Overview
This post discusses the different participants that are a part of AI Product Recommendation (AIPR), and their respect motifs to better understand the ecosystem overall for general awareness and for spotting strategic implication where available.
For this post, we aim to keep it simple.
Participants in AIPR
User
The user inputs their decision narrative into AI like ChatGPT for product recommendation.
Overall, the user is likely doing so due to their behavioral adaption of using AI for asking questions.
The most prominent reason for using AI and LLM instead of traditional search engines is to save manual energy and effort in doing the search, then reading and comparing multiple articles to find the answer. This can be a cognitive burden on people to find and process information.
Another reason for this behavior could be due to the overall belief that AI has access to “infinite knowledge”. As people become familiar with the fact that LLMs like ChatGPT can access the web directly, it could create a belief that we could “ask ChatGPT anything”.
Additionally, what makes this behavior sticky is that people can ask follow up questions. Compared with Google, where doing a separate search would be necessary.
User’s Motif in AIPR
As mentioned above, the user wants to save energy and effort – which is accomplished by the act of asking AI alone.
Even then, it should generally be assumed that doing any additional tasks consumes time and energy. The user must still read the AI’s response after asking the question, and decide if the answer is satisfactory.
The user wants to find relevant and accurate information from a credible source, while being able to easily digest the answer.
The user prefers to see information in their personal context. If AI can remember details about the user that is relevant and useful for the search (such as allergy, health conditions, preferences, professional context, etc.), this would add significant value to the user’s experience.
AI System (LLM)
At a high level, AI Product Recommendation is a part of AI’s higher authority protocol of answering questions.
Sometimes AI may have the answer from its internal knowledge model, and sometimes it may not. In the latter case, AI may seek information externally using some web search capability like Bing or Google, or their own indexed web information (in the case of ChatGPT’s OAI-SearchBot)
AI Developers’ Motif in AIPR
Much of the user’s motifs are aligned with the AI system.
AI developers want to see that their product is useful to users more than their competitors.
They want accurate information to be shown to users, and for users to find the information digestible and comprehensible.
They want to provide relevant, accurate, and credible information. Providing wrong information would risk their reputation greatly.
In the end however, it is important to keep in mind that AI developers are entities. There are stakeholders, board of directors, or investors – meaning that there are commercial motifs.
- Differentiation – one of the primary motif is the differentiation and/or company vision. Each AI company belongs to a different group of owners, with no two group of owners sharing identical long-term goals. For this reason, the overall roadmap and product vision for each respective AI system will be differentiated from another.
- Adoption – each AI’s differentiation will drive adoption in the intended commercial direction. Not all AI creators may target the same user base. Some may have a much more niche market. Going forward, in fact, it is possible that more AI systems enter the market than operating systems than we have seen before.
- User Experience is key to retaining adoption.
- For developers like OpenAI in context of shopping, they directly state it in this post.
- User Experience is key to retaining adoption.
- Monetization – when there is commercial intent, monetization motif is almost always present. Economic ROI is one of the most agreed upon measurements of success when organizations operate with investors, shareholders, or board of directors.
- Safety First – as the type of entity that humans look for answers with great amount of trust, safety is one of the major concerns of LLMs.
- Accuracy of Information – AI developers would want their users to get accurate information. Irrelevant, wrong, uncredible, or answers that are overly complicated will contribute to a negative user experience, damaging adoption.
- Ecosystem Expansion – AI developers seek to expand the breadth of their ecosystem through complementary products, services, APIs, and integrations. Each additional offering creates new touchpoints for users and developers while increasing the overall value of the ecosystem. (e.g. Google, Gemini, Android, Workspace, Cloud, etc.)
- Maintaining Balance of Power – some AI developers, like in the case of OpenAI have openly stated that they want to maintain balance of power on their platform as seen in their charter of principles and model spec documents. Google and Bing have shown similar ethical concerns in their own documents as these platforms all share the characteristic that they control the flow of world-wide information. Although, when it comes to shopping intent, search engines and AI tend to show signs of monetization, for the most part, they claim that they will not intrude on their own search algorithm.
- Control of Desired Paths – platforms tend to take control of how users interact with platform, especially when the interest are aligned with other motifs like monetization or user experience.
- ChatGPT has already rolled out their shopping research feature, as well as guidance documents for merchants to better align their products for the platform experience.
To keep this post simple, we will keep AI developer’s motifs to this list above.
Web Search System
As LLMs mature, they may develop their own web crawler to create their own index of knowledge. This gives better control for AI developers to manage how they search and find information.
For example, as of July 2026, ChatGPT uses Bing and Shopify in their web search. When a direct AIPR question is asked, ChatGPT then has to convert the user’s decision narrative into web searchable keywords.
This creates an additional step in the web retrieval process and sometimes causes ChatGPT to run multiple searches simultaneously. Sometimes, if the results don’t quiet match the user’s intent, this process will repeat until the results are more satisfactory.
Whereas if ChatGPT was to be able to do the same search in a more LLM-natural way (closer to the user’s query), it might be able to find information easier.
For this reason, ChatGPT is showing signs of developing their own web index as hinted by the introduction of their own crawlers as per this source, and their offline mode accessing the “ChatGPT crawled index” as per this help file.
Similarly, Google AI is embedded right into the search engine. Users can enter keywords (a dominant pre-existing behavior adoption), to which the Google AI can provide an explanation of its own to add value.
As humans increasingly adapt AI in asking questions, the web search capability may either be integrated with AI more closely, or get developed by the AI developer themselves.
Corroboration Sources
Corroboration is a method in which AI systems tend to use to establish experiential truth in “what product is best at XYZ”.
In this process, AI will retrieve multiple sources citing that “product X is recommended for XYZ” or “is best at XYZ” – then AI compare that to other sources to see if they also make the same claim, contradict it, or perhaps suggest a different product is superior.
AI tends to look through retailer sites, product review sites, forums (Reddit or a niche forum), bloggers, and even social media like YouTube and Instagram to find evidence in AIPR.
Though there are different types of corroborators on the web, the motifs are usually similar based on the type of content:
- Consumer content
- Commercial content
Motif of Consumer Corroborator
Consumer content in corroboration refers to reddit forum contributions, product reviews, or posts made by consumers on their personal social media. For consumer content, particularly the reddit and forum contributions are most utilized in AIPR corroboration.
Narr Theory hypothesizes that the main motifs for consumer-content such as these are usually one of the following:
- Need or desire for self-expression
- To share their experience with future buyers
- Delight or upsetting experience
- Incentivized review
Motif of Commercial Corroborator
Commercial content in corroboration refers to some kind of established organization. It can be an ecommerce site, a product review blog, YouTube channel, a lifestyle blog or channel, or some version of institutional existence.
- Monetization
- Traffic or views
- Useful product reviews (not a 100% of the time, but still a big factor)
Organizations that invest the time, effort, and perhaps capital to publish posts are likely to be doing so for some form of upside.
It is not easy to product and edit a YouTube video, let alone rank top in the algorithm.
The same goes for Instagram posts, product reviews, and even written content.
If their product reviews or content is poor quality, consumers will lose trust in their opinion and lose interest in their content. Their motif is aligned with providing quality insights to their audience, though the measurement of quality can vary.
Nonetheless, this type of corroborator is likely to invest resources into learning both the algorithm of platforms and how to produce better content.
“Better content”, however may not always be a more accurate truth or thoughtful opinion. Depending on how engagement is measured and rewarded, content can get morphed to “fit the algorithm” or reduced to only digestible bits when there can be a much more complex dynamic at play.
Nonetheless, the motifs of content creators is an important factor in AIPR, as AI often cites and corroborates from these sources.
Sources of corroboration should ideally have “truthfulness” at core, but this is often not the case in the day-to-day economy.
Client
Here, the client refers to the target product in discussion of AIPR.
The product can be hardware, software, or a service.
In AIPR, the main question at hand is whether or not the target LLM recommends the target product.
Client’s Motif in AIPR
As AI adoption of asking questions increases over time, potentially to the mainstream level, clients will have more to gain from their product being recommended by AI systems globally.
Through AI, products are discovered in perhaps a more natural, conversational and user-personalized way than traditional search engines. As AI keeps getting better, AI may be able to remember more personal details of each user, where solution recommendations become more relevant and therefore more useful, reducing friction to purchase.
The primary motif here is revenue from increasing recommendation share.
In addition, depending on how AIPR is harnessed, there may be an opportunity for clients to uncover product research & development opportunities. This can happen if tools like Profound AI, Peec, and Goodie AI indeed reveal what narratives consumers are entering in relation to their product and category.
The client has motif to become the top recommendation in their category and defend that position.
The client has motif to cooperate with corroborators that are major sources of authority in AIPR.
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