Former Trump advisor: ‘Conservatives’ risk killing America’s golden goose by taxing university research

Proposal to tax universities' licensing income on taxpayer-funded research would hit the tech-transfer ecosystem and VC deal flow; most universities collect only a few billion dollars annually in licensing revenues. University-driven research parks generated roughly $33B in federal tax revenue last year, university tech transfer has contributed nearly $2T to U.S. gross industrial output since 1996 and helped spawn ~20,000 companies (950 startups in 2024). If enacted, the tax risks materially reducing startups, jobs, and long-term tax receipts by discouraging R&D commercialization.

Analysis

Primary second-order effect is a shift in bargaining power and capitalization economics: if universities pull back on licensing they will either (a) take larger equity stakes up front to replace royalty streams or (b) push discoveries into acquisition pipelines dominated by incumbents with balance-sheet heft. Both outcomes favor large incumbents (deep-pocket corporates and Big Tech) over early-stage VC-backed startups because they raise the price of translating early IP into independent companies and increase investor required returns by raising execution risk.

Policy risk will act on two distinct horizons. Headlines, Commerce memos, or think-tank briefings can compress risk into days–weeks, repricing small-cap biotechs and VC-backed names that trade on dealflow signals; legislative action or regulatory guidance would take 6–18 months and drive more structural effects (reduced spinout formation, slower regional ecosystem growth) measurable over 2–5 years. A critical mechanical reversal would be simple: if universities can switch compensation from royalties to equity or upfront payments, much of the deterrent impact disappears quickly — that’s a likely near-term managerial response.

The consensus overlooks an important offset: universities and VCs can renegotiate contracting forms (equity-for-IP, sponsored research with pre-negotiated exit rights) or route commercialization through acquisition-friendly structures that sidestep royalty tax exposure; these fixes mute long-term damage but increase dilution and lower exit multiples for founders and early investors. For investors, that implies not an outright death of innovation but a re-pricing of early-stage risk and a multi-year transfer of value from founders/VCs to corporates and large-cap acquirers — a dynamic that creates asymmetric opportunities via relative-value and event-driven trades.

Key Decisions for Investors

• Pair trade (6-18 months): Long GOOGL (or GOOG) vs short BIIB, equal notional. Thesis: Big Tech benefits from weakened university-to-startup funnel while Biotech names reliant on academic licensing see higher pipeline risk. Risk/Reward: If proposal fails or universities pivot to equity quickly, unwind; target 15-25% relative return, stop-loss if spread narrows by 8-10%.
• Options hedge on BIIB (9-12 months): Buy BIIB 6–12 month put spread 25–30% OTM (debit). Rationale: caps max loss while paying for policy-driven volatility; payoff if licensing/activity disruption accelerates M&A delays or kills programs. Exit triggers: unwind if Biogen announces new direct licensing/equity program or >$XXXM of upfront deals that replace royalties (company disclosures).
• Bull call spread on GOOGL (9-12 months): Buy 12-month 15% OTM call spread, financed by selling nearer-term calls where implied volatility spikes on headlines. Rationale: captures defensive re-rating of Big Tech as acquirer-of-choice for university IP. Risk/Reward: limited premium outlay for 2–4x upside if rotation into scalable acquirers continues.
• Event hedge / monitoring rule: Maintain cash/hedge ratio for small-cap biotech exposure; step up protection (increase put spread notional or tighten stops) on passage of any Congressional language or OMB guidance advancing taxing authority — those events materially increase downside in 0–90 days.