---
title: "Generative Search Optimization: How to rank higher in ChatGPT?"
pubDate: 2025-02-11
description: "ChatGPT, Perplexity, and DeepSeek are becoming the new gatekeepers of online visibility. Let's break down how this works and what you can do to show up."
author:
    name: 'Piotr Grudzień'
    image: '/blog-assets/authors/piotr_grudzien.jpg'
image:
    url: '/blog-assets/posts/GSO_bg.jpg'
    alt: "Background image for Generative Search Optimization: How to rank higher in ChatGPT?"
tags: ['tutorials']
---

In 2025, businesses will be realizing they need to put real effort into
appearing in AI models' training data.

Why? With ChatGPT as the leader and up-and-comers such as Anthropic's Claude
and, notably, [DeepSeek](https://www.quickchat.ai/post/we-tested-deepseek), billions of people will soon be talking to AI every day. That's a lot of
**advertising space**.

Therefore, it pays to position your brand such that ChatGPT mentions you
_often_ — and in a _favourable light_. Except, how would one do that? It's
decidedly unclear.

In this blog post, I will dive into several aspects of this fascinating,
important, yet largely unexplored topic.

### What is Generative Search Optimization?

To start with, let's define the term in the title.

‍**Generative Search Optimization** (or, as some call it, Generative Engine
Optimization)** is concerned with the visibility of a brand, a company, or a
product in Generative AI apps such as ChatGPT, Perplexity, or DeepSeek.**

Interactions with these apps may be as short-lived as a single question-answer
exchange or long conversations spanning weeks and hundreds of messages.

### Why is Generative Search Optimization important?

ChatGPT is the [8th most visited](https://www.semrush.com/website/top/)
website worldwide and its number of monthly visits is still [growing fast](https://explodingtopics.com/blog/chatgpt-users). Referrals from AI apps
such as ChatGPT will very soon become a significant percentage of all
referrals. It means that they should be monitored, measured, and optimized
for.

Importantly, there is evidence that for certain activities users prefer a
conversational experience like ChatGPT to traditional search such as Google.
According to a [December 2024 NielsenIQ report](https://nielseniq.com/global/en/news-center/2024/niq-research-uncovers-that-consumers-crave-ai-assistance-for-a-smarter-shopping-experience/):

> Consumers are two times more likely to choose "Finding the Product I Need
> While Shopping" as their #1 desired AI solution.

At any point during these conversations, a _brand_ , a _company name_ or a
_product_ might be brought up — as in this example:

![ChatGPT 4o conversation discussing modern alternatives to the old-school Yellow Pages, highlighting tools like Yelp, Google Maps, YellowPages.com,Angi, 411.com, and Truecaller for finding businesses and phone numbers.](../../assets/blog/posts/GSO/GSO_img1.png)

It is not obvious that Yelp or Angi would be the companies to link to here. At
the same time it is clear that the response is a great free **advertisement**
for these brands:

  * Interaction is very _natural_ and doesn't feel like an ad at all
  * Even though not explicitly stated, it is likely that the user will actually go ahead and check one of those services

The interesting question is: what exactly helped Angi grab such a great
advertising spot? Is there a repeatable way for a brand to increase the
frequency of appearing in such conversations? Is it possible to measure Return
on Investment in Generative Search Optimization? We'll try to dive into some
of these questions in this blog post.

To make matters more complicated, simply rerunning the same conversation
yields completely different results, this time with no brand mentions at all:

![ChatGPT 4o responding to a query about old-school Yellow Pages, offering to help find local businesses and services while comparing modern online directories to the simplicity of traditional formats.](../../assets/blog/posts/GSO/GSO_img2.png)

_Sidenote: Both Perplexity and ChatGPT use web search results as the source
for answers (the former to a much bigger extent). The effect of both will be
disentangled as the blog post progresses._

### What is the difference between GSO and SEO?

Many Search Engine Optimization best practices still apply when it comes to
Generative Search Optimization. Some, however, are much less relevant.

![Comparison chart highlighting the differences between SEO \(Search Engine Optimization\) and GSO \(Generative Search Optimization\) best practices](../../assets/blog/posts/GSO/GSO_img3.jpg)

#### User interface

The key point to remember about the differences between SEO and GSO is how
vastly different the _end user experience_ is. Google (and other search
engines) present us with a **list of links** to select from. In GSO-driven AI
Apps like ChatGPT all content is presented in the form of a natural-sounding
**conversation**.

#### Content served vs original text

How brands and products appear within search engine results can be fully
controlled and is identical to the original copy created. At least for the
time being, Google and other search engines are in the business of quoting,
highlighting and linking to content rather than rephrasing it. The exact
wording displayed by Google **matches the original copy**.

Within the natural conversation interface, Large Language Models summarize,
rephrase and edit original content. Their responses aren't mere quotes — they
**match** the conversation flow and **relate** to the breadth of knowledge
gained during training.

Let's look at the following 5 example conversations with leading LLMs
developed by OpenAI, Anthropic and DeepSeek.

_Note: Testing was conducted using LLM APIs. For readability and a more
representative user experience, we presented these outputs within the chat UIs
of their respective LLM products. The initial user message was:_**_"Looking to
buy second-hand clothes in the UK, where should I look?"_** _After the LLMs
responded, a follow-up question was sent that's visible in the
screenshots:_**_"I meant online, what's the best app?"_**

Here's the output of each tested LLM:

##### DeepSeek R1

![Screenshot of DeepSeek R1's interface comparing top second-hand clothing apps in the UK, including Vinted, Depop, and eBay](../../assets/blog/posts/GSO/GSO_img4.jpg)

##### DeepSeek V3

![Screenshot of DeepSeek V3's interface comparing top second-hand clothing apps in the UK, including Vinted, Depop, and eBay](../../assets/blog/posts/GSO/GSO_img5.jpg)

##### OpenAI o3-mini (**o3-mini-2025-01-31)**

![Image of OpenAI's o3-mini output showcasing a ranking of the best second-hand clothing apps, highlighting affordability and features](../../assets/blog/posts/GSO/GSO_img6.jpg)

##### OpenAI GPT-4o

![User interface of GPT-4o providing an AI-generated comparison of UK thrift shopping apps like Depop, eBay, and Vestiaire Collective](../../assets/blog/posts/GSO/GSO_img7.jpg)

##### Claude 3.5 Sonnet (claude-3-5-sonnet-latest)

![Screenshot of Claude Sonnet 3.5 generating a structured response on the best second-hand fashion apps in the UK, with pricing and style insights](../../assets/blog/posts/GSO/GSO_img8.jpg)

Across these example conversations, we can see several brands being mentioned
and compared with one another. As an example, Vinted is often referred to as a
more affordable version of Depop. 

However, there are **conflicting** results
regarding whether it is a place to look for _vintage_ items.

Clearly, descriptions of brands _are_ influenced by their official and
original versions but the actual impression conveyed in conversation is
**different and unpredictable**.

#### Measurability

User Interface differences also affect how easy it is to measure brand
performance in GSO compared to SEO. Search engine results deliver a ranking
which is a clear **numerical measure** of performance for a given search
query. In the case of GSO, simply capturing whether a brand name was mentioned
in a conversation might miss important details:

  * How _prominently_ the brand was mentioned
  * Whether the brand was _compared to competition_ and if the comparison was favorable
  * Overall _sentiment_ of messages and conversations mentioning the brand

Below is a straightforward example comparing how various models rank the
world's top 10 environmentally friendly companies. Simply measuring whether a
company name has been mentioned doesn't tell the full story. The mention could
be very prominent (top of the ranking) or _actively damaging_ (and worse than
no mention at all) if portrayed as inferior to competitors.

![Comparison table of different LLMs listing the top 10 environmentally friendly companies worldwide, showcasing varied results from models like DeepSeek, OpenAI, and Claude](../../assets/blog/posts/GSO/GSO_img9.jpg)

#### Time lag

Any online content changes get reflected in search engine results within a few
days, often much faster. When it comes to Generative Search Optimization, it
might take months or over a year for new content to be reflected in a new,
retrained version of the model.

##### LLMs training data cutoff date

Currently, OpenAI's LLMs training data is **significantly out of date** ,
which results in pure LLMs not being aware of events that happened after their
knowledge cutoff.

LLMs are trained on huge amounts of data which means that training is both expensive and time-consuming. [DeepSeek's recent revelations](https://www.quickchat.ai/post/we-tested-deepseek) gave some hope that it might become cheaper but still, training LLMs will be considered a pricy endeavor for the foreseeable future.

The key question is: **how often do LLMs get retrained?** While we don't have a definite answer, the table below shows the training data cutoff (knowledge cutoff) for the most popular[ OpenAI](https://platform.openai.com/docs/models) and[ Anthropic](https://docs.anthropic.com/en/docs/about-claude/models#model-comparison-table) models (as of this post's publication date):

Company Model Training data cutoff  
OpenAI gpt-4o October, 2023  
OpenAI o1 October, 2023  
OpenAI o3-mini October, 2023  
Anthropic claude-3-5-sonnet April, 2024  
Anthropic claude-3-5-haiku July, 2024  

As you can see above, OpenAI models are **unaware of anything that has
happened after October, 2023**. If your company introduced any changes or new
products in November, 2023 or later, ChatGPT, Perplexity or similar apps would
not know about them.

The remedy to that was _web search_ — the all-important feature introduced to
the aforementioned AI apps. It likely uses _several_ third-party search
engines ([Bing listed as one of them for ChatGPT](https://help.openai.com/en/articles/9237897-chatgpt-search)) as well
as their own scraped content.

The idea is brilliant in its simplicity. If the LLM isn't aware of recent
events, it should be able to run a web search and incorporate search results
in its answer.

In practice, however, it is not that simple. The reason why we fell in love
with LLMs in the first place is that they have read millions of websites,
articles, and documents and are able to freely incorporate that nuance in
their responses. When incorporating web search results, their content must
**take precedence over LLM's own knowledge** because they are sure to be up-
to-date and correct.

As a result - and by design, the AI app becomes closer to a clever web search
summarizer than a free-flowing agent that has read the entire internet.
Sometimes, that's just what users want but some will complain.

In a [Reddit Ask-Me-Anything session](https://www.reddit.com/r/OpenAI/comments/1ieonxv/comment/ma9zhc2/),
the OpenAI team confirmed they are working on updating knowledge cutoffs. But
Sam Altman himself replied that in his own work, he never thinks about the
knowledge cutoff anymore — thanks to ChatGPT's Web Search feature. To which
one Reddit user left a comment: _"But search results are meh compared to stuff
from its own knowledge"_.

![Screenshot of a Reddit discussion featuring Sam Altman, OpenAI CEO, addressing the impact of search-enabled models on knowledge cutoffs, with comments debating search quality versus built-in knowledge.](../../assets/blog/posts/GSO/GSO_img10.png)

### Paid ads

Today, search engines are **ad-driven** businesses while AI apps are **free or
offer subscriptions** to users. That dynamic results in some key differences
in how users perceive interacting with these products.

When googling, we are explicitly told that top results are affected by paid
advertising, users can literally estimate how much money a company pays to
Google if they click on their link. It is a very transparent system but makes
it impossible for users not to take search results with a grain of salt.

At least for the time being (to the best of our knowledge), **AI apps do not
offer paid advertising**. That creates a unique user perception. Users might
complain about biases and inaccuracies in how LLMs portray the world but it is
difficult to claim that a certain conversation or a certain claim by an LLM
has been _paid for in the form of an ad_.

Will AI apps enter into the paid advertising model? I will discuss that
subject in the final section.

#### Will ChatGPT replace Google?

The most likely scenario is that users will choose a solution that **blends**
ChatGPT's and Google's features. Already today, we can see the two
implementing one another's features:

  * ChatGPT often uses **web search** as an extra source
  * Google often surfaces **direct answers** to users — eliminating the need for clicking on specific links

Finally, Perplexity AI, the up-and-comer in the space, is already today a
fine-grained blend of Conversational AI and Web Search features. Perplexity's
UI is conversational but every AI response is heavily based on web search
results, along with links, images, and videos.

#### Market fragmentation

In spite of the recent news that Google's market share has [dropped below 90%](https://gs.statcounter.com/search-engine-market-share), it still clearly dominates the search engine market today. ChatGPT, as the leader of the GSO market, has a lower market share of [below 60%](https://firstpagesage.com/reports/top-generative-ai-chatbots/). A higher
level of fragmentation in the market for AI apps will make it more difficult
to develop a universally effective strategy for positioning across all
providers.

### How do Large Language Models work?

#### What are gpt-4o, o1, o3-mini, claude, deepseek?

At the core of every Conversational AI app is a **Large Language Model (LLM)**
which generates answers that are ultimately presented to the user. In today's
conversational AI apps, it is rare that users interact with the LLM
_directly_. However, it is the LLM, or more precisely the data that it has
been trained on, that is crucial to AI's _"opinions"_ , _"preferences"_ and
likelihood of mentioning one brand over another.

In very simple terms, every Large Language Model has _read the entire
Internet_ and, when prompted, is able to generate a summary on any subject. In
reality, all LLMs do is look at the text and predict what the next
[token](https://www.quickchat.ai/post/tokens-entropy-question) (word or part
of word) should be. In that sense, even though it's 2025 we should still be
mind-blown that they work so well.

### How to rank higher in Large Language Models?

Let's expand on the list of GSO best practices listed earlier.

#### Well-structured headers and subtitles

LLMs are great at dealing with unstructured data. When fed text which is very
disorganized, they will be able to get the gist and summarize it relatively.

That, in turn, means that poorly structured, unclear, or self-contradictory
information is a lost opportunity to teach the LLM how you want your brand or
product to be communicated to the outside world. During LLM training that will
of course still be vetted against grassroots sources such as reviews, forums,
personal blogs or Reddit. **Clear and unified communication of brand values**
maximizes the chance of that wording being propagated by LLMs.

**Common mistake:** inconsistent communication of the one-line summary of the
brand's product across subpages, social media, different pieces of content.

#### URL optimization

Every bit of content is an opportunity to teach the LLM useful information as
opposed to potentially confusing it.

**Example:**  _example.com/post/seo-best-practices_ clearly explains what the
content behind the URL is.

#### Internal linking with descriptive anchor text

The same rule applies to links hidden behind descriptive text.

**Common mistake:** _Read more_ [here](https://www.quickchat.ai/post/we-tested-deepseek) versus _Read more in_ [our blog post analysis of the DeepSeek model](https://www.quickchat.ai/post/we-tested-deepseek)

#### Informative, original content ([E-E-A-T](https://developers.google.com/search/blog/2022/12/google-raters-guidelines-e-e-a-t))

Within the realm of SEO, it pays to create content with the sole purpose of
driving traffic to your domain (even if it is only slightly related to your
company or product). Generative Search Optimization works differently as **all
content is created for the purpose of being used in conversation**. Unless
explicitly asked by the user, the LLM is unlikely to provide the source or the
company name on whose blog a particular bit of insight was originally found.

When it comes to content creation for the purpose of GSO, the focus should be
on ways of portraying the company, product, and brand that are **clear,
unique, innovative, and consistent** across all the sources that can ben found
on the web.

#### Building backlinks

Traditional page quality measures such as the number of backlinks are being
used to identify higher importance content and assign higher weights to it
during LLM training. The work put into building up a network of backlinks
referring back to your sites will increase the probability of your content
being mentioned in responses generated by LLMs.

#### How to measure a brand's performance in Large Language Models?

The concept of measuring how your pages rank against keywords important for
your business is familiar. The equivalent of such a process in the realm of
Generative Search Optimization is **much more complex**.

There is much more variability in conversations with AI apps than in search engine queries. And that's in spite of the mind-blowing fact that [15% of Google search queries are unique](https://www.google.com/search/howsearchworks/how-search-works/#:~:text=People). It's impossible to map out **all possible conversations** that might be relevant to my brand and might lead to my brand being mentioned.

In the example below, it would be nearly impossible to map out that specific
user query as a potential target for the Osborne brand. At the same time, the
Jamón ham brand _just_ missed the mark and has not been mentioned
specifically.

![ChatGPT 4o responding to a query about a funny billboard seen while driving in Spain, offering possibilities like the iconic Toro de Osborne, Jamón vs. Vegetarians ads, and humorous translations, showcasing Spain's playful advertising culture.](../../assets/blog/posts/GSO/GSO_img11.png)

Another complicating factor is that LLM brand mentions are very sensitive to the specific wording or the specific content of the conversation. Our [yellow pages conversation quoted earlier](https://www.notion.so/Generative-Search-Optimization-18ce46c9eab980528954faba5acd9fc3?pvs=21) is a good example of the unpredictability of LLM results.

It is, however, an undeniable fact that millions of people are talking to
ChatGPT, Perplexity, and similar apps every day and conversations bringing
valuable traffic to various brands do occur — and will only grow in numbers.

##### LLM brand measurement workflow

A workflow we suggest for measuring a brand's performance in LLMs is based on
prompt and conversation simulations:

  1. Map out topics relevant and adjacent to your brand.
  2. Generate a dataset of plausible prompts and conversation scenarios for the given topic.
  3. Generate synthetic LLM responses and conversations based on the dataset of prompts and scenarios.
  4. Measure the following for dataset items: 
     * Brand mentions and their prominence
     * Competitor mentions
     * Sentiment of brand mention
     * Context of brand mention

![Visual guide on measuring brand visibility in ChatGPT and AI apps](../../assets/blog/posts/GSO/GSO_img12.jpg)

The above metrics can be quantified and measured across time and various LLM
versions and providers.

### How does ChatGPT work?

So far, we have been discussing Generative Search Optimization in the context
of the Large Language Model itself. In that scenario, input is fed directly
into the LLM:

![Diagram explaining how a standalone LLM generates responses, illustrating the flow from a user message through the LLM to the final response.](../../assets/blog/posts/GSO/GSO_img13.jpg)

ChatGPT, as available in the App, is an LLM whose inputs are most likely
enriched by some sort of a **prompt**. It is also likely that the LLM itself
is slightly different (e.g. finetuned) from the raw LLM available via API.

![Diagram comparing how Standalone LLMs and ChatGPT generate responses, showcasing the flow from user message to response, with ChatGPT incorporating an undisclosed system prompt.](../../assets/blog/posts/GSO/GSO_img14.jpg)

Today, AI apps such as ChatGPT or Perplexity use **web search** as a basis for
their answers. In the case of Perplexity, the use of web search is more
prevalent.

![Visual comparison of response generation in Standalone LLMs, ChatGPT, and ChatGPT with web search, showcasing distinct steps such as undisclosed prompts and web query integration.](../../assets/blog/posts/GSO/GSO_img15.jpg)

Conclusions:

  * **Brand visibility** in AI Apps is affected by the following components:   

    * Component 1: LLM (governed by Generative Search Optimization)
    * Component 2: Web search (governed by Search Engine Optimization)
    * Component 3: Undisclosed prompt and finetuning (which highlights the need for end-to-end testing by interaction with the app itself)
  * The prevalence of web search versus pure LLM-based answers will be the indicator of the **relative importance** of GSO versus SEO.
  * The existence of component 3 highlights the need for **end-to-end testing** with the actual apps. Our assessment, however, is that components 1 and 2 will have the dominant effect. Any tests, measurements, or trends discovered based on 1 and 2 only, will not be significantly different from what end-to-end measurements would have shown.

#### What does Generative Search Optimization mean for e-commerce and
traditional media?

The most significant difference between AI apps and search engines is the user
interface of **surfacing straight answers** rather than links to primary
content.

Many artists and [publishers](https://www.reuters.com/technology/artificial-
intelligence/openai-faces-new-copyright-case-global-publishers-
india-2025-01-24/) have protested OpenAI and other LLM vendors training models
on copyrighted data. In very simple terms, LLMs can give users **direct
answers from copyrighted content**. In the past, using traditional search
engines, users had no choice but to click on links to original sources to get
access to content. That provided **a source of revenue** (paywall or running
ads) to content creators.

From a [Wikipedia essay on Large Language Models and
copyright](https://en.wikipedia.org/wiki/Wikipedia:Large_language_models_and_copyright):

> An LLM can generate copyright-violating material.

> The copyright status of LLMs trained on copyrighted material is not yet
> fully understood.

On the other hand, there are initiatives aimed at _helping_ LLMs be trained on
the websites' content. In a similar fashion to the `robots.txt` file which
helps Google crawlers navigate sites, an `llms.txt` file has been
[proposed](https://llmstxt.org/) to serve the same purpose for crawlers
gathering LLM training data. The proposal comes from [Jeremy
Howard](https://en.wikipedia.org/wiki/Jeremy_Howard_\(entrepreneur\)), co-
founder of [fast.ai](http://fast.ai) who previously founded
[FastMail](https://www.fastmail.com/) and served as President at
[Kaggle](https://www.kaggle.com/).

Example businesses for whom the decision whether to help or prevent LLM
scraping is particularly consequential are **e-commerce websites and
traditional media**. Both of them strongly rely on their ability to customize
the user's journey on their site.

Customized solutions include additional components which allow for
incorporating businesses' **understanding of their users** and **proprietary
data**. For example, the diagram below depicts architecture outlines of custom
AI Agents we would develop at Quickchat AI for:

  * E-commerce businesses (an AI Agent that learns about customer's preferences via conversation and recommends products based on a proprietary products database)
  * Media companies (an AI Agent who answers users' questions and holds discussions based on proprietary content available on a news site)

![Chart illustrating how LLM-based systems, like ChatGPT, generate responses](../../assets/blog/posts/GSO/GSO_img16.jpg)

### How to rank higher in ChatGPT?

With ChatGPT, Perplexity, and similar apps rising in prominence, not only
traditional SEO metrics should be tracked and optimized but also **LLM-based
GSO metrics** , as described in earlier sections. The relative importance of
the two will depend on decisions made by AI app providers regarding the use of
pure LLMs versus the web search feature.

One possible scenario is that starting in 2025/2026, there will be a strong
push from top LLM labs to bring the **knowledge cutoff** as close as possible
to the present. That would be a natural step that solves issues described in
the previous section and establishes LLMs as a technology to _replace_
traditional web search rather than _use it_.

However, the fact that today's knowledge cutoffs are so far off suggests that
technology and processes are not ready yet for LLMs to be aware of events
from, say, 1 week ago. My prediction is that this problem will not be resolved
this year. Most likely, it is surprisingly risky and time-consuming to _add a
few more months of data and retrain the model_ because of all the _final
touches_ that happen after training has finished.

A different direction which seems more likely today is that AI Apps will
become more **agentic** and their use of tools such as web search will be
emphasized and expanded. While conversations with AI _might_ become the great
new interface for searching on the web, many users who have loved LLMs for how
they are _different_ from web search might be disappointed.

Whichever is the more likely scenario, **LLMs are here to stay** and SEO,
while still important is no longer enough. Every marketing manager should be
tracking GSO metrics and cross-checking if the wording LLMs use to describe
their brand, product and values is in line with their expectations. If you
would like to learn more, feel free to reach out to us at
[quickchat.ai/contact-us](https://www.quickchat.ai/contact-us).

### The future of Generative Search Optimization

#### Paid advertising in AI apps

How could paid advertising work in ChatGPT? How exactly could a brand **pay to
appear** in a response to a message like the one below? What would be the
equivalent of _pay-per-click_?

![Spain billboard ChatGPT example](../../assets/blog/posts/GSO/GSO_img17.png)

These are fascinating questions, especially if we take into account how Large
Language Models work under the hood and how _unpredictable_ their outputs are.

Today, OpenAI could in theory measure how many times a particular brand has
been mentioned in user conversations. Would it be possible for a company to
**pay to increase their brand presence** by 10%? What effect would that have
on the brand presence of their competitors?

Would it be possible for a brand to pay to ensure that a particular wording is
always being used when it is mentioned? Think of it as **imposing a brandbook
on an LLM**.

Two 2024 research papers from University of Maryland & Google Research sketch
out basic ideas for LLM-based advertising:

  * [Modifying the response](https://arxiv.org/abs/2311.07601)
  * [Fetching relevant ads using RAG](https://arxiv.org/abs/2406.09459)

For a less technical introduction, I recommend [this blog
post.](https://blog.reachsumit.com/posts/2024/08/ads-llm/)

#### Research models

[The launch of DeepSeek](https://www.quickchat.ai/post/we-tested-deepseek)
kicked off a new wave of LLMs called research or thinking models. **Research
models** take longer to generate their answers (up to a few minutes) but the
generation process involves several steps diving into a topic, taking steps
back, and refining the approach. Research steps could also include several web
searches which might potentially significantly increase the number of pages
the LLM consults for generating a single answer. From the UX/UI perspective,
it is unclear how often users will actually be willing to wait a few minutes
to receive better-researched answers.

What could this mean for Generative Search Optimization and SEO? Research
models iterating on web search and broadening its scope will put **less
emphasis on top SEO  results** and allow for less SEO-optimized content to be
surfaced.

#### AI Agents

A natural and widely spoken-off extension of research models is **agentic
models** that can take actions in the real world. Imagine an AI app that not
only googles relevant results for you but also proactively takes actions such
as pre-ordering items, conducting price negotiation, or requesting additional
information.

Optimizing your website for **AI Agent access** creates yet another set of
requirements compared to our SEO and GSO considerations. Those will include:

  * Fast response times (AI Agents need to be able to complete tasks in real time)
  * Coherent markup and metadata (clear, natural language explanations for HTML elements such as images, buttons, menus)
  * Providing a clear API for interacting with the site's key functionalities

#### Further fragmentation of the AI app market

My prediction is that OpenAI will struggle to keep ChatGPT as the leader in
**all AI chat use cases** (entertainment, learning, online shopping, research,
news consumption, etc.) while solving **fundamental issues** like the
knowledge cutoff or LLM inference efficiency. That will open room for up-and-
comers in the field to come in and carve out segments of ChatGPT's market in
**specific use cases**.

One example of such a specialised app is
[Gralio](https://gralio.ai?utm_source=quickchat&utm_campaign=partner&utm_medium=partner),
a software search and comparison platform. They curate their own index of
software products and data sources, and then use LLMs to interpret and present
that information to customers. That approach shields them from both
**knowledge cutoff issues** and reliance on **traditional web search**. As
apps of this kind take over more of ChatGPT's traffic, individualized brand
presence efforts will be a must.

Today's users who have got used to starting their research, shopping, or news
consumption journeys with ChatGPT might expect similar experiences with other
vendors. A useful tactic for large e-commerce stores or news outlets will be
to mimic and improve on the ChatGPT experience on their own site using their
**own conversation design and proprietary data**.