Even if digitized and born-digital audiovisual material today amounts to a steadily increasing body of data to work with and research, such media modalities are still relatively poorly represented in the field of DH. Streaming media is a case in point, and the purpose of this article is to provide some findings from an ongoing audio (and music) research project, that deals with experiments, interventions and the reverse engineering of Spotify’s algorithms, aggregation procedures, and valuation strategies. One such research experiment, the SpotiBot intervention, was set up at Humlab, Umeå University. Via multiple bots running in parallel our idea was to examine if it is possible to provoke — or even undermine — the Spotify business model (based on the so called “30 second royalty rule”). Essentially, the experiment resembled a Turing test, where we asked ourselves what happens when — not if — streaming bots approximate human listener behavior in such a way that it becomes impossible to distinguish between a human and a machine. Implemented in the Python programming language, and using a web UI testing frameworks, our so called SpotiBot engine automated the Spotify web client by simulating user interaction within the web interface. The SpotiBot engine was instructed to play a single track repeatedly (both self-produced music and Abba’s “Dancing Queen”), during less and more than 30 seconds, and with a fixed repetition scheme running from 100 to n times (simultaneously with different Spotify Free ‘bot accounts’). Our bots also logged all results. In short, our bots demonstrated the ability (at least sometimes) to continuously play tracks, indicating that the Spotify business model can be tampered with. Using a single virtual machine — hidden behind only one proxy IP — the results of the intervention hence stipulate that it is possible to automatically play tracks for thousands of repetitions that exceeds the royalty rule.