I recently built a Netflix clone without writing most of the code myself. Before you close this tab thinking I’m advocating for replacing human developers, hear me out. This experience with Google Antigravity taught me something nuanced about AI development tools and the future of junior developers.
My Experiment: Building Without Coding
Using Google Antigravity, I created a functional Netflix clone complete with a Django backend and a Simple frontend. I simply prompted Antigravity to add apps in Django, generate the frontend components, handle the backend logic, and tie everything together. The result? A working application that would have taken me an hour to build manually was ready in just 5 minutes.
So, Can Google Antigravity Replace Junior Developers?
The short answer: No, but it’s complicated.
Here’s what I learned from this experiment:
What Google Antigravity Excels At
First, you need to grant permission for him to control the Chrome browser and the terminal. Even if you provide a token, he will be able to push code to the GitHub repository by himself.
Speed and boilerplate generation. Antigravity churned out repetitive code, set up project structures, and handled standard CRUD operations faster than any human could. It’s like having a junior developer who never gets tired of writing the same patterns.
Pattern recognition. Need a login system? Authentication middleware? Antigravity has seen thousands of implementations and can generate one that follows best practices instantly.
Syntax and framework knowledge. The tool knew Django conventions, React patterns, and CSS frameworks without needing to Google documentation every five minutes.
What Google Antigravity Struggles With
When dealing with complex logic involving multiple apps and API integrations, he has to dry-run the process multiple times to fix issues. Sometimes, this takes too much time for a solution that a human could implement quickly.
Debugging complex issues. When things broke in unexpected ways, the AI often suggested generic fixes. Real problem-solving requires human intuition and understanding of how different parts of the system interact.
Architecture decisions. Should this be a microservice? How should we structure the database for future scaling? These strategic decisions still need human judgment.
Context and trade-offs. The AI doesn’t know your team’s coding standards, your company’s technical debt, or why certain “bad” solutions might actually be the right choice given real-world constraints.
The Real Question: What Does This Mean for Junior Developers?
Rather than asking “will AI replace junior developers,” we should ask “how will junior developer roles evolve?”
Junior Developers Who Will Struggle
If your value proposition is purely “I can write boilerplate code and implement straightforward features,” then yes, AI is coming for that work. Typing speed and memorizing syntax were never sustainable differentiators.
Junior Developers Who Will Thrive
The junior developers who will succeed are those who:
- Ask better questions. Understanding what to build and why is more valuable than knowing how to build it.
- Debug and problem-solve. AI-generated code still breaks. Knowing how to investigate, understand error messages, and fix issues is crucial.
- Understand systems thinking. Seeing how components interact, anticipating edge cases, and thinking about scalability can’t be automated.
- Communicate effectively. Translating business requirements into technical specifications and explaining technical concepts to non-technical stakeholders remains a human skill.
- Learn continuously. The tools are evolving rapidly. Adapting and learning to work with AI rather than competing against it is the key.
My Take: AI as a Force Multiplier
After building my Netflix clone, I don’t see AI as a replacement for junior developers. I see it as a tool that raises the bar for what “junior” means.
In the past, a junior developer spent months learning syntax, framework basics, and how to set up projects. Now, AI handles much of that grunt work. This means junior developers can (and must) focus on higher-level skills earlier in their careers.
The junior developer of the future isn’t someone who can slowly implement a feature spec. It’s someone who can:
- Use AI tools to rapidly prototype ideas
- Evaluate and improve AI-generated code
- Focus on the problems AI can’t solve: understanding user needs, making architectural decisions, and debugging complex systems
Conclusion
Can AI coding assistants replace a junior developer? Only if that junior developer refuses to evolve. The real opportunity is for junior developers to embrace these tools, level up faster, and focus on the irreplaceable human skills that make great developers great.
The Netflix clone I built proves that AI can generate code. But it also proved that without human judgment, context, and problem-solving, code is just a starting point, not a solution.
The question isn’t whether AI will replace junior developers. It’s whether junior developers will learn to make AI their superpower.