@joshgans and I did an internal talk on AI for research. Mostly demos, so no slides, but broadly: 1) Research should be efficient, open & replicable. 2) AI helps will all three. 3) Always use the best model. 4) Structure your processes/tools/etc. so you can continue to do 3. 1/15 What I mean by "structure your process/tools/etc" is first that everything you do - code, writing, editing, collabs, slides - should have plain text as the substrate. This means no matter what, you can always use every AI tool now and in the future to interact with it. 2/x

AI economists and AI researchers: this is *excellent*. Details below, but as I feel like I've said in every talk on this topic since my slides said "GPT-2", 1) AI technical capabilities are better and improving quicker than you think, 2) impact on economy *much* slower. 1/13 Hinton famously said (at our event!) a decade ago to not train radiologists. Today, radiologist hiring very strong w/ avg income of US$520k. What went wrong? As a technical matter, Hinton was right - anomalies on images very easy for AI today (this article undersells that!). 2/13

The "reasoning doesn't exist" Apple paper drives me crazy. Take logic puzzle like Tower of Hanoi w/ 10s to 1000000s of moves to solve correctly. Check first step where an LLM makes mistake. Long problems aren't solved. Fewer thought tokens/early mistakes on longer problems. 1/11 But fundamental problem is that there are foundation *models*, and then there is *the way we train these models to cutoff thought*. Imagine you say "hello". Try it on o3 and 4o. o3 takes longer because it "thinks" about other things you could mean. Unnecessary in this case. 2/x

Gobsmacked that the Toner-Rodgers neural network for scientific discovery paper has been withdrawn. This was a very strong claim about how 2023 era AI was improving the speed of science. I don't have details on the issues, but wanted to pass along this retraction. 1/2 That said, I am personally aware of 2025 automated lab scientific progress using LLMs and other techniques, so don't update too much in the other direction! MIT note here: economics.mit.edu/news/assuring-…

On the NBA and economic theory: dynamic mechanism design tells us how to redo the draft. A good rule of thumb (due to Reny?) is that stochastic mechanisms like draft lotteries usually aren't best. How to prevent tanking while still having worst team get top pick? Simple! 1/x Theory is beautiful. Myerson Revelation: look only at mechanisms that get teams to reveal quality "type" (we/ dynamic caveats!). Better teams will get some information rents else they tank. Let's find mechanism with no tanking that maximizes prob worst team gets top pick. 2/x

Not great for my comparative advantage, but from some experiments we have done at Rotman, I am totally convinced the vast majority of research that doesn't involve the physical world can be done more cheaply with AI & a little human intervention than by even good researchers. 1/7 Brainstorm interesting question, find all prior lit on this, grab necessary data inputs if any and clean/merge, write theory or statistical model, check extensions, prep writeup, prep replication file. This is 90% of work for papers of that type. What can't AI help with? 2/7

Back to econ in a second, but let's have fun: how about 50 correct moderate takes: 1) Ukrainians are heroes who suffered a ton. 2) Putin obviously covets Georgia and the Baltics also. 3) EU not in talks because they basically have no hard power; France even lost the Sahel. 1/x 4) The far right European parties are bad. 5) Pretending parties that win state and EU elections, and are in govt in NL and AT and IT, aren't legitimate will not end well. 6) And in fact EU & UK speech laws not on side of liberty. 7) (but EU food and kid culture is better!) 2/x

France just held a big AI summit. They want to build up their tech sector. Global prosperity would be enhanced by this. But I can't tell you how insane EU regulation is here, and how it inadvertently enhances market power of big US firms. Let's look at GDPR, for example. 1/x You may think this is just "accept cookie" banners and rules so, like, your personal medical information isn't sold. Not at all. Let's imagine you are a tiny US software company, doing a hundred bucks a month of sales in Europe. What's required? 2/x

Curious about the wide range of reactions to o3 Deep Research. I went through some cases where people found "bad results". I think I understand what is going on - part of this is just bad UX choices by OpenAI, part a misunderstanding of what the model is for. 1/x So o3 is a reasoning model - an LLM. Deep Research grafts web search, python tools, and a fine-tuned model to use those tools to gather additional data on top of o3. It can't read gated articles, has a mediocre OCR for public pdfs, can't read dynamically generated webpages. 2/x

The new OpenAI model announced today is quite wild. It is essentially Google's Deep Research idea with multistep reasoning, web search, *and* the o3 model underneath (as far as I know). It sometimes takes a half hour to answer. Let me show you an example. 1/x So related to the tariff nuttiness, what if you wanted to read about the economics of the 1890 McKinley tariff, drawing on modern trade theory? I asked Deep Research to spin up a draft, with a couple paragraphs of guidance, in Latex, with citations. 2/x

The tariffs are insane; everyone knows that if Congress voted in secret, Senate would be 95-5 against. This whole situation is based on the ignorance of one man, and the lack of fortitude among Congress to push back. It will harm all four countries. So what should Canada do? 1/x Facts: most N. America trade is intermediate goods, Canada is (much) more reliant on US intermediate than US is on Canada, "negotiation" tough b/c the policy is bad for the US as is (hard to threaten someone who isn't even maximizing US welfare!). 2/x

I have friends on that AEA committee on social media but seriously, read this report and tell me if you think it is a fair, scholarly, and nonpartisan look at evidence. There are like a half dozen potshots at people who disagree with a certain political consensus. 1/4 Evidence that doesn't support narrative is buried in appendices (eg, in every question there is no gap in harassment by race, so despite the text, no data on that factor is shown at all), "#econtwitter" is seen as neutral environment (it was very left wing applied micro) etc 2/4

Since immigration reform might be on the table: mostly family green cards and H1B via lottery is crazy. Just auction then off with age and field adjustment and a background check + some method for high grit strivers to come in numbers. Abundance! I'd also make it de facto unrestricted for developed countries where net migration isn't huge. This is status quo for Canadians, Chileans, Singaporeans, Australians by treaty (E3, TN, H1B1). No issues except less friction. If future Jensens, Katlins & Elons want to come let them!

Super excited to publicly launch "All Day TA", a product @joshgans and I have been working on with our team over the last year. Short version: if you teach in spring, you will want to use this! It's the future of higher education. A short thread: 1/x @joshgans Today, we lecture, give exams, see student gets 7/10, move on. Educ lit clear that individual tutoring, tracked to level of each student, much more effective - but costly. In Algebra 1 type courses, orgs like @khanacademy have great million $ tools. What about *your* course? 2/x

For new PhD students in social science (and some old professors - I sure needed this a few years ago!): prepared some notes on what the "2024 PhD Tech Stack" should look like. Goal: your research is replicable, accessible, and efficient. 1/9 Replicable: whether or not the journal requires a full replication file, there is *no reason* every table and result in your paper can't be created with one line of code, and *no reason* you can't find every change you or a coauthor made. It's called version control! 2/9

Job market candidates: a quick thread on how the econ market works, incl. at business schools. 1) It is not unusual to get many 100s of applications. Nobody reads them all first-pass. Most places take a quick glance at letter/cv/intro, and keep the top x%. 2) You may think there are well-thought-out ex-ante fields schools will hire in. Sometimes this is true - but sometimes "general" ads are not actually general due to internal dept bargaining. You can't know - and sometimes the dept doesn't know yet - so don't worry about it.

It's Nobel Day, and the prize goes to Acemoglu, Johnson and Robinson! I believe AJR 2001 is the most cited economics paper of the 21st century (~20k!). A brief thread on why this literature was so influential, and slightly controversial, and my read on how AJR got here. 🧵1/x Briefly: "AJR polecon" posits 1) institutions, laws & culture persist 2) even if their origins can be for somewhat random reasons 3) leading to differential growth 4) with no 'end of history' liberal democracy trend 5) but societies where broad talent is used productively prosper

It's Friday and I'm feeling a bit feisty, so...I think a lot of innovation economists don't realize just how much influence UCL IIPP is having on policy, esp. in Europe. They are super connected politically and I don't think politicians realize how nonstandard their work is. 1/x IIPP is basically Mazzucato and friends. Almost every economist there is super heterodox. Ex: a housing policy paper commissioned by UK govt by them popped up on my feed. It was a long "it's not supply" argument. Here is the final post; look at who it is "of interest" to! 2/x

Econ friends: if you don't code w/ LLM (Cursor + Sonnet is best right now), huge mistake. Seriously, just throw out your Stata license. Karpathy here is literally one of the world's best coders (Stanford PhD under Fei-fei, head of AI at Tesla, cofounder OpenAI, amazing teacher).

https://twitter.com/karpathy/status/1827143768459637073

Hacker News, Twitter, etc. are full of "I don't see how this is useful. *My* problems are harder." Dude, *Andrej Karpathy* writes his code this way now. It's not magic - you still need to think. But "2x improvement" from 2023 is now more like 4x productivity improvement.

The hardest thing to explain to non-academics is how different technical rigor is across fields. Here's (yet another) article in PNAS, of course handled by Fiske, of course in psych, that we would have desk rejected. Paper is "Can Names Shape Facial Appearance?" 1/n The question is (and I have no idea why we care, to be honest) whether names match facial appearance, even if we go beyond gender/race/obv religion. Their headline result? Yes, but only for adults, so people "grow into" their faces. 2/n

A lot of bad AI policy is being driven by the fact that one side of the debate is sometimes really odd. "I don't believe exercise, Ozempic or calorie counting work, so I do a crazy fast every 3rd month" is literally the science of the most famous AI alignment guy. 1/3

https://twitter.com/ESYudkowsky/status/1816925777377788295

Many EA and alignment folks are really thoughtful and reasonable. But there is a blind spot in their separation of better knowledge (they are often smart!) vs. different preferences. Two ppl may disagree on policy b/c one has better info or b/c they have different goals. 2/3