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.

In this short episode, I reconnect with Dr. Bill Bellows, a frequent guest and advocate for W. Edwards Deming's principles. The conversation focuses on reviving the In2:In Thinking Network, a nonprofit Dr. Bellows co-founded to improve how individuals and organizations work, learn, and think together. Dr. Bellows discusses the origins of the network and its 16-year legacy of bringing together diverse voices inspired by Deming, Russell Ackoff, and others to explore innovative ways of collaboration.

We also reveal exciting plans for a 2025 In2:In Thinking Network Conference in Santa Clarita, California. This event aims to merge communities from software development, systems thinking, and various industries to foster cross-pollination of ideas. The goal? An "oasis of sanity in a sea of madness," where passionate learners share experiences and challenge conventional approaches to problem-solving.

If you want to learn more or get on the mailing list, reach out to either of us.

In this second installment of my conversation with Dr. David Woods, we continue our exploration of resilience engineering and complexity science, focusing on practical applications and actionable strategies. Building on the foundational concepts from part one, Dr. Woods offers deeper insights into how organizations can thrive in unpredictable environments by embracing resilience as a core competency.

We dive into the nuts and bolts of designing systems that can adapt and recover, emphasizing the importance of fostering collaboration, continuous learning, and feedback loops. Dr. Woods connects these practices to W. Edwards Deming’s teachings, particularly the interplay between profound knowledge and operational flexibility. Our conversation also underscores the significance of learning from near misses and small failures, treating them as opportunities to strengthen systems rather than vulnerabilities to hide.

Key highlights include:

• The Adaptive Cycle: Dr. Woods introduces a powerful framework for understanding how systems evolve and adapt over time, offering lessons for IT, healthcare, and manufacturing.
• Learning from Disruption: Examples of organizations that turned crises into growth opportunities by leveraging resilience principles.
• Operationalizing Resilience: How leaders can embed resilience thinking into daily operations through deliberate design and cultural shifts, echoing Deming’s focus on systems thinking and constancy of purpose.

This episode serves as a practical guide for anyone seeking to bridge theoretical concepts with real-world applications. Dr. Woods leaves us with actionable takeaways on how to lead and thrive in an era of constant change, making this a must-listen for leaders and practitioners alike.

In this episode, I sit down with Dr. David Woods, a leading expert in resilience engineering and complexity science, for the first of a two-part conversation. Together, we explore the interplay between resilience, complexity, and systems thinking, diving into how these principles intersect with W. Edwards Deming’s transformative ideas on quality and continual improvement.

Dr. Woods introduces the foundational concepts of resilience and highlights its critical importance in an era marked by rapid digital transformation. We discuss how organizations in IT, healthcare, and manufacturing can adapt to unforeseen challenges by fostering resilient systems capable of anticipating, absorbing, and recovering from disruptions. Connecting these insights to Deming’s framework, Dr. Woods emphasizes the need for leadership to shift focus from static efficiency to dynamic adaptability.

Key themes in this episode include:

• Resilience in Action: Real-world examples of how organizations have successfully implemented strategies to thrive under complexity.
• Complexity and Systems Thinking: The dangers of oversimplifying complex systems and how embracing uncertainty can drive innovation.
• The Role of Leadership: How leaders can cultivate an environment that values learning, experimentation, and Systemic Thinking, echoing Deming’s principles of profound knowledge.

Dr. Woods’ expertise provides a thought-provoking lens for understanding how organizations can prepare for the unexpected while staying true to the pursuit of quality and improvement. Part one lays a strong foundation for the continuation of our discussion in the next episode, where we’ll delve deeper into practical strategies and case studies.

In this episode, I invite Tracy Ragan, CEO of DeployHub, on the podcast for an in-depth discussion on the evolution of DevOps, the complexities of modern software systems, and the cultural challenges women face in technology. Tracy's rich history in software development, from working on mainframes in the late 1980s to spearheading DevOps advancements, provides a fascinating lens through which to examine the industry.

Key Topics Discussed:

1. The Historical Context of DevOps: Tracy recounts the industry's shift from mainframe to distributed systems and the lessons learned (and forgotten) along the way. She highlights the recurring mistakes in adopting "shiny new objects" without addressing foundational issues, such as dependency chaos and scripting overuse.
2. AI and Long-Tail Productivity: Tracy and John explore AI's transformative potential, emphasizing that its real impact lies in long-term gains rather than short-term ROI. Tracy draws parallels to past transitions, like the adoption of relational databases, arguing that AI's value will be fully realized only with improved system architectures.
3. The Persistent Challenge of Women in Tech: Tracy candidly discusses her experiences as a woman in a male-dominated industry, noting a regression in gender diversity, particularly post-COVID. She highlights systemic issues, including exclusion from key networking opportunities and persistent biases, advocating for cultural shifts to empower women in tech.
4. DeployHub’s Role in Tackling DevOps Complexity: Tracy introduces DeployHub's innovative approach to managing software supply chains and SBOMs (Software Bill of Materials). By mapping dependencies and automating vulnerability remediation, DeployHub aims to reduce the time and complexity of patch management, addressing critical gaps in modern software pipelines.

In this episode of The Profound Podcast, I sit down with Dr. Jabe Bloom, a researcher and expert in systems thinking, AI, and digital transformation. We explore Eric Lawson’s book The Myth of AI, tackling the contentious debate around artificial general intelligence (AGI). Dr. Bloom offers insights from his dissertation and divides the ongoing discourse on AI into two camps: dogmatists and pragmatists. Dogmatists believe AGI is inevitable, while pragmatists focus on the practical impacts of current AI technology, such as large language models (LLMs), and how these will reshape businesses, education, and society.

Throughout the episode, Dr. Bloom explains his framework for thinking about AI, touching on proactionary versus precautionary approaches to its development and regulation. He also draws connections between these ideas and W. Edwards Deming’s principles, especially around abductive reasoning—a concept that links back to Dr. Bloom’s past discussions about AI’s potential in problem-solving.

The conversation takes a critical view of AGI's feasibility, with Dr. Bloom emphasizing the current challenges AI faces in replicating abductive reasoning, which involves making intelligent guesses—a capability he argues machines have yet to achieve. We also dive into examples from fields like DevOps, healthcare, and city planning, discussing where AI has shown great promise and where it still falls short.

Key takeaways from the episode include the importance of addressing present AI technologies and their immediate impacts on work and society, as well as the ongoing need for human oversight and critique when using AI systems.

In this episode of the Profound Podcast, I speak with Erik J. Larson, author of The Myth of Artificial Intelligence, about the speculative nature and real limitations of AI, particularly in relation to achieving Artificial General Intelligence (AGI). Larson delves into the philosophical and scientific misunderstandings surrounding AI, challenging the dominant narrative that AGI is just around the corner. Drawing from his expertise and experience in the field, Larson explains why much of the AI hype lacks empirical foundation. He emphasizes the limits of current AI models, particularly their reliance on inductive reasoning, which, though powerful, is insufficient for achieving human-like intelligence.

Larson discusses how the field of AI has historically blended speculative futurism with genuine technological advancements, often fueled by financial incentives rather than scientific rigor. He highlights how this approach has led to misconceptions about AI’s capabilities, especially in the context of AGI. Drawing connections to philosophical theories of inference, Larson introduces deductive, inductive, and abductive reasoning, explaining how current AI systems fall short in their over-reliance on inductive methods. The conversation touches on the challenges of abduction (the "broken" form of reasoning humans often use) and the difficulty of replicating this in AI systems.

Throughout the discussion, we explore the social and ethical implications of AI, including concerns about data limitations, the dangers of synthetic data, and the looming “data wall” that could hinder future AI progress. We also touch on broader societal impacts, such as how AI’s potential misuse and over-reliance might affect innovation and human intelligence.

In this episode of The Profound Podcast, I have an enlightening conversation with Dr. Jabe Bloom, a prominent voice in the fields of DevOps and digital transformation. The discussion revolves around the philosophical underpinnings of scientific reasoning and its application to complex systems, particularly through the lens of Charles Sanders Peirce's work on abductive reasoning.

Jabe Bloom begins by exploring Peirce’s contributions to philosophy, particularly how Peirce's concept of abductive reasoning offers a framework for making educated guesses in situations where data is incomplete or variables are unknown. This idea becomes especially pertinent when Bloom contrasts the scientific method typically used in complicated domains, like Lean manufacturing, with the unpredictability of complex systems, where multiple hypotheses might be equally valid.

The conversation further delves into how these ideas connect to digital transformation, especially in organizations navigating the complexities of modern IT and business environments. Bloom highlights the importance of fostering environments where experimentation and educated guessing are encouraged, as this aligns with Peirce's pragmatic approach, which values the usefulness of an idea over its absolute truth.

To wrap up, we also discuss the broader implications of Peirce’s work on modern AI and socio-technical systems, emphasizing the need for a deeper understanding of how these systems operate and how to integrate artificial intelligence into complex human processes.