The Luck of the Query

You type something into Google, skim past the AI Overview and sponsored content, glance at the blue links, and still come up empty. Whatever you were looking for is either buried on page 14 of the search results or simply not coming up the way you expected. 

You rephrase the query and try again. Still nothing, or at least, not what you need to find.

Then, on a whim, you toggle over to Claude or Copilot or ChatGPT—or any of the dozens of other options now available—and ask the same question, but this time, you’re provided with exactly the information you wanted to find.

This happened to me recently. I was trying to track down a medical provider who had moved away five years ago. I knew this person’s first and last name, the organization they used to work for, and where they said they were moving, but nothing else.

I thought that would be enough to get started, but Google was no help. I tried Boolean searches (using quotation marks around key phrases to limit results to exact matches), but I was stuck in a graveyard of irrelevant results.

So I turned to an AI chatbot—Claude, specifically—and within seconds, I had a link to a web page with the contact information I needed.

Why couldn’t Google help me in this example? More importantly, how common is my experience? Will #GoogleFail become a hashtag?

Similar experiences are leading millions of people to wonder if AI search outperforms Google. If that’s true, how will that affect digital marketing?

Different Engines, Different Instincts

Google is optimized for the world’s most common queries. It is extraordinarily good at finding authoritative, high-traffic pages on topics millions of people search for every day. News stories, products, Wikipedia entries, popular how-to content—with all of that, Google is essentially unbeatable. Its AI summaries and overviews, powered by Gemini and displayed prominently at the top of search results, are similarly tuned to these high-signal sources.

But optimization sometimes creates a blind spot. Pages that are technically public and indexed, but low in traffic and thin in content—such as healthcare provider directories, professional licensing databases, aggregated registry entries—don’t always rise to the surface. Those pages exist, and they’re certainly crawlable. But Google just doesn’t have much reason to rank them highly for the average search, and so they don’t turn up for most users.

AI chatbots like Claude approach the problem differently. Rather than ranking pages by authority and traffic, they synthesize across whatever sources their search layer surfaces, including those thin, low-ranked directory pages that Google effectively buries.

For obscure but legitimate research, an AI can sometimes outperform the world’s most powerful search engine simply by casting a wider net and connecting the dots across sources that wouldn’t individually rank well.

The Luck Part

Part of what makes AI search feel magical is also what makes it dangerous: it doesn’t always know when it doesn’t know something.

In that same search, the AI initially produced a middle name with initials that seemed oddly specific. That’s the kind of detail that can feel more constructed than found. In this case, it turned out to be accurate; I had never seen this person’s two middle initials before. But for a moment, it looked like a confident hallucination, which is something everyone using chatbots still must contend with.

That’s a clear tradeoff. AI assistants are willing to synthesize across thin sources and fill apparent gaps in ways that can produce correct answers—or plausible-sounding ones that are completely wrong—and both are delivered with an equal measure of confident authority.

Google, for all its limitations, shows you what it has found. An AI shows you what it has concluded related to your search prompt. Those are meaningfully different things.

When you need to quickly tighten a door knob that’s come loose, it’s probably faster to check the junk drawer where you last saw the screwdriver. But if you need a more specific tool for a focused purpose—a stud finder, let’s say, or the measuring tape—you don’t want to spend an hour rummaging through a three-car garage full of tools.

But if you had someone who could look in the garage and the junk drawer in a matter of seconds and let you know where you could find what you need, you’d save so much time—time you can spend measuring twice (three times!) and cutting once.

When to Use Which

For well-documented topics, high-profile people, recent news, and anything where authoritative sourcing matters, Google and its own AI summaries remain the stronger choice. The information density and source quality are simply higher.

But for tracking down specific professionals, maneuvering through niche databases, finding someone who has moved or changed affiliations, or synthesizing a fuzzy starting point into concrete leads? AI search is increasingly worth trying first. Its willingness to reason across low-authority sources and make connections is exactly the capability these searches require.

The catch is that you still need to verify what it finds. The answer might be exactly right, or it might be a confident near-miss. The AI can’t always tell the difference, which means you have to be the discerning human in the loop. And, in the end, getting useful results from any kind of search tool still comes down to how well you frame the question.

_{Photo by Jens Lelie on Unsplash}