With the completion of Q1 2026 earnings reports for S&P 500 constituent companies, one set of data stands out: over 80% of firms have reported earnings that beat market expectations, and overall EPS growth has been sharply revised upward—from the consensus estimate of 14.4% at the start of the year to 28.6% within a single quarter. This level of outperformance not only significantly exceeds the five-year historical average, but also marks the strongest earnings surge since the post-pandemic rebound in US equities in 2021.
The market is transitioning from an "AI narrative-driven" phase to a "data-driven earnings" phase. Investors are shifting focus from "Can AI deliver?" to "Which sectors are successfully converting massive capital expenditures into sustainable earnings growth?" Drawing on the latest data from FactSet, Zacks, and several major institutions, this article systematically breaks down the structural drivers behind Q1’s earnings surprises and explores how asset allocation strategies between traditional financial assets and digital assets are evolving amid the ongoing AI infrastructure race.
Over 80% Positive Earnings, Growth Revised Upward: Q1 Earnings Season Overview
Record Highs in Earnings Beats and Growth Rates
According to Fortune, citing FactSet data through early May, 84% of S&P 500 companies reporting have delivered EPS above market expectations—surpassing the five-year average of 78% and the ten-year average of 76%. If this ratio holds through the end of earnings season, it will be the highest since Q2 2021. On the revenue side, 81% of companies exceeded forecasts, also well above the five-year average of 70%.
More importantly, earnings growth has been revised upward dramatically. Bloomberg Industry Research reports that S&P 500 constituents saw Q1 earnings rise 27% year-over-year, far outpacing the roughly 12% predicted by analysts before earnings season began—more than doubling expectations and marking the fastest growth since 2004, excluding periods of recovery from major economic shocks. Citibank’s data shows S&P 500 companies achieved 27% year-over-year earnings growth, while previous forecasts were at 14%. In other words, within a single quarter, market expectations for corporate earnings growth nearly doubled.
Structural Analysis: The Magnificent 7 Lead, Earnings Growth Spreads Broadly
The structural characteristics of this round of earnings outperformance are also noteworthy. FactSet data shows the "Magnificent 7" saw Q1 earnings grow 63.2% year-over-year—their highest rate since Q2 2021. Meanwhile, the remaining 493 S&P 500 companies posted a combined earnings growth rate of 17.4% year-over-year, the highest since Q4 2021.
Citi’s latest research confirms that growth is broad-based, not limited to a single sector or industry, though tech and AI-related companies remain the primary drivers. Goldman Sachs estimates that about half of S&P 500’s earnings growth in 2026 will come from beneficiaries of AI infrastructure. In terms of contribution, Alphabet, Amazon, and Meta collectively accounted for 71% of the index’s total earnings increase last week.
Upward Revision Path: Incremental Changes from Q4 to Q1
Examining the timeline of earnings growth revisions provides insight into the incremental logic behind Q1’s earnings surprises. As of the end of March, FactSet tracked a consensus Q1 earnings growth forecast of 13.1%. As April earnings reports rolled out, several tech giants posted results far exceeding expectations, prompting continued upward revisions. By early May, overall growth had climbed to 27.1%.
This dynamic revision process shows that the market’s optimism was not fully priced in before earnings season began, but was adjusted upward week by week as actual data emerged. Looking at full-year forecasts from major investment banks, this upward trend may not be a one-off correction: FactSet data indicates analysts expect earnings growth for Q2 to Q4 2026 at 21.3%, 23%, and 20.6%, respectively—maintaining double-digit growth near or above 20% for the year.
AI Infrastructure: The Core Engine Driving Corporate Earnings Cycles
Capital Expenditure Scale: Jumping from Billions to Hundreds of Billions
The deeper driver behind this round of earnings outperformance is the continued expansion of AI-related infrastructure investment. In 2026, the scale of AI capital expenditures is no longer just an industry topic—it’s now a macroeconomic indicator.
According to S&P Global’s latest research, the five largest US hyperscalers are expected to spend over $700 billion in capital expenditures in 2026, up more than 60% year-over-year. Amazon’s 2026 capex guidance is $200 billion, Alphabet’s is $175–185 billion, Microsoft’s exceeds $140 billion, and Meta’s ranges from $115–135 billion. Wedbush further estimates Alphabet’s capex could double, and Meta’s could grow by more than 75%.
CNBC’s calculations show that total AI capital expenditures for 2026 may reach $700 billion—surpassing the estimated $636 billion spent globally on oil and gas in 2025. In other words, AI infrastructure investment is reshaping global investment structures with a capital intensity that exceeds traditional energy sectors.
Profit Margin Divergence and Market Pricing of "AI Monetization"
Capital markets are undergoing a subtle but important shift in their attitude toward AI capital expenditures. Wedbush’s post-Q1 earnings analysis notes that Q1 2026 marks a turning point—markets have moved from "tolerating conceptual investment" to "demanding verifiable profit outcomes."
This structural change is reflected clearly in profit margin data. FactSet reports that S&P 500’s composite net profit margin hit 13.4% in Q1 2026—the highest since FactSet began tracking this metric in 2009. By sector, information technology’s net margin reached 29.1%, well above last year’s 25.4%, and leading all other sectors.
However, not all companies ramping up capital expenditures are receiving positive market feedback. Investors’ evaluation of AI spending hinges on whether it can translate into revenue and profit growth in the short term. Companies able to demonstrate direct AI-driven revenue gains enjoy valuation premiums, while those with unclear revenue conversion paths despite increased spending face valuation compression. Wedbush calls this phenomenon the "AI monetization gap"—a clear market pricing difference between companies that can prove direct financial returns from AI and those that cannot.
This divergence affects not only individual stock performance in the short term, but also has a ripple effect on the broader market valuation system. FactSet data shows the S&P 500’s forward P/E is 20.9x, above the five-year average of 19.9x and the ten-year average of 18.9x, reflecting higher valuation levels based on earnings growth expectations. In this elevated valuation environment, the ability of companies to consistently deliver on earnings forecasts becomes the key variable supporting current market valuations.
Gate Stock Trading: A Bridge from Crypto Accounts to US Equities
As the structure of US equity earnings evolves, demand for cross-market asset allocation is rising. For digital asset users holding stablecoins, traditional securities investment poses significant conversion costs—such as USD exchange, opening overseas brokerage accounts, and cross-border settlement. Gate’s real stock trading service offers a solution for asset allocation in this context.
Compliance Architecture: Real Asset Ownership and Regulated Brokerage Custody
Gate stock trading is fundamentally different from typical "stock tokens" or "RWA-mapped assets." Gate uses an omnibus account structure, connecting with Alpaca—a compliant US broker-dealer with clearing qualifications—to directly access major US securities markets like NYSE and Nasdaq. Stocks purchased by users are real underlying assets traded on Nasdaq or NYSE, held in custody by SIPC-member brokers, and eligible for SIPC insurance protection (up to $500,000). Additionally, Gate stock accounts are managed entirely separately from contract and spot accounts, avoiding cross-collateralization risks.
From an asset ownership perspective, this architecture is fundamentally different from tokenized stocks, which typically do not confer real shareholder rights. Gate’s real stock trading provides genuine equity registered on exchanges, with users holding assets authenticated and backed by the custodian—not price-tracking derivatives.
Differentiated Advantages: USDT Settlement + Zero Holding Costs + Broad Asset Coverage
Gate stock trading offers three core differentiated features from a user perspective:
First, direct USDT settlement. Users can trade US equities and ETFs directly using USDT, without needing to open separate overseas brokerage accounts or convert to USD. For users allocating assets with stablecoins long-term, this extends USDT’s use case from crypto trading into global securities allocation.
Second, zero holding costs. Unlike perpetual contracts with funding rates or CFD products with swap and overnight fees, Gate spot stock trading does not involve funding or overnight holding costs, making it more suitable for long-term allocation strategies.
Third, asset coverage. Gate stock trading supports over 10,000 stocks and ETFs listed on NYSE, Nasdaq, and other major exchanges—spanning technology, finance, consumer, energy, healthcare, and more—enabling investors to diversify based on industry trends.
How to Trade: Four Steps to Complete Allocation
Gate stock trading is integrated into the existing app ecosystem. The steps are:
- App update and login: Update Gate App to the latest version (Android latest / iOS 8.21.5 or higher), then log in to your Gate account.
- Access the stock section: In the app, go to the "TradFi" section and select "Stocks."
- Asset transfer: Move USDT from your spot account to your stock trading account.
- Place trades: After completing KYC and meeting local access requirements, you can view real-time quotes and buy or sell US equities and ETFs.
The current service supports intraday market trading, with margin trading and securities lending features to be rolled out later. Gate also plans to support one-click stock asset transfers between brokers, further enhancing liquidity and cross-platform management efficiency.
Conclusion: After Earnings Validation, New Windows for Cross-Market Allocation
The Q1 2026 earnings season’s outperformance confirms a key thesis: AI investment is moving from the conceptual stage to earnings validation. The sharp upward revision in earnings growth—from 14.4% to 28.6%—shows that the market is entering an early phase of repricing corporate profitability.
Looking ahead, the capital expenditure cycle for AI infrastructure remains on an upward trajectory. S&P Global projects the top five hyperscalers will spend over $700 billion in capex in 2026, up more than 60% year-over-year. This underscores that AI infrastructure buildout is not slowing—it’s accelerating.
For investors across asset classes, this moment presents two opportunities: first, to focus on companies in the AI value chain that can convert capital spending into earnings; second, to reassess asset allocation structures and evaluate the interplay and complementarity between different asset types. Gate’s stock trading service gives digital asset users direct access to US equities with USDT settlement, helping reduce operational and friction costs for cross-market allocation. As the integration between digital assets and traditional financial markets deepens, the practical value and market fit of this functionality warrant ongoing attention.
