Inside the Edge: Reflections from MIT’s Imagination in Action Summit

There are moments when it feels like you’re sitting inside the future as it’s being imagined — and, in some cases, quietly engineered. The Imagination in Action summit at the Massachusetts Institute of Technology 's Media Lab that I attended this week was one of those moments.

Curated by John Werner and held in an intimate (read packed!), electric space filled with world-class scientists, technologists, ethicists, and builders, this wasn’t just a stage for showcasing what AI can do. It was a field of inquiry — into what kind of future we want to create, and what kind of leadership that will demand.

As an executive coach working with Chief AI Officers and leaders across the AI ecosystem, I came in listening for more than breakthroughs. I came listening for signals — about agency, ethics, experimentation, and what it means to lead through the unknown.

Many of the conversations stretched beyond my technical depth (am still processing Stephen Wolfram 's discussion about 'irreducible compute' building blocks). The pace of advancement, the scale of compute, the mathematical frameworks — much of it was designed for those building at the frontier of engineering. And yet, in the spaces between the keynotes and panels, I found something just as meaningful: a willingness to meet across disciplines. A curiosity that invited dialogue. A recognition that the future isn’t built in silos — and that we need each other to ask better questions.

Over 12 hours and more than 50 sessions (only 20 of which I attended), several themes kept surfacing:

• Agency in the age of autonomous agents
• Trust in systems we don’t fully understand
• Responsibility in a time of decentralization and open systems
• And the deep need for meaning in the midst of speed

This article is a reflection on those themes — not just as insights from the summit, but as signals for the kind of leadership we need now.

For those of you building, regulating, or funding the future of intelligent systems, the technical breakthroughs matter — but so do the questions behind them. This moment isn’t just about deploying more powerful models. It’s about cultivating the kind of leadership capable of navigating complexity, holding uncertainty, and making choices that serve not only performance — but people and the planet.

1. Agency in the Age of Agents

How do we lead when machines are learning to lead too?

At the heart of the summit was a question I’ve been sitting with for some time:

What does it mean to lead in an era where intelligence is increasingly agentic?

From Alex 'Sandy' Pentland and Dazza Greenwood ’s exploration of agentic AI — systems designed to serve individuals rather than corporations — to Ramesh Raskar ’s vision of NANDA as a decentralized internet for AI agents, a new kind of digital infrastructure is emerging. One where intelligent agents not only assist us, but negotiate, decide, and act on our behalf.

The promise is clear: agents that protect our data, simplify our decisions, personalize our experiences, and extend our capabilities. But so is the peril: the risk of outsourcing too much, too fast — and losing touch with the very agency that makes us human.

This isn’t just a technical shift. It’s a leadership challenge.

Will we shape these agents to serve our values — or will we find ourselves shaped by them?

And how do we develop leaders — not just systems — that can hold that line?

As AI systems become more autonomous, leadership becomes less about control and more about discernment: knowing what to delegate, what to retain, and what principles must anchor the decisions we no longer make ourselves.

2. Open Systems, Closed Incentives

Why the open-source debate is really about power, participation, and purpose.

One of the most consistent undercurrents throughout the summit was the tension between open and closed AI systems. It showed up in panels on foundation models, agentic platforms, and hallway conversations. But this wasn’t just a technical or licensing debate — it was a conversation about values.

In a gently provocative panel dialogue with Karl Zhao, PhD and Alvin Wang Graylin (汪丛青) , Bob Young — founder of Red Hat — reminded us that many of the systems we now take for granted were born out of a radically collaborative ethos. As he put it, “Proprietary software is an evolutionary dead end.”

But that future is not guaranteed. As models become larger and more expensive to train, there’s a gravitational pull toward centralized power — whether in closed labs or corporate-held APIs.

Are we building a future that’s open by design — or just open by marketing?

And can we build the leadership capacity to navigate this tension with integrity?

I found myself in one such conversation after a session with Ramesh Raskar. I asked about the economic incentives behind decentralized systems — and whether the existing wealth dynamics would simply replicate themselves. His response: “Join the conversation. Help me solve it.” Part invitation, part challenge.

That’s the work.

3. Leadership as Meaning-Making

What AI can’t replace — and why it matters more now than ever.

Throughout the day, a powerful truth surfaced again and again:

Leadership is no longer just about setting direction. It’s about helping people make sense of the terrain.

From Amy Edmondson ’s call for “safe-to-fail” environments to Jeremy Wertheimer ’s reminder that “everyone is now an AI grad student,” the message was clear: we are in a time of inquiry, not certainty.

Alisa Cohn ’s comment has stuck with me:

“The future of leadership development is the future of human development.”

In highly technical environments, the temptation is to optimize for precision. But human systems need story, safety, and meaning.

The skill sets of the future may be technical. But the soul work of leadership — presence, discernment, meaning-making — is what keeps us grounded while everything else accelerates.

This theme echoed later in the day when Andrew Ng reflected on how drastically the cost of AI prototyping has dropped. What used to take months and teams can now be tested in hours by a single engineer. The implications? Organizations can now run dozens of experiments in parallel — not to chase hype, but to discover what genuinely works.

Ng’s challenge to leaders: redesign your innovation process. Create sandboxes. Remove the friction. Enable more learning through doing — and don’t be afraid to let most of it fail.

“Smart organizations aren’t just scaling AI,” he said. “They’re scaling the number of ideas they’re willing to try.”

That’s a powerful reframe. In a world of accelerating possibility, leadership becomes less about control — and more about creating the conditions where smart failure can thrive.

4. Compute, Capital, and the Future of Access

Why the real bottlenecks may not be intelligence — but infrastructure and inequality.

A quieter but equally important thread emerged throughout the day:

AI’s scalability is bumping up against the physical limits of energy, infrastructure, and access.

This was made plain in Chase Lochmiller 's CEO session as he spoke about Crusoe ’s AI energy architecture — the single facility at ABilene, TX, is set to draw 1.2 gigawatts of power for a single datacenter. But Crusoe's energy first approach was also highlighted: bringing data centers to where Crusoe can produce clean energy (low cost clean and abundant wind and solar energy).

And in the Snowflake fireside chat, Sridhar Ramaswamy underscored how seamlessly AI is integrating into cloud-native ecosystems — but only for those with the resources to experiment.

If “AI is the new electricity,” as Andrew Ng said, then we must ask: who’s getting connected — and who’s still in the dark?

Open models may promise inclusion, but compute access remains uneven. This isn’t just a technical constraint — it’s a call to rethink how we allocate power in every sense of the word.

5. Governance, Safety, and the Work of Ethical Imagination

What kind of future are we willing to take responsibility for?

The most resonant moments of the day weren’t about scaling models — they were about holding values.

AI safety, as discussed by Jamie Metzl , Noelle R. , and others, isn’t just about aligning models. It’s about aligning systems. Aligning leadership. Aligning intent with impact.

Noelle’s metaphor of the “baby tiger” — a seemingly safe system that may grow into something unmanageable — reminded me that risk isn’t just what AI does. It’s what we ignore in how we use it. "Cute and interesting it may be at first but remember it has claws and teeth too."

One speaker asked, “Who bears the cost when AI gets it wrong?” That’s not just a policy question. It’s a leadership one.

“In a time of accelerating intelligence, our job as leaders isn’t just to keep up — it’s to slow down long enough to ask: to what end?”

Closing Reflection: Leading from the Edge

By the end of the day, I was full — not just with information, but with a deeper sense of the responsibility we carry as AI leaders, advisors, and builders.

This wasn’t a conference where you came away with simple answers. It was a space for honest tension: between speed and safety, between agency and automation, between infrastructure and inclusion.

What I left with wasn’t certainty — but clarity.

That this work isn’t just about scaling systems.

It’s about growing the kind of leadership capable of holding complexity with courage and humility.

We don’t just need smarter systems.

We need wiser stewards.

The kind of leaders who ask not only “Can we build this?” but “Who will it serve?”

Not just “How do we scale?” but “What do we want to sustain?”

If you’re reading this — you’re likely already asking these kinds of questions.

I hope this reflection has offered a few more to sit with.

And if you’d like to go deeper into what AI leaders are facing right now, I invite you to explore the

👉 CAIO Leadership Insights Report — a synthesis of over 30 interviews with senior leaders shaping the future of AI.

And if you’re navigating your own leadership questions — you’re not alone.

This is the work I do. And it’s work we can explore together.

Let’s stay in the conversation.