For decades, specifications served code. They were the scaffolding we built and then quietly abandoned once the "real work" of coding began. PRDs were written to guide development, architecture diagrams drawn to inform implementation, acceptance criteria drafted to give QA something to validate against. But these were always subordinate to the code itself. Code was truth. Everything else was, at best, good intentions.

That relationship is now being inverted. And the implications for how we build software, and who leads that process, are profound.

The end of vibe coding

The first wave of AI-assisted development was intoxicating in its simplicity. Describe what you want, let the model generate code, copy-paste, iterate. Developers called it "vibe coding," a term that captured both the speed and the chaos of the approach. It worked brilliantly for prototypes and demos. It fell apart spectacularly for anything that needed to be maintained, audited, or scaled.

The fundamental problem wasn't the AI models themselves. It was the absence of structure around them. Without a clear specification, AI agents are remarkably good at producing code that compiles, looks plausible, and subtly misses the point. Context rot sets in as conversations lengthen. Architectural decisions get buried in chat history. The model "forgets" what was agreed three prompts ago and quietly contradicts itself. Anyone who has tried to build a non-trivial application this way has experienced the moment where the cost of debugging AI-generated code exceeds the cost of having written it from scratch.

This is where spec-driven development enters the picture. Not as a return to waterfall-era documentation ceremonies, but as a necessary evolution that treats specifications as the primary artifact from which code is generated, validated, and regenerated.

Specifications as executable artifacts

Spec-Driven Development (SDD) inverts the traditional power structure. Specifications don't serve code. Code serves specifications. The PRD isn't a guide for implementation; it's the source that generates implementation. Technical plans aren't documents that inform coding; they're precise definitions that produce code.

Two frameworks have emerged at the forefront of this movement, each approaching the problem from a different angle.

BMAD Method (Breakthrough Method for Agile AI-Driven Development) is the more comprehensive of the two: a full agentic framework that deploys 21+ specialized AI agents mimicking an entire agile team: Analyst, Product Manager, Architect, Scrum Master, Developer, QA. These agents collaboratively generate and refine project artifacts through the entire lifecycle. The framework is scale-adaptive, adjusting its planning depth from quick bug fixes to enterprise-grade systems. Documentation becomes the source of truth, not code. Every architectural decision is versioned, traceable, and auditable, a property that has significant implications for regulated industries and compliance-sensitive environments.

GitHub Spec Kit takes a lighter, more agent-agnostic approach. Open-sourced in late 2024, it formalizes SDD through four gated phases: Specify, Plan, Tasks, and Implement. You don't advance to the next phase until the current one is validated, creating explicit human checkpoints that prevent the drift that makes vibe coding unreliable. Spec Kit works with GitHub Copilot, Claude Code, Gemini CLI, Cursor, and others, providing the scaffolding that ensures any AI coding agent has the structured context it needs to produce code aligned with intent.

These aren't competing approaches. They're complementary. BMAD's specialized agents can generate the detailed specs that Spec Kit's gated workflow then validates and executes. Together, they represent the maturation from ad-hoc prompting to systematic, auditable, enterprise-ready development.

The quiet revolution in project management

Here's where the story gets interesting for those of us who think about organizational dynamics and team structure.

If the specification is now the artifact that directly generates implementation, then the person who can articulate what to build, with precision, completeness, and domain insight, becomes the most valuable person in the pipeline. Title doesn't matter. Whether that person is a PM, a product owner, an architect, or a domain expert, the leverage belongs to whoever can translate business intent into unambiguous, executable specifications.

This is a profound inversion of status. For years, project management in technology has been drifting toward a coordination function: tracking tickets, facilitating standups, chasing status updates. AI is already automating much of that administrative layer. The PMs who define themselves by those activities are in trouble. But the PMs who understand how the SDD lifecycle works, who can orchestrate multiple AI agents across a leaner team, and who know how to drive work from spec to implementation through that pipeline? They've never been more essential.

The catch is that the bar for specification quality has gone up dramatically. When a human developer received a vague spec, they filled the gaps with judgment, experience, and tribal knowledge. They asked clarifying questions. They made reasonable assumptions. An AI agent does none of this. It executes what you specified, including your blind spots. If the AI builds the wrong thing, the spec was wrong. There's no misinterpretation to blame. There's only what you specified and what you didn't.

This forces a new accountability model that is uncomfortable for PMs accustomed to deferring implementation details. Specs now need version control and review rigor equivalent to code. They need pull requests, reviews, and history. Edge cases that PMs traditionally left to "the developers will figure it out" must be explicitly addressed. The feedback loop, however, tightens dramatically. You can generate code from a spec in minutes, test it, find the gaps, refine the spec, and regenerate. The iteration cycle that used to span weeks now happens in an afternoon.

Estimation, calibration, and parallel paths

The downstream effects ripple through every aspect of how projects are managed.

Traditional estimation (story points, velocity, t-shirt sizing) breaks down when AI coding agents can compress certain tasks by an order of magnitude while other tasks remain stubbornly human-intensive. The skill now is calibration: categorizing work as "AI-accelerable" versus "human-intensive" and tracking how actuals compare to estimates in each category. Within a few sprints, you have data to build a fundamentally different planning model.

Scope conversations change as well. "Can we build this?" becomes almost always "yes." The real question becomes: "Should we build this, and what's the maintenance cost of AI-generated code at scale?" Technical debt accumulates differently, and potentially faster, when code is generated rather than crafted.

Perhaps most significantly, AI enables parallel experimentation in ways that sequential delivery never could. Tools like Lovable and Replit allow rapid prototyping of full-stack applications from natural language descriptions in minutes, not weeks. A PM stuck in purely sequential delivery cadences misses the opportunity to run multiple solution paths simultaneously and converge on evidence rather than prediction.

The desktop agent dimension

The transformation extends beyond the development pipeline. Anthropic's recent launch of Claude Cowork, a desktop AI agent that reads local files, executes multi-step tasks, and integrates with external services through plugins, signals a broader shift in how knowledge work itself is performed. The tool was born from an observation that developers were using Claude Code, Anthropic's terminal-based coding agent, for distinctly non-coding work: vacation research, slide decks, email management, expense processing.

For PMs, this is directly relevant. The administrative burden that consumed hours each week (status report compilation, meeting note synthesis, stakeholder communication drafts, risk analysis documentation) can now be delegated to an agent that operates on your actual files and workflows. The time recovered can be reinvested into the work that actually differentiates a senior PM: stakeholder alignment, strategic judgment, and navigating the ambiguity that no agent can resolve.

What this means going forward

We stand at an inflection point that I find genuinely exciting. The emergence of spec-driven development is the clearest signal of where software engineering is heading: toward a world where the people who can articulate what to build, with precision and domain insight, matter more than ever. Code becomes the last-mile delivery mechanism. Debugging means fixing specifications. Refactoring means restructuring for clarity of intent.

The implication is clear: domain expertise, the ability to encode business rules and edge cases that a less experienced person would miss, an understanding of stakeholder dynamics and organizational constraints. These are the capabilities that become more valuable in an AI-augmented world, not less. But only if you actively adapt how you work.

The biggest trap is thinking AI is a tool your team uses while your own role stays the same.

It isn't. The role itself is being restructured. The question is whether you're steering that restructuring or being carried along by it.