Tuesday assorted links
1. Meta’s AI-powered Ray-Bans are life-enhancing for the blind (WSJ).
2. Removal of all NEPA regulations?
3. New betting odds for a Russia-Ukraine ceasefire.
4. Capybaras face vasectomies.
5. Stock market data for Estonia.
6. Andrej on Grok 3. And Ethan Mollick. So far I like it.
Talking with Johnathan Bi about AI
A very good dialogue, here is the YouTube:
Johnathan discusses it here on Twitter, plus it is on all the usual podcast services. This is an inaugural video for the Cosmos Institute, other people will follow in the series.
An Economic Approach to Homer’s Odyssey: Part II
My three-part essay for Liberty Fund continues, here is the opener:
In the previous article, I outlined what an economic approach to reading Homer’s epic, The Odyssey,1 might look like. I also noted that what most strikes me about The Odyssey is Homer’s treatment of comparative political regimes. Looking at the wide variety of regimes Odysseus encounters is the focus of this article.
Given that human behavior, at least in The Odyssey, can be understood in terms of the non-standard assumptions described in my previous essay, what are then the possible states of affairs? Which polities might we look to for arranging human interactions and maintaining political order? Utopia is not readily achieved, not only because of material constraints, but also because human behavior is too restless and too desirous of alternative states of affairs. A straightforward order based on political virtue is also beyond human grasp, again because it clashes with the nature of human beings as we understand them. What then might fit with a vision of humans as restless, intoxicating, deceiving, and self-deceiving creatures? The travel explorations of The Odyssey can be understood as, in part, an attempt to address this question.
I will now consider the major and some of the minor polities described by The Odyssey, roughly in the order they appear in the story.
The discussion starts with Pylos and Sparta…
Monday assorted links
It’s happening at The New York Times
The New York Times is greenlighting the use of AI for its product and editorial staff, saying that internal tools could eventually write social copy, SEO headlines, and some code.
In an email to newsroom staff, the company announced that it’s opening up AI training to the newsroom, and debuting a new internal AI tool called Echo to staff, Semafor has learned. The Times also shared documents and videos laying out editorial do’s and don’t for using AI, and shared a suite of AI products that staff could now use to develop web products and editorial ideas.
“Generative AI can assist our journalists in uncovering the truth and helping more people understand the world. Machine learning already helps us report stories we couldn’t otherwise, and generative AI has the potential to bolster our journalistic capabilities even more,” the company’s editorial guidelines said.
Here is the full story, via the excellent Samir Varma.
Italy’s Superbonus: The Dumbest Fiscal Policy in Recent Memory
Luis Garicano has an amazing post on “one of the dumbest fiscal policies in recent memory.” Launched in Italy during COVID by Prime Minister Conte, the “Superbonus” scheme subsidized 110% of housing renovation costs. Now if one were to use outdated, simplistic, Econ 101 type reasoning one would predict that such a scheme would be massively costly not only because people would rush to renovate their homes for free but because the more expensive the renovation on paper the bigger the bonus.
The proponents of the Superbonus, most notably Riccardo Fraccaro, were however, advocates of Monetary Monetary Theory so deficits were considered only an illusory barrier to government spending and resource constraints were far distant concerns. Italy still had to meet EU rules, however, so the deficit spending was concealed with creative accounting:
rather than direct cash grants, the government issued tax credits that could be transferred. A homeowner could claim these credits directly against their taxes, have contractors claim them against invoices, or sell them to banks. These credits became a kind of fiscal currency – a parallel financial instrument that functioned as off-the-books debt (Capone and Stagnaro, 2024). The setup purposefully created the illusion of a free lunch: it hid the cost to the government, as for European accounting purposes the credits would show up only as lost tax revenue rather than new spending.
In MMT terms, Fraccaro and his team effectively created money as a tax credit, putting into practice MMT’s notion that a sovereign issuer’s currency is ultimately a tax IOU.
So what were the results? The “free renovation” scheme quickly spiraled out of control. Initially projected to cost €35 billion, the program ballooned to around €220 billion—about 12% of Italy’s GDP! Did it drive a surge in energy-efficient renovations? Hardly. Massive fraud ensued as builders and homeowners inflated renovation costs to siphon off government funds. Beyond that, surging demand ran headlong into resource constraints. Econ 101 again: in the short run, marginal cost curves slope upward.
Construction costs sharply increased – the Construction Cost Index grew by roughly 20% after the pandemic and surged another 13% after September 2021, with the Superbonus directly responsible for about 7 percentage points of that rise, according to Corsello and Ercolani (2024). The price of setting up scaffolding, an essential first step for renovation, increased by 400% by the end of 2021.
…Even the program’s environmental benefits came at an astronomical cost – any calculation will yield far north of €1,000 per ton of carbon saved (versus an ETS Carbon price of around €80 per ton).
Moreover, as Garicano trenchantly notes once started the program’s structure made it fiendishly difficult to stop:
The benefits were concentrated among vocal constituencies: homeowners getting renovations, the environmental movement, and contractors seeing booming business. The costs, while enormous, were spread across all taxpayers and pushed into the future through the tax credit mechanism. No government—leftist, technocratic, or right-wing—was able to resist its logic. Parliament consistently pushed back against efforts to limit its scope, even after fraud estimates hit €16 billion. As prime minister, Mario Draghi, despite publicly criticizing the program for tripling construction costs, could not halt it — in fact, his initial action was to simplify access to it. When his government attempted to curb abuse, the Five Star Movement reacted with anger, and even modest controls on credit transfers were fought. By 2023, Giorgia Meloni’s right-wing government faced the same constraints—industry groups protested, coalition partners balked.
In normal times, the EU might have intervened to curb the reckless deficit spending—everyone knew what was going on, even if the numbers were temporarily kept off the books. But during COVID, the EU turned a blind eye, and the ECB kept interest rates low.
In fact, Garicano argues that the Superbonus story is merely the most blatant example of deeper systemic issues which now trouble the entire EU:
This erosion of discipline isn’t limited to Italy. France’s deficit has drifted to 6.1% of GDP. Spain reversed its post crisis pension reform right around the time Italy was passing the Superbonus, with much larger negative consequences for fiscal sustainability. In a world where the ECB will always intervene to prevent bond market pressure and Brussels cannot credibly enforce fiscal rules on large states, sustainable fiscal policy becomes politically almost impossible.
The very mechanisms designed to protect the euro may now be undermining it.
Naming AI models correctly
Are you confused by all the model names and terminology running around? Here is a simple guide to what I call them:
o1 pro — The Boss
4o — Little Boss
o3 mini — The Mini Boss
GPT 4o with scheduled tasks — Boss come back later
o1 — Cheapskates’ boss
Deep Research — My research assistant
GPT-4 — Former Boss
DeepSeek — China Boss
Claude — Claude
Llama 3.3, or whichever — Meta. I never quite got used to calling Facebook “Meta,” so I call the AI model Meta too. Hope that’s OK!
Grok 3 — Elon
Gemini 2.0 Flash — China Boss suggests “Laggy Larry,” but I don’t actually call it that.
Perplexity.ai — Google
Got that? Easy as pie!
Kevin Kelly’s fifty travel tips
Here is one of them, in part:
Here in brief is the method I’ve honed to optimize a two-week vacation: When you arrive in a new country, immediately proceed to the farthest, most remote, most distant place you intend to reach during the trip. If there is a small village, remote spa, a friend’s farm, or a wild place you plan on seeing on the trip, go there immediately. Do not stop near the airport. Do not rest overnight in the arrival city. Do not pause to acclimate. If at all possible proceed by plane, bus, jeep, car directly to the furthest point without interruption. Make it an overnight journey if you have to. Then once you reach your furthest point, unpack, explore, and work your way slowly back to the big city, wherever your international departure airport is.
In other words you make a laser-straight rush for the end, and then meander back. Laser out, meander back. This method is somewhat contrary to many people’s first instincts, which are to immediately get acclimated to the culture in the landing city before proceeding to the hinterlands. The thinking is: get a sense of what’s going on, stock up, size up the joint. Then slowly work up to the more challenging, more remote areas. That’s reasonable, but not optimal because most big cities around the world are more similar than different. All big cities these days feel same-same on first arrival. In Laser-Back travel what happens is that you are immediately thrown into Very Different Otherness, the maximum difference that you will get on this trip.
Here are the rest, mostly I agree.
Reforming the NIH
It seems the Trump proposal to simply cut overhead to fifteen percent will not stand up in the courts, at least not without Congressional approval? Nonetheless a few of you have asked me what I think of the idea.
My preferred reforms for the NIH include the following:
1. Cap pre-specified overhead at 25 percent, down from a range running up to 60 percent.
2. Encourage more coverage of overhead in the proposals themselves, where the researchers are accountable for how the overhead funds are spent. Severely limit how much the “overhead” cross-subsidizes other university functions, as is currently the case.
3. Fund a greater number of proposals, with the money coming from overhead reductions, as outlined in #1 and #2.
4. Set up a new, fully independent biomedical research arm of the federal government, based on DARPA-like principles. In fact this was seriously proposed a few years ago, with widespread (but insufficient) support.
I would note a few additional points, which have been covered in earlier MR posts over the years:
5. The NIH could not get its act together during Covid to make fast grants with sufficient rapidity during a time of crisis. They performed much worse than did say the NSF.
6. A while back the NIH set up a program to make riskier grants. The program did not in fact make riskier grants.
7. The NIH killed the idea of an independent DARPA-like biomedical research agency, fearing it would limit the size and influence of the NIH itself.
8. The submission forms, their length, and the associated processes are absurd. Whether or not the costs there are high in an absolute sense, it is a sign the current NIH is far too obsessed with process, as happens to just about every mature bureaucracy.
At this point it is obvious that the NIH cannot reform itself. It is also obvious that a slower, technocratic approach just gives the interest groups — in this case it is “the states” most of all — time to mobilize to protect the current NIH. There are universities in many Congressional districts and a fair amount of money at stake.
I do not per se favor a move to fifteen percent overhead, as I do understand the associated costs on scientific research. Nonetheless I take very seriously the possibility that a radical “thoughtless” cut now stands some chance of getting us to where we ought to be in the longer run, especially since subsequent administrations will get further cracks at this problem. They can up overhead to 25 percent, and set up the new DARPA-H. I just don’t see why that is impossible, and it may not even be unlikely. So what exactly is your discount rate and risk aversion here?
I feel the defenses of the NIH I am reading do not take the entire broader analysis seriously enough. They do not take sufficiently seriously that the writers themselves have failed to adequately reform the NIH. And over time, without serious reform, the bureaucratic stultification will only get worse.
What should I ask David Robertson?
Yes, David Robertson the conductor. He studied with Boulez and Messiaen, and arguably is the second best Boulez conductor ever. He also is famous for his recordings of John Adams. I find him consistently excellent, for instance his Unsuk Chin, Milhaud, or Porgy and Bess. Here is his Wikipedia page. Here is his TEDx talk on conducting. Here is his home page. He is very smart.
So what should I ask him?
Which economic tasks are performed with AI?
I have now read through the very impressive paper on AI tasks to have come out of Anthropic, with Kunal Handa as the lead author, including Dario, Jack, and quite a few others as well. Here is the paper and part of the abstract:
We leverage a recent privacy-preserving system [Tamkin et al., 2024] to analyze over four million Claude.ai conversations through the lens of tasks and occupations in the U.S. Department of Labor’s O*NET Database. Our analysis reveals that AI usage primarily concentrates in software development and writing tasks, which together account for nearly half of all total usage. However, usage of AI extends more broadly across the economy, with ∼ 36% of occupations using AI for at least a quarter of their associated tasks. We also analyze how AI is being used for tasks, finding 57% of usage suggests augmentation of human capabilities (e.g., learning or iterating on an output) while 43% suggests automation (e.g., fulfilling a request with minimal human involvement).
There is also a new paper on related topics by Jonathan Hartley, Filip Jolevski [former doctoral student of mine], Vitor Melo, and Brendan Moore:
We find, consistent with other surveys that Generative AI tools like large language models (LLMs) are most commonly used in the labor force by younger individuals, more highly educated individuals, higher income individuals, and those in particular industries such as customer service, marketing and information technology. Overall, we find that as of December 2024, 30.1% of survey respondents above 18 have used Generative AI at work since Generative AI tools became public.
Both recommended, the latter supported in part by Emergent Ventures as well.
Emergent Ventures winners, 40th cohort
Akhil Kumar, 19, Toronto, global health issues and general career development.
Janet Shin, Berkeley, neurotech and brain imaging.
Diana Leung, San Francisco, AI and bio and machine learning.
Kyle MacLeod, Oxford University, economics videos on YouTube.
Aarav Sharma, Singapore, high school, to work on exoskeletons and AI.
Megan Gafford, NYC, writings on aesthetics, Substack.
Alice Gribbin, Berkeley, to write a book on Correggio and beauty.
Kaivalya Hariharan, MIT, to work on man-machine collaboration and AI, with previous EV winner Uzay Girit.
Eve Ang, Singapore, high school, biosciences and building exoskeletons.
Alex Chalmers, London area, writing on tech, progress, and policy.
Elanu Karakus, Stanford, Turkey, a smart flower to help bees find flowers.
Ishan Sharma, Washington DC, policy work on geologic hydrogen.
Parker Whitfill, economics PhD student at MIT, evaluations of differing AI systems.
Sympatheticopposition.com, @sympatheticopp, San Francisco, writing and Substack.
Yes there are further EV winners and an additional cohort coming soon! Apologies for any delays.
Again, here is the AI engine, built by Nabeel Qureshi, for searching through the longer list. Here are previous cohorts of EV winners.
Saturday assorted links
European Union fact of the day
The IMF estimates that Europe’s internal barriers are equivalent to a tariff of 45 per cent for manufacturing and 110 per cent for services. These effectively shrink the market in which European companies operate: trade across EU countries is less than half the level of trade across US states. And as activity shifts more towards services, their overall drag on growth becomes worse…
Europe has been effectively raising tariffs within its borders and increasing regulation on a sector that makes up around 70 per cent of EU GDP.
This failure to lower internal barriers has also contributed to Europe’s unusually high trade openness. Since 1999, trade as a share of GDP has risen from 31 per cent to 55 per cent in the eurozone, whereas in China it rose from 34 per cent to 37 per cent and in the US from 23 per cent to just 25 per cent. This openness was an asset in a globalising world. But now it has become a vulnerability.
That is from Mario Draghi in the FT. Canada too! Even Trudeau is now saying there should be free trade across the provinces. I do appreciate the willingness of those political units to try to counter the Trump tariff proposals. But it would go better if they were themselves practitioners of free trade internally, never mind externally.
How should government disclosure be done?
That is my recent Bloomberg column on this all-important topic. Here is one part:
The risk is that Trump would hoard the most sensitive information and disclose selectively, to manipulate the news cycle or to distract attention from other events. It also could give him more political weapons to use against what he calls the “deep state.” The president himself is hardly a model of transparency, whether the questions concern his tax returns, his medical exams or the possession of classified documents after leaving office.
But again, the issue is governmental disclosure, and so far, Trump’s record is 0 for 1. Before assuming office, he suggested that the US military knew more than it was letting on about the drones that had been sighted above New Jersey and other Northeastern states. Then, after Trump took office, his press secretary said only that they were “authorized” by the government “for research and various other reasons.” There has been no subsequent attempt to clarify matters. Personally, I am more confused than I was a month ago.
Perhaps there are good national security reasons for this silence. The point is that it is foolish to expect full and open disclosure from the president, no matter what his executive order says or what he has earlier promised.
One way to improve the process would be to appoint some independent auditors on a bipartisan basis, perhaps selected from Congress. Ex post, those auditors could judge whether disclosure, with transparent explanations, had actually occurred. They could grade the degree of disclosure, but they would not have the power to prevent it. Otherwise, there is a risk that — to choose an example not quite at random — evidence favoring the “two gunmen” hypothesis for JFK’s assassination is released, but conflicting evidence for the “lone gunman” hypothesis is suppressed. The auditors would issue a report saying whether disclosure was unbalanced or unfair.
And this:
Another problem with the task force is that it is authorized for only six months. Bureaucracies are by nature slow-moving, and can be even more so when they wish to be. A six-month deadline creates incentives to wait things out. Trump could threaten to extend the mandate, and perhaps he will. But then the disclosure campaign would turn out to be just a bargaining chip, rather than a genuine attempt to bring the truth to light.
Definitely recommended.