Category: Web/Tech
Sentences to ponder
To provide some sense of scale, that means the equivalent of about $1,800 per person in America will be invested this year on A.I.
Here is more from Natasha Sarin at the NYT.
Thiel and Wolfe on the Antichrist in literature
Jonathan Swift tried to exorcise Baconian Antichrist-worship from England. Gulliver’s Travels agreed with New Atlantis on one point: The ancient hunger for knowledge of God had competition from the modern thirst for knowledge of science. In this quarrel between ancients and moderns, Swift sided with the former.
Gulliver’s Travels takes us on four voyages to fictional countries bearing scandalous similarities to eighteenth-century England. In his depictions of the Lilliputians, Brobdingnagians, Laputans, and Houyhnhnms, Swift lampoons the Whig party, the Tory party, English law, the city of London, Cartesian dualism, doctors, dancers, and many other people, movements, and institutions besides. Swift’s misanthropy borders on nihilism. But as is the case with all satirists, we learn as much from whom Swift spares as from whom he scorns—and Gulliver’s Travels never criticizes Christianity. Though in 2025 we think of Gulliver’s Travels as a comedy, for Swift’s friend Alexander Pope it was the work of an “Avenging Angel of wrath.” The Anglican clergyman Swift was a comedian in one breath and a fire-and-brimstone preacher in the next.
Gulliver claims he is a good Christian. We doubt him, as we doubt Bacon’s chaplain. Gulliver’s first name, Lemuel, translates from Hebrew as “devoted to God.” But “Gulliver” sounds like “gullible.” Swift quotes Lucretius on the title page of the 1735 edition: “vulgus abhorret ab his.” In its original context, Lucretius’s quote describes the horrors of a godless cosmos, horrors to which Swift will expose us. The words “splendide mendax” appear below Gulliver’s frontispiece portrait—“nobly untruthful.” In the novel’s final chapter, Gulliver reflects on an earlier promise to “strictly adhere to Truth” and quotes Sinon from Virgil’s Aeneid. Sinon was the Greek who convinced the Trojans to open their gates to the Trojan horse: one of literature’s great liars.
Here is the full article, interesting and varied throughout.
New data on social media
It has gone largely unnoticed that time spent on social media peaked in 2022 and has since gone into steady decline, according to an analysis of the online habits of 250,000 adults in more than 50 countries carried out for the FT by the digital audience insights company GWI. And this is not just the unwinding of a bump in screen time during pandemic lockdowns — usage has traced a smooth curve up and down over the past decade-plus.
Across the developed world, adults aged 16 and older spent an average of two hours and 20 minutes per day on social platforms at the end of 2024, down by almost 10 per cent since 2022. Notably, the decline is most pronounced among the erstwhile heaviest users — teens and 20-somethings…
Additional data from GWI trace the shift. The shares of people who report using social platforms to stay in touch with their friends, express themselves or meet new people have fallen by more than a quarter since 2014. Meanwhile, reflexively opening the apps to fill up spare time has risen, reflecting a broader pernicious shift from mindful to mindless browsing.
Here is more from John Burn-Murdoch in the FT. I was just doing as Aspen podcast two nights ago, where I spoke of social media as a problem that, in time, largely would solve itself. You also may recall my recent post about declining rates of depression for young adults. That said, you might wonder what exactly is the correct definition of social media (MR comments section?), and whether this study is tracking the proper conception of it.
For the pointer I thank Adrian Kelly.
Do LLMs favor outputs created by themselves?
Here is part of the abstract, I will not ask who or what wrote this:
We focus on the hiring context, where job applicants often rely on LLMs to refine resumes, while employers deploy them to screen those same resumes. Using a large-scale controlled resume correspondence experiment, we find that LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled. The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 68% to 88% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting. We further demonstrate that this bias can be reduced by more than 50% through simple interventions targeting LLMs’ self-recognition capabilities.
Here is the full paper by Jiannan Xu, Gujie Li, and Jane Yi Jiant, via the excellent Kevin Lewis.
Valuing free goods
There is a new AEJ Macro paper by Brynjolfsson, et.al. on how to value free goods. Here is one of the concrete measures:
Using this approach, we estimate the reservation price [for giving up Facebook] to be $2,152 in 2003 US dollars.
That is for the 2017 version of Facebook. Note this does not measure “whether Facebook is really good for you on net,” but it does indicate some fairly strong demand. And:
…the estimate contribution to welfare due to Facebook in the US over the period 2003-2017 is $231 billion (in 2017 dollars), which translates to $16 billion on average per year.
Some simple economics of Sora 2?
I do not have access or any kind of inside information on what it can do, or not. Still, from my distance it seems quite possible that the “slop” side of the equation is a simple way to fund AI “world-modeling” (and other) skills in a manner that is cross-subsidized by the consumers of the slop.
That is good, not bad. Let us hope it is true, and so shall all the glass bridges break yet again.
Markets in everything
A new app offering to record your phone calls and pay you for the audio so it can sell the data to AI companies is, unbelievably, the No. 2 app in Apple’s U.S. App Store’s Social Networking section.
The app, Neon Mobile, pitches itself as a moneymaking tool offering “hundreds or even thousands of dollars per year” for access to your audio conversations.
Neon’s website says the company pays 30¢ per minute when you call other Neon users and up to $30 per day maximum for making calls to anyone else. The app also pays for referrals. The app first ranked No. 476 in the Social Networking category of the U.S. App Store on September 18 but jumped to No. 10 at the end of yesterday, according to data from app intelligence firm Appfigures.
On Wednesday, Neon was spotted in the No. 2 position on the iPhone’s top free charts for social apps…
However, Neon’s marketing claims to only record your side of the call unless it’s with another Neon user.
Here is the full story, via Mark.
My excellent Conversation with Steven Pinker
Here is the audio, video, and transcript. Here is part of the episode summary:
Tyler and Steven probe these dimensions of common knowledge—Schelling points, differential knowledge, benign hypocrisies like a whisky bottle in a paper bag—before testing whether rational people can actually agree (spoiler: they can’t converge on Hitchcock rankings despite Aumann’s theorem), whether liberal enlightenment will reignite and why, what stirring liberal thinkers exist under the age 55, why only a quarter of Harvard students deserve A’s, how large language models implicitly use linguistic insights while ignoring linguistic theory, his favorite track on Rubber Soul, what he’ll do next, and more.
Excerpt:
COWEN: Surely there’s a difference between coordination and common knowledge. I think of common knowledge as an extremely recursive model that typically has an infinite number of loops. Most of the coordination that goes on in the real world is not like that. If I approach a traffic circle in Northern Virginia, I look at the other person, we trade glances. There’s a slight amount of recursion, but I doubt if it’s ever three loops. Maybe it’s one or two.
We also have to slow down our speeds precisely because there are not an infinite number of loops. We coordinate. What percentage of the coordination in the real world is like the traffic circle example or other examples, and what percentage of it is due to actual common knowledge?
PINKER: Common knowledge, in the technical sense, does involve this infinite number of arbitrarily embedded beliefs about beliefs about beliefs. Thank you for introducing the title with the three dots, dot, dot, dot, because that’s what signals that common knowledge is not just when everyone knows that everyone knows, but when everyone knows that everyone knows that and so on. The answer to your puzzle — and I devote a chapter in the book to what common knowledge — could actually consist of, and I’m a psychologist, I’m not an economist, a mathematician, a game theorist, so foremost in my mind is what’s going on in someone’s head when they have common knowledge.
You’re right. We couldn’t think through an infinite number of “I know that he knows” thoughts, and our mind starts to spin when we do three or four. Instead, common knowledge can be generated by something that is self-evident, that is conspicuous, that’s salient, that you can witness at the same time that you witness other people witnessing it and witnessing you witnessing it. That can grant common knowledge in a stroke. Now, it’s implicit common knowledge.
One way of putting it is you have reason to believe that he knows that I know that he knows that I know that he knows, et cetera, even if you don’t literally believe it in the sense that that thought is consciously running through your mind. I think there’s a lot of interplay in human life between this recursive mentalizing, that is, thinking about other people thinking about other people, and the intuitive sense that something is out there, and therefore people do know that other people know it, even if you don’t have to consciously work that through.
You gave the example of norms and laws, like who yields at an intersection. The eye contact, though, is crucial because I suggest that eye contact is an instant common knowledge generator. You’re looking at the part of the person looking at the part of you, looking at the part of them. You’ve got instant granting of common knowledge by the mere fact of making eye contact, which is why it’s so potent in human interaction and often in other species as well, where eye contact can be a potent signal.
There are even species that can coordinate without literally having common knowledge. I give the example of the lowly coral, which presumably not only has no beliefs, but doesn’t even have a brain with which to have beliefs. Coral have a coordination problem. They’re stuck to the ocean floor. Their sperm have to meet another coral’s eggs and vice versa. They can’t spew eggs and sperm into the water 24/7. It would just be too metabolically expensive. What they do is they coordinate on the full moon.
On the full moon or, depending on the species, a fixed number of days after the full moon, that’s the day where they all release their gametes into the water, which can then find each other. Of course, they don’t have common knowledge in knowing that the other will know. It’s implicit in the logic of their solution to a coordination problem, namely, the public signal of the full moon, which, over evolutionary time, it’s guaranteed that each of them can sense it at the same time.
Indeed, in the case of humans, we might do things that are like coral. That is, there’s some signal that just leads us to coordinate without thinking it through. The thing about humans is that because we do have or can have recursive mentalizing, it’s not just one signal, one response, full moon, shoot your wad. There’s no limit to the number of things that we can coordinate creatively in evolutionarily novel ways by setting up new conventions that allow us to coordinate.
COWEN: I’m not doubting that we coordinate. My worry is that common knowledge models have too many knife-edge properties. Whether or not there are timing frictions, whether or not there are differential interpretations of what’s going on, whether or not there’s an infinite number of messages or just an arbitrarily large number of messages, all those can matter a lot in the model. Yet actual coordination isn’t that fragile. Isn’t the common knowledge model a bad way to figure out how coordination comes about?
And this part might please Scott Sumner:
COWEN: I don’t like most ballet, but I admit I ought to. I just don’t have the time to learn enough to appreciate it. Take Alfred Hitchcock. I would say North by Northwest, while a fine film, is really considerably below Rear Window and Vertigo. Will you agree with me on that?
PINKER: I don’t agree with you on that.
COWEN: Or you think I’m not your epistemic peer on Hitchcock films?
PINKER: Your preferences are presumably different from beliefs.
COWEN: No. Quality relative to constructed standards of the canon…
COWEN: You’re going to budge now, and you’re going to agree that I’m right. We’re not doing too well on this Aumann thing, are we?
PINKER: We aren’t.
COWEN: Because I’m going to insist North by Northwest, again, while a very good movie is clearly below the other two.
PINKER: You’re going to insist, yes.
COWEN: I’m going to insist, and I thought that you might not agree with this, but I’m still convinced that if we had enough time, I could convince you. Hearing that from me, you should accede to the judgment.
I was very pleased to have read Steven’s new book
Pulse
Today we are launching my favorite feature of ChatGPT so far, called Pulse. It is initially available to Pro subscribers.
Pulse works for you overnight, and keeps thinking about your interests, your connected data, your recent chats, and more. Every morning, you get a custom-generated set of stuff you might be interested in. It performs super well if you tell ChatGPT more about what’s important to you.
In regular chat, you could mention “I’d like to go visit Bora Bora someday” or “My kid is 6 months old and I’m interested in developmental milestones” and in the future you might get useful updates.
Think of treating ChatGPT like a super-competent personal assistant: sometimes you ask for things you need in the moment, but if you share general preferences, it will do a good job for you proactively.
This also points to what I believe is the future of ChatGPT: a shift from being all reactive to being significantly proactive, and extremely personalized.
That is from Sam Altman.
What should I ask Sam Altman?
I will be having a conversation with him at a Roots of Progress event, though it will not be a formal CWT Conversation per se. So what should I ask him?
To be clear, this is the conversation with Sam I want to have…
AI and weather tracking as a very positive intervention
India’s monsoon season was unusual this year, but many farmers there had new AI weather-forecasting tools to help them ride out the storms.
Google’s open-source artificial intelligence model NeuralGCM and the European Center for Medium-Range Weather Forecasts’s AI systems are making sophisticated and granular forecasting data available to even the smallest farms in poor areas. Thanks to the open-source AI, and decades of rainfall data, the Indian government sent out forecasts to 38 million farmers to warn them about looming monsoons.
The initiative to help farmers adapt is the latest example of how companies are expanding their weather-tracking capabilities amid mounting concerns about extreme weather and climate change.
The effort is part of a growing “democratization of weather forecasting,” said Pedram Hassanzadeh, a researcher at the University of Chicago who focuses on machine learning and extreme weather. Researchers from the university partnered with the Indian government to gather and send out the monsoon predictions.
“Up until very recently, to run a weather model, you needed a 100 million-dollar supercomputer,” said Olivia Graham, a product manager at Google Research. But now, farmers in India can make better-informed agricultural decisions quickly, she said.
These projects seem to have very high benefit to cost ratios. Here is one relevant RCT, here is another. Here is more from the WSJ, via Michael Kremer. Here is a useful and informative press release.
From the comments
I am a high school teacher. As this study finds, banning phones hurts our best students. Unlike Tyler, I do have a problem with these policies. Studies like this dont even measure the ways that phones help our best students the most: they allow students to access real teachers, better teachers, sources of knowledge and learning that are beyond what they are stuck with in our public schools. There are many actions we could take that would boost grades. We could adopt singapore’s culture or the Japanese juku system. We could become as draconian as you like to boost grades for low-performing students. But to what end? Maybe there is one Peter Scholze who could boost his early learning by 5 pct, even 100 or more pct depending on what schhol he is in, with a phone. Is banning it from him worth boosting the algebra 1 scores of 20,000 future real estate salesman by 3 percent? Phones are new. Teachers have no idea how to use them. They are devices that contain the entire world’s knowledge and kids want to use them – and we are banning them? Any teacher who wants to ban phones is taking the easy way out.
That is from Frank. A broader but related point is that a school without smartphones probably cannot teach its students AI — one of the most useful things for a person to learn nowadays. As Frank indicates, you should be very suspicious that the smart phone banners take absolutely no interest in measuring their possible benefits. We economists call it cost-benefit analysis. If you wish to argue the costs are higher than the benefits, fine, that can be debated. If you are not trying to measure the benefits, I say you are not trying to do science or to approach the problem objectively.
And from a later post, again Frank:
Does automation reduce stigma?
By removing human cashiers, self-checkout registers may alter feelings of embarrassment experienced by customers. Using high-frequency scanner data from supermarkets in the Washington, D.C. area with staggered adoption of self-checkout, we conduct event study analyses on consumer purchasing behavior. On the extensive margin, we find positive but noisy effects of self-checkout adoption on sales of some stigmatized items. On the intensive margin, we show that stigmatized items are much more likely to be purchased at self-checkout than at cashier registers, especially condoms and pregnancy tests. We estimate that customers are willing to pay 8.5 cents in additional time cost for the privacy of purchasing stigmatized items at self-checkout.
Here is the full paper by Rebecca Cardinali., et.al. Via the excellent Kevin Lewis.
I even draw distinctions across automated models. For instance, if I have “a stupid question,” I am more likely to ask Grok, since I would rather GPT maintain a higher opinion of what I do and do not know.
Is it the phones?
Or perhaps we should just credit Sydney Sweeney? That is from Chris Said.
Summary of a new DeepMind paper
Super intriguing idea in this new @GoogleDeepMind paper – shows how to handle the rise of AI agents acting as independent players in the economy.
It says that if left unchecked, these agents will create their own economy that connects directly to the human one, which could bring both benefits and risks.
The authors suggest building a “sandbox economy,” which is a controlled space where agents can trade and coordinate without causing harm to the broader human economy.
A big focus is on permeability, which means how open or closed this sandbox is to the outside world. A fully open system risks crashes and instability spilling into the human economy, while a fully closed system may be safer but less useful.
They propose using auctions where agents fairly bid for resources like data, compute, or tools. Giving all agents equal starting budgets could help balance power and prevent unfair advantages.
For larger goals, they suggest mission economies, where many agents coordinate toward one shared outcome, such as solving a scientific or social problem.
The risks they flag include very fast agent negotiations that humans cannot keep up with, scams or prompt attacks against agents, and powerful groups dominating resources.
To reduce these risks, they call for identity and reputation systems using tools like digital credentials, proof of personhood, zero-knowledge proofs, and real-time audit trails.
The core message is that we should design the rules for these agent markets now, so they grow in a safe and fair way instead of by accident.
That is from Rohan Paul, though the paper is by Nenad Tomasev, et.al. It would be a shame if economists neglected what is perhaps the most important (and interesting) mechanism design problem facing us.
asdf wrote: