The moment teams accept that the syntax interview is broken, they immediately ask themselves the wrong question: “What should we replace it with?”
That question assumes the problem was the format. It wasn’t. The problem was the signal it tested for.
For 20 years, technical interviews relied on a simple assumption: If a candidate could produce correct code under pressure, they likely understood the underlying system. That assumption no longer holds. AI can now generate correct syntax instantly, which means code output is no longer a reliable proxy for competence.
In response, many teams are experimenting with so-called audit interviews. They hand candidates AI-generated code and ask them to critique it. This is directionally right, but most implementations fail for a predictable reason.
They still don’t know what they’re scoring.
What Should a Software Engineering Interview Assess?
AI code generation tools are fundamentally reshaping the work of software developers and, as a result, how we should hire them. These are the four essential qualities to assess in your interview process.
- Verification depth: Does the candidate look past surface-level correctness to make sure the code won’t break at scale?
- Architectural reasoning: Does the engineer understand not just how this code block works, but the system as a whole?
- Economic awareness: Does the candidate treat engineering resources as finite or do they throw everything and the kitchen sink at a problem without regard for cost?
- AI interrogation skill: Let candidates use AI during the interview process and gauge how well they interact with it. Do they treat it as an oracle or an intern?
Why ‘Realistic’ Interviews Still Fail
Many teams respond to the collapse of the syntax interview by making interviews more realistic. They allow IDEs. They allow Google. They allow AI. On paper, this looks like progress.
In practice, so-called realism without structure accelerates failure.
When everything is allowed but nothing concrete is measured, interviews drift toward performance. Candidates who speak confidently and move quickly are rewarded. Candidates who slow down to reason about edge cases, cost implications or long-term maintainability are penalized for not “shipping.”
This same failure mode exists in poorly governed engineering organizations. Output becomes the only visible signal as judgment becomes invisible. An audit interview that does not explicitly slow candidates down and force them to justify decisions under constraint is still a syntax interview. It has simply moved from whiteboards to laptops.
The Fallacy of the Take-Home Test
A common objection I hear is, “Why not just give them a take-home assignment?”
In the age of AI, the take-home test is the most dangerous signal in hiring. When you give a candidate 48 hours to solve a problem asynchronously, you’re no longer measuring their engineering ability. You are measuring their available time and their subscription tier to OpenAI.
A candidate with zero judgment but an expensive LLM subscription can generate a flawless take-home submission. They can generate unit tests, documentation and edge-case handling that looks senior level. But because you weren’t in the room to see the decision velocity, you can’t distinguish between the engineer who carefully architected the solution and the one who blindly pasted a prompt. The audit interview must be live because the signal isn’t the code itself. The signal is the hesitation. I want to see where they pause.
Verification Is More Valuable Than Generation
To fix this system, we must understand that AI has inverted the cognitive work of engineering.
Generation is cheap. Verification is expensive.
Reading and auditing a 500-line AI-generated module requires a completely different approach and skill set than writing it. It requires maintaining multiple execution paths in working memory, understanding implicit dependencies that the AI hallucinated or ignored and identifying where correctness today becomes failure tomorrow.
This is not junior work. It isn’t even mid-level work. It’s the core of senior-level engineering judgment. Audit interviews work because they force candidates to adopt this harder cognitive mode. Syntax interviews never did.
The 4 Dimensions of Engineering Judgment
After reviewing thousands of resumes and sitting in hundreds of technical interviews, I have found four dimensions that actually separate senior-level judgment from syntactic competence. If you are going to interview for verification, these are the four signals you must evaluate.
1. Verification Depth
The most common failure mode in an AI-assisted world is surface correctness. The code runs, the tests pass and the linter is happy. A junior engineer stops there. A senior engineer knows that correct is not the same as robust.
Low Score
The candidate points out syntax errors or style violations (e.g., “This variable should be camelCase”). They trust the logic because the code runs.
High Score
The candidate identifies failure modes that span across data, scale and time. They ask, “What happens to this function if the database latency spikes?” or “This logic holds for 100 users but breaks at 10,000.”
2. Architectural Reasoning
AI is excellent at solving local problems (writing a function) but terrible at solving global problems (designing a system). The interview must test if the candidate can see beyond the immediate code block.
Low Score
The candidate fixes the function in isolation. They optimize the loop but ignore where the data comes from.
High Score
The candidate reasons about the system the code lives in. They ask about upstream dependencies (“Is this data sanitized before it gets here?”) and downstream impact (“Will this retrying logic DDoS our own internal API?”).
3. Economic Awareness
This is the rarest signal but the most valuable. In a cloud-native environment, every line of code has a price tag. Choosing an expensive API, unnecessary compute or a fragile dependency is not just a technical error. It is a capital error.
Low Score
The candidate selects the most powerful tool or the newest library without regard for cost. They solve the problem using maximum resources.
High Score
The candidate explicitly mentions trade-offs. They might say, “We could use a vector database here, but for this data set size, a simple Postgres query is 10 times cheaper and sufficient.” They treat engineering resources as finite capital.
4. AI Interrogation Skill
If you allow candidates to use AI during the interview (and you should), watch how they use it.
Low Score
The candidate treats the AI as an oracle. They paste the prompt, accept the first output and then paste it back into the IDE.
High Score
The candidate treats the AI as an intern. They challenge the output. They add constraints (“Rewrite this, but assume memory is limited to 512MB”). They verify the AI’s logic before accepting it.
The Mechanics of the Audit Interview
Implementing this process requires more than just a rubric. It requires a structured simulation. I break the audit interview into three timed phases to force the candidate out of rehearsal mode and into decision mode.
Phase 1: Orientation (Five Minutes)
Before any code, I show the candidate a dashboard. It might indicate rising cloud costs, missed SLAs or a traffic spike. I ask, “What is the primary constraint here?” A candidate who dives into solutions without identifying the constraint is a liability. I want to see them orient themselves in the problem space before they touch the solution.
Phase 2: The Audit (20 Minutes)
I hand them a Python service generated by an AI. It works, but it is flawed. It might have a hidden N+1 query or a memory leak that only appears at scale. The prompt is simple: “This code passes tests but will fail in production. Tell me why.” This strips away the performance art of typing. It forces them to read, reason and simulate execution in their head.
Phase 3: The Defense (10 Minutes)
I ask them to fix one thing and explicitly defer the rest. “You can only deploy one change today. What is it?” This tests their ability to prioritize. A weak hire tries to fix everything. A strong hire fixes the critical failure and accepts the technical debt of the minor issues, explicitly noting them for later.
I built the Audit Interview System to automate this exact protocol. It runs candidates through a governed simulation — testing engineering for technical insolvency and product management for capital risk — and generates a leveled assessment (L3-L8) based on their judgment. You can run a live simulation here: The Audit Interview Protocol.
Solving the Junior Gap
The most common pushback to the audit interview is about mentorship. People ask, “If juniors don’t write code from scratch, how will they ever develop the intuition to audit it?”
This fear is misplaced. We are not removing the apprenticeship; we are inverting it. In the past, a senior engineer reviewed the junior’s code. In the AI era, the junior should be reviewing the AI’s code, with the senior engineer reviewing the review.
By testing for verification skills early, we select for juniors who are naturally skeptical and detail-oriented. We stop hiring typists and start hiring auditors. This actually accelerates their growth because they spend their first year analyzing high-volume code patterns rather than struggling with syntax errors.
What a Strong Hire Actually Looks Like
In an audit interview, a strong engineering hire doesn’t immediately propose solutions. That is a counterintuitive signal that trips up many hiring managers who are used to rewarding speed.
Instead, the strong hire:
- Restates the problem in their own words to verify alignment.
- Asks clarifying questions about scale and constraints before touching the keyboard.
- Identifies the most dangerous assumption in the AI-generated code.
- Explains trade-offs before writing or editing anything.
They are comfortable saying, “I would not ship this yet.” That discomfort with premature action is the signal you are hiring for. Senior engineers do not write better code; they write less fragile code.
