Kaushik Gopal

Most organizations don’t have an AI innovation problem. They have a scaling problem. Across industries, companies have spent the past few years experimenting with AI pilots, yet many are still struggling to move from promising proofs of concept to real-world deployment. The obstacle is rarely a lack of ambition — private global investment in AI grew more than thirteenfold between 2014 and 2024.

Everyone says they’re rebuilding their company in an AI-native way. But what does that mean? Most companies look at what their existing employees do and try to automate it with AI. That’s not it. That’s dabbling. A truly AI-native company rethinks every role and tears down the walls between them. None of us can 8-ball this but here’s my sketch. It’s not exhaustive but hopefully it sparks something: Quality Assurance The QA team typically finds bugs and dutifully files them in Jira.

Loop engineering convinced me. Not because it’s clever — because done right, it doesn’t bankrupt you. This post captures where I’ve landed: a responsible way to run loops at scale without burning a hole in your pocket. Naysayer → Believer I’ve been a vocal naysayer. Not because the approach doesn’t work — it works. The costs never justified it. No surprise — the people singing its praises usually aren’t the ones paying the API bills. But when Peter & Boris tell you something, you look closer.