Reflections from Mesh Lab Episode 1: The Future of Work & Learning

We built the wrong systems.

Our schools. Our training programs. Our credentialing frameworks. We optimized for content retention, compliance, and measurable outputs. We got exactly what we designed for. And now a machine does it all faster, cheaper, and at scale. This is the tension underneath every conversation about AI and learning: We need AI literacy. We also need human skills. And the systems we built make it nearly impossible to develop both.

I carried this tension into our first Mesh Lab conversation, a year-long expert series hosted by Insight Jam. Six of us from K-12, higher education, and workforce development gathered around one question: What is fundamentally broken in today’s learning frameworks now that AI can generate answers, content, and code on demand?

We did not find a clean answer. We found a fault line. And an unavoidable truth.

The Symptom and the Disease

Everyone wants to talk about AI-first. AI-first organizations. AI-first teams. AI-first learning. But AI-first thinking is just a symptom. The disease is deeper: We never had clarity about human outcomes in the first place. We defaulted to what we could measure. And what we could measure turned out to be what machines could automate.

Our learning systems were never designed to develop human capability. They were designed to sort, credential, and comply. AI did not break them. AI revealed they were already broken.

Automatable vs. Irreplaceable

For years, I have studied the gap between what we measure and what actually matters. Here is what I found: We perfected the measurement of what turned out to be automatable. Content knowledge. Technical procedures. Standardized outputs. We built sophisticated systems to assess and credential it all. Meanwhile, the irreplaceable skills got pushed to the margins. Curiosity. Adaptability. Agency. The capacity to navigate ambiguity, recover from failure, and create value in situations no one anticipated.

We called them soft because we couldn’t see them. What we couldn’t see, we couldn’t value. What we couldn’t value, we couldn’t fund. What we couldn’t fund, we couldn’t develop. The irreplaceable skills atrophied while we perfected the automatable ones.

Conditions, Not Content

Here is the part nobody wants to hear: The training industry is built on a lie.

You cannot lecture someone into curiosity. You cannot compliance-train your way to adaptability. You cannot competency-framework your way to agency. Human skills develop through the conditions we create. Not the content we deliver.

We built systems so obsessed with measurement and credentialing that we forgot learning is supposed to be enjoyable. We drained the joy out of development and wondered why engagement tanked. The process is the learning. When we skip the struggle, when we optimize for efficiency, when we automate the hard parts, we automate away the growth. Every shortcut we create is a developmental bypass. Every efficiency we gain is a capability we lose.

Agency does not get taught. It emerges. But only when the conditions are right. And we have spent decades building conditions that suppress it.

What Cannot Be Automated

Human presence is not a soft skill. It is infrastructure.

Trust forms in proximity. Collaboration requires reading the room. Leadership depends on sensing what cannot be said. Every act of connection depends on something that cannot be digitized, optimized, or scaled. But shaping AI requires knowing what human capability actually is. Which brings us back to the problem: we never intentionally built systems to develop it.

We built systems to sort humans. To rank them. To credential them. To make them compliant, predictable, and measurable. We did not build systems to make them curious, adaptable, and irreplaceable.

The Real Question

Before we redesign learning frameworks or AI strategies, we must be honest about what we are trying to produce. Compliant workers who can use AI tools? Or humans with the agency to solve problems we have not yet imagined?

Look at what we fund, measure, and reward. We are still building compliance machines. Still treating human skills as the soft stuff around the edges. Still acting as though content delivery is the point. It is not. The point is human capability. The point is developing people who can think, adapt, connect, and create value in a world changing faster than any curriculum can capture.

What We Are Building

This is the first of 12 Mesh Lab conversations. We are here to build an actionable blueprint.

The Future of Work and Learning Mesh brings together academics, technologists, corporate talent leaders, and futurists around one defining question: If AI can deliver instruction, write content, and solve problems faster than humans, what should education actually develop? Over the next year, this cross-sector group will design a practical framework for developing irreplaceable human capabilities from secondary education through workforce entry. 

Traditional education taught students to compete with each other. The AI economy requires humans who collaborate with each other and complement AI. This Mesh Lab is where we design the bridge between education and the workforce.

Join the ongoing discussion in the Insight Jam community. This is not work any of us can do alone.

The future is human; but only if we build it.