts a new one. Excellent. And so you, in your paper, you illustrate eight major historical, fundamental process innovations. And I would love to sort of frame the conversation by walking through them so that, a just because they're great history and B, so that everybody can sort of be anchored on the very concrete, examples while at the same time, I'll, I'll sort of poke at, The, the more sort of abstract questions and ideas around this. so the, the first, [00:09:00] the first one you talked about is the shift from the new Komen to the watt steam production process. So like, what was that? And, and why was that important? it was important because, what it did was it changed fundamentally how we could make power. So the newcomer engine had, the condensation of steam in the same vessel, as, as the, as what was being the vacuum was being pulled to enable the, Pulling of water up from the coal mines in Britain, turns out it's actually 10 mines rather than coal mines, where this was first developed. And what, what did was to factor that's one of a fundamental process schema factor, the two pieces so that the vacuum pulling and the condensation happened in different vessels. And as a result of that, he was able to increase the efficiency of the steam engine by, an or

A conversation with Professor Andrew Odlyzko about the forces that have driven the paradigm changes we’ve seen across the research world in the past several decades. Andrew is a professor at the University of Minnesota and worked at Bell Labs before that. The conversation centers around his paper “The Decline of Unfettered Research” which was written in 1995 but feels even more timely today.  Key Takeaway The decline of unfettered research is part of a complex web of causes - from incentives, to expectations, to specialization and demographic trends. The sobering consequence is that any single explanation is probably wrong and any single intervention probably won’t be able to shift the system. Links The Decline of Unfettered Research Andrew's Website A Twitter thread of my thoughts before this podcast (Automated, and thus mistake-filled) Transcript   audio_only [00:00:00]  In this conversation. I talked to professor Andrew Odlyzko about the forces that have driven the paradigm changes we've seen across the research world. In the past several decades. Andrew is a professor at the university of Minnesota and worked at bell labs for that our conversation centers around in his paper, the decline of unfettered research, which was written in 1995, but feels even more timely today. I've linked to it in the show notes and [00:01:00] also a Twitter thread that I wrote to get down my own thoughts. I highly recommend that you check out one of them either now or after listening to this conversation.  I realized that it might be a little weird to be talking about a paper that you wrote 25 years ago, but it, it seemed when I read it, it sort of blew my mind because it seemed so like all of it just seemed so true today. Um, and so I was, I was wondering, uh, like first do you, do you, do you sort of think that the, the core thesis of that paper still holds up? Like how would you amend it if you had to write it again today? Oh, absolutely. I'm convinced that the base thesis is correct. And as the last quarter century has provided much more evidence to support it. And basically if I were writing it today, I would just simply draw on this experience all those 25 years. Yeah. Yeah. Cause, okay, cool. So, so like, um, I sort of wanted to [00:02:00] establish the baseline of like asking questions about it is still, is still super relevant. Um, So, uh, just, uh, for, for the, for the listeners, um, would you sort of go through how you think of what unfettered research meets? Because, uh, I think many people have heard of, of sort of like, like basic or, or curiosity driven research, but I think that the distinction is actually really important. Mmm. Well, yes. So basically unfettered researchers, emotional curiosity, driven research, very closely related to maybe some shades of difference with the idea here is that you kind of find the best people. You can most promising researchers and give them essentially practically complete freedom. Give them resources, making them complete freedom to pursue the most interesting problems that they see. Um, and that was something which, uh, kind of many people still think of this as being the main mode of operations. And that's still thought [00:03:00] the best type of research in that case, but it's definitely been fading. Yeah. So, uh, would you, would you make the art? So what, like, what is the, is the most powerful argument that unfettered research is actually not the best kind of research. Well, so why is it not the best kind of research? So again, this is not so much an issue of world's best in some global optimization sense. And so on my essay. It wasn't really addressed to the forces that were influencing conduct of science technology research. Um, and, uh, I'm not quite saying that it's kind of ideal that it was happening. I said, well, here are the reasons. And given the society we live in and the institutions, the general framework here is what's happened and why it's happening. Yeah. [00:04:00] Now and a particular outfit. Yes, there was an argument coming out of my discussion was that, uh, this unfettered research was, uh, becoming a much smaller fraction of the total. And this was actually quite justified. But yes, uh, even so to a large extent, research did dominate for a certain period of time. Um, that era was ending now. It was likely to be the con kind of consigned to a few small niches. So evolving on the, a small number of people, much more of the work was going to be kind of oriented towards particular projects. Yeah, the, the, the thing that I really like about the term unfettered research that I feel like draws a distinction between it and curiosity European is that, uh, unfettered research, the idea of fettered versus unfettered, uh, feels like it refers to, um, Sort of like [00:05:00] external constraints on a researcher, whereas curiosity driven versus, uh, not curiosity driven is, uh, the motivation uh,  um, Where, where is like, curiosity? Do you have any, is like the internal, no motivation for a researcher. And I think it's, my whole framework is around  incentives. So it's like, what are the incentives on researchers and, and, uh, fettered versus unfettered really sort of, uh, touches on that. Yes. Um, personally, I don't draw a very sharp distinction between the two, I think has got into very fine gradations and so on. I'm not sure they kind of necessarily in most meaningful is our sons. Is that when we're talking, just driven around unfettered research, People are never kind of totally acting in isolation based on is our curiosity. They always react to the opportunities. They react to what they hear from other people. And very often also they are striving for recognition. Yeah, [00:06:00] invitations to stock home to receive about price and so on. That's something many people in the proper disciplines of course keep in mind or so, so there are always some constraints coming from particular group in that case, I kind of, I know these terms as almost synonymous. Yeah, that makes a lot of sense. And so sort of a, the upshot of the decline of unfair research for me was, uh, kind of mind blowing. And it makes so much sense when you put it this way, that research has become a commodity. And I'm not sure how much you've been paying attention to sort of what I would called, like the, the, um, stagnation literature, where there's been a lot of literature around the idea of, of scientistic stagnation. And I realized that sort of at the core of that was this assumption about [00:07:00] research being a commodity. Like you look at these economic models and it's just like, okay, well we need more researchers to produce more research and it's this undifferentiated. Thing. Um, and so, so like in your mind, what are the implications of something specifically research becoming a commodity, right. Let me maybe kick it back a little bit. I'm not sure commodities quite the right term. Uh, I think we can relate it to something that has been documented and discussed very extensively in various areas, such as sports. Sports or maybe music and so on named new that what happens is, well, it's becoming very music, becoming very competitive, uh, schools, cranking out people are selecting them for the ability to perform at a certain level, scolding them, and then letting them go on the stage and so on and compete. And so what you find, for [00:08:00] example, you sport typically the gap between the. Top, whereas leads say the gold medal winner as a silver medal winner has been narrowing performance has been increasing in practically all areas of sports people, jump throws that are higher. They run faster. So on again, that seemed to be leveling off in many cases. People studying human physiology, argue with some quantitative models that we're approaching the limits of what's possible to do with our human body, unless we go to some other planet and other environmental assaults. Uh, so you hire these people, but you still have the best ones in there. Um, you were saying bolt, you know, kind of, uh, sprint or repeatedly case is I got a good example. And so you, you, you couldn't, it's not quite. Correct to say is that the hundred meter [00:09:00] sprinters are a commodity. There is definitely a differentiation there, and there is a reason to encourage them to compete and get better and train to do better and better. On the other hand, you come to a situation losing anyone knows the top around nurse makes less and less of a difference to the performance. It should observe. And I think that something similar happening with the research, you said that she saw you. And so I think that presupposes something that I love your take on, which is that sort of, there are natural limits to human physiology. I think like that's a pretty clear, right? Like, um, but there's. Not as clearly a limit to  technological ability or the, the amount that we can know about how the universe works [00:10:00] like possibly. Um, and so, so this is, this is almost like, it feels almost philosophical, but so the, the analogy to sports, um, Would presuppose some, some natural limit on, uh, sort of like the amount of science and the amount of technology that we could do. Um, and so, so do you think that that's, that's the case. Okay. Yes, there definitely is a difference in those kind of general research in science. We don't have these very obvious, very obvious reasonably well defined limits. On the other hand, what we're coming up against is the fact that these fields still are becoming more and more competitive, soft sciences are sort of growing. Uh, it's also your current number of sub fields is growing. A volume of information that's available is growing while that also means that watch any single individual can master [00:11:00] smaller and smaller fraction of that total. So in some sense, you could say that human society is becoming much more knowledgeable. The algorithm  each individual we can say is becoming less, less knowledgeable, knows less and less about the world. And we depend much more on the information we got from others. Uh, there's this extensive concern right now about the postural world and al

A conversation with Eleonora Vella about getting the right people in the room, finding research on the cusp of commercializability, and generally how TandemLaunch’s unique system works. Eleonora is a Program director at TandemLaunch. Tandemlaunch is a startup foundry that builds companies from scratch around university research. This is not an easy task - check out Episode 15 with Errol Arkilic, Episode 19 with Mark Hammond, or Episode 21 with Eli Velazquez if you need convincing. Given the challenges, TandemLaunch’s successes suggest there’s a lot to learn from their processes. Key Takeaways - An under appreciated reason that commercialization is tricky because it involves a transfer from one skillsets to another - The timescales of business and patents seems to have become decoupled   Links TandemLaunch Homepage

A conversation with Dr Anton Howes about The Royal Society of Arts, cultural factors that drive innovation, and many aspects of historical innovation. Anton is a historian of innovation whose work is expansive, but focuses especially on England in the 18th and 19th centuries as a hotbed of technological creativity. He recently released an excellent book that details the history of the Royal Society of Arts called “Arts and Minds: How the Royal Society of Arts Changed a Nation” and he publishes an excellent newsletter at Age of Invention. Notes Aton on Twitter: @AntonHowes Arts and Minds: How the Royal Society of Arts Changed a Nation - Anton's Book Age of Invention - Anton's Newsletter The referenced post about Dungeons and Dragons We don't dig too much into the content of the book because Anton talked about it on other podcasts. He gives a good overview in this one. How much did a steam engine cost in today's dollars, these sources suggest it was roughly $100k , but as anton noted - it's complicated. Transcript (Rough+Experimental)  Ben: the place that I I'd love to start is the,society of arts did something that I feel like people don't discuss very much, which is focused on,  inventions that have positive externalities. So you, you talk a lot about how they, they would promote,Inventions that maybe people,couldn't make a lot of money off of they weren't going to patent. , and it's one of the few examples I've seen in history of like non-government forces really promoting,inventions with positive externalities. And so I was wondering , if you see that.  how could we get more of that today? And like, if there were other [00:02:00] things doing similar work at the time and maybe how that theme has like moved forward in time. Anton: Yeah. That's really interesting question. I'm trying to off the top of my head, think of any examples of other non-governmental ones. I suspect there's quite a few from that period, though, just for the simple reason that. I mean the context in which the society of arts and emerges right, is at a time when you have a very capable state, but a state that doesn't do very much. Right? So one of the, one of the things you see throughout it is actually the society kind of creating what you might call the sorts of institutions that States now take upon themselves all the time, voting positive externalities as you, as you, which is a very good way of putting it. , you know, Trying to identify inventions that the market itself wouldn't ordinarily provide. , later on in the night in the mid 19th century, trying to proper state into providing things [00:03:00] like public examinations or, you know, providing those things privately before you have a state education system. But I think one of the main reasons for that is that you don't really have that kind of role being taken up by the central state. Right. I mean, the other thing to bear in mind here of course, is that a lot of governance actually happens at the local level. And so when we talk about the government, we really mean the central government, but actually a lot of stuff would be, is happening, you know, amongst the, kind of the towns and cities. It seems with that written privileges, the various borrowers with their own often quite bizarre privileges and like the way they were structured,local authorities for want of a better word, although they kind of. Take all sorts of different forms. And I think you do see quite a lot of it. It's just, it wasn't all done by a single organization at the time. So I think that's kind of the main underlying context there. Ben: Yeah. And so I guess sort of riffing on that. , one thing that I was wondering, as I, as I read through the book was like, why don't we see [00:04:00] more of that sort of like non central, central state,Positive externality promoting work done. Now, like you think of philanthropy and it doesn't quite have that same flavor anymore. And I wonder like do, like, my bias would be, would be to think that sort of,there's almost like a crowding out by the centralized state now that people sort of expect that. , and I was wondering like, do you. W w how do you think of it, perhaps there's some crowding out. I mean, the interesting thing, right, is that Britain has actually kind of interesting in that it has quite a lot of these bottom up institutions. Whereas across the rest of Europe, you actually see quite a few top-down ones. Right? So I discussed in the book that there is actually not one, but two French societies of arts, sociology. Those are there's even a third one, which still exists, which is a kind of a later much later one from, I think the late 1938, early 19th, late [00:05:00] 18th, early 19th centuries. , part of the, kind of catch up with Britain project that Napoleon and others start pursuing,But yeah, you have a lot of these princely institutions, ones that depend on particular figures to be their patrons,to promote them,to, you know, provide a meeting space for them to provide them with funds, to provide up, to, to fund anyone who's doing fellowship of that, of that kind. Whereas in Britain, you seem to get basically those stuff that doesn't get funded by the particular patrons, even when they're promised that funding like the Royal society, which they always hoped we'd get some kind of government or, you know, some funds from Charles the second or something never does. , it obviously gets support that, you know, he gives them a Royal base that they can have on the table in front of them when they have that discussions. But that's about it. And the society of arts I guess, is, has to be set up because you have that lack of. , you have that lab because of state support. , I mean, what's interesting is I guess in certain [00:06:00] complex contexts, you do get state funding of these sorts of institutions. The Dublin society becomes the Royal Dublin society, but that one actually does get state funding as part of the kind of compact try and get Ireland to catch up with, with, with Britain in terms of its economy, same with Scotland, the society Scottish society of improvers does eventually get. I guess morphed into what becomes the Scottish board of trustees for fisheries and manufacturers, probably full title one. , so organizations like that, I guess become state ones. I mean, the idea that there, the fact that they're quite uncommon though, is, is interesting. And I wonder if Britain was just a bit better sometimes that they're organizing these things and keeping them going. , the Dublin society is. An outlier. So there's the society of arts. You see lots of these patriotic societies set up to emulate the society of arts across Europe, but very, [00:07:00] very few of them,assisted I think by the 1850s, the only one, like they're pretty much, refounded a bunch of them as kind of discussion clubs. And then since then, I think the only real one to keep going was it's the one on Malta for summary, bizarre reason. , I've kind of forgotten the original question now I've kind of gone. So, so the original question was just around,like why almost like why aren't there more,nongovernmental organizations sort of devoted to promoting,these positive externalities. Like that's, that's sort of the big question I have. So I guess my answer there is partially that. It seems as though if you did have crowding out it was happening just as much then, or at least had that potential. Right? Cause you have these Nobles who could be the patrons. You have the King, who could be the patron. , although potentially you're right in that, because British Monex worth giving their patronage. You end up with these actually ironically more robust institutions because they're [00:08:00] much more broad based and bottom up. Yeah. Being formed and then surviving. So perhaps it's the case that because we just expect the government to do it and the government's extremely rich and actually does give lots of money for lots of different things. We just say, well, it's easier to, just to kind of persuade a politician, to get some money set aside for a new agency in much the same way that you know, today Britain is trying to set up an ARPA. , I think just announced a few weeks ago. , because once the idea gets,enough currency, as long as you can persuade the panels that be the, maybe it's actually quite straightforward to do it. The reason I ask is actually based on something that, that Jomo cure has pointed out, which is how,Kind of like the federalisation of innovation makes it much more robust. , I'm sure you've seen the, the,sort of like the contrast between like the Chinese state. , and then how, like, in, in Europe, comer, [00:09:00] Copernicus could like go, go between Patriot to patron until they found someone who would actually support him. , and so I always wonder about like having multiple sources of innovation and like how to have that happen. , so that was that's, that's sort of something that, that I'm, I'm always thinking about. , I guess you could say that that's, that's present right on the European level. Certainly the big question then is why is it that you don't get it happening in other fractured States? , I think a very neglected part of the case thesis, right? Is that yes, fractured States is one thing, but the other half of that, of the, of the puzzle there is also having a kind of common culture. Yeah. Even if that's. Completely kind of invented right with Swedes who presumably is descended from whatever bar area, really fat calling themselves, you know, Albertus Magnus or, or, or whatever, you know, people who are certainly not Latin from, you know, in [00:10:00] any kind of. I guess ethics sense claiming a Latin heritage or Greek or Latin heritage for themselves. , I guess bricks as well. Right? , many of whom are probably Anglo, Anglo, Saxon, Germanic Anglo-Saxons or, or, or pre Roman council something. , you know, John D is actually referring to himself as the artist know. But, but that, that, that common language, you know, having a lingua franca, French of Latin then of French, and then I guess more, more recently of English having that common set of assumptions, you know, the Republic of le

A conversation with Ashish Arora about how and why the interlocking American institutions that support technological change have evolved over time, their current strengths and weaknesses, and how they might change in the future. Ashish Arora is the Rex D. Adams Professor of Business Administration at the Fuqua School of Business at Duke University. His research focuses on the economics of technology and technical change and we spend most of this conversation focused on his recent paper: “The changing structure of American Innovation - some cautionary remarks for economic growth.” I tried an experiment this episode and wrote notes on the paper before the interview.  Key Takeaways Ashish introduces a useful framework by breaking the innovation world down into four players : academia, incumbent companies, inventors, and government and then look at how their relationships evolve over time. The current innovation system is well equipped to enable new products with large technology risks and almost no market risk (like new cancer drugs) or high market risks and almost no technology risks (like most software) but falls short in between those two extremes. A fuzzy one but it’s important to marinate in the constant complexity of the answer to ‘How does technology happen? ’   Notes Ashish’s Home Page Ashish on Twitter The Changing Structure of American Innovation My notes on the paper Steve Usselman’s Website Transcript (Experiment and automatically transcribed) [00:00:00] [00:01:00] just to start us off, , would you give a summary of the paper? I'm going to direct everybody to go read it, but just for people who are, are listening, like what, what do you think are the key things that you would want people to take away from reading your paper? So the paper itself is descriptive, but our objective data is to, to make, make one argument, which is that the way in which innovation in America is organized? Has changed over time. And there's a sense in which the system we have now is closer to what we had say at the turn of the night of the 20th century. So, you know, a hundred years [00:02:00] ago there are important differences. So that's, that's one from a descriptive point of view. There are important differences too. And we, you know, we can talk more about that, Ken. The part, which I think is, is most interesting. And perhaps also most speculative is, you know, two things. One, why has, why, why, what, what caused this change? What caused this system to evolve? And the second is, well, you know, is it good or bad? And you know, what, what might, what should one do about it? What could we do about it? , and I suspect we would spend some time on that as well. Yeah. I thought the, the dividing up the paper into different areas was, was really important., and so actually, would you say a little bit more about how,, the way that innovation is structured now resembles the way that it did at the turn of the 20th century? [00:03:00] So if you think let's start with today, right? If we think about today, we have the, the. Big tech companies. , but most people would say, you know, if you think about the innovation system today, we have sort of three sets of players, maybe four, we have the universities where, which do a lot of the research produce a lot of the fundamental knowledge and importantly, a lot of the, what economists call human capital people that, that do it. so that's one. The second part is, is the startup community, right? The startups and the VCs that fund them and all that kind of stuff. And the third are the firms, the, the incumbent firms, as we call them in economics, the peanut, the Googles, the Facebooks, but also the IBM's Microsoft and so on. And these, these are the different components. And if you go back to Adam Smith, He talked about a division of labor as being the quintessential aspect of capitalism. That [00:04:00] capitalism is this relentless force towards specialization. And what we have, you might think of it as a division of labor in innovation there, the universities that produce the research, the startups that take it and make it more commercially applicable. And then the incumbents that apply it. If you go back. Say two 1860s, that's kind of the system we had. We didn't have the universities, but we had independent inventors and we had people that backed them. And then those inventors would sell that inventions for the most part to companies that were producing, you know, early ones were railroads, for example. And so there's a sense of, you know, in that sense, it's similar. You could think of this as a splinter or a fragment system. I prefer to think of this as, as specialization and a division of innovative labor. Does that make sense? Yeah, definitely. I think so. Something that, so I completely agree with that. , those similarities, the thing that strikes me, that's [00:05:00] different between that, like the technology then, and the technology now is. Sort of the level of complexity and the amount that it takes to integrate it. Like something that I noticed about, , 1850s technology, and maybe this is this, this might be like a cognitive bias where it's like a fish in the water sort of thing. But you look at like patents from 1850. And like, you could, you could take that. You could take that patent and you could like build the thing., whereas now. Everything is just, is so complex. And like, literally, if you like, even just like downloading software from get hub and try to get it to run, sometimes doesn't work. and, and so do you, do you think that that comparison breakdown at all there, you know, that's a fair point and that I've struggled with it? So, so there's a sense of what's surely things are much more complex now than they were earlier. , but, but let me offer you. , a sort of a counter example or two, please. So one, if you think about one [00:06:00] complex industry of the time was agricultural machinery, right? Those mechanical devices were complex and people did, , innovative parts of it. , and at some point, you know, the whole system became integrated. You can just sort of bolt on stuff. The second sort of, probably more compelling one is, is, , the railroads, which if you think about as a technical system, we're quite complex. And, , Steve  who's at Georgia tech has written up. He he's a historian of study science. He studied this extensively and I'm persuaded by his work that this was really complex. , but somehow the railroads managed to integrated. While, still relying on independent inventions. So if you think about track switching, these all came from different, different parts of, , you know, different people in different parts of the co , of the country. And, and they didn't really, the railroad companies themselves didn't really have a function whose [00:07:00] job it was to, to, to develop these innovations. This somehow had got managed. Yeah. So, you know, I mean, I find it depending on which side of the bed I wake up, I either agree with you or disagree with you. But yeah, I think the trick with all of this,, that I think is fascinating is that it's so multi causal and so nuanced that it's, it's very, it's tough to say like, okay, like this is. Exactly the same or exactly different., and so I, I think that conversations like this are actually really important for sort of exploring that nuance., actually like just something that I'm wondering about the railroads is, , my, my sense of modern corporations is that they are very hesitant to integrate. External systematic change. Right? So it was like in my, my mental model, if we've had a railroad today and someone came and said like, Oh, I have this, this like, great way to change the way that you do tracks, but you need to [00:08:00] do all your tracks this way. It would never adopt that., is, do you have a sense of whether there was like a cultural difference or a good point? I'm not an expert on this, but again, relying on Steve's work. Steve Musselman's work. There's an interesting case of, , of breaking, you know, when you have a, when you have a locomotive and you've got these, these are these bogeys that are coupled, how do you stop this thing? And so this was a complicated thing and it was, it was a system that had to be installed, , in, in, in all the, all the cars and the railroads were. So, so there's a sense in which they were very open to the system, , and Westinghouse. Was that was the guy who was one of the people who came up with the whole system. There were others who came up with different ways of accomplishing this. And the railroad said, fine. , you know, we'll, we'll take it, but we want to do it. And Westinghouse said, no, no, no, I'll supply you the whole system. And just, you just put it in. And there was a lot of friction around that and, and [00:09:00] eventually Westinghouse prevailed, , thanks in part to his, his patent position. And his willingness to take the railroads on. So,, but to go back to your big question, is there a cultural change? I mean, surely there has to be right. And we were talking about 150 years, right? Yeah. But you know, that, that particular axis. Yeah. I suspect, I suspect that that all particularly since companies now have an autumn D function or an engineering function, that's that, you know, build certain. Builds up such certain sort of preferences or biases or, or views. It would be hard to adopt something wholesale from the outside and give up what you have internally. If you didn't have such an entrance function, it might be easier. But you know, I'm really speculating on this one. Yeah, absolutely. That's, that's what we're here for. The, the, like, this is not, , we're not doing any sort of peer review or anything. and, and so I guess I, another. Big [00:10:00] theme that I was wondering about that you didn't. Like, I feel like you hinted at, but didn't quite touch on in the paper was sort of the nature of the technology in these different areas themselves, like, you know, , late 18 hundreds, you have a lot of sort of mechanical inventions and then sort of giving way to chemistry and then electronics,, and then eventually software. and, and do you, do you have

In this episode I talk to Venkatesh Narayanamurti about Bell Labs, running research organizations, and why the distinction between basic and applied research is totally wrong. Venkatesh has led organizations across the research landscape: he was a director at Bell Labs during its Golden Age, a VP at Sandia National Lab, the Dean of Engineering at UC Santa Barbara and started Harvard’s engineering school. Our discussion touches on the ideas in his book Cycles of Invention and Discovery. In it, he argues the the pipeline model of basic research leading to applied research leading to commercialization is not how good research actually works and that there are many negative consequences of most of our research institutions being either explicitly or implicitly operating around that model. Main Takeaways - Research depends on good people and trusting those people. - In order for the first point to happen, people who are responsible for research organizations need to grok the research - We should really stop using the terms basic and applied research Notes Cycles of Invention and Discovery Good overview of Cycles of Invention and Discovery's Thesis Venkatesh's full history Some Topics Touched On: - Fund people over projects - NSF structure - Bell Labs didn’t make the applied/basic distinction - Deep scholarly work - Frank Jewett and Bush - Agreements to license things from at&t - What would you do to start a research institute from scratch? - Why people went to Bell Labs - Just a smaller community - How do you nurture and lead research - Nothing nothing nothing nothing something - Tough love leadership - People who knew what was going on - Bayh-Dole act - How do you prevent things from becoming ossified - Research area not reporting to operating company - No metrics on managing research - Informal mentoring

In this episode I talk to Adam Marblestone about technology roadmapping, scientific gems hidden in plain sight, and systematically exploring complex systems. Adam is currently a research scientist at Google DeepMind and in the past has been the chief strategy officer at a brain-computer interface company and did research on brain mapping with Ed Boyden and did his PhD with George Church. He has a repeated pattern of pushing the frontiers in one discipline after another - physics, biology, neuroscience, and now artificial intelligence. I wanted to talk to Adam not just because it’s fascinating when people are able to push the frontier in multiple disciplines but because he does it through a system he calls technological roadmapping. Most of our discussion is framed around two of Adam’s works - a presentation about roadmapping biology and his primer on climate technology. The conversation stands on its own, but taking a glance at them will definitely enhance the context. Links below. Key Takeaways Technological roadmapping enables fields to escape local maxima It might be possible to systematically break down complex technical disciplines into basic constraints in order to construct these roadmaps Figuring out these constraints may also enable us to reboot stalled fields Links Road-mapping Biology presentation Architecting Discovery paper Adam’s Website Adam on Twitter The Longevity FAQ The Longevity FAQ - Making of Hypothes.is

In this episode I talk to Jude Gomilla about distributed innovation systems focused especially around the bottom-up response to the coronavirus crisis. Jude is a physicist, founder and CEO of the knowledge compilation platform Golden, and a prolific angel investor. He’s also been in the thick of the distributed response to the coronavirus response from day one. Key Takeaways - There’s a clear gap between market-based distributed systems and a top down systems coordinated by the government but it’s not clear how to fill it. - Twitter is shockingly important as a coordination tool. - The concept of centralized top-down problem statements coupled with distributed bottom up solutions may be under explored. Notes Gödel finding inconsistencies in the constitution Jude on Twitter Golden.com - [especially their cluster on the virus Feline Coronavirus Gilead - company working on treatment Balaji Srinivasan on Twitter Chris Dixon Idea Maze Article Cambridge Institute for Manufacturing paper on distributed manufacturing - Government as a giant flywheel - Claims and counter claims - How do you figure out what’s going on quickly without a centralized system? - Strategies based on timescales - hybrid strategies - Wave 1 - Ramp up for Wave 2 - How to respond to the [[‘Someone is working on that’]] problem - related - Too much explore vs too much exploit - Prizes for solving problems - Top down problem generation and bottom up solution generation