There is a meaningful difference between pasting a problem into ChatGPT and deploying an AI system that autonomously orchestrates multi-step workflows across a regulated enterprise. Karl has spent the last several years working in that gap — building what he calls “AI coworkers” for wealth management firms and manufacturers. This article unpacks the architectural decisions, engineering philosophy, and organizational implications behind that work.

Frank Vitetta, is the founder and CEO of Orchid Box, LLM Scout, and CodeScout. LLM Scout monitors how brands are cited and represented across major AI platforms. Krish Palaniappan is the CEO of Snowpal, an API platform helping businesses build software faster.

Frank points to a striking statistic: roughly 18% of Google’s traffic today comes from LLM bots. When you ask ChatGPT or Claude a question they can’t answer from training data, they perform live Google searches to gather information. They break your prompt into multiple search queries — a process called “query fan-out” — then crawl the top results in real time to synthesize an answer.

If you’ve been losing sleep wondering whether your job will survive the AI revolution, congratulations: you’re already doing more strategic thinking than most C-suites. That, in essence, is the bracing message Dr. Kelly Monahan brought to a recent Snowpal podcast conversation with founder Krish Palaniappan. Kelly, who studies the future of work and has done time in the research trenches at Deloitte, Accenture, and Meta, has the rare distinction of having started her HR career by laying people off because of robotic process automation. It is, as career origin stories go, the equivalent of a firefighter whose first day on the job involves lighting a match. Twenty years later, the technology is more polite about it (chatbots are nothing if not cheerful), but the underlying question is the same: what is a human worker actually for?