Hamel Husain identifies two mistakes AI teams make: shallow adoption where engineers just copy-paste ChatGPT prompts without building real intuition, and jumping to complex architectures like multi-agent systems before establishing simple foundations. Start simple, embed AI deeply in your workflow, and iterate toward complexity as you understand the system.
What goes wrong as part of this process when you step in? So the first thing that goes wrong is not even evals. The first question I ask is, are you using AI? And are you using AI in a deep way? Are your engineers coding with AI? Are you building things with AI that's beyond just like copy and paste into ChatGPT? If the answer is no, then you're not going to be successful building AI because you won't have a good intuition on what is possible and what's not. You're going to have really bad specification. You won't kind of zone in on what is a good idea and what's not. You won't have a good mental model. So I think that's the first failure point that people face. And the second failure mode that I see a lot is reaching for complexity too fast. So, okay, you want to build an AI product. Don't off the bat go for the most complex architecture and setup. Don't like on day one reach for the most complicated orchestration framework and a graph database and a multi-agent thing. Start simple, build your way up incrementally. I see a lot of teams that just go straight to the most complicated architecture and then they can't really reason about what is happening and they don't have a good mental model.
