“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“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
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute

Showing 78 database articles primarily about Innovation/Research

Currently filtering by:

Remove Innovation/Research

Remove "primary topics only" restriction

Remove 'Database'

Show all 7,361 articles

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

Include articles that mention this topic 369

Only articles primarily about this topic 78

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.

Challenging the Narrative of European Decline, Continued

Paul Krugman Paul Krugman Wonks Out

Date Posted:: May 19, 2026
Is Database:: Database

In 2021 prices, per capita GDP in the Euro area is falling relative to that of the US, but the opposite is true if one takes into account the benefits to Europe from falling tech prices. “The big benefits of IT come from applying it, [not] creating it.”

Let’s start by looking at GDP per capita in Europe (actually the euro area) as a percentage of GDP per capita in the US. If we do this using constant prices — the World Bank uses 2021 prices — we get the line in Chart 1 labeled “2021 prices.” This line shows Europe falling behind over the past 25 years. If, however, we simply use prices in each given year, we get the line labeled “PPP,” which shows Europe gaining on the US. We get a similar picture if we look at GDP per worker-hour, where the black line is calculated using 2021 prices and the blue line is calculated using PPP. IT progress is passed on to all consumers via lower prices. The big benefits of IT come from applying it, rather than creating it. And as I’ve tried to show, the data show Europe holding its own in the relative value of the goods it produces, indicating that European economies are doing fine when it comes to applying technological advances. What should worry Europe, instead, are the geopolitical implications of US/Chinese leadership in advanced technology. The risk of being cut off from strategically important technologies, once minimal, is now very real. And that risk, rather than misleading numbers about trends in real GDP per worker hour, is what should concern European policymakers.

Takeaways by Macro Roundup^® AI

Euro-area GDP per capita relative to the US declines over the past 25 years when measured in constant 2021 prices, as the World Bank does, but rises when measured at each year's prices (PPP).
The same divergence appears in GDP per worker-hour, where the constant-2021-price series and the PPP series point in opposite directions for Europe relative to the US.
The author argues that Europe is holding its own in the relative value of the goods it produces and in applying technological advances, and that the real concern is the risk of being cut off from strategically important technologies amid US and Chinese leadership.

How Long Do We Wait for New Inventions?

Brian Potter Construction Physics

Date Posted:: May 11, 2026
Is Database:: Database

An analysis using Claude finds that for more than half of 190 inventions, the delay between the earliest date at which the invention became possible and the invention’s realization was 10 years or less. Since 1900, the fraction rose from ~50 to 75%.

I used a list of 190 major inventions. For each invention, I asked Claude Opus 4.7 how much earlier it could have been invented. The graph shows how much earlier, on average, each invention could have appeared for both the “earliest plausible” and “earliest straightforward” date ranges. We can clearly see a few trends on this graph. One is that for most inventions, the gap between when they could have been invented and when they were actually invented is not particularly large. Of the 166 inventions Claude estimated a date for, 107 of them (64%) had an “earliest plausible” date 50 years or less from the actual date, and 150 of them (90%) had an “earliest straightforward” date 50 years or less from the actual date. For more than half the inventions, the average earliest straightforward date of invention was 10 years or less from the actual date.

Takeaways by Macro Roundup^® AI

Of 166 major inventions for which Claude Opus 4.7 estimated a date, 107 (64%) had an "earliest plausible" invention date within 50 years of the actual date, and 150 (90%) had an "earliest straightforward" date within that window.
For more than half of the inventions assessed, the average "earliest straightforward" date of invention was 10 years or less from the actual date of invention.
Asking Claude Opus 4.7 how much earlier each of 190 major inventions could have been invented suggests that for most inventions the gap between the earliest possible and actual date is not particularly large.

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?

Takeaways by Macro Roundup® AI

Related Articles:

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

Related Articles:

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

Related Articles:

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

Related Articles:

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

Related Articles:

In 2021 prices, per capita GDP in the Euro area is falling relative to that of the US, but the opposite is true if one takes into account the benefits to Europe from falling tech prices. “The big benefits of IT come from applying it, [not] creating it.”

Does Europe's income gap with the US depend on price measurement?

Takeaways by Macro Roundup® AI

Related Articles:

An analysis using Claude finds that for more than half of 190 inventions, the delay between the earliest date at which the invention became possible and the invention’s realization was 10 years or less. Since 1900, the fraction rose from ~50 to 75%.

How much earlier could major inventions plausibly have appeared?

Takeaways by Macro Roundup® AI

Related Articles:

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI

Takeaways by Macro Roundup^® AI