AI tools frequently produce incorrect information, with error rates as high as 30%. Relying on this technology to replace entry-level staff is a major risk, as newcomers are essential for learning and eventually providing the human oversight that fallible AI requires.

By replacing the foundational, detail-oriented work of junior analysts, AI prevents them from gaining the hands-on experience needed to build sophisticated mental models. This will lead to a future shortage of senior leaders with the deep judgment that only comes from being "in the weeds."

With a significant error rate of 20-30%, AI tools cannot be trusted to replace junior employees. This strategy is misguided because it removes the human learning process and introduces unreliable outputs, undermining a company's talent pipeline and quality of work.

Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.

Despite hype about full automation, AI's real-world application still has an approximate 80% success rate. The remaining 20% requires human intervention, positioning AI as a tool for human augmentation rather than complete job replacement for most business workflows today.

By replacing junior roles, AI eliminates the primary training ground for the next generation of experts. This creates a paradox: the very models that need expert data to improve are simultaneously destroying the mechanism that produces those experts, creating a future data bottleneck.

Don't blindly trust AI. The correct mental model is to view it as a super-smart intern fresh out of school. It has vast knowledge but no real-world experience, so its work requires constant verification, code reviews, and a human-in-the-loop process to catch errors.

AI coding tools disproportionately amplify the productivity of senior, sophisticated engineers who can effectively guide them and validate their output. For junior developers, these tools can be a liability, producing code they don't understand, which can introduce security bugs or fail code reviews. Success requires experience.

Despite the hype, AI is unreliable, with error rates as high as 20-30%. This makes it a poor substitute for junior employees. Companies attempting to replace newcomers with current AI risk significant operational failures and undermine their talent pipeline.