Something genuinely remarkable is happening in web development studios right now, and we're living it firsthand at Refined Element. AI isn't just making our work slightly faster—it's fundamentally changing what's possible in a day, and our clients are noticing.

Here's what that looks like in practice: Last week, a client needed urgent updates to their Xperience by Kentico multi-site deployment. The kind of task that used to mean late nights parsing through documentation and testing edge cases. Instead, our AI-assisted workflow handled the heavy lifting—generating boilerplate code, catching potential issues before they hit QA, and even drafting the technical documentation. What would have been a three-day sprint wrapped in six hours. The client was ecstatic. We were too, honestly.

This isn't about AI replacing developers—that's not what's happening here. It's about reclaiming time for the work that actually matters: solving complex problems, understanding client needs, architecting elegant solutions. The grunt work—the repetitive patterns, the documentation that always falls behind, the test coverage that never quite gets finished—AI handles that now.

The productivity gains are legitimately through the roof. But the real story isn't just speed; it's quality. When your AI assistant catches a subtle bug at 2 PM instead of your client discovering it at deployment, everyone wins. When comprehensive documentation writes itself alongside the code, knowledge transfer becomes effortless. When pattern matching across a massive codebase happens instantly rather than through hours of grep commands, developers stay in flow state longer.

Our clients are seeing this transformation too. Faster turnarounds, yes, but also more polished deliverables, better communication, and budgets that stretch further. One client recently told us their expectations for what's achievable in a sprint have completely reset—in the best way possible.

We're not at the "code that writes itself" sci-fi future yet, but we're somewhere equally interesting: a partnership between human expertise and machine efficiency that makes both sides better. The platform knowledge required for sophisticated Kentico implementations hasn't diminished—if anything, it's more valuable. But now that knowledge gets leveraged more effectively, applied more consistently, and documented more thoroughly.

The transformation is real, it's happening now, and honestly? It's thrilling to be part of it.

I'm using Claude to write about using Claude to write about using Claude to build features in Xperience by Kentico. If your head just tilted slightly, you're tracking correctly.

Here's what happened: I built AI-powered content and coding features into the CMS. Then I asked an AI to help me write about that work. The post resonated—readers loved it, executives forwarded it around. Success tastes sweet until you realize the weirdest part isn't what you built, but who wrote the story.

Now I'm back in the editor, one layer deeper into the recursion, asking the same AI to help me write about asking that AI to write about...you get it. We've created a documentation ouroboros, and honestly? It feels like glimpsing the future through a funhouse mirror.

The technical work is straightforward enough: machine learning models personalizing content delivery, natural language processing improving search, automated testing validating component outputs. Standard 2025 web development, really. But somewhere between implementing the features and explaining them to stakeholders, I crossed a threshold. The tools I use to build became the tools I use to document became the tools I use to reflect on documentation itself.

This is where it gets genuinely strange. Every prompt I write teaches the AI about my project. Every response shapes how I think about the work. The boundary between "doing development" and "explaining development" has dissolved into something like a collaborative improvisation where neither participant is entirely sure who's leading.

I keep wondering: when the AI helps me articulate what I built with AI, is the resulting clarity genuine insight or just really convincing recursion? Does it matter? The executives greenlit more budget. The developers on my team actually read the documentation. The features ship on schedule.

Maybe this is just what technical writing becomes when the tools achieve a certain capability threshold. Your documentation toolchain doesn't just record the work—it participates in how you understand the work. It's less "AI replacing writers" and more "writing becoming a real-time negotiation between human intention and machine articulation."

The recursive loop tightens. Next week I'll probably use this post as context for the AI to help plan the next feature sprint. The snake continues eating its tail, and somewhere in that spiral, we're building the future of content management and digital experience.

I just can't tell anymore who's holding the pen.