Group 1 - Alphabet reported Q2 total revenue of $96.4 billion, a 14% increase year-over-year, with search and other businesses generating $54.1 billion, up 12% [1] - Google's AI features in search, specifically AI Overviews, have been used by 2 billion people, with conversational search mode reaching over 100 million monthly active users in the US and India [2][3] - The traditional search model, which has remained unchanged for over two decades, is being disrupted by AI search, which directly generates answers and summaries for users [4][5][6] Group 2 - AI search is severing the traditional cycle of search, traffic, advertising, and monetization, leading to reduced clicks and traffic for websites, which could impact advertising spending [7][10] - The global search advertising market is projected to be around $205 billion in 2024, with Google holding 56% market share; however, AI search reduces the need for users to click on links, threatening this revenue model [9][10] - The shift from CPC (cost-per-click) to CPS (cost-per-sale) in advertising is anticipated, where advertisers pay for completed transactions rather than clicks [12][14] Group 3 - Google is testing embedded "sponsored recommendations" in AI answers, while Perplexity is exploring CPS experiments with airlines and e-commerce [13] - The future of search and advertising is expected to evolve into a transaction-oriented model, focusing on selling outcomes rather than traffic [14][15] - The transition to CPS is not yet fully established, and challenges remain in scaling, adapting to new models, and managing costs associated with AI search [16][18] Group 4 - AI search is expected to function more like an assistant, helping users clarify needs and execute tasks directly within the search interface [18][21] - The revenue structure for search is likely to resemble e-commerce, with income derived from transaction commissions, premium exposure, subscriptions, and distribution fees [21][22] - The supply side must adapt by structuring data to be machine-readable, enabling AI assistants to directly access and utilize information [25][26] Group 5 - The future of SEO will focus on creating data interfaces that machines can understand, moving away from traditional traffic-driven models [28] - New attribution models will emerge, emphasizing the entire conversation and user journey rather than just the final click [30][33] - The infrastructure supporting AI search will require significant investment and transformation, moving from traditional indexing to more complex systems [34][36] Group 6 - The search market may become fragmented, with different AI search applications tailored to specific industries, such as e-commerce, local services, and high-risk sectors like healthcare and law [37][38] - The transition to AI search may face resistance due to existing business interests, leading to a gradual evolution rather than an immediate overhaul of current models [38][39]