AI Impact on Revenue, Profits Strongest in IT

A new study by Torsten Slok, partner and chief economist at Apollo Global Management, finds evidence of artificial intelligence financial benefits in the information technology industry, but not elsewhere. “Revenue per worker is rising for the Mag 7 but is flat for the S&P 493,” he says. “Profit margins are rising for the Mag 7 but are flat for the S&P 493” as well. 

source: Apollo

 

source: Apollo

That might fuel concern that AI overinvestment is occurring. On the other hand, it seems also the case that AI investment is highly concentrated in relatively few IT firms. In other words, AI actually is not being deployed at scale outside IT. 

source: Apollo

So observers can take their pick of explanations: either AI is not generally producing measurable results or AI is not yet producing results because few firms outside IT have implemented at scale. 

At a high level, it also is likely that standard statistics miss some meaningful AI-related benefits. In other words, value is created, but not in ways that show up immediately, in every firm or industry, in the form of gross domestic product and productivity growth.

Type of benefit or measurement problem	Example involving AI	What conventional statistics may miss
Free or inexpensive services	A person uses a free chatbot instead of paying for tutoring, research, or basic professional advice.	Consumer value beyond the price paid.
Quality improvement	AI helps a physician explain a diagnosis more clearly or helps a lawyer produce a better analysis.	Better output when the number of services and their recorded prices barely change.
Time savings	A worker completes research in 20 minutes instead of two hours.	The value of saved time if it doesn't translate into recorded production, earnings, or leisure-related output.
New capabilities	A small business gains access to analytical capabilities previously affordable only to large firms.	New economic opportunities that have not yet generated recorded sales.
Intangible investment	A company spends months reorganizing workflows, developing proprietary data, and training employees.	Some of the investment and the resulting organizational capital.
Innovation spillovers	AI accelerates product development, scientific research, or software creation.	Future benefits that have not yet become market transactions or measurable output.
Unpaid or household activity	A consumer uses AI to plan a trip, learn a language, or manage household tasks.	Some of the resulting value because much household production lies outside GDP.

Measurement is itself a problem. The OECD, for example,  identifies intangible investment, cloud-computing accounting, and improvements in the quality and scope of services as important measurement challenges.

Also, value can be produced in economic welfare without showing up in GDP statistics. Such has been the case for many internet-supplied “no extra cost” services used by consumers, for example.

Economists call the difference between what a consumer would be willing to pay and what the consumer actually pays consumer surplus. So a2025 paper by Erik Brynjolfsson and coauthors, GDP-B: Accounting for the Value of New and Free Goods, develops an alternative approach to capturing benefits from free and new goods that standard GDP measures poorly. 

A 2026 Stanford Digital Economy Lab study, What Is Generative AI Worth?, used surveys and incentive-compatible choice experiments to estimate the value U.S. consumers place on access to generative AI tools. Its estimated aggregate consumer surplus rose from $116 billion in 2025 to $172 billion in 2026.

That is unlikely to satisfy skeptics insistent on visible AI impact on firm results. The International Labour Organization's May 2026 research brief, The Aggregation Paradox of AI , finds that strong task-level gains have not yet translated consistently into measurable gains at the firm, sector, or macroeconomic levels. Uneven adoption, workflow constraints, and the cost of organizational change help explain the gap. 

AI might produce large benefits for consumers and workers while capturing relatively little revenue for the companies providing it, as the Apollo analysis suggests is presently the case. 

Or, productivity gains might accrue primarily to a small number of firms, with limited diffusion to the rest of the economy, as the Apollo study also suggests. 

The absence of an immediate productivity boom is not strong evidence that AI is failing, or will fail. Past historical evidence suggests an important new technology takes some time to show benefits. 

But the existence of a historical productivity lag is not strong evidence that a productivity boom is inevitable, either.

Still, a delay in measured economic or firm benefits is not at all unusual. Past innovations such as steam power, electricity,  information technology and the internet have taken some time to generate clear economic value, because:

• Complementary investments in organizational processes, software, training, and new business models must be made

• Benefits may initially be concentrated in particular tasks, firms, and industries

• Some gains, particularly in consumer welfare and intangible assets, are imperfectly captured by conventional statistics.

Financial observers will still remain skeptical. But, as a historical matter, important new general-purpose technologies have almost-always, perhaps always, experienced a lag time between introduction and impact. AI is showing the same pattern.