With the rise of Large Language Models (LLMs) like ChatGPT and Bard, an increasing number of internet folk predict the end of search engines like Google and Bing. It is quite natural in any civilisation for newer technology to replace older technology.
But when it comes to search, things are not so straightforward. Neither are LLMs nor their implications linear. I would like to start with the prime reason behind the prediction that search is about to die due to LLMs.
How Are LLMs Killing Search?
Contrary to LLMs and their implications, the reason why internet-folk predict LLMs will kill search is quite simple: the proof is in the pudding.
Ever since ChatGPT 3, more and more users looking for information have been asking their questions to LLMs rather than search engines like Google.
Part of this issue arises form Search Engine Optimisation (SEO). Explained shortly, SEO is the process of manipulating the contents of a web page to “game” a search engine’s algorithm such that the web page is ranked higher by the search engine.
What happens when everybody tries to game a system? Not much good. Today’s Google search results, for instance, are filled with bloated and spammy web pages. I often find myself landing on pages that feature 1500-word articles filled with nonsense for a simple yes-or-no question!
Contrary to this ordeal, LLMs often provide relatively simple answers to questions. When you look at it this way, it feels that it is indeed quite natural that LLMs will eventually replace search. However, this is only part of the story; the facts make the playing field more complicated.
The Facts
For starters, ChatGPT set a growth-record for any consumer application by reaching 100 million monthly active users in just two months since launch.
For comparison, it took TikTok 9 months and Instagram 30 months to reach the same number.
However, as of November 2023, ChatGPT’s entire site traffic was only 2% of Google’s (the world’s top search engine with over 91% market share) web traffic. Furthermore, ChatGPT’s month-over-month user growth seems to be flattening over the past months (source: Similarweb).
We have facts tugging on both sides of the argument. What now?
Well, here is the least objective yet most interesting fact of the lot: ChatGPT has replaced 90% of my own search engine queries. Yet here I am arguing that search is here to stay. Why would that be?
Why Search is Here to Stay — Argument by Contradiction
For the sake of argument, imagine a future where LLMs give you all the information you need.
This means you don’t need to visit a search engine anymore. All the information you will ever need will be available without the hassle of SEO-bloated, spammy websites. But we are missing something here.
Search Vs. LLMs — Illustration created by the author
You don’t use search engines just for raw information. You also use them to find “services”. Imagine that you had forgotten all about the internet (except access to an LLM) and are new to it.
How would you “find” a product/service you are looking for? Sure, you could ask the LLM. But the LLM would only give you indexed information. For the LLM to give you up-to-date information, it has to keep indexing regularly.
In other words, the LLM will have to serve as a wrapper for a search engine underneath it.
Disovery and Visibility are Part of Human Civilisation
When an LLM starts to serve as a wrapper for a search engine, things start to get even more complicated. What the LLM does is synthesize the indexed and ranked information provided by the search engine.
This means that all websites that currently work as “information as a service” will likely die off. Of course, they won’t approve of this. They will likely stop allowing any sort of indexing/crawling of their websites and advertise their services heavily in other niche channels (dominantly social media). Note the word “advertising”.
This is another phenomenon that CANNOT be killed. Moreover, as the “search” experience improves with LLMs, advertising will evolve into more targeted and efficient streams.
Short history of search: Local information brokers turned into yellow pages. Yellow pages turned into online directories. Online directories turned into search engines.
Short history of advertisement: Print ads turned into radio ads. Radio ads turned into TV ads. TV ads turned into internet ads.
My Prediction: At the limit, LLMs will not replace search engines; they will merge with them to evolve and improve the “search” experience. Discovery (search) and visibility (advertising) will continue to evolve and thrive as long as advanced civilisation continues to exist.
If you’d like to get notified when interesting content gets published here, consider subscribing.
This is where retrieval augmented generation (RAG) architectures in the LLM space may play a hand – LLM model is trained and implemented once, but then it augments inference queries via current indexes.
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
Cookie
Duration
Description
cookielawinfo-checkbox-advertisement
1 year
Set by the GDPR Cookie Consent plugin, this cookie is used to record the user consent for the cookies in the "Advertisement" category .
cookielawinfo-checkbox-analytics
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional
11 months
The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
CookieLawInfoConsent
1 year
Records the default button state of the corresponding category & the status of CCPA. It works only in coordination with the primary cookie.
viewed_cookie_policy
11 months
The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Cookie
Duration
Description
_gat
1 minute
This cookie is installed by Google Universal Analytics to restrain request rate and thus limit the collection of data on high traffic sites.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Cookie
Duration
Description
__gads
1 year 24 days
The __gads cookie, set by Google, is stored under DoubleClick domain and tracks the number of times users see an advert, measures the success of the campaign and calculates its revenue. This cookie can only be read from the domain they are set on and will not track any data while browsing through other sites.
_ga
2 years
The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors.
_ga_R5WSNS3HKS
2 years
This cookie is installed by Google Analytics.
_gat_gtag_UA_131795354_1
1 minute
Set by Google to distinguish users.
_gid
1 day
Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously.
CONSENT
2 years
YouTube sets this cookie via embedded youtube-videos and registers anonymous statistical data.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Cookie
Duration
Description
IDE
1 year 24 days
Google DoubleClick IDE cookies are used to store information about how the user uses the website to present them with relevant ads and according to the user profile.
test_cookie
15 minutes
The test_cookie is set by doubleclick.net and is used to determine if the user's browser supports cookies.
VISITOR_INFO1_LIVE
5 months 27 days
A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.
YSC
session
YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.
yt-remote-connected-devices
never
YouTube sets this cookie to store the video preferences of the user using embedded YouTube video.
yt-remote-device-id
never
YouTube sets this cookie to store the video preferences of the user using embedded YouTube video.
This is where retrieval augmented generation (RAG) architectures in the LLM space may play a hand – LLM model is trained and implemented once, but then it augments inference queries via current indexes.