AI Literacy Is Now a Core Engineering Interview Skill

General
1

I’ve been running hiring loops for over a decade now, and I’ve never seen such a fundamental shift in what we’re actually evaluating in technical interviews. We’re not just adding a new question category - we’re redefining what “competent engineer” means in 2026.

The Numbers Don’t Lie

AI skills now command a 56% wage premium - that’s more than double the 25% premium from just a year ago. When I saw that data, I knew our interview process needed an overhaul, not a tweak. The market is telling us something, and it’s not subtle.

Even more telling: 75% of AI job listings now specifically seek engineers with deep, specialized AI capabilities rather than generalists. The “I’ve played with ChatGPT” crowd is no longer competitive.

What We Changed at Our EdTech Startup

When I joined as VP Eng six months ago, our interview loop was still testing whether candidates could implement merge sort from memory. We were optimizing for 2019 skills in a 2026 world.

Here’s what our new technical interview looks like:

AI-Paired Coding Round (90 min)
Candidates get access to Cursor, Copilot, Claude - whatever tools they normally use. We give them a realistic problem: take this existing codebase, understand it, and add a new feature. The twist? We’re watching how they use AI, not just that they use it.

What we’re actually evaluating:

  • Can they craft prompts that produce consistent, auditable results?
  • Do they validate AI output critically, or accept it blindly?
  • When AI generates something wrong, can they debug it?
  • Do they know when to stop asking AI and just write the code themselves?

The Distinguishing Patterns

After running dozens of these interviews, the patterns that separate exceptional candidates are crystal clear:

Strong candidates:

  • Use AI to scaffold complex data structures, then manually optimize critical paths
  • Summarize unfamiliar API docs with AI to free up mental bandwidth for architecture decisions
  • Catch bugs in AI-generated code that our interviewers initially missed
  • Know when a problem is simpler to just code than to explain to an AI

Weaker candidates:

  • Copy-paste AI output without reading it
  • Keep prompting when they don’t understand the output rather than stepping back
  • Can’t explain why the AI-generated solution works
  • Panic when the AI gives wrong answers (which it does, regularly)

The Concerning Pattern: Over-Delegation

Here’s what worries me: candidates who’ve relied too heavily on AI show gaps in fundamental understanding. There’s research showing that full delegation to AI improves short-term productivity but impairs conceptual understanding, code reading, and debugging abilities.

We had a candidate last month who could use Copilot to implement anything but couldn’t explain what Big O notation meant when the AI wasn’t available. That’s a red flag for us.

The New Minimum Standard

I keep coming back to this analogy: AI literacy in 2026 is like Microsoft Office literacy was in the 1990s. It’s not a bonus skill - it’s the baseline. If you can’t collaborate effectively with AI tools, you’re going to struggle in any modern engineering environment.

But here’s the nuance: knowing how to use AI isn’t enough. You need to know how to think alongside AI. That means:

  • Prompt engineering as system design - treating prompts as testable, iterable, documented artifacts
  • Guardrail building - understanding bias propagation, prompt injection, brittle reasoning
  • Critical output evaluation - treating AI like a brilliant but unreliable junior developer

Looking for Your Experiences

I’m curious how other teams are adapting:

  • Have you updated your interview loop to include AI tools?
  • What signals have you found that distinguish AI-literate candidates?
  • How do you balance “can use AI effectively” with “has solid fundamentals”?
  • Are candidates responding positively to these new formats?

The hiring landscape has changed. I’d love to hear how others are navigating it.

2

This resonates deeply, @vp_eng_keisha. We went through a similar transformation at our financial services company, and I want to share what worked for us - and what initially didn’t.

The Resistance We Faced

When I first proposed allowing AI tools in interviews, half my senior engineers pushed back hard. “We’ll just be testing who’s best at prompting” was the common objection. “How will we know if they actually understand the code?”

Those were fair concerns. We addressed them by designing evaluation criteria that explicitly test for understanding, not just output.

Our Evaluation Framework

We developed a rubric with three dimensions:

1. AI Collaboration Quality (30%)

  • Do they provide clear context to the AI?
  • Do they iterate on prompts effectively when results aren’t right?
  • Do they break complex problems into AI-sized chunks?

2. Critical Validation (40%)

  • Do they read and understand the generated code before using it?
  • Can they identify when AI output is wrong or suboptimal?
  • Do they test edge cases that AI often misses?

3. Fundamental Knowledge (30%)

  • Can they explain why the solution works?
  • Do they recognize algorithmic complexity issues?
  • Can they debug without AI assistance when needed?

Real Examples That Distinguish Candidates

Strong candidate example: Last month, a candidate used Claude to generate a rate-limiting implementation. The AI’s version worked but had a race condition in the sliding window logic. The candidate caught it, explained the issue to us, and fixed it manually. That’s exactly what we’re looking for.

Concerning candidate example: Another candidate generated similar code with Copilot, ran it, saw it worked for the test cases, and moved on. When we asked about thread safety, they tried to prompt the AI for an answer rather than reasoning through it. Red flag.

The Shift in What We’re Really Hiring For

Here’s what I’ve realized: we’re now hiring for AI supervision skills as much as coding skills. Engineers need to:

  • Recognize when AI is hallucinating plausible-sounding nonsense
  • Know which problems AI handles well vs. where it struggles
  • Maintain enough domain knowledge to validate AI output

Our hiring has improved. We’re finding engineers who ship faster because they use AI well, but who also catch bugs that AI introduces. Best of both worlds.

One Thing I’d Add

Make sure your interviewers are AI-literate too. We had interviewers who didn’t use AI tools themselves trying to evaluate AI collaboration - that was awkward. We now require all interviewers to be daily AI tool users.

3

As someone who’s been on the candidate side of these new interview formats recently, I can offer a different perspective.

The Range of Experiences

I interviewed at six companies in the past three months, and the approaches to AI in interviews ranged wildly:

Company A (Canva-style): “Use whatever tools you normally use - Cursor, Copilot, Claude, whatever. We just want to see how you work.” Felt natural, like my actual day-to-day.

Company B (Awkward hybrid): “You can use AI, but… we’ll dock points if you rely on it too much.” Unclear expectations. I spent half the interview second-guessing myself.

Company C (Traditional ban): “No AI tools allowed. We want to see YOUR skills.” Felt like being asked to code without internet access - technically doable, but why?

Company D (Best experience): “We’ll give you access to AI tools AND we’ll have you explain and modify AI-generated code we provide.” This tested both collaboration AND understanding.

What Actually Prepared Me

Here’s what I wish someone had told me before these interviews:

Helpful prep:

  • Practice narrating my thought process while using AI (“I’m asking Claude for a scaffold here because…”)
  • Getting comfortable saying “That doesn’t look right” and investigating AI output
  • Building intuition for what AI handles well (boilerplate, common patterns) vs. poorly (edge cases, system design)

Unhelpful prep:

  • Memorizing syntax (AI handles this fine)
  • Practicing algorithm problems without AI (not realistic)
  • Treating AI as a search engine (prompting requires different skills)

The Best Interview I Had

Company D had me work with a codebase that contained AI-generated code with subtle bugs intentionally introduced. My job was to:

  1. Identify what the code was supposed to do
  2. Find the bugs
  3. Fix them
  4. Extend the feature

This tested exactly what matters: can you work with AI-generated code in the real world, where you’re often inheriting or reviewing it?

My Takeaways for Other Candidates

  • Ask upfront about AI tool policies. If they’re vague, it’s a yellow flag about their engineering culture.
  • Narrate your thinking. Interviewers can’t see your intent - explain why you’re prompting vs. coding manually.
  • Don’t be afraid to critique AI output. Catching an AI mistake demonstrates more skill than accepting good output.
  • Know your fundamentals. Companies that let you use AI will test whether you understand what it generates.

The interview format signals a lot about what working at that company will be like. I ended up accepting an offer from Company D.

4

I want to zoom out and share why I pushed for AI-inclusive interviews at my company - and the longer-term concerns that keep me up at night.

Why I Made This a Priority

When I became CTO, I noticed a disconnect: our engineers used AI tools all day, but our interviews tested a world that no longer existed. We were hiring for the past, not the present.

The 56% wage premium @vp_eng_keisha mentioned isn’t just a market signal - it’s telling us that AI-literate engineers are genuinely more productive. If our interview process filters them out or can’t identify them, we’re leaving value on the table.

The Learning Paradox That Worries Me

Here’s what concerns me: there’s solid research showing that heavy AI delegation can impair fundamental skill development. Engineers who never struggle through problems don’t build the deep understanding needed to recognize when AI is wrong.

We’re seeing this play out. Some candidates have impressive portfolios of AI-assisted work but can’t debug when the AI isn’t available. They’ve built productivity without building depth.

How do we square this circle? We need AI-literate engineers, but we also need engineers with strong fundamentals. They’re not automatically the same thing.

Our Approach to Balancing Both

We’ve structured our interview loop to test both:

Round 1: AI-Assisted Problem Solving
Full tool access. We’re evaluating collaboration skills, validation instincts, and workflow efficiency.

Round 2: Fundamentals Deep-Dive
No AI tools. Whiteboard discussion of algorithms, system design, and trade-offs. Can they reason through problems, or do they need the AI crutch?

Round 3: Code Review
Review AI-generated code with intentional issues. Tests critical thinking without requiring implementation from scratch.

This combination gives us signal on both dimensions. Candidates need to pass all three.

What Skills Will Matter in 2-3 Years?

Here’s my prediction: the ability to verify AI output will become more valuable than the ability to generate code. As AI gets better at writing code, the bottleneck shifts to validation, testing, and understanding.

The engineers who thrive will be:

  • Excellent at articulating requirements to AI (prompt precision)
  • Ruthless about testing and edge cases
  • Deep enough in fundamentals to catch subtle bugs
  • Strong at system design (where AI still struggles)

Interview Format as Culture Signal

One last point: how a company interviews reflects how they work. If a company still bans AI in interviews, it might mean they’re behind on AI adoption generally. If they’re confused about expectations, their engineering processes might be similarly unclear.

@alex_dev’s point about Company D’s format being the best is telling - it tested real-world skills. The interview should mirror the job.

We’re all figuring this out together. The companies that get interviewing right will have a talent advantage for years.