AI CodeCon made one thing clear: AI-assisted code generation is not a novelty. It's already reshaping how software is built. Teams that understand this shift will outperform those that don't. Others will fall behind.

Tim O'Reilly opened the event with a sharp comparison. He called this moment similar to the birth of the internet. The pattern is familiar. The web changed how people built, communicated, and collaborated. AI is doing the same. The gains in productivity are undeniable. But they are uneven. Most tools get you 70 percent of the way. The remaining 30 percent reveals the limits. This is now called the "70% problem."

Tools like Bolt, V0, and Loveable help close that gap. They provide structure, speed, and focus. But human judgment is still required. The final 30 percent always matters more than people think.

AI handles accidental complexity well. It stumbles on essential complexity. This is where senior developers show their value. They understand maintainability, long-term thinking, and code quality. And now, non-engineers are also generating code. PMs, EMs, sometimes even designers. The result? Quality control becomes critical. Sloppy AI output creates debt faster than humans can repay it.

Bad code is easy to produce. Humans write it. AI writes it too. What separates robust systems from fragile ones is process. Good software comes from critical review, systematic testing, and rigorous refactoring. The tools, whether human or machine, only get you so far. What matters is the process behind the result. Consistent, deliberate effort builds good systems. Discipline makes the difference.

The term "vibe coding" came up often. Usually with a wince. Some used it with visible discomfort. Others offered caveats. Harper Reed said plainly, "I call it AI codegen, because that's what it is." Still, the term captures something important. This is a new mode of working. Fast. Iterative. Loosely structured. Kent Beck (who changed my life as a developer once before when he introduced me to test-driven development), was first to say what many of the presenters said, that AI has brought back the joy of coding for them. The model generates. The developer shapes. The loop continues, almost adictively.

Harper Reed delivered the most practical walkthrough of the event. His codegen workflow with LLMs is detailed, battle-tested, and real. It's not about chatbots and copy-paste. It's about rigorous scaffolding. Tight loops. Systematic review. His blog post documents it well. Seeing it live showed how deliberate every step is. Reed emphasized that Git is "save games for code." Used well, if something goes wrong, you can always reload. I love that framing.

There was disagreement about what this means for junior developers. Some say AI eliminates the grunt work that helped people learn. Others argue it accelerates growth. Both are possible. Either way, ignoring this shift isn't an option. It is already happening.

The event wasn't about glossy slides or overhyped roadmaps. It focused on real usage. People shared bugs. Failures. Dead ends. They also shared wins. This is what it looks like to build with AI. It's not clean. But it is powerful.

I am far less inclined now to believe this is a trend. It's a foundational change in how software is written. Teams that learn to work with AI deliberately and critically will move faster and build better. Everyone else will struggle to keep up.