Edward Conard

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  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
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Nicholas Bloom on Management, Productivity, and Scientific Progress

Tyler Cowen Scientific Progress
Date Posted:
August 12, 2020
Is Database:
Database

Nick Bloom finds research productivity is falling 5% a year, posing significant challenges for economic growth and innovation.

Research productivity is declining at a rate of 5% annually, posing significant challenges for economic growth and innovation. This trend is evident across various sectors, with the number of researchers required to achieve a given level of output increasing substantially. For instance, the number of researchers needed to double computer chip density has risen by a factor of 18 since the 1970s. This decline in productivity growth is concerning as it suggests diminishing returns on investment in research and development (R&D), which could impact GDP growth and technological advancement. The implications are profound for policymakers and business leaders who must adapt strategies to counteract these trends and sustain economic progress. Addressing this issue may require rethinking management practices, increasing collaboration, and leveraging new technologies to enhance efficiency and output in research activities.

Tyler Cowen, "Nicholas Bloom on Management, Productivity, and Scientific Progress," Mercatus Center, August 12, 2020, https://medium.com/conversations-with-tyler/nicholas-bloom-tyler-cowen-productivity-economics-b5714b05fc2b

Ed Comment:Ok to file for the factoid that research productivity is falling 5% a year. I’d like to see the calculation behind that. Beyond that, I don’t see much insight. For starters, I think an enormous and increasing share of workers are working in services. Much of the R&D may not be aimed at increasing the productivity and product offerings of services. Perhaps Slack is, or radical transparency, but not research into the number of planets in the universe. From my perspective at luxury vanity that is unlikely to help anyone in any relevant time frame. It’s a shame we fund it. I also worry that the great expansion of research is diverting talent from serving customers, which slows productivity, especially if the research doesn’t find something that increases productivity. I’m especially concerned about cancer research that will save a few productive people, but largely allows highly unproductivity old people (negative contributors) to live a few years longer on average.

COWEN: How much of the measured productivity edge of American multinationals is just tax arbitrage and where profits get assigned to?

BLOOM: I never really thought a huge amount of it was that. My personal view — I guess this is, again, biased by my research — is American firms are particularly just fantastically well managed.I’ve done a lot of work for many years, looking at management practices, trying to collect data in cross-country surveys.To explain what I mean, management practices — the basics are, do you collect information and use it to improve yourself? Think of lean, collecting information all the time, and having improvement processes. Secondly, do you train and promote employees, try to promote the best people, trying to avoid things like promoting family, friends, or long-serving employees? So meritocratic HR systems.I’m not going to say American firms are perfect. They’re definitely not perfect. There are many management scandals. But on average, American firms are much better managed, and they take that with them abroad. There’s this whole literature that has been sometimes called dark matter, or explaining why we seem to have this endless negative balance of trade but positive balance on our investments abroad. American companies seem to make huge profits abroad.One big explanation is they’re just exploiting lots of this intangible capital, which we think of as good management. American multinationals around the world are well managed, and they make a lot of profits where they’re located in the UK and France, in Ghana, in Thailand — wherever they are. That’s helping keep the US economy afloat, that return profits. I don’t see that as being related — transfer pricing and offshore tax manipulation. That is a factor, but I think American firms are primarily driven, actually, by better innovation and better management.

COWEN: But if you assign the blame to government, ideas are a global public good. Isn’t it true that global governmental expenditure on R&D in absolute terms is up, even if it may be down as a percentage of budgets for total R&D? Thus, scientific progress in the United States, which can draw upon governmental support in China, Japan, India, UK, Switzerland, should still be going up. It has to be within private scientific progress that there’s a diversion of effort away from public goods and toward more private goods. Or no?

BLOOM: No, it’s a good question. It’s certainly true, our paper only focused on the US. The puzzle gets much harder if you include global R&D. You see that productivity per researcher or research dollar is falling, in the sense of the rate of progress per dollar we’re spending….The puzzle gets even more extreme if you look globally. Sure, it’s tricky because Europe has become slightly less of a powerhouse, but obviously Asia has completely taken off in the amount of R&D being spent in, say, India, and China has exploded. Has that offset the reduction in US publicly funded R&D, certainly as a share of GDP? It’s not obvious. One reason is, there’s plenty of evidence on knowledge spillovers being localized. There’s a lot of evidence, for example, that you’re more likely to coauthor with your colleagues in your own university or the same firm. I guess the same firm is more obvious, but if that was true, you may think the transmission of ideas from China to the US is less effective than within the US. I also don’t know if the increase in Chinese and Indian R&D by their government sectors is enough to offset the reduction by the US, and whether it’s in the right areas. It may be that a lot of developing countries’ R&D is more, say, defense and national security focused, which I suspect has lower tradeoffs. The nice thing about the US and things like the National Science Foundation and the National Institute for Health is they would put huge amounts of funding on very basic research that had broad value. An MIT researcher goes to the NSF, gets funding for research. They tend to be focused on very basic things that are of interest to broad science, and that has, I suspect, the largest value in.

BLOOM: The big picture — just to make sure everyone’s on the same page — is, if you look in the US, productivity growth... In fact, I could go back a lot further. It’s interesting — you go much further, and you think of European and North American history. In the UK that has better data, there was very, very little productivity growth until the Industrial Revolution. Literally, from the time the Romans left in whatever, roughly 100 AD, until 1750, technological progress was very slow. Sure, the British were more advanced at that point, but not dramatically. The estimates were like 0.1 percent a year, so very low. Then the Industrial Revolution starts, and it starts to speed up and speed up and speed up. And technological progress, in terms of productivity growth, peaks in the 1950s at something like 3 to 4 percent a year, and then it’s been falling ever since. Then you ask that rate of fall — it’s 5 percent, roughly. It would have fallen if we held inputs constant. The one thing that’s been offsetting that fall in the rate of progress is we’ve put more and more resources into it. Again, if you think of the US, the number of research universities has exploded, the number of firms having research labs. Thomas Edison, for example, was the first lab about 100 years ago, but post-World War II, most large American companies have been pushing huge amounts of cash into R&D. But despite all of that increase in inputs, actually, productivity growth has been slowing over the last 50 years. That’s the sense in which it’s harder and harder to find new ideas. We’re putting more inputs into labs, but actually productivity growth is falling.

COWEN: Let’s say paperwork for researchers is increasing, bureaucratization is increasing. How do we get that to be negative 5 percent a year as an effect? Is it that we’re throwing kryptonite at our top people? Your productivity is not declining 5 percent a year, or is it? COVID aside.

BLOOM: COVID aside. Yeah, it’s hard to tell your own productivity. Oddly enough, I always feel like, “Ah, you know, the stuff that I did before was better research ideas.” And then something comes along. I’d say personally, it’s very stochastic. I find it very hard to predict it. Increasingly, it comes from working with basically great, and often younger, coauthors. Why is it happening at the aggregate level? I think there are three reasons going on. One is actually come back to Ben Jones, who had an important paper, which is called, I believe, “ Renaissance Man.” This came out 15 years ago or something. The idea was,it takes longer and longer for us to train. Just in economics — when I first started in economics, it was standard to do a four-year PhD. It’s now a six-year PhD, plus many of the PhD students have done a pre-doc, so they’ve done an extra two years. We’re taking three or four years longer just to get to the research frontier. There’s so much more knowledge before us, it just takes longer to train up. That’s one story. A second story I’ve heard is, research is getting more complicated. I remember I sat down with a former CEO of SRI, Stanford Research Institute, which is a big research lab out here that’s done many things. For example, Siri came out of SRI. He said, “Increasingly it’s interdisciplinary teams now.” It used to be you’d have one or two scientists could come up with great ideas. Now, you’re having to combine a couple. I can’t remember if he said for Siri, but he said there are three or four different research groups in SRI that were being pulled together to do that. That of course makes it more expensive. And when you think of biogenetics, combining biology and genetics, or bioengineering, there’s many more cross-field areas. Then finally, as you say, I suspect regulation costs, various other factors are making it harder to undertake research. A lot of that’s probably good. I’d have to look at individual regulations. Health and safety, for example, is probably a good idea, but in the same way, that is almost certainly making it more expensive to run labs. In fact, COVID is a huge pushback. I was talking just before the shutdown to a good friend of mine, and she said she has a big lab that has a number of animals and longer-running experiments going on. In fact, the shutdown has been extremely expensive. When we reopen with social distancing, of course, the costs are going to go up again. These are all factors pushing on your point of regulation. It’s just expensive running research.

COWEN: If I understand your estimates correctly, efficacy per researcher, as you measure it, is falling by about 5 percent a year. That seems phenomenally high. What’s the mechanism that could account for such a rapid decline?

Phenomenon Tyler Cowen interview with Nick Bloom

Steve Comment: Note Bloom’s calculation for research productivity is“…Research productivity is the ratio of idea output, measured as TFP growth, to the effective number of researchers.See Notes to Figure 1 and the online Appendix. Both research productivity and research effort are normalized to the value of 1 in the 1930s….”See attached paper

  • Productivity
    • Innovation/Research
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Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network
Date Posted:
September 4, 2026
Is Database:
Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup® AI

  1. The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

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Gross and Net US Investment

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

Timothy Taylor Conversable Economist
Date Posted:
September 4, 2026
Is Database:
Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup® AI

  1. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

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The AI Re-Acceleration That Wasn’t

AI Summary. 615). Claims of re-acceleration result from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Paul Kedrosky Applied Complexity
Date Posted:
September 3, 2026
Is Database:
Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Are AI performance gains accelerating or just appearing to through selective measurement?

Core argument: Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup® AI

  1. Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
  2. Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

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  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
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Understanding AI and Productivity

AI Summary. U.S. productivity growth has accelerated to ~2.2% annually since mid-2022, above the 2010s baseline, though pandemic-era labor market and business formation dynamics likely contributed alongside AI. Historical general-purpose technology booms sustained labor productivity growth above 2.5% for a decade or more, making the current acceleration substantial but not unprecedented.

Chad Syverson Economic Innovation Group
Date Posted:
August 28, 2026
Is Database:
Database

Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Is AI-driven productivity growth sustainable at historical technology boom levels?

Core argument: U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.

Productivity from mid-2022 on has maintained a faster-than-2010s trajectory involving annual growth of about 2.2%. Could this acceleration be due to AI? Perhaps. The timing leans against AI being the sole initial cause. Additionally, there were well-documented increases in economic dynamism (labor market churn and business formation) during the pandemic emergence whose timing matches the acceleration’s start. Regardless of AI’s current effect, the longer the aggregate productivity acceleration continues, the more plausible it is that AI is an important driver. As for the magnitude, a sustained increase from 1.5 to 2.2% annual productivity growth would be substantial (after a decade, GDP per capita would be 7% higher than otherwise), but hardly unprecedented. The 1995–2004 productivity boom saw annual productivity growth of nearly 3% per year, and other past general-purpose-technology-related productivity boosts saw labor productivity growth in excess of 2.5% for a decade or longer.

Takeaways by Macro Roundup® AI

  1. U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.
  2. The 1995–2004 productivity boom averaged nearly 3.0% annual growth, establishing that a durable AI-driven acceleration to 2.2% would be meaningful but well within historical precedent for general-purpose-technology cycles.
  3. Pandemic-era surges in labor market churn and business formation align more precisely with the productivity acceleration’s start date than AI adoption does, complicating AI-as-sole-cause narratives.

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US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times
Date Posted:
August 24, 2026
Is Database:
Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup® AI

  1. U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
  2. U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

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Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
August 17, 2026
Is Database:
Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Are technology companies hiding the true cost of artificial intelligence?

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

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