Genes, Plasticity, and Learning

When we learn we are drawing on something old and profoundly biological: not just our brains, which we sometimes overly credit for our ability to learn, but also the human nervous system which is constantly updating itself in real time. These systems are intertwined which sometimes makes learning easy and sometimes hard. Sometimes adrenaline supercharges our excitement and primes us for learning. Other times the flight or fight instinct kicks in and drives us away from that learning experience.

This article is about that capacity. We’ll disentangle three ideas that get conflated: evolution, adaptation, and learning. Then we will focus on the specific form that matters most to your career right now: phenotypic plasticity, our ability to change our behavior or function within a lifetime. We’ll cross the Baldwin Bridge (how learning today can shape tomorrow’s landscape), look at cultural inheritance (how groups pass on strategies faster than genes ever could), and we’ll explore the constraints and trade-offs (plasticity isn’t free). Along the way, we’ll connect how our bodies and brains function together with the specifics of our current environmental challenge, the rapid and massive shift which AI is bringing to our lives and our work.

Evolution vs. Adaptation vs. Learning: What’s the difference?

There are several different ways that humans (and all animals) change over different timescales. One of the things which has made our species more successful than any other on Earth is in the speed and efficiency with which we have been able to change ourselves over short spans of time. Many factors contribute to this capacity for change including language, tool use, and the collaborative communities we live within. There is a core biological truth about human capacity for change which emerged over a long period and which enables our shorter period capacity for change. Briefly, these mechanisms are:
Evolution which is a long-term population-level change mechanism that works across generations. It works on heritable variation; it’s slow compared to an individual human lifetime, but has set us up for success as a species.

Adaptation has two senses. In evolutionary biology, it’s the fit that results from selection over time; in everyday life, we also use it to mean how an individual adjusts to new conditions.

Learning is the individual mechanism such as neurons rewiring, habits forming, and mental models updating which makes within-life adaptation possible.

Why the fuss over definitions? When people talk about “adaptability,” they can mean two different kinds of change. One operates across many lifetimes; the other unfolds inside a single one. In an age of temporal compression, we don’t have centuries for traits to evolve. All of our leverage is in how we learn and how in learning we can then adapt. The key is in deliberately shaping how to take in feedback and change behaviors and habits. That’s the practical, within-lifetime meaning of adaptability we’ll use throughout: the trainable capacity to sense change, update mental models, and reconfigure behaviors and tools so you can thrive in new conditions.

Keeping these slow and the fast forms of adaptation distinct clarifies what we can influence today and what we inherit from yesterday and can allow us to ease our fear of the moment (the fight or flight instinct) by reframing how AI will affect us from a grand evolutionary drama (“My species is being replaced”) to a learning problem (“My brain can absorb a new grammar and make it useful”).

Start with the slow story. Over deep time, populations accumulate traits that made survival more likely in the environments our ancestors faced. This is the relationship between evolution and adaptation. It is the reason we have stereoscopic vision, why we balance upright on two legs, why our hands can both grip a hammer and thread a needle, and why the human brain comes pre-wired with a remarkable capacity for language. None of us “learned” to have opposable thumbs. We were born into a species that, over countless generations, was shaped by selection to have that physical advantage. Evolutionary adaptation is stable and species-typical and it’s the backdrop against which our lives play out.

Against that backdrop, each of us is constantly adjusting. This is the relationship adaptation and learning, adaptation in the everyday. Within-life changes that improve our fit to current conditions. That adjustment shows up through three channels.

First, our bodies acclimatize. Spend time at altitude and your breathing quickens; after days, your blood chemistry shifts; after weeks, your endurance improves. Work a summer outdoors and your skin darkens. Train in heat and your body learns to sweat sooner and conserve salts. These changes are reversible. They don’t rewrite your genome; they tune the dials you already have.

Second, we change developmentally, especially during sensitive windows. The foods you ate and the activities you practiced as a child shaped your frame, your voice, even the fine anatomy of your hands. Grow up immersed in a language and your ear tunes itself to its sounds; the accent you form early becomes a kind of cognitive imprint of that exposure. These changes are less reversible. They’re not genetic, but they can be long-lasting because they influence how the organism builds itself.

Third, and most visibly in adult life, we adapt behaviorally. We alter routines, adopt new strategies, and acquire skills in response to demand. A team under deadline moves to daily standups. A parent rearranges mornings when school start times change. A developer learns a new framework because the project requires it. This kind of adaptation is where learning does most of its work.

Adaptation is the observed improvement in fit and learning is the engine which powers that improvement. Learning changes behavior and mental models based on experience. In the brain, that means synapses strengthen or weaken, circuits reorganize, and patterns of attention and prediction shift. In communities, it means sharing symbols and rules such as language, tools, norms, and the playbooks we pass around. Learning spans a spectrum: the automatic tuning of habits through reinforcement; the deliberate construction of concepts and theories; and the social absorption of know-how from mentors, peers, books, code, and institutions.

You can feel these layers interacting in a single, ordinary transition. Imagine moving from sea level to Denver, “ the mile high city.” In the first days, you get winded climbing stairs. Without being asked, your body starts to adjust: you breathe a bit faster; after a while, you carry more oxygen in your blood. That’s acclimatization. If you had been born and raised there, your chest shape and capillary density might have developed slightly differently though since youthful developmental plasticity is more powerful than our later capacity for acclimatization. For both the young and old though, learning can contribute. Friends and colleagues may suggest that you slow your pace on runs, practice “pressure breathing,” drink more water; and be cautious of the more intense sun at altitude. Whether explicitly in a doctor’s advice or self-help article or through a layer of cultural learning about trail etiquette, coaching cues at the gym, or other city rhythms your brain can direct your body to adapt faster than it could do so alone.

None of this alters your DNA; all of it improves your fit to the place.

A musician’s life tells the same story from a different angle. The species-level evolutionary endowment gives us hands with extraordinary dexterity and a brain ready to detect rhythm and pattern. It is years of practice in childhood and adolescence though which shape bones, tendons, and neural pathways and other developmental changes that make certain movements effortless. Learning is important here as well as daily rehearsal and feedback from teachers polish timing and tone. Here again: evolution supplies the equipment; within-life adaptation fits the player to the instrument and the audience; learning does the heavy lifting.

Two quick rules of thumb keep the vocabulary honest. When you’re describing traits shaped across generations, call that evolutionary adaptation. When you’re describing the ways a person adjusts within a life, call it adaptation in the everyday sense, and be clear about which channel you mean: physiological, developmental, or behavioral. Some within-life adaptation is learning (new strategies, skills, concepts); some is not (your spleen’s behavior at altitude doesn’t improve because you studied, it improves because your physiology can tune itself).

What matters for this book is how these layers enable one another. Evolution didn’t just equip us with fixed tricks; it endowed us with a capacity to change. This is the plasticity that makes learning possible. And learning, in turn, makes rapid within-life adaptation routine; it lets a team adopt Scrum next month or a profession codify best practices over a decade. And through culture, individual learning can be shared with other learners. We store it in procedures, software, textbooks, and institutions, so that we can all accelerate the learning of the other people in our teams, companies, and societies.

Adaptability, then, isn’t a single mechanism. It’s a stack. At the base is the deep-time inheritance that made us the kind of creatures who can learn quickly. In the middle are the bodily and developmental systems that set the range and speed of our adjustments. At the top are the day-to-day learning processes, both personal and collective, that translate experience into better fit. When you feel yourself “getting the hang of” a new role, city, or technology, you are watching that stack in motion. Evolution designed the learners; learning does most of the adapting; culture carries the best of it forward.

Exercise 4: Learn one new tool per week

Set yourself a goal over the next month, in which each week you investigate and test a new AI tool. Define some aspect of your work that you would like to enhance using AI. Bring to this your sense of what should remain human, while the machine can make a contribution. Develop your ability to explain what works and what doesn’t work. And above all bring your patience. Learning is not easy, it takes time.

Before and after each investigation and experiment start with your physical state: three minutes of breath or a brisk walk to down-shift threat. Then do one focused learning block. End by writing the reward signal you’ll feed back into the system (or your next attempt).

Close your experiment by writing the three things you remember, from memory. Then test them. Retrieval strengthens the exact neural pathways you need under pressure. (We’ll formalize this in your learning sprints later.)

Design your network.

To help you in succeeding on this journey, surround yourself with people who’ll let you learn in public and be sure to have some connections to people who stretch your thinking. Find someone who will schedule a weekly 30-minute “apprentice slot” with data science and a monthly coffee with a product manager outside her lane.

Adaptation happens and brains rewire when signals are clear and energy is available. Learning creates those signals when the right balance is created between repetition, spacing, interleaving, and positive feedback loops. Organizations can define a culture of learning through artifacts and systems which spread ideas across teams. Managing overload while also maintaining a cadence which is inspirational is challenging both as individuals and as leaders. Biological adaptability provides the basic tools we need to learn and adapt, but organizations can help by structuring an environment in which we can move from coping to shaping.