Ross Douthat interviews Peter Thiel, who is clueless

Ross Douthat, Peter Thiel and the Antichrist, NYtimes, June 26, 2025.

They start out with economic stagnation:

Douthat: So I want to start by taking you back in time about 13 or 14 years. You wrote an essay for National Review, the conservative magazine, called “The End of the Future.” And basically, the argument in that essay was that the dynamic, fast-paced, ever-changing modern world was just not nearly as dynamic as people thought, and that actually, we’d entered a period of technological stagnation. That digital life was a breakthrough, but not as big a breakthrough as people had hoped, and that the world was stuck, basically.

Thiel: Yes.

Douthat: You weren’t the only person to make arguments like this, but it had a special potency coming from you because you were a Silicon Valley insider who had gotten rich in the digital revolution.

So I’m curious: In 2025, do you think that diagnosis still holds?

Thiel: Yes. I still broadly believe in the stagnation thesis. It was never an absolute thesis. The claim was not that we were absolutely, completely stuck; it was in some ways a claim about how the velocity had slowed. It wasn’t zero, but 1750 to 1970 — 200-plus years — were periods of accelerating change. We were relentlessly moving faster: The ships were faster, the railroads were faster, the cars were faster, the planes were faster. It culminates in the Concorde and the Apollo missions. But then, in all sorts of dimensions, things had slowed.

I always made an exception for the world of bits, so we had computers and software and internet and mobile internet. And then the last 10 to 15 years you had crypto and the A.I. revolution, which I think is in some sense pretty big. But the question is: Is it enough to really get out of this generalized sense of stagnation?

The conversation goes on in that vein, and then:

Thiel: Well, I think there are deep reasons the stagnation happened. There are always three questions you ask about history: What actually happened? And then you have another question : What should be done about it? But there’s also this intermediate question: Why did it happen?

People ran out of ideas. I think, to some extent, the institutions degraded and became risk averse, and some of these cultural transformations we can describe. But then I think to some extent people also had some very legitimate worries about the future, where if we continued to have accelerating progress, were you accelerating toward environmental apocalypse or nuclear apocalypse or things like that?

But I think if we don’t find a way back to the future, I do think that society — I don’t know. It unravels, it doesn’t work.

The middle class — I would define the middle class as the people who expect their kids to do better than themselves. And when that expectation collapses, we no longer have a middle-class society. Maybe there’s some way you can have a feudal society in which things are always static and stuck, or maybe there’s some way you can ship to some radically different society. But it’s not the way the Western world, it’s not the way the United States has functioned for the first 200 years of its existence.

Two things: 1) Back in the 1990s my friend Abbe Mowshowitz was talking about virtual feudalism, which is where he saw us headed. He hired me to ghost an article about that but, alas, he was unable to publish it, so I eventually posted it as one of my working papers: Virtual Feudalism in the Twenty-First Century.

2) I sorta' kinda' agree with Thiel about being stuck. But I think about history in a very different way. I think in terms of cultural evolution, specifically, the theory of cultural ranks that David Hays and I sketched out back in the 1990s. [Here's a brief guide: Mind-Culture Coevolution: Major Transitions in the Development of Human Culture and Society.] At the moment we're stuck "treading water," as it were, in Rank 4, but haven't made it to the next level. Just what that next level will turn out to be, that's not at all clear, & that's where I talk about the Fourth Arena, thus: Welcome to the Fourth Arena – The World is Gifted. When I say that Thiel is clueless, that's what I'm talking about; he has no sense of cultural evolution, of a succession of cognitive architectures, or the mind itself, taken collectively, as a driving force in history.

Back to the conversation, and Homo economicus:

Douthat: So you think that ordinary people won’t accept stagnation in the end? That they will rebel and pull things down around them in the course of that rebellion?

Thiel: They may rebel. Or maybe our institutions don’t work, since all of our institutions are predicated on growth.

Douthat: Our budgets are certainly predicated on growth.

Is that a law of nature, or a contingent matter of historical circumstance?

On the nexus of Silicon Valley, stagnation, and Trump:

Douthat: What did Trump do in his first term that you felt was anti-decadent or anti-stagnation? If anything — maybe the answer’s nothing.

Thiel: I think it took longer and it was slower than I would’ve liked, but we have gotten to the place where a lot of people think something’s gone wrong. And that was not the conversation I was having in 2012 to 2014. I had a debate with Eric Schmidt in 2012 and Marc Andreessen in 2013 and Bezos in 2014.

I was on “There’s a stagnation problem,” and all three of them were versions of “Everything’s going great.” And I think at least those three people have, to varying degrees, updated and adjusted. Silicon Valley’s adjusted.

Douthat: And Silicon Valley, though, has more than adjusted ——

Thiel: On the stagnation question.

Douthat: Right. But a big part of Silicon Valley ended up going in for Trump in 2024 — including, obviously, most famously, Elon Musk.

Thiel: Yeah. And this is deeply linked to the stagnation issue, in my telling. These things are always super complicated, but my telling is — and again, I’m so hesitant to speak for all these people — but someone like Mark Zuckerberg, or Facebook, Meta, in some ways I don’t think he was very ideological. He didn’t think this stuff through that much. The default was to be liberal, and it was always: If the liberalism isn’t working, what do you do? And for year after year after year, it was: You do more. If something doesn’t work, you just need to do more of it. You up the dose and you up the dose and you spend hundreds of millions of dollars and you go completely woke and everybody hates you.

And at some point, it’s like: OK, maybe this isn’t working.

On Mars, AI, and progress, with a conversation between Elon Musk and Demis Hassabis (CEO of DeepMind):

Thiel: Yeah. And the rough conversation was Demis telling Elon: I’m working on the most important project in the world. I’m building a superhuman A.I.

And Elon responds to Demis: Well, I’m working on the most important project in the world. I am turning us into interplanetary species. And then Demis said: Well, you know my A.I. will be able to follow you to Mars. And then Elon went quiet. But in my telling of the history, it took years for that to really hit Elon. It took him until 2024 to process it.

Douthat: But that doesn’t mean he doesn’t believe in Mars. It just means that he decided he had to win some battle over budget deficits or wokeness to get to Mars.

Thiel: Yeah, but what does Mars mean?

Douthat: What does Mars mean?

Thiel: Well, is it just a scientific project? Or is it like a Heinlein, the moon as a libertarian paradise or something like this?

Douthat: A vision of a new society. Populated by many, many people descended from Elon Musk.

Thiel: Well, I don’t know if it was concretized that specifically, but if you concretize things, then maybe you realize that Mars is supposed to be more than a science project. It’s supposed to be a political project. And then when you concretize it, you have to start thinking through: Well, the woke A.I. will follow you, the socialist government will follow you. And then maybe you have to do something other than just going to Mars.

Douthat: So the woke A.I., artificial intelligence, seems like, one, if we’re still stagnant, it’s the biggest exception to the place where there’s been remarkable progress — surprising, to many people, progress.

It’s also the place — we were just talking about politics — where the Trump administration is, I think, to a large degree, giving A.I. investors a lot of what they wanted in terms of both stepping back and doing public-private partnerships. So it’s a zone of progress and governmental engagement.

And you are an investor in A.I. What do you think you’re investing in?

Thiel: Well, I don’t know. There’s a lot of layers to this. One question we can frame is: Just how big a thing do I think A.I. is? And my stupid answer is: It’s more than a nothing burger, and it’s less than the total transformation of our society. My place holder is that it’s roughly on the scale of the internet in the late ’90s. I’m not sure it’s enough to really end the stagnation. It might be enough to create some great companies. And the internet added maybe a few percentage points to the G.D.P., maybe 1 percent to G.D.P. growth every year for 10, 15 years. It added some to productivity. So that’s roughly my place holder for A.I.

It’s the only thing we have. It’s a little bit unhealthy that it’s so unbalanced. This is the only thing we have. I’d like to have more multidimensional progress. I’d like us to be going to Mars. I’d like us to be having cures for dementia. If all we have is A.I., I will take it. There are risks with it. Obviously, there are dangers with this technology. But there are also ——

Now that was interesting, very interesting, Mars as a political project. Hmmmm. And then they launch into a conversation about smart people:

Thiel: But I share your intuition because I think we’ve had a lot of smart people and things have been stuck for other reasons. And so maybe the problems are unsolvable, which is the pessimistic view. Maybe there is no cure for dementia at all, and it’s a deeply unsolvable problem. There’s no cure for mortality. Maybe it’s an unsolvable problem.

Or maybe it’s these cultural things. So it’s not the individually smart person, but it’s how this fits into our society. Do we tolerate heterodox smart people? Maybe you need heterodox smart people to do crazy experiments. And if the A.I. is just conventionally smart, if we define wokeness — again, wokeness is too ideological — but if you just define it as conformist, maybe that’s not the smartness that’s going to make a difference.

I note that smart people aren't going to get anywhere if they're working with the wrong ideas. There were a lot of smart people in the 17th century, but they didn't have radios and airplanes. It's not that they weren't smart enough, rather, the intellectual foundations weren't there for them to work from.

And then they veer off into transhumanism and Christianity and cryonics and and ... the Antichrist? That's where I get off the bus, but you might find it interesting.

If the world's a mess, don't blame Donald Trump

Aaron Benanav, There’s a Reason the World Is a Mess, and It’s Not Trump, NYTimes, April 21, 2025.

The world is a mess.

As President Trump upends global trade through a punitive suite of tariffs and redraws America’s alliances, world leaders are scrambling to respond. They are badly placed to deal with such disruption: Across the world, governments have been losing elections — or barely holding on — in the face of rising discontent. From the United States to Uruguay, Britain to India, an anti-incumbent wave swept through democracies in 2024. But not only democracies are in crisis. China, too, is grappling with social unrest and economic instability. Strife, these days, is global.

There are many explanations for this sorry state of affairs. Some see rapid social change, especially around migration and gender identity, fueling a cultural backlash. Others argue that elites flubbed their pandemic responses or have grown detached from their populations, driving a surge in anti-establishment sentiment and support for strongmen. Another argument holds that algorithm-driven social media has made it easier for misinformation and conspiracy theories to spread, giving rise to greater volatility.

There’s something to each of these theories, to be sure. But there is a deeper force underlying today’s disarray: economic stagnation. The world is experiencing a long-term slowdown in growth rates that began in the 1970s, worsened after the 2008 global financial crisis and shows no sign of improving. Stuck with low growth, waning productivity and an aging work force, the world economy is in a rut. This shared economic predicament lies behind the political and social conflicts the world over.

I agree with this much, we can't blame the sorry state of the world on Donald Trump. He's just a power-mad grifter who's figured out how to exploit the mess for his own gain. And, yes, I believe that the economic stagnation is real. While I'm skeptical of the capitalist mantra of "growth, growth, growth, and more growth," I certainly don't think we've reached a point where a steady-state economy would be better (less strain on the environment). I've been following Tyler Cowen for well over a decade now, perhaps a decade and a half, and he called it in this 2011 book, The Great Stagnation. I note, however, that's he's recently come to believe that we're pulling out of it.

Let's go on with the article, which asks why growth has slowed:

One reason is the global shift from manufacturing to services. This has stalled the primary engine of economic expansion: productivity growth. Productivity — the output per hour worked — can rise quickly in manufacturing. A car factory that installs robotic assembly lines, for example, can double production without hiring more workers, perhaps even firing some. But in services, efficiency is much harder to improve. A restaurant that gets busier usually needs more servers. A hospital treating more patients will require more doctors and nurses. In service-based economies, productivity is always slower to rise.

This seismic shift, in the making for decades, has a name: deindustrialization. In America and Europe, we know what that looks like: lost manufacturing jobs, amid declining demand for industrial goods. But deindustrialization is not limited to wealthy economies. The move from manufacturing to services is happening across the G20, dragging down growth rates nearly everywhere. Today about 50 percent of the world’s work force is employed in the service sector.

There’s another reason for global stagnation: slowing population growth. Birthrates surged after World War II, creating strong demand for housing and infrastructure construction and spurring the postwar boom. Demographers once assumed birthrates would stabilize at replacement level, around two children per family. Instead, fertility rates have tended to fall below this threshold. [...]

This is a big problem for the economy.

Because everything shrinks. What to do? A.I. to "improve efficiency in labor-intensive service sectors like health care and education"? Hasn't worked so far. Reindustrialization, "under strict tariff protections"? That's Trump's gambit.

But here, too, there is cause for doubt. For one thing, the decline in manufacturing was not just about trade. Even manufacturing and export powerhouses like Germany and South Korea have seen industrial employment shrink. For another, the industries generally targeted for revival — semiconductors, electric vehicles and renewable energy — employ relatively few workers. The era when manufacturing could provide mass employment is over.

Benanav doubts that increasing the population is a way out, with which I'm sympathetic. He votes for more deficit spending (two paragraphs) and redistribution.

The second approach is redistribution. In the past, the primary rationale for policies that enriched wealthy households was to stimulate growth from the top down, but this strategy has evidently failed. Instead, governments could place much higher taxes on the rich and redistribute income to the rest of society. That would be an uphill battle in the United States and elsewhere, admittedly, but it would bring big benefits, improving consumer demand and strengthening markets both domestically and internationally.

The goal should be not just to raise income levels, which studies show are increasingly disconnected from happiness, but also to build more stable and equitable societies in a slower-growth world. That requires investing to improve people’s lives: repairing ecosystems, rebuilding infrastructure and expanding housing.

Color me sympathetic, deeply sympathetic. But still, I remain skeptical.

I keep thinking that the world is limping along on social, institutional, and political structures grounded in the 19th century, if not even earlier, and those structures are no longer adequate. Why not? That's a tricky one, and I'm not prepared to answer it. But my thinking is grounded in the cultural ranks theory that David Hays and I developed during the 1990s, and that tells me that we need another restructuring of, of, well, of everything. That's a hard case to argue.

Search as an interface between informatics and economics: How is the distribution of good ideas like the distribution of gold deposits?

The Answer: Both distributions are highly irregular. What that means in the case of gold deposits is pretty obvious: Gold ore is a physical substance that is found in the earth, a huge mass of physical substance. But there is no obvious order to just where you can find deposits of ore. So you have to go looking for them, which is called prospecting.

Ideas, though, are not things. What does it mean to talk about their distribution? Is there some kind of abstract space where ideas exist? If so, how do we map and describe that space.

So, first I’m going to talk a bit about locating ideas in space. Then I’m going to present a conversation I had with Claude 3.5. First I talk about locating ideas in some abstract space, then I present the conversation I had with Claude, which starts with gold and ends with AI.

Ideas in space

Well, think of a library. Libraries contain books and books contain ideas. Books are physical objects and so have locations in physical space, library shelves. So, how are books placed on those shelves? Off hand, there seems to be two principles: 1.) alphabetically by author name, and 2.) according to subject matter. Fiction tends to be organized according to the first principle while non-fiction is organized by the second. This means that novels placed on the same shelf might are likely to be very different in character. (Take a look at this alphabetized list in Wikipedia.) Non-fiction is arranged by subject matter, so books that occupy the same self will be generally about the same thing. But there is a limit to how far that principle takes us. For one thing, any given book can be about many different things. It can’t be on selves with all of them. Still, you get the idea. We have ideas arranged in space, albeit a space limited to three dimensions.

Things get more interesting when we have a collection of electronic documents. In this case, actual physical space is irrelevant. What matters is, well, idea space. Back in the 1960s and 1970s Gerard Salton came up with the idea of representing documents using vectors containing terms in the documents. Thus, each document will occupy a position in some high-dimensional space. Documents located close together in that space will be about similar topics. Salton’s Vector Space Model is one of the foundations of machine learning – something I discuss with Claude 3.5 in this post, Memory in machines and minds, a conversation with Claude 3.5 Sonnet.

OK, so that gets us a way of thinking about ideas as existing in an abstract space. But how do we motivate the idea that some ideas are closer to us than other ideas? Well, imagine that you occupy some position in idea space, a high dimensional space. There are a bunch of ideas you understand very well. We can say they are close to you. Other ideas are utterly unintelligible. By convention we will say that they are far from you. In order to reach one of those distance ideas you are going to have to learn the ideas that are on a path between where you are now and where that idea is.

Yeah, I know, it’s fuzzy and indistinct. But you get the idea. I explore that notion in these working papers: Stagnation and Beyond: Economic growth and the cost of knowledge in a complex world, and, What economic growth and statistical semantics tell us about the structure of the world. The second one is shorter and more directly to the point.

It seems pretty clear to me that the further development of machine learning and A.I. is going to teach us quite a bit about the structure of idea space. Every LLM is a high-dimensional space jam-packed with billions and billions of ideas. We’ve just barely begun to map that space, something I talk about in my recent report, ChatGPT: Exploring the Digital Wilderness, Findings and Prospects, see especially pp. 34-38, 42-44.

* * * * *

That’s enough of that. The rest of this post consists of a dialog I had with Claude 3.5 about the relationship between technology and the search for irregularly distributed things. We start with gold, move on to oil and diamonds, then documents, drug discovery, movies and finally new AI technology. As always, my prompts are bold face while Claude’s replies are plain face.

Gold

How have gold prospecting methods changed from the 19th century through the 20th century?

Let me walk you through the key changes in gold prospecting methods across these two centuries.

In the 19th century, prospecting was largely manual and relied on simple tools and techniques:

Panning was the most iconic method - prospectors would use flat pans to swirl river sediment, letting water wash away lighter materials while heavier gold settled at the bottom. This was supplemented by sluice boxes, which were long wooden channels that used flowing water and riffles to separate gold from sediment on a larger scale.

Placer mining dominated early operations, focusing on loose gold deposits in streams and riverbanks. As surface deposits became depleted, hydraulic mining emerged - using high-pressure water cannons to blast away hillsides, though this was later restricted due to environmental damage.

The 20th century brought significant technological advances:

Early in the century, dredging became widespread - floating mechanical dredges could process huge volumes of river sediment. Hardrock mining expanded with better drilling and crushing equipment, allowing miners to extract gold from solid rock formations more efficiently.

Later developments included:

- Geochemical prospecting using sophisticated analysis of soil and rock samples
- Aerial surveys and remote sensing to identify promising deposits
- More precise drilling techniques for sampling
- Advanced recovery methods like heap leaching and carbon-in-pulp processing
- Mercury-free extraction techniques addressing environmental concerns

Modern prospecting now combines traditional methods with high-tech approaches like:

- GPS mapping and GIS software for precise location data
- Ground-penetrating radar to detect deposits
- Portable XRF analyzers for rapid mineral identification
- Environmental monitoring systems
- Computer modeling of deposits

This progression shows how gold prospecting evolved from relatively simple manual techniques to a sophisticated industry incorporating advanced technology while becoming more environmentally conscious.

OK, so we have roughly three technological eras: 1) manual, 2) chemical sample analysis and aerial and remote sensing, and 3) high-tech computer-based methods for detection and mapping.

Yes, that's a good way to break it down! Let me build on that framework:

Era 1 (Manual) - roughly 1800s to early 1900s:

Primary methods were direct physical observation and mechanical separation. Prospectors relied on their understanding of geology, visible gold indicators, and mechanical processes like panning, sluicing, and early dredging. Skills were often passed down through experience and apprenticeship.

Era 2 (Chemical/Remote) - roughly 1920s to 1970s:

Brought systematic scientific analysis into prospecting. Geochemical sampling could detect trace elements indicating gold deposits. Aerial photography and later satellite imagery allowed prospectors to identify promising geological formations from above. This era marked the shift from prospecting as an art to a more systematic science.

Era 3 (Digital) - 1980s onward:

Computerization transformed the field. GPS, digital mapping, and data analysis tools allowed prospectors to integrate multiple data sources and create detailed geological models. Ground-penetrating radar and portable analyzers provided immediate feedback in the field. Environmental monitoring became increasingly sophisticated and integrated into prospecting practices.

What's particularly interesting is how these eras overlap and build on each other - modern prospectors often combine techniques from all three eras, using traditional knowledge alongside the latest technology.

Carry-over skills

What kinds of skills carried over from one era to the next? For example, what did highly skilled 19th century prospectors know that remained valuable in the second era? And then from second to third.

This is a fascinating question about the evolution of expertise! Let me trace these skill transfers:

From Era 1 to Era 2:

- Understanding of geological formations and how they relate to gold deposits remained crucial. Early prospectors' knowledge of where gold typically accumulated (like placer deposits in river bends or specific rock formations) helped inform where to conduct chemical sampling and aerial surveys
- Reading the landscape for signs of mineralization - knowledge of indicator minerals, rock types, and geological structures remained valuable even when supplemented by chemical analysis
- Water flow dynamics understanding - experience with how gold moved and concentrated in waterways helped inform sampling strategies
- Assessment of ore quality - the ability to visually evaluate mineral-bearing rock remained important even as chemical assays became available

From Era 2 to Era 3:

- Interpretation of indirect evidence - the skill of making conclusions from multiple indicators (developed during the chemical/remote sensing era) became even more valuable when working with computer models and multiple data streams
- Sampling methodology - understanding of how to collect representative samples remained crucial for computer analysis - Pattern recognition in geological features - this skill trans
ferred well to interpreting digital mapping and radar data
- Knowledge of seasonal and weather effects on prospecting - this remained important for planning surveys and interpreting remote sensing data

What's particularly interesting is how these generations of prospectors had to learn to integrate their traditional knowledge with new technologies - for example, using their understanding of geology to know where to deploy expensive new sensing equipment most effectively.

The core skill that seems to have remained valuable across all eras is the ability to think in three dimensions about underground structures and visualize how geological processes might have created and moved gold deposits. This spatial thinking was valuable whether using a gold pan, analyzing aerial photographs, or interpreting 3D computer models.

Hossenfelder on stagnation: “Science is in trouble and it worries me”

Show notes:

Innovation is slowing, research productivity is declining, scientific work is becoming more [less] disruptive. In this video I summarize what we know about the problem and what possible causes have been proposed. I also explain why this matters so much to me.

00:00 Intro
00:33 Numbers [1]
06:33 Causes
10:32 Speculations [2]
16:25 Bullshit Research
22:06 Epilogue

I've given a fair amount of attention to this topic under the label, stagnation. I've also written a working paper: Stagnation and Beyond: Economic growth and the cost of knowledge in a complex world, July 25, 2019.

[1] Here she opens by citing results from Bloom et al. Are Ideas Getting Harder to Find? (2017), and other more recent papers. 

[2] Mentions Fast Grants, with clips showing Patrick Collison and Tyler Cowen.

Ezra Klein and Jim Pethjokoukis on macro factors in economic growth [why not cultural rank]

Transcript: Ezra Klein Interviews James Pethokoukis, NYTimes, May 21, 2024.

EZRA KLEIN: I mean, but take South Korea, take the U.A.E., take China. I mean, you can pick your country here. The kind of question I’m trying to raise about your thesis, because it’s also relevant, frankly, to my thesis, is, if the problem is that America makes a series of policy mistakes in the ’70s, why, then, in the ensuing five decades, don’t a bunch of our competitor countries race past us.

There are theories that they would. Japan, in the ’80s and ’90s, seemed like maybe they were, right? Japan was going to be the future. There were a million books written in the ’90s about this. Germany at different times, right? But I don’t think you would look at anybody today, any rich country of significant size, and say, they really got it right, and we really got it wrong. So how do you understand that if the story is about mistakes we specifically made in the ’70s?

JIM PETHOKOUKIS: Well, we can make mistakes that are very specific to us. And other countries may have made different mistakes, even though there was, as you say, this great enthusiasm in the ’80s that Japan had it sort of figured out, that they could do economic growth and innovation in a brand new way, which turned out not to be the case. And then you mentioned Germany, and we seem to have this insatiable desire to find — at least some people do — to find some other model. I don’t think those models have turned out better than the American model.

EZRA KLEIN: If you were to try to make an argument about why things look not the same, but why nobody has achieved the Jim Pethokoukis world across Canada, across Western Europe, across Asia, right, all countries during this period that were rich enough to do much of what you’re talking about, do you have theories that unite the answer?

JIM PETHOKOUKIS: Yeah, I mean, listen, I don’t think it is wrong to do sort of a cross-country because this productivity slowdown didn’t just happen in the United States. Clearly, there was some sort of macro reasons. It’s just becoming harder and more expensive to do research. Those things affected everybody.

So once you’ve assumed, OK, there was sort of this umbrella effect that would make it difficult to do productivity and economic growth and faster tech progress everywhere. So that mattered. And then to what extent do our decisions matter? At first, we didn’t understand what happened. And then when we did, I think we’ve just underestimated the difficulty, at least certainly in the United States, of returning to fast growth.

And the ideas that we put forward, whether it was a little more spending on this program, a tax cut here, maybe those are individually great ideas, but given, I think, the headwinds from these macro factors, sort of the tailwinds need to be much, much stronger. And even now, when we’re talking about spending more money on R&D, I don’t think it’s enough.

EZRA KLEIN: Let me try some thesis on you that I think can work across countries. One is that as countries get richer, they become more risk averse. Some of the innovations you’re talking about, like colonies on the moon and flying cars, they require a high tolerance for risk. Maybe as societies get more affluent, people have enough. Their lives are good enough. They aren’t as motivated to take that risk. What do you think of that?

Why not cultural evolution in the sense that David Hays and I have argued in our work on cultural ranks? That's as macro as you can get. I addressed the issue of of economic growth in: Stagnation and Beyond: Economic growth and the cost of knowledge in a complex world, Working Paper, August 2, 2019.

Abstract: What economists have identified as stagnation over the last few decades can also be interpreted as the cost of continuing successful engagement with a complex world that is not set up to serve human interests. Two arguments: 1) The core argument holds that elasticity (ß) in the production function for economic growth is best interpreted as a function of the interaction between the economic entity (firm, industry, the economy as a whole) and particular aspects the larger world: physical scale in the case of semi-conductor development, biological organization in the case of drug discovery. 2) A larger argument interprets current stagnation as the shoulder of a growth curve in the evolution of culture through a succession of fundamental stages in underlying cognitive architecture. New stages develop over old through a process of reflective abstraction (Piaget) in which the mechanisms of earlier stages become objects for manipulation and deployment for the emerging stage.

Why is scientific progress slowing down? Science is making fewer and fewer breakthrough discoveries.[Hossenfelder]

What's going on? 

1. Nothing at all. Things are just fine. Most scientists prefer this.
2. There's nothing left to discover.
3. Current arrangements award productivity over usefulness.

What economic growth and statistical semantics tell us about the structure of the world

Bumping this to the top of the queue on general principle, and because it takes a very abstract view of economic development, which is front and center in Tyler Cowen's current conversation with Stephen Jennings, who is a developer working Kenya.

New working paper. Title above. Download at:

• Academia: https://www.academia.edu/43938531/What_economic_growth_and_statistical_semantics_tell_us_about_the_structure_of_the_world
• SSRN: http://ssrn.com/abstract=3680119
• ResearchGate: https://www.researchgate.net/publication/343836653_What_economic_growth_and_statistical_semantics_tell_us_about_the_structure_of_the_world

Abstract, contents, and first section below.

---

Abstract: The metaphysical structure of the world, as opposed to its physical structure, resides in the relationship between our cognitive capacities and the world itself. Because the world itself is “lumpy”, rather than “smooth” (as developed herein, but akin to “simple” vs. complex”), it is learnable and hence livable. Machine learning AI engines, such as GPT-3, are able to approximate the semantic structure of language, to the extent that that structure can be modeled in a high-dimensional space. That structure ultimately depends on the fact that the world is lumpy. It is the lumpiness that is captured in the statistics. Similarly, I argue, the American economy has entered a period of stagnation because the world is lumpy. In such a world good “ideas” become more and more difficult to find. Stagnation then reflects the increasing costs the learning required to develop economically useful ideas.

Contents

Wending our way in a complex world 2
World, mind, and learnability: On the metaphysical structure of the cosmos 5
Stagnation, Redux: Like diamonds, good ideas are not evenly distributed 10
The complex universe: Further reading 18

Wending our way in a complex world

This paper is based on two very different posts that I’ve written in the last month. One of them takes the statistical semantics of AI engines like GPT-3 as its starting point: “World, mind, and learnability: On the metaphysical structure of the cosmos” (revised considerably for this paper). The other is about economic growth and stagnation: “Stagnation, Redux: Like diamonds, good ideas are not evenly distributed”.

These two very different papers nonetheless share both substance and method. Methodologically, both argue that the situation we observe is intelligible if we assume that the world is structured in a certain way. Their core substance is about that structure: the world must be “lumpy” – a notion I discuss on pages 5 ff. Because the world is lumpy we can learn about it, live in it, talk about it, and write about it. By contrast, a “smooth” world would be unintelligible and hence unlivable. The exhaustive statistical analysis of a large body of text is, in effect, able to recover that structural lumpiness as reflected in language and use it to produce new texts.

However, because the world is lumpy, we begin by learning and benefiting from things close to hand. When those resources have been exhausted we and must travel deeper into the world, expending more and more effort to extract economic benefit. Our current economic stagnation reflects the increasing cost of learning more about the world. A smooth world would no doubt be more convenient, for there would be economic benefit at every turn, either that or economic disaster. That is, if the world were smooth, we wouldn’t be here.

That, I know, this talk of smoothness and lumpiness is very abstract and “featureless”. But then could a resonance between such disparate phenomena as statistical semantics and economic stagnation but be abstract? I’ll provide more substance later in this paper, some diagrams, and some arguments. But first I want to suggest that what I’ve been calling lumpiness is what Ilya Prigogine and many others have called complexity.

Our complex world

Some years ago David Hays and I wondered why natural selection leads to complexity [1]. We argued that, over the long run, natural selection favors organisms with increased ability to process information, and that ability yields benefits in a complex universe. But what did we mean by that, a complex universe? Here is what we said:

It is easy enough to assert that the universe is essentially complex, but what does that assertion mean? Biology is certainly accustomed to complexity. Biomolecules consist of many atoms arranged in complex configurations; organisms consist of complex arrangements of cells and tissues; ecosystems have complex pathways of dependency between organisms. These things, and more, are the complexity with which biology must deal. And yet such general examples have the wrong “feel;” they don't focus one's attention on what is essential. To use a metaphor, the complexity we have in mind is a complexity in the very fabric of the universe. That garments of complex design can be made of that fabric is interesting, but one can also make complex garments from simple fabrics. It is complexity in the fabric which we find essential.

We take as our touchstone the work of Ilya Prigogine, who won the Nobel prize for demonstrating that order can arise by accident (Prigogine and Stengers 1984; Prigogine 1980; Nicolis and Prigogine 1977). He showed that when certain kinds of thermodynamic systems get far from equilibrium order can arise spontaneously. These systems include, but are not limited to, living systems. In general, so-called dissipative systems are such that small fluctuations can be amplified to the point where they change the behavior of the system. These systems have very large numbers of parts and the spontaneous order they exhibit arises on the macroscopic temporal and spatial scales of the whole system rather than on the microscopic temporal and spatial scales of its very many component parts. Further, since these processes are irreversible, it follows that time is not simply an empty vessel in which things just happen. The passage of time, rather, is intrinsic to physical process.

We live in a world in which “evolutionary processes leading to diversification and increasing complexity” are intrinsic to the inanimate as well as the animate world (Nicolis and Prigogine 1977: 1; see also Prigogine and Stengers 1984: 297-298). That this complexity is a complexity inherent in the fabric of the universe is indicated in a passage where Prigogine (1980: xv) asserts “that living systems are far-from-equilibrium objects separated by instabilities from the world of equilibrium and that living organisms are necessarily ‘large,’ macroscopic objects requiring a coherent state of matter in order to produce the complex biomolecules that make the perpetuation of life possible.” Here Prigogine asserts that organisms are macroscopic objects, implicitly contrasting them with microscopic objects.

Peter Thiel’s second thoughts about funding Eliezer Yudkowsky and friends

That’s my speculative and somewhat polemical framing of a middle passage in this video, which presents a talk Peter Thiel recently gave before the Oxford Union. He’s talking about technology stagnation and so on and so forth. About Thiel, from the YouTube description:

Peter Thiel is an American technology entrepreneur and investor. He co-founded PayPal and Palantir, made the first outside investment in Facebook, and has funded companies like LinkedIn and Yelp. Thiel also started the Thiel Foundation, which works to advance technological progress and long-term thinking via funding non-profit research into artificial intelligence, life extension, and seasteading.

At about twenty minutes in (c. 20:07) there’s a striking passage where Thiel talks about a change in attitude that took place about a decade or so into the current millennium. Note that at the beginning when Thiel mentions “getting involved in all these things” that that involvement includes early funding for The Singularity Institute for Artificial Intelligence, which became the Machine Intelligence Research Institute in 2011.

Twenty years ago when I started getting involved in all these things the narrative was still generally a positive utopian it was people thought you know it’s kind of dangerous technology you know. If you build this computer that’s as smart or smarter than any human being in the world in the it’s kind of dangerous, but we’re gonna have to work really hard to make sure it’s friendly, that it’s aligned with humans and it was still sort of circa 2003 whatever misgivings people might have had about biotech or rockets or nuclear power, they did not yet have about AI and the AI narrative was still a generally positive utopian one.

And there’s sort of a strange way where this has completely flipped over the last decade or so. I was involved [with] a thing called The Singularity Institute which pushed a sort of accelerationist utopian technology. We’re progressing, we need to progress faster. We need of course to be a little bit careful and I sort of remember thinking to myself by 2015 I reconnected so many people and it didn’t feel like they were really pushing the AI thing as fast as before and it sort of devolved into you know some kind of escapist Burning Man camp.

You sort of got the sense that it had shifted from transhumanism to Luddite, something Luddite where no actually we want to slow this down. It feels kind of dangerous. It’s kind of a bad thing on net. And this finally this suspicion I think was finally confirmed you can look up on the Internet uh I’m gonna read this. It’s from April 2022 less than a year ago. Eliezer Yudkowsky, who’s one of the sort of thought leaders of the sort of futurist AI. It’s a post from the Machine Intelligence Research Institute and it’s announcing a new “Death with Dignity” strategy and so of the short version of this:

It's obvious at this point that humanity isn't going to solve the alignment problem, or even try very hard, or even go out with much of a fight. Since survival is unattainable, we should shift the focus of our efforts to helping humanity die with with [sic] slightly more dignity.

I want to underscore you don’t deserve to die with a lot of dignity because you’re not going to “try very hard, or even go out with might of a fight.” But it is an extraordinary, it’s an extraordinary way that the context is shifted.

What happened to bring about this shift in attitude? I’m wondering if it was some a failure of nerve. 

In any event we should note that having the business acumen needed to become rich by backing high tech ventures does not imply any deep insight into the future or, for that matter, technology itself. Technology is changing so fast that one can easily become rich on technology that will be obsolete a decade from the time the ink dries on the first check you cash.

Whoops! Science ain't what it used to be. Have the cookie-cutters and clerks taken over?

William J. Broad, What Happened to All of Science’s Big Breakthroughs? NYTimes, Jan. 17, 2023. From the opening:

Miracle vaccines. Videophones in our pockets. Reusable rockets. Our technological bounty and its related blur of scientific progress seem undeniable and unsurpassed. Yet analysts now report that the overall pace of real breakthroughs has fallen dramatically over the past almost three-quarters of a century.

This month in the journal Nature, the report’s researchers told how their study of millions of scientific papers and patents shows that investigators and inventors have made relatively few breakthroughs and innovations compared with the world’s growing mountain of science and technology research. The three analysts found a steady drop from 1945 through 2010 in disruptive finds as a share of the booming venture, suggesting that scientists today are more likely to push ahead incrementally than to make intellectual leaps.

“We should be in a golden age of new discoveries and innovations,” said Michael Park, an author of the paper and a doctoral candidate in entrepreneurship and strategic management at the University of Minnesota.

The new finding of Mr. Park and his colleagues suggests that investments in science are caught in a spiral of diminishing returns and that quantity in some respects is outpacing quality. While unaddressed in the study, it also raises questions about the extent to which science can open new frontiers and sustain the kind of boldness that unlocked the atom and the universe and what can be done to address the shift away from pioneering discovery. Earlier studies have pointed to slowdowns in scientific progress but typically with less rigor.

There's more at the link.

Scott Alexander on the argument from low-hanging fruit [stagnation, redux]

Scott Alexander, The Low-Hanging Fruit Argument: Models And Predictions, Astral Codex Ten, April 1, 2022.

It begins:

Imagine scientists venturing off in some research direction. At the dawn of history, they don’t need to venture very far before discovering a new truth. As time goes on, they need to go further and further.

Actually, scratch that, nobody has good intuitions for truth-space. Imagine some foragers who have just set up a new camp. The first day, they forage in the immediate vicinity of the camp, leaving the ground bare. The next day, they go a little further, and so on. There’s no point in traveling miles and miles away when there are still tasty roots and grubs nearby. But as time goes on, the radius of denuded ground will get wider and wider. Eventually, the foragers will have to embark on long expeditions with skilled guides just to make it to the nearest productive land.

Let’s add intelligence to this model. Imagine there are fruit trees scattered around, and especially tall people can pick fruits that shorter people can’t reach. If you are the first person ever to be seven feet tall, then even if the usual foraging horizon is very far from camp, you can forage very close to camp, picking the seven-foot-high-up fruits that no previous forager could get. So there are actually many different horizons: a distant horizon for ordinary-height people, a nearer horizon for tallish people, and a horizon so close as to be almost irrelevant for giants.

Finally, let’s add the human lifespan. At night, the wolves come out and eat anyone who hasn’t returned to camp. So the the maximum distance anyone will ever be able to forage is a day’s walk from camp (technically half a day, so I guess let’s imagine that everyone can teleport back to camp whenever they want).

This model can explain some otherwise confusing observations about the history of science:

1. Early scientists should make more (and larger) discoveries than later scientists.
2. Early scientists should be relatively more likely to be amateurs; later scientists, professionals.
3. Early scientists should make discoveries younger (on average) than later scientists.
4. These trends should move more slowly for the most brilliant scientists.
5. These trends should fail to apply in fields of science that were impossible for previous generations to practice.

Scott then goes on to elaborate on each of those five.

I've presented a somewhat more abstract version of this argument that takes a 1992 article by Paul Romer, Two Strategies for Economic Development (gated), as its point of departure: Stagnation, Redux: It’s the way of the world [good ideas are not evenly distributed, no more so than diamonds]. That blog post makes up the second part of my working paper, What economic growth and statistical semantics tell us about the structure of the world, August 24, 2020, 19 pp, https://www.academia.edu/43938531/What_economic_growth_and_statistical_semantics_tell_us_about_the_structure_of_the_world.

Here's the abstract from that working paper:

The metaphysical structure of the world, as opposed to its physical structure, resides in the relationship between our cognitive capacities and the world itself. Because the world itself is "lumpy, rather than "smooth" (as developed herein, but akin to "simple" vs. "complex"), it is learnable and hence livable. Machine learning AI engines, such as GPT-3, are able to approximate the semantic structure of language, to the extent that that structure can be modeled in a high-dimensional space. That structure ultimately depends on the fact that the world is lumpy. It is the lumpiness that is captured in the statistics. Similarly, I argue, the American economy has entered a period of stagnation because the world is lumpy. In such a world good "ideas" become more and more difficult to find. Stagnation then reflects the increasing costs the learning required to develop economically useful ideas.

Marc Andreessen: An existential crisis in science [Talent search]

Richard Hanania interviews Marc Andreessen, Flying X-Wings into the Death Star: Andreessen on Investing and Tech, August 16, 2021:

On basic research:

That said, something has gone very wrong in the basic research complex that we do have in the universities and in the federal funding system. We know something has gone very wrong because of the replication crisis. John Ioannidis at Stanford wrote this classic paper where he said “50% of published research is non-reproducible” which means you basically can’t reproduce the same results meaning you can’t do anything with the research, it’s basically fake. Interestingly he was studying biomedical research, which is an area of research you think would be very focused on getting things right. It subsequently turned out he might have been underestimating the problem. Fields like biomedicine might be as fake as 70%.

There’s this other amazing study that maybe we can link to for your listeners. It was a study of medical trials funded by a branch of the federal apparatus, heart and lung research. It’s this great chart that shows that new medicines stopped working at the point when the funders of the research require the researchers to register their experiments ahead of time. It’s basically this chart that shows for 30 years or something you had all of these new medicines coming out and the research results were like “wow this really works we should give it to patients,” and then there’s this point where the people running medical trials were forced to pre-register their hypothesis with the funding agency. They were forced to basically say upfront “this is exactly what we’re testing for; here’s exactly how we’re going to measure the results.” Subsequent to that point, new medicines have stopped working.

There’s two possible implications to this, right? The low-hanging fruit argument is that it used to be easier to make medicines that work and now we’ve harvested the low hanging fruit in science and technology, now it’s harder to do it and it’s just a coincidence. The other explanation is that these old medicines didn’t work either. They were basically fake research results based on data mining or p-hacking in such a way that the results were fraudulent. The implication is that there are a lot of drugs on the market today that don’t actually work.

Anyway, science right now is in an existential crisis. This is a real, real issue, and there’s now a generation of scientists who specialize in pointing this out and analyzing it. Andrew Gelman and others. I’ll give you an example: I had a conversation with the long-time head of one of the big federal funding agencies for healthcare research who is also a very accomplished entrepreneur, and I said, “do you really think it’s true that 50-70% of biomedical research is fake?” This is a guy who has spent his life in this world. And he said “oh no, that’s not true at all. It’s 90%.” [Richard laughs]. I was like “holy shit,” I was flabbergasted that it could be 90%.

He’s like “well look, 90% of everything is shit”, which is literally this thing called Sturgeon’s law which says that 90% of everything is bad. 90% of every novel written is bad, 90% of music, 90% of art… 90% of everything is bad. So his analysis was, anything you get in the field of medical experimentation, biomedical development, and this is going to be true of any field, there’s like five labs total in the world that are really good at what they’re doing and doing really cutting edge work. And this is true of quantum computing; pick any field for advanced technology you want.

So those five labs have a pretty good shot at doing interesting work, even some of that is going to reproduce and some of it isn’t. But once you get out of those top five labs, it’s pretty much make-work, incremental, marginal improvements at best, and a complete waste of time otherwise. And I said “good God, why does the other 90% continue to get funded if you know this?” And he said, “well, there are all these universities and professors who have tenure, there are all these journals, there are all these systems and people have been promised lifetime employment.” Anyway, a longwinded way of saying that we have pretty serious structural and incentive problems in the research complex.

Finding talent:

Richard: When Peter Thiel was doing his Thiel fellowship for people, it was seen as this huge eccentricity. That was 10, 20 years ago? You pay people to drop out of school. That’s fascinating that it’s now a better credential to have dropped out of Harvard in Silicon Valley than to have actually finished.

Marc: The Thiel fellowship is super interesting. I’m super open on these things, and even I was like “really? 19-year-olds?” Basically he paid a bunch of 19-year-olds to drop out of college, they all moved to a group house in San Francisco, and he told them all “go nuts.” On the surface you would say “oh my god! What are you doing? Let’s hope that 100% of them survive.” And if you look at what that cohort has done, it’s a very small number of kids… that’s a point Peter makes, it wasn’t that many kids! People really freaked out given the fact that it was 20-year-olds or something.

That tells you how important the social signal and status stuff is. He calls the existing universities the Catholic Church; part of the Reformation, sells indulgences. They know full well what they’re doing… they know they’re corrupt, they know their system is broken. So of course they’re going to disproportionately panic at the indication… they see Martin Luther walking down the street of course they’re going to freak out.

But you look at that cohort of kids, they’ve been successful beyond belief! Ethereum! Ethereum is a $300 billion outcome in ten years; a Thiel Fellow. Sigma, another company we’re invested in is extraordinarily successful… they’re revolutionizing computer design; Thiel Fellow. And there’s another dozen of these that are like these really spectacular breakthrough… these people are just doing incredibly fundamental work. Centered in tech, but of course that’s where people can do breakthrough work these days.

There it is. That worked. It begs this question, “OK, why are we not all doing that? There’s something there.”

Richard: I think Peter Thiel being able to select 20 people is probably the point. [laughs] I went to law school at the University of Chicago. It just showed me you could have so many smart people… and I just felt like law school was for people who didn’t know what to do with their lives. That was me too, I didn’t go to a great college, I didn’t know what to do. That's what they were there for. Selection is everything, so you’re right. Not everybody can found Ethereum, but people can think about starting a hardware store instead of getting some worthless degree, I think that’s realistic for people.

Marc: Selection is a big part of everything, I can see the general point. I will say I’m sure it’s just not 20. I’m sure it’s not 2 million, but I’m sure it’s not 20.

Richard: Well even if it’s 2,000…

Marc: Yeah! Well how about that? It’s not like the Harvard undergraduate class, how many kids per year? Probably a couple thousand freshmen a year. That’s not that big. We load onto those kids the responsibility apparently for determining every aspect of how our society works. We’re making some selection there already. What's the total reporting staff for the New York Times? 800?

Here’s the way we’re thinking about it from a tech standpoint. You can’t just go flat up against the bundle, you can’t do a frontal assault on the death star. Even if you could, and people have tried to full on create new universities, you run up against the accreditation cartel. It’s one of these government regulatory capture things. Universities are a government supported cartel, and quite literally they have access to federal student lending through the accreditation process, the accreditation process is formed by the incumbent. The government has delegated to these institutions the ability to decide who gets to compete against them, and the answer is nobody gets to compete against them. These are nominally non-profits but you would never know it looking at their financial statements.

It is this idealized, stagnant entrenchment of the status quo which is extremely powerful. You’re not going to frontal assault it. What you can do and what we’re doing is you can slowly strip away pieces of it; you can pull pieces out. You can for sure pull out the actual skills training part of it. You can pull out the dating part of it. You can pull out a lot of the logistical components of it. You can pull out housing, you can pull out the food component.

And then you get into the serious stuff. Can you re-credentialize? Can you create new forms of signal and status that are not just based on these legacy institutions? It’s the X-wing death star thing. Any individual effort probably won’t work, but if we run enough experiments over time and strip off enough pieces I think we’ll be able to start having an impact.

There's more at the link.

Ramble at the beginning of August 2021: We’re lost, looking for meaning, alas inequality [the Tractatus]

The inequality that sends billionaires into space is not an independent and isolated feature of the contemporary world. But let’s not start there. Let’s start with something ‘easy,’ Wittgenstein’s Tractatus, then we can think about the meaning of life, and then we can take a look at inequality – kith and kin to those billionaires. I end with a somewhat whimsical suggestion for a Twitter tax for the rich.

This is something of a grab bag and a parking lot. I’ve been thinking of these things and put them here so I know where they are. In time I’ll attempt to make sense of them. Maybe.

Wittgenstein’s Tractatus

I recently read a post by Rohit (The Architecture of Knowledge) that reminded me of Wittgenstein’s Tractatus Logico-Philosophicus. I asked him about it and he replied that, yes, he was familiar with it and that it was “an inspiration forever in its scope and ambition.”

I understand where he’s coming from. I read it early in my undergraduate years at Johns Hopkins and it had a strong effect on me. Yes, for its logical structure, but also for its last proposition: “7. Whereof one cannot speak, thereof one must be silent.” I’d almost say its appeal was evenly split by that proposition and by the rest of it.

Looking back, it is a peculiar work. I went on to study computational semantics in my graduate years at SUNY Buffalo where I studied computational semantics with David Hays (while working on a degree in English literature). There I certainly was interested in something one might as well call “the architecture of knowledge,” that is, the structure of human knowledge. For the most part we – members of Hays’s research group – were interested in general principle, on the one hand, and small scale structures on the other. Yet my first major piece of work, an analysis of Shakespeare’s Sonnet 129, certainly implied large scale structure, as did my dissertation, “Cognitive Science and Literary Theory” (1978). That work certainly looks very different from Wittgenstein’s Tractatus.

Wittgenstein was much taken with symbolic logic, which was relatively new at the time. He took its propositions to represent, that is, to be capable of representing, facts about the world. The world itself? Is that what he was chasing? Or did he think of those propositions as representing what Noam Chomsky came to call mentalese, the conceptual language of the human mind? Perhaps he did, but if so, he later came to rather different views on such subjects. In any event it certainly doesn’t look the work I did with David Hays or, for that, matter any of the roughly similar work others were doing at the time (see, e.g., John Sowa’s comprehensive website on knowledge representation).

Of course representing how we think about the world is very different from representing the world as it really is. That’s something fraught with metaphysical difficulties. And yet that’s something I’ve given some attention to in recent years, I’m thinking particularly about my work on pluralism, but also my more recent work, What economic growth and statistical semantics tell us about the structure of the world. This work, however, is quite different from, has a very different texture than, the earlier work on conceptual structures.

That there is a clear difference between these two bodies of work suggests that we’ve learned something in the decades that have passed since Wittgenstein wrote his Tractatus. That work, it seems to me in retrospect, is neither about the world nor about our representations of the world. Or it is indifferently about both. It is an undifferentiated metaphysical ether.

To live a meaningful life

Meanwhile, our visions of the future are vapid, a theme I’ve been exploring in a recent series of posts on billionaires-in-space and in a post on our visions of the future. I think this is linked to various posts I’ve made about living a meaningful life:

• Scott Sumner, Meaning in Life,
  
• Our Rage for Order and Coherence,
  
• Personality, understanding, and anxiety as the driver of cultural evolution [Tech Evol],
  
• Fear of death as a driving force in human (cultural) life

Our visions of the future are not coupled with, do not emerge from, a belief in living a meaningful life. Absent such a belief, billionaires going into space collapses into self-regarding joy-riding. These seem to be people unconnected with the world, holding themselves above and outside the world. They are hollow men.

But how does the human mind, the human spirit, find itself at home in the world? For that’s what’s at stake. In a post about Mark Moffett’s The Human Swarm I made some observations about identity that are relevant to the question of meaning:

The argument that needs to be made is that our nervous system affords us open-ended awareness of the world. I suspect that’s a joint product of the active nature of the nervous system and the emergence of language. On that active nature, the nervous system doesn’t passively take the world in, but rather actively probes the world through continuously projecting expectations – think, for example, of the model William Powers developed almost a half century ago in Behavior: The Control of Perception (1973). Thus perception is a process of verifying those projections (or, to use a more current language, updating Baysian priors).

The emergence of language leads to an endless curiosity about everything: What’s that? How does it work? Where’d it come from? Living becomes thus becomes a dialog with the world. And the question, Where did WE come from? will arise in that process. The answer initially takes the form of myth, of stories about origins. And those stories, in effect, establish the link between a society and world. That too is a matter of identity.

Those same myths and stories direct our search for meaning in life. Where do stories of billionaires joy-riding in space direct that search for meaning?

Inequality, executive pay, and stagnation

Back in 2019 Tyler Cowen published a short article in Time, Why CEOs Actually Deserve Their Gazillion-Dollar Salaries, which was excerpted from his recent book, Big Business: A Love Letter to an American Anti-Hero. After acknowledging that that top CEOs make 300 times as much as the average worker, Cowen argues:

While individual cases of overpayment definitely exist, in general, the determinants of CEO pay are not so mysterious and not so mired in corruption. In fact, overall CEO compensation for the top companies rises pretty much in lockstep with the value of those companies on the stock market.

The best model for understanding the growth of CEO pay, though, is that of limited CEO talent in a world where business opportunities for the top firms are growing rapidly. The efforts of America’s highest-earning 1% have been one of the more dynamic elements of the global economy. It’s not popular to say, but one reason their pay has gone up so much is that CEOs really have upped their game relative to many other workers in the U.S. economy.

Today’s CEO, at least for major American firms, must have many more skills than simply being able to “run the company.” CEOs must have a good sense of financial markets and maybe even how the company should trade in them. They also need better public relations skills than their predecessors, as the costs of even a minor slipup can be significant.

And so forth and so on, “yada yada,” to quote various characters from Seinfeld. I’m willing to grant that these highly compensated executives get their jobs through honest labor, rather than some form of corruption, and that, on the whole, they are more competent than the next lower tier of executive talent.

What I question is that ratio between CEO pay and the pay of the average worker, 300-to-1. Is that necessary? Wouldn’t a 30-to-1 compensation ratio leave plenty of room for these (mostly) guys to play “mine is bigger than yours”? Where did that ratio come from?

We know roughly when it arose, after the 1960s. In August of 2020 the Economic Policy Institute reported:

In 2019, a CEO at one of the top 350 firms in the U.S. was paid $21.3 million on average (using a “realized” measure of CEO pay that counts stock awards when vested and stock options when cashed in rather than when granted). This 14% increase from 2018 occurred because of rapid growth in vested stock awards and exercised stock options tied to stock market growth. Using a different “granted” measure of CEO pay, average top CEO compensation was $14.5 million in 2019. In 2019, the ratio of CEO-to-typical-worker compensation was 320-to-1 under the realized measure of CEO pay; that is up from 293-to-1 in 2018 and a big increase from 21-to-1 in 1965 and 61-to-1 in 1989. CEOs are even making a lot more—about six times as much—as other very high earners (wage earners in the top 0.1%). From 1978 to 2019, CEO pay based on realized compensation grew by 1,167%, far outstripping S&P stock market growth (741%) and top 0.1% earnings growth (which was 337% between 1978 and 2018, the latest data year available). In contrast, compensation of the typical worker grew by just 13.7% from 1978 to 2019.

Cowen has also written about something he calls The Great Stagnation (2011), when America’s productivity started slumping. As I recall he sees stagnation as beginning in the 1970s. So CEO pay starting galloping upward at roughly the same time economic productivity started slowing down. Is there a connection there?

I’m not suggesting that one caused the other, though I must admit the correlation is tempting. However, the CEO compensation system is not a system that is isolated from and thus independent of the overall economy. They are two aspects of the same economic system.

Peer review, what's it good for? All together now: Absolutely nothing!

Mark Humphries, The Absurdity of Peer Review: What the pandemic revealed about scientific publishing, Elemental, June 3, 2021.

Humphries begins by reporting that he "was reading my umpteenth news story about Covid-19 science" and noted that the story cited many pre-prints and, in each case, the mention

was immediately followed by the disclaimer that it had not yet been peer reviewed. As though to convey to the reader that the research therein, the research plastered all over the story, was somehow of less worth, less value, less meaning than the research in a published paper, a paper that had passed peer review.

Imagine reading about the discovery of the structure of DNA with that same reticence we use today: “In a recent Letter to the journal Nature, Cambridge University scientists James Watson and Francis Crick proposed a new structure for DNA (not yet peer reviewed). They claim their “double helix” model, a spiral of two strands of bases, both explains decades of experimental work, and provides a clear mechanism for copying genes. Their proposal drew heavily on data contained in Letters in the same issue of Nature from the teams of Rosalind Franklin (not yet peer reviewed) and Maurice Wilkins (not yet peer reviewed).”

Or consider this modern take on a certain scientist’s annus mirabilis: “The past year of 1905 has been a remarkable for one Herr Einstein, who proposed no less than four theories new to modern physics in a series of papers. His first was on a much-anticipated explanation of the photoelectric effect (not yet peer reviewed), the second on how Brownian motion arises from the collision of invisible particles (not yet peer reviewed), the third on the equivalence between mass and energy (not yet peer reviewed), and the final paper updates Newton’s mechanics to be more accurate for objects moving close to the speed of light (not yet peer reviewed).”

These imagined reports are both eye-wateringly ridiculous.

The papers they describe are works that have formed the foundations of biology and physics as we know them. None of the classic papers on DNA, nor Einstein’s four in his miracle year, were peer reviewed. Indeed, only one of Einstein’s 300 or so published papers was ever peer-reviewed, which so disgusted him that he never submitted a paper to that journal again. It did not matter that the DNA papers and Einstein’s papers were not peer-reviewed. Nor did it matter for the many thousands of the classic papers published in the past century that were not peer reviewed: the work in them is the foundations on which science stands today.

Whoops! Note that peer review didn't become the norm in science until the 1970s. How did science ever advance without it?

So, if peer review doesn't insure intellectual rigor and scientific interest, what's it good for?

I’d wager that it’s a combination of simple inertia, and because journals rely on it for gate-keeping. When it did appear, peer review’s job was to help out editors who received papers beyond their expertise, and wanted some advice on whether to publish or not. Now it has become all about who gets chosen to be published in the most selective journals, who can convince a handful of reviewers their paper is worthy of publishing in that journal, and so dramatically increase the chances of their paper being read: for the primary purpose of the selective journal is to say “hey, read our stuff, it’s been hand-picked just for you.” It is a way for editors to filter papers based on the personal biases of two to four individuals. Without that gate-keeping role, it seems we would have no need of peer review.

This then is why I was so bothered about how Covid-19 research is reported: peer review is no guard, is no gold standard, has little role beyond gate-keeping. It is noisy, biased, fickle.

That is to say, it's good for nothing beyond protecting the reputations of the gate keepers.

Horgan’s The End of Science, a reconsideration, Part 1: Introduction

Back in the ancient days of 1996 John Horgan published The End of Science: Facing the Limits of Knowledge in the Twilight of the Scientific Age and thereby earned the enmity of scientists and science lovers everywhere. But the book was not, as its title suggests, an exercise in the then fashionable exercise of science bashing. If anything it was born of a love of science, albeit a love disappointed at the current progress of science. Things seemed to be grinding to a halt, perhaps most noticeably in the foundations of physics, where elegant theorizing had far outstripped empirical evidence: What good is a theory if your can’t test it against the observational evidence?

Basic Books issued a new edition in 2015 and Horgan wrote a new preface in which he argued that his argument still holds. Judging from remarks he made on Twitter to Eric Weinstein he’s still skeptical about science’s prospects for future advance. Yes, “our descendants will learn much more about nature, and they will invent gadgets even cooler than smartphones,” but gone are the days of ideas as “cataclysmic as heliocentrism, evolution, quantum mechanics, relativity, [and] the big bang.”

I’m not so sure. Back in 1997 I published an essay-review in which I argued:

One can read The End of Science as evidence of the emergence of a new worldview. Those, like Horgan, who believe that science is indeed coming to an end are heirs of standard Western metaphysical assumptions, assumptions which are in a shambles. Those who believe that science has a splendid future are helping to forge an as-yet unnamed worldview, one grounded in different assumptions, though, perhaps more often than not, the people working toward this worldview think of it as but a continuation of the Western worldview they were taught in school.[1]

I still like that argument, but feel the need to acknowledge that years that have passed by saying a bit more.

For one thing, in 2011 economist Tyler Cowen published a book in which he argued that the economy is stagnating after two centuries of accelerating growth that lifted 100s of millions out of poverty into a comfortable middle class existence. Given that that economic growth has been driven by technological innovation, and that technological innovation is (loosely) coupled with scientific progress, Cowen’s argument resonates with Horgan’s. In 2018 Patrick Collison and Michael Nielson published an article in The Atlantic in which they argued that science seemed to slowing down [3]. A year after that Collison and Cowen surveyed the situation and issued a call for Progress Studies [4]. I replied to this work with a working paper where I argued, “What economists have identified as stagnation over the last few decades can also be interpreted as the cost of continuing successful engagement with a complex world that is not set up to serve human interests” [5]. That came from the same line of thinking and investigation I used in my reply to Horgan.

I now wish to add to that argumentation in a series of blog posts, though I’m not sure how many or on what schedule. My current plan is to begin by discussing what seems to me the clearest case for scientific stalemate, the foundations of physics. Then I’ll put on my literary critic’s hat and give Horgan’s words a close reading – e.g. “cataclysmic”? – suggesting that there’s a lot of wiggle-room in it. Then I want to take a personal look at one of Horgan’s big questions: “How, exactly, does a chunk of meat make a mind?” I’ve been tracking that ever since, say, I read a Scientific American article by Karl Pribram about the holographic brain in 1969; I continued on by studying cognitive science and computational semantics in graduate school in the 1970s with David Hays (while pursuing a degree in English Literature); some years later, in the 1990s, I had conversations about neurodynamics with the late Walter Freeman (some of which ended up in my 2001 book on music, Beethoven’s Anvil: Music in Mind and Culture), and, well, that’s enough for the moment. I’ll conclude by returning to my main line, the one I used in 1997 review of Horgan, used again in my thoughts on economic stagnation, but that actually dates to my graduate school days [6].

More later.

References

[1] William L. Benzon, Pursued by Knowledge in a Fecund Universe, Journal of Social and Evolutionary Systems 20(1): 93-100, 1997. https://www.academia.edu/8790205/Pursued_by_Knowledge_in_a_Fecund_Universe.

[2] Tyler Cowen, The Great Stagnation: How America Ate All The Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better: A Penguin eSpecial from Dutton, Dutton, 2011.

[3] Patrick Collison and Michael Nielsen, “Science Is Getting Less Bang for Its Buck”, The Atlantic, Nov 16, 2018, https://www.theatlantic.com/science/archive/2018/11/diminishing-returns-science/575665/.

[4] Patrick Collison and Tyler Cowen, We Need a New Science of Progress, The Atlantic, July 30, 2019, https://tinyurl.com/hbnd5w8a.

[5] Stagnation and Beyond: Economic growth and the cost of knowledge in a complex world, Version 2, Working Paper, Aug. 2, 2019, 62 pp., https://www.academia.edu/39927897/Stagnation_and_Beyond_Economic_growth_and_the_cost_of_knowledge_in_a_complex_world.

[6] This blog post is a guide to that work, much of which I developed in close conjunction with the late David Hays, Mind-Culture Coevolution: Major Transitions in the Development of Human Culture and Society, New Savanna, July 4, 2020, https://new-savanna.blogspot.com/2014/08/mind-culture-coevolution-major.html.