Liberal AI
Can AI be liberal? In what sense? One answer points to the liberal insistence on freedom of choice, understood as a product of the commitment to personal autonomy and individual dignity. Mill and Hayek are of course defining figures here, emphasizing the epistemic foundations for freedom of choice. “Choice Engines,” powered by AI and authorized or required by law, might promote liberal goals (and in the process, produce significant increases in human welfare). A key reason is that they can simultaneously (1) preserve autonomy, (2) respect dignity, and (3) help people to overcome inadequate information and behavioral biases, which can produce internalities, understood as costs that people impose on their future selves, and also externalities, understood as costs that people impose on others. Different consumers care about different things, of course, which is a reason to insist on a high degree of freedom of choice, even in the presence of internalities and externalities. AI-powered Choice Engines can respect that freedom, not least through personalization. Nonetheless, AI-powered Choice Engines might be enlisted by insufficiently informed or self-interested actors, who might exploit inadequate information or behavioral biases, and thus co5mpromise liberal goals. AI-powered Choice Engines might also be deceptive or manipulative, again compromising liberal goals, and legal safeguards are necessary to reduce the relevant risks. Illiberal or antiliberal AI is not merely imaginable; it is in place. Still, liberal AI is not an oxymoron. It could make life less nasty, less brutish, less short, and less hard – and more free.
The fertility asymptote?
From a recent paper by Sebastian Galiani and Raul A. Sosa:
Fertility rates have fallen below replacement in most countries, fueling predictions of demographic collapse. We show these forecasts overlook a crucial fact: societies are not homogeneous. Using the Bisin–Verdier model of cultural transmission with endogenous fertility and direct socialization, calibrated to U.S. and global data, we find that high-fertility, high-retention groups persist, gain share, and lead the total population to grow. Even if fertility remains below replacement in every country, extinction is unlikely. Simulations imply continued growth with pronounced compositional change, driven especially by religious communities with high fertility. In our ten-generation world calibration, Muslims become the largest tradition.
I am pleased to hear that extinction is unlikely.
Tuesday assorted links
1. The separating equilibrium.
2. Can speed revitalize American manufacturing?
3, Malmo real estate prices are doing fine.
4. AI and economics summer institute at University of Chicago.
5. Why total legal services costs may not fall with AI. Correct working link here.
6. Sylvia Plath in her journals.
7. Stephen Kinsella Substack on the economy of Ireland.
p.s. hbsk!
The Journal for AI Generated Papers
Science should be machine-readable
One of the leading tasks of our time:
We develop a machine-automated approach for extracting results from papers, which we assess via a comprehensive review of the entire eLife corpus. Our method facilitates a direct comparison of machine and peer review, and sheds light on key challenges that must be overcome in order to facilitate AI-assisted science. In particular, the results point the way towards a machine-readable framework for disseminating scientific information. We therefore argue that publication systems should optimize separately for the dissemination of data and results versus the conveying of novel ideas, and the former should be machine-readable.
Here is the paper by A. Sina Booeshagh, Laura Luebbert, and Lior Pachter. Via John Tierney.
Rebuilding our world, with reference to strong AI
When 2012 passed into 2013, we did not have to rebuild our world, not in most countries at least. It sufficed to make adjustments at the margin.
After the Roman Empire fell, parts of Europe had to rebuild their worlds. It took a long time, but they ended up doing pretty well.
After the American Revolution, the newly independent colonies had to rebuild their own world. They did so brutally, but with considerable success.
After WWII, Western Europe had the chance to rebuild its own world, and did a great job.
We moderns are not used to having to rebuild our world.
It is now the case that strong AI is here/coming, and we will have to rebuild our own world. Many of us are terrified at this prospect, others are just extremely pessimistic. It seems so impossible. How are all the new pieces supposed to fit together? Who amongst us can explain that process in a reassuring way?
Yet we have done it many times before. Not always with success, however. After WWI ended, Europe was supposed to rebuild its own world, but they came up with something far worse than what they had before. Nonetheless, in the broader sweep of history world rebuilding projects have had positive expected value.
And so we will rebuilding our world yet again. Or maybe you think we are simply incapable of that.
As this happens, it can be useful to distinguish “criticisms of AI” from “people who cannot imagine that world rebuilding will go well.” A lot of what parades as the former is actually the latter.
In any case, it all will be quite something to witness.
“You see tech and AI everywhere but in the productivity statistics”
How many times have I heard versions of that claim? Erik Brynjolfsson picks up the telephone in the FT:
While initial reports suggested a year of steady labour expansion in the US, the new figures reveal that total payroll growth was revised downward by approximately 403,000 jobs. Crucially, this downward revision occurred while real GDP remained robust, including a 3.7 per cent growth rate in the fourth quarter. This decoupling — maintaining high output with significantly lower labour input — is the hallmark of productivity growth.
My own updated analysis suggests a US productivity increase of roughly 2.7 per cent for 2025. This is a near doubling from the sluggish 1.4 per cent annual average that characterised the past decade.
It is fine to suggest caution in interpreting such statistics, but they hardly push the other way.
Monday assorted links
1. Andrew Hall on improving the operation of prediction markets.
2. A new aesthetic for San Francisco.
3. Intelligent AI delegation. And Seb Krier. And Abigail Shrier. All of this can change your life.
4. Krugman on tariff incidence.
5. The century of the maxxer (“How many apricots can fit in your mouth?”). Excellent piece.
6. Andy Goldsworthy (New Yorker). Ditto.
7. Claims.
Malthus had real influence
From a recent paper by Eric Robertson:
Public officials often fail to implement government policy as directed, yet the role of economic ideas in shaping these implementation choices is poorly understood. This paper provides causal evidence that exposure to economic ideas can durably influence bureaucrat behavior. I study British colonial bureaucrats in India, exploiting a natural experiment created by the abrupt death of Thomas Malthus in 1834, replacing his economics instruction at a bureaucrat training college for that of a contemporary critic, Richard Jones. Whereas Malthus regarded economic distress as a natural mechanism for restoring equilibrium by reducing population growth, Jones disagreed with this view. Linking rainfall shocks to district-level fiscal responses, I show that officials trained by Malthus delivered less relief during droughts, providing 0.10-0.25 SD less aid across all major measures compared with officials taught by Jones. The results reveal that exposure to abstract economic ideas can shape real-world policy implementation for decades.
This may be a case where using rainfall shocks in a paper actually makes sense. Via Krzysztof Tyszka-Drozdowski.
India’s AI wedding buffet
Shruti Rajagopalan surveys much of the AI policy debate in India. Excerpt:
If there is a single domain where India’s AI ambitions will succeed or fail, it is energy. And energy in India is not a technology problem. It is a political economy problem, arguably the most intractable one the country faces.
India’s peak electricity demand hit 250 GW in May 2024, up from 143 GW a decade earlier. The IEA forecasts 6.3 percent annual growth through 2027, faster than any major economy. Cooling demand alone could reach 140 GW of peak load by 2030. One number captures the trajectory. For each incremental degree in daily average temperature, peak demand now rises by more than 7 GW. In 2019 the figure was half that. India is getting hotter, richer, and more electricity-hungry simultaneously.
State-controlled distribution companies have accumulated $83.7 billion in debt because energy prices have been politically distorted for decades. Over 50 GW of renewable capacity sits underutilized. About 60 GW is stranded behind inadequate transmission. The shortage is financial and infrastructural, not resource-based. Without reforming distribution pricing, governance, and grid investment ($50 billion estimated by 2035), new renewable capacity will not become reliable electricity. It will become another line item on a DISCOM balance sheet no one wants to read.
India’s electricity reaches consumers through 72 distribution companies, 44 of them state-owned, collectively the most financially distressed utilities in the world. Accumulated losses stood at ₹6.92 trillion ($76.89 billion) as of March 2024, rising every year despite five government bailouts since 2002.
Substantive throughout.
At the Grand Egyptian Museum
Neal Spencer has a good review at the LRB, excerpt:
Over the past few decades, however, Egyptian museums have pivoted away from Europe and America. The National Museum of Egyptian Civilisation, which opened in 2021, rejected the traditional division of artefacts into pharaonic, Coptic, Greco-Roman and Islamic eras (a framework associated with European academic disciplines). The Grand Egyptian Museum, announced at the height of Hosni Mubarak’s rule and styled ‘the largest museum in the world dedicated to the people, history and culture of Ancient Egypt’, opened in November last year with a lavish ceremony broadcast round the world. It is estimated to have cost more than $1 billion ($300 million of which was a loan from Japan) and sprawls over an area the size of seventy football pitches. The financial crash of 2008, the Arab Spring and Covid meant that its construction took almost twenty years. Much has changed in that time. The last decade of construction took place under the military regime of Abdel Fattah el-Sisi, who installed one of his generals as its head – the first non-Egyptologist to direct a major Egyptian museum.
I saw the museum shortly after the opening and found it pretty spectacular, both the building/setting and the collection. It is worth making a trip to Cairo just to see this, and it now can be considered one of the world’s great museums and history sites (yes I had seen the earlier incarnation of the museum, years ago). The very wise Rasheed Griffith also gave the museum an A+.
Sunday assorted links
The economics of corporate espionage
Weprovide systematic evidence on the economic damages from espionage to US firms and industries. Compiling a comprehensive dataset of publicly disclosed espionage incidents from 1995-2024, we establish that espionage has substantial negative effects on targeted f irms. In an event-study design, revenues and R&D expenditures at targeted firms decline by roughly 40% within five years, with effects persisting for up to a decade. These effects do not appear for firms unsuccessfully targeted for espionage, supporting a causal interpretation. These firm-level damages translate into measurable aggregate effects on US industry: exports in targeted sectors decline by 60% over a decade. Given these substantial damages, we investigate whether firms restrict knowledge sharing in response to espionage. Across a wide range of outcomes, we find no evidence of such restrictions. Firms do not reduce their patenting with foreign inventors, and do not discriminate in employment based on perceived espionage risk. Overall, espionage has clear economic harms to targeted firms and US industry, but firms are puzzlingly unresponsive in how they manage innovation.
That is from a new paper by Andrew Kao and Karthik Tadepalli. Via Kris Gulati.
Taxing Beta, Exempting Alpha: A Benchmark-Based Inheritance Regime
This paper proposes a generational benchmark inheritance regime as a structural replacement for the federal estate tax. By distinguishing between systemic market returns (Beta) and active value creation (Alpha), the regime captures the passive growth of capital at generational boundaries while fully exempting idiosyncratic surplus. Using a Pareto tail interpolation (α ≈ 1.163) calibrated to Federal Reserve wealth data, we estimate baseline annual revenue of approximately $295 billion under conservative assumptions. This revenue is sufficient to finance a 2.1 percentage point reduction in the OASDI payroll tax, shifting the fiscal burden from labor to underperforming dynastic capital. Unlike continuous wealth taxes, the regime requires no new valuation machinery, relying exclusively on existing estate and gift tax procedures. We situate the proposal within the Jeffersonian principle of usufruct and the modern literature on optimal inheritance taxation.
Minimum wage hikes and robots
This paper studies how minimum wage policy affects firms’ adoption of automation technologies. Using both state-level measures of robot exposure and novel plant-level data on industrial robot imports linked to U.S. Census microdata from 1992-2021, we show that increases in minimum wages raise the likelihood of robot adoption in manufacturing. Our preferred identification exploits discontinuities at state borders, comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean.
That is from Erik Brynjolfsson, et.al., including Andrew Wang. Via the excellent Kevin Lewis.
By the way, a photo from our textbook Modern Principles of Economics:
