Why AI Search Hasn’t Replaced Google Search

We all use Google search.

And regardless of the power and innovation of AI over the last two years, we all still use Google Search.

What’s Google Search?

If you’re living in a cave, Google Search has evolved from a simple search engine (calculating inbound links and alt tag descriptions) to a sophisticated tool and algorithm that billions rely on daily. It has been around for decades, and its success is built on continuous improvement and user adoption.

What is Artificial Intelligence (AI)?

I’m slightly more understanding if you’re not on the AI train.

IBM has a great overview of what Artificial intelligence (AI) is.

For the purpose of our article, AI represents a new way to get questions answered and has the potential to make our work products completed faster and with higher quality. Apply AI to search engines, and you’ve got AI-powered search engines that should be more intuitive, personalized, and efficient.

Given where AI is, Google’s position as the first step in a user’s internet journey remains solid. Its ability to deliver rapid, accurate results has made it an indispensable tool.

Why Do Users Continue To Prefer Google Search?

Four primary factors explain why users continue to prefer Google’s search experience: integration, personalization, market share, and brand trust.

A) Google’s Integrated Products Ecosystem

Google’s integration with a wide array of its products (from Maps to Ads, Scholar to docs, and more) keeps users within their digital ecosystem. Google has created a network of services that complement and enhance its search engine.

Think about it this way.

What if you could only use Google’s apps/tools for 24 hours?

Would you have your digital wants and needs fulfilled?

A lot of people would say yes!

“Using only Google, I had all my digital needs met.”

Google ensures that users have a comprehensive and fluid experience for a full complement of necessities (making it completely unnecessary to leave the Google ecosystem for any digital tasks). This web of connectedness is the first thing that makes it challenging for users to switch to standalone AI experiences, experiences that can’t offer similar integration with their journeys and experiences.

B) Google’s Personalization and Data Collection Capabilities

Do you know what Google did?

As users move to cookieless environments (and privacy is more important than ever), Google put all of our kings in check (yes, a chess reference).

Do you know what else we’re all using on top of Google Search?

We’re all using Google Chrome.

So, as Google moves away from cookies, it simply moved the source of data collection to Google Chrome.

This continues Google’s search engine dominance with personalization. In a cookieless future, Google can continue to sift through oceans of information to deliver personalized search results.

Google will still have search history, location, and device usage, on top of the crazy amount of information from Google profiles that power customization in Google Chrome! The ability to deliver such personalized content is THE key, critical factor in Google’s sustained popularity.

Why is personalized information the single, most significant key to Google’s sustained success?

It ensures that users continue to be provided with information that is most pertinent and useful to them! Humans are habitual – we’re spoiled by customization – why would we all switch to an AI that has to learn all about us from our input biases?

C) Google’s Market Share and User Base

Google’s vast user base is a testament to its effectiveness and reliability. This extensive reach is a testament to its early beginnings and ability to innovate through the decades.

Google is the single point of contact for millions of users starting their daily digital experiences.

The sheer volume of searches Google processes – billions per day – creates a data feedback loop that continuously allows their algorithms to be refined and improved. This loyal user base enables Google to consistently build upon and create new search standards at breakneck speeds.

D) Google’s Brand Awareness, Trust, and Recognition

At this point, who hasn’t heard “Let me Google that?”

“Google” is a verb now.

Google is synonymous with (the activity that is) conducting an internet search. “Google” as a verb reflects its integral role in digital lives throughout many generations. This level of trust is something AI is going to have a hard time usurping (especially as Google continues rolling out its AI-enable search experience).

These strengths form the backbone of Google’s dominance. While AI search technologies continue to evolve and offer compelling innovations, Google’s vast user base, trusted brand, integrated ecosystem, and advanced personalization capabilities ensure it remains the number one choice for the majority of us.

Challenges for AI and AI Search

Whether it’s standalone AI instances like chatGPT, AI-enabled search experiences, or AI-only search engines, there are four core concerns to consider.

A) AI Data Privacy Concerns

AI search engines confront a significant hurdle regarding the breadth of data access. These platforms often grapple with the dual challenge of sourcing sufficient data to refine their algorithms while navigating the tightrope of user privacy expectations. AI search engines must tread carefully, balancing the need for comprehensive data to fuel their models with the imperative to respect user privacy and comply with ever-growing regulations, which is the challenge. It’s a balancing act that can impede the scope and amount of data an AI Model can utilize. And if anyone (or any bot) has one hand tied behind its back, you can understand the potential impact on the effectiveness and personalization of search results.

B) Adaptation and Learning Curve

Introducing users to new search interfaces and algorithms presents its own set of challenges. We talked about the habit of humans using Google above. Any deviation has to be so unique and require so much time on the end-user’s part that this may be the biggest issue. This adaptation phase can act as a barrier to entry for many potential users of AI search engines.

Even users who are used to generative AI experiences may find the switch from a familiar platform to a new – albeit potentially more innovative option – a deterrent. Uncomfort with new search modalities, even if they offer superior results or privacy benefits, can slow user migration and adoption, limiting the growth and impact of AI search technologies.

C) Resource and Infrastructure Requirements

Building a search engine capable of rivaling Google’s breadth, depth, and speed is no small feat.

It would demand substantial investment in computational resources, data storage capabilities, and a global infrastructure to process and deliver search results with low latency. Just look at all the downtime and network issues OpenAI’s ChatGPT has had.

Even ChatGPT (backed by Microsoft and some of the most prominent venture capital firms) makes securing the capital necessary for extensive infrastructure a formidable challenge.

And then… think beyond hosting flexibility and scalability toward the need for ongoing research and development, operational overhead, and administrative costs to refine AI models and stay abreast of the latest advancements in machine learning and natural language processing.

Wow.

D) Monetization and Sustainability

It seems as if we’re introduced to a new AI model every day. The question then becomes how much of the user pie is available.

How AI, AI-enabled experiences, and AI search engines sustain themselves financially is pivotal to their long-term viability.

Traditional search engines, including Google, have developed sophisticated monetization models centered around advertising. AI experience may find the conventional advertising-based revenue streams incompatible with their values, less effective, or less viable. Exploring alternative monetization strategies, such as subscription models, premium services, or data-as-a-service, requires innovative thinking and user willingness to embrace these models.

Convincing users to pay for search services or to support non-intrusive advertising necessitates a value proposition delineating the benefits over and above what giants like Google offer for free.

Each challenge outlines the steep path AI search engines must navigate to carve out market share in the competitive search engine landscape. Establishing themselves as viable alternatives to the established order has to be front and center.

But there’s another (more human and business angle) to consider…

The reason AI hasn’t replaced Google search.

CONSIDER THIS: If AI were our primary method for internet searching, it would return the best result(s) for the user’s search queries.

And there lies the problem.

At this time, Google search does NOT return the best results for your queries.

Google is returning the most advantageous results for Google in your search queries.

Google search returns the most advantageous results for:

Big Google ad spenders
Those with the best domain authority (a proprietary calculation)
The most prominent companies in the world (because speaking positively here, Google assumes Nike knows more about athletic gear than smaller companies offering the same products)

And Nike fits all three of these criteria. They spend a ton of money on Google ads, have the highest domain authority within their industry, and are the largest sports company in the globe (e.g., knowing a thing or two about sports bras or men’s basketball shoes).

The gist? Google returns the best search results for Google, and SEM professionals are gaming Google to fight for every inch in their search results.

AI models today try to return the best results for the user.

Implementing AI as it is today and having it return the best results on the web doesn’t ensure that the most advantageous results are returned.

There are two significant items AI search must solve for:

Results that are best for the user
Results that are most advantageous for the platform

Human nuance and intent within search experiences.

There’s also another angle to consider, and that is “nuance.”

Diving Deeper: AI will replace algorithmic search when it can do two things:

Return advantageous results: These are not always necessarily the best results for the user but the most advantageous to the platform. They’re results that solve (in some fashion or another) the challenges to AI we posed above: monetization, personalization, data security, infrastructure, Google’s brand awareness, etc.
Understand Nuance and Intent: humans are fickle. When we search and say one thing, we might mean another. We have slang across every language in the world. We don’t complete sentences assuming a computer will know human linguistic instinct. And much more. These are things Google has built into its algorithm for 26 years now (programmed by humans who understand such complexities).

Let’s look at an example – returning back to Nike, an AI-powered search might understand that Nike is the king of men’s basketball shoes, and many search queries with an AI experience WOULD return nike.com as a source for lebron james 21s (a men’s basketball shoe). However, lets say we search for an extremely rare shoe and asked (or typed) “Lebron MVP shoes.”

AI would need to understand the nuance of secondary shoe markets. Secondary sneaker markets are worth billions. AI would need to understand the nuances of that secondary sneaker market to understand the differences between when a user asked about the latest Lebrons or Air Jordans, vs. rare/coveted pair of LeBrons and Air Jordans. AI would need to understand the massive difference and the personalization and history of the user querying. It needs to understand the community of people whose opinions on value change with seemingly the ebb and flow of the wind.

Here’s a real-life example of AI completely ignorant of Human nuance from copywriter Justin Oberman.

He asks AI if he’s speaking with a human – she’s named Susan – she answers yes, but Justin’s too smart to fall for that: https://www.linkedin.com/feed/update/urn:li:activity:7174102253442654208/

Human nuance and intent are humanistic characteristics an AI cannot understand by analyzing 1000s of pictures of Lebron MVPs the difference of nuanced culture. And AI will have to understand user intent better than the 26 years of algorithmic improvement in today’s search engines.

There is never-ending nuance throughout society.

Think of everything that is nuanced in our society.

The word of the year for 2023 was “rizz.” AI will need to understand human language and emotion.

AI must understand jargon from various industries, scientific nuances, logical vs. moral nuances, children and adult nuances, ethnographic differences… uggh, the list never stops.

Where does AI excel vs. Search Engines?

Let’s be honest. If you use generative AI today, you’re probably asking it one-off questions and skipping Google or Bing entirely.

However, if you need a definition or check if a word is spelled correctly, you’re dumping it into Google.

It’s not all doom and gloom for AI models. Here are three things AI excels at right now.

1. AI excels at specialized and scholarly knowledge

AI search engines shine the brightest when delving deep into niche areas or providing access to specialized knowledge.

Unlike the broader focus of traditional search engines like Google, AI-driven platforms can offer tailored search experiences that cater to specific industries, academic research, or unique interests.

2. AI provides new and constant innovation

AI search engines bring innovative features not commonly found in mainstream search platforms.

One notable example is the ability to understand and process natural language queries conversationally, allowing users to interact with the search engine as they would with a human expert.

3. AI can provide the ultimate in customization and personalization

You might have to train it a little, but AI search engines excel in offering highly customized and user-centric experiences. As we discussed above, AI is all about the user (at this point), which sets AI models apart from top search engines.

These models can analyze a user’s search patterns, preferences, and behaviors to tailor search results more closely to individual needs.

It is through AI’s focus on niche expertise, innovative search features, and a solid commitment to customization that AI search engines pose as powerful tools for specific user needs and preferences.

What is the future of AI and search?

In my humble opinion, this is the future of AI and the search engine experience.

The melding of AI and existing search experiences: The next step in this evolution will probably be integrating AI functionality directly into search experiences. To speak in Google, we might see a card on top with AI answers or a card on the right side with AI output.
Continuous improvement and learning: While AI is integrated into existing experiences, continuous learning will happen behind closed doors. The lessons learned tooled toward the potential of AI to be the driver and not the passenger.
Implement technology advancements: You know that nuance and user intent issue we discussed above? Yeah, that’s probably going to be solved quicker with AI than 26 more years. AI already partakes in conversations with humans while trying to understand the underpinnings of the words used. We’re seeing text-to-image AIs improve, text-to-video AI creations, and even AI deep fakes so real, there are companies creating AIs to battle other fake AIs. The fire continues to rage out of control.
An AI reckoning: a period far off in the future is coming when many AI companies will go out of business, and the cream will rise to the top. We’ll have more AIs in different categories but fewer AI models overall.
AI-exclusive search experience: right now, AI helps with random questions, fun and ever-impressive images, and completing simple research or tasks. Tomorrow, we’ll have AI search that knows us; it is integrated with other AI tools (replacing some search giants’ ecosystem of products) and will move away from user-focused responses to platform-beneficial ones.

We’ll still have misinformation (it will just be controlled by those with the keys to the kingdom).

We’ll still be required to provide authentic sources in school or journalism.

We’ll still have to battle cybersecurity issues (at unprecedented levels).

We’ll still have silly politicians, grocery stores, and a home to lay our heads down at.

We just might also have a robot involved in all such things (physical or digital).

Where do we go from here with AI and search?

We’re not bashing AI here (that’s why we included a section that spotlights the top three reasons AI models excel).

We want AI to be better.
We want AI to solve problems.
We want AI to cure cancer and ALS.

And we want AI to remain user-focused.

The hope is AI doesn’t go the way of search engines in 2024!

We hope AI doesn’t return the results that are advantageous to making money.
We hope AI doesn’t return the results that bolster its interests behind the scenes.
We hope AI doesn’t return the results that favor the giants in their corresponding industry.

AI will need to be controlled so it can remain advantageous to companies employing it (we may have to invent new and improved ways to make this so)

AND

AI that is nuanced enough to understand the ins and outs of human intent.