Use the above game to generate interaction in a class setting. Students collectively form an LLM and have fun seeing the final sentence that gets produced. I call this game “LLM Telephone” based on the classic game of telephone. I suggest downloading the file LLM_Telephone_Game_Sheet and handing out printed copies. However, this game could be adapted to a virtual setting.
The nice thing about passing papers in the classroom is that you can have several sheets circulating in a quite room, so when the final sentence is read allowed it comes as a surprise to most people.
If you’d like to have a handout to follow the game with a more technical explanation, you can use this two-page PDF:
The game relies on a player presenting two tokens of which the next player can select their favorite. Participants should be bound by the rules of grammar and logic when making their selection and presenting two tokens to the next player.
This game works as a fun ice breaker for any type of class that touches on the topic of artificial intelligence. It is suitable for many ages and academic disciplines.
There was a seismic shift in the AI world recently. In case you didn’t know, a Claude Code update was released just before the Christmas break. It could code awesomely and had a bigger context window, which is sort of like memory and attention span. Scott Cunningham wrote a series of posts demonstrating the power of Claude Code in ways that made economists take notice. Then, ChatGPT Codex was updated and released in January as if to say ‘we are still on the frontier’. The battle between Claude Code and Codex is active as we speak.
The differentiation is becoming clearer, depending on who you talk to. Claude Code feels architectural. It designs a project or system and thrives when you hand it the blueprint and say “Design this properly.” It’s your amazingly productive partner. Codex feels like it’s for the specialist. You tell it exactly what you want. No fluff. No ornamental abstraction unless you request it.
Codex flourishes with prompts like “Refactor this function to eliminate recursion”, or “Take this response data and apply the Bayesian Dawid-Skene method”. It does exactly that. It assumes competence on your part and does not attempt to decorate the output. It assumes that you know what you’re doing. It’s like your RA that can do amazing things if you tell it what task you want completed. Having said all of this, I’ve heard the inverse evaluations too. It probably matters a lot what the programmer brings to the table.
Both Claude Code and Codex are remarkably adept at catching code and syntax errors. That is not mysterious. Code is valid or invalid. The AI writes something, and the environment immediately reveals whether it conforms to the rules. Truth is embedded in the logical structure. When a single error appears, correction is often trivial.
When multiple errors appear, the problem becomes combinatorial. Fix A? Fix B? Change the type? Modify the loop? There are potentially infinite branching possibilities. Even then, the space is constrained. The code must run, or time out. That constraint disciplines the search. The reason these models code so well is that the code itself is the truth. So long as the logic isn’t violated, the axioms lead to the result. The AI anchors on the code to be internally consistent. The model can triangulate because the target is stable and verifiable.
I’m trying to coin “Commodity Sports” as the term to refer to sports betting that takes place on exchanges regulated by the US Commodity Futures Trading Commission, as opposed to sports betting that takes place through casinos regulated by state gaming commissions. So far it seems to be working alright, I haven’t convinced Gemini but have got the top spot in traditional Google search:
That article- Will Commodity Sports Last?– is my first at EconLog. I’m happy to get a piece onto one of the oldest economics blogs, one where I was reading Arnold Kling’s takes on the Great Recession in real time, where I was introduced to Bryan Caplan’s writing before I read his books, and where Scott Sumner wrote for many years (though I started reading him at The Money Illusion before that).
The key idea of the piece, other than the legal oddity of sports betting sharing a legal category with corn futures, is that the Commodity Sports category is being pioneered by prediction markets like Kalshi. As readers here will know, I like prediction markets:
I love that CFTC-regulated exchanges like Kalshi and Polymarket are bringing prediction markets to the mainstream. The true value of prediction markets is to aggregate information dispersed across the world into a single number that represents the most accurate forecast of the future.
But I’m not so excited to see them expanding into sports:
Although I see huge value in prediction markets when they are offering more accurate forecasts on important issues that help policymakers, businesses, and individuals make more informed plans for our future (e.g., Which world leaders will leave office this year?, or Which countries will have a recession?)… I see much less value in having a more accurate forecast of how many receptions Jaxon Smith-Njigba will have.
Like Robin Hanson, I worry that the legal battles against Commodity Sports and the brewing cultural backlash against sports betting risk taking the most informative prediction markets down along with it.
A big narrative for the past fifteen years has been that “software is eating the world.” This described a transformative shift where digital software companies disrupted traditional industries, such as retail, transportation, entertainment and finance, by leveraging cloud computing, mobile technology, and scalable platforms. This prophecy has largely come true, with companies like Amazon, Netflix, Uber, and Airbnb redefining entire sectors. Who takes a taxi anymore?
However, the narrative is now evolving. As generative AI advances, a new phase is emerging: “AI is eating software.” Analysts predict that AI will replace traditional software applications by enabling natural language interfaces and autonomous agents that perform complex tasks without needing specialized tools. This shift threatens the $200 billion SaaS (Software-as-a-Service) industry, as AI reduces the need for dedicated software platforms and automates workflows previously reliant on human input.
A recent jolt here has been the January 30 release by Anthropic of plug-in modules for Claude, which allow a relatively untrained user to enter plain English commands (“vibe coding”) that direct Claude to perform role-specific tasks like contract review, financial modeling, CRM integration, and campaign drafting. (CRM integration is the process of connecting a Customer Relationship Management system with other business applications, such as marketing automation, ERP, e-commerce, accounting, and customer service platforms.)
That means Claude is doing some serious heavy lifting here. Currently, companies pay big bucks yearly to “enterprise software” firms like SAP and ServiceNow (NOW) and Salesforce to come in and integrate all their corporate data storage and flows. This must-have service is viewed as really hard to do, requiring highly trained specialists and proprietary software tools. Hence, high profit margins for these enterprise software firms.
Until recently, these firms been darlings of the stock market. For instance, as of June, 2025, NOW was up nearly 2000% over the past ten years. Imagine putting $20,000 into NOW in 2015, and seeing it mushroom to nearly $400,000. (AI tells me that $400,000 would currently buy you a “used yacht in the 40 to 50-foot range.”)
With the threat of AI, and probably with some general profit-taking in the overheated tech sector, the share price of these firms has plummeted. Here is a six-month chart for NOW:
Source: Seeking Alpha
NOW is down around 40% in the past six months. Most analysts seem positive, however, that this is a market overreaction. A key value-add of an enterprise software firm is the custody of the data itself, in various secure and tailored databases, and that seems to be something that an external AI program cannot replace, at least for now. The capability to pull data out and crunch it (which AI is offering) it is kind of icing on the cake.
Firms like NOW are adjusting to the new narrative, by offering pay-per-usage, as an alternative to pay-per-user (“seats”). But this does not seem to be hurting their revenues. These firms claim that they can harness the power of AI (either generic AI or their own software) to do pretty much everything that AI claims for itself. Earnings of these firms do not seem to be slowing down.
With the recent stock price crash, the P/E for NOW is around 24, with a projected earnings growth rate of around 25% per year. Compared to, say, Walmart with a P/E of 45 and a projected growth rate of around 10%, NOW looks pretty cheap to me at the moment.
(Disclosure: I just bought some NOW. Time will tell if that was wise.)
Usual disclaimer: Nothing here should be considered advice to buy or sell any security.
Yesterday’s super bowl was fun for a variety of reasons, but your 147th favorite economist was especially happy to see that markets continue to keep things interesting. The NFL was a “only teams with elite quarterbacks can win” league…until it wasn’t. After Brady, Manning, Brees, and Maholmes winning two decades of Super Bowls, we have back to back years of decidedly average quarterbacks winning (within-NFL average, to be clear. These are all objectively incredible athletes). How did this happen? Is it tactical evolution, flattening talent pools, institutional constraints, or markets updating? The answer is, of course, all of the above, but updating markets is the mechanistic straw that stirs the drink.
The NFL is a salary capped, which means each team can only spend so much money on total player salaries. As teams placed greater and greater value on quarterbacks, a larger share of their of their salary pool was dedicated accordingly. These markets are effectively auctions, which means eventually the winner’s curse kicks in, with the winner of the player auction being whoever overvalues the player the most. Iterate for enough seasons, and you eventually arrive at a point where the very best quarterbacks are cursed with their own contracts, condemned to work with ever decreasing quality teammates. Combine that with a little market and tactical awareness, and smart teams will start building their teams and tactics around the players and positions that market undervalues. And that (combined with rookie salary constraints), is how you arrive at a Super Bowl with the 18th and 28th salary ranked quarterbacks.
Whenever a market identifies an undervalued asset (i.e. quarterbacks 25 years ago) there will, overtime, be an update. Within that market updating, however, is a collective learning-as-imitation that eventually results in some amount of overshooting via the winners curse. This overshoot, of course, may only last seconds, as market pressure pushes towards equilibrium. In markets like long term sports contracts or 12 year aged whiskey, that overshoot can be considerable, as mistakes are calcified by contracts and high fixed cost capital.
What does this predict? In a market like NFL labor, I’d expect a cycle over time in the distribution of salaries, iterating between skewed top-heavy “star” rosters and depth-oriented evenly distributed rosters. At some point a high value position or subset of stars are identified and distproportionately committed to, but the success of those rosters eventually leads to over-committment, so much so that the advantage tilts towards teams that spread their resources wider across a larger number of players undervalued teams whose fixed pie of resources are overcommitted to a small number of players. That’s how you get the 2025 Eagles and 2026 Seahawks as super bowl champions.
I wonder when it will cycle back and what the currently undervalued position will be?
The title of this post, “everyone take copies,” comes from a conversation between the human subjects in an experiment in our lab, on which the paper is based. The experiment was studying how and when people take resources from one another.
Here’s a tip that doesn’t require any piracy. For those of you who are tired of the subscription economy fees, I think it’s safe to say in 2026 that anyone in the United States can find a local thrift store or annual rummage sale with oodles of nearly-free media. DVDs for a dollar. Used books for a dollar. Basically you are paying the transaction costs – the media itself is free. (I typed that dash myself, not AI!)
“Buying” a movie to stream on Amazon Prime can run over $20. Buying a used DVD is usually less than $10.
Once upon a time, eugenics was all the rage. It was nascent during the reconstruction era and persisted into the 20th century. It grew out of biological evolutionary theory and emphasized reproductive fitness. In brief, the theory asserted that there are differences in individual fitness and that the more fit living things will survive better and reproduce, eventually becoming a greater part of the population. The ability to compile and evaluate statistics about various human measurements made inferences hard to resist. Of course, researchers were plagued by small sample size, omitted variable bias, and social biases of the day (for example, phrenology inferred fitness characteristics from skull shape).
People employing eugenic thinking, overwhelmingly, supported theories that their own type of person was among the more fit. Eugenicists didn’t promote theories of their own un-fitness. In the progressive era of the early 20th century, eugenics met the prevailing attitude that government could be employed to resolve social and economic ills. This era is when the income tax emerged, prohibition was enacted, the Federal Reserve was formed, and various labor regulations were enacted.
The result was that policy sometimes pursued greater ‘fitness’ among its populations. Rather than systematically encouraging the supposedly more fit with economic incentives, most policy was geared toward reducing the reproductive success of supposedly less fit people. These included forced sterilization, institutionalization, and economic exclusion. Besides rejecting basics individual human dignity, the harm was all the more tragic given that fitness was often poorly specified. That is, policy criteria weren’t dependably related to fitness. Fatal conceit, indeed!
One of my favorite ways to argue is to grant premises and then change details on the margin to see whether the conclusion changes. Let’s do that. Let’s grant that there are innate differences between people that are related to biological success. Since survivability is related to resource acquisition, let’s grant also that economic success overlaps at least somewhat. Taking that as granted, does pursuit of the historical eugenic policy still follow?
It does not.
There are two mistakes that eugenicists and various sorts of racists and xenophobes made. They assert or imply 1) that fitness characteristics are stable and systematically identifiable, and 2) that policy needed to intentionally select for the fitness characteristics.
May you live in interesting times – apocryphal Chinese curse
In early 2025 I shared forecasts about the economy that turned out to be pretty good. This year, economic forecasts center around a boringly decent year (2.6% GDP growth, inflation below 3%, unemployment stays below 5%, no recession), though with high variance. But forecasts about politics and war foretell a turbulent year.
In the US, midterm elections have a 78% chance to flip control of the House and 35% chance to flip the Senate despite a tough map for Democrats. A midterm wave for the out-of-power party is typical in the US, given that the party in power always seems to over-play their hand and voters quickly get sick them. More surprising is that forecasters give a 44% chance that Donald Trump leaves office before his term is up, and a 16% chance that he leaves office this year. Markets give a 20% chance that he will be removed from office through the impeachment process, so the rest of the 44% would be from health issues or voluntary resignation.
Forecasters at Kalshi predict a greater than even chance that 4 notable world leaders leave office this year:
I find this especially notable because Viktor Orban is the only one who would be removed through regularly scheduled elections. In the UK, Keir Starmer was just elected Prime Minister in 2024 and doesn’t have to face reelection until 2029; but he is so unpopular that his own Labor Party is likely to kick him out of office if local elections in May go as badly as polls indicate. If so, he would join Boris Johnson and Liz Truss as the third British PM in four years to leave office without directly losing an election. The leaders of Cuba and Iran don’t face real elections and would presumably be pushed out by a popular uprising orUS military action.
Some other important world leaders will probably stay in office this year, but forecasters still think there is a significant chance they leave: Israel’s Netanyahu (49%), Ukraine’s Zelenskyy (32%), and Russia’s Putin (14%). For the latter two, this belief could be tied to the surprisingly high odds given to a ceasefire in the Russia-Ukraine war this year (45%). Orban leaving office could be tied into this, as Hungary has often vetoed EU support for Ukraine.
Myself, I find most of these market odds to be high, and I’m tempted to make the “nothing ever happens” trade and bet that everyone stays in office. But even if all these markets are 10pp high, it still implies quite an eventful year ahead. Prepare accordingly.
So what is the truth? I have put together what I think are the best economic indicators to judge how the economy is doing. And what does it tell us? I think the fairest read is that 2025 was a pretty good year, but based on most economic data it was almost identical to 2024.
The only indicator that is clearly better is private-sector job growth in 2024. We might add S&P 500 in 2024 growth too, although some other assets such as gold have performed better in 2025. Inflation in 2025 is a tad lower, but not the massive improvement Trump suggests. This is especially the case for one of his favorite prices, gasoline. Yes, 2025 is a little lower than 2024… just like 2024 was a little lower than 2023.
And what of that greatest of all macroeconomic indicators, GDP? We don’t yet have Q4 data for GDP, which means we don’t have full-year 2025 data yet. But the growth rate of real GDP in 2024 was 2.8%, and betting markets are currently predicting 2.3% for 2025. Betting markets could be wrong! But it seems unlikely it would be much above 2.8% (those same betting markets only think there is a 4% chance it will be over 3.0%).
None of this is to say that the 2024 and 2025 economies are exactly the same. Certainly there is more uncertainty due to the shifting tariff policy, but on the other hand even with that uncertainty the economy is still performing fairly well. And my table above only includes economic outcomes, not any changes to government budgets, nor important social indicators such as crime. These are important too, but my focus in this post is only on the economic data.
It seems that in those surveys about whether the economy is better now or under Biden, it would be useful to offer an “about the same” option. Of course, in 2021-2022 inflation was much worse under Biden — but job growth was much better. A lot of this was baked in from the pandemic, 2020 monetary and fiscal stimulus, etc. Once we were back to a semi-normal economy in 2024, it was a decent year. Not blockbuster, but decent. So was 2025.
Economist Writing Every Day 