The recent popularization of large language models (LLMs) has profoundly transformed the way we interact with the internet. Whereas search engines such as Google Search and Bing were once the primary gateway to finding information, today—whether due to convenience or the perceived accuracy of results—many users turn directly to the answers provided by artificial intelligence models to support everyday decisions and choices.
According to the survey “Consumption and Use of Artificial Intelligence in Brazil”, conducted by the Itaú Foundation Observatory and Datafolha, more than 55% of the Brazilian respondents stated that they use artificial intelligence tools to search for specific topics, find information quickly, or answer complex questions. These figures indicate that interaction with AI-based systems has already become part of daily life for a significant portion of the population, changing how content is consumed and valued on the internet.
In line with this growing shift in content consumption behavior, Answer Engine Optimization (AEO) has emerged—a set of practices aimed at optimizing content for AI-based answer engines. Unlike Search Engine Optimization (SEO), which seeks to improve rankings on search results pages, AEO focuses on increasing the chances that content will be used as a direct answer by conversational tools.
More than a natural evolution of digital tools, this new logic has the potential to redefine how users access information, products, and services, displacing the central role that traditional search engines have played over recent decades.
New search models
Projections reinforce this trend. A study by Semrush estimates that by 2028 more people will turn to models such as ChatGPT and Gemini to discover content than to traditional search engines. This represents a structural transformation whose effects go beyond technology, impacting business models, the circulation of advertising revenues, and the very informational dynamics of the internet.
At first glance, this shift may seem harmless. However, it raises important questions: what criteria does artificial intelligence use to select recommendations? To what extent can users rely on these answers without independent verification? And, above all, what is the level of transparency regarding possible biases or endorsements embedded in the results presented by LLMs?
We know that, at present, SEO logic still predominates. Content that ranks highest in search engines largely applies careful optimization techniques to gain relevance and visibility. However, this same process can obscure equally valuable material that is less optimized, relegating it to lower positions and often making it invisible to the broader public.
With the rise of AEO, the challenge becomes even more pronounced. Whereas users previously had at least a list of links to explore, they now often receive a single, direct answer, mediated by parameters that remain largely unclear. This shift not only reduces source diversity but may also transform how content is consumed on the internet: smaller pages that once depended on organic traffic may no longer be accessed, while attention and revenue become even more concentrated among a few players.
Moreover, content production tends to be shaped to favor algorithms that determine reach and visibility, encouraging formats optimized for automated systems rather than for the public’s informational needs. In this context, trust, transparency, analytical capacity, and source plurality emerge as essential aspects to consider in the development, strategy, and consumption of digital content.
Future perspectives and recommendations
The advancement of AEO is likely to proceed alongside discussions on ethics, regulation, and responsibility. Companies that produce content will need to understand how to adapt their strategies to reach not only search engines but also AI systems. This means investing in clarity, reliability, and constant updating, as well as considering how their information may be interpreted by algorithms.
For users, the recommendation is to cultivate a critical stance toward the answers received. As with traditional searches, it is essential to compare different sources, verify credibility, and not limit oneself to accepting the first suggestion provided by a language model.
For AI developers, there is a growing need to establish clear transparency parameters, explicitly indicating which sources were used, how information was selected, and what potential biases may be present.
In this context, the adoption of architectures such as Retrieval-Augmented Generation (RAG) stands out. RAG combines the capabilities of language models with active searches in external databases. Rather than relying solely on knowledge acquired during training, RAG incorporates up-to-date and verifiable information, making responses more accurate and better aligned with reality.
Applied to the AEO context, this mechanism can represent a significant asset: instead of answers built only from prior training, the model would be able to offer results that are more transparent, traceable, and aligned with multiple perspectives—benefiting both user trust and the quality of the informational ecosystem.
Ultimately, both SEO and AEO are moving toward coexistence within an increasingly complex digital environment. The true differentiator will be finding a balance between efficiency, diversity of perspectives, and information reliability, ensuring that artificial intelligence serves as a tool to support knowledge rather than a filter that limits users’ view of the world.
