The Hiring Crisis Nobody Saw Coming

Engineering managers across the industry are hitting the same wall: technical interviews have stopped working.

A candidate submits a take-home assignment that looks like it was written by a senior architect. Clean code, optimized algorithms, thoughtful abstractions. You bring them in for a live technical round, ask one question about trade-offs, and watch them freeze. Blank stares. The sound of frantic typing in another tab. They can't explain the decisions in code they supposedly wrote.

The pattern is unmistakable. Candidates are using Claude Code, Cursor, and other AI coding tools to complete interview assignments, then struggling to defend work they didn't actually understand. As one engineering manager put it: "It's becoming really hard to tell who's actually a solid engineer and who's just good at managing an LLM."

This isn't theoretical. Engineering leaders are discussing this problem openly in technical communities. The tools many of us use daily to be more productive have fundamentally broken the way we evaluate technical talent.

The Plausibility Trap

We call this the plausibility trap: AI-generated code is good enough to be convincing but reveals nothing about the person who submitted it. The standard technical interview loop was built on a simple premise -- if someone can write good code under observation, they're probably a competent engineer. Take-home assignments tested real-world problem-solving. Live coding rounds verified they could actually think through problems. AI coding tools destroyed both assumptions.

Take-home assignments are now completely unreliable. A candidate with minimal technical knowledge can prompt an AI to generate production-quality code and submit it as their own work. The code might be excellent, but it tells you nothing about the person's actual capabilities. Live coding rounds are harder to fake, but they're also increasingly disconnected from how engineers actually work. Nobody codes in a vacuum without access to documentation, Stack Overflow, or AI assistance anymore. Testing someone's ability to implement a binary tree on a whiteboard doesn't predict whether they can architect a scalable system or debug production issues.

The hiring signal you need has been drowned out by noise.

The Real Problem Isn't AI

Here's what makes this particularly difficult: AI coding tools are legitimate productivity multipliers for experienced engineers. We use them. Our teams use them. In many cases, we want candidates who know how to use them effectively.

The problem isn't that candidates use AI. The problem is that current interview processes can't distinguish between someone who uses AI as a force multiplier and someone who uses it as a crutch. An experienced engineer using Claude Code understands what they're asking the AI to do, evaluates the generated code critically, knows when the AI is wrong, and makes informed architectural decisions. When things break, they debug effectively. Someone who's just good at prompting does something fundamentally different -- they copy-paste AI output without understanding it, struggle to explain design decisions, have no intuition for when code will fail, and freeze when asked about trade-offs. The difference is night and day, but traditional interviews can't surface it.

What We've Learned From Hiring With AI in Mind

Across our collective experience building engineering teams, we've adapted our interview processes to account for this reality. The shift that matters most: stop testing code generation and start testing code evaluation.

Instead of asking candidates to write code from scratch, give them AI-generated code with deliberate flaws and ask them to review it. Can they spot the bugs? Identify the performance issues? Suggest better approaches? This tests what matters -- technical judgment, not typing speed. We call this "the code review interview," and it's the single most reliable signal we've found. An engineer who can tear apart bad code understands how good code works. Someone who can't critique it certainly can't write it.

The next shift: focus on architectural decision-making under constraints. Present real scenarios from your codebase -- "We need to handle 10x traffic by next quarter, walk me through how you'd approach this." Strong engineers ask clarifying questions, consider trade-offs, and explain their reasoning. Weak ones regurgitate generic patterns without context. The depth of the follow-up questions tells you everything.

Pair programming on real problems beats coding puzzles every time. Bring candidates into an actual bug from your system or a small feature request. Let them use whatever tools they normally use, including AI. Watch how they think through the problem, what questions they ask, how they navigate unfamiliar code. This reveals far more than any algorithm challenge because it mirrors the actual job.

Debugging skills are another reliable signal. Give them broken code and ask them to fix it. Debugging requires understanding how systems work at a level you can't fake. You can't AI your way through a race condition or a memory leak if you don't understand what's happening underneath. And in later rounds, have your most experienced engineers conduct deep dives into the candidate's actual work history. "Tell me about a system you designed that failed. What would you do differently?" Real experience has depth that fabricated stories don't.

The Broader Shift

This hiring challenge is a symptom of something larger. AI hasn't just changed how we code -- it's changed what engineering competency means.

Five years ago, a good engineer was someone who could write clean, efficient code. Today, a good engineer is someone who can architect systems that solve real business problems, make informed trade-offs under uncertainty, debug complex distributed systems, evaluate and integrate AI-generated solutions, and mentor other engineers effectively. The skill we're actually hiring for is technical judgment, not code production. Interview processes that haven't adapted to this reality are selecting for the wrong capabilities entirely.

What This Means for Your Team

If you're struggling to find good engineering talent, your interview process might be filtering for the wrong signals. The candidates who fail your algorithmic coding challenges might be exactly the experienced engineers you need, while the ones who pass might collapse under the complexity of real-world systems.

For startups and growing companies, this is particularly critical. You can't afford to hire people who can't operate independently. Someone who needs AI to write every function isn't going to survive on a fast-moving team where problems don't have Stack Overflow answers. But you also can't afford to dismiss AI-savvy candidates who use these tools effectively. The engineers who have mastered AI-assisted development are force multipliers -- the ones who ship faster without sacrificing quality.

The hiring process you need tests judgment, not memorization. System thinking, not syntax. Problem-solving under real constraints, not puzzle-solving in artificial conditions.

How Fractional Leadership Helps

This is where experienced technical leadership makes the difference. If you're a founder or executive trying to build an engineering team, you might not have the context to evaluate whether someone is actually a strong engineer or just good at talking.

We've been on both sides of this: hiring hundreds of engineers, getting hired by skeptical founders, watching teams succeed and fail based on talent decisions. We know what questions reveal real competency versus surface-level knowledge.

When we work with companies as fractional technology leaders, one of the first things we often do is audit the hiring process. Are you actually testing for the skills that matter? Are your interview loops filtering for experience or just filtering for people who are good at interviews?

Getting hiring right is one of the highest-leverage decisions technical leadership makes. Hire the wrong engineer and you'll spend six months realizing they can't deliver before spending another six months replacing them. Hire the right one and they'll 10x your team's output.

AI coding tools have made hiring harder, but they've also made experienced technical judgment more valuable. The companies that adapt their hiring processes to this new reality will build stronger teams. The ones that don't will keep wondering why their "talented" engineers can't ship.

If your technical interviews aren't producing strong hires anymore, you're not alone. The rules changed. Time to change your playbook.

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Need help building an engineering team that can actually deliver? The Bushido Collective provides fractional technology leadership to help startups and growing companies make better technical hiring decisions, build effective interview processes, and scale engineering teams that ship. Learn more about our services or get in touch.