Edward Conard

Top Ten New York Times Bestselling Author

  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…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
  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “…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
  • “…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
  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Tuesday, June 2, 2026

AI Summary. The authors' bottom-up tracking puts deduplicated artificial intelligence sector revenue at $25bn a quarter, up from $13bn under the same value-added method that strips out double-counting across cloud, model-lab, and application layers. They argue industry strain remains negative but should turn positive if revenue growth holds and capital spending forecasts don't rise.

Azeem Azhar and Nathan Warren Exponential View
Date Posted:

Azeem Azhar and Nathan Warren argue that while the large AI labs’ capex relative to revenue growth is “concerning…if revenue growth holds on trend and capex forecasts don’t revise upward, it should exit the red by the end of the year.”

Is AI revenue growth keeping pace with capital spending?

Core argument: Bottom-up tracking of deduplicated AI sector revenue puts the figure at $25 billion a quarter, up from $13 billion in September, using a value-added method that reduces double-counting across the cloud, model-lab, and application layers.

Quarterly revenues have roughly quintupled year on year: OpenAI from $1.7 billion to $6 billion, and Anthropic from $400 million to $4.8 billion. Based on our own bottom-up tracking of deduplicated sector revenue, we now put the figure at $25 billion a quarter, up from $13 billion in September. One note on that number. Since the first piece, we’ve refined how we calculate sector revenue, moving to a more nuanced value-added approach that reduces double-counting across the cloud, model-lab, and application layers. Our original September figure for Q3 2025 was $25 billion; under the new method, that same quarter comes out at $13 billion. This keeps industry strain in the red for now. But if revenue growth holds on trend – and capex forecasts don’t revise upward – it should exit the red by the end of the year.

Takeaways by Macro Roundup® AI

  1. Bottom-up tracking of deduplicated AI sector revenue puts the figure at $25 billion a quarter, up from $13 billion in September, using a value-added method that reduces double-counting across the cloud, model-lab, and application layers.
  2. Quarterly revenues have roughly quintupled year on year, with OpenAI rising from $1.7 billion to $6 billion and Anthropic from $400 million to $4.8 billion.
  3. Industry strain remains in the red, and is projected to exit the red by the end of the year if revenue growth holds on trend and capex forecasts are not revised upward.

Related Articles:

  • Everyone’s Looking For A Bubble. No One Sees The Stampede — A proprietary model estimates monthly artificial intelligence revenue grew roughly 18-fold in two years, from $772m to $13.8bn, with AI accounting for an estimated 23% of Google Cloud, 10% of Azure and 5% of Amazon Web Services. The model's ratio of investment to revenue fell from 6.1x to 4.7x, still historically high.
  • Abandon Ship! — 42 AI-related stocks have accounted for 75%-85% of S&P 500 returns, earnings and capital spending since generative AI chatbots launched, and 80%-100% over the more recent period. Unlike the dot-com era, rising tech capital spending has not been accompanied by increased debt financing in aggregate.
  • Commoditization, Orchestration, and the New AI Stack — The author argues that as incremental gains from larger AI training runs shrink, the return on additional computing power falls, weakening the case for the training spending that dominates data center demand. Survivors of model commoditization will be those owning the orchestration layer or the applications where outcomes are priced.

AI Summary. Alphabet is seeking $80bn to finance data-center expansion and computing capacity for training and running AI models, with Berkshire Hathaway buying a $10bn stake. The company has said its capital expenditures could reach $190bn in 2026 and expects a significant increase the following year.

Katherine Blunt Wall Street Journal
Date Posted:

Google is issuing $80B (upsized to $85B) in equity to finance its AI buildout. It expects capex to be ~$190B in 2026 and to rise from that in 2027; the stock fell on the news.

Are tech giants turning to outside financing for AI data centers?

Core argument: Alphabet is seeking $80 billion to finance data-center expansion and secure the computing capacity needed to train and run AI models, with Berkshire Hathaway buying a $10 billion stake.

Alphabet said the proceeds would be used to finance data-center expansion and secure computing capacity needed to train and run AI models. The company earlier this year said that its 2026 capital expenditures were expected to be as much as $190 billion and that it expects capital expenditures next year to significantly increase by comparison. Shares in Alphabet fell more than 1% in aftermarket trading.

Takeaways by Macro Roundup® AI

  1. Alphabet is seeking $80 billion to finance data-center expansion and secure the computing capacity needed to train and run AI models, with Berkshire Hathaway buying a $10 billion stake.
  2. Alphabet said its 2026 capital expenditures were expected to reach as much as $190 billion, and that it expects capital expenditures the following year to significantly increase by comparison.
  3. Alphabet shares fell more than 1% in aftermarket trading.

Related Articles:

  • The Macro Implications of the AI Capex Boom — Artificial intelligence capital spending is estimated to add roughly 1.4 percentage points to US growth in one year and about 1.5 points the next, comparable to business investment's contribution during the tech bubble. A material part of that estimate reflects profits at Nvidia and a few other chipmakers that are unlikely to be recycled back into the economy.
  • One Hundred Years in the U.S. Stock Markets — Over a century, U.S. publicly traded stocks generated roughly $91tn in shareholder wealth enhancement, with returns strongly positively skewed and dominated by a relatively small number of high-performing firms. Because most possible outcomes fall below the mean in a skewed distribution, the authors argue financial planning should focus on median outcomes or the full distribution.
  • How Much Is Donald Trump Costing America’s Economy? — Excluding artificial-intelligence-related categories, US non-residential fixed investment has contracted at roughly a 3% annual rate over the past year, versus average growth above 5% in the prior decade, running about $130bn below trend and cutting GDP growth by roughly 0.4 percentage points. Credit remains plentiful, with tight corporate bond spreads.

AI Summary. Goldman Sachs research finds that large companies floating 7% or less of their shares at initial public offering have 54% of shares in circulation two years later, while across its full sample of listings a 24% float at debut became 69% within 24 months.

John Authers Bloomberg
Date Posted:
Is Database:
Database

Goldman Sachs finds that large firms with low floats at IPO have 54% of their shares in circulation two years later. If SpaceX/Anthropic/OpenAI “follow this pattern, then the weight of new equity supply could well become unbearable next year or in 2028.”

How quickly do small initial public offering floats expand afterward?

Core argument: Goldman's research shows that large companies floating 7% or less of their shares at IPO have 54% of shares in circulation two years later.

Goldman’s research shows that big companies with a very low float — putting only 7% or less on the market at IPO — have 54% of their shares in circulation two years later. For the full sample of IPOs it examined, a 24% float at the market debut had turned to 69% within 24 months. History isn’t encouraging. In the dot-com cycle, the chart further up the page shows that the peak in IPOs came in 1999 — and the top of the market and subsequent crash hit the following year as lockups ended and founders tried to make some cash.

Takeaways by Macro Roundup® AI

  1. Goldman's research shows that large companies floating 7% or less of their shares at IPO have 54% of shares in circulation two years later.
  2. Across Goldman's full sample of IPOs, a 24% float at market debut had risen to 69% within 24 months.
  3. In the dot-com cycle, IPOs peaked in 1999, with the market top and subsequent crash arriving the following year as lockups ended and founders sought to cash out.

Related Articles:

  • An AI IPO Impact Update: The AnthroPix Effect May Be $5-Trillion+ — A closed-end fund holding small private stakes in OpenAI, SpaceX, and Anthropic has more than doubled in under two weeks and trades at nearly triple its last reported net asset value. The author argues this reflects investor appetite for any exposure to pre-IPO artificial intelligence companies, which will pull money out of other assets.
  • Trampled Underfoot by a Stampede of Unicorns — A database trawl counts 1,727 private companies valued above $1bn globally, and if private market valuations are accurate, the largest of them have outperformed public markets since the pandemic. Several, led by SpaceX and OpenAI, plan initial public offerings, which can signal trouble ahead since insiders are choosing to sell.
  • One Hundred Years in the U.S. Stock Markets — Over a century, U.S. publicly traded stocks generated roughly $91tn in shareholder wealth enhancement, with returns strongly positively skewed and dominated by a relatively small number of high-performing firms. Because most possible outcomes fall below the mean in a skewed distribution, the authors argue financial planning should focus on median outcomes or the full distribution.

AI Summary. Inequality between 1980 and 2000 mainly reflected divergence in wages and salaries, while the more recent rise reflects a growing share of income going to capital, especially corporate profits. Two candidate mechanisms are capital-biased technological change and growing monopoly power, alongside rising concentration of wealth among the top 0.1% of Americans.

Paul Krugman Krugman Wonks Out
Date Posted:
Is Database:
Database

Changes in American inequality btw 1980 and 2000 were largely due to changes in employee compensation, whereas changes in inequality since 2000 have largely been driven by changes in capital income, particularly that of the .1%.

Is inequality now driven by capital income rather than wages?

Core argument: The 1980–2000 surge in U.S. inequality mainly reflected divergence in wages and salaries, with big gains near the top coming from soaring executive compensation and large hedge fund bonuses rather than capital income.

Unlike the current period, the surge in inequality between 1980 and 2000 mainly reflected divergence in wages and salaries. Even near the top of the income distribution, big income gains largely came from big paychecks — soaring executive compensation, big bonuses for hedge fund managers, and so on. Capital income rose much less. In contrast, what we’re experiencing now is a huge rise in capital income, especially corporate profits, as a share of total income. There are two well-known mechanisms that can shift the distribution of income in a market economy away from labor and toward capital: 1) Capital-biased technological change, and 2) a rising share of income going to profits could be growing monopoly power, with big corporations exploiting their market dominance to raise prices and hold down wages. I won’t try today to figure out which of these stories is right about changes since 2000. However, let’s talk about something that has been happening within the capitalist class — the growing concentration of capital itself in the hands of an ever-smaller group. Here, for example, is the share of wealth held by the top 0.1% of Americans. In recent years the 0.1% haven’t just seen their share of total wealth rise, they’ve seen a sharp rise in their share of top 1% wealth.

Takeaways by Macro Roundup® AI

  1. The 1980–2000 surge in U.S. inequality mainly reflected divergence in wages and salaries, with big gains near the top coming from soaring executive compensation and large hedge fund bonuses rather than capital income.
  2. The current rise in inequality features a large increase in capital income, especially corporate profits, as a share of total income.
  3. Two well-known mechanisms can shift income in a market economy from labor toward capital: capital-biased technological change and growing monopoly power, with big corporations exploiting market dominance to raise prices and hold down wages.

Related Articles:

  • The Record Divide Between Corporate Profits and Worker Pay — Labor's share of gross domestic income fell to 51%, the lowest since records began in 1947, while profits' share rose to 12.1%, the highest since 1950. Inflation-adjusted hourly wages are up 3% since the end of 2019, compared with a 50% rise in profits.
  • The Gilded Age and Beyond: The Persistence of Elite Wealth in American History — The authors find that most extremely wealthy Americans fall out of the top tail within their lifetimes, and that grandparental wealth is associated with reaching the top 1% in a non-linear way. Still, over 90% of grandchildren of top 1% grandfathers did not reach that level.
  • The Economics of Inequality in High-Wage Economies — Information technology created investment opportunities exceeding the supply of properly trained talent, while trade with low-wage economies, low-skilled immigration, trade deficits, and aging demographics eased constraints on low-skilled labor and risk-averse savings. The author argues this framework, rather than rising cronyism, explains high returns to success, slowing productivity growth, and low interest rates.

AI Summary. Texas leads all US states in business investment and population growth, created roughly a fifth of all net new US jobs from 2020 to 2025, and is on course to overtake California as America's largest state economy. Other states are copying its approach, including plans to eliminate corporate income taxes and offer shovel-ready mega-sites.

Economist Staff The Economist
Date Posted:
Is Database:
Database

According to CBRE, at least 184 American firms, including Tesla and Caterpillar, moved their headquarters to Austin, Dallas or Houston btw 2020 and 2025. During that period, Texas drove ~20% of all net job creation in the US.

Is Texas becoming the center of American business investment and growth?

Core argument: Texas created roughly a fifth of all net new jobs in the United States from 2020 to 2025, and no state receives more business investment or adds more people to its population.

On May 27th the shareholders of ExxonMobil approved a plan to cut its ties with New Jersey and reincorporate in Texas, where it has long had its headquarters. The oil giant is not alone. Texas is steadily establishing itself as America Inc’s new centre of gravity. No state receives more business investment or is adding more people to its population. From 2020 to 2025 it created roughly a fifth of all net new jobs in the country. It is only a matter of time before Texas overtakes California as the largest economy in America. Texas’s success should worry those in New York and California monitoring their tax take. At the same time it has spawned a raft of imitators. Legislators in North Carolina have passed a plan to get rid of its corporate-income tax by 2030. Tennessee has copied Texas’s strategy of offering firms shovel-ready mega-sites. Nevada is trying to launch its own business court.

Takeaways by Macro Roundup® AI

  1. Texas created roughly a fifth of all net new jobs in the United States from 2020 to 2025, and no state receives more business investment or adds more people to its population.
  2. ExxonMobil's shareholders approved a plan to cut ties with New Jersey and reincorporate in Texas, where the oil giant has long had its headquarters.
  3. Other states are imitating Texas: North Carolina's legislators passed a plan to eliminate its corporate-income tax by 2030, Tennessee offers firms shovel-ready mega-sites, and Nevada is trying to launch its own business court.

Related Articles:

  • Where Americans Choose to Move and Where They Leave — County-level mapping of net domestic migration within the United States shows some counties gained net movers equal to 10% or more of their 2020 population while others lost an equivalent share, excluding international moves.
  • Fifty Shades of Growth — Boom Belt" states combine high birth rates with low death rates, led by Utah at 13.2 births and 6.3 deaths per 1,000 residents for a natural increase of 6.9 per 1,000. Texas records about 12.7 births versus 9.1 deaths per 1,000, with roughly half its population growth from natural increase.
  • As New Jobs In Finance Dry Up, New York City’s Fiscal Model Is Wilting — New York state's share of US taxpayers reporting over $1m in income fell from 12.7% in 2010 to 8.7% in 2022, and the Citizens Budget Commission estimates state and city income tax receipts from that group would have been $13bn higher had the share held. Job growth has concentrated in lower-paid health care and social assistance.

Wednesday, June 3, 2026

AI Summary. Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.

Paul Kedrosky Applied Complexity
Date Posted:
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.

Are small AI models closing the gap with frontier models?

Core argument: 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.

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

  1. 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.
  2. 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.
  3. 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.

Related Articles:

  • Commoditization, Orchestration, and the New AI Stack — The author argues that as incremental gains from larger AI training runs shrink, the return on additional computing power falls, weakening the case for the training spending that dominates data center demand. Survivors of model commoditization will be those owning the orchestration layer or the applications where outcomes are priced.
  • The Geopolitics of AI: Decoding the New Global Operating System — The performance gap between the top American and Chinese AI models narrowed from 9.3% to 1.7%, with Chinese firms relying on cheaper open-source models—DeepSeek prices usage 20-40x below OpenAI's ChatGPT. This price advantage could win adoption in developing countries and build dependency on Chinese AI ecosystems.
  • Misanthropic: On Mythos, Bad Human Behaviors and Systems Vulnerabilities — Anthropic reports that its Mythos model has detected thousands of high-severity cyber vulnerabilities, including ones found by chaining multiple obscure software weaknesses, and that these hacking skills are emergent rather than a designed capability. Mythos completed a corporate network attack simulation estimated to take an expert over 10 hours, which no other frontier model had solved.

AI Summary. A JPMorgan analysis found that more than 60% of US data-center capacity planned for completion in 2027 has not yet begun construction, with another 7% delayed. Analysts and power-industry experts say Google's strategy of owning generation—including its $4.75bn purchase of wind and solar developer Intersect—and shifting computing loads to follow power supply could speed its grid connections.

Katherine Blunt Wall Street Journal
Date Posted:
Is Database:
Database

Google is planning to build generation capacity alongside data centers to avoid power bottlenecks to bringing data centers online. The firm spent $4.75B to buy Intersect power, and signed a three-year deal with Voltus.

Is data-center construction falling behind schedule, and does owning power generation help?

Core argument: A JPMorgan analysis found that more than 60% of data-center capacity planned for completion in 2027 is not yet under construction, with another 7% delayed.

A JPMorgan analysis found that more than 60% of data-center capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed. Analysts and power-industry experts [said Google's] strategy of securing its own sources of generation and investing in the ability to shift computing loads to follow power supply could allow it to get its data centers connected to the grid faster than competitors and provide new avenues for deploying its swelling war chest. This year, Google became the only tech giant to own a power company with the $4.75 billion acquisition of Intersect, a wind and solar developer that in recent years pivoted to building such projects to support data centers. Intersect has energy projects under development to supply multiple gigawatts of electricity. [This week] Google announced a three-year agreement with demand-response company Voltus to help create more capacity in PJM, the nation’s largest power market.

Takeaways by Macro Roundup® AI

  1. A JPMorgan analysis found that more than 60% of data-center capacity planned for completion in 2027 is not yet under construction, with another 7% delayed.
  2. Google became the only tech giant to own a power company through its $4.75 billion acquisition of Intersect, a wind and solar developer that has energy projects under development to supply multiple gigawatts of electricity.
  3. Analysts and power-industry experts said Google's strategy of securing its own generation and shifting computing loads to follow power supply could connect its data centers to the grid faster than competitors.

Related Articles:

  • America’s Electricity Gap — US power consumption has risen more in the past two years than in the previous 15 combined, yet supply has not kept pace and electricity prices have climbed more in four years than in the prior 14. The US Energy Information Administration projects generation to grow another 4.6%, with commercial users including data centers driving most added demand.
  • Fighting Words: The Energy Transition in 2026 — Estimates based on primary energy overstate how much electricity is needed to replace oil in passenger cars, since electric motors convert 80%-90% of electricity into motion versus about 18% for gasoline cars. Measuring useful final energy net of waste heat avoids this "primary energy fallacy": the US consumed 45 exajoules of useful final energy versus 95 exajoules of primary energy.
  • U.S. Electricity Generation in 2025 Hit A Record, Again — U.S. electricity net generation reached a record 4.43 terawatt hours, up 2.8% from the prior year's high, contrasting with relatively flat generation from the mid-2000s to the early 2020s. Much of the recent demand growth comes from the commercial sector, which includes data centers, and the industrial sector.

AI Summary. A US executive order creates a voluntary framework under which artificial intelligence labs are asked to submit new models for government security reviews for up to 30 days before public release, down from the 90-day period in an earlier draft. The earlier draft was withheld over concerns that federal oversight of frontier models could hamper the growth of leading labs.

Joe Miller Financial Times
Date Posted:

The USG will review new AI models before their release. While some view the new process as “watered-down” relative to a previous proposal, Dean Ball argues the executive order is “clearly teeing up the infrastructure for a model licensing regime.”

Will AI labs voluntarily submit new models for government security reviews?

Core argument: Donald Trump's executive order asks AI labs to submit new models for security reviews for up to 30 days before public release, down from the 90-day review period in an earlier draft seen by the FT.

Donald Trump has signed a long-awaited executive order that creates a “voluntary framework” for the US to gain early access to cutting-edge AI models. The order is a watered-down version of an earlier draft, which was due to be signed last month but Trump pulled over concerns that federal oversight of frontier AI models might hamper the growth of leading labs. Under the order AI labs will be asked to submit their new models for security reviews “for a period of up to 30 days” before they are publicly released. The original executive order — a draft of which was seen by the FT — asked for a 90-day review period. Dean Ball, a former Trump adviser who last year wrote the administration’s AI Action Plan, called the order a “fairly major win for the safety contingent within the [administration]” and suggested it was “clearly teeing up the infrastructure for a model licensing regime”.

Takeaways by Macro Roundup® AI

  1. Donald Trump's executive order asks AI labs to submit new models for security reviews for up to 30 days before public release, down from the 90-day review period in an earlier draft seen by the FT.
  2. Trump pulled the earlier version of the order over concerns that federal oversight of frontier AI models might hamper the growth of leading labs, and signed instead a "voluntary framework" giving the US early access to cutting-edge models.
  3. Dean Ball, a former Trump adviser who wrote the administration's AI Action Plan, called the order a "fairly major win for the safety contingent" within the administration and suggested it was "clearly teeing up the infrastructure for a model licensing regime".

Related Articles:

  • Misanthropic: On Mythos, Bad Human Behaviors and Systems Vulnerabilities — Anthropic reports that its Mythos model has detected thousands of high-severity cyber vulnerabilities, including ones found by chaining multiple obscure software weaknesses, and that these hacking skills are emergent rather than a designed capability. Mythos completed a corporate network attack simulation estimated to take an expert over 10 hours, which no other frontier model had solved.
  • China’s Own Mythos Is Coming — America Must Prepare — The authors argue the US can extend its estimated seven-month lead in artificial intelligence by closing gaps in export controls: halting all AI chip exports to China including less advanced models, curbing smuggling networks, blocking cloud and remote access to controlled chips and US models, and restricting chipmaking equipment.
  • White House Opposes Anthropic’s Plan to Expand Access to Mythos Model — The White House opposes Anthropic's proposal to extend access to its Mythos artificial-intelligence model to roughly 70 more companies and organizations, which would raise the total with access to about 120. Officials cited security concerns about a model capable of conducting cyberattacks, and doubts that Anthropic has enough computing power to serve that many users without degrading government use.

AI Summary. Comparisons of living standards across countries can use "constant price" purchasing power parities, which extrapolate from a base year using national inflation measures, or "current price" parities recalculated annually; the debate over whether Europe has fallen behind the U.S. turns on which method is preferable. The two methods agree when inflation is measured consistently across countries.

Matt Klein The Overshoot
Date Posted:

Addressing whether intra-country growth comparisons should use constant or current price PPPs, Klein notes that for Japan, both give the same conclusion. ICT‑related price and quality‑adjustment asymmetries—while present—have not yielded large PPP drift.

Does the method of comparing prices across countries change living standards conclusions?

Core argument: The dispute over whether Europe's living standards have fallen relative to the U.S. turns on which purchasing power parity method is preferred: constant price PPPs anchored to a base year or current price PPPs recalculated each year.

One obvious approach would be to start from a given base year and then run the data forwards and backwards using national measures of inflation. This generates “constant price purchasing power parities”. In any given year, the relative purchasing power between two societies is sensitive to the starting benchmark, but the direction of travel always looks the same. The other obvious approach would be to recalculate the price parities each year, if possible, and then use those to compare incomes each year. This creates “current price purchasing power parities.” The argument about whether Europe’s living standards have fallen relative to the U.S. is an argument about whether one method is better than the other. In theory these should produce more or less the same answers as long as inflation is measured consistently across countries and as long as the mix of items tracked does not change. Sometimes, that is exactly what happens. Both methods produce essentially the same results when comparing Japanese and U.S. incomes over time, for example.

Takeaways by Macro Roundup® AI

  1. The dispute over whether Europe's living standards have fallen relative to the U.S. turns on which purchasing power parity method is preferred: constant price PPPs anchored to a base year or current price PPPs recalculated each year.
  2. Constant price purchasing power parities, built by running data forward and backward from a base year using national inflation measures, yield relative purchasing power that is sensitive to the starting benchmark, though the direction of travel looks the same.
  3. In theory the two purchasing power parity methods should produce roughly the same answers as long as inflation is measured consistently across countries and the mix of tracked items does not change, as happens when comparing Japanese and U.S. incomes.

Related Articles:

  • Europe Versus America: A Response to the Critics — The author argues that standard productivity measures do not carry the implications for cross-country comparisons of living standards and economic welfare that many economists assume, rather than that European productivity is mismeasured. Faster measured US productivity growth coexists with European relative productivity and purchasing power at current prices not declining.
  • The Mismeasurement of European Productivity — European productivity growth has trailed the United States for three decades, and measures using current purchasing-power-parity conversions versus national price deflators answer different questions. The authors argue the flat current-purchasing-power-parity series reflects changing the price yardstick each year rather than showing Europe is keeping pace.
  • The Future of European Competitiveness – A Competitiveness Strategy for Europe — A European Union competitiveness report attributes the EU's labour productivity growth gap with the US largely to weaker activity in information and communications technology and large-scale digital services; excluding main ICT sectors, EU productivity was broadly at par with the US from 2000-2019. It estimates Europe's investment share must rise about 5 percentage points of GDP.

AI Summary. Victoria's Secret shares hit a record high after the lingerie retailer beat earnings estimates and raised its annual net sales guidance to as much as $7.13bn. The company has posted four consecutive quarters of annual revenue growth, with its bras gaining market share among 18- to 44-year-olds and growth in both the under-$50,000 and over-$200,000 income categories.

Lily Meier Bloomberg
Date Posted:

As the use of GLP-1 drugs for weight loss expands, lingerie sales are rising. Victoria’s Secret has posted four consecutive quarters of annual revenue growth, and its stock is up ~236% since the fall of 2024.

Is a focus on core products lifting a lingerie retailer's sales?

Core argument: Victoria's Secret raised its annual net sales guidance to as much as $7.13 billion and reported quarterly net sales of $1.56 billion for the period ended May 2, exceeding analyst estimates and sending shares to a record high.

Victoria’s Secret shares soared after the company beat earnings estimates and boosted its outlook, adding to signs that Chief Executive Officer Hillary Super’s turnaround plan is on track. The lingerie retailer raised its annual net sales guidance to as much as $7.13 billion and reported net sales of $1.56 billion for the quarter ended May 2, exceeding analysts estimates. The company has posted four consecutive quarters of annual revenue growth, with Victoria’s Secret bras gaining market share with 18- to 44-year-olds, and the retailer seeing growth in the under-$50,000 and over-$200,000 annual income categories.

Takeaways by Macro Roundup® AI

  1. Victoria's Secret raised its annual net sales guidance to as much as $7.13 billion and reported quarterly net sales of $1.56 billion for the period ended May 2, exceeding analyst estimates and sending shares to a record high.
  2. Victoria's Secret has posted four consecutive quarters of annual revenue growth, with its bras gaining market share among 18- to 44-year-olds.
  3. Victoria's Secret is seeing growth in both the under-$50,000 and over-$200,000 annual income categories, adding to signs that Chief Executive Officer Hillary Super's turnaround plan is on track.

Related Articles:

  • Today’s Most Important Technology — The share of patients taking semaglutide for weight control rather than type 2 diabetes rose from under 10% in 2019 to 41.7%. Obesity and overweight, affecting 1 in 8 people worldwide, cost the global economy $2tn — nearly 70 cents on the dollar from premature mortality and lost productivity — an impact projected to double by 2035 absent intervention.
  • National-Level and State-Level Prevalence of Overweight and Obesity Among Children, Adolescents, and Adults in the USA — An estimated 15.1m US children aged 5–14, 21.4m older adolescents aged 15–24, and 172m adults aged 25 and over had overweight or obesity, with adult prevalence highest in North Dakota for males (80.6%) and Mississippi for females (79.9%). Obesity prevalence among adolescents rose roughly 158–186% over three decades.
  • Lilly and Novo Whipsaw Wall Street With Split in Obesity Outlook — Eli Lilly launched lower-priced vials of its obesity drug Zepbound to extend supply beyond injector pens, later cutting vial prices by 50%, and resolved its shortage while rival Novo Nordisk's persisted. Ending the shortage required compounding pharmacies to stop selling copies of Lilly's medicine.