How much did S&P 500 earnings grow in Q1 2026?

The S&P 500 reported approximately 28% year-over-year earnings growth in Q1 2026, with 84% of companies beating EPS estimates — the highest beat rate since Q2 2021. Net profit margins reached a record 13.4%, driven by operating leverage in AI-exposed mega-cap stocks.

What is hyperscaler capex and why does it matter for stocks?

Hyperscaler capex refers to capital expenditures by Amazon (AWS), Microsoft (Azure), Google (GCP), and Meta on cloud and AI infrastructure. In 2026, this spending reached $725 billion — a 77% year-over-year increase. This spending flows directly into semiconductor revenue (Nvidia, SK Hynix, TSMC) and supports earnings growth across the AI supply chain.

What is Goldman Sachs's S&P 500 year-end target for 2026?

Goldman Sachs projects the S&P 500 to reach approximately 7,600 by year-end 2026, representing roughly 6% gains from May levels near 7,400, backed by 12% EPS growth forecasts. The firm also warns that 45% market cap concentration in AI mega-caps increases volatility risk.

Is the S&P 500 overvalued based on current earnings?

The forward 12-month P/E ratio for the S&P 500 stood at 20.9 in late April 2026, above both the five-year average of 19.9 and the ten-year average of 18.9. This suggests modest overvaluation relative to historical norms, though the elevated earnings growth rate provides some justification for premium multiples.

What percentage of S&P 500 market cap is concentrated in AI stocks?

As of April 2026, 45% of S&P 500 market cap is concentrated in AI-exposed mega-cap stocks. Goldman Sachs has flagged this concentration as making the AI-fueled rally increasingly a 'one big trade,' raising the risk of correlated drawdowns if AI spending growth disappoints.

S&P 500's AI Earnings Boom: Q1 2026 Results Show 28% Growth and $725B Capex Wave

The Q1 2026 earnings season delivered numbers that would have seemed implausible two years ago. The S&P 500 posted 28% year-over-year earnings growth. Eighty-four percent of companies beat EPS estimates — the highest rate since Q2 2021. Net profit margins hit a record 13.4%. Behind all of it is a single structural driver: AI capital expenditures are flowing through the economy at unprecedented scale. Here's what the data shows and what it means for investors navigating the second half of 2026.

Q1 2026 Earnings: The Numbers

The headline figures from Q1 2026 reporting are striking on multiple dimensions.

Year-over-year earnings growth came in at approximately 28%, significantly ahead of the 15–18% analysts had projected entering the quarter. MUFG Americas research tracking the season noted that Q1 2026 S&P 500 earnings growth is currently expected to exceed 27%, with 88% of companies reporting actual EPS above estimates and the average beat clocking in at 10.8% above consensus estimates.

Net profit margins reached 13.4%, a record high. This reflects operating leverage across the AI-exposed technology sector: the incremental revenue from AI subscriptions, cloud compute, and software licensing is being generated at high margins, since the underlying infrastructure was largely already in place.

The 84% beat rate — highest since Q2 2021 — suggests that analyst estimates entering the quarter were still calibrated to a pre-AI-capex world. The market has been consistently underestimating the earnings impact of AI infrastructure buildout for four consecutive quarters.

The $725 Billion Capex Wave

The structural driver of the earnings boom is hyperscaler capital expenditure. In 2026, Amazon Web Services, Microsoft Azure, Google Cloud Platform, and Meta collectively are spending $725 billion on cloud and AI infrastructure — a 77% year-over-year increase from approximately $410 billion in 2025.

To put the number in context: $725 billion is larger than the GDP of Switzerland. It exceeds the combined annual revenue of Nvidia, TSMC, and Samsung Semiconductor combined. It is the largest single-year infrastructure investment in the history of computing.

This spending has a direct and traceable path through the earnings chain. Hyperscalers purchase GPU clusters from Nvidia, HBM memory from SK Hynix and Samsung, networking equipment from Broadcom and Arista, and data center construction from multiple industrial contractors. Each dollar of hyperscaler capex generates revenue for dozens of companies across the semiconductor, hardware, and infrastructure stack.

Goldman Sachs estimates that AI spending will drive approximately 40% of S&P 500 earnings-per-share growth in 2026. That is not an AI sector metric — it's a broad index claim. AI capital flows have become a macroeconomic variable.

AI Concentration Risk: The One Big Trade Warning

The same AI tailwind that is driving exceptional earnings is creating a concentration risk that Goldman Sachs flagged explicitly in May 2026. As of April 2026, 45% of the S&P 500's total market capitalization is concentrated in AI-exposed mega-cap stocks — primarily Microsoft, Nvidia, Alphabet, Apple, Meta, Amazon, and Tesla.

Goldman's warning is that the AI-fueled rally is becoming "one big trade." When a single thematic cluster accounts for nearly half of a major index's market cap, the behavior of the index begins to resemble the behavior of the cluster. Diversification through passive S&P 500 investing provides less protection against sector-specific drawdowns than historical data would suggest.

The mechanism for a correlated drawdown is not hypothetical: if AI infrastructure spending growth disappoints — either because enterprise customers slow AI adoption, because a major hyperscaler reduces capex guidance, or because AI utility fails to convert to measurable ROI at scale — the correction would hit the largest index weights simultaneously.

This risk is particularly relevant given that the market's reaction to Big Tech capital spending plans in Q1 2026 showed early signs of investor skepticism. Analysts noted that markets are no longer giving open-ended credit for AI investment announcements without evidence of return. Companies that reported high AI capex without clear path to revenue recognition were penalized in post-earnings trading even when aggregate earnings beat.

Sector Breakdown: Where the Growth Is Coming From

The 28% aggregate earnings growth is not evenly distributed. Technology and communications services are disproportionately responsible for the headline number, while consumer discretionary and real estate contributed at or below historical averages.

Within technology, the AI infrastructure layer — semiconductors, cloud compute, and enterprise software — grew earnings at rates significantly above the sector average. Companies with direct exposure to hyperscaler capex spending (Nvidia's data center segment, TSMC's advanced node capacity, Broadcom's networking ASIC business) reported quarter-on-quarter sequential growth that outpaced year-over-year comparisons.

AI-enabled software companies in the SaaS layer — Salesforce, ServiceNow, Workday — reported stronger-than-expected revenue retention as customers who had initially piloted AI features began expanding seats. This "land and expand" dynamic in enterprise software is a structural tailwind that should sustain earnings growth through 2026 even if new AI customer acquisition slows.

Healthcare and industrials reported AI-related earnings contributions through productivity improvements rather than revenue growth: lower claims processing costs, faster drug discovery pipelines, optimized manufacturing scheduling. These contributions are less visible in headline earnings beats but represent a broadening of AI's economic impact beyond the technology sector.

Valuation: Justified or Stretched?

The forward 12-month P/E ratio for the S&P 500 stood at 20.9 in late April 2026. This sits above both the five-year average of 19.9 and the ten-year average of 18.9, suggesting the market is pricing in above-average growth expectations.

Whether this valuation is justified depends on whether 12% annual EPS growth — Goldman Sachs's forecast for 2026 — is achievable and sustainable. The Q1 2026 beat rate argues it is achievable in the near term. The sustainability question is harder.

The bear case: $725 billion in annual AI capex cannot compound at 77% per year indefinitely. At some point, hyperscalers will face diminishing returns on infrastructure investment and reduce spending growth. When that happens, the 40% of S&P 500 EPS growth attributed to AI spending will compress, and the current P/E multiple will look increasingly stretched.

The bull case: AI is early enough in its enterprise adoption curve that hyperscaler capex will generate returns — in the form of recurring revenue from AI subscriptions, cost savings from automation, and new revenue streams from AI-native products — that justify sustained high capital spending for several more years. If AI ROI becomes demonstrable at scale, the P/E compression risk diminishes.

Goldman Sachs projects S&P 500 year-end 2026 target at 7,600 from May levels near 7,400 — approximately 3% upside from current levels. This relatively modest projection from a firm that tracks the earnings data closely suggests the firm believes much of the AI earnings upside is already priced in.

What to Watch in Q2 2026 Earnings Season

The Q2 2026 earnings season, which begins in mid-July, will be the first test of whether the Q1 beat rate was a structural shift or a one-quarter phenomenon. Key metrics to monitor:

Hyperscaler capex guidance updates will be the most market-moving disclosures. If Amazon, Microsoft, or Google revises AI infrastructure spending downward even modestly, the reaction in semiconductor and cloud stocks will be disproportionate.

Enterprise AI revenue conversion — the extent to which pilot customers are converting to paying subscriptions — will determine whether the AI software layer sustains its earnings momentum. Q1 showed early positive signals; Q2 will confirm or contradict them.

Margin sustainability at AI-native companies deserves attention. The record 13.4% net profit margins in Q1 reflect a favorable revenue mix; as AI products mature and competitive pricing pressure increases, margin pressure may emerge even in a strong revenue environment.

Operating leverage in non-tech sectors provides a less volatile signal of AI's broad economic impact. Industrials and healthcare companies reporting AI-driven efficiency gains with sustained or expanding margins would indicate AI is generating genuine economic value rather than speculative capital flows.