The case against conversational interfaces

Summary

01 Intro Conversational interfaces are a bit of a meme. Every couple of years a shiny new AI development emerges and people in tech go “This is it! The next computing paradigm is here! We’ll only use natural language going forward!”. But then nothing actually changes and we continue using computers the way we always have, until the debate resurfaces a few years later. We’ve gone through this cycle a couple of times now: Virtual assistants (Siri), smart speakers (Alexa, Google Home), chatbots (“conversational commerce”), AirPods-as-a-platform, and, most recently, large language models. I’m not entirely sure where this obsession with conversational interfaces comes from. Perhaps it’s a type of anemoia, a nostalgia for a future we saw in StarTrek that never became reality. Or maybe it’s simply that people look at the term “natural language” and think “well, if it’s natural then it must be the logical end state”. I’m here to tell you that it’s not. 02 Data transfer mechanisms When people say “natural language” what they mean is written or verbal communication. Natural language is a way to exchange ideas and knowledge between humans. In other words, it’s a data transfer mechanism. Data transfer mechanisms have two critical factors: speed and lossiness. Speed determines how quickly data is transferred from the sender to the receiver, while lossiness refers to how accurately the data is transferred. In an ideal state, you want data transfer to happen at maximum speed (instant) and with perfect fidelity (lossless), but these two attributes are often a bit of a trade-off. Let’s look at how well natural language does on the speed dimension: The first thing I should note is that these data points are very, very simplified averages. The important part to take away from this table is not the accuracy of individual numbers, but the overall pattern: We are significantly faster at receiving data (reading, listening) than sending it (writing, speaking). This is why we can listen to ...