"Vibe coding" means letting AI write most of your code from natural language prompts instead of coding everything yourself.
Popularized by Andrej Karpathy, vibe coding has helped developers—and even non-programmers—build apps much faster. But using it in production without a strategy can lead to security issues, bugs, and technical debt.
At Anthropic's Code with Claude event, researcher Eric Zelikman explained how to safely use AI in real production systems.
1. AI Can Handle Bigger Tasks Every Year
Today's AI can usually complete about 1 hour of coding work.
That capability is growing rapidly:
Today: ~1 hour of work
In 1 year: ~1 day of work
In 2 years: ~1 week of work
Soon, reviewing every line of AI-generated code won't be practical. Developers will need to focus on verifying results, not reading every line.
Think of it like a compiler: Developers once inspected assembly code generated by compilers. Today, nobody does—we trust the compiler. AI is becoming the next layer of abstraction.
2. Focus on the Product, Not Every Line of Code
Eric Zelikman's key advice:
"Forget that the code exists, but never forget that the product exists."
Instead of checking every line, verify that:
✅ The feature works correctly
✅ Requirements are met
✅ Security isn't compromised
✅ Performance is acceptable
✅ Users have a good experience
This is how CEOs, CTOs, and Product Managers already work—they validate outcomes, not every implementation detail.
3. Protect the Core, Let AI Build the Rest
Not all code is equally important.
🛡️ Core (Protect with Human Review)
Examples:
Authentication
Database schema
Payment systems
API architecture
Business logic
These parts affect the entire application and should always receive careful human review.
🚀 Leaf Features (Great for AI)
Examples:
UI components
Dashboard widgets
Utility functions
Individual pages
Small APIs
If AI makes mistakes here, they're easier to replace or rewrite without affecting the whole system.
Rule: Let AI build the edges—not the foundation.
4. Treat AI Like a New Team Member
Don't simply say:
"Build this feature."
Instead, provide clear context:
Explain the goal.
Point to the relevant files.
Describe coding standards.
List constraints.
Mention edge cases.
Spending 15–20 minutes preparing a clear specification often saves hours of debugging later.
Your role shifts from programmer to product manager.
5. Real-World Proof
Anthropic used Claude to generate a 22,000-line production code change.
They succeeded because they:
Planned requirements first
Limited AI to isolated parts of the system
Carefully reviewed core architecture
Used automated tests to verify everything worked
The result was a major feature delivered much faster while maintaining confidence in production quality.
Key Takeaways
🤖 AI is becoming capable of handling increasingly complex development tasks.
🎯 Judge AI by the quality of the product, not by reading every line of code.
🛡️ Protect core architecture with human oversight.
🚀 Let AI move quickly on isolated features and UI.
📋 Good planning and clear prompts produce far better results.
✅ Invest in automated testing and validation instead of relying solely on manual code reviews.
The future isn't developers vs. AI—it's developers who know how to manage AI effectively.