The Internet Is Splitting in Two and Here’s Which Side Your Brand Is On

In this week’s episode of Voices of Search, we spoke with Jeff Reine, co-founder at Everything Machines, about a problem most marketing teams haven’t fully confronted yet: their websites are increasingly unreadable to the bots doing the research that shapes what AI models say about them.

Jeff spent two decades in enterprise marketing and platform strategy before co-founding Everything Machines, a company built around the premise that the open web is splitting into two distinct audiences—human visitors and AI crawlers—and that most brands are only set up to serve one of them. To close that gap, Everything Machines built a product called the Everything Cache, a parallel, bot-readable version of a brand’s existing website that makes content accessible to LLM crawlers without touching or competing with the human-facing site.

Today, Jeff broke down why that gap exists, how the shift from traditional search to AI-driven discovery has fundamentally changed the buyer’s journey, and what brands need to build now before the volume shift fully arrives.

Key Takeaways From This Episode:

• The shift from search and discover to ask and answer changes the buyer’s mindset entirely. Users are no longer active agents spelunking through blue links. They’re receiving an answer that feels conversational and authoritative, and they’re treating it that way.
• LLMs are now mega-influencers. Brands tell them things, but don’t control what they say to their prompters. The only way to influence the output is to control what you put in front of the crawlers on your own properties.
• AI search is driving the fulfillment of one-to-one marketing, something the industry has been chasing for 25 years. The intermediary that finally gets us there isn’t a brand. It’s a chat assistant.
• Most enterprise websites are not readable by LLM bots. JavaScript, dynamic rendering, and bloated page structures are invisible to crawlers that want clean, fast markdown wrapped in structured data.
• The AEO stack has three distinct layers: monitoring, content publishing infrastructure, and content enrichment. Most teams are only thinking about the first one.
• In two years, the brands that win will be the ones that leaned into transparency and truthfulness. LLMs are increasingly able to spot and discount content designed to persuade rather than inform.

From Search and Discover to Ask and Answer

To understand why the infrastructure gap Jeff describes matters, you first have to understand how dramatically the discovery experience has changed for buyers.

Put simply, traditional search was a DIY project. You gave Google a few words, got blue links, and went spelunking. You were the active agent deciding what was relevant, following threads, opening tabs, and forming your own conclusions. 

Now you ask a question and receive an answer. It doesn’t come with arrows pointing to more links or an invitation to keep searching. It just lands, and because it arrives in a chat format, we’re conditioned to receive it the way we receive a text message. We read it, integrate it, and move on.

“It looks pretty discreet,” Jeff said. “It ends. And we’re humans. We’ve been trained for tens of years that when someone texts you, you respond. You’re now in a conversation.”

That shift has real implications for brands. In a search world, your job was to get a page in front of a human who would then evaluate it themselves. In an answer world, the evaluation happens before the human ever sees your brand. The LLM is doing the research, synthesizing the information, and presenting a conclusion. Your content either fed that conclusion or it didn’t—and whether it did depends heavily on whether the crawlers could read it in the first place.

Hallucinations Are a Brand Control Problem

That infrastructure gap is also what drives the hallucination problem, and Jeff reframed it in a way that makes the stakes concrete.

When an LLM gets something about your brand slightly wrong—not fabricated, just off—it isn’t necessarily a technical failure on the model’s part. It might simply reflect inconsistent or unclear content across your own properties. The model did its best with what it found. 

“The answer engines are now mega-influencers,” Jeff said. “We tell them things, and then we don’t decide what they say to their followers, their prompters. We have to rely on them to interpret it.”

In other words, brands need to treat their own controlled properties as the primary source of truth for what they want LLMs to say about them. Content needs to be specific, transparent, and as complete as possible. Anything vague or inconsistent becomes an open invitation for the model to fill in the gaps on its own terms.

The Three Layers of AEO Most Teams Haven’t Built

Understanding that the problem is partly an infrastructure problem leads to a question most teams haven’t asked yet: What does a complete answer engine optimization stack actually look like?

Jeff outlined three distinct layers:

The first is monitoring

First, you need to track how your brand shows up across AI platforms consistently over time. Tools like Otterly and Gumshoe operate here. 

This layer is necessary, but it isn’t sufficient on its own, and it’s where most teams stop.

The second layer is the content publishing infrastructure

Next, make sure your content is actually readable by LLM crawlers from the get-go. Most websites are built for humans, with JavaScript rendering, dynamic content, tracking scripts, and visual elements that LLM bots simply can’t process. 

The bots don’t care about brand colors or animated hero images. They want clean, fast markdown wrapped in JSON-LD, pre-rendered, and as noise-free as possible. 

If your site can’t serve that, the crawlers doing the groundwork that shapes what an LLM says about your brand are working with incomplete information at best.

The third layer is content enrichment

Lastly, publishing more specific, deeper content than most brands would ever consider putting on a human-facing page is a must. LLM bots have an unlimited appetite for detail, which is why support pages, highly specific use case documentation, anonymized customer chat logs, product origin stories—why a product was built and what problem it was designed to solve—are so important.

Early AI citation data consistently surfaces these functional, specific pages over homepage hero sections and marketing copy. “Forget pages,” Jeff said. “What we’re trying to do is produce questions and answers in conversation that are going to help the LLMs learn.”

What the Everything Cache Actually Is

Everything Machines’ answer to the infrastructure layer is the Everything Cache—a product that builds a parallel, bot-optimized version of a brand’s existing website without disrupting or competing with the human-facing site or its SEO performance.

Every cached page includes no-index and no-follow metatags, canonical links pointing back to the original URL, and clear cache identification markers. It’s designed to be completely invisible to Google while being highly readable to LLM crawlers. “You have a website, you have pages,” Jeff said. “You need to replicate that and mirror it so that bots can easily access it, because they’re not going to fight through your JavaScript.”

In practice, the process strips out JavaScript, CSS, tracking scripts, ads, navigation boilerplate, and interactive elements. What remains is a clean HTML5 semantic structure with a clear heading hierarchy, validated structured data, and the actual content served as fast and simply as possible to the crawlers doing the grounding work that ultimately shapes LLM responses.

The goal isn’t to rank. It’s to give LLM crawlers a version of your brand that they can actually read, and from which they can build an accurate picture of who you are and what you do.

One-to-One Marketing Has Finally Arrived (Via a Third Party)

Beyond the infrastructure argument, Jeff made a broader point about where this is all heading—one that reframes how brands should be thinking about content depth and specificity going forward.

AI search, Jeff argued, represents the completion of a 25-year journey toward genuine one-to-one marketing. Not because brands have gotten smarter about personalization, but because a third-party intermediary has stepped in to do it for them. The traditional constraint was always volume. You could build 10 personas, run 100 campaign variants, but at some point, the complexity outran what the team could manage. The buyer’s context always got compressed into a segment they were close enough to but never quite right for.

Now that context is baked directly into the prompt. A B2B buyer searching for HR software for a specific company type, headcount, location, and use case is giving the LLM everything it needs to fetch highly relevant, personalized information on their behalf. The question for brands shifts from “how many personas can we manage?” to “how well does our content address the specific intersections of persona and topic that buyers are actually prompting around?” 

“How would you build infinite landing pages if there’s a perfect landing page for every user?” Jeff asked. “That’s how we think about the Everything Cache.”

The Right Answer Isn’t a Compromise

Most enterprise websites are currently talking to humans and hoping the bots pick it up. That gap between what brands are publishing and what LLM crawlers can actually read is where AI visibility is being won and lost right now. Ultimately, most brands assume that optimizing for human visitors and AI crawlers at the same time means finding an uncomfortable middle ground.

Jeff’s position is that it doesn’t have to be that way. You just have to build two distinct experiences by keeping the human site exactly as it is, and build a separate, clean, fast, content-rich layer that speaks directly to the bots. Each audience gets exactly what it needs.

The brands that figure this out now won’t just be more visible in AI search—they’ll be the ones that shaped what the models say about their category before everyone else catches up.

Voices of Search is a daily SEO and content marketing podcast hosted by Jordan Keone and Tyson Stockton. The show delivers actionable strategies and data-driven insights to help marketers navigate the ever-evolving world of search engine optimization and content marketing. New episodes air weekly, covering everything from technical SEO to AI discovery, featuring industry leaders and practitioners sharing real-world frameworks and proven tactics.

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