Yesterday morning, I had a discussion on Mastodon about the business model of online search. But I need a little more space to lay out my reasoning.
In this article, I will be talking about the monetization of search, and therefore the monetization of users’ attention. I do not consider this to be desirable, I am explaining how it works today and how it will probably work tomorrow.
The monetization of online search has historically been based on a proven paradigm: the valorization of the traffic it generates. The model is primarily structured around two major axes:
On the results pages: companies invest to position their ads in response to specific user queries. For example, a search for “running shoes Paris” will prioritize targeted ads from Parisian retailers.
On websites: search engines collaborate with a vast network of websites to display advertising campaigns in various formats (text links, banner ads, videos, page skins, etc.). The revenue from these campaigns is then shared between the search engine and the website publisher.
The longevity of this system is ensured by a set of specialized technological tools:
- Platforms for purchasing positions, links, and advertising space: real-time bidding systems allow for the automated and optimized acquisition of ad placements.
- Performance measurement tools: Software solutions are dedicated to counting the number of clicks obtained or analyzing incoming traffic1.
- An advanced targeting system: tracking user journeys allows for the construction of detailed advertising profiles and the implementation of retargeting campaigns.
Over the past two decades, the results pages of major search engines, such as Google, have undergone notable adjustments.
On the one hand, the regulatory framework, particularly in certain countries, as well as public expectations regarding transparency, have led to a clearer visual identification of sponsored links compared to so-called “natural” results.
On the other hand, interface innovations have been introduced, such as the “zero position” (an informational box appearing before the organic results to provide a direct answer) or the algorithmic improvement of link titles.
Despite these surface-level evolutions, the structural foundations of the business model have remained the same: spaces or links, sold for clicks.
The growing integration of generative artificial intelligence at the core of search engines marks a potential disruption.
These AI systems now offer explanatory paragraphs, dynamically written, in direct response to user queries. In this new paradigm, traditional links may still exist, but they are contextualized by pre-existing semantics, which already carry part of the answer.
This transformation of user interaction is not a simple innovation in usage; it implies a profound and systemic change for a significant part of the search engine business model. If AI provides direct and complete answers, traffic to source websites could drop drastically. In return, these answers create considerable potential for influence, capable of determining future navigation or even a purchase.
How will influence be monetized in this new digital landscape?
On the results pages: let’s take the example of a search for “best floral perfume for women” which would have previously offered a product page for “Dior, J’adore” eau de parfum on the Sephora website as the first link. In an AI response, will the perfume be highlighted as “the most well-known,” “the best-selling,” or “the most appreciated,” or will the Sephora brand be highlighted in connection with one of its current campaigns? On what criteria will this semantic choice be based? Will the response be personalized based on the user’s profile information, and if so, with what level of granularity (loyalty to the perfume, loyalty to the seller, sensitivity to social proof)? Regardless of the client, will a monetary value be assigned to the exposure to this type of message?
Evolution of purchasing: how will advertising campaign purchasing platforms have to evolve? Will it be necessary for a brand to define the semantic fields with which it wants to be associated? Will it be necessary to define a specific semantic universe for each product or service? Consequently, how will the associated bidding mechanisms evolve and on what new criteria (qualitative, contextual, engagement) will they be based?
Evolution of measurement: Most analytics are gradually transforming from tracking raw traffic to analyzing user cohorts and their behaviors. It is therefore likely that analysts will find themselves comparing the impact of different AI-generated messaging on specific audience segments.
The objective will then be to identify the semantic fields that are most likely to drive engagement or conversion, and to determine at what frequency of exposure a message is likely to provoke an engagement or a conversion. From a project perspective, this means redefining key performance indicators beyond the simple click, to integrate measures of influence and persuasion.
Evolution of targeting: Profiling tools will, if they haven’t already, evolve to integrate people’s sensitivity to a particular lexical field, level of language, or type of argumentation. This prospect, while technologically appealing for advertising optimization, raises a major ethical concern: the risk of creating an internet that increasingly confines individuals to their cognitive biases or existing preferences (“filter bubbles”), rather than allowing them to express and develop their potential.
This aspect directly touches on the quality of the user experience and the societal responsibility of digital platforms, particularly in terms of equitable access to information.
Evolution of ad placements on sites: once the use of AI for advertising becomes widespread, it should gradually spread to all spheres. Instead of affiliate links, entire paragraphs of sites or articles should soon be written by AI in monetized placements within the content itself. I’m not looking forward to seeing the first press articles containing a sponsored-content piece within the article itself, with the same logic of evaluating the value of the paragraph based on its suitability for targeting.
Google, currently a hegemonic player in the search market, could manage to maintain its dominant position by adapting its model, but it’s not going to be easy. Google’s considerable growth over the past twenty years has been mainly built on capitalizing on and optimizing its existing business model. This structural inertia could paradoxically be a hindrance to its ability to reinvent itself in depth.
The key to its future success might lie more in mastering the ecosystem of exchanges and interactions between artificial intelligences than in owning and operating all the end services themselves. This would mean Google would have to work on standardizing the protocols and interfaces that allow intelligent agents, developed by various actors, to communicate and collaborate effectively. This is the stated objective of the Google Agentspace project: to define the rules of this new market rather than trying to dominate every specific application that would emerge from it.
This reminds me of the strategy historically adopted by Google with Chrome. By investing heavily in the development of Chrome and offering the browser to the public for free, Google gained considerable influence over the evolution of web standards, allowing it to steer many standardization efforts in a way that was favorable to its ecosystem.
Will Google be able to replicate this success in an AI ecosystem that is already much more fragmented and competitive than the browser market was at the time of Chrome’s launch?
And how will this be articulated with the evolution of the regulatory framework in certain regions, such as the EU, which imposes a “transparency obligation” (notably in “semantically optimized” responses, via the AI Act and the Digital Services Act)?
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I remain convinced that there is a conflict of interest when the tools belonging to the seller of the advertising are considered the only ones capable of judging its effectiveness. I have never known tools that praised the virtues of advertising on acquisition as much as Google Analytics itself. ↩