1 Reason Eli Lilly Stock Is Still a Buy

Shares are down 15% year-to-date, but Eli Lilly's gross and operating margins have improved since 2020 and, as of Q4 2025, sit above similarly sized peers. The company has invested $55 billion in manufacturing capacity since 2020 and built the largest pharma supercomputer with Nvidia; management attributes some margin gains to improved production costs, while acknowledging lower realized prices and rising GLP-1 competition as headwinds. Lilly says modest (~5%) reductions in time and cost from AI-driven drug discovery could meaningfully lower expenses and further boost margins over the medium term.

Analysis

The margin story here is not a pure productivity tale — it is a levered capacity story. Large factory investments convert variable COGS into fixed cost base, so the realized margin upside hinges on utilization moving materially above current run-rates; if throughput does not scale into the mid‑70s–80s percent range within ~24–36 months, the fixed-cost drag will outpace per-unit savings and compress margins. Watch throughput, batch yields and unit COGS per SKU as the key operational signals rather than headline gross-margin percentages.

AI creates optionality but also vendor concentration risk. Small percentage improvements in discovery/attrition compound across a multi-stage pipeline and can boost R&D ROI non-linearly — but those benefits require continued high GPU/accelerator spend, increasing dependency on a narrow set of suppliers and pushing up non-COGS operating spend. That means part of the margin story is transferable to hardware/software vendors; Nvidia stands to capture a disproportionate slice of pharma’s marginal IT dollar while incumbent CPU providers face an uncertain share battle.

Second-order supply-chain winners are specialist fill/finish and single-use consumables suppliers, and potential M&A targets, not necessarily the clinical-stage biotech cohort most discussed. Conversely, payor actions or formulary steering that reduce realized prices by mid‑teens would neutralize much of the scale benefit — that’s a 12–36 month policy/competitive risk that can flip the thesis quickly. Monitor realized price per treatment and payer coverage changes as higher-conviction sell signals.

Key Decisions for Investors

• LLY — Initiate a tactical long (2–4% NAV) over a 12–24 month horizon to capture margin realization and pipeline optionality; hedge with a 12‑month 10–15% OTM put (or put spread) sized to limit downside to ~10% of NAV. R/R: base-case 20–35% upside if utilization and AI gains materialize; tail risk: 20–30% downside if pricing/payer actions accelerate.
• NVDA — Buy a 9–12 month call spread 20–30% OTM (size 1–2% NAV) to express rising pharma AI compute demand without full delta exposure. R/R: 2–4x if pharma/biotech enterprise spend continues to drive GPU demand; risk: AI spending reallocation or cycle pause compresses premium.
• NDAQ — Small cyclical long (0.5–1% NAV) over 6–12 months to capture higher listings and trading flow from a buoyant biotech/drug-discovery wave; liquid equity or short-dated calls. R/R: modest upside from fee/flow expansion with limited downside; acts as a partial macro hedge on increased capital markets activity.
• Risk hedge — If concerned about accelerated pricing erosion, buy a 12–18 month LLY put spread (10–20% OTM) sized to cover 25–50% of the long position. This keeps upside exposure while capping losses if payor/regulatory pressure materially reduces realized prices.