Category: Data Source

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.

Earth fact of the day, #2

The shortages have gone on for so long that they are aggressively driving down how much carbon is being released into the atmosphere, a Washington Post analysis of data from the International Energy Agency shows. People worldwide are using significantly less oil and gas, which means less climate pollution…

Such an annual decline has not happened since the height of the coronavirus pandemic. Fossil fuel consumption dropped significantly more then, and consumption was lower in absolute terms, too. Crude oil demand averaged 91 million barrels per day in 2020, compared with 102 million barrels per day under the latest IEA forecast.

But this year’s drop — especially given the sharp increase that was initially forecast — is substantial.

Here is the full story.  Not a good thing overall, but there is a lesson in that to…

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.

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:

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Strange bedfellows, you might say…

CA 91326: A Public Choice Mystery Investigation

Carl Danner writes to me with a political economy mystery. On Sept 9, 2026, The California Public Utilities Commission announced a set of clean energy grants but only “for eligible organizations headquartered in ZIP Code 91326.”

I decided to investigate with ChatGPT. The first thing we discovered is that:

The ZIP-code restriction is about the Aliso Canyon gas leak. More specifically, this is money ultimately intended to benefit residents of Porter Ranch and nearby communities affected by the 2015–16 Aliso Canyon disaster, not a generic clean-energy grant that happens to be located there.

The Aliso Canyon gas leak was a big deal with thousands of families evacuated for months so it’s not surprising that money is allocated to that problem but note that the grants are not for organization serving the people or region affected by the gas leak (which is much larger than zip 91326) but for an organization headquartered in ZIP Code 91326. I smelled a payoff. So I pushed ChatGPT to investigate:

It may be [a payoff], but the evidence supports a narrower claim: the Round 3 rules look unusually tailored to make Save Porter Ranch a likely recipient.

The strongest facts are:

  • The legislature appropriated $2 million from the Aliso Canyon Recovery Account for CBO outreach in Los Angeles County, prioritizing a broad “Aliso Canyon Disaster Area” containing 11 communities—not Porter Ranch alone.
  • CPUC’s first round distributed roughly $1.3 million through a normal competitive process across multiple organizations.
  • For Round 3, CPUC suddenly restricts eligibility to organizations headquartered in the single ZIP code 91326, with a maximum award of $75,000 and exclusion of previous recipients.
  • Save Porter Ranch is headquartered at 19360 Rinaldi Street, Porter Ranch, CA 91326 and has been an active participant in CPUC proceedings concerning Aliso Canyon for years.

That is suggestive because the legislative authorization itself does not appear to require this hyper-local 91326 restriction. It defines the relevant disaster area much more broadly. CPUC therefore seems to have exercised administrative discretion to create a tiny eligibility pool.

I would distinguish three possibilities:

  1. Benign geographic set-aside: CPUC noticed that none of the first-round money had gone to an organization actually based in Porter Ranch and reserved the final $75,000 for a local group.
  2. De facto earmark: CPUC knew that Save Porter Ranch was one of very few qualifying CBOs in 91326 and wrote the eligibility rule sufficiently narrowly that it was highly likely to win.
  3. Quid pro quo/payoff: the money was deliberately directed to Save Porter Ranch in exchange for support, silence, settlement behavior, etc.

We have substantial circumstantial evidence for #2, but I have seen no evidence yet for #3. Calling it a “payoff” requires some evidence about why CPUC created the ZIP restriction or about communications with Save Porter Ranch.

I wasn’t satisfied so I pushed the investigation deeper and ChatGPT investigated the legislative history. It found that

The 91326 restriction did not originate with CPUC staff. The Legislature itself inserted it into the 2026 budget cleanup bill, and it is unusually specific….The evidence [for a payoff] is considerably stronger than I thought.

…If I were writing this up now, the sharp factual formulation would be: “In an August 2026 budget bill, the California Legislature quietly directed the CPUC to hold an otherwise-unplanned third grant round restricted to nonprofits headquartered in a single Porter Ranch ZIP code. One conspicuous beneficiary of that restriction is Save Porter Ranch, the local Aliso Canyon advocacy organization, which had previously helped lobby state officials over how Aliso Canyon penalty funds should be spent.”

I then asked ChatGPT to “look for overlap in Save Porter Ranch and CA legislatures, e.g. spouses, children, business partners.” It replied:

The strongest link is Richard Mathews. Save Porter Ranch’s IRS filings list Mathews as a director alongside Matt Pakucko and Kyoko Hibino. Mathews is also the longtime president of the North Valley Democratic Club. He previously served as a regional vice chair of the Los Angeles County Democratic Party and on the California Democratic Party executive board and its Legislation Committee.

That puts a Save Porter Ranch director directly inside the local/state Democratic Party apparatus, not merely in occasional contact with elected officials.

…the Mathews connection is stronger than I expected. This isn’t just an advocacy organization obtaining support from politicians. One of Save Porter Ranch’s three disclosed directors was simultaneously a fairly senior Democratic Party insider whose political club includes and supports legislators involved in the relevant legislative machinery.

…The missing piece remains the most important one: who requested the sentence “headquartered within ZIP Code 91326”?

I enjoyed the fact that ChatGPT seems excited by the new discoveries! Digging deeper:

That gives us a much tighter network than we had initially:

Save Porter Ranch
→ director Richard Mathews
→ president, North Valley Democratic Club
→ Schiavo and Gabriel are identified as club members / repeatedly endorsed by it
→ Schiavo sits on Budget + Utilities & Energy and represents the affected area
→ Gabriel chairs Assembly Budget
→ August 28 Assembly budget amendments insert a bespoke ZIP-code restriction
→ restriction makes Save Porter Ranch an unusually obvious potential recipient.

There is another revealing feature. The amendment was handled as part of a budget-bill junior rather than through ordinary policy legislation. The public legislative history is extraordinarily thin: amendment August 28, withdrawn from committee August 30, passed August 31. Neither Gabriel’s Assembly presentation nor Laird’s Senate presentation told legislators that the “third round” was actually limited to one ZIP code. Gabriel merely said CPUC must offer “a third round of unspent funds”; Laird similarly described a third round “as specified.” The Assembly passed it 72–0.

That doesn’t demonstrate corruption, but it is exactly how a highly localized legislative earmark can pass with effectively zero scrutiny.

Alas, “the remaining decisive evidence probably isn’t indexed on the web.” Still, you get the idea.

Of course, this is small potatoes by the standards of California political/regulatory friends and families but it’s a nice case study in political economy. How do we want government to work? Is this ethical? What if the sums were much larger? What if the funds went to an equally well-connected group but for a less justifiable cause?   

It’s always surprised me that most public choice is not done in this micro-history way. My early paper on the Separation of Commercial and Investment Banking: The Morgans vs. The Rockefellers (which was inspired by Burch’s magnificent Elites in American History which hardly any public choice scholars know) is one of the few examples. I could have done many more papers along those lines but archival history didn’t appeal to me although I did learn a lot digging through Carter Glass’s papers at UVA. I suppose economists like models and consider this sort of thing journalism or history at best.

If you want more details on CA 91326 you can read my whole conversation with ChatGPT.

The Price of Intelligence is Falling Rapidly

An amazing Epoch AI report from Emberson and Roodman:

  • Over the past three years, the cost of a given level of AI performance has fallen an average of some 47% per quarter. That is a 13-fold drop every year – a faster rate than any other transformative technology in history.

To give an example, OpenAI o3 cost about $0.30/question to attain 75% on GPQA Diamond in January 2025, while GPT-5.6 Luna attained roughly the same score for $0.0004 in mid-2026—a roughly 725-fold decline in under 18 months.

Thus, it’s not just that the models are getting smarter. A given level of intelligence is also requiring dramatically less inference expenditure. This is one reason the open-model threat is not as large as it appears: frontier models don’t merely outperform older models; they are rapidly becoming cheaper to run at any given level of performance. Smarter and cheaper.

The economics of late grandmotherhood

We show how fertility delays compound across generations to raise age at first grandmotherhood. Under the 1960 first-birth schedule median grandmotherhood came at 44. Under the 2024 schedule it is 57, cutting the healthy years with grandchildren from 28 to 15. Longer life does not make up the loss of years when the grandmother is healthy enough to supply money and care. Delay is most pronounced among the college-educated, with reproductive-control access accounting for over half the fall in grandmotherhood by age 55 among White college graduates. When a daughter times her first birth to maximize her own payoff, the loss her choice imposes on her mother is external to her calculation. We call this the Rotten Adult Kid Problem. Delay raises both the price of a grandmother’s care and wealth per grandchild.

That is from a new NBER working paper by Dora Costa and Matt Kahn.

Further results on AI and labor market reallocation

This paper documents how artificial intelligence has affected stocks and flows in the U.S. labor market. Combining CPS and JOLTS data with AI exposure and adoption measures from OpenAI and Lightcast, respectively, we construct labor force stocks, worker flows, and market tightness by AI exposure and adoption. Since the introduction of LLMs, workers with high AI exposure and adoption have experienced larger declines in job-finding and job-switching rates than other groups, while their within-job activity switching has increased noticeably relative to others. The positive effects of AI on nominal wage growth and hours worked attenuate markedly in the post-LLM period, suggesting that LLM-driven reallocation has operated through within-firm task reorganization and a reconfiguration of labor inputs, alongside weaker demand for workers most exposed to AI. To quantify LLM-driven reallocation pressure, we construct individual-level hirability and separability indices and showthat dispersion in job-finding prospects has risen markedly since 2023, largely driven by the AI factors. The natural rate of unemployment—recovered from the trend components of unemployment inflows and outflows by AI exposure and adoption, as well as from a theoretical model of structural unemployment—is estimated to have risen by about 0.1-0.2 percentage point since the introduction of LLMs, albeit with considerable uncertainty.

That is from a new NBER conference paper by Hie Joo Ahn and Nicholas A. Carollo.  Via Inclusive Productivity Network and Alex Imas.

UK productivity higher than we had thought

UK productivity rose at almost twice the pace previously estimated in the decade after the 2008 global financial crisis, according to new official figures that make Britain look much less of an international outlier.

Output per hour worked, the main measure of labour productivity, grew by an average of 1.3 per cent per year between 2009 and 2019, the Office for National Statistics said on Thursday. The ONS had previously estimated average annual growth of 0.7 per cent over the period.

The figure is based on a new “component-based” methodology that has already been adopted by many other national statistics institutes. It draws on a range of data sources to improve the ONS’s count of both the number of workers contributing to UK output and the number of hours worked.

Here is more from Delphine Strauss at the FT.

Internet diffusion and religious decline?

Using a panel of 81 countries across six World Values Survey waves (1990–2022), we examine whether growth in internet diffusion is associated with religious decline. We construct a six-item composite religiosity index, align annual macro series to wave periods, and estimate country fixed-effects models with one-wave lags and a broad set of modernization, demographic, welfare-state, and cultural controls. We also estimate models separately for individual dimensions of religiosity. Across specifications, lagged internet penetration is negatively associated with religiosity within countries over time. These results remain robust under period dummies and in models estimated without lags, although reverse-ordering tests indicate that prior religiosity can also predict later internet use. Associations are strongest for denominational belonging and self-identification as a religious person. Overall, internet diffusion emerges as a robust macro-level correlate of within-country religious decline.

Here is the full article by  and Via the excellent Kevin Lewis.

Callum Williams on cybersecurity prices

Share prices of cyber firms have jumped around a lot in recent weeks, leading one side or the other to claim victory. But the crucial point is that, relative to the historical norm, the market is not really pricing ANYTHING big to change. There was a much bigger move in cyber stocks in both 2020-22 (up) and 2022-23 (down) but no one read “AI x-risk” into this.

Here is the full post with graph.

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Hardly the final word, and I am myself more pessimistic than those numbers indicate.  But at least with this we are getting somewhere concrete and scientific rather than just scare stories.  As for meta-commentary on the discourse itself, you really should be asking who are the people insisting on data here, and who are the people trying to talk you away from focusing on the data so much.

Did the ACA reduce mortality?

Many of us brought up related points at the time, but basically we were booed off the reservation:

While recent research has provided evidence that the Medicaid expansions of the Affordable Care Act (ACA) reduced mortality, there is no evidence on the effect of the Affordable Care Act (ACA) net of the Medicaid expansions on mortality. This is an important gap in knowledge because the ACA significantly increased health insurance coverage in non-expansion states. In this article, we exploit the large increase in health insurance coverage brought forth by the ACA to examine the effect of the ACA and Medicaid expansions on mortality. Unlike prior studies that relied solely on geographic variation in Medicaid expansions to estimate the net effect of the expansion, we use a novel empirical approach that allows us to investigate the effect of the ACA net of Medicaid expansion on mortality, the incremental effect of the Medicaid expansion, and the overall effect of the ACA including Medicaid expansion. We use longitudinal data from the NHIS Linked Mortality Files (LMF) and a nationally representative sample of 40 to 58-year-olds combined with a difference-in-differences and a difference-in-differences-in-differences research design to obtain estimates of the effect of the ACA on mortality. We find no evidence that the Medicaid expansions had a beneficial effect on mortality but do find that the ACA net of Medicaid expansion reduced mortality.

That is from a new NBER working paper by Anuj Gangopadhyaya, Cuiping Schiman & Robert Kaestner.  Via Glenn Mercer.

Does AI assistance enhance or erode expertise?

From a new NBER working paper:

Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.

That is by David Autor, et.al.  Do note that over time the allocation of humans to tasks will evolve so that more of the humans become more productive, not less.  RCTs somehow have the odd disadvantage of requiring too many things to be held constant, and so they can miss the benefits of longer-term adjustments.

The Prediction Archive

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.