You’ve probably noticed the quiet panic taking over the tech hiring market. Hiring managers are terrified of making a bad hire in a landscape that shifts every week, while engineers are sweating bullets that their hard-earned coding skills are rapidly losing market value. The anxiety is palpable on both sides of the table. But here’s the truth nobody in the boardroom wants to say out loud: your “no-LLM” interview policy is making you look like a dinosaur.
Banning AI in a coding interview is like banning calculators in a calculus exam—you aren’t testing for genius, you’re testing for rote memorization.
In the agentic era, the value of an engineer has fundamentally shifted. We used to hire people who could memorize syntax and solve isolated puzzles. Now, AI commoditizes that instantly. The real value today is systems thinking, architectural trade-offs, and infrastructure design—skills that LLMs cannot autonomously orchestrate. But here’s the paradox that keeps hiring managers up at night: architectural judgment is notoriously difficult to assess in a standard 1-hour interview loop. So, we fall back on what’s easy to measure: raw coding ability. We measure the wrong thing simply because it’s convenient.
We aren’t hiring engineers anymore; we’re hiring syntax dictionaries and hoping they can think.
It’s time to take a side. A ‘no-LLM’ policy is as outdated as a ‘no-calculator’ policy in modern mathematics. We should be actively testing how well engineers prompt, review, and override AI, rather than testing if they can code without it. If an engineer can’t use an LLM to build a robust system, or worse, can’t spot the architectural hallucinations and security flaws the AI introduces, they are a liability in your modern tech stack. Stop asking them to invert a binary tree on a whiteboard. Ask them about the pros and cons of JWT. Ask them when orchestration is necessary versus message queues. Ask them to review an AI-generated API and tell you exactly why it will fail in production.
The best engineers don’t write perfect code from scratch; they know exactly when to trust the machine and when to rip the steering wheel from its hands.
Hiring managers, you need to overhaul your evaluation rubrics before your traditional filters weed out the actual talent and leave you with a team of memorizers. Engineers, stop grinding algorithmic puzzles and start learning how to orchestrate agents. The landscape has shifted, and if you’re still interviewing like it’s 2019, you’re already obsolete.
In a world where AI can write the code, the only humans worth hiring are the ones who know how to break it.
FAQ
Q: If engineers use AI during interviews, how do you know they actually understand the code?
A: You make them debug it. Force the AI to generate a flawed system and see if the engineer can identify the architectural bottlenecks and security holes. Understanding is proven through review and override, not from-scratch generation.
Q: What should a modern engineering interview actually look like?
A: Give them a broken, AI-generated codebase with a specific production issue. See how they prompt an LLM to help diagnose it, and more importantly, how they validate the AI's proposed fixes against your system's architectural constraints.
Q: Isn't this just letting mediocre engineers cheat their way into a job?
A: No, it's raising the bar. Memorizing leetcode tricks was the real cheat code. Now, you're testing for actual engineering judgment and systems thinking—the skills that actually matter when things break in production.