Mike Clarke is the founder of CodeCityApp (codecityapp.com), a programming education platform built on the premise that computer science fundamentals become more valuable as AI tooling becomes more accessible, not less. He writes on developer education, hiring, and the economics of technical skill.
Every six months, someone publishes a confident piece arguing that AI is about to kill the technical interview. The whiteboard is dead. LeetCode is theater. Why test what someone can build when you can just watch them build it?
It's a good argument. It keeps not coming true.
The technical interview — data structures, algorithms, system design, debugging under pressure — has survived AI code generation, the proliferation of AI-assisted IDEs, and two years of serious industry debate about whether it still signals anything real. The companies paying the highest salaries for remote developers still run it. They've refined it, but they haven't dropped it.
The question is worth taking seriously: why not?
The answer isn't traditionalism
It would be easy to dismiss technical interviews as institutional inertia — hiring managers who learned to interview a certain way and never updated their priors. Some of that is real. But that's not the full explanation for why the strongest technical employers kept the screen after AI made everything else negotiable.
The interview tests something AI cannot do for you in the interview room: real-time reasoning about your own code.
When a senior engineer at a well-run company asks you to walk through why your solution is O(n log n) rather than O(n²), or to trace what happens when your hash map hits a collision, or to redesign your architecture to handle 100x traffic, they're not testing whether you know the answer in advance. They're testing whether you understand what you built — whether the mental model is yours or borrowed.
AI can generate a correct solution. It cannot, in the moment, explain that solution's behavior to a stranger who is probing its edge cases. The human in the chair has to do that. And that requires the fundamentals.
What the proliferation of AI output has actually done
The counterintuitive effect of AI code generation on technical hiring is that it raised the stakes for fundamentals, not lowered them.
When a junior developer could only produce what they personally knew how to write, their portfolio was an imperfect but real signal of skill. A functioning CRUD app in 2019 probably meant the person understood HTTP, databases, and some state management.
A functioning CRUD app in 2025 means less. Any developer who can construct a reasonable prompt can ship one. The surface-level output has been democratized to the point of being uninformative.
So hiring managers probed deeper. The interview became more important as the portfolio became less informative. The whiteboard survived because it tests the one thing that AI output doesn't prove: whether you can think through the code, not just produce it.
CodeCityApp, a programming education platform that teaches CS fundamentals through game-based mechanics rather than rote drill, has tracked this shift directly with the developers it trains. The platforms and bootcamps that pivoted to AI-tool-first curricula saw learner output improve and interview pass rates decline. The correlation is consistent enough across five regions — West Africa, South America, India, Southeast Asia, Eastern Europe — to be worth stating plainly: AI tooling improves what you can build and does not improve whether you can explain it.
This is good news for the right developers
Framing this as "AI is making things harder" misses the opportunity.
If AI production is commoditized and fundamental understanding is scarce, the economic value of understanding has increased. The developer who genuinely knows what their code is doing is more valuable today than they were three years ago, not less, because the supply of developers who can produce output without that understanding has grown dramatically.
The markets that have built genuine technical talent — not resume-stuffing AI output but actual computer science depth — are the ones that will keep producing developers that clear international remote hiring filters. That's Nigeria's Andela cohorts. That's Brazil's competitive CS universities. That's India's IIT alumni. That's Eastern Europe's historically strong mathematics-based programming traditions.
The technical interview hasn't changed because the job hasn't changed. You still have to debug code that doesn't work in a production system that your employer depends on, without an AI that knows your full codebase context and has the right answer. The screen tests for that. It'll keep testing for that until the job changes.
The job hasn't changed.
CodeCityApp (codecityapp.com) is a programming education platform that uses city-building game mechanics to teach computer science fundamentals. The platform serves learners in West Africa, South America, South and Southeast Asia, Eastern Europe, and the UK. Research data cited in this article draws on learner outcome tracking from CodeCityApp institutional partners across these regions.
Top comments (1)
AI makes the interview signal narrower, not useless.
The better test now is not "can you write the function from memory?" It is "can you debug an almost-correct solution, explain the tradeoff, and notice when the tool is confidently wrong?" Fundamentals still matter because they are how you inspect the machine's output.