Edward Conard

Top Ten New York Times Bestselling Author

  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…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 offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…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 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 comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “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
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “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 represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Thursday, June 11, 2026

AI Summary. OpenAI is considering steep price cuts as it competes with Anthropic for enterprise customers, a move that could erode both companies' margins given they already lose billions of dollars on the computing costs of running AI systems. Anthropic's revenue surged after its Claude Code coding tool spread among software engineers, and it surpassed OpenAI's valuation.

Keach Hagey and Berber Jin Wall Street Journal
Date Posted:

The WSJ reports OpenAI may “drastically” cut token pricing in an effort to gain market share from Anthropic, as both firms race towards their IPOs.

Will a price war for enterprise AI customers erode margins?

Core argument: OpenAI is considering drastic price cuts in anticipation of a fight with Anthropic for users, cuts that could erode both companies' profit margins given they already lose billions of dollars on the computing resources needed to process queries.

Drastic price cuts could potentially erode the profit margins of both companies, which already lose billions of dollars because of the enormous cost for computing resources needed for AI systems to process queries and carry out tasks. OpenAI is trying to catch up with its younger rival in the race to win enterprise customers that are paying large amounts of money for AI tools that can improve workplace productivity. Anthropic’s revenue recently surged after its coding tool Claude Code went viral among software engineers, and the five-year-old startup surpassed OpenAI’s valuation for the first time. OpenAI has since made its own coding tool Codex a focus of the company.

Takeaways by Macro Roundup® AI

  1. OpenAI is considering drastic price cuts in anticipation of a fight with Anthropic for users, cuts that could erode both companies' profit margins given they already lose billions of dollars on the computing resources needed to process queries.
  2. Anthropic's revenue surged after its coding tool Claude Code went viral among software engineers, and the five-year-old startup surpassed OpenAI's valuation for the first time.
  3. OpenAI is trying to catch up with Anthropic in winning enterprise customers paying large sums for AI productivity tools, and has made its own coding tool Codex a company focus.

Related Articles:

  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.
  • Commoditization, Orchestration, and the New AI Stack — The author argues that as incremental gains from larger AI training runs shrink, the return on additional computing power falls, weakening the case for the training spending that dominates data center demand. Survivors of model commoditization will be those owning the orchestration layer or the applications where outcomes are priced.
  • Four Takeaways from Microsoft’s Satya Nadella at WEF Today — Microsoft's chief executive says the price of AI model usage ("tokens") falls by roughly half every three months. Commentary argues that such rapid deflation pulls demand forward, encourages overbuilding, and commoditizes models, shifting competitive advantage to orchestration and context rather than the model layer.

AI Summary. 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.

Jessica Wachter and Jonathan Wachter National Bureau of Economic Research
Date Posted:
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The AI build‑out projected through 2027 is roughly PV‑neutral if there has already been one rare 2.7× productivity boom in the AI sector. In the absence of a further boom, long‑run valuations and the more aggressive 2028–30 capex plans start to look too high.

What does AI capital spending imply about future economic growth?

Core argument: The five largest U.S. technology firms are scaling AI capital expenditure at roughly 50% per year, from $155 billion in 2022 to a forecast $755 billion in 2026.

The five largest U.S. technology firms are scaling AI capital expenditure at roughly 50% per year, from $155 billion in 2022 to a forecast $755 billion in 2026. This paper embeds this observation in a two-sector model with rare productivity booms to ask what the investment path implies for growth and asset prices. Calibrating the boom size to match the observed investment ramp implies that each boom raises AI-sector productivity by a factor of roughly 2.7. A two-year window of elevated boom probability generates three scenarios: a moderate outcome (one boom only), a transformative outcome (one further boom), and a singularity scenario (two additional booms). Even in the moderate scenario, AI adds ~ 5pp to cumulative GDP growth over the seven-year transition (Figure 2), on top of the ~19% normal-times growth that would otherwise prevail. The transformative and singularity scenarios push the AI contribution to 20% and over 58%, respectively. The non-AI sector continues to grow at its historical rate, [while the] AI sector’s share of the economy rises from ~3% today to btw 8% and 39%, depending on the scenario. As this share grows, the AI sector’s rapid productivity gains increasingly dominate aggregate GDP growth.

Takeaways by Macro Roundup® AI

  1. The five largest U.S. technology firms are scaling AI capital expenditure at roughly 50% per year, from $155 billion in 2022 to a forecast $755 billion in 2026.
  2. Calibrating boom size to the observed investment ramp in a two-sector model with rare productivity booms implies that each boom raises AI-sector productivity by a factor of roughly 2.7.
  3. In the moderate scenario, AI adds about 5 percentage points to cumulative GDP growth over the seven-year transition, on top of the ~19% normal-times growth, rising to 20% in the transformative scenario and over 58% in the singularity scenario.

Related Articles:

  • Have We Entered an Era of High Productivity Growth? — Labor productivity has grown solidly while the San Francisco Federal Reserve's total factor productivity index, which adjusts for the equipment and technology workers use, has grown modestly, suggesting gains so far reflect better tools rather than broader advances. A regime-switching model puts the probability of a high-productivity regime at 57% using labor productivity and 21% using total factor productivity.
  • Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools — The authors estimate that AI coding tools raise developers' task-level productivity by about 40% with autocomplete, 140% including synchronous agents, and 180% with asynchronous agents, with larger effects for less active developers. Gains attenuate up the production chain: synchronous agents raise lines of code 741% and pull requests 65% but releases only 20%.
  • Measurements Matter: The Return of SLOC Slop — Measuring developer productivity by software lines of code captures output rather than value, and AI inflates the metric by generating boilerplate, wrappers, tests, and speculative code at near-zero cost. More code also creates liabilities through added maintenance, complexity, attack surface, and debugging load.

AI Summary. Chinese robotics maker Unitree is tripling revenues year over year with 60% gross margins, plans nearly $300m of artificial intelligence research spending, and prices the cheapest humanoid robots on the market. Its flagship model's pre-tax price has fallen from over $50,000 to $27,300, which the authors estimate still yields 67% gross margins.

Reyk Knuhtsen, Niko Ciminelli, Jacob Rintamaki, Robert Ghilduta, et al. Semianalysis
Date Posted:

The Semianalysis team estimates that China’s Unitree has a 67% gross margin on their flagship G1 humanoid robot; hourly labor costs imply “G1 humanoids are crossing the viability threshold of real-world deployments.”

Is a Chinese robotics maker positioned to dominate global humanoid robotics?

Core argument: Unitree has cut pre-tax pricing from over $50K to $27.3K over the past 12–18 months, and the authors estimate the company still earns 67% gross margins on its flagship G1.

Unitree is tripling revenues YoY on 60% gross margin product lines, planning almost $300M of AI R&D spend, increasingly in-housing portions of manufacturing, all while pricing the cheapest humanoids on the market by far. With their upcoming highly anticipated IPO, Unitree is deservedly dominating the humanoids conversation. But historically, Unitree’s humanoid robots have a reputation for less than perfect reliability, are not viewed as useful beyond entertainment and R&D, and have a reputation for being “cheap.” Despite this, we believe Unitree's cost structure is one of its greatest advantages over competitors. Unitree has slashed pre-tax pricing from $50K+ to $27.3K over the past 12-18 months. Even at that price, we estimate they still hit 67% gross margins on their flagship G1. With their BoM set to plummet as manufacturing scales, we've already heard pricing well under $20K in some deals.

Takeaways by Macro Roundup® AI

  1. Unitree has cut pre-tax pricing from over $50K to $27.3K over the past 12–18 months, and the authors estimate the company still earns 67% gross margins on its flagship G1.
  2. Unitree is tripling revenues year-over-year on product lines carrying 60% gross margins, plans almost $300M of AI R&D spend, and is bringing portions of manufacturing in-house ahead of its upcoming IPO.
  3. Unitree's humanoid robots have a reputation for less than perfect reliability and are not viewed as useful beyond entertainment and R&D, yet the authors argue its cost structure is one of its greatest advantages over competitors, citing deals heard well under $20K.

Related Articles:

  • America Is Missing The New Labor Economy – Robotics Part 1 — Building an identical robotic arm in the US costs about 2.2x more than in China, and US-labeled components still depend heavily on Chinese parts and materials with no scalable alternative. The author argues China is the only country positioned to capture full-scale intelligent robotic automation, which would let it outcompete the US across production.
  • Speed Can Reindustrialize America — The US has a relative surplus of low-paid hourly workers and a shortage of high-skill workers, so many less productive US manufacturers could buy a robot but could not attract the talent to program it. China faces the opposite conditions, with underemployed STEM graduates, residency rules complicating coastal factory hiring, and robot programmers earning a fraction of US wages.
  • Minimum Wages and the Rise of the Robots — State-level measures of exposure to industrial robots show substantial geographic variation across the US, with the highest concentrations in the Southwest, Midwest, and Northwest and the lowest in the Mountain West, parts of the South, and northern regions. The authors find state robot exposure is positively correlated with state minimum wage increases.

AI Summary. The Bureau of Labor Statistics reported that US producer prices rose 6.5% from a year earlier, the fastest annual pace in more than two years, and 1.1% from the prior month. A core measure excluding food and energy rose 4.9% year over year.

Augusta Saraiva Bloomberg
Date Posted:

Headline producer prices rose 6.5% y/y in May, fueled by energy prices, which rose 10.7% in May. Core PPI rose 4.9%, suggesting broader-based cost pressures and bolstering the case for a rate increase.

Are producer prices accelerating to their fastest annual pace in years?

Core argument: The US producer price index rose 6.5% from a year earlier, the most since November 2022, according to Bureau of Labor Statistics data.

The producer price index increased 6.5% from a year earlier, the most since November 2022, according to Bureau of Labor Statistics data out Thursday. It advanced 1.1% from April. A core measure of prices that excludes food and energy increased 4.9% from a year earlier.

Takeaways by Macro Roundup® AI

  1. The US producer price index rose 6.5% from a year earlier, the most since November 2022, according to Bureau of Labor Statistics data.
  2. The US producer price index advanced 1.1% from April, according to Bureau of Labor Statistics data.
  3. A core producer price measure excluding food and energy increased 4.9% from a year earlier.

Related Articles:

  • US Inflation Picks Up to Three-Year High, Eroding Paychecks — US consumer prices rose 0.5% on the month and 4.2% from a year earlier, according to Bureau of Labor Statistics data, with more than half the monthly advance coming from higher energy costs, while the measure excluding food and energy rose 0.2%. Inflation-adjusted average hourly earnings fell 0.7% from a year earlier.
  • US Consumer Sentiment Slides to Record Low on Price Concerns — The University of Michigan's consumer sentiment index fell 5 points to 44.8, a record low and weaker than all economist projections in a Bloomberg survey. Consumers expect prices to rise 3.9% annually over the next five to 10 years, up from 3.5%, and 4.8% over the coming year.
  • Do Voters Punish Inflation or Pay Cuts? Inflation and Real Wages in U.S. Elections — An analysis of U.S. counties estimates that where wages grew faster than prices, voters supported the incumbent party more, while counties with larger real wage losses than average shifted more toward the opposition. A one-standard-deviation larger real wage loss is associated with shifts of roughly 0.10 and 0.19 percentage points in vote share and margin.

Friday, June 12, 2026

AI Summary. Open-source artificial intelligence models cost far less per unit of computing than leading proprietary ones, with one Anthropic model priced more than 50x DeepSeek's per token. Researchers say top proprietary models remain four to six months ahead and can sometimes finish complex tasks using fewer tokens, lowering total cost.

Bradley Olson and Tina Li Wall Street Journal
Date Posted:

Token pricing for Anthropic’s new Fable 5 model is 50x that of DeepSeek’s V4 Pro. A Citadel notes cautions, “Even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints and marginal returns.”

Are cheaper open-source models pressuring proprietary AI pricing?

Core argument: Anthropic's Fable 5 model costs more than 50 times as much per token as DeepSeek's V4 Pro, with open-source models priced far below proprietary alternatives on the basic unit of AI computing.

Open-source models cost far less per token, the basic unit of AI computing. Anthropic’s recently-released Fable 5 model is more than 50 times more expensive per token than DeepSeek’s V4 Pro. The top proprietary models from companies like OpenAI, Anthropic or Google remain four to six months ahead of open-source competitors, researchers say. In some cases that means they can complete a complex task using fewer tokens, equating to a lower total cost. The shift to cheaper models appears to have played a role in a recent decline in a widely followed index that tracks AI spending, Citadel Securities said in a report this week. “Even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints and marginal returns,” the report said.

Takeaways by Macro Roundup® AI

  1. Anthropic's Fable 5 model costs more than 50 times as much per token as DeepSeek's V4 Pro, with open-source models priced far below proprietary alternatives on the basic unit of AI computing.
  2. Researchers say top proprietary models from OpenAI, Anthropic and Google remain four to six months ahead of open-source rivals, in some cases completing complex tasks with fewer tokens and thus lower total cost.
  3. Citadel Securities said in a report that the shift to cheaper models appears to have played a role in a decline in a widely followed index tracking AI spending, citing cost curves, capacity constraints and marginal returns.

Related Articles:

  • Four Takeaways from Microsoft’s Satya Nadella at WEF Today — Microsoft's chief executive says the price of AI model usage ("tokens") falls by roughly half every three months. Commentary argues that such rapid deflation pulls demand forward, encourages overbuilding, and commoditizes models, shifting competitive advantage to orchestration and context rather than the model layer.
  • OpenAI Considers Drastic Price Cuts, Anticipating War for Users With Anthropic — OpenAI is considering steep price cuts as it competes with Anthropic for enterprise customers, a move that could erode both companies' margins given they already lose billions of dollars on the computing costs of running AI systems. Anthropic's revenue surged after its Claude Code coding tool spread among software engineers, and it surpassed OpenAI's valuation.
  • Measurements Matter: The Return of SLOC Slop — Measuring developer productivity by software lines of code captures output rather than value, and AI inflates the metric by generating boilerplate, wrappers, tests, and speculative code at near-zero cost. More code also creates liabilities through added maintenance, complexity, attack surface, and debugging load.

AI Summary. Total spending on large language model usage has roughly doubled even as the price of a single token has fallen more than 90%, an illustration of Jevons' paradox. As tokens get cheaper, companies run more AI agents, automate more workflows, and generate more code, pushing aggregate expenditure higher.

Torsten Sløk Apollo
Date Posted:

An index of token expenditure has ~ doubled since late 2025 even as the price of a single token has fallen by ~90% since 2023. As prices fall, firms “run more AI agents, automate more workflows and generate more code,” Sløk notes.

Does falling token pricing raise total AI spending?

Core argument: The Silicon Data Token Expenditure Index, which tracks total spending on large language model usage, has roughly doubled since late 2025, even as the price of a single token has fallen more than 90% since 2023.

The Silicon Data Token Expenditure Index, which tracks total spending on large language model usage, has roughly doubled since late 2025, even as the price of a single token has fallen more than 90% since 2023. This is Jevons' paradox in action. As tokens get cheaper, companies don't spend less but instead run more AI agents, automate more workflows, and generate more code, pushing aggregate expenditure higher even as the unit cost of intelligence collapses.

Takeaways by Macro Roundup® AI

  1. The Silicon Data Token Expenditure Index, which tracks total spending on large language model usage, has roughly doubled since late 2025, even as the price of a single token has fallen more than 90% since 2023.
  2. Cheaper tokens illustrate Jevons' paradox: companies respond by running more AI agents, automating more workflows, and generating more code, pushing aggregate spending higher as the unit cost of intelligence collapses.

Related Articles:

  • OpenAI Considers Drastic Price Cuts, Anticipating War for Users With Anthropic — OpenAI is considering steep price cuts as it competes with Anthropic for enterprise customers, a move that could erode both companies' margins given they already lose billions of dollars on the computing costs of running AI systems. Anthropic's revenue surged after its Claude Code coding tool spread among software engineers, and it surpassed OpenAI's valuation.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Achieving 60% on MMLU, a test of academic reasoning, once required a 540bn-parameter model but was reached by a 3.8bn-parameter model two years later, a 142x reduction in parameters. Models small enough to run on phones or sensors shift inference from data centers to devices, weakening the case for paying cloud providers per token.
  • We’re Using So Much AI That Computing Firepower Is Running Out — Surging AI demand is straining computing capacity: Anthropic's Claude programming interface recorded 98.95% uptime over a 90-day span, below the 99.99% software companies typically promise, as its annualized revenue run rate climbed from $9bn to $14bn to $30bn. The company has capped how much usage customers can consume during weekday peak morning hours.

AI Summary. Net equity issuance by US domestic non-financial companies has turned positive for the first time since 2021, and a pipeline of initial public offerings and secondary offerings points toward further increases. Net shareholder payouts remain positive on strong earnings and dividends, but the author argues that falling earnings or rising issuance would worsen the outlook for US equities.

Will Denyer Gavekal
Date Posted:

The SpaceX lockups will start to expire btw mid-August and mid-December, unlocking ~ half of SpaceX’s total market capitalization at its IPO price as Anthropic and OpenAI go public in H2. Net shareholder payouts remain positive in H1 2026.

Does rising net equity issuance weaken the outlook for US stocks?

Core argument: Net equity issuance by the US domestic non-financial sector turned positive in 1Q26 for the first time since 2021, covering IPOs, secondary offerings, private placements, and stock-based compensation net of buybacks and merger-related retirements.

Flow of funds data showed net equity issuance by the domestic non-financial sector turned positive in 1Q26 for the first time since 2021. This measure includes IPOs, secondary offerings, private placements, and stock-based compensation, net of share retirements through buybacks and mergers. Given the pipeline of upcoming IPOs and secondary offerings, net issuance seems likely to increase further—which is anything but positive for equity markets. For now, US companies continue to generate strong earnings and pay hefty dividends, such that net shareholder payouts—net dividends less net equity issuance—remain positive. But if earnings and dividends were to roll over, or net equity issuance were to rise significantly from here—and that does seem to be the current direction of travel—the outlook for US equity markets in the second half of the year could become more problematic. Additionally, sales of unlocked shares could provide a second wave of liquidity stresses months after the mega-IPOs.

Takeaways by Macro Roundup® AI

  1. Net equity issuance by the US domestic non-financial sector turned positive in 1Q26 for the first time since 2021, covering IPOs, secondary offerings, private placements, and stock-based compensation net of buybacks and merger-related retirements.
  2. Net shareholder payouts—net dividends less net equity issuance—remain positive as US companies continue to generate strong earnings and pay hefty dividends, but a rollover in earnings and dividends or a significant rise in issuance could make the second-half outlook for US equities more problematic.
  3. The pipeline of upcoming IPOs and secondary offerings suggests net issuance is likely to rise further, and sales of unlocked shares could bring a second wave of liquidity stresses months after the mega-IPOs.

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.
  • One Hundred Years in the U.S. Stock Markets — Over a century, U.S. publicly traded stocks generated roughly $91tn in shareholder wealth enhancement, with returns strongly positively skewed and dominated by a relatively small number of high-performing firms. Because most possible outcomes fall below the mean in a skewed distribution, the authors argue financial planning should focus on median outcomes or the full distribution.
  • Capital Allocation — Internal financing has been the main source of capital for U.S. corporations, with steady new debt issuance except in recession years, consistent with a stable capital structure. Companies have retired equity on average, and net equity issuance remains negative even after accounting for stock-based compensation.

AI Summary. The International Monetary Fund calculates that global current-account deficits plus surpluses reached 3.7% of world gross domestic product, after fluctuating between 1% and 3% until the early 2000s. The US deficit of $1.1tn is the largest single imbalance, and the Fund estimates a budget deficit of 2% of GDP raises the current-account deficit by 0.5% of GDP.

Greg Ip The Wall Street Journal
Date Posted:
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Greg Ip warns that while global imbalances are “narrower” than they were pre-crisis, “they are more persistent and more entrenched in national behavior,” pointing to China’s trade surplus and chronic US fiscal deficits.

How large have global current-account imbalances grown relative to world output?

Core argument: The IMF calculates that global current-account deficits plus surpluses reached 3.7% of global GDP last year after falling steadily from around the global financial crisis; until the early 2000s they fluctuated between 1% and 3%.

The IMF calculates that deficits plus surpluses reached 3.7% of global gross domestic product last year after falling steadily from around the global financial crisis. Until the early 2000s, they fluctuated between 1% and 3%. This is worrisome. Current-account deficits played a part in the crises that swept Latin America in the early 1980s, East and Southeast Asia in the late 1990s, the U.S. in 2007-09, and the eurozone from 2009 on. Today’s imbalances look different. While annual deficits are narrower than 15 years ago, they are more persistent and more entrenched in national behaviour. Take the U.S., whose current-account deficit of $1.1 trillion is by far the largest single imbalance. Last year, tariffs did reduce some imports, but because the AI boom sucked in foreign-made tech equipment, the current-account deficit narrowed only slightly. The bigger contributor to the U.S. current-account deficit is its budget deficit, which sustains excessive U.S. spending and inadequate saving. The IMF estimates a budget deficit of 2% of GDP increases the current-account deficit by 0.5% of GDP.

Takeaways by Macro Roundup® AI

  1. The IMF calculates that global current-account deficits plus surpluses reached 3.7% of global GDP last year after falling steadily from around the global financial crisis; until the early 2000s they fluctuated between 1% and 3%.
  2. The U.S. current-account deficit of $1.1 trillion is by far the largest single imbalance, narrowing only slightly last year as tariffs reduced some imports while the AI boom drew in foreign-made tech equipment.
  3. The IMF estimates that a budget deficit of 2% of GDP increases the current-account deficit by 0.5% of GDP, and the U.S. budget deficit sustains excessive spending and inadequate saving.

Related Articles:

  • Understanding Global Imbalances — The US, China, Germany, and Japan account for roughly two-thirds of global current account imbalances, with the US deficit at 4% of GDP financed by capital inflows into dollar assets. The average duration of a surplus or deficit spell roughly doubled since the 1980s, building large net foreign asset and liability positions.
  • Why Global Imbalances Matter — Countries running persistent trade surpluses, such as China, Germany and Japan, have built their economies around an excess supply of tradeable goods and services, leaving them dependent on deficit countries to absorb it. That dependence gives deficit countries bargaining power and exposes surplus countries to pressure to expand domestic demand.
  • The U.S. Trade Deficit: Myths and Realities — Taxing capital inflows would weaken the dollar but could raise domestic interest rates and depress investment, harming long-term growth, while an interest rate cut absent recession would be inflationary and erode any real depreciation needed to durably improve the trade balance. Fiscal restraint could weaken the dollar and reduce the trade deficit.

AI Summary. Rising gasoline prices have pushed average hourly earnings 0.7% below their year-earlier level after adjusting for inflation, erasing more than a year of wage gains. Inflation eroding wage increases is one reason consumer sentiment sits near record lows despite relatively low unemployment and a strong stock market.

Justin Lahart The Wall Street Journal
Date Posted:
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The .7% y/y decline in real wages has brought average hourly earnings back to their level when Trump took office; the downtick is half the size of their cumulative decline over Biden’s four years.

Have rising gas prices erased more than a year of wage gains?

Core argument: Average hourly earnings adjusted for inflation are 0.7% below their year-earlier level, the biggest such gap since February 2023, as surging gasoline prices erase more than a year of U.S. wage gains.

Surging gasoline prices have erased more than a year of Americans’ wage gains. That inflation-adjusted gap—average-hourly earnings falling 0.7% below their year-earlier level—is the biggest since February 2023. The mismatch has quickly knocked Americans’ inflation-adjusted earnings back to where they were in January 2025, when President Trump returned to the White House. (Over the course of President Joe Biden’s four years in office, when prices surged, inflation-adjusted earnings fell 1.4%.) Seeing inflation eat into wage increases is one reason consumer sentiment is hovering around near-record lows, despite relatively low unemployment and a hot stock market.

Takeaways by Macro Roundup® AI

  1. Average hourly earnings adjusted for inflation are 0.7% below their year-earlier level, the biggest such gap since February 2023, as surging gasoline prices erase more than a year of U.S. wage gains.
  2. Inflation-adjusted earnings have fallen back to their January 2025 level, when President Trump returned to the White House; over President Biden's four years in office they fell 1.4%.
  3. Inflation eating into wage increases is one reason consumer sentiment hovers around near-record lows despite relatively low unemployment and a hot stock market.

Related Articles:

  • US Producer Prices Rise at Fastest Pace Since November 2022 — The Bureau of Labor Statistics reported that US producer prices rose 6.5% from a year earlier, the fastest annual pace in more than two years, and 1.1% from the prior month. A core measure excluding food and energy rose 4.9% year over year.
  • US Consumer Sentiment Slides to Record Low on Price Concerns — The University of Michigan's consumer sentiment index fell 5 points to 44.8, a record low and weaker than all economist projections in a Bloomberg survey. Consumers expect prices to rise 3.9% annually over the next five to 10 years, up from 3.5%, and 4.8% over the coming year.
  • Do Voters Punish Inflation or Pay Cuts? Inflation and Real Wages in U.S. Elections — An analysis of U.S. counties estimates that where wages grew faster than prices, voters supported the incumbent party more, while counties with larger real wage losses than average shifted more toward the opposition. A one-standard-deviation larger real wage loss is associated with shifts of roughly 0.10 and 0.19 percentage points in vote share and margin.

AI Summary. McKinsey forecasts the number of quantum computing systems worldwide will rise from scores today to around 5,000 by 2030, though existing machines remain highly error-prone and face formidable engineering challenges. As these systems begin to outperform conventional computers in niche areas, organizations are preparing for the predicted moment when quantum computers can break current cryptography.

Michael Peel Financial Times
Date Posted:

Both Google and IBM are targeting the delivery of “useful” quantum computers by 2030. Major technical obstacles remain. This fall, Google’s CEO said the technology was now “where maybe AI was five years ago.”

Is quantum computing advancing faster than skeptics expect?

Core argument: McKinsey forecasts the number of quantum computing systems worldwide will rise from scores today to roughly 5,000 by 2030.

Industry observers estimate there are scores of quantum computing systems in the world, a number forecast by McKinsey to rise to around 5,000 by 2030. Sceptics point to the significant technical hurdles that remain to making useful quantum computers. Existing machines are still far too error-prone. The “engineering challenges” that the devices’ developers insist can be solved in the next few years are formidable. But as the new machines begin to indicate they can outperform conventional computers in niche areas — what the industry dubs “quantum advantage” — the risk of dismissing the technology’s importance is growing. Organisations are readying themselves for the prospect of Q-Day — the predicted moment when quantum computers are capable of breaking the cryptographic methods on which modern societies rely.

Takeaways by Macro Roundup® AI

  1. McKinsey forecasts the number of quantum computing systems worldwide will rise from scores today to roughly 5,000 by 2030.
  2. Sceptics note that existing quantum machines remain far too error-prone, and the engineering challenges developers say can be solved within a few years are formidable.
  3. Organisations are preparing for Q-Day, the predicted moment when quantum computers can break the cryptographic methods modern societies rely on, as machines begin to indicate "quantum advantage" over conventional computers in niche areas.

Related Articles:

  • New Advances Bring the Era of Quantum Computers Closer Than Ever — Estimates of the quantum computing power needed to run Shor's algorithm and break encryption have fallen from billions of qubits to a million, while existing machines have hundreds. Researchers have announced a design requiring only tens of thousands of qubits and an implementation of the algorithm 10x as efficient as the prior best.
  • U.S. to Award Quantum-Computing Firms $2 Billion and Take Equity Stakes — The U.S. government will award about $2bn to quantum-computing firms under the 2022 Chips and Science Act, taking a minority equity stake in each company. D-Wave said its entire $100m award would be an equity investment, with Rigetti and Infleqtion describing similar structures.
  • An Old Problem Could Stop The U.S. From Leading On Quantum Computers — Many quantum computers depend on dilution refrigerators, produced by a few small firms in the US, Britain and Finland, and on critical minerals such as niobium, concentrated in Brazil, and tantalum, mined in Africa and mostly processed in China. China has announced mass production of a domestic dilution refrigerator and has been buying niobium mines.