When three AI coding assistants give you the identical answer, that is not three independent confirmations. It is one answer wearing three costumes, because the models likely trained on overlapping slices of the same internet.
The Details:
Large language models absorb similar patterns from similar scraped datasets, so they inherit the same blind spots and outdated practices together. If a mistake exists broadly across public code and documentation, every model trained on that corpus tends to reproduce it, and cross-checking one model with another just confirms the shared error rather than catching it.
Real verification means going outside the model family entirely. Pull up official docs, RFC text, or the actual library source, then run the generated code in an isolated environment and watch what it actually does instead of trusting the explanation attached to it.
Assign someone on the team, even on a rotating basis, to actively argue against whatever the AI tools agreed on. Their job is to hunt for edge cases and contradicting evidence, and distributed teams have an advantage here because engineers in different regions carry different real-world exposure and different documentation habits.
Oversight only works if senior engineers actually have time to push back, not just glance and approve. When experienced reviewers are stretched too thin to dig in, teams are not moving faster, they are postponing the cost of a bad decision to a point where it is more expensive to fix.
Bottom Line: Treat every AI suggestion as a hypothesis to test, not a verdict to trust, because agreement between models is a symptom of shared training data, not proof of truth.
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