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We talk constantly about the future of work — AI agents, automation, leaner teams, productivity gains.
But what if the real drag on performance isn’t technology — it’s coordination?
Unproductive and unnecessary meetings cost companies up to $1.4 trillion every year. Seventy-one percent of senior leaders say meetings are inefficient. The average knowledge worker now spends around 11 hours a week in meetings. And nearly half admit to faking excuses to avoid them.
This isn’t a scheduling issue.
It’s a systems issue.
Dr. Rebecca Hinds — founder of the Work Innovation Lab at Asana, the Work AI Institute at Glean, and author of YOUR BEST MEETING EVER: 7 Principles for Designing Meetings That Get Things Done — argues that meetings are organizational “junk drawers.” Instead of asking whether a meeting is necessary, companies simply default to adding another recurring invite.
Her solution is radical in its simplicity: treat meetings like products.
Define the user. Clarify the outcome. Design the experience. Measure performance. Iterate.
In this episode, we zoom out beyond tactics and ask deeper questions:
Why are humans so inefficient at coordinating with one another?
 What do broken meetings reveal about incentives, trust, and accountability?
 Does AI meaningfully solve meeting dysfunction — or simply automate it?
 And in a world pushing toward automation, what is the human role in collaboration?
If coordination is broken, no productivity tool can save us.
And if meetings are the canary in the coal mine, we should probably pay attention.

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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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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.

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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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Healthcare is colliding with technology faster than most people realize.
In this episode of FUTUREPROOF., I sit down with analyst Rajiv Leventhal, who covers the intersection of healthcare, pharma, and tech, to unpack three forces reshaping the system at once: AI, GLP-1 weight loss drugs, and the mental health impact of digital life.
We start with AI as a health tool. Nearly a quarter of ChatGPT’s global weekly users now use it for health-related prompts. That’s not a niche behavior. It’s a mainstream one. The question isn’t whether people will turn to AI for medical guidance. They already are.
The real tension is trust and liability. General-purpose AI tools aren’t bound by HIPAA in the same way healthcare providers are. Yet they’re increasingly acting as digital concierges — answering late-night pediatric questions, explaining lab results, and helping people prepare for appointments in a system where access is strained.
And that system is strained. Even in major cities, patients can wait months — sometimes a year — to see specialists. When access gaps widen, alternative tools step in. AI isn’t replacing doctors. It’s filling holes.
We then turn to GLP-1 drugs and the weight-loss explosion. What began as diabetes treatment became a cultural and commercial wave driven by social media, FDA approvals, and aggressive advertising. But beneath the surface is a regulatory gray market of compounded versions, patent battles, and telehealth platforms monetizing demand.
Finally, we tackle social media’s impact on mental health. The evidence linking heavy use — especially among teens — to anxiety and depression is growing, even if causation remains complex. Is this a regulation problem? A parental problem? A public health issue? Or another example of technology moving faster than governance?
This episode isn’t about hype.
It’s about what happens when broken systems create openings — and tech companies move into the space.
Because when trust erodes and access declines, people don’t wait.
They improvise.