Selection is not exclusive to biology. It is a fundamental physical force that acts on matter, favoring configurations that persist over time. This process of 'selfish matter' battling for persistence was happening long before the first cells emerged, making life a natural consequence of physics.

Challenging traditional hierarchy, Dr. Levin argues that cognition—problem-solving in various spaces—is a fundamental property of the universe that is wider than life. He suggests the conventional view (intelligent life is a tiny subset of all matter) is backward, and that life is just one way cognition manifests.

Frances Arnold, an engineer by training, reframed biological evolution as a powerful optimization algorithm. Instead of a purely biological concept, she saw it as a process for iterative design that could be harnessed in the lab to build new enzymes far more effectively than traditional methods.

A tumor can be viewed as an evolving system within the body's environment. It progresses from stage to stage by "ratcheting up" its functional information—its ability to survive and grow. This evolutionary framework could inspire novel cancer treatments.

A more effective way to define life is not by its internal components (like RNA or metabolism) but by its unique capability. Life is any system that can recursively produce many identical copies of highly complex objects, a feat only achievable through evolution.

While biology (birds) provides initial inspiration for flight, progress eventually requires engineering machine-specific solutions (jet engines). Similarly, AI learned foundational principles from human cognition, but its recent breakthroughs come from non-biological methods like massive scaling. The focus should be on universal "laws of thought," not just mimicking biological hardware.

Traditional science failed to create equations for complex biological systems because biology is too "bespoke." AI succeeds by discerning patterns from vast datasets, effectively serving as the "language" for modeling biology, much like mathematics is the language of physics.

Counterbalancing the well-known arrow of time (entropy and decay), a proposed new law of nature suggests a second arrow. This law describes the universe's inherent tendency to build greater pattern, complexity, and functional information in all evolving systems.

AI models use simple, mathematically clean loss functions. The human brain's superior learning efficiency might stem from evolution hard-coding numerous, complex, and context-specific loss functions that activate at different developmental stages, creating a sophisticated learning curriculum.