“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
“A full-throated defense of economic dynamism.” - The Wall Street Journal
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute

For whatever topics you select (currently: Innovation/Research):

Include articles that mention this topic 375

Only articles primarily about this topic 79

Is Big Business Really Getting Too Big?

Economist Staff The Economist

Date Posted:: August 24, 2023

.@TheEconomist notes that American firms collect 41% of “excess profits” globally as measured by firms’ return on invested capital above a hurdle rate of 10%.

Out of some 900 sectors in America the number where the four biggest firms have a market share above two-thirds grew from 65 in 1997 to 97 by 2017. The Economist has come up with a crude estimate of “excess” profits for the world’s 3,000 largest listed companies by market value (excluding financial firms). Using reported figures from Bloomberg we calculate a firm’s return on invested capital above a hurdle rate of 10% (excluding goodwill and treating research and development, R&D, as an asset with a ten-year lifespan). This is the rate of return one might expect in a competitive market. In the past year excess profits reached $4trn, or nearly 4% of global GDP. American firms collect 41% of the total, with European ones taking 21%. The energy, technology and, in America, health-care industries stand out as excess-profit pools relative to their size.

Takeaways by Macro Roundup^® AI

The Economist estimates that "excess" profits — returns on invested capital above a 10% hurdle rate — for the world's 3,000 largest listed non-financial companies by market value reached $4trn over the past year, or nearly 4% of global GDP.
American firms collect 41% of the estimated excess profits and European firms 21%, with energy, technology and, in America, health care standing out as excess-profit pools relative to their size.
Among roughly 900 American sectors, the number where the four largest firms hold a market share above two-thirds rose from 65 in 1997 to 97 in 2017.

No Signs of AI Payoff Outside the Tech Sector

Torsten Slok Apollo

Date Posted:: October 26, 2026
Is Database:: Database

Citing flat revenue per worker, and profit margin growth for the S&P 493, Slok argues that so far, AI’s diffusion through the economy has delivered “zero financial payoff to corporate America outside of tech.”

With no impact on the bottom line, mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins. VC-backed companies are spending more and more on tokens, but mainstream corporate America does not need the latest model and is unwilling to pay for it. As long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will be very limited. The question looking ahead is whether, even as models become commoditized, mainstream corporate America can turn increasingly cheap intelligence into higher revenue growth and higher profit margins. That may be harder than it sounds. The AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.

Takeaways by Macro Roundup^® AI

Revenue per worker and profit margins are rising for the Magnificent 7 but flat for the S&P 493, with no signs of AI boosting productivity growth or delivering financial payoff outside the tech sector.
Mainstream corporate America is pulling back from AI and treating token spending as a cost center that weighs on margins, while VC-backed companies spend increasingly more on tokens.
The macroeconomic effects of AI will remain very limited as long as agentic AI and token spending stay confined to a small part of the economy.

The College Wage Premium in the Generative AI Era

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.

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

The U.S. college wage premium fell from 0.626 in 2022 to 0.575 in 2026 after expanding for four decades, with Current Population Survey data implying the first sustained negative relative demand growth for college labor.
By 2026, moving from zero occupational AI exposure to full exposure had a -0.086 effect on wages, and combined with the college–non-college exposure gap this mechanism accounts for roughly 28% of the 2022–2026 drop in the college wage premium.
Post-2022 wage growth slowed disproportionately in high-exposure occupations that employ a disproportionate share of college graduates, patterns that, while non-causal, indicate task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in compressing the skill premium.

The AI Re-Acceleration That Wasn’t

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.”

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

Using all observations from Epoch's Capabilities Index and controlling for developer and model family, a piecewise model shows no statistically significant breakpoint in AI capability trends (p = 0.615) and does not improve on a purely linear trend.
The estimated change in slope of the AI capabilities trend carries a confidence interval of -8.4 to +23.4 points per year.
Apparent AI acceleration results from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side of that breakpoint.

The AI Trade Is Losing One of Its Key Signals

Jan-Patrick Barnert and Michael Msika Bloomberg

Date Posted:: July 7, 2026
Is Database:: Database

The Silicon Data LLM Token Expenditure Index, which tracks AI token prices, is down ~20% from its recent peak in May. The metric is a rough “proxy for marginal willingness to pay.”

A softer index doesn’t mean AI is getting cheaper. The gauge blends prices and usage, meaning a dip can imply very different scenarios: either list prices are falling, or demand is shifting toward cheaper models. It could also point to a genuine softening in what buyers are prepared to shoulder. Each of these possibilities carries different implications. Let’s start with a benign read: While token prices have collapsed more than 90% since 2023, total spend has roughly doubled since last year. Cheaper tokens have expanded the market. This means that an index pause is simply digestion, while demand is real and capex is money well spent. The bull case for Nvidia rests here. Now for the interpretation that’s keeping people up at night: Bears warn that sustained weakness in the index could end the trade that saw nearly the entire AI cohort rally hard this cycle. It’s token spending that justifies the next capex order, and the bill is already looking stretched. Allianz Research said there’s nearly a 46% growth gap between AI investment and sales. That’s worse than the 32% divergence measured during the 2001 telecom bust.

Takeaways by Macro Roundup^® AI

Token prices have fallen more than 90% since 2023 while total token spend has roughly doubled over the past year, supporting a benign read in which cheaper tokens expand the market and an index pause reflects digestion.
Allianz Research put the growth gap between AI investment and sales at nearly 46%, wider than the 32% divergence measured during the 2001 telecom bust.
The index blends prices and usage, so a dip can imply falling list prices, demand shifting toward cheaper models, or a genuine softening in what buyers are prepared to shoulder.

The Age Of The Solopreneur

Ernie Tedeschi, Marisa Rama and Chris Cruickshank Stripe Economics

Date Posted:: June 22, 2026
Is Database:: Database

New US business applications have surged since late 2024, driven mainly by “likely nonemployer” filings representing “solopreneurs” rather than businesses expected to hire. Data suggest AI may be enabling this by filling capability gaps without hiring.

While the recent surge in US business formation is not reflected in high-propensity applications, the evidence points toward a structural increase in genuine small business activity over 2025 and 2026, driven by solopreneurs. Preliminary evidence of growth in AI tool usage and solopreneur growth in high-AI-adoption sectors suggests that advances in AI are responsible for a meaningful portion of this growth. We believe AI is lowering barriers to business formation and growth by expanding the capabilities of solopreneurs, further improving tools and platforms that cater to new businesses, and creating a new set of opportunities for entrepreneurs to pursue. Whether the magnitude of this effect is as large as the most optimistic readings of the data suggest remains to be seen, but we believe we might be in the early innings of a fundamental acceleration in business formation—which could have ripple effects throughout the economy.

Takeaways by Macro Roundup^® AI

The recent surge in US business formation is not reflected in high-propensity applications, but the evidence points toward a structural increase in genuine small business activity over 2025 and 2026, driven by solopreneurs.
Preliminary evidence of growth in AI tool usage and solopreneur growth in high-AI-adoption sectors suggests that advances in AI are responsible for a meaningful portion of this growth.
AI appears to be lowering barriers to business formation and growth by expanding solopreneur capabilities, improving tools and platforms serving new businesses, and creating opportunities for entrepreneurs, with possible ripple effects throughout the economy.

Chart of the Day: Small Models are Closing the Gap to Frontier AI

Paul Kedrosky Applied Complexity

Date Posted:: June 3, 2026
Is Database:: Database

Small models have increasingly closed the performance gap with frontier models. To Kedrosky, this implies “edge compute becomes real,” which in turn, at the margin, means reduced relative demand for data center-based versus local compute.

A quiet story in AI isn't that large models keep getting better, it's that small ones are catching up quickly. In 2022, reaching 60% on MMLU, a test of academic reasoning, required a 540-billion-parameter model. By 2024, a 3.8-billion-parameter model hit the same threshold — a 142-fold parameter reduction in two years. The direction is clear, and the implications are wide-ranging. Edge compute becomes real. Models that fit on a phone or a sensor don't need a data center. Inference shifts from hyperscaler to device, which is a different supply chain and a different competitive moat than anyone has built for. The data center API business model has a half-life. If a fine-tuned 7B model running locally can handle 80% of enterprise use cases, the case for paying per-token to a cloud provider shrinks fast. The most exposed providers are those whose moats are model quality rather than distribution, i.e., the capex-heavy frontier models. Compute becomes less scarce.

Takeaways by Macro Roundup^® AI

Reaching 60% on MMLU, a test of academic reasoning, required a 540bn-parameter model in 2022 but only a 3.8bn-parameter model by 2024, a 142-fold parameter reduction in two years.
Models small enough to run on a phone or sensor shift inference from hyperscalers to devices, which involves a different supply chain and competitive moat than providers have built for.
The case for paying per-token to a cloud provider shrinks if a fine-tuned 7B model running locally can handle 80% of enterprise use cases, leaving capex-heavy frontier providers whose moats rest on model quality rather than distribution most exposed.

.@TheEconomist notes that American firms collect 41% of “excess profits” globally as measured by firms’ return on invested capital above a hurdle rate of 10%.

Are markets becoming more concentrated and profits rising above competitive levels?

Takeaways by Macro Roundup® AI

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Citing flat revenue per worker, and profit margin growth for the S&P 493, Slok argues that so far, AI’s diffusion through the economy has delivered “zero financial payoff to corporate America outside of tech.”

Is artificial intelligence boosting revenue and margins outside big tech?

Takeaways by Macro Roundup® AI

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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 AI exposure driving the decline in the college wage premium?

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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.”

Has AI capability progress accelerated beyond its linear trend?

Takeaways by Macro Roundup® AI

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The Silicon Data LLM Token Expenditure Index, which tracks AI token prices, is down ~20% from its recent peak in May. The metric is a rough “proxy for marginal willingness to pay.”

Why does a softening AI token index send ambiguous signals?

Takeaways by Macro Roundup® AI

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New US business applications have surged since late 2024, driven mainly by “likely nonemployer” filings representing “solopreneurs” rather than businesses expected to hire. Data suggest AI may be enabling this by filling capability gaps without hiring.

Is AI driving the surge in one-person business formation?

Takeaways by Macro Roundup® AI

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Small models have increasingly closed the performance gap with frontier models. To Kedrosky, this implies “edge compute becomes real,” which in turn, at the margin, means reduced relative demand for data center-based versus local compute.

Are small AI models closing the gap with frontier models?

Takeaways by Macro Roundup® AI

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