Metascience

Scientific literature is plagued by fraudulent publications, undermining trust and slowing progress.

The volume of scientific publications is overwhelming, making it difficult for humans to read, comprehend, and synthesize the entire body of literature. How can AI-generated knowledge become cumulative? What should a machine-human shared Wikipedia look like? We should collect and synthesize all the world’s knowledge, accelerate its development, and make it universally available in a compelling form.

Traditional structures dominate in how research is conducted and how its outputs are disseminated. Expensive publishing practices restrict and slow the spread of knowledge. We should replace outdated publishing practices and complement research practices with new approaches that leverage frugal innovation, community-led platforms, and open access. We imagine a future where scientific discovery is more inclusive and dynamic.

Current clinical trial designs are not sufficiently optimized for gathering robust evidence, leading to inefficiencies and suboptimal outcomes.

This is more of a meta-bottleneck. But scientists are spending a lot of time not doing science, and the institutional structures in which they work are often set up with incentive structures that hinder certain kinds of outcomes, like more coordinated research.