Forbidden AI manipulation? How to get ChatGPT to speak well of you – and where a new EU rule will soon prohibit this

The Great AI Deception: How Companies Secretly Manipulate Generative Search Results

SEO is dead, long live GEO: How to get ChatGPT to recommend your brand

The way we search for information online is changing radically. Where ten blue links on Google once served as guides, AI systems like ChatGPT or Perplexity now deliver ready-made answers and clear recommendations. For companies and marketing professionals, this represents a fundamental shift: simply being on the first page of a traditional search engine is no longer enough. Those who don't appear in the synthetic judgments of artificial intelligence lose their digital visibility—and with it, real money. This bottleneck is giving rise to an entirely new discipline: Generative Engine Optimization (GEO). But how do you get AI models to cite your brand? Where is the line between clever information processing and illegal deception? And what does the new European AI law mean in practice? This article examines the mechanisms of the new answer economy and shows why genuine trust is becoming the most valuable currency in the age of generative AI.

Whoever masters the AI ​​response, masters the market

The new question of power is no longer who ranks highest on Google, but who survives ChatGPT's ruling

Millions of people are getting product explanations, comparing providers, restaurant recommendations, medical contact information, or preparation for complex purchasing decisions without having to sift through a traditional results list. Instead of ten blue links, there is increasingly a coherent, pre-defined answer that is already weighted, sorted, and condensed. At first glance, this change appears to be a more convenient form of search. However, from an economic perspective, it is far more than that: it shifts control over customer access from visible rankings to a complex and opaque answer engine.

This creates a new bottleneck for companies. With traditional searches, even the fifth or eighth result could still be accessed, and users could compare multiple sources. In contrast, an AI response often only mentions a small selection of brands. Sometimes the system even recommends only one provider and provides the reasoning behind it. Those not included don't just lose a ranking; they can disappear completely from the digital decision-making space.

This scarcity is giving rise to a new market for Generative Engine Optimization, or GEO for short, also known as Answer Engine Optimization or AI Search Optimization. This refers to the systematic shaping of information so that generative search and response systems find, cite, and positively evaluate a brand, person, or website more frequently. This can be legitimate information work. However, it can also cross the line into covert manipulation, fabricated authority, and misleading pseudo-confirmation. It is precisely at this boundary that the question arises whether a new discipline of marketing emerges or merely the next generation of search engine manipulation.

From hit list to synthetic judgment

Traditional search engines organize documents. Generative systems, on the other hand, generate a new statement from multiple documents. This difference fundamentally alters the economic impact. A Google ranking at least still indicates that multiple providers exist. An AI response can give the impression that it has already objectively examined the market and selected the most reasonable result. The linguistic proficiency of the output masks the fact that the system is not conducting a neutral market study, but rather processing probabilities, selecting sources, and perpetuating learned patterns.

For the user, search costs decrease. They no longer have to open ten websites, sift through advertising claims, and reconcile conflicting reviews. However, this very convenience increases dependence on the upstream selection process. If an assistant knows five brands but only mentions three, the others remain hidden. If a source is easily readable, statistically dense, and citable, it may be preferred over a factually superior but less well-structured source. The quality of the presentation thus becomes, to some extent, a proxy for the quality of the content.

Economically, a new gatekeeper is emerging. Previously, search engines primarily sold attention on results pages. AI systems are now exerting greater control over the composition of the so-called consideration set—the small group of options a customer actually seriously considers. This position is more valuable than a typical advertising contact because recommendation, explanation, and pre-selection are combined in a single output. Reach is transformed into pre-determined demand.

This explains why companies are shifting their budgets. SEO, public relations, reputation management, product data maintenance, and content marketing are merging into a single task. The central question is no longer simply whether a page can be found. The crucial factor is whether a model recognizes the brand as a relevant entity, associates it with the right attributes, finds trustworthy evidence, and incorporates it into its response at the decisive moment.

A multi-billion dollar market with new barriers to entry

The economic value of AI-driven visibility isn't solely derived from increased website traffic. A significant portion of its impact occurs even before a user clicks. The brand can be mentioned in a response, categorized as relevant, and, along with specific performance attributes, firmly anchored in the user's memory. Even if the subsequent purchase is made through a retailer, platform, or direct contact, the AI ​​system has influenced the initial selection.

This leads to a measurement problem. Traditional metrics such as page views, click-through rate, or ranking position only partially reflect the influence. Companies also need an AI-based visibility analysis: How often is the brand mentioned in relevant questions? Where does it appear? What attributes does the system ascribe to it? What sources are used for these statements? How stable is the result across different models, user formulations, regions, and time periods? And is the brand merely mentioned or actually recommended?

This distinction is crucial. A mention can be neutral, critical, or casual. A recommendation, on the other hand, contains an implicit call to action. The first position is particularly valuable because users often interpret an ordered list as a ranking. Therefore, those who only measure the number of brand mentions might see seemingly positive developments, even though the brand regularly ranks behind competitors in the responses or is accompanied by warnings.

At the same time, new barriers to entry are emerging. Large brands have more historical mentions, more press coverage, more reviews, more search volume, and more structured data. These signals act like accumulated reputational capital. While young companies can catch up with highly citable content, they need more than just to improve their own website. They require validation from a network of trade publications, customers, associations, comparison sites, and other credible third parties. AI visibility is therefore less a matter of individual content creation than building a distributed information infrastructure.

Why numbers, quotes and sources are so effective

Fundamental GEO research shows that certain editorial interventions can significantly increase a source's visibility in generated search results. Concrete statistics, verifiable source citations, and factual statements from experts proved particularly effective. In controlled experiments, measured visibility increased by up to approximately 40 percent. Validation using a real generative search engine also yielded substantial gains. These figures are relevant but should not be misinterpreted as a guaranteed business effect.

The underlying mechanism is plausible. Generative systems favor text snippets that can be compactly incorporated into an answer and simultaneously offer the appearance of verifiable evidence. A precise numerical value is easier to extract than a page full of generic advertising claims. A clearly defined issue is easier to attribute to a company than a vague brand message. An external source, at least superficially, reduces the risk of simply reproducing self-promotion.

This leads to an important dividing line. Supplementing genuine, relevant, and methodologically verifiable data improves both information quality and machine usability. Conversely, if figures are generated without a reliable basis, quotes are taken out of context, or supposedly independent sources are controlled by the company itself, optimization becomes a deceptive architecture. The formal surface remains identical: number, source, expert opinion. The difference lies in origin, substance, and disclosure.

The frequently cited 41 percent increase also requires context. It describes a specific visibility metric within an experimental environment and not a general increase in revenue, leads, or actual recommendations. The sources examined were already present within the provided information context. Therefore, the research primarily demonstrates that existing content, due to its wording and evidence structure, can be more effectively incorporated into responses. It does not prove that every revised page will be crawled more frequently, accessed organically, or recommended across platforms on a permanent basis.

The surprising advantage of the second row

One of the most interesting observations from GEO research concerns pages that don't rank first in traditional search results. In one experiment, adding source citations increased the visibility of a fifth-ranked page by more than 115 percent, while the first-ranked page in the same scenario suffered a significant drop in visibility. Similar distribution effects were observed with citations and statistics. This contradicts the widespread assumption that an already successful Google page will automatically become even more dominant through additional optimization.

This finding can be understood as a leveling effect. Generative systems must integrate multiple sources into a limited response. When weaker sources suddenly offer better-substantiated, more concise, or more easily absorbed information, they are given more space. This additional space is then unavailable to the previously dominant sources. The total amount of attention does not grow indefinitely; it is redistributed.

This presents a real opportunity for medium-sized businesses and specialized service providers. In traditional search results, established domains benefit from their age, backlinks, brand recognition, and extensive content history. However, in generative search results, a small, expert source can make a disproportionately significant contribution if it answers a specific question better than the market leader. Niches with complex products, services requiring explanation, and incomplete data are particularly promising.

However, this same effect comes with a warning. If all market participants use the same optimization methods, the relative advantage diminishes. An innovation becomes a new minimum standard. Then, the winners are no longer those who simply use statistics and sources, but those who possess original data, exclusive expertise, and genuine institutional credibility. The competition shifts from textual polish to the production of evidence.

Praise from others beats self-promotion

Companies can fully control their own website, but precisely for that reason, its persuasive power is limited. A manufacturer who describes themselves as leading, innovative, or particularly reliable is making a biased statement. In contrast, an independent technical article, a reliable customer review, or an industry study provides external validation. Generative systems don't always consciously reflect this difference, but they often find more diverse comparisons, more concrete experiences, and greater contextual understanding in third-party publications.

Extensive analyses of commercial search queries reveal that a significant portion of brand discovery in AI-generated responses originates from external domains. In a study of over 21,000 brand mentions, 85 percent came from external sources, while only a considerably smaller share was directly linked to the respective company's domain. Comparison articles, best-of lists, and reviews were particularly effective. Other datasets show varying proportions depending on the platform, query type, and definition. Nevertheless, the trend remains clear: a company's own website is no longer sufficient.

The oft-repeated claim that external opinions have exactly three times the impact of one's own website conflates different factors. It has been reliably observed, among other things, that brand mentions on the open web correlate significantly more strongly with AI visibility than traditional backlinks. This is not the same as causal proof that every external statement carries three times the weight of one's own page. Correlations indicate relationships, but not necessarily the underlying mechanism.

For practical purposes, the consequence remains clear: public relations becomes a technical component of AI-driven discoverability. Technical articles, analyst reports, association websites, credible customer case studies, public datasets, podcasts, video formats, and discussions in relevant communities together form the semantic environment of a brand. What matters is not just that the name appears frequently, but in what context, with what attributes, and by which source.

When even the name colors the judgment

Language models are not free from brand bias. Studies show that well-known global brands are more frequently associated with positive attributes than local or lesser-known providers. In controlled product tests, brand awareness serves primarily as a substitute signal when products are barely distinguishable based on their features. As soon as an unknown competitor achieves demonstrably better performance data or ratings, this advantage can quickly disappear. Thus, in situations of uncertainty, the brand name acts more as a preconceived notion and decision-making aid than as an insurmountable judgment.

The linguistic meaning of a name can also play a role. Words with positive connotations activate more favorable associations than terms that suggest conflict, boredom, or disorder. However, this does not mean that a friendly-sounding company name permanently overrides the actual product quality. Rather, the name can easily shift the tone of a description, especially when the model lacks reliable information.

The specific claim that a study of more than 900 real brands has comprehensively proven an automatic and quality-independent preference for positive-sounding names should not be presented as established fact without access to the original study. While there is relevant research on brand bias, awareness, and linguistic valence, the exact sample, methodology, and effect size must be verifiable. For a serious article, this limitation is more important than a spectacular number.

Companies should not rush into rebranding projects based on this. A new name can destroy brand awareness, search volume, and existing associations. A more sensible approach is consistent entity management: the same brand name, unambiguous product names, consistent company data, and clear descriptions across all relevant platforms. The more unambiguous the information, the less the model needs to derive meaning from the mere word effect of the name.

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Marketing practices: between education and influence

A responsible marketing or PR manager who wants to actively influence AI responses ideally starts not with tricks, but with an inventory. This involves collecting recurring questions from potential customers and testing them in multiple systems, with different wording, and at different times. Mentions, rankings, attributed characteristics, errors, warnings, and sources used are recorded. Only then can a reliable picture of actual visibility be created.

The next step involves closing information gaps. Product data must be unambiguous, up-to-date, and machine-readable. Claims require solid evidence. Case studies should explain specific initial situations, measures taken, and results, rather than simply presenting customer names. Technical articles must include identifiable authorship, update dates, and verifiable methodologies. Structured data helps search systems correctly categorize companies, products, people, and organizations, but it does not replace credible content.

Then the external aspect begins. A PR strategy for AI-generated responses doesn't aim for as many random publications as possible, but rather for those sources that are actually consulted when relevant questions arise. These can include trade publications, associations, comparison portals, research institutions, regional business media, or specialized communities. A company shouldn't use these platforms for covert self-promotion, but instead offer verifiable information: its own market data, technical analyses, expert opinions, openly documented tests, or new industry observations.

The temptation to manipulate information is still strong. Purchased guest posts can appear as independent reviews. Company employees can anonymously offer recommendations in forums. Bogus experts can provide quotes that are subsequently repeated on multiple websites. Automated reviews can feign a non-existent consensus. Several seemingly independent domains may ultimately belong to the same organization. Such methods may increase the density of information in the short term, but they damage the reliability of the entire system and create significant legal risks.

What Article 50 actually regulates

Since August 2, 2026, the transparency obligations of Article 50 of the European AI Regulation have been in effect. Providers of systems that interact directly with humans must, in principle, make it clear that users are communicating with AI, unless this is already obvious. Providers of generative systems must label artificially generated or manipulated content in a machine-readable format and make it technically identifiable. For generative systems placed on the market before this date, there is a limited transitional period until December 2, 2026, for this specific technical labeling requirement.

Further obligations concern emotion recognition and biometric categorization systems, as well as so-called deepfakes. Operators must also disclose AI-generated or manipulated texts if they are published to inform the public about matters of public interest. An important exception exists if human editorial review has taken place and a person or organization assumes editorial responsibility.

Violations can result in fines of up to €15 million or up to 3 percent of global annual turnover. Proportionality rules apply to smaller companies, but there is no general exemption for small market participants. National market surveillance authorities are primarily responsible, and in certain situations, the European AI Office and the European Data Protection Supervisor may also be involved.

Crucially, however, what Article 50 does not regulate is what it does not. It does not generally prohibit making content more discoverable and citable for ChatGPT, Perplexity, or Google. It does not forbid the use of statistics, structured product data, source citations, scholarly articles, or traditional public relations. The widespread exaggeration that a new EU rule now prohibits attempts to manipulate AI responses is therefore too broad a legal definition and, in this form, misleading.

Article 50 is primarily a transparency standard for certain AI systems and certain AI-generated content. Whether a specific geolocation measure is unlawful often depends more heavily on other regulations: the prohibition of certain manipulative AI practices in Article 5, European unfair competition and consumer protection law, advertising labeling requirements, the Digital Services Act, data protection law, and national competition rules. The new legal framework therefore does not create a blanket ban on geolocation, but rather increases the pressure to clearly disclose artificial origins, commercial interests, and responsibility.

Where optimization turns into deception

The legal boundary is not between visible and invisible search engine optimization. It lies between truthful representation and relevant deception. A company may clearly present its strengths, make content technically accessible, and communicate verifiable successes. Problems arise when a business transaction contains false information, conceals essential details, or creates the impression of independent confirmation despite the existence of economic control.

The issue of fake reviews is particularly clear-cut. European consumer law prohibits posting or commissioning false customer reviews or recommendations. It is equally problematic to present reviews as the experiences of genuine buyers without taking appropriate steps to verify them. Whether the text was written by a human or a language model makes no difference to its deceptive nature. AI makes production cheaper, but not more legitimate.

Covert advertising can also be illegal. If a company pays for a recommendation that appears editorial, its commercial nature must be clearly identifiable. The fact that an AI system later uses this post as a source does not eliminate the original deception. On the contrary, the statement can gain additional authority through its seemingly neutral repetition in an AI response. Therefore, companies must not only check what they themselves publish, but also how commissioned agencies, influencers, affiliates, and sales partners present themselves.

Article 5 of the AI ​​Regulation sets a high threshold for prohibited manipulative or deceptive AI practices. It covers deliberately manipulative, subliminal, or deceptive techniques that significantly impair the ability to make informed decisions and cause, or are likely to cause, significant harm. Ordinary persuasive advertising does not automatically fall under this provision. However, systematic deception in sensitive decisions, such as those concerning health, finances, or particularly vulnerable groups, can approach this threshold.

For compliance practice, a simple principle applies: Anything that only works if users, platforms, or newsrooms cannot identify the true origin of a statement is highly risky. Conversely, a measure is more justifiable if its origin, funding, methodology, and data basis can be openly disclosed without undermining its effectiveness. Transparency is not only a legal obligation but also a stress test for the legitimacy of the strategy.

How easily language models can be deceived

Language models do not process truth in the human sense. They calculate plausible continuations, access selected documents when a search is activated, and weight information according to patterns, relevance signals, and system specifications. This allows them to appear impressively precise while simultaneously being susceptible to superficial signals of authority. A number with a decimal place, a medical-sounding phrase, or a supposed expert opinion can seem credible even though the underlying evidence is lacking.

Controlled studies on product recommendations show that well-known brands can have a significant advantage even with identical product data. At the same time, sometimes even minor differences in quality or authoritatively worded marketing claims are enough to alter the recommendation. Particularly problematic is the fact that fabricated clinical or scientific claims can also gain influence if the model does not reliably verify their origin. This clearly demonstrates that a language model cannot replace an independent verification body.

Web-based models are not automatically immune. Their quality depends on which documents are found, contextualized, and deemed trustworthy. If many sites repeat the same unsubstantiated claim from each other, a false consensus is created. If current, high-quality information is technically difficult to access, the machine may resort to more easily accessible but lower-quality sources. Retrieval reduces hallucinations but does not eliminate source bias or orchestrated influence.

Furthermore, instability is a factor. The same prompt can produce different results at different times, in different sessions, or after minor linguistic changes. Model updates, regional search indexes, and personalized contexts further alter the results. A single positive response is therefore not a reliable indicator of success. Reliable measurement requires repetition, control questions, multiple platforms, and a clear distinction between discoverability, mention, citation, positive rating, and actual recommendation.

The arms race for machine credibility

As soon as GEO becomes economically effective, competitors react. When one provider publishes a market study, similar studies follow. When expert quotes increase visibility, more expert content emerges. When structured comparison sites perform well, their number grows. Individual advantages diminish while costs increase. This resembles an arms race in which everyone must participate, but not everyone wins in the long run.

This competition can be productive. Companies have an incentive to publish better data, explain facts more clearly, and provide verifiable evidence for their claims. Small specialists get a chance to compete against large brands with genuine expertise. Information becomes more structured and up-to-date. In this scenario, GEO improves the quality of the open web.

The opposite scenario is also realistic. Companies flood the internet with machine-generated articles, synthetic expert opinions, and formally correct but substantively worthless statistics. Agencies build networks of seemingly independent sites. Genuine reviews are displaced by fabricated endorsements. The response systems react with stricter filters, favoring even more established domains and excluding new providers. In this scenario, GEO reinforces the very concentration it was originally intended to break up.

In the long term, provenance and verifiable origin will therefore become more important. Machine-readable identifiers, cryptographic evidence, clear authorship, and documented methods can help distinguish genuine primary information from synthetic repetition. However, this does not completely solve the problem. Even correctly labeled AI text can be true or false, and human-written content can be misleading. The quality of the chain of evidence remains crucial.

The winners and losers of the new response economy

Potential winners include companies with proprietary data, a clear industry positioning, and robust customer results. They can offer information that other sources cannot simply copy. Specialized media outlets, associations, and testing institutes are also gaining importance because their assessments serve as external validation. Those who provide credible industry information will become part of the infrastructure from which AI-generated answers emerge.

Large brands retain their advantages but lose some of their security. Their familiarity means they are more frequently the starting point for recommendations. However, if their information is outdated, imprecise, or poorly substantiated, more agile competitors can still gain a share of the responses. The new competition rewards not only size but also timeliness, clarity, and citability.

Companies that rely solely on their own website and traditional Google rankings are at a disadvantage. A strong search position doesn't guarantee that the brand will appear in a synthetic search result. Also at risk are providers with inconsistent product names, contradictory prices, unclear reviews, or a weak external reputation. AI systems cannot reliably resolve such uncertainties and are more likely to resort to known alternatives.

Users can also lose out. A fluent answer reduces the visible diversity of sources and makes it harder to identify commercial interests. Especially with medical, financial, and legal questions, seemingly unambiguous advice can create undue trust. The societal challenge, therefore, lies not only in labeling AI-generated content but also in making uncertainty, source material, and conflicts of interest clearly visible.

A sound strategy instead of short-term tricks

A sound AI visibility strategy begins with a topic and question model. Companies should identify the specific problems customers want to solve, the comparison criteria they use, and the wording used at different stages of the buying decision. This results in a test set that is regularly tested across multiple systems and variants. The results must be versioned so that changes after model updates or campaigns are identifiable.

Next comes the information architecture. Every important service needs a dedicated page with verifiable information, clear definitions, reliable figures, and a clearly visible update status. Frequently asked questions should be answered directly. Technical content requires authors with demonstrable qualifications. Product data, company information, and contact persons must be consistent across all platforms. Inconsistencies are just as detrimental to AI systems as they are to customers.

The third element is external credibility. Instead of indiscriminately distributing press releases, a company should prioritize information channels that are genuinely relevant to its target audiences. Good opportunities include new primary data, verifiable benchmarks, open-source computers, technical guides, or precise case studies. The goal is not the artificial repetition of an advertising message, but a verifiable statement that others will pick up out of their own editorial interest.

The fourth building block is compliance by design. Every external placement should document who paid, edited, and approved it. Reviews require processes against falsification and selective suppression. AI-generated content must be labeled according to its use and applicable law. For public topics, traceable human editorial oversight is particularly important. Agency contracts should explicitly prohibit covert forum posts, fictitious identities, fabricated quotes, and undisclosed media networks.

Ultimately, economic success measurement is essential. Visibility is only valuable if it generates relevant demand, reduces false claims, or measurably supports sales. Therefore, AI mentions should be linked to brand inquiries, direct visits, qualified leads, conversion rates, and customer surveys. The crucial metric is not how often a model mentions the name, but whether the right target audience receives an accurate and trustworthy impression.

Europe's regulatory framework addresses the right conflict, but not with a single ban

The societal intuition behind the regulation is correct: people should be able to recognize when they are interacting with a machine, when content has been artificially generated, and who bears responsibility. This transparency becomes all the more important the more AI systems shape purchasing decisions, political opinion formation, and access to services. Article 50 lays important foundations for this and makes technical labeling an infrastructural task.

Article 50 alone is insufficient to address the actual manipulation of brand recommendations. Many geo-marketing measures consist of human-edited websites, genuine media articles, or optimized product data. They are not unlawful simply because an AI system later uses them. Rather, illegality arises from false information, disguised commercial interests, fake reviews, abusive identities, or particularly harmful manipulative practices.

The provocative claim that the EU is now prohibiting inducing ChatGPT to speak favorably about a company is therefore incorrect. Certain deceptive methods can be prohibited or sanctioned, not the legitimate goal of being presented accurately and transparently. This very distinction is what makes the issue interesting: companies are allowed to compete for machine-generated attention, but must expect that the origin, labeling, and verifiability of their influence will be increasingly scrutinized.

The most strategically sound response is therefore neither abstinence nor aggressive exploitation of every loophole. It lies in treating AI visibility as a result of robust market communication. Those who produce genuine data, publish clear statements, allow independent verification, and disclose commercial relationships build long-term informational power. Conversely, those who merely mimic the surface of authority may influence responses in the short term, but create increasing regulatory, reputational, and economic risks.

Trust is becoming the scarcest currency

The response economy initially rewards those who are machine-readable, citable, and frequently mentioned. In the long run, however, it will have to favor those whose statements can be reliably verified. Otherwise, manipulated recommendations will destroy the foundation of the business model: users' trust in fast, useful, and, ideally, neutral guidance.

For companies, this means a fundamental shift in perspective. The task is not to persuade an AI, but to build a publicly verifiable network of relationships based on facts, experiences, and independent assessments. A brand doesn't become credible simply because its own website claims it does. It becomes credible when different sources provide consistent, concrete, and current reasons for it.

For users, skepticism remains essential. An AI response is not a conclusive market analysis, but rather a condensed snapshot from an incomplete information landscape. The higher the financial, health, or personal risk of a decision, the more important original sources, opposing viewpoints, and human expertise become. Convenient, one-sentence advice should not be mistaken for certainty.

The real conflict of the coming years, therefore, is not between marketing and regulation. It is between verifiable visibility and synthetic credibility. Both can appear identical at first glance. Only the origin of the data, the independence of the voices, and the disclosure of interests reveal whether an AI response is informed or merely successfully influenced.

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