14# Simo Ahava (Co-founder: Simmer, Partner: 8-bit-sheep, Co-host: Standard Deviation Podcast) on Teaching Technical Marketers When AI Removes the Incentive to Learn, Why Critical Thinking Is the Skill AI Can’t Replace, and What Agentic Commerce Means for Data Layer Architecture

In this episode of Knowledge Distillation, Katrin Ribant speaks with Simo Ahava – quite simply the person the entire digital analytics and technical marketing community turns to when they need to understand how things actually work. Simo has been writing about web analytics, tag management, and the Google marketing stack since 2010, and his blog at simoahava.com has become the definitive technical reference for anyone implementing Google Analytics or Google Tag Manager. A Google Developer Expert in both platforms from 2014 to 2025, a multiple Digital Analytics Association award finalist, and one of the most generous knowledge sharers the industry has ever seen – if you’ve ever asked a question on Measure Slack, there’s a good chance Simo answered it, thoughtfully, for free. He co-founded Simmer with his wife Mari Ahava, an online learning platform for technical marketers that has become the gold standard for courses on server-side tagging and BigQuery. He is partner and co-founder at 8-bit-sheep, a Helsinki-based digital services consultancy, and co-hosts the Standard Deviation Podcast with Juliana Jackson.

The conversation opens with what Simo calls the educator’s dilemma: AI makes it trivially easy to get answers, which removes the incentive for deep learning. His students take course content to an LLM, get a conflicting answer, and bring the contradiction back – without the baseline knowledge to judge which is correct. Katrin pushes back: practitioners doing real analytics work need to understand fundamentals like context windows and attention mechanisms. They land on a distinction – Simo’s concern applies to learners seeking quick answers, Katrin’s to practitioners maintaining context continuity across complex workflows.

The episode then pivots to agentic commerce. Simo draws a direct line from his data layer and server-side tracking expertise to the challenge of designing websites for AI agent access. Tag management systems have let organizations survive with poorly structured data for years. Agentic commerce breaks that – agents need structured data by design, not retroactive patches. Simo warns against over-optimizing for agents at the expense of human UX, and raises the unsolved measurement problem: how do you track agentic traffic when AI agents have no reason to identify themselves?

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