Faster than the Speed of Thought: Virtual assistants, search and the logic of pre-emption

The rapid development of predictive technologies from simple pre-emptive text to voice-activated virtual assistants raises questions about how we engage with bodies of knowledge mediated by algorithms. Predictive technologies with increasingly adaptive algorithms supported by machine learning, have the capacity to learn alongside us, gleaning information to better understand behavioural patterns and predict human action and intention. These technologies are often promoted in terms of how they assist human users and are evaluated in terms of their speed and relevance. This valorisation of speed is underpinned by an algorithmic means-end logic that is not subject to the durational constraints of human perception and attention. Indeed, the inhuman time of an algorithm has to be adjusted to fit the lived time of human thought and action. Drawing on the work of Henri Bergson and Bernard Stiegler among others, this paper argues the quest for speed in the development of search technologies constructs a future in which time is reduced to discrete possibilities and disregards the lived delay immanent to human thought.