Edward Conard

Top Ten New York Times Bestselling Author

  • “…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 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
  • “…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 offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “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 must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…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
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Friday, January 24, 2025

AI Summary. Among early 20th-century US immigrants, the authors find that new Polish Catholic church openings are associated with slower economic assimilation for Polish residents — lower income scores and a shift toward manual occupations — alongside stronger communal ties such as more within-group marriage and Polish names. These patterns persist into the second generation and are not found for Italians.

Ran Abramitzky, Leah Platt Boustan and Osea Giuntella National Bureau of Economic Research
Date Posted:

.@leah_boustan @Osea82 find Polish church-centered ethnic enclaves established from 1900-30 in the US hindered immigrants’ economic mobility, with an estimated “7% decline in earnings associated with a 10pp increase in own-ethnicity population share.”

Do ethnic enclaves slow immigrants' economic assimilation?

Core argument: New Polish Catholic church openings in the early 20th-century US led Polish residents to assimilate more slowly economically, with falling income scores and a shift toward manual occupations.

We compare two Catholic ethnic groups that moved to the US in large numbers in the early 20th century: Italians and Poles. Historians argue that Poles were more likely to center their communal life around church activity. Consistent with this qualitative evidence, we find that new Polish Catholic church openings lead Polish residents to experience slower economic assimilation, including falling income scores and a shift toward manual occupations, counterbalanced with stronger communal ties, as measured by heightened within-group marriage and greater use of Polish names. These patterns persist into the second generation, and are not apparent for Italians, who were less focused on the church. We do not find any evidence that Polish church openings affect other residents of the neighborhood (Polish likely Jews or other Catholics), suggesting that the church was not placed in a neighborhood that was otherwise on a downswing. Our paper documents that living in an ethnic enclave can hinder the economic mobility of immigrants.

Takeaways by Macro Roundup® AI

  1. New Polish Catholic church openings in the early 20th-century US led Polish residents to assimilate more slowly economically, with falling income scores and a shift toward manual occupations.
  2. Polish church openings coincided with stronger communal ties among Polish residents, measured by heightened within-group marriage and greater use of Polish names, patterns that persisted into the second generation.
  3. Polish church openings show no effect on other neighborhood residents, including likely Polish Jews and other Catholics, suggesting churches were not placed in neighborhoods already on a downswing; no comparable patterns appear for Italians, who were less focused on the church.

Related Articles:

  • White Flight from Asian Immigration: Evidence from California Public Schools — In California public schools, the authors estimate that the arrival of one Asian student in a high-socioeconomic-status suburban district is associated with the departure of 1.5 white students, alongside rising academic performance. They suggest this reflects parental concerns about academic competition rather than housing market dynamics or direct racial animus.
  • Recent Immigration Surge Has Been Largest in U.S. History — An analysis of government data finds the recent U.S. immigration surge is the largest on record, with net migration averaging 2.4m people a year from 2021 to 2023 and the foreign-born share of the population reaching 15.2% in 2023, up from 13.6% in 2020. About 60% of immigrants arriving since 2021 entered without legal authorization.

AI Summary. US living standards have grown an average 2.5% per year since the cohort born in the late 1990s reached adulthood, giving this generation faster-improving living standards and more upward mobility than the millennials before them. Americans born in 1995 show more upward mobility relative to their parents than those born in 1965, and are also outpacing millennials in home ownership.

John Burn-Murdoch Financial Times
Date Posted:

Gen Z Americans aged 22-34 are seeing higher growth in mean actual individual consumption per capita than previous US generations and their European counterparts, even as their home ownership rates have decreased. @jburnmurdoch

Are younger Americans seeing faster income gains and more upward mobility?

Core argument: US living standards have grown an average 2.5% per year since the cohort born in the late 1990s reached adulthood, giving Gen Z Americans faster-improving living standards than young boomers had at the same age.

Gen Z are motoring ahead. US living standards have grown at an average 2.5% per year since the cohort born in the late 1990s entered adulthood, blessing this generation not only with far more upward mobility than their millennial elders, but with more rapidly improving living standards than young boomers had at the same age. And it’s not just incomes: Gen Z Americans are also outpacing millennials in their climb up the housing ladder. All the signs are that in the US, the decades-long slowdown in generation-on-generation economic progress has not only stopped but gone into reverse. Americans born in 1995 are enjoying even more upward mobility relative to their parents than those born in 1965. Zoomers by name, zoomers by socio-economic nature.

Takeaways by Macro Roundup® AI

  1. US living standards have grown an average 2.5% per year since the cohort born in the late 1990s reached adulthood, giving Gen Z Americans faster-improving living standards than young boomers had at the same age.
  2. Americans born in 1995 are enjoying more upward mobility relative to their parents than those born in 1965, a sign that the decades-long slowdown in generation-on-generation economic progress in the US has gone into reverse.
  3. Gen Z Americans are outpacing millennials in upward mobility and in their climb up the housing ladder.

Related Articles:

AI Summary. The Congressional Budget Office projects federal debt held by the public will climb from about 98% of GDP to 118% by 2035, with deficits totaling $22tn over the decade and interest costs rising to 4.1% of GDP. Real growth is projected to average 1.8%, with inflation near 2%.

CRFB Staff Committee For A Responsible Federal Budget
Date Posted:

CBO’s 2025 baseline, assuming the expiration of TCJA, projects debt growing from 98% of GDP today to 118% by 2035. Extension of the TCJA would bring debt to 129% of GDP by 2035. @MarcGoldwein

How high will federal debt and interest costs rise by 2035?

Core argument: CBO projects debt held by the public will rise from nearly $29 trillion, roughly 98% of GDP, to $52 trillion, or 118% of GDP, by 2035, with deficits totaling $22 trillion (5.8% of GDP) over the decade.

Debt held by the public will rise from nearly $29 trillion today, or roughly 98% of GDP, to $52 trillion by 2035, or 118% of GDP. Deficits will total $22 trillion (5.8% of GDP) over the next decade. Spending will grow from 23.3% of GDP in 2025 to 24% by 2035, while revenue will grow from 17.1% of GDP in 2025 to 18.3% by 2035 under current law, which assumes the expiration of the 2017 tax cuts at the end of this year. Interest costs will match their record as a share of GDP in 2025 – 3.2%– and rise to 4.1% of GDP by FY 2035. The economy will continue growing at a steady clip, with real GDP growth averaging 1.8% over the decade, unemployment and inflation normalizing around 4.4% and 2%, respectively, and 10-year Treasury notes averaging 3.8% by the end of the decade.

Takeaways by Macro Roundup® AI

  1. CBO projects debt held by the public will rise from nearly $29 trillion, roughly 98% of GDP, to $52 trillion, or 118% of GDP, by 2035, with deficits totaling $22 trillion (5.8% of GDP) over the decade.
  2. CBO projects interest costs will match their record share of GDP at 3.2% in 2025 and rise to 4.1% of GDP by fiscal year 2035.
  3. CBO projects spending will grow from 23.3% of GDP in 2025 to 24% by 2035 while revenue rises from 17.1% to 18.3%, under current law, which assumes the 2017 tax cuts expire at the end of this year.

Related Articles:

  • The Budget and Economic Outlook: 2025 to 2035 — The Congressional Budget Office projects the federal deficit will grow from $1.9tn (6.2% of GDP) to $2.7tn by 2035, well above the 3.8% of GDP average of the past 50 years. Outlays are projected to rise to 24.4% of GDP, driven by growth in Social Security and Medicare spending and rising net interest costs, while revenues reach 18.3%.
  • The Long-Term Budget Outlook Under Alternative Scenarios for the Economy and the Budget — The Congressional Budget Office projects that if private investment were more sensitive to federal deficits, debt held by the public would exceed 250% of GDP and real national income per person would be 18% ($21,300) lower than its baseline, with the average interest rate on federal debt 2pp higher.
  • Larry Summers and Bob Rubin Join Our CIOs to Discuss the Surprising Economic Cycle — Adopting the Simpson-Bowles fiscal consolidation when interest rates were at zero would have risked excessive disinflation, since long rates had no room to fall to provide an offsetting expansionary impulse. After the large run-up in debt and rise in real interest rates, the developing fiscal situation risks inflationary pressure, Treasury market disruption, or crowding out of private investment.

AI Summary. Meta plans to spend as much as $65bn on artificial intelligence projects in a single year, including a data center the company says would cover a significant part of Manhattan and increased hiring on AI teams. Meta expects to bring roughly a gigawatt of computing power online and to hold more than 1.3m graphics processing units.

Dana Wollman and Kurt Wagner Bloomberg
Date Posted:

Meta plans to spend $65B on AI-related projects in 2025. Mark Zuckerberg acknowledges the risk of over-investment, but argues the level is rational to avoid being “out of position for the most important technology for the next 10 to 15 years.”

How much is a major tech company investing in AI annually?

Core argument: Meta plans to spend as much as $65 billion on AI-related projects in 2025, including a data center Mark Zuckerberg said would be "so large that it would cover a significant part of Manhattan," plus increased AI hiring.

Meta plans to invest as much as $65 billion on projects related to artificial intelligence in 2025, including building a giant new data center and increasing hiring in AI teams. The company intends to use the funds to build a data center “so large that it would cover a significant part of Manhattan,” Mark Zuckerberg said. Meta plans to bring around a gigawatt of computing power online in 2025 and is projected to end the year with more than 1.3 million graphics processing units. Meta has invested significantly in AI over the last several years, and recently announced a new $10 billion data center in Louisiana. “I think that there’s a meaningful chance that a lot of the companies are over-building now, and that you’ll look back and you’re like, ‘oh, we maybe all spent some number of billions of dollars more than we had to,’” Zuckerberg told Bloomberg in July. “On the flip side, I actually think all the companies that are investing are making a rational decision, because the downside of being behind is that you’re out of position for like the most important technology for the next 10 to 15 years.”

Takeaways by Macro Roundup® AI

  1. Meta plans to spend as much as $65 billion on AI-related projects in 2025, including a data center Mark Zuckerberg said would be "so large that it would cover a significant part of Manhattan," plus increased AI hiring.
  2. Meta plans to bring around a gigawatt of computing power online in 2025 and is projected to end the year with more than 1.3 million graphics processing units, having recently announced a $10 billion data center in Louisiana.
  3. Mark Zuckerberg told Bloomberg in July that there is "a meaningful chance" many companies are over-building by some billions of dollars, while arguing the investments are rational because falling behind leaves a company out of position on the most important technology for the next 10 to 15 years.

Related Articles:

  • SoftBank and OpenAI Back Sweeping AI Infrastructure Project In US — OpenAI and SoftBank plan a US artificial intelligence infrastructure joint venture called Stargate, intending to spend $100bn initially and as much as $500bn over four years. SoftBank holds financial responsibility and OpenAI operational responsibility, with Abu Dhabi state fund MGX and Oracle also funding and Arm, Microsoft and Nvidia as technology partners.
  • A Severe Case of COVIDIA: Prognosis For An AI-Driven US Equity Market — Computing cycles move from infrastructure to platforms to applications, and NVIDIA data center revenues as a share of market-wide capital spending are projected to reach levels last seen at the 1969 mainframe peak and the dot-com boom. Large cloud providers spend more than twice as much training foundational models as running them for customers.
  • Business Spending Is Lining Up to Reinforce Growth — Around 70% of sectors are posting positive year-over-year profit growth, with big tech showing the largest acceleration in demand and profits but strength broadening across the economy. Companies are responding with rising capital spending growth and more optimistic investment plans, as firms typically add capacity with a slight lag after real demand picks up.

AI Summary. Chinese developer DeepSeek trained its V3 large language model using fewer than 3m chip-hours at an estimated cost under $6m—roughly a tenth of the computing power and expense of Meta's Llama 3.1—through a series of marginal efficiency improvements rather than one breakthrough. The model was trained on chips constrained by American export sanctions.

Economist Staff The Economist
Date Posted:

Despite US export controls, Chinese AI firms like Alibaba & DeepSeek have released open-source models rivaling OpenAI and Google, at much lower costs. DeepSeek plans to charge ~10% of the cost of leading-edge models from OpenAI, Google, and Anthropic

Can export controls stop Chinese AI models from matching American efficiency?

Core argument: DeepSeek trained V3's billions of parameters in fewer than 3m chip-hours at an estimated cost below $6m, roughly a tenth of the computing power and expense used for Llama 3.1, using chips that were not the most powerful because of American sanctions.

DeepSeek’s gargantuan LLM is notable not just for its scale, but for the efficiency of its training, whereby the model is fed data from which it infers its parameters. This success derived not from a single, big innovation, says Nic Lane of Cambridge University, but from a series of marginal improvements. The training process, for instance, often used rounding to make calculations easier, but kept numbers precise when necessary. The server farm was reconfigured to let individual chips speak to each other more efficiently. And after the model had been trained, it was fine-tuned on output from DeepSeek R1, the reasoning system, learning how to mimic its quality at a lower cost. Thanks to these and other innovations, coming up with V3’s billions of parameters took fewer than 3m chip-hours, at an estimated cost of less than $6m—about a tenth of the computing power and expense that went into Llama 3.1. And because of America’s sanctions, the chips v3 used weren’t even the most powerful ones. In February, when DeepSeek starts to let other firms create services that make use of V3, it will charge less than a tenth of what Anthropic does for use of Claude, its LLM. “If the models are indeed of equivalent quality this is a dramatic new twist in the ongoing LLM pricing wars,” says Simon Willison, an AI expert.

Takeaways by Macro Roundup® AI

  1. DeepSeek trained V3's billions of parameters in fewer than 3m chip-hours at an estimated cost below $6m, roughly a tenth of the computing power and expense used for Llama 3.1, using chips that were not the most powerful because of American sanctions.
  2. Nic Lane of Cambridge University attributes DeepSeek's success not to a single big innovation but to a series of marginal improvements, including selective rounding in calculations, server-farm reconfiguration for more efficient chip-to-chip communication, and fine-tuning on output from the R1 reasoning system.
  3. DeepSeek will charge firms building services on V3 less than a tenth of what Anthropic charges for Claude, which AI expert Simon Willison says would be a dramatic new twist in the LLM pricing wars if the models are of equivalent quality.

Related Articles:

  • The Year-End Wake-Up: The ‘Chinese Sputnik’ That Demands Our Attention — Chinese AI company DeepSeek trained its Version 3 large language model for roughly $5.6m, about 100x cheaper than Meta's Llama 3, with performance comparable to Claude 3.5 Sonnet and interface access costing 12x less than Anthropic's. The author argues US chip export controls backfired by spurring leaner engineering, and that the gap between open-source and closed-source models is narrowing rapidly.
  • The Global AI Talent Tracker 2.0 — The United States remains the leading destination for the world's most elite AI talent and hosts 60% of top AI institutions. China's share of top AI researchers working domestically rose from 29% in 2019 to 47% in 2022, and India retained one-fifth of its top AI researchers in 2022 versus nearly none in 2019.
  • 2025 AI Diffusion Export Controls – Microsoft Regulatory Capture, Oracle Tears, Impacts Quantified, Model Restrictions — The US Framework for Artificial Intelligence Diffusion imposes export restrictions aimed at preventing China from accessing the computing power needed to build frontier AI models. It sorts the world into three tiers — the US and 18 allies, 23 arms-embargoed nations plus Macau, and all other countries — with differing access to AI chips and caps on graphics processor purchases.

Monday, January 27, 2025

AI Summary. Consumers and businesses stand to gain most from a future of effectively free AI products, along with big consumer tech companies, since a world of free AI makes product and distribution the decisive advantages they already hold. China also benefits, and the United States faces a choice between expanding chip export restrictions and permission-based regulation or allowing itself to compete.

Ben Thompson Stratechery
Date Posted:

.@benthompson argues DeepSeek implies “Jevon’s Paradox will rule the day in the long run” as consumers and businesses deploy “effectively-free AI products and services.”

Who benefits most from a future of effectively free AI?

Core argument: The author argues consumers and businesses are the biggest winners from DeepSeek, anticipating effectively-free AI products and services, with Jevon's Paradox ruling in the long run so that everyone who uses AI wins.

The biggest winners are consumers and businesses who can anticipate a future of effectively-free AI products and services. Jevon’s Paradox will rule the day in the long run, and everyone who uses AI will be the biggest winners. Another set of winners are the big consumer tech companies. A world of free AI is a world where product and distribution matters most, and those companies already won that game. China is also a big winner, in ways that I suspect will only become apparent over time. Not only does the country have access to DeepSeek, but I suspect that DeepSeek’s relative success to America’s leading AI labs will result in a further unleashing of Chinese innovation as they realize they can compete. That leaves America, and a choice we have to make. We could, for very logical reasons, double down on defensive measures, like massively expanding the chip ban and imposing a permission-based regulatory regime on chips and semiconductor equipment that mirrors the EU’s approach to tech; alternatively, we could realize that we have real competition, and actually give ourself permission to compete.

Takeaways by Macro Roundup® AI

  1. The author argues consumers and businesses are the biggest winners from DeepSeek, anticipating effectively-free AI products and services, with Jevon's Paradox ruling in the long run so that everyone who uses AI wins.
  2. The author holds that big consumer tech companies win in a world of free AI, because product and distribution matter most there and those firms have already won that game.
  3. The author contends China is a major winner, suspecting DeepSeek's relative success against America's leading AI labs will further unleash Chinese innovation, leaving the U.S. to choose between expanded chip bans and permission-based regulation or permitting itself to compete.

Related Articles:

  • The Year-End Wake-Up: The ‘Chinese Sputnik’ That Demands Our Attention — Chinese AI company DeepSeek trained its Version 3 large language model for roughly $5.6m, about 100x cheaper than Meta's Llama 3, with performance comparable to Claude 3.5 Sonnet and interface access costing 12x less than Anthropic's. The author argues US chip export controls backfired by spurring leaner engineering, and that the gap between open-source and closed-source models is narrowing rapidly.
  • China’s AI Industry Has Almost Caught Up With America’s — Chinese developer DeepSeek trained its V3 large language model using fewer than 3m chip-hours at an estimated cost under $6m—roughly a tenth of the computing power and expense of Meta's Llama 3.1—through a series of marginal efficiency improvements rather than one breakthrough. The model was trained on chips constrained by American export sanctions.
  • 2025 AI Diffusion Export Controls – Microsoft Regulatory Capture, Oracle Tears, Impacts Quantified, Model Restrictions — The US Framework for Artificial Intelligence Diffusion imposes export restrictions aimed at preventing China from accessing the computing power needed to build frontier AI models. It sorts the world into three tiers — the US and 18 allies, 23 arms-embargoed nations plus Macau, and all other countries — with differing access to AI chips and caps on graphics processor purchases.

AI Summary. Chinese artificial intelligence start-up DeepSeek released a large language model with performance comparable to US rival OpenAI while claiming to use far fewer Nvidia chips, raising doubts about whether US AI leadership requires spending billions of dollars on chips. Nvidia lost almost $600bn in market value, the largest recorded drop for any company.

Arjun Neil Alim, Leo Lewis, Eleanor Olcott, Tim Bradshaw and Ian Smith Financial Times
Date Posted:

China’s DeepSeek shocked Silicon Valley with a model rivaling OpenAI’s, developed “with far less computing power than US rivals,” calling into question expectations about the AI capex cycle. Nvidia is down ~ 17% and Siemens fell ~ 20%.

Does US AI leadership require spending billions on chips?

Core argument: Nvidia lost nearly $600bn of market value, the biggest recorded drop for any company, as investors reassessed likely future investment in AI hardware following DeepSeek's model release.

Tech stocks tumbled as advances by Chinese artificial intelligence start-up DeepSeek cast doubt on whether the US could sustain its leadership in AI by spending billions of dollars on chips. DeepSeek last week released its latest large language AI model, which achieved a comparable performance to that of US rival OpenAI, even though the company has previously claimed to use far fewer Nvidia chips. Venture capital investor Marc Andreessen called the new Chinese model “AI’s Sputnik moment”, drawing a comparison with the Soviet Union stunning the US by putting the first satellite into orbit. The results sent a shockwave through markets on Monday, with Nvidia losing almost $600B of market value, the biggest recorded drop for any company, as investors reassessed the likely future investment in AI hardware. The rout extended well beyond traditional tech names. Siemens Energy, which supplies electrical hardware for AI infrastructure, plunged 20%.

Takeaways by Macro Roundup® AI

  1. Nvidia lost nearly $600bn of market value, the biggest recorded drop for any company, as investors reassessed likely future investment in AI hardware following DeepSeek's model release.
  2. DeepSeek's latest large language model achieved performance comparable to that of OpenAI, even though the Chinese start-up has previously claimed to use far fewer Nvidia chips.
  3. The selloff extended beyond traditional tech names, with Siemens Energy, a supplier of electrical hardware for AI infrastructure, plunging 20%, while venture capital investor Marc Andreessen called the Chinese model "AI's Sputnik moment".

Related Articles:

  • The Year-End Wake-Up: The ‘Chinese Sputnik’ That Demands Our Attention — Chinese AI company DeepSeek trained its Version 3 large language model for roughly $5.6m, about 100x cheaper than Meta's Llama 3, with performance comparable to Claude 3.5 Sonnet and interface access costing 12x less than Anthropic's. The author argues US chip export controls backfired by spurring leaner engineering, and that the gap between open-source and closed-source models is narrowing rapidly.
  • SoftBank and OpenAI Back Sweeping AI Infrastructure Project In US — OpenAI and SoftBank plan a US artificial intelligence infrastructure joint venture called Stargate, intending to spend $100bn initially and as much as $500bn over four years. SoftBank holds financial responsibility and OpenAI operational responsibility, with Abu Dhabi state fund MGX and Oracle also funding and Arm, Microsoft and Nvidia as technology partners.
  • A Severe Case of COVIDIA: Prognosis For An AI-Driven US Equity Market — Computing cycles move from infrastructure to platforms to applications, and NVIDIA data center revenues as a share of market-wide capital spending are projected to reach levels last seen at the 1969 mainframe peak and the dot-com boom. Large cloud providers spend more than twice as much training foundational models as running them for customers.

AI Summary. Technology stocks added almost $4.63T in market capitalization in a single year and $13.6T over a decade, with semiconductor and auto gains concentrated in standout firms such as Nvidia and Tesla. Seven companies—Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla—added $15.8T over ten years, about 40% of the total gain across US stocks.

Aswath Damodaran Musing on Markets
Date Posted:
Is Database:
Database

Between 2014 and 2024, the Mag 7 added $15.8T in market cap, approximately 40% of the increase in market capitalization across all US equities over the decade. @AswathDamodaran

How concentrated are US stock market gains among a few technology firms?

Core argument: The Magnificent Seven — Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla — added $5.6T in market cap in 2024, almost the entire market's gain for the year.

Technology stocks collectively added almost $4.63T in market cap just in 2024, and over the last five (ten) years, the sector has added $11.3T ($13.6T) in market cap. The top two performers, in 2024, on the best performing industry list, semiconductors and auto & truck, owe much of their overall performance to super-performers in each one (Nvidia with semiconductors and Tesla with auto & truck), but airline companies also had a good year, though it may be premature to conclude that they have finally found working business models that can deliver profitability on a continuous basis. In 2024, seven companies (Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla) increased in market cap by $5.6T, almost of the entire market's gain for the year. While it is not uncommon for stock market returns to be delivered by a few winners at the top, with the Mag Seven, the domination extends over a decade, and in the last ten years (2014-2024), these seven companies have added $15.8T in market cap, about 40% of the increase in market capitalization across all US stocks over the decade.

Takeaways by Macro Roundup® AI

  1. The Magnificent Seven — Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla — added $5.6T in market cap in 2024, almost the entire market's gain for the year.
  2. Over 2014–2024, Apple, Amazon, Meta, Alphabet, Microsoft, Nvidia and Tesla added $15.8T in market cap, about 40% of the increase in market capitalization across all US stocks over the decade.
  3. Technology stocks added almost $4.63T in market cap in 2024, and $11.3T and $13.6T over the last five and ten years respectively, with semiconductors and auto & truck the top 2024 performers, owing much to Nvidia and Tesla.

Related Articles:

  • Data Update 2 for 2025: The Party Continues (for US Equities)! — Applying an internal-rate-of-return approach to the S&P 500 using consensus earnings and payout estimates implies an expected stock return of 8.91% and an equity risk premium of 4.33%. Assuming the premium reverts to 4.5% yields an index value about 12% below current levels, with an estimated 80% chance stocks are overvalued.
  • On Bubble Watch — Data from J.P. Morgan Asset Management covering monthly observations over a 27-year period show a strong relationship between the S&P 500's starting price-to-earnings ratio and its annualized return over the following ten years, with higher starting valuations consistently followed by lower returns. Purchases at a multiple near today's 22x were followed by ten-year returns between +2% and -2%.
  • US Exceptionalism: Drivers of Equity Outperformance and What’s Needed for a Repeat — US companies need about 7% earnings-per-share growth to deliver a normal 4% equity risk premium above the risk-free rate, versus about 3% for other developed markets, with the highest expectations embedded in technology and the largest US tech firms. Chinese companies could generate a 4% risk premium over Chinese bonds with no earnings growth.

AI Summary. The argument holds that large US trade deficits reflect capital flowing in to take advantage of high returns as the US pulled ahead in information technology, since the trade balance plus net foreign investment inflows must sum to zero. On this view the deficits represent international macroeconomics working as intended, though the US has de-industrialized.

Paul Krugman Krugman Wonks Out
Date Posted:

Challenging @michaelxpettis @paulkrugman argues that trade deficits are the obverse side of capital account surpluses. As “the US economy was doing well, it was attracting a lot of foreign investment. And the balance of payments balances.”

Do large trade deficits reflect capital flowing toward high returns?

Core argument: The U.S. pulled ahead of the rest of the world during roughly 1990–2005 by figuring out how to use IT, and remains ahead, with much of the divergence occurring in that relatively brief period.

There was definitely a period of a decade or so [~1990-2005] when the U.S. was really figuring out what to do with IT and the rest of the world was not. And so the U.S. pulled ahead and is still ahead, although a lot of the divergence took place during this relatively brief period. You'll see that that's exactly when the United States began running big trade deficits. And the reason is that because the US economy was doing well, it was attracting a lot of inflows of foreign investment. And the balance of payments balances. The trade balance plus net inflows of foreign investment equals zero. So if we're getting a lot of money coming to America to take advantage of the opportunities, we run a big trade deficit. Hard to see how any of this is objectionable or a problem. This is international macroeconomics working the way it's supposed to. Capital flowing to areas that offer high returns to investment, which for a while was the United States. Okay, now the reason people think it's a problem is that we have definitely de-industrialized.

Takeaways by Macro Roundup® AI

  1. The U.S. pulled ahead of the rest of the world during roughly 1990–2005 by figuring out how to use IT, and remains ahead, with much of the divergence occurring in that relatively brief period.
  2. The onset of large U.S. trade deficits coincided with that period of outperformance, as a strong U.S. economy attracted large inflows of foreign investment and the balance of payments requires the trade balance plus net capital inflows to equal zero.
  3. Capital flowing to the United States in pursuit of high returns on investment represents international macroeconomics working as it is supposed to, and objections to the resulting trade deficits rest on the fact that the U.S. has de-industrialized.

Related Articles:

  • China’s Very Bad, No Good Trillion-Dollar Trade Surplus — China's high investment rates face diminishing returns and the government is unwilling to promote higher consumer spending, so it compensates for weak domestic demand with large trade surpluses supported by a weaker currency. The author argues this attempt to export its policy failure will provoke a protectionist backlash from Europe, the UK, and others.
  • Trade Intervention for Freer Trade — The authors argue the United States should counter surplus countries' trade policies either by restricting their goods exports through tariffs or by restricting their capital inflows through taxes, volume limits, reserve requirements, transaction approvals, or investor eligibility rules. They contend capital controls are a more targeted alternative to uniform tariffs, which impose equal costs on more and less mercantilist countries.
  • A User’s Guide to Restructuring the Global Trading System — During the 2018–2019 U.S.–China trade war, the effective tariff rate on Chinese imports rose 17.9 percentage points while the renminbi depreciated 13.7% against the dollar, leaving after-tariff import prices in dollars up just 4.1%. The author argues this currency offset of more than three-fourths of the tariff explains why inflation pressure was negligible.

AI Summary. German industrial output has fallen 15% since 2018, manufacturing employment is down 3%, and over €300bn of investment capital has flowed out of the country since 2021. Exports support roughly one in four German jobs and equal 43% of GDP, four times the US share and twice China's.

Tom Fairless and Bertrand Benoit Wall Street Journal
Date Posted:
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German industrial output has fallen by 15% since 2018 and manufacturing employment is down 3%, as German exports slow amid increasing global competition from Chinese manufacturers.

Is Germany's export-dependent economic model breaking down?

Core argument: German industrial output has fallen 15% since 2018 and manufacturing employment is down 3%, with over €300 billion in investment capital having flowed out of Germany since 2021.

Germany’s industrial output has fallen by 15% since 2018, and the total number of people employed in the manufacturing sector is down 3%. Manufacturers in Germany’s metal and electrical industry, weighed down by costs, could lay off as many as 300,000 workers over the next five years said Stefan Wolf, president of a lobby group for the sector, noting “Deindustrialization is in full swing.” Over €300 billion in investment capital has flowed out of Germany since 2021. Exports support roughly one in four German jobs. More than two-thirds of cars produced in Germany are exported. Since the mid-1990s, exports’ share of Germany’s GDP doubled, reaching 43% of GDP, four times the share in the U.S. and twice as high as China.

Takeaways by Macro Roundup® AI

  1. German industrial output has fallen 15% since 2018 and manufacturing employment is down 3%, with over €300 billion in investment capital having flowed out of Germany since 2021.
  2. Stefan Wolf, president of a lobby group for Germany's metal and electrical industry, said manufacturers weighed down by costs could lay off as many as 300,000 workers over the next five years, adding that "Deindustrialization is in full swing.".
  3. Exports have doubled as a share of German GDP since the mid-1990s to 43%, four times the U.S. share and twice China's, and support roughly one in four German jobs.

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