Learning from the Past: SEO's Lessons for AIO
The current evolution in marketing reminds me of the early days of SEO.
In the beginning, Google encouraged best-practice SEO—well-architected websites, relevant content, and strong backlinks. However, as black hat techniques emerged (gaming the algorithm for quick rank increases), Google drastically changed its approach. They deliberately gave less and less information about their ranking engine to prevent manipulation.
A major shift came when large brands complained that startups were gaming the system to outrank them for branded terms. Google responded with algorithmic changes that prioritized legacy brands and penalized the system-gamers. They even removed the ability for users to see organic keywords in Google Analytics, making optimization much harder and giving rise to third-party tools like SEMrush.
The Parallel to AIO
I see this exact dynamic playing out now with AIO (AI Optimization).
Historically, many large language models (LLMs) have used platforms like Reddit and Quora as part of their learning engine. This led to black hat AIO techniques where content was deliberately flooded onto these platforms, hoping it would be ingested by the LLMs and influence their answer engines.
However, just as with SEO, the LLMs are countering this spam. Recent reports indicate that some major LLMs are already using less data from sources like Reddit due to the proliferation of low-quality spam.
The Case for White Hat AIO
If you are an AIO practitioner, you must recognize that the LLMs will become:
More Secretive: They will grow quieter about their ranking factors and the channels they use for data acquisition. In fact, LLMs are already more secretive than Google ever was in the beginning. Not surprising as many LLM engineers are former Google / Bing engineers who know building more open tools will cause more issues for them without a clear benefit.
Less Accommodating: They likely won’t offer a suite of tools that makes it easy to reverse-engineer their ranking.
The successful path is the White Hat approach, focusing on high-quality signals that the answer engines will reward:
Great Content Architecture: Ensuring your site is technically developed to be easily crawled and provides the right metadata for LLMs.
Credible External Signals: Getting content published in high-credibility publications and focusing on listicle placement—since LLMs love high-quality sources that rank vendors.
Contextual Clarity: Brands must go out of their way to clearly communicate their unique value proposition versus competitors to the answer engines.
If a vendor proposes an AIO idea that feels like a hack, avoid it. Black hat techniques may offer a short-term bump, but they inevitably lead to penalties, de-listings, and years of recovery efforts. White Hat AIO offers slower, but consistent, growth that pays significant long-term dividends.