Microsoft Got AEO and GEO Half Right. Here's What They Missed.

The marketing world is buzzing about Microsoft's new playbook on AEO and GEO. The 15-page PDF is being shared, cited, and treated as a blueprint for AI-era visibility.

There's just one problem: it's incomplete. And if you build your strategy on an incomplete foundation, you're going to waste time, money, and opportunity.

Microsoft scratched at something real. But they couldn't—or didn't—fully realize what AEO and GEO actually mean, how they differ, and why both need something above them to make sense.

Let me fix that.

The Microsoft Framing (And Why It Falls Apart)

In their January 2026 playbook "From Discovery to Influence: A Guide to AEO and GEO," Microsoft defines the terms like this:

AEO (Answer Engine Optimization): "Optimizes content for AI agents and assistants so they can find, understand, and present answers effectively."

GEO (Generative Engine Optimization): "Optimizes content for generative AI search environments to make it discoverable, trustworthy, and authoritative."

Sounds reasonable. But then look at their examples:

AEO example: "Lightweight, packable waterproof rain jacket with stuff pocket, ventilated seams and reflective piping"

GEO example: "Best-rated waterproof jacket by Outdoor magazine, no-hassle returns allowed for 180 days, three year warranty, 4.8 star rating"

See the problem?

That's not two different optimization strategies for two different system types. That's product specs versus trust signals. Both matter to every AI system. Perplexity absolutely cares about ratings and warranties when it retrieves information. ChatGPT absolutely benefits from detailed specs when it generates a response.

Microsoft is conflating content type with system architecture. They're telling you what to include without explaining which systems need which approach and—critically—where that content needs to live.

That's the muddle. And it's spreading because everyone's treating this playbook as gospel.

The Real Distinction Microsoft Missed

Here's what AEO and GEO actually align to—and it has nothing to do with "specs vs. trust signals."

The distinction is mechanical. It's about how different AI systems retrieve and surface information.

AEO applies to retrieval-based systems.

These are AI platforms that pull information in real-time from live sources. They search, fetch, and cite. Think Google AI Overviews, Perplexity, and Bing Copilot—any system using RAG (Retrieval-Augmented Generation).

When someone asks Perplexity a question, it searches the web, pulls relevant sources, and synthesizes an answer with citations. Your visibility depends on being the source that gets retrieved.

AEO optimization means structured data, freshness signals, technical accessibility, and authority markers that retrieval systems prioritize when deciding what to pull. This is closer to traditional SEO thinking because there's still a fetch-and-cite mechanism at play.

GEO applies to training-based systems.

These are AI platforms that draw primarily from parametric knowledge—information baked into the model during training. Think ChatGPT (without web browsing enabled), Claude, and Gemini.

When someone asks ChatGPT a question without web search, it's not fetching anything. It's generating a response based on patterns learned during training. Your visibility depends on having influenced what the model learned—or being authoritative enough that the model "knows" you.

GEO optimization means presence in training corpora, entity authority, and high-authority citations that shape how models understand your brand, product, or category.

This is the real distinction. Retrieval vs. training. Live search vs. learned knowledge. Not specs vs. credibility.

The Content AND Placement Problem

Here's where Microsoft's playbook really falls short.

Their entire framework focuses on content structure: what fields to fill in, what schema to implement, what data to include in your product feeds. That's valuable, but it's only half the equation.

It all comes down to content, right? In either case, those details need to be available in some digital text form. But Microsoft treats "digital text form" as synonymous with "your website and your feeds."

That's a critical blind spot.

For retrieval-based systems (AEO), your content needs to be on your site AND structured for retrieval AND fresh AND technically accessible. Microsoft covers this reasonably well.

But for training-based systems (GEO), content on your site isn't enough. LLMs don't learn from your product feed. They learn from the broader web—Wikipedia, Reddit, news sites, industry publications, research papers, and countless other sources that make it into training data.

Microsoft's GEO advice is mostly about putting trust signals on your own site: reviews, ratings, certifications, schema markup. That's fine for retrieval systems that will fetch your page. But it doesn't address the fundamental GEO question: How do you influence what models learn about your brand when they're trained on data from across the web?

Real GEO strategy requires thinking about placement:

• Is your brand mentioned on Wikipedia with accurate information?

• Are you being discussed on Reddit, Quora, and industry forums?

• Are you cited in authoritative publications that likely appear in training data?

• Do high-authority third-party sources reference your brand accurately?

Microsoft's playbook treats your site as the center of gravity. But for training-based systems, the center of gravity is the entire corpus of text that models learn from. That's a fundamentally different optimization challenge.

The "Engine" Problem

While we're here, let's address something that's been bothering me since these terms emerged.

AEO. GEO. Answer Engine. Generative Engine.

LLMs are not engines.

Search engines crawl, index, and rank. They're mechanical retrieval systems. The "engine" metaphor made sense because it described a process: input query, turn crank, output results.

Large language models don't work that way. They reason. They generate. They synthesize. Calling them "engines" imports a mental model that doesn't fit—and it's causing people to approach AI optimization with the wrong framework entirely.

When you think "engine," you think about rankings and positions. You think about keywords and backlinks. You think about the mechanics of retrieval.

But AI systems—especially training-based ones—don't rank your content. They learn from it. They don't retrieve your page. They absorb your information and reconstruct it in novel ways.

The terminology matters because it shapes strategy. And "engine" is the wrong shape.

I won't belabor this point. But it's worth noting that the very terms AEO and GEO carry conceptual baggage that limits how people think about the problem.

Why AIO Rules Them All

So we have AEO for retrieval-based systems and GEO for training-based systems. Two different optimization approaches for two different AI architectures. Two different strategies for content AND placement.

But here's the reality: brands don't get to choose which systems their customers use.

When someone researches your product, they might ask Perplexity (retrieval), ChatGPT with web search (hybrid), ChatGPT without web search (training), Google AI Overview (retrieval), or Claude (training).

If you've only optimized for one pathway, you're invisible in the other.

This is why AI Optimization—AIO—is the necessary umbrella.

AIO isn't a replacement for AEO and GEO. It's the framework that contains them. It's the recognition that brands need a unified strategy for AI visibility that accounts for both retrieval-based and training-based systems, and that addresses both content and placement.

Under AIO:

• AEO becomes your retrieval strategy (content + placement for systems that fetch in real-time)

• GEO becomes your training strategy (content + placement for systems that learn from the web)

• Both roll up into a coherent approach to being visible wherever AI surfaces information about your category

[INSERT IMAGE: AIO Framework Diagram]

Microsoft's playbook talks about "three data pathways"—feeds, crawled data, and offsite data. That's fine as far as it goes. But it doesn't give you a framework for thinking about which pathway matters when, how to prioritize across fundamentally different AI architectures, or how to build presence in the sources that training-based systems actually learn from.

AIO does.

What This Means For Your Strategy

If you're building an AI visibility strategy based on Microsoft's framing, you're going to end up with a well-structured website that training-based AI systems may never have learned about. You'll have great product feeds that don't address where ChatGPT gets its information.

Here's what you should do instead:

1. Audit your visibility across both system types.

Check retrieval-based platforms: Are you showing up in Perplexity? Google AI Overviews? When these systems search for your category, do they find and cite you?

Check training-based platforms: Ask ChatGPT and Claude about your brand, product, or category. What do they "know"? Is it accurate? Are you even present? If they're wrong or silent, no amount of schema markup on your site will fix that.

2. Map your optimization efforts to system architecture.

For retrieval (AEO): Focus on structured data, schema markup, content freshness, and the authority signals that RAG systems use to select sources. This is about being retrievable from your own properties.

For training (GEO): Focus on entity authority, Wikipedia presence, high-authority citations, Reddit and forum presence, and ensuring your information exists in the sources that models actually learn from. This is about being learnable from across the web.

3. Address both content AND placement.

Don't just optimize what you say. Optimize where you say it. For AEO, that means your site and feeds. For GEO, that means the entire ecosystem of sources that inform how models understand your category.

4. Build a unified AIO strategy.

Don't treat these as separate workstreams with separate teams. The content that makes you retrievable often overlaps with the content that makes you learnable. But the emphasis differs, and you need to understand where to invest based on where your customers are discovering information.

The Stakes

Microsoft is right about one thing: the goal is no longer traffic. It's influence.

AI systems are becoming the primary interface between brands and customers. When someone asks an AI assistant for a recommendation, that AI doesn't show them a list of blue links to click. It tells them what to buy, who to trust, which option is best.

If you're not visible to AI systems—both retrieval-based and training-based—you're not in the consideration set. You're not being recommended. You're invisible.

The brands that figure this out now will be the benchmark everyone else is catching up to. The brands that build on incomplete foundations—that treat AEO and GEO as content-only problems—will wonder why their AI visibility isn't improving despite all their "optimization" efforts.

Microsoft got it half right. They identified that something new is needed. But they couldn't articulate the real distinction between system types, and they missed the critical role of placement in training-based optimization.

AIO is the framework that fills those gaps. And it's time the industry started using it.