The segment explains AI Deputization as a practical way to decide which parts of your work should be handed to AI, which should be done together with AI, and which should remain yours. It points to three emerging tools that make this easier: GrokBot, which can learn a task from demonstration; ChatGPT Computer Memory, which helps preserve context about how you work over time. The practical lesson is to combine better context capture with a structured five-dimension audit, then decide whether each recurring task should be deputised, done as a duet, or defended as human work.

Diana Hu argues that an AI-native company should be designed around AI from the beginning rather than adding AI tools to existing workflows. Her practical model is a closed-loop, queryable organisation: important work, decisions and results are captured so AI can observe what happened, propose the next action and continuously improve how work gets done. The deeper implication for knowledge workers is that AI changes not only individual productivity but also how responsibilities, information flow and even company structure can be designed.

This discussion explains the growing role of the Forward Deployed Engineer (FDE): someone who works close to a company’s real operations to decide where AI can create value, then helps turn general-purpose models into working systems. The practical framework is audit → evaluations → deployment: understand the workflow and its exceptions, test whether the AI performs reliably, then integrate it with the systems people already use. For knowledge workers, the useful point is that successful AI adoption depends less on access to the best model than on understanding work well enough to deploy AI appropriately.

Corey Ganim lays out a practical AI consulting model built around a paid assessment: interview a small business owner about repetitive work, use AI to analyse the problems and identify suitable tools, then present a short report with clear time-saving opportunities. The assessment is also a gateway to higher-value implementation and recurring support. For knowledge workers, the useful point is that valuable AI work can begin with diagnosing real workflow friction rather than building custom technology from scratch.

Greg Isenberg argues that AI agents represent a shift from software that helps people do work to systems that can perform parts of the work itself. His practical playbook is to find a repetitive workflow people already pay someone to do, study how the job actually works, build the smallest useful agent, and add approvals, logs and checks before increasing autonomy. The useful lesson is that good agent opportunities start with understanding the work, not with finding somewhere to apply AI.

Greg Isenberg argues that “learn AI” is too vague to be useful and proposes six practical skill areas that should become more valuable as AI improves: running agents and local models, building distribution, robotics, curation, becoming a builder-distributor, and creating real-world communities. The useful idea is to build a skill stack around AI rather than chase individual tools. For knowledge workers, the practical lesson is to choose capabilities where AI increases your leverage rather than simply learning whatever technology is newest.

This short walkthrough shows how a /teach skill can turn an AI agent into a more structured learning partner, not just a chatbot that explains things on demand. The useful idea is that the skill does not simply answer questions; it creates lessons, checks understanding, tracks progress, and tries to teach at the learner’s next reachable level. For knowledge workers, the value is a clearer way to use AI for learning: not asking for generic explanations, but designing a guided path that stretches without overwhelming.