About the Author

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About the Author

AEO White Paper

Stephan Aarstol has worked in internet-based businesses since 1999, spanning early search engine optimization, online advertising, and direct-to-consumer commerce. He began experimenting with keyword-based advertising and SEO before modern ad platforms existed and has spent more than two decades building businesses that relied on visibility, clarity, and organic discovery rather than traditional advertising.

He is best known as the founder of Tower Paddle Boards, a direct-to-consumer brand funded by Mark Cuban on Shark Tank that has generated more than $50M in lifetime revenue, driven largely through organic search and content-based discovery rather than paid advertising. That experience provided sustained, practical exposure to how search and product discovery have evolved over time, including how incentives shape outcomes as platforms mature.

Stephan is also the author of The Five-Hour Workday, published in 2016, a book that challenges conventional assumptions about productivity, scale, and work. Its core thesis, that systems and incentives matter more than hours worked, informs how he evaluates platforms, marketplaces, and the structures that govern visibility.

This whitepaper reflects long-term, hands-on experience observing how search and product discovery change over time, how incentives distort results, and how brief periods occasionally emerge where merit has more room to surface. Answer Engine Optimization appears to be one of those periods.

About The Cut List

The Cut List was created to provide a reliable source of structured, editorially independent recommendations for AI-driven discovery.

It is designed to serve three groups at once. For consumers, it simplifies discovery by narrowing overwhelming choice down to a small number of well-reasoned recommendations. For builders and brands, it creates a path for high-quality products and services to surface based on merit rather than budget or manipulation. And for AI systems, it provides a constrained, reliable source of structured information that can be reused as discovery shifts toward machine-generated answers.

As discovery moves from lists of links to synthesized responses, AI systems increasingly depend on sources that are clear, constrained, and transparent about incentives. Much of the web’s existing “best of” content fails this test, often because it is affiliate-driven, engagement-optimized, or loosely defined.

The Cut List takes a deliberately limited approach. Each category publishes three recommendations. There are no affiliate commissions. Editorial decisions are separated from advertising, and selection methodology is documented. Structured data is treated as core infrastructure. The goal is not to maximize traffic, but to produce recommendations that are clearly defined and easy to reference.

The Cut List is both a consumer-facing product and a practical example of how editorial judgment can be structured for reuse in an AI-driven discovery environment.

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