Contrary to fears of consolidation, AI agents are adept at finding small, specialized merchants that perfectly match complex user queries. This improved discoverability can help niche brands compete with larger players who previously dominated search and advertising channels.

While replacing Google search was an early goal, the most tangible and lucrative product-market fit for foundation models is in the software development lifecycle. This vertical is becoming the core battleground for enterprise revenue.

AI agents, unlike humans, need complete and exhaustive information (thousands of results) and use complex, controllable queries. A search engine built for human keyword simplicity and limited results will fail to serve them effectively.

Traditional SEO focuses on a limited set of keywords. AEO requires tracking a vast number of specific questions (prompts) that different customer personas ask AI engines, reflecting their unique challenges and buyer journey stage. This is a fundamental shift in content strategy.

Unlike passive consumption apps, where getting many users to try a feature once is key, high-intent products like Google Search measure success by user intensity. The critical question is not "how many people used it?" but "are individual users using it more intensely over time?"

OpenAI explicitly focuses on extreme user segments. Power users are particularly valuable because they push the empirical limits of the technology, effectively performing product discovery on OpenAI's behalf and revealing what's possible long before the core team can.

With model improvements showing diminishing returns and competitors like Google achieving parity, OpenAI is shifting focus to enterprise applications. The strategic battleground is moving from foundational model superiority to practical, valuable productization for businesses.

Users now ask AI models highly specific, long-form questions, not short search terms. HubSpot's CEO advises creating more detailed content with better citations and case studies to provide authoritative answers for these complex queries and remain visible.

Perplexity leverages its user-facing product to improve its core search technology. When the LLM reasons through search snippets and selects which ones to cite in an answer, that selection process acts as a powerful signal to refine and improve the underlying search ranking algorithm for future queries.