It seems like only yesterday that the combination of American conservatism and unchecked deregulation was bumbling, incompetent, and corrupt rather than outright horrifying. Ah yes, the Bush-Cheney years were most definitely the good old days. In hindsight, there were perhaps few clearer warning signs of the forthcoming widespread corporate malfeasance than the Enron scandal, which broke in 2001 and exposed the price fixing, market manipulation, and collusion that (seemingly) laid the groundwork for the 2008 subprime mortgage crisis. Drawing on the extensive paper trail that emerged in the aftermath of the scandal, Tega Brain and Sam Lavigne have created The Good Life, and ‘Enron email simulator’ that allows a (truly) dedicated reader to receive 500,000 emails from the Enron archive in chronological order. Produced with the support of a 2016 Rhizome microgrant, the artists recently elaborated on the significance of the emails and their project in an accompanying short essay for Rhizome:

The Enron email archive is a corpus of more than 500,000 emails, written between 158 senior executives of the Enron corporation during the last years of the company’s operation. In March of 2003, following revelations of Enron’s spectacularly corrupt business practices and subsequent demise, these emails were deemed public domain and released online by the Federal Energy Regulatory Commission (FERC). This was the first release of an email database of this size, and it remains one of the only large public domain email collections easily and freely accessible online. As Finn Brunton, author of Spam: A Shadow History of the Internet, observes, “The FERC had thus unintentionally produced a remarkable object: the public and private mailing activities of 158 people in the upper echelons of a major corporation, frozen in place like the ruins of Pompeii for future researchers.” The corpus has since become a uniquely valuable linguistic resource for computer scientists who have used it to train spam filters and other natural language machine learning systems.