AI is disruptive because it turns human intent into executable output at near-zero marginal cost, decoupling cognitive work from specialized tools, skills, and organizational scale.
That shift matters because it moves the bottleneck from making things to deciding things. When output becomes cheap, what remains expensive is the precision of intent: the constraints, boundaries, responsibilities, and invariants that keep a system coherent over time. That is a fundamental change. It affects law, finance, medicine, research — and software engineering. But not every application of AI participates in that disruption.
▶ AI is disruptive, but Vibe Coding isn’t.
Vibe Coding uses AI to optimize existing practices without fundamentally rethinking how software is conceived or built. Most AI tools in software development focus on acceleration. They help developers write code faster. They reduce boilerplate. They autocomplete functions, refactor blocks, generate tests, and explain unfamiliar code. All of this is useful. Much of it is impressive.
▶ But acceleration is not the same as disruption.
Vibe coding — prompting an AI to generate code and iterating until it works — accelerates the last mile. It applies AI to code production while leaving everything else unchanged. Making development more efficient isn’t the breakthrough.
▶ To be clear: vibe coding works. But …
Teams ship features faster. Prototypes appear in hours instead of days. Solo developers can build things that once required teams. Tools like Copilot, Q Developer, and Ghostwriter generate code quickly from informal prompts, conversations, or high-level scenarios.
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But across major platforms, the same pattern emerges: pull-request gates, automated reviews, security scans, test generation, agent feedback loops. These are compensating mechanisms. They exist because AI-generated output is fast — but not inherently correct. More guardrails. More reviews. More AI to check AI. Useful — but they don’t change the underlying game.
The real shift begins when intent becomes explicit, formal, and executable.
Constraints, responsibilities, and system boundaries that are defined upfront in a machine-verifiable way. AI no longer guesses what one means — it operates within strict, normative rules. Invalid states are excluded by design. Auditable, reproducible systems become the default, not the exception.
This isn’t about coding faster. It’s about deciding earlier and more precisely — so AI can materialize software as intended, not merely as inferred.
If you want to break the cycle of managing ambiguity and chasing correctness downstream, you need more than better AI. You need a new foundation: formal, executable intent. That is the real breakthrough.
A shift away from coding toward specification.
A shift away from code dependency toward executable requirements as first-class artifacts.
Vibe Coding speeds up the old game.
Executable intent changes the game.
Approaches like Secos Rocks envision a world where software is defined by explicit, formal intent rather than incidental implementation details.
Teams define what a system must be — clearly, formally, and testably. The specification becomes the source of truth. Delivery is reproducible by default, auditable by construction, and scalable through semantic definitions and formal requirements rather than individual effort.
This shifts the center of gravity from implementation detail to domain clarity. Meaning, behavior, dependencies, governance, and environments are captured as first-class artifacts. AI automates materialization; humans define intent, constraints, and responsibility.
Software is no longer defined by how it is coded, but by what is made explicit. When requirements are executable, validation, auditability, and governance become intrinsic properties of the build.
This is how future software gets built.
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