A 2019 study in the Quarterly Journal of Economics found that minimum wage increases did not affect the overall number of low-wage jobs in the five years following the wage increase. However, it did find disemployment in 'tradable' sectors, defined as those sectors most reliant on entry-level or low-skilled labor.[78]

A 2018 study published by the University of California agrees with the study in the Quarterly Journal of Economics; it finds that minimum wages actually lead to fewer jobs for low-skilled workers. The article discusses a trade-off for low- to high-skilled workers: when the minimum wage is increased, GDP is more heavily redistributed toward high-academia jobs.[79]

In another study, which shared authors with the above, published in the American Economic Review, found that a large and persistent increase in the minimum wage in Hungary produced some disemployment, with the large majority of additional cost being passed on to consumers. The authors also found that firms began substituting capital for labor over time.[80]

Card and Krueger expanded on this initial article in their 1995 book Myth and Measurement: The New Economics of the Minimum Wage.[82] They argued that the negative employment effects of minimum wage laws are minimal if not non-existent. For example, they look at the 1992 increase in New Jersey's minimum wage, the 1988 rise in California's minimum wage, and the 1990–91 increases in the federal minimum wage. In addition to their own findings, they reanalyzed earlier studies using updated data, generally finding that the earlier negative employment effects did not hold up in the larger datasets.[83] This had major implications on policy, challenging long-held economic views that increasing minimum wage led to deadweight loss.

A 2011 paper reconciled differences between datasets, showing positive employment effects for small restaurants but negative effects for large fast-food chains.[87] A 2014 analysis found that minimum wage reduces employment among teenagers.[88]

A 2017 study in Seattle found that raising the minimum wage to $13 per hour reduced the incomes of low-wage workers because they worked fewer hours as businesses adjusted to higher labor costs.[94] A 2019 study in Arizona suggested that smaller minimum wage increases might lead to slight economic growth without significantly distorting labor markets.[95]

For free-market types, including The Economist, fiddling with wages by fiat sets off alarm bells. In a competitive market anything that artificially raises the price of labour will curb demand for it, and the first to lose their jobs will be the least skilled—the people intervention is supposed to help. That is why Milton Friedman called minimum wages a form of discrimination against the low-skilled; and it is why he saw topping up the incomes of the working poor with public subsidies as a far more sensible means of alleviating poverty.

Scepticism about the merits of minimum wages remains this newspaper’s starting-point. But as income inequality widens and workers’ share of national income shrinks, the case for action to help the low-paid grows. Addressing the problem through subsidies for the working poor is harder in an era of austerity, when there are many other pressing claims on national coffers. Other policy options, such as confiscatory taxes, are unattractive.

Nor is a moderate minimum wage as undesirable as neoclassical purists suggest. Unlike those in textbooks, real labour markets are not perfectly competitive. Since workers who want to change jobs face costs and risks, employers may be able to set pay below its market-clearing rate. A minimum wage, providing it is not set too high, could thus boost pay with no ill effects on jobs.

Empirical evidence supports that argument. In flexible economies a low minimum wage seems to have little, if any, depressing effect on employment. America’s federal minimum wage, at 38% of median income, is one of the rich world’s lowest. Some studies find no harm to employment from federal or state minimum wages, others see a small one, but none finds any serious damage. Britain’s minimum wage, at around 47% of median income, with a lower rate for young people, also does not seem to have pushed many people out of work.

High minimum wages, however, particularly in rigid labour markets, do appear to hit employment. France has the rich world’s highest wage floor, at more than 60% of the median for adults and a far bigger fraction of the typical wage for the young. This helps explain why France also has shockingly high rates of youth unemployment: 26% for 15- to 24-year-olds.

A second lesson is that politicians should give the power to set minimum wages to technocrats. In Britain, the floor is adjusted annually on the advice of economists and statisticians in the Low Pay Commission; it has generally advanced gradually. In America, the federal floor is set by politicians and adjusted irregularly in huge increments. That does no favours to American workers or their employers.

Finally, governments should remember that minimum wages are a palliative. They should not distract attention from more fundamental causes of low wages—such as a lack of education and skills—and the efforts to address them.

Morgan Stanley, one of the lead underwriters, projects SpaceX revenue of $160 billion in 2028, $330 billion in 2030, and $3.4 trillion by 2040, with adjusted EBITDA projected to exceed $2.7 trillion at that point. Reaching those numbers from SpaceX’s $18.7 billion in 2025 revenue requires a compound annual growth rate of roughly 42%, which would outpace even Amazon’s fastest growth era. Morgan Stanley’s model places AI infrastructure as the heaviest revenue driver, projecting $190 billion from SpaceX’s AI business alone by 2030. That figure is anchored to xAI’s Grok platform and the Colossus supercomputer following the earlier merger.

Deficits are large by historical standards. The deficit totals $1.9 trillion in fiscal year 2026 and grows to $3.1 trillion in 2036. Relative to the size of the economy, the deficit is 5.8 percent of gross domestic product (GDP) in 2026 and increases to 6.7 percent in 2036. Deficits averaged 3.8 percent of GDP over the last 50 years (see Chapter 1).

Debt held by the public rises from 101 percent of GDP in 2026 to 120 percent in 2036, well above the previous record of 106 percent just after World War II.

Outlays are large by historical standards—and growing. They total 23.3 percent of GDP in 2026, exceeding their 50-year average of 21.2 percent. After being adjusted for shifts in the timing of certain payments, outlays remain at about that level through 2028 but then grow steadily, boosted by rising spending on mandatory programs and increasing net interest costs. Outlays in 2036 are 24.4 percent of GDP (see Chapter 3).

the cumulative deficit over the 2026–2035 period is $1.4 trillion (or 6 percent) greater (see Chapter 5). Three major policy developments contribute to those changes: The 2025 reconciliation act (see Appendix A) increased deficits by an estimated $4.7 trillion; higher tariffs reduced deficits by an estimated $3.0 trillion; and administrative actions related to immigration increased deficits by an estimated $0.5 trillion.

The law’s most significant tax changes were those that extended certain provisions of the 2017 tax act (P.L. 115-97) that had expired or were scheduled to expire after 2025. The reconciliation act lowered statutory tax rates and changed the amount of individual income subject to tax, thus reducing individual income tax liabilities for most households and for pass-through businesses (that is, businesses for which income is taxed under the individual income tax system). It also accelerated deductions for business investment and thus reduced effective marginal tax rates on that investment. (The effective marginal tax rate measures the tax burden on returns from a marginal investment—that is, one that is expected to earn just enough, after taxes, to attract investors.) The reconciliation act modified eligibility and financing for Medicaid and the Supplemental Nutrition Assistance Program (SNAP) and altered the terms of federal student loans, reducing federal spending on those programs. It also provided additional funding for defense, homeland security, and immigration-related activities.

The 2025 reconciliation act is estimated to increase total deficits over the 2025–2034 period by $4.2 trillion relative to CBO’s January 2025 baseline projections (see Table A-1).

To account for the effects of the 2025 reconciliation act, CBO increased its estimate of total deficits over the 2025–2034 period by $4.2 trillion relative to the agency’s January 2025 baseline projections (see Table A-1). That amount reflects CBO and JCT’s conventional estimate of the law’s effects on primary deficits (which exclude net outlays for interest) as well as effects of increased net outlays for interest and of budgetary feedback from macroeconomic changes. In total, the reconciliation act increases CBO’s estimate of federal debt as a percentage of GDP in 2034 by 9.0 percentage points, from 117.1 percent of GDP to 126.0 percent.

But new research suggests another culprit may be doing more of the damage: remote work.  “Remote work can explain 64% of the increase in unemployment for all young college graduates between 2017-19 and 2022-24,” researchers wrote in a blog post published Monday by the New York Federal Reserve Bank. Separately, a paper by economists Peter John Lambert and Yannick Schindler of the London School of Economics posted last month reached a similar conclusion. The authors found that work-from-home exposure was a stronger predictor of the pullback in early-career hiring than exposure to artificial intelligence, at least so far.

“Junior workers rely heavily on training, mentoring, supervision, informal feedback and internal networks,” Schindler said, “which are all things where in-person contact is really important.”

people in their 20s are generally eager to work in an office at least part time. The same is true for many workers older than 50, who tend to be more senior, are less likely to have young children at home, and whose jobs often involve more meetings and supervising.   “It’s the people in the middle of those ages — the ones who have families, who don’t need day-to-day mentoring because they’re already well on their career path,” he told Straight Arrow, “who want the flexibility of working from home.”

GDP, broadly speaking, is a measure of the value of an economy. Analyzing the debt in context of GDP makes it easier to track the debt alongside changes in economy and inflation, allowing for comparisons of the debt over time; it can also indicate a country's ability to repay its debt. When debt reaches 100% of a nation's GDP, it indicates that the country owes about as much as its economy generates annually.

Lenders, including JPMorgan and MUFG, have spent more than six months distributing $38bn of construction debt tied to a data centre project leased to Oracle in Texas and Wisconsin, people familiar with the matter said.

Some banks sought to sell the loans at a discount to non-bank lenders to offload the Oracle-linked debt, the people said.

Banks have in recent weeks sounded out investors about structures including a variant of a significant risk transfer, or SRT. SRTs have been commonly used by European banks to reduce their capital requirements by offloading the risk of losses on part of a loan portfolio to investors such as private credit funds and insurers in exchange for a return. North American banks have begun using the instruments more in recent years. Rather than a classic SRT that may be tied to dozens of loans, banks are exploring slicing and dicing large and concentrated data centre loans to shift the riskiest portions off their books, for example.

Companies have already started expanding to new debt markets beyond bank lending by issuing private credit, asset-backed securities, commercial mortgage-backed securities and privately placed bonds. “There’s a nervousness . . . [Banks] are having to find more counterparties in order to achieve for what’s in the market and in the pipeline,” said Carlos Mendez, co-founder at Crayhill Capital.

The U.S. Dollar Index, which measures the greenback against other major currencies, logged its steepest six-month drop in more than 50 years in the first half of 2025. Though the decline hasn’t deepened, the dollar index is still about 10% lower than the start of Trump’s term.

A strong dollar makes imports cheaper and can help keep inflation in check. A weak one can increase prices on foreign goods but boost American exports.

Trump has suggested a strong dollar puts the U.S. at a disadvantage and that a weak dollar helps American industry. And as with most things with Trump, he’s been blunter in his messaging.

“You make a hell of a lot more money with a weaker dollar,” he said last year, one of a number of public statements showing his preference for seeing the dollar decline.

Trump isn’t alone in seeing benefits of a weaker buck.

In recent months, corporate earnings calls have been peppered with talk of how a weaker dollar has helped companies from Philip Morris to Coca-Cola, with executives pulling out C-suite phrases like “favorable currency impact” to note how the dip brought tailwinds outside the U.S. that added to bottom lines.

Currency values are constantly moving and, while the dollar’s recent fall is notable, it has reached lower levels at points in the presidencies of each of Trump’s predecessors, back through the creation of the Dollar Index in 1973, when Richard Nixon was at the helm.

Kenneth Rogoff, a Harvard University economist and former chief economist at the International Monetary Fund, says while “a lot of policies that Trump is doing are something of a cancer for the dollar,” he believes that it was destined to fall no matter who was in charge.

“The dollar had been on a 15-year bull run,” he said. “I would argue the dollar is still wildly overvalued, and over the next maybe five or six years, it might fall 15%.”

What does that mean for American consumers? Rogoff says commodity prices are likely to rise, particularly with the impact of the Iran war on fuel prices.

“They’re just going to go up,” he says, “no matter what the dollar’s at.”

OpenAI and Anthropic are competing for partnerships with buyout firms that would allow them to quickly roll out their AI tools to ​potentially hundreds of private, established companies owned by buyout firms. This would boost adoption of their models and encourage customer stickiness at scale.

OpenAI is ‌offering private-equity firms a guaranteed minimum return of 17.5%, significantly higher than typical preferred instruments, two people familiar said. It is also offering early access to its newest AI models as it seeks to enlist investors like TPG and Advent for its joint venture, three sources said.

The rise of artificial intelligence has become a force propelling the economy and the stock market. But it is also fueling the U.S. trade deficit, as tech companies import expensive foreign computers and chips to fill their new data centers.

Unlike cars, steel and other goods, electronics were intentionally spared by Mr. Trump. Last April, the administration issued an exemption from tariffs for smartphones, computers, semiconductors and other electronics. It was a significant break for tech companies, like Apple, Nvidia and Dell, that have lobbied the president against broad tariffs.

The tariff exemption has increased demand for imports of computers, semiconductors and other equipment. So has rapid data center construction around the United States.

The A.I. boom has helped to prop up an otherwise lackluster U.S. economy. It is also powering growth in the stock market, which Mr. Trump has long seen as a metric of his administration’s success. Over the past three years, America’s largest tech stocks — a group known as the Magnificent Seven — have been responsible for more than half of the 88 percent gain in the S&P 500.

We did the math. At $185 billion a year, in eight years, Google would be spending $1.5 trillion, slightly more than OpenAI has committed to spend over the same time period. Extend that out to 10 years, as Vahdat noted, and Google would be spending $1.9 trillion.

Vahdat is clear that this is “not a promise” that Google would spend that much over the next 10 years. But the decade-long view he takes suggests the scope of Google’s bet. “The point here is that we are, at Google, investing at the highest levels,” he says.

There’s a big difference between Google’s data center ambitions and OpenAI’s: Google is a money-making machine. In the fourth quarter, Google parent Alphabet raked in $113 billion in revenue; for the full year, sales topped $400 billion for the first time in the company’s more than 25 year history. By comparison, OpenAI is spending at similar levels and only brought in about $13 billion in revenue last year — a tiny fraction of Google’s revenue, and less than half of Google’s cash reserves.

Google’s TPUs previously were only used in house for Google’s own infrastructure — to power consumer apps like Gmail and YouTube, and eventually train self-driving cars and develop and run AI models like Gemini. Now, they’re one of the industry’s go-tos: maybe not as popular as Nvidia’s top of the line Blackwells, but still useful for pretraining and operating AI models at scale. Google first started selling access to them through a cloud service in 2018, letting other companies rent out processing power. But more recently, Google has inked high profile deals, like a big contract with Anthropic, and has reportedly been in talks with Meta to use its chips. In December, Morgan Stanley estimated that TPUs could generate $13 billion for Google by 2027. “It is fair to say that the demand for cloud TPUs has been unprecedented,” Vahdat says, particularly in the last few years.

In August, Vahdat, Google Chief Scientist Jeff Dean, and 10 other researchers and execs at the company, co-published a paper aiming to contextualize AI’s power guzzling. The paper says that the median prompt for Google’s Gemini AI model uses the same amount of energy it takes to power 9 seconds of television and consumes around five drops of water, which they write is “substantially lower than many public estimates.” (One report says large data centers can consume up to 5 million gallons per day, equivalent to the water use of a town populated by up to 50,000 people.)

Coding After Coders: Summary

The New Reality of AI-Assisted Programming

• Elite software developers now rarely write code themselves — instead, they direct AI agents in plain English
• Tools like Claude Code deploy multiple agents simultaneously: one writes, one tests, one supervises
• Tasks that once took days now take under an hour

The Strange New Workflow

• Developers spend their days describing intent to AI, reviewing the AI's "plan," then letting agents execute
• When agents misbehave, developers have resorted to scolding, pleading, ALL-CAPS commands, and emotionally charged language ("embarrassing," "national security imperative") — and it seems to work
• Prompt files have become records of hard-won rules to constrain unpredictable AI behavior

Economic Stakes

• Coding was once considered near-guaranteed, high-paying employment ($200K+)
• It may be the first expensive white-collar skill AI can fully replace — unlike AI video or legal briefs, AI-generated code that passes tests is indistinguishable in value from human-written code
• Irony noted: Silicon Valley workers, who told others to "learn to code," got automated first

Developer Sentiment: Mostly Euphoric

• Most developers interviewed were energized, not demoralized — reporting 10x to 100x productivity gains
• Key insight from tech executive Anil Dash: unlike creative fields where AI removes the soulful work and leaves drudgery, in coding AI removes the drudgery and leaves the soulful parts

Historical Context: A Long Arc of Abstraction

• Each programming era simplified the one before: Assembly → high-level languages (Python) → open-source packages → now natural language intent
• AI represents the highest abstraction layer yet: developers no longer need to manage syntax, memory, or debugging minutiae
• The open question, now being asked at Anthropic itself: what is coding, fundamentally, when the code-writing is gone?

Total effective tax rates (includes all taxes: federal+state income tax, sales tax, property tax, etc) for the richest Americans declined by 2018 to a level beneath that of the bottom 50% of earners,[18] contributing to wealth inequality. Analysis by economists Emmanuel Saez and Gabriel Zucman.

The Federal Insurance Contributions Act (FICA /ˈfaɪkə/) is a United States federal payroll (or employment) tax payable by both employees and employers to fund Social Security and Medicare[1]—federal programs that provide benefits for retirees, people with disabilities, and children of deceased workers.

Since 1990, the employee's share of the Social Security portion of the FICA tax has been 6.2% of gross compensation up to a limit that adjusts with inflation.[a][9] The taxation limit in 2020 was $137,700 of gross compensation, resulting in a maximum Social Security tax for 2020 of $8,537.40.[7] This limit, known as the Social Security Wage Base, goes up each year based on average national wages and, in general, at a faster rate than the Consumer Price Index (CPI-U). The employee's share of the Medicare portion of the tax is 1.45% of wages, with no limit on the amount of wages subject to the Medicare portion of the tax.

So personal income tax in the US is ~30% for most of us (ranging from ~10%-37%), compared to Social Security's ~6.2% Medicare is 1.45% (or 12.4% + 2.9% if you count the employer portion). AND only the first ~$137K is taxable so our maximum tax amount to Social Security and Medicare is capped, while normal income tax that politicians can direct to anything from foreign wars to immigration enforcement to redistribution to different states or interest on debt driven by tax breaks to the rich that caused deficits.

An average of 9,000 refugees were admitted monthly between January 2024 to January 2025. From February to December 2025, there were 1,226 total admissions, 1,059 of whom were from South Africa.

It's quite disappointing that these policies - especially the H1B tax, which brings the best and brightest in the world to the US - all target legal immigrants.

I love this report!

This data-driven, impartial report contains historic metrics — how you use them to advocate for the changes you want to see in the country is up to you.

Most spending was on Social Security, national defense, grants to state and local governments, Medicare, and interest on the debt. Spending and revenue were both higher than their pre-pandemic levels, and the federal government ran another deficit as spending outpaced revenue.

Why do we always lump Social Security in with other national spending? Social Security is collected separately from all other tax revenue and goes directly to the Social Security trust fund. That money cannot be put anywhere else. Politicians can't direct Social Security goes into a trust fund and politicians can't change how it's spent, unlike defense spending and other spending. In my view, Social Security should be separate. It's not the government's money to spend, it's money that is given back to the people directly. So comparing national defense, which the government can choose to change the spending levels, reallocate it to other spending priorities, Social Security cannot be because it's a trust fund.

Public schools took in and spent more funds than ever before. It also had mixed impacts on teachers and students. The number of public-school teachers has increased each year since 2020 while the number of students has decreased or stayed the same. Meanwhile, test scores have fallen.

Well we have to do something about that and be drastic about it. However, I don't see how cutting funding alone - the current Republican priority - will help.

Prominent economists, including from Morgan Stanley and JPMorgan Chase, calculate that the AI buildup was directly responsible not for 92 percent or 39 percent of gains to the U.S. economy in 2025, but as little as zero.

It’s clear that the huge spending on AI is adding to the U.S. economy, but the available economic data doesn’t neatly capture its effects. The debating economists and the slippery data suggest that if the technology does start to reshape the economy, it may be challenging to detect and clearly measure. That may leave political and corporate leaders to choose the numbers that fit their preferred narratives on how AI is changing American life and work.

That’s because the $31 trillion in yearly U.S. gross domestic product, the widest measure of the economy, tallies only the final value of products and services produced domestically. Spending on imports and foreign made components is subtracted because it boosts the economies of other countries, not that of the United States.

Roughly three-quarters of the cost of an AI data center is for the computer gear and parts such as computer chips that go inside of it, technology analysts estimate. America’s AI champions, including the computer chip pioneer Nvidia, manufacture many of their products in Asia — despite efforts by the Biden and Trump administrations to reduce U.S. dependence on essential chips made overseas.

And some forecasters say that the U.S. government’s economic data is a poor measure of the impact of AI and that alternative calculations show the current boom is an even bigger boost to economic growth.

“This is a big deal, but not the be-all and end-all,” said Joseph Politano, an economic analyst who writes the Apricitas Economics newsletter. He calculates that AI-related spending contributed about 0.2 percentage points to the 2.2 percent U.S. economic growth last year.

The AI buildup is putting real money into the pockets of some Americans and U.S. businesses. Stock market gains from AI enthusiasm are plumping up Americans’ investment portfolios.

“The two engines of today’s economy are the AI ecosystem and wealthy consumers,” Richmond Fed President Tom Barkin said in a January speech.

Prominent economists, including from Morgan Stanley and JPMorgan Chase, calculate that the AI buildup was directly responsible not for 92 percent or 39 percent of gains to the U.S. economy in 2025, but as little as zero.

Prominent economists, including from Morgan Stanley and JPMorgan Chase, calculate that the AI buildup was directly responsible not for 92 percent or 39 percent of gains to the U.S. economy in 2025, but as little as zero.

Prominent economists, including from Morgan Stanley and JPMorgan Chase, calculate that the AI buildup was directly responsible not for 92 percent or 39 percent of gains to the U.S. economy in 2025, but as little as zero.