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.

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.

In this episode, I continue my conversation with Laksh Raghavan, a cybersecurity leader and systems thinker, diving into profound insights on applying multidisciplinary approaches to organizational challenges. Picking up from Part 1, this discussion illuminates the principles of W. Edwards Deming and other thought leaders in fostering organizational reliability, productivity, and innovation.

The episode opens with a discussion on Herbert Simon's "satisficing" and its organizational implications. Laksh emphasizes how businesses like McDonald's excel by prioritizing reliability over perfection, ensuring consistent experiences across global markets. He connects this to Deming's principles of variation reduction, explaining how psychological perceptions of quality—rather than objective measures—often dictate success. This theme extends to companies like Apple, which masterfully align human psychology with technological precision to command premium loyalty and profits.

We also explore behavioral science's role in technology and consumer behavior, from Uber's elimination of uncertainty in ride-hailing to Google's laser focus on search quality. They highlight the importance of understanding human psychology when solving organizational problems, as demonstrated by the famous "elevator mirrors" anecdote from Manhattan skyscrapers. Laksh masterfully ties these insights to modern developer productivity, arguing that reducing psychological friction, rather than merely optimizing technical processes, leads to sustainable performance improvements.

The conversation crescendos with a deep dive into systems thinking, advocating for leadership frameworks that address interconnected "messes" rather than isolated problems. Laksh shares the vital role of education and storytelling in cultivating systemic thinking within organizations, drawing parallels between Deming’s teachings and modern challenges in cybersecurity and software delivery.

You can learn more about the Cyb3rSyn community and join through the following links below:

https://www.cyb3rsyn.com/

https://www.cyb3rsyn.com/p/announcing-cyb3rsyn-labs

In this episode of The Profound Podcast, I welcome back cybersecurity expert and systems thinker Laksh Raghavan. Laksh explores how multidisciplinary thinking bridges the gap between theory and practice in solving complex problems.

The discussion begins with Laksh's professional journey, highlighting his early dissatisfaction with conventional consulting and his pivot toward systems thinking and complexity science. Inspired by figures like Kurt Lewin, Charlie Munger, and Russell Ackoff, Laksh outlines his approach to integrating insights from diverse disciplines to better address both technical and socio-technical challenges.

A central theme of the episode is multidisciplinary thinking, which Laksh describes as synthesizing insights from various fields—physics, psychology, and economics—to build a latticework of mental models. He compares this process to what Charlie Munger advocated for in investing, but applies it internally to organizational design and problem-solving.

We delve into foundational concepts like epistemic humility, the "blind men and the elephant" parable, and the challenges of communicating complex ideas to diverse audiences. Laksh critiques what he terms the "Silicon Valley syndrome," where reductive technical solutions often miss the human elements integral to sustainable success. Examples like Elon Musk's organizational strategies and the "doorman fallacy" underscore the need to see systems as adaptive and human-centered.

Laksh also highlights the importance of psychological insights in addressing organizational and cybersecurity challenges. He emphasizes education reform and the joy of learning as critical to fostering creativity and resilience in individuals and teams.

The conversation concludes with reflections on W. Edwards Deming's principles and their relevance to modern organizations, particularly in viewing systems holistically and embracing uncertainty. Laksh's current work with Cyb3rSyn embodies this vision, aiming to create environments where multidisciplinary learning thrives.

You can learn more about the Cyb3rSyn community and join through the following links below:

https://www.cyb3rsyn.com/

https://www.cyb3rsyn.com/p/announcing-cyb3rsyn-labs

In this second part of my conversation with Dr. Bill Bellows, we continue our deep dive into the profound concepts of W. Edwards Deming and their modern applications in leadership, systems thinking, and operational excellence. Dr. Bellows brings his wealth of experience and insight to tackle complex questions about embedding systems thinking within large organizations, balancing tools and strategies, and fostering meaningful collaboration.

We begin by examining the challenges of introducing Deming's principles in diverse organizational settings. Dr. Bellows shares his approach of starting small—providing a foundational vocabulary to diverse teams before scaling deeper understanding. He underscores the importance of translating concepts into actionable strategies that resonate within the unique contexts of organizations, using inspiring examples from industries like aerospace.

A significant theme in this episode is the interplay between analysis and synthesis in problem-solving. Dr. Bellows advocates for a shift from merely addressing isolated failures to improving system functions holistically—a perspective championed by Deming and Taguchi. We explore examples like defining the function of a weld or a sweater to demonstrate how function-focused improvement prevents trading one failure mode for another.

Dr. Bellows also reflects on the diminishing emphasis on human capital investment, such as the decline of leadership development facilities, and connects this to the broader need for fostering systems-level thinking in organizations. Together, we discuss how organizations can avoid reducing education to mere compliance training and instead inspire innovative thinking across all levels.

We conclude by revisiting key takeaways, including the critical role of operational definitions in achieving clarity, the importance of defining and measuring function, and the imperative to develop methods that inspire system-wide transformation.

In this episode, I engage with Dr. Bill Bellows in a deep dive into the application of W. Edwards Deming’s systems thinking in software development and DevOps. Dr. Bellows, a veteran in quality management and an expert in Deming’s principles, shares insights on variation, Taguchi loss functions, and the synthesis of parts in a system to highlight gaps in current industry metrics like DORA.

Key Topics:

1. Misconceptions About Managing Parts vs. Systems:
   • Dr. Bellows references Russell Ackoff’s assertion that managing individual parts optimally doesn’t guarantee an effective system. He relates this to the tendency in software and manufacturing to assess components in isolation rather than as part of a larger system.
2. The Role of Variation in Quality:
   • Building on Shewhart’s work, Dr. Bellows explains how statistical process control examines stability and variation within components. Taguchi’s insights are introduced to show how variation in individual parts impacts the whole system's functionality.
3. Applying Taguchi to Modern Metrics:
   • The conversation examines how DORA metrics, such as deployment frequency and mean time to recovery, serve as output measures but fail to address the underlying inputs driving these metrics. Dr. Bellows highlights the importance of understanding "failure" through operational definitions and its nuanced variations.
4. Systems Thinking in Feedback Loops:
   • Emphasizing tighter feedback loops, Dr. Bellows ties traditional Deming concepts to the promise of continuous improvement in DevOps. He advocates for a systemic view, where the interplay of individual variances contributes to collective outcomes.

Key Insights:

• Systems must be analyzed holistically to manage complexity and leverage opportunities effectively.
• Outputs like DORA metrics should inform adjustments to input characteristics rather than serve as the sole focus.
• Precision in defining failure and understanding its economic implications is critical to refining processes and delivering value.