Technology

I Found My Lost Wedding Ring with Cheap Metal Detectors (Bounty Hunter Tracker IV, SUNPOW OTMD12)

Scott BuchananLeave a comment

About two weeks ago, after my wife and I boarded our plane and settled into our seats, she (seated to my left) noticed that my wedding ring was missing. “Huh,” I said, “That’s weird. I never take it off.” Where could it be?  I remembered that just before we left, I had vigorously shaken the water out of a washed, paintbrush, using a whiplike motion of my arm. And that could have worked my ring off my finger, without me noticing. Maybe. If that’s what happened, my ring was lost somewhere in a 10 ft x 20 ft strip of dense grass. Some kind family members spent time on their hands and knees looking there on my behalf, without results.
A replacement genuine 14 karat gold ring might run close to $1000. Cheaper imitation gold rings are available, of course. But, being a techie, I wondered if I could find my ring using a cheap metal detector.

There is a fraternity of serious coin/jewelry hunters, who you see swinging detectors along at the beach. They also enjoy finding artifacts at historical sites, etc. They use more expensive metal detector gear, that really does perform better, especially at distinguishing signals from gold/silver/copper, versus iron/steel trash items. This is essential if you are trying to search large areas efficiently for gold/silver/copper-based coins and jewelry. But since I wasn’t sure my ring was actually lost in that grass, I wanted to minimize my investment. AI helpfully zeroed in on the Bounty Hunter Tracker IV as the cheapest regular metal detector that would perform reasonably well. I got it on Amazon for $71.



I refer the reader to more extended reviews, such as are here and here and here, for technical details on this device. I will just note here that it did exactly what I needed to do. In its “tone” mode, it is relatively insensitive to small steel objects, and puts out a distinctive low tone for gold (vs. a high beep for copper/silver). As with all such metal detectors, you have to swing it back-and-forth to generate a signal. I swept my 10 x 20‘ area, and got about a dozen hits (beeps). I marked these locations with tabs of blue painter’s tape, some of which are visible in this photo (click to enlarge):


This type of metal detector, with a flat coil that you swing over the ground, is good for detecting the approximate location of metal at or near the surface. But AI recommended also getting a “pinpointer” type detector for zeroing in to the nearest centimeter/ inch. Supposedly the cheapest such pinpointer that gives reasonable performance is the SUNPOW OTMD12, available for $20 on Amazon. It doesn’t discriminate among different metals, it just beeps and vibrates when the tip gets close to metal. See comments on Amazon, and reviews here and here:



I poked around with the pinpointer on the first ten or eleven locations found by the bounty hunter. There was no metal on the surface, but it still beeped on about half of the locations, suggesting there were fairly large pieces of metal buried at some depths.

Then, at the far edge of my 10’ x 20‘ search area, I heard a special low-pitched tone from the Bounty Hunter, indicative of gold or nickel. I poked around with the pinpointer, zeroed in with its beeps, and pulled aside the grass, and…there was my ring, bought by my sweetheart nearly fifty years ago:

A very welcome sight indeed, which was invisible until the grass over it was pulled aside. Besides my ring, I now have a couple safe, intriguing devices for my grandkids. A way to keep kids occupied is to throw a handful of pennies or other coins out on the lawn and turn the kids loose with the metal detectors.

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

Scott Buchanan2 Comments

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.

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.

Making Swiss Cheese Is Not So Easy; Role of Copper Kettles

Scott BuchananLeave a comment

Heidi is a charming story of a Swiss girl who is raised by her gruff but good-hearted grandfather in a hut high in the Alps. Microeconomics: he uses milk from his two goats to make cheese, which he sells down in town for necessities like nails and sausage and bread. The book mentioned him stirring the milk in a large kettle, and ending up with round cheeses. That makes it look pretty easy.

This got me curious about how Swiss cheese is actually made. I promptly fell down a rabbit-hole of cheese-making technology. It is actually a pretty involved process, where lots can go wrong if you are not on top of it. In a short post here, I can only summarize the steps involved:

1. Prepare the milk

Fresh cow’s milk is filtered and warmed in a large vat. Depending on the cheese and local tradition, the milk may be raw or pasteurized. Traditional Alpine cheeses commonly use raw milk, and some require the use of a copper kettle (more on that below).

2. Add starter cultures and rennet

Lactic-acid bacteria are added to begin converting lactose into lactic acid. Rennet, an enzyme preparation traditionally derived from calf stomachs, is then added to coagulate the milk into a soft gel. Modern producers also add bacteria that operate at high temperatures to convert lactic acid to propionic acid plus carbon dioxide; this reaction makes the holes in Swiss cheese and gives it its nutty flavor. Traditional producers rely on bacteria naturally present in the raw milk and on their equipment and the barn walls. (Side comment, the use of rennet has been known since ancient times; probably some Neolithic folks noted what happened to their milk when they stored it in a calf’s stomach, not having any stainless-steel Yeti bottles on hand.)

3. Cut the curd

The solidified milk is cut into small pieces. This releases whey, the watery portion of the milk.

4. Heat and stir the curds

The curds are stirred while the temperature is raised. This helps expel whey and produces the firm texture characteristic of the cheese.

5. Drain and mold

The whey is separated from the curds, and the curds are transferred into large circular molds, to give the familiar wheel shape.

6. Press the cheese

Pressure compacts the curds and helps expel remaining whey. The curds knit together into a solid wheel.

7. Salt the cheese

The wheel is salted, often by soaking it in brine. Salt adds flavor, draws out moisture, and helps regulate microbial activity during aging.

8. Age and develop the holes

The cheese first undergoes initial ripening and sometimes a warmer stage that encourages propionic-acid bacteria to convert fermentation products into propionic acid, acetic acid, and carbon dioxide. The gas accumulates in openings in the cheese, creating its familiar holes (“eyes”). The wheels are subsequently aged further to develop flavor and texture.

The Role of Copper Cauldrons in Traditional Swiss Cheese Making

It turns out that copper vats serve several different functions. First, copper heats quickly and evenly, and cools just as fast.  This lets cheesemakers swing the vat on and off the traditional wood fire with precise temperature control, which is critical during the curd-cooking stage. Second, trace copper ions (typically 5–20 mg/kg) dissolve from the vat into the acidic milk and affect the chemistry in various ways: they bind to milk proteins, remaining in the finished cheese. The copper shifts the balance of bacterial activities, giving cleaner flavor. Copper increases protein breakdown during ripening, which contributes to the rich, nutty flavor. It also, inhibits the release of nasty-smelling hydrogen sulfide.

Modern mass-producers of Swiss cheese try to mimic these chemistries using stainless-steel vats, by adding various chemicals (including sometimes copper). However, regulations require a copper vat for genuine Gruyère, Swiss Emmental, Sbrinz, Raclette, French Comté, and Italian Parmigiano Reggiano.  

Is AI Taking Our Jobs? Signal Boosting Noah Smith

Joy Buchanan3 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?

Continue reading →

Internal Audit of AI Agents and Risk

Joy 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/

What If AI Earnings Fall Far Short of Expectations?

Scott BuchananLeave a comment

Trillions of dollars are being plowed into building U.S. data centers to handle future AI compute demand. These are being paid for by rich people and organizations, anticipating juicy returns on their investments. Those juicy returns depend on consumers (individuals or businesses) being willing to pay enormous amounts for access to AI.  Commentators are so enamored with the glorious prospects of how AI will end poverty and maybe even death, that it is hard to find a clear statement of who exactly will pay how much for all this. Lots of folks are happy to pay $20/month for AI. $200/month? Not so much.

I predict that those earnings will fall far short of expectations. We observe that AI consumption is bifurcating into two main markets, a commodity tier and a premium tier (with, of course, sub-tiers within each of those broad categories).  Chinese models are readily available over the internet, and they have proven capable of performing well enough to handle most AI tasks. The Chinese models are priced far lower (on the order of 10X lower) than the big U.S. “frontier” models (ChatGPT from Open AI, and Claude suite from Anthropic). By far the most U.S. compute usage depends on these two labs. Western users are starting to use the Chinese models more and more. This keeps prices so low that the frontier labs are losing money on their AI sales. Sophisticated users automatically route their AI workload to the cheapest feasible provider.

Their will always be a subset of AI usage in the West that requires the highest level of performance, or freedom from Chinese government spying or manipulation, that will be directed to a premium tier. But if that premium tier ends up being only, say, 20% of AI usage in 2028, there is a real question as to whether the financial bases of the data centers being built now can be sustained. There are huge bear and bull arguments on both sides here, which are tough to balance. I got only equivocal “it depends” answers from AI on this.

If the data centers don’t make expected profits, what then? It all depends on how they were financed. Most of the build-out to date has been the big 4 hyperscalers, Google, Amazon, Microsoft, and Meta spending their free cash flow from their other business lines. If it turns out they simply flushed that money down the toilet, no big deal. Just a trillion-dollar whoopsie. The CEOs will still get their bonuses, don’t worry.

But now as more debt financing enters in, the stakes get higher. Analysis seems to show that the debt loads that the big 4 hyperscalers have incurred is manageable – -their base cash flows are so huge that they can manage their own debt. But in the past year we have seen the emergence of monstrous “Special Purpose Vehicles” (SPVs) with a mixture of equity, debt, and guarantees, to finance practically all the upcoming trillion dollars of data centers. This pushes the financing of the balance sheets of the hyperscalers.

If those newer data centers flop, their equity investors will take a hit, leaving their creditors in the hole and in control. One really needs to analyze exactly who those equity and debt holders are for the SPVs.  If the creditors decide to recoup some of their investment by selling the data center for say 60 cents on the dollar, the most likely buyers would be…Google and Amazon. There is a school of thought that this (let the SPVs fail, scoop up their assets at discount) has been their plan all along. World domination in AI compute!  Just like they have achieved world domination in online search and video and shopping. Maybe.

The resulting slowdown in data center investing would likely throw the U.S. economy into slowdown or recession, considering that it’s estimated that fully half of our recent GDP growth has been from circularly-financed AI buildout. Chipmakers’ (Nvidia, Micron, AMD, etc.) profits depend on continued acceleration in AI build-out. If that build-out stalls, or even slows down, chipmaker profits will crater. Whether this risk is already priced into their share prices is debated.

Boilerplate disclaimer: Nothing here should be considered advice to buy or sell any security.

Temperature vs thinking level for LLM products

Joy 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.
Continue reading →

Boy Wonder Leopold Aschenbrenner Blows Up His $45 Billion Situational Awareness Hedge Fund

Scott BuchananLeave a comment

Leopold Aschenbrenner is a very bright guy. Born in Germany to physician parents, he skipped enough grades to graduate from high school at age 15, allowing him to enroll at Columbia University in 2017 at that same age. He went on to graduate from Columbia at age 19 as valedictorian with a degree in economics and mathematics-statistics. An econ prof at the time said his “record of scholarship exceeds that of any student in the department in the previous 20 years.” The Mercatus Center, a think tank at George Mason University, recognized his potential, awarding him an Emergent Ventures grant.

Leopold Aschenbrenner, Columbia Class of 2021 Valedictorian

After graduation, Aschenbrenner moved into the effective-altruism research and grantmaking world, making notable contributions in various ways. In 2023, he joined the newly created “Superalignment” team at OpenAI, that was charged with making conceptual and engineering progress on aligning systems “much smarter than humans” before such systems were built. The next year he was discharged by OpenAI; the company said it was because of a security leak, but his version (which I find more credible) is that he was ousted as retaliation for circulating an internal memo arguing that the company’s protections against model-weight theft and algorithmic exfiltration were inadequate for AGI-relevant work.

Two months after his departure from OpenAI, he self-published Situational Awareness: The Decade Ahead, which you can download here.    This monograph synthesized scaling-law extrapolations, geopolitical analysis, and AI-lab security commentary into a single forecast: that the largest AI labs, on current trends, will plausibly reach AGI around 2027 and that an intelligence explosion to superintelligence could occur in the subsequent few years. This work went viral on Wall Street, and before you can say “monetization”, he was leading a hedge fund named, appropriately enough, Situational Awareness. There he put into practice his convictions that the demand for compute would be voracious, and would be limited by physical constraints such as electricity and chip fabs.

Thus, in his fund he went long companies like SanDisk (memory fab), Bloom Energy (makes solid oxide fuel cells), and Nebius (builds whole data centers). Very long, in fact, with leverage reportedly as high as 400%. He tried to hedge this long book by shorting software companies which are viewed as vulnerable to disruption by AI. This strategy worked fabulously for a while. His assets under management (AUM) climbed to $45 billion, with returns in the first 6-7 months of 2026 approaching 400%. Not bad for a 25-year-old.

But then, genius failed (yet again)- -in a stunning reversal, the market rebelled in late July against big AI capex spends, dumping memory fabs and infrastructure, and bought into the maligned software (SaaS) sector. So, BOTH his long and short legs went against him, followed in due course by the dreaded margin calls. Aschenbrenner’s $45 billion shrank to a measly $10 billion in a matter of days, as he was forced to sell off his public equities at a discount to those friendly capitalist sharks at Citadel. Wise old heads wagged, saying, yup, this sort of Black Swan event always happens sooner or later, and you then get carried out on a stretcher if you run a highly leveraged bet that is not truly hedged.

Down, but not out – – The word on the Street is that folks with money to invest are already lining up to entrust more bazillions to our altruistic wizard. We have not heard the last of Leopold Aschenbrenner.

Announcing the Disability Records Project

Zachary Bartsch1 Comment

Did you know that we have access to digital copies of the historical US census rolls? You can also find the digitized data at IPUMS. However, the data for people with disabilities is not great. It depends on the year, but those data have error rates on the order of 20% or higher.  We have the digital census rolls, the data just doesn’t match them.

So, I created a non-install windows computer application that lets people identify disabled people on those digital census rolls. Complemented with machine learning, my goal is to improve the accuracy of historical records about people with disabilities. Historical and quantitative research about disabled populations is relatively thin. We can do better. If you have students who would benefit from this research experience, then do please let me know! I can approve your institution’s email domain and we can get started.

The application is really straightforward with basically two user-facing features.

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