Introduction
Recently another topic has become popular in the AI space and in today’s post we will discuss what’s the buzz, why is it relevant and what you need to know to filter out the noise.
We understand that software has always been written in layers of abstraction, Assembly gave way to C, C to Python, and APIs to platforms. However, today a new layer is forming above them all: intent itself.
A human will typically describe their intent in natural language, while a large language model (LLM) generates, executes, and iterates on the code. Now we hear something new “Vibe Coding” which was popularized by Andrej Karpathy – This approach focuses on rapid, conversational prototyping rather than manual coding, treating AI as a pair programmer.
What are the key Aspects of “Intent” in Vibe Coding:
- Intent as Code: The developer’s articulated, high-level intent, or “vibe,” serves as the instructions, moving from “how to build” to “what to build”.
- Conversational Loop: It involves a continuous dialogue where the AI acts on user intent, and the user refines the output based on immediate visual/functional feedback.
- Shift in Skillset: The critical skill moves from knowing specific programming languages to precisely communicating vision and managing the AI’s output.
- “Code First, Refine Later”: Vibe coding prioritizes rapid prototyping, experimenting, and building functional prototypes quickly.
- Benefits & Risks: It significantly increases productivity and lowers the barrier to entry. However, it poses risks regarding code maintainability, security, and the need for human oversight to ensure the code’s quality.
Fortunately, “Vibe coding” is not simply about using AI to write code faster; it represents a structural shift in how digital systems are conceived, built, and governed. In this emerging model, natural language becomes the primary design surface, large language models act as real-time implementation engines, and engineers, product leaders, and domain experts converge around a single question: If anyone can build, who is now responsible for what gets built? This article explores how that question is reshaping the boundaries of software engineering, product strategy, and enterprise risk in an era where the distance between an idea and a deployed system has collapsed to a conversation.
Vibe Coding is one of the fastest-moving ideas in modern software delivery because it’s less a new programming language and more a new operating mode: you express intent in natural language, an LLM generates the implementation, and you iterate primarily through prompts + runtime feedback—often faster than you can “think in syntax.”
Karpathy popularized the term in early 2025 as a kind of “give in to the vibes” approach, where you focus on outcomes and let the model do much of the code writing. Merriam-Webster frames it similarly: building apps/web pages by telling an AI what you want, without necessarily understanding every line of code it produces. Google Cloud positions it as an emerging practice that uses natural language prompts to generate functional code and lower the barrier to building software.
What follows is a foundational, but deep guide: what vibe coding is, where it’s used, who’s using it, how it works in practice, and what capabilities you need to lead in this space (especially in enterprise environments where quality, security, and governance matter).
What “vibe coding” actually is (and what it isn’t)
A practical definition
At its core, vibe coding is a prompt-first development loop:
- Describe intent (feature, behavior, constraints, UX) in natural language
- Generate code (scaffolds, components, tests, configs, infra) via an LLM
- Run and observe (compile errors, logs, tests, UI behavior, perf)
- Refine by conversation (“fix this bug,” “make it accessible,” “optimize query”)
- Repeat until the result matches the “vibe” (the intended user experience)
IBM describes it as prompting AI tools to generate code rather than writing it manually, loosely defined, but consistently centered on natural language + AI-assisted creation. Cloudflare similarly frames it as an LLM-heavy way of building software, explicitly tied to the term’s 2025 origin.
The key nuance: spectrum, not a binary
In practice, “vibe coding” spans a spectrum:
- LLM as typing assistant (you still design, review, and own the code)
- LLM as pair programmer (you co-create: architecture + code + debugging)
- LLM as primary implementer (you steer via prompts, tests, and outcomes)
- “Code-agnostic” vibe coding (you barely read code; you judge by behavior)
That last end of the spectrum is the most controversial: when teams ship outputs they don’t fully understand. Wikipedia’s summary of the term emphasizes this “minimal code reading” interpretation (though real-world teams often adopt a more disciplined middle ground).
Leadership takeaway: in serious environments, vibe coding is best treated as an acceleration technique, not a replacement for engineering rigor.
Why vibe coding emerged now
Three forces converged:
- Models got good at full-stack glue work
LLMs are unusually strong at “integration code” (APIs, CRUD, UI scaffolding, config, tests, scripts) the stuff that consumes time but isn’t always intellectually novel. - Tooling moved from “completion” to “agents + context”
IDEs and platforms now feed models richer context: repo structure, dependency graphs, logs, test output, and sometimes multi-file refactors. This makes iterative prompting far more productive than early Copilot-era autocomplete. - Economics of prototyping changed
If you can get to a working prototype in hours (not weeks), more roles participate: PMs, designers, analysts, operators or anyone close to the business problem.
Microsoft’s reporting explicitly frames vibe coding as expanding “who can build apps” and speeding innovation for both novices and pros.
Where vibe coding is being used (patterns you can recognize)
1) “Software for one” and micro-automation
Individuals build personal tools: summarizers, trackers, small utilities, workflow automations. The Kevin Roose “not a coder” narrative became a mainstream example of the phenomenon.
Enterprise analog: internal “micro-tools” that never justified a full dev cycle, until now. Think:
- QA dashboard for a call center migration
- Ops console for exception handling
- Automated audit evidence pack generator
2) Product prototyping and UX experiments
Teams generate:
- clickable UI prototypes (React/Next.js)
- lightweight APIs (FastAPI/Express)
- synthetic datasets for demo flows
- instrumentation and analytics hooks
The value isn’t just speed, it’s optionality: you can explore 5 approaches quickly, then harden the best.
3) Startup formation and “AI-native” product development
Vibe coding has become a go-to motion for early-stage teams: prototype → iterate → validate → raise → harden later. Recent funding and “vibe coding platforms” underscore market pull for faster app creation, especially among non-traditional builders.
4) Non-engineer product building (PMs, designers, operators)
A particularly important shift is role collapse: people traditionally upstream of engineering can now implement slices of product. A recent example profiled a Meta PM describing vibe coding as “superpowers,” using tools like Cursor plus frontier models to build and iterate.
Enterprise implication: your highest-leverage builders may soon be domain experts who can also ship (with guardrails).
Who is using vibe coding (and why)
You’ll see four archetypes:
- Senior engineers: use vibe coding to compress grunt work (scaffolding, refactors, test generation), so they can spend time on architecture and risk.
- Founders and product teams: build prototypes to validate demand; reduce dependency bottlenecks.
- Domain experts (CX ops, finance, compliance, marketing ops): build tools closest to the workflow pain.
- New entrants: use vibe coding as an on-ramp, sometimes dangerously, because it can “feel” like competence before fundamentals are solid.
This is why some engineering leaders push back on the term: the risk isn’t that AI writes code; it’s that teams treat working output as proof of correctness. Recent commentary from industry leaders highlights this tension between speed and discipline.
How vibe coding is actually done (a disciplined workflow)
If you want results that scale beyond demos, the winning pattern is:
Step 1: Write a “north star” spec (before code)
A lightweight spec dramatically improves outcomes:
- user story + non-goals
- data model (entities, IDs, lifecycle)
- APIs (inputs/outputs, error semantics)
- UX constraints (latency, accessibility, devices)
- security constraints (authZ, PII handling)
Prompt template (conceptual):
- “Here is the spec. Propose architecture and data model. List risks. Then generate an implementation plan with milestones and tests.”
Step 2: Generate scaffolding + tests early
Ask the model to produce:
- project skeleton
- core domain types
- happy-path tests
- basic observability (logging, tracing hooks)
This anchors the build around verifiable behavior (not vibes).
Step 3: Iterate via “tight loops”
Run tests, capture stack traces, paste logs back, request fixes.
This is where vibe coding shines: high-frequency micro-iterations.
Step 4: Harden with engineering guardrails
Before anything production-adjacent:
- static analysis + linting
- dependency scanning (SCA)
- secret scanning
- SAST rules
- threat modeling for critical flows
- code review (yes, still)
This is the point: vibe coding accelerates implementation, but trust still comes from verification.
Concrete examples (so the reader can speak intelligently)
Example A: CX “deflection tuning” console
Problem: Contact center leaders want to tune virtual agent deflection without waiting two sprints.
Vibe-coded solution:
- A web console that pulls: intent match rates, containment, fallback reasons, top utterances
- A rules editor for routing thresholds
- A simulator that replays transcripts against updated rules
- Exportable change log for governance
Why vibe coding fits: UI scaffolding + API wiring + analytics views are LLM-friendly; the domain expert can steer outcomes quickly.
Where caution is required: permissioning, PII redaction, audit trails.
Example B: “Ops autopilot” for incident follow-ups
Problem: After incidents, teams manually compile timelines, metrics, and action items.
Vibe-coded solution:
- Ingest PagerDuty/Jira/Datadog events
- Auto-generate a draft PIR (post-incident review) doc
- Build a dashboard for recurring root causes
- Open follow-up tickets with prefilled context
Why vibe coding fits: integration-heavy work; lots of boilerplate.
Where caution is required: correctness of timeline inference and access control.
Tooling landscape (how it’s being executed)
You can group the ecosystem into:
- AI-first IDEs / coding environments (prompt + repo context + refactors)
- Agentic dev tools (multi-step planning, code edits, tool use)
- App platforms aimed at non-engineers (generate + deploy + manage lifecycle)
Google Cloud’s overview captures the broad framing: natural language prompts generate code, and iteration happens conversationally.
The most important “tool” conceptually is not a brand—it’s context management:
- what the model can see (repo, docs, logs)
- how it’s constrained (tests/specs/policies)
- how changes are validated (CI/CD gates)
The risks (and why leaders care)
Vibe coding changes the risk profile of delivery:
- Hidden correctness risk: code may “work” but be wrong under edge cases
- Security risk: authZ mistakes, injection surfaces, unsafe dependencies
- Maintainability risk: inconsistent patterns and architecture drift
- Operational risk: missing observability, brittle deployments
- IP/data risk: sensitive data in prompts, unclear training/exfil pathways
This is why mainstream commentary stresses: you still need expertise even if you “don’t need code” in the traditional sense.
What skill sets are required to be a leader in vibe coding
If you want to lead (not just dabble), the skill stack looks like this:
1) Product and problem framing (non-negotiable)
In a vibe coding environment, product and problem framing becomes the primary act of engineering.
- translating ambiguous needs into specs
- defining success metrics and failure modes
- designing experiments and iteration loops
When implementation can be generated in minutes, the true bottleneck shifts upstream to how well the problem is defined. Ambiguity is no longer absorbed by weeks of design reviews and iterative hand-coding; it is amplified by the model and reflected back as brittle logic, misaligned features, or superficially “working” systems that fail under real-world conditions.
Leaders in this space must therefore develop the discipline to express intent with the same rigor traditionally reserved for architecture diagrams and interface contracts. This means articulating not just what the system should do, but what it must never do, defining non-goals, edge cases, regulatory boundaries, and operational constraints as first-class inputs to the build process. In practice, a well-framed problem statement becomes a control surface for the AI itself, shaping how it interprets user needs, selects design patterns, and resolves trade-offs between performance, usability, and risk.
At the organizational level, strong framing capability also determines whether vibe coding becomes a strategic advantage or a source of systemic noise. Teams that treat prompts as casual instructions often end up with fragmented solutions optimized for local convenience rather than enterprise coherence. By contrast, mature organizations codify framing into lightweight but enforceable artifacts: outcome-driven user stories, domain models that define shared language, success metrics tied to business KPIs, and explicit failure modes that describe how the system should degrade under stress. These artifacts serve as both a governance layer and a collaboration bridge, enabling product leaders, engineers, security teams, and operators to align around a single “definition of done” before any code is generated. In this model, the leader’s role evolves from feature prioritizer to systems curator—ensuring that every AI-assisted build reinforces architectural integrity, regulatory compliance, and long-term platform strategy, rather than simply accelerating short-term delivery.
Vibe coding rewards the person who can define “good” precisely.
2) Software engineering fundamentals (still required)
Even if you don’t hand-write every file, you must understand:
- systems design (boundaries, contracts, coupling)
- data modeling and migrations
- concurrency and performance basics
- API design and versioning
- debugging discipline
You can delegate syntax to AI; you can’t delegate accountability.
3) Verification mastery (testing as strategy)
- test pyramid thinking (unit/integration/e2e)
- property-based testing where appropriate
- contract tests for APIs
- golden datasets for ML’ish behavior
In a vibe coding world, tests become your primary language of trust.
4) Secure-by-design delivery
- threat modeling (STRIDE-style is enough to start)
- least privilege and authZ patterns
- secret management
- dependency risk management
- secure prompt/data handling policies
5) AI literacy (practitioner-level, not research-level)
- strengths/limits of LLMs (hallucinations, shallow reasoning traps)
- prompting patterns (spec-first, constraints, exemplars)
- context windows and retrieval patterns
- evaluation approaches (what “good” looks like)
6) Operating model and governance
To scale vibe coding inside enterprises:
- SDLC gates tuned for AI-generated code
- policy for acceptable use (data, IP, regulated workflows)
- code ownership and review rules
- auditability and traceability for changes
What education helps most
You don’t need a PhD, but leaders typically benefit from:
- CS fundamentals: data structures, networking basics, databases
- Software architecture: modularity, distributed systems concepts
- Security fundamentals: OWASP Top 10, authN/authZ, secrets
- Cloud and DevOps: CI/CD, containers, observability
- AI fundamentals: how LLMs behave, evaluation and limitations
For non-traditional builders, a practical pathway is:
- learn to write specs
- learn to test
- learn to debug
- learn to secure
…then vibe code everything else.
Where this goes next (near / mid / long term)
- Near term: vibe coding becomes normal for prototyping and internal tools; engineering teams formalize guardrails.
- Mid term: more “full lifecycle” platforms emerge—generate, deploy, monitor, iterate—especially for SMB and departmental apps.
- Long term: roles continue blending: “product builder” becomes a common expectation, while deep engineers focus on platform reliability, security, and complex systems.
Bottom line
Vibe coding is best understood as a new interface to software creation—English (and intent) becomes the primary input, while code becomes an intermediate artifact that still must be validated. The teams that win will treat vibe coding as a force multiplier paired with verification, security, and architecture discipline—not as a shortcut around them.
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