Category: Web/Tech
Stanislaw Lem foresaw drones
This was published in English (and Polish) in 1986 under the title One Human Minute:
So it was not humanoid automata that former the new armies but synthetic insects (synsects) — ceramic microcrustacea, titanium annelids, and flying pseudo-hymenoptera with nerve centers made of arsenic compounds and with stingers of heavy, fissionable elements…The flying synsect combined plane, pilot, and missile in one miniature whole. but the operating unit was the microarmy, which possessed superior combat effectiveness only as a whole (just as a colony of bees was an independent, surviving unit while a single bee was nothing).
…The nonliving, synthetic “locust” was incomparably more lethal, since it was made that way by its designers. It possessed a preprogrammed autonomy, so that communication with a command center was unnecessary.
…the microarmy was one giant flowing or flying aggregate of self-assembling elements. It started out dispersed, approaching its objective from many different directions, as strategy or tactics demanded, in order to concentrate into a preprogrammed whole on the battlefield. For this fighting material did not leave the factory in final shape, read for use, like tanks or guns loaded on a railroad flatcar; the mechanisms were microproductive blocks designed to fuse together into a war machine at the designated place. For this reason, such armies were called “self-bonding.”
…Amid a swarm of self-guided, programmed microarms, a man in uniform was as helpless as a Roman legionary with sword and shield against a hail of bullets. In the face of special types of biotropic microarms capable of destroying everything that lived, human beings had no choice but to abandone the battlefield, for they would be killed in seconds…
A microarmy could easily penetrate all systems of defense and go deep into enemy territory. It had no more trouble accomplishing this than did rain or snow. Meanwhile, high-powered nuclear weapons were proving more and more useless on the battlefield.
Lem is always worth reading.
The new Mythos release
My prompt:
Write your own exam question and answer it, for microeconomics. Not a math question, but a high level PhD level question. You will be graded on the quality, interest, and creativity of the question as much as by your answer.
The answer. Here is Ethan Mollick on Mythos.
How well does current AI find errors in economics papers?
Can artificial intelligence (AI) refute economic theory? I document experiments in which I asked several AI models (Gemini, Refine, Claude, and ChatGPT) to check the correctness of four published papers in economic theory, each containing an error that I helped identify or correct. ChatGPT Pro performed best, occasionally constructing counterexamples and corrected proofs, while other models fared worse. However, no model located a true error without substantial human guidance, and data contamination complicates interpretation. I argue that a competent human paired with a frontier model can outperform current peer review, but AI cannot yet refute economic theory on its own.
That is from a new piece by Alexis Akira Toda.
New paper on the iPhone and fertility
The U.S. general fertility rate has fallen by 22% since 2007, a sustained decline not readily explained by economic conditions, contraceptive use, housing or childcare costs, or other commonly cited factors. We assess the potential role of a different shock: the diffusion of the smartphone. The U.S. rollout of the iPhone, the first modern smartphone, provides a natural experiment: from June 2007 through February 2011, the device was sold only on AT&T, allowing us to identify its effect from variation in AT&T’s mobile broadband coverage. Entropy-balanced Poisson and synthetic difference-in-differences event studies imply that access to the iPhone reduced births by 4.5–8.0% at ages 15–19 and 3.2–6.6% at ages 20–24, with statistically significant but smaller declines among older cohorts. Placebo analyses applied to Verizon and Sprint’s pre-2011 coverage footprint are null. Taken together, these cohort effects imply that the diffusion of the iPhone deepened the decline in births among women under 30 while suppressing the rise in births among older women. Overall, the diffusion of the iPhone explains 33–52% of the decline in the general fertility rate among women aged 15–44. National-survey evidence on time use and sexual behavior is consistent with the iPhone reducing in-person interactions, increasing pornography use, and reducing sexual frequency.
That is from
Note also that as this study is set up it does not discriminate against the ” the iPhone effect on fertility is mainly a thing of timing” hypothesis. And a Paul Novosad comment.
Might AI hurt corporate profits? (from my email)
From Clifford Sosin:
I loved your talk about AI and wanted to bounce an idea off you.
I think AI may be bad for corporate profit margins.
A lot of companies make money because their customers can’t be bothered to monitor them more closely, or to insource something. Customers let the company make some money in exchange for doing a decent-enough job and making the problem go away.
Bank of America has $2 trillion of deposits, not a penny of which is optimized. Most enterprise software vendors could be switched out far more often, or displaced by home-built software, but it’s too much of a pain. I could run a 12-party RFP for an Uber ride or a pair of socks, but I don’t.
In a sense, many professionals are an extension of the same idea. I could research my own real estate law, or my own insurance, whether business or personal, but I don’t because it would be too hard.
Google Search might be the biggest example. It makes money because advertisers know they need to be at the top of the results to be found. But my agent will happily search all the results across multiple search engines.
AI agents should change all this. By acting as incredibly rational and vigilant sourcing agents, CFOs, and experts for their users, they will take rents previously collected by these toll-takers and redistribute them to consumers.
And I don’t think the AI stack itself necessarily makes much profit. Commodity and open-weight models are hot on the heels of the major model companies, and competition in GPUs should intensify. Indeed, making a GPU is in some ways similar to making software, so perhaps it can commoditize substantially. Chip manufacturing may remain high-margin, but there are now plenty of entrants drawn in by the shortage who could make TSMC’s market more competitive over time.
Some companies will win. Low-cost providers may gain share as customers switch more often. Richer consumers may consume more high-end goods. Companies with genuinely advantaged business models and limited competition will be able to become more efficient. But my overriding sense is that the equilibrium outcome is lower margins for companies.
Of course, people will build new businesses, and maybe they will use AI to generate very high margins in ways I haven’t considered. That would prove me wrong.
But if this lower-margin hypothesis is true, the knock-on effects are probably positive for AI adoption, since it will make the models more popular with consumers.
And if your view is that AI drives GDP growth to be only 5–10% higher over the next decade, it’s possible that a 100–200 bp decline in corporate margins from roughly 12% would mean companies in aggregate don’t see much benefit — or in fact lose — even as consumers are better off.
Is work from home bad for your mental health?
From the “Results” section:
Relative to those in nonremotable jobs, workers in remotable jobs spent approximately one additional hour alone per workday after the pandemic. Those in remotable jobs also differentially increased days spent entirely alone and decreased after-work socializing. The rise in isolation was sharpest for those living alone, whose likelihood of spending the whole day without social contact rose by 7 percentage points (83%).
Mental distress simultaneously increased: Scores on the Kessler (K-6) measure of generalized psychological distress rose by 0.1 standard deviations for those in remotable jobs relative to those in nonremotable jobs. The increase in distress was roughly twice as large for those living alone compared with those living with family. Alternative measures of mental distress—such as the frequency of depression, mental health care utilization, and antidepressant prescriptions—show similar trends. In contrast, workers in remotable jobs did not differentially increase visits to non–mental health care providers or non–mental health prescriptions (statins, for example), suggesting that the change was not merely driven by increased flexibility for doctor visits.
That is from a recently published paper by Natalia Emanuel, Emma Harrington, and Amanda Pallais.
Barter markets in everything
A clean house in return for your data?:
We record first-person cleaning footage to help train the next generation of household robots. That data is valuable enough for us to offer cleaning services free of charge for a limited time.
Here is the link, via Glenn Mercer.
My twenty-minute AI talk for the Swedish company Sana
Law professors prefer AI over peer answers
Large language models (LLMs) are increasingly promoted as educational tutors, yet most evaluations focus on domains with a single ground truth. Many disciplines, however, hinge on judgment: reasoning, weighing ambiguity, and reaching defensible conclusions. Law provides a sharp test. We conducted a blinded evaluation of short-answer tutoring in contracts courses with sixteen U.S. law professors. Participants created 40 representative questions, wrote answers, and judged 2,918 anonymized comparisons between human and LLM responses. Professors rated LLMs far higher than their peers (average win rate = 75.33%), with models performing similarly to the best instructor. LLM responses were also rarely flagged as harmful (3.53%, vs 12.06% for professors). Preferences for LLM answers were consistent across evaluators and reflected shared professional standards. Our evaluation can be reliably extended to additional models by employing a separate LLM as a judge, rendering expert agreements an effective, scalable method to evaluate AI tutors in judgment-rich domains.
“far”. That is from a new paper by Alejandro Salinas, et.al. Via Andrew Curran. And via John Chamberlain:
Artificial intelligence (AI) and large language models (LLMs) tools are capable of mass-producing academic finance papers that are nearly indistinguishable from human-authored research, according to a new study published in the Journal of Economic Literature.
C’mon people, get ready. I know it is difficult to admit when your human capital has been devalued, but that time is upon us. In particular, being prolific is no longer such a comparative advantage in academia. You might run to the “but I know what questions to ask” cope, but I implore you to solve for the equilibrium. What is the equilibrium wage for merely asking questions?
Of course academic life and projects will continue, but the real rewards will go to people doing new, innovative, and hitherto impossible projects with AI.
The US Exports Intelligence
Most Americans work in the service sector so it’s not surprising that most export-related jobs are in the service sector (The U.S. exports about $2.2 trillion of goods and $1.2 trillion of services, but services are more labor intensive than manufacturing so they support more export jobs per dollar.)
Richard Baldwin writes:
In 2022, US service exports supported 8.9 million American jobs.
US manufacturing exports supported 2.2 million.
That’s four-to-one in favour of services. Yet in the national narrative, ‘export jobs’ almost always means things done in steel mills and factories.
…When a household in Germany pays for Netflix, that is an American export. When a Brazilian retailer buys Microsoft cloud capacity, that is an American export. When JPMorgan structures a financial deal in London, or an American consulting firm advises a company in Singapore, those are American exports too.
None of these is shipped in a container. No customs official records them as they clear the customshouse. Yet they are exports since they earn foreign income for America just as surely as the ‘Boeings, Beans and Beef’ that President Trump sold on his recent China trip.
Need I remind you that when OpenAI sells intelligence to people abroad, that is a US export? N.B. this is the future.
World trade in goods expanded roughly five-fold between 1990 and 2020. Trade in digitally enabled services expanded more than eleven-fold over the same period. These are the modern services.
The trade debate is fixated on manufacturing—where America is doing fine—while largely ignoring services, where America is crushing. Increasingly, our most valuable exports travel not on container ships but at the speed of light over fiber.
The returns to good data are rising
When we want A.I. to solve real problems for real people, we need to make sure the data exists. That means cleaning up government data sets that are currently in a shambles (a project that the province of Alberta’s government found A.I. could make much faster and easier). It may also mean funding the creation of novel data sets that could eventually give A.I. systems traction on scientific problems that are currently beyond our capability to solve. Those data sets — like the Protein Data Bank — would be public goods, and so would need to be funded by the public.
Here is a longer NYT column on AI from Ezra Klein. And this:
But much of the A.I. capacity will remain in the private sector. So a public agenda for A.I. should also give the private sector reason to work on public problems. Like in Operation Warp Speed, the government could define the outcomes it wants — a drug, a solution — and guarantee a market if it’s found and distributed equitably.
Negativism is not going to win in this sphere.
AI in gdp
- Quality-adjusted AI production in the United States grew at over 2,000 percent per year in 2024 and 2025, driven by three compounding forces: expanding data-center capacity, hardware efficiency gains, and—the largest of the three—algorithmic progress.
- Treating the AI sector as a coherent economic entity yields preliminary estimates of nominal AI GDP at approximately $250 billion in 2025, growing at roughly 2,600 percent per year in quality-adjusted real terms.
- National economic statistics accounts were not designed to track this kind of activity. Statistics agencies should begin developing AI-focused satellite accounts now, before the measurement gap becomes a policy gap.
Here is much more from Anton Korinek and Patrick McKelvey. Via the excellent Samir Varma.
Seven ways to avoid losing your job to AI
That is the theme of my latest Free Press column, here is one excerpt:
Principle five: Run experiments.
This is a more general version of the healthcare point. AI will generate so many new ideas and hypotheses, including for drugs and medical devices, but not only. Become a tester. Test new battery designs, new educational techniques, or new methods of conserving valued wildlife.
The demand for experiments will rise sharply, and most of those cannot be done by robots, at least not anytime soon.
Principle six: Gather data.
AI is a marvelous tool, but it relies on knowing lots about the world. That can stem from reading the internet, watching videos of people folding clothes, and hearing recordings of voices, among many other ways of absorbing information.
The more powerful the AI, the higher the returns from feeding it data, because it will make smart and useful inferences from those data. But most data in our world have never been put into AI models. Just consider corporate records, historical archives, referee reports for failed scientific papers, accounts of lab procedures, and much more. Most of that remains virgin territory.
The next few decades will bring an immense investment in feeding more data into the AIs. So there will be new jobs in gathering environmental data, job safety data, construction site data, corporate and management data, public health data, agricultural data, education data, and much more. Those jobs could be yours.
Recommended.
Robert Wright’s *The God Test*
The subtitle is Artificial Intelligence and Our Coming Cosmic Reckoning, due out June 23.
In the first chapter, Wright summarizes four of his perspectives, these are my paraphrases of his pp.5-6:
1. When it comes to AI, we should be somewhere on the awe spectrum.
2. We can create a future where the upside of AI far outweights the downside, though that involves steering human understanding toward the better side of the awe spectrum.
3. A major reorientation of human thought is required, and right now few people seem inclined to do that.
4. The worldviews of the current AI acclerationists and also doomers are not cosmic enough.
It is a good time for this book to be published, and I agree with much more of it than I disagree with. My main difference is that I am more focused on very small things — such as Rainier cherries and the forthcoming three to four hour Apichatpong movie — than on cosmic awe per se. For better or worse, I was not born with those genes, and unlike Wright I am far from Buddhism. I do think there will be a transformation of “observed awe,” and I am somewhat worried that it will not go well. Will we be good at building a fairly new world, if not from scratch, on the basis of some new premises about what is possible and what is not? I will in any case interpret the pending transformation through a Straussian lens, namely thinking that a lot of the observed transformation of awe will be about something other than what people are claiming. It will be about people arguing over relative status, but under different guises. Not as tasty as a good Rainier cherry, but interesting to follow as well.
But are we still good at steering and evolving grand visions? Christianity and the Enlightenment are a hard act to follow.
What should I ask Chase Koch?
Yes I will be doing a Conversation with him. Chase and Charles Koch have a new book out, namely