“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…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 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 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
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“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
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute

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Measurements Matter: The Return of SLOC Slop

Paul Kedrosky Applied Complexity

Date Posted:: June 8, 2026

Anthropic data shows Mythos drove an 8-fold increase in mean lines of code per developer. Kedrosky cautions against measuring output over value: “The best engineers create value by writing less code, not more.”

[Kedrosky warns a new Anthropic paper based on internal data on increased developer productivity, as measured by software lines of code (SLOC) per developer measures output, not value]. Ten thousand lines of generated code are often less valuable than deleting a thousand lines of technical debt. SLOC as a measure rewards quantity, not usefulness. AI systematically inflates the metric. Agents generate boilerplate, wrappers, tests, and what-if code at near-zero cost. An 8x increase in SLOC generally corresponds to a far smaller increase in actual engineering throughput. Code volume creates liabilities. More code means more maintenance, complexity, attack surface, and debugging load. The best engineers create value by writing less code, not more. Most evidence is about implementation, not automating research. The strongest results involve coding, experiment execution, and debugging. Recursive self-improvement depends on solving an unsolved problem.

Takeaways by Macro Roundup^® AI

Kedrosky argues an 8x increase in software lines of code generally corresponds to a far smaller increase in actual engineering throughput, since AI agents generate boilerplate, wrappers, tests, and what-if code at near-zero cost.
Kedrosky warns that an Anthropic paper using internal data measures developer productivity by lines of code per developer, a metric that captures output rather than value and rewards quantity over usefulness.
Kedrosky notes that more code brings more maintenance, complexity, attack surface, and debugging load, and that the strongest evidence covers coding, experiment execution, and debugging rather than automated research, leaving recursive self-improvement dependent on an unsolved problem.

US Labor Productivity Increases at Fastest Pace in a Year

Julia Fanzeres Bloomberg

Date Posted:: November 5, 2026
Is Database:: Database
Is Important:: Important

Nonfarm business productivity grew at a 3.6% annualized rate in Q3, the fastest pace in a year. Real hourly compensation rose 1.1%, while the labor share fell to 53%, the lowest in data going back to 1947.

US labor productivity accelerated in the third quarter at the fastest pace in a year, suggesting a capital spending boom is translating into greater efficiency at a time when input costs have soared. Productivity, or nonfarm business output per hour, increased at a 3.6% annualized rate after an upwardly revised 2.1% pace in the prior quarter, data from the Bureau of Labor Statistics showed Thursday. Unit labor costs — what businesses pay employees to produce one unit of output — fell 0.7%, the first decline since the second quarter of 2025.

Takeaways by Macro Roundup^® AI

US nonfarm business output per hour rose at a 3.6% annualized rate in the third quarter, the fastest pace in a year, following an upwardly revised 2.1% pace in the prior quarter, according to the Bureau of Labor Statistics.
Unit labor costs, what businesses pay employees to produce one unit of output, fell 0.7% in the third quarter, the first decline since the second quarter of 2025.
The third-quarter productivity acceleration suggests a capital spending boom is translating into greater efficiency at a time when input costs have soared.

Change Is Accelerating Because It’s All Interconnected

Greg Jensen Bridgewater Associates

Date Posted:: October 30, 2026
Is Database:: Database

Bridgewater estimates that AI equities are pricing in ~3.5% annual productivity growth, while real growth and productivity implied by breakeven earnings in the broader stock market are slightly below current levels. They conclude: “Either AI stocks or broader macro markets are very meaningfully mispriced.”

Different markets are pricing very different futures; most are far from pricing an unrecognizable world. The AI boom has contributed tens of trillions of dollars of market cap in AI companies. As in any gold rush, trillions of dollars of “picks and shovels” only make sense if a great deal of gold (profits) is found. The pricing of a broader universe of AI companies tells us something about the scale of transformation that is discounted. If we calculate the incremental revenue required to sustain the valuation of AI companies and estimate how much of that can be earned by cannibalizing the revenue of existing companies, we can back out a productivity boost required to generate the rest of the incremental revenues. Today, we estimate that AI equities are discounting something like 3.5% annual productivity growth. That’s more than we estimate is needed for the US to make its debts sustainable. Across a reasonable range of assumptions, we assess AI stocks to be pricing trend nominal GDP growth well above the recent past. By contrast, consensus estimates and broader markets imply a world that looks more like the recent past. Making sense of the pricing in AI stocks requires a boom in productivity and growth. Consensus estimates for growth and productivity (from economist and Fed estimates) are slightly lower than current measures, as are rates of real growth and productivity implied by breakeven earnings in the broader stock market.

Takeaways by Macro Roundup^® AI

AI equities are priced for roughly 3.5% annual productivity growth, more than is estimated as needed to make US debts sustainable, and across a reasonable range of assumptions imply trend nominal GDP growth well above the recent past.
Consensus growth and productivity estimates from economists and the Fed, along with real growth and productivity implied by breakeven earnings in the broader stock market, sit slightly below current measures, describing a world resembling the recent past.
Either AI stocks or broader macro markets are very meaningfully mispriced, since the AI boom has added tens of trillions of dollars of market cap that only makes sense if a great deal of profit is found.

How Much Revenue Does The AI Buildout Need In 2030?

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.

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

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.
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.

Tech's Trillion-Dollar Internal Inconsistency

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.”

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

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.
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.
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.

Nine Percent of GDP

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.

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

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.
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.
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.

Financing the AI Buildout

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.

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

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 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.
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.

Anthropic data shows Mythos drove an 8-fold increase in mean lines of code per developer. Kedrosky cautions against measuring output over value: “The best engineers create value by writing less code, not more.”

Do lines of code measure developer productivity?

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Nonfarm business productivity grew at a 3.6% annualized rate in Q3, the fastest pace in a year. Real hourly compensation rose 1.1%, while the labor share fell to 53%, the lowest in data going back to 1947.

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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.

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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.”

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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?

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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?

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