Have you vibe coded an app yet? You should, it’s the most fun you can have on a computer at the moment. For a $20 license to Claude Code or Github Copilot, a little product vision, and a weekend’s worth of time, can you build your own working app. Development experience not required, everyone’s a developer now… at least in a certain sense.
The real question is: Does vibe coding have a place in enterprise software development?
In This Blog
- What we mean by “vibe coding”
- The enterprise SDLC hasn’t gone away
- What AI is changing
- Ambiguity is no longer an immediate blocker
- What vibe coding actually shortcuts
- Discovery replaces definition
- Why this breaks down in production
- Where vibe coding is legitimate in the enterprise
- How Emergent is using Vibe Coding
- The real takeaway
- Frequently Asked Questions
What We Mean by “Vibe Coding”
Vibe coding focuses on getting working software running as quickly as possible. The requirements and specifications can be ambiguous and very little (or no) time is spent on security, quality gates, or formality.
The result is incredibly fast iterations. You try something, see the results, and ask for changes until you’re happy. The software emerges and changes to become what you need. Requirements run on vibes.
The Enterprise SDLC Hasn’t Gone Away
Despite all the hype, enterprise software development still follows a familiar shape:
- Decompose product vision into features and stories
- Decompose stories into specifications and acceptance criteria
- Translate specifications into code
- Apply quality guardrails (tests, security, review)
- Deploy
- Collect feedback and iterate
The expensive part of this lifecycle has never been typing code. It’s the decomposition: the hundreds of small, explicit decisions that turn intent into something precise enough to implement safely.
This is where enterprise rigor lives: not just in syntax, but in intent made explicit.
What AI is Changing
Large language models are great at language and translating from language to language is a strength. In the same way it can easily translate English to French, it can also translate English to C#, Python, or JavaScript. Programming languages are explicit, they’re some of the easiest languages to master.
The act of typing code, looking up syntax, and editing files is now solved. LLMs can do this much, much, faster. This has never been the largest aspect of a developer’s job, but this speed boost makes a difference.
This doesn’t remove the need for specifications, but it changes the economics of producing software once specifications exist.
Ambiguity is No Longer an Immediate Blocker
There’s a second shift happening alongside this one. Modern agents have become noticeably better at operating in the presence of ambiguity. When requirements are incomplete or underspecified, they don’t simply fail. They infer, assume, and proceed, potentially producing something useful.
This is an important shift because traditional enterprise processes treat ambiguity as risk. It’s expensive to build something incorrectly, so we specified before we built. Reducing the cost of writing code reduces the risk of ambiguity.
That doesn’t mean the assumptions are correct. It means progress can happen without waiting for clarity. This is either a good thing or terrible thing depending on whether you’re going for speed or correctness.
What Vibe Coding Actually Shortcuts
Vibe coding collapses specific areas of the SDLC in exchange for speed.
Decomposition still happens, but through assumptions. Instead of humans making each micro-decision deliberately, the model fills in gaps probabilistically. Architectural choices, edge cases, and data assumptions emerge as side effects rather than artifacts of explicit design.
Quality guardrails are not applied upfront. There are no deterministic acceptance criteria, no threat models, no intentional test strategies. Those concerns are deferred (or ignored entirely) in favor of momentum.
Communication and handoffs are removed. Vibe coding is usually a solo endeavor so there are no meetings, documentation, or knowledge transfer.
Feedback loops are immediate. You see changes to the software in minutes, not days. The result is speed, but also opacity. Decisions are made without a record of why they were made and many of those decisions are hidden from the user.
Discovery Replaces Definition
This collapse fundamentally changes how requirements are treated.
In vibe coding workflows, requirements are no longer fully defined and decomposed before implementation. They are discovered through interaction. You react to running software, clarifying which assumptions you like and rejecting the ones you don’t.
This can be dramatically faster than traditional upfront analysis, especially when you don’t know what you want yet.
Why This Breaks Down in Production
Vibe coding struggles in production not because it’s sloppy, but because it defers costs that enterprises cannot ignore.
Rapid iteration produces tangled codebases with accidental architectures. Security, performance, and compliance risks accumulate invisibly. There is no reliable audit trail tying business intent to implementation decisions.
The system works… until it needs to be understood, scaled, secured, or defended.
At that point, the missing decomposition reappears as technical and organizational debt.
Where Vibe Coding is Legitimate in the Enterprise
Used intentionally, vibe coding is extremely valuable in specific contexts.
It excels at accelerating requirements discovery, validating feasibility, and proving value early. It helps teams explore solution space before committing to architecture. It creates alignment by making ideas tangible quickly.
How Emergent is Using Vibe Coding
We’re finding a growing number of legitimate use-cases within enterprise software development:
- Proof of Value. We can quickly create light-weight applications that aren’t designed to be maintainable, scalable, or contain a finalized set of requirements at a fraction of the time and cost it would take to build a full production ready application. The goal is to get the application in front of a small number of users to discover where and how value is derived. This lets organizations test the waters will ideas to be sure they’re investing in the right applications.
- Feasibility. There are times our clients want to push the boundaries of what technology can do or integrate applications in ways they’ve never been integrated before. Our solution has always been to build a PoC to ensure it’s feasible before committing to the full project. These tests can now be done in hours instead of days.
- Feature decomposition. There are even useful times to vibe code within an enterprise application. When our client benefits from discovering requirements through experimentation instead of defining them up front, we can vibe code a feature within an existing project. The feature can then be reworked to become production ready. Even with the rework, this can be a faster way to gather requirements and feedback on new features.
The Real Takeaway
Vibe coding isn’t the future of enterprise software engineering, but it is a legitimate addition to how software gets designed and how vision become requirements.
The teams that succeed won’t argue about whether vibe coding is “real engineering.” They’ll be the ones who understand when ambiguity is acceptable, when assumptions must be made explicit, and when it’s time to stop vibing and start engineering.
Frequently Asked Questions
What is vibe coding, exactly?
Vibe coding is AI-assisted software development that prioritizes speed and iteration over upfront planning and specification. The developer (or non-developer) interacts with an AI tool, describes what they want in natural language, reviews what gets generated, and refines from there. Requirements emerge through the process rather than being defined before it starts. The name comes from the idea that the software takes shape based on feel and reaction rather than formal design.
Is vibe coding appropriate for enterprise software development?
It depends on the context. Vibe coding is well-suited to requirements discovery, early prototyping, and feasibility validation — situations where the goal is to make an idea tangible quickly rather than to produce a production-ready system. It’s not well-suited for building systems that need to be maintained, scaled, audited, or secured over time. The gap is in what gets deferred: quality guardrails, explicit architectural decisions, and documentation of why things were built the way they were.
Can a vibe-coded prototype become a production application?
Rarely without significant rework. The challenge isn’t that vibe-coded software is technically wrong — it often works fine as a proof of concept. The problem is that the decisions embedded in it were made implicitly, without review or documentation. Scaling, securing, or handing that system off to another team requires reconstructing the reasoning that was never captured. In most enterprise contexts, it’s more effective to use the prototype to inform a proper build than to try to harden it into production.
How does AI change the role of developers in enterprise software projects?
AI removes the mechanical parts of writing code — syntax lookup, boilerplate, file editing — and does so much faster than any individual. What it doesn’t change is the need for deliberate decomposition: breaking product vision into features, features into stories, stories into specifications precise enough to implement correctly. That work requires judgment about business intent, edge cases, and downstream consequences. Developers who focus here, rather than on typing speed, become significantly more valuable in an AI-augmented environment.
How should enterprise teams think about where vibe coding fits?
The most useful framing is to treat vibe coding as a frontend to the development lifecycle, not a replacement for it. Use it to accelerate discovery — to explore the solution space, build alignment, and surface what you actually want before committing to an architecture. Once the discovery work has produced something concrete enough to specify properly, the standard enterprise rigor takes over. The teams that get in trouble are the ones that skip that transition and let the prototype quietly become the system.
