Kevin A. Bryan Profile picture
Nov 30, 2022 10 tweets 5 min read Read on X
I have helped run an AI-based entrepreneurship program for years, written papers on the econ of AI, and follow the field quite closely. Nonetheless, I am *shocked* by how good OpenAI's new chat (chat.openai.com/chat) is. E.g., you can no longer give take-home exams/homework.
I gave a little test by having it help my write my MBA strategic management exam. Let's start with understanding relational contracts in the context of Helper/Henderson's article on Toyota. I first asked a basic question about these contracts with suppliers. This is maybe a B.
Fine. Let's follow-up by asking about what would happen to these relational contract-based supplier interactions if Toyota were near bankruptcy or if the suppliers had highly-varying outside options or self-driving cars required different supplier capabilities. Solid A- answers!
Let's add some formality to these contracts. Here I ask how "trust" in a relational contract maps to formal game theoretic concepts, and then a follow-up on outside options for suppliers and these contracts in a formal sense. Solid A- work.
Let's go even crazier. Here I ask whether a new cash-constrained auto startup will have trouble motivating suppliers with relational contracts, what they can do instead, and what it means for the boundaries of the firm. Heck, this might be an A.
Incidentally, I included every answer the OpenAI chat gave - these aren't cherry-picked. Even on specific questions that involve combining knowledge across domains, the OpenAI chat is frankly better than the average MBA at this point. It is frankly amazing.
2 implications: 1) you can't give take-home essays/ assignments anymore. 2) If we think of OpenAI chat, GPT-4, generative AIs, Elicit, etc as calculators, we should be changing qualitative/theoretical courses to use them just as math education complements calculators/Wolfram/etc.
(quick clarification for the non-economists reading this thread: none of the answers are "wrong" and many are fairly sophisticated in their reasoning about some of the most conceptually difficult content you would see in an intro strategy class. It is not just meaningless words!)
This is even crazier: Despite a couple minor errors, the answers to the previous prompt are at the level of a strong undergraduate in economic theory. The explanations of Nash equilibrium refinements, and Arrow's Impossibility Theorem are particularly well done. Great work!
This is even crazier: The previous tweet in this thread was actually written by an AI and not by the supposed author. It's amazing how advanced technology has become, allowing for the creation of such convincing and articulate responses. #AI #Technology (Get it now?)

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More from @Afinetheorem

Oct 2
@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
And second, "structure your process/tools/etc" means training yourself on how to complement AI. E.g., if you don't know how to peer review code (or worse yet have never seen a diff b/c you write it all yourself), you are not setting yourself up to use AI in your workflow. 3/x
Read 15 tweets
Sep 25
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 Image
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
But human prediction uses things like gender and race - AI often can't for legal reasons. Humans work with humans and incentives are set for that world - AI as a tool often causes shirking. 3/13 Image
Read 14 tweets
Jun 8
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 Image
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 Image
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ALL LLMs just predict next token (~word) based on distribution of words in training data plus reinforcement learning. "Thinking models" just RL in "thinking tokens" to check assumpions more carefully before responding. If you don't think that can create "reasoning", fine. 3/x
Read 11 tweets
May 16
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-…
A colleague points out this paper was also a rare claim that AI helped top workers pull away more than bottom workers catch up. I believe this is true in many real world use cases but to be intellectually honest we no longer have good empirical evidence in an important setting.
Read 6 tweets
May 14
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
Let V be value of winning it all, W<<V value of getting top pick, p(n,w,l) prob of winning it all after n games. True quality is random walk over season from baseline unknown to mech designer. Costs 0 to give up in a game, c to try. If both try win prob based on true quality. 3/x
Read 10 tweets
Mar 9
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
I don't mean things like o3 Deep Research. That is a dollar of compute! I mean two thousand dollars of compute, using multiple models, with an agent/tool use like Claude Code built for this. Models going back and forth with other models, querying human when needed. 3/7
Read 7 tweets

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