Edward Conard

Top Ten New York Times Bestselling Author

  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…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
  • “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
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “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 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
  • “…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
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
Upside of Inequality Oxford Unintended Consequences
Econ Summary Highlights Debate

Monday, August 17, 2026

AI Summary. 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.

Erik Brynjolfsson, Bharat Chandar and Ruyu Chen Stanford University
Date Posted:

If employment of the most AI-exposed workers aged 22–25 had kept pace with that of their less-exposed peers since January 2018, their employment would be 15% higher than at the July 2025 data vintage observed in our previous study, and 19% higher as of June 2026.

Is artificial intelligence reducing job opportunities for young workers in exposed occupations?

Core argument: no comparable decline appears in any older age group.

Figure 4 plots occupation event study estimates for the most exposed quintile (relative to quintile 1) for workers aged 22–25, tracing the long-difference coefficient as the endpoint of the difference is rolled forward month by month, with no controls and under four control sets: occupational interest rate exposure, education (college share), a work-from-home measure, and all three jointly. The relative decline grows to roughly 18pp by mid-2026. Other age groups show no comparable decline. This divergence, documented in prior versions of this study, has continued to widen; the shortfall was 15% below where it would have been had it kept pace with that of their less-exposed peers at the July 2025 data vintage, and 19% as of June 2026. It operates primarily through reduced hiring rather than increased separations, and declines are concentrated in occupations where AI usage substitutes for human tasks; complementary usage is associated with flat or rising employment.

Takeaways by Macro Roundup® AI

  1. no comparable decline appears in any older age group.

Related Articles:

AI Summary. 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.

Paul Kedrosky Applied Complexity
Date Posted:

US labs have “conceded commoditization” for models behind the frontier, while still trying to “preserve scarcity rents” for their frontier models. Kedrosky warns “this market-ceding tactic has failed in most markets historically, especially when competing with China.”

Does AI pricing collapse force a choice between growth and profitability?

Core argument: DeepSeek forced every Western lab to justify higher costs, then demonstrated retained pricing power by tripling effective output price while remaining within the low-cost band—exposing the fragility of Western labs’ commodity-tier positioning.

Good-enough models are converging in capability and price. That makes model switching easier and durable margins harder to defend. US labs are cutting lower- and mid-tier offerings while keeping their best models expensive. They are conceding commoditization below the frontier while trying to preserve scarcity rents at the top. This market-ceding tactic has failed in most markets historically, especially when competing with China. This is irreconcilable with trillion-dollar valuations and high & growing debt loads. OpenAI and Anthropic must finance enormous capital spending while competition pushes the models likely to generate the most volume toward commodity pricing. An 80% year-over-year price decline requires 400% unit growth just to stand still, and much more to deliver overall growth.

Takeaways by Macro Roundup® AI

  1. DeepSeek forced every Western lab to justify higher costs, then demonstrated retained pricing power by tripling effective output price while remaining within the low-cost band—exposing the fragility of Western labs’ commodity-tier positioning.
  2. U.S. labs ceding mid- and lower-tier segments to defend frontier scarcity rents repeats a market-retreat pattern that has historically failed against low-cost competitors, compounding capital-spending strain at OpenAI and Anthropic where an 80% annual price decline requires 400% unit growth merely to sustain revenue.

Related Articles:

  • 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.
  • Who’s Afraid of Chinese Models? — Frontier AI labs can sustain lower inference prices as inference revenue scales faster than training costs, making high per-token margins less necessary to fund model development.
  • 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

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:

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.

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:

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.

AI Summary. Japan's long-term government bond market is the most distorted among major economies, with the gap between future rate expectations and current 10-year yields at an extreme outlier relative to its own history and G10 peers.

Robin Brooks Robin Brooks Substack
Date Posted:

Citing the difference between Japan’s artificially depressed 10-year yield and its 10y10y fwd rate, Brooks notes “Japan’s ‘shadow yield’ is much higher than observed yields, which means Japan is – de facto – in a debt crisis.”

Is Japan's bond market pricing in an unrealistic future?

Core argument: Japan’s long-end yield curve is the most distorted in the G10, with its 10y10y-vs-10y spread z-score breaching the two-standard-deviation threshold that defines acute stress across the peer group.

The blue lines are my proxy for how “broken” yield curves are at the long end. This is the difference between the 10y10y forward [which is what markets price for the 10-year yield in 10 years’ time] and 10-year yield. I demean this difference and divide it by its historical standard deviation. The resulting z-score measures how unusual the slope of the yield curve is at the long end relative to history. The black line in each chart is the median across all G10 z-scores. The gray shaded area is a two standard deviation confidence interval around the black line. If you’re outside this area, something very worrying is going on. Japan sticks out like a sore thumb on this metric.

Takeaways by Macro Roundup® AI

  1. Japan’s long-end yield curve is the most distorted in the G10, with its 10y10y-vs-10y spread z-score breaching the two-standard-deviation threshold that defines acute stress across the peer group.
  2. Japan’s yield curve distortion is structural, not cyclical: its standardized 10y10y-vs-10y gap is a persistent outlier relative to the G10 median, not a transient deviation within normal historical bounds.

Related Articles:

  • Shadow Government Bond Yields in the G10 — Government bond yields across major economies are artificially suppressed by central bank intervention; if those interventions were removed, long-term yields would rise materially above current market levels.
  • Global Debt Report 2026 — Across the OECD last year, $13.5T of governmental debt needed refinancing, 70% ($9.5T) of which was US debt, up from 57% in 2020. The US and Japan were…
  • 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.

AI Summary. Tariff refunds are contributing an estimated 0.2 percentage points to US quarterly GDP growth, adding to existing economic tailwinds from AI investment, industrial expansion, and fiscal policy, keeping interest rates elevated for longer.

Torsten Sløk Apollo
Date Posted:

Net tariff receipts turned negative in May as refunds outpaced custom duties. The Atlanta Fed projects GDP growth of 4.3% at an annual rate in Q3, of which Sløk estimates ~.2pp will be driven by tariff refunds.

Are tariff refunds masking underlying economic weakness?

The tailwinds behind the US economy are not just AI spending, the industrial renaissance and the One Big Beautiful Bill. Tariff refunds have now joined the list. Not only are tariff refunds boosting corporate earnings, they are also boosting GDP growth. The Atlanta Fed's GDPNow currently points to 4.3% growth this quarter, of which we estimate roughly 0.2 percentage points come from tariff refunds. The bottom line is that the US economy continues to be supported by a growing set of tailwinds. Rates will stay higher for longer.

Related Articles:

  • The State of U.S. Tariffs July 24, 2026 — The average U.S. tariff rate stands at 11.1% and is set to rise to 11.8% by year-end, with current tariff policy estimated to raise consumer prices by 0.7% and generate $1.9tn in revenue over ten years, net of drag on economic output.
  • The AI Capex Boom Is Building Twice as Fast as the Housing Boom — Data-center investment is expanding at nearly twice the speed of the housing boom at its peak, rising from 1.4% to 3.1% of GDP in two years (~0.85 percentage points per year) versus housing's fastest pace of 0.5 percentage points per year.
  • The AI Investment Race — AI infrastructure investment is expanding faster than any previous technology boom, including the railway mania and dotcom bubble, within just three years. Debt-fueled buildout and circular financing arrangements are creating financial interconnectedness that makes the current cycle more fragile and prone to sharp correction.

Friday, August 14, 2026

AI Summary. 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.

Jamie John and Clara Murray Financial Times
Date Posted:

Token prices for the leading US AI labs are falling. OpenAI cut the price of its mid-tier GPT-5.6 Luna by 80%, while Anthropic reduced the price of its Opus 5 by 50% relative to its frontier Fable 5 model.

Does cheaper AI pricing actually reduce total costs for users?

Core argument: Headline token prices are an unreliable cost proxy, as higher-capability models completing tasks in fewer tokens or attempts can deliver lower total cost despite carrying a higher per-token list price.

OpenAI cut the price of GPT-5.6 Luna from $1 to $0.20 per mn input tokens and from $6 to $1.20 per mn output tokens. Anthropic launched Opus 5 at $5 per mn input tokens and $25 per mn output tokens — half the price of its Fable 5 model. This week, the company called off a planned rise in prices for its Sonnet 5 model, which had been due to take effect from September. Headline token prices do not provide a straightforward comparison between AI models, however. More capable models can sometimes complete a task using fewer tokens or with fewer attempts, meaning a model that appears more expensive based on the headline price of tokens can ultimately cost less.

Takeaways by Macro Roundup® AI

  1. Headline token prices are an unreliable cost proxy, as higher-capability models completing tasks in fewer tokens or attempts can deliver lower total cost despite carrying a higher per-token list price.

Related Articles:

  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Who’s Afraid of Chinese Models? — Frontier AI labs can sustain lower inference prices as inference revenue scales faster than training costs, making high per-token margins less necessary to fund model development.
  • 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

AI Summary. AI infrastructure spending by the largest technology companies now exceeds their combined operating cash flow, with ~$282bn of $863bn in planned 2026 buildout funded by debt, equity, and leases. As new capacity comes online, owners will compete to monetize it, increasing compute availability for buyers.

Hannah Petrovic, William Gildea and Marija Gavrilov Exponential View
Date Posted:

An Exponential View analysis of capex guidance suggests the 7 largest hyperscalers will spend ~$863B in 2026, exceeding their combined operating cash flow for the first time. Two-thirds of the AI buildout is still funded by cash, down from 85% in 2024.

Will artificial intelligence infrastructure spending finally match its economic returns?

Core argument: The Big Seven’s 2026 capex guidance of $863 billion exceeds their combined operating cash flow for the first time, with roughly $282 billion—one-third of total spend—financed through debt, equity, and leases rather than internal cash generation.

In 2024, cash funded 85% of Alphabet, Amazon, CoreWeave, Meta, Microsoft, Nebius, and Oracle's (the Big Seven's) capex; it dropped to 70% in 2025 and is at two-thirds in 2026. Capex guidance of $863 billion for 2026 exceeds the Big Seven’s combined operating cash flow for the first time. Some $282 billion – around a third – is funded by debt, equity, and leases. Current capex reading is great news for anyone buying compute. A third of the buildout is about to come online – and its owners will be looking to earn with it.

Takeaways by Macro Roundup® AI

  1. The Big Seven’s 2026 capex guidance of $863 billion exceeds their combined operating cash flow for the first time, with roughly $282 billion—one-third of total spend—financed through debt, equity, and leases rather than internal cash generation.

Related Articles:

  • The State of the AI Economy — AI infrastructure revenues across major cloud providers narrowly cover depreciation costs when compute assets are written down over 6 years. Demand for AI compute still exceeds supply, and every 10% price cut drives 12–18% more token usage, meaning total spending rises as prices fall.
  • 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.

AI Summary. 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.

Robin Wigglesworth Financial Times
Date Posted:

As of Q2, the hyperscalers have ~$2.5T lease and purchase commitments that are off balance sheet. GS warns “this treatment can understate leverage and future liquidity needs as these obligations are recognised and contractual payments come due.”

Are technology giants hiding trillions in future spending obligations?

Core argument: Alphabet, Microsoft, Amazon, Nvidia, and Oracle had collectively committed $982bn in contractual purchases—covering chips, compute, electricity, and equipment—as of Q1-end, compounding the $1.5tn in hyperscaler lease obligations identified by Goldman Sachs analysts.

Goldman Sachs analysts scoured through the footnotes of the hyperscalers’ regulatory filings and counted $1.5tn of lease commitments, of which $1tn hadn’t started yet and therefore didn’t appear in their financial accounts as conventional liabilities. As those analysts obliquely noted: "From a credit perspective, this treatment can understate leverage and future liquidity needs as these obligations are eventually recognised and contractual payments come due." [We noted] an interesting titbit from an earlier Morgan Stanley report which also totaled up the purchase commitments of Alphabet, Microsoft, Amazon, Nvidia and Oracle. These are typically contractual obligations to buy chips, compute, electricity to power data centres, and other equipment, and came to nearly $1.5T.

Takeaways by Macro Roundup® AI

  1. Alphabet, Microsoft, Amazon, Nvidia, and Oracle had collectively committed $982bn in contractual purchases—covering chips, compute, electricity, and equipment—as of Q1-end, compounding the $1.5tn in hyperscaler lease obligations identified by Goldman Sachs analysts.

Related Articles:

  • Big Tech AI Spending Spree Tops $1tn — 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.
  • 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.

AI Summary. 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.

Greg Ritchie Bloomberg
Date Posted:

Though a $25B auction of 30-Year Treasurys drew “decent” demand, the yield rose to 5.216% — the highest 30-year yield at auction since August 2001.

Are rising bond yields signaling investor alarm about fiscal sustainability?

Core argument: The U.S. Treasury’s $25 billion 30-year bond sale cleared at a 5.216% yield—the highest since 2001—as investors demanded elevated compensation to finance the nation’s expanding fiscal deficit.

The US government sold 30-year bonds at the highest interest rate in a quarter of a century, a testament to investors’ demand for compensation to finance the nation’s growing deficit. The yield at the $25 billion sale Thursday came in at 5.216%, the most since 2001, even as a drop in oil prices supported US debt in secondary-market trading. The sale was met with decent demand. The yield at Thursday’s 30-year sale was a little above the prevailing level seen in the market before the 1 p.m. bidding deadline in New York—a sign that demand slightly lagged expectations.

Takeaways by Macro Roundup® AI

  1. The U.S. Treasury’s $25 billion 30-year bond sale cleared at a 5.216% yield—the highest since 2001—as investors demanded elevated compensation to finance the nation’s expanding fiscal deficit.
  2. Demand at the 30-year auction slightly undershot expectations, with the clearing yield pricing above pre-deadline secondary-market levels, signaling investor reluctance at current deficit trajectories.

Related Articles:

  • If Anyone Needs an Intervention, It’s the BOJ — The 30-year inflation-protected US government bond yield has reached levels seen only briefly during the 2008 financial crisis, signaling severe stress in long-term inflation expectations and potential damage to central bank credibility.
  • US 30-Year Yield Soars to Highest Since ‘07 After Fed Stands Pat — Long-term government borrowing costs have risen to their highest level in nearly two decades, while inflation-adjusted yields signal that bond markets expect the economy's neutral interest rate to be structurally higher than previously assumed.
  • The Dangerous Brew That’s Rattling Bond Markets — Government borrowing across major economies has reached unprecedented peacetime levels, with U.S. deficits averaging 6.2% of GDP from 2023–2026 versus 4.1% in the early 2000s. Since 2020, economic shocks have consistently pushed inflation higher rather than lower, forcing long-term interest rates up and adding an estimated $

AI Summary. The gap between crude oil prices and refined fuel prices has widened by ~$35/barrel because global refining capacity is constrained, suppressing crude prices even as total energy costs rise.

Paul Krugman Krugman Wonks Out
Date Posted:

Krugman asks why crude is “only” ~$25 above its prewar level. Much of the world’s refining capacity is trapped behind Hormuz or offline, raising the “crack spread” – the premium on refined products like diesel – while depressing the crude oil price.

Is refining capacity the missing link in energy price inflation?

Core argument: The crack spread — the margin between crude oil and refined product prices — has widened by ~$35/barrel since the Strait of Hormuz closure, absorbing price rationing that would otherwise have driven crude prices sharply higher.

The difference between the price of a barrel of crude and the price of the products refined from that barrel — the “crack spread” — has exploded, rising about $35/barrel since the eve of the war. Why has the crack spread widened so much? The main answer is that a lot of the world’s refining capacity is either trapped inside the Strait or offline as a result of Ukraine’s drone campaign. It’s not all about Iran. The shortage of refining capacity has held crude prices down, as buyers won’t pay extremely high prices for crude they can’t refine. Or to put it differently but equivalently, the cutoff of oil shipments through the Strait of Hormuz, in effect, required a large rise in global prices [of petroleum products] to ration demand, but much of that rationing has taken place through a rise in the crack spread rather than a rise in crude oil prices.

Takeaways by Macro Roundup® AI

  1. The crack spread — the margin between crude oil and refined product prices — has widened by ~$35/barrel since the Strait of Hormuz closure, absorbing price rationing that would otherwise have driven crude prices sharply higher.
  2. Constrained global refining capacity, hobbled by both Hormuz-trapped facilities and Ukraine’s drone campaign against Russian infrastructure, suppresses crude demand and caps crude prices by limiting buyers’ willingness to pay for unrefinable oil.

Related Articles:

AI Summary. Global fertility has fallen below replacement level, meaning population will peak at roughly 9 billion around 2056 and then decline, driven by large existing generations masking the underlying shortfall in births.

Jesús Fernández-Villaverde and Patrick Norrick University of Pennsylvania
Date Posted:

The UN appears to systematically overestimate births; e.g. 33 of 37 countries with high-quality statistics registered fewer births in 2024 than the UN had forecast. Fernández-Villaverde and Norrick infer humanity is below replacement fertility in 2026.

Will declining birth rates eventually shrink the global economy?

Core argument: Global fertility has fallen below replacement level as of 2026, ending humanity’s ability to sustain long-run population stability without a reversal in trends.

In Table A1 we compare the World Population Prospects (WPP) estimates of births in 2022-2023 with the official numbers reported by several countries. [A2 shows the full sample with the deviations.] As of 2026, humanity is likely to be below the replacement fertility level: we are having fewer births than we need to keep population constant in the long run. This astonishing fact does not mean that population has stopped growing. Because of momentum effects (the large cohorts of women born two or three decades ago are having their children now, and their own parents have not died yet), world population will keep growing for another 30 years or so. But unless trends change, it will peak at roughly 9 billion around 2056 and then start falling, first slowly, then fast.

Takeaways by Macro Roundup® AI

  1. Global fertility has fallen below replacement level as of 2026, ending humanity’s ability to sustain long-run population stability without a reversal in trends.
  2. Population momentum—driven by large cohorts of women now in peak childbearing years—will sustain growth for roughly 30 more years before world population peaks at approximately 9 billion around 2056 and begins declining.

Related Articles:

Thursday, August 13, 2026

AI Summary. Corporate earnings are growing faster than stock prices, indicating the current market rally is driven by real profit gains rather than inflated valuations. Broad earnings growth across the economy is outpacing overall economic output, with inflation-adjusted revenue for large companies running well above GDP growth.

Greg Ip Wall Street Journal
Date Posted:

A Bank of America analysis finds the median earnings growth of S&P 500 firms has accelerated to 13% from 8% two years ago. Total revenue, adjusted for inflation and currency changes, excluding energy and financial firms, is up ~8%.

Are corporate profits growing faster than the overall economy?

Core argument: S&P 500 median earnings growth accelerated to 13% from 8% two years ago, while Q2 earnings excluding Amazon and Alphabet surged 32%, compressing price-to-earnings multiples even as equity prices rose—contradicting a bubble diagnosis.

There are several potential explanations for why the stock market has disconnected from GDP. One is that it’s a bubble. Another is that it tells us something about the future, namely that growth is going to accelerate. In a bubble, stock prices typically go up faster than earnings, inflating valuations. But in the last year, earnings have risen faster than prices. Exclude Amazon.com and Alphabet, whose results were inflated by investment gains, and earnings were up a stunning 32% in the second quarter so far. The boom isn’t just a tech or AI story. The median earnings growth of S&P 500 companies has accelerated to 13% from 8% two years ago, according to Bank of America. The bank calculates that total revenue for the index excluding energy and financial companies, and adjusted for inflation and currency changes, is up around 8%, much more than GDP.

Takeaways by Macro Roundup® AI

  1. S&P 500 median earnings growth accelerated to 13% from 8% two years ago, while Q2 earnings excluding Amazon and Alphabet surged 32%, compressing price-to-earnings multiples even as equity prices rose—contradicting a bubble diagnosis.
  2. Real, currency-adjusted S&P 500 revenue excluding energy and financials grew approximately 8%, materially outpacing GDP and indicating corporate profit generation has structurally decoupled from headline economic output.

Related Articles:

  • 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 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.
  • Momentum, Rotation and the Value in Growth — Noting US underperformance relative to the world since the start of 2025, and the fact that the 5 largest US stocks now have a P/E only marginally above that…

AI Summary. The US government sold $42bn in 10-year bonds at a 4.683% yield, the highest since 2007, as investors demanded greater compensation to finance federal borrowing.

Michael MacKenzie and Greg Ritchie Bloomberg
Date Posted:

An auction of $42B of 10-Year Treasuries drew “decent” demand, with the yield rising to 4.683% — the highest 10-year yield at auction since 2007.

Are rising bond yields signaling investor concern about US debt sustainability?

A $42 billion auction of 10-year US Treasuries resulted in the highest yield for the benchmark securities since 2007, luring decent appetite from investors who’ve been demanding more compensation to finance the US government. The yield at Wednesday’s sale came in at 4.683%, the most since the global financial crisis, and just above the prevailing level seen in the market before the 1 p.m. bidding deadline in New York — a sign that demand only slightly lagged expectations.

Related Articles:

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  • Are Government Bonds Safe in Times of War and Pandemic? — US government bonds, normally safe assets, become risky in times of war because of negative real returns due to bursts of inflation. As in the fiscal theory of…
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AI Summary. At least 5m barrels of oil per day continue to transit the Strait of Hormuz, with the true volume likely higher as growing oil spills from tanker attacks indicate ongoing vessel traffic despite efforts to close the waterway.

Javier Blas Bloomberg
Date Posted:

US Energy Secretary Chris Wright reported that ~9mm bpd crossed Hormuz over the previous week. Javier Blas’s baseline was ~7mm but finds Wright’s claim plausible: “If we’re missing two tankers rather than one, that raises it to 9 million barrels.”

Does the Strait of Hormuz remain open despite attempts to close it?

Core argument: Verified tanker tracking places Strait of Hormuz oil flows at a minimum of 5M barrels per day, with adjustments for untracked vessels pushing the plausible figure to 7–9M barrels per day.

By counting identified tankers using available data, we can safely say that, at the very least, 5 million barrels a day are transiting Hormuz. Then I like to add an adjustment factor for known unknowns — because I’m quite certain that more is happening. Say one supertanker a day is crossing in utmost secrecy; that pushes the count to 7 million barrels a day. If we’re missing two tankers rather than one, that raises it to 9 million barrels. It quickly adds up. There's a tell of what’s going on: Oil spills in the Strait of Hormuz, quite visible on the available satellite imagery, are growing by the day. It’s a sign that Iran is attacking tankers to keep the waterway closed, but also an indication that many are battling equally to keep it open — perhaps more successfully than we think.

Takeaways by Macro Roundup® AI

  1. Verified tanker tracking places Strait of Hormuz oil flows at a minimum of 5M barrels per day, with adjustments for untracked vessels pushing the plausible figure to 7–9M barrels per day.
  2. Proliferating oil spills visible on satellite imagery confirm active Iranian attacks on Hormuz tankers, yet sustained transit volumes demonstrate that interdiction efforts remain incomplete.

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John Authers Bloomberg
Date Posted:

Copper hit a new all-time high. The underlying driver is the accelerating pace of electrification, which S&P Global forecasts will increase copper demand by 50% over the next 15 years, which alongside supply constraints will likely create a supply shortfall of ~10mm metric tons by 2040.

AI-related demand is dwarfed by the huge energy transition — encompassing electric vehicles, renewables, and grid infrastructure. In general, EVs require nearly three times as much copper as conventional cars, and their rapidly growing sales add to demand. Global EV sales last year were 25% higher than total new-car sales in the US, the world's second-largest new-car market. Altogether, global copper demand is expected to shoot up by about 50%, rising to 42 million metric tons by 2040. Meanwhile, supply is expected to fall short by as much as 10 million. Freedom Broker argues that this will be driven by declining ore grades, limited large-scale discoveries — even as the global copper exploration budget has increased in recent years — and extended project development timelines averaging 17 years from discovery to production.

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AI Summary. Incumbent political parties face structural disadvantages when voter anger toward the status quo is high, making approval ratings and economic conditions more decisive than internal party organization in determining electoral outcomes.

Nate Silver and Eli McKown-Dawson Silver Bulletin
Date Posted:

Nate Silver’s midterm election model gives the Democrats an ~86% chance of flipping the House and a ~56% chance of flipping the Senate though Silver notes, “I’ll admit to being slightly surprised by that [Senate] number.”

Does voter anger toward the status quo matter more than party organization?

Core argument: Incumbent parties worldwide face persistent voter backlash against the status quo, compounding the electoral liability of an 80-year-old lame-duck president heading into a midterm cycle.

The Democratic Party has a whole host of problems; turning out their voters in midterms isn’t one of them. For midterm election purposes, you’d rather have a little internal disarray than have a president with a 38% approval rating on the basis of an unpopular quagmire in the Middle East and understandable voter anxiety about spiking gas prices. Not to mention voter fatigue over an 80-year-old, lame duck president at a time when the incumbency “advantage” has turned into persistent anger all around the world toward the status quo and whoever is in charge of it.

Takeaways by Macro Roundup® AI

  1. Incumbent parties worldwide face persistent voter backlash against the status quo, compounding the electoral liability of an 80-year-old lame-duck president heading into a midterm cycle.

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