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 should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “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
  • “…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 offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “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
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Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive

AI Summary. Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.

George Hammond Financial Times
Date Posted:
August 24, 2026

Ramp data suggest spending on Fable 5, Anthropic’s frontier model, has plateaued at ~11% of observed customers’ outlays on Anthropic’s tools. This is seemingly a behavior change – previously, corporate users defaulted to the most powerful available models.

Are businesses choosing cost efficiency over cutting-edge AI capabilities?

Core argument: Anthropic’s Fable 5 captures only 11% of corporate spending on Anthropic tools more than two months post-launch, breaking the historical pattern of enterprise users defaulting to a vendor’s most powerful model.

Spending on Fable 5, Anthropic’s largest and priciest model, has plateaued at only about 11% of overall outlay on the company’s tools, more than two months after its release, according to spending data from 70,000 companies collected by payments group Ramp. This breaks a pattern of corporate users defaulting to the most powerful models. Analysts and investors in Anthropic said the change was primarily driven by Fable’s high price and the fact that older models are capable of handling the bulk of business demands.

Takeaways by Macro Roundup® AI

  1. Anthropic’s Fable 5 captures only 11% of corporate spending on Anthropic tools more than two months post-launch, breaking the historical pattern of enterprise users defaulting to a vendor’s most powerful model.
  2. High pricing on Fable 5 drives corporate buyers toward older, cheaper Anthropic models that satisfy the bulk of business workloads, compressing revenue concentration at the premium tier.

Related Articles:

  • The AI Price Wars and Their Consequences — AI model pricing is converging toward commodity levels, where an 80% annual price decline requires 400% unit growth just to maintain flat revenue. Ceding lower-tier markets to defend premium pricing has historically failed against low-cost competitors, making trillion-dollar valuations difficult to sustain alongside heavy capital spending.
  • OpenAI and Anthropic In Price War as Chinese AI Rivals Gain Ground — AI model pricing is falling as competition intensifies, with leading models cutting token costs by up to 80%. Higher-priced models can deliver lower total costs by completing tasks in fewer tokens or attempts.
  • The AI Trade Is Losing One of Its Key Signals — AI token prices have fallen over 90% while total spending has roughly doubled, expanding the market overall. However, a 46% gap between AI investment and actual sales — wider than the 32% divergence seen during the 2001 telecom collapse — raises the risk that current infrastructure spending is outpacing real
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
Previous articleAugust 24, 2026How Potential AI Futures Would Play Out In The Current Tax SystemIselin and Nunn expect AI to increase output growth but to reduce labor’s share of income. “This bias in AI-induced growth reduces the revenue increase one would otherwise expect, given the preferential tax treatment afforded to capital income.”Next articleAugust 24, 2026An Elbow in the Face May Get America’s AttentionUS-Canada trade talks broke down over the weekend. Canadian Prime Minister Mark Carney announced a forthcoming domestic aid package in an effort to help the Canadian economy manage the fallout from a protracted trade war with the US.
Showing 89 database articles primarily about Investment

US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times
Date Posted:
August 24, 2026
Is Database:
Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup® AI

  1. U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
  2. U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

Related Articles:

  • The Two Europes — The European Union contains two divergent economies: a reforming frontier energized by security threats, and a stagnant interior where structural reform pressure remains absent.
  • Ed Conard Debates Furman On “The Expected Value of Risk Taking” — I debate @JasonFurman—Pres. Obama’s Chair of the Council of Economic Advisors—at Harvard over the effect of tax increases on the expected value of innovative…
  • The Future of European Competitiveness – A Competitiveness Strategy for Europe — An EC study of European competitiveness finds that EU gross value-added per hour worked increased by 0.7%/year from 2000-19, vs. 1.2%/year in the US. “Europe…
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  • Comparisons
    • Europe USA Relative Performance
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    • Innovation/Research

Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
August 17, 2026
Is Database:
Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Are technology companies hiding the true cost of artificial intelligence?

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

Related Articles:

  • The Hyperscalers’ Exploding ‘Purchase Commitments’ Reach $1.5tn — Major technology companies have accumulated $1.5tn in lease commitments and $982bn in purchase obligations for chips, computing power, and energy, totaling roughly $2.5tn in future spending. Much of this debt does not appear on standard financial statements, understating true leverage and future cash demands.
  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
  • Big Tech Credit Risks Rise Sharply As AI Spending Soars — The cost of insuring major technology companies' debt against default has reached record highs, driven by surging AI infrastructure spending that is straining balance sheets and pushing credit ratings toward junk status.
  • Investment
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AI Is Driving Up Treasury Yields: ‘It Just Touches Everything’

AI Summary. Heavy corporate bond issuance driven by AI investment has reduced demand for long-term government debt, pushing 10-year Treasury yields up ~0.3 percentage points as investors rotate into higher-yielding corporate bonds.

Davide Barbuscia, Ye Xie, and Michael MacKenzie Bloomberg
Date Posted:
August 17, 2026
Is Database:
Database

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 artificial intelligence investment reshaping the government bond market?

[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.

Related Articles:

  • Costliest US Bond Sale Since ‘01 Is Investor Warning to Bessent — The US government sold $25bn in 30-year bonds at a 5.216% yield, the highest rate since 2001, as investors demanded greater compensation to finance a growing national deficit.
  • U.S. Treasury Investors Are Long in AI — U.S. government debt acts as a leveraged bet on long-run productivity growth, because tax revenue rises automatically with faster growth while spending commitments stay flat. Each 0.1 percentage point increase in permanent productivity growth raises the fundamental value of government debt by $1.3tn, implying a 71 basis point decline in
  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
  • Investment
  • GDP
    • Financial Markets
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    • Innovation/Research

Big Tech AI Spending Spree Tops $1tn

AI Summary. Combined capital spending by Google, Amazon, Microsoft, and Meta on AI infrastructure has exceeded $1.1tn since 2023, with executives warning that continued investment will reduce the cash available to repay debt or return money to shareholders.

Ryan McMorrow, Rafe Rosner-Uddin and Hannah Murphy Financial Times
Date Posted:
July 31, 2026
Is Database:
Database

Google, Amazon, Microsoft and Meta’s combined free cash flow fell to a decadal low of ~$7B as their capex continues to rise.

Is Big Tech's trillion-dollar AI bet crowding out shareholder returns?

Combined capital spending by Google, Amazon, Microsoft and Meta from the beginning of the AI boom in 2023 to the end of June hit $1.1tn, according to earnings reports from the four companies in the past two weeks. Several top executives acknowledged to analysts that the outlay on AI would continue to sap free cash flow in coming quarters. The free cash flow metric is closely watched as a measure of the cash companies have left to service debt or return to shareholders after covering their operating costs and capital spending.

Related Articles:

  • 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 Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
  • The Magnificent Seven Are Riding Into the Sunset — When large technology companies issue new shares to fund capital spending, the resulting increase in equity supply puts downward pressure on stock prices, reversing the buyback-driven trend that has supported market gains for decades.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

AI and Productivity

AI Summary. 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.

Ernie Tedeschi Stripe Economics
Date Posted:
July 24, 2026
Is Database:
Database

AI is showing up in the macro numbers primarily in increased utilization and labor and capital deepening rather than true total factor productivity gains.

Is productivity growth coming from working harder or working smarter?

Core argument: Capital utilization and capital deepening are distinct mechanisms, but firms pushing existing assets harder typically accelerate capex simultaneously, linking near-term utilization gains to longer-run investment cycles.

What appears to be lifting US labor productivity, rather than microproductivity gains, are macroproductivity gains from AI—specifically companies running their existing capital harder. Think longer runs of factories already built, more utilization of server racks and GPU clusters already paid for, and more occupancy of existing hotel rooms. Economists call this “capital intensity” or “utilization.” Higher capital utilization represents real economic gains, but it’s not the same as microproductivity. This is not the same as “capital deepening” (growth in measured productivity from expanding capital supply—for example, through capital investment). What the current numbers do not yet support is the claim that this transmission is already well underway at the aggregate level: adoption is too thin, the cross-sectional signal is too weak, downstream bottlenecks remain, and the productivity pickup is too attributable to utilization to be confident in that conclusion. Instead, the acceleration we’re seeing so far is largely a result of companies trying to meet the demand for AI capacity by pushing the limits of their existing infrastructure.

Takeaways by Macro Roundup® AI

  1. Capital utilization and capital deepening are distinct mechanisms, but firms pushing existing assets harder typically accelerate capex simultaneously, linking near-term utilization gains to longer-run investment cycles.

Related Articles:

  • 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.
  • America Is Experiencing A Productivity Miracle — Productivity gains in America are concentrated in professional services and management—sectors that use technology intensively rather than produce it—with additional momentum from energy, where shale extraction and liquefied natural gas exports have expanded output and revenue.
  • AI and the Pitfalls of Innovation — Historical productivity booms driven by major technologies have been short-lived, typically lasting around a decade before growth slows again despite continued technological visibility.
  • Investment
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Midyear Outlook

AI Summary. AI investment is crowding out rival capital projects by absorbing scarce physical inputs—grid capacity, construction labor, metals, and engineering talent—while semiconductor prices surge where supply constraints meet surging demand. With hyperscaler free cash flow exhausted, over 80% of future AI capital spending requires external financing, including fresh equity issuance.

Jason Thomas Carlyle
Date Posted:
July 7, 2026
Is Database:
Database

Tech-related net fixed investment had risen 30% y/y by the end of H1, while non-tech investment fell, suggesting the datacenter buildout is crowding out non-tech related fixed investment.

Is artificial intelligence investment starving other industries of critical resources?

Core argument: Tech capex surged 30% vs. year-ago while non-tech investment fell, driving a 56 pts outperformance of semiconductor stocks over hyperscaler.

Overall tech-related investment increased 30% from year-ago levels, while all other capex fell. This record divergence implies that the AI boom is bidding away physical inputs – electricians, grid capacity and transformers, metals and materials, engineering talent, and construction labor – in quantities that stress the economics of rival projects elsewhere. Perhaps the best illustration of binding real resource constraints was observed inside the AI data center complex itself. In the first half of the year, the average price of semiconductors, labor for assembly and integration, and related materials exploded, as physical supply constraints interacted with insatiable demand. As more economic rent accrued to the compute layer, the stock prices of the semiconductor manufacturers upstream from this physical chokepoint outperformed those of their top customers by 56%. When AI spending gets financed out of internally generated cash flow, “crowding out” isn’t a pressing issue for investors. Credit spreads have not widened materially, financing conditions remain accommodative, and the cost of capital for non-software borrowers doesn’t suggest displacement. But the hyperscalers’ free cash flow has now been exhausted. Over 80% of the projected capex over the next few years will have to be externally financed, including fresh equity issuance.

Takeaways by Macro Roundup® AI

  1. Tech capex surged 30% vs. year-ago while non-tech investment fell, driving a 56 pts outperformance of semiconductor stocks over hyperscaler.
  2. Overall tech-related investment increased 30% from year-ago levels, while all other capex fell.
  3. Midyear Outlook.

Related Articles:

  • The Macro Implications of the AI Capex Boom — Bridgewater forecasts real US GDP growth of 2.8% in 2026, with AI capex contributing 1.4pp (50%) this year and 1.5pp in 2027.
  • The Other US Capex Question — Weak non-AI business investment in the U.S. is driven primarily by near-zero labor force growth from tightened immigration policy, not by AI spending crowding out capital, since corporate savings are sufficient to fund both simultaneously.
  • America’s Data Center Build-Out Is Falling Way Behind Schedule — Over 60% of U.S. data center capacity planned for 2027 is not yet under construction, signaling a significant supply shortfall as demand for computing power accelerates.
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