AI and the Principles of Evolution

Every major technology has promised to make our lives simpler. None of them did. The printing press, electricity, the internet, digitalization: each one was supposed to reduce complexity. Instead, we used every single one of them to build systems of ever higher complexity, pushing ourselves right back to the edge of what we can handle.

AI is no different. Understanding why tells us what will actually change for enterprises, and what will not.

Complexity Fills Available Capacity

Consider digitalization. Its promise was better, more reliable processes. Less paperwork, fewer errors, faster decisions. Look around any office today: people spend their days filling out spreadsheets, sending emails, managing workflows in tools they can barely keep track of. Digitalization did not reduce bureaucracy. We used it to create more rules, more processes, more administrative structures, because suddenly we could manage them.

This is not a failure of digitalization. It is a law of human systems.

We always operate at the maximum complexity our tools allow.

Before digitalization, we simply could not afford the level of bureaucratic control we have today. We would not have had the capacity to manage it. Digital tools raised the ceiling. We immediately moved in.

The same pattern repeats with AI. We will not use it to simplify our organizations. We will use it to build pharmaceutical research programs of unprecedented complexity, requiring entire Teams of specialists augmented by AI just to keep up with the science. We will use it to create financial products so intricate that no single human mind can fully comprehend them. We will use it to manage supply chains spanning continents with real-time optimization that would have been unthinkable five years ago.

We will use every bit of available AI capacity. Our data centers are already running at full throttle. When we build more, we will fill those too.

Life Creates Higher-Order Systems

Why does this happen? Because it is the fundamental principle of life itself.

Life, at its core, is a process that takes energy and uses it to create systems of higher order. A cell takes chemical energy and builds a structured organism. A plant takes sunlight and creates complex molecular structures. An ecosystem takes the energy flowing through it and produces ever more intricate webs of interdependence.

Left alone, systems tend toward disorder, toward a uniform distribution of energy. Increasing entropy is a fundamental law of the universe: the second law of thermodynamics.

Life to works against this tendency, locally. It takes energy and creates order, structure, complexity. In doing so, life actually increases the total entropy of its environment, releasing waste heat and disorder for every bit of order it builds. The local structure we see around us comes at the cost of greater disorder elsewhere. However, as long as energy flows in, life keeps building upward (see Life, Energy, and Entropy).

Our economy is nothing other than an expression of this same principle. Our organizations, our products, our markets: they are all systems of higher order that we maintain through continuous investment of energy. Leadership energy, capital, human effort.

Teams, business units, and enterprises are how humans create higher-order systems. They are the social structures through which we channel energy into organized complexity. This is why maintaining a team costs energy: leadership, coordination, motivation. Stop investing, and the team dissolves. Thermodynamics.

AI Is Just the Next Evolutionary Step

The history of human progress is a history of co-evolution between humans and their tools. We create tools. The tools extend our reach. We use the extended reach to tackle harder problems. The harder problems require better tools. The cycle continues.

Fire, agriculture, writing, printing, steam engines, electricity, computing, the internet: each of these was an evolutionary step that fundamentally expanded what humans could achieve. Each time, the expansion was not toward simplicity but toward greater complexity. Each time, the social structures of collaboration adapted but did not disappear.

AI is the current step in this sequence. It is a powerful one, certainly. But it follows the same pattern. We are already using AI to develop AI, which is simply the logical continuation of this co-evolutionary process (see Open Ended Evolution).

AI is not the end of evolution. It is the next chapter.

The question is not whether AI will change how we work. It will. The question is whether it will change the fundamental need for human collaboration. It will not.

If you follow the argument so far, you might expect that AI will at least make teams smaller or less necessary. The evidence from every previous technological revolution says otherwise.

Consider the discussion around team size and structure. Some argue that AI will dissolve traditional teams into fluid, dynamic configurations: pods, wings, pairs with AI assistants, dynamic reteaming within larger groups. These structures are not new. In well-functioning scaled organizations like LeSS, you already see groups of 50 to 60 people who know each other well enough to reconfigure their team structure from Match to Match (or Sprint to Sprint) as needed.

The concept of a “team” is deliberately broad in AME3. There is no fixed number. A hospital ward operating in shifts is a team, even though its members rarely see each other all at once. A software development group of seven is a team. A larger Arena of 50 people can function as a cohesive unit with dynamic sub-structures within it.

Think of a football squad. The team is not just the eleven players on the pitch. It includes substitutes who never play a single match but are essential to the squad’s readiness.

The club forms an Arena in terms of AME3. It includes the coaching staff, the marketing team, the fan and community liaison group. All of these Teams work together within one stable unit, each contributing to the club’s success in their own way, toward a common Ambition and shared Goals. The club itself is the Enterprise. A large one has multiple Arenas, focused on the women’s league, the junior program, the premier league.

The social pattern of the team predates every technology we have ever invented. It will outlast AI too.

What changes is what teams take responsibility for. As described in The AI-Enhanced Team of the Future, teams are evolving from component-focused groups to full-business teams that own end-to-end value chains. AI accelerates this evolution. It does not replace the team itself.

New Products Require Reorganization

There is one dimension where AI’s impact goes beyond previous technological shifts: the sheer speed at which new products and services can emerge.

When AI enables a team to build, test, and deploy a new product in weeks rather than months, the bottleneck shifts from development to organizational adaptation. Your enterprise must be able to absorb new products into its portfolio, spin up new Arenas, restructure value chains, and reallocate resources, all within the cadence of a Tournament

This is where Evolution Focus becomes critical. Every advancement along the evolution path requires reorganization. This is not dramatic in an AME3 organization. It is standard practice. The flexibility to reorganize is built into the system through Empirical Control and the lean governance structures of the Enterprise.

But make no mistake: the pace of reorganization will increase. Enterprises that treat organizational structure as fixed will fall behind those that treat it as a living system, one that evolves alongside its products and markets (see also Developing a Strategy for the GenAI Era).

The Principle

Life creates systems of ever higher order as long as energy is available. Humans always push their systems to the maximum complexity their tools allow. AI is the latest and most powerful tool in this sequence, but it follows the same evolutionary pattern as every tool before it.

Teams will not disappear. They will widen their scope, take on broader responsibility, and deliver outcomes that were previously impossible. The organizations that thrive will be those that understand this principle and design their structures accordingly: flexible, empirical, and always ready to evolve.

As long as we have energy, we will build. And as long as we build, we will build together.