AI is writing more code than ever.
That’s not hyperbole—it’s the reality I’m seeing across every bot project I touch. GitHub Copilot, ChatGPT, Claude, and a dozen other tools are churning out functions, classes, and entire modules at speeds that would’ve seemed impossible two years ago. But here’s what nobody talks about enough: who’s checking if that code actually works?
Enter Qodo, which just raised $70M in Series B funding to tackle exactly this problem. As someone who builds bots for a living, this news hit different. Because verification isn’t some abstract enterprise concern—it’s the difference between a bot that handles customer queries smoothly and one that crashes at 2 AM when you’re trying to sleep.
The Verification Gap
When I started building bots, code review meant me, a cup of coffee, and a careful read-through of what I’d written. Now? A significant chunk of my codebase comes from AI suggestions. I’m not complaining—it’s faster, often cleaner, and handles boilerplate like a dream. But speed creates its own problems.
AI-generated code looks right. It follows patterns. It compiles. But does it handle edge cases? Does it play nice with your existing architecture? Will it scale when your bot goes from 100 users to 10,000? These questions don’t have obvious answers when you’re moving fast.
Qodo’s bet is that verification needs to be automated, intelligent, and built specifically for the AI coding era. They’re not the first to think about code quality, but they’re among the first to design their tools around the assumption that humans aren’t writing most of the code anymore.
What This Means for Bot Development
Bot builders live in a weird space. We’re not building monolithic applications, but we’re not writing throwaway scripts either. Our code needs to be reliable, maintainable, and often runs in production environments where failures are visible to end users immediately.
When I integrate an AI-generated function into a conversation flow, I need confidence that it won’t break when a user types something unexpected. Traditional testing helps, but it’s reactive. You write tests for scenarios you think of. AI verification tools promise something different: proactive analysis that spots issues you didn’t anticipate.
The $70M Qodo raised suggests investors believe this problem is big enough—and urgent enough—to warrant serious attention. As AI coding tools become standard, verification can’t be an afterthought. It needs to be part of the development loop itself.
The Practical Reality
I’m cautiously optimistic. Not because I think Qodo or anyone else has solved verification completely, but because the conversation is finally happening. For years, the AI coding narrative was all about speed and productivity. Generate more, ship faster, automate everything. That’s valuable, but incomplete.
Quality matters. Especially in bot development, where your code is the interface. A buggy web app might have a clunky UI. A buggy bot just stops responding, or worse, responds incorrectly. Users don’t file bug reports—they leave.
What I want from tools like Qodo isn’t perfection. It’s partnership. Show me where my AI-generated code might fail. Flag the integration points that need human review. Help me understand what the AI wrote, not just accept it blindly. That’s the verification layer that makes AI coding sustainable.
Looking Ahead
The funding Qodo secured isn’t just about their product—it’s a signal about where the industry is heading. As AI writes more code, the tooling around that code needs to evolve. We need better ways to test, verify, and maintain what our AI assistants produce.
For bot builders specifically, this matters because our work sits at the intersection of code quality and user experience. Every function we ship is potentially customer-facing. Every integration point is a place where things can go wrong. AI helps us build faster, but we need verification tools to help us build better.
Qodo’s raise is a reminder that the AI coding revolution isn’t just about generation—it’s about the entire development lifecycle. And for those of us building bots in production, that’s exactly the kind of thinking we need more of.
đź•’ Published: