Decision-Making in Business & AI

If you’re here from the lecture, welcome! The slides and source are released under the CC BY-SA 4.0 license.

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Reading List

In general, I recommend Y Combinator’s Early Stage Advice. A few hours reading through it is time well spent. For sales and marketing, especially for high-tech products, read Magical Selling.

For the startup stages and taxonomy:

• The Six Startup Stages
• The Startup J Curve
• Y Combinator’s Startup Stages

For customer discovery, product-market fit, and eventually scaling:

• Mastering the Hype Cycle
• Crossing the Chasm
• Inside the Tornado

On not getting drunk on frameworks — the hinge of this lecture:

• Chesterton’s Fence, or in short: “Do not remove a fence until you know why it was put up in the first place.”
• The Men Who Killed Google, for how overreliance on metrics (rather than product quality) buys short-term gains at the cost of long-term goals.

On putting AI into a business, and on agents in particular:

• My AI Platforms Lecture, including open-source as a defensive and offensive maneuver
• Agentic AI for Beginners, if you want to actually run the agents we talked about
• Sizes of LLMs, for a sense of what “AI” actually refers to
• Specification gaming: the flip side of AI ingenuity, on agents doing exactly what you measured rather than what you meant

On governance — worth skimming before an enterprise customer asks you about it:

• IMDA’s Model AI Governance Framework for Agentic AI, and the earlier frameworks for traditional and generative AI
• ISO/IEC 42001, the AI management system standard — certifiable and audited, the way ISO 27001 is for security

As always, turn your critical thinking skills on and carefully engage with the claims each source makes.