Perplexity's Innovative Approach to Revenue Sharing and Advertising
Perplexity, an AI-driven search engine launched two years ago, is set to introduce an advertising platform with a revenue-sharing model by the end of September. This initiative aims to share a "meaningful double-digit percentage" of revenue with select publishers, addressing criticism of content scraping without permission. Unlike Google, which does not compensate publishers for content used in search results, Perplexity plans to create a sustainable revenue stream for publishers by citing sources and redesigning its interface. The company will pay publishers whenever their content is featured in an AI-generated response, distinguishing itself from other AI firms that pay for content used to train models.
In addition to the revenue-sharing model, Perplexity is exploring new ad formats, including video ads and brand-sponsored follow-up queries. The company does not train its proprietary AI models but allows users to choose from well-known models like OpenAI’s GPT-4. Despite the promising aspects of this initiative, the impact on publishers' revenue, particularly in offsetting potential losses from reduced traffic due to AI-generated responses, remains uncertain. Moreover, Perplexity faces challenges in establishing its ads business in a market dominated by giants like Google and Meta, and recent controversies have made it challenging to onboard publishers. Nonetheless, Perplexity is determined to move forward with its revenue-sharing commitment.
Key Takeaways
- Perplexity plans to launch an advertising platform and share revenue with publishers by the end of September.
- Initial partners include TIME, Fortune, The Texas Tribune, Der Spiegel, and Automattic.
- Revenue share will be a "meaningful double-digit percentage," according to Perplexity's chief business officer.
- Perplexity aims to provide more revenue to publishers than Google, which currently shares zero.
- Perplexity plans to allow brands to pay for specific follow-up questions in the "Related" section of AI-generated answers.
Analysis
Perplexity's introduction of a new ad platform, featuring revenue sharing with publishers, is positioned to counteract the backlash of content scraping and to compete with industry giants like Google. This strategic move could stabilize publisher revenues and fortify content partnerships. Yet, the dominance of Google and Meta presents formidable challenges. In the short term, publishers stand to gain immediate revenue; however, the long-term sustainability hinges on user engagement and ad model innovation. This approach bears the potential to redefine revenue sharing in AI-driven searches, influencing future tech partnerships and content monetization models.
Did You Know?
- Perplexity's Revenue-Sharing Model with Publishers:
- Perplexity plans to share a "meaningful double-digit percentage" of revenue with publishers each time their search engine uses a publisher's content in an AI-generated answer, significantly deviating from Google's model that does not compensate publishers for content used in search results. This initiative aims to create a sustainable revenue stream for publishers and forms part of Perplexity's strategy to address criticisms regarding unauthorized content scraping.
- New Ad Formats on Perplexity:
- Perplexity is exploring innovative ad formats, including video ads and the option for brands to pay for specific follow-up questions in the "Related" section of AI-generated answers. This approach diversifies the advertising options on the platform, aiming to enhance user engagement and provide more targeted advertising opportunities for brands.
- Perplexity's Use of External AI Models:
- Unlike other AI companies that train their own models using publisher content, Perplexity allows users to choose from leading external models like OpenAI’s GPT-40. By adopting this approach, Perplexity mitigates the costs and controversies associated with training proprietary AI models and capitalizes on the capabilities of established models, ensuring high-quality and relevant AI-generated responses.