AI in Development: Silver Bullet or Russian Roulette?

I’ve been building software for over a decade, and I’ve seen hype cycles come and go. But nothing compares to the current AI gold rush. Every week, there’s a new tool claiming to replace your entire dev team. But after running real experiments with my own team, I can tell you: AI in development is neither a silver bullet nor a guaranteed disaster. It’s a loaded gun — and how you handle the trigger makes all the difference. Let me break down my firsthand experience, the concrete results, and the hard lessons I’ve learned. This isn’t theory. This is what happened when we bet the house on AI for a production project. ## The Problem: Too Much Code, Too Little Time Last quarter, my startup needed to ship a complex feature — a real-time data pipeline that aggregates user behavior across multiple platforms and triggers automated workflows. The manual approach would have taken three senior engineers about six weeks. We had two weeks and a budget that couldn’t stretch. Desperate, I decided to go all-in on AI-assisted development. I didn’t just use it for boilerplate or code completion. I used it to architect, write, and debug the entire pipeline. The tools we used included GitHub Copilot for inline suggestions, ChatGPT for high-level architecture discussions, and a specialized code-generation tool called Cursor for refactoring. We set a clear goal: produce production-ready code with tests, documentation, and minimal human review. Here’s what actually happened. ## The Solution: What Worked First, the wins. AI drastically accelerated the initial scaffolding. I could describe the data model in plain English and get a working SQL schema in seconds. For the API layer, I gave a prompt like “create a FastAPI endpoint that accepts JSON, validates it with Pydantic, and stores it in PostgreSQL” — and it generated correct, runnable code. That saved us roughly 40% of the time we would have spent on boilerplate. Second, AI was surprisingly good at writing unit tests. We used a prompt that included the function signature and expected behavior. The generated tests covered edge cases I hadn’t even thought of. One test caught a null-pointer exception that would have crashed the pipeline in production. That alone was worth the experiment. Third, documentation. I hate writing README files. AI wrote a comprehensive one with setup instructions, environment variables, and example API calls. It wasn’t perfect, but it cut my doc time by 70%. Here’s a quick breakdown of the time savings we measured: | Task | Manual Time | AI-Assisted Time | Savings | |------|-------------|------------------|---------| | Database schema design | 8 hours | 1 hour | 87% | | API endpoint creation | 16 hours | 4 hours | 75% | | Unit tests (500+ cases) | 40 hours | 12 hours | 70% | | Debugging critical bugs | 24 hours | 8 hours | 67% | | Documentation | 12 hours | 3 hours | 75% | Overall, we shipped the feature in 28 hours of AI-assisted work versus an estimated 100 hours manually. That’s a 72% reduction in development time. But here’s the catch — and it’s a big one. ## The Russian Roulette: Where AI Almost Broke Us AI code is deceptively clean. It looks professional, uses proper naming conventions, and even adds comments. But it’s often subtly wrong. I spent three hours debugging a race condition that AI introduced. It had generated a perfectly logical async function — except it didn’t handle a mutex lock correctly, causing data corruption under load. A human would have caught it in code review, but AI-generated code bypassed my review because it looked “too good.” Another nightmare: security. AI generated an SQL query that was vulnerable to injection — even though I explicitly said “parameterize all inputs.” It didn’t. I only caught it because I ran a static analysis tool afterward. If I had trusted the output blindly, we would have shipped a security hole. Here’s what I learned the hard way: - **AI has no context of your infrastructure.** It doesn’t know your deployment environment, your existing codebase’s quirks, or your team’s coding standards. It generates generic solutions. - **AI hallucinates APIs.** It once suggested using a function from a library that doesn’t exist. When I asked it to verify, it insisted it was real. I wasted 30 minutes searching before realizing it was a hallucination. - **AI doesn’t understand business logic.** It can write a function that sends an email, but it can’t know that the email should only be sent if the user hasn’t opted out in the last 30 days. That’s human judgment. ## The Verdict: Silver Bullet or Russian Roulette? After this experiment, I’m convinced: AI in development is neither. It’s a high-powered tool that requires a skilled operator. If you treat it as a silver bullet — let it write everything and trust it blindly — you’re playing Russian roulette with your codebase. If you treat it as a junior developer who needs constant supervision, it becomes a force multiplier. Here are my concrete rules for using AI in development: 1. **Never commit AI-generated code without a human review.** Treat it like a pull request from a new intern. 2. **Use AI for scaffolding, not logic.** Let it write the boilerplate, but design the architecture yourself. 3. **Always run automated security scans on AI code.** Static analysis is non-negotiable. 4. **Test the hell out of it.** AI writes good tests, but it also writes tests that pass for the wrong reasons. Review them. 5. **Keep a human in the loop for critical paths.** Payment processing, authentication, data integrity — those are not for AI. ASI Biont supports connecting to GitHub and other development tools via API to automate your workflows — check it out at asibiont.com. But more importantly, build your own processes to validate AI output. ## Conclusion AI in development is not a magic wand. It’s a chainsaw. In the hands of a lumberjack, it cuts down a forest in hours. In the hands of someone who doesn’t respect its power, it cuts off a limb. The companies that will win are not the ones that adopt AI the fastest — they’re the ones that adopt it the smartest. With clear guardrails, rigorous testing, and human oversight, AI can be the best thing to happen to software engineering since version control. But without those safeguards, it’s a gamble I wouldn’t take. I’d love to hear your experiences. Have you used AI in production? What worked — and what almost broke you? [Source](https://habr.com/ru/companies/skbkontur/articles/1051432/?utm_campaign=1051432&utm_source=habrahabr&utm_medium=rss)

AI in Development: Silver Bullet or Russian Roulette?

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