The AI Trade Is Broadening: Stocks to Watch

Archive edition · Market data and company circumstances reflect 22 November 2025, when this newsletter was sent.

The Next Phase of the AI Trade

The latest earnings season has triggered another major upgrade in expectations for AI capital expenditure. After multiple quarters of upward revisions, the AI hyperscalers — Amazon (AMZN), Alphabet (GOOGL), Meta (META), Microsoft (MSFT), Oracle (ORCL) — have once again pushed spending forecasts higher.

What matters now is not just how much they spend, but which companies can translate that spending into real revenue, and which sectors become the next wave of beneficiaries as corporate AI adoption accelerates.

The market behaviour is shifting. The AI theme is no longer a single trade, it is becoming a multi-phase ecosystem with widening dispersion, clearer winners, and emerging new categories of opportunity.

Corporate AI Spending Is Going Parabolic

Wide variation in returns of AI infrastructure stocks during the last several weeks

Corporate spending on AI infrastructure remains one of the strongest investment cycles in US market history.

During earnings season, consensus 2026 capex forecasts for AI hyperscalers rose from $467B to $533B, reflecting another sharp upward revision.

Despite concerns about sustainability, actual capex has consistently exceeded forecasts for two straight years. Historical technology cycles show that when an adoption wave reaches this scale, spending rarely stops abruptly. It accelerates until clear monetization emerges, then gradually broadens across the economy.

Big Divergences Are Driving This Market

Even though the total spending pool is rising, the equity market has become far more selective. Infrastructure-related stocks are no longer moving in unison.

Companies showing strong revenue linkage to AI capex:

• Amazon (AMZN): Cloud demand inflection
• Alphabet (GOOGL): Platform-level AI services scaling
• Microsoft (MSFT): Enterprise AI productivity tools

Companies facing pressure:

The correlation between hyperscaler stocks has fallen from 80% to roughly 20%, highlighting that investors now care less about “AI as a theme” and more about AI delivering near-term revenue and margin contribution.

• Meta (META): Lower operating leverage despite AI spending
• Oracle (ORCL): Leverage and concerns around returns on capex
• Smaller data-center and AI infrastructure players with stretched balance sheets

The Valuation Gap

AI-exposed infrastructure names have delivered substantial returns this year, but fundamentals have not kept pace.

Many key AI infrastructure stocks are up 30–40%+ YTD yet their two-year forward EPS estimates are up less than 10%

This creates a valuation gap. The market is already pricing in years of future growth, making the group more sensitive to any slowdown in capex or supply-chain bottlenecks (chips, power, cooling capacity, and utility bottlenecks).

Can Hyperscalers Keep Spending?

The big question for investors is whether hyperscalers can realistically continue pouring massive amounts of capital into AI infrastructure.

Despite net debt for the group shifting from negative to slightly positive, leverage remains exceptionally low, and companies like AMZN, GOOGL, META, and MSFT still operate with some of the strongest balance sheets in global markets. These platforms have enormous financial flexibility and could add hundreds of billions in incremental debt without meaningfully impacting their credit quality. In other words, cash flow isn’t the limiting factor for AI capex.

The real constraints are more structural: Supply bottlenecks across chips, power availability, cooling, and datacenter build-outs; execution timelines; and investor tolerance if revenue growth doesn’t eventually keep pace with investment.

AI Platform & Productivity Beneficiary Stocks

Some AI Platform stocks have outperformed YTD. (Source: Goldman Sachs)

With infrastructure valuations stretched, investor attention is shifting toward the next layers of the AI value chain. Companies positioned to benefit as AI moves from infrastructure build-out to enterprise adoption and operational efficiency.

AI Platform Stocks (Direct Revenue Tailwinds)

These are software and services companies providing the databases, tools, and development environments needed for corporates to deploy AI inside their existing workflows.

These are companies referenced as outperforming or structurally well-positioned:

• Oracle (ORCL): Cloud + database stack tied directly to AI deployment
• MongoDB (MDB): Modern data storage tools used in AI application development
• Snowflake (SNOW): Data cloud used heavily in AI training pipelines
• ServiceNow (NOW): AI workflow automation within enterprise systems
• Palantir (PLTR): AI-enabled analytics platforms for operational use-cases

AI Productivity Beneficiaries (Indirect Revenue Tailwinds)

These companies are not building AI infrastructure, instead, they benefit from AI-driven efficiency, cost reduction, or workforce automation.

These companies represent the type of businesses highlighted in the AI labor-productivity framework:

• Walmart (WMT): Large labor base, AI-driven automation in supply chain + store ops
• Target (TGT): Inventory, logistics and demand-planning automation
• UPS (UPS): Routing, logistics optimization using AI
• Accenture (ACN): High exposure to AI-driven consulting and automation
• ADP (ADP): Workforce management automation using AI tools

What Investors Need to Know

Here’s how we think about positioning going forward:

1. Infrastructure is still the core of the AI trade, but not the only trade.Semiconductors, datacenter operators, and cloud platforms remain essential, but dispersion will continue to widen.

2. Balance sheet strength matters more than ever.Companies with debt-heavy structures or aggressive financing will face greater volatility.

3. AI Platforms are the next structural winners.Revenue growth from enterprise adoption is just beginning.

4. Productivity beneficiaries offer attractive risk-reward.These companies gain from AI without bearing the massive capex burden.

5. Broadening leadership is the next phase of the cycle.The AI trade is moving from infrastructure → platforms → downstream corporate productivity.

This is where long-term equity ideas begin to decouple from hype cycles and align with real earnings power.

Archive note

This article preserves the analysis in our weekly newsletter sent 22 November 2025. Market prices, forecasts and company circumstances reflect the time of publication and may have changed.

This material is general information, not personal financial advice or a recommendation to trade. Investing and trading involve risk, including loss of capital.