A Breakdown Of Microsoft’s Guide To AEO & GEO via @sejournal, @martinibuster

Microsoft published a sixteen page explainer guide about optimizing for AI search and chat. While many of the suggestions can be classified as SEO, some of the other tips relate exclusively to AI search surfaces. Here are the most helpful takeaways.

What AEO and GEO Are And Why They Matter

Microsoft explains that AI search surfaces have created an evolution from “ranking for clicks” to “being understood and recommended by AI.” Traditional SEO still provides a foundation for being cited in AI, but AEO and GEO determine whether content gets surfaced inside AI-driven experiences.

Here is how Microsoft distinguishes AEO and GEO. The first thing to notice is that they define AEO as Agentic Engine Optimization. That’s different from Answer Engine Optimization, which is how AEO is commonly understood.

• AEO (Answer/Agentic Engine Optimization) focuses on optimizing content and product information easy for AI assistants and agents to retrieve, interpret, and present as direct answers.
• GEO (Generative Engine Optimization) focuses on making your content discoverable and persuasive inside generative AI systems by increasing clarity, trustworthiness, and authoritativeness.

Microsoft views AEO and GEO as not limited to marketing, but multiple teams within an organization.

The guide says:

“This shift impacts every part of the organization. Marketing teams must rethink brand differentiation, growth teams need to adapt to AI-driven journeys, ecommerce teams must measure success differently, data teams must surface richer signals, and engineering teams must ensure systems are AI-readable and reliable.”

AI shopping is not one channel, it’s really a set of overlapping systems.

Microsoft describes AI shopping as three overlapping consumer touchpoints:

1. AI browsers that interpret what’s on a page and surface context while users browse.
2. AI assistants that answer questions and guide decisions in conversation.
3. AI agents that can take actions, like navigating, selecting options, and completing purchases.

The AI touchpoint matters less than whether the system can access accurate, structured, and trustworthy product information.

SEO Still Plays A Role

Microsoft’s guide says that the AEO and GEO competition changes from discovery over to influence. SEO is still important, but it is no longer the whole game.

The new competition is about influencing the AI recommendation layer, not just showing up in rankings.

Microsoft describes it like this:

• SEO helps the product get found.
• AEO helps the AI explain it clearly.
• GEO helps the AI trust it and recommend it.

Microsoft explains:

“Competition is shifting from discovery to influence (SEO to AEO/GEO).

If SEO focused on driving clicks, AEO is focused on driving clarity with enriched, real-time data, while GEO focuses on building credibility and trust so AI systems can confidently recommend your products.

SEO remains foundational, but winning in AI-powered shopping experiences requires helping AI systems understand not just what your product is, but why it should be chosen.”

How AI Systems Decide What To Recommend

Microsoft explains how an AI assistant, in this case Copilot, handles a user’s request. When a user asks for a recommendation, the AI assistant goes into a reasoning phase where the query is broken down using a combination of web and product feed data.

The web data provides:

• “General knowledge
• Category understanding
• Your brand positioning”

Feed data provides:

• “Current prices
• Availability
• Key specs”

The AI assistant may, based on the feed data, choose to surface the product with the lowest price that is also in stock.  When the user clicks through to the website, the AI Assistant scans the page for information that provides context.

Microsoft lists these as examples of context:

• Detailed reviews
• Video that explain the product
• Current promotions
• Delivery estimates

The agent aggregates this information and provides guidance on what it discovered in terms of the context of the product (delivery times, etc.).

Microsoft brings it all together like this:

First, there’s crawled data:
The information AI systems learned during training and retrieve from indexed web pages, which shapes your brand’s baseline perception and provides grounding for AI responses, including your product
categories, reputation and market position.

Second, there’s product feeds and APIs:
The structured data you actively push to AI platforms, giving you control over how your products are represented in comparisons and recommendations. Feeds provide accuracy, details and consistency.

Third, there’s live website data:
The real-time information AI agents see when they visit your actual site, from rich media and user reviews to dynamic pricing and transaction capabilities. Each data source plays a distinct role in the shopping journey — traditional SEO remains essential because AI systems perform real-time web searches frequently throughout the shopping journey, not just at purchase time, and your site must rank well to be discovered, evaluated, and recommended.

Microsoft recommends A Three-Part Action Plan

Strategy 1: Technical Foundations

The core idea for this strategy is that your product catalog must be machine-readable, consistent everywhere, and up to date.

Key actions:

• Use structured data (schema) for products, offers, reviews, lists, FAQs, and brand.
• Include dynamic fields like pricing and availability.
• Keep feed data and on-page structured data aligned with what users actually see.
• Avoid mismatches between visible content and what is served to crawlers.

Strategy 2: Optimize Content For Intent And Clarity

This strategy is about optimizing product content so that it answers typical user questions and is easy for AI to reuse.

Key actions:

• Write product descriptions that start with benefits and real use-case value.
• Use headings and phrasing that match how people ask questions.

Add modular content blocks:

• FAQs
• specs
• key features
• comparisons

Add Contextual Information

• Support multi-modal interpretation (good alt text, transcripts for video content, structured image metadata).
• Add complementary product context (pairings, bundles, “goes well with”).

Strategy 3: Trust Signals (Authority And Credibility)

The takeaway for this strategy is that AI assistants and agents prioritize content that looks verified and reputable.

Key actions:

• Strengthen review credibility (verified reviews, strong volumes, clear sentiment).
• Reinforce brand authority through real-world signals (press, certifications, partnerships).
• Keep claims grounded and consistent to avoid trust degradation.
• Use structured data to clarify legitimacy and identity.

Microsoft explains it like this:

“AI assistants prioritize content from sources they can trust. Signals such as verified reviews, review volume, and clear sentiment help establish credibility and influence recommendations.

Brand authority is reinforced through consistent identity, real-world validation such as press coverage, certifications, and partnerships, and the use of structured data to clearly define brand entities.

Claims should be factual, consistent, and verifiable, as exaggerated or misleading information can reduce trust and limit visibility in AI-powered experiences”

Takeaways

AI search changes the goal from winning rankings to earning recommendations. SEO still matters, but AEO and GEO determine how well content is interpreted, explained, and chosen inside AI assistants and agents.

AI shopping is not a single channel but an ecosystem of assistants, browsers, and agents that rely on authoritative signals across crawled content, structured feeds, and live site experiences. The brands that win are the ones with consistent, machine-readable data, and clear content that contains useful contextual information that can be easily summarized.

Microsoft published a blog post that is accompanied by a link to the downloadable explainer guide: From Discovery to Influence: A Guide to AEO and GEO.

Featured Image by Shutterstock/Kues

More Sites Blocking LLM Crawling – Could That Backfire On GEO? via @sejournal, @martinibuster

Hostinger released an analysis showing that businesses are blocking AI systems used to train large language models while allowing AI assistants to continue to read and summarize more websites. The company examined 66.7 billion bot interactions across 5 million websites and found that AI assistant crawlers used by tools such as ChatGPT now reach more sites even as companies restrict other forms of AI access.

Hostinger Analysis

Hostinger is a web host and also a no-code, AI agent-driven platform for building online businesses. The company said it analyzed anonymized website logs to measure how verified crawlers access sites at scale, allowing it to compare changes in how search engines and AI systems retrieve online content.

The analysis they published shows that AI assistant crawlers expanded their reach across websites during a five-month period. Data was collected during three six-day windows in June, August, and November 2025.

OpenAI’s SearchBot increased coverage from 52 percent to 68 percent of sites, while Applebot (which indexes content for powering Apple’s search features) doubled from 17 percent to 34 percent. During the same period, traditional search crawlers essentially remained constant. The data indicates that AI assistants are adding a new layer to how information reaches users rather than replacing search engines outright.

At the same time, the data shows that companies sharply reduced access for AI training crawlers. OpenAI’s GPTBot dropped from access on 84 percent of websites in August to 12 percent by November. Meta’s ExternalAgent dropped from 60 percent coverage to 41 percent website coverage. These crawlers collect data over time to improve AI models and update their Parametric Knowledge but many businesses are blocking them, either to limit data use or for fear of copyright infringement issues.

Parametric Knowledge

Parametric Knowledge, also known as Parametric Memory, is the information that is “hard-coded” into the model during training. It is called “parametric” because the knowledge is stored in the model’s parameters (the weights). Parametric Knowledge is long-term memory about entities, for example, people, things, and companies.

When a person asks an LLM a question, the LLM may recognize an entity like a business and then retrieve the the associated vectors (facts) that it learned during training. So, when a business or company blocks a training bot from their website, they’re keeping the LLM from knowing anything about them, which might not be the best thing for an organization that’s concerned about AI visibility.

Allowing an AI training bot to crawl a company website enables that company to exercise some control over what the LLM knows about it, including what it does, branding, whatever is in the About Us, and enables the LLM to know about the products or services offered. An informational site may benefit from being cited for answers.

Businesses Are Opting Out Of Parametric Knowledge

Hostinger’s analysis shows that businesses are “aggressively” blocking AI training crawlers. While Hostinger’s research doesn’t mention this, the effect of blocking AI training bots is that businesses are essentially opting out of LLM’s parametric knowledge because the LLM is prevented from learning directly from first-party content during training, removing the site’s ability to tell its own story and forcing the LLM to rely on third-party data or knowledge graphs.

Hostinger’s research shows:

“Based on tracking 66.7 billion bot interactions across 5 million websites, Hostinger uncovered a significant paradox:

Companies are aggressively blocking AI training bots, the systems that scrape content to build AI models. OpenAI’s GPTBot dropped from 84% to 12% of websites in three months.

However, AI assistant crawlers, the technology that ChatGPT, Apple, etc. use to answer customer questions, are expanding rapidly. OpenAI’s SearchBot grew from 52% to 68% of sites; Applebot doubled to 34%.”

A recent post on Reddit shows how blocking LLM access to content is normalized and understood as something to protect intellectual property (IP).

The post starts with an initial question asking how to block AIs:

“I want to make sure my site is continued to be indexed in Google Search, but do not want Gemini, ChatGPT, or others to scrape and use my content.

What’s the best way to do this?”

Screenshot Of A Reddit Conversation

Later on in that thread someone asked if they’re blocking LLMs to protect their intellectual property and the original poster responded affirmatively, that that was the reason.

The person who started the discussion responded:

“We publish unique content that doesn’t really exist elsewhere. LLMs often learn about things in this tiny niche from us. So we need Google traffic but not LLMs.”

That may be a valid reason. A site that publishes unique instructional information about a software product that does not exist elsewhere may want to block an LLM from indexing their content because if they don’t then the LLM will be able to answer questions while also removing the need to visit the site.

But for other sites with less unique content, like a product review and comparison site or an ecommerce site, it might not be the best strategy to block LLMs from adding information about those sites into their parametric memory.

Brand Messaging Is Lost To LLMs

As AI assistants answer questions directly, users may receive information without needing to visit a website. This can reduce direct traffic and limit the reach of a business’s pricing details, product context, and brand messaging. It’s possible that the customer journey ends inside the AI interface and the businesses that block LLMs from acquiring knowledge about their companies and offerings are essentially relying on the search crawler and search index to fill that gap (and maybe that works?).

The increasing use of AI assistants affects marketing and extends into revenue forecasting. When AI systems summarize offers and recommendations, companies that block LLMs have less control over how pricing and value appear. Advertising efforts lose visibility earlier in the decision process, and ecommerce attribution becomes harder when purchases follow AI-generated answers rather than direct site visits.

According to Hostinger, some organizations are becoming more selective about what which content is available to AI, especially AI assistants.

Tomas Rasymas, Head of AI at Hostinger commented:

“With AI assistants increasingly answering questions directly, the web is shifting from a click-driven model to an agent-mediated one. The real risk for businesses isn’t AI access itself, but losing control over how pricing, positioning, and value are presented when decisions are made.”

Takeaway

Blocking LLMs from using website data for training is not really the default position to take, even though many people feel real anger and annoyance of the idea of an LLM training on their content.  It may be useful to take a more considered response that weighs the benefits versus the disadvantages and to also consider whether those disadvantages are real or perceived.

Featured Image by Shutterstock/Lightspring

A Little Clarity On SEO, GEO, And AEO via @sejournal, @martinibuster

The debate about AEO/GEO centers on whether it’s a subset of SEO, a standalone discipline, or just standard SEO. Deciding on where to plant a flag is difficult because every argument makes a solid case. There’s no doubt that change is underway and it may be time find where all the competing ideas intersect and work from there.

The Case Against AEO/GEO

Many SEOs argue that AEO/GEO doesn’t differentiate itself enough to justify being anything other than a subset of SEO, sharing computers in the same office.

Harpreet Singh Chatha (X profile) of Harps Digital recently tweeted about AEO / GEO myths to leave behind in 2025.

Some of what he listed:

• “LLMs.txt
• Paying a GEO expert to do “chunk optimization.” Chunking content is just making your content readable.
• Thinking AEO / GEO have nothing in common with SEO. Ask your favourite GEO expert for 25 things that are unique to AI search and don’t overlap with SEO. They will block you.
• Saying SEO is dead. “

The legendary Greg Boser (LinkedIn profile), one of the original SEOs since 1996 tweeted this:

“At the end of the day, the core foundation of what we do always has been and always will be about understanding how humans use technology to gain knowledge.

We don’t need to come up with a bunch of new acronyms to continue to do what we do. All that needs to happen is we all agree to change the “E” in SEO from “Engine” to “Experience”.

Then everyone can stop wasting time writing all the ridiculous SEO/GEO/AEO posts, and get back to work.”

Inability To Articulate AEO/GEO

What contributes to the perception that AEO/GEO is not a real thing is that many proponents of AEO/GEO fail to differentiate it from standard SEO. We’ve all seen it where someone tweets their new tactic and the SEO peanut gallery chimes in, nah, that’s SEO.

Back in October Microsoft published a blog post about optimizing content for for AI where they asserted:

“While there’s no secret strategy for being selected by AI systems, success starts with content that is fresh, authoritative, structured, and semantically clear.”

The post goes on to affirm the importance of SEO fundamentals such as “Crawlability, metadata, internal linking, and backlinks” but then states that these are just starting points. Microsoft points out that AI search provides answers, not ranked list of pages. That’s correct and it changes a lot.

Microsoft says that now it’s about which pieces of content are being ranked:

“In AI search, ranking still happens, but it’s less about ordering entire pages and more about which pieces of content earn a place in the final answer.”

That kind of echoes what Jesse Dwyer of Perplexity AI recently said about AI Search and SEO:

“As for the index technology, the biggest difference in AI search right now comes down to whole-document vs. “sub-document” processing.

…The AI-first approach is known as “sub-document processing.” Instead of indexing whole pages, the engine indexes specific, granular snippets (not to be confused with what SEO’s know as “featured snippets”).”

Microsoft recently published an explainer called “From discovery to influence:A guide to AEO and GEO” that’s tellingly focused mostly on shopping, which is notable and remarkable because there’s a growing awareness that ecommerce stands to gain a lot from AI Search.

No such luck for informational sites because it’s also gradually becoming understood that Agentic AI is poised to strip informational sites of all branding and value-add and treating them as sources of data.

Common SEO Practices That Pass As GEO

Some of what some champion as GEO and AEO are actually longstanding SEO practices:

• Crafting content in the form of answers
  Good SEOs have been doing this since Featured Snippets came out in 2014.
• Chunking content
  Crafting content in tight paragraphs looks good in mobile devices and it’s something good SEOs and thoughtful content creators have been doing for well over a decade.
• Structured Content
  Headings and other elements that strongly disambiguate the content are also SEO.
• Structured Data
  Shut your mouth. This is SEO.

The Customer Is Always Right

Some of in the GEO Is Real campe tend to regard themselves as evolving with the times but they also acknowledge they’re just offering what the clients are demanding. SEO practioners are in a hard spot, what are you going to do? Plant your flag on traditional SEO and turn your back on what potential clients are begging for?

Googlers Insist It’s Still SEO

There are Googlers such as Robby Stein (VP of Product), Danny Sullivan, and John Mueller who say that SEO is 100% still relevant because under the hood AI is just firing off Google searches for top ranked sites to backfill into synthesized answers and links (Read: Google Downplays GEO – But Let’s Talk About Garbage AI SERPs). OpenAI was recently hiring a content strategist that is able to lean into to SEO (not GEO), which some say demonstrates that even OpenAI is focused on traditional SEO.

Optimization Is No Longer Just Google

Manick Bhan (LinkedIn profile), founder of the Search Atlas SEO suite, offered an interesting take on why we may be transitioning to a divided SEO and GEO path.

Manick shared:

“SEO has always meant ‘search engine optimization,’ but in practice it has historically meant ‘Google optimization.’ Google defined the interface, the ranking paradigm, the incentives, and the entire mental model the industry used.

The challenge with calling GEO a ‘sub-discipline’ of SEO is that the LLM ecosystem is not one ecosystem, and Google’s AI Mode is becoming a generative surface itself.”

Manick asserts that there is no one “GEO” because each of the AI search and answer engines use different methodologies. He observed that the underlying tactics remain the same but the “the interface, the retrieval model, and the answer surface” are all radically changed from anything that’s come before.

Manick believes that GEO is not SEO, offering the following insights:

“My position is clear: GEO is not just SEO with a fresh coat of paint, and reducing it to that misses the fundamental shift in how modern answer engines actually retrieve, rank, and assemble information.

Yes, the tactics still live in the same universe of on-page and off-page signals. Those fundamentals haven’t changed. But the machines we’re optimizing for have.

Today’s answer engines:

• Retrieve differently,
• Fuse and weight sources differently,
• Handle recency differently,
• Assign trust and authority differently,
• Fan out queries differently,
• And incorporate user behavior into their RAG corpora differently.

Even seemingly small mechanics — like logit calibration and temperature — produce practically different retrieval outputs, which is why identical prompts across engines show measurable semantic drift and citation divergence.

This is why we’re seeing quantifiable, repeatable differences in:

• Retrieved sources,
• Answer structures,
• Citation patterns,
• Semantic frames,
• And ranking behavior across LLMs, AI Mode surfaces, and classical Google results.

In this landscape, humility and experimentation matter more than dogma. Treating all of this as ‘just SEO’ ignores how different these systems already are, and how quickly they’re evolving.”

It’s Clear We Are In Transition

Maybe one of the reasons for the anti-GEO backlash is that there is a loud contingent of agencies and individuals who have very little experience with SEO, some who are fresh out of college with zero experience. And it’s not their lack of experience that gets some SEOs in ranting mode. It’s the things they purport are GEO/AEO that are clearly just SEO.

Yet, as Manick of Search Atlas pointed out, AI search and chat surfaces are wildly different from classic search and it’s kind of closing ones eyes to the obvious to deny that things are different and in transition.

Featured Image by Shutterstock/Natsmith1

Perplexity AI Interview Explains How AI Search Works via @sejournal, @martinibuster

I recently spoke with Jesse Dwyer of Perplexity about SEO and AI search about what SEOs should be focusing on in terms of optimizing for AI search. His answers offered useful feedback about what publishers and SEOs should be focusing on right now.

AI Search Today

An important takeaway that Jesse shared is that personalization is completely changing

“I’d have to say the biggest/simplest thing to remember about AEO vs SEO is it’s no longer a zero sum game. Two people with the same query can get a different answer on commercial search, if the AI tool they’re using loads personal memory into the context window (Perplexity, ChatGPT).

A lot of this comes down to the technology of the index (why there actually is a difference between GEO and AEO). But yes, it is currently accurate to say (most) traditional SEO best practices still apply.”

The takeaway from Dwyer’s response is that search visibility is no longer about a single consistent search result. Personal context as a role in AI answers means that two users can receive significantly different answers to the same query with possibly different underlying content sources.

While the underlying infrastructure is still a classic search index, SEO still plays a role in determining whether content is eligible to be retrieved at all. Perplexity AI is said to use a form of PageRank, which is a link-based method of determining the popularity and relevance of websites, so that provides a hint about some of what SEOs should be focusing on.

However, as you’ll see, what is retrieved is vastly different than in classic search.

I followed up with the following question:

So what you’re saying (and correct me if I’m wrong or slightly off) is that Classic Search tends to reliably show the same ten sites for a given query. But for AI search, because of the contextual nature of AI conversations, they’re more likely to provide a different answer for each user.

Jesse answered:

“That’s accurate yes.”

Sub-document Processing: Why AI Search Is Different

Jesse continued his answer by talking about what goes on behind the scenes to generate an answer in AI search.

He continued:

“As for the index technology, the biggest difference in AI search right now comes down to whole-document vs. “sub-document” processing.

Traditional search engines index at the whole document level. They look at a webpage, score it, and file it.

When you use an AI tool built on this architecture (like ChatGPT web search), it essentially performs a classic search, grabs the top 10–50 documents, then asks the LLM to generate a summary. That’s why GPT search gets described as “4 Bing searches in a trenchcoat” —the joke is directionally accurate, because the model is generating an output based on standard search results.

This is why we call the optimization strategy for this GEO (Generative Engine Optimization). That whole-document search is essentially still algorithmic search, not AI, since the data in the index is all the normal page scoring we’re used to in SEO. The AI-first approach is known as “sub-document processing.”

Instead of indexing whole pages, the engine indexes specific, granular snippets (not to be confused with what SEO’s know as “featured snippets”). A snippet, in AI parlance, is about 5-7 tokens, or 2-4 words, except the text has been converted into numbers, (by the fundamental AI process known as a “transformer”, which is the T in GPT). When you query a sub-document system, it doesn’t retrieve 50 documents; it retrieves about 130,000 tokens of the most relevant snippets (about 26K snippets) to feed the AI.

Those numbers aren’t precise, though. The actual number of snippets always equals a total number of tokens that matches the full capacity of the specific LLM’s context window. (Currently they average about 130K tokens). The goal is to completely fill the AI model’s context window with the most relevant information, because when you saturate that window, you leave the model no room to ‘hallucinate’ or make things up.

In other words, it stops being a creative generator and delivers a more accurate answer. This sub-document method is where the industry is moving, and why it is more accurate to be called AEO (Answer Engine Optimization).

Obviously this description is a bit of an oversimplification. But the personal context that makes each search no longer a universal result for every user is because the LLM can take everything it knows about the searcher and use that to help fill out the full context window. Which is a lot more info than a Google user profile.

The competitive differentiation of a company like Perplexity, or any other AI search company that moves to sub-document processing, takes place in the technology between the index and the 26K snippets. With techniques like modulating compute, query reformulation, and proprietary models that run across the index itself, we can get those snippets to be more relevant to the query, which is the biggest lever for getting a better, richer answer.

Btw, this is less relevant to SEO’s, but this whole concept is also why Perplexity’s search API is so legit. For devs building search into any product, the difference is night and day.”

Dwyer contrasts two fundamentally different indexing and retrieval approaches:

• Whole-document indexing, where pages are retrieved and ranked as complete units.
• Sub-document indexing, where meaning is stored and retrieved as granular fragments.

In the first version, AI sits on top of traditional search and summarizes ranked pages. In the second, the AI system retrieves fragments directly and never reasons over full documents at all.

He also described that answer quality is constrained by context-window saturation, that accuracy emerges from filling the model’s entire context window with relevant fragments. When retrieval succeeds at saturating that window, the model has little capacity to invent facts or hallucinate.

Lastly, he says that “modulating compute, query reformulation, and proprietary models” is part of their secret sauce for retrieving snippets that are highly relevant to the search query.

Featured Image by Shutterstock/Summit Art Creations

Why Agentic AI May Flatten Brand Differentiators via @sejournal, @martinibuster

James LePage, Dir Engineering AI, co-lead of the WordPress AI Team, described the future of the Agentic AI Web, where websites become interactive interfaces and data sources and the value add that any site brings to their site becomes flattened. Although he describes a way out of brand and voice getting flattened, the outcome for informational, service, and media sites may be “complex.”

Evolution To Autonomy

One of the points that LePage makes is that of agentic autonomy and how that will impact what it means to have an online presence. He maintains that humans will still be in the loop but at a higher and less granular level, where agentic AI interactions with websites are at the tree level dealing with the details and the humans are at the forest level dictating the outcome they’re looking for.

LePage writes:

“Instead of approving every action, users set guidelines and review outcomes.”

He sees agentic AI progressing on an evolutionary course toward greater freedom with less external control, also known as autonomy. This evolution is in three stages.

He describes the three levels of autonomy:

1. What exists now is essentially Perplexity-style web search with more steps: gather content, generate synthesis, present to user. The user still makes decisions and takes actions.
2. Near-term, users delegate specific tasks with explicit specifications, and agents can take actions like purchases or bookings within bounded authority.
3. Further out, agents operate more autonomously based on standing guidelines, becoming something closer to economic actors in their own right.”

AI Agents May Turn Sites Into Data Sources

LePage sees the web in terms of control, with Agentic AI experiences taking control of how the data is represented to the user. The user experience and branding is removed and the experience itself is refashioned by the AI Agent.

He writes:

“When an agent visits your website, that control diminishes. The agent extracts the information it needs and moves on. It synthesizes your content according to its own logic. It represents you to its user based on what it found, not necessarily how you’d want to be represented.

This is a real shift. The entity that creates the content loses some control over how that content is presented and interpreted. The agent becomes the interface between you and the user.

Your website becomes a data source rather than an experience.”

Does it sound problematic that websites will turn into data sources? As you’ll see in the next paragraph, LePage’s answer for that situation is to double down on interactions and personalization via AI, so that users can interact with the data in ways that are not possible with a static website.

These are important insights because they’re coming from the person who is the director of AI engineering at Automattic and co-leads the team in charge of coordinating AI integration within the WordPress core.

AI Will Redefine Website Interactions

LePage, who is the co-lead of WordPress’s AI Team, which coordinates AI-related contributions to the WordPress core, said that AI will enable websites to offer increasingly personalized and immersive experiences. Users will be able to interact with the website as a source of data refined and personalized for the individual’s goals, with website-side AI becoming the differentiator.

He explained:

“Humans who visit directly still want visual presentation. In fact, they’ll likely expect something more than just content now. AI actually unlocks this.

Sites can create more immersive and personalized experiences without needing a developer for every variation. Interactive data visualizations, product configurators, personalized content flows. The bar for what a “visit” should feel like is rising.

When AI handles the informational layer, the experiential layer becomes a differentiator.”

That’s an important point right there because it means that if AI can deliver the information anywhere (in an agent user interface, an AI generated comparison tool, a synthesized interactive application), then information alone stops separating you from everyone else.

In this kind of future, what becomes the differentiator, your value add, is the website experience itself.

How AI Agents May Negatively Impact Websites

LePage says that Agentic AI is a good fit for commercial websites because they are able to do comparisons and price checks and zip through the checkout. He says that it’s a different story for informational sites, calling it “more complex.”

Regarding the phrase “more complex,” I think that’s a euphemism that engineers use instead of what they really mean: “You’re probably screwed.”

Judge for yourself. Here’s how LePage explains websites lose control over the user experience:

“When an agent visits your website, that control diminishes. The agent extracts the information it needs and moves on. It synthesizes your content according to its own logic. It represents you to its user based on what it found, not necessarily how you’d want to be represented.

This is a real shift. The entity that creates the content loses some control over how that content is presented and interpreted. The agent becomes the interface between you and the user. Your website becomes a data source rather than an experience.

For media and services, it’s more complex. Your brand, your voice, your perspective, the things that differentiate you from competitors, these get flattened when an agent summarizes your content alongside everyone else’s.”

For informational websites, the website experience can be the value add but that advantage is eliminated by Agentic AI and unlike with ecommerce transactions where sales are the value exchange, there is zero value exchange since nobody is clicking on ads, much less viewing them.

Alternative To Flattened Branding

LePage goes on to present an alternative to brand flattening by imagining a scenario where websites themselves wield AI Agents so that users can interact with the information in ways that are helpful, engaging, and useful. This is an interesting thought because it represents what may be the biggest evolutionary step in website presence since responsive design made websites engaging regardless of device and browser.

He explains how this new paradigm may work:

“If agents are going to represent you to users, you might need your own agent to represent you to them.

Instead of just exposing static content and hoping the visiting agent interprets it well, the site could present a delegate of its own. Something that understands your content, your capabilities, your constraints, and your preferences. Something that can interact with the visiting agent, answer its questions, present information in the most effective way, and even negotiate.

The web evolves from a collection of static documents to a network of interacting agents, each representing the interests of their principal. The visiting agent represents the user. The site agent represents the entity. They communicate, they exchange information, they reach outcomes.

This isn’t science fiction. The protocols are being built. MCP is now under the Linux Foundation with support from Anthropic, OpenAI, Google, Microsoft, and others. Agent2Agent is being developed for agent-to-agent communication. The infrastructure for this kind of web is emerging.”

What do you think about the part where a site’s AI agent talks to a visitor’s AI agent and communicates “your capabilities, your constraints, and your preferences,” as well as how your information will be presented? There might be something here, and depending on how this is worked out, it may be something that benefits publishers and keeps them from becoming just a data source.

AI Agents May Force A Decision: Adaptation Versus Obsolescence

LePage insists that publishers, which he calls entities, that evolve along with the Agentic AI revolution will be the ones that will be able to have the most effective agent-to-agent interactions, while those that stay behind will become data waiting to be scraped .

He paints a bleak future for sites that decline to move forward with agent-to-agent interactions:

“The ones that don’t will still exist on the web. But they’ll be data to be scraped rather than participants in the conversation.”

What LePage describes is a future in which product and professional service sites can extract value from agent-to-agent interactions. But the same is not necessarily true for informational sites that users depend on for expert reviews, opinions, and news. The future for them looks “complex.”