Generative UI tools do more than just build apps. By allowing non-technical users to iterate on an idea through natural language, they naturally encounter and solve fundamental computer science problems like data modeling and abstraction without formal training.

For experienced leaders new to AI, building a custom GPT is an ideal starting point. A simple but high-value project is to feed a conference schedule into a GPT, allowing users to ask "Which sessions should I attend if I'm a senior PM?" This teaches core AI concepts without requiring coding.

Instead of being intimidated by technical tasks like creating animated GIFs, marketers can use AI platforms as an on-demand guide. Simply ask the AI to provide step-by-step instructions for a specific tool (e.g., MailChimp, Klaviyo) to overcome knowledge gaps without feeling inadequate or needing to ask colleagues.

For product managers not yet working on AI, the best way to gain experience is to build simple AI tools for personal use cases, like a parenting advisor or a board game timer. Using no-code prototyping tools, they can learn the entire development lifecycle—from ideation to prompting and user feedback—without needing an official AI project at work.

The most powerful use of AI for business owners isn't task automation, but leveraging it as an infinitely patient strategic advisor. The most advanced technique is asking AI what questions you should be asking about your business, turning it from a simple tool into a discovery engine for growth.

Vercel designer Pranati Perry advises viewing AI models as interns. This mindset shifts the focus from blindly accepting output to actively guiding the AI and reviewing its work. This collaborative approach helps designers build deeper technical understanding rather than just shipping code they don't comprehend.

To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.

AI development has evolved to where models can be directed using human-like language. Instead of complex prompt engineering or fine-tuning, developers can provide instructions, documentation, and context in plain English to guide the AI's behavior, democratizing access to sophisticated outcomes.

The skills of setting clear goals, understanding resource (model) strengths, and defining processes are the same for managing people and AI agents. Being a great manager makes you a great AI user, as both require clarifying outcomes and marshalling resources to achieve them.