AI model costs and rapid obsolescence are eating into margins—each new generation demands more compute, serves less time, and recoups less revenue. The only way to capture lasting value is by using closed-door models to drive high-value discoveries (like drug design) and own the instruments that generate proprietary data.

• GPT-4.5 only recouped 0.7× its $5.3B cost in five months before being surpassed, versus GPT-3's 3.6× return over 30 months—AI model economics are getting worse, not better, as inference costs now exceed training costs
• Labs are shifting from selling API tokens to using models for proprietary discovery (drugs, materials) whose value outlasts the model itself
• AI drug discovery cuts R&D costs 25-40% and timelines 30-40%, potentially saving $1B per drug against a backdrop of $6.7B average lifetime drug revenue
• Pairing closed models with owned physical instruments (like DeepMind's materials lab or OpenAI/Ginkgo's protein synthesis work) creates proprietary data flywheels competitors can't replicate