How to bring Google down: AI-powered search vs. traditional search engines

In the last two decades, Google has been the unphased leader in online search, which has shaped how billions of people access information on a daily basis. These iconic red-blue-and-yellow letters have defined the search experience, since even the student that is reading this was a kid. Here, attention would eventually be monetized to create one of the most profitable ecosystems ever created, while the user searches for watermark free powerpoint images, the new release of Drake, or some 2-am conspiracy theories (Weil & Woerner, 2015).

Logically, the search-giant’s model depends on users clicking through search results. Yet, this architecture is now being confronted by AI-powered search tools such as ChatGPT and Perplexity. Such tools essentially provide swift, direct and interactive answers, rather than the list of links we know from our old friend Google. This reduces the number of clicks and time spent on navigating through numerous websites. What does this change tell us? Lets Google it.

According to the insights of Iansiti and Lakhani, this shift reflects a “digital operating model”, which continually learns, scales, and refines its outputs as more users interact with such platforms (Massachusetts Institute of Technology, 2020). This means that AI search has potential to disrupt both user behavior as well as the advertisement-based revenue stream of Google. Since users can now bypass ads and obtain answers more easily from AI models, the traditional search economy could fade away and face major reorganizations. If we put this into recent statistics, reports have already indicated a 2.5% decline in Google Search traffic in the second quartile of 2025 (Similarweb, 2025). This would overlap with the strong rise in usage of AI-driven search platforms. But does this AI-development grow as smoothless as it seams?

This progress is indeed not without its challenges. Incumbents that face disruptive technologies have to, according to research, balance pragmatism with a healthy amount of paranoia, which in this case would make up a warning applying to Google as it races to integrate AI into its own search experience (Birkinshaw & Lancefield, 2023). In addition, AI-generated answers are still facing skepticism in their levels of accuracy and bias. Studies have implied that such Large Language Models are able to create “hallucinations”, which simply is confidently presented fabricated information. In essence, this would pose risks for the users that rely on such answers in critical decision making (McKinsey & Company, 2023).

In regards to data-access, AI-driven search could essentially concentrate informational power within a few proprietary models, which rises scrutiny on transparency, intellectual property, and the evident diversity of online knowledge (Massachusetts Institute of Technolog, 2020). Such a transition is likely to reshape SEO, the display of news, or even the relationship between businesses with their customers.

Though, it is important that, although such AI tools are becoming mainstream, we as a society must make the choice whether to prioritize speed and personalization over verifiability and diversity in information access. As this choice will undoubtedly gradually unfold itself in the upcoming years, we should ask ourselves whether to embrace AI-generated answers as the future of search, while centralizing control of information, or remain with our familiar transparency and diversity, though at the cost of slower innovation.

References

Birkinshaw, J., & Lancefield, D. (2023, June 13). How professional services firms dodged disruption. MIT Sloan Management Review. https://sloanreview.mit.edu/article/how-professional-services-firms-dodged-disruption/

Competing in the age of AI. (2020, January 1). Harvard Business Review. https://hbr.org/2020/01/competing-in-the-age-of-ai

Massachusetts Institute of Technology. (2020, March 3). From Disruption to Collision: The New Competitive Dynamics | MIT Sloan Management Review. MIT Sloan Management Review. https://sloanreview.mit.edu/article/from-disruption-to-collision-the-new-competitive-dynamics/

McKinsey & Company. (2023, June 14). The economic potential of generative AI: The next productivity frontier. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

Similarweb. (2025, August). ai.google website analysis: Global & category rank, traffic & engagement. Retrieved from https://www.similarweb.com/website/ai.google/

Weill, P., & Woerner, S. L. (2015, June 16). Thriving in an increasingly digital ecosystem. MIT Sloan Management Review. https://sloanreview.mit.edu/article/thriving-in-an-increasingly-digital-ecosystem/