What Will the GDP Report Say Tomorrow?

Jeremy HorpedahlLeave a comment

Based on all of the good predictors that I track, GDP growth for the 3rd quarter of 2026 will likely be right around 3.2%:

Table 1

Kalshi, the Atlanta Fed’s GDPNow model, and the WSJ survey of economists are typically the best predictors, and they are all right around 3.2%. I’ve also added a new column to the table, a Nowcast from the St. Louis Fed. This one tends to be a pretty poor predictor, so you might wonder why I’m adding it to my list. Read on below to see why, but for now it’s interesting to note that it is very close to the three other good predictors (the NY Fed model is generally not very good).

When I have tracked these predictors in the past, I have been comparing them to the advanced GDP estimate, the first one the BEA puts out. Being able to predict this release is a good and hard enough task, but ultimately we don’t actually care about the number that will come out tomorrow. This number will be revised many times with more data, not only the 2 months after it, but through subsequent benchmarking and revisions in later years. Ultimately, we care whether these predictors are getting the true number, not BEA’s initial release (even though they are calibrated to predict that initial release, for the most part as I understand them).

What does it look like if we compare those predictions to the current best data on GDP growth going back to 2023? On this measure, the St. Louis Fed Nowcast looks pretty good recently:

Table 2

While the St. Louis Fed Nowcast looks pretty bad over the entire time period — the worst of these 5 predictors — it has done very well in the past 3 quarters, including nailing the 1st quarter of 2026 exactly (though in one quarter, they got the sign wrong). So I’m now tracking it in my table to see if it will continue to perform well — maybe they improved the model? The 3rd quarter data won’t actually tell us much about the St. Louis Fed model relative to the others, since they all are saying the same thing, but it will be worth watching in the future.

If we tally up the best predictors using the final or most recent GDP growth estimate, the best predictor is Kalshi’s betting markets. Kalshi has the lowest average error, which is just slightly better than the WSJ survey of economists. But Kalshi was also the best predictor in 6 of the 14 quarters in Table 2. The WSJ survey was only the best in 3 quarters — and in two of those it had the same prediction as Kalshi. It was only best as a standalone in one quarter (the oldest one in these 14 quarters). The St. Louis Fed was the best predictor in 5 quarters, all of these by itself, but its average error (as well as standard deviation and variance) is the highest of these 5 predictors.

Based on different combinations of the predictors, the best pair of predictors is Kalshi and WSJ. Averaging those together gets you an average error of 0.68, better than any single predictor. That means my prediction based on all this information is 3.15% growth in the third quarter for the final figure, once we eventually have it. The average of the Atlanta Fed and Kalshi is the best pair for the advanced figure, which gives a slightly different prediction of 3.24%.

Holmes Whistleblower Claims “You Can See Everything” Film Is Misleading

Scott BuchananLeave a comment

Elizabeth Holmes is a very clever woman. She was an honors student in chemical engineering at Stanford, which demonstrates high intellectual engagement. Eager to start her own company, she dropped out to form Theranos, which sold a device purported to be able to run a multitude of medical analyses using just a drop of blood. Numerous professors told her the device could not work (and in fact it did not work), but she pressed ahead anyway and charmed bigwigs and VCs into backing her firm.

When WSJ reporter John Carreyrou started an investigation in 2015, Holmes initiated a hardball campaign to stop Carreyrou from publishing, which included legal and financial threats against both the Journal and the whistleblowers. In 2022, a federal jury convicted her of investment fraud, and she was sentenced to 11 years in prison. Holmes is an accomplished liar, as documented by Forbes (“Watch Elizabeth Holmes Lie To Forbes About Theranos”). There is no ambiguity here.

Enter equally clever filmmaker Nathan Fielder and his colleague Lance Oppenheim. In the month before she shipped out to prison, Holmes and her partner Billy Evans invited Fielder and Oppenheim to their home for extended interviews. Holmes used this as an opportunity to cultivate a sympathetic image of a sincere, vulnerable mother of two young children, who is facing tragic separation from her family. This was a trademark clever Elizabeth Holmes move to dispel the (true) image of the ruthless fraudster, knowing that at some point she would be likely requesting a reduction in her sentence.

Fielder exploits inconsistencies in her statements, and Evans’ unpleasant abusive behavior, to weave a web of “ambiguity” around the Elizabeth Holmes story. Did she actually lie, or was she just confused? Does her mind simply work differently than most people? Oh dear, it is so hard to know…Fielder parlayed this into in a critically-acclaimed documentary, “You Can See Everything.”

Fellow blogger Mike Makowsky captures the strong impressions on audiences made by this movie: The singular dominant theme, for my money, was the grand telephone game that “truth” is distilled through as it skips from source to source, medium to medium. By the end of the film you may find that, like me, you have updated your priors a half dozen times, left with your hands up in the air as the crushing weight of subjective reality threatens to abandon you in a fog of nihilism.

But Mike also supplies a reality check: “Then you get home, rewatch the documentary (“The Inventor: Out for Blood”), scroll through a few bullet points of the actual case and remind yourself that no, this person is very much a crook.”

People who actually know the truth are dismayed. The WSJ investigative journalist John Carreyrou acknowledges that the film is “great” art, but Forbes reports that in a post on X:

Carreyrou pushed back against viewers who find Holmes to be “unknowable” or “ambiguous,” noting she went after himself and Theranos whistleblowers “with everything she had,” saying she ran a “ruthless cover-up of a breathtaking fraud, and she was at the controls.”

Tyler Schultz was a scientist in his early twenties, just starting his career, when he joined Theranos. Within a few months he discovered that the device the company was hawking did not do what it was claimed; this put patients’ health at risk. He first tried to address the problems through internal channels and government oversight, before finally going to the press. Theranos counterattacked viciously, threatening to blacklist him from ever working again in his field and costing him $400,000 in legal bills to defend himself.

Schultz has responded to the film in an accessible 6-minute interview on YouTube, Theranos whistleblower says he nearly jumped out of his seat watching Elizabeth Holmes documentary. He states that the woman portrayed on the screen is absolutely not the actual Elizabeth Holmes of Theranos. He states that Holmes knowingly lied (he gives example). A centerpiece of his criticism is that the medical device that Holmes shows on the film is NOT the device that Theranos sold back in the day. The original device was indisputably a complete fake. By trotting out some completely new device and claiming that it “could” (what does THAT mean??) do wonderful things, Holmes deftly muddies the waters. And Fielder is completely complicit in this muddying. Indeed, the muddiness is what makes the film. Schultz lays out his case in detail in an article in STAT. The Schultz article starts off:

“You Can See Everything” is a hilarious, thrilling, fascinating, beautiful piece of art. What it certainly is not is a documentary, and it’s dangerous to pretend that it is.

I’ve had the displeasure of watching disgraced Theranos founder Elizabeth Holmes at work not just over a three-hour film, but over the entirety of my adult life. I was an employee turned whistleblower at Theranos, my grandfather sat on her board, she came to my family holidays and birthday parties. I met her when I was 20, she went to prison when I was 32. I wish I could have told director Nathan Fielder what I told John Carreyrou when he started investigating her: Do not underestimate Elizabeth Holmes.

The rest of the article is paywalled, but AI will summarize it for you if you ask. See also a Forbes article on the subject.

It seems the only real ambiguity here is whether filmmaker Fielder was too dumb to know Holmes was gaming him, or too ambitious to pass up an opportunity to exploit his encounter with a set of intriguing, dishonest people. Claude summarized it for me this way: Holmes gets a softened image, Fielder gets an enigma, and the audience gets a fog that the court record and the whistleblowers had already cleared.

Some film critics have run out of superlatives in gushing over this film (e.g., “An Astonishing Achievement That Changes How We Experience Cinema“ ). So, let’s give credit to Rick Romancito of the Taos News for calling it straight:

“You Can See Everything” has been praised for reimagining the documentary genre, but to me it merely muddies the waters between fiction and truth.

“You Can See Everything” and the Confidence Trap

mdmakowsky2 Comments

“You Can See Everything” is one of the great film-making experiences of my lifetime. That’s not even a particilularly hot take these days. If you need a recommendation, yes, four stars, you should see it. The singular dominant theme, for my money, was the grand telephone game that “truth” is distilled through as it skips from source to source, medium to medium. By the end of the film you may find that, like me, you have updated your priors a half dozen times, left with your hands up in the air as the crushing weight of subjective reality threatens to abandon you in a fog of nihilism. Then you get home, rewatch the documentary (“The Inventor: Out for Blood”), scroll through a few bullet points of the actual case and remind yourself that no, this person is very much a crook.

But that’s not what I want to talk about. What I want to talk about is confidence.

We are told, repeatedly and with hardly any caveat, that confidence is a good thing. An admirable thing. A sexy thing. But confidence can very much be a trap, particularly if confidence becomes your comparative advantage.

The parable of the young genius has become rooted in the tale of frustrated and constrained greatness. They are precocious and unruly, possibly with a history of mild behavioral issues or sullen solitude. They follow the “normal” track for a few years, often to a prestigious college, where they catch a glimpse of great possibility and simply can’t be held back any longer. They drop out and become an entrepreneur. They pursue investment and promise unblinking greatness and unfathomable innovation with the kind of a committed sureness that only youth can provide.

They are not tradditional frauds, at least not yet, because they believe it everything they say. And why wouldn’t they? They’ve never been wrong. The first time they stepped into an environment where they might be challenged they left before any incontrovertible truths might be controverted. Let’s stop the story here because it rarely actually starts in such a way and, if it does, it usually ends quickly. Which is for the best. It’s not crazy to suggest that the best thing that can happen at this stage is failure. Whether it’s an expert in your field explaining the fallacy underlying your grand promises or venture capitalists simply sniffing out a bad investment, the train stops and you can return to building yourself. You had a good life experience, a new perspective on your own education and skills, and the a greater appreciation for what it means to have something to contribute. No harm, no foul.

But that’s not what happens to a lot of people. Not just undergraduate drop outs either. It’s what can happy to anyone that a market anoints as special. That hype can fuel you until you’ve been running on it for so long you realize, perhaps too late, that you’re no longer selling a product or even yourself. Your selling the story of you. You change your title on LinkedIn to “thought leader”, “visionary”, or “futurist”. You think you are selling yourself, but you’re not because there’s nothing actually there to sell.

And this where the trap is. What you are selling is your confidence in the words coming out of your mouth. The problem is that the words themselves don’t have any value. You don’t know how things work. You don’t know, actually know, biochemistry. Or stochastic modeling. Or tokenization and neural network modeling. You know of them and how to reference them in a manner that obscures your own lack of understanding. The only thing you know, to your creeping dread, is your continued existence in this market is dependent on continuing to dazzle others with the kind of confidence they can only dream of posessing. You’re a carnival barker without a carnival. And eventually someone’s going to walk into an empty tent and they’re going to be pissed.

And that is terrifying.

When you watch “You Can See Everything”, pay attention to how hard they are working to maintain the image that they know what they are talking about. The 3X speed videos and obsession with rapid vocabularly adoption. They know of chemical reagents. And mechanism design. And blood. And lasers. And whatever it is they desperately need to convince people they know about. And notice to whom they scrupulously avoid ever interacting with: anyone with actual scientific or technical expertise. They are always cramming for a exam they didn’t study for. I’d call them dilettantes, but dilettante implies non-trivial, if thin, knowledge about a lot of things. Genuine knowledge. They aren’t dilletantes, they’re frauds.

Imagine Elizabeth Holmes was never sent to prison. What would she be doing right now? It’s pretty obvious to me that she’d be doing exactly the same thing. Selling grand visions wrapped unwavering confidence because that is the only thing she knows how to do. The film trope of the villain confessing to the hero (and the audience) “I’ve come too far” comes to mind.

I’m not saying being confident is going to turn you into a fraud. What I’m saying is that you need to have the skills, knowledge, and other forms of human capital first so that you can offer those skills to the world confidently. Confidence is a useful social lubricant in a market full of imperfect information. It’s useful as a complement, often necessary but never sufficient. To sell naked, untethered confidence itself is to be a confidence person.

You know. A con man.

Aspen Trees in the Fall

Joy BuchananLeave a comment

Even though I have been to the American West and even lived in California for 5 years, my Fall Break 2026 trip to New Mexico was my first encounter with Aspen trees in the Fall. I am the last person to discover this but happy to be on the train. The piercing blue sky of the High Desert makes a stunning frame for the gold trees. Double the beauty by going into local art galleries in Santa Fe to see paintings of the same. I found my phone camera roll to be disappointing compared to the real thing. ChatGPT helped me produce this map for you.

Here’s a .gov https://www.fs.usda.gov/wildflowers/beauty/aspen/fallcolors.shtml

Some people have a similar encounter with New England in the fall, but I already knew about that.

Fall in the Southeast means getting a break from the heat and being able to enjoy sunshine again outside of water sports. I don’t mean to undersell it. There are many really nice days in the Southeast, it’s just hard to plan ahead of time when they will occur.

Disability: Income, Unemployment, & Education

Zachary BartschLeave a comment

Let’s talk about disability. The American Community Survey (ACS) identifies seven broad categories, not including the baseline case of ‘No Disability’. I’ve written before about how disability designations are difficult because many qualifying conditions are a matter of degree. Surely you’ve met a person who is legally blind but can see *some*. How much sight ability can remain while still being considered blind? If a person can identify the sun’s location, but nothing else, then is that person still blind? Similarly, who’s to say whether some physical or cognitive characteristic is disabling? Why not enabling – depending on the task?

Wisely, the ACS side-steps this entire discussion and simply asks respondents whether they have “difficulty”.  We can be agnostic about the cause and degree while still trying to get at the heart of the matter. One downside is that people tend to have more difficulty as they age. Since that’s not relevant for earlier life economic outcomes, I only examine ages 24-45. Difficulty with “self care” is too small of a category in the 2024 ACS for the analysis here. Below are the other six difficulties identified:

  • Hearing Difficulty (n=6,609)
  • Seeing Difficulty (n=8,307)
  • Cognitive Difficulty (n=26,792)
  • Ambulatory Difficulty (physically getting around) (n=4,568)
  • Independent Living Difficulty (n=5,997)
  • No Disability (n=885,302)

We should say early on that many of the people who participate in “Deaf” (capitalized) culture do not consider deafness to be a disability and that any such suggestion is motivated by bias and hearing culture. I won’t engage with that here. But I can say that there is a long tradition going back through the 1800s that rejected alms-giving for deafness and instead supported independent living for deaf people. Historically, this manifested as outlawing panhandling while featuring deafness and rejecting government programs that provide simple transfers due merely to deafness. More recently, the push to reject the ‘disability’ label and its baggage has even been the motivation for some deaf people to oppose cochlear implants being covered by medical insurance. For ease, I’ll be using the colloquial terms for all of the above difficulties. Now that you have the greater context, let’s jump into the data (unweighted).

The baseline income chart is above. Given the above description, it should come as no surprise that deaf people have the highest average income among disability groups, conditional on employment. However, it’s also surprising that most of the above disabilities have incomes that I wouldn’t exactly call destitute. Many people who have a disability look much like everyone else. From an economic point of view, we all have a comparative advantage and it’s perfectly consistent with gainful employment and flourishing otherwise.

Continue reading →

A Simple Way States Can Reduce Healthcare Spending

James BaileyLeave a comment

Repeal Certificate of Need requirements, assuming you are one of the ~35 states that have them.

That’s what I find in a paper with Shishir Shakya just published in Health Economics.

Certificate of Need laws require healthcare providers to obtain government approval before building new facilities or expanding services. The original intent of the laws was that less building would mean less spending, but critics worry that it means less competition and higher prices leading to higher spending. Our paper finds that the critics are right, as states repealing their Certificate of Need laws see their healthcare spending fall relative to those that maintain them.

Longtime readers may recall that back in 2022 I posted about another paper of mine with a similar result, and even that wasn’t the first word on the subject. So what’s new in this paper? Besides newer data, we measure health spending as a percentage of the state economy rather than in raw dollars, to try to account for how CON changes the true burden of healthcare spending. We also use newer regression techniques that are probably more accurate but definitely help make prettier graphs.

Here’s what we found happening to health spending when states repeal Certificate of Need:

Full published paper is here, ungated working paper version here.

Has Mexico’s “Canasta Básica” Policy Made Food More Affordable?

Jeremy HorpedahlLeave a comment

A recent article in The Guardian profiles economist Isabella Weber. She doesn’t seem to like other economists very much, supposedly because we “obsess over ideological purity and are inflexible when searching for solutions.” Weber wants economists to come up with solutions that work even if… I guess, even if they don’t work well in theory? Maybe they will work well in practice?

Anyway, so far as I can tell, the article only gets into details on two concrete solutions to affordability. I’m sure she has more in her book, but let’s see what these are. The first for governments to create “publicly owned entities that can buy and sell grain into its market,” presumably to smooth out fluctuations in grain prices. The second is “the Mexican government’s canasta básica policy, through which retailers voluntarily cap the total price of a basket of two-dozen essential goods.”

Let’s look at the second one, since I think for developed countries like the US, fluctuating grain prices aren’t the primary source of affordability concerns. But perhaps American consumers would like a basket of grocery essentials to be cheaper.

Has this policy in Mexico worked? It was first implemented in 2022, and food inflation has certainly fallen in Mexico since then. But it has also fallen everywhere else in the world — 2022 was peak inflation not just in the US, but in most countries — so we can’t judge the success of the program based on that alone.

Instead, we can look at internationally comparable data on food spending, which comes from the USDA’s Economic Research Service. Our World in Data has a nice chart based on this data, and even though it is now two years out of date (I will provide 2025 data below), the 2023 data makes a case against the success of the “price controls” in Mexico (I put “price controls” in quotes because it is not a true price control, but an attempt to control prices via incentives):

Figure 1

It will probably not surprise you that the US spends much less on food as a percent of total spending than Mexico, given that we are a high-income country. But it is still worth pointing out just how low food spending is in the US. However, this chart still paints Mexico in a very poor light: for countries with about the same income level in the Americas (as measured by consumer expenditures), Mexicans spend much more on food than Argentina, Chile, or Panama.

If we use the 2025 USDA data, and focus on the 10 countries with consumption expenditure closest to Mexico (between about $8,000 and $12,000 of consumption), we see that Mexico is very much on the high end: only Argentina was clearly higher:

Table 1

Finally, we can compare Mexico against itself in this data, looking at food spending going back to 2017:

Figure 2

Food spending actually peaked in 2020, though be cautious of any data from 2020 as the pandemic made data collection difficult. After declining in 2021 and then into 2022 (the policy seems to have been put in place in mid-2022), there haven’t been any further declines. And food spending is still well above pre-pandemic levels (unlike the US, where the 2025 figure of 6.4% is the same as 2017-18).

Ultimately, we would want a more sophisticated analysis than my simple tables and charts here. But it doesn’t appear that Mexico’s 2022 policy really made food much more affordable, either relative to peer countries with similar income levels, or relative to Mexico’s own recent history. And that’s even before we get into any unintended consequences of the “price controls.” Of course, the policy may have made that basic basket of two-dozen items more affordable than it otherwise might be — for that we would need to compare individual item prices to peer countries — but it hasn’t obviously made food overall more affordable in Mexico.

WWII Strategic Initiatives 7. The Man Who Built the T-34 Tank That Helped Save Russia.

Scott BuchananLeave a comment

This is the seventh in a series of occasional blog posts on individual initiatives that made a strategic (not just tactical) difference in the course of the second world war.

History’s most important machines often have a stubborn, slightly obsessive person behind them. For the Soviet T-34 tank, that was Mikhail Koshkin. Koshkin ran the design bureau at the Kharkov tank and locomotive plant. What made him special was that he paid attention to how tanks died. The fighting against the Japanese at Khalkhin Gol in 1939, along with the lessons of Spanish civil war, showed that Soviet tanks were too thinly armored and caught fire too easily. Koshkin pushed for a diesel engine, thick sloped armor, wide tracks that could handle mud and snow, and a big gun. He didn’t just copy what already existed. He took the lessons of real combat and built a tank around them.

He made it very simple.  For instance, in most tanks, there is a whole interior compartment, holding the tank crewmen, which rotates along with the turret. But with the T-34, only the upper visible turret (with its gun) rotated. That minimalist turret simplified construction, even though it meant the gun loader had to scramble around on the fixed floor to keep up with turret rotation. The T-34 was easy to build in staggering numbers, by workers who were often barely trained, and it cost far less than the elaborate German machines. Stalin supposedly said that quantity has a quality all its own. The Soviets eventually built over 50,000 T-34s. Here are links to an original  (in Russian) T-34 gunnery manual (1942), and a complete maintenance manual (1944) with cutaway views of its construction.

Soviet T-34 Tank (Looks like museum model, not combat-ready)

From 1939 through late 1942, the Germans looked unstoppable. Poland, France, and the Balkans all fell quickly. However, in the opening weeks of their 1941 invasion of the U.S.S.R, Barbarossa, the T-34 proved a nasty surprise for the German Army. German antitank shots bounced off its sloped front armor and its gun could take out most German vehicles.

The Germans were impressed. Field Marshal Ewald von Kleist later called it “the finest tank in the world.” Armor strategist General Guderian said the news was “very worrying” and that German tank superiority had ended for the moment. So, early in the war, the relatively few but potent T-34’s helped to slow down the Nazi onslaught enough to give the Soviets time to recover from their staggering losses. Later in the war, the sheer numbers of T-34’s gave the Soviets resilience and offensive punch. By 1944-45 the Germans were fielding gigantic Pather and Tiger tanks which could easily take out two or three T-34’s, but not a swarm of ten or twenty up-gunned T-34’s which could surround a Tiger and shoot it from the side.

Sadly, Koshkin never saw the vindication of his design. In early 1940, he insisted on proving his prototypes by driving them himself 500 miles from Kharkov (in today’s Ukraine) to Moscow, through cold and mud, to show the brass what they could do. The tanks made it. Koshkin didn’t. He caught a bad cold that turned into pneumonia and died in September 1940, at just 42, nine months before the Germans invaded. The man who gave the Red Army its best weapon spent himself on the proof that it worked.

The modal number of citations is zero (in political science)

mdmakowskyLeave a comment

I was told when I was a graduate student that the modal published research paper was cited zero times. They then went on to say that the median was one or two, while the mean depended on how good the journal was. Well, someone went looking in political science:

“The modal number of citations to a political science article is zero is true, but most political science articles have more than zero citations is also true.” — @tompepinsky.com

— Omar Wasow (@owasow.bsky.social) 2026-10-19T00:07:13.264Z

The article provides more basic statistics:

So, at least in political science the median appears to be 26 and the mean 71. That’s a lot higher than I expected, but then poltiical science articles tend to cite far more papers.

My only real thought is this: what question is each number answering? The mode really just let’s you know that the distribution is truncated at zero. It’s a mechanical artifact more than anything. The median, or any of the quantiles, on the other hand, seems only informative if you have a benchmark in your head of at what point an article seems to to be moving the literature. Is 26 substantial, or is it just inframarginal noise for the 90% of articles that don’t really matter? It seems to me all these numbers only matter as fractions of the maximum. If 8123 is the ceiling, how does that change how we interpret a paper with 7 citations?

Most of this seems more like academic appointment accounting, which is almost by definition uninteresting, but it’s a great reminder that statistics have no meaning absent a context that goes beyond other statistics. That said, with just a minimal context, it’s amazing how far knowing nothing but the mean, median, and mode can take you.

Paper Published on Persistent LLM Hallucinations

Joy Buchanan1 Comment

I am pleased to announce the publication of

“LLM Hallucination of Citations in Economics Persists With Web-Enabled Models”

By Joy Buchanan, Stephen Hill, and Olga Shapoval

ABSTRACT: Despite significant advances since 2023 in large language model (LLM) capabilities and the introduction of web-enabled search functionality, citation hallucination remains a persistent problem in AI-generated academic content. We test GPT-4o, DeepSeek V3, web-enabled GPT-4o, and web-enabled GPT-5.1 across prompts derived from Journal of Economic Literature categories. Using a hierarchical citation validation framework, we find that even an advanced web-enabled model produces non-existent citations. While web-enabled models show improvement over their predecessors, the error rates remain high for scholarly applications. Our findings suggest that the fundamental challenge of citation hallucination persists despite technological advances, with implications for academic integrity and the reliable use of AI in research contexts. We provide an online tool for researchers to run LLMs through the benchmark test using our prompts.

Suggested citation:

Buchanan, J., Hill, S. and Shapoval, O. (2026), LLM Hallucination of Citations in Economics Persists With Web-Enabled Models. Southern Economic J. https://doi.org/10.1002/soej.70069

After some time went by since we published our 2024 paper on this topic and GPT became web-enabled, we thought that this silly problem with hallucinating up fake citations might have gone away. So, we went back to do our count again. We found that a significant number of references are still made up.

I believe that this particular problem of fake citations will go away soon. However, I think the starkness of fake references illustrates something more general about the LLM technology and highlights the importance of verification. Feel free to cite our 2026 paper as evidence that hallucinations are still happening, despite improvements along many margins. Since we started down this track, OpenAI released a paper titled “Why language models hallucinate” at https://openai.com/index/why-language-models-hallucinate/ Excerpt:

Hallucinations persist partly because current evaluation methods set the wrong incentives. While evaluations themselves do not directly cause hallucinations, most evaluations measure model performance in a way that encourages guessing rather than honesty about uncertainty.

You can access our working paper of “LLM Hallucination of Citations in Economics Persists with Web-Enabled Models” on SSRN at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5318314

We are not in the slow-lane “LLMs are stochastic parrots” group. Stephen Hill does even more with AI in his personal workflow than I do. AI will probably soon qualify for all of the academic prizes we used to award to human writers and scientists. However, as we hand more responsibility over to AI, keep the issue of hallucinations in mind.