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The politeness problem
“You’re absolutely right!”
If you’ve spent any time working with Claude Code or other AI coding assistants, you’ve heard this phrase more times than you can count. It’s become the unofficial catchphrase of AI-assisted development, appearing whenever you correct the AI’s approach, point out a bug, or suggest a different implementation.
At first, this politeness feels refreshing. Here’s an assistant that actually listens, acknowledges mistakes, and pivots gracefully. But after the hundredth correction followed by that same cheerful affirmation, something deeper becomes apparent. The AI’s excessive politeness reveals a fundamental shift in how software development works.
The AI’s willingness to be corrected reveals something important: the real power in AI-assisted coding doesn’t come from the AI’s ability to write perfect code. It comes from your ability to recognize what’s wrong, articulate why it’s wrong, and communicate exactly what needs to change.
The communication imperative
We’re witnessing a profound transformation in software engineering, and most people are focusing on the wrong part. AI can indeed write code, but the more significant shift is that effective coding now requires a level of technical communication that many developers have never needed before.
Think about how you’ve always worked. You’d start with a vague idea, hack around until something worked, refactor as you went, and gradually arrive at a solution. The process was iterative, exploratory, and largely internal. You could muddle through with intuition, trial and error, and gradual refinement.
AI-assisted development demands something different. You can’t just say “make it work better” or “fix the performance issue.” You need to articulate precisely what’s wrong, why it’s wrong, and what specific pattern or principle should guide the solution. The AI isn’t going to intuit your way through the problem—it needs explicit, technical direction.
This shift has created a new kind of skill gap. Understanding computer science fundamentals well enough to communicate technical concepts clearly and precisely matters far more than memorizing syntax or API documentation.
The fundamentals filter
Here’s where things get uncomfortable for the industry. The developers who will thrive in an AI-assisted world are those who truly understand the foundational concepts of computer science. Not the ones who memorized them for interviews, but the ones who internalized them so deeply they can explain why certain approaches work and others don’t.
Consider a simple example: optimizing a database query. In the pre-AI world, you might have tried different indexes, experimented with query plans, and eventually found something that worked. With AI assistance, you need to be able to say something like: “This query is doing a full table scan because we’re filtering on a non-indexed column. We need a composite index on user_id and created_date, ordered to match our WHERE clause and support the ORDER BY.”
The difference is profound. The AI can generate the DDL statement, but you need to understand indexing theory, query optimization, and database internals well enough to diagnose the problem and specify the solution.
This pattern repeats across every domain in software engineering. Want the AI to help with distributed systems? You better understand CAP theorem, eventual consistency, and partition tolerance well enough to explain why your current architecture is creating race conditions. Need help with object-oriented design? You should be able to articulate why your current approach violates the single responsibility principle and how to restructure it using composition over inheritance.
The bootcamp reckoning
This shift is particularly challenging for developers who learned through bootcamps or other accelerated programs. These programs excel at getting people productive quickly, teaching them to build functional applications and solve immediate problems. But they often don’t have time to cover the deep theoretical foundations that become critical when directing AI assistants.
I’m not throwing bootcamp graduates under the bus here. Many are incredibly talented developers who’ve built impressive careers. But the nature of AI-assisted development exposes a specific vulnerability: if you learned primarily through pattern matching and trial-and-error rather than understanding underlying principles, you’ll struggle to communicate effectively with AI tools.
The problem is that you don’t know what you don’t know. When an AI suggests an approach that seems to work but violates fundamental principles you’ve never learned, you can’t provide the correction. When performance problems arise from architectural decisions you don’t fully understand, you can’t guide the AI toward better solutions.
This creates a strange inversion. Junior developers who have solid CS fundamentals might be more effective with AI assistance than senior developers who learned through experience but lack theoretical depth. The ability to say “that approach will create a memory leak because we’re holding references to objects that should be garbage collected” becomes more valuable than years of experience shipping features.
The new senior engineer
The definition of a “senior” software engineer is evolving. In the past, seniority often meant having seen lots of codebases, debugged countless production issues, and developed intuition about what works in practice. Experience mattered more than theory.
AI assistance is changing this calculus. The most effective senior engineers in an AI-driven world will be those who can serve as technical translators—taking business requirements and architectural constraints and translating them into precise, actionable guidance for AI tools.
This requires a different kind of expertise. You need to understand not just how to solve problems, but why certain solutions are better than others. You need to be able to articulate the trade-offs between different approaches, explain the implications of architectural decisions, and communicate the reasoning behind coding standards and best practices.
The senior engineer becomes less of a code producer and more of a technical architect and communicator. Instead of writing the implementation, you’re writing the specification. Instead of debugging the details, you’re diagnosing the conceptual issues. Instead of optimizing the algorithms, you’re explaining why the current approach is suboptimal and what principles should guide the optimization.
The communication gap
What’s fascinating is how this reveals gaps in our own understanding. When you can’t clearly explain to an AI why its approach is wrong, it often means you don’t fully understand the problem yourself. The AI’s willingness to accept correction becomes a mirror, reflecting back your own knowledge gaps.
I’ve had countless moments where I knew something was wrong with the AI’s suggestion but struggled to articulate why. The code looked reasonable, might even work in simple cases, but violated some principle I felt intuitively but couldn’t express clearly. These moments are humbling—and educational.
The AI forces you to be precise about your reasoning. You can’t just say “that feels wrong” or “we don’t usually do it that way.” You need to explain the underlying principle being violated, the potential consequences, and the better approach. This level of precision in technical communication is a skill many developers have never needed to develop.
The path forward
So what does this mean for individual developers and the industry as a whole? First, it suggests that investing in computer science fundamentals is more valuable than ever. Not for the sake of passing interviews, but for the sake of being able to communicate effectively with AI tools.
If you find yourself struggling to direct AI assistants, the solution isn’t to avoid them—it’s to deepen your understanding of the underlying concepts. Study algorithms and data structures, not to memorize them, but to understand when and why to apply them. Learn design patterns, not as recipes to follow, but as solutions to recurring problems with specific trade-offs.
For the industry, this shift suggests we need to rethink how we evaluate and develop technical talent. Code-writing skills matter less; technical communication and conceptual understanding matter more. The ability to mentor an AI through complex problems becomes as important as the ability to solve those problems yourself.
Beyond the catchphrase
“You’re absolutely right!” will keep echoing through our development environments as we correct, guide, and redirect our AI assistants. But each instance represents something larger: a successful act of technical communication, a moment where deep understanding translated into actionable guidance.
The developers who master this new skill—the ability to communicate technical concepts clearly and precisely—will find AI assistance transformative. Those who struggle with it will find these tools more frustrating than helpful.
The irony is beautiful: in an age of artificial intelligence, the most valuable skill might be the most human one of all—the ability to think clearly and communicate precisely about complex technical concepts. The AI can generate the code, but only if you can generate the insight.
The future belongs to developers who can say not just “you’re wrong,” but “you’re wrong because of this specific principle, and here’s exactly what you should do instead.” The ones who can turn that polite “You’re absolutely right!” into a collaborative moment of technical precision.