In this episode, I sit down with Mark Graban, a leading voice in Lean and continuous improvement, to explore the enduring relevance of W. Edwards Deming’s principles in modern industries. Mark shares his decades of work in healthcare, manufacturing, and leadership consulting.

We dive into key themes from Mark’s career and writing, particularly his latest book, The Mistakes That Make Us: Cultivating a Culture of Learning and Innovation. He emphasizes how Deming’s ideas, such as eliminating fear and focusing on systemic improvement, remain critical today—especially in healthcare, where Lean and quality management have taken root in pockets but struggle to become the prevailing management philosophy.

A major focus of our discussion is the power of learning from mistakes. Mark explains how organizations like Toyota have embedded problem-solving into their culture, emphasizing that true improvement starts with surfacing problems, not hiding them. We also touch on psychological safety—how creating an environment where people feel safe to speak up is foundational for innovation and systemic learning.

Mark shares insights from running Deming’s famous Red Bead Experiment and why it still resonates today, illustrating how poor management practices persist despite decades of evidence against them. We also discuss corporate scandals like Wells Fargo’s account fraud scandal, where systemic pressures—not individual failings—led to widespread unethical behavior.

From his experiences in Japan studying Lean firsthand to the importance of small-scale experimentation in driving innovation, Mark offers a compelling argument for why organizations must rethink their approach to mistakes. Instead of punishing failures, companies should view them as opportunities to refine their systems and foster real innovation.

In this episode, I have a fascinating conversation with Reuven Cohen, someone who I believe is one of the most important voices in AI today.

Reuven recounts his journey in technology, from being an early advocate of cloud computing to now working at the cutting edge of AI and reasoning models. He shares insights into how AI is shifting the nature of work, particularly in fields like software development, business operations, and decision-making. He describes AI as "cloud computing 2.0, but with intelligence," emphasizing its role in cognitive offloading—augmenting human capability rather than merely automating tasks.

A key theme of the discussion is AI’s impact on productivity and workforce structure. Reuven shares staggering personal metrics—writing nearly 10 million lines of code in a year, something that would take a traditional developer thousands of lifetimes. He argues that AI is not replacing jobs outright, but fundamentally changing who remains valuable in an organization. He suggests that companies must decide whether to empower their top 10% to become exponentially more productive or replace the bottom 90% with AI-driven automation.

The conversation also dives into reasoning models versus instruct models, discussing when to use each in business applications. Reuven explains neurosymbolic AI, a new frontier where AI models don't just process natural language but interact with the world using symbolic logic and mathematics. He believes this approach will be essential for future breakthroughs in AI comprehension and decision-making.

As the episode progresses, John and Reuven reflect on the geopolitical landscape of AI, noting that China has become a dominant force in AI development. They discuss DeepSeek, the Chinese-developed reasoning model, and how it has disrupted traditional players like OpenAI and Google.

To wrap up, Reuven shares his latest projects, including an AI-driven truth detection system, which sparked ethical debates about transparency, privacy, and misinformation. He envisions a future where AI is not just an assistant but an autonomous force that reshapes industries, economies, and even the nature of work itself.

In this episode, I talk with returning guest Joseph Enochs about the artificial intelligence (AI) world and its implications for businesses and innovation. A major highlight of the conversation is an analysis of DeepSeek, an open-source AI model developed by a Chinese company. Joseph explains how DeepSeek and similar models demonstrate that AI development is becoming increasingly accessible globally. With only a fraction of the computing resources used by giants like OpenAI and Meta, DeepSeek has replicated the performance of cutting-edge models like GPT-4. This, Joseph notes, is a clear example of how creativity and resourcefulness can overcome technological constraints, further accelerating the democratization of AI.

The conversation also dives into emergent behaviors, where AI models demonstrate the ability to reason about new and unseen data, similar to human problem-solving. Joseph discusses critical benchmarks like GPQA (Google-Proof Question Answering) and the ARC Prize, which measure these capabilities. He highlights how modern models use reinforcement learning to develop reasoning skills, making them capable of tackling complex tasks at an unprecedented level of sophistication.

We also touch on practical business considerations, such as how organizations can evaluate AI models for cost-efficiency and task-specific performance. Joseph advises leaders to use AI-driven frameworks to determine when to invest in high-cost, high-performance models like GPT-4 Omni versus smaller, fine-tuned models for less complex problems. He underscores that open-source innovations will continue to push costs down and improve accessibility for businesses of all sizes.

The discussion wraps up with a reflection on the importance of knowledge sharing, applied research, and collaborative learning to accelerate the adoption of AI in solving real-world problems.