Vibe coding and AI coding agents: a guide for non-technical CEOs

What is vibe coding?

Vibe coding is building software by telling an AI what you want in natural language, running what it produces, and describing what to fix, without reading or fully understanding the code yourself.

The term was popularised by Andrej Karpathy, former head of AI at Tesla and a founding member of OpenAI, in early 2025, and Collins Dictionary named it Word of the Year 2025, defining it as an emerging form of software development that turns natural language into computer code using AI. The word "vibe" is doing real work: it means you accept the output on feel rather than on inspection.

That distinction matters for a CEO. There is a spectrum. At one end, a professional engineer uses an AI coding agent to write most of the code but reads, tests and reviews it: that is AI-assisted engineering. At the other end, someone with no technical background chats an app into existence and ships it: that is vibe coding in the strict sense. The tools are the same. The risk profile is not.

Which AI coding tools matter in 2026?

In 2026 the tools split into 2 families: coding agents built for engineers, such as Claude Code, Cursor and OpenAI's Codex, and app builders for non-technical users, such as Lovable, v0 and Replit.

These are the 6 I see most in my companies and in the founders I talk to, with what each is really for.

• Claude Code (Anthropic): an agentic coding tool that reads your whole codebase, edits files, runs commands and tests, and opens pull requests, available in the terminal, in IDEs, as a desktop app and in the browser; it connects to your tools through the Model Context Protocol (MCP) and can run scheduled tasks and automated code review. Built for engineers, and the one my teams use most.
• Cursor: an AI-first code editor for developers, where the agent works inside a professional editor; every feature assumes you can read code.
• Codex (OpenAI): OpenAI's coding agent for developers, working on tasks in your repositories from the terminal, editor or cloud.
• Lovable: a chat-based builder that produces a complete, deployed web application with database and login, and the tool most non-technical founders can genuinely use alone.
• v0 (Vercel): generates user interfaces and front-ends from a description; excellent for screens, but you still need a backend for data, payments and logic.
• Replit: a browser-based environment where the Replit Agent builds, hosts and runs full applications; powerful, and the setting of the best-known vibe coding accident of 2025.

What can a non-technical founder build, and what not?

A non-technical founder can build a convincing prototype, an internal tool, a landing page with a waitlist, or a first version of a simple SaaS in days, and should not ship anything that handles payments, personal data at scale, or a team of paying customers without an engineer reviewing it first.

The good news is real. In my Aurum VOS project, where the ambition is +100 companies at €1M ARR each run by very small teams, coding agents are what make a 1-person or 2-person company plausible. A founder can validate demand with a working product instead of a slide, iterate on the interface daily and stop paying an agency €20,000 for a prototype.

The bad news is equally real. A vibe-coded app tends to work for the person who built it and break for the 10th customer. Authentication, permissions, backups, error handling, cost control and data privacy are exactly the parts a non-technical founder does not know to ask for, and the AI will not add them unless asked. My rule: vibe code to learn and to sell, but before the first euro or the first customer record goes through it, have an engineer spend 2 days reviewing what was built. That review is the cheapest insurance in software.

What are the security and maintenance risks?

The risks are insecure code, agents taking destructive actions, nobody understanding the system, and a maintenance bill that arrives 6 months later, and all 4 are documented, not hypothetical.

According to Veracode's 2026 GenAI Code Security Report, published in July 2026, about 44% of AI code generation tasks introduced a risky vulnerability, the average security pass rate across models was 56% against 55% a year earlier, and models passed only 15% of cross-site scripting tests and 12% of log injection tests. AI now writes roughly half of committed code in adopting organisations, and its security has not improved. The agent risk is also on record: in July 2025 Replit's agent deleted a production database during an explicit code freeze in an experiment run by investor Jason Lemkin, then produced misleading reports about it, as The Register documented; Replit responded with separate development and production databases, a planning-only mode and better backups.

The human side is measured too. Stack Overflow's 2025 Developer Survey found 84% of developers using or planning to use AI tools, but only 29% trusting their accuracy and 66% naming "almost right, but not quite" output as their top frustration. And METR's 2025 randomised trial found experienced developers were 19% slower with AI tools on mature codebases while believing they were 20% faster. Translate that to a founder with no code training and the gap between perceived and actual quality is wider.

• Insecure defaults: missing input validation, secrets in the code, open database rules; run an automated security scan before anything goes live.
• Destructive agents: never give an agent production credentials; separate development from production and require approval for deletions and migrations.
• Orphan systems: if nobody can explain how the app works, nobody can fix it at 2 a.m.; keep documentation the agent writes and updates.
• Maintenance debt: dependencies age, providers change APIs, and a codebase nobody understands rots quickly; budget review time from day 1.
• Data and compliance: personal data in an AI-built app still falls under the GDPR and, since August 2026, the AI Act's transparency rules if the app talks to users as an AI.

How do I use coding agents in the companies I run?

In my companies coding agents write most of the first draft of new code, engineers own the design and the review, and every change passes automated tests, security checks and a human before it ships.

Concretely, our teams work with Claude Code in the terminal and in the IDE. Every repository has a CLAUDE.md file with the coding standards, the architecture decisions and the review checklist, so the agent starts each session knowing how we work. We connect it through MCP to our issue tracker, documentation and monitoring, so a bug report can become a reviewed pull request with the engineer supervising rather than typing. Automated code review runs on every pull request, and scheduled routines handle the boring work: dependency audits, failing test triage, release notes.

What I personally do is different. I use agents to prototype product ideas in an evening, to read unfamiliar codebases before a due diligence, and to write internal tools that would never justify an engineer's week. I read the code, because I can, and I still get surprised by what it gets wrong. The gains are large where the task is well specified and the tests exist; they shrink fast where either is missing. Review is now the bottleneck, so we made pull requests smaller and invested in the review tooling rather than in more generation.

What changes for founders and non-technical CEOs?

The founder's job shifts from buying engineering hours to specifying what the product must do, testing whether it does it, and owning the decisions the AI cannot make: what to build, for whom, and what must never break.

3 practical consequences follow. First, learn to write a specification: a clear description of users, flows, data and edge cases is now the most valuable technical skill a non-technical CEO can have, because it is the input to every agent. Second, hire differently: the first technical hire is a reviewer and architect who makes agents safe, not a typist, and 1 strong engineer with agents now covers what 3 did in 2023. Third, budget for maintenance from the start, because the cost of software moved from writing it to keeping it correct.

I am optimistic and careful in equal measure. Coding agents are the reason the one-person unicorn is no longer a fantasy, and they are also the reason a founder can lose a customer database in an afternoon. Use them like a junior engineer who is brilliant, tireless and occasionally confidently wrong: give clear instructions, check the work, and never hand over the keys to production.