There’s an excellent discussion in Nick Srnicek’s Platform Capitalism of the immense cash reserves that technology companies have built up in recent years. As he notes, the headline figures don’t tell the whole story because these reserves don’t take into account the other debts and liabilities of these corporations. But the broader financial context is one in which, due to low corporate yields, it’s cheaper to take on new debt rather than bringing these cash reserves back on shore and having them be subject to corporation tax.
Recognising these points seems extremely important to understanding this corporate behaviour. Much of the ambition of the book is to see technology companies in terms of a broader post-crisis political economy and this is why the caveats on the headline figures of cash reserves are so crucial. These behaviours do not emerge sui generis from the technology sector but rather reflect corporations acting rationally within a more expansive context.
The public perception of the corporations in question, as well as the shiny and attention-grabbing investments they make with these cash reserves, create a tendency to evaluate them in their own terms. But these behaviours reflect economic mechanisms which are not unique to the technology sector. As Srnicek notes, “the use of corporate debt by these companies therefore needs to be set in the context of a tax avoidance strategy” (loc 442). These two conditions are crucial to these behaviour: (a) low corporate yields and the capacity to take on debt afforded by them (b) off-shoring of wealth and large scale avoidance of corporation tax. Both conditions are central to our post-crisis political economy rather than being sectoral phenomena.
Understanding this macro-economic context helps avoid the aforementioned trend of seeing the technology sector in sui generis terms. Yes, it’s new and shiny, but these are still corporations within a capitalist system, albeit one currently undergoing systemic change. To understand these changes, what Srnicek calls ‘platform capitalism’ and what I tend to think of as ‘digital capitalism’, requires that we cut through the thickets of bullshit which are being promulgated about the ‘digital age’. He writes on loc 536:
Since the 2008 crisis, has there been a similar shift? The dominant narrative in the advanced capitalist countries has been one of change. In particular, there has been a renewed focus on the rise of technology: automation, the sharing economy, endless stories about the ‘Uber for X’, and, since around 2010, proclamations about the internet of things. These changes have received labels such as ‘paradigm shift’ from McKinsey1 and ‘fourth industrial revolution’ from the executive chairman of the World Economic Forum and, in more ridiculous formulations, have been compared in importance to the Renaissance and the Enlightenment. 2 We have witnessed a massive proliferation of new terms: the gig economy, the sharing economy, the on-demand economy, the next industrial revolution, the surveillance economy, the app economy, the attention economy, and so on. The task of this chapter is to examine these changes.
Understanding cash hoarding is central to moving beyond this breathless discursive explosion because it’s what facilitates many of the shiniest investments which appear to be at frontier of the ‘digital revolution’. It also facilitates the early acquisition of potential competitors, bringing them into the fold and often liberating them of their technology long before they might come to rival the platform giant. Cash hoarding protects from project uncertainty, facilitating open-ended investments in developments that lack a quantifiable market. But all the these factors which operate at the level of innovation need to be seen in terms of a political economy within which this corporate ‘autonomy’ becomes feasible and widespread.
It would be a mistake however to dismiss talk of ‘disruption’ and ‘innovation’ as epiphenomenal. Firstly, real innovations are underway, albeit ones which are pervasively mischaracterised as the linear unfolding of technological mastery rather than an uneven and lop-sided progress driven by the weird dynamics of the tech sector, distorted by the aforementioned vortex created by the new platform overlords. Secondly, innovation talk has become all pervasive within organisations, performing an important culture role that can’t be adequately understood if we simply subsume it under the category of ‘ideology’:
A search of annual and quarterly reports filed with the Securities and Exchange Commission shows companies mentioned some form of the word “innovation” 33,528 times last year, which was a 64% increase from five years before that.
More than 250 books with “innovation” in the title have been published in the last three months, most of them dealing with business, according to a search of Amazon.com.
Technology concerns aren’t necessarily the worst offenders. AppleInc. and Google Inc. mentioned innovation 22 times and 14 times, respectively, in their most recent annual reports. But they were matched by Procter & Gamble Co. (22 times), Scotts Miracle-Gro Co.(21 times) and Campbell Soup Co. (18 times).
The pervasive discourse of ‘innovation’ and ‘disruption’ helps mystify fundamental changes in capitalism, propped up by even more pervasive ideas of open/closed, fast/slow and modern/traditional. But it also does important work at a meso-social level, not least of all within higher education:
Equally, in a world where academics are obliged to offer up each piece of work to be evaluated as internationally significant, world leading etc., they will seek to signal such a rating discursively. A study by Vinkers et al. in the British Medical Journal uncovered a new tendency towards hyperbole in scientific reports. They found the absolute frequency of positive words increased from 2.0% (1974-80) to 17.5% (2014), which amounts to a relative increase of 880% over four decades. 25 individual positive words contributed to the increase, particularly the words “robust,” “novel,” “innovative,” and “unprecedented,” which increased in relative frequency up to 15 000%”). The authors comment upon an apparent evolution in scientific writing to ‘look on the bright side of life’.
We need to cut through this rhetoric, understanding its cumulative macro-cultural effects while also recognising the performative work it does across organisations and civil society. Doing so will inevitably be a complex exercise but it’s an important one. Doing this goes hand-in-hand with an account of digitalisation at the level of political economy rather than simply technology. This is what I take Srnicek to be doing on loc 568:
Data are not immaterial, as any glance at the energy consumption of data centres will quickly prove (and the internet as a whole is responsible for about 9.2 per cent of the world’s electricity consumption). 6 We should also be wary of thinking that data collection and analysis are frictionless or automated processes. Most data must be cleaned and organised into standardised formats in order to be usable. Likewise, generating the proper algorithms can involve the manual entry of learning sets into a system. Altogether, this means that the collection of data today is dependent on a vast infrastructure to sense, record, and analyse. 7 What is recorded? Simply put, we should consider data to be the raw material that must be extracted, and the activities of users to be the natural source of this raw material. 8 Just like oil, data are a material to be extracted, refined, and used in a variety of ways. The more data one has, the more uses one can make of them. Data were a resource that had been available for some time and used to lesser degrees in previous business models (particularly in coordinating the global logistics of lean production). In the twenty-first century, however, the technology needed for turning simple activities into recorded data became increasingly cheap; and the move to digital-based communications made recording exceedingly simple. Massive new expanses of potential data were opened up, and new industries arose to extract these data and to use them so as to optimise production processes, give insight into consumer preferences, control workers, provide the foundation for new products and services (e.g. Google Maps, self-driving cars, Siri), and sell to advertisers.
But what I find odd about this is it how it adopts the trope of ‘data as new oil’ without critically examining its embedding in the aforementioned rhetoric of disruption and innovation. I’m not yet sure if this is a disagreement with Srnicek’s argument or simply a request for further analysis. But I’m thus far finding the book thought provoking and highly recommend it.