Last week’s Economist had an excellent report on the data economy and its growing importance to contemporary capitalism. Data is now the most valuable commodity in the world, ahead of oil, and the five most valuable firms in the world are all technology companies: Alphabet (Google), Amazon, Apple, Facebook, and Microsoft.
It is generally understood that all the free online websites we use aren’t actually “free”, but rather are paid for via data collection on our activities on these sites, which are used for targeted advertisement. Less understood is the fact that increasingly, the real value is not in data and advertising, but in using the data to improve artificial intelligence programs that are much more valuable and create much more complex services, like language translation and self-driving vehicle algorithms. Hence, why Tesla is valued so high, despite selling so few cars relative to companies like Ford: the real value is in their massive pool of data that they collect from Tesla drivers, and their increasingly proficient self-driving vehicle AI.
Data-collection and AI has spread across the entire economy, beyond just what we think of as “tech”. Transportation is the obvious sector that is fusing with data/AI, but less obvious sectors include the health care industry, and traditional sectors in manufacturing and industrial processing.
GE, for instance, has developed an “operating system for the industrial internet”, called Predix, to help customers control their machinery. Predix is also a data-collection system: it pools data from devices it is connected to, mixes these with other data, and then trains algorithms that can help improve the operations of a power plant, when to maintain a jet engine before it breaks down and the like.
Of course, creating a system for data acquisition and monitoring is hardly new for plants, its not like you can run a modern plant without some kind of control system; what is actually new here is the application of AI to analyze plant data and suggest improvements that plant operators might not realize could be done.
The finance-data intersection is also important to grasp. A primary point of discussion in the Economist report was around data markets — specifically, the lack of one. Despite data being so valuable, there exists no real market for companies to trade data-sets and data-streams; instead, they simply buy up the whole company so that they can silo off data for themselves. There doesn’t appear to be much enthusiasm from tech companies to create such a market; on the other hand, one would imagine that Wall St. is watering at the mouth at the prospect of financializing data, in the same way that manufactured commodities were in the ’80s and ’90s. Wall St. already makes use of financial data and AI, to the point where engineers and programmers seem more important than traditional traders, but to bring the data itself into the realm of trading and financial speculation is whole other story.
Lastly, and perhaps most importantly, there is the issue of the amount of control that we average schmucks have over our data. The report discusses many ideas around how to give us back control, such as tools and regulations that allow us to view and control all the data that is out there about ourselves, and even get compensation for its use. Amusingly, the report’s final section is titled “Data workers of the world, unite!” and discusses arguments for a “digital labor movement” by those of us (which is basically all of us) whose data is being fed into increasingly powerful AIs, and monopolized by massive and unaccountable corporations. References are made to Jaron Lanier’s Who Owns the Future?, and the way immense amounts of value is being spun out of day-to-day activities– mostly online, but increasingly offline as well.
This idea is a callback to the Marxist analysis of machines and labor, where technology is said to be “dead labor” insofar as it is a mechanized interpretation of what living labor (the human worker) does. The power loom took the labor of weavers and interpreted it through a machine, locking in the knowledge and skills of the workers into a system controlled by capitalists. Machine-tool automation did the same for machinists and metal-workers. Today, we can imagine this trend reaching toward its logical conclusion, where capital soaks up human knowledge and skill in general and uses it to build AI that could perhaps for people entirely — and beyond.
All of this should emphasize the importance of organizing within the tech industry. All of these data and AI-centered processes are not driving themselves. They are dependent on armies of engineers, programmers, scientists, and all the other workers within the tech industry. They are also increasingly political, even radical. Contemporary movements to overthrow capitalism will necessarily have to synthesize the workers who are laboring at its core.