by Ashutosh Jogalekar
AI is collapsing barriers in scientific disciplines, most notably in math. An 87-year old conjecture named the Jacobian conjecture was recently solved with help from AI. An AI model solved another 80-year old conjecture, the distance conjecture. Many Erdős problems, named after the prolific late mathematician Paul Erdős, are swiftly succumbing to AI. Some mathematicians like Terence Tao – regarded by some as the greatest living mathematician – have simply come to terms with using AI in their research and think that AI will be as integral to math as were slide rules and calculators. Nor is math the only field that is is seeing striking inroads made with AI. Other theoretical disciplines, including theoretical computer science, are also being rattled. Theorist Henry Yuen sounded a bit shaken to see how many problems in his discipline were being solved by AI, and said that he could “feel the importance of these problems in his bones.”
Given this spectacular progress of AI in math and science, it does not seem unreasonable to ask the next question: how long before AI solves outstanding problems in all sciences: physics, chemistry, biology, astronomy, geology? And then how long before it also solves outstanding problems in other adjacent disciplines: neuroscience, psychology, anthropology, sociology, philosophy? Will we soon live in a post-human world, at least so far as science is concerned, which considering that science is what helps us make sense of the universe, means a post-human world, period?
First of all, let’s state the obvious. Any kind of optimism that AI will solve problems in applied sciences like chemistry and biology anytime soon is misplaced for obvious reasons: unlike math and theoretical computer science and even parts of theoretical physics, these disciplines need experimental verification and the right data. Unlike math, you simply can’t think your way to a new drug or material. In fact if you look at most of the Nobel-winning discoveries in these disciplines, you will realize that while AI could likely have helped accelerate them by generating ideas, most of them involved the serendipitous discovery of new biological molecules or mechanisms. An equal number involved the invention of new tools and techniques. It’s hard to see how AI could have possibly done this by itself. At least in the near future, I don’t see how AI can simply think its way to a new cancer drug, a new mechanism of memory or a new technique to unravel the mysteries of life, hype from AI leaders notwithstanding.
But beyond these specific domains, the entire debate around AI revolves around a more profound question. Read more »






There has long been a temptation in science to imagine one system that can explain everything. For a while, that dream belonged to physics, whose practitioners, armed with a handful of equations, could describe the orbits of planets and the spin of electrons. In recent years, the torch has been seized by artificial intelligence. With enough data, we are told, the machine will learn the world. If this sounds like a passing of the crown, it has also become, in a curious way, a rivalry. Like the cinematic conflict between vampires and werewolves in the Underworld franchise, AI and physics have been cast as two immortal powers fighting for dominion over knowledge. AI enthusiasts claim that the laws of nature will simply fall out of sufficiently large data sets. Physicists counter that data without principle is merely glorified curve-fitting.
In recent years chatbots powered by large language models have been slowing moving to the pulpit. Tools like 










