In this episode, I have a fascinating conversation with Jim Highsmith. We dive into Jim’s six-decade career in software development, his role in the Agile movement, and how his early influences continue to shape his thinking on digital transformation today. Jim shares stories from the punch card era to the Agile Manifesto, offering insight into the evolution of our industry.

We begin by exploring Jim's early work at Exxon, debugging code with hexadecimal printouts, and his eventual pivot into structured methods and adaptive development showing a career built on embracing risk, fostering change, and learning through experience.

Jim recounts the serendipitous path that led him to the Agile Manifesto in 2001, where he collaborated with figures like Kent Beck and Martin Fowler. He shares how his early thinking around adaptive methodologies aligned with what became known as Agile, even before the term existed. Throughout, Jim highlights how technological shifts, especially the rise of the internet, fundamentally altered software's purpose requiring new development paradigms.

In reflecting on Agile’s legacy, Jim contrasts optimization (à la Deming’s statistical process control) with adaptation (rooted in people, learning, and responsiveness). He emphasizes the importance of context in applying any methodology, whether Agile, Lean, or DevOps, and cautions against rigid orthodoxy in favor of flexible thinking. The conversation also touches on Deming’s influence, the missed opportunity for Agile and DevOps convergence, and Jim’s role in fostering integration between the Agile Alliance and the Project Management Institute (PMI).

Looking to the future, Jim sees AI as a transformation on the scale of the internet, requiring organizations to adopt adaptive mindsets or risk irrelevance. He warns that those who fail at Agile will likely fail at AI if they don’t build adaptive learning into their culture. He advocates for reimagining agility not as a fixed set of practices, but as a living, philosophical approach responsive to continual change.

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.