The striking possibility is that increasingly intelligent AI may not make Hayek obsolete. It may give us a new reason to appreciate him. Intelligence does not abolish the economic problem created by dispersed knowledge. A world filled with capable artificial minds may actually contain more decision-makers, more specialization, more private information, and more discoveries that no central intelligence anticipated.
For those minds, as for ours, prices would be a map of a world too complicated for any one mind to know.
It’s not every day that you get to present to 36 genuine internal auditors for an hour. Not the rowdiest crowd, true to form, but they did answer my poll questions. Many of them work at a regional bank here in Birmingham, AL. Here is the result of an (unscientific) poll from the session:
Opinion Poll: Who should own the risk appetite built into an AI model?
Claude has a better understanding of internal audit procedures than I do and actually helped me come up with the question. The interpretation of the graph is a collaboration between me and Claude, so I will not suppress the em dashes.
The plurality (first line) is the “textbook-correct” instinct — but it’s hollow without capability. Putting ownership on the business unit that deploys the model matches the Three Lines Model cleanly: the first line owns and manages the risks it takes. Good instinct, and worth affirming. The catch is the whole premise of your talk: the deployers usually can’t see the risk preference embedded in their model, let alone measure or set it. So “the business owns it” is right in principle but nominal in practice unless that owner is given the tools to actually recover and govern the appetite. That’s the gap between the poll’s ideal and the room’s reality.
The committee vote (31%) reflects real emerging practice, with a trap. Nearly a third reached for a dedicated AI-governance body — consistent with where NIST’s AI RMF and ISO 42001 point. But a committee can quietly dilute accountability: “everyone owns it” becomes “no one owns it” if it isn’t paired with a clearly accountable first line. Worth naming that risk out loud.
The most important result is the one that isn’t in any single bar: there’s no consensus. If you had asked this room “who owns credit risk?” you’d have gotten a tight, near-unanimous answer. The fact that ownership of a model’s risk appetite scatters across all four choices tells you this accountability is genuinely unsettled in their organizations.
The settled view: credit risk is owned by the first line — the business that originates the exposure. The lending or client-facing unit that decides to extend credit owns the risk of that decision. This is the textbook first-line ownership case, and it’s why credit risk is often the cleanest example used to teach the Three Lines Model.
So, congrats to me and Claude for coming up with a question that split opinion in a room of expert practitioners?
Members of the Institute of Internal Auditors know that they can “Join the Birmingham IIA on September 23, 2026 for an insightful CPE webinar featuring Dr. Joy Buchanan… “
I am pleased to get a chance to translate Buchanan and Foster (2026) to an industry audience. I have been reading up on audit controls to prepare.
Some help from Claude with the following: Enterprise risk management rests on a simple discipline: an organization decides how much risk it is willing to take in pursuit of its objectives — its risk appetite — sets tolerances around that level, and then works to keep actual decisions inside those limits. Under COSO ERM, the appetite statement and its tolerances are the structure; the ongoing question is one of conformance. It’s part of the IIA’s AI Auditing Framework and the Three Lines Model: management sets and owns the appetite, risk and compliance build guardrails and monitor, and internal audit provides independent assurance that what the organization actually does matches what it said it would tolerate. A systematic gap between the two is a finding.
For human decision-makers, we’ve built machinery to check this — credit policies, delegated authorities, four-eyes review, documented rationale, an auditable paper trail. We know how to reconstruct whether a loan officer’s or portfolio manager’s judgment stayed inside the lines.
Here is where our paper connects. When an LLM makes a risk-and-return decision — approving credit, weighting a portfolio, ranking procurement options — it too has a risk appetite. But almost none of that oversight infrastructure exists for it. We argue that AI agents are already making decisions with economic consequences and an element of risk.
By showing that the softmax mechanism inside an LLM is McFadden’s random utility model, Buchanan and Foster (2026) establish that the model’s choices reveal a genuine utility function — a measurable risk preference. Our portfolio experiment then recovers the parameters: the slope of the indifference curve we report is the model’s risk appetite, quantified.
I know an Internal Auditor. Some of their old functions will probably get automated. But they have new work to do: auditing the AI agents! Our paper is a step toward both measuring and manipulating the risk appetites of AI agents.
This is per the 2026 discussion of AI “slop” writing.
One of the things I buy at an annual local rummage sale is cheap physical media like books. This year, I picked up a book by a cartoonist who I like and respect. I thought his book would be funny and prescient from the standpoint of the publication date (1995). The book is 250 pages of mostly slop. Humans wrote lots of slop and it got printed by publishers who had a captive audience.
Why did I have such high expectations for a printed book? I think it is because, as of 2026, we are more selective about what we print. A filtering has happened. Many novels printed 100 years ago were junk.
When I think of “books” today, what it really makes me think of is “classics” or the top 0.01% of books.
So, score one point for the slopistas. Human writing was not universally smart or inspiring.
What I hate about slop is seeing it in spaces I used to trust. There was a time when I could log in to LinkedIn and see human writing from people who I had chosen to follow because I like them as people. There was a contract for my attention that is broken with slop.
I sense some push and pull in the algorithm whereby the sites might be suppressing slop, right now, relative to what I was seeing weeks ago. I went to LinkedIn on 7/17/26 to do a slop check and saw none. They might be trying to preserve the lead that James identified earlier this year: The Hot Social Network Is… LinkedIn?
Oddly, one of the worst bot-infested spaces I tread into is Facebook groups about sourdough bread making. I think the space is not important enough for Facebook to police, and the human users are not very sophisticated when it comes to tech. I logged this observation back in January.
Claude Fable is Anthropic’s most capable publicly available “Mythos-class” model. It is optimized for long-running autonomous tasks and deep knowledge work. The roll out of this product has been dramatic. Little people like me have access to it for only two weeks, and I doubt I will be able to afford it thereafter. With my window of access, I posed it the following prompt:
“This article indicates that a much smarter model might not be possible because the universe is opaque. Since Tyler Cowen made this statement, AI has helped people make breakthroughs in math and biology. Write this again in 2026 using the latest state of technology. Is the smartest LLM today much smarter than GPT-4? And is the universe legible?” and I copied in my blog post Is the Universe Legible to Intelligence?
One line from Fable’s response: “Notice where the wins clustered: mathematics with checkable proofs, protein structures with experimental ground truth, contest problems with known answers. These are the maximally legible domains — places with a fixed target and a way to verify that you hit it.”
The writing is coherent and contains no obvious hallucinations. Is the answer true, and does it tell the whole truth?
Whenever you read something think about who wrote it, and keep in mind that every author/model has a bias and limitations. In my paper with Will Hickman, we found that just reminding people that a paragraph has an author (whether the author is human or AI) increased the demand for fact checking from readers. LLMs will become more persuasive and closer to (but never completely) correct. Keep reading all things with some skepticism whether they are written by scientists, politicians, or AI.
Regardless, there is definitely such a thing as making the known world more legible to AI today. Thus, people are talking about increasing funding for data availability and the possible demise of the “research paper.”
One of my strongest AI/research takes is that it dramatically increases the value of building new descriptive datasets, digitizing archives etc.
Analysis might be free now, but Fable can’t deduce historical city sewer budgets from first principles. https://t.co/PiJngivhqK
Research papers are more like stories than facts. The demand for stories is not going away, but I definitely cannot predict the future of the write-for-pay scientist.
AI, potentially, could go and get its own new data, instead of waiting for humans to archive it. Thus, the self-improving AI might take us beyond the current models… unless they run up against something that is not legible to intelligence…
Robbins emphasizes class size and teaching load against the time of an instructor. An instructor teaching 4 sections with 100 students each is very limited in their ability to monitor and prosecute AI teaching. It’s worse if this instructor is on a temporary contract.
Limited eyes and hands and human attention really are a constraint here, at least for now. Some people see AI tools in the hands of students as the end of education itself.
I have been tweeting my replies to this:
I don’t do remote exams, but I hear about improvements to remote proctoring technology. The arms race is not over.
I don’t do remote exams, but I hear about improvements to remote proctoring technology. The arms race is not over.
Technology goes both ways. The phone students were using to cheat are now being marshalled as a “second camera” for remote test proctoring. Instructors are going to largely win this year if they take current technology seriously, for multiple choice and short answer evaluations.
The commercial Respondus program has just added Word extensions. This technology already exists and can run on the students’ laptops.
Right now, a clever student might still be able to shift their carbon-based eyes to a direction where the answer is displayed illicitly. And the instructor’s eyes can only monitor so many eyes. This is all so 2024. This conversation may be over soon. Human students can be placed under the supervision of machine eyes. Right now, we are still dealing with issues of false positives when machines flag students for cheating, but the machines are improving.
I believe that the roads will eventually be dominated by machine drivers and their unblinking eyes. Humans might drive cars for fun in the hinterlands, but it will no longer be considered a serious thing humans to do for work. Monitoring student cheating will become like truck driving. Human eyes are on the way out. We are going to become more cheat-proof than college has ever been before.
As a college professor, that will have implications for my job, although I can imagine a not-completely-negative future. Maybe I could do more fun work with students because the work of proctoring will be handled automatically. I have spent many many hours constructing tests that would be hard to cheat on and watching students take them. I take cheating seriously, and all the faculty at my business school work hard to protect the value of our degree. I predict that this will become a trivial part of teaching within 10 years.
Will students respond with various forms of hacking and deep fakes against such a system? Maybe. So far, in any arms race, Uncle Sam has been winning in the end for a century now.
If there is a will to do so, we could even bring back the research paper by having students work on a monitored computer that does not let them use AI to write. (We could almost do that already, but perhaps the true limiting factor is that, as I like to say, readers are that which is scarce.)
[Credit to my colleagues Art Carden and Anna Leigh Stone who have talked with me about test proctoring this semester.]
If you are already struggling to meet your deadlines for referee reports you owe to editors, should you take the time? If you don’t have time to indulge your curiosity about the 18th century and dead thinkers, right in the middle of the semester, should you look at it now or maybe browse it over the summer?
I think it’s worth going straight to the last chapter right now.
“Chapter 4: Why Marginalism Will Dwindle, and What Will Replace It?“
It was written for you and released quickly for this moment. Tyler does not personally have to worry about his job, but you might.
You might face mental resistance to reading this chapter, because you don’t want to hear the message. If that’s you, then it’s especially useful to read this chapter. He’s not correct about everything. Develop your counter argument, to go forth and save marginalism. You can only do that if you understand and name the threats. This is more about methods/professions and less about ideology than you might think from the title.
Here are some quotes that stood out to me
The ties of empirical work in economics to economic theory are evolving, and in particular the explicit ties to intuitive microeconomic reasoning, and marginalist thinking, are being cut. In much of traditional econometrics, the emphasis is on testing pre-existing models…
in machine learning, we let the algorithm build the “theory” for us, noting it may have tens of millions of variables and thus not count as a theory…
So much for prediction, what about hypothesis generation? Well, there is a new approach to that too, using machine learning.
A lot of economists do not regularly describe what they actually do for work. Yes, we are saving the world by writing papers, but what exactly do you do? Do you generate hypotheses? Is that what you are teaching your students to do?
It’s not fun to think of how the econ profession might need to reposition, but we owe it to students. Who better to work on this than tenured professors?
I think the case for undergraduates students to major in economics is strong. I also think the case for doing 4 years of college is strong for students who want to learn.
If economics is “more interesting” than hard science, then it might serve to scoop up good thinkers at the undergraduate level and get them doing something more technical than what they would end up doing in a humanities program. When I graduated from college, the fact that most econ student had accidentally learned to code was a benefit to them.
College graduate humans ought to be able to read and pass the Turing Test if they are going to be effective complements to AI.
Let me plug Mike as well for thinking about what research econs do in 2026: The actual AI problem in academic economics “Oh, what shall all the candlemakers do now that the sun has risen?” made me laugh.
If you’ve been on LinkedIn recently, then you may have seen the chatter about teaching your artificial intelligence to have various skills. I saw one post by a guy who claimed to have created several skills, each representing a tech billionaire.
At first, I thought “I am behind the 8-ball. What is this new thing?”. Obviously I know what the word “skill” is and how people use it, but I had not encountered its use in the context of AI having it. What does it mean for an AI to have a skill? I somewhat dreaded the the work of learning the new skill of teaching my AI skills.
Then I had lunch with a computer scientist and I learned that skills are nothing new.
Two things the white-collar chattering class fears is that their jobs will disappear or their stock portfolios will crash. The Citrini note put that feared scenario in a picture frame so we could stare at it, like Annie Jacobsen’s book on nuclear war. The post imagines a 2028 scenario: AI automates white-collar work, companies collapse, private credit blows up, mortgages default, unemployment hits 10%.
Even cognitive automation faces coordination frictions, liability constraints, and trust barriers. It seems more likely that AI will be a complement rather than a substitute for labor is many areas.
One barrier to AI taking all the white-collar jobs as quickly as 2028 is just physical scaling constraints.
Having done research on “learn to code” (Buchanan 2022), I always watch new developments with interest. In 2023, I told an auditorium full of students in Indiana to learn to code if they don’t hate the work too much. At that time I had forecast that AI tools would make coding less miserable but not eliminate the need for technical human workers. Even if that was good advice at the time, is it still good advice today? I wish I had time to put up a blog on this topic every week.
Adjustments can happen along the margin of price as well as quantity. Wages to programmers can come down from their previously exalted heights, which could help the market absorb some of the young professionals who listened to “learn to code” in 2023.
So, now that the value of coding skills is in question, people are turning back to the value of the maligned English degree. It has been true for a long time that employers felt soft skills were more scarce than STEM degrees. I might add that an economics degree conveys a highly marketable blend of hard and soft skills.
Buchanan, Joy (2022). “Willingness to be paid: Who trains for tech jobs?” Labour Economics, 79, Article 102267.
At the link, I speculate on doom, hardware, human jobs, the jagged edge (via a Joshua Gans working paper), and the Manhattan Project. The fun thing about being 6 years late to a seminal paper is that you can consider how its predictions are doing.
Sutton draws from decades of AI history to argue that researchers have learned a “bitter” truth. Researchers repeatedly assume that computers will make the next advance in intelligence by relying on specialized human expertise. Recent history shows that methods that scale with computation outperform those reliant on human expertise. For example, in computer chess, brute-force search on specialized hardware triumphed over knowledge-based approaches. Sutton warns that researchers resist learning this lesson because building in knowledge feels satisfying, but true breakthroughs come from computation’s relentless scaling.
The article has been up for a week and some intelligent comments have already come in. Folks are pointing out that I might be underrating the models’ ability to improve themselves going forward.
Second, with the frontier AI labs driving toward automating AI research the direct human involvement in developing such algorithms/architectures may be much less than it seems that you’re positing.
If that commenter is correct, there will be less need for humans than I said.
Also, Jim Caton over on LinkedIn (James, are we all there now?) pointed out that more efficient models might not need more hardware. If the AIs figure out ways to make themselves more efficient, then is “scaling” even going to be the right word anymore for improvement? The fun thing about writing about AI is that you will probably be wrong within weeks.
Between the time I proposed this to Econlog and publication, Ilya Sutskever suggested on Dwarkesh that “We’re moving from the age of scaling to the age of research“.
