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. 

How Unethical are Dynamic or Personalized Pricing Practices?

Dynamic and personalized pricing of retail consumer goods raises some ethical questions, but arguably mostly amplifies the already-existing issues. 

Neither practice is new. Consumers encounter variable prices in airline fares, hotel rooms, ride-hailing, event tickets, gasoline, online promotions; in  loyalty programs, coupons, clearance pricing, and targeted offers. 

What AI changes is the speed, granularity, and data intensity of pricing decisions.

So dynamic pricing is a scaled-up version of familiar supply-and-demand pricing. Airlines raise fares as seats fill. A  retailer may discount excess inventory. 

Likewise, personalized pricing extends familiar couponing and loyalty discounts into potentially individualized prices. The issue is whether this is “fair.” And it is fair to note that personalized pricing raises the most issues. 

Supporters argue that AI pricing can improve matching between prices, demand, and consumer circumstances, such as:

• Lower prices for price-sensitive shoppers (personalized pricing)

• Better inventory and availability (less waste)

• More relevant offers

• Convenience and responsiveness, potentially giving consumers better deals during off-peak periods or when demand is low.

Critics of personalized pricing Critics focus on opacity, exploitation and the possibility that AI turns ordinary shopping data into a tool for extracting more from each customer. Issues include:

• Loss of price transparency: A listed price may no longer mean “the price everyone sees,” but rather an algorithmic estimate of what a particular shopper will accept.

• Privacy and surveillance

• Discrimination and proxy harms, such as systematically higher prices for vulnerable groups or neighborhoods.

• Exploiting urgency or hardship (higher hotel rates when the algorithm predicts funeral or other non-discretionary travel)

• Harder comparison shopping

• Algorithmic coordination risk (across the board price increases by all models without any explicit human agreement, raising competition-policy issues. 

Both sets of arguments are logical. There could be benefits or costs for consumers, depending on how the pricing algorithms are used. 

Practice	More defensible if…	More troubling if…
Dynamic pricing	Rules are understandable, prices reflect real scarcity or cost, and changes are not designed to exploit panic	Prices surge opportunistically during emergencies or change unpredictably to pressure quick purchases
Personalized pricing	It delivers clear, disclosed discounts and consumers can opt out or understand the basis	It silently raises an individual’s price using inferred willingness to pay or sensitive proxies

The important question is not simply whether prices differ. It is who benefits, who loses, and whether the practice expands access or extracts surplus.

AI makes it increasingly feasible for a business to estimate the maximum price each individual customer is willing or able to pay, and then charge accordingly. 

That can be economically efficient in some circumstances, but it raises serious ethical questions about fairness, privacy and the exercise of market power. 

The central issue is that personalized pricing can shift value from consumers to sellers without necessarily creating any additional value for society.

The challenge is that price discrimination is not always exploitation. In some industries, such as passenger airline travel, prices are set dynamically, based on demand. 

The important question is not simply whether prices differ. It is who benefits, who loses, and whether the practice expands access or extracts surplus.

Pricing approach	Example	Ethical assessment
Cost-based differentiation	Different delivery fees based on distance	Generally defensible when costs genuinely differ
Volume discounts	Lower unit prices for larger purchases	Often defensible, although they may disadvantage low-income customers who cannot buy in bulk
Group-based discounts	Student, senior, or low-income fares	Can improve access and equity
Demand-based pricing	Higher hotel rates during a major event	Often defensible if transparent and not exploitative
Individualized discounts	AI identifies a shopper likely to abandon a cart and offers a discount	Can benefit consumers, although it may disadvantage loyal customers who do not receive the offer
Individualized price increases	AI predicts a shopper will pay more and raises their price	Ethically questionable, especially without disclosure or meaningful alternatives
Vulnerability-based pricing	A model predicts desperation or urgency and charges a premium	Very ethically difficult to justify
Discriminatory pricing	An algorithm systematically charges a protected group more without a legitimate basis	Potentially unlawful as well as unethical

Consider a product that “costs” $40. Assume a price-sensitive customer is willing to pay as much as $55, but is offered the product for $50. The seller makes $10 on that transaction. 

Another customer (not price sensitive) is offered the product at $90, and is willing to pay up to $100. The seller's gross margin for that sale is $50. 

So personalized pricing nets the seller $60 in gross profit. 

Alternatively, assume the seller uses an advertised rate of $90. The first customer would not buy, while the second does. The seller makes $40 in gross profit. 

If the seller charged everyone $50, both would buy, but the seller makes $20 in gross profit.

Personalized pricing might permit both customers to buy at different prices, potentially increasing total output and seller revenue. But a seller capable of extracting nearly every customer's maximum willingness to pay can capture much of the surplus itself.

The point is that  greater economic efficiency does not automatically imply greater fairness, or benefit for all consumers.

Potential consumers might be harmed in other ways. Common market prices set by each seller enable comparison shopping by potential consumers. 

If every customer receives a different price, several things become harder:

• Comparison shopping itself

• Collective bargaining by consumers

• Market transparency

• Competition could suffer, as competitors may have less incentive to lower prices broadly

• Loss of trust if consumers see such practices as efforts to exploit their personal circumstances.

The paradox is that AI can make prices more individually efficient while making markets less transparent and potentially less competitive. 

Safeguard	What it would accomplish
Price transparency	Disclose when individualized pricing is being used, especially for material price differences
Limits on sensitive data	Restrict using health, financial distress, precise location, or other sensitive information to set prices
Anti-discrimination testing	Test for unjustified price disparities across protected and vulnerable groups
Essential-service protections	Restrict exploitation of emergencies or captive customers purchasing necessities
Meaningful competition	Make switching providers and comparing prices easier
Consumer recourse	Allow customers to challenge erroneous data or suspicious price differences
Auditable algorithms	Maintain records that permit regulators to examine pricing practices and their effects
Stable public prices	Require common, accessible baseline prices in selected markets while allowing clearly defined discounts

But transparency alone is not a sufficient protection.  A consumer who needs emergency transportation, for example, may have no practical choice even if the price calculation is fully disclosed.

Likewise, banning the use of explicit sensitive attributes may not prevent discrimination if an algorithm reconstructs those attributes from other information.

Perhaps the strongest ethical objection is not that AI enables price discrimination but the fact that AI enables one-sided, largely invisible exploitation of information asymmetry.

The seller knows more about the buyer's circumstances and limits than the buyer knows about the seller's pricing strategy.

Using AI to find customers who would benefit from a lower price is easier to justify than using it to identify customers who can be made to pay more. The former arguably expands access. The latter only increases profit margins for the seller. 

SpaceXSI, AT&T, Verizon, T-Mobile and Beauty Contests

AT&T, Verizon and T-Mobile have been under pressure since SpaceXSI suggested it would be competing in the mobile phone business, and has followed up by acquiring 800-MHz low-band spectrum ideally suited to inside-building connectivity, an important consideration since satellite-to-phone only works when there is line-of-sight. 

According to Elon Musk, the purchase is "the last critical piece of the spectrum puzzle needed for SpaceX to provide complete phone coverage in America".

On October 9, 2026, T-Mobile US shares plunged 13 percent. the company's worst single-day drop since 2013.

AT&T shares fell approximately almost 10 percent, the worst single-day drop since 2000. Meanwhile, Verizon Communications declined about nine percent,  Verizon's worst single-day loss since July 2002.

Skeptics will argue the impact is out of proportion to the nature of the threat, as SpaceXSI still has to acquire or build a terrestrial transmission infrastructure. 

The impact on SpaceXSI was the opposite. Based on SpaceXSI's current market capitalization of $2.14 trillion, 3.5 percent of its market value is approximately $74.94 billion. So it might be argued that the 3.7-percent increase in market valuation on Oct. 9, 2026, which was about $75 billion, reflects the $8 billion investment in spectrum. 

In other words, SpaceXSI valuation increased about 10 times more than the cost of spectrum acquisition. 

On Oct. 9, SpaceXSI gained $27 billion, even using the low figure of 1.24 percent increase for the day.

The mobile service providers lost about $50 billion. 

You might argue that is an example of the “Keynesian beauty contest” at work. The Keynesian beauty contest is an economic and behavioral finance concept introduced by John Maynard Keynes in 1936 to explain how speculative markets and asset prices fluctuate.

The basic idea is that investors buy some stocks not because of strong company fundamentals, but because they believe others will buy them and drive the price higher.

The analogy is a beauty contest in which judges are rewarded for selecting the most popular choices among all judges, rather than those they may personally find the most attractive. 

As it pertains to SpaceXSI, at least some investors arguably price the company not on its current cash flows or fundamentals, but on what they believe other market participants will pay. 

It is, in other words, a narrative-driven market. We saw this during the dot-com bubble. But it has happened before then, as well. We call these periods “manias” or “bubbles.” 

I’m not arguing we are in a similar bubble at the moment. Maybe we are, maybe we will be, but it is not certain we are at the moment. 

Instead, we do see a beauty contest logic at work, where valuation is less on fundamentals and more on expectations. It might be unfair, but SpaceXSI currently benefits from the beauty contest, while the big three mobile service providers suffer.

Era and Mania	The "Prettiest Face" (The Hype Asset)	The Real Underlying Catalyst	The Beauty Contest Driver	The Collapse Trigger
1636–1637 Tulip Mania	Rare, variegated tulip bulbs (e.g., Semper Augustus).	The introduction of exotic flowers to the Dutch merchant class and early futures contract innovations.	Buyers knew a single bulb couldn't generate income. They bought purely because the broader Dutch public was highly coordinated in bidding up bulb futures, ensuring a "greater fool" would buy it next.	In February 1637, buyers at a routine auction in Haarlem simply failed to show up, breaking the psychological chain of assumed demand.
1720 South Sea Bubble	Shares of the South Sea Company.	A British government debt-for-equity swap scheme wrapped in a theoretical monopoly over South American trade routes.	Investors recognized that actual trade was non-existent. However, because the King and high-ranking politicians were buying and promoting it, citizens bought shares assuming the royal endorsement would force the public to keep bidding it up.	The company's own insiders realized the stock was dramatically overvalued and quietly began liquidating their positions, sparking a mass panic.
1840s Railway Mania	Shares of newly proposed railway companies.	The industrial revolution and the British government relaxing rules on corporate formation, combined with cheap credit.	While railways were revolutionary, the market funded thousands of overlapping, impossible routes. Investors didn't look at track viability; they chased the "railway" tag because newspaper columns constantly validated it.	The Bank of England raised interest rates to combat inflation, draining the cheap credit that speculators relied on to buy shares on leverage.
1920s The Roaring Twenties	Industrial conglomerates, radio technology stocks, and investment trusts.	Widespread adoption of electricity, automobiles, mass media, and the creation of retail margin accounts.	Speculators freely admitted stocks were expensive. They bought because loose margin requirements (borrowing up to 90% of the stock price) meant a massive wave of new retail money was constantly entering the market.	Corporate earnings began cooling in mid-1929, leading to minor margin calls that snowballed into the catastrophic Black Thursday crash of October 1929.
1997–2001 Dot-com Bubble	Any company with a ".com" suffix or internet business plan.	The commercial birth of the World Wide Web and massive telecom network buildouts.	Traditional valuation metrics (P/E ratios) were abandoned. Investors bought companies with zero revenue because they knew institutional mutual funds were suffering from intense FOMO and felt forced to chase internet listings.	Major tech backbones (like Cisco and Intel) reported spending slowdowns, revealing that startup capital was burning out, which ended the assumption of infinite demand.
2024–Present AI Infrastructure Boom	Hardware providers (GPUs), liquid cooling firms, and next-generation data center networks.	The emergence of highly scalable Large Language Models (LLMs) requiring massive parallel computing power.	High-frequency trading algorithms and retail momentum traders target companies based on the size of their "AI capital expenditures." Traders buy not for the immediate dividend yield, but because tech giants are locked in an arms race and must keep buying hardware.	Highly variable; heavily tied to micro-catalysts, structural demand shifts (such as ultra-efficient compute open-source models), or data center budget fatigue.