Friday, October 02, 2026
Opinion | Six Charts That Show Just How Much We Need A.I. - The New York Times
(Another View Of AI ?) - Six Charts That Show Just How Much We Need A.I.
The walls are closing in on artificial intelligence: moratoriums on new data center construction, job-loss concerns and rising alarm over safety.
All valid worries. But none are sufficient to turn away from one of the greatest technological innovations in history, one with the potential to reinvigorate our slow-growth economy and make it easier to address the country’s increasingly challenging problems. For all the concern about the technology, we need A.I. to deliver.
A quick Economics 101 refresh: Growth — and the increases in incomes and living standards that come with it — is a function of just two things, a larger labor force and how much each worker produces. More workers increase the overall size of the pie. But individuals don’t get a larger share unless each worker produces more.
Greater national income, particularly if it comes from higher productivity, gives the nation more legroom to redistribute wealth fairly among citizens and reduce inequality. It also provides resources to hold down the national debt and fund public services such as health care and education.
On their own, workers generally can’t become more productive. Investment helps, such as by replacing old manufacturing equipment. Training can benefit, too. But the key driver of efficiency is technological innovation. Think robots in factories, self-checkout in a drugstore or automated lawn mowers.
Inventions like these lower the amount of labor required to perform tasks, which reduces companies’ costs and keeps a lid on prices. Workers whose time was saved can move on to other tasks, also helping the economy expand.
For thousands of years, humans suffered from little innovation and saw little change in standards of living. Then, around 1500, productivity started to grow as farming methods became modestly more modern. Beginning in the mid-19th century, the beneficent effects of the Industrial Revolution took hold thanks to extraordinary innovations such as mechanized looms (famously attacked by the Luddites) and steam power.
Later, innovations such as the telegraph, the telephone, railroads, the internal combustion engine and the commercialization of electricity produced a golden age of productivity growth that carried on into the 20th century.
In the early 1970s, productivity growth slowed and has remained depressed for most of the past half-century, with notable exceptions such as the internet boom. Ending the productivity sluggishness should be a top national priority, and using A.I. is the most promising way.
Productivity Growth Has Been Stuck Below Its Postwar Pace for 50 Years
Growth in U.S. productivity
Source: Bureau of Labor Statistics.
Examples of its power already abound. Siemens uses its A.I. co-pilot to walk technicians through repairs of factory machines, decreasing time spent on unplanned equipment maintenance by 25 percent. Kaiser Permanente has deployed an A.I. scribe to streamline note-taking, cutting documentation time for physicians by nearly 16,000 hours. John Deere has an A.I.-equipped camera system that saved farmers 31 million gallons of herbicide mix in 2025 by spraying the substance only where the system detects weeds.
More will come. History suggests that innovation’s effects on the economy can take years, even decades, to play out. The surge in productivity growth after World War II was driven in part by the continuing deployment of electricity generators and other 19th-century inventions.
Soon after Henry Ford introduced the assembly line in 1913, he famously raised the pay of his workers to $5 a day. Because of his factories’ high productivity, the company’s profits still soared, and the price of a Model T fell to just $260 in 1925 from as much as $600 in 1913.
In 1987, the economist Robert Solow, who would later win a Nobel Prize, said, “You can see the computer age everywhere but in the productivity statistics.” He spoke prematurely. Productivity rose sharply in the late 1990s, stimulating growth, adding jobs and leading the federal government under President Bill Clinton to an unusual (and never matched) string of budget surpluses.
Anticipating the unfolding prosperity of his time, John Maynard Keynes wrote in his 1930 essay, “Economic Possibilities for Our Grandchildren,” that within a century, the average worker would toil for only 15 hours a week. Keynes got that wrong, but not for the seemingly obvious reason. Productivity — and accordingly, inflation-adjusted wages — has risen so much since then that Americans could labor just seven hours a week to afford the same lifestyle they enjoyed in 1930. Instead, the average American works38.3 hours a week, down only 20 percent from 1930. But workers now have more time for leisure and family, and the hours they do spend on the job support bigger incomes and more comfortable lifestyles than their grandparents enjoyed — more goods, bigger houses, pleasure travel and the like.
Working Less, Earning More
Note: G.D.P. is inflation-adjusted. Sources: Whaples, Huberman and Minns, Bureau of Labor Statistics, Maddison Project and Bureau of Economic Analysis.
A.I. has the potential to enhance the way we live in the same way. It saved me and my colleague, Will McGrew, many hours of prowling the internet to research this essay. That left us with time to do other tasks.
Of course there are risks. A.I. will eliminate jobs in some sectors — just like telephone operators disappeared in the last century — even as the productivity gains it generates will create demand for new jobs in others. The historical record has borne this out time and time again. No technological improvement in the history of the world has ultimately failed to create more jobs than it destroyed.
When I started on Wall Street in 1982, we had only hand calculators — no personal computers, no Excel for running models, just pencils and large sheets of paper on which to scribble our calculations. Those “spreadsheets” then had to be typed by a secretarial pool. If my boss found a mistake or wanted a different scenario prepared, we started from scratch. As the technology evolved, lots of those menial tasks disappeared, and yet employment in the financial sector is vastly greater today.
The Computer Era
Change in U.S. employment by occupation, 1990 to 2025
Note: Computer occupations include developers, programmers and analysts. Sources: Census and Bureau of Labor Statistics.
To date, A.I. has killed a limited number of jobs while boosting employment for plumbers, electricians, data scientists and market research analysts. More job losses — and offsetting employment gains — are probably to come, though. The country’s leaders need to anticipate the possibility of an A.I. “rust belt” and create policies to help workers adjust. The government can provide retraining and educational subsidies to younger workers. For older ones, the nation needs to shore up the social safety net, potentially embracing ideas such as wage insurance for those displaced. And securing the solvency of Social Security and Medicare is essential.
A.I.’s Early Winners and Losers
Percentage change in U.S. employment by occupation, May 2023 to May 2025
Sources: Bureau of Labor Statistics.
The country has dealt with challenges like these before. In 1900, 41 percent of the American work force was on the farm. By 2022, that had dropped to 1.2 percent. The transition succeeded because many workers migrated to cities and the expanding manufacturing sector. Yes, the economies of agricultural states suffered, but we mitigated that with a wide array of government programs aimed at shoring up rural America.
The Farm Shock
Change in U.S. employment by industry, 1900 to 2000
Note: Service industry includes health, education, business, leisure and information. Sources: Historical Statistics of the United States and the Bureau of Labor Statistics.
The cost of these government investments should be paid by the beneficiaries of A.I. Thus far, the gains from artificial intelligence have accrued not to workers, but to investors who have reaped soaring profits from the explosive increase in the value of companies in the A.I. industry.
Workers’ Slice Shrinks, Profits’ Slice Grows
Note: Wages and corporate profits through the second quarter of 2026. Source: Bureau of Economic Analysis.
We have many ways to make that right. Some have suggested a “token tax.” (Tokens are a measure of A.I. usage.) The wildly generous tax benefits A.I. companies receive for their capital expenditures — enacted in President Trump’s first term and made permanent in his second — should be curbed, as should the sales and property tax exemptions that many states provide to data centers. And with corporate profits generally booming, Washington should edge the corporate tax rate back up toward the 35 percent that existed before Donald Trump’s presidency.
The more daunting challenge A.I. poses is to ensure that the technology is developed and used safely, a necessity that tech C.E.O.s and independent experts have spoken about with increasing urgency. On this, too, I believe humans can succeed in managing the risk.
The United States and other nations have safely regulated food, medicines, aviation, nuclear energy and many other potentially dangerous items. I remember the widespread fears that the Nuclear Age brought. I participated in air raid drills in the basement of my suburban elementary school, huddled near barrels of emergency rations. Movie thrillers such as “Fail Safe” and “On the Beach” dramatized nuclear catastrophe, and later “The China Syndrome” warned about nuclear power. Yet many nations have continued to develop atomic energy and nuclear weapons with a remarkable record of safety.
As with other powerful technologies, new A.I. products must be tested thoroughly by a governmental entity before they can be deployed. Among other things, governments should consider mandating kill switches in A.I. products, which would provide humans a final defense against models that have gone rogue. Containing the spread of A.I. technology will be more difficult than, say, limiting nuclear proliferation, because A.I. technology is easier to transfer across borders. This is why international cooperation, particularly with China, is critical for global A.I. security. At the same time, powerful models depend on physical inputs — microchips and data centers — and the U.S. lead in the A.I. build-out will therefore be an asset for keeping A.I. under control.
Those calling to stop A.I. development and those insisting that no regulation is necessary are both wrong. As with many promising technologies, the risks of A.I. will never be eliminated. But they should be manageable. And the payoff could be immense.
Thursday, October 01, 2026
Wednesday, September 30, 2026
How Meta Uses A.I. Data Centers to Avoid Billions in Federal Taxes - The New York Times
How Meta Uses A.I. Data Centers to Avoid Billions in Federal Taxes
"Meta is exploiting a lucrative tax break intended to support research and experimentation. Its own accountants say the gambit is risky.
Mark Zuckerberg says Meta’s A.I. push is a tremendous success. “Our investments in A.I. are accelerating every major part of our core business,” he has told investors. “Every sign that we’re seeing in our own work and across the industry gives us confidence in this investment.”
But when Meta files its taxes, it tells the Internal Revenue Service a different story. It claims that its A.I. data centers are a giant experiment that could fail, according to four people with knowledge of the company’s operations.
It does this so it can tap into a tax credit intended for research and experimentation. It’s an aggressive interpretation of the tax break, which Meta embraced to claim billions of dollars in tax credits for data center expansion.
Characterizing its A.I. data centers as experimental is “kind of wild and out there,” said Andre Shevchuck, a partner at the advisory firm BPM who specializes in the research and experimentation tax credit.
Indeed, Meta’s own accountants recognize that the strategy is on shaky legal ground. In disclosures buried in securities filings, the tech giant warns that billions in tax savings are vulnerable to being overturned by the I.R.S., in large part because of “uncertainties with our research tax credits.”
Here’s what Meta is doing: For tax purposes, the company classifies its enormous, multibillion-dollar data centers as “pilot models.” Under a tax credit created in the 1980s to spur innovation, companies can get a rebate for supplies, but only if they are being tested in an experimental effort, not standard business operations. Meta is claiming that the costly A.I. computer chips it buys from companies, including Nvidia, are entitled to a taxpayer-provided discount as part of the experiment.
The move caused some unease within Meta’s finance department. The I.R.S. in the past has challenged companies that claimed the credit for basic supplies. While thousands of companies, including other tech giants, get huge benefits from the research tax credit, they do so overwhelmingly for salaries paid to researchers and engineers — the people carrying out innovation.
How Meta’s research tax credits have exploded in recent years
Meta’s use of the tax break for the data centers has not been previously reported. The New York Times reviewed securities filings and conducted multiple interviews, including with the people familiar with Meta’s operations, who spoke on the condition of anonymity to discuss proprietary matters.
The company started claiming the credit for the data centers two years ago. Since then, Meta’s savings from the credit have soared, trimming almost $4 billion off its tax bill last year, filings show. Meta is now the biggest beneficiary of the tax credit among publicly traded companies, a Times review of securities filings found.
Aggressive bets like these often pan out for big companies: Even if the I.R.S. balks, companies can settle disputes and still wind up ahead.
Meta is already in one sizable dispute with the I.R.S. over this tax break, for using it to subsidize its chief executive’s multibillion-dollar pay package. In 2013, Meta claimed that $4.1 billion of stock options exercised by Mr. Zuckerberg counted as a research expense because he helped invent new software, such as Facebook’s News Feed. The I.R.S. is trying to claw back the company’s resulting $355 million in tax savings, court filings show.
The social media company’s stock is soaring, and it is now worth nearly $2 trillion thanks in part to how its A.I. efforts have improved Instagram, WhatsApp and Facebook. This month, Meta released Muse, a personal A.I. agent that immediately became the most downloaded app for iPhone and Android users. Meta and other tech giants’ A.I. efforts have also been helped by an accelerated write-off for research and development expenses that stemmed from the One Big Beautiful Bill Act, passed in 2025.
“Meta is one of the largest investors in research and development in the United States,” said Andy Stone, a company spokesman. “Over the last five years, Meta invested $200 billion in R&D — $57 billion in the last year alone, advancing frontier research, building new technology and supporting American jobs. Like other companies that invest at this scale, we use the tax incentives Congress established decades ago to encourage this type of domestic investment.”
A Very Favorable Tax Credit
The tax break dates to the first year of the Reagan administration, when Japan was a global leader in technological innovation. Business lobbyists and legislators were worried that America’s fledgling tech sector would fall behind, so Congress created the Research & Experimentation Tax Credit as an incentive to take risks and invest in inventions that might not pan out commercially. A few years after the credit was created, a government report said it had been used to develop, among other things, electronic banking equipment and drugs to treat cancer.
Tax rules already permitted tech companies to write off research expenses from their taxable income. The new credit was even more generous and could be taken on top of the deduction. But it was harder to qualify for. Companies have to meet a complex four-part test to prove they are running an experiment, not just rolling out a new product.
In the summer of 2024, Meta was ramping up its efforts to compete in the Silicon Valley A.I. race, breaking ground on tens of thousands of acres of data centers across the country. This was an expensive endeavor. One of the biggest expenses of any A.I. data center was computer chips, which are made by companies like Nvidia and cost thousands of dollars each. A Meta employee overseeing the build-out had a creative idea to offset the costs: Tap the credit.
The credit is meant to spur innovation by encouraging companies to tackle unsolved problems and technical challenges. While Meta is testing different physical layouts for server racks and looking for the best way to network thousands of chips for A.I. training, the chips themselves are known to work. They have been at the center of A.I.’s progress for the last decade and turned Nvidia into the world’s most valuable company.
Some in Meta’s finance department questioned whether this tactic would pass muster with the I.R.S., according to a person familiar with the matter. The I.R.S. has rejectedother companies’ efforts to claim the credit for “proven and commercially available equipment and technology.”
The company sought advice from lawyers at multiple firms, who pointed to a relevant case from 2021, in which a federal judge denied the research tax break to an Indiana shipbuilder for the cost of building new types of vessels. Simply creating a new product wasn’t enough; a company must pinpoint the specific components of a project that were technically uncertain and prove it used scientific experiments to overcome that uncertainty.
One of the lawyers Meta consulted was Jeffrey Moeller at Ivins, Phillips and Barker, people familiar with the conversations said. Mr. Moeller represented the pharmaceutical maker Bayer in a $200 million dispute with the I.R.S. over research tax credits. In an interview, he would not comment specifically on Meta. But he did say the rules could permit claiming the credit on commercially available, proven products — if they were supplies required to resolve the uncertainty of a project.
Another lawyer consulted by Meta, those people said, was Alex Sadler, a former Department of Justice tax lawyer and now a partner at Morgan Lewis, which declined a request to interview him. But when he spoke at a tax conference in Virginia this month, Mr. Sadler said that pilot models were an “area of controversy” and that the I.R.S. “doesn’t like” when companies characterize commercial production as research to claim the credit. The I.R.S. takes issue with the use of the research tax credit for “big things,” he said.
“What if we have a $10 billion data center that does cool stuff that hasn’t really been done?” he said. “Is all the cost a research expenditure?”
After a few months of deliberation, Meta took the plunge. It started labeling chips bound for A.I. data centers differently for tax purposes from those sent to standard data centers, two people with knowledge of the matter said.
Risky Business
The strategy has been lucrative. Meta said the research tax credit shaved $2 billion off its taxes in 2024, and then $3.9 billion in 2025.
That is a significant increase from the $700 million the company reported in 2023, the year before it embarked on its data center strategy.
At the very top of the company, Meta executives kept the tax strategy close to the vest, two people with knowledge of the discussions said.
But because the I.R.S. was likely to challenge this new and untested accounting magic, the company had to acknowledge the risk in a securities disclosure called “unrecognized tax benefits.” That is essentially the gap between what Meta paid to the I.R.S. and how much it might owe if tax authorities challenge its maneuvers. It’s a warning to investors that Meta made a bet, and the amount of the gap reflects the odds of losing, as determined by a company’s tax advisers.
Since Meta began its research credit A.I. strategy, the amount set aside to cover those tax bets increased 45 percent — to $18.74 billion today from $12.9 billion two years ago. The contributing factor listed first in its annual financial filing is “uncertainties with our research tax credits.”
Other major tech companies, including Apple, Amazon, Alphabet and Microsoft, also report research tax credits of more than $1 billion a year. But none of them have flagged the research tax credit as a risk in their financial reports to investors or disclosed whether they have used it for A.I. data centers.
“Meta is claiming billions of dollars in tax benefits that its own accountants are telling investors are at risk of being overturned by the I.R.S.,” said Lisa De Simone, a former tax adviser at EY who teaches accounting at the University of Texas business school.
Mr. Stone, the Meta spokesman, said that “unrecognized tax benefits are simply a mandated accounting measure of uncertainty.” He called them a “snapshot in time reflecting the status of unresolved issues and reflect many different types of uncertainties.”
Another of Meta’s unresolved issues concerns one of the biggest U.S. Tax Court disputes in the country’s history: The I.R.S. is seeking nearly $16 billion in taxes and penalties on profits it says the company shifted to the Cayman Islands from the United States.
Tax credit experts said Meta was again entering choppy waters by taking this huge tax break on data centers. Shawn Marchant, who runs the credit and incentives practice at Tanner and advised on the research incentive for more than a decade at EY, said he would be “skeptical” of claiming it for all the computer chips in all the data centers. Mr. Shevchuck, the tax adviser at BPM, proposed one way it might work: “If you had a data center that you’re building out to cure cancer.”
Meta declined to answer questions about what made its A.I. data centers experimental, and why tens of billions of dollars of chips and computing equipment qualified for the research tax credit.
Meta’s auditor, EY, had to sign off on the plan. The global tax and accounting firm was among the firms that Meta consulted on using the tax credit in the first place. EY has since pitched other companies on using the research credit to buy computer chips for A.I. training.
Innovation or Creative Accounting?
James Shannon, a former U.S. representative from Massachusetts who sponsored the research tax credit in 1981, said it had been intended to support “people power, knowledge, information,” and not “making things.” He was surprised to hear that a technology company would use the credit for supplying A.I. data centers.
“This has gone way, way beyond what anybody could have imagined,” Mr. Shannon said.
Whether the tax break inspires the innovation that he and other lawmakers sought is a matter of debate. Some companies appear to use the credit for spending they would do anyway, according to a study last year by economists at the University of Southern California. If companies “simply reclassify existing spending as R&D,” the researchers wrote, they are getting the tax breaks “without meaningfully financing innovation.”
The credit has become the second-most expensive federal corporate tax break, behind only the reduced rate applied to offshore profits. In its most recent estimate, the congressional Joint Committee on Taxation projected the credit would cost the government $32.1 billion in 2025. Meta alone would be responsible for more than a tenth of that.
Dylan Freedman and Kitty Bennett contributed research.
Kashmir Hill writes about technology and how it is changing people’s everyday lives with a particular focus on privacy. She has been covering technology for more than a decade.
Jesse Drucker is an investigative reporter for the Business section and has written extensively on the world of high end tax avoidance.
Eli Tan covers the technology industry for The Times from San Francisco.
Mike Isaac is The Times’s Silicon Valley correspondent, based in San Francisco. He covers the world’s most consequential tech companies, and how they shape culture both online and offline."
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