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ABSTRACT
This paper examines the lived experiences and ethical dilemmas of investors and staff in London’s digital music startup culture. Startups often rely on what I term ‘imagined metrics’ to attract investment and to measure the efficacy of their technologies. However, this stands in stark contrast to the qualitative ways music is understood within these organisations and subsequently experienced via the technologies they build. Drawing on ethnographic observations alongside interview data, I suggest that these metrics have few true believers. Instead, critiques of imagined metrics and their susceptibility to exaggeration and misrepresentation are ubiquitous. This culture of scepticism is not incidental: it is a crucial pathway through which otherwise volatile startup culture is normalised. Investors, founders and staff often publicly acknowledge the unreliability of the numbers with which they work, even as metrics continue to underpin the decision-making process. Metrics thus do not require true belief to secure their effects. Yet, against this backdrop, processes of quantification increasingly shape digital music consumption. Resisting the classic equation of quantification with Weberian rationalisation, this study instead shows that metrics are imbued with emotions and interpretive narratives that extend well beyond the older trust in numbers (Porter [1995]. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Knorr-Cetina (Citation2006) in her work on financial markets describes these kinds of metrics as ‘provisional numbers’, placeholders for anticipated future transactions.
2 Data for this paper were gathered during an ethnography of London’s music startup scene between 2014 and 2018. This involved the participation of seven startups at various stages of development – from demo nights and angel investment, to Series A funding, to acquisition – as well as five angel investors. Data were collected through participant observation in startup incubators, shared offices and other social contexts (demo nights, evening drinks, coffees, dinners, phone calls). Alongside participant observation, and as part of a larger project looking at how new technologies are shaping musical creativity, I also conducted structured and semi-structured interviews with over twenty musicians, startup founders, employees, record label and streaming platform executives.
3 For metrics being deployed as marketing materials see, for example, Spotify’s annual marketing campaigns in which they invite users to review their listening habits from the previous year. Branded ‘Spotify Wrapped’ in reference to its release over the Christmas period, users are presented with colourful slides detailing the music they listened to via a dazzling array of numbers and graphics. Users are cheered as ‘World Citizens’ in relation to their geographically eclectic taste in music and congratulated on precisely how many new artists they ‘discovered’ over the course of a year. On one slide, users are informed which particular artist they ‘vibed’ to the most, according to number of times they listened to particular songs, how many of their albums they ‘explored’, even how many hours they had spent ‘with them’. ‘We’ve been so lucky to have you’, the final graphic pined, before inviting users to share their Spotify Wrapped with the world via social media. All these feelings and emotions are backed up by cold, hard numbers. For a discussion on the ‘commodification of taste’ in digital music, see Timothy Taylor (Citation2014).
4 The Government tightened its focus of the scheme by introducing a test to exclude companies set up for the purpose of accessing relief, exclude acquisition of shares in another company and exclude investment in Feed-in-Tariffs businesses.
5 Though the scheme was available nationwide, over two thirds of all SEIS investment (67%) in the 2017/18 tax year was concentrated in London and the South East. Just 25 startups received investment in the North East, compared with 1415 in the South East and London, suggesting an unequal level of investment distribution to the regions. In some respects this is hardly surprising. As a global financial city there was already a concentration of private wealth in the capital. Moreover, plans to create a tech centre to rival San Francisco’s Silicon Valley, most notably centred on the ‘Silicon Roundabout’ in Old Street area of London, have been longer established and more deeply entrenched than elsewhere in the UK.
6 Scholars of accounting, such as Tinker (Citation1991) and Morgan (Citation1988), have long observed that such ‘optimism bias’ is an important component of entrepreneurship rituals.
7 The performance of metrics at demo nights bears striking resemblance to how Robert Prey (Citation2020) describes musicians engaging with their metrics on digital music platforms. Musicians in the DIY digital economy are described, for example, as ‘reluctant entrepreneurs’, who ‘perform’ their metrics to audiences (see Haynes and Marshall (2018), quoted in Prey (Citation2020: 253)).
8 Angel investors and venture capitalists typically must wait many years before making a profit, during which time there is a high likelihood that the venture will fail (Barkoczy and Wilkinson Citation2019). Moreover, these studies have shown that the success rate for EIS and VCT supported companies is actually lower than those recorded in matched but unsupported companies.
9 For more on the anthropology of scale, scalability and ‘scale-making’ projects, see Tsing (Citation2012, Citation2013), Gal (Citation2016), Silverstein (Citation2016). For an ethnomusicological analysis of scalability in music streaming platforms, see Hodgson (Citationforthcoming).
10 For more on the ‘performativity’ of numbers in tech cultures, particularly as they relate to economic spheres, see MacKenzie (Citation2005) and Law and Singleton (Citation2000). Observing tech culture at the dawn of the digital age, for example, Law and Singleton, suggested that technologies develop in particular ways because the stories that surround them are continuously ‘performed’ by engineers and company executives: ‘For much of the work of making technologies – much of the growth of technology knowledge – arises within projects, project-work, and the telling of project-related stories, stories that are then enacted into reality. Our argument is that the difference between telling stories and acting realities isn’t so large’ (Law and Singleton Citation2000: 769).
11 Source, Office for National Statistics: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2014-11-19#regional-earnings (accessed: 22 September 2020).
12 More widely, virtually every music company I encountered, from early stage startups to large streaming platforms such as Spotify, similarly indexed, or equated, ‘success’ through metrical frameworks such as KPIs and marketing materials that measured artist-fan relations in numbers.
13 Mark Zuckerberg’s famous maxim ‘move fast and break things’ speaks to the fragility of startup culture as much as the established order it was intending to disrupt.
14 Such observations have been made in other contexts, where ‘faith … in statistical expertise’ runs deep (Merry Citation2016) and how users ‘tend to sanctify’ the metrics produced in audit culture (Strathern Citation2000). Numbers become increasingly ‘seductive’ to the extent that they acquire seemingly ‘magical’ properties (Merry Citation2016; Merry Citation2011: 84).
15 Lorraine Daston has made the point that quantification is an affective process that produces what she terms a Gefühlskollektiv, that is, a collective of feeling (Citation1995: 5).
16 The anthropologist Sally Engle Merry recognised that role that metrics play in political decision making, ‘even though the users recognize that these simplified numerical forms are superficial, often misleading, and very possibly wrong’ (Citation2011: 87). The accountancy theorist Michael Power (Citation1997) identified these processes as ‘rituals of verification’, there primarily as a means to an end, rather than representing anything meaningful in and of themselves. Cris Shore and Susan Wright (Citation2015) took this argument further, demonstrating how these metrical regimes, unmoored from the pretence of objectivity, are exposed to intense gaming.
17 See, for example, the Centre for Digital Music’s (C4DM) new Centre for Doctoral Training in Artificial Intelligence and Music, at Queen Mary University of London, which was recently awarded £6.2m by the UKRI.
18 In many respects this is hardly surprising. In the UK, the marketization of academia brought about by the introduction of tuition fees in 1998 heralded the kind of metrics driven approach to business more often found in the private sector. Initially, these processes of quantification were confined to the managerial strata of universities. Recent disciplinary shifts towards economic- and computer science-led methodologies suggest that the quantification of music is starting to shape research projects too.
19 Some data on UKRI here.
20 This fragility is felt not just by startups, but across the environments in which they operate: Tech Hub – the incubator mentioned earlier in this paper – itself went bust in August 2020. See: https://www.ft.com/content/6375f8ff-c306-4e56-8175-65c8f7b917f7 (accessed: 22 September 2020).
Additional information
Notes on contributors
Thomas Hodgson
Thomas Hodgson is a Departmental Lecturer in Music at the University of Oxford. He is interested in the ethnomusicology of migration, creativity, and AI in the Global South, with a particular focus on Pakistan. He has published in various international journals, including Les Cahiers d’Ethnomusicologie, Sound Studies, Popular Music, and Performing Islam. He is also a practicing musician, having recorded and toured extensively around the world with the UK Top 20 band Stornoway. Along with the band’s bassist, Oliver Steadman, he went on to establish the music tech platform Tigmus (This is Good Music), which used data and analytics to match artists with venues and promoters.