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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.

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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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For years, organizations have poured millions into DEI training.

And yet most employees still report discrimination. Promotion gaps persist. Trust remains uneven.

So what’s going on?

In this episode of FUTUREPROOF., I sit down with Lily Zheng — strategist and author of Fixing Fairness — to interrogate a hard truth: much of what we call DEI doesn’t work. Not because fairness is unpopular. Not because inclusion is misguided. But because we keep trying to fix people instead of fixing systems.

Lily introduces the FAIR framework — Fairness, Access, Inclusion, and Representation — and argues that the real leverage isn’t in workshops. It’s in incentives, evaluation criteria, hiring processes, and executive accountability.

We explore:

• Why standalone DEI training can backfire
• The “missing stair” metaphor — and how organizations normalize dysfunction
• The Cobra Effect of poorly designed diversity incentives
• Why representation is ultimately about trust, not optics
• What meritocracy gets wrong about itself
• And why rebranding DEI won’t solve structural problems

At a moment when DEI faces political backlash and corporate retrenchment, Lily makes a counterintuitive claim: the future of workplace inclusion will be more rigorous, more measured, and more accountable — not less.

This is a systems conversation.

Not about slogans.
Not about performative commitments.
About incentives, power, and what actually moves outcomes.

If you care about leadership, governance, and the second-order effects of institutional design, this episode will challenge you.