Category: Web/Tech

What should I ask Tom Griffiths?

Yes I will be doing a Conversation with him.  Looking at Wikipedia:

Thomas L. Griffiths (born c. 1978) is an Australian academic who is the Henry R. Luce Professor of Information Technology, Consciousness, and Culture at Princeton University. He studies human decision-making and its connection to problem-solving methods in computation. His book with Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions, was named one of the “Best Books of 2016” by MIT Technology Review…

Griffiths released The Laws of Thought: The Quest for a Mathematical Theory of the Mind in 2026. Siobhan Roberts describes it as “a rigorous and captivating account of how cognition can be modeled via three mathematical frameworks: logic, artificial neural networks (“mathematical systems that emulate the operation of the brain”) and probability theory.”

Here is his research page, here is his new book on Amazon, I thought the book was excellent.

So what should I ask him?

My excellent Conversation with Luis Garicano

Here is the audio, video, and transcript.  Here is part of the episode summary:

Tyler and Luis start their conversation with Spain — housing, NIMBYism, and the productivity crisis; Spanish literature and why the Civil War still looms so large; and what Chicago taught Luis about party discipline in European politics. Then to the EU’s unanimity problem, capital markets, and Denmark’s flexicurity model; a round of overrated-versus-underrated on Rosalía, Penélope Cruz, and Sgt. Pepper’s; and finally into Messy Jobs — why retraining programs fail, whether AI’s leisure dividend has arrived, and whether Spain could ever achieve AI sovereignty.

Excerpt:

COWEN: Why is there a current productivity crisis in Spain?

GARICANO: Productivity indeed hasn’t grown for three decades, more or less. We’re currently having extensive growth. We’re having immigration, we’re having tourism, but we don’t really have productivity. A lot of it has to do with the political economy. I think if you want to explain the West, not just Spain, you have to understand who is voting and who is this being governed for. Spain is a particularly low-fertility, high life-expectancy country. We have the fifth-lowest fertility and the fifth-highest life expectancy in the world grosso modo, and very high pensions.

Essentially, all the GDP growth we’ve had has gone, 100 percent of the GDP growth we’ve had since 2008 has gone to pensions, to the pensioners. In terms of investment, there is very little in terms of productive investment. We have this fantastic highway network and this high-speed rail network. It’s not really getting the maintenance it needs. Just as one example, the country is basically being governed by and for the older retired people.

COWEN: What does the optimistic scenario look like? You don’t have to predict it’s going to happen, but lay out for me how it could all go well. You would get productivity growth of 1.5 percent a year, and economic growth a bit higher than that.

GARICANO: Spain has amazing fundamentals for the current situation, meaning we could easily be very electricity-energy rich due to solar, wind, and nuclear. We have all this empty space where you could put nuclear plants without much resistance. In fact, we are closing them for political reasons. The energy could be a big advantage. It’s a really amazing place to live. There is no more diverse geography and climate and nature anywhere in Europe, I think, or close to, and beautiful. You could easily see a situation where Spain becomes Florida, or Austin, Texas. Think of Texas. It attracts technology, attracts talent who wants to live there, attracts energy, builds the data centers, et cetera. That scenario is not impossible. The political economy is the tricky part.

COWEN: Doesn’t that mean you actually don’t have good fundamentals? You said you don’t even have a YIMBY movement. Life there really is quite good. I’ve been many times. It’s one of my favorite countries to visit. Isn’t that like a resource curse where there’s no sense of crisis? Old people live for a long time. It’s very comfortable. The weather’s great. The food is amazing. Aren’t those, in fact, liabilities in a time of very rapid change?

And another, on a very different topic:

COWEN: On Messy Jobs, your new and excellent book, you argue very persuasively, “In my view, AI will not lead to anything like mass unemployment, maybe not even to a rise in unemployment, because jobs will become messier and the AIs won’t be able to do them.” Is that a fair description of part of your argument?

GARICANO: I think that’s completely right. Yes.

COWEN: Now, I agree with you, but I think my worry is the opposite, that I know a lot of people, they want simple jobs, they’re okay with some measure of tedium, and that if most jobs become messy jobs, for them, that’s quite stressful, and they’re upset and disoriented. Do you worry about that?

GARICANO: I think that we will have to have a tolerance for human relations. Is that what your friends don’t want? We have a tolerance for relational work that is complex, that has politics in it, that has a big human component, and that’s the part that is going to stay. If people just want to be in their desk typing away, I think two good ways to think about it is work from home and outsourcing. If you think of which jobs were offshored, let me say offshored, in the big offshoring wave to India, those are jobs that are not messy. They’re clean. The company specifies the jobs. They say, “Okay, we can specify this job perfectly. Let’s ship you up.”

Those jobs are the same exact ones that are under complete threat of disruption. A lot of the work from home, when it doesn’t involve a lot of submittings, I guess, has the same feature. Those jobs are clean, single-task, and very often verifiable. You can just see how the performance is going and have your RL, your reinforcement learning loop work on those. I think those are gone.

The messy component that I want to emphasize, many people will say, “Oh, AI will tend to the messiness as well.” I think that there is some messiness that is contingent and that AI can streamline, but there is a lot of messiness that is both relational and has to do with a deeper aspect of the knowledge problem that doesn’t really go away. As AI advances, many aspects of Hayek, Polanyi, and all these knowledge problems are still there.

COWEN: How much retraining will be required and how frequent will that retraining have to be? If I think of me working with agents, I have to retrain myself every month or two. That’s difficult for me. It’s not stressful given my position, but I can imagine it would be stressful. Can we really just put a big chunk of the labor force through that?

Definitely recommended.

What should I ask Moxie Marlinspike?

Yes I will be doing a Conversation with him, live at the Roots of Progress event next week.  From Wikipedia:

Moxie Marlinspike is an American entrepreneur, cryptographer, and computer security researcher. Marlinspike is the creator of Signal, co-founder of the Signal Technology Foundation, and served as the first CEO of Signal Messenger LLC. He is also a co-author of the Signal Protocol encryption used by Signal, WhatsApp, Google Messages, Facebook Messenger and Skype.

There is much more at the link, for instance he is also an anarchist of some kind or another.  So what should I ask him?

The polity that is Singapore

Police in Singapore have charged a man who is accused of posting an AI-generated image of a saltwater crocodile in a popular reservoir.

Ye Lin was charged with communicating a false message and obstructing the course of justice for allegedly deleting the picture and the application he used.

The fake image caused public concern, authorities allege. The national water agency suspended its work at the city-state’s largest reservoir for two days last month after receiving information that a crocodile had been spotted.

Here is the full story, via Kyle.

Don’t let AI make you dumber

That is the topic of my latest Free Press column, here is one excerpt:

I do not think the skeptics would put it this way, but as I read Conti, I find he has a pretty bleak fundamental view of humanity. Are we all really just looking to veg out and abandon curiosity and inquiry, at least once the machines have taken care of both the basic functions of life and certain higher aims such as scientific research? I think some people are like that—indeed you might say many people—but it does not reflect what I take to be the general human condition.

If I look at most people who might fit into the “middle class” when it comes to intellectual pursuits or educational status, I observe they have a lot of strong interests. This might play with their pets, improve their performance at sports, or learn how to cook better. You do not have to identify those preferences with “the new Athens” or “the next Mozart” to think they are perfectly good and noble ways for people to spend their time.

Most of us want to do something interesting and stimulating with our leisure time, and if we do not, it is often because our jobs are so busy and stressful that we just wish to decompress. Of course, in this radical vision of our AI future, fewer jobs will be so all-consuming and so more of us will use vacations and leisure time to explore and learn rather than to just sit on the beach scrolling our phones. And to the extent some jobs do remain hectic, or become even more so (such as cybersecurity), they will continue to be challenging and intellectually stimulating.

A related worry is that humans may feel they simply cannot compete with the AIs, and thus they might turn away from creative pursuits. It is true that I, more than ever, have given up all hope of proving new theorems in mathematical economics. But many of my intellectual and creative pursuits do not involve competition at all. For instance, I use AI to understand classical music better, asking the models questions before I sit down to listen to a piece. (Such as “which are the best recordings?” and “what should I listen for in the second movement?”) As the models get better and smarter, I am not going to be discouraged in this endeavor, as I was not “competing” with the models to see which of us knew more. Rather, I will gratefully end up much better informed about classical music—my increasing knowledge has already induced me to see more live concerts.

Recommended, and AI saved me time on the proofreading and fact-checking (not the writing!), so I could return to reading China Mieville…

The Macroeconomic Effect of AI through software engineering

We measure how artificial intelligence (AI) affects the economy through its impact on software engineering productivity. We use information from financial markets to develop a forward-looking measure that is available in real time. We estimate the sensitivity of each firm’s stock return to an AI stock market index, and how this sensitivity depends on the share of firm payroll in software engineering. We use a model to map this cross-sectional relationship into software engineering productivity gains. From November 2022 to December 2025, AI increased the market’s expected present value of software engineering productivity by the equivalent of a permanent 32.6% productivity increase. The corresponding effect on the level of GDP is 3.6% in the baseline and 6.5% when higher software engineering productivity also raises R&D productivity. By mid-2026, amid rapid progress in coding agents, the effect of AI on productivity and GDP had more than doubled relative to the end of 2025.

That is a new NBER working paper by Alex Blumenfeld, Jonathon Hazell, Chen Lian & Andreas Schaab.  This is also a simple way of showing that markets do indeed price in the effects of AI.

AI in science

Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements—LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.

That is from a new paper by Mihai Codreanu, et.al.

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.

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:

Image

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.

Nicholas Decker throughout.

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.