What is life?

As to the question, “What is life?” the first answer is that life is rare—or at least appears so far to be. As far as we know, there is only one place in the entirety of existence that has this thing called life. Second, life is fragile: Earth suffered four mass extinctions even before the dinosaurs. Third, life is persistent—once the seed is sown, it is hard to weed out. Lastly, life is adaptable; its reach spans geography, scale, and scope. Because these four observations crop up repeatedly in later chapters—fragility in ecological collapse, persistence in mycelial networks, adaptability in technological diffusion—they form the foundation on which the rest of the book builds.

Life, in this schema, is simply “the self” within self-organising matter—a re-normalised boundary at which internal information flow (and the energy-/material gradients that sustain it) outstrips communication with the outside world. A living system’s own feedback loops, regulatory networks, and continually refreshed patterns dominate its behaviour more than any external disturbance. When matter sustains its own order through internal recirculation of information, we call it alive. This definition will later let us compare biological markets (fungi, bees) with human markets (stocks, crypto) on the same information-theoretic footing.

1.1 Evolution—Life Learning to Live

Evolution is a single, open-ended learning process: the gradual amelioration of matter’s innate tendency to self-organise, where amelioration simply means greater persistence. Because survival demands richer internal models, the evolution of life is simultaneously the evolution of intelligence. Early organisms had no sight, smell, or hearing; those capacities emerged for navigating an uncertain reality. The longer a pattern endures, the more confidence we have that it will endure further. No mystical “will to survive” is required—self-organisation begets persistent structures, while collapse-prone ones vanish. Natural selection thus acts as an information-theoretic filter, amplifying enduring patterns over transient ones. This same filter will reappear, scaled up, when we later explore why certain business models, computer interfaces, or monetary regimes outlast others.

1.2 Why Mutation?

Imperfect replication—mutation—is inevitable: the more complex a self-organising structure, the higher the chance that copying introduces subtle errors. Far from being a bug, those slips power diversity, expanding the repertoire of patterns on which selection can act. Speciation is simply the branching of attractors in pattern-space—new basins of stability into which imperfect replicas occasionally wander. In financial language (Chapter 10), mutation plays the role of volatility: small random moves that allow the system to explore new equilibria.

1.3 Homeostasis—the Will to Power

Intelligent life succeeds by navigating reality’s complexity and uncertainty. Homeostasis measures that ability. It is an order-keeping process that counters entropy. Ageing is the information loss accumulated through repeated maintenance of order (López-Otín et al. 2013; Sinclair et al. 2023). Later, when we meet battery constraints in smartphones or liquidity droughts on Wall Street, we will see those, too, as homeostatic ceilings—points where maintenance costs swamp adaptive capacity.

1.4 A Nervous System

The nervous system is the locus where homeostasis is actively processed. It compresses high-dimensional sensory data into a workable world-model and predates the neocortex as the primary information system. Communication within that system is mediated by feelings: I see a tiger, I feel fear, so I run. Feelings map the state of life inside and outside the organism. Of course, not every control signal reaches consciousness; spinal reflex arcs and hormone bursts can trigger rapid action with no felt qualia. This built-in compression anticipates Chapter 9’s claim that every technological medium—from telegram to broadband—wins by shrinking the cost of transmitting relevant signals.

1.5 The Brain—Biological Motivation for the Fantastical

The brain began as an aid to the senses but evolved into a generator of internal worlds. Imagining is metabolically cheaper than perceiving—a core claim of predictive-coding and active-inference theory (Friston 2010; Clark 2013)—so the brain fills sensory gaps with top-down forecasts. Humans sample the slice of the electromagnetic spectrum most useful for survival; other species sample differently; none perceives it all. Mental imagery fuses many inputs into a higher-dimensional meta-model and lets organisms project homeostatic needs through time (“How will I feel later?”). This “offline simulation” will resurface when we frame cyberspace (Chapter 11) as humanity’s collective imagination running on silicon.

1.6 Feelings as an Information System

Aside from Antonio Damasio, few have explored feelings in computation, yet economics offers a parallel: Ludwig von Mises framed human action as the attempt to relieve felt unease. The nervous system generates unease; the brain strategises to quell it. Consciously reducing unease is how societies scale across generations—foreshadowed by Mises in Human Action (1949) and expanded by Damasio in The Strange Order of Things. By translating “unease” into prediction error, we unify neuroscience, cybernetics, and Austrian economics—a trinity that sets the stage for later critiques of central-bank policy.

1.7 Human Action—the Economics of Mises

To Mises, action is computation over time: forecasting how long a choice will keep unease at bay. In AI terms, this is reinforcement learning: Ice-cream eased hunger but caused nausea—negative reward. Steak satisfied—positive reward. Ferrari solved nothing—zero reward. The nervous system records binary feedback: Did action reduce unease? Repeat or avoid accordingly, and learning occurs. Chapter 10 will show how false price signals hijack this reward loop at the scale of entire economies.

1.8 Capital, Time, and Computation

Economics studies how scarce means serve desired ends through time at a price. Modern curricula often downplay time: trade-cycle theory ceded ground to static macro models, and time preference survives mostly in behavioural sidebars. Restoring temporal calculus realigns economics with the unease-minimising, model-based intelligence that defines living systems. In the next chapter we scale that temporal calculus from single organisms to ecosystems, watching how mycelial webs and bee colonies invest “capital” (nutrients, labour) with horizons as long as forests.

Notes & Anchors

1. Schrödinger, E. (1944) What Is Life? — rarity vs. entropy
2. Drake, F. (1961) “Drake Equation” — concurrent-civilisation probability
3. Simard, S. (2021) Finding the Mother Tree — mycorrhizal “Wood-Wide Web”
4. Holland, J. (2014) Signals and Boundaries — OLS limits in complex systems
5. Wolfram, S. (2002) A New Kind of Science — computational irreducibility