When turnover is low, leadership loves to call it stability. Jay Caldwell makes the more uncomfortable point: sometimes it is just fear with better optics. In this conversation, he and David unpack why “quiet staying” can become a serious organizational liability in an AI era—especially when people are still hitting goals, still showing up, and still slowly draining the place of experimentation, risk-taking, and fresh thinking.

They also get into the deeper workforce consequences of AI adoption: why broad rollouts often create anxiety instead of momentum, why the most AI-engaged employees may be the most likely to leave, and why cutting entry-level hiring might solve a short-term budget problem while quietly wrecking your future talent pipeline.

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If your organization looks successful on paper but feels strangely tense in practice, there’s a good chance fear—not excellence—is quietly running the show. In this episode, David Rice talks with Brave Together author Chris Deaver about how fear disguises itself in high-performing workplaces: polished presentations, perfect metrics, and meetings where nobody laughs—and nobody challenges anything either.

Their conversation explores what happens when organizations reward superhero behavior, visibility over collaboration, and certainty over curiosity. The result is brittle excellence and teams that quietly fracture under pressure. The alternative, Deaver argues, is building cultures of co-creation—where leaders shift from being the smartest person in the room to becoming integrators, connectors, and context builders. In a world reshaped by AI and constant disruption, the real advantage isn’t information anymore—it’s shared wisdom, deep empathy, and the courage to build together.

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Businesses are pouring millions into generative AI—chatbots, copilots, “agents”—while quietly ignoring the other half of the AI stack that’s been delivering measurable value for decades. Predictive AI doesn’t write poetry. It predicts who’s going to churn, which transaction is fraud, and which customer is worth contacting. It calculates probabilities and helps you act on them at scale. Not glamorous. Just effective.

In this conversation, Eric Siegel—author of The AI Playbook and founder of Machine Learning Week—makes a subversive claim: most organizations should be investing at least as much in predictive AI as generative AI. The problem isn’t the math. It’s the gap between tech and business. Companies celebrate models as value. But the model isn’t the value. Acting on predictions is.

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You were told AI would clear your calendar. Instead, you’re answering 800 chats a day and wondering what, exactly, you accomplished. Productivity is up. So is the volume. You 10x your output and somehow inherit 10x the work. Welcome to the hamster wheel.

In this episode, Eliza Jackson, COO at ButcherBox, and I unpack the real transformation behind AI at work. It’s not about learning a new tool. It’s about unlearning how you work. It’s about rethinking what you own versus what you delegate. And it’s about building the kind of resilience and mindset that don’t show up on a résumé—but determine whether your team can survive what’s coming.

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Most employees are using about 1% of what AI can actually do. Not because they’re lazy. Not because they lack access. But because no one has shown them how to think with it. Meanwhile, somewhere in Silicon Valley, a 23-year-old is running a startup like they’ve got 28 PhDs sitting beside them—for a penny a minute. That gap isn’t theoretical. It’s operational. And it’s widening by the hour.

In this conversation, Kevin Surace and I dig into what that gap really means—for your productivity, your profession, and your relevance. From three-paragraph prompts to million-dollar consulting projects replicated in minutes, we explore why this wave looks familiar (desktop computers, the internet, Excel) and why it’s moving faster than all of them. Resistance isn’t noble. It’s career-limiting.

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So yeah—your dashboards look great. Your team’s shipping faster, summarizing more, “getting leverage” with AI… and all the while you might be quietly trading away the one asset you can’t buy back on a subscription plan: human judgment.

In this episode, I’m joined by Dr. Vivienne Ming—neuroscientist, entrepreneur, and an “AI realist” who has zero patience for utopian hype or Skynet fan fiction. Vivienne lays out a clean fork in the road: cognitive automation (AI does the thinking for you) vs. cognitive augmentation (AI makes you think better—often by making the work harder). If your AI strategy is mostly about convenience, this is your gentle-ish warning that convenience is not a strategy. It’s a sedative.

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AI didn’t replace your job—it replaced your value proposition. In this episode, we sit down with Taylor Blake, SVP of AI Labs at Degreed, to talk about the uncomfortable truth facing L&D teams: if your job is framed as delivering content, unblocking employees, or feeding answers in the flow of work, AI is already doing it better, faster, and without your calendar invite.

But where AI stops short is precisely where L&D’s future begins. Taylor shares how her team at Degreed lives as “customer zero,” using their own tools before shipping them to clients—which means they’re embedded in the mess, not just pitching the promise. From readiness over responsiveness to the emotional toll of relentless efficiency, this conversation explores what it really means to build capability in a world where one employee now has the power—and pressure—of ten.

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Most business leaders are still talking about AI as if it’s just another “productivity upgrade.” Meanwhile, the world building the future — massive data centers, AGI R&D, and winner‑take‑all investment — is sprinting ahead without guardrails, ethics, or broad societal input. In this conversation, researcher Christopher DiCarlo pushes executives to confront a reality most aren’t prepared for: AI isn’t a tool you add to the org chart, it’s a paradigm shift that will redefine work, power, human purpose, and morality.

This episode is part wake‑up call, part philosophical intervention. It challenges HR leaders and executives to stop asking “how much more productive do we need to be?” and start asking “what kind of future are we building — and at what cost?” If you’re still waiting for a miracle app to solve everything, this conversation will shift your perspective on AI strategy, ethics, and leadership responsibility.

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