Everyone’s Writing Like a Fancy Algorithm Now—Here’s How to Spot It and Build a Fix

I spent fifteen minutes last Tuesday trying to understand a software update changelog that could have been a single sentence. "We fixed the redirect loop issue that occurred when users navigated backwards through multi-step forms under certain session conditions."

Translation: "Fixed a bug where the back button broke."

This wasn't an AI hallucination. A human wrote it. And they probably thought it made them sound smart.

Jargon-riddled communication is a pervasive pain, and it's not limited to chatbots. While Pieter Levels' recent post about Claude's overly verbose output gaining over 300,000 views shows many people relate, the problem is much bigger than one AI model. Our tracking reveals 21 distinct problems in the Communication category alone. "Unclear technical documentation" sits at a severity score of 4.2 out of 5. That's not just annoying; it's actively hindering work.

And this isn't a new complaint. Jargon has always been a gatekeeping mechanism. The difference now is that AI can amplify it at scale, generating reams of prose that sound authoritative but say very little. The result? More people are drowning in text that feels like it was written by a committee of academics trying to impress each other.

But here's what gets me excited as a builder: widespread pain equals a massive opportunity. This isn't about hating on one AI. It's about recognizing that the world needs a layer of translation between complexity and clarity. Think about it—if an experienced developer like Levels is pulling out a dictionary for AI-generated text, imagine what it's like for someone new to a field.

The idea isn't to make everything dumbed-down. It's to give people the option to choose their level of simplicity. Sometimes you need the precision of "asynchronous state reconciliation," and sometimes you just need "syncing." The tools we use should respect that context.

Already, some builders are connecting these dots. One app idea we've seen, a Plain Language Summarizer for Legal Jargon, tackles exactly this translation layer for a notoriously impenetrable domain. But why stop there? A real-time jargon buster for meetings, an email assistant that highlights sentences that could be cut in half, a documentation grader that flags reading-level spikes—the space is wide open.

This isn't about waiting for Claude or any other model to "learn" to be concise. It's about creating tools that sit between the output and the reader, giving users agency over comprehension. The technology to do this exists right now. A combination of LLMs fine-tuned on readability rules, a solid heuristics engine, and a clean UI could turn any wall of text into a skimmable insight.

The market is screaming for it. Over 23,000 problems are tracked in our system, and a disproportionate number circle back to communication friction. Whether you're a solo developer looking for your next project or an indie hacker searching for a bootstrap idea, the signal is clear: help people understand each other, and you'll never be short of customers.

And let's be honest, the first time your tool translates a 500-word policy update into "Your data stays private, here's the one thing that changed," you'll feel like a hero.

So next time you find yourself re-reading a paragraph for the third time, don't just sigh. Open your note-taking app. That frustration is a feature request.