There’s a funny and slightly painful pattern emerging in modern tech companies. A PM or stakeholder asks ChatGPT a question, gets a clean confident answer, and suddenly they feel like they’ve unlocked the “truth” of the problem. Not only that, they’re convinced that this answer outranks the judgment of someone who’s spent years actually working with real datasets, messy pipelines, production constraints, and domain complexity. This isn’t arrogance, it’s misunderstanding. Because there’s a difference between an answer and understanding.
A language model can produce a plausible solution, but a data scientist has to determine whether it’s statistically valid, supported by the data, implementable, ethical, resilient in real production environments, and aligned with the actual business context. And that last one, the context, is often invisible to people who think “the model said A so we should do A.” There’s also a trend where companies aggressively brand everything as “AI-driven,” even if the application of AI is superficial or misaligned with reality. They want fast answers, fast prototypes, fast “innovation”. But speed isn’t the bottleneck, clarity, reasoning, and validation are. I’ve seen this dynamic more and more: someone copies a generic suggestion from an AI tool and presents it as a strategic direction, but that answer is only as strong as the assumptions behind it, the data it was trained on, and its ignorance of real-world constraints.
A data scientist’s job isn’t to select canned responses. It’s to interrogate, refine, adapt, and reason, to evaluate uncertainty, not just mask it behind confident text. AI can simulate confidence. Humans have to deliver correctness. And here’s the deeper issue: when AI outputs become accepted without scrutiny, realistic approaches get sidelined. Thoughtful skepticism gets mistaken for “slowing things down.” And genuine expertise gets overshadowed by instant, polished responses that sound intelligent even when they’re wrong. The irony? The best technical people I know are using AI, but as a tool, not a source of truth. They pair AI’s breadth with human depth. AI can suggest but humans must judge. If you’ve ever been in a meeting where someone insists “Well ChatGPT said…” and you had to push back with real evidence, domain understanding, and reasoning, then you know exactly what I’m talking about.
AI can accelerate thinking, but it can’t replace thinking and we shouldn’t pretend otherwise. The real skill isn’t asking a model for an answer, it’s knowing when that answer is incomplete, biased, or just plain wrong. And as long as products are built in the real world with real consequences human judgment isn’t optional, it’s essential.