Edward Conard

Top Ten New York Times Bestselling Author

  • “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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
  • “…a 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
  • “…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 very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “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
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
Upside of Inequality Oxford Unintended Consequences

AI Summary. The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.

Stijn Van Nieuwerburgh Brookings Papers on Economic Activity
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Stijn Van Nieuwerburgh projects ~$10.3T will be spent on the AI buildout over 2025–2032, averaging 3.63% of GDP. He estimates 2/3 of that spending reflects IT equipment and one-third real estate and associated power infrastructure.

How much of the planned data center pipeline gets built?

Core argument: Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.

The required investment is enormous. A representative 200 MW AI training campus costs roughly $8.2 billion, about two-thirds of which reflects IT equipment and one-third the real estate and associated power infrastructure. We estimate that 183 GW is completed by 2032 with a further 118 GW completed after 2032. This pipeline will trigger about $10.3 trillion dollars of investment over 2025–2032. At an average of 3.63% of GDP per year, the projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms. It would double the electricity consumption of the entire U.S. residential sector.

Takeaways by Macro Roundup® AI

  1. Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.
  2. The authors’ central scenario allocates the announced pipeline so that 182.8 GW of additional U.S. capacity becomes operational during 2025–32, including 19.0 GW tied to projects already operational in 2025 or 2026.
  3. The authors’ scenario has 117.2 GW of planned data center capacity completed after 2032 and 226.9 GW never completed, applying lower realization rates to more distant and less fully documented projects.

Related Articles:

  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed — Hyperscaler and data-center investment is tracking toward $1.5tn annually by 2030, which in real terms would exceed the 2005-06 housing boom, the 1990s fiber peak, and the 1882 railroad peak combined. External debt and equity are projected to fund close to 90% of the buildout, making it dependent on credit markets.
  • The Summer I Turned Pretty — A warning sign in a boom cycle is when companies closest to final demand weaken while capital spending beneficiaries keep thriving, as when telecom operators' shares rolled over while communications equipment stocks kept rising. Semiconductor stock prices and free cash flow are soaring while large cloud providers' shares stagnate and their free cash flow plummets.

AI Summary. Global investment in "deep tech" — companies whose products rest on major scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn invested in the decade through 2019, excluding sums poured into AI start-ups. Failed battery and electric-vehicle bets from an earlier peak illustrate the risks of such moonshot dealmaking.

Tim Bradshaw Financial Times
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Since the start of 2024, more than $150B of venture capital has been invested into non-AI “deep tech” firms whose products are rooted in significant engineering advances, exceeding the $133B invested in such firms btw 2010 and 2019.

Is the AI boom pushing venture capital toward riskier moonshot bets?

Core argument: Global "deep tech" investment excluding AI start-ups has exceeded $150bn since the start of 2024, more than the $133bn invested in the entire decade through 2019, according to Dealroom.

The AI boom is fuelling a resurgence in ambitious “moonshot” bets, as early SpaceX backers’ huge returns and falling valuations for traditional software companies force tech investors to embrace riskier and more capital-intensive dealmaking. Excluding the giant sums ploughed into AI start-ups, global investment in “deep tech” — companies whose products are rooted in big scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn in the entire decade to the end of 2019, according to Dealroom. This year’s deep-tech investments have not yet surpassed 2021’s peak, which was propelled by battery and electric vehicle deals for the likes of Rivian and Northvolt — many of which turned sour, highlighting the risks involved in moonshot dealmaking.

Takeaways by Macro Roundup® AI

  1. Global "deep tech" investment excluding AI start-ups has exceeded $150bn since the start of 2024, more than the $133bn invested in the entire decade through 2019, according to Dealroom.
  2. Huge returns for early SpaceX backers and falling valuations for traditional software companies are pushing tech investors toward riskier, more capital-intensive "moonshot" dealmaking.
  3. Deep-tech investment has not yet surpassed the 2021 peak, which was propelled by battery and electric vehicle deals for companies such as Rivian and Northvolt, many of which turned sour.

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  • Capital Is Making a Comeback — Tangible investment in property, factories and equipment fell from 12.5% to 8.5% of private gross domestic product, while intangible investment in intellectual property, software and process knowledge rose from about 11.5% to 16.75%. The author argues demand for tangible capital investment will now have to rise.
  • Public to Private Equity in the United States: A Long-Term Look — An analysis of more than 31,000 global venture capital deals finds 62% lost money and over half lost 50–100% of invested capital, though the distribution of returns has much fatter tails than buyouts or public equities. Venture funds averaged a public market equivalent ratio of about 1.4, indicating returns above public markets.
  • Gross and Net US Investment — Net US investment has fallen from roughly 40% of gross investment in the 1970s to about 25% in the past decade, meaning about three-quarters of gross investment now just replaces depreciating capital. The author attributes this gap to modern investment being more information-technology-related, which depreciates faster and requires more frequent replacement.

AI Summary. Net US investment has fallen from roughly 40% of gross investment in the 1970s to about 25% in the past decade, meaning about three-quarters of gross investment now just replaces depreciating capital. The author attributes this gap to modern investment being more information-technology-related, which depreciates faster and requires more frequent replacement.

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

Why does less of US gross investment add net capital?

Core argument: US net investment has run at about 25% of gross investment over the past decade, down from roughly 40% in the 1970s, meaning about three-quarters of gross investment merely replaces depreciated capital.

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

Takeaways by Macro Roundup® AI

  1. US net investment has run at about 25% of gross investment over the past decade, down from roughly 40% in the 1970s, meaning about three-quarters of gross investment merely replaces depreciated capital.
  2. The likely reason for the widening gap between gross and net US investment is that modern investment is more tied to information technology, which depreciates more rapidly and must be replaced and updated more often.
  3. Raising the capital used by the average US worker—a key driver of past labor productivity growth—now requires a larger rise in gross investment to achieve a given rise in net investment.

Related Articles:

  • US Stock Market To Stop Shrinking For First Time In 23 Years — Goldman Sachs estimates that net supply of US equity — new shares issued less shares removed by buybacks and take-privates — will be roughly flat in 2026 after being negative since 2003, with a larger influx expected in 2027 as lock-up periods on this year's initial public offerings expire.
  • Capital Is Making a Comeback — Tangible investment in property, factories and equipment fell from 12.5% to 8.5% of private gross domestic product, while intangible investment in intellectual property, software and process knowledge rose from about 11.5% to 16.75%. The author argues demand for tangible capital investment will now have to rise.
  • The Transition to a Higher Cost of Capital — Bond yields near 4.5% sit below the 5.5% short-term interest rate, leaving little risk premium, and those yields have been enough to draw buyers back toward normal bond holdings. The author argues yields still need to rise to a sustainable cost of capital given structurally higher fiscal and inflationary spending.

AI Summary. The U.S. college wage premium fell from 0.626 to 0.575 after four decades of expansion, implying the first sustained negative growth in relative demand for college-educated labor. The authors estimate that weaker wage growth in occupations highly exposed to artificial intelligence accounts for roughly 28% of that decline, though the patterns are not causal.

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network
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The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is AI exposure driving the decline in the college wage premium?

Core argument: 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.

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

Takeaways by Macro Roundup® AI

  1. The U.S. college wage premium fell from 0.626 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.
  2. 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.
  3. 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.

Related Articles:

  • Looking for the Ladder — The article argues that the hiring downturn in occupations most exposed to generative AI reflects interest-rate sensitivity rather than AI itself, since roughly 38% of workers in the most AI-exposed quintile work in information, finance and insurance, and professional and technical services, versus under 2% in the least-exposed quintile.
  • How Students and Recent Grads are Responding to the Rise of AI — Undergraduate enrollment in degrees tied to the most AI-exposed jobs has risen 8%, indicating students are moving toward rather than away from fields exposed to artificial intelligence. A decline in computer science degrees is more than offset by growth in other exposed fields such as engineering.
  • AI and Young-adult Jobs: The Real Mystery — Employment rates have declined for young adults both with and without college degrees, with non-degree holders faring marginally worse, and employment growth for young workers of all education levels has lagged that of other workers. Artificial intelligence can replace some lower-skilled jobs, but those jobs are too few to drive youth labor market weakness.

AI Summary. Investment-grade companies have sold nearly $1.5tn of bonds, up 36% from a year earlier, and some investors have sold US Treasuries to buy higher-yielding corporate debt. Bank of America economists estimate the surge in corporate-debt sales, along with more mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point.

Davide Barbuscia, Ye Xie, and Michael MacKenzie Bloomberg
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US investment-grade debt issuance is up 36% y/y to ~$1.5T. A BofA analysis argues the surge in this debt, alongside increased MBS issuance, has driven up the 10-year Treasury yield by ~.3pp this year.

Is a surge in corporate bond issuance raising Treasury yields?

Core argument: Investment-grade companies have sold nearly $1.5 trillion of bonds in 2026, a 36% jump from a year earlier, putting them on pace to eclipse the record set in 2020, when businesses rushed to seize on near-zero interest rates.

[In 2026] investment-grade companies have sold nearly $1.5 trillion of bonds, a 36% jump from a year earlier, putting them on pace to eclipse the record from 2020, when businesses were rushing to seize on near-zero interest rates. As the slew of longer-dated bonds keeps hitting the market, some investors have sold US Treasuries. That freed up cash to buy higher-yielding debt from immensely profitable companies like Alphabet, whose recent 30-year debt was issued at a yield of nearly 6.4%, 1.15 percentage points more than comparable Treasuries. Quantifying the precise impact on interest rates is difficult and estimates vary. Bank of America economists said the AI borrowing is “potentially crowding out long-end Treasury demand” and has played a major role in the rise of bond yields. They estimated that the surge in corporate-debt sales — along with a rise in issuance of mortgage-backed securities — pushed up 10-year rates by about 0.3 percentage point this year.

Takeaways by Macro Roundup® AI

  1. Investment-grade companies have sold nearly $1.5 trillion of bonds in 2026, a 36% jump from a year earlier, putting them on pace to eclipse the record set in 2020, when businesses rushed to seize on near-zero interest rates.
  2. Bank of America economists estimated that the surge in corporate-debt sales, along with a rise in mortgage-backed securities issuance, pushed 10-year rates up by about 0.3 percentage point this year, and said AI borrowing is "potentially crowding out long-end Treasury demand.".
  3. Alphabet issued 30-year debt at a yield of nearly 6.4%, 1.15 percentage points above comparable Treasuries, as some investors sold US Treasuries to free up cash for higher-yielding corporate debt.

Related Articles:

  • Costliest US Bond Sale Since ‘01 Is Investor Warning to Bessent — The US government sold $25bn of 30-year bonds at a yield of 5.216%, the highest at such a sale since 2001, reflecting investors' demand for compensation to finance a growing deficit. The yield came in slightly above the prevailing market level before the bidding deadline, indicating demand modestly lagged expectations.
  • U.S. Treasury Investors Are Long in AI — The authors find U.S. government debt has a large positive productivity beta, because the tax code makes federal revenue highly sensitive to productivity growth while spending commitments change little, leaving bondholders implicitly long productivity growth. They argue greater market confidence in sustained productivity gains from AI could substantially reprice government debt, lowering nominal yields.
  • The Market Is Asking Questions — Markets are weighing whether the trillions committed upfront to AI data centers, chips, and power will generate returns above the cost of capital before those assets depreciate. With the buildout increasingly funded by debt rather than internal cash flow and borrowing spreads widening, financing costs could rise until new data-center spending no longer clears its return hurdle.

AI Summary. Artificial intelligence models' capability gains are shrinking in percentage terms, falling from about 15% relative to the baseline early on to roughly 10% for the same absolute increment, narrowing the gap between frontier and good-enough models. The author argues this points to a commoditizing market where price, not minor model differences, becomes the main differentiator, pressuring frontier firms' margins.

Paul Kedrosky Applied Complexity
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Citing the Epoch’s Capabilities Index, Kedrosky notes “the gap from the model frontier to the 90th percentile is under 6%. The gap from the #1 to the #2 model is roughly zero. The premium that the best models command [are] collapsing with declining variance.”

Do shrinking relative AI capability gains turn models into a commodity?

Core argument: AI model gains that once delivered about 15% improvement relative to the baseline now buy roughly 10%, so progress continues while its rate declines.

Even if the absolute capability gain per unit of time is constant, the percentage gain is not. Early in the data, models improved by about 15% relative to the baseline. At the end of the data, the same increment buys a 10% relative gain. Progress is real, but its rate is declining. The economic implications are clear. When the variance is wide, being the frontier model meant something. You could, after all, be almost 50% better than the average model. And you could, in theory, price like it. The distance between frontier and good-enough was wide enough that it looked like an early moat. This is what maturing commodity markets look like. Price will become the main differentiator, not minor model differences. This will have huge implications, from the continuing and inexorable rise of China in AI, as we are already seeing with DeepSeek's massive pricing advantage, to the resulting margin pressure on frontier companies, and the implications for said companies' post-IPO performance.

Takeaways by Macro Roundup® AI

  1. AI model gains that once delivered about 15% improvement relative to the baseline now buy roughly 10%, so progress continues while its rate declines.
  2. Wide variance across AI models once let a frontier model be nearly 50% better than the average and, in theory, price accordingly, a gap that looked like an early moat.
  3. The author argues AI is maturing into a commodity market where price, not minor model differences, becomes the main differentiator, citing DeepSeek's pricing advantage, margin pressure on frontier companies, and implications for their post-IPO performance.

Related Articles:

  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — 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.
  • Four Takeaways from Microsoft’s Satya Nadella at WEF Today — Microsoft's chief executive says the price of AI model usage ("tokens") falls by roughly half every three months. Commentary argues that such rapid deflation pulls demand forward, encourages overbuilding, and commoditizes models, shifting competitive advantage to orchestration and context rather than the model layer.
  • 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. Firms that begin supplying a foreign multinational for the first time show productivity gains of about 8% after three years, along with higher sales to other customers. The authors find similar performance gains when firms start supplying other "superstar firms," defined as the largest or most export-intensive firms.

Mary Amiti, Cédric Duprez, Jozef Konings and John Van Reenen National Bureau of Economic Research
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Evidence from Belgian btw 2002-14 suggests that large firms generate significant TFP spillovers to domestic suppliers. Firms that start a serious relationship with a superstar firm increase their TFP by 8% three or more years after the relationship is formed.

Does supplying a large firm raise a domestic supplier's productivity?

Core argument: Firms that begin supplying a foreign multinational for the first time see productivity rise about 8% after three years, alongside gains in other performance measures such as sales to firms other than the new multinational partner.

We use an event study approach, examining what happens when a firm begins supplying a foreign multinational for the first time. We uncover a sharp increase in productivity (which rises by about 8% after three years) and other performance measures (e.g. sales to firms other than the new multinational partner). The fraction of aggregate value accounted for by multinationals declined by about ten percentage points in Belgium in our sample period (2002 and 2014), which would suggest a strong headwind against productivity growth. However, in a novel result we are also able to document that when we look at similar events of starting a serious relationship with other “superstar firms” - defined as those who are in the top thousandth of the size distribution and/or export intensively - we find very similar performance impacts.

Takeaways by Macro Roundup® AI

  1. Firms that begin supplying a foreign multinational for the first time see productivity rise about 8% after three years, alongside gains in other performance measures such as sales to firms other than the new multinational partner.
  2. The share of aggregate value accounted for by multinationals in Belgium fell about ten percentage points between 2002 and 2014, which would suggest a strong headwind against productivity growth.
  3. Beginning a serious relationship with other “superstar firms” — those in the top thousandth of the size distribution and/or intensive exporters — yields very similar performance impacts to supplying foreign multinationals.

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