Category: Web/Tech
Should you text more?
Here, in five waves of panel data (N = 1,966 US adults), we examined associations between life satisfaction and self-reported use of ten common social technologies measured every 3 months on a six-point frequency scale from ‘I did not use’ to ‘multiple times daily’. At this measurement level and timescale, Bayesian and frequentist random-intercept cross-lagged panel models showed little credible evidence that any social technology use predicts subsequent life satisfaction. In the reverse direction, increases in life satisfaction predicted only modest increases in (video) calling in select demographic groups. In analyses comparing different people, frequency of texting was associated with higher life satisfaction, whereas frequency of YouTube and TikTok use was associated with lower life satisfaction. Despite limited ability to detect within-person change due to temporal stability in responses, the absence of cross-lagged effects is informative: there is scant evidence of a meaningful relationship between social technology use and subsequent life satisfaction.
That is from a new Nature article by Kostadin Kushlev, Kibum Moon, Matt Motyl, Nathanael J. Fast & Juliana Schroeder. Via the excellent Kevin Lewis.
A doomsday scenario for American AI
That is the title of my latest Free Press column, here is the closing bit:
Sick and elderly Americans will go to Chinese companies for their AI-invented and AI-tested medical devices and drugs. America still will be a wealthy country, so China will charge the highest prices possible, yet prioritize Chinese citizens for treatment. Large numbers of Americans will die prematurely, at least compared to a world in which many of those innovations came from the U.S. My colleague Alex Tabarrok has coined the phrase invisible graveyard to refer to these lost lives, invisible because we do not observe the state of the world where they get treatment readily and cheaply. Over time, this invisible graveyard will swell into the many millions.
Finally, we will ask what went wrong.
The postmortem will be this. Many people panicked about the possibility of strong AI models killing us all. That fear was not based on peer-reviewed scientific research showing a high chance of doom, nor was doom indicated in any market prices of the time, including measures of risk. It was a story, just like this is a story, and it was spread on social media. The key point of the doom story was that, if America keeps the No. 1 spot in AI models, the models will be so strong they will do us all in, or lead to unimaginable catastrophes. We were too afraid to have America keep the lead, forgetting that if truly destructive AI is our fate, the doomer scenario can come from Chinese AI as well.
Today, we cannot say for sure that the AI doom scenario is false. But is it a story we wish to live by? Is belief in it a good way to protect and extend life, liberty, and the pursuit of happiness? Will it help us much, or for long, if it is Chinese AI that turns on us and does us in?
In my view, successful societies accept challenges and meet them. Solving problems, bit by bit, is the best way to ensure that we have the capabilities to meet big and truly existential risks, should those risks come along. Debating the chances of our doom, ex ante, on a highly speculative basis, is unlikely to provide the same kind of expertise and talent cultivation. It is instead more likely to demoralize and immobilize us.
So which America are we going to choose?
Recommended, do read the whole thing.
Peter Thiel on AI and tech stagnation and democracy
Lots of fresh material, one of the very best Peter Thiel outputs.
Earth fact of the day
An average of 72 percent of respondents said they felt curious, happy or excited about A.I., compared with 41 percent who felt worried, sad or angry.’@nytimes‘s Damien Cave uses @Gallup data to remind us the rest of the world digs AI.
It is 93 percent positive from China. That is from Nick Gillespie. Snap out of it you sad sacks, hope you can recognize negative emotional contagion when you see it! You do not all need to be regional thinkers. Here are the full rankings. Here is another visualization of the numbers:
Strange bedfellows, you might say…
*Fear of Data*
The author is Omri Ben-Shahar, and the subtitle is How Privacy Panic Led Tech Regulation Astray — and How to Fix It. I would describe this book as bracing, and full of substantive engagement. Basically the author wishes to give privacy considerations less weight in social decisions. Excerpt:
What is the concrete evidence for the benefits of facial recognition technology in investigation of human-trafficking crimes? I would love to have found global estimates of the magnitude — of the trafficking victims rescued through the most advanced facial recognition methods — but all I have is a collage of reports [reports are then described].
One chapter is entitled “The Futility of Personal Rights.” Agree or not, this book is full of actual arguments, so I approve.
From the Master of Industrial Organization
Believing that AI will be incorrigible leads us to proposing the wrong solutions, including ones which are likely to increase the danger than decrease it. I think that an AI pause is using the wrong method to fix an ill-posed problem. The point of a pause would be to research AI until we have a deep enough theoretical understanding as to predict what an AI will do. I think this goal will always elude us. I cannot conceive of what this deep theoretical understanding would look like; I don’t think anyone actually does. It’s a call for a stop, not a pause.
The world would not stand still, though. The companies far behind the frontier now would doubtless learn more about what it is that the frontier AI companies are doing. If cooperation were to break down, we would have restarted the race with many more competitors. This is no improvement.
Optimal liability for offensive and defensive AI
How much liability should AI providers bear when their services enable both attack and defence? Liability can improve welfare while increasing harm. Providers sell a common input to productive users, attackers and defenders. Within a defended contest, a higher common price reduces effort without changing attack success or attacker profits, saving resources and improving the target’s security payoff. Compensation weakens defence and raises attacker profits. Optimal liability balances these effects against productive exclusion. Greater competition can lower optimal liability; every such decline must end at an outcome retaining defence. With cybersecurity access fixed, monopoly can warrant partial liability but never full liability when provision is worthwhile. When guardrails preserving productive uses are available, strong competition favours universal guarding socially but encourages unilateral removal at insufficient liability. At a fixed provider count, sufficiently many productive users ensure a pure equilibrium with universal guarding under high liability. A universal-guarding requirement makes liability redundant. Under monopoly, adoption follows a unique liability threshold, while zero liability remains uniquely optimal for a range of parameters with sufficiently many productive users.
That is from a new paper by Joshua Gans.
Dartmouth Provost okie-dokie
On Aug. 26, a Semafor investigation using Pangram to determine whether recent guest columns in major newspapers were written using artificial intelligence identified a column by Schnell as “100% AI-written.” Schnell’s column in The Washington Post — titled “Universities are fighting AI cheating. But there’s a deeper problem.” — argued that “a degree should distinguish what students can do independently from what they can accomplish with AI.
”The Dartmouth subsequently reviewed publications written by Schnell before and after the November 2022 public release of ChatGPT. Pangram 4.0, the latest publicly available version of the AI detector, labeled all of Schnell’s written works prior to 2022 as “100% human-written,” while a test of nine of his works published this year returned a median “AI-written” percentage of 96%. Between 2019 and 2026, Schnell did not publish any academic works or op-eds for which he was the sole author credited, according to his online curriculum vitae.Pangram 4.0 assesses whether a work is AI-written by separating the submitted text into smaller segments, which are then identified as “human,” “AI-assisted” or “AI-generated,” according to a Pangram technical report.
…Schnell’s Sept. 18 email statement to The Dartmouth was itself “100% AI-written,” according to a Pangram test.
Here is the full story, you people crack me up…
Banning self-recursive improvement in AI models?
Some people are suggesting this, including Ezra Klein.
I do not understand how it is supposed to work. Put aside the issue of foregone innovations, let us say I seek to access an AI from overseas. Am I allowed to visit their website? So many sites have AI behind them, including Chinese open source AI. Do all of those sites get banned? And how? Firewall imposed on Americans? And we cannot put the good open source models on our hard drives? How enforced? Does the government have to read the creation logs of the model (how can they?), and then decide whether I can use that model or not?
What if Anthropic licenses its IP to an independent subsidiary in the Cayman Islands and they keep on using code to improve the AI? Or maybe only foreign nations are allowed to have the best models and the really fast rates of improvement? How are multinationals supposed to operate across borders? (Presumably you cannot just use the RSI of your foreign affiliate, but then the whole MNC is crippled and eventually rendered uncompetitive?) How are we to keep “AI sovereignty” for Americans?
If the top U.S. labs have to give up on what is supposed to be really important, they will look very “catchable.” The policy creates a big incentive for some other parts of the world to invest a lot more in compute. How safe is that? And in the short run, do we also have to stop American firms from selling their compute abroad?
Does this whole thing mean that regular companies can use AI to write code, but the main labs cannot? Solve for the equilibrium there. Or the whole code writing function goes away for everybody?
What is the political mechanism through which RSI progress gets turned back on again, and when? Is it as rational as our current debate over data centers? If the top companies are no longer building RSI models, how are they supposed to know how safe or unsafe they might be?
At current margins, are humans even capable of writing the next steps of code that are required for further progress? At whatever speed?
I do not find this to be a workable idea. I think it is “people wanting to do something that sounds reasonable and less scary,” without thinking through the practical details. I have made a related point before, but one does best with AI policy when you start with the stuff that otherwise you might put into your last two or three paragraphs of an essay (“It remains to be seen how…but we are all human beings and surely we should not give up hope…”). Even if you believe in a deal with China, and I do not, we (and others) now know competitors can catch up more quickly than we had thought, at least if they think they have a chance of winning. They might even buy some space compute from Elon.
C’mon people, I know for many of you this is a difficult take to swallow, because it feels like “we don’t care enough” if we do not take some dramatic steps. Nonetheless the reality is that, a long time ago, we made a whole series of decisions that imply…we have no other choice than to just see this one through and get it more or less right. The longer we deny that truth, the harder it will be for all of us.
Further results on AI and labor market reallocation
This paper documents how artificial intelligence has affected stocks and flows in the U.S. labor market. Combining CPS and JOLTS data with AI exposure and adoption measures from OpenAI and Lightcast, respectively, we construct labor force stocks, worker flows, and market tightness by AI exposure and adoption. Since the introduction of LLMs, workers with high AI exposure and adoption have experienced larger declines in job-finding and job-switching rates than other groups, while their within-job activity switching has increased noticeably relative to others. The positive effects of AI on nominal wage growth and hours worked attenuate markedly in the post-LLM period, suggesting that LLM-driven reallocation has operated through within-firm task reorganization and a reconfiguration of labor inputs, alongside weaker demand for workers most exposed to AI. To quantify LLM-driven reallocation pressure, we construct individual-level hirability and separability indices and showthat dispersion in job-finding prospects has risen markedly since 2023, largely driven by the AI factors. The natural rate of unemployment—recovered from the trend components of unemployment inflows and outflows by AI exposure and adoption, as well as from a theoretical model of structural unemployment—is estimated to have risen by about 0.1-0.2 percentage point since the introduction of LLMs, albeit with considerable uncertainty.
That is from a new NBER conference paper by Hie Joo Ahn and Nicholas A. Carollo. Via Inclusive Productivity Network and Alex Imas.
Obama on agentic AI
Obama recently said:
“If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet.”
As Roon noted: “this sounds completely incoherent to me”
From Rob Saker:
An agent is a system that can plan, use tools, write code, query data, run experiments, check its own work, and keep going. That is how the work gets done. Treating “agentic” as optional decoration, as if the serious version of AI is a polite chatbot locked in a box while the unserious version “roams free on the internet”, is the kind of sentence you write when you’ve heard the buzzwords and never watched a lab actually use the technology. You do not cure cancer with a model that only answers questions. You cure cancer with systems that can read the literature, propose hypotheses, design assays, analyze results, rewrite the next experiment, and do that loop a thousand times faster than a human postdoc. That loop is agentic. Strip the agency out and you are left with a very expensive autocomplete.
I agree with those points, but my main concern is different. How would we enforce such a prohibition on commercial agents? Set up a Chinese-like firewall that bans Americans from accessing sites with foreign agents? Monitor all those sites over time, so we know which suppliers to ban? Ban VPN as well? Give our government the power to inspect hard drives, in case agentic functions might be embedded there? Set up FBI “phishes,” luring Americans in with the prospect of agent access from abroad, and then arresting them, as we do with child ****?
Something else? How about insisting that all American (and foreign?) web sites set up tough captcha problems, so that agents may not be used (ha ha)?
You might also ask how an AI “agent” is to be defined, after all even O3 had some “agentic” abilities, such as opening up museum web sites to see which exhibits are on. Is it typing things into boxes and filling out forms that is to be prohibited? How much regulation of software would that require, how would that regulation actually occur, and what else would end up being restricted?
I do not think Obama intends to be supporting massive restrictions on freedom of speech and civil liberties. Rather this is a good example of how some political factions will simply have “ideas about what might be good,” without having freedom concerns — or for that matter practicality and civil liberties concerns — center of mind in the first place.
And this would cause the immediate bankruptcy of both Anthropic and OpenAI, right?
I can readily imagine that, upon the advent of agentic AI, we need some significant changes in our cyber laws. Now would be a good time to both commission and also cite some peer-reviewed academic literature on this matter!? That is not an impossible thing to do, yet our public discourse seems oddly resistant to the notion.
Addendum: And do not forget the classic Gwern piece, remarkably early in its prescience.
Muse
It is excellent. It has one of the best user interfaces I have seen on any product. The model is good and the speed and clarity exemplary. It integrates with your calendar and email, if that is want you want, very easily and smoothly. It all feels very…American.
Kudos to the team!
Internet diffusion and religious decline?
Using a panel of 81 countries across six World Values Survey waves (1990–2022), we examine whether growth in internet diffusion is associated with religious decline. We construct a six-item composite religiosity index, align annual macro series to wave periods, and estimate country fixed-effects models with one-wave lags and a broad set of modernization, demographic, welfare-state, and cultural controls. We also estimate models separately for individual dimensions of religiosity. Across specifications, lagged internet penetration is negatively associated with religiosity within countries over time. These results remain robust under period dummies and in models estimated without lags, although reverse-ordering tests indicate that prior religiosity can also predict later internet use. Associations are strongest for denominational belonging and self-identification as a religious person. Overall, internet diffusion emerges as a robust macro-level correlate of within-country religious decline.
Here is the full article by Miran Lavrič and Tibor Rutar. Via the excellent Kevin Lewis.
Scott Beaulier interviews me, in part about Wyoming
Reimagining research papers as interactive and reliable AI agents
That is a new Nature paper by Jiacheng Miao, Joe R. Davis, Yaohui Zhang, Jonathan K. Pritchard, and James Zou. Here goes:
Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper’s code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent’s effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.
As I have been saying for a while now, there is much more of this coming down the pike. Via Charles Klingman.