Not long ago, a prevailing pessimism gripped the scientific community. Respected scholars published papers with titles like “Are Ideas Getting Harder to Find?” and “Papers and patents are becoming less disruptive over time.” In 1996, John Horgan’s book The End of Science captured the mood, suggesting that humanity had already plucked the low-hanging fruit of nature’s secrets.
The underlying worry was that the universe offers a finite set of discoveries, and we had found the easy ones. As the mathematician Joseph-Louis Lagrange once remarked, “we cannot find more than once a system of the world to establish.” With each passing year, it seemed, more brains were needed just to maintain a fraction of the old pace of progress.
Then came artificial intelligence. While it is too early to measure the full impact on productivity, early signs are striking. Jack Clark, co-founder of Anthropic, recently noted that across different scientific fields, “progress is accelerating, and it has started to accelerate in the last two years.” His observation echoes a broader trend visible in laboratories from Tokyo to Bengaluru.
AI’s first fruits: from drug discovery to fusion
Anthropic has ramped up investment in drug development, reporting preliminary results from six collaborations powered by its Claude model. These include a 70% shorter CAR-T signaling domain, roughly 35,000 antibody designs, and more than tenfold tighter antibody binding. While still preclinical, the numbers hint at a new pace of biomedical innovation.
OpenAI, meanwhile, has announced a possible breakthrough treatment for neurodegenerative disease. Google Cloud’s chief scientist, Pushmeet Kohli, lists recent advances enabled by its Gemini model, including progress in nuclear fusion reactor design, rare disease research, and quantum computing. These are early, but they suggest that AI is not just automating tasks—it is opening new avenues of inquiry.
For Asia, this matters deeply. Countries like China, Japan, and South Korea have long invested in research and development, but they face aging populations and a shrinking pool of young scientists. AI could help offset these demographic headwinds, as Chinese universities surge in global rankings, yet the pressure to maintain momentum is intense.
The end of the “burden of knowledge”
One of the biggest constraints on scientific progress has been the sheer time it takes to train a human researcher. By the time a scientist masters the literature and reaches the frontier, decades have passed. This “burden of knowledge” has been cited as a reason ideas were getting harder to find.
AI annihilates that constraint. An AI agent can absorb the entire corpus of a field in days, not decades, and can work around the clock. Moreover, the number of such “researchers” is no longer limited by the number of talented humans. As long as we can build more chips and data centers, we can scale up research capacity in parallel.
This is not just a Western story. In India, startups are using AI to accelerate drug discovery for neglected tropical diseases. In Singapore, researchers are applying machine learning to materials science. The potential is global, but the competition is fierce. As Asia’s AI future hinges on open models and smart regulation, the region’s policymakers are watching closely.
Of course, there are risks. A golden age of science could also become a golden age of bioterrorism if safeguards fail. But for now, the trajectory is hopeful. The next few decades could be the most exciting period for human knowledge since the scientific revolution.
