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We live in a moment where disagreement feels dangerous.

Politics is polarized. Social media amplifies outrage. Inside companies, dissent is often muted — not because people agree, but because they assume speaking up will damage relationships or reputations.

But what if most of that fear is wrong?

Julia Minson, decision scientist at Harvard Kennedy School, studies the psychology of disagreement. Her research on “conversational receptiveness” reveals something counterintuitive: people systematically overestimate how much disagreement will harm a relationship and underestimate how much thoughtful dissent earns respect.

That miscalculation has consequences.

When leaders avoid disagreement, bad ideas survive. When teams confuse persuasion with understanding, trust erodes. When we treat conflict as a character flaw rather than a cognitive process, we weaken our institutions.

In this episode, we explore why humans are wired to assume they’re objectively right, how subtle language shifts can dramatically increase receptiveness, and why polarization may be less about ideology and more about judgment errors.

And in an era where AI systems increasingly summarize, mediate, and even “assist” in conflict, what happens if our tools inherit our biases? And if healthy disagreement is essential to good decision-making, how do we preserve it inside organizations that prize alignment over friction?

This isn’t a conversation about compromise.

It’s about whether we still know how to disagree in ways that make us smarter.

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In this episode of FUTUREPROOF., we sit down with Kevin Ashton—the technologist who coined the term Internet of Things and helped usher in the smartphone era—to talk about something even more foundational than AI.

Stories.

In his new book, The Story of Stories, Kevin traces a million-year arc—from the first fires where early humans gathered, to the invention of writing and printing, to electricity, electronics, and the smartphone. His thesis is provocative: language did not create stories. Stories created language.

Every major storytelling revolution has followed a simple pattern: it increases the number of people who can tell stories—and the number of people who can hear them.

For the first time in history, anyone can tell stories to everyone.

But there’s a catch.

While AI cannot understand meaning, algorithms now determine which stories we see, amplifying bias, shaping belief, and influencing behavior at scale. The power of storytelling has never been more democratized—or more intermediated.

We explore:

• Why storytelling is innate, not cultural
• The eight great revolutions of human communication
• Why machines can generate content but not meaning
• The risks of algorithmic amplification
• The role of critical thinking in a post-scarcity information world
• Whether the next storytelling revolution is technological—or cognitive

This conversation isn’t about nostalgia.
It’s about understanding the oldest human technology in a moment when the newest one is accelerating everything.

If we think in stories—and we always will—the question becomes:
Who shapes the stories that shape us?

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Everyone says they’re “AI-first.”

Very few organizations are AI-ready.

In this episode of FUTUREPROOF., we sit down with Ben Tasker, who is leading one of the largest workforce-scale AI education efforts in the public utility sector — upskilling 36,000 employees while advising global organizations on certification and governance.

Ben calls this moment the “AI Between Times.” The tools are evolving rapidly, but the AI-driven economy they promise hasn’t fully stabilized. That gap creates risk — and opportunity.

We unpack what actually breaks when companies try to move beyond pilot projects:

• Why buying AI tools is easy — and building internal capability isn’t
• The tension between augmentation and displacement
• What the 70/30 rule means in cost-constrained environments
• Why governance must precede implementation
• And how AI fluency is quietly becoming a new form of institutional power

Ben argues that AI strategy lives or dies at the human level. Not because technology isn’t powerful, but because incentives, culture, and leadership determine whether that power compounds or fractures an organization.

This conversation isn’t about hype cycles.

It’s about whether institutions can transform fast enough — without breaking trust in the process.

Because the future of work won’t be defined by who bought the best tools.

It will be defined by who prepared their people.