Joshua Gans

It worked.

To save entry-level jobs from AI, look to the medical residency model | Brookings

Good policy direction from Brookings on how to deal with entry level job disruption that may come from AI. www.brookings.edu/articles/to-...

Excellent

Sure you have read short histories of innovation before but this one is the shortest.

The strategy of proposing to destroy the status quo and then claiming victory for restoring the status quo is an interesting one.

YouTube video by Geo News English Davos: Canadian PM Mark Carney speaks at World Economic Forum | Geo News English

You've heard about the @MarkJCarney speech. You should listen to it all. It is one of great speeches of all time. We will talk of this for decades. www.youtube.com/watch?v=CQOr...

In strategic management and wanting to move to Canada. We have a senior slot this year in Strategic Management. Job ad and application details are here jobs.utoronto.ca/job/Toronto-...

Bottom line: Jagged intelligence isn't a bug that scaling will fix—it's a fundamental feature of how these models work. Better adoption strategies require either much denser knowledge coverage, effective calibration, or users developing expertise about where models fail.

For reasoning-intensive tasks, the model predicts that extended reasoning (like chain-of-thought) helps most in the largest knowledge gaps—exactly where users encounter problems most often. This explains why reasoning modes improve user experience disproportionately.

The paper shows that reducing variation in gap sizes (making performance more regular) is more valuable than just increasing average quality. A model with consistent mediocre performance can be more useful than one with erratic brilliance.

"Mastery" means learning where the model is reliable through repeated use. But this is harder than it sounds. The reliability landscape has complex patterns, and you need extensive experience across many tasks to build an accurate mental map.

Scaling up models (adding more knowledge points) improves average quality but doesn't eliminate jaggedness. You get denser coverage, but gaps remain. The fundamental unevenness persists even as models get larger and more capable.

Calibration helps but has limits. If users could see reliability scores for each specific task, they'd delegate more effectively. But even with perfect calibration, the inspection paradox means users still encounter errors at higher rates than the model's average.

The model shows that "blind adoption"—using AI without checking local reliability—works only when coverage is extremely dense. For most current models, blind adoption leads to unacceptable error rates even when average quality seems high.

This creates a threshold problem. Users adopt AI when global quality signals (like benchmark scores) look good enough. But the errors they actually experience are amplified by the inspection paradox, leading to disappointment and distrust.

Think of it like waiting for a bus. Longer gaps between buses mean you spend more time waiting in those gaps. Similarly, you encounter AI's biggest knowledge gaps most frequently—making the model seem worse than its average performance.

Here's the key insight: users experience AI weaknesses more than its strengths due to an "inspection paradox." When you try many tasks, you're statistically more likely to hit the large gaps in knowledge rather than the small ones.

The paper models AI knowledge as scattered points on a landscape. Between these knowledge points, the AI extrapolates—essentially guessing based on nearby information. The further you get from a knowledge point, the less reliable the output becomes.

Generative AI shows a puzzling pattern: it excels at some tasks but fails at seemingly similar ones. This "jagged" performance creates a fundamental problem for users trying to decide when to rely on AI. New research explains why this happens and what it means.

A Model of Artificial Jagged Intelligence Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

New paper out today. A thread. www.nber.org/papers/w3471...

I think that was its greatest episode.

Greenland Invasion Game - Interactive Strategy Simulator Play a humorous interactive strategy game where you deploy US forces to occupy Greenland. Educational parody with real geographic data and satirical military simulation mechanics.

In light of the current global situation, I believed that AI could be used to create a playable simulation that allows you to map out the nuances of the military and other trade-offs associated with a US invasion of Greenland. You can play it here claude.ai/public/artif...

Prediction Machines

This is incredible.

(More legible version here www.npr.org/sections/the...)

Venezuelan Opposition Leader Gives Trump Her Peace Prize

Americans. Sigh. www.nytimes.com/live/2026/01...

I'm with Ben Edelman here. The lawsuit documents appear to focus on what was odd in HBS' review of his candidacy. Not sure you can learn about your own tenure process from reading this. (And always better to write something or teach someone than to worry about the process.)

So junior faculty are always wanting more information on the tenure process, and we usually can’t give them more than advice. Whatever else is happening in Edelman v. Harvard, the documents are a window on this. www.edelman-v-harvard.org/documents/

The Microeconomics of Artificial Intelligence A comprehensive treatment of the microeconomics associated with the adoption and use of artificial intelligence.It is well recognized that recent advances

I am pleased to announce that the OPEN ACCESS link for The Microeconomics of AI is now available. Click here to download for free. direct.mit.edu/books/oa-mon...

Price Discrimination with Costless Resale Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

Anyhow, here is the paper. It doesn't involve any fancy techniques. Indeed, it could have easily been written in the 1950s. www.nber.org/papers/w34669

Is this just a theoretical curiosity? It certainly is that. But I think that a range of different prices based on income-related factors does arise in things like car sales and pharmaceuticals (especially internationally).