Don’t Paint Over Rust
Summary: The verbatim LinkedIn post behind the wiki concept of the same name — reproduced word for word as it was published.
Sources: LinkedIn Posts.md (“Don’t Paint Over Rust,” posted 2025-10-08), verbatim
Last updated: 2026-07-10
Originally posted October 8, 2025.
As a young Naval officer, my very first assignment put me in a leadership position with accountability for vital equipment and a team of sailors operating in a harsh environment under extreme stress. I had a head full of theory—Leadership, Engineering, Philosophy—but little real-world experience. My lifeline, like many young officers before me, was my senior enlisted partner: my Chief.
The Chief (Chief Petty Officer in the fleet) had risen through the ranks to achieve an equivalent leadership role. He had some of the theoretical grounding I did, but far more importantly, he carried a lifetime’s worth of hard-earned, practical knowledge—distilled from relentless problem-solving and the daily grind of “getting the job done.”
The Chief possessed many pithy sayings, inherited across generations of sailors, that compressed deep wisdom into a handful of words. One of those sayings has stayed with me ever since:
“Don’t paint over rust.”
In the Navy, it meant that before you try to improve something, you must first ensure you’re building from a solid foundation. Painting over rust only hides the problem—the corrosion will continue, and eventually the weakened steel will fail, possibly at a critical moment. I remember more than one sailor getting “coached” for slapping fresh paint over compromised metal, thinking it looked good while the underlying structure was deteriorating in silence.
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This lesson is just as relevant in AI and data-driven organizations today. Too often, we see businesses race to adopt machine learning models or AI solutions on top of processes, datasets, or infrastructure that are already “rusting.” Poor data quality, incomplete governance, brittle workflows, and hidden bias are all forms of corrosion. Advanced algorithms built on top of these issues may look impressive at first (fresh paint), but they are destined to fail under real operational stress.
Before training the next model or deploying the next AI system, leaders must strip away the rust:
- Audit and clean the data—ensure it is accurate, complete, and relevant.
- Validate the processes feeding that data—remove inefficiencies, fix bottlenecks, and address gaps.
- Identify ethical and compliance risks—surface bias before it undermines trust.
Once the “steel” of your AI foundation is solid, you must apply the “paint” to protect it:
- Establish strong policies, governance frameworks, and documentation.
- Train teams on proper AI usage and oversight.
- Continuously monitor and maintain systems to prevent new corrosion from creeping in.
In the Navy, ships survive the ocean because their steel is strong and their protection is constant. In AI leadership, the same is true: success depends on building from an unshakable core and safeguarding that strength over time. Do that, and your crew—whether sailors or data scientists—can be confident that your systems will perform when it matters most.