Defense, Infrastructure, and Transition Materials: A Lower-Volatility Route into the AI Boom

Play the AI Boom Without the Bubble: Bank of America's 'Transition' Route

Hook: You need AI upside for portfolios — but you don’t want the extreme valuation, headline-driven swings that come with pure-play AI names. Bank of America’s 2025-26 thesis points to a practical alternative: transition stocks — defense contractors, infrastructure operators, and transition materials providers that gain durable exposure to AI without the bubble risk. This guide explains the thesis, gives stock-selection criteria, outlines valuation guardrails, and models what happens if AI demand disappoints.

The problem investors face in 2026

Since late 2023, a concentrated group of semiconductor and cloud-native AI leaders drove enormous returns. By 2025 large-cap AI multiples stretched, and volatility rose as investors debated the speed of adoption, regulation, and chip-cycle supply. By early 2026 many investors — especially those managing cash flows and liabilities like pensions, taxable investors, and active traders — ask: how do I capture structural AI upside while preserving capital and generating income?

Understanding Bank of America’s 'Transition' Thesis

Bank of America frames a set of industries as indirect AI plays: defense contractors, infrastructure companies, and transition materials suppliers. The logic: AI adoption drives increased demand for compute, sensors, secure communications, hardened infrastructure, next-generation power materials and specialty chemicals for chips. These demands flow to companies that supply systems, build networks, or mine and refine materials — businesses with stronger balance sheets, contract visibility, and lower multiple expansion risk than pure-software or chip plays.

"The direct winners (chips, hyperscalers) may have higher upside, but transition stocks offer durable, lower-volatility exposure to AI-driven capex and modernization."

Key 2025–2026 drivers that strengthen the thesis:

• Heightened defense budgets and modernization programs tied to autonomy, drones, ISR (intelligence, surveillance, reconnaissance), and resilient communications.
• Mass rollout of edge-compute and fiber/5G infrastructure supporting AI inference outside data centers.
• Regulatory and geopolitical moves (e.g., CHIPS Act funding rollout in the US, EU critical-raw-materials and synthetic media guidance) that accelerate domestic supply chains for advanced materials.
• Corporate capex shifts from R&D-only to systems integration and deployment requiring specialized hardware and materials.

Three Transition Buckets — How Each Gains from AI

1) Defense contractors

Why: Modern military systems increasingly integrate AI for autonomy, target recognition, cyber, and electronic warfare. Governments seeking technological superiority invest in platforms and upgrade cycles rather than consumer-facing chips.

• Revenue drivers: long-term contracts, upgrade programs, services and sustainment tied to new sensors and compute modules.
• Advantages: strong backlog visibility, higher free cash flow, dividends, and buybacks provide downside protection.

2) Infrastructure operators and builders

Why: AI needs ubiquitous bandwidth and edge compute — fiber, data center interconnects, power distribution upgrades, telecom towers, and civil infrastructure upgrades are all beneficiary categories.

• Revenue drivers: recurring services (connectivity), capex cycles to expand fiber and edge facilities, government infrastructure grants funneling into networks.
• Advantages: stable cash flows, inflation-linked contracts in some utilities, regulatory protections.

3) Transition materials suppliers

Why: Advanced semiconductors, power electronics, and AI hardware require specialty materials — silicon carbide (SiC), gallium nitride (GaN), rare-earth magnets, copper, lithium, and advanced chemicals for chip fabrication.

• Revenue drivers: multi-year supply agreements, capacity expansions, and localized processing driven by policy incentives.
• Advantages: pricing power when demand tightens, diversification across industrial and tech end-markets.

Stock Selection Criteria: How to Pick the Best Transition Names

Not all companies in these buckets are equal. Use a disciplined checklist to identify transition stocks that offer real AI exposure with defensive characteristics.

1. Quantify indirect AI exposure: Look for explicit product lines or contracts touching autonomy, sensors, edge compute, or specialized materials for semiconductors. Ideally 10–30% of near-term revenue tied to AI-related programs.
2. Backlog and contract visibility: Defense and infrastructure companies with multi-year backlog reduce execution risk. Favor firms with >2 years’ revenue visibility.
3. Free cash flow and balance-sheet strength: Net cash or manageable leverage allows companies to weather a cyclical slowdown and to fund expansion without dilutive equity raises.
4. Pricing power and long-term offtake agreements: For materials suppliers, long-term supply contracts or integration into customer roadmaps provides resilience to demand swings.
5. Valuation discipline: Target companies trading below trough-adjusted multiples for their sector, or trading at a discount to peers after normalizing margins.
6. Dividend/buyback track record: Income generation reduces downside and signals management confidence.
7. Operational leverage metrics: Understand how incremental AI-related revenue converts to EBIT and FCF. High incremental margins are preferable.
8. Geopolitical and regulatory resilience: Favor firms with diversified supply chains or local manufacturing investments that align with 2025–26 policy shifts; monitor regulatory guidance such as the EU’s media and device rules for possible cross-sector impacts (regulatory watch).

Valuation Considerations: Avoiding the Bubble Pitfall

Transition stocks can rerate, but the core appeal is a lower starting multiple and better downside protection. Here are valuation guardrails to apply in 2026.

• Use normalized earnings: Adjust for one-off defense contract phasing or commodity spikes. Look at cyclically adjusted P/E or EV/EBITDA over a 3–5 year window.
• Free cash flow yield: Target names with FCF yield above the sector median, especially if the company invests in growth capex tied to AI expansion.
• Multiples vs. growth expectations: Calculate PEG-like metrics but replace near-term revenue growth with the percentage of revenue exposed to AI-driven capex to better capture durable tailwinds.
• Scenario valuations: Value the company under base, upside, and downside AI-adoption cases (see next section). Price targets should reflect weighted scenario probabilities.
• Margin and cash conversion sensitivities: Model how a 10–20% reduction in AI-related revenue affects margins and FCF — if downside multiples compress significantly, a stock may not be a good buffer play.

Scenario Analysis: What If AI Demand Slows?

Scenario planning is essential. Below are three stylized scenarios to stress-test transition allocations. Use numbers as illustrative sensitivities; customize to your holdings.

Base Case (50% probability): AI demand grows as consensus — steady capex

• Assumption: AI-related capex grows 15–25% annually through 2027, benefiting system integrators and materials suppliers.
• Impact on transition stocks: Organic revenue growth of 5–12% for defense and infrastructure, 8–18% for materials with higher cyclicality. Margins improve modestly from scale and pricing.
• Portfolio outcome: Lower volatility than pure AI leaders, with 1.0–1.5x beta relative to the market and positive alpha from durable cash flows.

Bull Case (25% probability): AI adoption accelerates and broadens

• Assumption: Edge deployments and defense modernization accelerate; materials tightness leads to pricing power.
• Impact: Revenue growth above historic peaks, margins expand meaningfully, multiples re-rate as forward visibility increases.
• Portfolio outcome: Transition names capture significant upside — potentially 20–40% over 12 months — while still lagging hypergrowth AI leaders, but with more stability.

Bear Case (25% probability): AI demand disappoints — macro and policy slow adoption

• Assumption: Slower enterprise adoption, budget cutbacks, or rapid commoditization of key AI components reduce capex.
• Impact: Materials sees oversupply and price pressure; infrastructure rollouts delayed; defense programs may absorb budget but shift timelines.
• Portfolio outcome: Transition stocks decline, but typically less than pure-play AI equities, particularly for companies with strong secular defense backlogs or diversified end-markets.

Practical stress test: For a representative transition materials stock, simulate a 20% reduction in AI-related revenue and measure the impact on FCF yield. If FCF yield remains attractive (<6–8% compression), the name retains its buffer utility in the portfolio. Also stress operational resilience — deployments and releases should be verified against operational playbooks like zero-downtime release pipelines and TLS hardening.

Portfolio Construction: How to Allocate to Transition Stocks

Transition stocks should be a complement, not a substitute, for core AI exposure. Below are pragmatic allocation frameworks depending on risk profile.

Conservative (income / liability-driven)

• Allocation: 8–12% of equity sleeve to transition stocks.
• Composition: 50% defense, 30% infrastructure, 20% transition materials.
• Tools: Prefer dividend-paying names, use covered-call overlays to enhance yield.

Balanced (growth + protection)

• Allocation: 12–20% of equity sleeve.
• Composition: 40% defense, 30% infrastructure, 30% materials.
• Tools: Pair with selective direct AI exposure (chips, cloud) and use options for tail risk hedging.

Opportunistic (high conviction)

• Allocation: 20–30% of equity sleeve, tilting to materials and infrastructure if valuations justify.
• Composition: dynamic; increase materials when commodity tightness is visible, rotate to defense where backlog growth is explicit.
• Tools: Use staged buys on contract awards and capital-spend announcements; take small options positions to leverage upside.

Practical Implementation: Trade Ideas and Risk Controls

Implementation matters. Here are tactical steps that translate the thesis into executable portfolio actions.

1. Start with a watchlist: Identify 8–12 names across the three buckets that meet the checklist above. Monitor contract announcements, backlog updates, and offtake agreements — use field reviews of portfolio ops & edge distribution for execution signals.
2. Stagger entries: Use dollar-cost averaging over 3–6 months to reduce timing risk around earnings and budget cycles.
3. Set scenario triggers: Predefine sell or trim thresholds (e.g., price appreciation >35% or a negative revision in backlog visibility by >10%).
4. Hedge tail risks: Use index puts, or buy-put spreads on the heaviest-weighted transition names to cap drawdowns without a large premium outlay.
5. Monitor policy signals: Watch budget hearings, CHIPS Act rollouts, and EU materials policy — these materially affect order timing. Also track low-latency and execution infrastructure trends in market data stacks (market data & execution stacks).
6. Tax-aware rebalancing: For taxable investors, harvest losses in volatile names and prioritize long-term holdings for tax efficiency.

Case Studies: Real-World Examples from 2025–26

Below are short case studies illustrating how transition stocks performed amid the 2025–26 market backdrop.

Defense contractor — Upgrades and Homogeneous Backlog

A large defense prime secured multi-year sensor and autonomy upgrade contracts in late 2025. The company exhibited strong backlog visibility and continued buybacks. Result: share price held up better during AI-chips selloffs because revenue was tied to government budgets rather than hyperscaler capex cycles.

Infrastructure operator — Edge and Fiber

A fiber-network operator that announced a multi-year edge-compute partnership with cloud providers in early 2026 saw steady margin expansions from recurring revenue. This company benefited from municipal grants and predictable churn, offering lower beta to the portfolio.

Transition materials supplier — SiC capacity expansions

A specialty materials manufacturer that invested in SiC wafer capacity in 2024–25 signed multi-year offtake with auto and AI hardware customers. Despite materials cyclicality, the long-term contracts insulated the firm in a slower AI-demand scenario.

Risks and Warning Signs

No strategy is risk-free. Watch for these warning signs that could undermine the transition thesis:

• Policy reversals or budget cuts that materially reduce defense or infrastructure spending.
• Rapid commoditization of specialty materials leading to price collapses and margin erosion.
• Supply-chain disruptions that prevent capacity ramps for materials or delay infrastructure projects — consider logistics and edge datastore constraints when modeling delivery timelines.
• Valuation complacency: if transition names re-rate to growth multiples similar to AI leaders, the ‘lower-volatility’ advantage fades.

Actionable Takeaways

• Use transition stocks as a lower-volatility overlay to capture structural AI adoption without the idiosyncratic bubble risks of pure-play AI equities.
• Apply a strict selection checklist: quantify AI exposure, prioritize backlog visibility, cash generation, and contractual protections.
• Stress-test valuations: model base, bull, and bear AI-demand scenarios and use FCF yield as a primary guardrail.
• Construct allocations to match risk objectives — conservative, balanced, or opportunistic — and implement layered entries and hedges.
• Monitor policy and supply-chain signals in 2026 — CHIPS Act rollouts, EU minerals rules, and defense budget cycles will drive real-order timing. For operational readiness, consult field playbooks on hybrid edge workflows and data center design for AI.

Conclusion — Why This Matters in 2026

Bank of America’s transition thesis offers a pragmatic path to retain exposure to one of the largest technological shifts of our era while managing downside risk and delivering income. In 2026, when markets price growth expectations with more nuance, transition stocks provide a balance: exposure to AI-driven capex and materials demand combined with contract visibility, cash-flow resilience, and lower re-rating risk.

If you need an implementation plan tailored to your portfolio — scaling allocations, hedging, and tax-aware rebalancing — consider systematic screening against the checklist above and staging capital deploys around policy and contract milestones. Use field reviews of operational stacks and edge distribution to align execution with thesis timing (portfolio ops & edge distribution).

Call to Action

Ready to incorporate transition stocks into your AI strategy? Start by creating a watchlist using the 8-point selection checklist in this article. If you want a customized allocation or a modeled scenario analysis for your holdings, subscribe to our premium forecasting service for portfolio-level stress testing and real-time alerts tied to defense, infrastructure, and materials catalysts.

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