AI Search Isn’t Replacing Google, It’s Layering On Top – Similarweb Data via @sejournal, @gregjarboe

Rand Fishkin, the co-founder and CEO of SparkToro, posted on LinkedIn on July 24, 2026, to say that Similarweb’s newest report would “probably infuriate” two groups at once. The AI zealots who are certain every marketing dollar should already be chasing chatbot visibility. And the AI skeptics who’ve suspected the hype was overblown but never had the receipts to prove it to a boss suffering from what Rand calls AI derangement syndrome.

Most industry reports comfort one camp and annoy the other. This one, Similarweb’s “2026 Generative AI Landscape: The Evolution of AI Search,” manages to hand both sides a data point that undercuts their certainty. I read all 38 pages because I’m into ground-truthing, and I want to walk through why Rand is right, what the report actually shows, and what you should do differently tomorrow morning because of it.

The Number That Should Worry The AI Zealots

Similarweb tracked audience overlap between ChatGPT and Google between March and May 2026, and 461 million of ChatGPT’s 494 million users (95%) also use Google in the same window. Almost nobody has left Google for ChatGPT. They’ve added ChatGPT to a Google habit that hasn’t budged.

Zoom out further, and the gap gets starker. Search still pulls 3.3 billion average monthly unique visitors worldwide. AI chatbots, even after growing 57% year over year, sit at 655 million. So, search is still roughly five times the size of the entire AI chatbot category combined. If your 2026 budget deck assumes AI search has already eclipsed traditional search, the math in this report says otherwise.

Citations tell the same story from a different angle. Only 6.8% of ChatGPT answers in the U.S. included a link to an external source as of May 2026. That’s up more than fivefold from around 1% a year earlier, which is genuinely fast growth, but it also means 93 out of every 100 ChatGPT answers still send nobody anywhere. Ethan Smith of Graphite makes a sharper point in the report about how users are folding those same prompting habits back into Google itself, with average search length climbing steadily since AI Mode launched. People aren’t abandoning search boxes. They’re just typing longer sentences into them.

The Number That Should Worry The AI Skeptics

Now for the half of the report that punctures the other side’s confidence. Average monthly web visits across generative AI platforms hit 9.5 billion between June 2025 and May 2026, up 70% year over year. App downloads worldwide climbed to 2.7 billion, up 134%. Half of all generative AI users are now 35 or older, compared to 61% under 34 just two years ago, which is the clearest signal I’ve seen that this isn’t a Gen Z fad running out its trend cycle. Michael Horrocks of Miro puts it plainly in the report: “Growth concentrated in younger demographics can fade with trends; growth spreading into older generations is often what durable, mainstream adoption looks like.”

Meta AI’s own disclosed numbers back that up from a completely different angle. Publicly reported monthly active users went from 384 million in September 2024 to 1.2 billion by March 2026, more than tripling in 18 months, entirely by riding inside Instagram, Facebook, WhatsApp, and Messenger rather than as a standalone destination anyone had to seek out. And ChatGPT ad penetration in the U.S. jumped from 14% of desktop chats in May 2026 to 26% just one month later. Whatever you think about the maturity of AI search, advertisers clearly don’t think it’s a toy.

My read is that both camps are pattern-matching off the piece of the data that confirms what they already believed, and both are ignoring the half that complicates it. AI search hasn’t replaced anything. It has stacked a new, fast-growing, unevenly distributed layer on top of a search ecosystem that was already there, and the practitioners who will win the next two years are the ones measuring the stack instead of arguing about which layer matters more.

Why The Disconnect Between Citations And Clicks Matters More Than Either Camp Realizes

The most useful chart in the whole deck, and the one I think gets underappreciated in the LinkedIn debate, comes from Aleyda Solis of Orainti. She points out that 65% of the URLs ChatGPT cites sit two or three folders deep, the pages doing the actual evidentiary work behind an AI answer. But 58.8% of the referral traffic that AI sends back to sites lands on the homepage, not the cited page at all. Cited pages and clicked pages are almost entirely different populations of URLs.

That single data point should reorganize how agencies report AI performance to clients. If you’re only tracking whether your deep product or blog content gets cited, you’re missing the fact that the humans who actually click through are landing somewhere else entirely and need their own conversion path. Rand makes a related point in the report itself, comparing this to how 20th-century advertisers proved billboard and radio spend worked by measuring lift in store visits rather than counting who glanced at the sign. The mechanism has changed. The discipline of measuring downstream behavior instead of surface impressions hasn’t.

3 Things To Change In Your Strategy This Week

1. Split your reporting into two separate metrics. Track citation rate and citation folder depth as one key performance indicator that measures whether AI trusts your content enough to use it as evidence. Track referral landing pages and downstream conversion as a completely separate KPI that measures what actually happens once a human clicks through. Conflating the two in a single dashboard is how brands miss both problems at once.

2. Stop treating “AI visibility” as a single category. Similarweb’s brand visibility index shows how category-specific this already is. CeraVe leads beauty at an index of 100 while NYX Cosmetics sits at 19 in the same category. Kevin Indig of Growth Memo argues in the report that share of voice is the metric that matters here because it’s a relative comparison in a stochastic system, not an absolute score. Pull your own category’s leaderboard before you assume you’re winning or losing.

3. Match your content to the platform’s actual audience, not the platform’s overall size. The affinity data shows ChatGPT skews toward everyday consumers researching restaurants, health, and fashion; Claude users are 25 times more likely than the average searcher to visit university sites and skew heavily toward professionals and students, and Gemini users over-index on graphics, security, and hardware content. A single piece of “AI-optimized” content aimed at all three is aimed at none of them.

The Bottom Line

I don’t think the AI zealots are wrong that something structural is shifting. Nine and a half billion monthly visits and a fivefold jump in citation rates in under a year are not a rounding error. But I also don’t think the skeptics are wrong that most of the industry’s AI panic is running well ahead of the actual traffic numbers, given that Google still commands five times the audience of every AI chatbot combined and 95 out of every 100 ChatGPT users never left Google in the first place. Rand’s post nailed the discomfort because the data refuses to let either side keep its story simple. The brands that will actually benefit from this report aren’t the ones picking a side. They’re the ones pulling the citation and referral numbers for their own category this quarter and building a strategy around what the data says rather than what the argument on LinkedIn says.

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