2Q26 U.S. Earnings update: Promising young adopters

2Q26 U.S. earnings are the strongest in a while

U.S. earnings just keep getting stronger. After reaching 27% last quarter, S&P 500 2Q26 earnings per share (EPS) growth is on track to hit 38% year-over-year (y/y), bringing the estimate for the full year up to 28%. This would mark the strongest year since COVID and the third year of double-digit earnings growth since the 2000s. Profit margins, too, are at a record high.

But that strength is concentrated in two themes: artificial intelligence (AI) and oil. Just 10 companies are responsible for 77% of the earnings growth expected in 2Q26. The hyperscaler numbers are also being skewed by enormous gains from their investments in private AI companies. Excluding those, 2Q26 EPS growth would still be strong at 19%, in line with last quarter.

There are industries outside of AI seeing genuinely strong growth: aerospace & defense, luxury apparel, personal care products, and medical equipment, to name a few. However, their impact on the S&P 500 index is obscured by their lower weight and the magnitude of the AI and energy gains.

• Tech is once again behind the strength in S&P 500 earnings, but it is not coming from the hyperscalers. After adjusting for investment gains, the hyperscalers are contributing to just 8% of EPS growth this quarter, down from 21% in 2025 and 41% in 2024, as capex continues to pressure profitability. Indeed, for the first time ever, their collective cash flow is expected to turn negative next quarter. But a lot of that cash has flowed right into the semiconductor industry, which is on track to grow EPS by 134% y/y, driving 61% of the index-level growth, as shown in Exhibit 1. But narrative, not fundamentals, is driving returns. Semis earnings have come in 20% above expectations, but the industry is down 8% since the first report.
• Energy earnings growth estimates shot up from 0% before the conflict to 128% currently, thanks to the increase in oil prices. Brent rose an average of 53% y/y over the quarter, but management teams remain focused on shareholder returns and the long-term cycle rather than adding capacity to chase a short-term spike.
• Financials upside continues to be driven by AI. The boom in IPOs, equity & debt issuance, and stock gains is driving fee revenues in investment banking and asset & wealth management. Investment banking fees were up 26% y/y, driven by equity issuance, IPOs, and M&A. Elsewhere, tax refunds and elevated gas prices are boosting consumer spending and credit card revenues. According to Chase data, credit card spending rose an average of 5.3% y/y in 2Q and a still-strong 4.7% excluding gas stations.

Eventually, earnings growth will need to broaden out. The continued profitability of the semi-industry depends on the continued profitability of the hyperscalers, which depends on monetizing consumer and enterprise AI. 

Consumers are rapidly adopting AI 

Everybody loves generative AI. In less than four years since ChatGPT’s launch, 1 out of every 10 people in the world use it each week. It is contributing over a third of U.S. gross domestic product (GDP) growth and has driven the equity market up 100% over the past three years, creating USD 30 trillion in wealth.

While its impact is undeniable, AI’s value remains the subject of much debate. Everyone is using it, but not everyone is paying for it. Even those who are might not be paying for all their usage. Only 6% of users are paying for ChatGPT; the USD 20/month plan gives them access to about USD 700 worth of inference, and for just USD 200/month, you get a whopping USD 14,000 in inference.¹ So, while OpenAI’s revenue more than tripled between 2023 and 2025, paying for inference eats up more than half of it. Throw in the exorbitant cost of training new models, and management does not expect to make a dime until 2030.²

Enterprise adoption is much slower

Ultimately, companies, not consumers, will probably foot the bill. If the buck indeed stops with companies, the future of the AI trade rests heavily on their adoption.

Unfortunately, it takes a lot more than a snappy response to automate real work. Enterprise AI agents need access to companies’ proprietary data, documents, code, workflows, and operational systems. All that information needs to be migrated onto the cloud and organized into databases that models can understand. Agents need to be connected to those databases, as well as operating systems like Windows, ERP, and CRM. They also need to be provisioned with the correct data access, protected with cybersecurity and monitored constantly. In short, the real challenge in enterprise AI is not the model’s capabilities; it is the system around it. This complexity helps explain why enterprise adoption has not progressed as quickly as consumer adoption.

Even though companies are not ready to scale AI broadly, they are using it to reduce operational costs. S&P Global is targeting a 20% reduction in costs by 2027 through both AI and traditional productivity measures and is already 60% of the way there. It is also boosting productivity in areas like customer service and document processing. Importantly, a lot of these AI savings are being reinvested back into the businesses, rather than boosting margins in the short term.

Almost every company talks about the ways they are incorporating AI into products. The early results are always very promising, but it’s still too early to see tangible impacts on earnings. 

Investment implications

Much of AI’s return on investment (ROI) depends on enterprise adoption, which is still in the early stages. Enterprise adoption is less about the models and more about the data layers around them, which creates an opportunity for enterprise software companies.

Companies are realizing some cost savings through AI automation but reinvesting most of it back into their businesses.

These cost savings applications are likely low-hanging fruit in the medium term, so their benefit to margins will likely be competed away. S&P 500 margins are already at 20-year highs, and companies cannot keep accreting operating leverage forever. Some of the benefits will need to accrue to consumers.

Agentic commerce may be the first AI use case driving material revenue growth. Investing in AI is a risk, and thus a view on each company’s management team and strategy is essential. 

¹SemiAnalysis. “The Math Doesn’t Work.” June 11, 2026.
²Berber Jin and Nate Rattner. “An Inside Look at OpenAI and Anthropic’s Finances Ahead of Their IPOs.” Wall Street Journal. April 5, 2026.

The companies/securities mentioned are for illustrative purposes only. Their inclusion should not be interpreted as a recommendation to buy or sell. J.P. Morgan Asset Management may or may not hold positions on behalf of its clients in any or all of the aforementioned securities

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