Former Trump official: the U.S. can win the AI race — if it gets patent policy right

Key takeaway: Uncertainty over Section 101 patent eligibility for applied AI risks diverting investment in AI-enabled manufacturing, energy, defense and supply-chain systems to jurisdictions with clearer IP enforcement. The author urges three policy priorities: preserve USPTO clarity and examiner training, congressional clarification of subject-matter eligibility, and aligning IP incentives with strategic sectors to keep commercialization and capital onshore. Monitor legislative activity on Section 101 and further USPTO guidance—positive action would reduce legal risk for AI investments, while continued uncertainty is a measurable downside for U.S. AI commercialization.

Analysis

A meaningful, underpriced channel by which AI policy alters capital flows is patent enforceability — not just issuance. If Section 101 is legislatively clarified in a pro-enforcement direction over the next 6–24 months, expect a re-rating of firms whose business models monetize embedded AI through licensing or capture value through proprietary stacks; that re-rating will come through higher acquisition multiples, more non-dilutive licensing deals, and easier debt financing for hardware-heavy fabs.

Second-order winners are those that convert legal certainty into tangible supply‑chain commitments: semiconductor equipment vendors, domestic fabs, and defense primes that internalize complex AI-IP into systems-of-systems. Conversely, startups whose valuations rest primarily on network effects or open-model commoditization (rather than defensible process IP) face longer, costlier paths to exits as investors demand stronger freedom-to-operate evidence and insurance against invalidation.

A likely tactical consequence in the next 12–36 months is a surge in patent diligence spend and litigation activity — more PTAB challenges, more district-court cases, and a brisk market for assertion/licensing deals. That increases short-term headline risk but also creates optionality: firms with deep, high‑quality portfolios can monetize through licensing or M&A at premium multiples, while uncertainty favors large incumbents with scale to absorb litigation expense.

Key Decisions for Investors

• Long RMBS (Rambus) — buy 9–15 month call spread (LEAP-style if available) to express asymmetric upside from improved IP enforceability and licensing leverage. Rationale: RMBS has licensing/IP monetization optionality that re-prices higher if courts/legislation increase patent durability. Risk/reward: limited downside to premium (max loss ≈ 100%), upside potential 2–4x if licensing deals or settlements accelerate; monitor PTAB/district court outcomes and Congress activity on Section 101.
• Long LRCX (Lam Research) or AMAT (Applied Materials) — accumulate shares or 9–18 month slightly OTM calls to play domestic fab buildout driven by onshore capex tied to secure IP regimes. Rationale: stronger IP certainty increases willingness to localize manufacturing (fab orders lag policy by 6–18 months). Risk/reward: cyclical downside if chip demand weakens (expect 20–30% volatility); target 30–60% upside if fab investments sustain.
• Long RTX or LMT (defense primes) — buy 12–24 month calls or a modest equity overweight. Rationale: defense systems integrating applied AI favor primes with robust internal IP and secure domestic supply chains; procurement cycles and authorizations are multi-year catalysts. Risk/reward: downside tied to budget politics; expect 20–40% upside in base-case policy alignment, mitigate with 20% stop-loss or hedged collar.
• Long CLVT (Clarivate) — buy 6–12 month calls or shares to capture near-term growth in patent-diligence, analytics, and IP subscription spend as investors and acquirers demand defensibility checks. Rationale: increased patent uncertainty drives outsized spending on analytics and freedom-to-operate services. Risk/reward: modest execution risk in subscription growth; potential 2x upside if enterprise pipeline expands materially within 12 months.