In this episode of In Demand, Asia and Kim unpack how they think about and using AI today, and what founders and operators should think about when using AI in their day-to-day work. They explore when AI meaningfully speeds up analysis and deliverables, why human interpretation still matters for strategy, and the common mistake of outsourcing thinking to an LLM too early.

If you are experimenting with AI in product, marketing, or operations and trying to figure out where it actually adds value, this episode offers a grounded framework for thinking about it.

Got a question you’d like Asia to unpack on the podcast? Record a voicemail here.

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Chapters

  • (00:02:30) - What Kim has been finding exciting with AI today and why understanding its strengths matters.
  • (00:07:15) - Practical use cases: deliverable creation and analyzing large datasets.
  • (00:12:00) - Why AI-assisted workflows outperform fully outsourced thinking and how LLMs can get things wrong.
  • (00:18:30) - Why LLMs struggle with nuance, emotion, and deeper strategic interpretation of research interviews.
  • (00:26:15) - How companies can build an internal brain by aggregating sales calls, interviews, and support data.
  • (00:32:30) - Why AI raises the floor for many skills but still rewards real expertise.
  • (00:40:30) - Wildest AI tools and experiments: LennyBot, synthetic users, and Granola.

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