Some data suggest artificial
intelligence is already causing job losses. Other sources show the opposite.
Why is it so hard to figure out what’s going on?
·
Artificial
Intelligence (AI) is expected to reshape the economy, but its
current economic impact remains unclear and difficult to measure.
·
Studies
offer conflicting evidence on
AI's effects, with some indicating job losses, especially among new graduates,
while others show increased hiring by AI-adopting firms.
·
Economists
also disagree on whether AI is boosting productivity, influencing inflation, or
significantly changing employment patterns.
·
The
rapid spread of generative AI since 2022 has
outpaced the ability of traditional economic statistics to capture its effects.
·
Experts
warn that accurate measurement is essential for
effective policymaking, as delayed
understanding could lead to poor policy decisions.
·
Nathan
Goldschlag of
the Economic Innovation Group has called for greater investment in measuring
AI's economic impact.
·
A bipartisan
bill in the U.S. Senate proposes expanding
AI-related data collection and publishing an annual report on AI's impact on
the labour market.
·
Since
2023, the U.S. Census Bureau has
surveyed businesses on AI adoption, but data remain limited and inconsistent.
·
Researchers
use various AI exposure indices to
identify occupations likely to be affected, but different methodologies often
produce contradictory conclusions.
·
Existing
government labour statistics were designed before the AI era and lack detailed
data on technology occupations and AI-related job changes.
·
The Yale
Budget Lab has introduced a monthly
"occupational churn" indicator to monitor changes in hiring patterns
as an early signal of AI's labour market effects.
·
Budget
constraints and declining survey response rates are weakening the U.S.
statistical system, limiting its ability to track AI-driven changes.
·
Private-sector
datasets are increasingly supplementing government statistics:
o Stanford University's Digital Economy Lab,
using ADP payroll data, found sharp declines in entry-level jobs in AI-exposed
sectors since ChatGPT's launch.
o Ramp and Revelio
Labs found that firms investing heavily in AI
are adding jobs faster than slower adopters.
·
Researchers
note that effective AI adoption requires sustained
investment, and simply subscribing to AI tools does
not automatically improve productivity.
·
Private
datasets, while more timely, may not fully represent
the broader economy because they often cover specific types of businesses.
·
Most
economists agree that AI's overall impact on the broader
economy remains limited so far, as
many firms are still experimenting with the technology.
·
Experts
describe AI adoption as following a J-shaped
productivity curve, with initial productivity declines
followed by gains after organizations adapt.
·
Measuring
AI's true impact is complicated by other major economic factors, including the COVID-19
pandemic, inflation, high interest rates, immigration policy, and trade changes.
·
While
future data may better isolate AI's effects, economists caution that data
alone cannot reliably predict AI's long-term impact on jobs and the economy
over the next decade.
[ABS News Service/02.07.2026]
Pretty
much everyone agrees that artificial intelligence has the potential to reshape the
economy in the coming decades. But no one is sure what effect the technology is
having right now.
According
to some measures, A.I. is contributing to high unemployment rates among new
graduates and might already have destroyed tens of thousands of jobs. Other sources
suggest companies might actually be adding workers as a result of the technology.
A.I.
might be contributing to the U.S. inflation problem, or part of the solution to
it. It might be responsible for a recent pickup in productivity growth, or might
be playing virtually no role — or the productivity boom itself might be a mirage.
Researchers
can’t even agree on basic questions like how many companies are using A.I. or which
workers are most vulnerable to the disruptions it could cause.
The
conflicting signals partly reflect the challenge of detecting economic shifts in
real time. Government statistics are inherently backward looking, and they are better
at measuring broad trends than developments in specific sectors or regions. New
technologies that might lead to the emergence of new products, jobs or entire industries
can be particularly difficult to measure.
What
makes A.I. different is the speed of its spread through the economy. It has taken
less than fours years for generative A.I. to go from a
novelty useful mostly for writing limericks to a powerful tool adopted by the world’s
largest corporations. Economists have become convinced that the technology will
have profound implications for workers and the economy, even as they disagree about
what those implications will be. By the time the data is clear, they warn, it could
be too late for policymakers to figure out how to respond.
“The
stakes are super high,” said Nathan Goldschlag, director of research at the Economic
Innovation Group, a think tank. “Getting the policy right is going to depend on
getting the measurement right. You can’t get the policy right if you don’t know
what’s happening.”
Mr.
Goldschlag on Thursday published a report documenting
the challenge of A.I. measurement and proposing steps to improve it. He and other
experts argue that the government and the private sector should be devoting more
resources to the problem.
They
are getting at least a hearing in Washington. In June, a bipartisan group of senators
introduced a bill that would expand data collection and require the federal government
to produce an annual report on A.I.’s effect on the labor
force.
“The government’s got to make some big decisions
about A.I. and about the economy, and if you’re doing that in a vacuum, you’re going
to make mistakes,” said Senator Mark Kelly, an Arizona Democrat and one of the bill’s
sponsors. “This affects millions of Americans’ lives and millions of businesses.
And you can’t do this smartly without reliable data.”
Mixed
Signals
Policymakers
aren’t flying completely blind. Since 2023, the Census Bureau has asked companies
about their A.I. use in a biweekly survey. It has also included questions about
the technology in an annual business survey, although only intermittently.
Researchers
have developed several measures of “A.I. exposure,” many of which use a government
database of job descriptions to assess which occupations will be most affected.
Economists can use those measures to figure out whether the most exposed occupations
are adding jobs more slowly, for example, or experiencing different rates of wage
growth.
The
trouble is that the sources often tell confusing or contradictory stories. Surveys
reach wildly different estimates of companies’ A.I. use based on how questions are
asked. A.I. exposure measures tell different stories about which jobs will be most
affected. In one study, economists at Northwestern University and American University
found that when they used different exposure measures, those could influence not
just the scale of A.I.’s effect on jobs but the direction. A.I. was hurting employment
according to some measures, and helping according to others.
“It’s
like going to the doctor and getting three different diagnoses for the same condition,”
said Michelle Yin, a Northwestern University economist who was one of the study’s
authors.
Part
of the problem is that the best-known measures of the economy were developed for
an era before personal computers and the internet, let alone A.I. The monthly jobs
report from the Bureau of Labor Statistics, for example, provides breakdowns of
job growth in manufacturing, retail and construction, but not in technology, which
has adopted A.I. tools most aggressively. Instead, tech is spread across several
categories, including information, a broad sector that also includes newspapers
and film studios.
The
jobs report provides even less information on occupations that might be vulnerable
to displacement, such as software developers, accountants and customer service agents.
The most recent breakdown of detailed occupations is from May 2025, an eternity
ago in the fast-evolving world of A.I.
Still,
economists say that for all its shortcomings, government data will be crucial for
understanding A.I.’s effect over time. Researchers at the Yale Budget Lab, for example,
have begun publishing a monthly analysis based on government data that tracks “occupational
churn,” how quickly the types of jobs that make up a given industry are changing.
The measure is designed to be something of an early warning system for A.I.’s effects.
As companies begin adopting the technology, the researchers theorize, they are likely
to begin hiring for different roles even if their total number of employees doesn’t
change right away.
“It’s
easy to pick up case studies in retrospect,” said Martha Gimbel, executive director
of the lab. “What makes this time different is we are actually trying to measure
this and figure this out in real time.”
But
those efforts could be hampered by a federal statistical system that has been plagued
by falling response rates to government surveys. Shrinking budgets have made it
hard for statistical agencies to fill the gaps. Erika McEntarfer, who led the Bureau
of Labor Statistics until President Trump fired her last year, said an additional
$10 million a year in funding would allow the agency to expand the sample size of
its monthly labor market survey so that it could do a
better job of capturing economic shifts.
“The
data we’re currently using to understand A.I.’s impact on the labor market is in jeopardy because of funding shortfalls,”
she said. “It would take only some very modest investments to shore them up.”
Private Data
Many
economists aren’t waiting for the government to catch up. Several research teams
have released A.I. measures based on private-sector data that is more detailed and
more timely, albeit less comprehensive, than what is available
from the government.
The
Stanford University Digital Economy Lab last month released a dashboard of A.I.
indicators based partly on data from ADP, the payroll processor. That data shows
that entry-level jobs have declined sharply in the most A.I.-exposed sectors since
ChatGPT debuted in 2022. Erik Brynjolfsson, the lab’s director, called the trend
a canary in the coal mine for A.I.-driven job losses.
“I
think it’s comparable to the Industrial Revolution in terms of how it’s going to
affect the labor market,” Mr. Brynjolfsson said. “I wish
the federal government was investing more in it. But meanwhile, there’s some great
private data sources that we’re pulling together, and that’s what I think is helping
to fill that gap.”
But
the private data is just as muddy as the government statistics. Research published
this week by Ramp, an expense management company, and Revelio
Labs, a labor market data firm, found that the companies
using A.I. most intensely were adding jobs more quickly than those that had been
slower to adopt the tools.
Ramp
has access to data on which A.I. tools its customers are buying and how much they
are spending on them. That allows it to distinguish heavy users from more cautious
adopters — a crucial distinction, because it takes time and investment for companies
to figure out how to use the tools effectively, said Ara Kharazian, lead economist
at Ramp.
“It’s
difficult to measure A.I.’s impact on a business, because it requires sustained
adoption,” he said. “It’s clear in our work that a simple chat subscription does
not drive productivity for a firm.”
Such
data isn’t necessarily representative of the entire economy, however. ADP’s clients
tend to be relatively large and well established. Ramp’s clients tend to be tech
savvy. But if A.I. is going to have the effects that its biggest boosters promise,
it will need to be adopted by companies of all shapes and sizes.
Work in Progress
Researchers
generally agree on one thing: A.I.’s effect on the broad economy has been limited
so far.
That
isn’t necessarily surprising. Mr. Brynjolfsson and other economists have found that
technological innovations often follow a J-shaped pattern, in which companies initially
become less productive as they experiment with new tools, then experience rapid
gains once they figure out how to take advantage of them.
The
confusing economic evidence suggests that many companies are still on the downward
part of the J.
“The
signals are mixed because, probably, the underlying economics are mixed, because
we’re still in a period of experimentation,” said Mr. Goldschlag,
the Economic Innovation Group economist. “The tools themselves are still becoming
useful.”
If
white-collar jobs really do begin disappearing en masse,
as some in Silicon Valley predict, it won’t take long for the losses to show up
in the government’s data. But even then, it may not be obvious that A.I. is to blame.
The
U.S. economy has undergone a series of shocks in recent years that have nothing
to do with A.I.: the Covid-19 pandemic and its ripple effects, including the return-to-office
battles that persist to the present day; inflation and the high interest rates that
the Federal Reserve has adopted to fight it; and drastic swings in government policy
on immigration, trade and other areas. If a company has cut jobs since 2022, it
isn’t easy to tell whether that is the result of A.I., high interest rates or both.
Over
time, it should become easier for researchers to separate the effects of A.I. from
other forces. But they still won’t be able to resolve the question that policymakers
and everyday citizens want most to answer: What comes next?
“What
the data can almost never tell us is where we’re going to be in five to 10 years,”
said Ms. McEntarfer, the former commissioner of labor
statistics. “People are looking to data to answer that question, and it’s just too
difficult.”