Discovering AI-Generated Code
A couple of months ago, I decided to switch from standard VS Code to Windsurf. I had been using VS Code since about 2015, although since Copilot autocomplete came into the picture a couple years ago, coding became much easier. The autocomplete feature finished my thoughts, and I could even write a docstring for a class or function and Copilot would give me a usable solution, most of the time.
And then came editors that used the entire codebase as context and were able to generate full projects. I decided to try out Windsurf and see what happened. Within an hour I had a full Django REST framework server foundation ready for a stock trading application. This was no basic implementation—it contained authentication, interactive documentation, and was fully tested.
The next day I built out a personal finance application that I had in my head for years, but had never built. As opposed to the day before, this time I built out both the frontend and the backend. Everything was working great, and I built it in a day. What would have taken months of traditional SaaS development—user authentication, subscription management, admin dashboards—was scaffolded and functional in hours.
This is incredible, I thought. And my career is toast.
The Fundamentals Haven't Changed
A client of mine sent me a message. Through his code editor, he had accidentally terminated the entire production environment, including the database. He'd been experimenting with an AI coding assistant, got confident with the rapid development speed, and made a destructive command. Here's the thing—AI is super confident, and if something doesn't work, one idea to get to its goal is to terminate your environment.
The recovery took a few days, and served as a stark reminder that while AI can accelerate development, it doesn't replace the fundamental need for proper development practices, staging environments, and human intervention (at least for now).
Even though I was able to build these projects with incredibly different timelines than before, that didn't mean that anyone could. The speed of AI-assisted development can mask the complexity of the decisions being made. When you can generate a working solution in minutes, it's easy to skip the critical thinking about whether it's the right solution. You still need to review the code, and know when something smells wrong.
The core process remains unchanged: understand requirements, build, gather feedback, and iterate. What's transformed is the speed and efficiency of execution, particularly in that crucial "build" phase, but the other phases remain important.
Wearing Many Hats Just Got Easier
Throughout my career, I've always juggled multiple roles—product manager understanding user needs, project manager coordinating timelines, and as an engineer implementing solutions. This wasn't by deliberate choice; as a consultant working with startups and growing companies, wearing multiple hats was simply necessary. My business background has always driven me to approach solutions from a strategic perspective, focusing on what creates real value rather than just what's technically interesting.
What's fascinating is how AI code generation has shifted the balance of these responsibilities. The engineering portion, which used to consume about 90% of my time on a project, has become dramatically more efficient. I can now stand up complete SaaS applications in weeks instead of months, prototype business ideas in hours instead of days, implement complex features in days instead of weeks.
But here's the key insight: this efficiency gain doesn't make the other roles less important—it makes them more critical.
Fast Code, Wrong Direction
The "build" phase is undeniably faster now. I can describe a component, API endpoint, or a database migration, and have working code in minutes. Features that would have taken me a full day to implement—like setting up user authentication, subscription billing, or admin dashboards for a SaaS product—can now be scaffolded and functioning in an hour.
However, this speed comes with a significant caveat: you must know what you're building and why. AI is incredibly powerful at execution, but it will enthusiastically code you into a corner if you're not providing clear direction.
I've watched AI dig deeper and deeper into architectural rabbit holes, creating increasingly complex solutions to problems that didn't need solving. Without experience to recognize these patterns, it's easy to end up with a codebase that works but is unmaintainable.
The key is finding the right balance on the spectrum of human involvement. On one extreme, you could write every line of code manually, gaining complete control but sacrificing the enormous efficiency gains AI offers. On the other extreme, you could create a completely autonomous coding agent, give it broad directives, and let it make all implementation decisions before pushing directly to production. Both approaches miss the sweet spot.
The optimal approach lies somewhere in the middle: leveraging AI for rapid implementation while maintaining human oversight of architectural decisions, code quality, and strategic direction. You want to be involved enough to catch problems early, but not so involved that you're micromanaging every function the AI writes.
The Product Management Mindset
What I've discovered is that successful AI-assisted development requires thinking like a product manager creating requirements for a very capable but literal team member. Instead of thinking in terms of "how do I implement this function," I'm now crafting detailed product requirements to relay to my AI agent for accurate implementation.
The shift is fundamental: I'm no longer building the solution directly—I'm defining the problem and success criteria so clearly that the AI can build it correctly. The questions I ask now are:
- What exactly should this feature accomplish for the user?
- What are the specific acceptance criteria and edge cases?
- How should it behave in different scenarios?
- How does this integrate with existing functionality?
These detailed requirements aren't just helpful—they're essential. The more precise and comprehensive my "product brief" to the AI, the better the resulting code. I've learned that vague instructions lead to elegant solutions to the wrong problems, while clear requirements lead to exactly what I need.
What This Means for Developers
For developers early in their careers, my advice is to focus on building solid engineering fundamentals alongside your product sense. You still need to recognize bad patterns when AI applies them, understand how to debug issues properly, and know when the code "smells" wrong. AI can generate code quickly, but you need the technical foundation to evaluate whether that code is maintainable, performant, and follows good practices.
For experienced developers, embrace the efficiency gains but double down on your strategic thinking. Use the time saved on implementation to better understand your users and market. To improve your efficiency, you must expand into more of a product perspective, or else your role might be not needed in the near future.
For organizations, recognize that while development speed has increased dramatically, the need for experienced technical leadership hasn't diminished—it's evolved into something more strategic and product-focused. This is especially critical for SaaS companies where rapid iteration and market validation are essential for success.
Looking Forward
We're still in the early days of this transformation. AI tools will continue improving, and development will become even more efficient. But the fundamental skills of product thinking and strategic decision-making will only become more valuable.
This added efficiency creates new opportunities. When you can build SaaS products in days instead of weeks, the ROI calculation changes dramatically. Ideas that were previously too expensive or risky to pursue become viable. Prototypes that would have cost thousands can now be built for hundreds. This opens up entirely new categories of business models and makes validating SaaS concepts exponentially more affordable.
The future belongs to developers who can effectively orchestrate AI tools while maintaining a clear vision of what they're building and why. We're transitioning from being primarily implementers to being primarily strategists—and that's an exciting evolution.
