The field can no longer ignore the fundamental mystery posed by living things.

December 15, 2025, 9 AM ET

On October 8, 2024, the field of physics was plunged into controversy. That day, the Nobel Prize in Physics was awarded for discoveries not involving black holes, cosmology, or strange new subatomic particles, but about AI. How could the discipline’s highest award go to research about machines designed to mimic human brains? Where was the physics in that?

For most of the 20th century, physicists largely ignored living systems. They understood living things as machines, albeit ones made of gooey parts. A subfield called biophysics uncovered specific physical mechanisms behind those molecular machines. Organisms as a whole, however, were not a major concern.

But today, many of my colleagues in physics no longer agree with such dismissals. Instead, we have come to believe that a mystery is unfolding in every microbe, animal, and human—one that challenges basic assumptions physicists have held for centuries, and could answer essential questions about AI. It may even help redefine the field for the next generation.

The central hubris of physics has long been the idea that it is the most “fundamental” of all sciences. Physics students learn about the basic stuff of reality—space and time, energy and matter—and are told that all other scientific disciplines must reduce back down to the fundamental particles and laws that physics has generated. This philosophy, called “reductionism,” worked pretty well from Newton’s laws through much of the 20th century as physicists discovered electrons, quarks, the theory of relativity, and so on. But over the past few decades, progress in the most reductionist branches of physics has slowed. For example, 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. An early pioneer of this approach was the physicist Philip W. Anderson, who succinctly framed the nascent anti-reductionist perspective with the phrase “More is different.” Complex-systems science has grown rapidly in the 21st century, and researchers in the field won the Nobel Prize for Physics in 2021.

From a physicist’s perspective, no complex system is weirder or more challenging than life. For one thing, the organization of living matter defies physicists’ usual expectations about the universe. Your body is made of matter, just like everything else. But the atoms you’re built from today won’t be the atoms you’re built from in a year. That means you and every other living thing aren’t an inert object, like a rock, but a dynamic pattern playing out over time. The real challenge for physics, however, is that the patterns that make up life are self-organized. Living systems both create and maintain themselves in a strange kind of loop that no existing machine can replicate. Think about the cell membrane, which enables a cell to stay alive by letting some chemicals in while keeping others out. The cell creates and continually maintains the membrane, but the membrane is also itself a process that makes the cell.

That chicken-and-egg problem challenges the dream of the old physics: that once the universe’s fundamental particles were cataloged, everything else could be explicitly described and predicted. Give me a young star, and I can use the reductionist laws of physics to predict that star’s future: It will live a million years rather than a billion years; it will die as a black hole rather than as a white dwarf. But the components of a living organism yield something new and unexpected, a phenomenon called “emergence.” Give me a simple cell from the early days of Earth’s history, and I could never predict that some 4 billion years later it would evolve into a giant rabbit that can punch you in the face. Kangaroos—like humans—are an unpredictable, emergent consequence of life’s evolution.

Read: The black hole that could rewrite cosmology

The fundamental laws that govern matter and energy cannot predict another fundamental property of life: It is the only system in the universe that uses information for its own purposes. Plants grow toward light, microbes swim toward rich food sources, animals hide from predators, humans send giant metal contraptions into outer space. Although one can, say, program a robot to search for a wall plug when its battery gets low, a living thing (a human programmer, for example) must hard-code that need into the machine. Life, by contrast, is both agential and autonomous. From microbes to crabs to people, all living things have their own itches to scratch.

To truly understand living systems as self-organized, autonomous agents, physicists need to abandon their “just the particles, ma’am” mentality. One of physicists’ great talents—starting with the laws of simple parts (such as atoms) and working up to a complex whole—cannot fully account for cells, animals, or people. Luckily, the field has another elemental skill that can help: a particular way of asking questions and building models to make predictions. Physicists have always been good at capturing the essential aspects of a system and casting those essentials in the language of mathematics. *How much useful energy flows through a cell membrane? Which arrangement of neurons maximizes the information in a flatworm’s nervous system? *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.

Read: The alien intelligence in your pocket

As the 21st century continues to unfold, my fellow physicists will undoubtedly continue to advance the study of black holes, quantum mechanics, and other traditional domains. The study of life, however, will take us to places we’ve never imagined, opening a path for the future of our field that, for once, unfolds on a level playing field with biologists, ecologists, neuroscientists, and sociologists. At its best, the pursuit of fundamental answers about the nature of living things might lead physicists not only to new scientific marvels, but also to an entirely new way of doing science.

Explore More Topics