Category: Web/Tech

Differentiation drives the erosion of positivity on social media

by  Tyler Cowen in

We live in a digital age, where billions of people engage in dialogue within topic-bound communities and threads. In an archival analysis of over 2 billion Reddit comments and an experiment, we show that this dialogue becomes more negative over time. Further analyses suggest that negativity rises over time because social media users seek to make unique comments on the same topic, and it is easier to differentiate oneself through negative comments than through positive comments. As threads and communities evolve, and it becomes more difficult to make unique observations, users turn to negativity. Our studies show how basic human motives interact with the structure of social media platforms, posing an acute challenge for sustaining healthy online dialogue.

Here is the article by Hongkai Mao, et.al. Via the excellent Kevin Lewis.  For some of you commenters, how does it feel to be a puppet in the unfolding of this game?

Jackson Dahl podcasts with me and Nabeel on aesthetics

by  Tyler Cowen in

Filmed at home, this ran about two hours, and yes that is Nabeel Qureshi, with a cameo from Spinoza toward the very end.  From Jackson:

Links

From the episode summary:

Tyler and Nabeel are good friends, and given how prolific Tyler is, I decided to use Nabeel as an entry point and interview them together. We discuss sacred commitments, AI acceleration, mentorship, friendship, and more, but I focused the majority of the conversation on art and aesthetics. Tyler and Nabeel are unlikely aesthetes given their day jobs, but in fact take art deeply seriously. They have a shared love for and similar tastes in art, music, and film, in particular. We discuss strange and beautiful art, aesthetic stagnation, and a wide range of favorites: The Beatles, Mozart, Mondrian, Springsteen, Lana Del Rey, Kanye West, Cassavetes, The Sopranos, Apichatpong Weerasethakul, and more.

https://www.youtube.com/watch?si=wC78q_BeD27XDnLN&v=qPHV-BezoIc&feature=youtu.be

Excerpt:

Tyler: (18:31) I think I’m very mundane in many ways. When Marc Andreessen had that famous tweet about not being too introspective, I know he got slammed for that, but I sympathize with that in many ways. I have my work. I focus on it. I want to go see places I haven’t seen before. That really drives me. I feel pretty well motivated. I do think all kinds of deep thoughts, but to me those deep thoughts feel more superficial than my so-called superficial urges to go around doing things. And I’m fine with that.

…Jackson: (23:25) Do you experience art primarily by thinking or by feeling?

Tyler: (23:29) I don’t even know what those words mean. I experience it by looking at it. I don’t think I have very deep emotional responses. I think it’s pleasure and I feel I learn a lot from it. When I go out and look at other works of art or just the world, I see a lot more than people who don’t live with art. I don’t think I feel that much. I’ve never cried in front of a painting. When I read these accounts of someone seeing a Madonna and weeping, it makes no sense to me. It’s like people who do sports gambling. Why do you do that? There are positive-sum gambles for you. Here are a few.

There is much more of interest, self-recommending!

AI cheating on math econ at Brown

by  Tyler Cowen in

The temptation to use artificial intelligence (AI) to cheat is shaking up elite universities in the United States. Professor Roberto Serrano, who is the Harrison S. Kravis University Professor of Economics at Brown University, has detected a massive fraud in one of the classes he teaches, ECON 1170, an advanced undergraduate course in mathematical economics. He has conclusive evidence that at least 50 students cheated on the March midterm exam, making it the biggest known scandal at Brown and in the entire Ivy League, which brings together the East Coast’s eight most elite private universities, including Princeton, Harvard, Yale, Columbia, Cornell, Dartmouth College and University of Pennsylvania.

When he reported the case to high-ranking officials at Brown, he got a cold reaction. The response from the president, he said, was absolute silence. The dean did not comment either until Serrano took the case before the Academic Code Committee.

Here is the full story, via Anecdotal.

Typewriters and fertility

by  Tyler Cowen in

Workplace technological changes were instrumental in creating new tasks for women over the last century. This paper studies the adoption of the typewriter into US workplaces. Exploiting exogenous variation in typist demand across sectors, I document that the typewriter increased women’s labor force participation, leading to lower rates of marriage and fertility. These developments stemmed from a transition of White women from households into office work and an indirect crowding-in effect drawing Black women into household services. Acting as a “meeting technology,” the typewriter reshaped social interactions, enabling White women to marry above their socioeconomic backgrounds and achieve upward mobility.

That is from a recent paper by Myera Rashid.  Via Kris Gulati.

An infovore shares his chats

by  Tyler Cowen in

In conjunction with OpenAI, here is a short piece by me on how to use GPT Pro for travel and a few other matters.  Excerpt:

6. Learn what to look for in an art museum

I’m visiting the Detroit Institute of Arts. Which pictures in particular should I be looking for, and what is some useful context for viewing them?

Why was this chat valuable? “After two visits in three days, I would second the advice I got from this chat.”

Here is the link to the answer.  And know when to ask it to read the local newspapers for you, to learn about a place.

There are some good photos in the piece, including some hitherto unpublished photos of Spinoza.

Aaron Levie on current implicit AI regulation

by  Tyler Cowen in

We now have de facto AI regulation. It’s not obvious why from here on out models that have certain levels of capability or are trained on certain compute sizes won’t have to be reviewed by the government before release.

Realistically, as AI models became more and more powerful this was going to be inevitable (I think it’s too early, but here we are). So now it’s mostly just interesting to think about the implications and scenarios from here. A few would be:

* America gets to control who gets access to frontier intelligence and when. This generally works as long as we remain at the frontier at all times and don’t have a risk of being surpassed. At the moment we have a clear lead in frontier intelligence so this is a good bet, but lots of motivated parties would love to change that.

* This likely creates backlog of AI releases which means that we will see less rapid fire back and forth jumps in model progress. Bull/fine case is that we just get bigger step functions per release at a slower rate and we end up at the same point we would have. Bear case is those incremental smaller jumps were necessary for the continued flywheel of innovation.

* Other countries likely have even more incentive to at least hedge their bets with sovereign AI strategies so aren’t dependent on access to US AI all times. Previously this was relatively moot because the alternative wasn’t good enough, but that could change out of necessity and what we’re seeing in China.

* Open weights obviously a big winner here as it becomes what likely sovereign AI gets built out on, and what (for now) can still be released to the market without the same controls. One interesting question would be how regulation eventually extends to open models, which would have its own set of long term consequences.

Anyway some big updates to everyone’s mental models of AI regulation as a result of the capabilities we’re now seeing in AI. Wild times.

Here is the link.  I would say I have long thought something like this was coming, but am pleased we got in so much early progress “under the wire” up until now.  And here is more from Aaron.

Translated from the Chinese

by  Tyler Cowen in

I think this is the Cursor moment for academia.

The Stanford REAP team has made their move, CoPaper.AI is mass-terminating the manual labor of traditional empirical papers. Link: copaper.ai/landing

If using large models to write papers before was just about polishing and compiling references for you, then this Project from Professor Ross Griebenow’s team at Stanford is like dropping a nuclear bomb in the empirical circles of social sciences and economics.

The greatest truth is the simplest; the heaviest sword has no edge. Its functions are straightforward. Feed in the raw dataset, and within 30 minutes, it can generate a complete DOCX paper complete with full Stata/R code and publication-quality charts.

It chains together EDA, variable definition, econometric model building (from OLS to advanced DID, regression discontinuity, causal forests) all using an Agent workflow.

Every chart it produces comes with 100% reproducible Stata, R, EViews source code underneath. How many low-quality paper mills and data drones’ jobs will this smash?

Data drones and paper ghostwriters are collectively facing unemployment countdown. Because from now on, for social science papers, AI handles all the entropy-increasing drudgery—humans only need to define the problem.

Here is the link.  Mostly that is not true, so perhaps the Chinese are trying to demoralize us.  But will it never ever be true?  In two years be true?  Less?

How research in math will change (from my email)

by  Tyler Cowen in

From GA:

I am a mathematician…and some of your recent comments on MR about the role of AI in Econ research as well as the (disappearing?) role of academic papers inspired this response. (It is partially but not exclusively about academia, so I hope it is ok that I’m sending it to your GMU address. Also, I’m hoping it doesn’t get flagged as spam because of the ai in the title…)

In no particular order:

-In the course of a math career, one accumulates lots of computational guesses, now one can test those with minimal effort.

-One also accumulates lots of incomplete and half formed drafts, proofs of special cases, etc, etc. Running those past claude and chatgpt can (does!) pay off. A lot of math is cleverly applying linear algebra and while I’m very good at linear algebra, I’m not as good at it as the AI’s are.

-The lower hanging fruit here are slightly off the beaten track, but not esoteric subjects. If you have a good overview of such, you can pretty quickly prod ai’s into making progress on them. (Before, you needed to have a school of grad students for that). Basic techniques (graph theory, algebra, calculus..) that ai’s are already good at can push these forward already. Making progress on truly hot topics is harder.

-There are some quite smart people trying to measure just how good autonomous ai’s are at math (e.g. the first batch project). That’s a fun game, but for practical purposes right now, what is relevant is how good an ai is when guided by a motivated human. I suspect we’ll see some remarkable things on that front in the next few years once math people really grok the good routines.

-For instance, getting claude and chatgpt to referee each other’s arguments is fun, and they genuinely have different insights on parts of the same problem.

-The kids will be all right. Right now, they are making pocket change doing ai training developed a better “feel” for the different ai’s that I probably ever will. And they learn things by asking the ai to explain an argument to them instead of trying to decipher a math book or paper.

-Which brings me to your papers point. I notice that a project informed with the right context is much more informative to me than the physical pdf of a math paper, and much easier to extract information out of by just asking the thing.

-Refereeing will look very different very soon. All the referee reports that have been collected by the journals should be valuable, hard to get data. And running all the accepted and published math papers through ai’s as the `control’ will end with quite a few people having egg on their face. It’s like self-driving cars, but there is no refereeing union.

-The last really big math revolution was all the stuff in the wake of Witten and 4-manifold stuff predicted by string theory in the early 90’s. This is going to be so much bigger than that. Buckle up.

AI-Native Firms

by  Tyler Cowen in

Very important work from Hyunjin Kim and Rembrand Koning. Insead and HBS respectively:

We study how firms built around AI capabilities-“AI-native” firms-are organized. Drawing on Y Combinator batches W20-F24 and U.S. venture-backed startups whose first financing closed between 2020 and 2024, we classify each firm’s AI-native status and link it to workforce microdata on team size, function, seniority, and hierarchy. Relative to non-AI startups in the same industry-cohort, AI-native firms are 25% smaller. Their share of engineers is 13% greater, and the shares of entry-level workers and managers are each roughly 15% lower. Their hierarchies are half a seniority level flatter-yet valuations are comparable, implying more value created per employee. We argue these patterns reflect two channels: a process channel, in which AI changes how people work inside the firm, and a product channel, in which AI capabilities are built into what the firm sells. Using text from product descriptions and job postings, we find that embedding AI into the product, beyond layering on AI tools into existing workflows, is a primary way startups are scaling “knowledge work” without large teams of knowledge workers.

The tweet storm on the new paper is especially useful.  Via Luis Garicano.  And note those results predate the very latest and best tools.

My Conversation with Dave Baszucki

by  Tyler Cowen in

Dave is CEO and co-founder of Roblox, and here is the audio, video, and transcript.  From the episode summary:

With over 100 million daily active users and projected revenue bookings of $7 billion this year, it is one of the largest gaming economies in the world—and one that has made millionaires out of teenage developers in Argentina, South Korea, and everywhere in between.

Tyler and Dave explore why Roblox decided early against prioritizing advertising revenue, why Dave thinks the main competition of Roblox is its own execution speed rather than Fortnite, whether every mega platform inevitably becomes an everything app, how falling token costs will change the platform, why he insists all the games on Roblox are beautiful, whether Robux should have a floating exchange rate, why admitting you have kids under 13 on your platform turns out to be a competitive advantage, why he’s skeptical of blanket social media bans, what his son’s experience with bipolar disorder taught him about metabolic health, his two-year sabbatical between companies that involved a motorhome trip across North America and a stint hosting talk radio in Santa Cruz, why Mutiny on the Bounty remains one of his favorite books, what he’ll learn next, and much more.

Excerpt:

COWEN: What percentage of your games now do you feel are beautiful?

BASZUCKI: All of them.

COWEN: Some look just quite ordinary. They might be fun, but I wouldn’t say they’re beautiful, right?

BASZUCKI: Well, I was trying to go a couple levels out of the box on you there. The reason I feel they’re beautiful is when you said that, I immediately went to look and feel, but then I tried to imagine the 12-year-old or the 18-year-old or the 30-year-old struggling to build something wonderful and the human connection to those games. By that definition, I think they’re all beautiful. They are all the efforts of creation of real people trying to pour their hearts out to make something that other people love to play.

On an artistic basis, I think you could ask me what percent of paintings in the MoMA do I think are beautiful. I’d probably say 20 percent. If I had to look at 1,000 Roblox games, I wouldn’t name which is more beautiful to me because I think that’s less important than really the heartfelt work of all the creators.

COWEN: I’ve been struck when I look at gaming at how much people don’t seem to care much about the visual beauty of their games. I would have expected something different, say, 15 years ago, and they just want a game that engages them somehow. Normal standards of visual beauty seem to have fallen away. Is that incorrect? Would you correct that impression in some manner?

BASZUCKI: I think you’re absolutely correct. What I feel you may actually be describing, if we looked into other disciplines, the evolution of story from the campfire to written to audio to a movie, and the increasing fidelity; all of those stories, in a way, are beautiful, but at the time, for the vast majority of the creators, it may be that writing is just easier than producing a 4K Hollywood movie. I feel that’s a little bit like the metaphor you’re talking about right now in gaming.

For the vast majority of people, their story or their idea for their game is actually pretty beautiful. Whether it’s a fashion game like Dress to Impress or it’s a grow garden game, the games are arguably beautiful, even if they don’t look photorealistic. What I think we’ll see is, over time, as AI helps accelerate the ability to make games look really polished in any style the creator wants—could be photorealistic, could be anime, could be a Warner Brothers 2D cartoon look—you and I might say that looks more beautiful, but the core gameplay is still somewhat the original gameplay. I think we are going to see games arguably look more beautiful, even though I think they’re all beautiful.

The dialogue is a bit slow to get underway, but there are many interesting parts.

Do teens regret their social media use?

by  Tyler Cowen in

A new study by Irish researcher Eoin Whelan attempts to answer this. Dr. Whelan told me he was specifically inspired by Haidt’s 2024 claims and sought to examine them rigorously and in the context of other regrets. This is a great use of science…testing dramatic public claims. So…do they hold up?

In Dr. Whelan’s study, 389 young adult participants (20-24) who were social media users as teens were asked about their regrets regarding their teenage years. A list of 20 possible teenage regrets was asked of all participants, with degree of regret marked on a 7-point Likert scale. This is an interesting design…testing social media regrets against other possible regrets, putting them in better context than the crude survey Haidt relied on.

So how did social media regrets hold up? Out of 20 possible regrets, too much time on social media ranked 13th. The top regrets were 1.) not sticking up for oneself, 2.) being too self-conscious, 3.) not documenting memories, 4.) not learning practical life skills and 5.) not getting help with mental health. Girls were slightly more likely to regret time on social media than boys (ranking 11th vs 13th) though this effect was very small (I estimated it at about r = .11) so hardly the big “vulnerable girls” narrative some have peddled.

Further, regrets over time spent on social media as a teen did not predict current young adult life satisfaction for either boys or girls. Thus such regrets may be more a symptom of current panics over social media than anything of actual life importance2. Of the regrets, only not working harder in school and not exercising negatively predicted young adult life satisfaction. Interestingly, having regrets over socializing with friends positively predicted life satisfaction.

As Dr. Whelan noted in his study, “The objective of this study was to critically examine the commonly held belief that social media use during teenage years is a significant source of regret and a predictor of diminished well-being in early adulthood…Contrary to dominant narratives in the public domain, our results suggest that regrets over time spent on social media are not among the most potent regrets reported by young adults…As such, these results align with prior research indicating that the harmful effects of social media may be overstated.”

Here is the full Chris Ferguson Substack.

Can Online Activity Be Regulated? Evidence from Adult Websites

by  Tyler Cowen in

The consequences of online regulations depend on the extent to which users can circumvent restrictions or substitute toward noncompliant platforms. Since 2023, 25 U.S. states have implemented age verification laws that caused prominent adult websites (including Pornhub) to restrict local access for all users. We study how these restrictions affected browsing activity using individual-level panel data. Access restrictions reduced overall time spent on adult sites by roughly 10%. Specifically, for every 100 hours spent on top adult sites before restrictions, about 50 hours remained accessible at noncompliant sites that never restricted access, 30 hours persisted through VPN-based circumvention, 10 hours were substituted from compliant sites to noncompliant sites, and 10 hours were no longer spent on adult sites.

That is from a new NBER working paper by Matthew Brown, Emily J. Davis, and Devin G. Pope.