Morningstar Says Software Stocks Haven't Been This Undervalued In 3 Years. These 3 Look Like Good Deals.

The article argues Microsoft, Salesforce, and Alphabet are undervalued AI beneficiaries rather than disruption victims, citing Microsoft’s 26.5 trailing P/E, Salesforce’s 12% revenue growth to $11.2B and 169% jump in Agentforce ARR to over $800M, and Alphabet’s 48% Google Cloud sales growth to $17.7B. It also highlights Microsoft’s OpenAI/Copilot integration, Salesforce’s 2.4 billion AI agent work units, and Alphabet’s Gemini traction plus a potential $1B annual Apple deal. Overall, the piece is bullish on large-cap software stocks with AI exposure.

Analysis

The market is still pricing AI as a binary threat to software, but the more durable read is that AI is becoming a distribution and monetization layer on top of entrenched enterprise workflows. That favors firms with high switching costs, proprietary customer data, and the ability to bundle inference into existing contracts. In that framing, the real losers are not the large incumbents named here, but smaller point-solution SaaS vendors and generic middleware names that get commoditized as customers consolidate spend into a few platform vendors.

Microsoft and Salesforce have a second-order advantage that is easy to miss: AI increases the value of their installed base by making them the default orchestration layer for employees and customer-facing agents. That should support net retention and cross-sell more than top-line seat growth, which matters because the next leg of upside is likely margin expansion rather than explosive revenue acceleration. Alphabet is different: its AI advantage is less about enterprise lock-in and more about controlling distribution at the browser, mobile, and cloud layers, which could pull ad budgets and developer workloads back toward its ecosystem if model quality remains competitive.

The contrarian issue is valuation dispersion, not absolute valuation. The crowded consensus is that AI winners must be pure-play model companies; the better trade is that the beneficiaries are the incumbents who can monetize AI through existing cash flows while absorbing capex. The main risk is timing: infrastructure spend is immediate, but enterprise AI budget conversion can take quarters, so near-term multiple compression is still possible if customers demand proof of ROI before scaling deployments.

The cleanest read-through is that AI capex should keep benefiting the picks-and-shovels ecosystem and could pressure weaker software peers with no data moat or distribution. If Gemini/Siri or Copilot-style bundling drives usage materially higher over the next 6-12 months, the platform layer gains pricing power, while the long tail of standalone app vendors faces slower seat expansion and higher churn. This makes the dispersion trade more attractive than a broad long software basket.

Key Decisions for Investors

• Long MSFT and GOOGL into earnings season; use a 6-12 month horizon to express the view that AI monetization shows up in margin expansion before it shows up in headline revenue. Prefer call spreads over outright stock to reduce multiple-compression risk.
• Long CRM vs. short a basket of lower-moat SaaS names over 3-6 months; the thesis is that AI consolidates spend into systems of record rather than replacing them. This is a quality/dispersion trade with asymmetric downside protection in CRM from switching costs.
• Add GOOGL on weakness if cloud growth remains >40% and capex intensity does not trigger guidance cuts; the risk/reward improves when the market over-discounts infrastructure spend relative to future monetization. Favor a staged entry rather than full-size at once.
• Avoid chasing small-cap AI application vendors with no proprietary data or workflow lock-in; they are the most likely margin losers as model costs fall and platform vendors bundle features for free. Use them as short candidates on any post-earnings strength.
• If you want convexity, buy 6-9 month MSFT call spreads financed by selling near-dated downside puts only if the name pulls back on capex fear. The setup is a classic 'invest through fear of spend' trade, with upside driven by recurring AI attach rate rather than hero assumptions.