Bio Ai

”Bio Bucks” - We’ll see an uptick in pharma AI partnerships but more scrutiny over deal details. Investors will discount commercial milestones more heavily and value quality over quantity in partnered programs.

“Public-Private Chasm” - The pace of AI breakthroughs in the lab compared to the glacial speed of trials will further the chasm between early and late stage companies and how public and private investors perceive (and value) AI-native biotechs.

“Insilico Assays” - As AI achieves experimental accuracy on a more measurements, diagnostic and tool companies will take AI more seriously with a string of acquisitions + investments.

“Multi-Modal” - Large, multi-modal models (LMMs) will steal the spotlight from language + diffusion models - opening opportunities in complex biology and non-generative applications like target discovery.

“AI-Native SaaS” - Open source models will continue to dominate bio and chemistry, but scientists will struggle to integrate them into their work. Software companies that build delightful UX on open source AI will own the workflows and capture much of the value.

“Explainability” - As foundation models get bigger and more powerful, the ability to learn from their internal representations will become increasingly important for drug discovery. Expect explainability to become a hot area of AI research, though likely under a different name.

“RLXF” - Reinforcement Learning through Experimental Feedback (RLXF) and other forms of supervision will prove powerful in aligning biological foundation models and will drive the biggest gains in utility and adoption this year.

“High-Context Data” - The move to self-supervised learning will change how companies run experiments and upend what training data is valuable. The specifics will vary by application, but expect an emphasis on data with higher dimensionality and more relevant “biological context”.

“Service Enabled Tech” - More AI companies will go to market as full-stack services, adopting service-based business models and competing against existing CROs. AI services will be able to charge more than software and fit more easily into existing budgets - but scope and operational efficiency will be important.

“Platform-Pipeline Fit” - AI biotechs will be forced to think about drug development sooner and face more scrutiny over the programs they pursue. The best companies will be incorporate clinical development into their platforms from the beginning and prioritize programs that are scientifically and commercially attractive while being maximally aligned with their platforms.