Specs Became Valuable Again When AI Made Junior Developers Obsolete

The Documentation Nobody Wanted to Write Just Became Your Competitive Advantage

AI coding assistants killed the junior developer pipeline and accidentally made specs worth writing again. When Copilot or Cursor can generate a function from a comment, the quality of that comment determines whether you ship working code or plausible garbage. Spec-driven development went from "annoying overhead" to "the only way to maintain control."

Nir Kaufman's conversation with Roi Kes and Muli Gottlieb on Israeli Tech Radar nails something most AI coding discussions miss: the problem isn't whether AI can write code. It obviously can. The problem is that AI without context writes code that compiles but doesn't solve the actual problem. And the gap between "it runs" and "it works" is exactly where specs live.

AI Works Like a Talented Junior Who Never Asks Clarifying Questions

The podcast frames AI assistants as talented juniors who can execute well but lack context. That's the perfect mental model, except for one critical difference: actual juniors eventually learn your codebase and ask fewer stupid questions. AI assistants reset to zero context every time.

This creates a weird inversion. With human juniors, you invest time upfront explaining things poorly, then gradually reduce oversight as they learn the system. With AI, you need maximum clarity on every single interaction because it never builds institutional knowledge. The assistant that helped you yesterday has no memory of the architectural decisions you made or the bugs you've already fixed.

The implication: specs aren't documentation for future developers anymore. They're instructions for an infinitely patient but contextually blind execution engine that you're going to invoke hundreds of times per day. Writing "implement user authentication" gets you a security nightmare. Writing a spec that defines token structure, refresh logic, rate limiting, and error states gets you something you might actually ship.

Documentation Stopped Being Technical Debt When It Started Controlling AI Output

For the last decade, documentation was the thing you wrote after shipping (which means never). The code was the source of truth. Comments were lies waiting to happen. Every team had some variation of "the code documents itself" in their engineering principles.

AI coding tools broke that assumption completely. Code doesn't document itself when you're generating it from natural language prompts. The prompt is the documentation now. And if your prompt is vague, your code is vague. If your prompt has implicit assumptions, your code has bugs.

This flips the entire economics of documentation. Writing specs used to be pure overhead—time spent not shipping. Now specs are leverage. A well-written spec generates correct code across multiple files, multiple developers, and multiple refactoring cycles. The spec becomes the durable asset. The generated code is disposable.

The teams winning with AI aren't the ones using it to skip planning. They're the ones using it to enforce planning. Spec-driven development means you can't start coding until you've articulated what you're building clearly enough that an AI can implement it correctly. That constraint is a feature, not a bug.

The Real Shift: From Code Review to Spec Review

Here's what the podcast implies but doesn't quite say: code review is becoming obsolete for AI-generated code. Not because the code doesn't need review—it absolutely does—but because reviewing AI output is like proofreading a photocopy. The errors are upstream.

If the spec was wrong, the code will be wrong in predictable ways. If the spec was ambiguous, the code will be confidently incorrect. If the spec missed edge cases, the code will fail in production. Reviewing the generated code catches syntax errors and obvious bugs, but it can't catch "this solves the wrong problem."

Smart teams are shifting review cycles earlier. Spec review before generation, not code review after. The question changes from "does this code work?" to "does this spec describe what we actually need?" That's a harder question, but it's the right question.

This also changes what senior developers do. You're not reviewing pull requests anymore. You're reviewing problem definitions. You're not fixing implementation bugs. You're fixing specification bugs. The skill that matters isn't writing clean code—AI can do that. It's writing unambiguous requirements that generate clean code.

Why This Matters More Than the Productivity Hype

Every AI coding tool pitches productivity gains: "10x faster development!" "Ship features in hours!" That's true but useless. Shipping the wrong thing faster is just expensive failure with better velocity metrics.

The actual transformation is about control and accountability. When humans write code, you can trace bugs back to decisions and learn from mistakes. When AI writes code, bugs trace back to prompt ambiguity. The only way to maintain accountability is to make the spec explicit enough that failures are obviously specification failures, not mysterious AI hallucinations.

Spec-driven development with AI isn't about going faster. It's about maintaining the ability to understand what you built and why. It's about preserving the decision trail when the implementation is generated. It's about keeping humans in the loop at the point where judgment actually matters—defining the problem—instead of the mechanical work of typing syntax.

The Uncomfortable Truth

Developers spent twenty years arguing that specs were waterfall thinking, that agile meant less documentation, that working software was the only documentation that mattered. We were right for the constraints we had: human developers who could interpret vague requirements and ask clarifying questions.

AI assistants don't have those constraints. They'll happily implement exactly what you said, even when what you said isn't what you meant. The only defense is saying exactly what you mean, in writing, before you generate a single line of code.

Specs became valuable again not because the old process people were right all along. They became valuable because the fundamental constraint changed. When your "junior developer" is an AI that never learns context, the spec isn't overhead. It's the product.