Profound

I have a conversation with Diane Kulisek in this episode. Diane, a veteran in quality systems and regulatory affairs, shares her journey from Gillette to Rocketdyne to Johnson & Johnson, weaving in the principles of Deming and the realities of complex, high-stakes industries. We dive into W. Edwards Deming’s seminal perspectives on quality and how they’ve shaped Diane’s extraordinary career across aerospace, consumer products, and medical device manufacturing. We start with Diane’s early work at Gillette, where she first encountered military-grade quality standards, and move into her groundbreaking experience at Rocketdyne. There, she managed space shuttle main engine avionics and led self-managed teams. Diane highlights the power of elected management and the deep cultural dysfunctions she observed, drawing sharp analogies to adult children of alcoholics and the normalization of deviance in corporate environments.Our conversation then pivots to regulatory complexity. Diane explains how compliance efforts in medical device manufacturing must transcend minimum standards to uphold the priceless value of human life. She critiques the profit-centric motives of insurance companies and exposes the structural misalignments that can compromise quality in favor of greed and speed.We also explore the limitations and potential of AI in auditing, with Diane emphasizing the importance of human experience in identifying risk and systemic failures. She proposes the provocative idea of creating an “AI Deming,” using Deming’s extensive body of work to model principled decision-making.Diane’s reflections bring a critical eye to regulatory frameworks, the ethics of risk management, and the potential of technology to augment human insight. Through it all, she remains grounded in Deming’s enduring vision that quality is a moral imperative and a societal good.This is Diane's LinkedIn Page:https://www.linkedin.com/in/dkulisekShow Notes:1: https://www.scribd.com/document/451480272/MIL-Q-9858A-Quality-Program-Requirements-pdf2: https://adultchildren.org3: https://www.acquisition.gov/far/16.3054: https://www.wsj.com/articles/SB9918626065751548435: https://www.nasa.gov/wp-content/uploads/2015/04/708066main_Shuttle_Bibliography_2-ebook.pdf?emrc=c67e146: https://www.irvinestandard.com/2023/johnson-johnsons-innovation-irvine-roots-and-credo-to-give-back/7: https://asq.org/-/media/ASQ-Supplemental-Media-Import/1/3/9/2/6/ar_1106_105018.pdf8: https://www.dol.gov/sites/dolgov/files/oalj/PUBLIC/WHISTLEBLOWER/REFERENCES/STATUTES/SARBANES_OXLEY_ACT_OF_2002.PDF9: https://store.pda.org/TableOfContents/Risk_Assessment_Ch01.pdf10: https://www.healthcarefinancenews.com/news/class-action-lawsuit-against-unitedhealths-ai-claim-denials-advances11: 

In this episode, I have a conversation with Doug Finke. A 16-time Microsoft MVP and long-time technologist, Doug’s experience ranges from mainframe assembly programming to pioneering AI integrations in software development. We reminisce about the early days of system programming and explore how those foundational skills have enabled technologists like Doug to excel in the rapidly evolving world of AI.Doug shares his journey from COBOL and assembler to becoming a PowerShell innovator, highlighting how his early exposure to structured programming and domain-specific languages now serves as a superpower in working with large language models (LLMs). Our discussion pivots to the game-changing potential of AI, specifically the rise of function calling, modular design, and the Model-Context-Protocol (MCP). Doug explains how these patterns transform AI from simple copilots into powerful collaborators capable of orchestrating entire software systems.We also look at the implications of AI-driven software development for enterprises, examining whether vertical SaaS solutions may soon be disrupted by in-house, AI-built alternatives. Doug emphasizes how AI accelerates both code creation and maintenance, challenging long-held assumptions about whether to buy or build software.Our conversation concludes with a philosophical lens on teaching AI, the evolving role of junior developers, and the importance of naming, structure, and design patterns in crafting software that AI can effectively understand and extend. Whether you're deep into AI tooling or just starting to explore, Doug's perspective offers a clear bridge between classical computing and today's transformative technologies.

I have a fantastic conversation with Lonnie Wilson in this episode, diving deep into W. Edwards Deming’s seminal perspectives on quality, systems thinking, and the enduring challenges of operationalizing his philosophy in the real world. Lonnie also shares how his initial admiration for Chevron's management practices began to unravel after encountering Deming's writings in the mid-1980s.The episode opens with Lonnie recounting his experience attending one of Deming's four-day seminars. His anecdote about Deming’s instantaneous answer to a deceptively simple question. Lonnie reflects on Deming's often enigmatic style, particularly his refusal to provide concrete answers to complex management questions, such as what to replace performance appraisals with.We explore the tension between Deming’s abstract guidance and the practical application of his ideas in corporate environments. Lonnie shares his journey of attempting to implement cultural change through Deming’s 14 Points, only to face organizational resistance and surface-level interest. This led him to develop pragmatic systems that bridged the gap between Deming's theory and the day-to-day realities of business operations.Lonnie and I delve into critical questions: Was Deming’s refusal to provide prescriptive answers a form of intellectual elitism or a deliberate nudge toward self-discovery? How did his lack of direct management experience influence his worldview? Lonnie argues that while Deming's concepts were revolutionary, they were often delivered without a “method for method,” leaving followers to forge their paths.The conversation concludes on a reflective note, with both of us acknowledging that, despite the depth and accuracy of Deming’s ideas, much of the corporate world has either misunderstood or ignored them. If Deming were alive today, Lonnie believes he would be dismayed by the lack of progress in management thinking and would challenge his disciples to evolve and iterate on his foundational work.

I'm still trying to figure out how to produce the audiobook for Rebels of Reason. This is my first experiment with 11Labs. It's not great. I will have to work on the voice equipment; however, it's a start. I hope you enjoy it. 

In this thought-provoking discussion, Dr. Bill Bellows, co-founder of the In2:InThinking Network, shares his insights on systems thinking inspired by W. Edwards Deming. He delves into the significance of the upcoming In2:InThinking Forum, emphasizing the necessity of psychological safety for innovation. Dr. Bellows highlights how systems thinking can revolutionize fields beyond manufacturing, including software and education. He advocates for a culture of humility and collaboration, aiming to empower professionals to rethink traditional approaches and drive meaningful change.

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