The review of Gemini highlights a critical lesson: a powerful AI model can be completely undermined by a poor user experience. Despite Gemini 3's speed and intelligence, the app's bugs, poor voice transcription, and disconnection issues create significant friction. In consumer AI, flawless product execution is just as important as the underlying technology.
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When deploying AI tools, especially in sales, users exhibit no patience for mistakes. While a human making an error receives coaching and a second chance, an AI's single failure can cause users to abandon the tool permanently due to a complete loss of trust.
Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
Even with comparable model quality, user experience details create significant product stickiness for LLMs. Google's Gemini feels much slower than ChatGPT, and ChatGPT's mobile app includes satisfying haptic feedback. This superior, faster-feeling UX is a key differentiator that causes users to churn back from competitors.
Despite access to state-of-the-art models, most ChatGPT users defaulted to older versions. The cognitive load of using a "model picker" and uncertainty about speed/quality trade-offs were bigger barriers than price. Automating this choice is key to driving mass adoption of advanced AI reasoning.
Despite Google Gemini's impressive benchmarks, its mobile app is reportedly struggling with basic connectivity issues. This cedes the critical ground of user habit to ChatGPT's reliable mobile experience. In the AI race, a seamless, stable user interface can be a more powerful retention tool than raw model performance.
As foundational AI models become commoditized, the key differentiator is shifting from marginal improvements in model capability to superior user experience and productization. Companies that focus on polish, ease of use, and thoughtful integration will win, making product managers the new heroes of the AI race.
Recent dips in AI tool subscriptions are not due to a technology bubble. The real bottleneck is a lack of 'AI fluency'—users don't know how to provide the right prompts and context to get valuable results. The problem isn't the AI; it's the user's ability to communicate effectively.
Gemini is converting daily ChatGPT users not just with model capabilities, but with superior UX like better response sizing and perceived speed. Crucially, the trust in the Google brand for search is transferring to its AI, making users more confident in its reliability, even with less complex reasoning.
Despite models being technically multimodal, the user experience often falls short. Gemini's app, for example, requires users to manually switch between text and image modes. This clumsy UI breaks the illusion of a seamless, intelligent agent and reveals a disconnect between powerful backend capabilities and intuitive front-end design.
Customers are so accustomed to the perfect accuracy of deterministic, pre-AI software that they reject AI solutions if they aren't 100% flawless. They would rather do the entire task manually than accept an AI assistant that is 90% correct, a mindset that serial entrepreneur Elias Torres finds dangerous for businesses.
