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Here is another strong argument on something that has been quietly breaking beneath the surface of the AI wave - the relationship between knowledge and its origin.

Humans naturally resist sharing the reasoning behind their decisions because context and memory have historically been a source of influence and professional advantage. As a result, many critical decisions remain undocumented and live only in conversations or individual memory. In the AI-augmented era this becomes a serious governance problem, because systems evolve faster and the reasoning behind changes disappears even more quickly. Without preserved decision context, organizations lose the ability to explain, audit, or safely evolve their systems. The AI shift therefore turns decision provenance from a cultural preference into a structural requirement for organizational governance.

Once decisions become first-class artifacts, something fundamentally changes. When the environment evolves, we are no longer forced to rediscover the reasoning behind the system through archaeology and speculation. Instead, we can revisit the original decision, update the assumptions that are no longer valid, and regenerate the implementation in a way that reflects the new context.

Let's imagine a "Provenance-native company" in 2030 - an organization built from the beginning around decision lineage, SDLC memory, and AI execution traceability rather than trying to retrofit it later.

Following the release of the initial version of the Provenance Manifesto, I began examining whether existing market solutions align with principles outlined therein.

The article explores a simple but overlooked problem: software organizations rarely preserve the reasoning behind their decisions, even though those decisions shape everything they build. It argues that AI retrieval and documentation alone cannot solve this, because what’s missing is a structured system that records the relationships between decisions, assumptions, and outcomes. The Provenance Manifesto proposes treating decisions as first-class artifacts so organizations can preserve intent, accountability, and decision lineage as AI accelerates software development.

AI is rapidly taking over the “What” layer of software development — generating architectures, code, optimizations, and alternative solutions faster than humans ever could. As a result, implementation and solution exploration are becoming cheap, scalable, and increasingly automated. But the real strategic layer of engineering has never been the “What.” The critical questions are the “Why” — why a solution exists, why a trade-off was accepted, why a risk is tolerable, and why a particular outcome matters for the business. These questions define intent, not implementation.

As AI becomes capable of proposing architectures, writing code, and optimizing systems, the real danger is not malicious AI but losing track of the human intent behind the systems we build. Organizations already struggle to remember why decisions were made; in an AI-augmented environment this problem becomes much more serious because machines can optimize solutions faster than humans can understand them. To avoid building systems that perfectly optimize the wrong goals, we need a new infrastructure layer called Provenance—a structured record of decisions, constraints, trade-offs, and intent that links system behavior back to human purpose. Without such a memory layer, organizations risk becoming highly efficient but strategically misaligned, gradually losing the ability to explain or control the systems they create.

Software delivery organizations repeatedly lose the context behind their decisions. Months after implementation, teams often cannot explain why something was built, what trade-offs were made, or what was originally promised. This happens because SDLC tools track artifacts like tickets, commits, hours, and costs — but not the intent, commitments, and reasoning behind them. The result is “Context Amnesia : teams rebuild solutions, repeat decisions, argue about scope, and incur rework, margin loss, and burnout. The core problem is not careless teams but a systemic gap — SDLC has no built-in memory of decision rationale. The uncomfortable question the article raises is: why, in modern software development, do we rigorously track execution but not the reasoning that shaped it?