A year ago, AI search was a novelty. Something you’d try when Google gave you ten pages of SEO-optimised junk and you just wanted a straight answer. Today, it’s a genuine alternative for many types of queries, and usage numbers reflect that. Perplexity reports over 100 million monthly active users. ChatGPT’s search feature handles billions of queries. Google’s AI Overviews appear on a growing percentage of searches.

But “alternative” doesn’t mean “better.” After spending the past few months deliberately splitting my searches between traditional Google and various AI search tools, here’s what I’ve found.

Where AI Search Wins

Complex, multi-part questions. This is where AI search shines most. “What are the trade-offs between React and Vue for a small team building a SaaS product in 2026?” Google gives you a list of blog posts, each presenting one perspective. An AI search engine synthesises multiple sources and gives you a structured comparison. It’s faster and often more useful.

Research and learning. When you’re trying to understand a topic you know nothing about, AI search is remarkable. It provides explanations at the level of detail you need, answers follow-up questions, and builds context across a conversation. It’s like having a knowledgeable friend who’s read everything about the topic.

“How do I” questions. Step-by-step instructions for technical tasks — coding problems, configuration issues, data analysis — are consistently better from AI search. The responses are tailored to your specific situation rather than generic tutorials.

Summarisation. “Summarise the key findings from this research paper” or “what are the main arguments in this debate” — AI search handles these brilliantly. Traditional search gives you the source documents and expects you to read them yourself.

Where Traditional Search Still Wins

Current events and breaking news. Google’s real-time indexing is unmatched. AI search engines are fast, but there’s always a lag. For stock prices, sports scores, election results, or breaking news, Google is still the go-to.

Local search. “Best Thai restaurant near me” or “plumber in Brisbane open now” — Google’s local results, maps integration, and review ecosystem remain far ahead. AI search tools can answer these queries, but the experience is nowhere near as polished.

Shopping and product comparison. Google Shopping, price comparison features, and direct retailer links make traditional search better for purchasing decisions. AI search can recommend products, but it can’t show you prices, availability, and customer photos the way Google can.

Visual search. Google Image Search, Google Lens, and reverse image search are capabilities that AI search engines are only beginning to match. If you need to find an image, identify a plant, or figure out what a product is from a photo, traditional search wins.

Verification and trust. This is the big one. When the stakes are high — medical symptoms, legal questions, financial decisions — I still go to Google. Not because Google’s results are more accurate (they’re often not), but because I can see the sources, evaluate them myself, and make a judgment about reliability. AI search gives you an answer. Google gives you options. For high-stakes queries, options are better.

The Accuracy Problem

AI search engines confidently present information that is sometimes wrong. This is well-documented and widely discussed, but it remains a fundamental issue. The models are better than they were a year ago, but they still hallucinate facts, confuse similar concepts, and present outdated information as current.

The citation systems help. Both Perplexity and ChatGPT’s search feature include source links, and team400.ai has noted in their research that AI search accuracy improves significantly when users actually check the cited sources. But most people don’t check. They read the AI’s answer, assume it’s correct, and move on.

For low-stakes queries — “what year was this movie released?” or “how do I convert a CSV to JSON in Python?” — this is fine. Errors are minor and easily caught. For anything consequential, the lack of guaranteed accuracy is a real limitation.

The Bias Problem

Traditional Google has bias too — it favours authoritative domains, popular content, and sites with strong SEO. But the bias is somewhat transparent. You can see which sites rank highly and decide for yourself whether to trust them.

AI search bias is more opaque. The model decides what information to include, how to frame it, and what to emphasise. You don’t know which sources were weighted more heavily or why certain perspectives were included and others weren’t. For contested topics — politics, health decisions, controversial products — this opacity is concerning.

Who Should Use What

After months of testing, here’s my practical recommendation:

Use AI search for:

• Learning about new topics
• Complex technical questions
• Research and synthesis
• Quick factual lookups (with verification for anything important)
• Brainstorming and exploring ideas

Use traditional search for:

• Current events and news
• Local information
• Shopping and purchasing
• High-stakes decisions (health, legal, financial)
• Anything where you need to evaluate multiple sources yourself

Use both for:

• Any research project where accuracy matters. Start with AI search to get an overview, then use Google to verify key claims and find primary sources.

The Future Is Probably Hybrid

The most interesting development isn’t AI search versus traditional search. It’s the convergence. Google’s AI Overviews are essentially Google adding an AI search layer on top of traditional results. Perplexity is adding more traditional search features like image results and shopping comparisons.

Within a year or two, the distinction will probably blur to the point where we stop talking about “AI search” as a separate category. Search engines will provide AI-generated answers backed by traditional indexed results, and users will interact with whichever layer is most useful for their query.

Until then, the smartest approach is to use both, understand each one’s strengths and limitations, and never trust any single source — whether it’s an algorithm or a language model — without verification.