At Weave, we interviewed 134 people to find our 2 founding engineers. One key thing we looked for was AI competence, and it completely changed how we approached technical interviews.

Most companies are still hiring like it's 2019. They're testing skills that matter less and missing the engineers who will actually thrive in an AI-first world. The best engineers today aren't the ones who can recite sorting algorithms from memory. They're the ones who combine raw intelligence with AI mastery to ship code faster than ever before.

AI has fundamentally changed how we write software. If your hiring process hasn't adapted, you're probably hiring the wrong people.

The Problem with Traditional Technical Interviews

Traditional coding interviews test memorization and pattern matching. Can you implement quicksort on a whiteboard? Do you remember the optimal solution to the two-sum problem? These skills were valuable when engineers spent most of their time writing boilerplate code and implementing well-known algorithms.

But that's not how modern software development works. Today's engineers use AI to handle routine coding tasks and focus their energy on higher-level problem solving, architecture decisions, and understanding complex systems. The engineers who excel are the ones who know how to effectively collaborate with AI tools.

Yet most companies still ban AI during technical interviews. They're optimizing for skills that matter less while ignoring the skills that actually determine success on the job.

A Better Approach: 3 Steps to Screen for AI Competence

Here's how we restructured our technical interview process to identify engineers who can thrive in an AI-powered development environment.

Step 1: The AI-Enabled Take-Home Assignment

Give candidates a realistic coding challenge with explicit permission to use any tools they want. This means AI assistants, documentation, Stack Overflow, or whatever they would normally use in their day-to-day work.

The key is what happens next. Schedule a 30-minute follow-up where they walk through their solution. Ask them to explain their approach, then drill deeper on their reasoning. Ask "Why did you make that choice?" or "How does this part work?" Keep going until you reach the limits of their understanding.

This approach works because everyone uses AI on take-homes anyway. By allowing it explicitly, you can focus on what actually matters. Do they understand the code they submitted? Can they explain design decisions and trade-offs? How deep is their knowledge of the technologies they chose?

The drilling process reveals whether they have genuine technical understanding or just copy-pasted AI output. You'll quickly separate the engineers who used AI as a sophisticated thinking partner from those who treated it like a magic code generator.

Plus, this approach makes the take-home assignment more accurately reflect how their actual work will look. Your engineers will use AI tools on the job, so you should evaluate how well candidates can work with those tools.

Step 2: Split Your Live Interview Into Two Phases

Structure your in-person technical interview as two distinct phases:

Phase 1: Problem-solving without AI (40 minutes) Test their ability to think through complex problems and design solutions. Focus on system design, architectural decisions, and reasoning through trade-offs. This phase reveals their fundamental engineering thinking.

Phase 2: Implementation with AI (20 minutes) Give them a coding task and watch how they work with AI tools. Observe their prompting strategies, how they iterate on AI output, and whether they can effectively guide the AI toward good solutions.

You need both skills. Phase 1 shows whether they can think through complex problems and architect solutions from scratch. Phase 2 reveals whether they can efficiently leverage modern development tools.

Engineers who excel at only one phase will struggle in real work environments. The ones who can't think without AI will hit walls when they encounter novel problems. The ones who resist AI will be outpaced by their peers who embrace these tools.

Step 3: Evaluate Their AI Strategy

Ask candidates to walk through their typical development workflow. Have them explain when they choose AI assistance versus manual coding. Ask about specific projects they've built and how they incorporated AI tools into their process.

The best engineers have developed intentional approaches to AI usage. They understand when AI helps and when it hurts. They can articulate their prompting strategies and recognize AI limitations. They've thought deeply about how to integrate these tools into their workflow rather than just using them occasionally.

Listen for specificity in their answers. Vague responses like "I use ChatGPT sometimes" indicate limited experience. Look for engineers who can explain their reasoning behind different AI tools, describe specific prompting techniques, or share examples of when they chose not to use AI.

Why This Matters More Than Ever

The gap between AI-competent engineers and everyone else is widening rapidly. Engineers who know how to effectively work with AI are shipping features 3-5x faster than those who don't. They're also better at learning new technologies because they can use AI to accelerate their learning process.

Meanwhile, engineers who resist AI or use it ineffectively are falling behind. They spend time on tasks that could be automated while missing opportunities to tackle more interesting and valuable problems.

If you want to build a team that can compete in 2025 and beyond, you need to hire engineers who can thrive in an AI-augmented environment. That means updating your interview process to test the skills that actually matter.

Making the Change

Start by allowing AI in your take-home assignments. Most companies resist this change because they worry about cheating, but you're not looking for the person who can write the most code from scratch. You're looking for the person who can solve problems effectively using all available tools.

Then restructure your live interviews to include both AI and non-AI phases. This gives you a complete picture of how candidates think and work.

Finally, ask direct questions about AI strategy. Make it a normal part of your technical conversation rather than an afterthought.

The companies that adapt their hiring process first will have access to the best AI-native talent while everyone else is still stuck evaluating candidates with outdated criteria. The engineering teams of the future won't be built on LeetCode performance. They'll be built on the ability to effectively collaborate with AI to solve real problems.

Don't let your hiring process become the bottleneck that prevents you from building a world-class engineering team.

And of course, use Weave to benchmark them to your current engineers and industry standards :)