At a glance
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AI models can reason, but we still force users to submit a single prompt and wait—a batch protocol inherited from punch cards. That mismatch means the real bottleneck is an interface that doesn’t share the conversational load.
AI usability and adoption will be decided by interface protocol, not just model capability. Teams that ignore this will build frustrating tools.
Summary
While LLM capability has surged, the interface protocol hasn’t changed. We still package our entire request, hit submit, and wait—the same batch pattern as punch cards, even with voice. This mismatch forces users to shoulder all the cognitive work of encoding intent and repairing output.
The talk introduces three concepts: channel (keyboard, voice), expression (what meaning can pass through), and protocol (the rules of exchange). Expression exploded with natural language, but the protocol stayed batch. Features like ‘thinking step-by-step’ are just packaging tricks, not interactive conversation.
Examples like Nvidia’s Personal Plex and the speaker’s own demo show machines that backchannel, take turns, and understand who’s speaking, moving from batch to genuine participation. This shift reduces the human’s burden: the AI notices ambiguity, asks follow-ups, and decides when to act.
For engineering leaders, the message is that AI’s adoption bottleneck is shifting from model quality to interface design. Teams building AI features need to treat the protocol as a first-class product investment, not a UI skin. The goal is to stop making humans reshape their communication for the machine.
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