FUTUREPROOF.

Send us Fan Mail
We talk a lot about AI reshaping the future.
We talk less about who gets to participate in it.
In this episode of FUTUREPROOF., I sit down with Corbb O’Connor, who leads accessibility advocacy at Level Access. Corbb is blind. He’s spent years consulting enterprise teams — from financial institutions to global brands — helping them design digital experiences that are actually usable by people with disabilities.
This isn’t a compliance conversation.
It’s a systems conversation.
As AI systems increasingly generate interfaces, content, decisions, and workflows at scale, accessibility can no longer be an afterthought. If accessibility isn’t embedded upstream — in product design, in data pipelines, in AI outputs — exclusion compounds just as quickly as innovation.
Corbb argues that inclusion is not a moral add-on. It’s infrastructure. It’s economics. It’s risk management. And increasingly, it’s competitive advantage.
We explore:
 Why accessibility should be treated like cybersecurity — a non-negotiable requirement, not a retroactive fix 
 The difference between “AI for accessibility” and “accessible AI” 
 Why automated scanning tools can’t replace human testing 
 How poor product design quietly excludes users without teams even realizing it 
 Why psychological safety and culture matter just as much as tooling 
 And whether AI will widen or narrow accessibility gaps over the next five years 
If digital products define access to banking, healthcare, employment, and civic life, then accessibility isn’t a feature.
It’s participation.
And as AI becomes core infrastructure, the question becomes sharper:
Are we scaling inclusion — or scaling exclusion?

Send us Fan Mail
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.

Send us Fan Mail
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.

Send us Fan Mail
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.

Send us Fan Mail
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?