Part 6/11:
Skepticism and Challenges in AI-Driven Science
Not everyone on the sidelines shares unreserved optimism. Bojan Tunguz, a data scientist, comments that scientific frontier breakthroughs have historically been limited more by our ability to evaluate and validate results than by the raw intelligence of tools used. While AI could enhance validation processes, monetizing such capabilities remains problematic, especially when incremental scientific advancements lack commercial appeal.
Moreover, critics argue that real scientific progress requires more than computational prowess; it involves rigorous validation, experimental replication, and nuanced interpretation—tasks that AI may struggle to master outside controlled settings.