Candles, Not Lightbulbs
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 (“Candles, not lightbulbs,” posted 2026-03-03), verbatim
Last updated: 2026-07-10
Originally posted March 3, 2026.
In most conversations about analytics and AI, I notice how quickly we slide into the language of defensibility: accuracy, robustness, governance, corner cases, attack surfaces, cost to scale. Those are all necessary, especially as solutions move closer to core decisions and regulatory scrutiny sharpens. But if I am not careful, that focus can crowd out the original purpose of the work: to help a human make a better decision in the moment it matters.
Over time I’ve tried to keep a simple hierarchy in my head. The models, data pipelines, and integrations are all in service to a human operator who is often sitting in the dark — staring at an unclear baseline, fragmented information, and competing goals, and still expected to choose a path with confidence. The purpose of insight is not to be impressive; it is to illuminate the choice. If the decision-maker is still standing in shadow, I don’t think we have finished the job.
That’s where “Candles Not Lightbulbs” came from for me. When an underwriter, claims handler, clinician, or risk officer is effectively navigating in the dark, even a modest, imperfect signal can be transformative. The right question is not “Is this the perfect model?” but “Is this enough light to make today’s decision meaningfully better than yesterday’s?” If the answer is yes, I rarely see a good reason to leave people in darkness while we chase theoretical perfection.
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The pushback comes fast: candles are fickle, they can mislead, and they leave plenty of room for improvement. All true. But the difference between operating in darkness and having a first, flickering light source is profound. Once a human operator can see the contours of the problem, interrogate the recommendation, and connect it to policy and judgment, real learning begins — both for the model AND for the organization that has to live with its implications.
And that learning is where genuine defensibility is built. Early deployment under “candlelight” surfaces edge cases, failure modes, workflow friction, and trust thresholds that no amount of whiteboarding will fully anticipate. When you pair that with clear governance, human accountability, and a visible commitment to evolution, you get something far more powerful than a one-time, perfectly documented model: you get an integrated, trusted tool that keeps getting better because real people are using it, questioning it, and shaping its future.
So the balance, at least as I’ve experienced it, is not speed versus safety. It is speed in service of human judgment, anchored by structures that keep us honest about where the light is strong, where it is weak, and when it is time to upgrade from candle to bulb. The leaders I’ve seen succeed in AI don’t ask, “When will this be flawless?” They ask, “When is this good enough to start helping our people decide with eyes more open than they are today — and how will we keep earning that trust over time?”