Category: Web/Tech
Sentences to ponder
This matters for the AI question, and the book leaves it unfinished. If the breakthroughs of the past required social conditions, not just cognitive capacity, then what does it mean when the next breakthroughs are produced by systems that have no social conditions at all? A neural net does not need a university chair or financial independence from the church. It does not need to reorganize its commitments. It does not, in any recognizable sense, have commitments. The machine that replaces the marginalist is not a better marginalist. It is a different kind of thing entirely.
That is from Jônadas Techio, presumably with LLMs, this review of The Marginal Revolution is interesting throughout. And this:
Maybe the book demonstrates only that Cowen personally remains good at something the field no longer needs.
*The AI Doc*
The subtitle of the movie is Or How I Became an Apocaloptimist, and here is the trailer.
Overall this film was better and smarter than I was expecting. Intelligent people were allowed to speak, and to present various sides of the issue. It was also interesting to see how various people one knows come across on the big screen.
It is easy enough to mock the final section of the movie, which calls for a participatory “civil rights” movement on AI, negotiations with China, and a big voice for trade unions in the decisions. What Dan Klein calls “the people’s romance.” The Straussian read there is correct, even though it probably was not intended by the moviemakers. In reality, for better or worse, the final decisions will continue to be made by the national security establishment.
On a weekend, there were five other people in the theater.
Is Tinder actually OK?
Online dating apps have transformed the dating market, yet their broader effects remain unclear. We study Tinder’s impact on college students using its initial marketing focus on Greek organizations for identification. We show that the full-scale launch of Tinder led to a sharp, persistent increase in sexual activity, but with little corresponding impact on the formation of long-term relationships or relationship quality. Dating outcome inequality, especially among men, rose, alongside rates of sexual assault and STDs. However, despite these changes, Tinder’s introduction did not worsen students’ mental health on average and may have even led to improvements for female students.
That is from a new paper published in AEJ: Applied Economics, by Berkeren Büyükeren, Alexey Makarin, and Heyu Xiong.
A bilateral AI pause?
Dean ball has some thoughts and hesitations:
Here are some questions I wish “Pause” and “Stop” advocates would address:
1. Assuming we achieve the desired policy goal through a bilateral US/China agreement, what would be the specific metric or objective we would say needs to be satisfied in advance? Who decides whether we have satisfied them? What if one one party believes we have satisfied them but the other does not?
2. If the goal is achieved through a bilateral US/China agreement, would we need capital controls to ensure that U.S. investors cannot fund semiconductor fabs, data centers, or AI research labs in countries other than the U.S. and China?
3. Would we need to revoke the passports of U.S.-based AI researchers and semiconductor engineers to prevent them leaving America to join AI-related ventures elsewhere? How else would the U.S. and China keep researchers within their borders?
4. How should we grapple with the fact that (2) and (3) are common features of autocratic regimes?
5. Do the above questions mean that this really should be a global agreement, signed by all countries on Earth, or at least those with the theoretical ability to host large-scale data centers (probably Vanuatu doesn’t need to be on board)?
*The Marginal Revolution: Rise and Decline, and the Pending AI Revolution*
I am offering a new piece of work — I do not quite call it a book — online and free. It has four chapters, is about 40,000 words, is fully written by me (not a word from the AIs), and it is attached to an AI with a dual page display, in this case Claude. Think of it as a non-fiction novella of sorts, you can access it here. You can read it on the screen, turn it into a pdf (and upload into your own AI), send it to your Kindle, or discuss it with Claude.
Here is the Table of Contents:
1. What Is Marginalism?
2. William Stanley Jevons, Builder and Destroyer of Marginalism
3. Why Did It Take So Long for the Science of Economics to Develop?
4. Why Marginalism Will Dwindle, and What Will Replace It?
Here are the first few paragraphs of the work:
How is it that ideas, and human capabilities, become lost? And how is that new insights come to pass? If eventually the insight seems obvious, why didn’t we see it before? Or maybe we did see it before, but didn’t really know we were on to something important? Why do new insights arrive suddenly, in a kind of flood? How do new worldviews replace older ones?
And what does all of that have to do with the future of science, the future of research, and the future of economics in particular? Especially when we try to understand how the ongoing artificial intelligence revolution is going to reshape human knowledge, and the all-important question of what economists should do.
Those are the motivating questions behind this work, but I will address them in what is initially an indirect fashion. I will start by considering a case study, namely the most important revolution in economics, the Marginal Revolution (to be defined shortly). The Marginal Revolution made modern economics possible. What was the Marginal Revolution? How did it start? Why did it take so very long to come to fruition? From those investigations we will get a sense of how economic ideas, and sometimes ideas more generally, develop. And that in turn will help us see where the science, art, and practice of economics is headed today.
Recommended! I will be covering it more soon.
Solve for the China tech equilibrium
Authorities in Beijing have barred two executives from a Singapore-based AI firm from leaving China amid a review of the company’s $2 billion acquisition by U.S. social media giant Meta, according to a report by the Financial Times on Wednesday.
Xiao Hong and Ji Yichao — the CEO and chief scientist, respectively, of Manus — were summoned to Beijing this month and questioned over a possible violation of foreign direct investment reporting rules related to the acquisition before being told they could not leave the country, the report said.
Here is more from The Washington Post. In my view, the American lead in AI is somewhat larger than a model comparison alone might suggest.
Ryan Hauser interviews me in print
Here is the link, here is one excerpt:
What was your path into AI, and what are you working on now?
I first became interested in AI when I saw the chess computer Tinker Belle wheeled into a New Jersey chess tournament in I think 1975. I followed the Kasparov matches closely, and the more general progress of AI in chess. I read chess master David Levy telling me that chess was far too intuitive for computers ever to do well. He was wrong, and then I realized that AI could be intuitive and creative too. That was a long time ago.
In 2013 I published a book on the future of AI called Average is Over. I feel it has predicted our current time very accurately. I also taught Asimov’s I, Robot – a work far ahead of its time – for twenty years.
Right now I am simply working to keep afloat and to stay abreast of recent AI developments. I blog and write columns on the topic frequently, and have regular visits to the major labs. I encourage universities to experiment with AI education.
I mention William Byrd and Paul McCartney as well.
What should I ask David Baszucki?
Yes I will be doing a Conversation with him. From Wikipedia:
David Brent Baszucki (/bəˈzuːki/ buh-ZOO-ki; born January 20, 1963) is a Canadian-born American entrepreneur, engineer, and software developer. He is best known as the co-founder and CEO of Roblox Corporation. He co-founded and was the CEO of Knowledge Revolution, which was acquired by MSC Software in December 1998.
Roblox (/ˈroʊ.blɒks/ ⓘ, ROH-bloks) is an online game platform and game creation system developed by Roblox Corporation that allows users to program and play games created by themselves or other users. It was created by David Baszucki and Erik Cassel in 2004, and released to the public in 2006. As of February 2025, the platform has reported an average of 85.3 million daily active users. According to the company, their monthly player base includes half of all American children under the age of 16.
So what should I ask him?
Dwarkesh chats with Terence Tao
Some more slow take-off, driven by start-ups
So far, however, the predictions that the mass automation of coding will leave outsourcing firms obsolete seem overblown. Their clients often hope AI will create huge productivity gains by, for example, using the technology to quickly and cheaply build a new internal HR tool. But such improvements in productivity are only possible in “greenfield” environments with “clean architecture”, argues Atul Soneja, chief operating officer at Tech Mahindra, an IT firm. Deploying AI in “brownfield” environments—with legacy code, a lack of documentation and multiple systems that must all continue to operate in real time—is far trickier. In the end, clients often realise that their AI dreams were too ambitious and end up hiring as many outsourced coders as before, say executives.
What is more, the AI boom may present an opportunity for the consultancy arms of India’s outsourcers. They argue that they can now fulfil more of a strategic role for their clients: getting the most out of AI requires understanding all of the context around the problem, something that consultants with experience across businesses can offer. Nandan Nilekani, one of the founders of Infosys, reckons that such services related to AI could be worth $300bn-400bn by 2030.
Here is more from The Economist.
Those new service sector jobs?
An AI memory startup called Memvid is offering $800 for a one-day, eight-hour shift for one candidate to “bully” AI chatbots by telling them what to do on camera.
Business Insider reported this week that Memvid wants someone to spend eight hours testing and critiquing the memory of popular AI chatbots, effectively paying $100 an hour for what they have branded as a “professional AI bully” role. The worker’s job is to examine where chatbots lose track of details, forget context or misrepresent data, and then feed those findings back to Memvid so the startup can improve its products.
“You’ll spend a full 8-hour day interacting with leading AI chatbots — and your only job is to be brutally honest about how frustrating they are,” the job listing reads.
The draw is that the role doesn’t require a computer science background, AI credentials or any kind of work experience. “No prior AI bullying experience required — we all start somewhere,” the listing reads.
The requirements are deeply personal. The first requirement is an “extensive personal history of being let down by technology,” and the second desired trait is “the patience to ask a chatbot the same question four times (and the rage when it still gets it wrong).”
Here is the full article, via the excellent Samir Varma.
Is AI currently helping economic research?
The third possibility, that AI helps to weed out mistakes, is trickier for the discipline. This stage could become even more important if journals do start to be hit by a wave of AI-generated slop — or, perhaps more likely, good papers with so many appendices and robustness checks that even the most dedicated referee is defeated. (The real “Dr Robust” does not have infinite energy.)
Eager to embrace the new technology, several of the top five economics journals are already experimenting with Refine, an impressive AI-powered reviewing tool that scours economics papers for errors. Ben Golub, one of its creators, shared that even with papers that had been through referees at top journals, Refine was picking up problems in at least a third of cases.
Here is more from Soumaya Keynes at the FT.
Some simple economics of AI?
AI lowers the cost of building businesses. But it raises the bar for sustaining advantage. More companies can start. Fewer can dominate.
That implies greater dispersion. More volatility. Less structural concentration. A market that rewards adaptability rather than mere size.
And it raises the question that follows logically from duration compression: if software moats erode faster, where does durable advantage reconcentrate? The answer may be in the places that resist compression, physical infrastructure, energy constraints, material bottlenecks, regulatory barriers. The assets that cannot be replicated with model access and API credits. The things that still require time.
Equity does not disappear in this world.
It transforms.
From ownership of stability to exposure to speed.
From franchises to call options.
And that is the structural shift beneath the surface panic, the real story unfolding in the Age of Agents.
Here is more from Jordi Visser.
The AI arms race
That is the topic of my latest column for The Free Press, here is the closing tag:
The biggest risk is not from the AI companies, but rather that the government with the most powerful AI systems becomes the bad guy itself. The U.S., on the world stage, is not always a force for good, and we might become worse to the extent we can act without constraint. The Vietnam War is perhaps the least politically controversial way of demonstrating that point.
So today we need an odd and complex mix of not entirely consistent ideologies for the current arms race to go well. How about some tech accelerationism mixed with capitalism, and then a prudent technocratic approach to military procurement, to make sure those advances serve national security ends? On the precautionary side, we need a dash of the 1960s and ’70s New Left and libertarian anti-war ideologies, skeptical of Uncle Sam himself. We do not want to become the bad guys.
Do you think we can pull that off? The new American challenge is underway.
Worth a ponder.
Why you should work much harder RIGHT NOW
If strong AI will lower the value of your human capital, your current wage is relatively high compared to your future wage. That is an argument for working harder now, at least if your current and pending pay can rise with greater effort (not true for all jobs).
If strong AI can at least potentially boost the value of your human capital, you should be investing in learning AI skills right now. No need to fall behind on something so important. You also might have the chance to use that money and buy into the proper capital and land assets.
So…WORK HARDER!
Addendum: From Ricardo in the comments:
Suppose you are the best maker of horse carriages in Belgium around the time the automobile is invented. You might want to take on as many orders as possible for new carriages because you know your future is precarious. Or, maybe you get your hands on one of these new-fangled automobiles as soon as possible and learn how fix them. Both options require you to WORK HARDER but these seem to be the two best options available. Paradoxical but true.