The Dark Side of Vibe Coding: What Nobody Tells You

The most serious challenge with vibe coding is security. The statistics are sobering: **45% of AI-generated code contains security flaws** according to automated security scanning. This includes: - SQL injection vulnerabilities - Cross-site scripting (XSS) weaknesses - Improper authentication implementations - Insecure data handling - Missing input validation - Hard-coded secrets and API keys Why is AI so bad at security? Because security requires adversarial thinking—imagining how someone might misuse or attack your code. AI models generate plausible-looking code based on patterns they've seen, but many examples in training data have security flaws. **Real-World Security Failures** In late 2025, a YC-backed startup discovered their AI-generated authentication system was vulnerable to token reuse attacks. The code looked correct—it implemented JWT tokens, refresh tokens, and all standard patterns. But a specific edge case in the refresh logic allowed attackers to reuse old tokens. The company had to notify users, rotate all sessions, and implement emergency fixes. Their Series A was delayed while they underwent security audits. The cost: estimated $2.3M in lost valuation and three months of work. Another case: an e-commerce site built entirely with vibe coding tools had AI-generated payment processing that didn't properly validate transaction amounts on the server side. A security researcher found they could modify prices during checkout. By the time the vulnerability was discovered, $47,000 in fraudulent transactions had occurred. **The Pattern Problem** AI learns from existing code. Much of that code has security issues. When AI replicates those patterns, it replicates the vulnerabilities. And because AI-generated code is consistent, when it makes a security mistake, it makes it everywhere consistently. Traditional development might have security issues in 37-42% of code (human developers aren't perfect either), but those issues are usually varied. AI's issues are systematic, making them easier to exploit at scale. **What You Must Do** 1. **Always run security scanning**: Tools like Snyk, Semgrep, or GitHub Advanced Security should scan all AI-generated code 2. **Get professional security review**: Before launching anything handling sensitive data 3. **Assume AI security is wrong**: Validate every authentication, authorisation, and input handling implementation 4. **Test adversarially**: Don't just test if code works; test if it can be broken 5. **Implement defense in depth**: Multiple layers of security, assuming any single layer might fail The security challenges of vibe coding aren't insurmountable, but they require explicit attention. Treating AI-generated code as secure by default is a recipe for breaches.

Beyond security, code quality presents long-term challenges: **The Consistency Problem** When you build an application over weeks or months using AI tools, you'll likely use different prompts, different tools, or different AI models. Each generates code in slightly different styles with different assumptions. The result: codebases that look like they were written by five different developers who never talked to each other. Different state management patterns, inconsistent naming conventions, varied error handling approaches, and conflicting architectural decisions. One startup's CTO described their AI-generated codebase as "a collection of beautifully written functions that don't agree on how to work together." **The Black Box Problem** When AI generates code, you often don't fully understand how it works. For simple code, this is fine. For complex business logic, it's dangerous. Developers report spending hours debugging AI-generated code because they don't understand the implementation well enough to identify issues. The code works... until it doesn't. Then troubleshooting becomes archaeological investigation. **The Over-Engineering Problem** AI tools often generate more complex solutions than necessary. Asked for a simple feature, they might implement: - Abstract factories when a simple function would work - Complex state management for trivial UI - Over-generalised solutions for specific problems - Dependency injection where direct instantiation is fine This isn't just academic concern—complex code is harder to modify, debug, and maintain. When requirements change (and they always do), the over-engineered solution becomes a liability. **The Technical Debt Accumulation** Vibe coding makes it easy to add features quickly without refactoring. The result: technical debt accumulates faster than with traditional development. Studies tracking AI-generated codebases over 6-12 months find: - Feature additions slow down by 40-60% as complexity grows - Bug density increases faster than in traditionally developed code - Refactoring becomes harder because no one deeply understands the codebase - New team members take longer to become productive **The Documentation Gap** AI generates code, not explanations of architectural decisions. Why was this approach chosen? What alternatives were considered? What trade-offs were made? This context loss makes future modifications more difficult. Developers working on AI-generated code six months later often don't understand the reasoning behind decisions, leading to modifications that break unstated assumptions. **What You Can Do** 1. **Establish architectural standards before generating code**: Define patterns, naming conventions, and approaches 2. **Review and refactor regularly**: Don't just accept AI output—improve it 3. **Document the why, not just the what**: Explain decisions AI made 4. **Keep it simple**: Prompt for simple solutions, not clever ones 5. **Use consistent tools and prompts**: Reduce stylistic variation 6. **Read the generated code**: Understanding what AI built prevents future problems

A more subtle but serious concern: what happens when developers stop writing code themselves? **The Fundamentals Gap** Developers who learn primarily through vibe coding often have weak fundamentals: - Understanding of algorithms and data structures - Ability to debug complex issues - Knowledge of how underlying systems work - Capacity to optimise performance - Skills to architect complex systems One engineering director described interviewing candidates with impressive portfolios built with AI tools who couldn't explain basic concepts like how authentication works or what a database index does. **The Investigation Skill Atrophy** When AI handles implementation, developers lose practice: - Reading documentation carefully - Debugging step by step - Investigating root causes - Understanding error messages - Searching effectively for solutions These skills seem mundane, but they're essential when AI can't solve a problem or generates incorrect code. **The Over-Reliance Problem** 'AI dependency' is emerging as a recognised issue. Developers report: - Feeling lost without AI tools - Inability to solve problems when AI fails - Reduced confidence in their own abilities - Anxiety about working in environments without AI access One developer described it as 'losing the ability to ride a bike because you always drive.' The skill is still there theoretically, but the fluency is gone. **The Junior Developer Crisis** This is particularly acute for early-career developers. Traditional progression involved: 1. Learning fundamentals through practice 2. Building muscle memory through repetition 3. Developing debugging intuition through errors 4. Understanding systems through implementation Vibe coding shortcuts this progression. Junior developers can build impressive projects without developing foundational skills. When they encounter problems AI can't solve, they lack the depth to troubleshoot. One startup CTO described hiring a developer with an impressive GitHub portfolio of AI-built projects who couldn't debug a simple null pointer error without AI assistance. **The Expertise Plateau** There's concern that widespread vibe coding adoption might prevent developers from reaching true expertise. Mastery comes from deep practice, deliberate struggle, and extensive experience with details. If AI handles those details, do developers ever develop deep expertise? Some researchers worry about a future with many developers who can direct AI tools but few who truly understand software systems at a deep level. When novel problems arise, who will solve them? **The Counterargument** It's worth noting the alternative perspective: skills naturally evolve with tools. Modern developers don't hand-optimise assembly code or manage memory at the bit level—those skills became less relevant as abstractions improved. Perhaps deep implementation knowledge is similarly becoming less critical, whilst architecture, product thinking, and systems design become more important. The truth is likely somewhere between: some traditional skills matter less, others remain critical, and new skills are emerging. But the transition period creates genuine competency challenges.

One of vibe coding's most overlooked impacts: the potential damage to open source software development. **The Contribution Collapse** Open source thrives on developers contributing back improvements, bug fixes, and new features. But if developers are using AI to generate code rather than learning from and improving existing libraries, contribution rates might decline. Early data suggests this may be happening: - Pull request numbers to major open source projects increased 12% in 2025, below the 20-25% growth of prior years - First-time contributors decreased 8% - Documentation improvements (often entry-point contributions) declined 15% One open source maintainer described the change: 'People used to dig into my library's code to understand how it worked, find bugs, and submit fixes. Now they just ask AI to solve their problem without engaging with the underlying code.' **The Maintenance Burden** Open source maintainers are already overwhelmed. If user bases grow (because AI tools make adoption easier) whilst contributors decline, maintainer burden increases. Several high-profile maintainers have warned about burnout as they see: - More users (AI makes their libraries more accessible) - Fewer contributors (AI reduces need to understand internals) - More AI-generated bug reports (often lower quality) - Less community engagement **The Training Data Paradox** AI models are trained on open source code. If developers stop contributing to open source because AI handles their needs, the training data for future AI models becomes stagnant. This creates a weird loop: AI is trained on human-written open source → developers use AI instead of writing code → less high-quality code for future AI training. Some researchers call this 'data exhaustion'—the risk that AI consumes the open source ecosystem it depends on. **The Licensing Confusion** AI models trained on open source code raise licensing questions: - Is AI-generated code derivative of its training data? - Do open source licenses apply to AI outputs? - Should AI-generated code credit the projects it learned from? These questions are unsettled legally, creating uncertainty for both AI tool providers and users. **The Quality Feedback Loop** Open source benefits from many eyes reviewing code, finding issues, and suggesting improvements. If that code is AI-generated and less reviewed (because humans trust AI output), does quality decline? A few open source projects have started requiring disclosure when contributions are AI-generated, specifically because they've noticed quality differences. **What Can Be Done** 1. **Actively contribute to open source**: Even when using AI tools, contribute fixes and improvements back 2. **Learn from libraries you use**: Study implementations, not just APIs 3. **Support maintainers**: Financially back the projects you depend on 4. **Engage with communities**: Don't just consume; participate 5. **Use AI to accelerate contributions**: Let AI help you contribute more, not contribute less

The economic implications of vibe coding extend beyond individual developers: **The Skill Valuation Shift** Traditional developer skill valuations are disrupting: - Junior implementation roles declining in value - Senior architecture roles increasing in value - Gap between AI-proficient and non-proficient developers widening This creates economic winners and losers. Developers who adapt thrive; those who resist face declining prospects. **The Geographic Disruption** Offshore development centres thrived on cost arbitrage—cheaper developers implementing specs from expensive markets. If AI can implement those specs, what happens to those jobs? Early data from India and Eastern Europe shows: - Junior developer roles declining - Pressure on rates for implementation work - Shift toward higher-value architecture and product roles This isn't necessarily negative long-term (regions adapt and move up the value chain), but the transition creates disruption. **The Agency Model Disruption** Development agencies traditionally charged $100-200/hour for developers. If clients can use AI tools at $20-50/month, why hire agencies for standard projects? Agencies report: - Declining demand for simple implementations - Pressure to move upmarket to complex projects - Need to justify value beyond code generation - Adoption of AI tools themselves to remain competitive **The Education Crisis** Computer science education is struggling to adapt: - Teaching syntax seems less relevant when AI handles it - But fundamentals remain important - Curriculum redesigns lag industry reality - Graduates may have skills mismatch with market needs Universities are caught between teaching traditional CS (deep fundamentals, theory) and practical AI-assisted development. Many are doing neither particularly well. **The Wealth Concentration** AI tools amplify individual capability but concentrate value: - Solo developers can build what required teams - Small teams can compete with large companies - But AI tool providers capture significant value - Winner-take-most dynamics may intensify This could lead to more inequality even as absolute capabilities increase.

Beyond practical challenges, vibe coding raises ethical questions: **The Attribution Problem** When AI generates code based on training data including millions of developers' work, who deserves credit? This isn't just philosophical—it affects: - Career portfolios (did you write this or did AI?) - Academic integrity (students using AI for assignments) - Professional reputation (can you do this without AI?) **The Accessibility Paradox** Vibe coding democratizes software creation, enabling non-developers to build. This is positive. But it may also: - Reduce entry-level developer job opportunities - Change what 'learning to code' means - Shift who has power in tech organisations - Alter career paths into software development These changes aren't inherently bad, but they redistribute opportunities in ways that affect real people's lives and livelihoods. **The Environmental Cost** AI model training and inference consume significant energy. Vibe coding means: - Constant AI API calls during development - Energy for training ever-larger models - Infrastructure to serve millions of developers The environmental cost of AI-assisted development is rarely discussed but isn't zero. **The Bias Amplification** AI models trained on existing code inherit that code's biases: - Naming patterns that assume male developers - Examples that default to Western contexts - Accessibility patterns that may neglect edge cases - Security assumptions that work for some threat models but not others When AI replicates these patterns at scale, it amplifies existing biases rather than correcting them.

Understanding risks is only useful if you can manage them. Here's how: **For Individual Developers** 1. **Maintain fundamentals**: Regularly solve problems without AI to keep skills sharp 2. **Review everything**: Never trust AI output without understanding it 3. **Learn security**: Study common vulnerabilities and test for them 4. **Contribute to open source**: Stay engaged with the broader ecosystem 5. **Build hybrid skills**: Combine AI proficiency with traditional expertise **For Development Teams** 1. **Establish AI usage guidelines**: When to use AI, when not to, what reviews are required 2. **Require security scanning**: Automated tools on all AI-generated code 3. **Implement code review**: Focus on logic, security, and architecture 4. **Maintain architectural standards**: Don't let AI dictate system design 5. **Invest in testing**: Comprehensive test suites catch AI mistakes **For Companies** 1. **Professional security review**: Especially for customer-facing applications 2. **Maintain expertise**: Don't eliminate all senior developers 3. **Document decisions**: Capture why, not just what AI built 4. **Plan for maintenance**: AI-generated code still needs long-term support 5. **Balance productivity and quality**: Speed isn't everything **For the Ecosystem** 1. **Support open source**: Financially and through contributions 2. **Share learnings**: Publish challenges and solutions 3. **Develop standards**: Industry best practices for AI-assisted development 4. **Advocate for responsible AI**: Push tool providers for better security, attribution, and quality 5. **Educate**: Help others understand both benefits and risks

The dark side of vibe coding is real but not insurmountable. Like any powerful technology, AI-assisted development creates both opportunities and challenges. **The Balanced View** Vibe coding isn't evil, and it isn't perfect. It's a tool with: - Genuine productivity benefits - Real security and quality risks - Impacts on skills, careers, and ecosystems - Ethical considerations worth examining The key is using it thoughtfully rather than uncritically. **The Path Forward** Successful AI-assisted development requires: - Understanding limitations alongside capabilities - Implementing safeguards for security and quality - Maintaining skills even as tools change - Contributing back to the ecosystem you benefit from - Thinking critically about impacts Developers and companies that acknowledge and manage risks will benefit from AI tools whilst avoiding pitfalls. Those who ignore risks will learn through painful experience. **For Melbourne Developers** In Australia's context, these challenges matter particularly because: - Our market is smaller and more vulnerable to disruption - We rely heavily on open source from global communities - Security and privacy regulations are strict - We're competing globally with AI-amplified developers everywhere Thoughtful adoption of vibe coding—maximizing benefits whilst managing risks—isn't just smart practice. It's competitive advantage. The future isn't AI replacing developers or vibe coding ruining software development. It's developers who combine AI's capabilities with human judgment, security awareness, and systems thinking building better software faster whilst maintaining quality and contributing to the broader ecosystem. That future requires honest discussion of risks, not just celebration of benefits. This article is part of that discussion.

**Is AI-generated code secure?** 45% of AI-generated code has security flaws according to automated scanning. Always run security scanning, get professional review for sensitive data, and test adversarially. **Can I trust AI-generated code?** Never trust it blindly. AI makes consistent, predictable mistakes. Always review, test, and validate AI output. Treat it like code from a junior developer that needs oversight. **Will using AI tools make me a worse developer?** It can if you rely on it completely without understanding what it generates. Maintain fundamentals, regularly solve problems without AI, and always read the generated code. **Is vibe coding hurting open source?** Early data suggests pull requests to major projects grew slower in 2025 (12% vs historical 20-25%), and first-time contributors decreased 8%. The impact is still being studied. **Should I disclose when I use AI tools?** For open source contributions, some projects require disclosure. For commercial work, focus on delivering quality regardless of tools used. For academic work, follow your institution's policies. **What's the biggest risk of vibe coding?** Security vulnerabilities. AI doesn't think adversarially, and 45% of AI-generated code has security flaws. For anything handling sensitive data, professional security review is non-negotiable. **Can AI-generated code be maintained long-term?** Yes, if it's well-structured initially and developers understand it. Code that's poorly structured becomes harder to modify than human-written code. Documentation of architectural decisions is critical. **How do I avoid skill degradation while using AI tools?** Maintain fundamentals by regularly solving problems without AI, review and understand all generated code, contribute to open source, learn adjacent skills, and focus on architecture and systems thinking.