This briefing sets out a practical view of how software development is changing, and where the real value is likely to sit over the next few years. It argues that the most effective model is not “AI replaces developers”, nor the current fashion for loosely directed prompt-driven “vibe coding”, but a partnership: an experienced architect or senior developer providing structure, judgement, and intent, with an AI coding assistant handling research-heavy and execution-heavy work.
The distinction matters. Used well, AI compresses delivery timelines and raises baseline quality. Used badly, it produces brittle systems that look complete until they fail under real use.
At its core, professional software development has always been about decision-making under constraint. Requirements are incomplete, trade-offs are unavoidable, and the consequences of early choices often surface months later. Senior developers and architects earn their keep by shaping those decisions: choosing boundaries, defining contracts, sequencing work, and knowing which problems deserve precision and which can be left flexible. None of that disappears with AI. In fact, it becomes more important.
What does change is where time is spent. A capable AI assistant is exceptionally good at tasks that traditionally consumed disproportionate effort: scanning API documentation, comparing libraries, recalling edge-case behaviour, generating idiomatic code in unfamiliar frameworks, and stitching together boilerplate that is correct but tedious. It can draft service clients, data models, validation layers, and test scaffolding faster than any human, provided it is given clear direction and constraints.
The senior developer’s role shifts upward. Instead of writing every line, they define the shape of the system, the invariants that must hold, and the failure modes that must be avoided. They tell the AI not just what to build, but how and why: which architectural pattern to follow, how state flows through the system, what performance or security assumptions apply, and what “done” actually means. The AI then executes within those bounds, pulling in the right APIs, examples, and documentation to produce working code that aligns with the intent.
This is a very different proposition from what is often described as “vibe coding”. In that model, the human input is vague and outcome-focused: “the user needs to log in”, “add a dashboard”, “make it scalable”. The AI fills in the gaps as best it can. Sometimes the result looks impressive on first run. More often, it’s a tangle of assumptions: authentication logic mixed into UI code, hard-coded secrets, undocumented state transitions, and dependencies chosen because they were common in training data rather than appropriate for the problem.
The issue with vibe coding is not that the AI writes bad code in isolation. It’s that the system has no spine. There is no explicit model of the domain, no clear separation of concerns, and no shared understanding of what must remain stable over time. Each new prompt layers more behaviour on top of an already fragile base. The codebase becomes difficult to reason about, which means difficult to change safely. That is exactly the opposite of what most organisations need.
By contrast, a directed AI workflow starts with structure. An experienced developer will articulate things that feel obvious to them but are critical for the AI: where authentication lives, how identity is represented, what guarantees the API makes to clients, how errors propagate, and which parts of the system are allowed to know about which others. They will specify non-functional requirements early, because they know those are expensive to retrofit. They will review output not line by line, but at the level of intent: does this code respect the architecture, or has it quietly undermined it?
There is also a governance benefit. When the human is clearly accountable for design decisions and the AI is treated as an implementation tool, review and audit become tractable. You can explain why a library was chosen, why a pattern was followed, and where responsibility lies. That is much harder when the development process is effectively a series of improvised prompts.
None of this is to say the model is risk-free. AI output still needs scrutiny, especially around security, licensing, and subtle correctness issues. It can be confidently wrong. The difference is that an experienced developer knows where to look and what questions to ask. They understand which parts of the system are safety-critical and which are not. That judgement remains a human responsibility for the foreseeable future.
The direction of travel is clear. Teams that treat AI as a junior but extremely fast contributor, guided by senior hands, will build better systems more quickly. Teams that treat it as an oracle and replace design thinking with vague intent will accumulate problems they don’t see until production. The technology is the same in both cases. The outcome depends on whether experience is used to steer it, or sidelined in favour of convenience.