Collins Announces ‘Vibe Coding’ as the Coding Word of 2025

It’s official — and more than a little on the nose, given the AI era in which we are already knee-deep: Collins Dictionary has named “vibe coding” as its designated Word of 2025, a moniker designed to codify what they are seeing from an emerging trend toward development via describing intent to an AI rather than doing so through writing out every single line by hand. Popularized by the artificial intelligence researcher Andrej Karpathy in a post on X, it has jumped from an obscure in-joke to something like mainstream shorthand for natural-language-driven coding.

Collins’ lexicographers say the choice reflects how people describe technology when machine assistance goes from novelty to default. It signals a larger cultural shift, as well: software isn’t just typed anymore — it’s conjured up by talking to models that can draft code, wire up interfaces, and iterate on feedback within minutes.

What ‘Vibe Coding’ Really Means for Software Development

In operation, vibe coding is the process by which a developer, founder, or hobbyist tells an AI assistant about goals and constraints — then guides its output with examples, test cases, and clarifications. Think prompts such as “build a dashboard that ingests CSVs, charts out the sales by region, and exports to PDF,” then iterative tuning instead of yak shaving manual boilerplate and API plumbing.

It falls at the juncture of traditional programming, no-code tooling, and prompt engineering. Unlike classical no-code, which confines users to a set of prebuilt blocks, vibe coding speculates that AI can produce any novel code that you ask it to make through either gluing components or generating tests and even suggesting architectural changes as the vision progresses.

Why Collins’ Pick Matters for Developers and the Public

Collins usually picks up words that have broken out beyond niche circles. Recent honorees “AI,” “permacrisis,” and “NFT” signaled technological and social inflection points. In ushering in “vibe coding,” Collins is effectively saying that AI-native building has moved beyond the lab and into the popular discourse — from boardrooms to bootcamps to TikTok tutorials.

Lexicographers track frequency and context by monitoring a multi-billion-word corpus of news, social media, and broadcast material. “Vibe coding” was meritoriously chosen for its rapid surge in frequency across developer forums, product marketing, and the mainstream press, besting other neologisms on the shortlist like “clanker,” “aura farming,” or “broligarchy.”

The Data on the Trend: Adoption and Productivity Signals

Developer behavior has been leaning in this direction for some time. GitHub found that in controlled studies, developers using AI pair-programming assistants finished coding tasks up to 55% faster and reported lower cognitive load. Stack Overflow’s developer surveys show a significant majority are experimenting with or using AI assistants for code generation, documentation, and debugging. Enterprises are hearing that call: large consultancies note huge growth of AI-enabled software delivery, particularly around internal tools and data workflows.

Real-world examples abound. Startups are getting MVPs on keel by gumming model prompts together with little frameworks, then fixing fragile chunks with their own hand-tuned code when they have to. Big teams are putting AI to work in drafting tests, migration scripts, and telemetry hooks — jobs that traditionally soak up time, but not smarts. Design and product teams are even prototyping with conversational specs that AI can transform into clickable demos in hours.

Power and Pitfalls in Equal Measure for AI-Led Coding

The lure is clear: speed, convenience, and a creative loop that feels more like directing than typing. But vibe coding is not sorcery — and it’s not without its consequences. Scholarly research has demonstrated, from universities and companies such as Stanford and UC Berkeley among others, that AI-generated code can obfuscate security bugs, and model outputs are subject to hallucinations, license contamination, and hidden performance costs.

Experienced teams now use AI as an accelerator, not an autopilot. Best practices are to enforce linters and policy checks, run unit and property-based tests over every change produced, and have humans-in-the-loop for threat modeling and architectural decisions. In regulated settings, governance and audit trails are table stakes: logs, model versions, and dependency lineage must be monitored as stringently as the code.

How It’s Different from No-Code and Low-Code in Practice

No-code platforms democratized the creation of software, but often by sacrificing flexibility. Vibe coding, on the other hand, aims to synthesize special code that should be accessible for reviewability (as testable and extendable as any other repository). That inspectability is critical: teams can iteratively harden AI-drafted models, replace flaky portions, and standardize around known-good templates.

The forthcoming workflow feels hybrid: describe the intention in natural language, allow the model to scaffold components, then harden critical paths through traditional engineering rigor.

Vibes to get into, variables to tie up.

A Linguistic Picture of the AI Age and Its Implications

The fact that a playful phrase like “vibe coding” would now find its way into the hallowed hall of a dictionary says something about where we are. Technical change is speeding up, yes — but so is the human impulse to package it in popular terms. The label may be sassy, but it nicely captures the hope and ambiguity of AI-native creation: software that starts as a conversation and ends as a system someone will have to own.

For a builder, the takeaway is less in jargon than it is in discipline. Use the vibes to go fast; trust in engineering to build it tough. Collins’ choice is really just a recognition of what most developers already understand: that this is how modern software and infrastructure gets built.