Joy Buchanan

Is AI Taking Our Jobs? Signal Boosting Noah Smith

October 10, 2026October 7, 2026Joy Buchanan2 Comments

Artificial intelligence is supposed to transform the labor market. Depending on whom you ask, AI is either about to eliminate millions of jobs or usher in a new era of productivity and prosperity. I am reviewing the evidence before I teach an “unemployment” chapter…

I want to give a signal boost to economist Noah Smith, who has been following the emerging evidence about AI and employment at his blog, Noahpinion. His answer, so far, is that the evidence for widespread displacement is weak.

In his September 7 post, “AI keeps stubbornly refusing to take our jobs,” Smith points out a contrast. Many technology executives expect AI to make human workers obsolete, and public opinion surveys from Pew Research Center indicate that Americans share these concerns. Yet employment has held up remarkably well, especially among prime-age workers. AI might still take our jobs soon, but we have not seen widespread displacement yet. Economists have been saying for a long time that automating tasks is not the same thing as eliminating jobs.

In “Automation and New Tasks: How Technology Displaces and Reinstates Labor,” Daron Acemoglu and Pascual Restrepo explain that automation can reduce demand for labor performing particular tasks while also increasing productivity and creating new tasks for workers.

Consider software developers. AI can now write substantial amounts of code. One might conclude that fewer programmers will be needed. But programmers also design systems and decide what software should do. Making code less expensive could expand the amount of software that firms want to produce. Greater productivity may increase rather than decrease the demand for skilled programmers. (my old paper on who wants these jobs is “Willingness to be Paid: Who Trains for Tech Jobs?“)

Smith points readers to Guy Berger’s analysis of software developer employment and reporting on firms that have reconsidered replacing programmers with AI. These examples challenge the assumption that better coding tools necessarily mean fewer coding jobs.

One piece of evidence comes from the U.S. Census Bureau’s Business Trends and Outlook Survey, highlighted by Alex Tabarrok at Marginal Revolution and discussed by Smith. Among firms using AI, 44 percent reported that it supplemented or enhanced existing employee tasks. Ten percent reported that AI performed tasks previously done by employees, while 11 percent reported that AI introduced tasks nobody had previously performed. Most firms reported no employment change attributable to AI.

Smith also highlights research by Ara Kharazian, Jonathon Simon, and David Stevens who find that firms adopting AI tend to expand their employment, including entry-level employment. These findings contrast with the popular narrative that businesses are replacing junior employees with chatbots.

What about young workers?

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Prices as a Map for AI Agents

October 3, 2026October 1, 2026Joy BuchananLeave a comment

New from me at EconLog:

Prices as a Map for Minds That Can’t Know Everything

Who owns model risk?

September 26, 2026September 23, 2026Joy BuchananLeave a comment

Update to readers after doing the CPE (read Internal Audit of AI Agents and Risk for background )

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?

Internal Audit of AI Agents and Risk

September 19, 2026September 19, 2026Joy Buchanan2 Comments

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.

Buchanan, J., & Foster, J. (2026). The innate economic preferences of language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.26288

Image by Grok. I don’t sell that mug but EWED does have merch at https://shop.spreadshirt.com/economist-writing-every-day/

Korea So Far

September 12, 2026September 11, 2026Joy BuchananLeave a comment

I am going to Korea next year. I look forward to being able to relay travel observations. Here’s what I can say so far.

I have several distinct personal points of connection to Korea through my family, my husband’s family, and my next-door neighbor here in Alabama. There is a George Mason U. campus in Korea. I hope to explore all of those connections when I go.

KPop Demon Hunters was an absolute phenomenon on Netflix last year. Every school child in America, across every race and state, knew the songs. As for the soundtrack, I determine:
“Golden” – correctly rated
“Soda Pop” – overrated
“What it Sounds Like” – underrated
And what is interesting in September of 2026 is how fast the whole thing died out. Trends come and then they really go in the monoculture (is this the “unicontext”?).

I am learning Korean by doing one lesson per day on Duolingo. Progress is slow, partly because I do not expect to need to become fluent ever. I am enjoying this. I have frittered away a good number of hours on cognitive trivia like computer Solitaire and Wordle. On the margin, I encourage a few of you English speakers to make learning a Non-Romance Language your new game. I have also used Duolingo to try to brush up on Spanish and French, but it’s fun to enter a totally different mindset altogether. It’s also difficult and might be frustrating if I had a higher bar for my performance.

If any readers have a connection in Seoul, please let me know.

Temperature vs thinking level for LLM products

September 5, 2026September 4, 2026Joy Buchanan2 Comments

If using ChatGPT or an API in 2023, you might have followed the advice “turn the temperature down” when you wanted a more serious answer. That is going out of date. The following is a collaboration between me and Grok to lay this out simply:

What temperature was

Imagine the model is always choosing the next word from a long list of options, ranked from “very likely” to “weird but possible.” Temperature changes how adventurous that choice is.

Low temperature: almost always pick the obvious next word. Answers feel steady, repetitive, “corporate.” Good when there is basically one right output, like extracting a date from a contract.
High temperature: more willing to pick a less obvious word. Answers feel livelier but sometimes incorrect.

On older products you could often set this yourself.

What thinking level is

Newer models (Gemini 3, OpenAI’s reasoning models) do extra work before they talk to you. They draft a private scratchpad: break the problem into steps, check themselves, then write the answer you see. You don’t see the scratchpad even though you pay for it. That hidden draft is the “thinking.”

Thinking level (sometimes called reasoning effort) is not “how random should the next word be?” It is “how long is the model allowed to work on that draft?”

Low / minimal: answer quickly and cheaply. Fine for “what’s the status of ticket 1842?”
High: spend more time (and more tokens) on hard things — a gnarly spreadsheet, a multi-step plan, a tricky piece of code.

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Universal Basic Yosemite and the Parton Fertility Rate

August 29, 2026August 29, 2026Joy BuchananLeave a comment

A Twitter(X) exchange caused me to revisit my post from 2020 Affording a second child

A childhood friend and Facebook contact of mine pleaded to her friends, “How do people afford multiple children?”

I checked up on her and saw delightful pictures of a second child who, one way or another, they are now financially supporting along with that firstborn girl, in 2026. I obviously won’t post her family photos, but they took a summer trip to Yosemite National Park that looked something like this

Based on how she felt about supporting a second child, I would be surprised if they have a third, a “big family.” Two is about where most people can still afford to take trips across the country in the summer.

This caused me to reflect on Dolly Parton, who we all miss. The U.S. flag is at half-staff in recognition of Dolly’s passing.

Part of her charming narrative is the fact that she was born into poverty. Famously, Dolly grew up with 11 siblings in a two-room Tennessee cabin. I doubt their family made it out to Yosemite.

So, with some help from ChatGPT, I inquired about the Total Fertility Rate of those Parton children. In one generation, everything dramatically changed. The number of children born to the Parton clan ranges from three to zero. Chat writes:

Dolly and her five sisters appear to have had only about six biological children among them—an average of roughly one child per woman. Dolly herself had none. Including their brothers, the twelve Parton siblings seem to have produced only about sixteen children altogether.

The Parton family therefore compresses a major American demographic change into just two generations. Avie Lee had twelve children; her daughters averaged about one each.

Journal Crisis at Games and Economic Behavior

August 24, 2026August 25, 2026Joy Buchanan3 Comments

I used Grok for this query because economists were discussing the issue on X(Twitter) this week and Grok would have quick access to that discourse. Note that Grok provided links to legitimate web sources, not just Twitter speculation. I am providing those same links in the place where Grok put them. I do not know how much the chatter of the week affected the response below.

Prompt from J. B. on 8/22/26 to Grok: What happened to the economics journal Games and Economic Behavior?

The leading game theory journal Games and Economic Behavior (GEB), published by Elsevier since its founding in 1989, remains active and is still publishing issues (including volumes into 2026), but it is in a major editorial crisis due to a mass resignation of its leadership. (https://www.sciencedirect.com/journal/games-and-economic-behavior)

In late July 2026, Editor-in-Chief Hervé Moulin (who had held the role since January 2021) was informed by Elsevier that his 3-year contract would not be renewed, despite his willingness to continue. Elsevier cited a desire to move “towards a closer alignment with Elsevier strategic priorities and publishing policies.” Requests for further explanation (from Moulin and then collectively from the other six editors) were dismissed; Elsevier simply asserted its contractual right to make the decision and began searching for new editors without input from the existing board. (https://gametheorysociety.org/message-from-herve-moulin-geb-editor/)

In response, by August 14, 2026, all seven editors (Moulin plus Sushil Bikhchandani, Aislinn Bohren, Nicole Immorlica, Roberto Serrano, Emanuel Vespa, and Nicolas Vieille) resigned with the required three-month notice. Nearly all of the advisory editors (the equivalent of associate editors) also resigned in solidarity. The outgoing team is handling papers already under review and new submissions until November 14, 2026 (with some flexibility for papers already assigned by mid-August); after that, they will only finish evaluations already in progress. Elsevier has publicly thanked Moulin for his service. (https://gametheorysociety.org/message-from-herve-moulin-geb-editor/)

The journal continues under Elsevier ownership (it is one of the two official journals of the Game Theory Society, alongside the International Journal of Game Theory). Community discussion has focused on the implications for the field, the role of commercial publishers, and possibilities such as launching a successor journal. Similar recent mass resignations have occurred at other Elsevier titles over governance and editorial control issues. (https://casrai.org/news/2026-editorial-board-mass-resignations)

In short, GEB has not shut down, but its longstanding editorial team has largely departed over Elsevier’s non-renewal of the Editor-in-Chief and related decisions.

Prompt from J. B. on 8/22/26 to Grok: What will be the implications for the reputation of the journal?

The reputation of Games and Economic Behavior (GEB) is likely to face significant short- to medium-term damage, with longer-term outcomes depending heavily on how Elsevier handles the transition and whether a strong successor journal emerges.

GEB has long been viewed as the leading specialized journal in game theory (and one of the stronger outlets in theoretical economics more broadly). Its prestige rests on attracting high-quality, creative work; the expertise and independence of its editorial team; rigorous standards; and community trust. The mass resignation of all seven editors (including Editor-in-Chief Hervé Moulin) plus nearly the entire advisory board in August 2026, following Elsevier’s non-renewal of Moulin’s contract, directly undermines those foundations. (https://gametheorysociety.org/message-from-herve-moulin-geb-editor/)

Short-term effects

Loss of trust and submissions: Top researchers often choose journals partly based on the known editors and the expectation of fair, expert handling. With the board depleted (current editors only fully process papers until mid-November 2026), many authors are likely to pause or divert submissions. Community discussion already includes calls for boycotts of the Elsevier GEB and support for alternatives. @Afinetheorem
Perception of publisher interference: Elsevier’s stated rationale (“closer alignment with Elsevier strategic priorities and publishing policies”) and its decision to search for new editors without board input have fueled views that commercial priorities overrode academic ones. This echoes other recent mass resignations (e.g., Journal of Approximation Theory at Elsevier, where resigning editors declared the journal “as we have known it has effectively ceased to exist”). casrai.org
Operational disruption: Delays in reviews, potential quality variation under interim or new leadership, and uncertainty can further erode confidence among authors, referees, and readers.

Metrics such as impact factor or AJG ranking (currently 3) change slowly and will not immediately reflect the crisis, but subjective reputation in the game theory and economics communities can shift quickly.

Medium- to long-term scenarios

Historical patterns from similar mass resignations offer useful analogies:

“Zombie journal” risk: The title, ISSN, indexing, and historical impact factor stay with Elsevier. If the new editorial team is perceived as less distinguished or more aligned with volume/publisher goals, submissions from leading researchers can decline, citation patterns can shift (sometimes toward more geographically concentrated or lower-prestige work), and the journal’s standing within the field can permanently weaken. Cases like Lingua (Elsevier, 2015) and others show that the brand can persist while the scholarly niche contracts. sciencedirect.com
Recovery path: If Elsevier rapidly appoints highly respected new editors who maintain rigorous standards and rebuild community confidence, the damage can be limited. Some journals have stabilized after board changes, though full restoration of prior prestige is uncommon when the exit was acrimonious.
Successor journal effect: Strong community interest already exists in launching a new outlet (possible names floated include variations on “Theory of Games and Economic Behavior” or society-backed alternatives; comparisons are drawn to how JEEA or other breakaways have functioned). If a credible successor is established—especially one endorsed by the departing editors and the Game Theory Society—and is treated as the “true” continuation by tenure/promotion committees, the original GEB’s relative prestige would likely decline further. Successful breakaways (e.g., Glossa after Lingua, or Imaging Neuroscience after NeuroImage) have often captured much of the field’s attention and high-quality papers. journalmetrics.org

**end of Grok responses **
If you want some chatter, you can put “GEB” into the search bar at x.com, LinkedIn, or BlueSky this week and see some more speculation.

There is a lot happening with publishing, including a huge shock of strangeness from the ability to both write and review papers with LLMs. Tumult will be normal. Read Mike on Will AI kill the research paper? or Mike on The actual AI problem in academic economics or Mike on Academic economists are overcommitted . The real perennial problem is that nothing is free but no one wants to pay.

Lastly, dear reader, you might wonder why so much of my post was AI-generated this week. I see this as burning my tokens so you don’t have to. You can find this with a free web search. Some people predict that “cheap AI” is coming to an end, so we might find it worthwhile to publish responses as well as prompts for each other. Regards, fellow humans and AI readers.

Predicting Social Media from 1997

August 15, 2026August 6, 2026Joy Buchanan3 Comments

At a rummage sale, I picked up a book by cartoonist Scott Adams called The Dilbert Future: Thriving on Stupidity in the 21^st Century published in 1997. I thought I might find a clever prediction about the future, which we can now verify from the standpoint of 2026.

The text of the book is mostly dumb. I get the impression that Scott Adams was making easy money with a guaranteed humor book contract. I don’t recommend the book to anyone.

HOWEVER, with my paper copy I kept skimming ahead to see if any of his predictions about the future were impressive. Finally, on page 200 I found something.

Recall, the internet only became publicly available in the early 90’s. Respectable newspapers might have started to lose out to cable news in the mid-90’s. Blogs did not start until after Adams’ book was published. Social media proper (marked by the launch of Facebook) started in 2004. (Let millennials quietly walk away from Xanga journals and pretend that never happened.) So, my interest in this passage hinges on the fact that this book has a publication date of 1997.

The following is copied from Adams’ humor book.

I predict that news outlets will try to compensate for the loss of relevant news by focusing on stories that are more shocking and depressing than ever. At least that way they’ll get your attention and sell advertising even if the stories aren’t “news” in the traditional sense.

This will limit the reporting to a few stories per year about famous people who are killing other famous people. And if there are not enough of those stories to sell advertising slots, the media will…

Prediction 51: In the future, the media will k*** famous people to generate news that people will care about.

The end of traditional news outlets will not limit people’s access to information. Thanks to the ubiquity of video cameras and the Internet, every citizen will be a reporter. If something happens in your neighborhood, you’ll tape it, stick it on the Internet with your own commentary and make it available to the world… The weather reports will be computer-generated and constantly available by computer, pager, voice-mail… All news gathering will be disaggregated.

Prediction 52: In the future, everyone will be a news reporter.

People will have access to software that constantly combs the internet for “small” news that is relevant to them.

…

your software will be able to do a sort of “credibility credit check” on any person who posts information to the Internet… This won’t be foolproof, but nothing is.

This new model depends on people being willing to take the time to put information on the Net without the benefits of payment. Why will people do that? They will do it because that’s our most basic human nature: People like to talk more than they like to listen.

Joy again: Not bad as predictions go. Notice the quaint terminology, such as “tape it” and pagers. (Pagers use radio networks instead of cell towers.) Attention is scarce, and writing is not (even pre-LLM). Adams predicted what I call poastmodernism.

Joy on Severance and Wild Problems

August 8, 2026August 6, 2026Joy BuchananLeave a comment

For EconLog, I wrote a reflective piece on Season 2 of Severance and how it relates to the book Wild Problems by Russ Roberts.

You Cannot Outsource Life: Severance and Wild Problems (EconLog link)

The essay is about pain and a meaningful life.

Rather than accepting that work, grief, and love may transform us, Lumon divides experience from identity. An adult who is primarily asking, “Who do I want to become?” likely would reject the Lumon…

Read more at the link above. Remember that the first words spoken in Season 1 were “Who are you?” Maybe Russ Roberts should do a whole podcast on this show.

During a rare slower week in the summer (thanks to my sister) I was able to binge Season 2. The genre could be described as Science Fiction. S2 does answer some of the questions raised in S1 but ends with a new cliffhanger to bring you back for Season 3. If you want to enjoy the show, the trick for me was not to take it too seriously. Ben Stiller is a producer and you can see traces of what feels like Zoolander humor to me.

I think the dialog is great. The sibling relationship and marital disputes and office inside jokes feel realistic.

As I said about Season 1, this show could be, among other things, a meditation on AI alignment. When you think enough about AI alignment, I guess you start seeing it in your TV shows. I wrote about that previously in: Artificial Intelligence in the Basement of Lumon Industries

Other previous posts on Severance, based on Season 1:

Lumon Industries and Drudgery (Joy)

Perks in Severance (Joy)

Severance and the Disutility of Work (Mike)