The piece below is a (heavily) abridged version of a paper I have co-written with Adam Badger, who's doing his PhD research on food delivery platforms at Royal Holloway (London). A previous version was presented at the "Assetization of Work: Varieties of Human Capital" workshop that took place in Sydney last April. As we were preparing the manuscript for journal submission, we thought it would be good to already publish a "trailer" here, to share some of our ideas and arguments and create a space for discussion. Hopefully, the full article will be out some time next year.
Gig work as data work
What kind of work is platform-mediated “gig work”? Phrased differently: what kinds of value are created through platform labor? While it is to a certain extent true that gig platforms enable workers to capitalize on their cars, bikes, or other physical assets they may use during service provision, the wear and tear that comes with repeated (often intensive) use actually depreciates these assets over time and such depreciation is not taken into account in platform-governed service pricing. Uber and Lyft drivers are responsible for repairs and maintenance costs; to which many have to add monthly interest bearing car payments because they do not own the high-value assets they ostensibly “rent out” to riders. Meanwhile, the (e)bikes and scooters used by large groups of food delivery workers are relatively low-value assets that do not generate rent and are frequently subject to theft or (in New York City) confiscation. In the face of such insecurity and financial strain, what are low-income and asset-poor gig workers to do? Are there other resources they could meaningfully use to their advantage?
For our purposes, it is strategically useful to momentarily accept the standard argument, restated by companies in various court cases, that they are in fact not themselves service providers (and thus cannot be held accountable as employers) but merely provide the technical platform on which service providers find access to their customer base. From this perspective, platform companies provide a service that is categorically distinct from the service provided by the gig worker – i.e. they provide an “informational service”. In return for this service, platform companies charge a commission on each service transaction conducted via the platform. Crucially, however, besides extracting rent from each transaction they orchestrate, platforms also extract data about these transactions, which means that gig workers can likewise be understood to provide an “informational service” to the platforms they use.
Accordingly, we argue that the governance of platform-mediated gig work is characterized by a process that we call “dual value production”: the monetary value produced by the service provided is augmented by the use and speculative value of the data produced before, during, and after service provision. Yet what may at first sight seem like a supplementary component of the central servicing activity is actually key to understanding what gig economies, like the platform economy at large, are about: data-driven product innovation, service optimization, and synergistic expansion. When considering the assetization of work across different local-serving industries that have been “disrupted” by platform companies, what thus stands out as the common denominator is not the gig worker as a precarious rentier capitalizing on minor assets but rather the apparatus through which gig work is rendered productive of computationally processed data as a particular asset class (cf. Sadowski 2019).
Varieties of human (data) capital
According to Luke Stark (2018: 207), the “subject of digital control is not only plastic but also scalable: shaped and made legible at different orders of technical analysis” whose juxtaposition and integration can generate new insights and applications. By rendering gig workers intelligible from different angles and at varying levels of granularity, labor platforms enact algorithmically induced “classification situations” that are not just central to service optimization but also give rise to “consequential forms of social categorization and price-differentiated opportunities” affecting the livelihoods of each signed up “partner” (Fourcade and Healy 2017: 10).
The scoring, rating, and ranking methods that algorithmically bestow “übercapital” on gig workers are not only widely recognized and accepted formats of contemporary digital culture but also function as calculative devices that commensurate a great variety of worker skills, conducts, behaviors, and activities by translating them into “portfolios” of evaluative metrics and indices. To the extent that these portfolios are made accessible and legible to gig workers, they provide some measure of insight into one’s performance vis-a-vis other workers and thereby encourage the cultivation of behaviors and conducts deemed conducive to one’s self-appreciation on the platform. As Michel Feher notes, it is possible “to govern subjects seeking to increase the value of their human capital, or, more precisely, to act on the way they govern themselves, by inciting them to adopt conducts deemed valorizing and to follow models for self-valuation that modify their priorities and inflect their strategic choices” (2009: 28).
However, it should be emphasized here that, unlike Feher’s ideal type neoliberal subject preoccupied with “capital growth or appreciation rather than income, stock value rather than commercial profit” (ibid.: 27), many gig workers cannot afford to defer the more immediate returns on their investment (particularly income) in favor of the ongoing appreciation of “the capital to which he or she is identified” on the platform (ibid.). This utilitarian behavior stems from the simple fact that most are low-income and asset-poor, only having signed up to the platform because they have bills to pay and mouths to feed. If food delivery workers wouldn’t get paid (bi)-weekly and did not have the additional option to “cash out” instantly, whenever they need their money, they wouldn’t work so hard at learning when and where to go online, which delivery offers to reject, what restaurants to avoid, what routes to take, where to wait in between deliveries, and how to approach a customer to increase the chance of a cash tip. All these investments in their human capital (i.e. the knowledge and skills that are needed to make a halfway decent living from this work) are only worth the effort and the strain on both bike and body as long as there are concrete monetary returns. In other words, you satisfy the platform’s algorithms and improve your metrics only for a long as this satisfies your needs.
Beyond food delivery, such immediate satisfaction is not the only force that drives gig workers to self-appreciate in competitive platform-orchestrated markets that are frequently oversaturated on the supply side. There are other aspirations of human capital, to cite Feher again; ones that express a longer temporal horizon and reach beyond the domain of one single platform. For top-rated Handy cleaners or nannies marketing their services on Care.com, for instance, customer-facing reputation systems can present a valuable measure of their human capital that could potentially be leveraged outside the platform as well, when these workers decide to seek other employment opportunities and career paths. At the moment such potential is technically limited by the fact that most reputation systems are tied to the specific platform architectures within which they have been engineered, meaning that they cannot be transferred to other environments. After all, platform companies do not want to lose their user base, hence they limit users’ mobility. This has led some particularly entrepreneurial Handy cleaners to take screenshots of their ratings and reviews so that they could host these images on their own website or at least save them for future reference.
This makeshift practice may no longer be needed in the near future, however, given that various gig economy stakeholders - including platform companies, progressive think tanks, and labor advocates – have been promoting the development of portable reputation systems that transcend platform boundaries and thereby would detach valuable data assets from the realm of exclusive corporate ownership. The intention is to give gig workers the ability – and the legal right – to aggregate and mobilize their reputational data as they see fit, in order to grant them more agency over the appreciation of their human capital. This intention, in turn, follows from the broader assumption that reputational indices derived from gig platforms will play an increasingly important role in gauging as well as boosting a worker’s competitive position and employability on various labor markets.
While this indeed may be the case in some labor markets, it is less likely to be salient or effective in others, let alone that it works across labor markets. A cleaner who markets his services across Handy and TaskRabbit but aspires to start his own cleaning company without being reliant on these rent-seeking platforms will probably welcome the opportunity to aggregate his ratings and reviews, as these evaluative indices are commensurable with the criteria that inform a customer’s selection of possible service providers. Yet it would be harder to imagine an Uber driver getting the same mileage out of his ratings when attempting to build a career path out of the ride-hailing business. Overall, the problem with portable reputation initiatives is that they conflate technical and social commensurability, something endemic to the technological solutionism of Silicon Valley’s policy entrepreneurship. This is the fallacy of the “plugin”: you can plug Uber ratings into a new technical environment but you cannot likewise “plug” an Uber driver into a new professional environment, especially when the orders of worth governing this environment are not commensurable to those of ride-hailing. Just because Uber ratings can be integrated into driver profiles on Jobcase – a social media platform for “work life” – does not mean that such ratings will be of value (i.e. that they will form meaningful, relevant, or appealing indices) to potential employers.
More fundamentally, such initiatives problematically disavow a truth that is generally avoided in neoliberal perspectives on the opportunities and challenges of the gig economy but can hardly be overlooked: the human capital of low-wage gig workers performing fungible labor is valued very poorly in societies across the world. This brings us to the objective of Feher’s political project, which is to strategically “embrace the neoliberal condition” and to “allow it to express aspirations and demands that its neoliberal promoters had neither intended nor foreseen” (2009: 25). Such a strategy, according to Feher, enables a political struggle over “the question of what constitutes an appreciable life” (ibid.: 41). While we are generally inclined to support such a crucial project, there are nevertheless reasons to hesitate and think twice before adopting its immanent critique.
First, as we’ve indicated above, the evaluative architectures through which self-appreciation becomes both possible and mandatory are owned by venture-backed corporate platforms that set the terms and conditions determining contemporary modes of valuation and devaluation. As such, we fear that the range of progressive or alternative demands and aspirations that can be expressed within this framework of financialized platform capitalism will be severely limited, given that these will be tethered to practices, logics, and languages rooted in stark inequalities with respect to wealth and power. This leads us to our second hesitation: by merely demanding better resources for self-appreciation we still tacitly accept, or continue to adhere to its associated measures of valuation, which makes it difficult to relinquish the moral economy (i.e. the order of worth) on which such measures have historically been predicated. This is a long history of racialized, gendered, and classed subordination that originated when capital first sought to scale globally, one that continues to cast a long shadow over today’s gig economies despite their protagonists’ best efforts to re-brand low-wage service work as a colorblind site of opportunity and an entrepreneurial side hustle. Only by tracing this history from the plantation, via the factory and the firm, to the platform, does it become possible to fully grasp the low value attributed to fungible gig work and those who service others on demand.
Finally, it should also be highlighted that – besides (data) expropriation and exploitation – perhaps the main form of structural violence inflicted on gig workers is registered as a pervasive sense of being superfluous. Uber drivers and Uber Eats couriers are acutely aware that they are replaceable: on the short term by a standing reserve of hundreds if not thousands of similarly situated workers attracted to Uber’s promising figures and hefty referral bonuses; and on the long term by the machine learning algorithms trained on their expropriated data. This condition of superfluity translates most directly into poor remuneration and a lack of support, which explains why so many gig workers quit within a year after signing up with a platform. When you cannot establish a reliable, much less a sustainable, income stream, you have little choice but to look for the nearest exit and find another way to make money.
From the platform to the meta-platform
Meanwhile, the absence of profitability and long-term sustainability are much less of an acute problem for platform companies. This is because the profitability of a company’s business model is secondary to its capitalization, or the appreciation of its market value, to the extent that revenues only need to be ample enough to provide would-be investors with a “proof of concept” regarding the platform’s ability to scale and thus to become profitable at some stage in the future. As long as there is investor confidence that a platform will at one point attain monopoly-like status and can thereby start extracting monopoly rents, it can expect new capital injections that bankroll its continuing efforts at gaining market share and improving its financial performance, which should lead to a further inflation of its market value.
To be sure, a platform company’s data assets play a crucial role in both boosting investor confidence and in improving its financial performance – given that the latter at least partly depends on using data analytics to cut labor-related costs. For instance, during Lyft’s recent IPO road shows the company maintained its assertion that prioritizing data-driven growth and innovation, especially with respect to its autonomous vehicle project, is the right strategy and that the lack of profitability in the present – and, according to its SEC filings, potentially also in the future – should not dissuade investors from purchasing shares.
Moreover, Lyft and Uber’s IPOs remind us that the aforementioned cycles of capital appreciation are punctuated by so-called “liquidity events”, when founders and early investors (e.g. venture capital firms) get an opportunity to “exit” by cashing out a portion of their shares. Reminiscent of food delivery workers’ refusal to forfeit their income in exchange for the speculative appreciation of their human capital, these investors are likewise unwilling to indefinitely absolve a platform company from its obligation to generate monetary returns. In this sense, as Langley and Leyshon (2017: 24) suggest, “the platform business model performs the temporal structure of venture capital funds”, which is an observation that pushes us to expand the purview of our analysis beyond the realm of the platform.
Ultimately, the origins of its data-driven monopolistic aspirations can be traced up one level, to the top tier of the rent-seeking value chain constitutive of financialized platform capitalism. This tier is the domain of what we call “meta-platforms”: venture capital firms and investment funds looking to exploit the network effects and synergetic possibilities that emerge when managing a large and varied portfolio of investments in platform companies and other data-centric businesses, each intent on “disrupting” different industries by leveraging its analytics capacities. We use the term “meta-platform” because the growing power of these financial institutions stems from how they effectively operate as higher-order platforms whose profits are constituted by the rents extracted every time it matches investors (including institutional investors such as pension funds, sovereign wealth funds) with tech companies looking for capital injections that will allow them to scale quickly. Paying critical attention to meta-platforms also moves us beyond a concern with “shareholder value”, insofar as the stakes of our analysis do not just pertain to the influence of shareholder objectives on a company’s daily operations but demand that we account for the strategic governance of mutually reinforcing monopoly formations across sectors.
The meta-platform par excellence is SoftBank, the conglomerate that manages the $100 billion Vision Fund, nearly half of which is financed by Saudi Arabia’s sovereign wealth fund. According to SoftBank’s founder and CEO, Masayoshi Son, Vision Fund’s portfolio companies control 90% of the ride-hailing market worldwide, which is a percentage that should surely give us pause. Son’s approach, especially since the inauguration of the Vision Fund, has been to “over-invest” in particular platform companies and thereby basically pre-ordain a winner in various competitive markets. This then sets up Son’s “cluster of number ones” strategy, which revolves around the creation of productive synergies between portfolio companies “whose whole is theoretically greater than the sum of its parts – an added value derived from the partnerships and business opportunities that come with being a part of the SoftBank family”. Such partnerships and business opportunities largely center on finding ways to actualize the potential of massive amounts of data captured from a great variety of sources. As a recent Wired article, from which we just quoted, summarizes Son’s vision:
a future where every time that we use our smartphone, or call a taxi, or order a meal, or stay in a hotel, or make a payment, or receive medical treatment, we will be doing so in a data-transaction with a company that belongs to the SoftBank family. And, as Son likes to say: “Whoever controls data controls the world.”
Meta-platforms seek to control the world, or at least the platform ecosystem that increasingly reshapes the world in its image. Having learned their lesson in the wake of the dot.com collapse, during which Son and his peers lost immense amounts of money, meta-platform CEOs now aim to construct data-centric architectures of sustainability that will protect them in case the next tech bubble bursts – a bubble that they themselves will have helped to create. Even in the event that Uber would fold, for instance because governments around the world miraculously agree that the company is in fact an employer and investors would consequently lose interest in its shares, its IPO has offered SoftBank an opportunity to cash out some of its equity and use these returns to invest in – and thereby anoint – the next Uber, or perhaps rather the next Palantir or Arm. Platforms may come and go, but meta-platforms allocating the wealth of nations are becoming too big to fail.
Doganova, L. & F. Muniesa (2015) Capitalization devices, in M. Kornberger, L. Justesen, A.K. Madsen & J. Mouritsen (eds.) Making things valuable. Oxford: Oxford University Press, pp. 109-25.
Feher, M. (2009) Self-appreciation; or, the aspirations of human capital. Public Culture, 21(1): 21-41.
Fourcade, M. & K. Healy (2016) Seeing like a market. Socio-Economic Review, 15(1): 9-29.
Langley, P. & A. Leyshon (2017) Platform capitalism: the intermediation and capitalisation of digital economic circulation. Finance and society, 3(1): 11-31.
Sadowski, J. (2019) When data is capital: Datafication, accumulation, and extraction. Big Data & Society, 6(1): https://doi.org/10.1177/2053951718820549
Stark, L. (2018) Algorithmic psychometrics and the scalable subject. Social studies of science, 48(2): 204-231.