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Existential risk, AI, and the inevitable turn in human history
In several of my books and many of my talks, I take great care to spell out just how special recent times have been, for most Americans at least. For my entire life, and a bit more, there have been two essential features of the basic landscape:
1. American hegemony over much of the world, and relative physical safety for Americans.
2. An absence of truly radical technological change.
Unless you are very old, old enough to have taken in some of WWII, or were drafted into Korea or Vietnam, probably those features describe your entire life as well.
In other words, virtually all of us have been living in a bubble “outside of history.”
Now, circa 2023, at least one of those assumptions is going to unravel, namely #2. AI represents a truly major, transformational technological advance. Biomedicine might too, but for this post I’ll stick to the AI topic, as I wish to consider existential risk.
#1 might unravel soon as well, depending how Ukraine and Taiwan fare. It is fair to say we don’t know, nonetheless #1 also is under increasing strain.
Hardly anyone you know, including yourself, is prepared to live in actual “moving” history. It will panic many of us, disorient the rest of us, and cause great upheavals in our fortunes, both good and bad. In my view the good will considerably outweigh the bad (at least from losing #2, not #1), but I do understand that the absolute quantity of the bad disruptions will be high.
I am reminded of the advent of the printing press, after Gutenberg. Of course the press brought an immense amount of good, enabling the scientific and industrial revolutions, among many other benefits. But it also created writings by Lenin, Hitler, and Mao’s Red Book. It is a moot point whether you can “blame” those on the printing press, nonetheless the press brought (in combination with some other innovations) a remarkable amount of true, moving history. How about the Wars of Religion and the bloody 17th century to boot? Still, if you were redoing world history you would take the printing press in a heartbeat. Who needs poverty, squalor, and recurrences of Ghenghis Khan-like figures?
But since we are not used to living in moving history, and indeed most of us are psychologically unable to truly imagine living in moving history, all these new AI developments pose a great conundrum. We don’t know how to respond psychologically, or for that matter substantively. And just about all of the responses I am seeing I interpret as “copes,” whether from the optimists, the pessimists, or the extreme pessimists (e.g., Eliezer). No matter how positive or negative the overall calculus of cost and benefit, AI is very likely to overturn most of our apple carts, most of all for the so-called chattering classes.
The reality is that no one at the beginning of the printing press had any real idea of the changes it would bring. No one at the beginning of the fossil fuel era had much of an idea of the changes it would bring. No one is good at predicting the longer-term or even medium-term outcomes of these radical technological changes (we can do the short term, albeit imperfectly). No one. Not you, not Eliezer, not Sam Altman, and not your next door neighbor.
How well did people predict the final impacts of the printing press? How well did people predict the final impacts of fire? We even have an expression “playing with fire.” Yet it is, on net, a good thing we proceeded with the deployment of fire (“Fire? You can’t do that! Everything will burn! You can kill people with fire! All of them! What if someone yells “fire” in a crowded theater!?”).
So when people predict a high degree of existential risk from AGI, I don’t actually think “arguing back” on their chosen terms is the correct response. Radical agnosticism is the correct response, where all specific scenarios are pretty unlikely. Nonetheless I am still for people doing constructive work on the problem of alignment, just as we do with all other technologies, to improve them. I have even funded some of this work through Emergent Ventures.
I am a bit distressed each time I read an account of a person “arguing himself” or “arguing herself” into existential risk from AI being a major concern. No one can foresee those futures! Once you keep up the arguing, you also are talking yourself into an illusion of predictability. Since it is easier to destroy than create, once you start considering the future in a tabula rasa way, the longer you talk about it, the more pessimistic you will become. It will be harder and harder to see how everything hangs together, whereas the argument that destruction is imminent is easy by comparison. The case for destruction is so much more readily articulable — “boom!” Yet at some point your inner Hayekian (Popperian?) has to take over and pull you away from those concerns. (Especially when you hear a nine-part argument based upon eight new conceptual categories that were first discussed on LessWrong eleven years ago.) Existential risk from AI is indeed a distant possibility, just like every other future you might be trying to imagine. All the possibilities are distant, I cannot stress that enough. The mere fact that AGI risk can be put on a par with those other also distant possibilities simply should not impress you very much.
Given this radical uncertainty, you still might ask whether we should halt or slow down AI advances. “Would you step into a plane if you had radical uncertainty as to whether it could land safely?” I hear some of you saying.
I would put it this way. Our previous stasis, as represented by my #1 and #2, is going to end anyway. We are going to face that radical uncertainty anyway. And probably pretty soon. So there is no “ongoing stasis” option on the table.
I find this reframing helps me come to terms with current AI developments. The question is no longer “go ahead?” but rather “given that we are going ahead with something (if only chaos) and leaving the stasis anyway, do we at least get something for our trouble?” And believe me, if we do nothing yes we will re-enter living history and quite possibly get nothing in return for our trouble.
With AI, do we get positives? Absolutely, there can be immense benefits from making intelligence more freely available. It also can help us deal with other existential risks. Importantly, AI offers the potential promise of extending American hegemony just a bit more, a factor of critical importance, as Americans are right now the AI leaders. And should we wait, and get a “more Chinese” version of the alignment problem? I just don’t see the case for that, and no I really don’t think any international cooperation options are on the table. We can’t even resurrect WTO or make the UN work or stop the Ukraine war.
Besides, what kind of civilization is it that turns away from the challenge of dealing with more…intelligence? That has not the self-confidence to confidently confront a big dose of more intelligence? Dare I wonder if such societies might not perish under their current watch, with or without AI? Do you really want to press the button, giving us that kind of American civilization?
So we should take the plunge. If someone is obsessively arguing about the details of AI technology today, and the arguments on LessWrong from eleven years ago, they won’t see this. Don’t be suckered into taking their bait. The longer a historical perspective you take, the more obvious this point will be. We should take the plunge. We already have taken the plunge. We designed/tolerated our decentralized society so we could take the plunge.
See you all on the other side.
My excellent Conversation with Tom Holland
Here is the transcript, audio, and video. Here is part of the summary:
Historian Tom Holland joined Tyler to discuss in what ways his Christianity is influenced by Lord Byron, how the Book of Revelation precipitated a revolutionary tradition, which book of the Bible is most foundational for Western liberalism, the political differences between Paul and Jesus, why America is more pro-technology than Europe, why Herodotus is his favorite writer, why the Greeks and Persians didn’t industrialize despite having advanced technology, how he feels about devolution in the United Kingdom and the potential of Irish unification, what existential problem the Church of England faces, how the music of Ennio Morricone helps him write for a popular audience, why Jurassic Park is his favorite movie, and more.
Here is one excerpt:
COWEN: Which Gospel do you view as most foundational for Western liberalism and why?
HOLLAND: I think that that is a treacherous question to ask because it implies that there would be a coherent line of descent from any one text that can be traced like that. I think that the line of descent that leads from the Gospels and from the New Testament and from the Bible and, indeed, from the entire corpus of early Christian texts to modern liberalism is too confused, too much of a swirl of influences for us to trace it back to a particular text.
If I had to choose any one book from the Bible, it wouldn’t be a Gospel. It would probably be Paul’s Letter to the Galatians because Paul’s Letter to the Galatians contains the famous verse that there is no Jew or Greek, there is no slave or free, there is no man or woman in Christ. In a way, that text — even if you bracket out and remove the “in Christ” from it — that idea that, properly, there should be no discrimination between people of different cultural and ethnic backgrounds, based on gender, based on class, remains pretty foundational for liberalism to this day.
I think that liberalism, in so many ways, is a secularized rendering of that extraordinary verse. But I think it’s almost impossible to avoid metaphor when thinking about what the relationship is of these biblical texts, these biblical verses to the present day. I variously compared Paul, in particular in his letters and his writings, rather unoriginally, to an acorn from which a mighty oak grows.
But I think actually, more appropriately, of a depth charge released beneath the vast fabric of classical civilization. And the ripples, the reverberations of it are faint to begin with, and they become louder and louder and more and more disruptive. Those echoes from that depth charge continue to reverberate to this day.
And:
COWEN: In Genesis and Exodus, why does the older son so frequently catch it hard?
HOLLAND: Well, I’m an elder son.
COWEN: I know. Your brother’s younger, and he’s a historian.
HOLLAND: My brother is younger. It’s a question on which I’ve often pondered, because I was going to church.
COWEN: What do you expect from your brother?
HOLLAND: The truth is, I have no idea. I don’t know. I’ve often worried about it.
Quite a good CWT.
Chat Law Goes Global
PricewaterhouseCoopers (PWC), the global business services firm, has signed a deal with OpenAI for access to “Harvey”, OpenAI’s Chatbot for legal services.
Reuters: PricewaterhouseCoopers said Wednesday that it will give 4,000 of its legal professionals access to an artificial intelligence platform, becoming the latest firm to introduce generative AI technology for legal work.
PwC said it partnered with AI startup Harvey for an initial 12-month contract, which the accounting and consulting firm said will help lawyers with contract analysis, regulatory compliance work, due diligence and other legal advisory and consulting services.
PwC said it will also determine ways for tax professionals to use the technology.
IBM’s Watson was a failure so we will see but, yeah I will say it, this time feels different. For one, lawyers deal with text where GPTs excel. Second, GPTs have already revolutionized software coding and unlike Watson I am using GPTs every day for writing and researching and it works. The entire world of white collar work is going to be transformed over the next year. See also my paper with Tyler, How to Learn and Teach Economics with Large Language Models, Including GPT.
Teaching and Learning Economics with the AIs
Tyler and I have a new paper, How to Learn and Teach Economics with Large Language Models, Including GPT:
GPTs, such as ChatGPT and Bing Chat, are capable of answering economics questions, solving specific economic models, creating exams, assisting with research, generating ideas, and enhancing writing, among other tasks. This paper highlights how these innovative tools differ from prior software and necessitate novel methods of interaction. By providing examples, tips, and guidance, we aim to optimize the use of GPTs and LLMs for learning and teaching economics effectively.
Most of the paper is about how to use GPTs effectively but we also make some substantive points that many people are missing:
GPTs are not simply a chatty front end to the internet. Some GPTs like ChatGPT have no ability to search the internet. Others, like Bing Chat, can search the internet and might do so to aid in answering a question, but that is not fundamentally how they work. It is possible to ask a GPT questions that no one has ever asked before. For example, we asked how Fred Flintstone was like Hamlet, and ChatGPT responded (in part):
Fred Flintstone and Hamlet are two vastly different characters from different time periods, cultures, and mediums of storytelling. It is difficult to draw direct comparisons between the two.
However, one possible point of similarity is that both characters face existential dilemmas and struggles with their sense of purpose and identity. Hamlet is plagued by doubts about his ability to avenge his father’s murder, and his own worthiness as a human being. Similarly, Fred Flintstone often grapples with his place in society and his ability to provide for his family and live up to his own expectations.
Not a bad answer for a silly question and one that (as far as we can tell) cannot be found on the internet.
GPTs have “read” or “absorbed” a great amount of text but that text isn’t stored in a database; instead the text was used to weight the billions of parameters in the neural net. It is thus possible to run a GPT on a powerful home computer. It would be very slow, since computing each word requires billions of calculations, but unlike storing the internet on your home computer, it is feasible to run a GPT on a home computer or even (fairly soon) on a mobile device.
GPTs work by predicting the next word in a sequence. If you hear the phrase “the Star-Spangled”, for example, you and a GPT might predict that the word “Banner” is likely to come next. This is what GPTs are doing but it would be a mistake to conclude that GPTs are simply “autocompletes” or even autocompletes on steroids.
Autocompletes are primarily statistical guesses based on previously asked questions. GPTs in contrast have some understanding (recall the as if modifier) of the meaning of words. Thus GPTs understand that Red, Green, and Blue are related concepts that King, Queen, Man and Woman are related in a specific way such that a woman cannot be a King. It also understands that fast and slow are related concepts, such that a car cannot be going fast and slow at the same time but can be fast and red and so forth. Thus GPTs are able to “autocomplete” sentences which have never been written before, as we described earlier.2 More generally, it seems likely that GPTs are building internal models to help them predict the next word in a sentence (e.g. Li et al. 2023).
The paper is a work in progress so comments are welcome.
What should I ask Jonathan Swift?
Yes, I would like to do a Conversation with Jonathan “G.P.T.” Swift. Here is Wikipedia on Swift, excerpt:
Jonathan Swift (30 November 1667 – 19 October 1745) was an Anglo-Irish satirist, author, essayist, political pamphleteer (first for the Whigs, then for the Tories), poet, and Anglican cleric who became Dean of St Patrick’s Cathedral, Dublin, hence his common sobriquet, “Dean Swift”.
Swift is remembered for works such as A Tale of a Tub (1704), An Argument Against Abolishing Christianity (1712), Gulliver’s Travels (1726), and A Modest Proposal (1729). He is regarded by the Encyclopædia Britannica as the foremost prose satirist in the English language.[1] He originally published all of his works under pseudonyms—such as Lemuel Gulliver, Isaac Bickerstaff, M. B. Drapier—or anonymously. He was a master of two styles of satire, the Horatian and Juvenalian styles.
His deadpan, ironic writing style, particularly in A Modest Proposal, has led to such satire being subsequently termed “Swiftian”.
So what should I ask him? I thank you in advance for your suggestions.
That was then, this is now
From Taylor C. Sherman’s useful Nehru’s India: A History in Seven Myths:
Although Hindu nationalists had gained prominence in the run-up to partition, the new Congress leaders of the Government of India tried to sideline them. After Gandhi’s assassination on 30 January 1948, members of the Rashtriya Swayamsevak Sangh were arrested, and the Hindu Mahasabha declared it would not take part in politics. In short, though raging before partition, the flames of Hindu chauvinism were quickly doused after independence, at least according to the old nationalist narrative. Secondly, the reform of Hinduism was seen as an essential element of secularism. To this end, a prominent Dalit, Bhimrao Ramji Ambedkar, was put in charge of both writing the Constitution and overseeing reform of Hindu personal law. Within a short time after independence, so the myth goes, India had a secular state, and was on course to establish a sense of security and belonging for the two groups who had raised the loudest objections to Congress’s nationalism: Muslims and Dalits.
As with so many of the myths that have arisen about this period after independence, the myth of India secularism owes a great deal to Jawaharlal Nehru.
The book is both a good focused view of the Nehru era, but excellent background for current disputes.
Statement of Commitment to Academic Freedom and to Intellectual Merit
Academic freedom and intellectual merit are under attack in the United States, from both the left and the right. The norms of the university and intellectual life are fragile and need protecting because such norms are always in tension with political and economic power.
The undersigned members of the GMU Department of Economics express their commitment to academic freedom and to intellectual merit.
Addressed to the George Mason University (GMU) community and the public at large
~~~
American universities have professed allegiance to two ideals. First, the ideal of academic freedom – the right of students and faculty to express any idea in speech or writing, without fear of university punishment, and secure in the knowledge that the university will protect dissenters from threats and violence on campus.
Second, the ideal of intellectual merit – the right and duty of academic departments to hire and promote the most brilliant, creative, and productive faculty in their fields, and admit the most intellectually promising students, without pressures from the administration.
These ideals are the cornerstones of liberal education. They protect faculty and students who hold views unpopular on university campuses. Academic freedom protects existing students and faculty who dissent from current dominant academic opinion and ideology. No matter how unpopular their views, they know the university will protect them. As stated in the University of Chicago Statement on freedom of expression and as quoted in GMU’s “Free Speech at Mason” Statement:
[We must hold a fundamental commitment to] the principle that debate or deliberation may not be suppressed because the ideas put forth are thought by some or even by most members of the University community to be offensive, unwise, immoral, or wrong-headed.
Intellectual merit protects prospective students and faculty who speak and write against current dominant viewpoints. No matter how unpopular their views, they know that university administration will not obstruct or prejudice their admission, hiring, or promotion.
Recently, both of these ideals have come under attack. Pressure for conformity has intensified and universities have increasingly interfered with departments’ personnel decisions. For example, at some universities, one of the more egregious new practices is the requiring of written “diversity” statements by prospective students, staff, or faculty, then used to discriminate among candidates, often by quarters of the university with interests other than those of the department or unit. Such methods recall arrogations of the past, such as The Levering Act of 1950, used against radicals.
We strongly believe the attacks on academic freedom and intellectual merit are deeply mistaken. The classic rationales in favor of these ideals are sound. To protect them, viewpoint diversity must be celebrated and academic departments must maintain their ability to select, hire, and promote students and personnel based on intellectual merit. We insist that the degree of institutional autonomy that the GMU Department of Economics has traditionally enjoyed is vital to the health of viewpoint diversity not only within the university but within the academy writ large.
It is vital that every department in a university enjoys independence, so it can dare to be different and keep viewpoint diversity alive. George Mason University has excelled in supporting viewpoint diversity with a variety of diverse departments, centers and organizations. Viewpoint diversity at George Mason has benefited the university, the United States, and the wider intellectual world.
Indeed, some of the Department’s chief contributions have taught that all forms of authority can exert power to excess, and that guarding against such excess calls for the very ideals affirmed here, respect for dissent and intellectual merit.
We, the undersigned members of the GMU Department of Economics, look forward to continuing our independence to do good economics according to our judgment, guided by the ideals of academic freedom and intellectual merit.
Signed by the following GMU Department of Economics faculty (full-time & emeritus):
1. Jonathan P. Beauchamp
2. James T. Bennett
3. Donald J. Boudreaux
4. Bryan D. Caplan
5. Vincent J. Geloso
6. Timothy Groseclose
7. Robin D. Hanson
8. Garett Jones
9. Daniel B. Klein
10. Mark Koyama
11. David M. Levy
12. Cesar A. Martinelli
13. John V.C. Nye
14. Thomas C. Rustici
15. Vernon L. Smith
16. Alex Tabarrok
17. Karen I. Vaughn
18. Richard E. Wagner
19. Lawrence H. White
Why AI will not create unimaginable fortunes
From my Bloomberg column from last week:
A small number of AI services, possibly even a single one, likely will end up better than the others for a wide variety of purposes. Such companies might buy the best hardware, hire the best talent and manage their brands relatively well. But they will face competition from other companies offering lesser (but still good) services at a lower price. When it comes to LLMs, there is already a proliferation of services, with Baidu, Google and Anthropic products due in the market. The market for AI image generation is more crowded yet.
In economic terms, the dominant AI company might turn out to be something like Salesforce. Salesforce is a major seller of business and institutional software, and its products are extremely popular. Yet the valuation of the company, as of this writing, is about $170 billion. That’s hardly chump change, but it does not come close to the $1 trillion valuations elsewhere in the tech sector.
OpenAI, a current market leader, has received a private valuation of $29 billion. Again, that’s not a reason to feel sorry for anyone — but there are plenty of companies you might not have heard of that are worth far more. AbbVie, a biopharmaceutical corporation, has a valuation of about $271 billion, almost 10 times higher than OpenAI’s.
To be clear, none of this is evidence that AI will peter out. Instead, AI services will enter almost everyone’s workflow and percolate through the entire economy. Everyone will be wealthier, most of all the workers and consumers who use the thing. The key ideas behind AI will spread and be replicated — and the major AI companies of the future will face plenty of competition, limiting their profits.
In fact, AI’s ubiquity may degrade its value, at least from a market perspective. It’s likely the AI boom has yet to peak, but the speculative fervor is almost palpable. Share prices have responded to AI developments enthusiastically. Buzzfeed shares rose 150% in one day last month, for example, after the company announced it would use AI to generate content. Does that really make sense, given all the competition BuzzFeed faces?
It’s when those prices and valuations start falling that you will know the AI revolution has truly arrived. In the end, the greatest impact of AI may be on its users, not its investors or even its inventors.
We’ll see how those predictions hold up.
Wednesday assorted links
1. Along at least one dimension, Musk’s Twitter takeover hasn’t mattered much.
2. Which personalities are best suited for training dogs? This is in fact also an excellent essay on who is good at working with ChatGPT. And Chinese views on ChatGPT. And long Stephen Wolfram piece on ChatGPT and neural nets. And top London law firm is hiring a GPT prompt legal engineer.
3. Lina Khan update (WSJ). Ouch. And Joshua Wright on the implications for the FTC, double ouch.
4. Michelin stars make restaurants snobbier.
Language Models and Cognitive Automation for Economic Research
From a new and very good NBER paper by Anton Korinek:
Large language models (LLMs) such as ChatGPT have the potential to revolutionize research in economics and other disciplines. I describe 25 use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples for how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I hypothesize that ongoing advances will improve the performance of LLMs across all of these domains, and that economic researchers who take advantage of LLMs to automate micro tasks will become significantly more productive. Finally, I speculate on the longer-term implications of cognitive automation via LLMs for economic research.
Recommended.
My Conversation with Glenn Loury
Moving throughout, here is the audio, video, and transcript. Here is part of the summary:
Economist and public intellectual Glenn Loury joined Tyler to discuss the soundtrack of Glenn’s life, Glenn’s early career in theoretical economics, his favorite Thomas Schelling story, the best place to raise a family in the US, the seeming worsening mental health issues among undergraduates, what he learned about himself while writing his memoir, what his right-wing fans most misunderstand about race, the key difference he has with John McWhorter, his evolving relationship with Christianity, the lasting influence of his late wife, his favorite novels and movies, how well he thinks he will face death, and more.
Here is one excerpt:
COWEN: What’s your favorite Thomas Schelling story?
LOURY: [laughs] This is a story about me as much as it is about Tom Schelling. The year is 1984. I’ve been at Harvard for two years. I’m appointed a professor of economics and of Afro-American studies, and I’m having a crisis of confidence, thinking I’m never going to write another paper worth reading again.
Tom is a friend. He helped to recruit me because he was on the committee that Henry Rosovsky, the famous and powerful dean of the college of the Faculty of Arts and Sciences at Harvard, who hired me — the committee that Rosovsky put together to try to find someone who could fill the position that I was hired into: professor of economics and of Afro-American Studies. They said Afro-American in those years.
Tom was my connection. He’s the guy who called me up when I was sitting at Michigan in Ann Arbor in early ’82, and said, “Do you think you might be interested in a job out here?” He had helped to recruit me.
So, I had this crisis of confidence. “Am I ever going to write another paper? I’m never going to write another paper.” I’m saying this to Tom, and he’s sitting, sober, listening, nodding, and suddenly starts laughing, and he can’t stop, and the laughing becomes uncontrollable. I am completely flummoxed by this. What the hell is he laughing at? What’s so funny? I just told him something I wouldn’t even tell my wife, which is, I was afraid I was a failure, that it was an imposter syndrome situation, that I could never measure up.
Everybody in the faculty meeting at Harvard’s economics department in 1982 was famous. Everybody. I was six years out of graduate school, and I didn’t know if I could fit in. He’s laughing, and I couldn’t get it. After a while, he regains his composure, and he says, “You think you’re the only one? This place is full of neurotics hiding behind their secretaries and their 10-foot oak doors, fearing the dreaded question, ‘What have you done for me lately?’ Why don’t you just put your head down and do your work? Believe me, everything will be okay.” That was Tom Schelling.
COWEN: He was great. I still miss him.
And the final question:
COWEN: Very last question. Do you think you will do a good job facing death?
Interesting and revealing throughout.
Klein on Construction
Here’s Klein writing about construction productivity in the New York Times:
Here’s something odd: We’re getting worse at construction. Think of the technology we have today that we didn’t in the 1970s. The new generations of power tools and computer modeling and teleconferencing and advanced machinery and prefab materials and global shipping. You’d think we could build much more, much faster, for less money, than in the past. But we can’t. Or, at least, we don’t.
…A construction worker in 2020 produced less than a construction worker in 1970, at least according to the official statistics. Contrast that with the economy overall, where labor productivity rose by 290 percent between 1950 and 2020, or to the manufacturing sector, which saw a stunning ninefold increase in productivity.
In the piquantly titled “The Strange and Awful Path of Productivity in the U.S. Construction Sector,” Austan Goolsbee, the newly appointed chairman of the Chicago Federal Reserve and the former chairman of the Council of Economic Advisers under President Barack Obama, and Chad Syverson, an economist at the University of Chicago’s Booth School of Business, set out to uncover whether this is all just a trick of statistics, and if not, what has gone wrong.
After eliminating mismeasurement and some other possibilities following Goolsbee and Syverson, Klein harkens back to our discussion of Mancur Olson’s Rise and Decline of Nations and offers a modified Olson thesis, namely too may veto points.
…It’s relatively easy to build things that exist only in computer code. It’s harder, but manageable, to manipulate matter within the four walls of a factory. When you construct a new building or subway tunnel or highway, you have to navigate neighbors and communities and existing roads and emergency access vehicles and politicians and beloved views of the park and the possibility of earthquakes and on and on. Construction may well be the industry with the most exposure to Olson’s thesis. And since Olson’s thesis is about affluent countries generally, it fits the international data, too.
I ran this argument by Zarenski. As I finished, he told me that I couldn’t see it over the phone, but he was nodding his head up and down enthusiastically. “There are so many people who want to have some say over a project,” he said. “You have to meet so many parking spaces, per unit. It needs to be this far back from the sight lines. You have to use this much reclaimed water. You didn’t have 30 people sitting in an hearing room for the approval of a permit 40 years ago.”
This also explains why measured regulation isn’t necessarily determinative. Regulation provides the fulcrum but it’s interest groups that man the lever.
Some of this is expressed through regulation. Anyone who has tracked housing construction in high-income and low-income areas knows that power operates informally, too. There’s a reason so much recent construction in Washington, D.C., has happened in the city’s Southwest, rather than in Georgetown. When richer residents want something stopped, they know how to organize — and they often already have the organizations, to say nothing of the lobbyists and access, needed to stop it.
This, Syverson said, was closest to his view on the construction slowdown, though he didn’t know how to test it against the data. “There are a million veto points,” he said. “There are a lot of mouths at the trough that need to be fed to get anything started or done. So many people can gum up the works.”
Read the whole thing.
Sunday assorted links
1. Colombian judge uses ChatGPT to make a court decision. And use ChatGPT on your own pdfs (breakthroughs every day, people…). And how to build LLM apps that are more factual. And more on Bing/ChatGPT integration.
2. Transcript of my 2009 Bloggingheads episode with Robin Hanson. TC: “What I find funny about your view is that you’re a skeptic about medical science, about almost everything — except freezing your head. You think that’s the one thing that works.” There is audio too, and note this comes from the period when Robin and I were talking a lot (and writing together) on the phenomenon of disagreement.
3. Jupiter keeps on adding moons.
4. Ezra Klein on construction productivity (NYT).
5. How open source software shapes AI. Paper here.
6. Shift in the mean center of U.S. population over the centuries.
Real Return Bonds–Not a Loony Idea
The Canadian government has said it will stop issuing real return bonds, i.e. inflation indexed bonds. Real return bonds are extremely useful to anyone who wants a steady stream of income that keeps up with inflation—retirees, for example. A real return bond would also be ideal for funding an endowment such as a university chair or scholarship program. I agree with John Cochrane and Jon Hartley writing in the G&M that ending sales of these bonds is a bad signal.
So why stop issuing real return bonds? The government may suspect that inflation will go up a lot more, and it will then have to pay more to bondholders. Non-indexed debt can be inflated away if the fiscal situation worsens. The cumulative 11-per-cent inflation since January, 2021, has inflated away 11 per cent of the debt already. Argentines have seen a lot more.
But issuing indexed debt makes sense if the government plans to be responsible. Tax payments and budget costs rise with inflation, and fall with disinflation, so the budget is stabilized if inflation-indexed bond payments do the same. And issuing indexed debt that can’t be inflated away is a good incentive not to turn around and inflate debt away.
Will Chinese LLMs be much worse?
Presumably these are being built right now. But which texts will they be trained upon? Let’s say you can keep out any talk of T. Square. What about broader Chinese history? Do you allow English-language sources? Japanese-language accounts of the war with Japan? Do you allow economics blogs in English? JStor? Discussions of John Stuart Mill on free speech?
Just how good is the Chinese-language, censorship-passed body of training data? Does China end up with a much worse set of LLMs? Or do they in essence anglicize most of what they learn and in time know?
Pre-LLM news censorship was an easier problem, because you could let the stock sit in a library somewhere, mostly neglected, while regulating the flow. But when the new flow is so directly derived from the stock, statistically speaking that is? What then?
Much hangs in the balance here. What was it that Paul Samuelson said about writing a nation’s textbooks?