The AI Tax and “legal” ways to minimise it
AI tools feel productive. That’s the problem.
A 2025 METR study found experienced developers were 19% slower using AI on their own codebases, yet believed they were 20% faster. I didn’t need a study to tell me something was off. I could feel it: more code shipping, more bugs slipping through, reviewing functions I couldn’t quite explain, and a growing sense that I was managing a workflow rather than doing engineering.
That’s the AI tax. The hidden overhead that accumulates every time you prompt, verify, redirect, and re-contextualise. It doesn’t show up in your commit count. It shows up in your cognitive load, your code familiarity, and eventually your production incidents.
This talk is about what that tax actually looks like day to day, why it’s hardest to spot when you’re most productive-feeling, and the practical strategies I’ve used to minimise it without throwing away the tools.
Krishna kanth Mundada
Krishna is a senior full-stack engineer at Versent working across cloud, AI, and enterprise platforms, with 12 years of experience building systems that actually have to work in production. He’s spent the last two years quietly accumulating an AI tax he didn’t know he was paying and building workflows to stop. An active voice in Melbourne’s tech community as a speaker and organiser, he has a habit of turning uncomfortable engineering observations into talks that make rooms go quiet before they start nodding.