AEO Vs. SEO: Why the “AEO is just SEO” pundits are in for a surprise.

Not too long ago, during the initial days of AEO, practitioners found comfort in interpreting the AEO process as the well-understood SEO process with a couple more steps in the workflow. Their understanding wasn’t too off.

After all, LLMs were still learning. Beyond text, they were no good. They sucked at math. Images generated were unacceptably absurd. Information was invented (better known as hallucination). Emojis and em-dashes were easy tells. Quite often, and most importantly, the likes of ChatGPT apologetically confessed that their learning was stale and limited.

AI chat engines began competing with search engines when the former became more agentic in their approach. The agentic approach powered ChatGPT, Claude, and their likes, to journey to a greater extent to fulfil the curiosity of their users by connecting to the live internet through search engine tools. No more humble apologies or disclaimers. For the users, the search engines suddenly seemed like relics of yore. Why tolerate a list of links when the precise answer is just a click away?

AI chat engines began competing with search engines when the former became more agentic in their approach. The agentic approach powered ChatGPT, Claude, and their likes, to journey to a greater extent to fulfil the curiosity of their users by connecting to the live internet through search engine tools.

For us, the SEO practitioners, that was possibly the moment of reckoning. We realized that the same traffic trends, impression-click tensions, and rank tracking that kept us useful would become our primary source of anguish. Ranking high no longer meant scoring a click. So we worked out a new narrative: “Ranking high in search implies that your content will be used to craft the AI answer. It may not lead to site traffic, but it will surely build your brand.”

I personally saw quite a few client websites experience a sharp drop in organic traffic without a commensurate decline in online conversions. That saved us from a few difficult conversations.

For a moment I felt that the impact of the AI-pocalypse would be limited to changing our measurement framework. The core of SEO, the optimization strategy that enables us to rank above our competition, stays unchanged. 

I was possibly right at that moment. But the moment passed too soon…

u/trustmebro knocks off F500 SAAS Giant 

Any SEO worth their salt would tell you that overall SEO performance is derived from two basic pillars: Content (what do you have to say) and Authority (why should we listen to you). Over the years, search engines refined methods to score sites based on these two factors. As a result, good SEO is also about helping a site do well on both.

Building Authority is a time-consuming process, and a lot of it depends on how well a brand is known in the public domain. Google’s documentation lists the E-E-A-T process in great detail, and you definitely aren’t SEO enough if you haven’t slept through at least one webinar detailing the same (we have all been there). For a particular topic, a brand with demonstrated expertise, experience, authoritativeness, and trustworthiness will beat the rest. It’s a known truth in the search engine world. As a result, people are not concerned when a lacklustre blog by IBM ranks right on top for a competitive tech keyword. Even great content is unable to win on search without sufficient authority.

AI is a different battlefield altogether.

Here, a messy Reddit comment by u/trustmebro could easily trump a well-researched op-ed by a leading company and get cited. Authenticity wins over authority. And most importantly, there are few factors that determine citability of content and these diverge sharply from SEO best practices. 

At No Nirvana Digital, we use a 9-step workflow for AEO. The goal of this article is not to present the workflow because tbh quite a few of these steps are already well-discussed and understood. I don’t want to add to the noise. 

I will instead focus on two concepts that are less understood today and are extremely critical for successful AEO. At least four of the nine steps in our workflow stem directly from these two ideas. 

These are Information Gain and Content Chunking. 

Your Content’s Goal: Help AI guess better

Information gain unfortunately isn’t an easy concept to understand if you are like me and advanced math is not your forte. In fact, when the theory was first proposed in 1948 (that long ago!) by Claude Shannon, it was bitterly criticized. Engineers criticized it for being too mathematical, and mathematicians critiqued it for lacking academic rigor. Yet today, it’s suddenly all very important. Important enough for a leading AI engine to be named Claude. Interestingly, Claude helped me understand the paper.

When a user asks an AI engine a question, the engine essentially checks multiple sources (its own memory and external links) and tries to guess an answer. Yes, that’s right. It will guess 100% of the time. Sometimes it will be right and sometimes wrong (like any system that operates on guesses). The goal of the AI engine is to become a trusted source of information by guessing correctly most of the time. If your content can help AI guess better — congrats! You are rewarded with a citation.

Information gain is the underlying mathematics that helps AI understand whether a particular content source can help it guess better.

One Prompt, three Sources

Let’s understand with an example. 

Here’s a sample prompt: What is the average churn rate for B2B SaaS companies?

The first source that an AI engine will check is its own LLM memory. For this example, let’s imagine that the LLM memory provides a distribution of answers instead of something specific:

Answer	Probability
2-4%	15%
5-7%	35%
8-10%	30%
11-15%	15%
16%+	5%

Can AI answer the user? As a user, would you like it if the answer to your question was, “There’s a 35% chance the churn rate is 5-7%, and a 30% chance it’s 8-10%”? I wouldn’t.

So instead of answering at this stage (and possibly risking user churn), the AI would check external sources. Now let’s consider three different sources, each with an answer ready:

Source A: “SaaS churn rates vary by company size and segment. Most sources suggest somewhere between 5% and 15% annually depending on the market.” 

Source B: “Based on analysis of 600 B2B SaaS companies between 2021–2023, median annual net revenue churn was 6.2%, with 80% of companies falling between 4% and 9%.” 

Source C: “Most cited benchmarks put B2B SaaS churn at 5–7%, but these figures conflate gross and net revenue churn. When measured correctly as gross logo churn, median rates are closer to 10–13% for SMB-focused SaaS, and 4–6% for enterprise-focused. The 5–7% figure is a composite average that obscures this split.” 

Source A’s information doesn’t help AI answer any better. If we go with the initial estimate, there was an 80% chance (30%+35%+15%) that the churn rate was in the range of 5-15%. Source A reiterates the same. Not a good reason for the AI to cite Source A as the source of the final information.

Source B does a lot more. Firstly, it qualifies the answer with primary research. This gives AI more to work with. Then it narrows the 80%-probability range to 4-9%. It definitely stands a better chance. So a precise AI answer of 4-9% wouldn’t be too off.

Source C is the clear winner. It doesn’t answer the question directly. Instead, it lays out different contexts and gives a tailored answer for each scenario. The AI engine can pass this exact framing on to the user. In the way, Source C, also ends up controlling the narrative for the AI answer.

In technical terms, one can say that information gain is highest for Source C and lowest for Source A. Source B is somewhere in between.

Death of Chegg

Let’s consider another scenario where the prompt has a universal correct answer — what is the scientific formula of water?

The answer in the LLM memory is likely to be H₂O and not a probabilistic distribution. In such scenarios, the AI engine will quickly reply with an answer without checking external sources. There is no information to be gained, so why ask around?

If a site’s content has nothing more to offer than universal truths (e.g., EdTech platforms like Chegg) — it is likely to face a bleak future. In 2021, chegg.com had a peak valuation of $14.7 billion. Today it is valued at around $100 million, and the free-fall continues.

AEO punishes what SEO rewardeth

The good news is that AEO strategists need not start enrolling in university math courses. We can keep the math aside and still manage by ensuring that every piece of content we create has elements that make it stand out and unique. If you are writing an article on “Best CRM tools in 2026,” make sure that you have something that other blogs don’t. It could be a useful framework that could help a business quickly shortlist vendors, or it could be a price estimation table built using your own primary research. If you have nothing to add, then maybe you should skip the topic.

In the SEO world, that was definitely not the case. SEO strategists spent hours researching top-ranking blogs to figure out what the right coverage was for a particular keyword. Search algorithms used advanced NLP techniques to score content for relevance. Most of these techniques ended up looking for a set of topics or phrases. In the SEO world, nothing was too saturated. Even if there were a thousand blogs covering every detail one needs to choose a CRM, there was always room for another. Adding angles or details that were new wouldn’t get rewarded and could potentially backfire (punishment for confusing the algo). Fearing the wrath of the algo, good writing quietly got sidelined to make way for keyword-heavy information blobs. The internet got populated with articles that were overly informative and genuinely boring.

u/trustmebro may lack an enterprise content writer’s sophistication, but when they recommend CRMs or video editing tools to fellow Redditors, they bring to the table something that was long forgotten: authenticity. Their authentic take on why HubSpot worked better for them than Pipedrive (not my take, at No Nirvana Digital, we use an embarrassingly basic homemade CRM) was something that wasn’t covered by the countless “Best CRMs for 2026” listicles. For the LLMs, that is information gain!

I mentioned earlier in the article that AI answers a question by making educated guesses. I must admit that it’s an extremely simplistic take, but kind of sufficient for the current discussion. The method that AI uses for its education is RAG (Retrieval-Augmented Generation). Explaining how RAG works in detail is beyond the scope of this article. If I tried, I might end up pissing everybody off in Claude Shannon style.

We do, however, need to acknowledge how RAG works to understand Content Chunking: the next important idea behind successful AEO.

RAG and the riches

An LLM (Large Language Model) can be imagined as a large library of documents. How large? Think billions. For context, the New York Public Library holds just 56 million items. Every time you submit a prompt, the LLM is checking these billions of documents to find an answer. Even a programmatic search of such a huge repository could take an unacceptably long time. That would beat the very purpose of AI engines. After all, getting the answer quickly is as important as getting the answer.

So how does AI manage to scan billions of documents to fetch an answer in seconds? The answer lies in RAG. 

These documents are not stored as doc files or PDFs like we commonly understand. Instead, each of them is embedded into an array of numbers. In geek speak, these arrays are called vectors. When your prompt is submitted, it also gets embedded into a vector. Then your prompt vector is compared against the individual document vectors. Once that is done, the document with the highest match is selected as the one supposed to have the answer.

Once the document is retrieved, its content is used to generate the answer to your prompt.

I personally find the very idea of matching embedded numbers to get the answer fascinating. More so because the underlying maths eludes me. It definitely works most of the time, or else AI would have been limited to niche academic circles.

Chunking 

What are these documents that together make an LLM so wise? Would each play by Shakespeare be a separate document, or would all of them together be a part of the same document? Is it possible that a single blog is split into multiple documents?

These questions led me to the concept of content chunking.

In the RAG process, the embedded prompt is matched with embedded documents. The very premise that the highest match score leads to finding the right answer is probabilistic (fancy guesswork) and prone to error. The error will be higher if a document covers too many things. The corresponding embedded version will not correctly reflect the actual content it contains. Since AI engines are getting better by the day, it can be assumed that the embedding process ensures that the sanctity of the documents is preserved, which again would be possible if the document covers something very specific.

A blog, a play, or the constitution of a country is anything but specific. So to connect the dots, it’s only natural that the original content would be “Chunked” into sub-documents (each containing specific coverage). The chunking process is the step that precedes embedding.

u/trustmebro Vs You 

Your article on “Top 10 CRM in 2026” could get chunked into five documents, ten documents, or maybe more. On the other hand, u/trustmebro’s lazy rant is possibly going in as a single chunk. Reddit comments are usually direct and to the point since fellow Redditors have notoriously low attention spans and appetite for bullshit.

If you are striving for AI citation, content chunking may or may not work in your favor. Let’s consider a simple example:

You wrote an article on Top 10 CRM in 2026. The article spanned 3,000 words and had four H2s (denoting four sections). For ease of understanding, let’s assume that the article gets chunked into four different documents, each carrying a single sub-section. Sub-section 2 contains your entire list (with products listed as H3s). Now whether or not your article is considered at all for any related prompt depends strongly on Sub-section 2 staying together in a single document. What if the sub-section covered 80% of the article word count (2,400 words)? There is a chance that it gets split and stored as separate documents. By the way, once chunked, the chunks will function entirely independently of each other.

When a user prompts “best CRM for my business,” the battle for AI’s attention is not between individual articles but between individual chunks (some from your article and some from elsewhere, including Reddit comments by u/trustmebro).

Can you win ?

AEO = Optimizing Chunks

When it comes to SEO, the whole article mattered. In the case of AEO, it’s a battle of chunks. Even if we keep information gain aside, the battles are different. This is exactly the bit that most SEO pundits are refusing to come to terms with.

If you want to do AEO the proper way, you need to think in chunks. Unfortunately, there is no documented way that explains how the chunking happens. We can only guess and try tactics to keep our content safe.

In No Nirvana Digital’s nine-step workflow, there is no single step dedicated to chunking optimization. Instead, at least three steps talk about tactics that can help your chunks retain context when chunked.

Here’s an example: we focus a great deal on having elaborate entity schemas. Now a lot of SEO experts take pride in dismissing schemas as a non-ranking factor. From our tests, we found that having a schema is equivalent to passing on a “this is what I am about” cheat sheet to AI. The great part is that a schema is a single block, and thus it’s highly probable that it will go in as a single chunk.

Having a TLDR section or a Key Takeaways section also helps you pass on full context about your article to the AI engines (as these are likely to go in unchunked). Most SEO practitioners agree that adding a TLDR helps AEO. Few understand why. 

So to sum it all up

The goal of this article was definitely not to belittle SEO practitioners. I am one too, and believe it or not, SEO is not dead. Yet. Even today, we do more SEO work than AEO.

The point of this article was solely to point out that both tracks are pretty different. It’s still early days.

Should blogs start sounding like Reddit comments? Should we have a separate content plan for SEO and AEO? Can an article be optimized for both SEO and AEO?

A lot of testing ahead. It’s certainly going to be interesting and busy for content strategists. Meanwhile, somewhere in a nearby universe, u/trustmebro is busy debating whether Jennifer Aniston lied about Botox.