Scaling Software with AI: Considerations for Teams and Tech Leaders
At 7Factor, we’re not anti-AI. In fact, we’re actively experimenting with it across internal teams. We’re watching how it reshapes workflows, speeds up boilerplate, and supports developers in writing code faster. It’s exciting. It’s promising.
At the current moment with where Generative AI is at it won’t write your entire codebase and maintain it for the next decade.
Yes, AI Is Powerful—But Let’s Get Real About What It’s Doing
Some tools act like helpful co-pilots, and others try to write entire functions for you. And for teams cranking out MVPs or testing ideas fast, this feels like rocket fuel.
But here’s the catch: AI isn’t solving your problem. It’s guessing. Based on what it’s seen before.
Most models are trained on massive public data sets; Stack Overflow copy-pastes, GitHub snippets, and everything in between. And while quantity is there, quality often isn’t.
We’ve seen it in action:
- Recommending unsafe implementations like naïve implementations with security vulnerabilities.
- Failing to understand system architecture or code structure at a level of static analysis algorithms.
AI doesn’t reason. It doesn’t understand. It just predicts. Yet, anyway.
If You Can’t Trust the Code, You Can’t Scale It
At 7Factor, we build secure, scalable, and built to drive your success. We care about what happens to your systems two years from now; not just how fast they go live.
That means our engineers aren’t just code writers. They’re system thinkers, collaborators, and yes—AI experimenters.
We’re seeing a pattern: teams getting early productivity boosts from AI, then hitting diminishing returns. Fast. Why? Because AI doesn’t think in terms of technical debt, maintenance, or onboarding new developers. Humans do.
And here’s the danger: if your team can’t understand what the AI wrote, you’re one bug away from a nightmare. That’s not a future we’re excited about.
Good Software Still Needs Taste
Writing code isn’t just a technical skill. It’s a craft.
Anyone can learn a language. But learning taste; and how to write maintainable, scalable, elegant software; takes time, mentorship, and experience.
AI doesn’t have taste, at this moment. It doesn’t know if what it wrote is aligned with your business goals or your infrastructure constraints. It doesn’t translate strategy into logic. People do that.
At 7Factor, our engineers practices:
- Translating real business problems into clean technical requirements.
- Understanding algorithmic complexity and system behaviors.
- Making trade-offs consciously; not accidentally.
The better we understand these fundamentals, the better we can direct AI — rather than being directed by it.
The Path Forward: Cautious Optimism
We’re not here to fearmonger. AI isn’t going away. And when used responsibly, it’s a powerful tool.
But you won’t find us blindly adopting every new AI tool that hits the market. We’re here to ask better questions:
- Where does AI increase speed without compromising quality?
- What happens when product owners rely on AI without understanding the architectural trade-offs it makes?
- What guardrails do we need in place to ensure AI doesn’t introduce future tech debt?
AI will get better. We believe that. But until it’s great; and until we’ve proven long-term ROI. We’re focused on what we do best:
Building high-quality, sustainable, custom software for clients
Let’s Have a Conversation
Curious how we’re using AI in real-world software teams? Want to explore what sustainable engineering looks like in your organization?
Let’s have that conversation. We’ll bring the real talk—and a roadmap that scales.
Contact us to learn more about building future-proof software with a team that knows the difference between hype and high value.