The conversation around AI and interviews has drifted toward a convenient myth: that automation has somehow leveled the playing field or made preparation obsolete. From where we sit, that narrative is not just wrong, it is dangerously complacent. The interviewer in that post speaks plainly about what is actually happening: the questions are getting harder, more layered, and more focused on reasoning than recall. That is not a small shift. It is a fundamental redefinition of what it means to be qualified for a data role.
What this means for candidates is straightforward. If you have been practicing by memorizing query patterns or model selections, you are preparing for a test that no longer exists. The SQL question is no longer about whether you can write a join; it is about whether you can explain why you chose that join, what assumptions you made about the data, and how you would communicate those limitations to a product manager who does not care about syntax. The modeling question is no longer about naming the right algorithm; it is about articulating failure modes, trade-offs, and the judgment calls that separate a useful analysis from a technically correct but useless one. AI can generate the answer. It cannot generate the reasoning behind it, and that is exactly where the bar has moved.
This should not be read as a reason to panic. It is a reason to practice differently. Instead of running through sample questions until the answers feel automatic, spend time interrogating your own process. Take a query you wrote and ask yourself: what would break if the data was dirty? What would you do if the business changed the definition of a metric mid-project? How would you explain a model's limitations to a non-technical stakeholder without dumbing it down? Those are the conversations that now dominate hiring loops, and they are not ones you can fake with a chatbot.
The takeaway is not that AI has made interviews obsolete. It is that AI has made shallow preparation obsolete. If you want to stand out, you need to go deeper than the answer. You need to understand the trade-offs, the constraints, and the context. The candidates who will succeed are the ones who treat every technical question as a chance to show how they think, not just what they know. That is a higher bar, but it is also a fairer one. And it is the only one that matters now.