"Google has had two years to kill perplexity and hasn't": Aravind Srinivas on Google’s AI reluctance

As Google intensifies its push into AI with Gemini 2.5 — touted as its "most intelligent AI model" — the AI search startup Perplexity has become a focal point of online debate.

In a recent Reddit AMA, Perplexity AI CEO Aravind Srinivas fielded a wave of questions about whether Google’s release of its Gemini 2.5 models and expanding lineup of AI-powered search tools could make Perplexity obsolete. And he isn’t pulling punches when it comes to Google’s role in the AI race.

In a pointed remark, he reportedly said, “Google have had two years to kill Perplexity and haven’t,” implying that the tech giant’s slow pivot to AI-powered search has left space for challengers like Perplexity to thrive.

Srinivas argued that Google’s reluctance to fully embrace AI is rooted in its business model. “Google can’t lead the AI search revolution due to ad revenue dependence,” he said, suggesting that the company’s massive earnings from traditional search ads create a conflict when it comes to prioritizing user-first, AI-driven search experiences.

While Google has invested heavily in AI— rolling out tools like Gemini and incorporating generative AI into its search engine— Srinivas believes these efforts remain constrained by the need to protect existing revenue streams. In contrast, Perplexity, which offers direct AI-powered answers without ads, is designed from the ground up to deliver a different kind of search experience.

Srinivas’s comments highlight a broader shift in the AI space, where smaller and more agile players are increasingly challenging Big Tech’s dominance.

Last month, Google introduced Gemini 2.5 and said, "Gemini 2.5 models are thinking models, capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.

In the field of AI, a system’s capacity for “reasoning” refers to more than just classification and prediction. It refers to its ability to analyze information, draw logical conclusions, incorporate context and nuance, and make informed decisions. For a long time, we’ve explored ways of making AI smarter and more capable of reasoning through techniques like reinforcement learning and chain-of-thought prompting. Building on this, we recently introduced our first thinking model, Gemini 2.0 Flash Thinking."

"Now, with Gemini 2.5, we've achieved a new level of performance by combining a significantly enhanced base model with improved post-training. Going forward, we’re building these thinking capabilities directly into all of our models, so they can handle more complex problems and support even more capable, context-aware agents," Google said in a blog.