I read an email this week about the Singularity Executive Program, and it’s lingered in my mind. Not for its usual “AI is coming” warning—we’ve all seen those—but for their next point: AI converging with quantum computing, biotech, robotics, spatial computing, and brain-machine interfaces simultaneously.

What struck me wasn’t the ambition. The program is for CEOs and senior executives, after all. What caught my attention was the assumption: leaders must recognize exponential change before their institutions have to react. This anticipatory, not reactive, mindset is central to my concern: education must rethink its approach to keep pace with accelerating technological convergence. That frame—anticipatory rather than reactive—has stayed with me, especially as I consider my work in education.

The reality is that our educational systems are still largely designed around linear improvement cycles, static credentialing models, slow curriculum revision timelines, and compliance-driven accountability structures. I see this regularly in my consulting work with districts. A curriculum committee meets to revise math standards on a five-year cycle. A district adopts a new learning management system after an eighteen-month evaluation process. A state develops AI literacy guidelines that, by the time they’re published, reference tools and capabilities already outdated.

Meanwhile, the technologies shaping society are advancing exponentially and converging in ways that blur traditional boundaries: human and machine, physical and digital, biological and computational. The core problem: our educational systems remain linear, while the world now changes exponentially. The gap between these two realities—one linear, one exponential—isn’t just widening. It’s fundamentally different in kind.

This tension has left me with questions I’m still wrestling with:

This question merits unpacking, as it’s more provocative than it seems. Education has always prepared young people for futures not yet formed, but those futures once extended existing patterns. If you learned to read, write, and calculate in 1950, those skills remained relevant decades later. Education, with its history and stable structures, counted on continuity.

What’s different now is the rate and nature of change. We’re not just preparing students for unknown careers; we’re preparing them for futures in which the cognitive tasks we teach them may be performed differently—or by machines. The information landscape they navigate will be shaped by technologies that don’t distinguish between synthetic and human-generated content. Where biological, digital, and physical realities increasingly overlap in ways we’re only beginning to understand.

The interesting question isn’t whether education can predict these futures. It’s about cultivating in learners—and in ourselves—the capacities to navigate profound uncertainty, to ask better questions, to engage ethical complexity, and to continuously learn and unlearn.

What I appreciate about the Singularity framing, whether one agrees with its conclusions or not, is that it emphasizes early pattern recognition, ethical tension, and hands-on engagement—not just abstract strategic planning. It asks leaders to develop literacy in convergent technologies before crises force hasty responses.

Education needs similar spaces for this kind of anticipatory work. Not another initiative or compliance mandate, but genuine opportunities to rethink some of our most fundamental assumptions. The school calendar mirrors an agrarian past. Standardized testing is the primary measure of learning and accountability. Age-based grade levels. The Carnegie unit. These aren’t inherently wrong, but they are assumptions—and assumptions that made sense in one era may constrain possibilities in another.

I’m writing this not with answers, but as an invitation to think alongside one another. In my consulting work, I’m exploring these questions with the same uncertainty many of you likely feel.