The full resource stack needed for Amazon's Echo to "turn on the lights"
In a novel new project, KATE CRAWFORD and VLADAN JOLER present an "anatomical case study" of the human labor, data, and planetary resources necessary for the functioning of an Amazon Echo. A 21-part essay accompanies an anatomical map (pictured above), making the case for the importance of understanding the complex resource networks that make up the "technical infrastructures" threaded through daily life:
"At this moment in the 21st century, we see a new form of extractivism that is well underway: one that reaches into the furthest corners of the biosphere and the deepest layers of human cognitive and affective being. Many of the assumptions about human life made by machine learning systems are narrow, normative and laden with error. Yet they are inscribing and building those assumptions into a new world, and will increasingly play a role in how opportunities, wealth, and knowledge are distributed.
The stack that is required to interact with an Amazon Echo goes well beyond the multi-layered 'technical stack' of data modeling, hardware, servers and networks. The full stack reaches much further into capital, labor and nature, and demands an enormous amount of each. Put simply: each small moment of convenience – be it answering a question, turning on a light, or playing a song – requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch."
Link to the full essay and map.
- More on the nuanced ethical dilemmas of digital technology: "Instead of being passive victims of (digital) technology, we create technology and the material, conceptual, or ethical environments, possibilities, or affordances for its production of use; this makes us also responsible for the space of possibilities that we create." Link.
- As shared in our April newsletter, Tim Hwang discusses how hardware influences the progress and development of AI. Link.