Unlocking Collective Intelligence
Every time we solve the same problem in isolation, we delay patient access.
In this piece, Alexander Seyf, founder of Act for Hope and CEO of Autolomous, argues that responsible data sharing is not just desirable, it is essential for accelerating the development of cell and gene therapies. He highlights a stark reality: many companies are solving the same problems in isolation, essentially reinventing the wheel rather than building on one another’s experience. This duplication of effort can add three to six months to development timelines and generate significant additional cost, delays that ultimately hurt patients.
Seyf explains that structured cooperation could dramatically reduce these inefficiencies. By sharing anonymised process data in a pre competitive context, organizations could avoid repeating known failures, identify optimal process parameters more quickly, and shorten their path to scale. Such collaboration would create a foundation of collective intelligence that benefits the entire sector.
However, he acknowledges that concerns over intellectual property, competitive advantage, and data misuse often block meaningful collaboration. To overcome this, he proposes robust governance frameworks and secure digital platforms with access controls, audit trails, and clear legal agreements, ensuring that sensitive information remains protected while non proprietary insights can be shared responsibly.
Trust, built through transparency, traceability, and strong data governance models, is another crucial element. According to Seyf, trust must be intentionally developed if the industry is to move toward more open data ecosystems.
To help advance this vision, Autolomous launched Act for Hope, a cross sector initiative focused on physician education, payer engagement, manufacturing optimisation, and knowledge sharing. Seyf emphasises opportunities such as sharing optimised manufacturing parameters, harmonising quality control methods, and co developing regulatory approaches. By responsibly pooling data, the industry can reduce risk, compress timelines, and scale more effectively, ultimately getting transformative therapies to patients faster.
