“Business Model” and “Monetisation”: On the Uses of Buzzwords
Western University, London, Canada, firstname.lastname@example.org, https://streeter.fims.uwo.ca
Abstract: This essay explores the function of corporate buzzwords by investigating the early histories of “business model” and “monetisation”. It analyses the terms as examples of managerial argot, and argues that at key moments in the formation of digital capitalism, the terms helped create a field of action where management communities could envision, discuss, and coordinate, in a safely depoliticised way, the fact that markets and property are not natural, that social and political choices are necessary to create markets and property out of human relations that are not self-evidently things. Analysing the terms, not as ideologies, but as what Raymond Williams called “social experiences in solution”, the essay focuses on the terms’ emergence before the regimes of accumulation with which they are now associated. This analysis suggests that close attention to language in historical context can help illuminate the emergence of political economic changes, showing that the rise of digital capitalism can be seen as, at the outset, as an effect, as much as a cause, of particular structures of feeling. It also suggests that scholars of current trends should not take for granted current buzzwords, but should treat their use and definition as sites of struggle.
Keywords: Keywords, discourse, managerial argot, culture and political economy, business model, monetisation, Raymond Williams
Acknowledgement: Thanks to the many individuals who provided immensely helpful comments on this work, especially Pier-Pascale Boulanger, Eric Guthey, Lisa Henderson, and tripleC’s anonymous reviewers.
The world of media management has its buzzwords. From synergy to innovation to disruption, buzzwords are uttered with great seriousness and scoffed at in equal measure. While individual terms rise and fall in popularity, the general presence at upper levels of management of vaguely defined but enthusiastically repeated buzzwords is a constant. The media industries are hardly unique in this; consider academic administration. Yet it remains an empirical fact that those who govern media institutions regularly use unique terms and phrases to describe aspects of industry strategy and structure coloured more by enthusiasm than precision. The words are part of doing business, and one of the tasks of scholarship is to understand how business gets done.
This essay explores the early careers of two of these buzzwords, “business model” and “monetise.” The question it seeks to answer is, why, in spite of their vagueness, do certain terms become common in management, and what does this tell us about the organisation of digital industries? It describes the terms as examples of managerial argot, and argues they have functioned to help enable and naturalise the collective work necessary to carving market relations and capital flows out of social activities like computer networking and digital media. Using textual analysis as a way to capture managerial structures of feeling, and contextualising them with a contradictions-of-capital sense of industrial evolution, this essay argues that the terms have helped create a field of action where management communities could envision, discuss, and coordinate, in a safely depoliticised way, the fact that markets and property are not natural, that it takes premarket institutional structures and collective effort – that it takes social and political choices – to create markets and property out of human relations that are not self-evidently things. Significantly, the terms rose to popularity before new industrial practices fell clearly into place; the terms did not appear to describe a set of practices already in existence, but appeared prior to, and thus helped set the conditions for, those new patterns to settle in. The peculiarities of everyday language are in this case shown to have a causal, rather than reflective, role in the emergence of digital capitalist social relations.
The next section lays out an approach to analysing language and social relations rooted in Raymond Williams, and distinguishes managerial argot from jargon, terms of art, ideology, and keywords. Section 3 begins with the existing literature on business models, noting that most works seek to clarify the term rather than analysing it as it is actually used. The section then goes on to explore the emergence of the term alongside the internet, using readings of early uses of the term to illustrate how, from the point of view of managers, it helped generate a terrain for action in the face of the deep uncertainties created by digitalisation and resultant tensions with classical ideas about markets. Section 4 explores the rise of “monetise” in the 2000-2017 period, showing how it performed a similar function around the rise of social media, but in a post-dotcom bubble environment with heightened anxieties concerning profit generation. Section 5 concludes that more work on managerial argot could further contribute to our understandings of economic structures, and that rather than uncritically using current, emerging forms of argot (e.g., “AI”), scholars would do well to approach them as sites of struggle.
Words are, in small and large ways, world-making, and often enough the worlds they make are capitalist. But how exactly is the role of language best understood? Raymond Williams, throughout his work, was concerned that we not mistake “terms of analysis for terms of substance” (Williams 1978, 179), i.e., that we not point to abstractions – e.g., neoliberalism, platform capitalism – as causes of social change when they might be better understood as outcomes. Towards that end, Williams famously defined structures of feeling as distinct from and typically appearing prior to formal systems of thought or ideologies. He called attention specifically to “social experiences in solution, as distinct from other social semantic formations which have been precipitated and are more evidently and immediately available” (1978, 133–34). Williams thus advocated that, to grasp “meanings and values as they are actively lived and felt,” we should seek out “social experiences in solution” before they have precipitated into institutionalised processes and ideologies. He argued that “forms and conventions – semantic figures – [...] are often among the very first indications that such a new structure is forming” (1978, 132–33). The terms business model and monetisation, I hope to show, were in their early years such instruments of structures-in-formation. They need not be understood as false consciousness, as merely ruses that pulled the wool over the eyes of the gullible for purposes originating elsewhere. Rather, they were generative of institutional possibilities.
In contrast with the more formally inclined methods of critical discourse analysis (Jessop 2004) or the “distant reading” (Oberhelman 2015) of quantitative corpus analysis, Williams insists on the necessity of a kind of close reading attuned to lived experience, “actively lived and felt” meanings, which involves seeking to grasp words as used by individuals acting with specific concerns in historical context. Stuart Hall’s oft-quoted admonition to begin qualitative analysis with “a long preliminary soak” (Steiner 2016) in the materials is evocative, but the process can be understood as less vague than that phrase makes it sound. The “point of view” of a speaker is something specific: the speakers – in the examples here largely high tech and media managers quoted by business journalists – are trying to solve specific problems in specific contexts, and are speaking to specific, though often multiple, audiences (e.g., other managers and a broader public) as they do so. Seeking to reconstruct what it felt like to be that type of person at that moment in history, the question then becomes, why these words and phrases and not others? Done iteratively, one can then tease out the play of meanings, with an eye towards relations of power. In sum, the method is to ask, what are the circumstances in which specific terms might seem useful rather than vague or obfuscatory? From what point of view did these terms solve problems?
2.1 Specifying Managerial Argot
“Managerial argot”, as I use the term here, is like most jargon in that it involves frequently used specialised insider terms one of whose functions is to indicate membership in a group. But managerial argot has its own distinct characteristics. It is not the same as highly technical terminology with very specific meanings originating in law (e.g. “residuals”) or engineering (e.g. “4K HD”), though it sometimes masquerades as such. Managerial argot, furthermore, is distinct from craft terms of art (e.g., “pulling focus”) which are for internal use. Managerial argot has a public function. In this sense, it shares something with the often-disparaged tradition of inventive terminology for firing workers (Yen 2008). Renaming a mass firing a “downsizing” or “rightsizing” or “re-engineering” clarifies nothing for those being laid off, but may soften the meanings of the action for those outside the company.
Managerial argot, however, is less self-evidently obscurantist than are euphemisms for layoffs. It is frequently enunciated with some enthusiasm, and is associated with ambition and vision. Managerial argot thus shares something with what Williams identified as a “key word”, that is, a word in which “the problems of its meanings seemed [...] inextricably bound up with the problems it was being used to discuss” (Williams 2014; Ghaziani and Ventresca 2005). Keywords and managerial argot share the quality of lacking consensus about precise definitions or referents, precisely because they are embedded in historical tensions and quandaries. In both cases, there is little point to searching for fixed, singular meanings, or trying to determine correct or incorrect uses. If the meaning seems shifting or tangled or contested, this is because of the circumstances being grappled with, not a simple lack of clarity on the part of the speaker.
However, Williams’ key words such as “democracy” and “culture” are broadly public, involving grand issues of the day, are used in the context of efforts to enunciate general or universal principles, and emerge over centuries. A statement about democracy, whether or not clearly defined, generally presents the term to its audience as something that can be universally understood. Managerial argot, in contrast, appears and disappears over periods of a decade or two, and asserts authority more than a claim to universal understanding; if its meaning is somewhat obscure to outsiders, that is part of its appeal. A statement about monetisation is uttered from a position of special expertise where it is not assumed that all listeners should be able to understand, only in the hope that all listeners should respect the speaker’s expertise, while those listeners sharing the speaker’s goals – other managers – are expected to accept the meanings in context. Media industry managerial argot, in sum, while enunciated with an eye towards public reception, operates in narrower contexts and works to mark the speaker’s expertise rather than to assert universal legibility.
3. The Emergence of “Business Model” 1990-2000
The pop investor website Investopedia.com defines “business model” as “a high-level plan for profitably operating a particular business in a specific marketplace” (Kopp 2020). That definition reflects contemporary popular usage, but does not really distinguish the term from a business strategy, business plan, or just “business” in the sense of “my business is . . .”. In 1999, this vagueness around the then-newly popular term famously prompted journalist Michael Lewis to quip, “’[b]usiness model’ is one of those terms of art that were central to the Internet boom: it glorified all manner of half-baked plans. All it really meant was how you planned to make money” (Lewis 1999, 256–57).
Much of the literature about the meaning of business model reacts to its chronic vagueness by offering more precise definitions (Baden-Fuller and Morgan 2010; Hedman and Kalling 2003; Magretta 2002; Nielsen and Lund 2014; Ovans 2015; Zott, Amit, and Massa 2011). For example, Magretta (2002) responded to Lewis’ quip by arguing that “business model” is in fact a useful term that can be distinguished from business strategy, but only if used a particular way. In developing her more specific definition, however, Magretta ends up not only distinguishing her definition from many uses of the term, but also includes examples from business history that were not called business models at the time, such as Sears’ efforts in the 1980s to offer financial services. Ovan’s similar 2015 summary of such efforts to clarify the term goes further, including, for example, Gillette’s early 20th century strategy of selling-razors-to-sell-razor-blades as a classic illustration of a business model. Such efforts to clarify have their value, but they essentially seek to generate a new meaning for the term, detaching it from common uses while ahistorically attaching it to phenomena which were not called business models in the past. In the name of clarity, they seek to take the term out of history. Here, rather than trying to swim against the tide of common usage, I hope to show that management buzzwords are best understood in the flow of history.
Studies of books, news media, and academic business journals agree that the phrase business model began to proliferate around 1990 and that it has multiple meanings in everyday use (Codrea‑Rado 2013; Doganova and Muniesa 2015; Ghaziani and Ventresca 2005; Zott, Amit, and Massa 2011). This is confirmed by a simple year-by-year count in mainstream news outlets of articles containing “business model.” Figure 1 shows that the term started to appear with some frequency in 1990, just as computer networking began to move from a specialised research context into broader public consciousness.
One can see in figure 1 that, after first appearing around 1990, growth in use of the term takes off substantially around the time of the 1995 Netscape Initial Public Offering (IPO), which launched the dotcom stock bubble. The slight downturn in number of articles using the phrase from 2000-2003 coincides rather precisely with the collapse of the dotcom stock bubble, which further hints at an enduring connection of the phrase to the rise of the internet and digital networking, something the efforts to give the term a precise meaning have difficulty explaining.
3.1 Deciphering Meaning: Textual Analysis, Context, and Experience
Word frequency counts alone cannot explain why terms suddenly become meaningful or, more importantly, the structures of feeling in which terms are experienced as useful. To better understand why it would make sense to assertively use a vaguely defined term in management contexts, some historical context combined with close reading are in order.
In the 1980s, the rise of the microcomputer generated much confusion in the computer industry, as things moved from a handful of corporations selling a narrow range of hugely expensive devices to a proliferation of start-ups selling a flood of devices with different but overlapping capacities. As one 1984 overview dryly put it, “With today's multitiered, overlapping set of programmable computer classes, where and how computing can be done and how much it will cost can vary considerably” (Bell 1984, 14). A 1990 review of the computing industry as a whole addressed the confusion in this way:
“Mainframe, minicomputer and personal computers have therefore been mapped onto a business model consisting of three tiers; corporate activities (mainframe), departmental activities (minicomputer or local area network) and personal computing” (Lawrence 1990).
Significantly, this early use of the term business model does not involve explaining exactly how firms would be making profits; it did not specify what would be the razors and what would be the razor blades. Rather, it was more an effort to assert a kind of abstract coherence in the face of managerial uncertainty. The industry is uncertain about which computers should be used for what purpose, the argument went, so they are using a distinction between centralised corporate activities, departmental activities, and personal computing in the hope that that will clarify things. (The distinction would not hold; within a few years the boundaries between the categories of mainframes, minicomputers, LANS, and PCs would become more blurry, not less.)
That same month, in another early and revealing example, the New York Times quoted Sun Microsystems’ Chief Financial Officer:
“We have a sensitive business model [...] When you're a growing company, and you're laying on expenses at the rate that we are, you have to be very sensitive to changes in projected revenue” (Markoff 1990).
Why a “sensitive business model” instead of the then-more-common “business plan” or “strategy”? Streeter (2011) has noted that, in the 1980s, particularly in North America, the dramatic rise of microcomputers was generally framed within an entrepreneurial narrative, with heavy emphasis on new start-ups and young entrepreneurs like Bill Gates and Steve Jobs. Meanwhile, most of the news coverage paid little attention to the necessary background industrial contexts (e.g. Intel, Xerox PARC) and for the most part ignored the very important technological developments in computer networking of the period, such as the shift of Arpanet technologies towards NSFNET and what would become the internet, the spread of ethernet local area networks, and the development of relatively horizontal collaborative design strategies that led to innovations like VLSI computer chips and software systems understood as open collaborative environments, particularly around Unix.
Sun Microsystems, founded in 1982, was unique in computer start-ups of the 1980s in that it was not just selling boxes or packaged software. Sun Microsystems had an initial customer base in research universities which valued collaboration. It based its “workstations” – more capable and expensive than PCs, but cheaper than minicomputers – on Berkeley’s Unix and the open collaborative style it brought with it. Sun therefore adopted the then-counterintuitive strategy of using open standards for both hardware and software, encouraging others outside the firm to use Sun’s standards to build their own, compatible but competing systems. As the decade progressed, this focus on building open standards would lead Sun to develop the Java programming language, the Network File System (NFS), virtualised computing, and other building blocks of today’s internet-connected world.
At the time, however, this approach went against the grain of business thinking. As one analyst put it, “An integral part of competition is to deny rivals access to proprietary technical knowledge. [...] Why, then, would a firm provide rivals easy access to its technical knowledge and encourage entry into its market?” (Garud and Kumaraswamy 1993, 351).
Because systems and interconnections were Sun’s focus, it did not present a clear set of objects, e.g., stand-alone computers or floppy disks in boxes, that could be sold on a per unit basis. In fact it looked like Sun was giving away knowledge of a type that most companies kept as a proprietary. How was this supposed to work? What exactly was being bought and sold: hardware, software, services, interconnected systems, or access? In the world that Sun was helping to create, who would be selling what to whom? While revenue from various sources seemed to be coming in, the entire process looked vaporous to many observers at the time. The phrases “business plan” or “business strategy”, which demanded a clear plan for future profits, did not seem adequate to Sun’s exploratory, university-adjacent approach.
In this context, when Sun’s CFO reassured the New York Times that Sun had “a sensitive business model”, he was not offering an explanation for exactly how Sun would make a profit in the future. He was not presenting a clear, uniform, business plan or strategy, a road map for what would be sold to whom for how much; he was not invoking the meaning that later commenters like Magretta have since sought to associate with the term. Instead, speaking of a business model “sensitive to changes in projected revenue” acknowledged that future revenue was uncertain, and Sun’s management had to be ready to accommodate that unpredictability. Sun’s CFO did not want to suggest that given the unpredictability of the situation, management would merely wing it. Calling it a “business model”, therefore, allowed him to claim to have something like a plan, but something more flexible, more ready for a fluid unpredictable situation, while avoiding the danger of sounding like there was no direction whatsoever. And importantly the term implied it was still a business – that it was not going to become too much like the non-profit university research programs that were its original customers – even if it was unclear who was going to be selling what to whom.
Most agree that business model’s full flowering came with the Netscape IPO and the 1990s dotcom stock bubble that followed in its wake. A financial analyst active in the Netscape IPO, Jen van der Meer, has offered a frank and vivid retrospective sense of the structure of feeling at the time:
“[A]s a tech equity analyst scrub in the 90’s, the moment that Netscape went public was a defining and seismic shift in how we all thought about business, models, and technology. [...] Before Netscape, I had an obscure job. No one knew or cared what I did when I said I was an equity analyst covering early stage tech companies. [...] Then all of a sudden, everyone knew about Netscape, and my job was super interesting to people. A taxi driver taking me to JFK for a flight to San Jose asked me if he could get in on the IPO. Netscape was beyond technology – it was an experience that enlivened the imagination and created massive possibilities for what was about to happen. [...] I was suddenly popular at dinner parties (but only for that short blip of time). The fact that the IPO was even accomplished proved that there was an appetite for investors to buy into companies that were not yet profitable. [...] Netscape was the first of its kind. As an analyst, it was difficult to estimate revenue, cost, and profit – because of all of the potential scenarios. It was beyond complicated. [...] I can’t remember those original projections, but I do remember staring at that blank screen of zeros, and thinking that the story would be much more complicated than anything that came before it” (van der Meer 2015).
To be fair, van der Meer goes on to argue that the multiple meanings of “business model” are a strength; as a business consultant, the term still works for her precisely because its multiple meanings enables moving forward in the face of an uncertain future. But for the purposes of this analysis, the point is that “business model” did not signal a eureka moment of great clarity or precision, but allowed people to go forward without that clarity, in the face of deep ambiguity: “I was suddenly popular at dinner parties” and “it was beyond complicated”. The word emerged in the face of large amounts of capital flowing in a context of wild ambiguity. This was the soil in which the term business model blossomed.
The phrase business model was useful to managers against a backdrop of ingrained structural uncertainty for two primary reasons. First, “model” is more tentative than “plan” or “strategy”. It allows for the speaker to admit that they are speaking hypothetically, in a world where rapid change is inevitable, including in the character of and boundaries around the “things” being sold. Second, prefacing “model” with the word “business”, ensures that everything stays under the umbrella of capitalism; that much need not be brought into question. Options being explored elsewhere in the world at the time, like France’s post-office-sponsored Minitel, were thus kept largely off the table (Mailland and Driscoll 2017), and the views of clever countercultural anti-capitalist internet experts (e.g., Hauben, Hauben, and Truscott 1997; Moglen 1999) could be safely ignored. The term “business model” thus allowed one to speak hypothetically about the future in a world where rapid change is inevitable and fundamental questions about who will be selling what to whom are uncertain, but it allows users of the term to avoid the political implications of that uncertainty: “this is after all a business”. It allows and encourages private businesses to adopt to blurry and shifting contexts, to allow for pre-market coordination and experimental flexibility organised across sectors while leaving unquestioned the assumption that this will go forward on a for-profit basis.
To see how this use of the phrase settled in, fast forward a few years to the moment when what was called “open source” software started to catch the attention of the business community around 1998. The surprising emergence of open source software, specifically the GNU/Linux systems, presented an obvious problem for people in the computing business: better software was being created without anyone being directly paid, and thanks to “copyleft”, on terms that prevented the creation of conventional property boundaries, of “things” that could be exchanged for money; open source licenses required the sharing of source code free of cost.
Given the Silicon Valley business culture of the day – which generally assumed the market is everything, and privatisation and commodification inevitably more efficient – it was perhaps predictable that many would loudly reject the idea entirely. Bill Gates somewhat hyperbolically claimed, “There are some new modern-day sort of communists, who want to get rid of the incentive for musicians and moviemakers and software makers under various guises. [...] Intellectual property is the incentive system for the products of the future” (Kanellos 2005). Microsoft co-founder Steve Ballmer echoed Gates, decrying Linux as “a cancer” (Greene 2001). Forbes magazine shared this line of reasoning, scoffing at the open software movement’s “usual public image of happy software proles linking arms and singing the ‘Internationale’ while freely sharing the fruits of their code-writing labour” (Lyons 2003).
The online libertarian movement at first predictably embraced Gates’ line of reasoning. Wayne Crews of the Competitive Enterprise Institute published an online critique of the open source movement, arguing that “like free love, open-source code is fun, but it’s probably not a way to run the world. . . . for the most part, the prospect of becoming fabulously wealthy, not the desire to give things away, drives software innovation” (Gattuso 1998).
But the problem with this scoffing was that Linux worked – it crashed less often than the contemporary Windows 95 – and it solved coordination problems for industry, working as a kind of shared standard on top of which other software services could be delivered. In 1998, under pressure from the Microsoft monopoly, Netscape open sourced its browser to much acclaim, various prominent businesses, like Apple and IBM, began flirting with open source operating systems, and the governments of China and Brazil would explore Linux as an alternative to dependence on the Microsoft monopoly. According to orthodox libertarian theory, Linux should not be successful, but it was. And perhaps just as importantly, Linux gained a kind of countercultural cachet. It was cool.
In 1998, leading internet libertarian Esther Dyson sensed the political danger for libertarian principles here, and intervened. Responding to Crewes, she wrote,
“There's a fundamental misunderstanding here. There is a lot of value – and money – floating around the world of [Open Source]. And yes, Netscape's use of OS to make its other services attractive is a legitimate, acknowledged and sensible business model. […] (It seems to me that there are religious extremists on both sides of what ought to be an argument about business models, not morality)” (Dyson 1998).
The word “business model” here performs a specific function: it allows Dyson to talk favourably about open source software without appearing to qualify her long standing libertarian free market principles. Again, her use of the term “model” clarifies little about how money was to be made from open source beyond “to make its other services attractive”, but it allows for a kind of organised imprecision. Netscape’s strategy at the time was at best blurry (and ultimately would fail; the company is gone and its browser is now distributed by the non-profit Mozilla). But the point was precisely not to have to argue the political economic specifics. Rather, the phrase “business model” allowed Dyson to shift the terms of discussion into a different zone: this is all still a “business” (somehow), but we are discussing the zone of modelling, a zone of abstract experimentation. The word “model” kept it all safely non-concrete, while modifying it with the adjective “business” reassured speakers and listeners alike that, whatever it was, it was still a business. Contra both Bill Gates and internet communitarians, Dyson assured her readers, basic principles of capitalism were not under question.
The phrase “business model”, in sum, had a naturalising effect, allowing for all the fluidity and the many non-proprietary, shared processes and forms of coordination across the digital industries to proceed without politicising them. The vagueness of the term was key to its effectiveness.
The term business model thus paved the way for the digitalisation of the media industries. For the media industries, the uncertainties are endemic: is the “thing” being sold music or copyrights or clicks or subscriptions or access or audience data? One could frame this as a political question: how should we as a society organise the production of information and media? But as long as the initial question is “what’s the business model?” the discussion gets channelled into a largely for-profit universe, keeping non-profit alternatives off the table. “Model” allows for the speculative and fluid character of things: something that is proprietary today might be given away for free tomorrow and vice versa. But keeping the idea that it is in any case a “business” structured into the language forecloses the possibility of construing the decisions as the political choices they are.
If the phrase business model took off alongside the internet, monetise took off alongside social media. And just as “business model” saw a dip in use as the collapse of the dot com bubble cast a darker light on internet businesses, “monetise” saw a dip after the 2016 U.S. election and related events which cast a cloud over social media. Like “business model”, “monetise” began to appear with new meanings in the 1990s, but it did not become widely popular until around 2004.
Monetisation did have a pre-internet meaning which is still sometimes invoked: in traditional economics to monetise is to turn something into some form of currency, into something that can be used as legal tender. This is what governments do when they monetise their debt; it’s an indirect way of selling off their debt by increasing the money supply.
But starting in the early 1990s a new, less specific use started to become popular in the media industries, reflected in press coverage. It now is often used to mean something like “earn money from”, as in “strategies for monetising user activity” or “rules for monetising a YouTube channel”.
The contemporary use of the term seems to have appeared in embryonic form around 1992, at first in the context of media industries rather than computer networking. That year, Time Warner announced it wanted to “monetise its investment in Turner Broadcasting”, with whom Time Warner had recently merged (Wollenberg 1992). The term seemed to mean that Time Warner intended to start exploiting Turner’s libraries of old movies and TV shows, a relatively new way to think about the media business at the time, as essentially an intellectual property business rather than a media production business.
After that announcement, the term started appearing in articles about the Time Warner/Turner deal, but reflecting some scepticism, usually by way of scare quotes. A 1994 piece, for example, noted that, “Time Warner has said for more than a year that it would like to 'monetise' its Turner Broadcasting stake, and the two companies have had talks about ways to accomplish that” (Dow Jones 1994). A year later, Variety put it even more snidely:
“Corporate types also busy themselves framing “strategic alliances,” which often involve entities with which their own companies have no common interests. If such an alliance doesn't work, the corporatists can always “monetise” it – they like to monetise things, which is a polite way of making it disappear into fiscal cyberspace” (Bart 1995).
In 2000, the NYT was still using scare quotes: “Mr. Jobs stressed that [Apple] believed that it could ‘monetise’ its new Internet strategy easily” (Markoff 2000). But by then the term was coming to be accepted to mean, roughly, a hope of turning vaporous entities and activities into a revenue stream, into a way to make money. It was typically expressed more as a hope than a plan. The term expressed a self-fulfilling confidence that a plan would emerge, while absolving the managerial community of any obligation to provide a description of what that plan would be.
As the decade progressed, the scare quotes began to lose their sense of scepticism, and eventually disappeared. Much of this had to do with the rise of Facebook. In 2007, an article about a social media software developers’ conference noted, “Apps are just one way mainstream companies are looking to profit off [Facebook], or ‘monetise’ it - a term constantly thrown around by e-commerce types” (Barmak 2007). The following year, the scare quotes were largely gone. For example, in October 2008, Facebook CEO Zuckerberg was quoted “I don't think social networks can be monetised in the same way that search did [...] In three years from now we have to figure out what the optimum model is. But that is not our primary focus today” (Kafka 2008).
Significantly, Facebook was hugely capitalised and rapidly growing in membership at the time, but it was not making a profit, and as the quote from Zuckerberg makes clear, did not have a clear plan about how it would do so. “Monetise” became a commonplace term around the rise of social media, not because it explained how anyone was making money. Like business model, the word was most often invoked precisely when the way people would make money was uncertain, not when it was known. At the same time, “monetise” addressed the anxieties about profitability generated by the dotcom bubble collapse: it wasn’t just a model. The word implied that this time, money would be made, somehow. Monetise invoked an unknown but expected future state where buying and selling, the making of money, would no longer be unclear. Monetise also had a continued life inside Hollywood, a life which was energised by the collision of Hollywood with the internet. For example, a 2009 article about coming changes in “the film industry’s business model” warned, “even when the studios figure out how to monetise movie delivery via the Internet, they won't be making anywhere near $15 per download” (Strauss 2009). In 2011, in an article about NBC’s strategy shift towards “higher quality content”, The New York Post quoted an industry executive saying “[NBC] is putting ‘content before commerce [...] Content will be the top priority before seeing how you monetise it’” (Atkinson 2011).
Significantly, very often the subject of the verb monetise was not individuals or firms, but industries and industry segments. Social networks monetised, not Facebook. “The studios” would figure out how to monetise streaming. NBC would have to “see how you monetise it”, i.e., see how it is monetised industry-wide, not monetise it by itself. A specific firm or individual business person may be looking for a business model, but does not “monetise.” Monetisation, at the time it came into widespread use, was generally imagined as a collective action.
Monetise in this period was roughly synonymous with the Marxist sense of “commodify”, meaning a process of abstracting human activities into quantifiable units that generate surplus value (Frow 2021). But unlike commodify, monetise sounded technical and financial, not political. It allowed for a public discussion of how to turn non-capitalist, collective activities into something that could be bought and sold. It’s not just that the business world would rather create their own terms instead of use dreaded Marxist ones; it’s that there was a felt need for a term that gives voice to the collective project of turning human activities into things that can be bought and sold, the collective effort to find shared ways to draw property and contract boundaries in a way that allows for capital accumulation, without opening the door to the politicisation of those acts, to a truly open discussion about what should be done with our media industries. The word monetisation makes available the social architectures of commodification while keeping the necessary collective action safely inside capitalist bounds.
What the term monetise enabled in the end was a context for firms like Facebook to expend staggering amounts of investor capital to establish monopoly-sized user bases by offering services for “free” prior to establishing any concrete plan for profit and the political difficulties that such plans can bring. Only after that monopoly of users is established need a firm bring down the walls of commodification, at a point when the firms’ dominance seems a fait accompli, when neither competitors nor users seem to have any choice in the matter.
The goals of this essay have been twofold. First, I have shown how industry buzzwords need not be taken as transparent. As media industries scholar Amanda Lotz put it, “[o]ur remit as critical scholars is to make it strange” (Lotz 2019). At a minimum, we should consider alternative terminologies with clearer meanings, such as commodify instead of monetise, or regime of accumulation (Lipietz 1986) instead of business model; both critical terms bring needed attention to the contingent political conditions necessary to specific economic relations. Beyond that, we should focus on the contradictory dynamics of how the terms are used, e.g., how the vagueness of terms may not be a simple flaw but useful to industrial communities struggling to organise the commodification of intangibles.
The second goal of this essay has been to suggest that attention to the specificities of discourse might sharpen the understandings of cause and effect in the analysis of political economic change. If my claims about the potential functions of business model and monetisation bear weight, if the terms’ causal role was not one of reflection or obfuscation but of function, then business argot is a site of political struggles over the development of economic relations over time. “Business model” did not arise as an explanation for, say, how Amazon.com transformed retail; it became popular several years before Amazon did, and by creating a terrain within which experiments with radical new ways of conducting business could take place, contributed to the conditions in which businesses like Amazon could arise. Vaguely defined buzzwords do not merely legitimate or obscure after the fact; in these cases, they build particular kinds of terrain for addressing uncertainties, opening up some possibilities while shutting down others. The buzzwords were not created by neoliberal ideologies. Rather, they helped create the conditions for the ideologies’ rise to dominance.
Since the proliferation of “business model” in 1990, it and related buzzwords helped smooth the way for digital media technologies to be thoroughly integrated with global capitalism. From Google to Facebook to Zoom and beyond, giving away services for free to establish large user bases before having any clear plan about how to make a profit is now considered entirely routine rather than deeply irrational. Under the umbrella of “monetisation”, digital delivery of audio visual media over the internet, not long ago considered a crisis, has become a driver of the global economy.
Yet the exact nature of what has transpired is still open to debate. While it is clear that digitalisation has been associated with new patterns for organising political economic relations, there is no consensus on how best to characterise them. The literature seeking to explain the peculiarities of contemporary “capitalism without capital” is characterised by a proliferation of theories and terminologies: platform capitalism, surveillance capitalism, financialisation, assetisation, and more (e.g., Srnicek 2016; Haskel and Westlake 2017; Boltanski and Chiapello 2018; Birch and Muniesa 2020; Zuboff 2020). While Raymond Williams would agree that is essential to understand dominant social patterns and structures, he might caution us not to make our theories more precise than the phenomena they are trying to describe, and to remember the necessary connection to lived experience. Marxist analysis is not inevitably functionalist and teleological, but the effort to specify structures does risk assuming more stability than is really there (Wright 1983). Sometimes capitalism involves a certain amount of “muddling through”.
A focus on managerial argot thus helps foreground the contingencies of capitalism. The success of internet fuelled patterns of investment, ownership, and control, from Google through Facebook to Netflix and beyond, was not unprecedented. As historians of the media industries are generally aware, over the long term, the construction of what gets sold to whom is often an open question, subject to uncertainty and political struggles. Strategies have ranged widely over the decades, from ticket booths to license fees to advertising to subscriptions to copyright collectives and more. When digitalisation began to introduce new possibilities of reproduction and dissemination in the 1980s, this was just another in a long line of industrial conundrums that get gradually resolved, often a bit uneasily, over periods of years or decades.
Nor should we reproduce the dominant view that those resolutions were inevitable. Historically, the commodification of communications has often been awkward and incomplete; think of the many things from recipes to fashion designs to book titles that have escaped the enclosures of copyright expansion, or the awkward political compromises sometimes embedded in institutions like copyright collectives or radio spectrum regulations in the public interest. And today, in the wake of the political crises associated with social media since the political crises of 2016 and since, the “business models” of the platform giants are almost certain to be subject to political intervention in many parts of the world. Given the fact of capital accumulation, pressure to commodify is perhaps constant. But it is not always unstoppable, and the ways struggles play out are in part shaped by the peculiarities of human circumstances, by things like language and everyday experience.
This analysis is intended to be suggestive, not complete. We need to look at other examples of managerial argot – Moore’s law, intellectual property, branding, synergy, innovation, and disruption all come to mind – and explore some of the relations between them and particular industry behaviours. The analysis of argot would have to be weighed alongside other kinds of explanations, especially economic ones, to shed sharper light on how argot does and does not function in particular contexts and how it works alongside other forces.
Finally, as scholars we might keep our eyes on the role of argot as a site of contemporary struggles. It is not inevitable that “monetisation” stays depoliticising. A recent best seller, for example, discusses the “monetisation” of the self in critical detail, and does tie it to “runaway” capitalism (Tolentino 2019). Similarly, Kate Crawford has convincingly argued that AI “is neither artificial nor intelligent” (Crawford 2021; Simonite 2021). If that is true, we might ask, what about the term and the current context makes it nonetheless useful, and for whom? And rather than invoking the term uncritically as part of an effort to intervene (by calling for, say, “ethical AI”), might it make more sense to inject alternate terminologies into the discussion (e.g., machine learning), to make word choice a strategy in building alternate futures? Similar concerns might be applied to any number of vague terms currently attracting managerial enthusiasm: the smart home, innovation, stakeholders, internet of things, influencer, analytics, and more.
David Harvey has pointed to the ”foundational contradictions of capital”, including the contradictions between capital and state and between capital as process and capital as thing, i.e., “the tension between fixity and motion within the circulation of capital” (Harvey 2015, 38, 75). Harvey is well aware that over time capitalism has proven adept at managing those contradictions. But flagging them as fundamental contradictions reminds us they will not go away. Markets, private property, and other keystones of capitalism require ongoing, elaborate, extra-market social coordination which predictably creates ongoing contradictions or tensions. Digital media technologies make vivid the fact that markets are made, not born, and their making and continued existence require elaborate non-market social structures, which is inherently troubling to the notion that markets, property, and the laws of contract are somehow free or natural or inevitable. The historical patterns of the emergence and flowering of business model and monetisation suggest that these tensions are constantly struggled over inside industry as well as out, and that, as analysts and users of industries and of language, we have our own parts to play in those struggles. The vagueness of these terms might serve as a hint that more, not less, is going on.
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 “Argot”, “jargon”, “technolect”, and “terms of art” are often used to refer to specialized language peculiar to a specific occupation or community involved in specialized tasks. I prefer the term “argot” in this context to avoid the narrowly functional and positive connotations of “terms of art” or “technolect” and the often derisive and negative connotations of “jargon”.
 The Oxford English Dictionary finds an example of “business model” in the sense of a “type of product tailored towards business use” dating back to 1832. However, the earliest example it lists for the contemporary meaning of “a plan for the successful operation of a business” is 2009 (“Business, n.” n.d.).
 Before 1990, less than ten articles per year contained the phrase “business model,” and most of those were not in anything like the contemporary sense. For example, the “business model of X” could refer to a version of a device intended for business instead of for consumers. Another pre-1990 meaning was “doing things like businesses do” as in a university adopting a “business model” for organizing its finances instead of a traditional university model.
 Figure 1 stops in 2006 because after that, “business model” became so common and the numbers so large that the vague meanings lampooned by Michael Lewis would seem to have won out in common discourse, making year by year measurements both difficult and fairly meaningless. Ngram analysis suggests that “business model” was becoming interchangeable with “business plan” by 2006 (van der Meer 2015).
 Magretta (2002) links the rise of “business model” to the rise of spreadsheet software, which with the appearance of cheap personal computers, made relatively complex modeling of business inputs and outputs more widely accessible.
 Doganova and Muniesa (2015) argue that business models are devices with the capacity “to orchestrate encounters and, in so doing, to transform a priori non-economic entities (such as a genetic engineering technology) into assets that generate streams of future revenues, that is, into capital.” This is in some ways close to the analysis being offered here, though it focuses on a few successful outcomes rather than generalized use, which risks tautology.
 The discussion that follows uses the term “open source” rather than FOSS because it was the term “open source” that the business community was reacting to. The philosophical differences between open source and FOSS, while significant, are a separate matter.
 For examples of excellent critical scholarship that sometimes takes industry terms for granted, consider the passing references to business models in Caldwell (2008) or Couldry and Mejias (2019), or to monetise/monetisation in Lotz (2022).
 One could also frame this in terms of the Weberian tradition of economic sociology, which explores cultural and social preconditions for the creation of capitalist economic conditions. See, e.g., Trigilia (2008)