Google’s AI struggles to match search engine revenue

Google’s search revenue growth slowed to 12 percent in the third quarter, marking a deceleration from the roughly 17 percent year-over-year increase seen in the previous period. This trend persisted even as search queries expanded at their fastest pace in five years, according to Alphabet’s recent earnings report. The decline in monetization efficiency is evident in paid clicks, which grew by only 8 percent, down from 13 percent prior. While these figures represent substantial expansion, they fell short of investor expectations for a company projected to spend approximately $200 billion this year on infrastructure primarily to support its artificial intelligence initiatives. Consequently, Alphabet shares have dropped about 14 percent since the earnings announcement, reflecting market skepticism regarding the return on investment for these massive AI expenditures.

The Impact of AI Mode on Monetization

Chief Business Officer Philipp Schindler attributed the revenue-growth slowdown largely to currency fluctuations and shifts in advertiser spending within specific industries. However, he acknowledged that Google’s transition toward an AI-centric interface also played a role. Queries made through AI Mode are growing rapidly but currently monetize at a lower rate than traditional text-based searches. Despite this, Schindler expressed confidence in the company’s direction, noting that internal tests have yielded promising results for new advertising formats. Nevertheless, it remains ambiguous whether these results stem from a strategic rollout of ads or indicate a deeper structural issue with integrating advertising into conversational AI interfaces.

Nate Elliott, a principal analyst at eMarketer covering AI and advertising, highlighted the severity of this shift. “This is the first major disruption that Google is facing to its core business in decades,” Elliott told The Verge. He emphasized that AI is fundamentally altering how users discover information, which directly impacts the click-through rates that sustain Google’s primary revenue stream. The nature of the search experience is evolving; while some queries still yield familiar shopping listings and text ads, others trigger AI Overviews or turn the process into a multi-turn conversation. This transformation challenges the traditional model where high commercial intent leads directly to ad engagement.

Advertiser Perspectives and Data Limitations

From the perspective of advertisers, the current landscape offers mixed signals. Andy Taylor, vice president of research at digital advertising agency Tinuiti, noted that many users searching for specific products like footwear remain focused on purchasing rather than seeking AI-generated explanations. Google argues that AI Mode queries are mostly incremental, meaning they add to total search volume rather than replacing existing ones. These queries often occur in the “upper funnel,” where users are researching potential purchases without a defined product in mind. The company views this as an opportunity to capture long-term advertising value, even if immediate conversion rates are lower.

Currently, Google is expanding ad placements in AI Overviews and across multiple turns in US-based AI Mode conversations. There are also discussions about introducing ads to the standalone Gemini app, though no timeline has been set. For now, advertisers report stable performance. Tinuiti’s same-store advertisers increased their Google paid search spending by 14 percent year-over-year in the third quarter while maintaining their return-on-ad-spend targets. “We’re still seeing a good return, and we’re not seeing performance take a hit,” Taylor said.

However, a significant limitation exists: advertisers cannot isolate the performance of AI-specific ads. Google does not provide separate targeting options or distinct performance metrics for this inventory. Steady overall returns could imply that AI ad traffic is too small to impact aggregate results, or that it performs comparably to traditional ads. Taylor suspects the former, leaving uncertainty about how much revenue AI will ultimately generate versus how much it might cannibalize from higher-yielding traditional searches.

Challenges in Conversational Advertising

The core challenge lies in translating rich conversational data into effective advertising. While conversational platforms allow users to share more context than simple keywords, identifying which parts of a dialogue are relevant for ad matching remains difficult. Elliott warned that success is not guaranteed. “It’s not a slam dunk that it’s going to turn into more effective advertising,” he stated.

Early data from competitors provides cautionary insights. An October working paper analyzing ChatGPT ads found that visits generated from the platform produced 64 percent less revenue per session for retailers compared to Google Search ads ($0.89 versus $2.45). However, this study measured retailer sales rather than platform revenue and may reflect differences in user intent rather than ad effectiveness. Users asking AI tools for recommendations are often in the research phase, whereas those searching Google for specific items may be ready to buy.

Historical precedents suggest that finding viable ad formats takes time. Google successfully navigated the transition to mobile despite initial lower value perceptions, and other platforms underwent years of experimentation before settling on dominant models. Today’s AI ad formats may evolve significantly before reaching maturity. With mounting AI infrastructure costs and double-digit search revenue growth slowing, Google faces pressure to demonstrate how its new search paradigm can sustain profitability. As Elliott concluded, “They absolutely do not have it all figured out.”