AI Fluent, Fundamentally Lost
The Dual Bar for Hiring in 2026
Last week, Gene Kim and Steve Yegge published a piece on vibe coding titled Hiring in the Age of AI: What to Interview For.1 Their central question is one every engineering leader must confront. If AI has reshaped how software is built, how should we evaluate talent today.
They argue that modern interviews must identify candidates who have embraced AI, engineers who can prompt, manage context, and direct tools toward outcomes. I agree. But this view overlaps with a concerning pattern I described in my recent article, When AI Isn’t Enough.2
We are at a crossroads where two truths coexist: AI fluency is no longer optional, but it is not enough to make someone an engineer.
The “AI Crutch” Phenomenon
In recent software engineering interviews, I’ve noticed a recurring pattern. Candidates breeze through screens using AI assistants, producing clean, working code. But the moment the conversation shifts to fundamentals, they collapse.
In one instance, a candidate couldn’t explain why they chose composition over inheritance in the code they had just generated. The code was solid, but the engineer lacked a mental model of why it worked or what would break if the requirements changed.
This was a lack of foundation. AI had become a crutch, allowing them to produce strong output while masking a hollow understanding of the system.
The Great Divergence: Acceleration vs. Noise
A pattern is emerging across the industry. Software engineering is splitting into two groups, and the results are counterintuitive.
Group 1: The Architects. Senior engineers (and those with strong instincts) are achieving massive productivity gains. They can guide AI, spot hallucinations, and explain clean architecture to the tool. For them, AI is an accelerator.
Group 2: The Prompters. Engineers without fundamentals are actually getting slower. They cannot evaluate the AI’s suggestions. When the model drifts, they lack the intuition to course-correct, turning the tool into noise rather than augmentation.
This second group creates a hidden enterprise risk: The Glass Cannon.
They build systems that look impressive and powerful but shatter under the pressure of real-world constraints. The risks are invisible at first, but devastating over time:
The Black Box Problem: Because they cannot explain their own output, they treat their code as a third-party library. When it breaks, recovery time skyrockets.
Debt at Machine Speed: They may ship features, but they generate technical debt at an accelerated rate. They cannot optimize for cloud costs, architecture, performance, resilience,or spot silent security vulnerabilities because they assume “working” means “correct.”
Team Burden: They shift significant pressure onto team or senior engineers who must catch flawed designs, brittle patterns, and AI driven errors during code reviews.
This shifts the cost of software development from creation (which becomes cheap) to maintenance (which becomes prohibitively expensive).
The Dual Bar for Modern Talent
Effective hiring in 2026 requires us to stop picking one lens over the other. We must test for The Dual Bar:
Can the candidate reason through a problem without the aid of AI? (To ensure they aren’t building glass cannons.)
Can they intentionally use AI to accelerate their work? (To ensure they remain competitive.)
We aren’t hiring for what AI might be able to do in 2030. We are hiring for what teams need to ship and maintain now. That requires a new hiring rubric.
A New Hiring Model
To surface the engineers who can think, not just the ones who can prompt, consider your interview process around these five signals:
Fundamentals: Test this with at least one session where AI tools are off the table. Focus on fundamentals, design reasoning, and trade-offs, not syntax recall.
AI Fluency: Ask them to walk through a recent AI-assisted project. How did they prompt? How did they debug model mistakes? Or have them work through a challenge in real time using AI on a shared screen.
Communication: In an AI world, muddled explanations lead to muddled prompts. Can they articulate technical context with precision?
Systems Thinking: Present a scenario with competing trade-offs (e.g., latency vs. consistency). See if they can connect decisions to the broader architecture.
Curiosity: Ask what they’ve experimented with in the last 90 days. Engineers thriving in this era are climbing the learning curve with intention.
Acceleration vs. Illusion
There is a fine line between acceleration and illusion. If we hire based on the wrong signals, we risk building teams with strong output but weak understanding.
The current generation of great engineers will be those who use AI as a collaborator, not a substitute for thinking. They will use these tools to amplify their strengths rather than hide their gaps.
The question every leader should ask now: Does our interview process surface the engineers who can think, or just the ones who can prompt?
References
Kim, G., & Yegge, S. (2025, December 1). Hiring in the Age of AI: What to Interview For. IT Revolution. https://itrevolution.com/articles/hiring-in-the-age-of-ai-what-to-interview-for/
Phil Clark. (2025, November 29). When AI Isn’t Enough. Rethink Your Understanding. https://rethinkyourunderstanding.com/2025/11/when-ai-isnt-enough/
This article was originally published on December 07, 2025.
