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. 

Is Sustainable Frontier Model Market Share Leadership Already Emerging?

Has market leadership in the frontier artificial intelligence model market already begun to take shape? And are calls to slow the development of the latest frontier models going to help solidify that market pattern? It seems possible. 

Consider the advantages of a slowing of frontier model releases for existing leaders. If new release pace slows to “a new model every 18 months to 24 months,” then the capital intensity of the business might well slow from the breakneck pace of “a new model every six months to 12 months.”

The caveat, of course, is that all the leading contenders need to agree to respect the new pattern. But such agreements can temper the competitive-investment dilemma where each leading firm has to respond if a major competitor ups investment, even if all the leading models are relatively comparable at each stage of development, as suggested by Stanford's Human-Centered Artificial Intelligence institute. 

Savings would develop as model developers spent less on training costs, for example, which might also drive less spending on graphics processing units and data center facilities. That, in turn, would help developers reach revenue targets more easily. 

On the other hand, such slowing could also enhance the value of existing high-performance computing assets.  If capability scaling slows, the economic life of existing infrastructure increases. In other words, older GPUs should have extended economic life and value, for example. 

Longer frontier model cycles also should mean the useful economic life of each existing model lengthens, allowing the existing models to remain near the performance frontier for a longer period, enhancing the value of each model. 

And all of that might actually help leaders remain leaders, while making it harder for upstarts to break into leadership, as costs related to governance and testing will increase. 

As seen in other industries, large compliance costs raise the costs of doing business, and tend to favor larger firms better able to bear the costs of such compliance. 

Also, if the frontier model releases slow their pace, while more money and effort has to be spent on safety and testing, there is less opportunity for an upstart to move faster and take share, assuming the upstarts have less capital to commit, compared to the model leaders. 

At the same time, the market leaders retain a lead in monetizing their products, for a longer time, at greater scale, while upstarts are disadvantaged, in that regard. 

In fact, most capital-intensive industries tend to have a small number of leaders. Perhaps the only surprise for the capital-intensive frontier model business is that we might be seeing market structure stabilization already. 

 

In addition, in part because of network effects, software industries also tend to have a small number of leaders. The rule is so common as to have a name: “The rule of three.”

“A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest,” BCG founder Bruce Henderson said in 1976. 

And such structures have profitability implications as well. “Costs characteristically decline by 20 percent to 30 percent in real terms each time accumulated experience doubles," Henderson posited in 1968. 

In most markets, argue Bain consultants, two firms have 80 percent of the profit. In other words, market share also often is a proxy for profitability. “On average, 80 percent of the economic profit pool was concentrated in the hands of just one or two players in each market,” say Bain and Company consultants. In other words, it really matters if a firm is number three in any market.

In mature, competitive markets where there are barriers to entry; economies of scale or are capital intensive, three companies tend to dominate, with the largest player holding 40-50 percent of the market, the second-largest holding 20-30 percent, and the third-largest holding 10-20 percent market share, the rule suggests. 

That virtually perfectly corresponds to a market share pattern of 4:2:1, as the number-three provider tends to have less than 10 percent share, in that pattern. Of course, real life does not always confirm to our predictions, but the theory is often quite useful. 

Industry / market	Approximate structure	Classification	Why the structure develops
Rental cars	48 / 22 / 14% historically	4:2:1	Fleet scale, purchasing, utilization, locations
Agricultural machinery	John Deere / AGCO / CNH	Rule of Three	Manufacturing scale, dealer networks, installed base
Household appliances	Whirlpool / Electrolux / GE	Rule of Three	Manufacturing scale + distribution
Credit ratings	S&P / Moody's / Fitch	Rule of Three, highly concentrated	Reputation, regulatory recognition, scale
Automobiles	Several firms with relatively comparable shares	Oligopoly	Huge fixed costs, but brands, geography and product segmentation prevent 4:2:1
Commercial aircraft	Boeing / Airbus / Embraer	Rule of Three-ish	Enormous R&D, certification and manufacturing barriers
Large commercial engines	GE Aerospace / Rolls-Royce / Pratt & Whitney	Rule of Three-ish	Massive R&D, installed base, certification and aftermarket
U.S. wireless	Verizon / AT&T / T-Mobile	Rule of Three	Spectrum scarcity + network scale + capital intensity
Desktop operating systems	Windows overwhelmingly dominant	Winner takes most	Developer ecosystem + applications + compatibility + switching costs
Search engines	Google overwhelmingly dominant, Bing distant #2	Winner takes most	Query data, relevance, advertiser ecosystem, defaults
Social networking	A few enormous platforms	Winner takes most within individual networks	Network effects are extremely powerful
Mobile operating systems	Android / iOS dominate	Two-sided winner-take-most	Developers + users + apps + hardware ecosystems
Enterprise CRM	Salesforce + Microsoft + Oracle/SAP/etc.	Oligopoly / emerging winner-take-most	Switching costs and installed applications matter, but multiple ecosystems survive
Cloud infrastructure	AWS / Azure / Google	Rule of Three-ish	Enormous capital investment + scale + enterprise relationships
GPUs for AI	Nvidia overwhelmingly ahead, AMD/others behind	Winner takes most—so far	Software ecosystem + CUDA + performance + supply scale
AI frontier models	OpenAI / Anthropic / Google currently prominent, with others competing	Hybrid	Huge compute costs favor concentration, but rapid model improvement and low switching costs favor competition
AI model APIs	Several major suppliers + open-weight models	Not yet settled	Models can be substituted relatively easily; price/performance changes rapidly
AI applications	Many thousands of potential providers	Potentially winner-takes-most at the platform level, fragmented underneath	Distribution, workflow integration and proprietary data may matter more than model ownership
AI agents	Not yet established	Potentially winner-takes-most	Persistent user context, permissions, transactions and ecosystem integration could create powerful switching costs

The “rule of three” seems to explain outcomes in capital intensive industries. But network effects might explain outcomes in software markets that are not so capital intensive, but are highly reliant on scale effects. 

Market structure	Typical share pattern	Main source of advantage	What happens to #3 and smaller firms?	Examples
Rule of Three / 4:2:1	~57% / 29% / 14%	Scale economies, experience curve, fixed costs	#3 remains viable and profitable	Rental cars, machinery, appliances, credit ratings
Oligopoly, but not 4:2:1	~35% / 30% / 20% / 15%	Scale + differentiation + regulation	Several firms remain viable	Autos, parcel delivery, airlines
Winner takes most	60–90% / 10–30% / tiny	Network effects, ecosystem, standards, switching costs, data	#2 can survive, but #3+ often become marginal	Search, desktop OS, social networks, dominant software platforms
Winner takes all	80–100% / near zero	Strong network effects + high switching costs + standardization	Alternative platforms largely disappear	Some historical OS/platform markets
Fragmented / turbulent	Many firms	Innovation, differentiation, low switching costs	Entrants continually replace incumbents	Much of software, consumer technology, early-stage AI

AI is interesting because it contains both forces simultaneously. Data center, cloud computing as a service and frontier AI models are the most capital intensive, and most likely to take a “Rule of Three” shape. 

On the other hand, graphics processing units and agentic AI might tend to take a “winner takes most” structure. Vertical applications might not fit either pattern. 

Layer	Likely structure	Economic reason
Power/data centers	Rule of Three / oligopoly	Capital intensity and physical constraints
GPU/accelerators	Winner-takes-most	Software ecosystem + performance + scale
Cloud	Rule of Three	Capital intensity + enterprise relationships
Frontier models	Three-to-five major competitors	Massive fixed costs but rapid technological change
Model APIs	Competitive oligopoly	Low switching costs and rapid price/performance improvement
AI assistants	Potential winner-takes-most	User relationships, memory, distribution
AI agents	Potentially winner-takes-most	Workflow integration, permissions, transactions
Vertical AI applications	Many winners	Industry specialization and differentiated workflows