{"id":355,"date":"2026-09-18T11:03:42","date_gmt":"2026-09-18T11:03:42","guid":{"rendered":"https:\/\/blogs.hanken.fi\/research\/?p=355"},"modified":"2026-09-18T11:08:37","modified_gmt":"2026-09-18T11:08:37","slug":"advertising-is-coming-to-ai-chatbots-and-it-could-influence-the-answers-you-get","status":"publish","type":"post","link":"https:\/\/blogs.hanken.fi\/research\/2026\/09\/18\/advertising-is-coming-to-ai-chatbots-and-it-could-influence-the-answers-you-get\/","title":{"rendered":"Advertising is coming to AI chatbots \u2014 and it could influence the answers you get"},"content":{"rendered":"

When Google founders Sergey Brin and Larry Page were still university researchers, they warned that advertising could undermine the trustworthiness of search engines. In their landmark paper<\/a> presenting Google, they also wrote:<\/p>\n

\u201cThe goals of the advertising business model do not always correspond to providing quality search to users[\u2026]We expect that advertising-funded search engines will be inherently biased toward the advertisers and away from the needs of the consumers.\u201d<\/em><\/p>\n

More than two decades later, a similar question is emerging around artificial intelligence. As companies race to commercialize AI systems to offset the amount of cash they are burning through<\/a>, advertising is becoming an increasingly attractive source of revenue.<\/p>\n

But advertising within AI systems presents a new challenge. Unlike online ads, which generally appear alongside information as banners or sponsored links, AI advertising is being integrated into the conversation<\/a> itself, potentially changing the answers you receive.<\/p>\n

AI\u2019s advertising model<\/h2>\n

Today, around half of consumers<\/a> report using AI conversations for search. Indeed, conversations with AI are becoming so common that some even develop dependencies<\/a> with AI companions. As advertising merges with AI, it could reshape these conversations.<\/p>\n

OpenAI has introduced ChatGPT ads<\/a> designed around the way people interact with AI assistants, while Google is testing sponsored answers<\/a> within Gemini conversations. Both approaches appear to follow closely how advertising technologies currently work, including real-time bidding<\/a> and micro-targeting<\/a> users, which allow advertisers to reach individual users based on behavioural data.<\/p>\n

Organizational theorist Henry Chesbrough<\/a> has argued that \u201ctechnology by itself has no inherent value; that value only arises when it is commercialized through a business model.\u201d His insight suggests that emerging technologies can only be examined as commercial systems shaped by financial incentives.<\/p>\n

AI companies say advertising will not change answers<\/a>. Yet advertising technology is difficult to audit<\/a> because outsiders often lack access to the data, and AdTech companies weaponize complexity<\/a> to prevent oversight.<\/p>\n

Preventing paid influence from shaping AI-generated answers remains largely voluntary and depends on AI companies honouring their promises.<\/p>\n

Some AI providers have already raised concerns about the impact of advertising on trust. Perplexity, an AI-powered answer engine, tested sponsored conversations but discontinued the program<\/a>.<\/p>\n

Even with clear labels, users may struggle to distinguish advertising from organically generated information when both appear within an AI conversation.<\/p>\n

New risks in the zero-click internet<\/h2>\n

What happens when advertising no longer directs traffic to a website, but instead becomes part of a monetized AI conversation?<\/p>\n

Commercial AI systems are heading towards a \u201czero-click internet<\/a>\u201d in which users make purchases within a corporate-controlled AI environment, not independent websites.<\/p>\n

This represents a new form of e-commerce where advertising and transactions are integrated directly into AI chats. As a result, valuable user attention and commercial opportunities remain under the control of AI providers.<\/p>\n

Our research<\/a> explores the implications of merging advertising into AI environments. It identifies risks for free speech and free markets. AI-generated answers compress competing perspectives and may make alternative viewpoints less visible. This could create challenges for minority voices and dissenting perspectives.<\/p>\n

It also raises questions about whether large corporations could purchase preferential visibility within AI systems. Smaller firms may face pay-to-play barriers, potentially increasing market concentration.<\/p>\n

AI and disinformation<\/h2>\n

Another concern is the amplification of disinformation<\/a> through AI. The United Nations warns<\/a> that embedding micro-targeted adverts into AI-generated content could amplify misinformation and polarizing material.<\/p>\n

We know that AI answers can be steered by their controlling corporations. One example is SpaceXAI\u2019s Grok chatbot, which generated false \u201cwhite genocide\u201d claims<\/a> in response to unrelated prompts and produced other extremist content<\/a> reportedly due to an unauthorized code change an employee had made<\/a>.<\/p>\n

Merging advertising with AI adds another layer of risk. Existing digital advertising technologies allow messages to be targeted at specific users based on their personal data.<\/p>\n

If similar techniques are integrated into AI conversations, misleading or manipulative messages could be delivered privately, without the public scrutiny that accompanies traditional political advertising.<\/p>\n

This is one concern associated with \u201cdark money<\/a>\u201d in AdTech: when third-party entities use digital advertising technologies for illegitimate purposes, such as election interference.<\/p>\n

Why current defences fail<\/h2>\n

Current defences against disinformation were not designed for AI conversations.<\/p>\n

Some of the most advanced legislation to counter disinformation, such as the European Union\u2019s Digital Services Act<\/a>, was designed for social media and search engines, not chatbots. ChatGPT falls under the act\u2019s scope if<\/em> it works as a search engine<\/a>. However, the act\u2019s rules do not guarantee the accuracy of each chat<\/a>.<\/p>\n

The principle responses to disinformation<\/a>, namely fact-checking and media literacy, were developed in an information environment where misleading claims were public and persistent.<\/p>\n

Fact-checkers assess claims with public relevance<\/a>, such as statements from politicians or hoaxes circulating widely on social media<\/a>. AI conversations, by contrast, are personalized and ephemeral, meaning misleading claims made to individual users may never enter the public information environment where they can be examined.<\/p>\n

Imagine a chatbot repeating a fabricated allegation about a candidate in a private conversation with a voter. The practical challenge is that a personalized falsehood may remain invisible to fact-checkers, leaving the voter misled.<\/p>\n

Media literacy<\/a> faces similar limitations. Users are encouraged to examine sources and compare evidence. However, while search engines presented users with ranked sources, AI systems increasingly provide a single generated response: not simply \u201can\u201d answer, but \u201cthe\u201d answer.<\/p>\n

For example, a company may flood the web with fabricated reports<\/a>. The reports can poison or taint the sources an AI system retrieves. When a consumer asks about the product, the AI turns those planted claims into an authoritative answer, complete with citations.<\/p>\n

Setting boundaries<\/h2>\n

Advertising-funded social media is addictive<\/a> and often rewards conflict<\/a>. Because controversy attracts attention, platforms can turn it into engagement metrics that advertisers value. Advertisers are also not limited to commercial brands; anyone willing to pay can attempt to increase their reach.<\/p>\n

While policymakers are increasingly focused on the governance of digital platforms<\/a>, there are still few guidelines for sponsored AI conversations.<\/p>\n

Policymakers should establish clear boundaries preventing sponsors from shaping the evidence and substance of AI-generated conversations. A label alone is unlikely to be enough.<\/p>\n

AI companies promise that advertising revenue will not influence the substance of their answers. However, such promises may come with a \u201cfor now\u201d<\/a> qualification.<\/p>\n

Writers:<\/em><\/p>\n

Carlos Diaz Ruiz<\/i><\/strong>, associate professor of marketing, Hanken School of Economics
\n<\/i>Broderick Turner<\/i><\/b>, assistant professor of marketing, Virginia Tech
\n<\/i>Erick M. Mas<\/i><\/b>, assistant professor of marketing, Muma College of Business, University of South Florida
\n<\/i>Zeynep Arsel<\/i><\/b>, professor, management, University of Bath and Concordia University chair in consumption, markets, and society, Concordia University<\/i><\/p>\n

The text was originally published in <\/i>The Conversation 16 September 2026.<\/u><\/i><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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