Edward Conard

Top Ten New York Times Bestselling Author

  • “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
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “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
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…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
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Generational Growth AI, Data Centers and the Coming US Power Demand Surge

AI Summary. Data center power demand is projected to grow 15% annually through 2030, lifting data centers to 8% of US power demand from about 3% and raising overall US power demand growth to 2.4% a year versus roughly 0% over the prior decade.

Carly Davenport, Brian Singer, Et al. Goldman Sachs
Date Posted:
April 29, 2024
Is Database:
Database

A @GoldmanSachs analysis argues US power demand will grow at 2.4%/year through 2030 versus 0% over the last decade. They forecast data centers will be 8% of power demand by 2030 from 3% currently.

Will data center growth end a decade of flat US power demand?

Core argument: Data center power demand is forecast to grow at a 15% CAGR over 2023–2030, lifting data centers to 8% of total US power demand by 2030 from about 3% currently.

We forecast a 15% CAGR in data center power demand from 2023-2030, driving data centers to make up 8% of total US power demand by 2030 from about 3% currently. We now see a 2.4% CAGR in US power demand growth through 2030 from 2022 levels vs. ~0% over the last decade. Of the 2.4%, about 90 bps of that is tied to data centers. We estimate about 47 GW of incremental power generation capacity will be required to support US data center power demand growth cumulatively through 2030, met with about 60% gas and 40% renewable sources. We expect this to drive about $50 bn of capital investment in US power generation capacity cumulatively through 2030.

Takeaways by Macro Roundup® AI

  1. Data center power demand is forecast to grow at a 15% CAGR over 2023–2030, lifting data centers to 8% of total US power demand by 2030 from about 3% currently.
  2. US power demand is projected to grow at a 2.4% CAGR through 2030 from 2022 levels, versus roughly 0% over the prior decade, with about 90 bps of that tied to data centers.
  3. About 47 GW of incremental US generation capacity is estimated as required cumulatively through 2030 to support data center demand growth, met with about 60% gas and 40% renewables, driving about $50 bn of capital investment.

Related Articles:

  • Electricity 2024 — More than 8,000 data centers operate globally, with about 33% in the United States, 16% in Europe, and close to 10% in China. US data center electricity use is projected to rise from roughly 200 TWh in 2022, about 4% of US electricity demand, to almost 260 TWh, or 6% of total demand.
  • Electricity Grids and Secure Energy Transitions — Under a scenario based on announced government pledges, global electricity demand is projected to grow 2.7% per year, more than doubling from just under 25,000 terawatt-hours to nearly 54,000 terawatt-hours by 2050. Hydrogen production via electrolysis and electric vehicles account for more than half of that projected growth.
  • The AI Transition One Year Later: On Track, but Macro Impact Still Several Years Off — Investment in AI hardware is projected to rise substantially as a precursor to integrating AI into business processes, with the investment cycle estimated to peak at 2–2½% of GDP and early signs visible among AI-enabling companies. The authors estimate generative AI could raise developed-market labor productivity growth by 1.5pp annually over a 10-year adoption period, roughly 15% cumulatively.
  • Investment
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Previous articleApril 29, 2024How to Tax WealthThe IMF argues that a well-designed tax on income from capital, at a lower rate than labor income, would be less distortive and more equitable than a wealth tax if countries seek to raise more revenue from the rich.Next articleApril 29, 2024China's Unremitted Export EarningsIn the 12 months ending March 2024, China ran a goods trade surplus of $844B, however only $411B has been repatriated. Two of the three drivers of this form of capital flight are unlikely to be reversed. @martin_lynge
Showing 96 database articles primarily about Investment

How Much Revenue Does The AI Buildout Need In 2030?

AI Summary. The authors estimate AI capital spending of $560bn in 2026 and, in a high-investment case, $3.5tn of net AI capital in service by 2030, requiring roughly $1.3tn in annual infrastructure revenue once depreciation, operating costs and a 15% return are covered. That implies 7.4x growth from current annualized revenues, with downstream revenues of $1.9–2.6tn.

William Gildea and Azeem Azhar Exponential View
Date Posted:
October 1, 2026
Is Database:
Database

Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

How much annual revenue must AI infrastructure generate to justify investment?

Core argument: In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.

We estimate AI CapEx to be $560B in 2026, 64% of the total declared by the hyperscalers. Unfinished facilities can’t generate revenue, so the investment bill and the productive asset base will grow at different speeds. [In the high investment case], by 2030 we see $3.5T of net AI capital in service. The largest component [of the revenue hurdle] is the $555B in compute depreciation. We model lifespans at the current 5-6 years. Shell depreciation and OpEx combined contribute about a further $216B to the required revenue hurdle. [Adding a 15% return on net capital brings required annual infrastructure revenue to $1.3T]. Reaching the $1.3T hurdle would represent 7.4x growth (59% CAGR) from today’s annualized revenues. For its customers to earn 30-50% gross margins, downstream revenues need to be $1.9-2.6T. [Editor’s note: Van Nieuwerburgh’s far larger figure of $3.7T covers mature revenues from projects completed through 2032, using commissioning capital and a 50% infrastructure operating cash-flow margin. This estimate instead charges depreciation and operating costs plus 15% on net capital in service in 2030. Different horizons and cost conventions prevent a direct comparison. Van Nieuwerburgh also has a much higher capex estimate.]

Takeaways by Macro Roundup® AI

  1. In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.
  2. Compute depreciation of $555bn, modeled on 5-6 year lifespans, is the largest component of the 2030 revenue hurdle, with shell depreciation and operating expenses adding about $216bn and a 15% return on net capital completing the $1.3tn total.

Related Articles:

  • Financing the AI Buildout — 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.
  • What Investment Data Implies about the AI Transition — The five largest U.S. technology firms are scaling AI capital spending about 50% a year, from $155bn to a forecast $755bn. Embedding this in a two-sector model with rare productivity booms, the authors estimate AI adds roughly 5, 20, or 58pp to cumulative growth over a seven-year transition, depending on scenario.
  • 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.
  • Investment
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Tech's Trillion-Dollar Internal Inconsistency

AI Summary. Wall Street analysts covering technology project the sector's operating cash flow will more than double to roughly $2.4tn by 2028, a gain of over $1.2tn, while analysts covering the rest of the S&P 500 — technology's customers — project those firms will add far less cash flow. These sector-by-sector forecasts are internally inconsistent.

Torsten Sløk Apollo
Date Posted:
September 29, 2026
Is Database:
Database

Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Are tech cash flow forecasts inconsistent with customers' projected growth?

Core argument: Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.

Wall Street equity analysts work in sector silos, and when you add up their forecasts, the numbers are internally inconsistent. The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow. In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time. The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.

Takeaways by Macro Roundup® AI

  1. Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.
  2. Analysts covering the other S&P 500 sectors, which are tech's customers, expect those companies to add much less operating cash flow, leaving the combined forecasts internally inconsistent.
  3. Either tech's customers will generate much more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who will pay for AI services.

Related Articles:

  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Financing the AI Buildout — 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.
  • 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.
  • Investment
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Nine Percent of GDP

AI Summary. An analysis estimates that for US data center owners to break even on AI investments, the sector would need about $3.725tn in annual compute revenue by 2032 — roughly 9.2% of projected 2032 GDP. The estimate assumes 183 GW of US capacity completed, a $9.61tn capital base, a 10% discount rate, and a 50% operating cash flow margin.

Hanno Lustig The Two Cents
Date Posted:
September 28, 2026
Is Database:
Database

The US AI-buildout is projected to average ~3.6% of GDP over 2025–32. Lustig notes data-center operators will need ~$3.725T in annual compute revenue by 2032, 9.2% of projected GDP, to earn a 10% unlevered return on the buildout.

How much annual revenue must AI data centers generate to break even?

Core argument: Stijn’s baseline estimate holds that data center owners would need $3.725 trillion of annual revenue by 2032 — roughly 9.2% of projected 2032 GDP — to break even on AI investment at a 10% discount rate.

What do you have to believe about future revenue in order for all data center owners to to break even on their AI investment? Using a 10% discount rate, Stijn’s baseline number is 3.725 trillion of annual revenue by 2032, which would amount to roughly 9.2% of projected 2032 GDP. This number is the top line earned from selling compute in aggregate by whoever operates the 182.7 GW of US capacity completed by 2032.2. The assumptions behind that number: 183 GW of U.S. data-center capacity completed between 2025 and 2032. $8.54 trillion of construction cost, grossed up to a $9.61 trillion capital base to account for the return on capital tied up during construction. A 10% discount rate. A 6-year life for IT equipment (68% of cost) and a 20-year life for everything else (32%). A 50% operating cash flow margin.

Takeaways by Macro Roundup® AI

  1. Stijn’s baseline estimate holds that data center owners would need $3.725 trillion of annual revenue by 2032 — roughly 9.2% of projected 2032 GDP — to break even on AI investment at a 10% discount rate.
  2. The break-even calculation assumes 183 GW of U.S. data-center capacity completed between 2025 and 2032 at $8.54 trillion of construction cost, grossed up to a $9.61 trillion capital base for return on capital tied up during construction.
  3. The estimate assumes a 6-year life for IT equipment, which accounts for 68% of cost, a 20-year life for the remaining 32%, and a 50% operating cash flow margin.

Related Articles:

  • Financing the AI Buildout — 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.
  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • 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.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

Financing the AI Buildout

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
Date Posted:
September 24, 2026
Is Database:
Database
Is Important:
Important

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.
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

Moonshot Capitalism: AI Rewrites The Venture Capital Playbook

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
Date Posted:
September 10, 2026
Is Database:
Database

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.

Related Articles:

  • 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.
  • Investment
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Gross and Net US Investment

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
Date Posted:
September 4, 2026
Is Database:
Database

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.
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