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As the GenAI chatbot market surges, Google's Gemini capitalizes on user demand, nearly doubling its share to 25.2%, while ChatGPT's lead narrows due to stiff competition from newcomers like Anthropic's Claude.
The landscape of GenAI chatbot apps has seen a significant surge in the past year, with the market growing by 152% year-over-year (YoY) since January 2025. This rapid expansion is marked by shifting market shares among major players, with Google's Gemini and Anthropic's Claude making notable gains while ChatGPT's dominance wanes.
The market is beginning to stabilize, which is a positive sign for the industry's long-term health. While the technology itself is advancing rapidly, widespread adoption remains limited. According to Apptopia, most people have yet to use a GenAI chatbot app, highlighting the potential for further growth.
Interestingly, about 20% of AI users are now using at least two apps. This trend suggests that users are not only experimenting with different tools but also integrating them into their daily workflows. Each app is finding its niche based on specific strengths and use cases.

“ChatGPT built the category, but as viable alternatives have scaled, users are naturally diversifying their toolkit,” said Tom Grant, VP of Research at Apptopia. “The multi-app usage figure is the number I’d watch here. It tells me that people aren’t just experimenting anymore; they’re building workflows around specific products for specific use cases. The market could end up looking like streaming where a few major players own the market but multiple players can carve out niches based on product differentiation rather than pure network effects.”
The GenAI chatbot market is evolving rapidly, with new players gaining traction and established leaders adapting to the competition. As users continue to explore and integrate these tools into their lives, the market is likely to see further diversification and specialization. For developers and businesses, this presents both challenges and opportunities to innovate and capture user attention.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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4 February 2026
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