The Biggest AI Mistake Enterprises Keep Making

AI didn't eliminate the hardest part of building a startup—it moved it. Today, the challenge isn't writing code; it's finding the people who can validate your idea before you race to market. Design partner discovery has become the new go-to-market problem, and the founders who solve it first will build companies that matter.

Every company I talk to is building agents, AI-native tools, and therefore integrating LLMs into their customer-facing production systems. The pace of adoption has been staggering. OpenAI’s rapid growth and Anthropic’s imminent IPO are clear validation points. But there’s a pattern I keep seeing: the demo works, the executive buyers are excited, the board is bought in—and then it hits production, and everybody is surprised with the LLM costs, in the form of credits or tokens consumed. Often this is because of lack of control and foresight with LLM usage as adoption and usage grows. Often there are little to no guardrails or notifications built in to inform users on the degree of usage and consumption over time. Also, there are no guardrails for engineering teams building the AI native solutions for downstream end users.

Who's going to build the next era of AI-driven software? Kimber Robbins, VP of Product at portfolio company Ketch, and Mochamad Asri, leading architect at Nvidia, explain the concept of the "Renaissance Engineer": their traits, their makeup, and their goals.