Introduction
We are told
today that we are living in an age of
massive transformation. Terms like the sharing economy, the gig economy,
and the fourth industrial revolution are tossed around, with enticing images of
entrepreneurial spirit and flexibility bandied about. As workers, we are to be liberated from the constraints of a
permanent career and given the opportunity to make our own way by selling
whatever goods and services we might like to offer.
As consumers, we are presented with a
cornucopia of on-demand services and with the promise of a network of connected
devices that cater to our every whim. This is a book on this contemporary
moment and its avatars in emerging technologies: platforms, big data, additive manufacturing, advanced robotics, machine
learning, and the internet of things.
It is not the
first book to look at these topics, but it takes a different approach from
others. In the existing literature,
one group of commentaries focuses on the politics of emerging technology,
emphasising privacy and state surveillance but leaving aside economic issues
around ownership and profitability. Another group looks at how corporations are
embodiments of particular ideas and values and criticises them for not acting
humanely – but, again, it neglects the economic context and the imperatives of
a capitalist system.[1]
Other scholars
do examine these emerging economic trends but present them as sui generis phenomena,
disconnected from their history. They never ask why we have this economy today,
nor do they recognise how today’s economy responds to yesterday’s problems.
Finally, a number of analyses report on how poor the smart economy is for
workers and how digital labour represents a shift in the relationship between
workers and capital, but they leave aside any analysis of broader economic
trends and intercapitalist competition.[2]
The present book
aims to supplement these other perspectives by giving an economic history of capitalism and digital technology, while
recognising the diversity of economic forms and the competitive tensions
inherent in the contemporary economy. The simple wager of the book is that we
can learn a lot about major tech
companies by taking them to be economic actors within a capitalist mode of
production.
This means abstracting
from them as cultural actors defined by the values of the Californian ideology, or as political actors seeking to wield
power. By contrast, these actors are compelled
to seek out profits in order to fend off competition. This places strict
limits on what constitutes possible and predictable expectations of what is
likely to occur.
Most notably, capitalism demands that firms constantly
seek out new avenues for profit, new markets, new commodities, and new means of
exploitation. For some, this focus on capital rather than labour may
suggest a vulgar economism; but, in a world where the labour movement has been significantly
weakened, giving capital a priority of agency seems only to reflect reality.
Where, then, do
we focus our attention if we wish to see the effects of digital technology on
capitalism? We might turn to the technology sector,[3]
but, strictly speaking, this sector remains a relatively small part of the
economy.
In the United
States it currently contributes around 6.8 per cent of the value added from
private companies and employs about 2.5 per cent of the labour force.[4] By
comparison, manufacturing in the
deindustrialised United States employs four times as many people. In the
United Kingdom manufacturing employs nearly three times as many people as the
tech sector.[5]
This is in part because
tech companies are notoriously small.
Google has around 60,000 direct employees, Facebook has 12,000, while WhatsApp
had 55 employees when it was sold to Facebook for $19 billion and Instagram had
13 when it was purchased for $1 billion. By comparison, in 1962 the most
significant companies employed far larger numbers of workers: AT&T had
564,000 employees, Exxon had 150,000 workers, and GM had 605,000 employees.[6]
Thus, when we
discuss the digital economy, we should bear in mind that it is something broader than just the tech sector
defined according to standard classifications.
As a preliminary
definition, we can say that the digital
economy refers to those businesses
that increasingly rely upon information technology, data, and the internet for
their business models. This is an area that cuts across traditional sectors
– including manufacturing, services,
transportation, mining, and telecommunications – and is in fact becoming
essential to much of the economy today.
Understood in
this way, the digital economy is far more important than a simple sectoral
analysis might suggest. In the first place, it appears to be the most dynamic sector of the contemporary
economy– an area from which constant innovation is purportedly emerging and
that seems to be guiding economic growth forward. The digital economy appears to
be a leading light in an otherwise rather stagnant economic context.
Secondly, digital technology is becoming systematically
important, much in the same way as finance. As the digital economy is an
increasingly pervasive infrastructure for the contemporary economy, its
collapse would be economically devastating. Lastly, because of its dynamism,
the digital economy is presented as an ideal that can legitimate contemporary
capitalism more broadly.
The digital economy is becoming a hegemonic
model: cities are to become smart, businesses must be disruptive, workers
are to become flexible, and governments must be lean and intelligent. In this
environment those who work hard can take advantage of the changes and win out.
Or so we are told.
The argument of this book is that, with a long
decline in manufacturing profitability, capitalism has turned to data as one
way to maintain economic growth and vitality in the face of a sluggish
production sector. In the twentyfirst century,
on the basis of changes in digital technologies, data have become increasingly central to firms and their relations with
workers, customers, and other capitalists.
The platform has emerged as a new business model,
capable of extracting and controlling immense amounts of data, andwith this shift we have seen the rise
of large monopolistic firms. Today thecapitalism
of the high- and middle-income economies is increasinglydominated by these firms, and the dynamics outlined in this book
suggestthat the trend is only going
to continue.
The aim here is
to set these platformsin the
context of a larger economic history, understand them as means togenerate profit, and outline some of
the tendencies they produce as a result.
In part, this
book is a synthesis of existing work. The discussion in Chapter 1 should be
familiar to economic historians, as it outlines the various crises that have
laid the groundwork for today’s
post-2008 economy. It attempts to historicise emerging technologies as an
outcome of deeper capitalist tendencies, showing how they are implicated within
a system of exploitation, exclusion, and competition.
The material in Chapter
2 should be fairly well known to those who follow the business of technology.
In many ways, the chapter is an attempt to give clarity to various ongoing
discussions in that world, as it lays out a typology and genesis of platforms.
By contrast, Chapter 3 hopefully offers something new to everyone. On the basis
of the preceding chapters, it attempts to draw out some likely tendencies and
to make some broad-brush predictions about the future of platform capitalism.
These forward-looking prognoses are essential to
any political project. How we conceptualise the past and the future is
important for how we think strategically and develop political tactics to
transform society today. In short, it makes a difference whether we see
emerging technologies as inaugurating a new regime of accumulation or as continuing
earlier regimes. This has consequences on the possibility of a crisis and on
deciding where that crisis might emerge from; and it has consequences on our
envisaging the likely future of labour under capitalism.
Part of the
argument of this book is that the apparent novelties of the situation obscure
the persistence of longer term trends, but also that today presents important
changes that must be grasped by a
twenty-first-century left. Understanding our position in a broader context is
the first step to creating strategies for transforming it.
Notes
2. Huws, 2014.
3. Since the
phrase ‘technology sector’ is so often thrown around with little clarification,
we will here define the sector using the North American Industry Classification
System (NAICS) and its associated codes. Under that system, the tech sector can be considered to include computer
and electronic product manufacturing (334), telecommunications (517), data processing,
hosting, and related services (518), other information services (519), and
computer systems design and related services (5415).
4. Klein, 2016.
5. Office for
National Statistics, 2016b.
6. Davis, 2015:
7.
1 The Long Downturn
To understand
our contemporary situation, it is necessary to see how it links in with what
preceded it. Phenomena that appear to be radical novelties may, in historical
light, reveal themselves to be simple continuities. In this chapter I will
argue that there are three moments in the
relatively recent history of capitalism that are particularly relevant to the
current conjuncture: the
response to the 1970s downturn; the boom and
bust of the 1990s; and the response
to the 2008 crisis. Each of these moments
has set the stage for the new digital economy and has determined the ways in
which it has developed.
All of this must
first be set in the context of our broad economic system of capitalism and of
the imperatives and constraints it imposes upon enterprises and workers. While
capitalism is an incredibly flexible system, it also has certain invariant
features, which function as broad parameters for any given historical period.
If we are to understand the causes, dynamics, and consequences of today’s
situation, we must first understand how capitalism operates.
Capitalism,
uniquely among all modes of production to date, is immensely successful at raising productivity levels.[7]
This is the key dynamic that expresses
capitalist economies’ unprecedented capacity to grow at a rapid pace and to
raise living standards. What makes capitalism different?[8]
This cannot be explained through
psychological mechanisms, as though at some time we collectively decided to
become greedier or more efficient at producing than our ancestors did. Instead,
what explains capitalism’s productivity growth
is a change in social relationships, particularly property relationships.
In precapitalist
societies, producers had direct access
to their means of subsistence: land for farming and housing. Under those
conditions, survival did not systematically depend on how efficiently one’s
production process was. The vagaries of natural cycles may mean that a crop did
not grow at adequate levels for one year, but these were contingent constraints
rather than systemic ones. Working sufficiently hard to gain the resources
necessary for survival was all that was needed. Under capitalism, this changes.
Economic agents
are now separated from the means of subsistence and, in order to secure the goods they need for survival, they must now turn to
the market. While markets had existed for thousands of years, under
capitalism economic agents were uniquely faced with generalised market dependence.
Production
therefore became oriented towards the market: one had to sell goods in order to make the money needed for purchasing
subsistence goods.
But, as vast
numbers of people were now relying upon selling on the market, producers faced competitive pressures.
If too costly, their goods would not sell, and they would quickly face the
collapse of their business. As a result, generalised
market dependency led to a systemic imperative to reduce production costs in
relation to prices. This can be done
in a variety of ways; but the most significant methods were the adoption of
efficient technologies and techniques in the labour process, specialisation,
and the sabotage of competitors.
The outcome of
these competitive actions was eventually expressed in the mediumterm tendencies
of capitalism: prices tangentially declined
to the level of costs, profits
across different industries tended to become equal, and relentless growth
imposed itself as the ultimate logic of capitalism. This logic of accumulation
became an implicit and taken-for granted element embedded within every business
decision: whom to hire, where to invest, what to build, what to produce, who to
sell to, and so on.
One of the most
important consequences of this schematic model of capitalism is that it demands constant technological change.
In the effort to cut costs, beat out competitors, control workers, reduce
turnover time, and gain market share, capitalists are incentivised to
continually transform the labour process. This
was the source of capitalism’s immense dynamism, as capitalists tend to
increase labour productivity constantly and to outdo one another in generating profits efficiently.
But technology
is also central to capitalism for other reasons, which we will examine in more
detail later on. It has often been used to deskill workers and undermine the
power of skilled labourers (though there are countertendencies towards
reskilling as well).[9]
These deskilling
technologies enable cheaper and more pliable workers to come in and replace the
skilled ones, as well as transferring the mental processes of work to
management rather than leaving it in the hands of workers on the shop floor.
Behind these technical changes, however, lies competition and struggle – both between classes, in their struggle
to gain strength at one another’s expense, and between capitalists, in their
efforts to lower the costs of production
below the social average. It is the latter dynamic, in particular, that
will play a key role in the changes that lie at heart of this book. But before
we can understand the digital economy we must look back to an earlier period.
The End of the Postwar Exception
It is
increasingly obvious to many that we live in a time still coming to terms with
the breakdown of the postwar settlement.
Thomas Piketty argues that the
reduction in inequality after the Second World War was an exception to the
general rule of capitalism; Robert Gordon
sees high productivity growth in the middle of the twentieth century as an
exception to the historical norm; and numerous thinkers on the left have long
argued that the postwar period was an unsustainably good period for capitalism.[10]
That exceptional
moment –broadly defined at the international level by embedded liberalism, at
the national level by social democratic consensus, and at the economic level by
Fordism – has been falling apart since the 1970s.
What
characterised the postwar situation of the high-income economies? For our
purposes, two elements are crucial
(though not exhaustive): the business
model and the nature of employment.
After the devastation of the Second World War, American manufacturing was in a globally dominant position. It was
marked by large manufacturing plants built along Fordist lines, with the automobile industry functioning as the paradigm.
These factories were
oriented towards
mass production, top-down managerial
control, and a ‘just in case’ approach that demanded extra workers and
extra inventories in case of surges in demand. The labour process was organised
along Taylorist principles, which sought to break tasks down into smaller
deskilled pieces and to reorganise them in the most efficient way; and workers
were gathered together in large numbers in single factories. This gave rise to
the mass worker, capable of developing a collective identity on the basis of
fellow workers’ sharing in the same conditions. Workers in this period were represented by trade unions that reached a
balance with capital and repressed more radical initiatives.[11]
Collective
bargaining ensured that wages grew at a healthy pace, and workers were
increasingly bundled into manufacturing industries with relatively permanent jobs,
high wages, and guaranteed pensions. Meanwhile the welfare state redistributed
money to those left outside the labour market.
As its nearest
competitors were devastated by the war, American manufacturing profited and was
the powerhouse of the postwar era.[12]
Yet Japan and Germany had their own comparative advantages – notably relatively
low labour costs, skilled labour forces, advantageous exchange rates, and, in
Japan’s case, a highly supportive institutional structure between government,
banks, and key firms. Furthermore, the American Marshall Plan laid the
groundwork for expanding export markets and for rising investment levels across
these countries.
Between the 1950s
and the 1960s Japanese and German manufacturing grew rapidly in terms of output
and productivity.
Most
importantly, as the world market developed and global demand grew, Japanese and
German firms began to cut into the share of American firms. Suddenly there were
multiple major manufacturers that produced for the world market. The
consequence was that global manufacturing reached a point of overcapacity and
overproduction that put downward pressure on the prices of manufactured goods.
By the mid-1960s, American manufacturing was being undercut in terms of prices
by its Japanese and German competitors, which led to a crisis of profitability
for domestic firms.
The high, fixed
costs of the United States were simply no longer able to beat the prices of its
competitors. Through a series of exchange rate adaptations, this crisis of profitability
was eventually transmitted to Japan and Germany, and the global crisis of the
1970s was underway.
In the face of
declining profitability, manufacturers made efforts to revive their businesses.
In the first place, firms turned to their successful competitors and began to
model themselves after them. The American Fordist model was to be replaced by
the Japanese Toyotist model.[13]
In terms of the labour
process, production was to be streamlined.
A sort of hyper-Taylorism aimed to break the process down into its smallest
components and to ensure that as few impediments and downtime entered into the
sequence. The entire process was reorganised to be as lean as possible.
Companies were increasingly
told by shareholders and management consultants to cut back to their core
competencies, any excess workers being laid off and inventories kept to a
minimum. This was mandated and enabled by the rise of increasingly
sophisticated supply chain software, as manufacturers would demand and expect
supplies to arrive as needed. And there was a move away from the mass
production of homogeneous goods and towards increasingly customised goods that
responded to consumer demand.
Yet these efforts
met with counterattempts by Japanese and German competitors to increase their own
profitability, along with the introduction of new competitors (Korea, Taiwan, Singapore, and eventually China).
The result was continued international competition, overcapacity, and downward
pressures on prices.
The second major attempt to revive profitability
was through an attack on the power of labour. Unions across the western world
faced an all-out assault and were eventually broken. Trade unions faced new
legal hurdles, the deregulation of various industries, and a subsequent decline
in membership.
Businesses took
advantage of this to reduce wages and increasingly to outsource jobs. Early outsourcing involved jobs with goods that
could be shipped (e.g. small consumer goods), while non-tradable services (e.g.
administration) and non-tradable goods (e.g. houses) remained. Yet in the 1990s
information and communications technologies enabled a number of those services
to be offshored, and the relevant distinction came to be the one between services that required face-to-face
encounters (e.g. haircuts, care work) and
impersonal services that did not (e.g. data entry, customer service, radiologists,
etc.).[14]
The former were
contracted out domestically where possible, while the latter were under increasing
pressure from global labour markets. Hospitality provides one illuminating
example of this general trend: the percentage of franchised hotels in the
United States raised from a marginal figure in the 1960s to over 76 per cent by
2006. Alongside this, there was a move towards contracting all other work
associated with hospitality: cleaning, management, maintenance, and janitorial
services.[15]
The drivers behind this shift were to reduce benefits and liability costs, in
an effort to maintain profitability levels. These changes inaugurated the
secular trends we have seen since, with employment being increasingly flexible,
low wage, and subject to pressures from management.
The Dot-com Boom and Bust
The 1970s
therefore set the stage for the lengthy slump in manufacturing profitability
that has since been the baseline of advanced economies. A period of healthy
manufacturing growth in the United States began when the dollar was devalued in
the Plaza Accord (1985); but manufacturing slumped again when the yen and the
mark were devalued over fears of Japanese collapse.[16]
And, while
economic growth recovered from its 1970s lows, nevertheless the G7 countries
have all seen both economic and productivity growth trend downwards.[17]
The one notable exception was the dot-com boom in the 1990s
and its
associated frenzy of interest in the possibilities of the internet. In fact the
1990s’ boom is redolent of much of today’s fascination with the sharing economy,
the internet of things, and other tech-enabled businesses. It will remain to
the next chapter to show us whether the fate of these recent developments will
follow the same downward path as well. For our present purposes, the most
significant aspects of the 1990s’ boom and bust are the installation of an infrastructural basis for the digital economy and
the turn to an ultraaccommodative monetary policy in response to economic
problems.
The boom in the
1990s amounted effectively to the fateful commercialization of what had been,
until that point, a largely non-commercial internet. It was an era driven by
financial speculation, which was in turn fostered by large amounts of venture
capital (VC) and expressed in high levels of stock valuation. As US
manufacturing began to stall after the reversal of the Plaza Accord, the
telecommunications sector became the favoured outlet of financial capital in
the late 1990s.
It was a vast
new sector, and the imperative for profit latched onto the possibilities
afforded by getting people and businesses online. When this sector was at its height,
nearly 1 per cent of US gross domestic product (GDP) consisted of VC invested in tech companies; and
the average size of VC deals quadrupled between 1996 and 2000.[18]
All told, more than 50,000 companies were formed to commercialise the internet and
more than $256 billion was provided to them.[19]
Investors chased
hopes for future profitability and companies adopted a ‘growth before profits’ model.
While many of these businesses lacked a revenue source and, even more, lacked
any profits, the hope was that through rapid growth they would be able to grab
market share and eventually dominate what was assumed to be a major new
industry. In what would come to characterise the internetbased sector to this
day, it appeared a requirement that companies aim for monopolistic dominance.
In the
cut-throat early stages investors enthusiastically joined, in hopes of picking
the eventual winner. Many companies did not have to rely on VC either, as the
equity markets swooned over tech stocks. Initially driven by declining
borrowing costs and rising corporate profits,[20]
the stock market boom came unmoored from the real economy when it latched onto
the ‘new economy’ promised by internet-based companies. During its peak period
between 1997 and 2000, technology stocks rose 300 per cent and took on a market
capitalisation of $5 trillion.[21]
This excitement
about the new industry translated into a massive injection of capital into the
fixed assets of the internet. While investment in computers and information
technology had been going on for decades, the level of investment in the period
between 1995 and 2000 remains unprecedented to this day. In 1980 the level of
annual investment in computers and peripheral equipment was $50.1 billion; by
1990 it had reached $154.6 billion; and at the height of the bubble, in 2000,
it reached an unsurpassed peak of $412.8 billion.[22]
This was a global
shift as well: in the low-income
economies, telecommunications was the largest sector for foreign direct
investment in the 1990s – with over $331 billion invested in it.[23]
Companies began spending extraordinary amounts to modernise their computing
infrastructure and, in conjunction with a series of regulatory changes
introduced by the US government, this laid the basis for the mainstreaming of
the internet in the early years of the new millennium.
Concretely, this
investment meant that millions of miles of fibre-optic and submarine cables
were laid out, major advances in software and network design were established,
and large investments in databases and servers were made. This process also accelerated
the outsourcing tendency initiated in the 1970s, when coordination costs were
drastically cut as global communication and supply chains became easier to
build and manage.[24]
Companies pushed
more and more of their components outwards and Nike became an emblem of the lean firm: branding and design were managed
in the high-income economies, while manufacturing
and assembly were outsourced to sweatshops in the low-income economies. In
all of these ways, the 1990s tech boom was abubble that laid the groundwork for the digital economy to come.
In 1998, as the
East Asian crisis gathered pace, the US boom began to stumble as well. The bust was staved off through a series of
rapid interest rate reductions made by the US Federal Reserve; and these
reductions marked the beginning of a lengthy period of ultra-easy monetary
policy. Implicitly the goal was to let equity markets continue to rise despite
their ‘irrational exuberance’,[25]
in an effort to increase the nominal wealth of companies and households and
hence their propensity to invest and consume.
In a world where
the US government was trying to reduce its deficits, fiscal stimulus was out of
the question. This ‘asset-price Keynesianism’ offered an alternative way to get
the economy growing in the absence of deficit spending and competitive
manufacturing.[26]
It was a signal shift in the US
economy: without a revival of US
manufacturing, profitability was necessarily sought in other sectors.
And it worked
for a time, as it facilitated further investment in new dot-com companies and
kept the asset bubble running until 2000, when the National Association of
Securities Dealers Automated Quotations (NASDAQ) stock market peaked. Reliance
on an accommodative monetary policy continued after the 2001 crash as well,[27]
including through lowered interest rates and through a new liquidity provision
in the wake of the 9/11 attacks.
One of the
effects of these central bank interventions was to lower mortgage rates,
thereby fostering conditions for a housing bubble. Lowered interest rates also
lowered the return on financial investments and compelled a search for new
investments – a search that eventually landed on the high returns available
from subprime mortgages and set the stage for the next crisis.
Loose monetary
policy is one of the key consequences of the 1990s bust, and one that continues
on to this day.
The Crisis of 2008
In 2006 US
housing prices reached a turning point, and their decline began to weigh on the
rest of the economy. Household wealth decreased in tandem, leading to lowered
consumption and eventually to a series of mortgage nonpayments.
As the financial
system had become increasingly tied to the mortgage market, it was inevitable
that the decline in housing prices would wreak havoc on the financial sector.
Strains began to emerge in 2007, when two hedge funds collapsed after being
heavily involved in mortgage-backed securities. The entire structure buckled in
September 2008, when Lehman Brothers
collapsed and a full-blown crisis burst asunder.
The immediate
response was quick and massive. The US Federal Reserve moved to bail out banks
to the tune of $700 billion, provided liquidity assistance, extended the scope
of deposit insurance, and even took partial ownership of key banks. Through
massive bailouts, support for faltering companies, emergency tax cuts, and a series
of automatic stabilisers, governments undertook the burden of increasing their
deficits in order to ward off the worst of the crisis.
As a result, the
high levels of private debt before the
crisis were transformed into high levels of public debt after the crisis.
Simultaneously, central banks stepped in to try and prevent a breakdown of the
global financial order. The United States initiated a number of liquidity
actions designed to make sure that the pipelines of credit kept running.
Emergency lending was made to banks, and currency swap agreements were drawn up
with 14 different countries in order to ensure that they had access to the
dollars they needed. The most important action, however, was that key interest
rates across the world dropped precipitously: the US federal funds target rate
went from 5.25 per cent in August 2007 to a 0–0.25 per cent target by December
2008.
Likewise, the
Bank of England dropped its primary interest rate from 5.0 per cent in October
2008 to 0.5 per cent by March 2009. October 2008 saw the crisis intensify,
which led to an internationally coordinated interest rate cut by six major
central banks. By 2016 monetary policymakers had dropped interest rates 637
times.[28]
This has continued through the postcrisis period and has established a low
interest rate environment for the global economy – a key enabling condition for
parts of today’s digital economy to arise.
But, when the
immediate threat of collapse was gone, governments were suddenly left with a
massive bill. After decades of increasing government deficits, the 2008 crisis
pushed a number of governments into a seemingly more precarious position. The
United States saw its deficit rise from $160 million to $1,412 million over
2007–9. In part from fears of the effects of high government debt, in part as a
means to build up the fiscal resources for any future crisis, and in part as a
class project intended to continue the privatisation and reduction of the
state, austerity became the watchword in advanced capitalist nations.
Governments were
to eliminate their deficits and reduce their debts. While other countries have
faced deeper cuts to government spending, the United States has not escaped the
dominance of austerity ideology. At the end of 2012 a series of tax raises and
spending cuts were brought in, while at the same time tax cuts that had been
implemented in response to the crisis were allowed to expire. Since 2011 the
deficit has been reduced every year. Perhaps the biggest influence of austerity
ideas on America, however, was the political impossibility of getting any major
new fiscal stimulus.
The United
States has a significantly decaying infrastructure, but even here the argument
for government spending falls on deaf ears. This has reached its peak in the
political posturing that occurs increasingly frequently over the US debt
ceiling. This congressionally approved ceiling sets a limit on how much debt
the US Treasury can issue and has become a major point of contention between
those who think that the US debt is too high and those who think that spending
is necessary.
Since fiscal
stimulus is politically unpalatable, governments have been left with only one
mechanism for reviving their sluggish economies: monetary
policy. The
result has been a series of extraordinary and unprecedented central bank
interventions. We have already noted a continuation of low interest rate
policies. But, stuck at the zero lower bound, policymakers have been forced to
turn toward more unconventional monetary instruments.[29]
The most
important of these has been ‘quantitative easing’: the creation of money by the
central bank, which then uses that money to purchase various assets (e.g.
government bonds, corporate bonds, mortgages) from the banks.
The United
States led the way in using quantitative easing in November 2008, while the
United Kingdom followed suit in March 2009. The European Central Bank (ECB),
due to its unique situation as a central bank of numerous countries, was slower
to act, although it eventually began purchasing government bonds in January 2015.
By the beginning of 2016, central banks across the world had purchased more
than $12.3 trillion worth of assets.[30]
The primary
argument for using quantitative easing is that it should lower the yields of
other assets. If traditional monetary policy operates primarily by altering the
short-term interest rate, quantitative easing seeks to affect the interest
rates of longer term and alternative assets. The key idea here is a ‘portfolio
balance channel’.
Given that
assets are not perfect substitutes for one another (they have different values,
different risks, different returns), taking away or restricting supply of one
asset should have an effect on demand for other assets. In particular, reducing
the supply of government bonds should increase the demand for other financial
assets. It should both lower the yield of bonds (e.g. corporate debt), thereby
easing credit, and raise the asset prices of stocks (e.g. corporate equities)
and subsequently create a wealth effect to spur spending.
While the
evidence is still preliminary, it does seem that quantitative easing has had an
effect in this way: corporate yields have declined and stock markets have
surged upwards.[31]
It may have had
an effect on the non-financial sectors of the economy as well, by making much
of the economic recovery dependent on $4.7 trillion of new corporate debt since
2007.[32]
Most important for our purpose is the fact that the generalised low interest
rate environment built by central banks has reduced the rate of return on a
wide range of financial assets. The result is that investors seeking higher
yields have had to turn to increasingly risky assets – by investing in
unprofitable and unproven tech companies, for instance.
In addition to a
loose monetary policy, there has been a significant growth in corporate cash
hoarding and tax havens in recent years. In the United States, as of January
2016, $1.9 trillion is being held by companies in cash and cashlike investments
– that is, in low-interest, liquid securities.[33]
This is part of a long-term and global trend towards higher levels of corporate
savings;[34]but
the rise in cash hoarding has accelerated with the surge in corporate profits
after the crisis.
Moreover, with a
few exceptions such as General Motors, it is a phenomenon dominated by tech
companies. Since these companies only need to move intellectual property
(rather than entire factories) to different tax jurisdictions, tax evasion is
particularly easy for them. Table 1.1 outlines the amount of reserves[35]
held by some of the major tech companies, and also the amount held offshore by
foreign subsidiaries.
Table 1. 1 Reserves, onshore and offshore
Source: 10-Q or 10-K Securities and Exchange Commission
(SEC) filings from March 2016
These figures
are enormous: Google’s total is enough to purchase Uber or Goldman Sachs, while
Apple’s reserves are enough to buy Samsung, Pfizer, or Shell. To properly
understand these figures, however, some caveats are in order. In the first
place, they do not take into account the respective companies’ liabilities and
debt. However, with historically low corporate yields, many companies find it
cheaper to take on new debt instead of repatriating these offshore funds and
paying corporate tax on them. In their SEC filings tax avoidance is explicitly
given as a reason for holding such high levels of offshore reserves. The use of
corporate debt by these companies therefore needs to be set in the context of a
tax avoidance strategy. This is also part of a broader trend towards the
growing use of tax havens. In the wake of the crisis, offshore wealth grew by
25 per cent between 2008 and 2014,[36]
which resulted in an estimated $7.6 trillion of household financial wealth
being held in tax havens.[37]
The point of all
this is twofold. At one end, tax evasion and cash hoarding have left US
companies – particularly tech companies – with a vast amount of money to
invest. This glut of corporate savings has – both directly and indirectly –
combined with a loose monetary policy to strengthen the pursuit of riskier
investments for the sake of a decent return.
At the other
end, tax evasion is, by definition, a drain on government revenues and
therefore has exacerbated austerity. The vast amount of tax money that goes
missing in tax havens must be made up elsewhere. The result is further
limitations on fiscal stimulus and a greater need for unorthodox monetary
policies. Tax evasion, austerity, and extraordinary monetary policies are all
mutually reinforcing.
To define the
present conjuncture, we must add one further element: the employment situation.
With the collapse of communism, there has been a long-term trend towards both
greater proletarianisation and greater number of surplus populations.[38]
Much of the
world today receives a marketmediated income through precarious and informal
work. This reserve army was significantly expanded after the 2008 crisis. The
initial shock of the crisis meant that unemployment jumped drastically across
the board. In the United States it doubled, going from 5.0 per cent before the
crisis to 10.0 per cent at its height. Among the unemployed, long-term
unemployment escalated from 17.4 per cent to 45.5 per cent: not only did many
people lose their jobs, they did so for long periods of time. Even today,
long-term unemployment remains at levels higher than anything seen before the
crisis. The effect of all this has been pressure on the remaining employed
population – lower weekly earnings, fewer household savings, and increased
household debt.
In the United
States personal savings have been declining from above 10.0 per cent in the
1970s to around 5.0 per cent after the crisis.[39]
In the United Kingdom household savings have decreased to 3.8 per cent – a
50-year low and a secular trend since the 1990s.[40]
In this context, many have been forced to take whatever job is available.
Conclusion
The conjuncture today is therefore a product of
long-term trends and cyclical movements. We continue to live in a capitalist society where competition and profit
seeking provide the general parameters of our world. But the 1970s created
a major shift within these general conditions, away from secure employment and
unwieldy industrial behemoths and towards flexible labour and lean business
models.
During the 1990s a technological revolution was laid
out when finance drove a bubble in the new internet industry that led to massive
investment in the built environment. This phenomenon also heralded a turn towards a new model of growth: America was
definitively giving up on its manufacturing base and turning towards
asset-price Keynesianism as the best viable option. This new model of growth
led to the housing bubble of the early twenty-first century and has driven the
response to the 2008 crisis. Plagued by global concerns over public debt,
governments have turned to monetary policy in order to ease economic
conditions.
This, combined
with increases in corporate savings and with the expansion of tax havens, has
let loose a vast glut of cash, which has been seeking out decent rates of
investment in a low-interest rate world. Finally, workers have suffered
immensely in the wake of the crisis and
have been highly vulnerable to exploitative working conditions as a result of
their need to earn an income.
All this sets
the scene for today’s economy.
Notes
1. Unless
otherwise stated in the text, ‘productivity’ will refer to labour productivity
rather than total factor productivity.
2. The following
paragraph summarises Robert Brenner’s insights in Brenner, 2007.
3. Braverman,
1999.
4. Piketty,
2014; Gordon, 2000; Glyn, Hughes, Lipietz, and Singh, 1990.
5. In many ways,
this balance was the result of the defeat of radical labour and shop floor
agitation rather than reflecting the success of the labour movement.
6. The following
three paragraphs draw heavily on the account in Brenner, 2006.
7.
Dyer-Witheford, 2015: 49–50.
8. Blinder,
2016.
9. Scheiber,
2015.
10. Brenner,
2002: 59–78, 128–33.
11.
Antolin-Diaz, Drechsel, and Petrella, 2015; Bergeaud, Cette, and Lecat,
2015.
12. Perez, 2009;
Goldfarb, Kirsch, and Miller, 2007: 115.
13. Goldfarb,
Pfarrer, and Kirsch, 2005: 2.
14. Brenner,
2009: 21.
15. Perez, 2009.
16. Federal
Reserve Bank of St Louis, 2016b.
17. Comments of
Verizon and Verizon Wireless, 2010: 8n12.
18. Schiller,
2014: 80.
19.
Dyer-Witheford, 2015: 82–4.
20. Greenspan,
1996.
21. Brenner,
2009: 23.
22. Rachel and
Smith, 2015.
23. Khan, 2016.
24. The zero
lower bound, or liquidity trap, argues that nominal interest rates cannot go
below zero (otherwise savers would take their money out and put it under the
proverbial mattress). The result is that policymakers cannot push nominal
interest rates below zero. For more, see Krugman, 1998. Recently some countries
have begun imposing negative rates on reserves held at the central bank, though
the effects of this action appear so far to be minimal and possibly contrary to
what is intended (e.g. decreasing lending, rather than increasing lending).
25. Khan, 2016.
26. Joyce, Tong,
and Woods, 2011; Gagnon, Raskin, Remache, and Sack, 2011; Bernanke, 2012: 7.
27. Dobbs, Lund,
Woetzel, and Mutafchieva, 2015: 8.
28. Spross,
2016.
29.
Karabarbounis and Neiman, 2012.
30. Reserves
refers to their holdings of cash, cash equivalents, and marketable securities.
31. Zucman,
2015: 46.
32. Ibid., 35.
Notably this estimate excludes banknotes (estimated around $400 billion) and
physical assets like art, jewellery, and real estate, which are also used to
avoid taxes.
33. Srnicek and
Williams, 2015: ch. 5.
34. Federal
Reserve Bank of St Louis. 2016a.
35. Office for
National Statistics, 2016b.
2 Platform Capitalism
Capitalism, when a crisis hits, tends to be restructured.
New technologies, new organisational forms, new modes of exploitation, new
types of jobs, and new markets all emerge to create a new way of accumulating
capital.
As we saw with
the crisis of overcapacity in the 1970s,
manufacturing attempted to recover by attacking labour and by turning towards
increasingly lean business models. In the wake of the 1990s bust, internet-based
companies shifted to business models that monetised the free resources
available to them.
While the
dot-com bust placed a pall over investor enthusiasm for internet-based firms,
the subsequent decade saw technology firms significantly progressing in terms
of the amount of power and capital at their disposal. 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 McKinsey[41]
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.[42]
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.
Numerous
theorists have argued that these changes mean we live in a cognitive, or
informational, or immaterial, or knowledge economy. But what does this mean?
Here we can find a number of interconnected but distinct claims. In Italian
autonomism, this would be a claim about the ‘general intellect’, where
collective cooperation and knowledge become a source of value.[43]
Such an argument also entails that the labour process is increasingly immaterial,
oriented towards the use and manipulation of symbols and affects.
Likewise, the traditional industrial working class is
increasingly replaced by knowledge workers or the ‘cognitariat’.
Simultaneously, the generalised deindustrialisation
of the high-income economies means that the product of work becomes immaterial: cultural content, knowledge, affects, and
services.
This includes media content like YouTube and
blogs, as well as broader contributions in the form of creating websites,
participating in online forums, and producing software.[44]
A related claim
is that material commodities contain an
increasing amount of knowledge, which is embodied in them. The production
process of even the most basic agricultural commodities, for instance, is
reliant upon a vast array of scientific and technical knowledges. On the other
side of the class relation, some argue that the economy today is dominated by a new class, which does not own the means
of production but rather has ownership over information.[45]
There is some
truth in this, but the argument goes awry when it situates this class outside
of capitalism. Given that the imperatives of capitalism hold for these companies
as much as for any other, the companies remain capitalist. Yet there is something new here, and it is worth trying to discern exactly what it is.
A key argument
of this chapter is that in the twenty-first century advanced capitalism came to be centred upon
extracting and using a particular kind of raw material: data.
But
it is important to be clear about what
data are. In the first place, we will
distinguish data (information that something happened) from knowledge (information about why something happened). Data may
involve knowledge, but this is not a necessary condition. Data also entail recording, and therefore a material medium of some kind.
As a recorded entity,
any datum requires sensors to capture it
and massive storage systems to maintain it. 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).[46]
We should also
be wary of thinking that data collection and analysis are frictionless or
automated processes. Most data must be
cleaned and organized 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.[47]
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.[48]
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. All of this had historical precedents in earlier periods of
capitalism, but what was novel with the shift in technology was the sheer
amount of data that could now be used.
From
representing a peripheral aspect of businesses, data increasingly became a central resource. In the early years of
the century it was hardly clear, however, that data would become the raw material
to jumpstart a major shift in capitalism.[49]
The incipient efforts by Google simply used
data to draw advertising revenues away from traditional media outlets like
newspapers and television. Google was performing a valuable service in
organising the internet, but this was hardly a revolutionary change at an
economic level.
However, as the
internet expanded and firms became dependent on digital communications for all
aspects of their business, data became increasingly relevant. As I will attempt
to show in this chapter, data have come to serve a number of key capitalist
functions: they educate and give competitive advantage to algorithms; they
enable the coordination and outsourcing of workers; they allow for the
optimisation and flexibility of productive processes; they make possible the
transformation of low-margin goods into high-margin services; and data analysis
is itself generative of data, in a virtuous cycle.
Given the
significant advantages of recording and using data and the competitive pressures
of capitalism, it was perhaps inevitable that this raw material would come to
represent a vast new resource to be extracted from.
The problem for
capitalist firms that continues to the present day is that old business models
were not particularly well designed to extract and use data. Their method of
operating was to produce a good in a factory where most of the information was
lost, then to sell it, and never to learn anything about the customer or how
the product was being used. While the global logistics network of lean
production was an improvement in this respect, with few exceptions it remained
a lossy model as well. A different business model was necessary if capitalist
firms were to take full advantage of dwindling recording costs. This chapter argues
that the new business model that
eventually emerged is a powerful new
type of firm: the platform.[50]
Often arising
out of internal needs to handle data, platforms
became an efficient way to monopolise, extract, analyse, and use the increasingly
large amounts of data that were being
recorded.
Now this model has come to expand across the economy,
as numerous companies incorporate
platforms: powerful technology companies (Google, Facebook, and Amazon),
dynamic start-ups (Uber, Airbnb), industrial leaders (GE, Siemens), and
agricultural powerhouses (John Deere, Monsanto), to name just a few.
What are
platforms?[51]
At the most general level, platforms are digital infrastructures that enable
two or more groups to interact.[52]
They therefore position themselves as intermediaries that bring together
different users: customers, advertisers,
service providers, producers, suppliers, and even physical objects.[53]
More often than not, these platforms also come with a series of tools that
enable their users to build their own products, services, and marketplaces.[54]
Microsoft’s Windows operating system enables
software developers to create applications for it and sell them to consumers;
Apple’s App Store and its associated
ecosystem (XCode and the iOS SDK) enable developers to build and sell new apps
to users; Google’s search engine provides
a platform for advertisers and content providers to target people searching
for information; and Uber’s taxi app
enables drivers and passengers to exchange rides for cash.
Rather than having
to build a marketplace from the ground up, a
platform provides the basic infrastructure to mediate between different groups.
This is the key to its advantage over
traditional business models when it comes to data, since a platform
positions itself (1) between users, and (2) as the ground upon which their
activities occur, which thus gives it privileged access to record them.
Google, as the platform for searching, draws on vast amounts of search activity (which
express the fluctuating desires of individuals). Uber, as the platform for taxis, draws on traffic data and the
activities of drivers and riders. Facebook, as the platform for social networking, brings in a variety of intimate social interactions that can
then be recorded. And, as more and more industries move their interactions
online (e.g. Uber shifting the taxi industry into a digital form), more and
more businesses will be subject to platform development.
Platforms are, as a result, far more than internet companies or tech
companies, since they can operate
anywhere, wherever digital interaction takes place.
The second essential characteristic is that
digital platforms produce and are reliant on ‘network effects’: the more numerous the users who use a platform,
the more valuable that platform becomes for everyone else.
Facebook, for example, has become the default social networking platform simply
by virtue of the sheer number of people on it. If you want to join a platform
for socialising, you join the platform where most of your friends and family
already are. Likewise, the more numerous the users who search on Google, the
better their search algorithms become, and the more useful Google becomes to
users.
But this generates
a cycle whereby more users beget more users, which leads to platforms having a
natural tendency towards monopolisation.
It also lends platforms a dynamic of ever-increasing access to more activities,
and therefore to more data. Moreover, the ability to rapidly scale many
platform businesses by relying on pre-existing infrastructure and cheap
marginal costs means that there are few natural limits to growth.
One reason for Uber’s rapid growth, for instance, is
that it does not need to build new factories – it just needs to rent more
servers. Combined with network effects, this means that platforms can grow very
big very quickly.
The importance
of network effects means that platforms must deploy a range of tactics to
ensure that more and more users come on board. For example – and this is the third characteristic – platforms often
use cross-subsidisation: one arm of the firm reduces the price of a service
or good (even providing it for free), but
another arm raises prices in order to make up for these losses.
The price structure
of the platform matters significantly
for how many users become involved and how often they use the platform.[55]
Google, for instance, provides service likes email for free in order to get
users on board, but raises money through its advertising arm. Since platforms
have to attract a number of different groups, part of their business is fine-tuning
the balance between what is paid, what is not paid, what is subsidised, and
what is not subsidised. This is a far cry from the lean model, which aimed to
reduce a company down to its core competencies and sell off any unprofitable
ventures.[56]
Finally, platforms are also designed in a way that makes them attractive to its varied users.
While often presenting themselves as empty spaces for others to interact on,
they in fact embody a politics. The rules of product and service development,
as well as marketplace interactions, are set by the platform owner. Uber,
despite presenting itself as an empty vessel for market forces, shapes the
appearance of a market. It predicts where the demand for drivers will be and
raises surge prices in advance of actual demand, while also creating phantom
cabs to give an illusion of greater supply.[57]
In their
position as an intermediary, platforms gain not only access to more data but
also control and governance over the rules of the game. The core architecture
of fixed rules, however, is also generative, enabling others to build upon them
in unexpected ways. The core
architecture of Facebook, for instance, has allowed developers to produce
apps, companies to create pages, and users to share information in a way that
brings in even more users.
The same holds
for Apple’s App Store, which enabled the production of numerous useful apps that
tied users and software developers increasingly into its ecosystem. The challenge
of maintaining platforms is, in part, to revise the crosssubsidisation links
and the rules of the platform in order to sustain user interest. While network
effects strongly support existing platform leaders, these positions are not
unassailable.
Platforms, in sum, are a new type of firm; they are characterised by providing the infrastructure to intermediate between different user
groups, by displaying monopoly tendencies driven by network effects, by
employing cross-subsidisation to draw in different user groups, and by having a
designed core architecture that governs the interaction possibilities.
Platform ownership, in turn, is essentially ownership of
software (the 2 billion
lines of code for Google, or the 20 million lines of code for Facebook)[58] and hardware (servers, data centres,
smartphones, etc.), built upon open-source material (e.g. Hadoop’s data
management system is used by Facebook).[59]
All these
characteristics make platforms key business models for extracting and
controlling data. By providing a digital
space for others to interact in, platforms position themselves so as to extract
data from natural processes (weather conditions, crop cycles, etc.), from production processes (assembly
lines, continuous flow manufacturing, etc.), and from other businesses and users (web tracking, usage data, etc.). They are an extractive apparatus for data.
The remainder of
this chapter will give an overview of the emerging platform landscape by way of presenting five different types
of platforms. In each of these areas, the important element is that the
capitalist class owns the platform, not necessarily that it produces a physical
product.
The first type
is that of advertising platforms (e.g.
Google, Facebook), which extract information
on users, undertake a labour of analysis, and then use the products of that process to sell ad space. The second type
is that of cloud platforms (e.g.
AWS, Salesforce), which own the hardware and software of digital-dependent
businesses and are renting them out as needed.
The third type
is that of industrial platforms (e.g.
GE, Siemens), which build the hardware and software necessary to transform
traditional manufacturing into internet-connected processes that lower the
costs of production and transform goods into services. The fourth type is that of product platforms (e.g. Rolls Royce,
Spotify), which generate revenue by using other platforms to transform a traditional good into a service and by collecting
rent orsubscription fees on them.
Finally, the
fifth type is that of lean platforms
(e.g.Uber, Airbnb), which attempt
to reduce their ownership of assets to aminimum and to profit by reducing costs as much as possible. These analytical divisions can, and often
do, run together within any one firm.
Amazon, for example, is often seen as an e-commerce company, yet it
rapidly broadened out into a logistics company. Today it is spreading into the
ondemand market with a Home Services program in partnership with TaskRabbit,
while the infamous Mechanical Turk (AMT) was in many ways a pioneer for the gig
economy and, perhaps most importantly, is developing Amazon Web Services as a
cloud-based service. Amazon therefore spans nearly all of the above categories.
Advertising Platforms
The elders of
this new enterprise form, advertising
platforms are the initial attempts at building a model adequate to the digital
age. As we will see, they have directly and indirectly fostered the
emergence of the most recent technological trends – from the sharing economy to the industrial internet.
They emerged out
of the easy creditfuelled dot-com bust, whose effect was twofold. One
aspect of it was that many competitors collapsed, leaving the various areas of
the tech industry increasingly under the control of the remaining enterprises.
The sudden unwillingness of venture capital (VC) to finance new entries meant
that entry into the competitive landscape remained closed as well. The monopoly
tendencies of the early tech boom were solidified here, as a new range of dominant
companies emerged from the ashes and have continued to dominate ever since. The
other important consequence of the bust was that the drying up of VC and equity
financing placed new pressure on internet-based companies to generate revenues.
In the midst of the boom there was no clearly dominant way to raise a sustainable
revenue stream – companies were relatively equally divided among different
proposals.[60]
However, the centrality
of marketing to finance capital’s ‘growth before profits’ strategy meant that dot-com firms had already built
the basis for a business model oriented towards advertising and attracting
users. As a percentage of revenues, these firms spent 3–4 times more than
other sectors on advertising, and they
were the pioneers in purchasing online advertising as well.[61]
When the bubble
burst, it was perhaps inevitable that these companies would turn towards
advertising as their major revenue source. In this endeavour, Google and Facebook have come to represent
the leading edges of this process.
Created in 1997, Google was an early recipient of
venture funding in 1998 and received a major $25 million funding round in 1999. At this point Google had been collecting user data
from searches and using these data to improve searches.[62]
This was an example of the classic use of data within
capitalism: it was meant to improve one’s services for customers and users. But there was no value leftover from which Google
could generate revenue. In the wake of the dot-com bust, Google increasingly
needed a way to generate revenues, yet a fee-based service risked alienating
the users who were the basis of its success.
Eventually it
began to use the search data, along with cookies and other bits of information,
to sell targeted ad space to advertisers through an increasingly automated
auction system.[63]
When the National Association of Securities Dealers Automated Quotations (NASDAQ)
market peaked in March 2000, Google unveiled AdWords in October 2000 and began
its transformation into a revenue-generating company. The extracted data moved from
being a way to improve services to becoming a way to collect advertising revenues.
Today Google and Facebook remain almost
entirely dependent on them: in the first quarter of 2016, 89.0 per cent of
Google’s and 96.6 per cent of Facebook’s revenues
came from advertisers.
This was part
and parcel of the broader shift, in the
early years of the new millennium, to Web 2.0, which was premised more on
user generated content than on digital storefronts and on multimedia interfaces
rather than on static text. In the press, this shift came packaged with a
rhetoric of democratizing communication in which anyone would be able to create
and share content online. No longer would newspapers and other mass media
outlets have a monopoly over what was voiced in society. For critical theorists
of the web, this rhetoric obscured a shift to business models premised upon the
exploitation of ‘free labour’.[64]
From this perspective,
the story of how Google and Facebook generate profit has been a simple one: users are unwaged labourers who produce
goods (data and content) that are then taken and sold by the companies to
advertisers and other interested parties. There are a number of problems
with this account, however. A first issue with the freelabour argument is that it often slides into grand metaphysical
claims. All social interaction becomes
free labour for capitalism, and we begin to worry that there is no outside
to capitalism. Work becomes inseparable from nonworkand precise categories become blunt banalities. It is important,
however,to draw distinctions
between interactions done on platforms and interactionsdone elsewhere, as well as between interactions done on
profit-orientedplatforms and
interactions done on other platforms.[65]
Not all – and
not evenmost – of our social
interactions are co-opted into a system of profitgeneration. In fact one of
the reasons why companies must compete to build platforms is that most of our
social interactions do not enter into a valorisation process. If all of our
actions were already captured withincapitalist
valorisation, it is hard to see why there would be a need to build theextractive apparatus of platforms.
More broadly,
‘free labour’ is only a portionof
the multitude of data sources that a company like Google relies upon:economic transactions, information
collected by sensors in the internet ofthings,
corporate and government data (such as credit records and financialrecords), and public and private
surveillance (such as the cars used to buildup Google Maps).[66]
Yet even limiting our attention to user-created data,
it is right to call this activity labour? Within a Marxist framework, labour has a very
particular meaning: it is an activity
that generates a surplus value within a context of markets for labour and a
production process oriented towards exchange. The debate over whether or
not online social interaction is part of capitalist production is not just a
tedious scholarly debate over definitions. The relevance of whether this interaction is free labour or
not has to do with consequences.
If it is capitalist, then it will be pressured by
all the standard capitalist imperatives: to rationalise the production
processes, to lower costs, to increase productivity, and so on. If it is
not, then those demands will not be imposed. In examining the activities of users online, it is hard to make the case
that what they do is labour, properly speaking. Beyond the intuitive hesitation
to think that messaging friends is labour, any idea of socially necessary
labour time – the implicit standard against which production processes are set
– is lacking. This means there are no competitive pressures for getting users
to do more, even if there are pressures to get them to do more online.
More broadly, if our online interactions are
free labour, then these companies must be a significant boon to capitalism
overall – a whole new landscape of exploited labour has been opened up. On
the other hand, if this is not free labour, then these firms are parasitical on
other value producing industries and global capitalism is in a more dire state.
A quick glance at the stagnating global
economy suggests that the latter is more likely.
Rather than exploiting free labour, the position taken
here is that advertising platforms appropriate data as a raw material. The
activities of users and institutions,
if they are recorded and transformed into data, become a rawmaterial that can be refined and used
in a variety of ways by platforms. With advertising
platforms in particular, revenue is generated through the extraction of data
from users’ activities online, from the analysis of thosedata, and from the auctioning of ad
space to advertisers.
This involvesachieving two processes. First, advertising platforms need to
monitor and record online activities. The more users interact with a site,
the moreinformation can be
collected and used. Equally, as users wander around theinternet, they are tracked via cookies and other means, and these
data becomeever more extensive and
valuable to advertisers. There is a convergence
ofsurveillance and profit making in
the digital economy, which leads some tospeak of ‘surveillance capitalism’.[67]
Key to revenues, however, is not just the collection
of data, but also the analysis of data. Advertisers are interested lessin unorganised data and more in data that give them insights or
match themto likely consumers.
These are data that have been worked on.[68]
They havehad some process applied to them,
whether through the skilled labour of adata
scientist or the automated labour of a machine-learning algorithm. Whatis sold to advertisers is therefore
not the data themselves (advertisers do notreceive personalised data), but rather the promise that Google’s software will adeptly match an
advertiser with the correct users when needed.
While the data
extraction model has been prominent in the online world, it has also migrated
into the offline world. Tesco, one of
the world’s largest retailers, owns Dunnhumby, a UK-based ‘consumer
insights’ business valued at around $2 billion. (The US arm of the company was
recently sold to Kroger, one of America’s largest employers.) The company is premised upon tracking consumers
both online and offline and using that information to sell to clients such as
Coca-Cola, Macy’s, and Office Depot. It has attempted to build a
monopolistic platform for itself as well, through a loyalty card that channels customers
into Tesco stores with the promise of rewards.
Simultaneously, more
and more diverse information about customers is being tracked (to the point
where the company is even suggesting using wearables as a source of customer
health data).[69]
Non-tech firms are also developing user databases and using data to adapt to
customer trends and effectively market goods to consumers. Data extraction is
becoming a key method of building a monopolistic platform and of siphoning off
revenue from advertisers.
These advertising platforms are currently the
most successful of the new platform businesses, with high revenues, significant profits, and a vigorous dynamism. But
what have they been doing with their revenues? Investment levels remain low in
the United States, United Kingdom, and Germany, so there has been little growth in fixed capital. Instead these
companies have tended to do three things with their cash. One was to save it, and high levels of corporate cash have been an
odd phenomenon of the post-2008 era. As we saw in Chapter 1, tech companies
have taken up a disproportionately large amount of this cash glut.
The leaders of tax evasion have also been tech companies:
Google, Apple, Facebook, Amazon, and Uber. The second use of this cash was in high levels of mergers and acquisitions – a process
that centralises existing capacity rather than building new capacity. Among the
big tech companies, Google has made the most acquisitions over the past five years
(on average, it purchases a new company every week),[70]
while Facebook has some of the biggest acquisitions (e.g. it bought WhatsApp
for $22 billion).[71]
Google’s creation of the Alphabet
Holding Company in 2015 is part and parcel of this process; this was an
effort designed to enable Google to purchase firms in other industries while
giving them a clear delineation from its core business.
Thirdly, these
companies have funnelled their money into tech start-ups, many of the
advertising platforms being large investors in this area. As we will see, they
have set the conditions for the latest tech boom.
Most importantly, however, they have provided a
business model – the platform – that is now being replicated across a variety
of industries.
Cloud Platforms
If advertising
platforms like Google and Facebook laid the groundwork for extracting and using
massive amounts of data, then the
emerging cloud platforms are the step that has consolidated the platform as a
unique and powerful business model. The story of corporate cloud rental begins
with ecommerce in the 1990s. During the late 1990s, e-commerce companies thought
they could outsource the material aspects
of exchange to others. But this proved to be insufficient, and companies
ended up taking on the tasks of building warehouses and logistical networks and
hiring large numbers of workers.[72]
By 2016 Amazon
has invested in vast data centres, robotic warehouse movers, and massive
computer systems, had pioneered the use of drones for deliveries, and recently
began leasing airplanes for its shipping section.[73]
It is also by far the largest employer
in the digital economy, employing over 230,000 workers and tens of thousands of
seasonal workers, most of whom do low-wage and highly stressful jobs in
warehouses. To grow as an e-commerce platform, Amazon has sought to gain as
many users as possible through cross-subsidisation.
By all accounts,
the Amazon Prime delivery service loses money on every order, and the Kindle
e-book reader is sold at cost.[74]
On traditional metrics for lean businesses, this is unintelligible: unprofitable
ventures should be cut off. Yet rapid and cheap
delivery is one of the main ways in which Amazon entices users onto its
platform in order to make revenues elsewhere.
In the process
of building a massive logistical network, Amazon Web Services (AWS) was
developed as an internal platform, to handle the increasingly complex logistics
of the company. Indeed, a common theme in the genesis of platforms is that they
often emerge out of internal company needs. Amazon required ways to get new
services up and running quickly, and the answer was to build up the basic
infrastructure in a way that enabled new services to use it easily.[75]
It was quickly
recognised that this could also be rented to other firms. In effect AWS rents
out cloud computing services, which include ondemand services for servers,
storage and computing power, software development tools and operating systems,
and ready-made applications.[76]
The utility of this practice for other businesses is
that they do not need to spend the time and money to build up their own
hardware system, their own software development kit, or their own applications.
They can simply rent these on an ‘as needed’ basis. Software, for instance, is increasingly
deployed on a subscription basis; Adobe, Google, and Microsoft have all started
to incorporate this practice. Likewise, the sophisticated analytical tools that
Google has developed are now beginning to be rented out as part of its AWS
competitor.[77]
Other businesses
can now rent the ability to use pattern recognition algorithms and audio
transcription services. In other words, Google is selling its machine-learning
processes (and this is precisely where Google sees its advantage over its
competitors in the cloud computing field).
Microsoft,
meanwhile, has built an artificial intelligence platform that gives businesses
the software development tools to build their own bots (‘intelligence as a
service’, in the contemporary lingo). And International Business Machines (IBM)
is moving to make quantum cloud computing a reality.[78]
Cloud platforms
ultimately enable the outsourcing of much of a company’s information technology
(IT) department. This process pushes knowledge workers out and often enables
the automation of their work as well. Data analysis, storage of customer
information, maintenance of a company’s servers – all of this can be pushed to
the cloud and provides the capitalist rationale for using these platforms.
The logic behind
them is akin to how utilities function. Jeff Bezos, Amazon’s chief executive
officer, compares it to electricity provision: whereas early factories had each
its own power generator, eventually electricity generation became centralised
and rented out on an ‘as needed’ basis. Today every area of the economy is
increasingly integrated with a digital layer; therefore owning the
infrastructure that is necessary to every other industry is an immensely
powerful and profitable position to be in.
Moreover, the significance
of the cloud platform for data extraction is that its rental model enables it
to constantly collect data, whereas the older purchasing model involved selling
these as goods that were then separated from the company.
By moving
businesses’ activities onto cloud platforms, companies like Amazon gain direct
access to whole new datasets (even if some remain occluded to the platform). It
is unsurprising, then, that AWS is now estimated to be worth around $70
billion,[79]
and major competitors like Microsoft
and Google are moving into the field,
as well as Chinese competitors like
Alibaba.
AWS is now the most rapidly growing part of Amazon –
and also the most profitable, with about 30 per cent margins and nearly $8
billion in revenue in
2015. In the
first quarter of 2016, AWS generated more profit for Amazon than its core
retail service.[80]
If Google and
Facebook built the first data extraction platforms, Amazon built the first major cloud platform in order to rent
out an increasingly basic means of production for contemporary businesses.
Rather than relying on advertisers’ buying data, these cloud platforms are
building up the basic infrastructure of the digital economy in a way that can
be rented out profitably to others, while they collect data for their own uses.
Industrial Platforms
As data
collection, storage, and analysis have become increasingly cheaper, more and
more companies have attempted to bring platforms into the field of traditional
manufacturing. The most significant of these attempts goes under the rubric of
‘the industrial internet of things’, or simply ‘the industrial internet’.
At the most
basic level, the industrial internet involves the embedding of sensors and
computer chips into the production process and of trackers (e.g. RFID) into the
logistics process, all linked together through connections over the internet.
In Germany, this process is being heralded as ‘Industry 4.0’. The idea is that
each component in the production process becomes able to communicate with
assembly machines and other components, without the guidance of workers or
managers. Data about the position and state of these components are constantly
shared with other elements in the production process.
In this vision,
material goods become inseparable from their informational representations. For
its proponents, the industrial internet will optimise the production process:
they argue that it is capable of reducing labour costs by 25 per cent, of
reducing energy costs by 20 per cent (e.g. data centres would distribute energy
where it is needed and when), of reducing maintenance costs by 40 per cent by
issuing warnings of wear and tear, of reducing downtime by scheduling it for
appropriate times, and of reducing errors and increasing quality.[81]
The industrial
internet promises, in effect, to make the production process more efficient,
primarily by doing what competitive manufacturing has been doing for some time
now: reducing costs and downtime. But it also aims to link the production
process more closely to the realisation process. Rather than relying on focus
groups or surveys, manufacturers are hoping to develop new products and design new
features on the basis of usage data drawn from existing products (even by using
online methodologies like A/B testing to do so).[82]
The industrial internet
also enables mass customisation. In one test factory from BASF SE, the largest chemicals producer in the world, the assembly
line is capable of individually
customising every unit that comes down the line: individual soap bottles
can have different fragrances, colours, labels, and soaps, all being automatically
produced once a customer places an order.[83]
Product lifecycles can be significantly reduced as a result.
As factories
begin to implement the components for the industrial internet, one major
challenge is establishing a common standard for communication; interoperability
between components needs to be ensured, particularly in the case of older
machinery. This is where industrial platforms come in, functioning as the basic
core framework for linking together sensors and
actuators, factories and suppliers, producers and consumers, software
and hardware.
These are the
developing powerhouses of industry, which are building the hardware and
software to run the industrial internet across turbines, oil wells, motors,
factory floors, trucking fleets, and many more applications. As one report puts
it, with the industrial internet ‘the big winners will be platform owners’.[84]
It is therefore
no surprise to see traditional
manufacturing powerhouses like General Electric (GE) and Siemens, as well as
traditional tech titans like Intel and Microsoft, make a major push to
develop industrial internet platforms. Siemens has spent over€4 billion to acquire smart manufacturing
capabilities and to build itsindustrial
platform MindSphere,[85]
while GE has been working rapidly todevelop
its own platform, Predix.
The field has so
far been dominated bythese
established companies rather than being subject to an influx of newstart-ups. And even the industrial
internet start-ups are primarily funded bythe old guard (four of the top five investors), keeping funding for the
sectorstrong in 2016 despite a
general slowdown in other start-up areas.[86]
The shift to industrial platforms is also an
expression of national economic competition, as Germany (a traditional
manufacturing powerhouse represented by
Siemens) and the United States (a technology powerhouserepresented by GE) are
the primary supporters of this shift. Germany hasenthusiastically bought into this idea and developed its own
consortium tosupport the project,
as has the United States, where companies like GE, Intel,Cisco, and IBM have partnered with the government in a similar
non-profitconsortium to push for smart
manufacturing. At the moment the Germanconsortium
aims simply to raise awareness and support for the industrialInternet, while the American
consortium is actively expanding trials with thetechnology.
The competition
here is ultimately over the ability to build the monopolistic platform for
manufacturing: ‘It’s winner takes all,’ says GE’s chief digital officer.[87]
Predix and MindSphere both already offer infrastructural services (cloud-based
computing), development tools, and applications for managing the industrial
internet (i.e. an app store for factories). Rather than companies developing
their own software to manage the internal internet, these platforms license out
the tools needed.
Expertise is
necessary, for instance, in order to cope with the massive amounts of data that
will be produced and to develop new analytical tools for things like time
series data and geographical data. GE’s liquid natural gas business alone is already
collecting as many data as Facebook and requires a series of specialised tools
to manage the influx of data.[88]
The same holds
for software designed to collect and analyse big data, for the modelling of
physical-based systems, or for software that makes changes in factories and
power plants. These platforms also
provide the hardware (servers, storage, etc.) needed to operate an industrial
internet. In competition with more generic platforms like AWS, industrial
platforms promote themselves as having insider knowledge of manufacturing and
the security necessary to run such a system.
Like other
platforms, these industrial firms rely
on extracting data as a competitive tool against their rivals, a tool that
ensures quicker, cheaper, more flexible services. By positioning themselves as
the intermediary between factories, consumers, and app developers, these
platforms are ideally placed to monitor much of how global manufacturing
operates, from the smallest actuator to the largest factory, and they draw upon
these data to further solidify their monopoly position.
Deploying a standard
platform strategy, both Siemens and GE also maintain openness in terms of who
can connect to the platform, where data are stored (on site or in the cloud),
and who can build apps for it. Network effects are, as always, essential to
gaining a monopoly position, and this openness enables them to incorporate more
and more users. These platforms already are strong revenue sources for the companies:
Predix currently brings GE $5 billion and is expected to triple this revenue by
2020.[89]
Predictions are that
the sector will be worth $225 billion by
2020 – more than both the consumer internet of things and enterprise cloud
computing.[90]
Nevertheless, demonstrating the power of monopolies, GE continues to use AWS
for its internal needs.[91]
Product Platforms
Importantly, the
preceding developments – particularly the internet of things and cloud
computing – have enabled a new type of on-demand platform. They are two closely
related but distinct business models: the product platform and the lean
platform. Take, for example, Uber and
Zipcar – both platforms designed for consumers who wish to rent some asset
for a time. While they are similar in this respect, their business models are
significantly different.
Zipcar owns the assets it rents out – the vehicles;
Uber does not. The former is a product platform, while the
latter is a lean platform that attempts to outsource nearly every possible cost.
(Uber aims, however, eventually to command a fleet of self-driving cars, which
would transform it into a product platform.) Zipcar, by contrast, might be
considered a ‘goods as a service’ type of platform.
Product platforms are perhaps one of the biggest means by which companies attempt to recuperate the tendency
to zero marginal costs in some goods.
Music is the best example, as in the late 1990s downloading music for free became
as simple as installing a small program. Record labels’ revenues took a major
dip, as consumers stopped purchasing compact discs (CDs) and other physical
copies of music. Yet, in spite of its numerous obituaries, the music industry has been revived in recent years by platforms
(Spotify, Pandora) that siphon off fees from music listeners, record
labels, and advertisers alike.
Between 2010 and
2014 subscription services have seen user numbers rise up from 8 million to 41
million, and subscription revenues are set to overtake download revenues as the
highest source of digital music.[92]
After years of decline, the music industry is poised to see its revenue grow
once again in 2016. While subscription
models have been around for centuries, for example in newspapers, what is
novel today is their expansion to new realms: housing, cars, toothbrushes,
razors, even private jets. Part of what has enabled these product platforms to
flourish in recent years is the stagnation in wages and the decline in savings
that we noted in Chapter 1.
As less money is
saved up, big-ticket purchases like cars and houses become nearly impossible
and seemingly cheaper upfront fees appear more enticing. In the United Kingdom,
for instance, household ownership has declined since 2008, while private
rentals have skyrocketed.[93]
On-demand
platforms are not affecting just software and consumer goods, though. One of
the earliest stabs at an on-demand
economy centred on manufactured goods, particularly durable goods. The most
influential of these efforts was the
transformation of the jet engine business from one that sold engines into one
that rented thrust.
The three big manufacturers – Rolls Royce, GE,
and Pratt & Whitney – have all moved to this business model, with Rolls
Royce leading the way in the late 1990s. The classic model of building an
engine and then selling it to an airline was a relatively low margin business
with high levels of competition. The competitive dynamics outlined in Chapter 1
are on full display here. Over the past 40 years the jet engine industry has
been characterised by very few new companies, and no companies leaving the
industry.[94]
Instead the
three major firms have competed intensely among themselves by introducing
incremental technological improvements, in an effort to gain an edge. This
technological competition continues today, when the jet engine industry
pioneers the use of additive manufacturing. (For instance, GE’s most popular jet
engine has a number of parts that are now 3D printed rather than welded
together out of different components.[95])
But margins on the engines themselves remain small, and competition tight.
By contrast, the maintenance of these engines involves
much higher profit margins – seven times higher, according to estimates.[96]
The challenge with maintenance is that it is quite easy for outside competitors
to come in to the market and take the profits away. This prompted Rolls Royce
to introduce the ‘goods as a service’ model, whereby airlines do not purchase the jet engine but pay a fee for every
hour one is used.
In turn, Rolls Royce provides maintenance and
replacement parts.
The raw material of data remains as central to
this platform as to any other.
Sensors are placed on all the engines and massive amounts of data are extracted from every
flight, combined with weather data and information on air traffic control, and
sent to a command centre in the United Kingdom.
Information on
the wear and tear on engines, possible problems, and times for scheduling
maintenance are all derived. These data are immensely useful in blocking out
competitors and in securing a competitive advantage against any outside
maintenance firm that may hope to break into the market. Data on how the
engines perform have also been crucial for developing new models: they enabled
Rolls Royce to improve fuel efficiency and to increase the life of the engines,
and generated another competitive advantage over other jet engine
manufacturers. Once again, platforms appear as an optimal form for extracting
data and using them to gain an edge over competitors.
Data and the
network effects of extracting them have enabled the company to establish
dominance.
Lean Platforms
In the context
of everything that has just been described, it is hard not to regard the new
lean platforms as a retrogression to the earliest stages of the internet-enabled
economy. Whereas the previous platforms have all developed business models that
generate profits in some way, today’s
lean platforms have returned to the ‘growth before profit’ model of the 1990s.
Companies like Uber and Airbnb have rapidly become
household names and have come to epitomise this revived business model. These
platforms range from specialised firms
for a variety of services (cleaning, house calls from physicians, grocery
shopping, plumbing, and so on) to more general marketplaces like TaskRabbit and
Mechanical Turk, which provide a variety of services. All of them, however,
attempt to establish themselves as the platform upon which users, customers, and workers can meet.
Why are they ‘lean’
platforms? The answer lies in an oft-quoted observation: ‘Uber, the world’s
largest taxi company, owns no vehicles […] and Airbnb, the largest accommodation
provider, owns no property.’[97]
It would seem that these are asset-less companies; we might call them virtual
platforms.[98]
Yet the key is that they do own the most important
asset: the platform of software and data analytics. Lean platforms operate
through a hyper-outsourced model, whereby
workers are outsourced, fixed capital is outsourced, maintenance costs are
outsourced, and training is outsourced. All that remains is a bare extractive minimum – control over the platform
that enables a monopoly rent to be gained.
The most
notorious part of these firms is their outsourcing
of workers. In America, these
platforms legally understand their workers as ‘independent contractors’ rather
than ‘employees’. This enables the companies to save around 30 per cent on
labour costs by cutting out benefits, overtime, sick days, and other costs.[99] It also means outsourcing training costs,
since training is only permitted for employees; and this process has led to
alternatives forms of control via reputation systems, which often transmit the gendered
and racist biases of society.
Contractors are then paid by the task: a cut of every
ride from Uber, of every rental from Airbnb, of every task fulfilled on
Mechanical Turk. Given
the reduction in labour costs provided by such an approach, it is no wonder
that Marx wrote that the ‘piece-wage is the form of wages most in harmony with
the capitalist mode of production’.[100]
Yet, as we have
seen, this outsourcing of labour is part
of a broader and longer outsourcing trend, which took hold in the 1970s. Jobs
involving tradable goods were the first to be outsourced, while impersonal
services were the next to go. In the
1990s Nike became a corporate ideal for contracting out, in that it contracted
much of its labour to others. Rather than adopting vertical integration,
Nike was premised upon the existence of a small core of designers and branders,
who then outsourced the manufacturing of their goods to other companies.
As a result, by
1996 people were already voicing concerns that we were transitioning to ‘a “just-in-time”
age of “disposable” workers’.[101]
But the issue involves more than lean platforms. Apple, for instance, directly
employs less than 10 per cent of the workers who contribute to the production
of its products.[102]
Likewise, a
quick glance at the US Department of Labor can find a vast number of non-Uber
cases involving the mislabelling of
workers as independent contractors: cases related to construction workers,
security guards, baristas, plumbers, and restaurantworkers – to name just a few.[103]
In fact the traditional labour market thatmost closely approximates the lean platform model is an old and low-techone: the market of day labourers – agricultural workers, dock workers, or other
low-wage workers – who would show up at
a site in the morning in the hope of finding a job for the day.
Likewise, a
major reason why mobile phoneshave
become essential in developing countries is that they are nowindispensable in the process of
finding work on informal labour markets.[104]
The gig economy simply moves these sites online
and adds a layer of
pervasive surveillance. A tool of survival is being marketed by Silicon
Valley as a tool of liberation.
We can also find
this broader shift to non-traditional jobs in economic statistics. In 2005[105]
the Bureau of Labour Statistics (BLS) found that nearly 15 million US workers
(10.1 per cent of the labour force) were in alternative employment.[106]
This category includes employees hired under alternative contract arrangements
(on-call work, independent contractors) and employees hired through intermediaries
(temp agencies, contract companies). By 2015 this category had grown to 15.8
per cent of the labour force.[107]
Nearly half of this rise (2.5 per cent) was due to an increase in contracting
out, as education, healthcare, and administration jobs were often at risk.
Most strikingly,
between 2005 and 2015, the US labour market added 9.1 million jobs – including
9.4 million alternative arrangement jobs. This means that the net increase in
US jobs since 2005 has been solely from these sorts of (often precarious)
positions.[108]
Similar trends can be seen in selfemployment.
While the number
of people who identify as self-employed has decreased, the number of people who
filed the 1099 tax form for selfemployment in the United States has increased.[109]
What we see here is effectively an acceleration of the long-term tendency towards more precarious employment,
particularly after 2008. The same trends are observable in the United Kingdom,
where self-employment has created 66.5 per cent of net employment after 2008
and is the only thing that has staved off much higher levels of unemployment.[110]
Where do lean
platforms fit into this? The most
obvious point is the category of independent contractors and freelancers. This
category has registered an increase of 1.7 per cent (2.9 million) between 2005
and 2015,[111]
but most of these increases have been for offline work. Given that no direct
measures of the sharing economy are currently available, surveys and other
indirect measures have been used instead. Nearly all of the estimates suggest
that around 1 per cent of the US labour force is involved in the online sharing
economy formed by lean platforms.[112]
Even here, the
results have to take into account that Uber drivers probably form the majority
of these workers.[113]
The sharing economy outside of Uber is tiny. In the United Kingdom less
evidence is presently available, but the most thorough survey done so far
suggests that a slightly higher number of people routinely sell their labour
through lean platforms. It is estimated that approximately 1.3 million UK
workers (3.9 per cent of the labour force) work through them at least once a
week, while other estimates range from 3 to 6 per cent of the labour force.[114]
Other surveys suggest
slightly higher numbers, but those problematically include a much larger range
of activities.[115]
What we can therefore conclude is that the sharing
economy is but a small tip of a much larger trend.
Moreover, it is
a small sector, which is premised upon the vast growth in the levels of unemployment
after the 2008 crisis. Building on the trends towards more precarious work that
were outlined earlier, the crisis caused
unemployment in the United States to double, while long-term unemployment
nearly tripled.
Moreover, the aftermath of the crisis was a jobless
recovery – a phenomenon where economic growth returns, but job growth does
not. As a result, numerous workers were forced to find whatever desperate means
they could to survive. In this context,
self-employment is not a freely chosen path, but rather a forced imposition.
A look at the demographics of lean platform workers seems to support this. Of
the workers on TaskRabbit, 70 per cent have Bachelor’s degrees, while 5 per
cent have PhDs.[116]
An International
Labour Organization (ILO) survey found that workers on Amazon’s Mechanical Turk
(AMT) also tend to be highly educated, 37 per cent using crowd work as their
main job.[117]
And Uber admits that around a third of its drivers in London come from
neighbourhoods with unemployment rates of more than 10 per cent.[118]
In a healthy economy these people would
have no need to be microtasking, as they would have proper jobs.
While the other
platform types have all developed novel elements, is there anything new about
lean platforms? Given the broader context just outlined, we can see that they
are simply extending earlier trends into new areas.
Whereas outsourcing once primarily took place in
manufacturing, administration , and hospitality, today it is extending to a
range of new jobs: cabs, haircuts, stylists, cleaning, plumbing, painting,
moving, content moderation, and so on. It is even pushing into white-collar
jobs – copyediting, programming and management, for instance. And, in terms of
the labour market, lean platforms have turned what was once non-tradable services
into tradable services, effectively expanding the labour supply to a near-global
level.
A multitude of
novel tasks can now be carried out online through Mechanical Turk and similar
platforms. This enables business, again, to cut costs by exploiting cheap
labour in developing countries and places more downward pressure on wages by
placing these jobs into global labour markets. The extent to which lean
platform firms have outsourced other costs is also notable (though not novel);
these are perhaps the purest attempts at a virtual platform to date.
In doing so,
these companies have been dependent upon the capacities offered by cloud platforms.
Whereas firms once had to spend large amounts to invest in the computing
equipment and expertise needed for their businesses, today’s start-ups have flourished because they can
simply rent hardware and software from the cloud. As a result, Airbnb, Slack,
Uber, and many other start-ups use AWS.[119]
Uber further relies on Google for mapping, Twilio for
texting, SendGrid for emailing, and Braintree for payments: it is a lean
platform built on other platforms. These companies have also offloaded costs from their balance sheets
and shifted them to their workers: things like investment costs (accommodations
for Airbnb, vehicles for Uber and Lyft), maintenance costs, insurance costs,
and depreciation costs. Firms such as Instacart (which delivers groceries) have
also outsourced delivery costs to food suppliers (e.g. Pepsi) and to retailers
(e.g. Whole Foods) in return for advertising space.[120]
However, even
with this support, Instacart remains unprofitable on 60 per cent of its business,
and that is before the rather large costs of office space or the salaries of
its core team are taken into account.[121]
The lack of profitability has led to the predictable measure of cutting back on
wages – a notably widespread phenomenon among lean platforms.
This has also
prompted companies to compete on data extraction – again, a process optimised
by the access afforded by platforms. Uber
is perhaps the best example of this development, as it collects data on all of
its rides, as well as data on drivers, even when they are not receiving a
fare.[122]
Data about what drivers are doing and how they are driving are used in a
variety of ways in order to beat out competitors.
For instance, Uber
uses the data to ensure that its drivers are not working for other taxi platforms;
and its routing algorithms use the data on traffic patterns to plot out the
most efficient path for a trip.
Data are fed
into other algorithms to match passengers with nearby drivers, as well as to
make predictions about where demand is likely to arise. In China, Uber monitors even whether drivers go to
protests. All of this enables Uber to have a service that is quick and
efficient from the passenger’s point of view, thereby drawing users away from
competitors. Data are one of the primary
means of competition for lean platforms.
Nevertheless,
these firms are still struggling to be profitable and the money to support them
has to come from the outside. As we saw earlier, one of the important
consequences of the 2008 crisis has been the intensification of an easy
monetary policy and the growing corporate cash glut. The lean platform boom is, fundamentally, a post-2008 phenomenon. The
growth of this sector is reflected most clearly in the number of deals made
for start-up companies: VC deals have tripled since 2009.[123]
Even after
excluding Uber (which has an outsized position in the market), on-demand mobile
services raised $1.7 billion over the course of 2014 – a 316 per cent increase
from 2013.[124]
And 2015 continued this trend towards more deals and higher volumes. But it is worth
taking a moment to put the funding of lean platforms in context. When we look
at the lean platforms for on-demand mobile services, we are primarily
discussing Uber. In terms of funding, in 2014 Uber outpaced all the other
service companies, taken together, by 39 per cent.[125]
In 2015 Uber, Airbnb,
and Uber’s Chinese competitor, Didi Chuxing, combined to take 59 per cent of
all the funding for on-demand start-ups.[126]
And, while the enthusiasm for new tech start-ups has reached a fever pitch,
funding in 2015 ($59 billion) still paled in comparison to the highs of 2000
(nearly $100 billion).[127]
Where is the
money coming from? Broadly speaking, it is surplus capital seeking higher rates
of return in a low interest rate environment. The low interest rates have
depressed the returns on traditional financial investments, forcing investors
to seek out new avenues for yield. Rather than a finance boom or a housing
boom, surplus capital today appears to be building a technology boom. Such is
the level of compulsion that even nontraditional funding from hedge funds,
mutual funds, and investment banks is
playing a major
role in the tech boom.
In fact, in the
technology start-up sector, most investment financing comes from hedge funds
and mutual funds.[128]
Larger companies are also involved, Google being a major investor in the
ill-fated Homejoy, while the logistics company DHL has created its own on-demand
service MyWays, and firms like Intel and Google are also purchasing equity in a
variety of new start-ups.
Companies like
Uber, deploying more than 135 subsidiary companies across the world, are also helped
by tax evasion techniques.[129]
Yet the profitability of these lean platforms remains largely unproven. Just
like the earlier dot-com boom, growth in
the lean platform sector is premised on expectations of future profits rather
than on actual profits. The hope is that the low margin business of taxis
will eventually pay off once Uber has gained a monopoly position.
Until these
firms reach monopoly status (and possibly even then), their profitability
appears to be generated solely by the removal of costs and the lowering of
wages and not by anything substantial.
In summary, lean
platforms appear as the product of a few tendencies and moments: the tendencies towards outsourcing, surplus
populations, and the digitisation of life, along with the post-2008 surge
in unemployment and rise of an accommodative monetary policy, surplus capital,
and cloud platforms that enable rapid scaling. While the lean model has
garnered a large amount of hype and, in the case of Uber, a large amount of VC,
there are few signs that it will inaugurate a major shift in advanced
capitalist countries. In terms of outsourcing, the lean model remains a minor
player in a long-term trend.
The
profit-making capacity of most lean models likewise appears to be minimal and
limited to a few specialised tasks. And, even there, the most successful of the
lean models has been supported by VC welfare rather than by any meaningful
revenue generation. Far from representing the future of work or that of the
economy, these models seem likely to fall apart in the coming years.
Conclusion
We began this
chapter by arguing that twenty-first-century capitalism has found a massive new raw material to
appropriate: data. Through a series of developments, the platform has
become an increasingly dominant way of organising businesses so as to
monopolise these data, then extract, analyse, use, and sell them. The old
business models of the Fordist era had only a rudimentary capacity to extract
data from the production process or from customer usage.
The era of lean
production modified this slightly, as global ‘just in time’ supply chains
demanded data about the status of inventories and the location of supplies. Yet
data outside the firm remained nearly impossible to attain; and, even inside
the firm, most of the activities went unrecorded.
The platform, on the other hand, has data
extraction built into its DNA, as a model that enables other services and
goods and technologies to be built on top of it, as a model that demands more
users in order to gain network effects, and as a digitally based medium that
makes recording and storage simple. All of
these characteristics make platforms a central model for extracting data as raw
material to be used in various ways.
As we have seen
in this brief overview of some different platform types, data can be used in a variety
of ways to generate revenues. For companies like Google and Facebook, data are, primarily, a resource that can be used
to lure in advertisers and other interested parties. For firms like Rolls
Royce and Uber, data are at the heart of beating the competition: they enable
such firms to offer better products and services, control workers, and optimise
their algorithms for a more competitive business.
Likewise,
platforms like AWS and Predix are oriented towards building (and owning) the
basic infrastructures necessary to collect, analyse, and deploy data for other companies
to use, and a rent is extracted for these platform services. In every case, collecting massive amounts of data is
central to the business model and the platform provides the ideal extractive
apparatus.
This new
business form has intertwined with a series of long-term trends and short-term
cyclical movements. The shift towards lean production and ‘just in time’ supply
chains has been an ongoing process since the 1970s, and digital platforms
continue it in heightened form today. The
same goes for the trend towards outsourcing. Even companies that are not
normally associated with outsourcing are still involved. For instance, content moderation for Google and Facebook
is typically done in the Philippines, where an estimated 100,000 workers
search through the content on social media and in cloud storage.[130]
And Amazon has a
notoriously low-paid workforce of warehouse workers who are subject to
incredibly comprehensive systems of surveillance and control. These firms
simply continue the secular trend of
outsourcing low-skill workers while retaining a core of well-paid high-skill
labourers.
On a broader
scale, all of the post-2008 net employment gains in America have come from
workers in non-traditional employment, such as contractors and on-call workers.
This process of outsourcing and building lean business models gets taken to an
extreme in firms like Uber, which rely on a virtually asset-less form to
generate profits.
As we have seen,
though, much of their profitability after the crisis has stemmed from holding
wages down. Even the Economist is forced to admit that, since 2008, ‘if the
share of domestic gross earnings paid in wages were to rise back to the average
level of the 1990s, the profits of American firms would drop by a fifth’.[131]
An increasingly
desperate surplus population has therefore provided a considerable supply of
workers in low-wage, low-skill work. This group of exploitable workers has
intersected with a vast amount of surplus capital set in a low interest rate
world. Tax evasion, high corporate savings, and easy monetary policies have all
combined, so that a large amount of capital seeks out returns in various ways.
It is no
surprise, then, that funding for tech start-ups has massively surged since
2010. Set in context, the lean platform economy ultimately appears as an outlet
for surplus capital in an era of ultra-low interest rates and dire investment
opportunities rather than the vanguard destined to revive capitalism.
While lean
platforms seem to be a short-lived phenomenon, the other examples set out in this chapter seem to point to an important shift in
how capitalist firms operate. Enabled
by digital technology, platforms emerge as the means to lead and control
industries. At their pinnacle, they have prominence over manufacturing,
logistics, and design, by providing the basic landscape upon which the rest of
the industry operates. They have enabled
a shift from products to services in a variety of new industries, leading
some to declare that the age of ownership is over.
Let us be clear,
though: this is not the end of
ownership, but rather the concentration of ownership. Pieties about an ‘age
of access’ are just empty rhetoric that obscures the realities of the
situation. Likewise, while lean platforms have aimed to be virtually assetless,
the most significant platforms are all
building large infrastructures and spending significant amounts of money to
purchase other companies and to invest in their own capacities. Far from being mere owners of information, these
companies are becoming owners of the infrastructures of society.
Hence the
monopolistic tendencies of these platforms must be taken into account in any
analysis of their effects on the broader economy.
Notes
1. Löffler and
Tschiesner, 2013.
2. Kaminska,
2016a.
3. Vercellone,
2007.
4. Terranova,
2000.
5. Wark, 2004.
6. Author’s
calculation on the basis of data from Andrae and Corcoran, 2013 and US Energy
Information Administration, n.d.; for more, see Maxwell and Miller, 2012.
7. One
particularly illuminating example of this comes from climate science; see
Edwards, 2010.
8. I draw here
upon Marx’s definition of raw material: ‘The land (and this, economically
speaking, includes water) in the virgin state in which it supplies man with
necessaries or the means of subsistence ready to hand, exists independently of
him, and is the universal subject of human labour. All those things which
labour merely separates from immediate connexion with their environment, are
subjects of labour spontaneously provided by Nature. Such are fish which we
catch and take from their element, water, timber which we fell in the virgin
forest, and ores which we extract from their veins. If, on the other hand, the
subject of labour has, so to say, been filtered through previous labour, we call it raw material; such is ore already
extracted and ready for washing’ (Marx, 1990: 284–5, emphasis added).
9. A useful
relation could perhaps be drawn to Jason Moore’s concept of cheap inputs,
although this lies outside the scope of this study; see ch. 2 in Moore, 2015.
10. Apple is one example of a major company excluded
by this focus, as it is primarily a traditional consumer electronics producer
with now standard practices of outsourcing manufacturing. It has some platform
elements to its business (iTunes, the App Store), but these only generate 8.0
per cent of the revenues that Apple is famous for. The vast majority (68.0 per cent) of revenues come from iPhone sales.
Apple is more akin to the 1990s Nike business model than to the 2010s
Google business model.
11. For useful
complementary approaches to platforms, see Bratton, 2015: ch. 9 and Rochet and
Tirole, 2003.
12. While technically platforms can exist in non-digital
forms (e.g. a shopping mall), the ease of recording activities online makes
digital platforms the ideal model for data extraction in today’s economy.
13. By ‘user’ we
also include machines – an important addition when considering the internet of
things. See: Bratton, 2015: 251–89.
14. Gawer, 2009:
54.
15. Rochet and
Tirole, 2003.
16. Kaminska,
2016b.
17. Hwang and
Elish, 2015.
18. Metz, 2012.
19. We can imagine a scenario where a firm owns
the code of a platform but rents all of its computing needs from a cloud-based
service. Hardware is therefore not essential to the ownership of a
platform. But, given the competitive demands that we will outline later on, the
largest platforms have all moved towards proprietary hardware. In other words, ownership of fixed capital remains
important to these firms, if not essential.
20. Goldfarb,
Kirsch, and Miller, 2007: 128.
21. Crain, 2014:
377–8.
22. Zuboff,
2016.
23. Varian,
2009.
24. Terranova,
2000.
25. Wittel,
2016: 86.
26. Zuboff,
2015: 78.
27. Ibid.
28. For one
example of a data value chain, see Dumbill, 2014.
29. Finnegan,
2014.
30. Davidson,
2016.
31. CB Insights,
2016b.
32. Henwood,
2003: 30.
33. Hook, 2016.
34. Clark and
Young, 2013.
35. Burrington,
2016.
36. In the
industry, these are known respectively as ‘infrastructure as a service’ (IaaS),
‘platform as a service’ (Paas), and ‘software as a service’ (SaaS).
37. Clark, 2016.
38. Miller,
2016.
39. Asay, 2015.
40. McBride and
Medhora, 2016.
41. Webb, 2015;
Bughin, Chui, and Manyika, 2015.
42. Bughin,
Chui, and Manyika, 2015.
43. Alessi,
2014.
44. World
Economic Forum, 2015: 4.
45. Zaske, 2015.
46. CB Insights,
2016c.
47. Waters,
2016.
48. Murray,
2016.
49. Miller,
2015b.
50. Waters,
2016.
51. Miller,
2015a.
52.
International Federation of the Phonographic Industry, 2015: 6–7.
53. Office for
National Statistics, 2016a.
54. Bonaccorsi
and Giuri, 2000: 16–21.
55. Dishman,
2015.
56. ‘Britain’s
Lonely High-Flier’, 2009.
57. Goodwin,
2015.
58.
Incidentally, they appear to be owned by what McKenzie Wark calls the vectoralist
class; see Wark, 2004.
59. Kamdar,
2016; Kosoff, 2015.
60. Marx, 1990:
697–8.
61. Polivka,
1996: 3.
62. Scheiber,
2015.
63. US
Department of Labor, n.d.
64.
Dyer-Witheford, 2015: 112–14.
65. The BLS
measures the gig economy indirectly, through ‘contingent and alternative
employment’ – but stopped in 2005, after funding was cut. They are, however, set
to carry out another survey in 2017; see BLS Commissioner, 2016.
66. US
Department of Labor, 2005: 17.
67. This
estimate is based on an attempt to duplicate the BLS surveys as closely as
possible. See Katz and Krueger, 2016.
68. Ibid.
69. Wile, 2016.
70. Office for
National Statistics, 2014: 3.
71. Katz and
Krueger, 2016.
72. Various
estimates include: 0.5% of the labour force (Katz and Kreuger, 2016); 0.4–1.3%
(Harris and Kreuger, 2015: 12); 1.0% (McKinsey: see Manyika, Lund, Robinson,
Valentino, and Dobbs, 2015); 2.0% (Intuit: see Business Wire, 2015). One
outlier survey from Burson-Marsteller suggests that 28.6% of the US labour
force has provided services through the gig economy (see Burson-Marsteller,
Aspen Institute, and TIME, 2016).
73. Harris and
Krueger, 2015: 12.
74. Various
estimates are: 3.0% of the labour force (Coyle, 2016: 7); 3.9% (Huws and Joyce,
2016); 6.0% (Business Wire, 2015). See also Hesse, 2015.
75. A Nesta
survey found that 25% of Brits had been involved in internetenabled collaborative
activity, but this category includes people who purchase from the internet
rather than just workers. It also includes people who donate goods or purchase
media online. An Intuit survey, on the other hand, reportedly found that 6% of
the population in Britain is working in the sharing economy, but the actual
data do not appear to be available. See Stokes, Clarence, Anderson, and Rinne,
2014: 25; Hesse, 2015.
76. Henwood,
2015.
77. Berg, 2016.
78. Knight,
2016.
79. See many
more examples at Amazon Web Services, 2016.
80. Huet, 2016.
81. Ibid.
82. While
government surveillance is often the focus of public attention today, corporate
surveillance is just as pernicious a phenomenon. Pasquale, 2015.
83. ‘Reinventing
the Deal’, 2015.
84. CB Insights,
2015.
85. Ibid.
86. CB Insights,
2016a.
87. National
Venture Capital Association, 2016: 9; Crain, 2014: 374.
88. CB Insights,
2016d.
89. O’Keefe and
Jones, 2015.
90. Chen, 2014.
91. ‘The Age of
the Torporation’, 2015.
3 Great
Platform Wars
If platforms are the emerging business
model for the digital economy, how do they appear when set in the longer
history of capitalism? In particular, up to this point we have largely left out
one of the fundamental drivers of capitalism: intracapitalist competition.
In Chapter 1 we
set out the context of the long downturn – that period since the 1970s when the
global economy has been saddled by overcapacity and overproduction in the
manufacturing sector. As companies were unwilling and unable to destroy their
fixed capital or to invest in new lines, international competition has steadily
continued and, alongside it, the crisis of overcapacity in manufacturing.
Unable to generate
growth in this situation, in the 1990s the United States began trying to
stimulate the economy through an asset-price Keynesianism that operated by
inducing low interest rates in order to generate higher asset prices and a wealth
effect that would spark broader economic growth. This led to the dotcom boom of
the 1990s and to the housing bubble of the early years of the twenty-first
century.
Today, as we saw
in the previous chapter, asset-price Keynesianism continues apace and is one of
the fundamental drivers behind the current mania for tech start-ups. Yet, behind
the shiny new technology and slick façade of app interfaces, what broader
consequences do these new firms hold for capitalism?
In this chapter we will step back to look at the tendencies
unleashed by these new firms into the broader economic environment of the long
downturn. Some argue
that capitalism renews itself through the creation and adoption of new
technological complexes: steam and
railways, steel and heavy engineering, automobiles and petrochemicals – and
now information and communications technologies.[132]
Are we
witnessing the adoption of a new infrastructure that might revive capitalism’s
moribund growth? Will competition survive in the digital era, or are we headed
for a new monopoly capitalism?
With network effects, a tendency towards monopolisation
is built into the DNA of platforms: the more numerous the users who interact on a platform, the more
valuable the entire platform becomes for each one of them. Network effects,
moreover, tend to mean that early advantages become solidified as permanent
positions of industry leadership. Platforms
also have a unique ability to link together and consolidate multiple network
effects. Uber, for instance, benefits from the network effects of more and
more drivers as well as from the network effects of more and more riders.[133]
Leading platforms
tend consciously to perpetuate themselves in other ways as well. Advantages in data
collection mean that the more activities a firm has access to, the more data it
can extract and the more value it can generate from those data, and therefore
the more activities it can gain access to. Equally, access to a multitude of
data from different areas of our life makes prediction more useful, and this
stimulates the centralisation of data within one platform.
We give Google access to our email, our calendars, our
video histories, our search histories, our locations – and, with each aspect
provided to Google, we get better predictive services as a result. Likewise, platforms aim to facilitate complementary
products: useful software built for Android leads more users to use Android,
which leads more developers to develop for Android, and so on, in a virtuous circle.
Platforms also
seek to build up ecosystems of goods and services that close off competitors: apps that only work with Android, services
that require Facebook logins. All these
dynamics turn platforms into monopolies with centralised control over increasingly
vast numbers of users and the data they generate. We can get a sense of how
significant these monopolies already are by looking at how they consolidate ad
revenue: in 2016 Facebook, Google, and Alibaba alone will take half of the
world’s digital advertising.[134]
In the United States, Facebook and
Google receive 76 per cent of online advertising revenue and are taking 85
per cent of every new advertising dollar.[135]
Yet it is also true that capitalism develops not only
greater means for monopoly but also greater means for competition. The
emergence of the corporation form, the rise of large financial institutions,
and the monetary resources behind states all point to its capacity to initiate
new lines of industry and to topple existing monopolies.[136] Equally importantly, digital platforms tend to
arise in industries that are subject to disruption by new competitors.[137]
Monopolies, in this view, should only ever be temporary. The challenge today,
however, is that capital investment is not sufficient to overturn monopolies;
access to data, network effects, and path dependency place even higher hurdles
in the way of overcoming a monopoly like Google.
This does not
mean the end of competition or of the struggle for market power, but it means a change in the form of
competition.[138]
In particular, this is a shift away from
competition over prices (e.g. many services are offered for free). Here we
come to an essential point. Unlike in manufacturing, in platforms
competitiveness is not judged solely by the criterion of a maximal difference
between costs and prices; data collection and analysis also contribute to how
competitiveness is judged and ranked.
This means that,
if these platforms wish to remain
competitive, they must intensify their extraction, analysis, and control of
data – and they must invest in the fixed capital to do so. And while their
genetic drive is towards monopolisation, at present they are faced with an
increasingly competitive environment comprised of other great platforms.
Tendencies
Since platforms are grounded upon the extraction
of data and the generation of network effects, certain tendencies emerge
from the competitive dynamics of these large platforms: expansion of extraction, positioning as a gatekeeper, convergence of
markets, and enclosure of ecosystems. These tendencies then go on to be
installed in our economic systems.
At one level,
the expansion of platforms is driven by the cross-subsidisation of services used to draw users into a network. If a
service appears likely to draw consumers or suppliers into the platform, then a
company may develop the tools to do so. Yet expansion is also driven by factors
other than user demand. One such factor
is the drive for further data extraction. If collecting and analysing this
raw material is the primary revenue source for these companies and gives them
competitive advantages, there is an imperative to collect more and more.
As one report
notes, echoing colonialist ventures: ‘From a data-production perspective,
activities are like lands waiting to be discovered. Whoever gets there first
and holds them gets their resources – in this case, their data riches.’[139]
For many of these
platforms, the quality of the data is of less interest than their quantity and
diversity.[140]
Every action performed by a user, no matter how minute, is useful for
reconfiguring algorithms and optimising processes. Such is the importance of
data that many companies could make all of their software open-source and still
maintain their dominant position due to their data.[141]
Unsurprisingly,
then, these companies have been prolific purchasers and developers of assets
that enable them to expand their capacity for gaining information. Mergers relating
to big data, for instance, have doubled between 2008 and 2013.[142]
Their vast cash
glut and frequent use of tax havens contributed to making this possible. A
large surplus of capital sitting idle has enabled these companies to build and
expand an infrastructure of data extraction.
This is the
context in which we should understand the significant investments made in the consumer internet of things (IoT),
where sensors are placed in consumer goods and homes.[143]
For example, Google’s investment in Nest, a heating system for residential
homes, makes much more sense when it is understood as the extension of data
extraction. The same goes for Amazon’s new device, Echo, an always-on device that
consumers place in their homes.
At the mention
of its name, Echo will respond to questions; but it is also capable of
recording activities around it. It is not difficult to see how this might be
useful for a company trying to understand consumer preferences. Similar devices
already exist in many phones – Siri for Apple, Google Now for Android, not to
mention the emergence of smart TVs.[144]
Wearable
technologies are another major element of consumer IoT. Nike, for instance, is
using wearables and fitness technology to bring users onto its platform and
extract their data. While all these devices may have some use value for
consumers, the field has not been driven by consumers clamouring for them.
Instead, consumer IoT is only fully intelligible as a platform-driven extension
of data recording into everyday activities.
With consumer IoT, our everyday behaviours start to be
recorded: how we drive, how many steps we take, how active we are, what we say,
where we go, and so on. This
is simply an expression of an innate tendency within platforms. It is therefore
no surprise that one of Facebook’s most recent acquisitions, the Oculus Rift VR system, is able to
collect all sorts of data on its users and uses this information as part of the
sales pitch to advertisers.[145]
The fact that
the information platform requires an extension of sensors means that it is
countering the tendency towards a lean platform. These are not asset-less
companies – far from it; they spend billions of dollars to purchase fixed
capital and take other companies over. Importantly, ‘once we understand this
, it becomes clear that demanding privacy from surveillance
capitalists or lobbying for an end to commercial surveillance on the Internet
is like asking Henry Ford to make each Model T by hand’.[146]
Calls for privacy miss how the suppression of privacy is at the heart of this business model. This tendency involves constantly pressing against the limits of what is socially and legally acceptable in terms of data collection. For the most part, the strategy has been to collect data, then apologise and roll back programs if there is an uproar, rather than consulting with users beforehand.[147] This is why we will continue to see frequent uproars over the collection of data by these companies. If data collection is a key task of platforms, analysis is the necessary correlate.
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