Help users identify unique distribution advantages and master the lifecycle of acquisition channels to build a sustainable engine for growth and retention.
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Help users design and execute a modern SEO strategy that balances technical site health with programmatic content scaling and AI-driven discovery.
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Turn organic search into a scalable acquisition engine using programmatic data and systematic experimentation.
Help the user with seo growth strategy using insights from 9 guests and posts across Lenny's Podcast and Newsletter.
Eli Schwartz: "And now you're in the mid-funnel and that's where SEO begins to matter. So the reason why I think this disruption is so big is because the journey changes, the discovery changes. So whereas before if you were a travel site and you were able to rank on best beach vacation within two hours of the United States, that's your ranking result."
As AI summaries satisfy high-level discovery, businesses must focus on deeper research queries where users need detailed comparisons and specific data.
Eli Schwartz: "Think of SEO as a product. The product managers are the people that should be thinking about this SEO question because it's a product question. Product people need to think about how do we position this to the user that is not going to find out about this from a social channel, that's not going to be attracted by an ad?"
Search visibility should be led by product managers and integrated into the core product positioning rather than treated as a separate content exercise.
Eli Schwartz: "Product people need to think about how do we position this to the user that is not going to find out about this from a social channel, that's not going to be attracted by an ad? This is a user that's doing their own self-discovery journey. If you can't answer the question about what is it that someone's going to do a search on, then don't do SEO."
Before investing, verify that your specific target audience actually uses search for discovery and that you can identify their exact intent.
Ethan Smith: "In order to win something like what's the best website builder? At Google, they would win if their blue link showed up first. But that's not the case in the LLM, because the LLM is summarizing many citations, and so you need to get mentioned as many times as possible."
Success in the age of AI search depends on being mentioned across multiple authoritative sources like Reddit and YouTube to influence LLM summaries.
Ethan Smith: "Significantly more valuable. Webflow saw a 6X conversion rate difference between LLM traffic and Google Search traffic."
Traffic from AI engines often converts at a higher rate because users arrive deeper into their research journey compared to traditional search.
From "Content-driven growth": "Research topics where searchers are underserved, and create content that delivers compelling, valuable, and differentiated value to those searchers."
Building a successful editorial program requires finding specific niches where existing search results fail to provide high-quality or differentiated value.
From "How to run SEO experiments": "When you’re running an SEO experiment you’re testing on the page level. Thus, you need to have an experimentation system that can do your bucketing on the page level."
SEO experiments must group by URL rather than user to ensure search engine crawlers receive a consistent experience for every crawl.
From "Winning at SEO": "Instead, you want to create pages that capture a specific and valuable user intent. SEO isn’t about capturing the top keywords — it’s about capturing the long-tail of keywords. Sure, you won’t catch the white whale of “flowers”, but if you can capture all of the keywords that follow a common formula such as “{flower type} for sale in {location}”, you’re going to be capturing valuable, high converting traffic and see a much higher ROI."
Target long-tail keyword formulas where your proprietary business data provides a distinct advantage that incumbents cannot easily replicate.
See references/artifacts.md for the full list with details.
For all 35 sourced insights from 9 guests, see references/guest-insights.md
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Help users identify unique distribution advantages and master the lifecycle of acquisition channels to build a sustainable engine for growth and retention.
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Help users build functional product prototypes from natural language or visual mocks using AI coding tools. This skill enables product leaders to bypass engineering bottlenecks and validate ideas through hands-on building.
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Help users build robust infrastructure for measuring, monitoring, and iterating on AI product performance using human, code-based, and LLM-as-a-judge methodologies.
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Help users interact with probabilistic models by designing interfaces that manage fluidity, intent, and agency while maintaining trust and control.
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Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.
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Help users convert massive volumes of qualitative and quantitative feedback into high-confidence product decisions through rigorous synthesis and internal immersion.
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