Founders keep asking a version of the same question every week: Should a company let AI handle the bulk of software creation? The question sounds tactical, like a tooling choice. In reality, it reshapes hiring, processes, and governance. I reached that point last year after a string of late-night postmortems. Our roadmap moved, the market moved faster, and our review queue piled up with changes spanning hundreds of files that a human-centric workflow could not clear.
We adopted a strict ‘no manual coding’ policy and restructured the team around a single mandate: operate AI systems with precision. The headcount adjustment was substantial. We parted with more than 40 colleagues who preferred traditional coding, focusing on a 17-person group that could master the new discipline of design, specification, verification, and shipping. People own the intent; models perform the implementation. That division of labor sets the tempo.
Redefining ‘A Day’s Work’
This approach, often called vibe coding, gives the team a clear cadence. The people guiding the models write rigorous PRDs, break work into units, supply context windows that models can digest, and enforce test gates before any merge. The cadence feels closer to air traffic control than hand-typing. Work moves in measured waves: plan, generate, verify, ship, observe, adjust.
The opening weeks felt unfamiliar as even skilled engineers learned a new craft. Keybindings stayed idle while the team engaged in intense debates. Stand-ups centered on the core disciplines of specification quality and test coverage. Throughput told the story: we could build 10x faster and fail 10x faster, learning and pivoting within days instead of quarters. This experience confirmed a company-building decision with long-term implications.
This model is proven in production, where our quant systems operate under these exact rules. These are low-latency, high-reliability environments where a single mistake is extremely costly. Aside from tiny surgical edits, humans design, specify, review, and operate. Guardrails prevent drift back to keystroke-driven habits and keep throughput high.
We run the model like a factory line. Every change must clear a fixed gate: static analysis, secret scanning, all unit tests, a performance budget per service, and a red-team prompt set for regression. Lead time fell from weeks to days, and the change-failure rate dropped materially.
The New Supply Chain for Software
Today’s IDEs and dev environments assume one person edits a few files for hours. The result is UI clutter and workflow bottlenecks. Conversely, vibe coding supervises models that can generate or modify hundreds of files in minutes.
Teams need an operations console for generation, verification, and release: persistent context across runs, provenance tracking, model routing, automatic test gates, and safe deployment by default. The platforms that deliver this “mission control” will shape the next decade of software.
Speed with control creates confidence. Generated code expands the attack surface, and scalable systems require prompts that encode performance budgets and failure modes. Large projects challenge context management; models can wander or refactor unintentionally. Governance ties it together with deterministic gates that block bad diffs before they merge.
From Coder to Conductor: The Engineer’s New Role
The engineer’s focus elevates to four key areas: intent, defining what the system must do with unambiguous clarity; architecture, designing robust structures that models can populate with code; verification, creating rigorous automated tests that act as the ultimate guardrail; and operations, managing the AI as a production system.
Vibe coding turns product intent into working software quickly. Product managers and designers can express flows, generate UI, and iterate with users in hours. Communication improves because the spec becomes the product.
According to estimations, about 3 out of every 1,000 people are software engineers. With vibe coding lowering the barrier to entry, we estimate that number could grow to 100 out of every 1,000 in the next five years. This massive influx of builders will create a market for platforms and tools that analysts project could reach $264 billion by 2032.
Where This Trend Is Headed
Founders can reframe the org chart around specification clarity and operational discipline. Establish a “manual coding by exception” rule with narrow, explicit cases. Measure throughput and quality per unit of specification. Adopt tools that preserve context at scale and enforce security gates at generation speed. Hire roles that make the new cadence safe: security engineering, reliability, test architecture, data curation, and model operations.
Vibe coding defines how companies will ship software in this cycle. The largest obstacle is mindset. Teams that align process, people, and platforms to this reality convert speed into a durable advantage. Teams that cling to keystroke-centric workflows face accelerating obsolescence as models absorb more of the implementation layer. The decision now sits with founders: design for intent-driven development and mission-control operations, or compete against those who do.
