Revolutionizing Search: The Impact and Future of AI-Powered Chat Interfaces

Today we’re talking about something that’s been on my mind lately. With the rise of AI-powered chat interfaces, I expect to see a shift in the way people search online. And that got me thinking – what does this mean for the search engines we’re all familiar with, like Google?

As someone who works in SEO, I’m curious about how this might change the way we approach search in general. So, in this episode, we’re going to explore the idea that AI chats could start to erode the market share of traditional search engines. We’ll take a look at an example of an AI search engine,, and discuss its strengths and weaknesses. Then, we’ll compare it to a more traditional Google search and see how they stack up.

The Rise of AI Chats

So, what’s behind this potential shift in search behavior? Well, it’s all about AI chats. You’ve probably heard of them, or even used them yourself. They’re these conversational interfaces that can understand natural language and respond with helpful answers.
Now, AI chats are becoming increasingly popular, and it’s not hard to see why. They offer a more personalized and interactive experience compared to traditional search engines. You can ask follow-up questions, get more detailed explanations, and even have a conversation that feels more human-like.
But what really sets AI chats apart is their ability to be trained on specific data sets. This means you can have a highly tailored discussion that’s focused on a particular topic or industry. And the best part is, you can refine your questions and get more precise answers without having to sift through a ton of irrelevant search results. It’s like having a personal expert or researcher at your fingertips, providing exactly the information you need.
For example, I saw a great example of a chat that was trained on a camera’s manual, and the person was asking questions to the chat bot that didn’t quite align with the right terminology. The chat bot understood the similarity with the topic and knew the information they were looking for. It’s like having a personal assistant that can help you find exactly what you’re looking for, without having to dig through a bunch of irrelevant search results.
And that’s what’s so exciting about AI chats. They have the potential to revolutionize the way we search for information online. Instead of relying on broad search queries, we can have more focused and targeted conversations that get us exactly what we need.
So, with the rise of these powerful AI chat interfaces, it got me thinking – what does this mean for the future of traditional search engines like Google? Well, one platform that’s trying to blend the best of both worlds is
Perplexity is an AI-powered search engine designed to provide a more conversational and interactive search experience. Unlike a typical Google search, Perplexity aims to understand the context and nuance behind your queries, and deliver tailored responses that feel more akin to talking to a knowledgeable expert.
Let’s take a closer look at what has to offer, and how it compares to the search experience we’re all familiar with on Google. – Overview

As I mentioned, is an AI-powered search engine that aims to provide a more conversational and interactive search experience. But what does that actually look like in practice?
One of Perplexity’s key strengths is its ability to excel at in-depth research and analysis. Unlike a traditional search engine that may just return a list of links, Perplexity can provide answers by synthesizing information from multiple sources in real-time.
For example, let’s say you need to do some thorough research on a complex topic like the environmental impact of cryptocurrency mining. With Perplexity, you could ask a detailed question, and it would respond with a well-structured summary, citing relevant data and studies to give you a holistic understanding of the issue. It can then help you understand it better by teaching you what certain terms are as well as giving you the information in different ways.
This is how the tool shines in these types of conversational queries. Its natural language processing abilities allow you to ask follow-up questions and engage in a back-and-forth dialog to fully explore a topic. This is great for those times when you have a lot of nuanced questions on your mind and want a more guided research experience, or don’t quite understand the topic and need additional guidance to truly understand.
So, to give an idea of how a search might be performed that ends up tailoring to your specific needs – let’s say you have an upcoming job interview for a senior marketing manager role at a tech company. You could ask Perplexity something like: ” What are the most important things I should know and prepare for in a job interview for a senior marketing manager position at a tech company? I’m currently a senior manager and looking at moving to a different company.”
Rather than just returning a generic list of interview tips, Perplexity would try to understand the specific context of your situation – the seniority level, the industry, and your own background. The content is much different based on the context of your question, and it allows for follow ups that continue to adhere to that context.
However, Perplexity is not without its limitations. As an AI-powered system, it can sometimes exhibit biases or blindspots in its knowledge base. And while it pulls from a wide range of sources, there may be certain niche or specialized topics where its coverage is not as comprehensive as a traditional search engine.
So while Perplexity represents an interesting evolution in search, it’s important to be aware of both its strengths and weaknesses, and when it might be the optimal tool compared to a more traditional approach like Google Search. You are also getting, mostly, summarized data from 3rd party sources, and you have to fact check with the citations given. In many cases, this isn’t an issue, but in more critical areas, it may be better to just go straight to the source.
For example, if you’re looking to do a quick factual lookup – like finding business hours, addresses, or weather forecasts – Google’s search results may be more straightforward and immediately useful. Perplexity excels at providing nuanced, synthesized information, but sometimes you just need a simple, direct answer found on a website.
Google’s search also tends to be better suited for localized searches, like finding nearby restaurants, shops, or services. Its deep integration with maps, reviews, and other local data makes it the go-to for those kinds of location-based queries.
And for certain tasks like online shopping or travel planning, Google’s search results are often more directly actionable. Its tight connections to e-commerce platforms and travel booking sites can make it more convenient for those types of use cases.
If you’re trying to access a specific website or online resource, Google can usually get you there more efficiently than an AI chat interface like Perplexity. The direct search results and website links are often the quickest path. Don’t know the right domain for a brand or restaurant, Google may be the fastest solution.
Perplexity shines for in-depth research, analysis, and conversational queries where you need a more tailored, nuanced response. But Google Search remains the optimal choice for quick factual lookups, localized searches, e-commerce, and directly accessing websites. Understanding the strengths and limitations of each approach will help you determine the right tool for the job.

Comparison to Google Search and General Knowledge AI Chats

So while and Google Search each have their own strengths and weaknesses, there’s another category of AI-powered interfaces that are worth exploring – the rise of general knowledge chatbots.
Platforms like ChatGPT and Anthropic’s Claude have captured a lot of attention for their ability to engage in free-flowing conversations on a wide range of topics. Unlike search engines that return lists of results, or more specialized AI assistants like Perplexity which works to summarize those results, these general knowledge chatbots are designed to be knowledgeable companions that can help you explore ideas, answer questions, and even tackle complex tasks.
The key differentiator is the breadth of their knowledge base. Rather than being trained on a specific domain or data set, these chatbots have been exposed to a massive amount of information spanning science, history, current events, creative writing, and more. This allows them to converse on just about any subject, drawing insights and making connections that a traditional search might miss.
However, it’s important to note that while these general chatbots have impressive depths of knowledge, that information is not necessarily as up-to-date as Perplexity’s real-time data pulled directly from the web. The free versions of these chatbots, in particular, may have knowledge bases that can become stale over time, especially when it comes to rapidly evolving current events or time-sensitive information.
That said, these general knowledge chatbots aren’t without their own strengths. Their ability to converse on a vast array of subjects, making connections and providing nuanced insights, can be incredibly valuable for open-ended, exploratory queries where you’re looking to dive deep into a topic.
So when weighing the different AI-powered search and conversational options, it’s important to understand the unique strengths and tradeoffs of each approach. Perplexity, Google Search, and general chatbots all have their place in the evolving landscape of how we find and engage with information online.

RAG (Retrieval Augmented Generation) Custom Chats

So far, we’ve explored the capabilities of, which provides a more specialized, data-driven search experience, as well as the broad knowledge and conversational abilities of general AI chatbots. But there’s another interesting category of AI-powered interfaces worth discussing – RAG custom chats.
RAG stands for “Retrieval Augmented Generation”, and it refers to a type of AI system that can take a specific piece of knowledge or information, and then engage in a customized, question-and-answer dialogue around that content.
For example, let’s say there’s a detailed industry report on the impact of artificial intelligence on business strategy and operations. With a RAG custom chat interface, you could upload that report and then ask the AI a series of follow-up questions – everything from high-level summaries to drilling down into specific data points, emerging trends, and recommended approaches.
The AI would leverage the information in the report to provide answers, clarifications, and even suggest additional areas for exploration. It could walk you through key findings, help you interpret the data, and even brainstorm how the insights from the report could be applied to your own business challenges.
[Provide a specific example of a RAG custom chat in action, demonstrating how it could engage with a user around a business/technology-focused knowledge base]
The key strength of RAG custom chats is their ability to provide a truly personalized, interactive experience. Unlike a general search engine or chatbot, which may struggle to give comprehensive, contextual responses, these AI assistants are laser-focused on a specific knowledge domain. This allows them to have more nuanced, back-and-forth discussions and really help users get the most out of the available information.
However, RAG custom chats are not without their limitations. For one, they require the upfront work of curating and formatting the knowledge base that the AI will draw from. This can be time-consuming and may limit the breadth of topics available compared to a more open-ended chatbot.
Additionally, the quality of the responses is still dependent on the depth and accuracy of the underlying data. While RAG systems can provide more tailored insights, they are not immune to potential biases or gaps in the knowledge base.
So when comparing RAG custom chats to the other AI-powered tools we’ve discussed, the key distinction is the level of focus and customization. excels at providing wide-ranging, up-to-date information, while general chatbots thrive on open-ended exploration. RAG custom chats, on the other hand, aim to deliver a highly curated, interactive experience centered around a specific knowledge domain.
Ultimately, each approach has its own strengths and weaknesses, and the optimal choice will depend on the user’s specific needs and the type of information they’re seeking. Understanding the unique capabilities of RAG, Perplexity, and general chatbots can help you determine the right tool for the job.
Section 5: The Future of Search and Blending Approaches (10-12 minutes)
So, we’ve covered a lot of ground – from the rise of AI chatbots like Perplexity and ChatGPT, to the specialized capabilities of RAG custom interfaces. It’s been a deep dive into the evolving world of information discovery and search. But the big question is, what does all of this mean for the future?
I mean, let’s be real – traditional search engines like Google have pretty much dominated the game for the past couple of decades. And they’ve certainly tried to stay ahead of the curve, rolling out their own AI-powered search features like SGE and AI Overviews. But the reception to these efforts has been lukewarm at best.
A big part of the challenge is that these AI-infused search results don’t quite capture the nuance and conversational flow of a true chatbot experience. The responses can still feel a bit stilted and disconnected, lacking the fluidity and contextual understanding that makes platforms like Perplexity or RAG custom chats so compelling.
There’s also the issue of trust and credibility. Google’s search results have long been seen as the gold standard for authoritative, up-to-date information. But when you start injecting more AI-generated content into that mix, users may start to question the reliability and accuracy of what they’re seeing.
After all, the AI powering these search features, while impressive, can still exhibit biases, gaps in knowledge, or a lack of nuance that traditional search engines have largely avoided. And users may be wary of relying on AI-curated results, especially for important decisions or high-stakes queries.
So while the idea of blending the best of traditional search and conversational AI is an enticing one, the execution so far has left something to be desired. The user experience just doesn’t feel as seamless or trustworthy as a truly dedicated AI chat interface or a well-established search engine.
At the end of the day, I think the key is having a diverse ecosystem of search and discovery tools, each catering to different needs and preferences. AI chatbots may never fully replace traditional search, but they could become powerful companions – enhancing our ability to find, understand, and apply information in more personalized, efficient ways.
The search landscape is evolving, and it’s going to be fascinating to see how Google and other players adapt. But for now, the melding of these two approaches doesn’t quite seem to be resonating with users in the way that the tech giants had likely hoped. It’s a challenge they’ll need to continue refining and perfecting.


Well, there you have it – a deep dive into the evolving world of AI-powered search and discovery. From the rise of conversational chatbots to the specialized capabilities of tools like Perplexity and RAG custom interfaces, it’s clear that the way we find information online is going through some big changes.
The key takeaway is that while traditional search engines like Google still have their place, these new AI-powered tools are offering compelling alternatives that could start chipping away at their market share. The ability to have natural, back-and-forth dialogues, get tailored insights, and explore knowledge in more interactive ways is really exciting.
But of course, each approach has its own strengths and weaknesses. Understanding the nuances between platforms like Perplexity, general chatbots, and RAG custom interfaces will be crucial in determining the right tool for the job. The future of search is likely to involve a blend of these different capabilities.
Personally, I think it would be a mistake for Google to double down on AI overviews in their search results. The data they’re pulling from isn’t always vetted, and anyone can post their opinion or even satirical content. That kind of unfiltered information can be downright dangerous, especially for queries around important, time-sensitive topics.
Generalized chatbots, on the other hand, are great for getting custom information on less time-critical subjects. And specialized AI search engines like Perplexity have a real place where you want more nuanced responses that leverage real-time data.
In the end, I believe Google is going to lose double-digit market share as people migrate away from them for many types of queries. The appeal of having a conversational, contextual search experience is just too strong. And I wouldn’t be surprised to see more and more users creating their own custom chatbots, trained on their emails, company wikis, or other personal knowledge bases.
The future of search is exciting, but it’s also a bit unnerving. We need to be vigilant about the quality and integrity of the information we’re relying on. But the potential of these AI-powered tools to revolutionize how we find, understand, and apply knowledge is undeniable.
So what do you think? Are you as bullish as I am on the rise of AI chats and custom search experiences? Or do you think the old-school search engine will remain king? I’d love to hear your thoughts and opinions. Be sure to reach out and let me know.
In the meantime, stay tuned for future episodes where we’ll dive even deeper into the evolving search landscape, explore more AI-powered innovations, and unpack what it all means for digital marketing and SEO.