The “Long Division” of Tech
Why your interview process is obsolete
If you’re still testing candidates on their ability to write “clean code” from memory or draft a standard marketing plan from scratch, you’re testing for a world that no longer exists.
AI is now so good at “hard skills” that evaluating them is like testing an accountant on their ability to do long division by hand. It’s a baseline, not a differentiator.
To find the true 1% of talent in the AI era, hiring managers must shift from evaluating execution to evaluating judgment.
From “What You Did” to “How You Think”
Stop asking for a list of past achievements. Start asking for the why behind the what.
In the AI era, the most valuable trait is the ability to navigate trade-offs, identify constraints, and explain the logic behind an imperfect choice. Any candidate can list what they shipped. The ones you want can explain why they chose path A over path B when both paths had real downsides — and what they’d do differently with what they know now.
The shift: Ask candidates to walk you through a decision where there was no “perfect” answer. What trade-offs did they make? How did they manage the risk? How did they know when to stop deliberating and commit?
The answer reveals more about a candidate’s value than any portfolio ever could.
Evaluate “System Orchestration” Over “Manual Implementation”
We no longer need “code monkeys.” We need system architects.
The modern candidate’s value lies in their ability to use AI as an amplifier — to build, audit, and maintain complex systems where they’re the conductor, not the sole instrumentalist. The person who can prompt an LLM to scaffold a service, then immediately spot the three ways it’ll break under load, is worth ten people who can hand-write the same service from scratch.
The shift: Instead of a whiteboard coding test, give candidates an AI-generated solution and ask them to find the critical architectural flaws that will cause it to fail at scale. Watch how they reason about failure modes, data integrity, and operational complexity. That’s the skill that matters now.
This applies beyond engineering. In marketing, give them an AI-generated campaign brief and ask where the messaging will fall flat with your actual customer base. In finance, hand them a model and ask what assumptions will blow up first.
Test for “Seeing Around Corners”
AI is excellent at processing the past. Humans excel at predicting the human-centric future.
Evaluating intuition means looking for a candidate’s ability to anticipate cultural shifts, team friction, or market pivots that data hasn’t captured yet. The best operators I’ve worked with share a common trait: they can feel when something is about to go wrong before the metrics confirm it. That instinct is built from pattern-matching across years of lived experience — and it’s the one thing AI can’t replicate.
The shift: Present a real-world scenario with deliberately missing data. The goal isn’t to get the “right” answer. It’s to see if they can intuitively spot the “unknown unknowns” that would derail the project. Do they ask the questions no one else thinks to ask? Do they flag the risks that aren’t in the brief?
The Race Has Already Been Won
The goal isn’t to find the person who can work the fastest — AI has already won that race.
It’s to find the person who knows where to go.
The companies that figure this out first will build teams that are radically more effective than their competitors. The ones that keep running LeetCode gauntlets and take-home assignments will keep hiring fast typists while the best talent walks out the door.
Your interview process is your first product. Make sure it’s built for the era you’re actually hiring into.
If this resonated, I’d love to hear how you’re rethinking hiring at your company. Reach out here.