In 1951, philosopher Martin Heidegger told a small audience, “The most thought-provoking thing in our thought-provoking time is that we are still not thinking.” Few understood him then. Seventy-five years later, the observation has become unavoidable because AI has forced every leader to confront a question about the nature of intelligence and thinking itself. If thinking is nothing but what machines can do, only faster, we have no case: We outsource it to machines. But if thinking is something else — an embodied, attentive activity through which reality reveals itself — then leadership in the age of AI is the task of cultivating a generative capacity no machine can replicate.

Consider the doctor who treats screens instead of patients, the teacher constrained by standardized testing, or the World Cup referee whose real-time decisions are repeatedly overturned by a video assistant referee. Everywhere, situation-sensitive judgment is being replaced by what Hartmut Rosa calls execution logic: prestructured parameters that turn decision makers into mere executors.1 As spheres of discretion disappear, the creativity of human agency drains away. Beneath these surface symptoms sits the deeper question now beginning to surface in boardrooms: What is irreplaceable about us, and which intelligence will be the foundation of durable advantage once everything codifiable has been automated?

Every leader I work with — in business, government, international institutions, and nongovernmental organizations — reports the same thing. The machine is spinning faster than they can process and think. The acceleration extends far beyond AI: the inbox, the KPIs, escalating disruptions, tools meant to save time that consume more. Overwhelm has become a shared planetary experience. It is also an early warning signal that something essential is being eroded, precisely when we most need it.

This erosion has a name, a diagnosis, and a response. The name is intelligence monoculture: the assumption that AI is the only intelligence worth investing in. The diagnosis is that monocultures, sooner or later, collapse. Our response should be to create a second infrastructure, running in parallel to the agentic AI-enabled IT stack: a deep-sensing leadership infrastructure that cultivates the collective capacities to co-sense and cocreate at the level of the whole system. With it, AI becomes survivable and useful. Without it, the first infrastructure depletes the very soil it is rooted in — heading toward erosion and, eventually, collapse.

This is the blind spot. Leaders have a strong grasp of what they do (the actions they take, the strategies they execute) and how they do it (the processes, the systems, the tools). What remains hidden is the inner place from which they operate: the source of attention, intention, and creativity that no machine can replicate.

The age of AI forces us to clarify our assumptions about intelligence. Can thinking be reduced to computation and pattern recognition? Or is human thinking qualitatively different? And underlying this looms a deeper question: Who are we as human beings? Are we mere extensions of increasingly powerful algorithms — or genuine sources of awareness, intention, and agency?

Three Intelligences for the Age of AI

Intelligence is not one thing. At a minimum, three forms must be differentiated and integrated.

AI in the form of large language models (LLMs) is a pattern-prediction machine, matching and meshing existing human knowledge at a superhuman speed. Trained on existing data, AI deals extraordinarily well with dynamic complexity. It’s powerful — but structurally backward-looking, even when it appears to look forward.

Organic intelligence (OI) is the intelligence of structurally coupled living systems in ecologies of relationships. It senses multiple perspectives and orients to see with rather than just look at.2 This is where empathic listening lives. OI handles social complexity — the texture of multiple worldviews, cultures, and interests.

Source intelligence (SI) is the intelligence of the whole social field — the social soil from which all perspectives emerge. It is sourced not only from what is but from what is emerging. SI also stands for soil intelligence: the intelligence of the social mycelium running through that soil that connects what looks separate aboveground. Examples are entrepreneurs and leaders who sense and create a future that does not yet exist.

SI is grounded in what Eva Pomeroy and I have called fourth-person knowing: the source from which collective action arises.3 It handles emerging complexity: Where the solution is unknown, the problem keeps changing, and it is unclear who needs to be at the table.

The three intelligences are highly interwoven and nested, with source intelligence at the core and organic and artificial intelligences in the surrounding spheres. An intelligence monoculture — almost entirely dominated by AI — would look like an empty shell. There would still be some hardware. But the living, breathing inner core would be gone, turning the shell into a superhardened iron cage for those trapped inside.

The standard fear about AI runs one way: Machines are becoming more like humans. The real danger, though, may run in the opposite direction. We are becoming more like machines — not physically, but epistemically: We see thinking as computation, learning as data processing, creativity as recombination, decision-making as optimization, and the human self as algorithm. That epistemic conversion is what makes LLMs so seductive: They do not need to actually understand. They only need us to have already redefined understanding as what they do.

Perhaps the ultimate gift of AI is this: It holds up a mirror that forces us to see ourselves and ask, “Who are we? And who do we want to become?”