The Use of Knowledge in a Digital Economy

A supercluster of economically useful and powerful new digital technologies (crypto, generative AI, AR/VR, IoT, et al.) have arrived all at once in the past decade. These technologies are disrupting economic organisation and institutions (e.g. money, contracting, administration, choice). The arrival of this supercluster marks the historical transition from an industrial to a digital economy. This paper explores how these innovations affect the way knowledge is created and used in a digital economy. I develop three approaches. The first is institutional technologies, as defined by Davidson et al. (2018), in which blockchains ‘industrialise trust’ and scale ‘common knowledge’. These institutional technologies are ‘hyperobjects’ (Morton 2013). This is how a digital economy now exists, as a compute layer over all physical objects. The result is digital money, smart contracts, computable capital, etc (Potts 2023). The second is generative AI as an innovation commons (Potts 2019), as knowledge production with low agency costs (Potts et al. 2023). Generative AI platforms combine pooled specialist and general knowledge (the trained model) with local knowledge (prompts) into a powerful feedback loop. This builds on the theory of toolkits and user innovation (von Hippel 2016). The third approach is ‘demic institutional hyperreality’ (Baudrillard 1981, Hartley and Potts 2014) which uses generative AI and VR to create digital economies (i.e. institutional metaverses) under evolutionary competition. Here, the supercluster of digital technologies creates a shared virtual reality of strong institutional presence in competition with other ‘deep fake’ institutional worlds. I conclude that in a digital economy the use of knowledge shifts into platforms and commons, and toward the edges. This is complementary with the way markets process knowledge. It also points toward much greater opportunities for economic design of private orders