“The real question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without them.” - Marvin Minsky, AI pioneer
Google’s AI versus OpenAI’s ChatGPT
In November 2025, the new version of Google’s Gemini surged in benchmarks and impressed early testers and users, but ChatGPT remained far ahead in adoption. Despite Gemini 3 drawing praise for its speed and reasoning, Google still struggles to turn strong performance into mass user engagement. Legacy reasons are responsible.
Engagement engine
OpenAI’s push toward what Sam Altman calls the next phase of growth is reshaping the chatbot market. New features are arriving, like persistent memory, group chats and personality modes, ChatGPT is designed to keep users returning. This focus on habit formation has helped it cross 800 million weekly users. Google has faced difficulty building social products that retain users. Its past failed attempts such as Google Plus and Google Buzz are proof. Strong engineering does not always translate to sticky consumer engagement in the real world. Gemini inherits this challenge even as Google integrates it into search and Android.
Today's scene
Even with Gemini’s rise, ChatGPT’s reach remains far larger, recording more than a billion visits in October 2025. Reasons include OpenAI’s speed of iteration and Altman’s startup instincts. The goal for Altman now is converting free users into paid subscribers. Google continues to invest heavily in cloud and AI chips, giving Gemini room to grow without pressure for immediate dominance. OpenAI, meanwhile, is betting on deepening user loyalty. The contest is not only about building a better model but creating an AI people find hard to give up.
Summary
ChatGPT maintains a wide lead over Google’s Gemini despite Gemini’s technical strengths. OpenAI’s emphasis on engagement, rapid iteration and sticky features keeps users loyal, while Google refines Gemini within its broader ecosystem. Technical superiority alone is not enough to secure dominance in the chatbot market.
Food for thought
If better performance cannot guarantee user loyalty, what truly makes an AI assistant indispensable?
AI concept to learn: Network Effects
Network effects describe how a product becomes more valuable when more people use it. In AI chatbots, this can mean faster feedback loops, richer data and stronger user habits. Understanding network effects helps explain why some platforms grow rapidly while others struggle to scale.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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