person / likes
James Burnham
“Oh, yes, my favorite is James Burnham. He’s my favorite.”
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Published podcast speaker
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person / likes
“Oh, yes, my favorite is James Burnham. He’s my favorite.”
person / recommends
“Then I read this profile that I recommend to everybody, which is, Tom Wolfe, the great novelist, journalist, wrote a profile of Bob Noyce, who was the original founder of Intel and basically the father of the chip industry.”
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94 transcript-backed records
01 / prediction
“And so if you sort of combine in a country like the US or any country in Europe, if you combine declining population with less immigration, the remaining human workers are going to be at a premium, not at a discount. And so I think that combination of faster productivity growth, faster economic growth, and then slower population growth and less immigration actually means there's going to be much less of this kind of dystopian no jobs' thing.”
02 / belief
“We could debate that, we could talk about that more. But the prosaic definition of AGI that at least I think the industry purchases but it's kind of conversed on, and tell me if you agree with this, is when the AI could do every economically-relevant task as good as a person.”
03 / belief
“I think we're used to living in a world where we just don't understand how good good can get, because we've been capped by our own biology.”
04 / belief
“One is, the trust that a lot of people have had and kind of what you described as kind of legacy institutions around the world is, I think, in kind of full-scale collapse right now.”
05 / belief
“" People can just do this today. And so I think there's this massive opportunity for parents in many walks of life, with a little bit of time at focus, to be able to say, " Okay, my kid's probably still going to go through a traditional education system, but I'm going to augment this with AI tutoring.”
06 / belief
“People who really want to improve themselves and develop their career should be spending every spare hour, in my view, at this point, talking to AI, being like, "All right, train me up.”
07 / belief
“I think to your point, your T-shaped thing, I think that's going to be true basically across the entire economy.”
08 / belief
“Take it this way. This is actually worth talking about because people, I think, get sideways on this issue.”
09 / belief
“I think 2025 was maybe the most interesting year in my entire career and probably life, and I think I would expect 2026 to exceed that.”
10 / evaluation
“If it doesn't work, or if it's not doing what you expect, or it's not fast enough or whatever, you need to be able to understand the results of what the AI is giving you.”
11 / uncertainty
“I don't know, my view is I need to put a big discount on my forecasting ability on this one.”
12 / prediction
“Everybody's materially much better off very quickly. And then by the way, to the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the social safety net to prevent people from being immiserated because the prices of all the goods and services that a welfare program has to pay from, they're all collapsing.”
13 / evaluation
“It's always kind of relative in comparison to a human worker, right? And it's, like, I don't know, human skill level caps out at a certain point, but that's because of the inherent biological limitations of the human organism.”
14 / evaluation
“Because you get to hire people with all these skills and experiences, right, and you're in this ecosystem that adapts and channels talents and skill and knowledge and people into the new fields.”
15 / uncertainty
“I would just say, there may be, I don't know, there may be like one particularly brilliant, I don't know, hedge fund manager or something who has this all figured out, but I guess I would say if they exist I haven't met them yet.”
16 / prediction
“All of that translates to, okay, probably at the end of this, there's going to be two or three companies that are going to end up with like 100%, I don't know, whatever, 50/50 or 30/30/30 or 90/10 and one, or whatever it is, market share and then they're going to have whatever profitability they have and it's going to be kind of a classic oligopoly, or maybe one company's going to win definitively and it'll be a monopoly.”
17 / recommendation
“There's this massive question in the field of education, which is, how do you improve educational outcomes? And basically, it turns out it's very hard to improve educational outcomes except there's one method that always does it, which is called the Bloom's 2 Sigma effect, which is there's one method of education that routinely raises student outcomes by two standards of deviation and will take a kid from the 50th percentile to the 99th percentile and that's one-on-one tutoring.”
18 / evaluation
“" What I found is if you look back on those predictions a few years later, and you can do this by the way, if you pull up coverage of the internet from 1993 through 1997, or for that matter even through 2005 or 2010, and you look at the kinds of confidence statements people were making in the first 10 or 15 years, I would say almost all of them were wrong, generally quite badly wrong.”
19 / recommendation
“You're always more productive if you know how the machine works when you use the machine. And so the super-empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100% want to understand this thing all the way down the stack because you want to be able to understand what it's giving you.”
20 / prediction
“Specifically, the following, which is every coder now believes they can also be a product manager and a designer because they have AI, every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager and a coder.”
21 / evaluation
“The way I think about this, we have a 10-year-old, we actually homeschool and so we think a lot about this. So I think the way to think about the impact of AI on, specifically, people as individuals, it's actually, a lot of people just focus on this kind of very, I would say, straightforward or overly simplistic view of just literally job losses, which we can talk about.”
22 / prediction
“They got really bad on many fronts at the same time. And so just relieving that and getting back to a reasonably optimistic, constructive, pro-growth frame of mind, there’s so much pent-up energy and potentially in the American system, that alone is going to, I think cause growth and spirit to take off.”
23 / belief
“We’ve got the best artists in the world, creative professionals, the best movies. So yeah, I would say all of our problems, I think are basically, in my view, to some extent, attempts to basically sand all that off and make everything basically boring and mediocre.”
24 / belief
“We have our issues and we have, I would say particular issue with manufacturing, which we could talk about.”
25 / belief
“I think what I realized is that I just have a very different perspective on some of these things, and the reason is because of the combination of where I came from and then where I ended up.”
26 / observation
“The way to actually think about how to make a system work and maintain any shred of freedom is to actually understand that that is actually what’s happening.”
27 / belief
“Number one, it was obviously the rise of smartphones, then it was the rise of the new messaging services, then it was the rise specifically of I would say combination of WhatsApp and Signal.”
28 / uncertainty
“I feel like I spent my first whatever, 30 years figuring out machines, and then now I’m spending 30 years figuring out people, which turns out to be quite a bit more complicated. And then, I don’t know, maybe God’s the last 30 years or something.”
29 / belief
“I think anybody listening to this could name a series of slogans that we’ve all been forced to chant for the last decade that everybody knows at this point are just simply not true.”
30 / belief
“I think the taxpayers do not understand this level of crisis, and I think if the taxpayers come to understand it, I think the funding evaporates. And so I think the fuse is going through no fault of any of ours, but the fuse is going and there’s some window of time here to fix this and address it and justify the money because just normal taxpayers sitting in normal towns in normal jobs are not going to tolerate this for that much longer.”
31 / belief
“I agree with that, up to a point. So, I think, for sure, for quite a long time, the people who are good at coding are going to be the best at actually having AIs code things, because they’re going to understand what, very basic, they’re going to understand what’s happening.”
32 / belief
“Balance the powers. But the other way they did it was they echoing what had been done earlier I think in the UK Parliament, they created the two different bodies of the legislature.”
33 / belief
“Individualism in America probably peaked, I think between roughly call it the end of the Civil War, 1865 through to probably call it 1931 or something.”
34 / belief
“I think what happens is they conform to the belief system around them, and I think most of the time they’re not even aware that they’re basically part of a herd.”
35 / belief
“The counter argument to that is I do think a lot of how Elon is causing change in the world right now … There’s the companies he’s running directly where I think he’s doing very well, and we’re investors in multiple of them and doing very well.”
36 / belief
“I would just say, I think the way to look at that … And look, like I said, I don’t want to predict what’s going to happen once this whole thing starts unfolding.”
37 / belief
“Actually, that’s worth noting is that’s another trillion-dollar question on AI, which is in a world of pervasive AI, and especially in a world of AI agents, and imagine a world of billions or trillions of AI agents running around, they need an economy. And crypto, in our view, happens to be the ideal economic system for that, because it’s a programmable money.”
38 / uncertainty
“If you have, the people I know who feel that way are pretty centered and generally seem very, I don’t know how to put it, pleased, proud, calm, at peace.”
39 / evaluation
“The thing that the tenured professors at all these places know is it doesn’t matter who the president is because they can outlast them because they cannot get fired.”
40 / evaluation
“About 20, 30-year-old book, but it’s completely modern and current in what it talks about as well as very deeply historically informed. So it’s called Private Truths, Public Lies, and it’s written by a social science professor named Timur Kuran, at I think Duke, and his definitive work on this.”
41 / commitment
“We built an ossified system, an ossified, centralized, corrupt system, where we’re surprised by the results.”
42 / prediction
“I would say, I’m not going to predict, I’m going to say there’s questions all over the place. And we have this category of question we call the trillion-dollar question, which is literally, depending on how it’s answered, people make or lose a trillion dollars, and I think there’s, I don’t know, five or six trillion questions right now, that are hanging out there, which is an unusually large number.”
43 / evaluation
“My serious observation would be it’s the thing I’ve talked to him about for a long time, and I keep trying to read and follow everything he does, is he’s probably, he is the, I think, see if you agree with this, he is the smartest and most credible critic of LLMs as the path for AI.”
44 / evaluation
“The voters actually have a voice and they actually exercise it, and they don’t just listen to ads. And so again, there, I would say, yeah, clearly there’s some power there, but I don’t know if it’s some weapon that he can just turn on and use in a definitive way.”
45 / evaluation
“I think you said something actually quite profound there, which is the ring of power is infinitely tempting.”
46 / evaluation
“I think that most people just go along, and I think even most high status people just go along. And I think maybe the most high status people are the most prone to just go along because they’re the most focused on status.”
47 / recommendation
“Oh, yes, my favorite is James Burnham. He’s my favorite.”
48 / belief
“Having said that, I think attacking from the edges is the thing that can be done, which is basically what we do, what Silicon Valley does.”
49 / evaluation
“Like I said, I think the best of the startups today are more aggressive, more ambitious, more capable.”
50 / evaluation
“Now, we’re dealing with a minority, not a majority, but I think there’s quite a bit — every hour I get that I can spend at 20-year-olds is actually very encouraging.”
51 / evaluation
“The interesting thing he said about that is, he said, “Look, managerialism is, basically, it’s not that it’s good or bad. It just is necessary because companies and institutions and governments and all the rest of it get to the point where they’re just too big and too complicated for one person to run everything.”
52 / prediction
“Cybersecurity people are quite, I think, legitimately concerned that AI’s going to make it easier to actually create and launch cybersecurity attacks.”
53 / evaluation
“Then there’s a somewhat separate question of who’s in the best position to deploy because it doesn’t help you that much to invent if you can’t deploy it.”
54 / evaluation
“Then over the course of the last 30 or 40 years, basically, what’s evolved is the realization in the field (and I think very broadly) that actually, that’s a mistake.”
55 / evaluation
“The good news of AI — and the good news also, by the way, with crypto because there’s always a lot of controversy around crypto and Web3 and blockchain around energy use.”
56 / prediction
“I think kids are going to grow up with basically — you could use various terms, assistant, friend, coach, mentor, tutor, but kids are going to grow up in this amazing back-and-forth relationship with AI.”
57 / prediction
“We will now have, I think, a relatively protracted process of many regulators or many agencies without explicit authority in the domain basically inserting themselves into the space.”
58 / disagreement
“They've got these crypto assets they're trading frequently, and then they'll back a startup and then they'll trade that startup's token just like they trade Bitcoin or Ethereum. But in our view that's the wrong way.”
59 / evaluation
“A big reason I'm more optimistic about a broader set of categories is because computer science in particular, now applies across more categories.”
60 / prediction
“ptimistic view on that is that it's the transition from these companies being in the bourgeois capitalist model to the managerial model that creates the opportunity for the new generation of startups. Because then the counterfactual, if these companies remained bourgeois capitalist companies for 100 years, then they would be the companies to create all the new products, and then we wouldn't necessarily need to exist because those companies would just do what startups do.”
61 / evaluation
“I view it as — we're an enabling agent for at least enough of a resumption of bourgeois capitalism to be able to get new things built, even if most of the companies that we built ultimately themselves end up being run in the managerial model.”
62 / belief
“I think there's a real possibility that basically every application category gets upended in the next five years.”
63 / belief
“As a firm, we have a big focus on software, we think software is a wedge across each of those verticals.”
64 / belief
“Education is a great example. I think the incumbent education system is trying to destroy itself.”
65 / belief
“I think he would say that we're a hybrid, we're a managerial entity that is in the business of catalyzing and supporting bourgeois capitalist companies.”
66 / belief
“I think 100% of the people we back have the intelligence to do it, maybe half of them have the temperament to do it, and then maybe half of those have the intelligence and the temperament and they really want to do it.”
67 / uncertainty
“There’s clearly going to be an educational revolution. Does that happen today or five years or ten years, I don’t know.”
68 / belief
“By the way, I have a strategy and a theory for where the whale is. And maybe one guy is like — look, I'm gonna go where everybody knows there are whales and other guy’s gonna be like — no, that place is overfished, I'm gonna go to some other place where nobody thinks there's a whale, but I think there is.”
69 / evaluation
“A consequence of that, that I think is pretty obvious, is that managerial capitalism has a big advantage that Burnham identified, which is that the managers are often very good at running things at scale.”
70 / evaluation
“People I know who spend a lot of time revisiting old decisions are less effective because they mire themselves in what ifs and counterfactuals.”
71 / uncertainty
“I'd also say this — I don't know that there's actually a dividing line between that form of speculation, and speculation on what people call investments.”
72 / evaluation
“That work really started kicking in in the 60s and 70s, a little bit later. And then he said — Look, the problem is there aren't other sectors that have had these huge investments in basic research.”
73 / recommendation
“I always love meeting with new nuclear entrepreneurs, because it's just so obvious that we should have this big investment in nuclear energy and there's all these new designs.”
74 / evaluation
“My favorite all time quote on being a startup founder is from Sean Parker, who says —“Starting a company is like chewing glass.”
75 / prediction
“If we hit five clean-tech sectors in a row or something like that, the whole thing just doesn't work.”
76 / evaluation
“Erdoğan came out and he said, “I think Twitter is the primary challenge to the survival of any political regime in the modern world,” And I was like — Okay, all the smart analysts all think this is worthless and then a guy who's actually trying to keep control of a country is like, this is my number one threat.”
77 / evaluation
“People are wired to respond to stress in different ways. And I think there are people who are wired to be extremely productive and get very happy under extreme levels of stress.”
78 / preference
“I like nothing more at the end of the day than having a couple hours to be able to get outside of my own head and watch a really good movie.”
79 / belief
“Again, this is the macro human-behavior question of all time, but the biggest question is, I think, and continues to be, and will probably always be — it’s a little bit the nature-nurture question, or let’s say it’s inherent capability of whatever form for whatever reason, and then training.”
80 / evaluation
“Necessary disclaimer on that: in my line of work, a false negative — saying something is not going to work and then it works — is a much bigger mistake than the false positive of saying something’s going to work and then it doesn’t work.”
81 / belief
“Being part of that banking system, I think, would have been very exciting at that time.”
82 / belief
“There were a set of these shows that basically propelled you — I think the line they would always use is “20 minutes into the future.”
83 / uncertainty
“Exactly. I don’t know. It’s one of these things you learn in venture capital. It’s like 90 percent of the battle is over by the time it starts.”
84 / belief
“Then it opens up this exit option, and I think that, as usual with any human system, it turns out that matters a lot.”
85 / belief
“I would say the case studies of Tesla and SpaceX — and this is something we’re spending a lot of time on in our firm — really leads me to wonder if the Janeway thesis is actually wrong, and if, actually, we should be much more open-minded about this.”
86 / evaluation
“Everything today is filtered through our politics. It’s really hard to understand, in my view, how people thought, especially before the 1960s, and again, I think, even before the 1930s, through our political lens.”
87 / prediction
“Maybe, I think, it grows the media business, but it doesn’t have to cause it to explode like that, but having it be a better proposition for creators, having it be a better proposition for consumers, having content come into existence that didn’t exist before.”
88 / preference
“Incentives matter, economics matter. It is better, in general, when there is a way for there to be monetary value assigned to productive work.”
89 / evaluation
“I think a lot of it is the authority figures just basically showing up and exposing the emperor, quite literally, in a lot of cases has no clothes.”
90 / evaluation
“I think that’s why California has the tech industry in the north and the media industry in the south. It’s because those are the two parts of the virtual frontier: the networks and then the content in the imaginary world.”
91 / recommendation
“Then I read this profile that I recommend to everybody, which is, Tom Wolfe, the great novelist, journalist, wrote a profile of Bob Noyce, who was the original founder of Intel and basically the father of the chip industry.”
92 / evaluation
“The reason I say that is because, one, it’s reductive, or it’s looking backwards, which is to say, we used to have in-person meetings, and now we have some people remote, so now we need hybrid meetings.”
93 / evaluation
“The reason I think that that’s probably right is because at least the history in the Valley is, if we have a sharp entrepreneur with the ability to attract a team, the ability to tell a story and have a vision, and then they’ve got insight into a technological dislocation — an actual change to the technology foundation of the field that they’re working in — then you have a shot for a successful start-up.”
94 / preference
“That stuff, I don’t tend to read more than once. It’s almost entirely, I would say, broadly speaking, it’s political theory, it’s history, it’s economic theory a little bit, although it’s probably more economic history.”