Into the Impossible With Brian Keating - Part 1 of 2: David Friedberg is All-In on Science (#297)

Episode Date: February 10, 2023

Please support the podcast by taking our short listener survey: https://www.surveymonkey.com/r/intotheimpossible David Friedberg believes that science is the best hope to save humanity. He is an American entrepreneur, businessman, and angel investor. After several years in investment banking and private equity, Friedberg joined Google in March 2004 as one of the first 1,000 employees and a founding member of Google’s Corporate Development group. As Corporate Development and Business Product Manager, Friedberg helped run Google's online advertising platform, AdWords, and negotiated acquisitions and worked with Google co-founder Larry Page. David appears each week as one of the four Besties on the @allin Podcast - one of Apple and Spotify’s Top podcasts — alongside fellow investors and pundits David Sacks, Chamath Palihapitiya, and Jason Calicanis. He founded and was chief executive of The Climate Corporation, whose $1.1 billion sale to Monsanto in 2013 made it the first unicorn (finance) in the agricultural technology space. He is founder and CEO of The Production Board (TPB). He is a co-host of the All-In podcast. Spanning his career, he has contributed to 32 patents. His investment portfolio includes Afterparty, Dave, The Every Company, Soylent, Supergut, Medico and many more. In this extended discussion, the indefatigable Friedberg weighs in entrepreneurship styles, investing, how to incentivize research and the problems with commercialization of institutional research. David provides some rare insight into his strategies for his almost superhuman productivity, his venture capital philosophy for successful investments, a tutorial on the microbiome, his thoughts on AI and so much more! twitter.com/friedberg www.tpb.co https://podcasts.apple.com/us/podcast/all-in-with-chamath-jason-sacks-friedberg/id1502871393 Connect with Professor Keating: 🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating 📸 Instagram: https://instagram.com/DrBrianKeating  🔔 Subscribe https://www.youtube.com/DrBrianKeating?sub_confirmation=1 📝 Join my mailing list; just click here http://briankeating.com/list ✍️ Detailed Blog posts here: https://briankeating.com/blog.php 🎙️ Listen on audio-only platforms: https://briankeating.com/podcast Subscribe to the Jordan Harbinger Show for amazing content from Apple’s best podcast of 2018! https://www.jordanharbinger.com/podcasts  🎧 On Apple devices, click here, https://apple.co/39UaHlB scroll down to the ratings and leave a 5 star rating and review The INTO THE IMPOSSIBLE Podcast. Other ways to rate here: https://briankeating.com/podcast Support the podcast on Patreon https://www.patreon.com/drbriankeating  or become a Member on YouTube- https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Learn more about your ad choices. Visit megaphone.fm/adchoices

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deprioritization during times of high network usage. how many people I sat at dinner with or at a poker table with or had drinks with or sat next to an airplane. And everyone said, I had that idea. I came up with that thing. And it was the thing that someone else turned into a big business. And even when you have the same thing, you can give the same IP or the same technology or the same thing to 10 different entrepreneurs or 10 different businesses or 10 different investors. And nine of them won't work. And maybe one of them will work.

So you never know what path, what team and what it's going to take to take a concept or a discovery or an invention. and make it into a business that generates money. Welcome everyone to this two-part episode of Into the Impossible with special guest, visionary investor and business leader David Friedberg. You're going to find out why companies like Alphabet and BlackRock trust this guest to invest over $300 billion of their capital to transform agriculture and other vital industries. Be forewarned, dear listeners, this two-parter may feel a bit like drinking from a fire hose. Our host, Brian Keating, dives deep with David into a wide range of top.

topics, from successful entrepreneurship styles, investing strategies, how to incentivize science, and the problems with commercializing institutional research and inventions. Mr. Friedberg provides rare insight into his strategies for achieving his almost superhuman productivity, his investing strategies, a tutorial on the microbiome, his thoughts on AI, the biology of aging, and so much more. Stay in touch with Professor Keating by signing up for his business. mailing list at briankeating.com slash list. And if you have a dot edu email, we'll send you a piece of deep space in the form of a rare meteorite fragment. While you're pondering this immersive

discussion, please consider investing in us with a five-star rating and sharing your thoughts in a review, like this one from T.J. in New York City. Dr. Keating does a great job of making complex interesting and entertaining. I enjoy the guests in every episode has an interesting takeaway. Definitely worth checking out. Now, get ready to stretch your perspectives with host Brian Keating going into the impossible with the inimitable David Friedberg. Any sufficiently advanced technology is indistinguishable from magic. Open the pod bay doors, please, help. Welcome everybody to a very special episode of the interesting.

The Impossible podcast featuring a fellow attendee of the University of California, in this case, Berkeley, way up north. And it is David Friedberg, who I've been a fan of for many years. He's an entrepreneur, an investor. He's worked with Google. He's worked on solving the problems of climate change. He's worked in agriculture. But like I said, most important thing is that he's a proud son of the University of California,

even if it is that one up north and not here in San Diego. David, welcome to Into the Impossible. Thanks for having me. Excited to talk. Yeah, it's been a great amount of fun kind of talking with friends and getting them to solicit questions for you. And hopefully I'll have a fun conversation about science, about the future of sustainability in science,

about nuclear energy, about biotech. We're going to dive deep because you were an astrophysics. You were an astrophysics major. at Cal. And you, I think it's probably true, David, that you've probably taught more people some really deep and beautifully, delightfully nerdy science on your podcast, which you're one of the four besties on the All-In podcast. I'm not going to speak very much about that fair, warning, full disclosure, but through the science corner, which I think we're going to start with, you have really brought to light to the masses, to millions of people. Your podcast is one of the most

popular in the whole world, really deep dives into things like fusion, decarbonization. And most recently, you've taught, you know, probably 10 million people what ribosomes do for a living. So the first question I want to ask you is, what drives your curiosity? What kinds of things spur your curiosity that then become a, you know, part of the science corner? What kind of things does it take to get your attention on a scientific topic? I don't know. I mean, I have a lot of subscriptions to journals. And, you know, they all come to my inbox every week and I kind of scroll through articles. Then there's certain topics that I tend to have kind of deep dives in where I'm spending time on something.

And then something kind of catches my interest and I go deeper on it and further on it. And then, you know, all of a sudden I come across something that I'm like, holy crap, does the world know about this? This is amazing. And I feel like sometimes I've talked about it. I will say like on the science corner on our podcast, I very rarely get to talk about the things I want to talk about or talk about them as much as I'd like to about it. I feel like we barely scratched the surface on the awe and wonderment of the universe and all that's out there and all that people should be thinking about and the humbling nature of the discoveries made through scientific endeavor. So yeah, I mean, it's great to share some of

those moments with people and, you know, get people intrigued and whatnot. But man, we live really just at the surface of the lake, if you will. Yeah, it is. Yeah, I mean, yeah, a lot of papers, a lot of research, a lot of blogs, whatever I can kind of come across books, et cetera. You mentioned journal. And then aside, by the way, like I will say like speaking to scientists, they'll often make recommendations on books and then you read these books and takes you down a whole other rabbit hole. Yeah, I'm going to, I'm going to send you some books that I think you'll enjoy or at least

will cure your insomnia because they're my books. But, but no, there are other books that I'd love to get your eyeballs on. So you mentioned journals, you subscribe to journals. I got into trouble recently on Twitter because I tweeted out that, you know, peer review is the worst form of scientific fact-checking, except for all the others, you know, kind of aping Winston Churchill's sober K on democracy. What do you think about peer review?

I know your forte especially is, obviously, you're trained in physics, astrophysics, but you have this special panache for biotech and biology, and we'll get to that in great depth later. But on the subject of peer review, what do you think of the replication crisis, the kind of pay to play journals. What do you see as a future?

What would you do differently than peer review, if anything? Yeah, I know a lot of people want to see these kind of decentralized models emerge. I hope that there's more experimentation around them and that there is some resolution. I don't love the fact that these journals charge these subscription fees and that they're really, you know, you have to get in a name brand journal in order to get recognition. I think that, you know, a lot of the preprint publishing, options are a really great way to get started. But I would say that there's probably one thing that's critical that's lacking, which is there's often an amplification of something, maybe a little

early in the cycle of it kind of achieving some degree of consensus. Let's use the word consensus, because there should never be absolute consensus in science. So as you make progress towards having greater consensus, maybe there's a moment at which point it makes sense to have some amplification. And often we see these kind of preprints that maybe aren't replicable, get a ton of amplification, they become kind of mainstay, they become accepted, that becomes fact. And then anything against it is, you know, hey, you're disputing fact, you're disputing science. So one of the things that I think is deeply missing in the amplifiers, whether they're Twitter people or journalists or what have you, is a kind of a better sense of scientific literacy and the ability to kind of

interpret scientific data and results, and then recognize that that data and results that can and should change under maybe different conditions or should be demonstrated by other testers. Because often I'll see someone pick something out of a paper or they'll miss the real point of the data and they'll say something that maybe isn't quite true or they'll jump on amplifying something that isn't quite there yet. So certainly from a process perspective, we need to kind of get scientific literacy rates and have a peer review system

that isn't contingent on this kind of commercial piping where you kind of have to pay to get access to the right journals. And then you have to pay to get access for the content in those journals. It's all, you know, science should be open. The data should be open. And there should be kind of a much more free-for-all

open-source voting process to support these things. what form that takes, I don't have a strong opinion. I haven't spent much time thinking about it. I know that there's lots of different ideas out there. Yeah, one idea that's been floated by, you know, hopefully future guests on the podcast, former UCSD professor Andrew Huberman, you know, is like NFTs and science, you know, where you pay, you know, to the Freedberg of microscope and you look down and you get the first, you know, picture of some, you know, the next thing you're going to dump into super gut. I don't know, I'm just making this up. But the point is you could use, an NFT model to support science? What do you think about that kind of suggestion? And could you see that, reverting back to the patronage of the Medici's? And could you see a downside of that potentially? Yeah, I don't know. That feels a little quirky to me because I think it implies decentralized funding. I don't think you need decentralized funding. Like, you know, all these systems of decentralized networks, they're not necessarily the best way to solve a problem. Like, you know, if you remember back

in the day, Skype was like a peer-to-peer communication system, and the latency was high, and a lot of bits were lost along the way. And so Skype didn't end up winning, right? Like, Zoom and Google Meet and Microsoft Teams won because they had a more robust, centralized network model that worked. The data would be transmitted to the center of the network and more efficiently routed back to the consumer. And same with a company called Juist, which made this peer-to-peer video sharing and I think Caza on Napster all these peer to peer systems were

less efficient. They're kind of trying to solve a problem in the least efficient way or in a less efficient way than maybe a centralized model. So I don't think that it's about decentralized funding. I think it's about, you know, we can aggregate more money through a central source like some federal

grants or what have you. There's another interesting model I'll share in a minute which is kind of private enterprises. that can aggregate large pools of capital and then allocate them within that pool in a way that maybe has smarter capital allocators than a faster turnaround time on allocating capital than, say, a government source. The problem with decentralized networks is often you see adverse selection. You see this in like the insurance industry. The term is often used the adverse selection where it's the best storyteller that wins, not necessarily the best candidate. And so we saw this with like, you know, all these ICOs, these initial coin offerings, or even with FTX, it's easy to take advantage of people. It's easy to take advantage of a distributed

network of funders versus having someone who has the criteria and the ability to kind of screen things in a smarter way than say a lot of people who then get drawn into some story. And then they lose a lot of money and things don't go well. So I'm not sold on the decentralized funding model as much as I am on just, you know, having more transparency and functioning around. How do we fund things? through centralized, aggregated pools of capital. Yeah, to that extent there, there's a lot of the, you know, journal kind of peer review process that, you know, resembles the academic proposal and grant writing process

in that it's not transparent. There's no feedback. I just had a young student, brilliant young man, and he applied for a fellowship, and they actually wanted him to apply for this fellowship. And he just got summarily rejected, and he got no feedback. And at least with a journal system,

You do get feedback. There are at least two people that typically will review. I've been a reviewer for nature and science and all these journals. And there's usually at least two people, but not much more than that. And at the proposal level, it's much worse even. And that sort of makes me want to pivot. Although you did say, did you have another model that you wanted to suggest or in that front? Or is that part of that? Well, you know, there's a there's a group that got funded last year called Altos Lass. They raised about $3 billion in startup funding. And they're pursuing this kind of, you know, of epigenetic partial reprogramming of cells to make them more youthful, right? These Yamanaka factors, which won the Nobel Prize a number of years ago, demonstrated the ability to kind of induce pluripotent stem cells from any cell. So you can kind of apply these factors and they kind of rewrite the epigenome of the cell and the cell starts to act like a pluripotent stem cell. And it turns out that what being called partial reprogramming allows the same cell to act more youthful and kind of resets the epigenome not all the way to being a stem cell,

but to being the type of cell it is. And as a result, and that cell actually starts to function more effectively. So it's kind of this youthful indicators are through the roof. It's really an incredible set of demonstrations. It often ends up causing cancer, however, because these cells, some of the cells go all the way to being stem cells and then they start proliferating uncontrollably and you have these kind of cancer problems. So it's a very difficult kind of bioengineer. engineering problem to figure out how we're going to make this work therapeutically. So this company, Altos Labs got stood up. I think they raised $3 billion from Bob Nelson, who's like one of the best biotech investors in the world, and Yuri Milner and Jeff Bezos.

A lot of people put money in. And they've been going out and just grabbing some of the best academic and research teams out there that are like they brought a ton of people out from UCSF. And I think part of the pitch to them was, look, you guys can make a decent salary working here and you don't need to go through this multi-year grant writing process. We have good smart capital allocators will make very quick decisions at the top. Everyone will see it. It'll all be very open and transparent. You'll get this funding.

And then your team and your work can continue to do their research. And 100% of the IP will be owned by the holding company. And right now, all the IP would technically be owned by UC or UCSF in this case. Now it's going to be held by this holding company. But the benefit to the research team to the scientist is you get very fast turnaround times. You don't have to wait. You don't have to kind of get dolled out little small checks here and there from these grants, spending a third of your time writing grants.

and then reporting back in a way that allows you to get your next grant and maybe doing things that are a little bit disincentivizing. So I think it's a really interesting model. I'm really curious to see what comes out of Altos Labs. Given that the number of teams that they've gone out and vacuumed up from academia, the probability of that portfolio of teams having a breakthrough or a set of breakthroughs that has commercial success is very high. And so it's enough capital to give you enough shock.

on goal that one of these things will work, one of these teams will come up with something that you can kind of develop, and then they own 100% of it instead of going to UCSF in front of an IT license and waiting years for that and so on, which I think is the traditional model of these biotech companies getting stood up. Right. And I look at that and I contrast it with basic physics, the type of research I do, which is also becoming more and more supported by private foundations, my observatory that I co-lead, and it's called the Simons Observatory after Cal grad, Jim Simons.

is the long-term supporter of cosmology and basic science as well as autism and many other fields. But he's often criticized, you know, like, well, what's this privatization of science and basic, you know, it should be free? Everything should be free. And aren't these billionaires just going to get whatever they want? I mean, you hear Bill Gates criticized incessantly for the work that, you know, kind of, I wonder in, and that's in cosmology, you know, what I'm doing or, you know, Jim Simon's is supporting, you know, just personally. or, you know, the team, not me personally. But the point being in a field like biotech where you have, you know, implications on human health and you have, you know, the risks and human subjects,

volunteering, is there a risk of, you know, the privatization kind of attracting, you know, really the unwanted attention, you know, that you didn't build this kind of ethos that, well, yeah, you're just coming in and skimming the cream off the top after these universities, public universities, have put so much into it. And how is it fair that some billionaire, can, you know, profiteer off of this. So what do you, how would you answer such critics? Okay. So I'm a big believer in the efficiency of markets. And a market has a buyer and a seller. And if a buyer is willing to pay what a seller wants for something, a transaction will take place.

And so you have to have a market where there are buyers and sellers coming together for things to sustain themselves. Otherwise, they're being force fed and they're not real. So we'll talk about this, I think, a little bit later, but the term sustainability for me, I think needs to be thought about in the context of how sustainable is this sustainability endeavor. And sustainable meaning how much will the market support it without some massive intervention driving it? Because otherwise, it's not what the customer wants. It's not what the buyers want. It's what some side of the market is trying to push into the market and they're subsidizing it or reducing the price.

And so those are all really bad things. And on the flip side, if they're holding things back, that the market, wants to buy, that's also really bad if the sellers aren't willing to sell what the market wants to buy. So I am all for private enterprises and people allocating their own capital and having hundreds or thousands or millions of businesses and entrepreneurs coming up with ideas, bringing them to the market and seeing if there's buyers and investing capital and making those pursuits. I am absolutely in support of basic research, product development happening in private enterprises to see that there is a marketplace for things. Because once

money starts coming in, it funds the next cycle and these things to kind of keep going. Now, in the academic and government sense, there are some early stage efforts in pure science and research that are unfundable from a capital markets perspective because the time horizons are too far out and the uncertainty is too great. And you have to spend a hundred billion dollars or $20 billion. And then maybe you have a 5% chance at a breakthrough. I mean, look at how much the ITAR facility in France is going to cost. I think it's at $30 billion at this point. There is no marketplace to support that.

Look, I'm a donor to Lick Observatory at UC. Alex Villopenko, who you may know is a good friend of behind. And yeah, and so I am very much in support of funding peer science. There's a lot of work that's gone on at UCSF. One of the advantages UCSF has, for example, is they also have access to a patient community where they can get incredible data. and get in field very quickly.

So they do have an advantage in being able to kind of generate new science and new discoveries. The challenge arises that that science then either gets licensed out in a way that UC doesn't benefit enough from the discovery that was made where they own the IP,

or they're just not open sourcing it. Those are the two problems. And what happens often is something in the middle. And that thing in the middle is where like, you end up with some guy who thinks he's too smart and he's the licensing guy and he goes out and he tries to negotiate one deal, and the smart VC comes to him and he takes him to dinner and drinks and he's like,

okay, I'll take 6% of your company and you get the license to this thing. 6% of the company for the IP, UC should own 100% on day one. And then the VCs should kind of compete for who's willing to pay what to get access to that IP. There's no competitive auction model. There's no model that really maximizes the value of the IP that was generated by UC in that case or a research institution. That's one way is that the research institution can either maximize the value. value for the investment that they're making. And if they did and they could capture a piece of that, there would be incredible returns. The Cystic Fibrosis Foundation funded a biotech startup that did

peer research, came through with a breakthrough therapeutic. That company sold the royalties and the license on that. And Cystic Fibrosis Foundation, I think, made $3 billion or something out of this outcome. And I think that's the model that the academics and the research institutions should be taking, which is maximizing value and doing it in the way that's really structured. So it's a auction, it's a market-based model versus one guy kind of, you know, and I've tried to license stuff from universities. I've tried from Harvard

from MIT from the UCSF. And I've got to tell you all these guys operate differently. It's impossible to get through to anyone. It's impossible to wrangle these things out. BIP just kind of sits there in languishes. They have these incredible breakthroughs. They publish on them. And then you as an enterprising business that's willing to put a billion dollars behind it

to bring it to market and make it real, can't get access to it. And when you do get access to it where someone gets access to it. They're usually an insider who gets a good deal on it. And then the university doesn't benefit as much as they should. The other alternative is to just open source everything. And that's another way to say, look, the government's going to fund or the university is going to fund X billion dollars over this amount of time. And everything that comes out is available to everyone and let the market proliferate and figure out how do you take that IP forward and build businesses on top of it. I think both of those models can work. The problem is we

have a model that kind of doesn't provide it. Yeah, we have the Schrodinger's model. just to push back with love and respect. So we had an exchange of, you know, capital for labor, you know, here and it resulted in a strike, a very well-publicized strike at the University of California, including up there Cal and down here in San Diego. And graduate students and postdocs and kind of the lifeblood of the university went on strike, essentially saying, you know, that their contract was unfair. And yes, people had agreed to, you know, to sell something. And there was a willing buyer and a willing seller, but no more.

and I wonder, you know, maybe pivoting that to to this notion that, well, okay, so if capitalism is the best system, you know, then scientific capitalism, it's always going to rely on labor at some level. Now, the problem I had with the graduate students, as much as I love them, and they do become part of your family. I mean, I had my graduates, some of my graduate students at my wedding. You know, I've been to, you know, funerals of people that were my mentor. So it's very familial. And the strike kind of brought some possible enmity between what should be kind of like a family. I notice I'm rambling a little bit, but the point being, when you have labor that is also getting an education and getting training and getting professional development, unlike medical students, right, who go into debt, you know, hundreds of thousands of dollars, is there like kind of an, what are the obligations, say, of the researchers that are, you know, some cases in biology, they do get kind of exploited in physics. It's much less so. We have to pay basically the rate of the highest, you know, NASA fellowship to get a postdoc here. But what are the obligations of the of the, of the, of the, of the, of the, of the, laborers, if you like, in science at the academic level versus the mentor or the capital class

versus the labor class. I know it's not a perfect analogy, but what would you see as the obligations? We're all in the same effort to try to benefit scientific knowledge. But what are the obligations of, say, the student researcher class versus the PI class? Who should benefit as well? You know, should the graduate student who makes this discovery that gets monetized and the PI's name is on the patent? You know, what are the obligations as well as the responsibilities? Your summer starts now with Memorial Day deals at the Home Depot. It's time to fire up summer cookouts with the next grill, four-burner gas grill, on special buy for only $199.

And entertain all season with the Hampton Bay West Grove seven-piece outdoor dining set for only $499. This Memorial Day get low prices guaranteed at the Home Depot. While supplies last, price invalid May 14th or May 27th, U.S. only exclusions apply. See Home Depot.com slash price match for details. Yeah, there's an important question there also as it relates to like athletes in universities, right? And there's been a lot of controversy lately on the NCAA and athletes can't take endorsements.

And it's kind of a similar model where you're given more than just a salary. Look, this is a broader philosophical question. What does someone who gets to be a doctor get versus someone who has another job that maybe isn't as fulfilling or rewarding to them? You know, there's an element of why people take the job of being a teacher when it doesn't pay as much because it's rewarding and it's what they want to do with their time and with their life. And I think that there's an important kind of judgment call about, you know, what someone wants to get out of a job. And it's often not just the salary. It's the salary plus. And the plus, I think, if it's enough to make up for the delta on salary relative to other alternatives or other expectations. it kind of gets you there.

But I think I'll come back to your other question in a second. I view unions to be almost like startups. It's like if the entire organization is dependent on the labor and without the labor, the organization doesn't work, then the labor is the company. The laborer is the startup. Service, yeah. Students as a service.

Yeah, the shareholder. Yeah, and that's exactly what it is. And by the way, in the model of kind of decentralized worlds, decentralized networks, we do see this concept of instead of everyone working for a company or working for a thing, everyone works for themselves. And then they can plug in and out of different things that they work for. And it's an individual as a service provider instead of a group of people as an employee base. And that also creates a more efficient market where kind of individuals find themselves.

The bigger issue with unions generally, I think it works well in a market. It works well for the laborers where there is almost a commoditization of the labor, like everyone working on a factory line. But like I've been on the board of the SF Symphony and there's a union for the members of the orchestra. Obviously in universities, graduates units are not all created equal. You know, I think that that the idea of having a union there, you know, it can kind of work against some people in that market. I do think one of the ways to resolve that salary plus gap is to create a model where you do

share the IP value with the research team of the research team. discovery team that worked on something, let them participate in ultimately the commercial success of that output. Again, if there was a really great engine at all of these government or academic labs that published in an open-source way, the IP, they wouldn't benefit. But if it was like a bid model and anytime new IP came to market within six months, it's going to be an auction, people can come in, they can bid on the IP, they can get access to it.

And it doesn't have to be this kind. And I know that's a little naive because all the IPs clustered in different ways and it comes out in a long line of stuff over time. so on. So universities, it can get very complex in what IP you're licensing now. But to have a value share model with the research team may solve that salary gap problem. But as you know, then you have the problem where some researchers aren't putting out IP that industry wants to license. You know, if you're working in anthropology, there's not going to get a lot of value coming out of your lab versus if you're working in pure physics. Or even in physics,

I have two patents. They've never made me a penny. And Elon, I know, has said, you know, patents are basically lawsuit bait. And, you know, in physics, there's, there's precious little, you know, as opposed to biotech where it's like you basically sits, you know, there's a lawyer in your laboratory and many of my colleagues' laboratories and maybe that's a sign they're doing more useful research than, you know, pure astrophysics. But, but anyway, continue. Look, I'm not sure like the right answer on the graduate student strike and, you know, whether they have an obligation to do research, I think.

For them, if the benefit of the work and the benefit they get from doing the work, plus the salary they make isn't enough, like any human, they're going to want to look for other things or try and make up the gap. So I don't know, it just seems like a pretty natural kind of marketplace dynamic to me. So, you know, we see it everywhere. Yeah, so pivoting just back to pure science for a second, just taking a question from one of my audience members that's been submitted to me. So Leif wants to know what question in science would David give his right arm to know the answer to? What material will superconduct at room temperature? Ah, awesome.

Okay, great. So that, I think, you know, as folks that are probably listening know, you know, superconducting material has no resistance and has this ability to kind of be a perfect magnetic field reflector. And it's long been theorized. And as you know, there's several theories for how we get there that we should be able to realize some sort of structure or some sort of molecule or some sort of, ceramic or something that can actually superconduct at room temperature because the cost of cooling makes superconducting materials, you know, not very extensible, right? We could reduce the loss of

electricity and transmission lines. We could create levitating trains and reduce friction and energy needs for transportation. I mean, the applications, you know, the incredible applications in computing, you know, imagine having, you know, the equivalent of a quantum computer in your iPhone. Like, I mean, there's just so much that could be realized if we can get a material that superconducts at room temperature. And there's all these phone-on pairing theories and not a lot that's been proved or disproven really at this stage. And it's like we always kind of walk a little bit halfway closer to the wall. And no one knows how or why we're getting where we're going. But that's a really exciting area.

I mean, I was 13 years old and I had a science fair project at school. And I did it on superconductors. And I actually got the superconducting disc from UCLA a couple bucks or something. some liquid nitrogen from UCLA and poured it and did a magnet floated it. And I said, I have my board. And I said, here's all the things you're going to be able to do with superconductors in the future. And it's going to totally change the world. And we're going to have, you know, it started with this etriam, baryum, copper oxide ceramic. And then everything is going to change. And, you know, here we are. I was 13. How old in mine? I'm 42. So, you know,

30 years later. And not much has changed. But yeah, that would be my big question. Yeah. It's certainly fascinating. I always point out, you know, superconductivity is the one problem that could have been solved by Feynman that he basically failed to solve. It was solved by my graduate student, one of my graduate school professors, Leon Cooper and Bardeen and Shrefer. Right. Shrefer, the Cooper pairing. Yeah, Cooper pairing. That's right. Leon is still alive and kicking. I saw him back in May when I spoke at Brown University commencement. And he's a, character. But pivoting to Shrefer, who is one of his co-loriates, so Shreifer won the Nobel Prize twice. And his first Nobel Prize he won it was with William Shockley, who was a denizen of the Bay Area.

and one of the founders of Fairchild. Yeah, Fairchild Semiconductor, which then the traitorous eight, I think it was called, by him, including Gordon Moore, went off and started, T.I. and then Intel. But I want to bring up Shockley, not for his, you know, kind of reprehensible eugenicist ideas and stuff involving the Nobel Prize sperm bank, which he allegedly contributed to down here in San Diego County. But instead on the transistor that, you know, he co-invented and is responsible for us having this conversation. There's about 10 billion transistors on each side of our screens just alone. And the fact that he and basically all, you know, I've interviewed 14 Nobel laureates and I've asked them, you know, maybe sort of questions along this front.

But I think you're not Nobel laureate, but you, I think in the realm of sustainability and kind of financial remuneration. Look, it's important to say. scientists. Everyone says, oh, I would do it if I didn't get the prize and I do it if I didn't get the money, and I believe them. But the fact is, David, that there's not a single Nobel Prize winner in any field, maybe economics. I don't even think so. I interviewed Wido M. Benz at Stanford across the bay from you, and he said, you know, basically he did it for the fun of it, not for the money. But anyway, there's not a single laureate, to my knowledge, that died with a net worth greater than 10x of his,

mostly his Nobel Prize winnings. And some are in fact, we're almost destitute by medical illnesses. Leon Letterman, who is an experimental particle physicist. He had to sell his Nobel Prize to fund his dementia treatments and Alzheimer's treatments. Watson, James Watson, I think, sold his for several million dollars for finance. In other words, these Nobel laureates are responsible for pure technology, for great discoveries, DNA, the transistor, the laser, Charlie Towns, who I knew briefly at your alma mater. He invented a laser, which is, you know, contributes a trillion dollars every year to world GDP.

Died, again, not destitute, but how do you envision or how could we envision a model where physics could be sustainable? And that the physicists who do contribute could receive financial remuneration. Is there a kind of a marketplace solution to the problem of sustainability? In other words, should we tax every email, you know, 0.1 cents or every web, you know, hyperlink that's clicked because it was a CERN byproduct. Do you have any thoughts on this day, but how we can make it sustainable? I mean, we already, you know, NFT, I don't think that's a starter in many, for many reasons. But can you think of ways that we can make it sustainable and remunerative to the inventor class? I just want to challenge the notion that the one thing is the value creator for the

whole of the market, that the laser is the trillion dollar market. And I'll say it like this, there is this kind of motif that there is this rock star entrepreneur who builds a business and makes all this money. And the reality is that that individual is a small member of a very large team that is tackling a very large set of problems on a daily basis, using a lot of capital to get there to figure out how do you build a business and create value, create market value, and create a product. And that whole team is dedicating their lives to the work. And that whole, and all the capital that went in came from pension funds and retirement funds because it's managed, you know, that's what DCs typically manage and so on and so forth. Or it's like large investors that

are mutual funds that are putting money in the public company. When you play this all out, we all think that it's an individual running across the field and then they got. Right. The idolatry of individuals. It's a, it's a rugby scrum. Okay. And the rugby scrum is pushing the ball down the field, trying to get this thing in the other end before they get their heads off. That's the reality of like building products and building businesses in a marketplace. And the unfortunate reality of being a human

is our brains are wired in such a way that we all think that we're the outsized contributor to that success. I elbowed that guy in the head. I took him out. That's how you got to the end zone. Only three people can win the Nobel Prize, right? Yeah.

Exactly. And the Nobel Prize problem is the same problem in entrepreneurship. and in scientific research. And I'll give you kind of a, I'll say two things. One, both Kanye West and Taylor Swift complained about the fact that they sold all of these original tracks that they had, I forgot what they, the masters to the record label. At the time that each of them sold their masters to the record label, they were struggling,

emerging artists. And they said, for giving up all of my future rights to this music, it may or may not work. And that moment, we had no idea what would come with that music. and the record label took a risk and they put a bunch of money in their pockets. And Kanye and Taylor Swift said, oh my God, that money just changed my life. Yeah, they sold some call-up. It just so happened to be. They sold some call-up.

Yeah, just so happened to be. Exactly. And then the record label happened to make a ton of money. And then they come back and they're like, wait, wait, wait, I want more of that money. And all the artists that lost, that the record label spent money on that didn't work out, they don't get to come back and demand a lot of money because their music didn't work out. And they got paid. And the record label doesn't get to go to those artists and say, give me back my money.

your music didn't sell. And I use that as an example, not to be this capitalist pigger to be disparaging. But the reality is, number one, we don't know what's going to work ultimately. And number two is there's a scrum to get there. There's so much that goes into, I mean, think about the original transistor. How much does the original transistor look like the 10 billion transistors? It's a coat hanger, David. It's a coat hanger with some chicken wire. And then there's a piece of chewing gum in there. Oh, look, we'll put a clip of that up here in the, and the video that if you're watching on YouTube. Yeah.

And I've like, I can't tell you how many people I've sat at dinner with or at a poker table with or had drinks with or sat next to an airplane. And everyone said, I had that idea. I came up with that thing. And it was the thing that someone else turned into a big business. And even when you have the same thing, you can give the same IP or the same technology or the same thing to 10 different entrepreneurs or 10 different businesses or 10 different investors.

And nine of them won't work. And maybe one of them will work. So you never know what path, what team. and what it's going to take to take a concept or a discovery or an invention and make it into a business that generates money. And 99% of the value, unfortunately, comes from that business building exercise because that's where the scrum happens. That's where you're hiring and firing people, burning money, iterating on product market fit, trying to figure out what customers want. Taking that original coat hanger and turning it into this incredible LCD monitor that cost $1,500 that I can sit in front of today and talk to you through the air. It's like, you know what it took?

How many people it took to go from that to this? How much money it took? Yeah. You know, and as much as I think that that invention is a really, like, powerful moment of a breakthrough, it is the guy in the coffee shop with the napkin that has the idea. And then the other guy goes and builds the e-commerce site and or 15 guys take that idea and 14 of them fail and the one guy builds it, the guy with the napkin, you know, he had a great idea of, but 99% of the value is.

So it's hard to say, I don't believe in this whole thing about glorifying either the entrepreneur or old or the idea person. And I hate this idea of the term founder, frankly. There are so many, quote, founders in Silicon Valley who start a company. And then they're not there when the company ultimately figures out its product. Or when the company ultimately starts making money or finds a customer or goes public. And like from the first 20 people to the next 20,000 people, it's a completely different work. It's a completely different company.

It's a completely different skill set. And 20,000 people worked on it for 10 years. Yeah. And those 20,000 people that. worked on it for 10 years, all contributed time and all these investors contributed money. So, you know, for me, it's not about like, how do we, you know, get things back. I do think that we need to make sure that IP rights end up in the right way, that there's a marketplace to get these things going and get people to participate.

And all the universities are, you know, Caltech has a friend of mine, um, runs a VC at Caltech. And it's like all, it's all about bringing Caltech, uh, physicists and biologists out into the market twice and giving them ownership of their IP. and they've got a great licensing deal with Caltech. They can take that IP transferred into startups and then they can go and build their business. Yeah, and across the country. And making it equity and ownership.

Yeah, across the country, the other school with a beaver mascot, in addition to my fair beloved Caltech where I was a postdoc for a few years is MIT. And they have the Bose, you know, the Bose family of those sound products and medical products and so forth. They have this, you know, their MIT is one of their biggest, you know, effective shareholders. I wouldn't know how you describe it.

But part of the problem is, you know, I would say that, you know, one of the main challenges for someone doing fundamental physics is that or fundamental science of any kind is that sometimes it produces technology, you know, and so then people really have this expectation that you're, you know, you're the goose that's going to keep laying these golden eggs. And as you said, there's a completely different, you know, kind of toolkit to get from, you know, one customer or just the prototype, you know, the chewing gum and, and coat hanger to the, you know, LCD monitor. At the same time, I remember listening to a talk by Jim Simons, again, you know, proud grad of Cal.

And he, you know, and he said, you know, he famously runs one of the most profitable hedge funds in history. It might even be the most profitable one, Renaissance technologies. And he said, you know, for a second month. Yeah, for a, yeah, right now it's Griffin, right? But he said, you know, for a long time, they famously only hired PhDs. And it was only like astrophysics. So I sent some of my students to him and he would review them. But now they don't do that anymore because they found that like they they don't know anything about business it's a very different skill. I found even with you know doing this podcast, you know, in my spare time, which is, which is so lovely, I get to talk to so many brilliant people, including, including yourself.

And it's really brought a lot of joy to me. But it's a completely different skill set. And I'm sure you know that. Like there's no skill set. There's no job description podcaster, right? I mean, it's a billion micro skills. And I don't think we do a great job of teaching. You know, there's no job, there's no single job professor. I mean, I have like 80 different things from research, teaching research, fundraising, you know, outreach. But oftentimes, David, the kind of non, you know, technical lab work, you know, grunge work in the lab is if you don't do that, you're kind of looked down upon. When I wrote my first book, losing the Nobel Prize, I went in to ask my department chair, who was the son of a Nobel Prize winner, who

co-invented the laser, Nikolai Bassov. And I said, you know, Dimitri, can I have some time off you know, to write this book. He said, we won't give you any time off, but we won't explicitly punish you for writing this book. In other words, this is not something that, you know, that a serious scientist should be spending his or her time on. And I've had that from my friend, John 11 at Columbia and Barnard and other places. But to what extent are we maybe compromising the future output of science? I'll just focus on physics by basically ascribing maybe not the best, you know, kind of attitude or cultural attitude towards those people that want to monetize or want to start businesses as well as be scientists. In other words, we're not teaching them those skills,

but we might also be stigmatizing. I don't think it's a big secret that, you know, 99% of my colleagues are, you know, are Democrat or very liberal and they might look down upon, they supported the strikes and so forth. They might look down upon, you know, any attempt to monetize or something like that as being detractive to pure physics and the reason they, quote, unquote, got into it. Do you see it like a, benefit to teaching entrepreneurship or, you know, some kind of skill set within a physics department? I've seen it at a lot of places and it generally like has not worked well. Okay, so why is?

Yeah, why are they negative out of? Yeah. So my three biggest predictors for entrepreneurial success are grit, bias to action and narrative. Ability to kind of tell narrative. So grit is perseverance through failure. which many scientists actually do have because most of the work you're doing is iterating, continuing to kind of develop even after something fails. Bias to action is a little different where so much of, you know,

depending on kind of what science we're talking about, you're doing careful planning. You don't get to do careful planning when you're building a startup or a growth stage business. You have to have a biased act. I always tell people the absolute index of bias to action is the movie Groundhog Day. By the way, happy Groundhog Day. Because, yeah, yeah. So you get to basically live the same, if you could live the same day a million times in a day,

you could make that day perfect. And that's what a startup is. You don't know what path is going to get you to the other side of the mountain. And you have to get over there before you run out of food and water. And so you've got to constantly be looking, realizing you're wrong, turning around going back,

realizing you're wrong, turning around, going back. The faster you can do that, the more of a bias you have to responding to what the environment or the market or your product development cycle are telling you, the more likely you are to succeed before you run out of capital or time. And then narrative, the person who indexes infinitely on narrative, so let me just say, each one of these three categories you can index infinitely on.

If you index infinitely on grit, it means you will never give up. You're out of water. You're out of food. You're out of money. And you convince everyone to keep going and not and not, you know, not 400. The movie 300. The movie 300. Exactly.

Exactly. That's the absolute. The absolute on bias to action is the movie. is a movie Groundhog Day. And the absolute on narrative is a guy like Elon Musk who could show up, tell you he's going to do something and then not do it and still get you to give him more money and do it over and over again for 10 years. And then finally he delivers a product.

But the ability that he has and Steve Jobs had is this ability to storytell people about what's possible in the future. And the power of narrative is that you can attract employees to come and help you build, getting that scrum with you on the rugby field. You can attract capital. Investors will keep giving you money. so you actually have more time to get there, and ability to attract customers,

sell them on the vision and the dream of what you're building. And those three skill sets are independent skill sets of, and I'm not saying that there's no overlap. There can certainly be overlap with scientists that are working in academia. But I'm saying those are the skill sets that predict entrepreneurial success. And there's a real question on how much of that comes learned through behavior and experience, how much of it is innate, and how much of it can be taught. And that's where I think there's a challenge of like,

let me just throw up an entrepreneurship school and teach people how to be entrepreneurs. I think that these things that are necessary for entrepreneurship, all the rest of it's tactical. It's textbook stuff. It's like, do this, right? This plan, you know, find your customer. Like there's a lot of people that help and support entrepreneurship and development.

But without those three things or having a strong index on some set of those three things, I think, you know, the probability of success is generally very low. Yeah. And so that's why I think these things are really hard to kind of find. Yeah, there are to find. And then there's also the kind of, you know, check your own kind of some cost fallacies and biases, too. Because everything you described described, you know, the systematic, you know, behavior of someone like Elizabeth Holmes, right? I mean, good storyteller, lots of grid, hard work, or bias to action, you know, influential customer seeking.

And you couldn't get, like I said, a better story. And that brings me to my, you know, I was going to ask you, you know, kind of another frivolous question. But I'd like to know, kind of in terms of how you systematize. Ray Dalio is famous. He has a quote that you should have, you should, you should a good entrepreneur in his case. He's saying should have many things that he systematizes

or she systematizes very well. And I wanted to ask you, you know, what things in your investment strategy or the way your thought process is, do you strategize in the mold of that famous black turtleneck wearing entrepreneur,

inventor, Elizabeth Holmes. So are there things, you know, do you have like a routine? Do you have a daily routine? Do you have, you know, monitoring things? How do you system? What do you systematize? And what's your rubric for systemization, if you will? Of the work I do.

Yeah, the work you do. And also, I'd be interesting in your personal, you know, habits if you want to share like sleep and things like that, but then you're into an investment. Yeah. My challenge is probably one of orientation towards creativity. So I bias towards creativity. So if I'm not feeling like I'm endeavoring into a new space, like I have almost like an interest or an addiction for new things,

for a new space, you know, maybe there's this intellectual curiosity problem that drives me to do that. But it's coupled with this idea that I have to make stuff or enable the making of stuff too. And so that makes it hard to have, although someone did once tell me who actually wrote a book on the psychology of creativity, that having a system for creativity can actually enable more creativity. than having the absence where a lot of people want to kind of have the absence.

Having a framework, having a painting size, right. Those are constraints, the constraint model, you know. Yeah. So look, I mean, my world is fairly, I mean, if I were to kind of try and classify it, I've got companies that I'm an investor in or that I help start that I'm an investor. So I own stakes in companies that I'm involved in. And they have different need states. And they are very dynamic.

And as their need states kind of increase, I need to spend more time with them. And that's why it's not as easy as just every week. I check in with each one. Sometimes I don't need to spend time with them. Sometimes there's a crisis and I got to spend all week with one of them. And then the work I can help them do is either recruiting or raising capital or product and strategy and customer stuff. And so that's kind of spending time with companies.

Then the other one is making sure that I have adequate capital to do the work I want to do. So, you know, we raise capital from other investors and we use that capital to support the work we're doing. And then the third is the most interesting, which is kind of exploration of new opportunities. And exploration of new opportunities is all about thesis development. So I tell people, like, our job is to connect the dots where we spend time reading papers or meeting with scientists or academics or researchers or engineers. Then we separately spend time with interesting executives or entrepreneurs.

And we separately spend time in the markets, understanding the market, the businesses that are operating in these markets very deeply. And then if we can see a connection, like if you took this engineering capability and this scientific discovery, you put them to. together with this team, you could change that market in that way. And that's the kind of connections that we try and make, which is what are we seeing the connections between that other people maybe aren't seeing? And that then forms the basis of a thesis. That thesis can then go be tested or invested against. And so we would then deploy capital into an existing team because

they map to our thesis and then they can go and kind of test it or they're executing well against the thesis and we want to support them and fund them. Or we can help kind of build a team and start a company to go execute against that thesis. So that's kind of how I would classify how I spend my time. Yeah, it's a scientific method. You're basically applying a scientific method approach to it. I want to ask you, I've interviewed a few billionaires on the podcast, Jim Simons, Michael Saylor, Tom Billue and others, unicorn founders and hedge fund, you know, all different, all different types of personalities. And I always like to ask them, you know, from your perspective, what is the purpose of wealth? You know, you often, you remember the original Wall Street movie,

with Gordon Gecko and Charlie Sheen. You know, Charlie just wants to, you know, he's making all this money. He wants to become wealthy so he can ride a motorcycle across China, which Ralph Potts is a well-known author, podcaster, et cetera. And he said, it costs about $5,000 to drive a motorcycle across China. Like, in other words, you could do that right now. You don't need to become a multimillionaire as Charlie Sheen wanted to do in Wall Street, the original one.

But he asked Gordon in that movie, if you remember, he asked him, you know, like, what are you doing all this for? Why are you ruining so many lives? I'm not accusing you of that thing. But there's only so many yachts you can water ski behind. So I want to ask you, from your perception, what is the purpose of wealth? Like, I think that there's, I'm a believer and I'm aligned with this concept that humans are wired. Living organisms, and you could actually trace this back to principles of physics, are oriented around desire.

that we are programmed to have this Zen Buddhists talk about it as dualism, like this idea that there's two sides to everything, when in fact, they are the same, okay? Yinyang, or they have elements of the same. Yeah, and the whole purpose of Buddhism is to see the unity of everything and not what the human brain is wired to do. And we're wired in that way because we have a body and a not body, right? We're wired to see what we are and what we are not. We are wired to feel what we have and what we have not.

And as a result, we see what we have not and intend to have more. There's this innate human desire for more. Yeah. And there's always something on the other side. There's always something more. And that's why we are successful in the sense of kind of thermodynamics. Like we're a feedback loop. Like we're really good energy consumers.

And as a result, we multiply. And that's what light. is kind of predicted around. And so on that premise, I think that there are three kind of intangibles that humans desire in a way that's fairly unconstrained. One is fame, which is recognition, the other one is influence.

And the third is wealth or assets, whether that's food or money or whatever. And so that's more like, you know, how much stuff do I have? And those three things are, they can be traded for one another. Like if you have a lot of influence, you can get paid by people to influence a group of people. You're a definition of an influencer. You know, if you have a lot of fame, you know, you can turn that into money, right?

You can get paid to do things. If you have a lot of money, you can buy ads and put yourself on the TV and become famous, right? And so I think that, you know, there is this kind of innate human orientation around having more stuff. And then we rationalize why we have those things. And we rationalize all humans rationalize all their behavior in some like ultra-a-weight. Like no one's innately evil. No one thinks I'm going to do a bad thing today. Everyone thinks they're doing things for a good reason.

And so what we do. Yeah, good intention. So we have this kind of unconscious orientation. And I would call it kind of almost programming. I mean, I don't know. You probably know Jeremy England. You know, would be cast on the podcast. Oh, he was.

Yeah. And I think like there's this idea that in, in physics, we're just oriented around consuming more energy. And then Nick Lane, I talked about his book on my podcast where, you know, if you think about it, it's quite simple. The feedback loop will dominate in a kind of chaotic system, the feedback loop that can self-replicate. Yeah.

So the self-replicating feedback loop wins. And so it absorbs all the energy and so on. Anyway, we're oriented that way. And then we rationalize our orientation, our desire for more stuff, whether it's fame or influence or money, because we think we're doing a good thing. And then everyone's got their own story on that. And I could tell you my bullshit story, but someone else will tell you another bullshit story around what do I intend to do with my wealth. What do I intend to do with my fame? What do I intend to do with my influence?

Why do I want to be president? I want to help people. Why do I want to have all this money? I want to help people. Why do I want to have all this fame? Because then I can help X, Y, or Z. Everyone's got this kind of altruistic intent that they rationalize this orientation around. How much of it is ultimately realized or not is honestly there's a lot of motivation and energy that

they put into it and luck is associated with it on what they can actually realize in terms of impacting the world. But like most people, we want to have an influence on the world. We want to see the things around us, the things that are not us, be influenced by us. And we use one of those three kind of vectors of aggregation to intend to do that. So, you know, I could tell you a bunch of stories around nonprofits. I could tell you about my interest in creativity and changing the world, finding problems, solving problems. That's what I want to do with my wealth.

Move humanity forward, have less suffering. Those are all the things I'm super excited and interested by. And, you know, I think like my, my, again, I try and be very honest about how much of this is me rationalizing my role in the universe. Like we always, like we all do, right? Like, I want to, like, even scientists, I want to have a discovery that can move the, world forward that can help us understand the universe better. I mean, that was always my intention as a kid. My interest as a kid was like, I want to understand how the universe works. Right. And then I want everyone to know that. And then, you know, and that, that's my fan, right?

Like, if I look really deeply as a kid, I wanted to be famous for being Einstein. Like, I wanted to discover stuff and then be famous. And if we all examine our innate kind of, right, if we all examine this innately, I think that there is one of those three vectors that we orient ourselves around. And then we rationalize the altruistic intent of it. Right. We backfill in the narrative to support the innate desires that we have, which I don't think are bad. I mean, I talked to a very, very famous astrophysicist at Cornell, Lisa Kaltanager, recently on the podcast. And she's the director of the Carl Sagan Institute there, which is focused on looking at approaches. You know, we've got to get you guys some besties, eventually get you some bestie finger puppets. This is the greatest swag. I'll thank you. But I said to her, Lisa, if you had a choice between finding slime mold on, you know, some exoplanet proximus and tury B at the end of your career or, and then getting a letter from God that says there's no other form of life in the universe, especially technological advanced aliens. And that would be what choice number A, letter A is you get to discover slime mold and you're the first person in history to ever discover life on another exosolians.

planet, or B, your great, great, great, great, great, grand PhD daughter, she discovers aliens, you know, with extraterrestrial technology a thousand years from now, which would you choose? And she was like, both. And I'm like, you can't choose both. But I get, you know, it's the instant gratification that goes to the fame. And, you know, as famous, I think Bill Murray once said, you know, if you want to be, you know, famous, try being rich first, you know, because that gives you most of the benefits of being, of being famous without any of the unwanted benefits

or without many of the unwanted benefits. So I want to pivot from well to an audience question. Good friend Kelly G. from Brooklyn, who's a huge fan of yours. She wants to talk me to ask you about Supergut, which I got a glass of it here. I actually have Munique, which I, you know, still has such good shelf life that I can still drink it, even though it's been rebranded as Supergut, I believe.

Is that right? Yes, it's called Supergut now. Better name, easier. Yeah, it's much better. Yeah, super. Anything that was super in the name is going to be good. And then Grand Unified Theory, I'm glad that you put the gut in there too because, you know, we all want a grand Unified.

You go. So she's wasted, you know, millions of dollars on probiotics. And she wants to ask about prebiotics versus probiotics in the microbiome. What about that? What are the opportunities there? Why did you invest in this company as a case study maybe for my, for my listeners? Can you talk, walk us through our thought process there? You said this place was steps from the water.

We just haven't found the steps yet. How much did we save? Enough. Enough to get lost! Or you could book a stay with Hilton. Welcome to your oceanfront room. Just steps from the water. The Hilton sale is on now.

Book on Hilton.com or the Hilton app and save up to 20% to get the stay you expected. When you want savings, not surprises. It matters where you stay. Hilton, for the stay. There's a ton of, so 20 years ago, the human genome was fully sequenced. And it was published, I think, 2003, right? 2002, 2003.

And it cost like $100 million to sequence the human genome at that time and took months or years to put all the sequences together. Now, digital technologies have driven the cost down of DNA sequencing faster than Moore's law in the past 20 years. And that's because DNA sequencers are optical scanners. And so you're generating gigabytes of data, transmitting that data, storing it, computing it, all those underlying digital technologies have dropped in cost by substantially. And so the cost of DNA sequencing has gotten really low, the speed has gotten really high, et cetera. As a result, we are able to see the physical world, the DNA in the physical world, at a resolution that was never possible before.

much like telescopes or observatories have allowed us to peer farther back in time, deeper into space, wider, you know, wider kind of point of view than we've ever seen had before. We can do the same by looking down into the physical world. And the physical world is just littered with microorganisms, with living biology that does stuff and creates molecules and consumes molecules and does stuff all around them. In the human gut, there's about 40 trillion bacterial cells plus or minus. a lot of kind of controversy on how many there are, roughly 10 trillion human cells in the body, also a little bit of controversy on that number, but call it roughly equivalent order of

magnitude number of cells of bacteria and humans. Those bacterial cells are making molecules and absorbing molecules. And then they're also interacting with and regulating human cells in a really powerful way. And we never had the ability to see these bacterial cells and understand what they are and what they're doing. So in just the last couple of years, because the cost of DNA sequencing has come down, we can now peer into the gut. and see what bacterial populations are there and what they're doing. And this is not just true in the human gut. It's also true in agriculture and soil.

So we can now see in the soil what microbes are maybe fixing nitrogen from the atmosphere, making it available to plants and improving plant health and productivity, what microbes are stimulating to the plant, what microbes are keeping bugs away, et cetera, et cetera, what microbes are bad. And so the cost of DNA sequencing now, you can get a sample done for five bucks, let's say. And it used to cost $5,000. Okay?

So now all of a sudden we have this insight with large data sets. And with large data sets, we have the ability to mine those data sets and identify trends and identify, you know, what's going on and start to test and prove things, scientific method. So the number of papers on the gut biome has kind of grown geometrically last couple of years. And so it turns out that the human gut microbiome, which is all the microbes that live in our gut are very significant and important influences of our health in our body. And they are doing all sorts of things. They are absorbing molecules.

They're consuming molecules. They're converting molecules. And they can also have a profoundly negative effect on human health. There are certain microbes that have proteins on their surface that look like proteins of the human cells, of certain human cells. And for certain genetically inclined people, your immune system attacks those microbes in your gut. And as a result, it makes antibodies to those proteins that mimic microbes in your body, and that's called protein mimicry. And then it causes an autoimmune reaction. And you have

autoimmune disease or inflammation or what have you. So there's all these discoveries that are being made in multiple sclerosis, in lupus and chogrin, and like all these kind of autoimmune diseases. And then there's also all these benefits that we're discovering that certain types microbes can profoundly improve our sleep, improve our mood, that there's this whole gut brain axis that we can actually regulate dopamine and serotonin and these important neurochemicals by stimulating certain. Right. The whole thing, gut decision, let me go with your gut, right.

I mean, that has wisdom. Yeah. Yeah. Go on. So we asked ourselves a couple years ago, I think it was 2017 or 2018. I was reading all these papers and I was talking about this guy in the space. We're talking with lots of people that understood the gut microbiome and what's going on. And we were like, maybe let's do a personalized probiotics business.

So we'll take your poop, we'll measure it, and then we'll give you personalized probiotic. And, you know, a bunch of things happen. We realize, number one, probiotics generally do not inoculate the gut. Because the gut microbiome is like a jungle. There's all these trees, and, you know, the monkey climbs the tree, eats the nuts. The monkey poops. The poop grows the tree. The jaguar comes in and eats the monkey.

There's a whole ecosystem going on of, you know, a regulatory, what's called microbial consortium that's kind of regulating one another. So if you throw a house cat in the jungle, the house cat's going to, get eaten. It's not going to survive. You can't just throw a single bug in the gut and expect it to inoculate. So paper after paper after paper has demonstrated that probiotic, that actually putting microbial organisms, even if they're alive in your gut, they don't stay there, they don't grow. One thing that did show up quite significantly was you can actually change the feedstock in your gut and profoundly change that ecosystem as a whole. That's this prebiotic term. And one of the

big prebiotic kind of concepts that people had done a lot of papers on, a lot of work on was resistant starch. No one had built a business selling resistant starch, you know, in a good, easy, consumable way for consumers. And when you consume resistant starch, it increases these gram positive bacteria in your population in your gut. They secrete short chain fatty acids in your bloodstream. Short chain fatty acids, drop your blood sugar, regulate insulin sensitivity, all these sorts of, you know, reducing inflammation. So I was like, this is the better business. We don't need to do this crazy poop testing thing. Like, you know, let's, so that's how we ended up starting the super gut.

But it really is this kind of, we have another business in the soil microbiome space where we actually measure farmer's soil and then we give them a readout that isn't just giving them all the Latin names and the population counts. They have no idea what that is. We translate that into a very specific set of decisions

that they should make to drive better outcomes in their farm. And that company is called Patternack. So, you know, that's, I think, all enabled by this trend. So, you know, speaking to our point earlier on, we kind of looked at that scientific trend of what's going on with DNA sequencing costs and all these discoveries that are being made. separately we looked at the markets and then we found the right teams to go and execute against

these business ideas. So that's kind of the connecting the doc concept. Awesome. Great. Okay. So the next thing that would be really interesting to talk about is, again, in the biology space. And I want to push back on some of the pushback maybe that you got recently on All In for your conversation about COVID-19. I should just say, just to confuse YouTube. because I had, I had, put it this way, this is just, you know, the world smallest violin will be used. But I did an episode with Charles Seif, was a professor at NYU, and a big critic of Berkeley Labs, nuclear fusion, you know, claims and so forth. We'll get to that in a minute.

But we did a podcast about that result and the laser fusion last month announced by the DOE, et cetera. And it got slapped with a climate warning and, and, and, uh, and it got slapped with a climate warning and, uh, implication that we should consult with Wikipedia and the United Nations about climate change. And all we said was, you know, if fusion would work, then it could be, as you pointed out, two months ago or so on the podcast, you know, could be one of the vectors that we used to decarbonize the atmosphere and actually desalinate the oceans, etc. It could have vast positive impact. But anyway, I want to touch another hot button third rail issue with a superconducting room

temperature, maglev train, and that's gain of function. What's wrong with gain of function? I had COVID over the summer. You know, I have a bunch of kids running around. They all got caught. My wife didn't get it, which was kind of interesting. And she swears it's because of this rabbit's foot that she wears on her fate. No, she wears a mask like 24-7. It's a thing. But again, I lost my sense of taste and smell. And I lost five pounds. I dropped five pounds, David, from my double chin to my stomach. Now, I'm just kidding. I, I, I, I, I lost a couple pounds. And I thought, well, this is great.

You know, what if, what if, like, they did us a solid? What if this will be the new, what are they, OZIMPIC that Jason Calicanus is so infatuated with? Anyway, what's wrong with gain of function? I mean, could we use it to benefit to do things like weight loss and so forth? So gain of function, I would say, is this kind of narrow view of a broader activity that goes on in protein engineering. Okay, let's call it that. And it is a ubiquitous tool set that is used in labs, academic, research, and commercial labs around the world to discover and create proteins that can kind of evolve our species with new therapeutics and so on. So let me just kind of dig into what goes on for a second.

take a um uh like how is protein made we talked about this on on our podcast i'll just just do a quick yeah uh five seconds on this brash business does come it down for theoretical cosmologists please yeah right okay so um you know proteins are coated in genes um in DNA and uh every three letters of DNA can be deemed a codon um and there are you know call it 20 amino acids and you can kind of think about a segment of DNA being a copy of it kind of coming off as RNA and that RNA goes into a ribosome and every three letters are selected and an amino acid is printed. And so you get this bead of amino acids and that beat of amino acids because of the kind of electric potential of the different molecules kind of collapses on itself and forms this

three-dimensional complicated molecule called a protein. And that protein actually is a machine. It does stuff physically, biophysically, it does stuff in the body. Yeah. It can break molecules apart. It can stick molecules together. It can carry molecules around. So all these different proteins have different function. It can bind to stuff. So the right surface on a protein can stick to say a cancer cell or stick to say a sick cell or, you know, do something interesting in the body. So evolution has driven the discovery and the development of, or sorry, the development of proteins that do all of these incredible things in biology. And we now have the toolkit to create novel proteins and use those to try and do new things. So we have created novel enzymes that we use in laundry detergent that are really good at breaking up dirt. We have created novel biologic drugs that can bind to cancer cells and then the body's natural immune system will come in and kill those cancer cells.

We have created novel proteins that can replace dysfunctional proteins in our body and have a profound benefit on people on sick population. The way that we kind of design these proteins and the way that we kind of explore what protein mutations might do biophysically or what protein mutations might allow us to do is we change the DNA. And we change the DNA very rapidly. Either we mutated or we specifically edit the DNA because we have tools to do that now where we can edit DNA very specifically.

And we express lots of different proteins. And then we assay them or measure them and we see what they can do. And that allows us to do drug discovery, all this other sort of stuff. And Francis Arnold won the Nobel Prize a number of years ago. You may know her from Caltech. She was a widow of my postdoctoral advisor, Andrew Lang, who unfortunately took his life in 2010. Yeah, I know, Francis. Yeah.

And yeah, and, you know, her directed evolution, work is, you know, almost mainstay now in a lot of research labs around the world for doing things like protein discovery and the exploration of where proteins are going to go and what they can do. So the term gain of function is the very narrow version of that, which is how do we evolve proteins or change proteins to see how the proteins on a virus are going to change what that virus does. And so it's about exploring the domain space of the potential of a virus as it evolved on its own because the little DNA that makes up that virus or the RNA that makes up that virus is going to evolve on its own random mutations will cause new viral proteins to emerge in nature

and then those new viral proteins will the ones that are really good at you know infecting their host and you know persisting will end up dominating evolutionarily and they will you know and that that DNA or that RNA will start to persist and kind of become dominant and so we have the toolkit to kind of explore that ourselves without waiting for nature to do it So it is a good philosophical question on should we do that and to what extent and what are the limitations and how do we control that. But the fundamental toolkits to do that are ubiquitous. This work of evolutionary design is ubiquitous. It's used in many labs from industrial biotech labs making enzymes for laundry detergent all the way through to biologic labs, all the way through to, you know, viral academic research labs all over the world.

So the idea that this was like a special crazy one-off idea that these guys did this gain of function research is like totally missing the broader picture of what goes on in biology today, which is that this is ubiquitous evolutionary kind of protein exploration and so on. And the particular application to seeing where a virus is headed is kind of a narrow view of that. And I'm not going to like, I mean, we could debate the merits and whatnot of doing that. merits and demerits is that the of doing that work for viral evolution and predicting viral evolution is there a benefit in doing so and getting ahead of the problem

and figuring out how we can stop the virus from evolving or having a vaccine that's ready for if the virus evolves maybe but then you have a Brad Pitt 12 monkeys problem where maybe you create the problem by doing that and that's what a lot of people are theorizing happen with SARS-CoV-2 which is that it was kind of a you know an evolution kind of designed virus that got out of the lab. And so, you know, I don't know if you saw the movie 12 monkeys, but like, you know,

you basically like in reverse time, you're trying to solve the problem and you create it in the process. And so it's an important question. Can you put guardrails around this? Can you put barriers around this? Certainly it makes sense to say that, yes, you should and yes, you can. And, you know, this idea that the Wuhan lab in China, the French, who were supposed to be kind of partners in building this lab out came along and said, this lab is not.

safe. It should not be open. They stepped out of it. There was a lot of warning signs and, you know, should more action be taken? Should we have a tighter set of guardrails? Almost like we have the nuclear regulatory commission. Should we have kind of a viral regulatory commission? And then have a, you know, a much kind of tighter framework for how we do this work. But remember, the Pandora's box is open. I mean, these tools are out there. And I think, you know, in a world where these tools can be harnessed in an offensive way, we have to be thought. about what's the right defensive approach. Yeah. Yeah, I saw Babylon B, you know, story, which is kind of like a conservative version of the onion. It said, you know, Pfizer announces new vaccine, effective against virus it created in the lab. But, you know, Jay Batichari, who's past guest on the podcast, professor at Stanford, MD, PhD, he said, you know, gain of function is fine, more or less, but you shouldn't be, you know, on a base layer, on a platform, that's a human pathogenic, you know, as susceptible to human pathogenesis. So I think, That's an interesting division line.

So I want to check, David, you're still good on time because I'd love to keep going. Okay, great. So you mentioned the proteins and how they evolve and how they react and what they do. Recently, we've had things with alpha fold and all sorts of breakthroughs involving AI and that can do such incredible things. Of course, a big thing that you have talked about a lot is GPT and artificial intelligence. I want to ask you, you know, based on your physics, you know, perspective. Do you remember from undergraduate or, you know, your encounters with Alex, our friend Alex Villopenko, what Einstein called the happiest thought of his life? Do you know

what that? Do you remember what that was? I don't know the answer now. Yeah, it was that an observer in free fall would experience no gravitational force. He called that the happiest thought of his life. And I hear all the stuff from from friend and past gas max tagmark in MIT that we're going to have AI, AE, we're going to have artificial Einstein's and so forth. But I'm always kind of not very sanguine about that because, you know, you look at that statement by Einstein, there's a lot packed in there. But two things in there are, you know, he felt happy. You know, he actually enjoyed the sensation of having this new knowledge for the first time that the sensation of freefall would experience the result in that experience would be no gravitational force. And that led to Einstein equivalence principle, which underpins all of GR. But the question is, you know, to what extent is that replicable in a huge? human, both the happiest thought and a visceral sensation that we as scientists do get guided by our senses. So in other words, can we expect that we would have a happy thought thinking computer or a computer that could visualize free fall? Is that, or is that just completely beyond

what we can do and that effectively what these computers are doing is just a level of computation that no human can match? But the creativity that you mentioned earlier is never going to be there. I'm not convinced about this notion of creativity per se. I always point people to this video by this guy who calls himself an illusionist. His name's Darren Brown out of the UK. Oh, yeah, yeah. And it's on YouTube, and there's an episode where, or a clip of this episode that he did years ago, where he took these ad agency executives.

These guys are like creative directors and they make ads. And they're like the best in the industry and they're super creative. and they come up with all these great ideas. And he goes and picks them up, puts them in a cab, takes him into his office. And in his office, he's got a whiteboard. And then he's got another whiteboard covered in a curtain. And he says, guys, I need you to create like an ad for me,

come up with the name for a pet cemetery company, come up with a logo, come up with a catchphrase. And they spend all day. And the cameras capture them ideating and going back and forth and like theorizing and like exploring all the domains of their creativity. And they finally settle on this great. concept of a logo and a name and a catchphrase. And then he comes back hours later and he flipped up the curtain and he had it identically correct. And the way he did it is on the cab right

over. And I don't know how real this all is, but it really spoke to me. On the cab right over, he kind of had these kids walk in front of the cab with his t-shirt that had this logo on it. He had them drive past the sign. He repeated the same word over and over on the buildings. And so there was this like subliminal programming that happened where by the time they got to the building, they were unconsciously, deeply influenced by this image, this name, and this catchphrase. And they kind of felt like they were discovering that through creative process. I think, like, there is this notion of, like, the human mind having, to some degree, you know, I mean, we'll use like a Jewish term of like, Kabad, which is like knowledge, understanding,

and wisdom, right? We absorb this data. We gather this knowledge. We synthesize it together. And then we have this kind of outlook. And there's these sleep and dream theorists or scientists that say it's the synthesis, the output that ends up being stored in your brain, not the actual data. And that's effectively the neural model, right?

That's the neural network. So to some degree, humans are programmable and we are programmed. And we are programmed to have these kind of synthetic outputs from the inputs that we receive and so on. What I think is missing from AI that I never really have had anyone explain well to me in terms of AGI, you know, artificial general intelligence is the association of the training data set being the limbic system of the human body. Right.

The reinforcement, they talk about, sorry to interrupt, but they talk about reinforcement learning. Yeah. But they never mentioned that reinforcement is coming from some human day, you know, overlords. No, that's right. And the training data, by the way, just to be clear, like, if you watch a baby, we have kids, you have, you know, I say we, because I know you have kids. dive kids. And you watch your baby, the baby first starts interacting because there's a physical visceral reaction. There's a limbic emotional reaction through their physical body to some stimulus

from the outside. And then we kind of build these much more kind of complicated thoughts and complicated networks, but it's all still driven by the physical limbic system, by the reaction to the exterior that we physically interact with. That's right. That training data ultimately realizes as these kind of concepts and then we have conceptual training. But that's often the output of of like these core engines. And I think like so many of these data sets that I, you know, that we use as training data are often the actual outputs. And then we train on the output so we don't train on the actual human input.

What is the right kind of human data set to train on? So if I could wrap a human, an MRI around a human brain from the point of development through to the point of being an adult and I could, you know, capture all the things that I believe that, computing, by the way, and digital technology allow us to capture all the things that the human body captured.

We just don't have the response mechanism as data today to train against that. And so I can capture feeling, I can capture temperature, I can capture light, I can capture smell, I can capture molecules, all these things I can capture as an input to my model. What I'm not capturing is the training data

on what happens in the lending system that makes me say, ow, that burned, you know? Right, they always say, oh, and by the way, yeah. Yeah. Like, why does the baby run out, move out of the way when someone comes running at them. So, you know, the AI can be trained on there's a body coming at me.

But the association of I can get hit and that will hurt is missing. Right. And I think that's the underlying that's kind of lacking of that training data. How do we get that training? Does that ever actually resolve and does that actually build models? There's going to be a lot of stuff that AI is going to be incredible at. And like this whole synthesis of creative process and creative thought, all the higher level abstraction stuff, it's just data processing, and the AI will beat us at that.

There's something, however, that's missing in terms of AGI that is deeply associated with the human physical self. I don't know. I'm not convinced. Yeah, they used to say on Twitter, you know, I'm also, I should also say I'm very naive on this stuff. Yeah, yeah, as I am I. I mean, I just like to be, you know, put on my pundit hat, but, you know, I used to say, on Twitter, it used to be forbidden to say learn to code, but now journalists are just going to learn to prompt. And furthermore, when you talk about these training sets, they'll say, like, this AI solve this problem that would take humans 100 million years or whatever, 10 years to solve. And I'm like, well, did you include the 10 years of 10 PhDs that it took to come up with the level of input programming that was used to make this model? And then, you know, my last kind of thought on this subject before we turn to just a side application of AI, which I am more sanguine about.

But the notion that a computer can beat a human at chess or Go or protein folding or whatever is well known. But my question is that can a computer create the game of chess? Can a computer create the game of Go? In that same way, can the computer create Einstein tensors and the Einstein field equations? I'm not convinced. But I am sanguine about one thing. But I'm also kind of anti-sanguine. We use at NERSC, which is a National Energy Research supercomputer up there at Berkeley,

a close colleague, Julian Burrill is one of the directors. We use it for all the cosmology data sets. It's kind of input processing. They have the world's third fastest computer, which tomorrow will be the 18th fastest computer, all sorts of really amazing processors up there. And it's a facility for the government, but we get to use it on energy subslice. But we found that actually a lot of computing things are getting slower. because even though the raw horsepower is increasing as Moore's law,

but the problem is that the demand is also increasing faster than Moore's law at some level because these things become so both, both, you know, attractive for researchers to use and so much more powerful they can solve more problems. So it's this positive feedback cycle in a way. But the actual raw, like if you just translate it to what we academics care about, papers, citations, H-Indese, whatever, those are kind of saturating based on, even given that these computers are improving. And I start to think about why that is, and maybe in the health space I had on Eric Topol a year or two ago, and he's a big proponent of, you know, we got to get computers out of the patient doctor interaction because you just have like a doctor looking at a screen and a patient looking at another screen.

But I'm a private pilot. I fly little tiny Cessna's around Southern California. And it's not really well known. But every time I take off, I have to listen to this like FM radio station for about a minute. And every time I come in for a landing, I also have to list called the Notam Cis. system, it used to be called notice to airmen. Pete Buttigieg and others changed that to notice to air missions recently. And it was famously in the news. It shut down for a couple hours in January and that led to snarls in the aviation administration. But that thing you actually

have to listen to on a radio, an analog radio, in the cockpit. And you have to tune in the radio with a dial or type into a keypad. And I'm like, why don't I have this Alexa, you know, device or something, you know, I have one in my room right now. I have a different word than Alexis, so I won't trigger, but I'm probably triggering millions of people around the world listening to this. But, you know, I should have an virtual co-pilot, right? The number

one contributor to safety of the airlines versus general pilots like me is that there's always two pilots, every plane you've ever flown on, David, has two pilots in it unless you've been the pilot, right? So, but why not augment for both professional pilots, commercial pilots, and private pilots like me, have a little assistant in the cockpit that knows

oh, Brian's coming up on Oakland airport. He's probably going to land there. He put it in his flight plan, which I have on a computer. I should really dial in the notams and listen to it for him, decoded visually, which we can read 10 times faster than listening or writing. So why don't we have those things? And it came up as well in the medical. Why isn't there an AI Alexa in the room listening to Dr. Topol, you know, prescribed whatever Vicodin. I don't know what he does. But I should really check in on him. But the point is, why don't we have that? And I think it comes down to lawyers. I think like the aviation's, you know, there's hundreds of lives on every commercial airliner.

What do you think is the impediment? Why aren't we actually scaling out? So these things are actually useful. So I have a virtual lab assistant or, you know, virtual co-pilot or virtual physicians assistant. What's the impediment? Is it, you know, legality? And if so, should we be hopeful that's going to improve with passage of time? Yeah, look. I mean, these are, I mean, I told people if you, I think about the next evolution of software. AI is just the current catchphrase. There have been these catchphrases over the years and data science and big data. Machine learning.

Algorithms and, yeah, machine learning. There's always some hypey bullshit that goes on. Sorry, no bad words allowed. So a lot of people are kind of using it. But at the end of the day, it's all about tools are about providing leverage to give. So there's an evolution of a tool set that gives us more leverage. And I give people the analogy, like people that used to make visual images were painters. Then there were photographers.

And then there was Adobe Photoshop. And Adobe Photoshop would kind of digitize and pixel-like images, and you would kind of change the pixels. But then there was this higher order tool set called Kai's Power Tools developed by another UC team out of UC Santa Barbara. I believe they were UCTN. I know they were based in Santa Barbara. And Kai's Power Tools was this plugin for Photoshop. It was a set of plugins. And what it was, you know, a Photoshop, an image is just a matrix.

And then it would do some transformation on that matrix and generate a new matrix. And, you know, the statistics were coded into software. And those statistics were used, the statistical formula were used to transform the matrix. And now you have what looks like a motion blur or a Gaussian blur or, you know, a pixelation effect or a sharpening effect or a softening effect. So there was all these Kai's power tools effect. special effects you could add to your photo. And prior to this, an Adobe Photoshop photo editor would have to go in and manually change the pixels

and kind of use a bunch of these Adobe Photoshop tools to make those changes. So Photoshop totally transformed the leverage potential enabled by Photoshop. Now the best users of Kai's power tools weren't necessarily the best photographers or even the best pixel editors. It was the person who had the best ideas of how to use the tool. And the tools, now you could do all this other cool stuff and we could extend ourselves into doing higher order stuff. And so I think, you know, we've seen software introduced that allowed us to do things like

autopilot and flying and now autopilot in driving. Yeah. And, you know, it's not necessarily fully self-driving, but it's not you have to do as much driving anymore. It's providing you more leverage. And I think that it's not about kind of on or off. All of these tools are going to integrate into our everyday life. Commercial markets, capital markets will drive interest.

You as a pilot will be willing to pay for that. AI-driven kind of capability. It will find its way into your cockpit because you do have a regulatory problem with flying and with driving and so on. So some of these things take a little bit longer to get approved than stuff that can just make its way in the market, like a new software editor or photo editor and so on. But I think that's my general kind of orientation around what AI is. It's a series of software and statistics and algorithms enabled in software provide us this kind of ever-extending leverage with our time and with our ability. as a human, and we always end up leveling up, right?

And we're going to level up going forward. I tell people we're kind of entering the narrator economy where we no longer need creators, people that create stuff. The software will create the script, the music, the movies, the visuals. And I as a narrator will simply tell the software, you know, pan left to right, go up 30 degrees, change the character, make her a little angrier right now, give her a Brooklyn accent. I mean, I will now be directing my computer to make a movie for me. that provides me leverage.

And it also enables more people to become directors of movies. There's only probably 50 people that direct a movie or 150 or probably 5,000 people a year that'll direct a movie. Now there can be 5 million. And the software will render the movie for you. And prior, you know, there were only maybe 5,000 people that could edit photos. And now there's 5 million people that are editing photos using this toolkit daily or using 5 billion or a billion people that are using Google Photos and Apple Photos

to edit their photos every day. And so, you know, when humans level up through kind of the evolution of software, the current iteration is AI, not only does it provide us kind of more leverage, but it also provides more access and more people. And then we all kind of transform our lifestyles. So I don't know. That's my, that's my orientation around the time. Yeah, everything will unfortunately eventually devolved to pornography, right?

So that's been, you know, the history of technology from TV to VHS, DVDs, and then the internet. now. Unfortunately, they're going to be making movies, David. I think you're right, but I think it's going to be personalized in a non-coacher direction, shall we say. Okay, quick question from an audience member. Just a reminder, you can always submit questions to me and guess when I post them on Twitter, Dr. Brian Keating, YouTube, Dr. Brian Keating as well. Any sufficiently advanced technology is indistinguishable from magic. Thanks for listening to Part 1 of this two-part episode of Into the Imposte

possible. And don't forget to come back for part two, where David Friedberg answers Brian's existential and audience questions. For a chance to win your very own bit of space dust in the form of a meteorite fragment, subscribe to Brian's mailing list at briankeeting.com slash list. Thanks for listening. And remember, always be curious. Ambition comes in all shapes and sizes. At first citizen, Bank, we roll with your goals because we're built for what you're building. Fit for your ambition for Citizens Bank.