Idea Machines Benjamin Reinhardt

Nadia Asparouhova talks about idea machines on idea machines! Idea machines, of course, being her framework around societal organisms that turn ideas into outcomes. We also talk about  the relationship between philanthropy and status, public goods and more. 
Nadia is a hard-to-categorize doer of many things: In the past, she spent many years exploring the funding, governance, and social dynamics of open source software, both writing a book about it called “Working in Public” and putting those ideas into practice at GitHub, where she worked to improve the developer experience. She explored parasocial communities and reputation-based economies as an independent researcher at Protocol Labs and put those ideas into practice as employee number two at Substack, focusing on the writer experience. She’s currently researching what the new tech elite will look like, which forms the base of a lot of our conversation. 
Completely independently, the two of us came up with the term “idea machines” to describe same thing — in her words: “self-sustaining organisms that contains all the parts needed to turn ideas into outcomes.” I hope you enjoy my conversation with Nadia Asparouhova. 
Links
Nadia's Idea Machines Piece
Nadia's Website
Working in Public: The Making and Maintenance of Open Source Software
Transcript
[00:01:59] Ben: I really like your way of, of defining things and sort of bringing clarity to a lot of these very fuzzy words that get thrown around. So, so I'd love to sort of just get your take on how we should think about so a few definitions to start off with. So I, in your mind, what, what is tech, when we talk about like tech and philanthropy what, what is that, what is that entity.
[00:02:23] Nadia: Yeah, tech is definitely a fuzzy term. I think it's best to find as a culture, more than a business industry. And I think, yeah, I mean, tech has been [00:02:35] associated with startups historically, but But like, I think it's transitioning from being this like pure software industry to being more like, more like a, a way of thinking.
But personally, I don't think I've come across a good definition for tech anywhere. It's kind, you know?
[00:02:52] Ben: Yeah. Do, do you think you could point to some like very sort of like characteristic mindsets of tech that you think really sort of set it.
[00:03:06] Nadia: Yeah. I think the probably best known would be, you know, failing fast and moving fast and breaking things. I think like the interest in the sort of like David and gly model of an individual that is going up against an institution or some sort of. Complex bureaucracy that needs to be broken apart.
Like the notion of disrupting, I think, is a very tech sort of mindset of looking at a problem and saying like, how can we do this better? So it, in a [00:03:35] weird way, tech is, I feel like it's sort of like, especially in relation, in contrast to crypto, I feel like it's often about iterating upon the way things are or improving things, even though I don't know that tech would like to be defined that way necessarily, but when I, yeah.
Sort of compare it to like the crypto mindset, I feel like tech is kind of more about breaking apart institutions or, or doing yeah. Trying to do things better.
[00:04:00] Ben: A a as opposed. So, so could you then dig into the, the crypto mindset by, by contrast? That's a, I think that's a, a subtle difference that a lot of people don't go into.
[00:04:10] Nadia: Yeah. Like I think the crypto mindset is a little bit more about building a parallel universe entirely. It's about, I mean, well, one, I don't see the same drive towards creating monopolies in the way that and I don't know if that was like always a, you know, core value of tech, but I think in practice, that's kind of what it's been of.
You try to be like the one thing that is like dominating a market. Whereas with crypto, I think people are [00:04:35] because they have sort of like decentralization

Seemay Chou talks about the process of building a new research organization, ticks, hiring and managing entrepreneurial scientists, non-model organisms, institutional experiments and a lot more!
Seemay is the co-founder and CEO of Arcadia Science —  a research and development company focusing on underesearched areas in biology and specifically new organisms that haven't been traditionally studied in the lab.  She’s also the co-founder of Trove Biolabs — a startup focused on harnessing molecules in tick saliva for skin therapies and was previously an assistant professor at UCSF. 
She has thought deeply not just about scientific problems themselves, but the meta questions of how we can build better processes and institutions for discovery and invention. I hope you enjoy my conversation with Seemay Chou  
Links
Seemay on Twitter (@seemaychou)
Arcadia's Research
Trove Biolabs
Seemay's essay about building Arcadia 
Transcript
[00:02:02] Ben: So since a lot of our conversation is going to be about it how do you describe Arcadia to a smart well-read person who has never actually heard of it before?
[00:02:12] Seemay: Okay. I, I actually don't have a singular answer to this smart and educated in what realm.
[00:02:19] Ben: oh, good question. Let's assume they have taken some undergraduate science classes, but perhaps are not deeply enmeshed in, in academia. So, so like,
[00:02:31] Seemay: enmeshed in the meta science community.[00:02:35] 
[00:02:35] Ben: No, no, no, no, but they've, they, they, they, they they're aware that it's a thing, but
[00:02:40] Seemay: Yeah. Okay. So for that person, I would say we're a research and development company that is interested in thinking about how we explore under researched areas in biology, new organisms that haven't been traditionally studied in the lab.
And we're thinking from first principal polls about all the different ways we can structure the organization around this to also yield outcomes around innovation and commercialization.
[00:03:07] Ben: Nice. And how would you describe it to someone who is enmeshed in the, the meta science community?
[00:03:13] Seemay: In the meta science community, I would, I would say Arcadias are meta science experiment on how we enable more science in the realm of discovery, exploration and innovation. And it's, you know, that that's where I would start. And then there's so much more that we could click into on that.
Right.
[00:03:31] Ben: And we will, we will absolutely do that. But before we get there I'm actually really [00:03:35] interested in, in Arcadia's backstory. Cuz cuz when we met, I feel like you were already , well down the, the path of spinning it up. So what's, there's, there's always a good story there. What made you wanna go do this crazy thing?
[00:03:47] Seemay: So, so the backstory of Arcadia is actually trove. Soro was my first startup that I spun out together with my co-founder of Kira post. started from a point of frustration around a set of scientific questions that I found challenging to answer in my own lab in academia. So we were very interested in my lab in thinking about all the different molecules and tick saliva that manipulate the skin barrier when a tick is feeding, but basically the, the ideal form of a team around this was, you know, like a very collaborative, highly skilled team that was, you know, strike team for like biochemical, fractionation, math spec, developing itch assays to get this done.
It was [00:04:35] not a PhD style project of like one person sort of open-endedly exploring a question. So I was struggling to figure out how to get funding for this, but that wasn't even the right question because even with the right money, like it's still very challenging to set up the right team for this in academia.
And so it was during this frustration that I started exploring with Kira about like, what is even the right way to solve this problem, because it's not gonna be through writing

William Bonvillian does a deep dive about his decades of research on how DARPA works and his more recent work on advanced manufacturing. 
William is a Lecturer at MIT and the Senior Director of Special Projects,at MIT’s Office of Digital Learning. Before joining MIT he spent almost two decades as a senior policy advisor for the US senate. He’s also published many papers and a detailed book exploring the DARPA model. 
Links
William's Website
The DARPA Model for Transformative Technologies
Transcript
[00:00:35]
In this podcast, William Bonvillian, and I do a deep dive about his decades of research about how DARPA works and his more recent work on advanced manufacturing. Well humans, a lecturer at MIT and a senior director of special projects at MIT is office of digital learning. Before joining MIT. He spent almost two decades as a senior policy advisor for the us Senate.
He's published many papers and a detailed book exploring the DARPA model. I've wanted [00:01:35] to compare notes with him for years. And it was a pleasure. And an honor to finally catch up with him. Here's my conversation with William
[00:01:42] Ben: The place that I I'd love to start off is how did you get interested in, in DARPA and the DARPA model in the first place you've been writing about it for more than a decade now. And, and you're probably one of the, the foremost people who who've explored it.
So how'd you get there in the first.
[00:01:58] William: You know, I, I I worked for the us Senate as a advisor in the Senate for for about 15 years before coming to MIT then. And I I worked for a us Senator who is on the on the armed services committee. And so I began doing a substantial amount of that staffing, given my interest in science technology, R and D and you know, got early contact with DARPA with some of DARPA's both program managers and the DARPA directors, and kind of got to know the agency that way spent some time with them over in their [00:02:35] offices.
You know, really kind of got to know the program and began to realize what a, what a dynamic force it was. And, you know, we're talking 20, 20 plus years ago when frankly DARPA was a lot less known than it is now. So yeah, just like you know, kind of suddenly finding this, this Jewelbox varied in the.
It was it was a real discovery for me and I became very, very interested in the, kind of the model they had, which was so different than the other federal R and D agencies.
[00:03:05] Ben: Yeah. And, and actually um, It sort of in your mind, what is the for, for people who I, I think tend to see different federal agencies that give money to researchers as, as all being in the same bucket.
What, what do you, what would you describe the difference between DARPA and the NSF as being
[00:03:24] William: well? I mean, there's a big difference. So the NSF model is to support basic research. And they have, you know, the equivalent of project [00:03:35] managers there and they, they don't do the selecting of the research projects.
Instead they queue up applicants for funds and then they supervise a peer review process. Of experts, you know, largely from academia who evaluate, you know, a host of proposals in a, in a given R and D area mm-hmm and and make valuations as to which ones would qualify. What are the kind of best most competitive applicants for NSFs basic research.
So DARPA's got a different project going on, so it doesn't work from the bottom up. It, it has strong program managers who are in effect kind of empowered to go out and create new things. So they're not just, you know, responding to. Grant applications for basic research, they come into DARPA and develop a [00:04:35] vision of a new breakthrough technology area.
They wanna stand up. And so it's, and there's no peer review here. It's really, you hire talented program managers. And you unleash them, you turn them loose, you empower them to go out and find the best work that's going on in the country. And tha

In this conversation, Adam Falk and I talk about running research programs with impact over long timescales, creating new fields, philanthropic science funding, and so much more. 
Adam is the president of the Alfred P. Sloan Foundation,  which was started by the eponymous founder of General Motors and has been funding science and education efforts for almost nine decades. 
They’ve funded everything from iPython Notebooks to the Wikimedia foundation to an astronomical survey of the entire sky. If you’re like me, their name is familiar from the acknowledgement part of PBS science shows.
Before becoming the president of the Sloan Foundation, Adam was the president of Williams College and a high energy physicist focused on elementary particle physics and quantum field theory. His combined experience in research, academic administration, and philanthropic funding give him a unique and fascinating perspective on the innovation ecosystem. I hope you enjoy this as much as I did. 
Links
- The Sloan Foundation
- Adam Falk on Wikipedia 
- Philanthropy and the Future of Science and Technology
Highlight Timestamps
- How do you measure success in science? [00:01:31]
- Thinking about programs on long timescales [00:05:27]
-  How does the Sloan Foundation decide which programs to do? [00:08:08]
- Sloan's Matter to Life Program [00:12:54]
-  How does the Sloan Foundation think about coordination? [00:18:24]
-  Finding and incentivizing program directors [00:22:32]
- What should academics know about the funding world and what should the funding world know about academics? [00:28:03]
- Grants and academics as the primary way research happens [00:33:42]
- Problems with grants and common grant applications [00:44:49]
- Addressing the criticism of philanthropy being inefficient because it lacks market mechanisms [00:47:16]
- Engaging with the idea that people who create value should be able to capture that value [00:53:05]
 
Transcript
[00:00:35]
In this conversation, Adam Falk, and I talk about running research programs with impact over long timescales, creating new fields, philanthropic science funding, and so much more. Adam is the president of the Alfred P Sloan foundation, which was started by the eponymous founder of general motors. And has been funding science and education efforts for almost nine decades. They funded everything from IP.
I fond [00:01:35] notebooks to Wikimedia foundation. To an astronomical survey of the entire sky. If you're like me, their name is familiar from the acknowledgement part of PBS science shows. Before becoming the president of the Sloan foundation. Adam was the president of Williams college and I high energy physicist focused on elementary particle physics in quantum field theory.
His combined experience in research. Uh, Academic administration and philanthropic funding give him a unique and fascinating perspective on the innovation ecosystem i hope you enjoy this as much as i did
[00:02:06] Ben: Let's start with like a, sort of a really tricky thing that I'm, I'm myself always thinking about is that, you know, it's really hard to like measure success in science, right?
Like you, you know, this better than anybody. And so just like at, at the foundation, how do you, how do you think about success? Like, what is, what does success look like? What is the difference between. Success and failure mean to
[00:02:34] Adam: you? [00:02:35] I mean, I think that's a, that's a really good question. And I think it's a mistake to think that there are some magic metrics that if only you are clever enough to come up with build them out of citations and publications you could get some fine tune measure of success.
I mean, obviously if we fund in a scientific area, we're funding investigators who we think are going to have a real impact with their work individually, and then collectively. And so of course, you know, if they're not publishing, it's a failure. We expect them to publi

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 ki

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 co