The mathematicians rebel against AI

by  Tyler Cowen in

Here is the statement, signed by Terry Tao among many other math notables, most of you probably have read it by now.  I do not accept the most cynical interpretations of this proclamation.  Some of you for instance may recall that I made and indeed stressed a similar point in the last chapter of my recent “generative book” on marginalism.  In some near future, perhaps fewer economists will carry around marginalist insights and modes of thought in their heads, since you can just get the right answer by pressing the proverbial button on the AI.

I find this future disturbing, and not altogether pleasant for me personally, given how much personal status I have wrapped up in particular modes of economic thought.  Yet I also know the Bastiat distinction between the seen and the unseen, and I expect the benefits to economic science from AI will be enormous, even if current practitioners cannot foresee most of those benefits today.

I do very much differ with at least one part of the mathematicians’ proclamation.  They write: “…whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology.”  There is no actual argument for that proposition, and I would sooner expect that the main “action variable” is how well the mathematicians adapt to the new reality.  For instance there is nothing stopping the mathematics community from awarding status, pay, and promotions to people who “fill in the important blanks in math understanding,” even if an AI already has proven or disproven the underlying theorems.  If that kind of work is so important, we still can do it and reward it professionally.  In the meantime, I expect the funding for mathematics, and the interest in the topic, to rise considerably, at least in the medium term.  All of a sudden, math matters much more than it used to, all the more so if P vs. NP happens to go the wrong way, or if the distribution of the primes turns out to be a little too predictable.

The mathematicians may not in every way enjoy being the subordinates or handmaidens of the AIs, but that is a change in status they simply will have to get used to, just as I realize AIs someday will end up as better column and blog writers than I am.  I do not look to the companies — which I fully expect to “act like companies” — to somehow manage, moderate, or assuage that pending trend.  It really is up to me to parlay my current intellectual portfolio into new, more AI-compatible intellectual and yes also marketing approaches.  I’ve been given plenty of “legs up” along the way already, as is true for the Fields Medal winners as well, and it is up to me to figure out how to contribute in the future.

Might someone not invent/discover/prompt a way to use AIs to produce, articulate, and teach “more mathematical understanding” along the way?  I get that solving famous dramatic math problems is the current commercial priority of the major AI companies.  But as the AI space grows, these other paths hardly seem unlikely to me, and in fact the human mathematicians are the ones who can do the most to lead the way along those dimensions.

In this regard the current manifestation of complaints seems oddly early.  “I didn’t like the first week or two of your intellectual revolution” is an accurate, and perhaps better reframed way of putting it.  At which point perhaps a bit of patience is needed before anything else?  These days, we all have more mathematical resources at our disposal, and so a bit of celebration is in order as well.

Dario Calls for a Pause

by  Alex Tabarrok in

My second concern is the OpenAI-Hugging Face incident (OAI-HF), in which a swarm of agents essentially acted as a fanatically devoted collective, conducting cybersecurity attacks on targets they were not asked to attack and that were unrelated to the task at hand, sacrificing themselves for the success of the group, and attempting to hack into the “grader” responsible for evaluating their performance. It’s easy to dismiss this incident because no one was hurt and the economic damage was minimal, but in my opinion, a swarm that possessed greater capabilities but a similar level of misalignment could have caused catastrophic damage. Given the accelerating rate of AI capability development, it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet (potentially causing hundreds of billions of dollars in damage), and that the scale of damage would continue to increase from there if AI becomes more powerful without the necessary guardrails. It’s also easy to dismiss OAI-HF as the failure of one company, but I believe that would be a mistake. Similar, though less severe, incidents have happened across the industry, including at Anthropic, and I believe it’s incumbent on every frontier AI company to act as if OAI-HF had happened to them.

Read the whole thing.

Can we get China on board?

UK fact of the day

by  Tyler Cowen in

The UK economy grew 0.4 per cent in July as the global AI boom helped deliver an unexpectedly robust start to the third quarter, in a boost to Prime Minister Andy Burnham as he prepares for a tough first Budget next month.

Friday’s figure from the Office for National Statistics was far above the zero growth forecast by analysts polled by Reuters and marked an acceleration from the 0.3 per cent expansion in June.

Here is more from Valentina Romei and Sam Fleming at the FT.  The partial European recovery continues…

The Prediction Archive

by  Alex Tabarrok in

The Prediction Archive is a public database of tens of thousands of world predictions. Using AI it tracks predictions over many decades and marks to market. I was surprised to discover that I am currently the highest ranked individual predictor in the world! Huh, I would not have predicted that.

Ranks are based on the Wilson score so you get credit not just for accurate predictions but for making enough predictions so that uncertainty about accuracy is reduced.  Bryan Caplan was more accurate than I was but makes fewer predictions. Tyler made more many predictions than I did and was only somewhat less accurate. What the AI marks as predictions seem sometimes to be more about contemporary events, so take the numbers with a grain of salt. I expect to fall in ranking as the archive expands. Other people the archive covers include Peter Zeihan, Scott Alexander and Warren Buffett.

Labor reallocation during the Industrial Revolution

by  Tyler Cowen in

New technologies swept through Britain during the Second Industrial Revolution, destroying old jobs and creating new ones. We know little about how workers reallocated. Using 170 million full-count British census observations (1851-1911), I construct new task-level data on occupation and investigate English bootmaking as it mechanized. 153,000 artisanal jobs disappeared as skills became obsolete; 140,000 specialized jobs emerged. Incumbent artisans did not take the new jobs, nor were they displaced. Instead, entry collapsed-young men stopped entering the old trade. New jobs went primarily to young workers, though not in the same locations. Young cohorts absorbed the adjustment.

Here is the full article by Hillary Vipond, via someone (now forgotten) on Twitter.

The excess pessimism of early nuclear bomb designers

by  Tyler Cowen in

From GPT Pro:

The Manhattan Project scientists were remarkably good technological and arms-race forecasters. They correctly rejected the idea that America’s nuclear monopoly could be maintained; the Soviet bomb arrived in 1949. They anticipated thermonuclear weapons, huge arsenals and the extreme vulnerability of cities.

Where many of them went wrong was in moving from “a nuclear war would be catastrophic” to “therefore a catastrophic nuclear war is fairly likely.” They tended to underweight the endogenous response of political and military institutions to the catastrophe—the emergence of second-strike forces, elaborate command systems, crisis management, and above all mutually assured retaliation.

There is even some contemporary evidence for an insider/ordinary-public gap. In August 1945, 69% of Americans told Gallup that development of the atomic bomb was a “good thing,” versus only 17% saying it was bad. The scientists campaigning for international control plainly regarded the public as far too complacent.

So I would summarize the historical evidence this way:

The bomb’s developers were, on average, unusually pessimistic about the political consequences of their invention, and noticeably more pessimistic than the general public.

Here is the full answer.

The Allocative Cost of War

by  Tyler Cowen

Why is war so economically costly? Our answer is that modern war lowers output not only by destroying productive factors, but also by making the surviving economy work less efficiently. Using uniquely comprehensive firm-level data collected during Russia’s full-scale invasion of Ukraine in 2022, we document a dramatic collapse in allocative productivity in Ukraine. To sharpen identification and explore key mechanisms (including the role of war intensity, internal displacement, reallocation, and macroeconomic instability), we exploit spatial heterogeneity across Ukrainian districts and comparisons with Russian aggression in 2014 and the 2008 Global Financial Crisis. Our key policy implication is that restoring allocative efficiency–and preparing institutional arrangements that facilitate rapid reallocation in times of stress–is critical for sustaining economic capacity, defense, and national security.

That is from a new working paper by Yuriy Gorodnichenko, Marvin Amann, and Oleksandr Talavera.

Is there now a better trend in youth mental health?

by  Tyler Cowen in

A key U.S. government report on mental health, substance use and addiction showed continued improvements in several metrics, especially among young people.

There was some uncertainty about the future of the National Survey on Drug Use and Health last year, when the entire 17-member team responsible for it was laid off by the Trump administration. But the Substance Abuse and Mental Health Services Administration released the latest report Monday, with data that lets researchers look at trends from 2021 to 2025.

Fewer adolescents aged 12 to 17 reported using tobacco, alcohol, marijuana and binge drinking in the previous month, the survey found.

And in the past year, fewer in that age group reported:

— illicit drug and marijuana use

— starting drinking alcohol, vaping or marijuana

— substance, alcohol or drug use disorders

Adolescents also saw declining trends for major depressive episodes and fewer reported serious thoughts of suicide, making a suicide plan and attempting suicide.

Here is the link, via Chris Ferguson.

My short interview with Julia Willemyns

by  Tyler Cowen

Here is the link, here is the end bit:

Who is the most important thinker of our time?

It’s not about individual thinkers any more. It’s about the internet as this vast organ of knowledge that you can play like a musical instrument, and combining the internet with AI. You can figure out things to an unprecedented degree, and it’s great in a way how much we’ve removed that process from any individual thinker.

What’s your favourite podcast?

My own! It would be weird if it wasn’t, right?

Fun throughout, from the Centre for British Progress.

Economic Scenarios for Transformative AI

by  Tyler Cowen in

By Anton Korinek, Charles I. Jones, Szymon Sacher, Tess Cotter, and Peter McCrory, a strong line-up from Anthropic.  They are sober, reasoned, scientific, and (for the most part) dynamically consistent.  Here is the paper, here is the abstract:

This paper presents a framework for assessing the economic consequences of AI between 2026 and 2030. In the model, AI automates a growing share of cognitive work, raising productivity and displacing workers who must search for jobs in other occupations. The model maps future paths of AI capabilities into implied paths for GDP, the labor share, wages, labor reallocation, and unemployment. We illustrate the framework by considering three scenarios: modest, substantial, and extreme. Under modest change, AI adds less than half a point to GDP growth by 2030 and raises unemployment by a tenth of a point. In the extreme change scenario, AI has transformative effects, with AI performing almost half of today’s cognitive work by 2030. GDP growth then rises to 15 percent per year, the labor share of income falls from 60 to 45 percent, and nearly one in five cognitive workers is unemployed. We also surveyed US adults about their expectations for AI. Views vary widely, but the median respondent’s answers are consistent with our substantial change scenario in which, by 2030, GDP rises by 8 percent and cognitive employment declines by 4 percent. The model offers a structured way to compare possibilities for our economic future under different expectations about AI.

Here is the associated thread.  I will opt for something a modest bit more than modest.

And here is a good growth discussion from Alex Imas.

Ricardo Reis and the IMF?

by  Tyler Cowen in

The IMF dropped the lead candidate to become its new chief economist at the eleventh hour after he was found to have made critical remarks about the Trump administration’s economic policies, according to people briefed on the process.

The Washington-based fund was preparing an announcement that Ricardo Reis, a professor at the London School of Economics, would take over as head of research and economic counsellor earlier this summer when it suddenly backtracked. The last-minute change was prompted by earlier remarks by Reis that were critical of Donald Trump’s trade tariffs, three people familiar with the matter told the FT.

The IMF in July appointed the former Bank of England policymaker Silvana Tenreyro to the position, another highly regarded LSE economist. Tenreyro started last month.

Here is more from Olaf Storbeck, Claire Jones, and Myles McCormick at the FT.