Recent advances in artificial intelligence (AI) have opened unprecedented opportunities to humans to think and operate the world and its increasing complexity with digital technologies. Striking examples are deep neural networks (DNNs) (Lecun et al., 2015; Mnih et al., 2015), which can be trained quickly on large datasets either self-generated or already available from human experience. In particular, algorithms can become more efficient than humans on specific tasks after relatively short training periods compared to the time that humans need to learn (few hours or days, as compared to years). Their technical efficiency has for instance been demonstrated for optimizing financial transactions, speech or text recognition (Hinton et al., 2012), language translation (Hassan et al., 2018), real-time image content analysis, autonomous driving (Chen et al., 2015), or playing chess or go (Silver et al., 2017). They also start to see use in medicine to reach diagnoses (Lehman et al., 2019; Ye et al., 2019) and improve neuroprosthetics (Bocquelet et al., 2016; Schwemmer et al., 2018; Anumanchipalli et al., 2019). This multiplicity of technical demonstrations is thus progressively bringing AI central and ubiquitous in human life. Yet, the effectiveness of algorithms in bringing more and more relevant recommendations to humans may start to compete with human-alone decisions based on values other than pure efficacy. Here, we examine this tension in light of the emergence of several forms of digital normativity, and analyze how this normative role of AI may influence the ability of humans to remain subject of their life.