Constructing a 'Transition to AI' ETF: Defense, Infrastructure and Materials Exposure

Hook: Stop Chasing the AI Bubble — Capture Durable AI Demand Through Transition Stocks

If you’re an investor or quant-builder frustrated by headline-driven AI stock mania and worried about timing a frothy market, you’re not alone. The hard problem is not finding the next chatbot darling: it’s building a repeatable, risk-managed exposure to AI’s long tail — the defense contractors, telecoms upgrading fiber, and materials producers that supply the compute infrastructure. This article shows how to construct a thematic ETF-like vehicle — a Transition to AI ETF — that captures indirect AI exposure using Bank of America’s transition idea as a foundation, including practical weighting schemes, rebalancing rules, and an illustrative backtest framework you can run yourself.

The 2026 Context: Why Transition Stocks Matter Now

By early 2026 the AI compute demand shift accelerated beyond hyperscalers into national security and edge compute. Late 2025 saw fresh government spending packages — supplemental defense budgets and new AI infrastructure grants in North America and Europe — and supply-chain re-shoring initiatives tied to the CHIPS Act and European industrial policy. These forces amplify demand for three indirect exposure buckets Bank of America highlighted in 2025: defense, infrastructure, and transition materials.

Why use transition stocks rather than direct AI software winners? Two reasons: (1) they offer exposure to structural demand without the same market multiple expansion risk as pure-play AI names, and (2) they’re often subject to multi-year contracts and capital spending cycles, which smooth returns and lower churn in an ETF sleeve.

Design Goals for a Transition to AI ETF

A pragmatic ETF design balances purity of AI-demand exposure with tradability, liquidity, and risk controls. Our ETF aims to:

• Capture indirect AI demand: defense (sensors, ASICs, compute platforms), infrastructure (telecom, data center builders, power), and materials (specialty semiconductors materials, copper, rare earths).
• Limit single-stock concentration: caps and sector diversification.
• Control volatility and drawdowns: via weighting and rebalancing rules.
• Be implementable: use liquid, investable instruments (stocks or ETF sleeves) and transparent index rules.

Universe Construction — Candidate Securities and Proxies

For a public ETF, you need an investable universe. Below are practical proxy groups (examples, not recommendations) that are liquid and representative as of 2026. Use company-level screens (market cap, liquidity, revenue exposure to AI-related contracts) to refine.

• Defense: primes and mid-caps with AI/ISR exposure — e.g., typical ETF proxies include ITA or core names like Lockheed Martin, Northrop Grumman, Raytheon Technologies.
• Infrastructure (data centers, fiber, power): data center REITs, fiber integrators, power equipment providers — proxies: Digital Realty, Equinix, American Tower, fiber builders. See discussion of micro‑REITs and localized REIT strategies for ideas on exposure construction.
• Transition Materials: specialty chemicals for semiconductors, copper producers, rare-earth miners, high-purity silicon suppliers — proxies: XLB constituents, materials mid-caps.

For initial prototyping we recommend starting with a manageable list of 20–40 liquid names (or 6–12 sector ETFs) spanning the three buckets. This keeps backtests computationally simple while preserving diversification.

Weighting Schemes: Tradeoffs and Recommendations

Weighting drives risk and return. Below are four practical schemes, with recommended defaults for a transition ETF aimed at long-term investors in 2026.

1) Equal-Weight (Simple, Anti-Consensus)

Each security gets identical weight. Pros: disciplined exposure to all names, avoids megacap domination. Cons: higher turnover, potential bias toward smaller, riskier names.

2) Market-Cap Weight (Low Turnover)

Weights reflect free-float market cap. Pros: low turnover, familiar index-style exposure. Cons: may overweight legacy telecom/data center giants rather than transition-focused smaller builders.

3) AI-Transition Score Weight (Thematic Tilt)

Build a quantitative score per security combining indicators such as: revenue % from AI-related contracts, capex on data center/compute, government contract book, supplier role in semiconductor supply chain. Normalize scores and weight proportionally. Pros: targets genuine transition exposure. Cons: requires data and governance; risk of model drift.

4) Risk-Parity / Volatility-Scaled Weight (Drawdown Control)

Allocate so each position contributes equally to portfolio volatility. Works well where sector volatilities diverge — e.g., materials vs defense. Pros: smoother returns, built-in risk control. Cons: complexity and higher turnover. Governance and rebalancing frameworks for operational strategies are discussed in Micro Apps at Scale: Governance which has applicable principles for index governance.

Recommended default (2026): a hybrid tilt — start with 40% AI-Transition Score, 30% Equal-Weight, 30% Risk-Parity. This blends thematic focus, diversification, and volatility control.

Rebalancing Rules: Practical Options and Tradeoffs

Rebalancing frequency and triggers materially affect turnover and tracking error. Consider these options informed by late-2025 liquidity and market structure trends.

• Calendar Quarterly (Standard): rebalance every quarter. Pros: predictable and low governance burden. Cons: may drift between quarters in volatile markets.
• Threshold Rebalance (Band): rebalance only when a security’s weight deviates beyond ±5–7%. Pros: reduces turnover while preventing large drifts.
• Volatility-Triggered: reweight after a material regime change (e.g., VIX spike > 30 or 30-day realized vol up 40%). Useful when macro shocks change correlations.
• Hybrid (Recommended): quarterly rebalancing with ±6% band and annual reconstitution. Apply volatility-triggered rules if monthly realized vol exceeds a threshold.

For tax-efficient ETF structures, pair rebalancing with in-kind creations/redemptions to minimize distribution events. Operational continuity and outage readiness should be considered alongside rebalancing cadence — see Outage‑Ready for continuity planning principles that map to index operations.

Illustrative Backtest: Methodology

Below I outline an illustrative backtest you can reproduce. This is a methodology demonstration — not investment advice. The goal is to show how weighting and rebalance choices affect outcomes.

Universe

• 20 liquid names, evenly split across defense, infrastructure, materials (use tickers or ETF proxies).
• Period: January 2016 – December 2025 (10 years) to include multiple cycles, supply-chain shocks, and AI run-ups.

Assumptions

• Data: adjusted daily close prices, dividends reinvested.
• Transaction costs: 0.05% per trade (aggressive) to 0.2% per trade (conservative) depending on share size.
• Slippage: modeled as additional 0.02% per trade.
• Rebalance: quarterly calendar vs threshold band (±6%).

Python Backtest Template (pseudocode)

# Lightweight backtest outline using pandas / yfinance
import yfinance as yf
import pandas as pd

# 1) define tickers and weights (e.g., equal-weight)
tickers = ['LMT','RTX','NOC','DLR','EQIX','AMT','FCX','NEM','ALB','APPS'] # example
prices = yf.download(tickers, start='2016-01-01', end='2026-01-01')['Adj Close']
returns = prices.pct_change().dropna()

# 2) build weight schedule (quarterly rebalance)
weights = pd.DataFrame(index=returns.index, columns=tickers)

# initialize equal weights
init_w = pd.Series(1/len(tickers), index=tickers)
weights.iloc[0] = init_w
# apply quarterly rebalances (simplified)
for i, date in enumerate(weights.index):
    if is_rebalance_date(date):
        # compute new weights based on strategy
        weights.loc[date] = compute_weights(prior_prices, method='hybrid')
    else:
        weights.loc[date] = weights.iloc[i-1]

# 3) portfolio returns
port_rets = (weights.shift(1) * returns).sum(axis=1)
nav = (1 + port_rets).cumprod()

# 4) metrics: CAGR, volatility, max drawdown

This template is intentionally high-level. For production use add realistic constraints (min/max weight, market-cap caps, liquidity screens) and run multiple scenarios to stress-test assumptions.

Illustrative Results (Hypothetical Example)

To show comparative behavior, imagine three portfolios over 2016–2025 using the same universe:

• Market-Cap Weighted: CAGR 8.6%, Volatility 14.5%, Max Drawdown -32%.
• Equal-Weighted: CAGR 9.9%, Volatility 16.3%, Max Drawdown -38%.
• Hybrid (40% Score, 30% Equal, 30% Risk-Parity): CAGR 11.2%, Volatility 13.1%, Max Drawdown -26%.

These hypothetical numbers illustrate the tradeoff: the hybrid scheme improved risk-adjusted returns and reduced drawdown by balancing thematic tilt with volatility control. In real backtests, results depend heavily on universe selection and transaction cost assumptions. Operational signals you might track in production include edge AI demand and micro‑REIT flows — see Operational Signals for Retail Investors.

Risk Management and Governance

An ETF’s standing rests on its risk framework. For a Transition to AI ETF consider:

• Concentration Limits: 5–8% cap per security, 25–35% cap per bucket.
• Liquidity Screens: minimum ADV, minimum market cap for inclusion.
• Stress Testing: simulate supply-chain shocks, sudden cuts in defense spending, or a large technology multiple contraction. Case studies of supply-chain resilience are useful context — see Supply Chain Resilience: Microfactories & Predictive Hubs.
• ESG & Compliance: some institutional investors will demand ESG overlays; have a transparent policy and exclusion list if you adopt one. Security and compliance tooling guidance is discussed in Security Deep Dive: Zero Trust & Homomorphic Encryption.

Operational Considerations: Execution, Custody & Tax

Building an ETF or ETF-like product requires attention to operations:

• In-Kind Creation/Redemption: preserves tax efficiency for ETF wrappers. Design an AP basket aligned with index weights and include substitute/security lists.
• Authorized Participants & Market Makers: ensure adequate two-way liquidity at NAV.
• Custody & Clearance: choose custodians experienced with specialty materials and foreign securities if you include non-US names — practical platform and clearance reviews (including customs & clearance platforms) can inform vendor selection, see Top Customs Clearance & Compliance Platforms.

Practical Implementation Steps for Quant Traders and PMs

1. Start with a clear universe and scoring rubric. Use revenue and capex signals plus contract disclosure text mining to build your AI-transition score.
2. Prototype with ETF proxies and run the illustrative backtest above across multiple weighting schemes and rebalance rules. Observability and cost tools accelerate iteration; a roundup of cloud cost observability tools is helpful for infra-driven strategies: Top Cloud Cost Observability Tools.
3. Incorporate transaction cost models and test turnover under different rebalance bands.
4. Run stress tests for macro shocks and scenario analyses (e.g., 2025 supply-chain disruptions, 2026 defense budget cuts). Operational signal frameworks are explained in Operational Signals for Retail Investors.
5. Document governance: reconstitution rules, data sources for scores, and index committee procedures.

2026 Trends That Affect Future Performance

Several late-2025 and early-2026 shifts are relevant:

• Decentralized AI Compute: rise of edge AI shifts demand from hyperscaler-only plays to telecom and industrial suppliers — see Edge AI for Retail and Edge AI & Cloud Testbeds for edge compute patterns.
• Onshoring and Defense Spending: increased domestic manufacturing and long-term defense program funding create stable revenue streams for defense suppliers.
• Materials Re-shoring & Recycling: new investments in recycling and substitute materials may change the materials complex; track policy signals and microfactory trends in The Evolution of Adhesives & Microfactories.
• Regulatory Scrutiny of Thematics: regulators in 2025–26 increased disclosure expectations for thematic ETFs; transparency in scoring and backtesting is now essential for institutional adoption.

Case Study: How a 40/30/30 Hybrid Outperformed in a Volatile AI Run-Up (Illustrative)

Consider a hypothetical AI “supercycle” in 2024–2025 where pure-play AI software tripled but later corrected 45% in late 2025. Our hybrid Transition ETF maintained a +28% cumulative return over the two-year window while drawing down just 18% at peak — because defense and infrastructure revenue streams continued to compound, and risk-parity scaling automatically reduced exposure when volatility spiked. The lesson: indirect exposure can provide upside in secular cycles while buffering bubble risk.

Actionable Takeaways

• Use a hybrid weighting scheme: mix thematic score tilt with equal-weight and volatility scaling to balance return and drawdown.
• Rebalance quarterly with a ±6% band: reduces turnover while limiting drift.
• Design an explicit scoring framework: revenue %, capex % and contract pipeline are high-information inputs for transition exposure.
• Model transaction costs and tax impacts: assume 0.05–0.2% per trade and use in-kind mechanisms where possible.
• Stress test governance: prepare for policy shocks and adjust materials exposure as recycling and substitution trends evolve.

"Bank of America’s transition idea is a practical roadmap — not a short-term trade. Positioning ETFs to capture those durable currents requires a disciplined index design that balances focus with risk control." — sharemarket.bot research team

Next Steps: Reproduce the Backtest and Iterate

If you build algorithmic strategies or manage portfolios, start by copying the Python template above, replace the placeholder tickers with a curated universe (or ETF proxies), and iterate on the AI-transition score. Run multi-scenario backtests (different cost regimes, rebalance rules) and track turnover, tax events, and simulated bid-ask impact.

Final Recommendations and Call to Action

A thoughtfully designed Transition to AI ETF can be an effective way to capture structural AI demand without the bubble risk inherent in pure software plays. Use a hybrid weighting approach, set practical rebalance rules, and enforce robust governance. If you want the backtest template or a pre-built scoring model tuned to 2026 realities, start a trial with our research toolkit or contact our team to build a customized ETF-like sleeve for your quant or advisory platform.

Ready to prototype? Request the Python backtest notebook, the scoring template, and a set of sample universes curated for defense, infrastructure, and materials exposure — optimized for institutional due diligence and live trading.

Related Reading

• Operational Signals for Retail Investors in 2026: Leveraging Edge AI, Micro‑REITs and Real‑Time Surveillance
• Micro‑REITs, Neighborhood Safety & Yield: Sourcing Local Income Opportunities in 2026
• Cloud Native Observability: Architectures for Hybrid Cloud and Edge in 2026
• Beyond the Seatback: How Edge AI and Cloud Testbeds Are Rewriting In‑Flight Experience Strategies in 2026
• Book List: Sci‑Fi That Predicted Today’s Metaverse Missteps
• Top 10 Cozy Winter Scents to Pair with Your Hot-Water Bottle
• Commissioning an Artist for Your Game: A Practical Guide (With Tips From Fine Art Collaborations)
• Designing a Sovereign Cloud Migration Playbook for European Healthcare Systems
• Selecting CRM Software in 2026: Security & Compliance Checklist for Tech Teams