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Answering AI Questions
How to prepare for the AI questions now common in behavioral interviews: the five areas interviewers evaluate (Work, Trust, Iteration, Growth, Scaling), how to inventory your own AI practice, and how to answer values questions about AI with judgment.
You will be asked about AI in your next behavioral interview. Companies are facing top-down direction to accelerate development through AI and sideways pressure from competitors who are improving the velocity of their own development lifecycle. Naturally, interviews have begun to probe for signals that you too are staying on top of development trends and are leveraging AI effectively.
They may have an entire interview dedicated to this or at least ask questions like:
- "Tell me about a time you used AI to deliver something you couldn't have delivered otherwise."
- "Tell me about a tool or workflow you adopted in the last six months."
- "How do you think about the tradeoffs between speed and verification when working with AI?"
This section covers what interviewers are really probing for when they ask these types of AI questions, how to inventory your practice to find relevant stories, and how to develop the philosophical positions you'll need to show judgement.
It probably goes without saying, but whatever you develop and prepare as responses on AI need to be updated pretty frequently given the pace of change.
The Five AI Areas Interviewers Evaluate
When interviewers ask AI questions, they're examining five distinct areas, even if they don't frame it exactly this way. Understanding these areas helps you inventory your practice and recognize what evidence a question is really seeking.
- Work: What you actually build with AI
- Trust: How you verify and stay safe
- Iteration: How you make AI better over time
- Growth: How you learn and stay current with latest AI best-practices
- Scaling: How you spread AI best practices outside of yourself
If you think about these, most of them overlap with the Signals Areas we've discussed before:
- Work is part of Scope: what work you choose to tackle with AI and what results you produce are clearly signs of scope, as AI is similar to deploying other technologies to achieve outcomes. Of course, you'll need to mine for very recent stories since model capabilities evolve rapidly.
- Iteration is part of Perseverance and Growth: You've already prepared stories about learning from failures and refining your approach. For AI iteration, the specific context might be different, but the underlying narrative is the same: "I encountered a problem, I devised a solution, and the outcomes improved."
- Growth is part of, well, Growth: our field has always changed quickly so staying on top of best practices is something managers have occasionally asked about, but right now we're experiencing a level of change in our industry that is truly unprecedented, so this becomes a must-have in new hires.
- Scaling is part of Leadership: Multiplying your impact through others is a core senior signal. AI scaling stories follow the same arc: "I helped someone adopt a practice, I navigated some resistance, and the team improved."
While these are parts of existing Signal Areas, Trust seems like a new thing: it's a form of judgment and familiarity that doesn't transfer from other domains. For example, you might not have used a specific debugger in the past, or even coded in a certain language, but your experience with other debuggers and other languages would transfer. With AI, you have to have actually caught AI mistakes, developed verification practices, and understood failure modes specific to this technology to do that on the job.
These five areas map roughly to seniority. Junior candidates need strong Work and Trust answers. Senior candidates should demonstrate all five, not neglecting Scaling.
Inventorying Your AI Practice
Work: What you actually build with AI
Trust: How you verify and stay safe
Iteration: How you make AI better over time
Growth: How you learn and stay current with latest AI best-practices
Scaling: How you spread AI best practices outside of yourself
Answering "Values Questions" About AI
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The Five AI Areas Interviewers Evaluate
Inventorying Your AI Practice
Work: What you actually build with AI
Trust: How you verify and stay safe
Iteration: How you make AI better over time
Growth: How you learn and stay current with latest AI best-practices
Scaling: How you spread AI best practices outside of yourself
Answering "Values Questions" About AI