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
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “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 very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “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 fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
Upside of Inequality Oxford Unintended Consequences

AI Summary. Deep-tech investment outside AI has exceeded $150bn since early 2024, surpassing the $133bn invested across the entire prior decade. Falling valuations for traditional software companies and outsized returns from early bets on capital-intensive ventures are pushing investors toward riskier, science-driven deals.

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.

Are investors abandoning software for capital-intensive science bets?

Core argument: Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.

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. Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.
  2. The 2021 deep-tech peak — driven by battery and electric vehicle deals including Rivian and Northvolt — has not yet been surpassed, and the subsequent losses from those deals underscore the capital destruction risk inherent in moonshot dealmaking.

Related Articles:

  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
  • Public to Private Equity in the United States: A Long-Term Look — Global venture capital returns are highly skewed: 62% of deals lose money, more than half lose 50–100% of invested capital, but fat-tailed outliers drive overall returns. This pattern mirrors historical whaling voyages, where payoffs were similarly variable and driven by rare outsized outcomes.
  • Gross and Net US Investment — Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

AI Summary. AI-driven data-center expansion and related professional hiring have added roughly 1.05m jobs above trend since 2022–2023, spanning electrical contracting, equipment manufacturing, software development, and data science. The job gains exceed what broader construction, manufacturing, and professional employment trends would predict.

Economist Staff The Economist
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The Economist estimates that so far the AI boom has created ~1mm new jobs in the US, exceeding their estimate of ~200,000 layoffs attributed to AI since mid-2023.

Is artificial intelligence creating a genuine employment boom or temporary hiring surge?

Core argument: AI-linked demand has generated roughly 730,000 above-trend jobs in engineering, software development, and data science since 2022, substantially outpacing near-term displacement effects.

[We] tracked five industries at the heart of the data-centre build-out, from electrical contracting to equipment manufacturing. Since 2023 employment in them has risen by roughly 320,000 more than broader construction and manufacturing trends would suggest. Not all of those jobs owe their existence to AI—grid upgrades and other factory building matters too. [We also] tracked employment in professional occupations closest to the AI boom—engineers, software developers, mathematicians and data scientists—and compared their growth since 2022 with professional employment overall. These roles have added roughly 730,000 jobs above trend in recent years. AI will not have created every single one of them. But it has almost certainly created quite a few.

Takeaways by Macro Roundup® AI

  1. AI-linked demand has generated roughly 730,000 above-trend jobs in engineering, software development, and data science since 2022, substantially outpacing near-term displacement effects.
  2. Data-centre construction has added approximately 320,000 above-trend jobs across electrical contracting and equipment manufacturing since 2023, with grid upgrades and broader factory-building contributing alongside AI demand.

Related Articles:

  • The College Wage Premium in the Generative AI Era — S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.
  • Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence — Young workers in the most AI-exposed occupations face an employment shortfall ~19% below less-exposed peers, driven by reduced hiring rather than job losses, and concentrated in roles where AI replaces rather than complements human tasks.
  • Looking for the Ladder — The downtick in hiring in AI-exposed occupations started 6 months prior to the release of ChatGPT, and is “perfectly” aligned with the start of Fed rate hikes…

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

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 the college degree losing its economic value to artificial intelligence?

Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.

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 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

Related Articles:

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

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.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

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. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

Related Articles:

  • US Stock Market To Stop Shrinking For First Time In 23 Years — US equity supply is turning positive for the first time in over two decades, as a surge in IPOs and large share sales by major technology companies outweighs the buybacks and privatizations that have shrunk the stock market since 2003.
  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
  • The Transition to a Higher Cost of Capital — Bridgewater Associates co-CIO Karen Karniol-Tambour expects 10-year Treasury yields to rise from the current ~4.5% to compensate for structurally higher fiscal…

AI Summary. Global venture capital returns are highly skewed: 62% of deals lose money, more than half lose 50–100% of invested capital, but fat-tailed outliers drive overall returns. This pattern mirrors historical whaling voyages, where payoffs were similarly variable and driven by rare outsized outcomes.

Michael Mauboussin and Dan Callahan Morgan Stanley
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Btw the mid-90s and 2018, 62% of global venture capital investments lost money, and more than half of the deals lost 50–100% of invested capital. Yet US VC returned ~40% higher mean wealth btw 1984 and 2020 than a parallel investment path in the S&P 500.

Does venture capital's extreme inequality in returns justify its economic role?

Core argument: Across 31,000+ global venture capital deals from the mid-1990s to 2018, 62% lost money and more than half destroyed 50–100% of invested capital, yet fat-tailed winners generate returns sufficient to offset the majority of losses.

Exhibit 8 shows in excess of 31,000 observations of returns, measured as multiples of invested capital at the beginning of the period, for global venture capital deals. These results are from the mid-1990s to 2018. 62% lost money and more than one-half of all deals lost 50 to 100% of invested capital. The offset is that the tails are much fatter than those for buyouts or public equities. Public market equivalent (PME) is generally reflected as a ratio between private equity and public market returns. A ratio above 1 reveals relative outperformance and below 1 means underperformance. Here’s an example of how PME works. Say a fund drew $200 million from its investors in January 2021 and paid out $470 million in December 2025. An investor could have invested the $200 million in the S&P 500, which returned $392 million over the same period. The PME would be 1.2 ($470/$392). For venture funds, the average over [1984-2020] was about 1.4.

Takeaways by Macro Roundup® AI

  1. Across 31,000+ global venture capital deals from the mid-1990s to 2018, 62% lost money and more than half destroyed 50–100% of invested capital, yet fat-tailed winners generate returns sufficient to offset the majority of losses.
  2. Harvard Business School professor Tom Nicholas finds venture capital return distributions mirror those of historical whaling voyages, where payoffs were determined by highly variable oil and whalebone yields — confirming that extreme skewness in risk capital is a durable structural feature, not a modern anomaly.

Related Articles:

AI Summary. Hyperscaler cloud revenue is growing rapidly (AWS +37%, Azure +43%, Google Cloud +82%), with combined capital spending and R&D rising from $460bn to $870bn over the last 12 months.

Michael Cembalest J.P. Morgan
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Since the start of the data-center buildout, S&P earnings breadth has “eclipsed” that which would be historically implied by the ISM manufacturing survey. Cembalest argues earnings growth “appears highly contingent” on continued hyperscaler spending.

Are hyperscalers investing faster than their revenue growth justifies?

Core argument: Hyperscaler capital spending and R&D nearly doubled from $460B in 2024 to $870B over the trailing twelve months, driving S&P 500 earnings breadth well beyond levels that ISM manufacturing surveys alone would predict—evidence of a powerful AI-led trickle-down across sectors.

The Q2 earnings season was positive for hyperscaler annual revenue growth: AWS 37%, Microsoft Azure 43% and Google Cloud 82%. Rising revenue backlogs reinforce evidence of AI monetization: Google Cloud’s backlog increased $52B to $514B, and the AWS backlog rose to $496B, up 36% q/q. Earnings growth is also more broad-based than it was in 2024-2025 when AI was the primary driver. Earnings growth appears highly contingent on continued hyperscaler capital spending and R&D which rose from $460B in 2024 to $870B over the last 12 months. The share of S&P 500 companies with positive expected earnings growth is normally well-predicted by the ISM manufacturing survey. But since the AI capital spending boom began, earnings breadth has eclipsed the improvement in manufacturing surveys; this suggests a powerful trickle-down effect from hyperscalers to other sectors.

Takeaways by Macro Roundup® AI

  1. Hyperscaler capital spending and R&D nearly doubled from $460B in 2024 to $870B over the trailing twelve months, driving S&P 500 earnings breadth well beyond levels that ISM manufacturing surveys alone would predict—evidence of a powerful AI-led trickle-down across sectors.

Related Articles:

  • AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed — AI infrastructure investment is tracking toward $1.5tn annually by 2030, with ~90% externally financed — making the cycle's fate dependent on debt market conditions rather than the underlying technology's commercial viability. At that scale, a credit contraction stops the buildout, and $1.5tn in annual spending requires AI services to generate
  • The Summer I Turned Pretty — Capital spending booms historically peak when end-demand companies stagnate while equipment suppliers still thrive; today, semiconductor profits are rising even as the large cloud companies funding AI infrastructure see earnings and cash flow decline, raising doubt over who will sustain AI investment.
  • The Buyers of AI Are Still Waiting for the Payoff — AI capital spending is generating profit margin gains for technology sellers but not for the companies buying and deploying AI across other sectors of the economy.

AI Summary. Corporate profit margins have expanded ~250 basis points over the past year, approaching all-time highs, as 23% profit growth far outpaced 8% growth in corporate value added. Labor's share of income is hitting new lows, confirming that margin expansion—not faster economic growth—is the primary driver of record profit levels.

Abiel Reinhart J.P. Morgan
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US corporate profit margins rose ~250bp y/y in Q2 and are approaching an all-time high. Reinhart notes that tech and communications services drove ~58% of recent S&P 500 profit growth, even as the sectors have been “steadily losing employment since late 2022.”

Are record corporate profits driven by growth or margin expansion?

Core argument: Corporate profit margins expanded nearly 250 bps over the past year and are approaching all-time highs, as domestic profit growth of 23% dwarfed the 8% rise in corporate value added, compressing labor’s share of income to record lows.

Nominal pre-tax corporate profits in the national income and product accounts (NIPA) were very robust in both 2Q (41% [annual rate]) and over the last year (23%). Excluding post-recession spikes, we haven’t seen a year this strong since the mid-2000s. Higher margins [were] the key driver [of profit growth], as 23% y/y domestic profit growth was far in excess of the 8% increase in corporate value added. Profit margins (pre-tax profits divided by value added) increased close to 250bp over the last year, and are approaching all-time highs, whereas the labor share is hitting new lows.

Takeaways by Macro Roundup® AI

  1. Corporate profit margins expanded nearly 250 bps over the past year and are approaching all-time highs, as domestic profit growth of 23% dwarfed the 8% rise in corporate value added, compressing labor’s share of income to record lows.

Related Articles:

  • US Corporate Profits Surge To Record As Worker Payouts Wilt — U.S. corporate pre-tax profits reached an annualized $4.8tn, or 18% of national income—the highest share since the post-WWII era—while workers' wages and benefits fell to 60% of national income, the lowest since the 1950s.
  • Are US Corporate Profit Margins Too High? — In Q1 2026, US after-tax non-financial margins were estimated at 7.6%, just short of the post-1949 high of 8.2% in Q2 of 2021. Tan Kai Xian argues US corporate…
  • The Record Divide Between Corporate Profits and Worker Pay — Labor's share of national income has fallen to 51%—its lowest recorded level—while corporate profits have reached 12.1% of national income, their highest share since 1950. Inflation-adjusted hourly wages have risen 3% since 2019, while inflation-adjusted corporate profits have risen 50% over the same period.

AI Summary. Real long-term inflation-adjusted yields reached 4.5% during the late-1990s productivity boom, compared to 2.4%–3.0% today, implying significant upward pressure on rates if AI drives a comparable acceleration in productivity growth.

Matt Klein The Overshoot
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Klein compares measures of real rates from the 1990s productivity boom to today’s real rates, using TIPS, as well as “synthetic TIPS” derived by subtracting inflation expectations from nominal rates. Current real rates are very low by comparison.

Does artificial intelligence productivity require substantially higher interest rates?

Real yields on actual TIPS were ~3.5% when they first arrived at the beginning of 1997, and then rose gradually to ~4.5% by the peak of the bubble in 2000. Yields on “synthetic” TIPS derived by removing estimates of inflation expectations from nominal yields were also around 3.5% in early 1997 and 4.5% in the first half of 2000, though they were more volatile in between. The yield on the benchmark 10y TIPS is currently 2.4% while the yield on the 30y TIPS is around 3%. Knowing what actually happened in the 1990s is helpful for calibrating expectations about what could happen in the next decade, if new software technologies lead to a comparable acceleration in productivity growth.

Related Articles:

  • AI, Productivity, and Rates: Part I — Resource utilization growth from late 2024 through early 2026 ranks among the fastest 5-quarter stretches since 1947, driven by a rise in average weekly hours worked rather than improvements in unemployment or manufacturing capacity.
  • AI and Productivity — Rising US labor productivity is driven by higher capital utilization—factories, servers, and hotel rooms running harder—rather than new investment or efficiency gains at the individual task level.
  • Have We Entered an Era of High Productivity Growth? — Labor productivity data show a 57% probability the U.S. economy has entered a high-growth regime, but efficiency-based measures show only 21%, mirroring the mixed signals seen in the mid-1990s technology boom before sustained productivity gains became clear.