Idea Machines Benjamin Reinhardt

Peter van Hardenberg talks about Industrialists vs. Academics, Ink&Switch's evolution over time, the Hollywood Model, internal lab infrastructure, and more!
Peter is the lab director and CEO of Ink&Switch, a private, creator oriented, computing research lab. 
References
Ink&Switch (and their many publications) The Hollywood Model in R&D Idea Machines Episode with Adam Wiggins Paul Erdós Transcript
Peter Van Hardenberg [00:01:21] Ben: Today I have the pleasure of speaking with Peter van Hardenbergh. Peter is the lab director and CEO of Inkin switch. Private creator oriented, competing research lab. I talked to Adam Wiggins, one of inkind switches founders, [00:01:35] way back in episode number four. It's amazing to see the progress they've made as an organization.
They've built up an incredible community of fellow travelers and consistently released research reports that gesture at possibilities for competing that are orthogonal to the current hype cycles. Peter frequently destroys my complacency with his ability to step outside the way that research has normally done and ask, how should we be operating, given our constraints and goals. I hope you enjoy my conversation with Peter.
Would you break down your distinction between academics and industrialists
[00:02:08] Peter: Okay. Academics are people whose incentive structure is connected to the institutional rewards of the publishing industry, right? You, you publish papers. And you get tenure and like, it's a, it's, it's not so cynical or reductive, but like fundamentally the time cycles are long, right? Like you have to finish work according to when, you know, submission deadlines for a conference are, you know, you're [00:02:35] working on something now.
You might come back to it next quarter or next year or in five years, right? Whereas when you're in industry, you're connected to users, you're connected to people at the end of the day who need to touch and hold and use the thing. And you know, you have to get money from them to keep going. And so you have a very different perspective on like time and money and space and what's possible.
And the real challenge in terms of connecting these two, you know, I didn't invent the idea of pace layers, right? They, they operate at different pace layers. Academia is often intergenerational, right? Whereas industry is like, you have to make enough money every quarter. To keep the bank account from going below zero or everybody goes home,
[00:03:17] Ben: Right. Did. Was it Stuart Brand who invented
pace
[00:03:22] Peter: believe it was Stewart Brand. Pace layers. Yeah.
[00:03:25] Ben: That actually I, I'd never put these two them together, but the, the idea I, I, I think about impedance mismatches
between [00:03:35] organizations a lot. And that really sort of like clicks with pace layers Exactly. Right. Where it's like
[00:03:39] Peter: Yeah, absolutely. And, and I think in a big way what we're doing at, Ink& Switch on some level is trying to provide like synchro mesh between academia and industry, right? Because they, the academics are moving on a time scale and with an ambition that's hard for industry to match, right? But also,
Academics.
Often I think in computer science are like, have a shortage of good understanding about what the real problems people are facing in the world today are. They're not disinterested.
[00:04:07] Ben: just computer
[00:04:08] Peter: Those communication channels don't exist cuz they don't speak the same language, they don't use the same terminology, they don't go to the same conferences, they don't read the same publications.
Right.
[00:04:18] Ben: Yeah.
[00:04:18] Peter: so vice versa, you know, we find things in industry that are problems and then it's like you go read the papers and talk to some scientists. I was like, oh dang. Like. We know how to solve this. It's just nobody's built it.
[00:04:31] Ben: Yeah.
[00:04:32] Peter: Or more accurately it would be to say [00

A conversation with Tim Hwang about historical simulations, the interaction of policy and science, analogies between research ecosystems and the economy, and so much more. 

Topics
Historical Simulations Macroscience Macro-metrics for science Long science The interaction between science and policy Creative destruction in research “Regulation” for scientific markets Indicators for the health of a field or science as a whole “Metabolism of Science” Science rotation programs Clock speeds of Regulation vs Clock Speeds of Technology References
Macroscience Substack Ada Palmer’s Papal Simulation Think Tank Tycoon Universal Paperclips (Paperclip maximizer html game) Pitt Rivers Museum  
Transcript
[00:02:02] Ben: Wait, so tell me more about the historical LARP that you're doing. Oh,
[00:02:07] Tim: yeah. So this comes from like something I've been thinking about for a really long time, which is You know in high school, I did model UN and model Congress, and you know, I really I actually, this is still on my to do list is to like look into the back history of like what it was in American history, where we're like, this is going to become an extracurricular, we're going to model the UN, like it has all the vibe of like, after World War II, the UN is a new thing, we got to teach kids about international institutions.
Anyways, like, it started as a joke where I was telling my [00:02:35] friend, like, we should have, like, model administrative agency. You know, you should, like, kids should do, like, model EPA. Like, we're gonna do a rulemaking. Kids need to submit. And, like, you know, there'll be Chevron deference and you can challenge the rule.
And, like, to do that whole thing. Anyways, it kind of led me down this idea that, like, our, our notion of simulation, particularly for institutions, is, like, Interestingly narrow, right? And particularly when it comes to historical simulation, where like, well we have civil war reenactors, they're kind of like a weird dying breed, but they're there, right?
But we don't have like other types of historical reenactments, but like, it might be really valuable and interesting to create communities around that. And so like I was saying before we started recording, is I really want to do one that's a simulation of the Cuban Missile Crisis. But like a serious, like you would like a historical reenactment, right?
Yeah. Yeah. It's like everybody would really know their characters. You know, if you're McNamara, you really know what your motivations are and your background. And literally a dream would be a weekend simulation where you have three teams. One would be the Kennedy administration. The other would be, you know, Khrushchev [00:03:35] and the Presidium.
And the final one would be the, the Cuban government. Yeah. And to really just blow by blow, simulate that entire thing. You know, the players would attempt to not blow up the world, would be the idea.
[00:03:46] Ben: I guess that's actually the thing to poke, in contrast to Civil War reenactment. Sure, like you know how
[00:03:51] Tim: that's gonna end.
Right,
[00:03:52] Ben: and it, I think it, that's the difference maybe between, in my head, a simulation and a reenactment, where I could imagine a simulation going
[00:04:01] Tim: differently. Sure, right.
[00:04:03] Ben: Right, and, and maybe like, is the goal to make sure the same thing happened that did happen, or is the goal to like, act? faithfully to
[00:04:14] Tim: the character as possible.
Yeah, I think that's right, and I think both are interesting and valuable, right? But I think one of the things I'm really interested in is, you know, I want to simulate all the characters, but like, I think one of the most interesting things reading, like, the historical record is just, like, operating under deep uncertainty about what's even going on, right?
Like, for a period of time, the American [00:04:35] government is not even sure what's going on in Cuba

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