Vibe coding and AI engineers

The AI Engineer role became visible in 2023, followed by “vibe coding” as a named practice in early 2025. The groups overlap, but they bring different levels of software experience and need different forms of education and support.

The AI Engineer

Coined by Shawn “swyx” Wang in his June 2023 essay The Rise of the AI Engineer. The argument:

A new role is forming between traditional ML engineers (who train models from scratch) and traditional software engineers (who don’t touch ML at all).
This role uses foundation-model APIs, prompt engineering, retrieval-augmented generation (RAG), evals, and agentic frameworks to build AI-powered applications.

“A wide range of AI tasks that used to take 5 years and a research team to accomplish in 2013, now just require API docs and a spare afternoon in 2023.”

Andrej Karpathy endorsed the framing publicly shortly after publication, increasing its visibility.

By 2026, “AI Engineer” is an established career identity:

The AI Engineer Summit (founded by swyx) is a specialist conference for the role.
The Latent Space podcast and newsletter (swyx + Alessio Fanelli) covers the people, tools, and working practices around AI engineering.
The AI Engineer Foundation institutionalises the identity.
Job postings titled “AI Engineer” appear at most developer-product companies; pay tends to be competitive with senior software engineering.

For DevRel teams, AI Engineers are a distinct audience with distinct preferences:

They consume content that assumes familiarity with embeddings, vector databases, RAG, agentic patterns, prompt engineering, evals, and the broader ML application stack.
They evaluate products primarily through hands-on experimentation in notebook environments (Jupyter, Colab, Cursor, Claude Code).
They participate in distinct community spaces (Latent Space Discord, OpenAI Discord, LangChain Discord, Hugging Face Hub).
They encounter substantial AI-product marketing. Runnable examples, evaluations, latency, and cost data give them evidence with which to compare products.

Products for AI engineers need examples built around model evaluation, retrieval, agents, latency, and cost. The people teaching those examples need enough experience to discuss the trade-offs.

Vibe coding

Coined by Andrej Karpathy on February 2, 2025 in a public post:

“There’s a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”

In the practice Karpathy described, a developer states an intent, runs the generated code, returns errors to the agent, and iterates without examining much of the implementation.

Karpathy explicitly framed this as appropriate for “throwaway weekend projects” rather than production systems. The framing got picked up far beyond its original scope: by late 2025, “vibe coding” was being used to describe everything from quick scripts to substantial production work, with mixed outcomes.

Audience effects for DevRel

Vibe coding adds two audience patterns relevant to DevRel:

Pattern 1: Existing developers vibe-coding adjacent work.

Existing developers may use agents for adjacent work: a backend engineer producing an admin panel, a frontend developer producing a CLI, or a senior engineer testing a prototype.

For DevRel, this means that more developers evaluate a product by asking an AI agent to build something with it in 30 minutes. Confusing documentation and weak API ergonomics now interrupt that evaluation before a person has read the full quickstart.

Pattern 2: New developers who only vibe-code.

Designers, domain experts, and founders may use AI agents to build software without prior experience writing code independently.

For DevRel, this is a new audience that needs a different style of engagement:

They don’t read technical reference docs the way traditional developers do.
They evaluate products by whether ChatGPT or Claude can successfully integrate them.
They participate in different communities (Replit, Bolt, Lovable, Cursor-adjacent spaces).
When generated code fails, they may not know which layer produced the error or how to inspect it without further assistance.

Reports of failed vibe-coded projects describe stripped tests, ignored security advice, leaked secrets, and code the author cannot maintain. Developer products serving this audience need safer defaults and recovery material for those failure modes.

Implications for DevRel content

Useful DevRel material for vibe-coders includes:

Quickstarts that an AI agent can read and faithfully execute (see Agent-facing documentation).
“Build this with AI” tutorial format: explicitly assume the reader is using Claude Code, Cursor, or ChatGPT.
Sample apps that exhibit best practices the AI agent will copy.
Plain-language explanations of concepts that agents tend to get wrong.
Recovery guidance for diagnosing and correcting generated code when it fails.

Most teams in 2026 have not yet figured out the right balance. Teams serving vibe-coders are iterating on quickstarts, sample apps, and AI-readable docs while preserving deeper material for traditional developers.

The JetBrains Human-AI Experience (HAX) research

In April 2026, JetBrains published the Human-AI Experience study analysing two years of telemetry from approximately 800 developers using AI coding assistants. Headline findings relevant to DevRel:

Developers using AI assistants write more code overall.
They spend more than one-third of their time double-checking and editing AI suggestions.
Editing frequency increased substantially among AI users, even though developers themselves perceived minimal change.
Time savings averaged 3.6 hours per week for regular users; daily users showed 60% higher pull-request throughput.
Significant trust gaps remained: even regular users reported incomplete trust in AI-generated code.

The study found differences between self-reported experience and observed editing behaviour. DevRel teams should report survey and telemetry results separately and investigate where they diverge.

The finding does not make surveys useless, but it prevents teams from treating reported productivity as a behavioural measure.

The skill-formation question

Anthropic’s early-2026 research reports faster work on familiar skills alongside a risk that AI assistance may hinder the acquisition of new ones.

The skill-formation concern is that a developer may complete an unfamiliar task without learning how to explain or debug the result. Teams can test that concern directly by asking people to modify the code after the assistant is removed.

For education products such as JetBrains Academy, courses by Wes Bos and Scott Tolinski, and Frontend Masters, the open question is how to teach AI use while testing whether learners can still explain, modify, and debug the result. Practices were still developing in 2026.

Infrastructure and tool documentation should not assume that every customer can diagnose generated code. Provide precise errors, accessible support, and explanations of the mechanisms an assistant may omit.

Changes in developer practice

A few specific cultural shifts visible in the developer population around AI work:

Disclosure of AI involvement. Developers vary in how much code they delegate and how they describe that involvement.
Evals and observability. AI Engineers increasingly use tools such as Braintrust, LangSmith, Helicone, and Langfuse to evaluate model output. DevRel material for these products needs to cover evaluation practice, not only generation.
Shared prompts. Developers publish prompt libraries and DevRel teams provide tested prompt examples alongside code.
The “skill collapse” anxiety. Some senior developers worry that the next generation will not develop the depth they did. Clear teaching material, transparent agent behaviour, and debugging-with-AI guides help people inspect what the agent produced and recover when it fails.

Practical takeaways for DevRel teams

Identify whether your product serves AI Engineers, vibe-coders, traditional developers, or some mix. Each requires different content.
Maintain agent-readable surfaces. Publish complete docs, schemas, examples, and error guidance for the agents developers already use.
Continue publishing for developers who work without agents. They still need complete reference, conceptual explanation, and conventional examples.
Evaluate AI-engineering channels. If the product serves AI Engineers, compare options such as Latent Space and AI Engineer Summit using audience fit and campaign results.
Track vibe-coding-mediated activation as a separate cohort. It behaves differently from traditional developer activation.
Measure skill formation. Check whether people can explain, debug, and maintain the code they produced with AI assistance.

Primary sources

Shawn Wang, The Rise of the AI Engineer, June 2023 (latent.space / swyx.io).
Andrej Karpathy, post on “vibe coding” via X / personal blog, February 2, 2025.
JetBrains, Understanding AI’s Impact on Developer Workflows, April 2026 (HAX study).
Anthropic, How AI assistance impacts the formation of coding skills, 2026.
Latent Space podcast and newsletter, 2023 to 2026.