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A sociotechnical perspective on what makes code legacy, and what actually keeps it alive. In the organization where I work, there is a mainframe COBOL system that has been running at the core of operations for decades. By every conventional definition, it should be legacy: old language, old architecture, a technology that most developers under forty have never touched. It is not legacy. The team responsible for it is among the most mature and capable in the organization. They understand every decision embedded in that system: the constraints that shaped it, the business rules it encodes, the edge cases it handles. They can explain why a specific field is structured a certain way, what regulatory requirement drove a particular branching condition, and exactly what would happen downstream if you changed it. There is no question about the system they cannot answer. ...

A first series of four articles on technology governance is already published on LinkedIn and established a diagnosis: inherited governance models are structurally misaligned with the pace of AI-driven change. This new series addresses a prior question: how do we diagnose organizational pathologies rigorously in the first place? The answer proposed here is methodological: structural analogy across distant domains. Why I Am Writing This I have been thinking about analogy since 2005, longer than I have been thinking about most things professionally. ...

Part IV of a series on rethinking technology governance foundations, and a pivot toward what comes next The first three articles in this series examined specific symptoms: the instability of the People-Process-Tools model under high human variability, the strategic liability of tool positioning in a fast-moving AI market, and the asymmetric risk that regulated organizations are structurally failing to measure. This fourth article steps back to name the underlying condition, the diagnosis from which those symptoms follow. ...

Rethinking the foundations of technology governance in an era of structural human variability A Time-Tested Model Confronting an Unprecedented Context The People, Process, Tools (PPT) model has shaped technology governance for decades. Its founding premise rests on the stability of human capital: skilled individuals, carrying a shared culture, apply defined processes using adapted tools. In its mature form, this model has always recognized that processes and tools encode organizational knowledge, with the human dimension serving as the activation vector. ...

Part II of a series on rethinking technology governance foundations In a previous article, I argued that the People-Process-Tools model needs to be rebalanced toward platform engineering in contexts of high resource variability. This article extends that argument one step further: in a world where AI is redefining the available tools every few months, even the ‘Tools’ layer of that platform deserves to be rethought. The Paradox of Tool Positioning In large organizations, the process of selecting, evaluating, approving, and deploying a new tool is rarely a lightweight exercise. Between security reviews, architecture assessments, procurement cycles, integration planning, and training programs, the full lifecycle from initial evaluation to production readiness commonly spans six to eighteen months. For highly regulated environments, that timeline can extend further. ...

Part III of a series on rethinking technology governance foundations The first two articles in this series examined how to rebalance technology governance toward platform engineering in contexts of high human variability, and why tool positioning is becoming a strategic liability in a world of accelerating AI. This third article steps back from the technical layer to address a more basic challenge: the organizational resistance that makes any of these transformations genuinely difficult, and the asymmetric risk of not moving fast enough. ...

On Creativity, Metaphor, and What AI Still Needs to Learn A Theory of Creativity First Before building any system, I needed to understand what creativity actually is. Here is the formulation I arrived at around 2005, working as a researcher in Québec City: Humans cannot create something entirely new from nothing. We can only perceive reality through our senses. You cannot imagine a color that does not exist. You cannot conceive a sound you have never heard. The entire domain available to human creativity is bounded by accumulated sensory experience. ...