Vibe Coding: Revolution or Danger?

Meta description Vibe coding boosts productivity, but at what cost? Analysis of the risks (security, technical debt) and best practices for coding with AI in 2026.

AI inside the IDE: a double-edged revolution

In 2026, generative AI has transformed developer environments. Between vibe coding, where large language models generate most of the code with minimal supervision, and a more incremental AI assistance that suggests contextualized snippets under human control, teams face a strategic choice: how to maximize productivity without sacrificing quality, security, or technical mastery? In this article, we explore both approaches. We will first look at what vibe coding is, its promises and limits, then dive into the associated risks (technical debt, security, environmental impact) and propose ways to integrate AI responsibly into software development.

What is vibe coding?

The term vibe coding was popularized in February 2025 by Andrej Karpathy, cofounder of OpenAI and former director of AI at Tesla. In a post on X (formerly Twitter), he described it as:

"There is a new way to code that I call 'vibe coding,' where you fully lean into the vibes, embrace the exponentials, and forget that code even exists."

Practically, vibe coding consists of describing a project or feature in natural language (written or spoken) and letting a large language model generate the corresponding code with minimal supervision. This approach could take an idea to a functional application in a few minutes.

Early assistance tools, such as GitHub Copilot (launched in 2021) and Cursor (2023), paved the way by offering contextual suggestions. Since then, many platforms have emerged to facilitate vibe coding: Lovable, Bolt, Replit, V0, or Gemini Canvas. To compare these tools and the underlying models, benchmarks like LiveBench.ai help evaluate performance. At the time of writing (11/2025), the three top-performing models according to LiveBench are:

• Claude 4.5 Opus Thinking High Effort (Anthropic)
• Claude 4.5 Opus Thinking Medium Effort (Anthropic)
• Gemini 3 Pro Preview High (Google)

Vibe coding: productivity or illusion?

Iconic example: Journalist Kevin Roose (New York Times) built multiple websites and apps without any coding background using ChatGPT and Claude. "Just having an idea, and a little patience, is usually enough," he wrote, marking a new era.

Here are a few examples illustrating the potential of vibe coding:

Concrete use cases:

• Agency built in a few weeks: A non-technical founder used vibe coding with ChatGPT and Cursor to launch a publishing agency, going from zero to paying clients. He still had to spend time fixing bugs and optimizing automated processes (source: Reddit examples).
• Service management system: A developer created a full Field Service Management system using AI-generated code, launching it as a side project. It worked initially but required refactors to handle scalability (source: Reddit).

For an MVP— a simplified product version with only essential features to test a concept and gather early feedback without excessive investment—the time savings are obvious.

For critical systems (banking, medical, air traffic control), where outages have severe consequences for safety, finances, or compliance, these initial gains often become traps. Accumulated technical debt—caused by complexity, trade-offs, or lack of documentation—makes every change more expensive and risky, as highlighted by GitClear.

Key concerns include:

• Loss of technical mastery: Developers may lose detailed understanding of generated code, making maintenance and evolution harder.
• Accumulation of technical debt: AI-generated code may lack structure, documentation, and consistency, making it difficult to maintain and evolve.
• Security risks: AI can introduce undetected vulnerabilities, especially if it is not trained on secure practices.

Technical debt: a build-up of quality issues (complexity, missing documentation) that makes future changes more costly and risky. It stems from decisions made to accelerate early development but that create long-term difficulties, such as duplicated code requiring multiple edits or outdated dependencies exposing security vulnerabilities. Debt accumulates like interest on a loan, making maintenance more expensive if left unmanaged.

Despite these challenges, there are clear benefits. More and more companies report significant reductions in development time thanks to AI while maintaining solid test coverage. Many startups and tech firms adopt vibe coding to speed up non-critical features and cut time-to-market.

But if AI handles maintenance and evolution, will we gradually lose the ability to understand and control the systems we create?

Accessibility for non-developers

Tools like GitHub Copilot, v0, and Cursor let product managers and designers build functional apps.

Example: A founder of a web solutions company used vibe coding to enhance his Shopify solution with limited programming knowledge. With precise prompts, he generated custom features that improved the user experience while saving the time and budget needed to hire developers.

Vibe coding can therefore help explore ideas quickly without getting stuck on technical details. Users just need to focus on describing what they want, as Bob Hutchins explains.

Risks and pitfalls

Exploding technical debt

GitClear (2025): "I don't think I've ever seen so much technical debt created in so little time." GitClear is a software analytics platform that measures team productivity and accumulated technical debt.

Technical debt is not exclusive to AI-generated code. Human developers can also write hasty code that is poorly structured or insufficiently tested. However, AI amplifies this phenomenon through the speed at which it generates code. The real challenge lies in the difficulty of conducting thorough code reviews: with teams often understaffed and volumes of produced code sharply increasing, it becomes complex to maintain the required quality level. This is why, as mentioned earlier, the loss of technical mastery becomes a major risk.

• Code duplication: Models reproduce similar snippets, making maintenance complex. However, in my tests, explicitly asking the AI to check and avoid duplication in the initial prompt or during a review phase greatly reduces this problem.

METR (2025): Experienced developers take 19% longer to solve tasks with AI than by coding manually, due to time spent correcting and validating generated code.

During a discussion I had with a teacher in several engineering or computer science schools, they observed that students using AI tools to generate code tend to produce projects with higher technical debt, as they focus on rapid generation rather than code design and structure. This does not mean they cannot code, but that AI can mask students' gaps in development best practices.

This observation is evolving, however. Many computer science professors and trainers are adapting their teaching methods: rather than evaluating only the deliverable or final project, they now focus on what the student has actually understood and retained from the development process. Assessment methods are shifting toward oral defenses, code explanations, and architecture justifications, allowing verification of effective mastery of concepts, regardless of the tool used to produce the code.

Concrete examples:

A startup had to fully refactor its backend after a year of vibe coding without structured reviews, which cost six months of additional work and delayed its launch.Personal experience: I used an agent (Claude Sonnet 4 via GitHub Copilot) to generate a complete web application (C# backend, React TS front) from natural-language prompts. While a functional application emerged quickly, the code lacked coherence and structure, making maintenance difficult. I then spent about three hours fixing subtle errors introduced by the AI, which reduced the initial gain. Starting from scratch, I regained control and quality.Conclusion: balance the use of AI and avoid blindly delegating generation for complex projects. Some repetitive tasks (simple unit tests, documentation) can nevertheless be effectively delegated to AI, freeing time to focus on architectural and business concerns.

Technical debt is not exclusive to AI-generated code. Human developers can also write hasty code that is poorly structured or insufficiently tested. However, the speed and volume enabled by AI can amplify the problem if there is no strong architecture or review process.

Security risks

AI-generated code can contain undetected vulnerabilities. Models can also hallucinate nonexistent dependencies, introducing errors and potential vulnerabilities.

Concrete dangers in production and environment variables

One of the worst pitfalls of vibe coding is careless access to production environments and environment variables: API keys, database passwords, certificates, and tokens can be exposed through copy-pasted snippets, logs, or misconfigured CI pipelines.

• In a military context, leaking or compromising an access token could let a hostile actor spy, falsify orders, or disrupt critical operations with potentially dramatic consequences for safety and lives.
• In banking, an exposed API key or production service identity could allow fraudulent transactions, exfiltrate sensitive customer data (PII), or bypass anti-fraud controls, leading to financial loss, regulatory penalties, and collapse of trust. Automation amplifies the risk: a secret committed or available in a CI runner can be exploited at scale within minutes. To limit these risks, apply least privilege, store secrets in dedicated managers (Vault, Secrets Manager), inject secrets only at runtime, monitor and audit all access, and automate rotation and rapid revocation in case of compromise.

Example

• Vulnerable Python script: A developer asked GitHub Copilot to generate a script to upload a file to GCS using default credentials. The generated code used google-cloud-storage==1.30.0, an outdated version with known vulnerabilities (CVE-2021-22570).
  • Solution: integrate scanners like Safety or Dependabot into CI/CD pipelines to detect vulnerable dependencies before deployment.
• Exposed API key: A script generated by Copilot to export customer data contained a hardcoded API key: const apiKey = 'sk-123456789';.
  • Solution: use environment variables and tools like GitGuardian to scan for secrets in code.
• Overly permissive security rules: A prompt to generate Terraform ("Create a Terraform configuration for an S3 bucket accessible from the internet") produced rules with 0.0.0.0/0, exposing the bucket publicly.
  • Solution: validate with Checkov or TFLint before deployment.
• IaC with excessive firewall rules: Infrastructure as code generated by an LLM created overly permissive firewall rules, exposing internal services. Prompt: "Configure a firewall for a Kubernetes cluster."
  • Result: rules open to everyone. Solution: manual review and automated security tests.
• A CI/CD workflow generated by an LLM introduced deployment steps without validation, pushing partially tested code to production.

These examples highlight a fundamental need: formalize your SDLC and security procedures. By documenting development steps, validation criteria, and security controls, you create a framework that AI agents can follow reliably. Tools like GitGuardian, Checkov, or Dependabot can then be integrated automatically into pipelines to detect issues before they reach production.

Licensing issues

AI models are often trained on massive datasets that include open-source code under various licenses (MIT, GPL, Apache, etc.). This raises questions about the intellectual property of generated code, especially for MVPs intended to become commercial products:

• License of generated code: Code produced by tools like GitHub Copilot or Claude may be considered derivative work, potentially subject to the licenses of the training data. For example, if the model was trained on GPL code, the generated code might need a compatible license.
• Commercial use: Companies must verify whether generated code can be used in proprietary products without violating open-source licenses.
• Transparency: Providers such as OpenAI or Anthropic do not always disclose the exact sources of their datasets, complicating legal compliance.

Example: A company had to rethink its licensing strategy after discovering Copilot-generated code included snippets under GPL, making their commercial product incompatible.

Fortunately, these risks are manageable. Several strategies allow teams to benefit from vibe coding while minimizing pitfalls.

Ideas for solutions to implement

• A team at Netflix uses AI assistance to generate code snippets for microservice management. Each suggestion is automatically analyzed by a security scanner and reviewed by a peer before being merged.
• Every AI-generated code change undergoes mandatory review, focusing on security and adherence to internal standards. Developers must justify technical choices and security implications.
• Result: the Netflix team reduced development time without increasing technical debt or vulnerabilities. Challenging AI suggestions also sparked richer discussions and continuous improvement of practices and knowledge.

These solutions make it possible to harness AI's benefits without sacrificing quality or security. They also foster closer collaboration: suggestions become a catalyst for analysis and collective improvement.

Beyond technical and security aspects, vibe coding also raises important environmental questions.

A danger: environmental impact

Heavy use of AI for code generation has a significant ecological impact. Data centers hosting LLMs consume vast amounts of electricity and water, contributing to carbon emissions and pressure on natural resources. For example, a 2025 UNEP study found that AI could consume almost as much energy as Japan by the end of the decade, with only half of that demand covered by renewable sources.

This trajectory is not inevitable. If AI writes our code, nothing prevents us from using it responsibly, questioning the systematic use of oversized models for mundane tasks. Take code generation: a developer using GPT-4o to write a basic Python function or debug a loop consumes up to 10x more energy than using a lightweight open model like Mistral 7B or DeepSeek Coder, which is just as effective for 90% of use cases. Worse, background AI copilots that analyze every keystroke can turn a vibe coding session into an invisible energy sink. According to CodeCarbon, a one-hour session with GPT-4o consumes ~0.5 kWh (about 250 g of CO₂ in the EU). With Mistral 7B locally: 25 g CO₂ (≈10x less).

The "green coding" paradox

Data centers already account for 1–1.5% of global electricity consumption, and their appetite is surging with tools like GitHub Copilot or Cursor. According to UNEP, without change, AI could represent 20% of global electricity demand by 2030, with direct consequences:

• Increased pressure on power grids, especially in regions still dominated by fossil fuels (e.g., Virginia, where 60% of U.S. data centers are concentrated).
• Hidden water stress: cooling GPUs requires millions of liters of water; a critical issue as droughts multiply (in 2024, Google used recycled water for its servers in Arizona).
• Resource waste: less than 50% of energy consumed by LLMs goes to computation; the rest is lost as heat or software inefficiencies (poor GPU use, redundant requests, etc.).

Rethink your workflow to reduce impact

Fortunately, alternatives exist to balance productivity and sobriety:

• Local first: Running models like Mistral or CodeLlama locally (via Ollama or LM Studio) cuts network footprint by 10x and avoids round-trips to the cloud. A MacBook M3 can already run a 7B model with acceptable latency for only marginally higher energy consumption than a classic IDE.

Note the rebound effect: using lighter models such as Mistral 7B can encourage heavier usage (more requests, longer sessions), potentially requiring more powerful GPUs and negating energy gains. This is why hyperscalers invest heavily in renewable energy, especially nuclear, to power their data centers. Microsoft, Google, and Amazon have all announced partnerships to develop new nuclear plants to secure long-term low-carbon energy.

Vibe coding is not just about personal flow; it is also a collective ethic. Just as we optimize code to avoid bottlenecks, we must optimize AI usage to avoid energy leaks.

This also means:

• Measuring impact with tools such as Experiments Impact Tracker or Scaphandre.
• Documenting hidden costs: add a line like # Carbon cost: ~0.1 Wh in comments for AI-generated snippets, just as we note algorithmic complexity (via CodeCarbon).
• Promoting a "just enough" culture: instead of asking AI to rewrite an entire file, submit precise microtasks ("Optimize this regex" rather than "Rewrite this module").

The goal is not to renounce AI but to use it as a talent multiplier rather than an energy crutch. The real vibe of code comes from human intelligence; AI is just an amplifier. It is up to us to make it a sustainable one.

Toward responsible integration: AI assistance proposal

Unlike vibe coding, which delegates most code generation to AI, AI assistance acts as an intelligent copilot. It does not replace the developer; it helps by suggesting lines, functions, or fixes under human supervision. Tools like GitHub Copilot (contextual completions) or Cursor (targeted refactoring) analyze ongoing code and offer relevant proposals, allowing developers to stay in control.

Here is an overview of the main AI assistance tools, according to Leptitdigital and the LiveBench.ai benchmark:

Using AI assistance responsibly

Model selection

Pick models suited to the task: for simple suggestions, lightweight models like Mistral 7B or CodeLlama 7B are enough. Reserve heavier LLMs (GPT-5, Claude 4) for complex needs (architecture, optimization). Use local open-source solutions (Ollama, LM Studio, internal models) to reduce network and energy footprint.

Prompt engineering

Prompt engineering is more than crafting precise requests; it is a holistic approach to thinking, learning, and guiding technical decisions. Instead of immediately asking for code, start by exploring options with the AI.

Example of a structured approach:

"I want to build an API to manage meeting-room bookings with Python/FastAPI.

Constraints:
- Need to handle 100 requests per second at peak
- Exhaustive tests (unit, integration, e2e)
- In production: well-placed logs and per-request metrics are enough
- In tests: full trace extraction required

What are the options for architecture and stack?
What are the main selection criteria (performance, maintainability, cost)?"

This approach lets you:

1. Understand alternatives before committing
2. Document decisions with their justifications
3. Learn by confronting AI recommendations with your expertise
4. Refine gradually by iterating on responses

Once architectural choices are validated, you can ask for specific code:

"Following option 2 (hexagonal architecture with PostgreSQL), generate the SQLAlchemy data model for the Reservation entity with fields: id, room_id, user_id, start_time, end_time, status. Follow PEP 8 conventions."

This process reduces unnecessary iterations, improves generated code quality, and strengthens your understanding of the project.

Validation and review

All AI-generated code must be systematically reviewed and tested. AI itself can make this easier. Integrate validation steps into the workflow:

• Mandatory code reviews: focus on security and adherence to internal standards. Use AI to automate a first review pass by asking it to flag potential issues, convention violations, or obvious vulnerabilities.
• Prompt engineering for reviews: explicitly ask AI to check its own code. For example: "Review the previous code and identify security flaws, performance problems, and Python best-practice violations."
• Use AI for code reviews: tools such as GitHub Copilot or CodeRabbit can analyze pull requests and suggest improvements automatically.
• Unit and integration tests for each generated snippet
• Documentation of technical choices and security implications

Recommended practice: organize pair-programming sessions where a developer is challenged by peers on AI suggestions. This confronts ideas, improves quality, and strengthens a culture of continuous learning.

Controlling debt

Integrating AI into development must not come at the expense of code quality or security. Keep the limits of AI in mind and avoid leaning on it as a constant crutch. Key principles:

• Transparency: document decisions made when using AI, including prompts and results.
• Responsibility: developers remain accountable for produced code, even if generated or assisted by AI.
• Ethics: assess the impact of AI use on end users and the environment, minimizing bias and respecting ethical norms.

A less-discussed aspect is cognitive debt: overreliance on AI can erode core skills (problem-solving, algorithmic understanding, language mastery). Keep investing in human training, positioning AI as a learning lever rather than a crutch.

Avoiding obsolete or vulnerable code

LLMs are trained on publicly available data, which may include outdated libraries, deprecated functions, or obsolete security practices. For example:

• A snippet generated by Copilot using requests.get() without SSL verification (disabled by default in older versions).
• A developer used Copilot-generated code to handle dates in Python that relied on datetime.datetime.utcnow() (deprecated since Python 3.12 in favor of datetime.datetime.now(timezone.utc)). Result: production warnings and risk of unexpected behavior after an update.
• Using an outdated or vulnerable package can introduce security flaws. In 2025, a team discovered AI-generated code that relied on a deprecated version, degrading performance and exposing known vulnerabilities. These examples show the importance of staying vigilant.

To avoid these traps:

1. Update dependencies regularly: use tools like Dependabot or Renovate to automate library updates.
2. Check maintenance status: review the latest release date, contributor activity, and open issues (GitHub, PyPI, npm, etc.).
3. Scan generated code: use tools like SonarQube to detect vulnerabilities in AI-generated code.
4. Train developers: raise awareness of AI-related risks and best practices for validating generated code.

Control the context you provide

Limit the context you share with your LLM or agent because leaking sensitive data is a real risk. If you work on a sensitive project and share environment variables, security configurations, or internal architecture details in prompts, you risk exposing them to third parties—especially if you mention your company and the scope you work on.

Best practices:

• Anonymize sensitive data in prompts.
• Use isolated environments for AI tests.
• Never include secrets, API keys, or personal information in prompts.
• Prefer generic descriptions rather than company- or project-specific details.

A double-edged tool

Generative AI—whether for vibe coding or assistance—offers unprecedented opportunities to accelerate software development. But like any powerful tool, it demands discernment and responsibility. By adopting ethical practices, investing in continuous training, and staying vigilant about security and technical debt, teams can benefit without sacrificing quality or control.

The real challenge in 2026 is not just using AI, but integrating it as an ally rather than a substitute. Contrary to some fears, AI will not replace developers; it will amplify their value (judgment, creativity, expertise). Traditional code remains the norm in most projects, but generative AI is already an essential companion.

Its impact depends on the choices we make today.

• For developers: AI can free time to focus on innovation, provided we do not become dependent on it.
• For organizations: it can speed up delivery, but only if usage is governed by solid processes (code reviews, automated tests, ecological impact measurement).
• For tech overall: it can democratize access to development if it remains accessible, transparent, and sustainable.

One thing is certain: this technology will profoundly transform how we code. Its impact depends entirely on you and your choices. What should you do?

1. Take control: try AI assistance on a personal project and enforce a golden rule for every generated line of code:

   • Do I understand what this code does?
   • Will I be able to maintain it in six months?
   • Why was this code generated this way? Could it be better?

   Doing so ensures AI remains a tool serving your expertise, not a substitute.

2. Share your experiences: vibecoding and AI assistance are not universal practices—discuss them with peers and the community:

   • What successes or failures have you encountered?
   • Which best practices have you adopted?

3. Pass on your knowledge: AI should not widen gaps. Train users of these tools on associated opportunities and risks:

   • Organize internal workshops.
   • Share resources and feedback.

By applying these principles, avoiding pitfalls, and keeping humans at the center, we can make AI a talent multiplier rather than a threat to our profession.