I recently read a story on Reddit that perfectly captures what happens when entrepreneurs entrust their entire tech stack to AI without understanding the code it produces.
The redditer goes onto explain how a non‑technical fintech founder built her MVP entirely through prompts: she generated screens, stitched together APIs and even added “credit scoring,” AI agents and dashboards. She showed the prototype to a bank, got the green light and then hired a dev team to refactor the AI‑generated code. The quote? Three hundred plus hours, basically the cost of building it properly from scratch…
It got worse. The dev team she hired also relied on vibe coding. They asked ChatGPT how to set up the server and ended up with SSH open to the world, a root password set to admin123, and no firewall. Ransomware encrypted everything, forcing the founder to shut down the project, rotate API keys and rebuild.
She lost money, time, a client’s trust and her credibility…
Would you sign a contract you hadn’t read just because an AI wrote it? Would you send a pitch deck you didn’t understand? Of course not. So why deploy code you can’t read?
AI is an amplifier: it makes you faster at what you already know. If you understand business, AI can help you scale. If you understand code, AI can accelerate your development. But if you understand neither, you’re just increasing your risk of failure…
Industry Data: Hidden Costs of AI‑Generated Code
- Security flaws are common: Veracode’s 2025 GenAI Code Security report found that 45 percent of AI‑generated code samples contained security vulnerabilities. Only 55 percent of AI‑generated code was deemed secure across four programming languages and four critical vulnerability types (src: Veracode).
- Half of the code is buggy: Researchers at Georgetown University’s Center for Security and Emerging Technology (CSET) evaluated code snippets from five large language models and discovered that almost half of the generated snippets contained bugs that could lead to exploitation. The report notes that AI code generation models can also be attacked directly and that insecure code can pollute the software supply chain (src: CSET).
- Productivity comes with instability: Google’s 2025 DevOps Research and Assessment (DORA) report showed that developers who used AI for code generation reported a 17 percent boost in individual effectiveness but also saw software delivery instability climb by nearly 10 percent. Overall, 60 percent of teams experienced slower development speeds or greater instability when relying heavily on AI (src: Dark Reading).
- Everyone’s using it – and paying the price: Surveys indicate that 84 percent to 97 percent of developers use AI tools for code generation. Yet widespread adoption has led to bloat and duplicated packages; security experts note that AI often duplicates dependencies, creating “code slop” that is verbose and brittle (src: Dark Reading).
- Breaches are happening now: A recent survey of 450 security leaders and developers found that one in five organizations have already suffered a serious cybersecurity incident tied to AI‑generated code, and 69 percent uncovered flaws created by AI. More than 90 percent worry about vulnerabilities introduced by AI (src: DevOps).
Five Red Flags to Watch Out For
- Blind trust in AI output: If your team accepts AI‑generated code without review, you’re walking into trouble. Experts have shown that nearly half of AI‑generated code contains bugs or security flaws. Incorporate static analysis and dynamic testing to catch vulnerabilities before they reach production (src: CSET, Veracode).
- Hard‑coded credentials and insecure defaults: AI models sometimes produce insecure configurations such as hard‑coding API keys or setting weak passwords (e.g., using “admin123” for root). Veracode’s analysis noted that AI tools frequently mishandle cryptographic practices and authentication. Always audit and replace default secrets (src: Veracode).
- No firewall or network segmentation: The Reddit case illustrated how an open SSH port and lack of firewalls let ransomware walk right in. Relying on AI for infrastructure configuration can expose your system if you don’t layer in proper security controls. Use secure baselines and have a security engineer review any AI‑generated infrastructure.
- Absence of accountability and processes: When no one is responsible for reviewing AI output, mistakes slip through. In a recent survey, 53 percent of respondents blamed security teams for AI‑related breaches, while 45 percent blamed the people who prompted the AI. Track AI usage, share accountability and build review checklists to prevent the “blame game” (src: DevOps).
- Explosion of “code slop” and technical debt: Dark Reading reports that AI amplifies flaws in training code and generates verbose, duplicated packages. When developers check in 75 percent more code because of AI, they accumulate technical debt that slows them down later. Watch for uncontrolled growth in your codebase and enforce refactoring (src: Dark Reading).
What we do Instead
AI doesn’t have to be your enemy. Used wisely, it can boost productivity without compromising security. Here are some safeguards:
- Combine expertise with automation: Pair AI with human oversight. Use AI for boilerplate code but have experienced engineers review architecture and security.
- Integrate security scanning: Run static application security testing (SAST), dynamic testing and software composition analysis on all AI‑generated code (src: Veracode).
- Track and audit AI usage: Keep logs of when and where AI is used in your pipelines, as suggested by the DevOps survey’s accountability findings (src: DevOps).
- Establish a culture of learning: Encourage team members to understand the code they deploy. AI is a tool, not a substitute for knowledge.
- Be transparent with clients: If you’re showing an MVP built with AI, disclose how it was created and what still needs to be hardened. This builds trust instead of eroding it.
- Follow strict policies on AI: To build responsibly, we abide by a strict AI usage policy and AI transparency policy. These policies ensure our clients understand how AI influences our work and outline clear guidelines for when and how we use AI.
Ready to Build the Right Way?
We specialize in helping founders and teams harness AI without sacrificing security, quality or trust. If you’re tired of the AI echo chamber and want real, data‑backed solutions, let’s work together!
Need help auditing an AI‑built prototype or preparing for a secure launch? Get in touch – I’m here to help you navigate the new frontier responsibly.