Google’s latest Gemini update highlights a broader problem in consumer AI: companies are increasingly turning internal tools and capabilities into separate products that users must learn to navigate.
In its announcement of new Gemini Live voice features, Google said users should not have to determine whether a task requires Spark, Daily Brief or a simple inbox search. Yet those are precisely the distinctions the Gemini app currently asks users to make.
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Too Many Features, Too Many Names
Gemini users can switch between Chat, Spark and Daily Brief, each with its own icon and place in the app. Rather than simplifying the experience, the growing list of branded features risks making the underlying technology more visible than it needs to be.
Daily Brief illustrates the problem. Google describes it as a source of personalised, proactive updates based on information from services such as Gmail and Calendar. In practice, however, some of its suggestions can feel less like useful assistance and more like unsolicited reminders about previous searches or unfinished research.
Spark has almost the opposite problem. The feature can act as an AI agent capable of completing tasks on a user’s behalf, but packaging that capability under a separate brand forces users to understand when and where they should use it.
A simpler approach would be to let users describe what they need and allow Gemini to determine whether a standard response, an agent or another capability is appropriate.
Gemini Is Not Alone
Google’s approach reflects a wider trend across the AI industry, where companies increasingly expose the architecture of their products through separate modes and branded features.
Anthropic, for example, asks users to distinguish between Claude’s standard chat experience and Cowork. ChatGPT similarly separates Chat and Work. For consumers, these distinctions can turn what should be a simple interaction into a question about which product or mode to use.
That approach is largely driven by how AI systems are built, rather than by how people naturally think about using them.
Apple Takes A Different Approach
Apple’s strategy for Siri offers a contrasting model. Rather than requiring users to learn a new AI interface, the company is integrating AI capabilities into tools people already use, including Spotlight, Photos, the camera and voice requests.
That approach could prove more effective as AI becomes a mainstream consumer technology. Users do not necessarily need to understand which model, agent or feature is handling a request; they simply need the system to complete the task.
Text-Based AI Offers A Simpler Model
The popularity of text-based AI assistants points in the same direction. Services such as Poke, Ollie, Lindy, Orchid, Lucas, Folk, Tomo and Instinct largely reduce the interaction to a familiar interface: send a message and let the assistant determine what needs to happen next.
That simplicity removes an additional layer of decision-making. Users do not need to choose between Chat, an agent or a specialised feature before asking for help.
As a16z investment partner Justine Moore recently argued, consumers increasingly want an AI assistant to feel like a contact they can message rather than another application they must learn.
For Google and its competitors, the challenge may therefore be less about adding capabilities and more about hiding the complexity behind them. The AI that wins mainstream adoption may ultimately be the one that asks users to understand the least.
