CityReads | Platform Capitalism’s Hidden Abode

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Platform Capitalism’s Hidden Abode

Data asset is central to platform capitalism.

Niels van Doorn, Adam Badger, 2020. Platform Capitalism’s Hidden Abode: Producing Data Assets in the Gig Economy, Antipode, https://doi.org/10.1111/anti.12641

Source: https://onlinelibrary.wiley.com/doi/10.1111/anti.12641

Picture source:https://socialistproject.ca/2017/12/workers-heart-algorithm/

Platform-mediated gig work has emerged as a new way of employment and livelihood in China as well as other countries in the world. For instance, there are over 5 million food delivery workers, 3 million couriers, 20 million ride-hailing drivers in China.

What kind of work is platform‐mediated “gig work”? Phrased differently, what kinds of value are created through platform labor?

A recent paper published in Antipode, “Platform Capitalism’s Hidden Abode: Producing Data Assets in the Gig Economy”, explains the nature of gig work and argues that the governance of gig work under conditions of financialized platform capitalism is characterized by a process of“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. App‐governed gig workers hence function as pivotal conduits in software systems that produce digital data as a particular asset class.

What is platform‐mediated gig work?

To answer this question, it may be strategically useful to momentarily accept the position defended by “gig economy” companies in various court cases, namely that they merely provide the technical platform on which service providers find access to their customer base. From this perspective, these companies provide an “informational service” that is categorically distinct from the service provided by the gig worker and as such they should not—indeed cannot —be legally held accountable as employers. In return for this service, the argument continues, gig economy companies charge a commission on each service transaction conducted via their platform. Crucially, however, besides extracting rent from each transaction they orchestrate, platforms also extract data about these transactions and usually about a lot more, which means that gig workers can likewise be understood to provide an “informational service” to the platforms they use. The fact that this service is neither optional nor remunerated suggests that such data extraction “continues to open up new frontiers for the expansion of the logics of property and to blur the borders between processes of governance and dynamics of capitalist valorization”.

In other words, gig work is, among other things, essentially data work and the gig economy should be understood as but one salient phenomenon within the more comprehensive constellation that is financialized platform capitalism. The digital platform is one of capital’s “new frontiers” in its fight to counter declining profitability rates, allowing it to expand into previously uncharted areas of life through data‐ and finance‐driven modes of accumulation. To be sure, we realize that data‐generative activities occur across gig platforms’ multi‐sided markets (i.e. valuable data is generated when customers browse their apps and rate the services provided, or when restaurants fulfil orders). However, the analytical scope of our contribution limits itself to the data work undertaken by (under)paid gig workers, even if this work cannot be neatly disentangled from the data generated by other “partners” in a platform’s ecosystem.

Gig work under conditions of platform capitalism 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.  Platforms capture part of this monetary value by charging rent, in the form of a commission, while capturing all the value of the data produced by gig workers. That is to say, using Sadowski’s pithy formulation, “platforms collect monetary rent and data rent”. Yet whereas the value of this monetary rent can be dynamically determined by the platform, the value of data rent is fundamentally indeterminate insofar as it derives from speculative and performative practices. Platforms engage in constant data accumulation because of the potential value this data, once processed by their analytics software, might embody or give rise to.  This value derives in part from data’s expected or actual practical utility in operational processes. Yet captured data also attracts venture capital and grows financial valuations, to the extent that investors expect data‐rich platform companies to achieve competitive advantages by creating data‐driven cost efficiencies, cross‐industry synergies, and new markets. In this way, it becomes possible “to convert data into money”, which is then again invested in activities and technologies that increase the capture of data.

While data may at first seem like a supplementary component of the service provided, it is thus actually key to understanding what gig platforms are about. Focusing on datafication allows us to grasp how app‐governed gig workers function as pivotal conduits in software systems that combine distributed data generation and centralized analytics, depending on layers of existing (public and private) urban infrastructure—from free Wi‐Fi networks to roads and bike lanes. In practice, a courier’s phone and physical labor become a site of translation through which complex urban environments are formatted into machine‐readable data streams. These apparatuses thereby produce digital data as a particular asset class, one that is central to platform capitalism “as a mode of accumulation that is simultaneously a system of domination”

The data asset

For gig platforms, captured data is not merely an asset generating economic rent for its owner, who monetizes third parties’ controlled access to this asset. Rather, data can potentially become a “catalyzing” asset insofar as it enables the optimization of a platform company’s software assets (e.g. its algorithms), which enhances its capacity to govern and extract rent from platform‐mediated transactions. As noted earlier, this subsequently allows the company to attract more investment capital.

Furthermore, a main distinguishing feature of the data asset is its high value elasticity, meaning that both its operational use value and its speculative financial value tend to increase significantly as it scales. To elucidate this elasticity, it is helpful to return to our notion of dual value production and our example of food delivery platforms. On the level of service provision, a platform company’s “bottom line” (i.e. net income) consists of the rent the platform extracts from each completed food order (i.e. the commission it takes from the restaurant plus the delivery fee it charges the customer, together forming its top line revenue) minus the piece‐rate labor costs associated with each order (from which some platforms extract another service fee) and other expenses. In traditional Marxist analyses, this is the scene of exploitation: “recompensed only for the socially necessary cost of their own reproduction, … [food delivery workers] have no claim on the surplus value their labor generates, which accrues instead to the … [platform company]”.

Essentially, expropriation “works by confiscating capacities and resources and conscripting them into capital’s circuits of self‐expansion”, which rather accurately describes the globe‐spanning capture of data assets derived from platform‐mediated food delivery work performed largely by (im)migrant men who lack ownership or meaningful control over these assets. Computational data expropriation makes it possible for food delivery platforms to continually optimize their accumulation strategies based on exploitation, for instance by dynamically adjusting—while progressively decreasing—riders’ delivery fees based on aggregated market data in order to increase profit margins.

As such, captured data expropriation is a practice characterized by alienation and unfreedom, which forms the condition of possibility for the exploited free labor of food delivery workers and in this sense constitutes platform capitalism’s true hidden abode. 6 While independent contractors can nominally choose when/how much they work and which orders they accept, it is precisely these sequences of decision‐making activities from which data assets can be derived. This means that couriers’ freedom of choice can be strategically leveraged as a behavioral “informational service” that is not freely provided and that can be used against their best interests.

Reclaiming the Data Asset

However, the afterlife of platform workers’ data assets has come to feature as a new frontier for organized resistance. No longer satisfied with the rewards of individual self‐appreciation supported by data‐derived assets such as performance metrics and reputation systems, workers are seeking new ways to directly leverage their data in efforts to gain control over their position and counter the massive inequalities discussed above. It is important to note here that labor organizing in the gig economy is no longer a novel phenomenon and neither is it short‐lived, despite high rates of worker turnover (Cant 2019). Over time, gig workers are finding new, more sophisticated avenues for organising their resistance efforts, as they learn which tactics are most effective. Moreover, recent efforts focused on gig workers’ data‐rights are part of a wider range of data‐based struggles, which includes the use of data obfuscation techniques such as “fake GPS”, where workers disguise their actual location.

We discuss three examples of such resistance efforts in order of their relative efficacy and collectivizing ambition: (1) the tactical use of Subject Access Requests (SARs) under the EU’s General Data Protection Regulation (GDPR) to contest the sudden deactivation of worker accounts; (2) the weaponization of such SARs in court cases against platform companies, to push back against pervasive information asymmetries; and (3) the creation of a federation of platform cooperatives that seeks to scale a worker‐owned software infrastructure and treats data as an asset to be deployed for the benefit of workers rather than platform capitalists. However, significant challenges lie ahead for any struggle to improve conditions for platform workers. Many of these challenges pertain to the limited sustainability of collective actions in the face of resource scarcity and persistent asymmetrical power relations.

From Platform to Meta‐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 their 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 and sovereign wealth funds, with tech companies looking for capital injections that will allow them to continue to scale quickly. Paying critical attention to meta‐platforms also moves us beyond a narrow 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.

Meta‐platforms seek to control the world, or at least the platform ecosystems that increasingly reshape the world in their image. Ultimately, it is the massive wealth and synergetic capacities of meta‐platforms that constitute the most formidable obstacle to worker power and social justice in increasingly data‐driven societies.

Conclusion

While platforms come and go, meta‐platforms allocating the wealth of nations are becoming too big to fail. It is this massive privatization of public wealth that returns us, one more time, to the position and plight of gig workers under conditions of financialized platform capitalism. While it is true that finance capital subsidizes a large share of gig workers’ daily wages, it is equally true that it ultimately seeks to render their labor obsolete. Meanwhile, its investment comes with stipulated expectations and constraints with respect to how a platform company can run its business, pushing a high risk/high gain model that has valued rapid growth and limited liability. In times of crisis, as this model becomes destabilized, we see how platforms that cannot weather the strain become expendable in a manner that mimics the disposability of gig workers—just further upstream.

Despite becoming subject to increasing public scrutiny and legislative challenges in some jurisdictions, this risk‐offloading and labor squeezing model thus continues to be hegemonic. Accordingly, it is crucial that scholars of platform‐mediated gig economies develop a more comprehensive critique of the structural relation between finance capital, data assetization, and job precarization. Conceiving of gig work as data work and elucidating its dual value production helps to bring this relation into clearer view, by entering platform capitalism’s hidden abode: the capture and valorization of data expropriated from disposable workers.

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