Every time we open a laptop lately, the search box looks a little different. Google now leads with an AI Overview before it shows a single blue link. Perplexity has built an entire company on the idea that you shouldn’t have to click ten tabs to answer one question. And a growing chunk of our own team now starts research tasks in ChatGPT search rather than a search engine at all. The question we keep getting asked by readers is a simple one: is AI search actually replacing Google, or is it just a shinier layer sitting on top of the same old results? We spent the last few weeks testing both approaches against real, everyday queries to find out.
The short answer is that it depends entirely on what you’re trying to find. For some tasks, AI-native tools like Perplexity are genuinely faster and more useful than a traditional search results page. For others, especially anything involving recency, location or buying something, the old model still wins comfortably, and Google’s AI layer knows it, which is why it so often falls back to conventional results underneath its summary.
What we mean by “AI search” in 2026
It’s worth separating two things that get lumped together under the same banner. The first is Google AI Overviews, the AI-generated summary that now sits above organic results for a huge share of queries. It’s built on Gemini, draws on Google’s existing index and Knowledge Graph, and is designed to keep you inside the Google ecosystem rather than send you elsewhere. The second is AI-native search, meaning tools built from the ground up around a language model doing the searching, reading and summarising for you: Perplexity, ChatGPT’s search mode, and to a lesser extent Copilot’s web-grounded answers.
The distinction matters because the two approaches have different incentives. Google still makes its money from ads and from keeping people inside its own properties, so its AI layer is bolted onto a search engine. Perplexity and OpenAI’s search products were built with the explicit goal of answering the question directly, with citations, and getting you off the page faster. That difference in design philosophy shows up in almost every test we ran.
How Google’s AI Overviews actually work
Google has been reasonably transparent about the mechanics here: AI Overviews are triggered by a mix of query intent, the model’s confidence in having a reliable answer, and whether Google judges the topic sensitive enough to hold back on (health and finance queries get treated far more conservatively than, say, recipe or trivia questions). If you want the full breakdown of how the trigger logic works, Search Engine Land’s guide to AI Overviews is a solid technical explainer of what’s happening behind the scenes.
In practice, we found Overviews genuinely useful for quick factual lookups, unit conversions, simple how-to questions and anything where Google’s index is deep and current. Where they got shaky was anything requiring nuance or synthesis across conflicting sources, things like emerging news stories, product comparisons with genuine trade-offs, or questions where the “correct” answer depends on context Google’s summary tends to flatten out. We also noticed Overviews occasionally citing a source that didn’t actually support the claim being made, a pattern that’s been documented at scale by independent researchers, not just anecdotal to our testing.
Where Perplexity and ChatGPT search do things differently
Perplexity’s whole pitch is that it behaves more like a research assistant than a search engine. Ask it a question and it runs several searches in the background, reads a handful of sources, and builds an answer with inline citations you can click through individually. ChatGPT’s search mode works similarly, and increasingly overlaps with the kind of agentic behaviour we’ve covered before, where the assistant doesn’t just answer once but plans out a small sequence of searches, checks its own answer, and refines it. We go into how that step-by-step planning actually works in our explainer on what AI agents actually do in 2026, and it’s the same underlying mechanism powering the better AI search tools.
The upside of this approach is obvious once you’ve used it for a while: for research-heavy questions, comparison shopping between concepts (not products), or “explain this topic to me properly” queries, Perplexity in particular produces a genuinely more readable, better-sourced answer than scrolling through five Google results and piecing it together yourself. The citations are also more consistently visible and clickable than Google’s, which tends to bury its sources in small expandable chips.
The downside is that these tools are still fundamentally language models doing retrieval, and retrieval errors compound. If the model misreads a source, or a source itself is wrong, the polished, confident-sounding answer doesn’t necessarily flag that uncertainty to you the way a page full of competing search results naturally does.
The citation and hallucination problem, honestly assessed
This is the part that doesn’t get talked about enough in the excitement over AI search. Independent research has repeatedly shown that AI search tools get their sourcing wrong more often than most people assume. A widely cited study from Columbia’s Tow Center for Digital Journalism tested eight AI search engines, including Perplexity and ChatGPT search, against known news articles and found the tools failed to correctly identify the source more than 60 percent of the time across the full test set, with Perplexity actually performing the best of the group tested and some competitors performing dramatically worse. You can read the full methodology and results in Columbia Journalism Review’s write-up of the study, and it’s worth sitting with those numbers before treating any AI search answer as gospel.
Google’s Overviews aren’t immune either. Because they’re generated fresh for each query rather than pulled verbatim from a single page, they can misstate details, conflate two different sources, or present an outdated fact as current. The failure mode is different to Perplexity’s (Google’s errors tend to be subtler misstatements rather than wrong sources entirely) but the underlying issue is the same: a language model summarising is not the same thing as a fact being verified.
Our practical takeaway, and what we’d genuinely recommend to readers, is to treat any AI-generated answer as a strong first draft rather than a finished one. For anything that matters, whether that’s medical information, financial decisions or anything you’d repeat to someone else as fact, click through to the actual source before you rely on it. This is exactly the same discipline we’d apply when comparing model outputs generally, which is part of why we did a proper side-by-side in our ChatGPT vs Claude vs Gemini comparison for Australian users rather than just taking each vendor’s marketing at face value.
Where traditional search still wins outright
For all the noise about AI search “replacing” Google, there are entire categories of query where the classic ten blue links approach still beats anything AI-native tools currently offer.
- Breaking news and anything time-sensitive: AI models have training cut-offs and retrieval lag, and even with live web access, a fast-moving story is still better served by a news search or a live blog than a generated summary.
- Local results: “cafes near me” or “plumber open now” queries depend on real-time location data, opening hours and map integration that Google’s local pack still handles far better than a chat-style answer.
- Shopping and price comparison: seeing multiple listings, current prices, stock availability and reviews side by side is still a fundamentally visual, comparative task that a single generated paragraph doesn’t do well.
- Anything requiring you to see the original page in full context: legal documents, government forms, or anything where the surrounding page content changes how you’d interpret a single quoted line.
- Niche or highly specific queries where the AI simply doesn’t have enough reliable source material to draw from, and either hedges heavily or guesses.
Google clearly knows this too, which is exactly why Overviews frequently sit above a full page of normal results rather than replacing them outright. It’s a hybrid model because a pure AI answer genuinely can’t cover the full range of what people search for in a day.
How Australians are actually searching, day to day
What we’re seeing in practice, both anecdotally and reflected in broader adoption data, is less a wholesale switch and more a splitting of habits by task. People are increasingly reaching for ChatGPT or Perplexity for research, learning and “explain this to me” questions, while still defaulting to Google for anything transactional, local or urgent. We covered the broader shift in everyday AI habits in our state of consumer AI in 2026 report, and search behaviour follows the same pattern: adoption of AI tools is real and growing quickly, but it’s additive to existing search habits rather than a full replacement of them, at least for now.
There’s also a generational and use-case split worth noting. Younger users and anyone doing research-heavy work (students, professionals writing reports, anyone comparing complex options) have shifted more of their behaviour toward AI-native tools. Meanwhile local business searches, shopping and anything with a map attached remain overwhelmingly a Google habit, largely because Google’s local and shopping infrastructure is years ahead of what any AI-native competitor has built.
For businesses and publishers, this split matters more than the headline “AI vs Google” framing suggests. Being cited accurately inside an AI Overview or a Perplexity answer is becoming its own visibility channel, separate from ranking in traditional organic results, and the two don’t always correlate. A page can rank well in Google’s classic results while being completely ignored by its own AI Overview, or vice versa, which is a genuinely new wrinkle for anyone thinking about how people find their content.
Final thoughts
AI search isn’t replacing Google in 2026, and based on everything we’ve tested, it isn’t close to doing so for a meaningful share of everyday queries. What’s actually happening is a genuine split by task: AI-native tools like Perplexity and ChatGPT search have become the better option for research, explanation and synthesis, with cleaner citations and less scrolling, while Google (AI Overview and all) remains the stronger choice for anything local, time-sensitive or transactional, largely because no AI-native competitor has matched its map data, shopping index or news freshness.
The bigger story, and the one we think matters more for everyday users, is accuracy. Both approaches can and do get things wrong, and the citation problems documented in AI search tools generally are serious enough that we’d encourage treating any AI-generated answer as a starting point rather than a verified fact, particularly for anything health, money or safety related. The tools are genuinely useful, arguably more useful for the right tasks than search has been in years, but “useful” and “reliably accurate” aren’t the same claim, and it’s worth keeping that distinction in mind every time you ask one of these tools a question that actually matters.
