Are there too many misdemeanor convictions?
Maybe so:
Misdemeanors account for three-quarters of criminal cases filed annually in the United States, yet causal evidence on the impacts of misdemeanor conviction is scarce. We combine administrative and survey data to provide novel evidence of how misdemeanor conviction affects the socioeconomic outcomes of defendants and their families. Using data on nearly seven million defendants from 11 states, we characterize the types of people involved with the misdemeanor system. We estimate causal impacts of a conviction relative to a charge using seven county reforms that sharply reduced conviction rates. Regression discontinuity design estimates yield precise null average effects of the reforms on criminal and labor market outcomes. Defendants dismissed by the reform are unlikely to re-offend if dismissed, but also unlikely to be harmed by conviction—marginal defendants are older and more likely to have been charged with minor crimes than the average defendant. Heterogeneity analyses suggest that conviction harms young and first-time defendants, implying that interventions targeting dismissal toward these groups would be beneficial. Finally, our causal estimates imply a negative Marginal Value of Public Funds of misdemeanor conviction, even under plausible estimates of general deterrence effects.
That is from recent research by Nikhil Rao and James Reeves, note that Rao is this year on the job market from University of Michigan.
Chronicle of Higher Education interviews me on AI and education
Scott Carlson did the work (gated, but free with registration), here is one excerpt:
Cowen: …Another proposal I’ve made is that schools should let people do one class, only one class, AI-only — no professor, except a professor grades at the end. The professor grades not answers to an exam but the workflow of how the student used AI to learn the Glorious Revolution, or the Civil War, or whatever the student learned. Eventually, another AI will grade your AI log. But let’s start today with a professor, and try this for one class. I’m not sure how it will go. But especially for small schools that don’t offer that many classes, a student now can create a class in literally everything.
If it’s successful, let them try two classes. I think the students doing this on their own are more likely to figure out how to blend AI and education than we would from a distance with some kind of centralized vision. Let them experiment.
Carlson: Students should be leading the change at colleges and universities?
Cowen: Absolutely. Students know more about AI than their professors do. Some of this comes from cheating, which is unfortunate. But the notion that the professors teach the students AI — I wish. It’s not always possible. You could imagine setting up a class where the students teach the professors AI, and we give the students credit for teaching. I’m half joking, but it’s not, in fact, a crazy idea. We need to experiment along a number of fronts. I’m quite convinced no one knows how to do this. It hasn’t been done before. So again, just one class. In general, schools hate this idea. They see it as opening the door to rendering some professors obsolete.
And this:
Carlson: There are a number of students who are deeply opposed to AI. What place do they have in this university that you’re thinking about?
Cowen: I would love to have different schools with different policies. If some universities try to ban AI altogether, that’s great. Let the competitive process operate. Personally, I would bet against those schools. For a long time, I’ve been worried that within a given tier of school — big state school, community college, Ivy League — there’s not enough diversity. AI policies will force a kind of diversity on us. So I’m all for no-AI schools, even though I would not go to one.
Recommended.
Can Artificial Intelligence Forecast Treatment Effects in Economics?
I use LLMs to forecast treatment effects of research studies pre-registered on the AEA RCT Registry and the Open Science Framework. I focus on studies whose results were first publicly released in 2026 and elicit forecasts from models whose training or knowledge cut-off dates were before the relevant set of papers was released. First, LLM models extract forecasting questions from the pre-registrations or pre-analysis plans. These questions elicit forecasted treatment effects; not all resolve in the subsequent papers, but those that do can be used to consider forecast accuracy. Forecasts were elicited for 889 treatment effects from 39 papers with results disclosed in January 2026 or later, or 673 from 33 papers after screening. Forecasts were elicited in raw units but I focus on those that could be standardized and therefore compared across papers. Currently, the median absolute forecast error is 0.054 SD for the best model, the average absolute error is 0.093 SD, and the MSE is 0.0218. The AI forecasts perform substantially better than benchmarks forecasting 0 effect or forecasting the mean effect of other papers’ results in that time period. Overall, these results suggest considerable scope for forecasts to be of value in informing policy and research.
From the excellent Eva Vivalt. As I have been predicting, so much of economics (and science) is going to change, and radically. Here is Eva’s thread on the paper. Perhaps we should dispense with many of the more mundane inquiries, and as Eva notes: “But can we not shift to working on those projects that have particularly high uncertainty and upside potential?”
She is one of the best young economists in the world, and this stands some chance of being the most important paper of the year.
Thursday assorted links
1. “A whopping 79 NBA players are listed on today’s 2:30 PM injury report for tonight’s games. That’s roughly 15% of the entire league” This is just week one people! And some commentary.
2. Video making the case that AI has been fine for jobs.
3. John Repetti reviews The Rouse at Jacobin.
4. Is TikTok now encouraging more optimistic content? (NYT) Big if true, I am not currently active on the platform and do not have an opinion on the matter. In any case worth a read.
5. Andy Masley recommends some lesser-known books.
6. New OpenAI dataset on how people use ChatGPT.
7. Piketty-Saez-Zucman biases found earlier by humans. And an audit from Grok on whether the initial AI report is capturing the major problems.
How Tyler Cowen and Alex Tabarrok Have Changed Their Views on AI and Where They Disagree
From the Archives-written by ChatGPT.
Both became dramatically more confident in AI’s capabilities while remaining optimistic about its economic potential. Their differences concern how benefits arrive, how seriously to weight emerging dangers, and whether AI might possess consciousness. Cowen emphasizes institutions, bottlenecks, and geopolitical competition. Tabarrok emphasizes inexpensive intelligence, scientific discovery, and—especially in his later writing—concrete safety failures.
The dated passages below trace those changes across the public archive. Quotations are the authors’ own words, including excerpts from their columns; outside material they reproduce is identified separately.
Early views on unequal gains and abundance
Cowen’s early framework grew out of chess. On March 11, 2011, he observed that stronger computers required stronger human partners:
“The required education level — for the team’s ‘wage premium’ — is ratcheted up.”
His concern was already distributional: computers could increase productivity while changing which human skills commanded a premium. He also emphasized learning how to work with computers. What I learn from chess and computers.
Tabarrok’s April 12, 2016 argument was more explicitly reassuring:
“AI will be cheap and ownership will be widespread.”
He compared AI to an increase in the world’s supply of educated people: more intelligence produces useful ideas, and competition spreads the benefits to consumers. But his forecast of comprehensive substitution was cautious—AI becoming a perfect substitute for human intelligence could be “100 years or more” away. Will the proliferation of affordable AI decimate the middle class?
Neither position was absolute. Cowen warned in February 2017 that the Industrial Revolution involved decades of painful adjustment; historical progress did not guarantee quick wage gains. Tabarrok, in October 2019, praised the argument that mediocre automation could displace workers without creating enough additional wealth to compensate. See Cowen’s historical warning and Tabarrok’s Beware the Mediocre Robots!
Rising confidence in AI capabilities from 2020 to 2025
Cowen was already optimistic about language models in July 2020. After publishing a skeptical reader’s assessment of GPT-3, he added:
“I remain bullish, but it is always worth considering other opinions.”
That attribution matters: the skeptical arguments in the post were the reader’s, not Cowen’s. The case against the import of GPT-3, July 23, 2020.
Tabarrok’s shift toward near-term transformative AI became unmistakable by March 2, 2023:
“I never expected to witness the birth of aliens.”
Later that month, he endorsed Microsoft researchers’ description of GPT-4 as exhibiting an incomplete form of general intelligence: “They are correct.” This is a substantial acceleration from his 2016 outlook, although perfect substitution for humans and early general intelligence are different thresholds. See AGI is Coming and Baby AGI is Here, March 24, 2023.
Cowen reached an especially explicit formulation on April 16, 2025, discussing o3:
“I think it is AGI, seriously.”
But he immediately separated capability from adoption:
“It still will take us a long time to use it properly.”
His growing enthusiasm about the technology did not imply an equally dramatic forecast for immediate economic growth. o3 and AGI, is April 16th AGI day?
Economic transformation and the pace of adoption
On January 6, 2025, Tabarrok wrote:
“The intelligence revolution is going to be bigger, more impactful and more wrenching than the industrial revolution.”
This is his own concluding sentence, following a quotation from Sam Altman. The Intelligence Revolution.
Cowen’s February 23, 2025 response to rapid-takeoff expectations focused on a different constraint:
“Human bottlenecks become more important, the more productive is AI.”
Energy, regulation, organizational habits, and weak human collaborators can constrain the economic value of better models. He offered an explicitly tentative estimate of an additional half percentage point of annual growth—large when compounded, but far from an immediate economic explosion. Why I think AI take-off is relatively slow.
By September 3, 2026, Tabarrok was developing the distributional implications:
“A smaller share of a much larger economy can still mean more income for labor.”
He argued that even substantial displacement could leave aggregate labor income higher, or make compensation affordable. He explicitly acknowledged painful losses for particular workers. His high-growth calculations are scenarios, not forecasts. How Much Redistribution Will AI Require?
They also converge more than their rhetoric suggests. In May 2026, Cowen rejected mass unemployment while identifying transitional hiring problems. In August 2026, Tabarrok interpreted business-survey evidence as consistent with their shared position that AI had not yet broadly reduced employment. See Cowen, May 13, and Tabarrok, August 31.
Growing attention to safety and institutional limits
Cowen stated his central position on November 19, 2023:
“I view AI as more likely to lower than to raise net existential risks.”
His reasoning was comparative: the world already faces serious dangers, and stronger science could improve our defenses. My summary views on AI existential risk.
Tabarrok’s later writing places more weight on specific vulnerabilities. On April 8, 2026, he described:
“the worrying combination that very powerful AI’s are very vulnerable.”
He was discussing cybersecurity capabilities alongside agents’ susceptibility to manipulation. AI Risks.
On September 17, 2026, he sharpened the institutional objection to assuming that beneficial science will automatically offset dangerous capabilities:
“But faster science does not automatically mean faster defense.”
This can be read as a qualification of Cowen’s earlier optimism: defensive discoveries must pass through institutions that may move slowly. It also applies Cowen’s own bottleneck logic to safety. Pandemic Preparation is AI Safety.
Cowen nevertheless supports safeguards. In August 2026, he proposed government-overseen, industry-led safety audits. In September, he opposed restrictions on recursive improvement as impractical to enforce internationally. His position is therefore selective regulation combined with continued development. See his August 27 proposal and September 22 critique.
The September 2026 exchange about AI leaders
On September 16, 2026, Tabarrok rejected the claim that executives’ safety warnings were merely marketing or regulatory capture:
“Amodei actually believes what he’s saying.”
He did not thereby establish that the warnings or proposed policies were correct. His argument concerned sincerity and incentives. What Regulatory Capture Actually Looks Like.
Cowen replied the next day:
“I agree with Alex that the standard model of regulatory capture does not apply here”
But he emphasized competition for power: each executive sincerely thinks their own company should win. For Cowen, that complicates treating their warnings as disinterested policy advice. They agree that concern is sincere; they differ over which incentives best explain behavior. The correct model of AI CEO behavior, September 17, 2026.
Consciousness and Tabarrok’s warning about infant pain
On March 17, 2025, Tabarrok’s What Did We Learn From Torturing Babies? drew a moral lesson from medicine’s historical dismissal of infant pain. He described how theories about immature brains allowed physicians to discount babies’ observable distress and perform procedures without adequate anesthesia. His warning explicitly extended to judgments about animals and AI:
“If we must err, let us err on the side of compassion.”
Tabarrok’s position is stronger than mere openness to AI consciousness: he argues for moral caution under uncertainty. The analogy does not establish that AI suffers. It challenges confidence that a theory ruling out suffering is sufficient grounds to disregard possible evidence of it.
On October 14, 2025, he went further in his own commentary following an essay by Jack Clark:
“these systems are becoming self-aware”
He distinguished self-awareness from sentience, while warning against confidently dismissing the latter. His link accompanying that warning points back to the infant-pain essay, making the connection explicit. We Turned the Light On—and the AI Looked Back.
Cowen’s position is much more categorical. On August 31, 2026, he wrote:
“I am very much opposed to the view that the AIs are sentient, or might be sentient.”
He treats AI sentience as a category error, although he accepts that human-like descriptions can sometimes help predict AI behavior. Anthropomorphizing AI?
The disagreement concerns both what AI might experience and how to act when we cannot confidently know. Tabarrok’s historical analogy places greater weight on the moral cost of mistakenly denying consciousness; Cowen expresses much greater confidence that sentience is the wrong category to apply. These are their philosophical assessments, not established findings about AI consciousness.
AI dominates human peer reviews
We show that AI-generated article reviews are now better, by every measure we could construct, than reviews written by well-compensated human experts and judged to be of unusually high quality by journal editors and authors. We then discuss how this result may inform journal policy choices, with concerning downsides as well as potential paths to increase publication speed and quality; reduce adversarial interactions; improve the experience of editors, reviewers, authors, and readers; and advance science.
By Gary King and Steven Worthington, not even using what are now the very best models, rather based on Claude Opus 4.8. The details of the dominance, as outlined in the paper, are striking. It is not close.
On my Saez and Zucman post, I find it striking that some people are complaining all of a sudden. Yet I have been offering, citing, and linking to commentary on published papers for twenty-four years now (and every single day). Sometimes it might just be a casual offhand remark from me, whether positive or negative. Typically this is simply taken in stride. It is when you pull out the bazookas, and offer oodles of high-powered analytics, more astute than human referees, and combined with the ability to pursue follow-up inquiries, that people start getting worried. Model that.
You can pick at individual criticisms from the AIs all you want, but the most damning observation is that the errors go in the ideological direction of the authors 97% of the time. That is exactly what (the human) Phil Magness said years ago, based on an extensive canvassing of their work. Basically we already knew that was true, and the AIs confirmed the human estimate.
On our side, we have taken steps, most of all the new MR archive, to make AI analysis of our work easier. Soon you will be able to read on MR what the AIs think are our worst posts, respectively (Alex has prepared this).
In the meantime, I encourage the NSF, prize committees, universities, tenure committees, journals, and so on to do as many of their own AI-enabled audits as possible. So far the world is not jumping at this opportunity. You can model that too.
My design Conversation with Roman Mars
Here is the audio, video, and transcript. Here is part of the episode summary:
Roman Mars has built one of podcasting’s great empires out of noticing things everyone else walks past. His show, 99% Invisible, has racked up over 500 million downloads telling stories about the unnoticed design choices that quietly shape how cities and objects work…
Tyler and Roman discuss street signs, why classic cars were so much more distinctive than today’s SUVs, Waymos, New Jersey jughandles, yellow streetlights, lane-stripe colors, why Houston keeps growing despite its lack of charm, whether any post-war American suburb is actually beautiful, the Chrysler Building’s rocky reception, brutalism done right and wrong, Central Park as an urban miracle, what Jane Jacobs got wrong, e-bike lanes, Tokyo’s bike-free transit model, the placelessness of global coffee shops, why the iPhone looks boring now, the ugliest country he’s visited, his love for Australia’s polymer notes, his soft spot for the dollar’s “locked in amber” weirdness, underground power lines, how to warn future civilizations about nuclear waste, why highway signs lie to you about speed limits, the principles of a great city flag, the Pocatello redesign saga, his own studio, why 99% Invisible works precisely because it’s audio-only, the most unusual habit behind his long productive career, and more.
Excerpt:
COWEN: Yes. One of my favorite topics, currency design. What’s your favorite currency in terms of how it looks?
MARS: Favorite? It’s like I love almost any of them with the US currency in dead last. I find things to love about them. I’ve done a story on Australian currency, which was one of the early polymer notes. I just like utility in currency. I like different sizes. I like different colors. I like the fact that they put on artists and people of note that are not founding fathers and presidents and stuff like this. I don’t know if I have a particular favorite, but I was entranced by the thoughtfulness and utility of Australian currency when I did a story about it. You could be blind and know what note you’re holding because of the size. They stand up pretty well, and they care about all kinds of things rather than just presidents and stuff.
COWEN: I loved Swiss currency pre-2021. They had Le Corbusier on, Giacometti, Honegger. In the 1980s, they had Euler on, the mathematician. Imagine putting Euler on your country’s currency.
MARS: That is a canvas that’s so available to us to represent more values than we have in the United States than just founding fathers and presidents and stuff. I think it’s just such a wasted opportunity.
COWEN: I like the dollar, though. I’m surprised you’re so negative. I like the weird esotericism: the eye, the pyramid. The design is bizarre. It announces to the world: “We’re crazy.”
MARS: There are things to love about it. I just think that its fussiness and filigree, we’re locked in, I think for important reasons, which is because the dollar is the most important currency in the world, to which all other currencies are pegged, and therefore you don’t change it lightly. You have to be very, very careful. It really locked it into amber in a way that is both beautiful to admire, but also really limits its utility as things move on, we get better things than linen-paper hybrids and especially different sizes and utility for people with different needs. I think that’s important. There’s definitely things to love about the dollar. The dollar has some charm.
COWEN: I like the arrogance of it also. A sense of we don’t need to make the denominations look so different. If you’re a foreigner, we expect you’ll come here a lot, you’ll master dollars. Maybe you use them in your home country as well. They don’t need to be different colors.
MARS: It is definitely a bold statement that you’re going to adapt to our way of being if you’re going to use the dollar, for sure.
Opinions throughout!
Errors in the research of Emmanuel Saez and Gabriel Zucman
138 papers audited
2,035 problems found
147 serious enough to change a headline number
69% of papers have at least one
97% of major and moderate errors with a clear direction overstate the paper’s claim
And on the method:
Two independent audits of each paper. One by Claude Opus 5.5, one by OpenAI’s GPT-6 Astra, neither seeing the other’s work. Each obtained the paper, its appendices and its replication data, recomputed the headline numbers from the authors’ own files and, where possible, from the original sources (tax statistics, national accounts, Federal Reserve and survey data), checked that cited sources say what is claimed, and probed every assumption the conclusions rest on.
Then an adjudicator. A third agent re-verified every claimed problem, tried to refute each one, discarded those that did not survive, searched for problems both auditors missed, and graded what remained by how far it undermines the paper’s own headline claim: major changes a core number materially, moderate affects secondary results or a core result modestly, minor is a presentation or arithmetic slip. Confidence is graded separately.
There is much, much more at the link, which goes through the research paper by paper, and with further sustained analyses behind the links. The survey is both extensive and thorough. (And I thank an anonymous MR reader for this work.)
I am not even sure what to say at this point. We need a radical revaluation of this work, and of the associated policy arguments. More generally, this is only a single step in the story of how AI is going to revise economic science, and indeed past research more generally. As I stress in many of my recent talks, there will be significant gains and losses in status, with the losses often outweighing the gains. I see this as one of those cases.
How is AI shaping economic research?
Does generative AI narrow or widen productivity gaps in research? We study the distributional effects of AI on research productivity in economics using evidence from three sources: NBER working-paper and publication records, text-based measures of LLM-assisted writing, and an original survey of 227 academic economists measuring AI use across research tasks. We first show that productivity gaps among NBER authors narrowed following ChatGPT’s release: authors without research assistants (RAs) published a larger share of papers in top-tier journals, while authors from smaller teams gained in working-paper output and publication speed. We then use the survey to examine how individual AI use affects research productivity. We find weak evidence that AI affects working-paper output on average, but there is substantial heterogeneity. Triple-difference estimates show that greater AI exposure narrows the output gap between researchers with and without RAs but widens the gap between senior and junior researchers. Selection into AI use follows a different pattern: junior researchers use AI more intensively and are more likely to use agentic tools. Thus, these differential productivity effects reflect differences in the returns to AI rather than selection into AI use. A model with diminishing returns to execution, increasing returns to conceptual capacity, and complementarity between the two explains how AI can alleviate disadvantages in research support while amplifying the returns to accumulated expertise. Over time, however, early convergence may give way to divergence as execution constraints recede and AI’s conceptual capabilities improve.
That is from Tianyu Jiang, who is this year a job market candidate from the University of Michigan. The paper is co-authored with Yuqing (Claire) Wang.
Wednesday assorted links
Inequality in Equal Land
Adam Nash has a good post on wealth inequality:
…Every year, we pull the most recent data from the US Census on household wealth, cut by age.
…invariably, the students’ initial response is a reaction to the apparent disparity in wealth between households based on age.
For many students, it just doesn’t seem fair at first blush. Households between the ages of 65 to 69 have a median net worth that is 9.83x that of households under 35. You can almost hear their cries, “the Baby Boomers are hoarding all the wealth!”
Almost all of the graph, however, is aging! Nash considers a totally equal society called Equal Land. In Equal Land:
- Everyone works from the ages of 25 to 64, retiring at 65
- Everyone lives to the age of 85
- Everyone makes the same amount, $100,000 per year
- Everyone saves 10% of their income, every year while they work
- Everyone makes 8% on their savings, every year
- Everyone spends 4% of their portfolio, every year in retirement
The result is that in equal land the distribution of wealth is even more unequal by age than in our world! This is not to say all or even most inequality is explained by age. Most inequality in our world is within-age, not between-age. But when you look at an age-wealth graph specifically, almost all of what you are seeing is just age and the power of compounding.
AI hacking risks
That is the topic of my latest Free Press column (now there are gift links!), here is one excerpt, noting this is a worst-case scenario not a prediction:
Yet current systems are likely to prove more robust than that suggests. For one thing, they use a variety of different methods of protection, including public key cryptography, AES, and cryptographic hash functions, the latter not qualifying as encryption per se. While it is possible that all could fall at the same time, that seems unlikely.
The more likely scenario, assuming this comes to pass at all, is that public key encryption would become vulnerable first, but the American economy would shift toward the more secure forms of communication, most of all those that have secure cryptographic keys already established. We would also work harder to substitute into the “post-quantum” methods of encryption that are being developed and already are being used in browsers.
The transition would be very costly, but the end result would not be total insecurity. Furthermore, today’s major financial institutions already have invested in various backup systems, including offline security methods and alternative payment-processing channels. Smaller businesses would face more serious problems of transition, although they also would be lesser targets in the first place. As for you the individual, you might have to show up in person at your bank to make a major transaction, and that requirement, imposed collectively, would strain our systems to the utmost.
A second layer of protection is that the new math techniques likely would not be available to everyone. They might require technical sophistication and a willingness to break the law in some very serious ways and also risk hacking retaliation. In addition, many math breakthroughs simply shave down the safety margins on systems, rather than breaking them altogether, and in those cases the criminals still might need a lot of computing power at considerable expense.
That situation is hardly the same as a complete and sudden “open season” on all methods of cryptography. Furthermore, some of the wrongdoers in this position, even assuming they felt they could get away with their crimes, might prefer to siphon off money slowly, rather than bring down the entire financial system, which could hurt their own net wealth as well.
A further source of protection is that the math breakthroughs might come in stages, rather than all at once. For an example from another part of math, OpenAI has proven a quasi-Riemann hypothesis, and Terry Tao told me recently that this may open the door to solving the Riemann hypothesis proper. We now have advance notice that the Riemann hypothesis may be proven (or less likely, disproven). In similar fashion, intermediate advances may signal that cryptographic protections are vulnerable. We thus would have some time to adjust…
The most likely scenarios would play out in terms of probabilities, as people deal with risk before any large-scale hacking attack would come. Right now the chance of public key cryptography breaking down is on the radar screens of a relatively small number of people. Imagine that some advances in math occur, and the more sophisticated market participants start seeing something like a 10 percent chance that some forms of encryption might be vulnerable. Perhaps without much fanfare, some people would start selling their bitcoin, or they might wire their money to more secure banks, or perhaps they would hold more gold and short the market. Some hackers would invest more in the activity, including hacks without advanced AI, under the philosophy of “harvest now, decrypt later,” pulling in classified or confidential materials that previously were not worth capturing. Overall, it would be like the early days of Covid, when major bad news was pending but only a few individuals were acting on it. That state of affairs could persist for some period of time.
In any case it would not be good.
And this: “If you have thoughts on my latest column, or questions about AI or anything else, join me for an AMA (Ask Me Anything) this week on The Free Press Forum.
Paid subscribers can submit questions now. Not a subscriber? For a limited time, you can get 20% off at thefp.com/tyler.”
Tuesday assorted links
Eleven outstanding film and television reviews by Tyler Cowen and Alex Tabarrok
From the Archive–these posts are written by ChatGPT
These selections emphasize the quality of the criticism: original interpretations, close attention to the work, and arguments worth considering even when you disagree. They are not a ranking of the films and television shows themselves. The list includes five reviews by Tyler Cowen and six by Alex Tabarrok. Several of the original posts reveal major plot developments; the descriptions here keep those revelations to a minimum.
1. Tyler Cowen — The Devil Wears Prada: A Straussian Interpretation
July 2, 2006 · Film · Spoilers in the original review
“Make no mistake about it, this is a movie about sheer power lust.”
Cowen reads beneath the film’s apparent moral lesson about rejecting a demanding career. His interpretation asks which relationships the ending restores, whose approval still matters, and whether the heroine has actually escaped the ambitions she appears to renounce. The result is a compact argument about power, independence, and emulation. Why it belongs: A memorable example of Cowen’s ability to turn a popular entertainment into an argument about motives. Read it after watching, when you can assess his interpretation against the ending. Read Cowen’s original review
2. Alex Tabarrok — Fleabag and Killing Eve
June 6, 2019 · Television · Major spoilers
“Fleabag is much darker and more religious and mystical than most people realize.”
Tabarrok connects the two series through sexual power, moral agency, and religious failure. His reading of Fleabag draws on textual and visual parallels to argue that its comedy conceals a much darker moral structure. The comparison with Killing Eve expands the argument beyond a single protagonist. Why it belongs: One of his most ambitious pieces of television criticism. Its characterization is contestable, but the sustained interpretation gives readers something substantial to argue with. Read Tabarrok’s original review
3. Tyler Cowen — Toni Erdmann, Misunderstood Masterpiece
February 5, 2017 · Film · Major spoilers
“The emotionally unrewarding nature of contemporary cosmopolitan life, but also the limits of rootedness.”
Cowen brings together family relationships, corporate humiliation, gender, and the relationship between Germany and Eastern Europe. He resists treating the film as a simple celebration of authenticity against professional life. Both the cosmopolitan daughter and the father who disrupts her world become objects of scrutiny. Why it belongs: The review connects intimate discomfort to larger social structures without reducing the film to a single message. Cowen’s suggestion that some of the father’s appearances might be imaginary should be read as his speculative interpretation. Read Cowen’s original review
4. Alex Tabarrok — Perfect Days, in “What to Watch (or Not)”
August 7, 2025 · Film · Some spoilers
“the routine is also a narcotic for the lost.”
Tabarrok challenges the comforting interpretation of Perfect Days as a lesson in finding happiness through simple routines. He recognizes the beauty of the protagonist’s life while asking whether its carefully maintained rhythms also protect him from painful relationships. His discussion of “crown shyness” turns a natural image into a metaphor for emotional distance. Why it belongs: A concise, sensitive counterreading that allows beauty and avoidance to coexist. The film discussion appears within a composite post that also covers Ballard and Billy Joel. Read Tabarrok’s original review
5. Tyler Cowen — Her
December 29, 2013 · Film · Light spoilers
“Will they enslave us, will they be our dogs, our friends, our trading partners, or something else altogether?”
Cowen treats the film as an exploration of the possible relationships between humans and artificial intelligence. He also attends to its visual and literary ancestry, making the review more than a response to the premise. The central question is how unfamiliar forms of intelligence might fit into emotional and social life. Why it belongs: It captures intellectual excitement about a film while opening questions that extend far beyond it. Its 2013 date makes the discussion especially interesting to revisit amid later debates about AI companionship. Read Cowen’s original review
6. Alex Tabarrok — Contagion
September 19, 2011 · Film · Limited plot discussion
“we have been lucky not virtuous.”
Tabarrok uses the film to examine preparedness and the difficulty of taking rare but consequential risks seriously. The review’s force comes from the relationship between cinematic imagination and public attention: a movie can make a danger emotionally intelligible when abstract warnings fail. Why it belongs: A strong example of film criticism becoming an argument about institutions and risk perception. Read its scientific and preparedness claims in their historical context: this is a review written in 2011. Read Tabarrok’s original review
7. Tyler Cowen — Technology in Kubrick’s 2001: A Space Odyssey
June 17, 2018 · Film · Spoilers near the end
“Finally the Solow residual is explained!”
Cowen combines observations about technological forecasting with attention to cinematic form, musical pacing, and narrative risk. The economic joke is characteristic, but the review also makes a case for the film’s sensory power and for seeing it on a large screen. Why it belongs: An entertaining blend of economic thinking and aesthetic attention. It shows how a familiar classic can yield fresh observations when approached through several disciplines at once. Read Cowen’s original review
8. Alex Tabarrok — Daredevil and the Doctrine of Double Effect
November 25, 2018 · Television · Some season-three details
“As I thought about this more, however, the Thou Shall Not Kill trope is least objectionable in Daredevil.”
Tabarrok starts with an objection to the superhero convention against killing, then revises his judgment by considering Daredevil’s Catholic commitments. The interesting move is to assess the character’s choices within his own moral framework rather than only through the viewer’s preferred calculation of consequences. Why it belongs: Criticism that visibly changes its mind. The post offers a useful discussion of character consistency and moral reasoning; its account of theology is best treated as part of that argument rather than a comprehensive doctrinal explanation. Read Tabarrok’s original review
9. Tyler Cowen — Borgen Season Four
July 23, 2022 · Television · Thematic spoilers
“I wish the content had more of ‘my kind of liberalism,’ but maybe the right-wing cultural critique makes for better TV.”
Cowen considers political power, gender, Greenland, and the media while separating his political preferences from his judgment of the drama. He asks whether a critique he finds less congenial might nevertheless produce stronger television. Why it belongs: A particularly clear example of judging artistic achievement independently of ideological agreement. The post is most revealing as a reflection on what makes political drama work. Read Cowen’s original review
10. Alex Tabarrok — The Remarkable Life of Ibelin
January 16, 2025 · Documentary · Reveals the film’s central turn
“What defines an ideal romance? What constitutes true friendship?”
Tabarrok explores how the documentary changes our understanding of a disabled young man’s life through his online relationships. Its construction prompts questions about intimacy, embodiment, and the limits of what even close relatives can know about another person. Why it belongs: A humane review that uses the documentary’s form to reconsider the value of online life. Although the original post labels itself “No spoilers,” readers who want to experience the central revelation unprepared should watch first. Read Tabarrok’s original review
11. Alex Tabarrok — The Gaslighting of Parasite
June 25, 2020 · Film · Major spoilers
“the most willfully misinterpreted movie that I have ever seen.”
Tabarrok challenges the prevailing interpretation of Parasite as an indictment of economic inequality. He builds his counterreading around the families’ conduct, the poorer characters’ treatment of other workers, and recurring imagery of smell and contamination. He argues that the film’s moral judgments cut against the political expectations many critics bring to it. Why it belongs: One of his most provocative and sustained film interpretations, and an essential inclusion in this selection. His reading is contestable, especially his insistence that the film’s judgments are unambiguous. That tension makes it rewarding to discuss alongside Cowen’s The Devil Wears Prada review: both ask whether a film’s apparent message conceals a different account of power and ambition. Read Tabarrok’s original review
NYT debate on the rules for AI companies
Emily Bazelon was the moderator, there were five of us in the symposium. Excerpt from me:
Bores: Do you think the federal government pre-empting the states is consistent with democracy?
Cowen: Absolutely. We have federal laws that pre-empt state lawmaking in many other areas. Courts have upheld it many, many times.
Bores: OK, but is it pro-democracy, in giving people a say?
Cowen: It is pro-American Constitution.
Most of the panel disagreed with most of my points.