AI-enabled scientific discovery is rapidly evolving from hype to reality, though significant limitations remain. The article traces the journey from Adam, a robot that made the first fully automated scientific discoveries in the 2000s, to today's powerful AI agents like ChatGPT running on GPT-5 pro, which helped physicist Alex Lupsasca independently rediscover black hole symmetries and mathematician Ernest Ryu prove a new optimization theorem. Both researchers have since joined OpenAI's new Science team. Meanwhile, specialized tools like AlphaFold (which earned a 2024 Nobel Prize) are enabling drug discovery breakthroughs, and companies like Insilico Medicine have used AI to identify both a disease-causing protein and a blocking drug for idiopathic pulmonary fibrosis, now in clinical trials.

Skeptics like NYU cognitive scientist Gary Marcus warn that large language models are fundamentally limited — they excel at searching within defined boundaries but lack the causal understanding and creativity needed for paradigm-shifting discoveries like relativity or continental drift. LLMs have also fueled a rise in junk science and paper mills. The most promising approaches appear to combine general-purpose AI agents with specialized, verifiable tools like knowledge graphs and domain-specific models, rather than relying on any single system.

Looking ahead, researchers are building systems that can construct their own experimental frameworks — like AutoRA for social science and Code Scientist for computer science — though these still lack true creativity and sometimes fabricate results. Google DeepMind's Demis Hassabis estimates genuine AI creativity is five to ten years away. A key bottleneck remains the physical world: AI-generated hypotheses must still be validated in real laboratories, and bridging that gap with robotic labs and tools like XR glasses for human-AI collaboration represents the next frontier.