Jay Bhatti has 20+ years of experience across SEO, NLP, machine learning, and large-scale data systems. He was an early founder in semantic search (Spock) and spent nearly five years at Microsoft. He writes on AI, search, and the future of digital commerce at SEOisDead.com.
Let me tell you what AI Marketing is not.
It is not adding an AI chatbot to your website.
It is not using ChatGPT to write your blog posts faster.
It is not putting ‘AI-powered’ in your agency’s pitch deck.
It is not hiring a ‘prompt engineer.’
I’ve spent 20 years building at the intersection of search, NLP, and machine learning. I was building semantic search systems before most marketers knew what an embedding was. I’ve watched the agency world absorb every technology shift by repackaging their existing services with new vocabulary and calling it transformation.
What’s happening right now is not a vocabulary update. It is a systems-level change in how consumer discovery works. And most of the industry is getting it dangerously wrong.
The Two Things AIM Actually Is
AIM — AI Marketing — has two distinct disciplines. Understanding the difference between them is the starting point for everything else.
1. Performance AI Marketing (Paid AIM)
Traditional paid search matches ads to declared intent — a user types a query and you bid on the keyword. Traditional paid social matches ads to behavioral profiles — you define an audience and buy impressions against it.
Paid AIM does something more sophisticated than either. It matches ads to the live conversational context of an active LLM session — the specific topic, intent layer, and semantic content of what a user is actually discussing in real time.
I’ve had early access to OpenAI’s self-serve ads beta. Here is what the targeting actually looks like: instead of a keyword list, you feed the system conversational prompts. Leading statements like: ‘They’re researching the best running recovery tools after a marathon.’ ‘They’re comparing home security systems for a first-time homeowner.’
The LLM’s core engine handles the semantic matching. You provide natural language intent. The system maps it to user conversations in real time. It is the highest-resolution targeting mechanism ever built — because it operates at the level of what someone is actively thinking, not what demographic they belong to.
2. Organic AIM — AEO, GEO, AIO
The organic side of AIM is what most people mean when they say ‘AEO’ or ‘GEO’ — the discipline of structuring your brand’s digital presence so that LLMs organically cite, trust, and recommend you when users ask relevant questions.
Here is where I see the most dangerous misconception. Agencies are treating AEO as SEO with different vocabulary. They’re running content audits, producing more blog posts, and calling it AI optimization.
That is exactly wrong. And here’s why.
Why ‘SEO with AI Labels’ Will Fail
LLMs don’t read your content the way Google’s crawler reads it. They are not indexing pages and ranking links. They are:
Breaking text into tokens and building mathematical vector representations
Evaluating the semantic coherence and entity clarity of what you publish
Cross-referencing your claims against the third-party sources they’ve already indexed
Building a probabilistic model of whether your brand is a trusted authority in its category
This means the hook that pulls a human reader into your article is invisible to the process that determines whether you get cited. The keyword density that moved your rankings is irrelevant to the embedding that represents your brand’s authority. The backlink profile that built your domain authority is necessary but insufficient — because the LLM also needs to be able to parse, understand, and trust what’s on the page it’s crawling.
The SEO agency that built you a beautiful content calendar and a clean backlink profile has done good work. But that work optimized for a system that is no longer the primary driver of consumer discovery. The question is whether they know how to optimize for the system that replaced it.
The Four Things That Actually Determine AI Visibility
Based on 20+ years of systems-building and direct experience with LLM platforms, here is what actually determines whether an LLM cites your brand:
Does the LLM have a clear, consistent, well-sourced understanding of what your brand is? This means Wikipedia presence, schema.org markup, consistent NAP data, and authoritative third-party coverage — not just content volume.: Entity authority
Can AI crawlers parse your content cleanly? Schema markup for products, FAQs, organizations, and people. JSON-LD implementation. Clean HTML over PDF-heavy archives.: Structured data quality
Does your content answer the follow-up questions, not just the headline? LLMs are looking for sources that can serve as reference documents, not marketing copy.: Semantic depth
Does the wider web corroborate what you say about yourself? Wikipedia proximity, tier-1 press coverage, academic citations, and review platform presence all signal trust to LLMs in ways that self-published content cannot.: Third-party corroboration
None of these are new ideas. What is new is that they now determine your discoverability in the channel where your next buyer is making decisions.
The Paid and Organic Relationship
One thing I want to be clear about: paid AIM and organic AIM are not in competition. They compound each other.
Think of it this way. When a user asks ChatGPT for a product recommendation and your brand appears organically in the AI’s answer, that organic recommendation creates the trust signal. Your paid ad placement in the same response captures the conversion. The organic result is the Rotten Tomatoes score. The paid unit is the ticket button.
The brands that will win the AI Marketing era will do both. The ones that do only paid will see performance erode as they compete against brands with stronger organic authority. The ones that do only organic will leave direct conversion on the table.
Next week: Mitch Stoller on the four declarations that every marketer must now accept.