AI markups are not a line item in S&P 500 earnings. In economics, a markup is the price a company charges relative to the marginal cost of producing one more unit. Artificial intelligence can change that ratio by improving demand forecasts, pricing decisions, personalization, automation and product quality. It can also reduce costs or require large investments that delay profit gains.
Those effects matter for valuation because stock prices reflect both expected earnings and the multiple investors are willing to pay for those earnings. Current public evidence supports plausible AI-related pricing and productivity channels, plus firm-level associations, but it does not quantify how much of total S&P 500 earnings growth comes specifically from AI markups. Nor does AI revenue automatically mean a company has achieved a higher markup.
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What an AI markup actually measures
A markup compares a product’s price with its marginal cost—the additional cost of making or delivering one more unit. A simple markup ratio is:
Markup ratio = price ÷ marginal cost
If a digital service sells for $10 and serving one additional customer costs $2, its markup ratio is 5. In percentage terms, the price is 400% above marginal cost. The calculation concerns incremental economics, not the company’s total revenue or accounting profit.
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Markup is different from revenue
Revenue is the money collected from customers. A business can generate rapidly rising AI revenue while paying heavily for specialized chips, cloud capacity, data licensing, engineers, sales staff and support. Those expenses may leave its price-cost markup unchanged or even lower.
Markup is different from gross margin and net margin
Gross margin subtracts cost of goods sold from revenue and divides by revenue. Net profit margin subtracts a much wider set of expenses, including operating costs, interest and taxes. Both are accounting measures. A price-cost markup is an economic measure based on the marginal cost of one additional unit, so the figures are not interchangeable. A company can have a high gross margin but a modest markup if its incremental costs are understated or difficult to allocate; it can also have a high markup while reporting a low net margin because it is spending aggressively on research, sales or infrastructure.
How AI can increase or reduce a company’s markup
AI affects markups through several channels, and the direction is not automatic. The same technology may raise a firm’s pricing power in one market while intensifying competition in another.
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| Channel | Potential effect on markup | What can offset the gain |
|---|---|---|
| Demand prediction | Better forecasts can help a firm set prices closer to each customer segment’s willingness to pay, reducing unsold capacity and discounting. | Competitors may obtain similar tools, making the advantage temporary. |
| Personalized or dynamic pricing | Prices can adjust to demand, timing, inventory and customer characteristics, potentially increasing the price collected above marginal cost. | Customer backlash, regulation, privacy limits or transparent rivals can constrain price differences. |
| Lower variable costs | Automation of support, coding, logistics or content production can reduce the cost of serving another customer, increasing the price-cost ratio if prices hold. | Model fees, data costs, human review and reliability controls may keep marginal costs high. |
| Improved product quality | AI features may make a product more valuable, allowing a higher price or reducing churn. | Features can become standard, and customers may resist paying separately for them. |
| AI-enabled competition | New entrants can use common models to match incumbents, forcing prices down and compressing markups. | Proprietary data, distribution, switching costs or trust can protect an incumbent. |
| Investment and implementation | Long-run productivity may improve after systems are integrated into operations. | Training, data cleanup, hardware, software and organizational changes can depress near-term earnings. |
Demand conditions matter as much as technology. Strong demand can let firms pass productivity gains through as higher prices or retain them as wider margins. Weak demand can force discounts even when AI lowers costs.
AI pricing tools are not the same as selling AI
Two businesses may both be described as “AI beneficiaries,” but their economics differ.
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Using AI to set prices
A retailer, airline, software provider or manufacturer may use machine-learning systems to forecast demand, segment customers, recommend prices or manage inventory. The relevant question is whether the tool changes the price relative to the marginal cost of serving the customer. Evidence of a higher markup would require economic measurement, not merely disclosure that an algorithm is in use.
Selling AI infrastructure or services
A company may sell processors, cloud capacity, models, consulting or applications. Its AI-related revenue shows that customers are buying those offerings; it does not show that the seller can maintain a higher price-cost markup. Scarcity, capacity expansion and competition among suppliers determine how much of that revenue becomes profit.
These activities can overlap. A cloud provider might use AI internally while selling AI capacity, and a software company might embed a model in a subscription product. Analyze each activity’s pricing, incremental cost and investment burden separately.
What the Federal Reserve evidence says—and does not say
Firm-level association from AI-pricing adoption
A San Francisco Fed working paper examined firms whose job postings required both AI-related skills and pricing-related work. That job-posting measure is a proxy for adoption, not a direct observation of every pricing decision. The paper reports that adopting firms experienced faster growth in sales, employment, assets and markups than comparison firms.
This is an association. It does not prove that AI caused the differences. Firms that adopt early may already have stronger management, better data, faster growth prospects or greater resources. The result also does not estimate a specific contribution to S&P 500 earnings, and it does not imply that every constituent receives the same benefit.
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AI diffusion, concentration and average markups
In an August 10, 2026 working paper, San Francisco Fed authors Hamid Firooz, Sylvain Leduc and Zheng Liu model AI adoption and competition. They report a non-monotonic relationship between AI diffusion and industry concentration. Their abstract states: “In comparison, the relation of AI with the average markup depends on whether increased AI usage is driven by demand or supply factors.”
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Putting AI markups in S&P 500 context
The S&P 500 contains 500 leading companies and covers approximately 80% of available U.S. market capitalization, according to S&P Dow Jones Indices page characteristics dated August 31, 2026. It is therefore a broad benchmark, but it is not an equal-weighted survey of businesses. The largest constituents can have an outsized effect on index earnings and valuation.
A New York Stock Exchange earnings preview dated July 14, 2026, using FactSet data as of July 10, estimated a 14.2% S&P 500 net profit margin for the second quarter of 2026 and a forward 12-month price-to-earnings ratio of about 20.5. These are dated estimates, not live October 2026 readings. They also describe the index as a whole: they do not identify what portion came from AI, pricing power, cost reductions or other factors.
Why an index average can conceal different experiences
- A software or semiconductor company may earn substantial AI-related revenue while another constituent uses AI mainly to lower service costs.
- Some firms may expand margins; others may spend heavily on data centers, models, hiring and implementation.
- Large technology constituents can influence aggregate earnings even when most companies have limited AI exposure.
- Industry concentration can change as AI lowers entry barriers in some markets and raises scale advantages in others.
Consequently, an S&P 500 margin increase is not evidence that every company achieved a higher markup, and a strong index quarter cannot be translated into an AI-markup percentage without company-level measurement.
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How AI markups feed into valuation
Equity value depends on the earnings investors expect and the valuation multiple they apply to those earnings. A simplified relationship is:
Share price = expected earnings per share × P/E multiple
AI can affect both terms. If better pricing or lower costs produce durable earnings growth, expected earnings may rise. If investors believe that growth is sustainable, they may also pay a higher multiple. But a high multiple makes the share price more sensitive to disappointment: a delay in adoption, stronger competition, lower prices or higher capital spending can reduce the earnings forecast or the multiple, or both.
| Question | Evidence that supports a higher valuation | Risk to the valuation |
|---|---|---|
| Are earnings rising? | Reported sales, operating income or cash flow improve after accounting for AI costs. | Revenue rises while infrastructure, personnel or acquisition costs rise faster. |
| Are gains durable? | Customer retention, proprietary data, workflow integration or switching costs protect pricing. | Rivals can copy the feature, models become cheaper, or customers negotiate prices down. |
| Is the market large enough? | AI expands the addressable market or increases usage per customer. | Adoption remains limited, demand is cyclical, or products cannibalize existing sales. |
| Is the multiple reasonable? | Expected growth, return on invested capital and cash generation support the P/E. | The multiple assumes near-perfect execution and leaves little room for slower growth. |
A higher P/E is therefore not justified merely because a company mentions AI. Investors must connect the technology to incremental revenue, incremental cost, competitive behavior and the duration of the advantage.
A practical framework for analyzing an AI-related earnings claim
- Identify the economic activity. Determine whether the company is using AI for pricing, selling AI products, reducing internal costs, or some combination.
- Separate price from volume. Check whether sales growth comes from higher prices, more units, new customers, currency effects or acquisitions.
- Estimate incremental cost. Look for disclosed spending on compute, cloud services, data, model licensing, specialized hardware, employees and human oversight.
- Check operating leverage. Compare revenue growth with gross profit, operating income and cash-flow growth. A markup claim is weaker if costs rise proportionally or faster.
- Test competitive durability. Assess patents, proprietary data, distribution, switching costs and the ease with which rivals can offer similar AI features.
- Review capital intensity. Large data-center or hardware commitments can absorb cash for years even when accounting earnings improve.
- Use dated valuation inputs. Record the earnings estimate date, the forward P/E date and the assumptions behind them. Do not combine a historical multiple with a current earnings forecast without labeling the mismatch.
- Run downside cases. Model slower adoption, lower prices, higher inference costs, additional regulation and a lower exit multiple.
Costs and pitfalls that can erase an apparent markup
Implementation friction
AI systems need usable data, secure access, integration with existing software and employee training. Pilots can look inexpensive while production deployment requires monitoring, testing, legal review and ongoing maintenance.
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Capital expenditure and depreciation
Companies building computing capacity may incur large capital expenditures before demand is certain. Depreciation and financing costs can reduce future earnings even if the service eventually attracts customers.
Price competition
If every competitor adopts similar pricing software, the technology may become a cost of doing business rather than a source of lasting pricing power. Savings may flow to customers through lower prices instead of remaining with shareholders.
Demand and quality risk
Incorrect recommendations, biased outputs, outages or poor customer experiences can increase refunds, support costs and regulatory exposure. A theoretical ability to personalize prices does not guarantee customers will accept those prices.
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What can be concluded today
AI can plausibly increase markups by improving demand information, allowing more precise pricing and lowering incremental production or service costs. It can also compress markups by enabling entry, standardizing features or triggering price competition. The San Francisco Fed evidence shows an association between AI-pricing adoption and faster growth in several firm outcomes, while its competition model emphasizes that demand and supply forces determine the effect on average markups.
For the S&P 500, the defensible conclusion is narrower. The index’s size and concentration mean that AI may materially influence aggregate earnings, but no cited statistic establishes the share of earnings growth attributable specifically to AI markups. Investors should therefore value identifiable cash flows and competitive advantages, use dated margin and P/E data, and treat broad AI narratives as hypotheses to test rather than as proof of a higher sustainable multiple.
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