Week 3 – McLuhan and AI

Innovation is a fact of life for the media industry, inextricably interwoven with technological advances – innovation is the motor of technological advance, and all organisations must innovate in order to respond to the advances (Kung, 2013; Dogruel, 2013). A wealth of scholars (Boyden, 2015; Collins, 2016; Snickars, 2016) have begun to speculate on a particular technological advance that carries the potential for significant innovation within the music industries: AI – or more simply, software that makes music autonomously through programmed algorithms.

With prototypes and start-ups such as Jukedeck – an algorithm-based software that creates songs for users, particularly independent brands looking for background music – offering inexpensive services at competitive rates, the economic potential of artificial intelligence within the music industry is as large as the potential it has to transform the way in which the industry runs.

McLuhan (1988) developed a means of examining and explaining the social processes underlying the adoption of a technology within the media through adopting a tetrad of media effects. McLuhan created four categories, and posed four questions:

  1. What does the medium enhance?
  2. What dos the medium make obsolete?
  3. What does the medium retrieve that had been obsolesced previously?
  4. What does the medium reverse/flip into when pushed to extremes?

Whilst scholars have attempted to use McLuhan’s tetrad to understand its relation to the creation of particular music (Cheyunski, 2003; Inglis, 2013), there has been a lack of recent research into utilising the tetrad to understand the true potential of artificial intelligence within the music industries.

Firstly, artificial intelligence, in the context of it being used as an algorithmic tool to essentially rewire the way in which music is produced, enhances the way in which record labels can capitalise on particular trends within the industry by programming instant-hits using algorithms built on the models of previous hits of the trend. Furthermore, it enhances the way in which creating music becomes accessible – the idea of the bedroom producer becomes even more of a reality in the sense that they no longer need a technologically-based set-up to create music, they simply need a level of coding ability.

Whilst its potential for enhancement on a number of levels is high, equally so is the potential for it begin the process of obsoleting areas of the industry, particularly that of the songwriting industry. Whilst I doubt that recent creations such as the artificially-created algorithmic-based Daddy’s Car (built out of Beatles songs) will completely eradicate an age-old industry, I do believe that it will begin to narrow it, as if preparing it to become obsolete at a later date.

Furthermore to this, artificial intelligence within the music industry poses the risk of reinvigorating the relationship between the songwriter and the industry, inspiring a focus upon writing unique music to counteract the potential of the ‘automatic hit-maker’, which could reverse the potential artificial intelligence has, making it somewhat redundant yet entirely purposeful in fixing the area it was targeting in an entirely different way.

Considering the technological scope and advancement in which artificial intelligence is acting under, it is becoming considerably more difficult to pinpoint a part of industry practice in the past in which it retrieves, as it is a contemporary concept which is still largely under-development.

Whilst this blog is small in scope, by using McLuhan’s tetrad, we are able to open up the area of artificial intelligence within the music industry for a more coherent and thought-out debate, acknowledging the depths of its potential impact as well as its benefits (Zhang, 2014; Turner, 2015)


Kung, L. (2013). Innovation, Technology and Organisational Change: Legacy Media’s Big Challenges. In: T. Storsul and A. Krumsvik, ed., Media Innovations: A Multidisciplinary Study of Change, 1st ed. Gothenburg: Nordicom.

Dogruel, L. (2013). Opening the Black Box: The Conceptualising of Media Innovation. In: T. Storsul and A. Krumsvik, ed., Media Innovations: A Multidisciplinary Study of Change, 1st ed. Gothenburg: Nordicom.

Boyden, B.E. (2015). Emergent Works. Colum. JL & Arts39, p.377.

Collins, N. (2016). A Funny Thing Happened on the Way to the Formula: Algorithmic Composition for Musical Theater. Computer Music Journal.

Snickars, P. (2016). More Music is Better Music. In: P. Wikstrom and R. DeFillippi, ed., Business Innovation and Disruption in the Music Industry, 1st ed. Cheltenham: Edward Elgar Publishing Limited.

Inglis, I. (2013). The Beatles and McLuhan: Understanding the Electric Age. Toronto and Plymouth: Scarecrow Press, 2013. 169 pp. ISBN: 978-0-8108-8432-8. Popular Music32(03), pp.523-525.

Cheyunski, F. (2003). A Cybernetic Session for Re-Mapping Communication Environments and How Technology is Reshaping Our Media Landscape or “A Boogie Woogie Bugle Ploy for Helping Our Companies. In Proceedings of the Media Ecology Association (Vol. 4, p. 19).

Turner, A. (2015). McLuhan in the Library. Art Libraries Journal40(01), pp.5-10.

Zhang, P. (2014). McLuhan and I Ching: An interological inquiry. Canadian Journal of Communication39(3), p.449.


1 thought on “Week 3 – McLuhan and AI

  1. A blog on Artificial Intelligence in relation to the Music Industry is a difficult thing to do. This one is a primer for anyone who wants a taster on the debates and issues on artificial intelligence within the music industry.
    This blog flows and though succinct, says a lot without going too deep.


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s