What revenue potential exists for embedding ads directly into AI user interfaces, like conversational agents or assistant platforms, based on current data and early experiments such as ChatGPT advertising tests and startups like Nexad and Dappier? Can ad placements in AI applications alone cover the high ongoing compute costs and achieve profitability?
Currently, the prospect of AI-integrated ads appears encouraging, although they aren’t a one-size-fits-all solution. Performance marketing trends demonstrate that high-intent and contextually aligned ad placements consistently yield superior engagement rates. This trend spans various formats, from search ads and retargeting to in-app placements and interstitials, including the emerging field of conversational environments.
However, even with elevated CPMs, relying solely on advertising for the substantial compute costs of advanced AI is unrealistic. This situation mirrors the dynamics seen in other rapidly growing channels, such as Telegram Mini Apps, where strong performance still necessitates a diverse monetization strategy.
Research indicates that ads woven into conversations can be perceived as manipulative once disclosed. From a product development and user retention viewpoint, what are the actual boundaries for ad saturation in AI environments before trust or user engagement deteriorates?
In our experience, building and maintaining user trust has emerged as a critical theme over the past two years. By 2025, both advertisers and users have distanced themselves from anything that feels manipulative. Consequently, maintaining clean traffic, transparent logs, and robust anti-fraud measures has become essential. If an ad begins to feel like advice, user trust may be irrevocably lost.
Thus, the key guideline is straightforward: advertisements must remain distinctly separate from the assistant’s voice. Blurring this line can lead to a collapse of trust, wherein no amount of contextual relevance can salvage the format. Sustainable advertising formats are those that are candid about their intent, where alignment between the audience and the offer remains the main factor driving performance.
What advertising formats are currently being tested within products (e.g., bottom-of-response ads, contextual placements, sponsored prompts), and which of these have demonstrated measurable success without compromising the fundamental AI experience?
Across various testing scenarios in emerging channels, the most effective formats tend to replicate existing successful models—featuring high intent and clearly delineated sponsored units such as in-app ads, interstitials, and Telegram Mini App placements. These formats are effective as they engage users at the opportune moment, without disrupting the overall experience.
Conversational advertising should adopt a similar principle. Adjacent placements are effective, while in-conversation steering tends to disrupt the natural dialogue, resulting in user disengagement.
If advertising becomes a significant revenue source for AI solutions, what are the potential impacts on adjacent markets (e.g., search ads, enterprise AI pricing, consumer expectations, regulatory scrutiny)? What external factors should business and policy creators consider?
Should conversational AI emerge as a major advertising channel, it could fundamentally alter the marketing funnel, much like how push notifications and pop-up formats did in the past. The focus of brand discovery could shift to earlier stages of the user journey, where brands optimize their strategies around conversational states rather than relying solely on keyword searches.
For performance-driven platforms, this evolution necessitates immediate adaptation. We must reassess ad formats and client strategies, fundamentally redefining our roles within the purchasing funnel, as conversational AI changes user interactions. Experiences with AI tools are unique, and advertising strategies must evolve accordingly, moving beyond simple replication of traditional models.
Simultaneously, the importance of traffic quality, fraud prevention, and results-oriented optimization will always hold significance, regardless of the advertising channel.
What implications will advertising in platforms like ChatGPT have for retail media?
There has been much speculation about how advertising in ChatGPT could impact retail media networks, but this is unlikely to create a zero-sum scenario. Retail media thrives because it is positioned close to the point of purchase, where consumer intent is high and conversions are more probable. In contrast, ChatGPT advertising would play a role earlier in the user journey during the consideration stage, when users are seeking assistance like ‘what’s the best option?’ or ‘help me decide between…’, influencing choices before they reach a marketplace or retailer site.
In fact, this could enhance the value of retail media by driving more qualified traffic into those environments. The real change will manifest in how brands allocate budgets and tailor messaging throughout the consumer journey. Advertisers will need to carefully consider which channels facilitate consideration and which ones close the sale.
An even more significant shift is behavioral. Discovery is becoming increasingly conversational instead of navigational. Users are asking, refining, and deciding within dialogues rather than merely searching and clicking. This evolution does not diminish retail media networks; instead, it places them within a more intricate, multi-touch advertising ecosystem.
For advertisers, the real question shifts from ‘ChatGPT or retail media’ to ‘how can we develop a strategy that leverages both channels effectively and ensures our brand appears at crucial conversion points’.
Concluding Thoughts on AI in Advertising
By 2026, AI will cease to be considered merely a performance enhancement and will evolve into the backbone of contemporary advertising systems. E-commerce is progressively leaning toward marketplaces and app-based shopping, requiring advertisers to manage numerous variables, including product specifics, pricing, device compatibility, geographic targeting, creative assets, and timing. No team can manually navigate all these elements in real-time anymore. AI will take on the responsibility of managing these micro-decisions—adjusting spend, testing creatives, and fine-tuning campaigns as circumstances evolve.
Human teams will remain vital, but their roles will transform. The most effective advertisers will leverage AI to manage routine optimizations, allowing their teams to focus on strategic planning, product selection, and long-term growth. In an increasingly competitive landscape, AI will delineate those advertisers who can scale effectively from those who stagnate.
