An astrophysicist at the University of Rochester writes that "many" of his colleagues in physics "have come to believe that a mystery is unfolding in every microbe, animal, and human." And it’s a mystery that:

• "Challenges basic assumptions physicists have held for centuries"
• "May even help redefine the field for the next generation"
• "Could answer essential questions about AI."

In short, while physicists have favored a "reductionist" philosophy about the fundamental laws controlling the universe (energy, mattery, space, and time), "long-promised ‘theories of everything’ such as string theory, have not borne significant fruit: * There are, however, ways other than reductionism to think about what’s fundamental in the universe. Beginning in the 1980s, physicists (along with researchers in other fields) began developing new mathematical tools to study what’s called "complexity" — systems in which the whole is far more than the sum of its parts. The end goal of reductionism was to explain everything in the universe as the result of particles and their interactions. Complexity, by contrast, recognizes that once lots of particles come together to produce macroscopic things — such as organisms — knowing everything about particles isn’t enough to understand reality...

Physicists have always been good at capturing the essential aspects of a system and casting those essentials in the language of mathematics... Now those skills must be brought to bear on an age-old question that is only just getting its proper due: What is life? Using these skills, physicists — working together with representatives of all the other disciplines that make up complexity science — may crack open the question of how life formed on Earth billions of years ago and how it might have formed on the distant alien worlds we can now explore with cutting-edge telescopes. Just as important, understanding why life, as an organized system, is different at a fundamental level from all the other stuff in the universe may help astronomers design new strategies for finding it in places bearing little resemblance to Earth. Analyzing life — no matter how alien — as a self-organizing information-driven system may provide the key to detecting biosignatures on planets hundreds of light-years away.

Closer to home, studying the nature of life is likely essential to fully understanding intelligence — and building artificial versions. Throughout the current AI boom, researchers and philosophers have debated whether and when large language models might achieve general intelligence or even become conscious — or whether, in fact, some already have. The only way to properly assess such claims is to study, by any means possible, the sole agreed-upon source of general intelligence: life. Bringing the new physics of life to problems of AI may not only help researchers predict what software engineers can build; it may also reveal the limits of trying to capture life’s essential character in silicon.