🦋🤖 Robo-Spun by IBF 🦋🤖

🌊➰🧭 AKIŞ 🌊➰🧭

(Turkish, Sources: Sexual Powers on Surplus-Information, Waves of Women at the Edge of Obsolescence: Profit, Panic, and the Pendulum, Stitching Feminist Waves into the History of Technology, Knotwork after the Quilting Point: Lacan, Cybernetics, and the Four Feminist Waves, Christian Atheism as the Loom: Why the Waves of Women Needed Žižek’s Background Condition, Factory of Womanhood on an Iconophile Loom: Four Waves, Four Betrayals, and the Peace that Fails)

Introduction

Modern history has been marked by alternating regimes of power that align with two different symbolic skillsets. On one side is the power of navigating desires and attention – the intuitive, relational craft of sensing what others want and keeping social situations in balance. On the other side is the power of applying formal rules and procedures – the analytical craft of creating standards, metrics, and algorithms to organize the world. These two modes are often culturally coded as “feminine” and “masculine” ways of knowing (though real individuals and organizations blend them). This report explores how each mode has risen to dominance in turn, how new technologies drove these shifts, and how women’s roles and labor have been made and unmade in the process.

We will see a recurring pattern: each era perfects one mode of coordination (attention or rules) to a point of over-saturation, and then a new technology emerges that exposes the limits of that old approach and re-centers power on the opposite mode. In other words, when a system built on personal tact and tacit know-how reaches a breaking point, a more formal, automated system takes over – and when a system built on formal rules becomes brittle or meaningless, human relational skills surge back to address what’s missing. This pendulum has swung repeatedly from the dawn of industrialization to today’s digital age.

Thesis in brief: Communications and management technologies alternate between rewarding two kinds of “surplus” power. One is what we might call surplus-enjoyment, rooted in desire and attention – the power of creating social energy, guiding collective passions, and intuitively coordinating others. The other is surplus-value, rooted in formalization – the power of structuring processes, enforcing consistency, and optimizing for efficiency and profit. Whenever one of these dominates to an extreme, the cracks begin to show: the system produces an excess of signals or complexity that the current methods can’t quite handle, which we can call surplus-information. At that breaking point, a new technology appears that leverages the other mode of power to capture that surplus-information[1][2]. The result is a reorganization of work, status, and even how we define “competence” – often disadvantaging those (frequently women) whose skills were central under the previous regime, while elevating a new set of experts.

Crucially, these patterns are not biologically determined. When we speak of “feminine-coded” or “masculine-coded” competencies, we refer to symbols and social roles, not inherent traits. In practice, people of all genders can and do cross these lines. But historically, societies have tended to associate the intuitive, affective, interpersonal domain with women and the structured, quantitative, impersonal domain with men. This report will show how those associations have played out in the labor and media systems of each era – from the spinning rooms of the 18th century to the social networks and AI labs of the 21st. We will also examine how these shifts have affected women’s cultural standing (sometimes giving them new leverage, other times rendering their contributions invisible), and how women have repeatedly adapted to and even prefigured the next wave of change.

In the end, understanding this pendulum of power suggests a strategy: neither purely human charisma nor pure algorithmic rationality is sufficient. The people and institutions that thrive are those who learn to be bilingual – who can infuse human insight into procedures without losing the “pulse” of real desire, and who can harness technical systems while remembering what truly matters. The “making and breaking of womanhood” in the title refers to how each wave has constructed a new ideal of feminine competence and then undone it – but also how the core human faculties traditionally exercised by women (care, mediation, contextual judgment) persist and return in new guises. To tell this story in a comprehensive way, we will proceed through multiple layers of analysis:

• First, we define the key concepts of surplus-enjoyment and surplus-value, and outline the general mechanism by which one technological regime gives way to another.
• Next, we dive into four historical case studies or “waves”, each illustrating the pendulum swing: from the cottage industries to the factory in the late 18th century; from the telephone switchboard and secretarial office to early computers in the early 20th; from public relations and broadcast culture to data-driven marketing in the late 20th; and from social media networks to generative AI in the present day. In each case, we’ll see how women’s roles were pivotal in the old order and how a new machine logic redefined expertise. We include vivid contemporary voices (imagined speeches or protests from those eras) to capture the human element of these transitions.
• Then we examine recurring patterns in how people respond to an impending transition – including a tendency for those in power (often women in these contexts) to double down on their tactics in ways that can backfire once conditions change.
• We consider how seemingly “obsolete” skills are preserved in institutions like academia, libraries, and guilds, acting as cultural shelters that keep older crafts alive until they are needed again.
• We then connect these waves to the familiar “four waves” of feminism – showing how each surge in women’s rights and cultural agency coincided with (and was underpinned by) a change in the information infrastructure.
• Going deeper, we explore a mythic perspective, drawing on the ancient imagery of weaving fate (the Greek Fates, the Norse Norns) to see how the tasks of spinning, measuring, and cutting threads map onto the modern tasks of handling information – and how women have been analogized to the weavers of society’s fabric.
• We also employ a psychoanalytic lens (Lacanian theory) to interpret these shifts in terms of the Symbolic structures that determine whose “speech” counts – looking at concepts like the “quilting point” that fixes meaning, the death of the “Big Other” (ultimate authority), and the idea that “the Woman does not exist” as a fixed essence, only as positions in a system of differences.
• We examine the religious and cultural background (what Slavoj Žižek calls Christian atheism) that made Western society especially prone to these cycles – essentially, how the Christian legacy of a universal but internally self-questioning order set the stage for secular procedures to replace personal mediation.
• We identify two enduring female archetypes – the figures of Mary the Mother (the nurturer) and Mary Magdalene (the witness) – that appear in different guises in each wave, representing women’s roles as guarantors of trust and as truth-tellers, and how each of these roles has been first exalted and then partially “mechanized” by new systems.
• We discuss the role of media and imagery, including how each era created an idealized “image of womanhood” (often a media-driven fetish) that ultimately collapsed – often through some act of betrayal or revelation (symbolized by the figure of Judas in a curious parallel to religious narrative). In this context, we’ll touch on a very recent historical rupture – the attacks of October 7, 2023 – as an example of a brutal reality shattering a utopian image, and what that implies for how we treat images and information moving forward[3][4].
• Finally, we look ahead to the emerging next wave, which many argue will need to correct the current excesses. If the recent era was dominated by hyper-visibility, constant engagement, and algorithmic amplification (to the point of exhaustion), the next era may value restraint, meaningful gaps, privacy, and human judgment as counter-forces. We will outline concrete suggestions (some drawn from psychoanalytic insights) for designing technology and institutions that protect space for human subjectivity – essentially moving “from spectacle to symbolic infrastructure.”

Throughout, we will cite historical facts and scholarly analyses to back up this sweeping narrative. But much of this report also draws on a synthetic interpretation of history – connecting dots between economics, gender studies, media theory, and mythology. The goal is to present a coherent story of how womanhood – as a symbolic role in society – has been continually constructed, deconstructed, and reconstructed alongside technological change, and what that means for everyone navigating the current landscape.

Before diving into the historical cases, let us clarify the central concepts of surplus-information, surplus-enjoyment, and surplus-value, and how a new technology can turn one type of human skill into a new kind of machine-readable procedure.

Two Kinds of “Surplus” Power in a Media System

Modern complex societies generate more information and signals than anyone can fully make sense of. This excess of circulating messages, impressions, and data – which we will call surplus-information – is like a byproduct of our communication networks. Whenever a new medium or system speeds up the flow of information (think of gossip moving faster than official reports, or social media posts outpacing traditional news), it creates a gap between what is being transmitted and what can be understood or managed. That gap represents potential power for those who can capture it: Who can make use of that extra information? Who can exploit the chaos for gain or control?

Historically, two different strategies – two symbolic competencies – have been employed to harness surplus-information:

• Surplus-enjoyment (Desire/Attention Navigation). This is the skill of working with the intangible, affective aspects of communication – essentially, managing other people’s attention, desire, and trust. It involves reading the room, knowing the unspoken social rules, mediating conflicts, curating the vibe, and making information feel meaningful or exciting to people. The term enjoyment here (drawn from Lacanian psychoanalysis) doesn’t mean simple pleasure, but the deeper phenomenon of how people get invested in certain images, narratives, or social bonds – even to the point of obsession. Surplus-enjoyment is created when someone amplifies attention and desire beyond the immediately useful. For example, a talented hostess in a salon or a community manager online can take a bare exchange of facts and turn it into a captivating experience or a loyal network – generating value by making bits of information socially magnetic. These skills are often coded as feminine because they resemble traditional expectations of women’s work: caring for others’ feelings, smoothing over tensions, understanding without being told – in essence, emotional and social intelligence.
• Surplus-value (Rule-based Processing). This is the skill of taking variability and turning it into order – formalizing messy flows into clear, repeatable, countable units that can be optimized. The classic example is in industrial production or accounting: setting up standards, schedules, metrics, or algorithms so that what used to be handled case-by-case or intuitively can now be handled by following a rule or running a machine. The term value here echoes Marx’s concept of surplus value extracted in production, but we use it more broadly to mean any gain from systematization. Surplus-value is captured by quantifying and streamlining. For instance, a skilled factory manager or software engineer creates value by implementing a process that works regardless of who is operating it, or by spotting a pattern in raw data that can be exploited reliably. These competencies align with what is culturally coded as masculine: analytical reasoning, control over the environment, precision, and impersonal judgment according to abstract rules.

It’s important to stress that these are symbolic positions, not inherent qualities of men and women. In reality, many women excel at analytical formalization and many men are gifted at emotional and social attunement. But over and over, societies have rhetorically or institutionally associated the first set of skills with femininity and the second with masculinity – in part because of historical divisions of labor. Women, often excluded from formal power, became the “invisible glue” holding interpersonal networks together (through communication, caregiving, coordinating behind the scenes). Men, given formal authority, tended to be credited with designing and enforcing systems (laws, machines, bureaucracies). This cultural coding then reinforces itself: e.g. if reading subtle social cues is expected of women, men may neglect developing that skill, and vice versa for formal technical training.

Surplus-information refers to the raw excess of signification produced when communication accelerates or expands without an immediate structure to absorb it. For example, when the first newspapers and telegraphs appeared, suddenly news and rumors traveled faster than the old institutions (like the local church or the town elders) could process – an information surplus. Similarly, in our time, billions of social media posts create an ocean of signals that traditional media literacy or education can’t fully organize. Someone will try to grab that surplus. Historically, those who succeed fall into one of the two categories above: either they harness it qualitatively (making sense of it through narrative, hype, relationships – the surplus-enjoyment path) or quantitatively (finding a way to structure and mine it – the surplus-value path).

To make this more concrete:

• Surplus-enjoyment approach: Navigate the glut of information by using human intuition and charisma. Imagine a skilled moderator or influencer today who can ride the waves of online trends, picking up what multiple audiences care about and weaving it into a story that captivates people. They don’t reduce the information – they amplify the meaningful bits, spin compelling interpretations, and create feedback loops of attention. In doing so, they harness surplus-information by turning it into collective emotional energy (followers, engagement, loyalty). Historically, think of the salon host in the 18th century who gathered writers and politicians and curated conversations that weren’t happening in official forums – she captured the surplus gossip and ideas circulating outside formal channels and gave them shape (often these salonnières were women who wielded considerable soft power[5]). These skills thrive in environments where ambiguity and personal influence are central – where it’s less about having the right answer and more about having the right touch.
• Surplus-value approach: Tame the glut of information by imposing structure and extracting consistent results. Picture a data engineer or operations manager who sets up a dashboard to track hundreds of metrics, or writes an algorithm that can parse thousands of social media posts for sentiment. They handle surplus-information by slicing through noise with formal rules – filtering, categorizing, and optimizing. In doing so, they turn what was a chaotic flood into a source of value (maybe identifying a trend in consumer behavior that can be monetized, or speeding up a process from days to seconds). Historically, an example is the development of the Hollerith punch card tabulator for the 1890 U.S. Census: the government was drowning in census data that could take a decade to compile by hand, so Herman Hollerith invented a machine to count and sort data on punched cards, completing the task in a fraction of the time[6][5]. That was a surplus-value move – formalizing citizens’ information into machine-readable bits and leaping ahead in efficiency. These skills dominate in environments where consistency, scale, and prediction matter most – where following the right procedure is more valued than individual flair.

We can already sense how these might cycle. The intuition/attention regime can generate a lot of informal complexity and dependence on particular people’s talents (e.g. “only Jane knows how to smooth things over with that client” or “our community thrives because of a few key moderators [motherator!] with uncanny tact”). That works until scale or speed makes it unsustainable – you can’t have a million one-on-one careful mediations per day; you can’t trust global supply chains entirely to personal relationships. At some point, the system tips: someone says “we need a standard protocol” or “let’s automate this,” and then the formal/procedural regime takes command. But that regime, in turn, can overshoot – it can strip away too much nuance, or create its own kind of chaotic excess (like information overload, or engagement metrics that optimize for the wrong thing, leading to meaningless or harmful content). Then it’s time for a return of human judgment: someone needs to “read between the lines” again and restore sense where the algorithm’s outputs stop making sense.

The Pendulum Mechanism: A useful way to think of these transitions is as a reductio ad absurdum triggered by technology. Reductio ad absurdum is a term meaning “reduction to absurdity,” often used in logic and math to describe pushing a premise to its extreme until it breaks. In our context, each dominant media or management paradigm gets pushed by new technical capabilities until it reaches a point of absurdity – an internal contradiction or runaway effect that it cannot resolve on its own terms. At that point, a new technical system doesn’t just appear out of nowhere; it comes to exploit the absurdity. It both exposes the old way’s limits and subordinates the old way under a new set of rules.

This usually happens in two steps:

1. Over-saturation of the Current Code: The ruling way of doing things (whether it’s informal networked management or rigid bureaucratic control) is stretched to its limits by increasing demands, volume, or speed. Marginal returns start turning negative. For example, a social media manager finds that posting more and more to chase engagement eventually just floods followers with noise, yielding diminishing returns – maximizing the strategy starts to undermine itself. Or consider earlier: by the late 1700s, cottage-based spinners with spinning jennies found themselves struggling to meet exploding textile demand; they could add more spindles and work more hours (often children joining in), but quality control and coordination were slipping. The old method was becoming absurd – threads were breaking, schedules failing – not because people were incompetent, but because the scale had surpassed what their tacit coordination could handle.
2. New Machine Logic Arrives: A new kind of machine or system is introduced that thrives on exactly the thing that was overwhelming the old system. It effectively says, “What you saw as an unmanageable mess, I see as raw fuel.” This new regime both mocks the old one by automating or trivializing its highest skill, and then establishes a new order in which those old skills are no longer the bottleneck for power. In our example, the introduction of the spinning mule (a partly automated, water-powered spinning machine) in the late 18th century did exactly that: it took the delicate, many-thread juggling act that women did on the jenny and mechanized a large part of it. It exposed that human spinners, no matter how nimble, couldn’t match the uniform tension and throughput of a well-calibrated machine[1]. Once the mule and its factory system took over, the basis of competence shifted to things like machine setting, maintenance, scheduling, and quality metrics – domains in which male engineers and managers then claimed expertise. The old improvisational virtuosity of the cottage spinner was now both unnecessary (much of it was in the machine) and insufficient for success (the market and production volume had moved on).

In short, technology drives a pendulum swing by first accelerating the reigning paradigm to a breaking point, and then offering the next paradigm as a solution. It’s not a smooth evolution but more like a series of stress fractures and leaps. Each leap reconfigures who has authority and how value is created.

Let’s put some terminology from earlier in this context: surplus-information grows when a paradigm saturates. At first, those adept in the current paradigm capture it (surplus-enjoyment or surplus-value, depending). But beyond a threshold, their methods produce absurd results – noise, contradictions, or runaway side effects. The incoming tech capitalizes on that surplus-information in a different way, turning what was noise for the old system into signal for a new system. The feminine-coded skills of the prior era often get characterized as “noise” or inefficiency at this point, even if they were the backbone of the system’s success in its earlier phase. The masculine-coded approach comes in as a savior that streamlines or cleans up the mess – but in doing so, it also relegates the previously core skills to a secondary status.

To fully ground this abstract description, we will now walk through the major historical “waves” or case studies of this phenomenon. Each case will show a concrete instance of the pendulum swinging from a surplus-enjoyment regime to a surplus-value regime (or in one case, vice versa) under the pressure of new technology. We start with the Industrial Revolution, often seen as the archetype of mechanization displacing skilled female labor and reorganizing production around male-led engineering – but as we’ll see, it’s more than just a story of machines replacing people; it’s about a shift in what counted as skill and who got to define “competence.”

Wave 1: Cottage Industry to Factory – The Fall of the Spinning “Jenny”

Context (late 18th – early 19th century): The textile industry is booming in Britain. For generations, spinning yarn was a domestic or cottage enterprise: women (and children) spun thread on simple wheels, often in their homes, and provided this thread to weavers or merchants. It was an archetypal “feminine” labor: dexterous, rhythmic, done alongside caregiving and other housework, and coordinated through informal community norms. Each spinner handled multiple spools, adjusting tension by hand and by feel. In the 1760s, James Hargreaves invented the spinning jenny, a hand-powered machine that allowed one person to spin multiple threads at once. The jenny greatly increased output, and it was small and cheap enough to be used in cottages and small workshops. As a result, spinning capacity expanded rapidly across villages – often with women and children operating dozens of spindles simultaneously on these jennies. Production was dispersed, semi-formal, and still reliant on the tacit skills of the spinners to keep threads from tangling or breaking. Demand for cotton thread kept rising, spurred by new mechanized looms and growing markets. Spinners responded by adding more jennies and working longer, training their daughters, coordinating with neighbors to fill large orders. This was the surplus-enjoyment phase in many respects: the knowledge of how to manage many threads, how to maintain quality by sight and touch, how to juggle household and production demands – all that lived in the community of women spinners and their families. They prided themselves on an almost musical coordination with their simple machines, “feeling” the subtle differences in fiber and adapting on the fly. Their ability to keep production going despite irregular supplies, uneven fiber quality, and fluctuating demand was a kind of social choreography. It wasn’t written down in any manual; it was embodied. It was said that a good spinner could “hear” when a spindle was wobbling or a thread was about to snap. This was an era when competence meant attentiveness, adaptability, and trustworthiness – virtues cultivated in domestic life.

However, by the 1780s and 1790s, this cottage system hit serious limits. Merchants complained that they couldn’t get consistent quality or large enough batches on time. A village’s output might be fragmented among many households; if one family fell ill or a quarrel broke out, an entire delivery could be delayed. Scaling up meant hiring many more spinners, which meant coordinating more homes – quickly becoming a logistical nightmare. The pace of surplus-information (in this case, the sheer number of threads and orders flying around) exceeded what these informal networks could manage. The absurdity of the culture, as one might put it, was becoming apparent: there were too many threads, too many small negotiations and custom adjustments needed, for the system to hold together efficiently. Production sometimes literally got tangled. A skilled spinner with a dozen jennies in her cottage was running a mini-factory without the formal tools of factory management – it worked, but just barely, and only through constant personal attention.

Into this situation came a new invention: Samuel Crompton’s spinning mule, first introduced in the 1770s and improved in subsequent decades. The “mule” was so named because it was a hybrid of two earlier machines (the jenny and Richard Arkwright’s water frame). It was larger and could only be operated in a mill or factory, typically powered by a waterwheel (and later steam engine). Crucially, the mule could spin hundreds of spindles at once under mechanized control, producing finer and stronger thread with far less human intervention in the spinning process. However, it did require a different kind of human skill: setting it up, monitoring mechanical parts, and doing maintenance tasks like “piecing” broken threads. Initially, the mule still needed skilled operators to manage the carriage and adjust tensions at certain points in the cycle, but over time even these functions were increasingly automated (like the self-acting mule developed by Richard Roberts in the 1820s)[1][7].

The key point is that the spinning mule centralized and standardized spinning. Instead of dozens of cottages producing unevenly, you had one mill with rows of mules producing vast quantities of uniform thread. And an interesting social flip happened: strength and engineering acumen suddenly mattered more than delicate finger feel. The early mules required physical strength to operate their wheels and carriage – something adult men were better positioned for than women or children (one had to draw out and push a 50-pound carriage repeatedly). As a result, spinning work largely shifted from women to men: “Home spinning was the occupation of women and girls, but the strength needed to operate a mule caused it to be the activity of men,” notes one historical account[1]. In the old days, weaving cloth on a handloom was considered a man’s job and spinning was women’s; with mechanization, it flipped – weaving eventually became a lighter, machine-assisted job women could do in factories, while operating the early spinning mules was a job for “mule spinners” who were often men[1]. Those mule spinners, often barefoot in the mills, became a sort of elite of the factory floor – sometimes called the “bare-foot aristocrats” of the cotton industry[1]. They commanded relatively high wages and formed strong craft unions (because the initial mule still required expertise to get good output; it wasn’t fully automated – it just changed the nature of expertise).

So, in terms of our framework: the spinning mule represented the surplus-value paradigm supplanting the surplus-enjoyment paradigm. The surplus-information (all those threads, orders, and quality issues) that had overwhelmed the cottage system was now tackled by formal procedures and mechanisms. Quality was measured and enforced by machine settings and by new supervisory roles (like overlookers in factories). Throughput was controlled by schedules and the turning of waterwheels. The attentiveness and interpersonal coordination of the cottage spinners – their surplus-enjoyment skill – was partly absorbed into the machine (literally in the form of cams, weights, and gears ensuring tension) and partly shifted upward into management (male overseers who set standards for how many breaks were acceptable, how long a spinner could pause, etc.). What counted as “skill” moved from a diffuse, relational knowledge to a procedural, specialized knowledge. The women who had been at the center of spinning either had to adapt by entering the factories in subordinate roles (many women did work in textile mills, but often in tasks like piecing or carding, generally lower-paid and under male supervision), or they were pushed out entirely as the cottage system dissolved.

Let’s hear a hypothetical voice from this transition. Imagine a master spinner’s wife or a community elder from a parish that has long relied on cottage spinning, addressing a gathering of merchants or local officials around 1795, when pressure to adopt the new mule technology is mounting. We’ll construct this as an impassioned plea, capturing the ethos of those whose traditional world is being labeled outdated:

Parish Hall, Lancashire, circa 1795 – A Spinner’s Appeal: “Gentle sirs, we do not spin mere thread; we bind the households of this parish to their good name. A skein has its conscience, and it rests in hands that know the temper of flax as they know the temper of a daughter. You would have us answer to the clock, and to gauges that mock the living feel of fiber. But should price alone govern, you unmake the very trust that calls your cloth ‘fine.’ We have kept your promises when storms broke roads and fevers emptied rooms; we have suffered the night-wake of broken yarn so that your mark might not be shamed in market. Do not suppose a new iron contrivance will keep faith better than a mother who has staked her bread upon it. If you would quicken us, then pay honest measure and let our order stand; else you will have speed without honesty, and cloth that will not hold a seam.”

This fictional speech encapsulates the moral economy that cottage spinners believed in. They are essentially saying: we provide quality and trust through our relationships and dedication; if you force us into the mold of a machine (the clock, the gauge), you will lose integrity. There’s a clear sense of absurdity recognized: “gauges that mock the living feel of fiber” – the speaker sees the idea of a mere dial substituting for a woman’s tactile knowledge as insulting and dangerous (absurd from her point of view). She warns that focusing solely on “price” (efficiency) could destroy the social trust that underlies the trade (“speed without honesty”).

From our vantage point, we know what happened: the mill system did largely prevail, and indeed it created immense wealth and output. But the speaker’s concerns weren’t entirely wrong – early factory cloth sometimes had quality issues until standards were refined, and the human cost in dislocation and deskilling was real. The cottage spinners’ way of life was broken. They responded initially with some resistance (there were instances of machine-breaking riots, like the Luddite uprisings a bit later, where communities destroyed textile machines in protest). But beyond protest, there were also adaptation strategies. Some women found new roles, like becoming inspectors or trainers in the textile mills (where their experience was still valuable if they could translate it to the new context). Others shifted to different domestic crafts or took in weaving (until that too mechanized).

A “smarter pivot” (to use the sources’ term) for someone in that era, hypothetically, would have been: take the tacit knowledge of spinning and codify it into the new system. For example, if a wise spinner became an early quality supervisor in a factory, she could help set the acceptable limits for thread variation, or design training programs for young mill workers (“this is how you join a broken thread so it doesn’t show”). In some cases, this did happen – women became teachers in the emerging domestic science or textile schools later in the 19th century, essentially preserving and formalizing the knowledge of fabric and craft. Another adaptation was forming or joining guilds and standards bodies that defined grades of cloth. The content we’ll cover later, about institutions sheltering human craft, applies here: what had been tacit became explicit and teachable in new forums (though typically after a period of loss and devaluation).

The transition from the spinning jenny regime to the spinning mule regime illustrates the general mechanism: the feminine-coded art of managing threads and domestic production achieved wonders (surplus-information was handled by surplus-enjoyment skill), but as production needs exploded, its limits became apparent (saturation). The new masculine-coded technology (the mule and the factory discipline around it) exposed those limits by automating a large part of that art, making the old way look slow, inconsistent, even “absurd.” It then reorganized the industry such that formal procedures, machine maintenance, and managerial control captured most of the surplus-information (surplus-value), and the previous tacit skills were either marginalized or pushed into supporting roles.

It’s worth noting labor politics followed this shift. Where cottage spinners had a kind of informal negotiating power (they could refuse work or set their own pace to some extent, and they had community standing), the new mule spinners in factories formed trade unions to protect their relatively privileged status. In 1870, the first national union of cotton spinners was formed in Britain[8]. Interestingly, part of the impetus for fully automating the mule (creating the “self-acting” mule) was that mill owners wanted to break the dependency on these skilled (male) spinners who were becoming labor aristocrats[8]. In other words, once power concentrates around rule-based male-coded expertise, the drive to further mechanize and even reduce that human element continues. By the late 19th century, even the mule spinners were being partly displaced by ring spinning frames and other innovations that required less craft – a preview of the pendulum swinging yet again.

Summary of Wave 1: The cottage industry era made heroines of women who could juggle threads and neighbors’ needs in equal measure – a feminized craft of navigating desire (here, the “desires” might be read as the needs of merchants, the schedules of families, the quality expectations of buyers – all balanced informally). The spinning mule’s arrival around 1780–1800 turned that craft into a mechanical process governed by settings and overseers. Symbolically, one might say the “Mother” figure of production – the nurturing spinner ensuring each thread and each family stays whole – was supplanted by the “Engineer” figure – the (male) mechanic who imposes order through apparatus. Women’s economic agency in spinning was broken, but their underlying sensibility (attention to quality, to the nitty-gritty of fibers) did not disappear; it re-emerged in new forms later (like women inspectors, teachers of textile arts, etc., which we will revisit in the section on shelters).

Next, we move to a different arena but a strikingly parallel story: the world of offices, telephones, and clerical work in the late 19th and early 20th centuries. There, we will see a feminine-coded communication web (think of armies of secretaries and telephone operators deftly routing information by intuition and protocol) give way to the early “computing” machines and bureaucratic scientific management – a shift from social coordination to formal procedure much like our first wave, but in the realm of information rather than textiles.

Wave 2: The Office & Switchboard – From Social Coordination to Mechanical Tabulation

Context (1890s – 1960s): By the late 19th century, businesses and governments had grown in scale and complexity. An explosion of paperwork and correspondence defined this era – letters, ledgers, memos, phone calls, and files flooded large organizations. In this pre-digital information age, the critical infrastructure was human: clerks, secretaries, typists, and telephone operators (a workforce largely made up of women by the early 20th century) formed vast clerical networks that kept the economy and administration running.

Consider a city office circa 1910: A team of young women sits in front of a telephone switchboard, manually connecting calls by inserting plugs into jacks to patch lines through. They must work quickly, but also courteously – a wrong connection or a rude tone can derail business. In the next room, stenographers and typists (again, mostly women, often called “typewriter girls”) are taking dictation from male managers and turning it into typed documents, making multiple carbon copies for distribution. Down the hall, a filing clerk (perhaps a woman or a lower-ranking man) meticulously organizes incoming paperwork into cabinets, maintaining a system so that any document can be retrieved. And at the elbow of the executive sits his private secretary (likely a woman if we’re in the U.S. or UK by this time), who manages his appointments, drafts his routine correspondence for him to sign, remembers which client needs a congratulations letter for a new baby, keeps the office running smoothly by informal means (a favor here, a quiet word there), and guards access to her boss like a dutiful gatekeeper.

This clerical web was a prime example of surplus-enjoyment based power – albeit often unacknowledged as power. These women (and some men in higher clerical positions) commanded the flows of information by means of social skill and discretion. A good telephone operator in 1920 didn’t just mechanically connect lines; she might recognize a repeat caller’s voice and give a personalized greeting, or subtly prioritize an urgent call by tone of voice. A good secretary knew which calls to put through and which to screen (“tell him I’m in a meeting”), effectively controlling who gets to speak to the decision-maker. She also mastered the tone of letters: how to phrase a polite refusal versus a welcoming invite. In a large company, the informal network of secretaries and clerks often knew how to get things done despite the formal hierarchy – they traded information (“The boss is in a good mood today, now’s the time to ask for that raise” or “Don’t rely on that inventory number in the ledger; I heard there was a counting error, better double-check”). Tacit knowledge and personal relationships greased the wheels.

Notably, during this period, bringing women into clerical work was seen as a form of automation in itself. Employers realized they could hire educated women to do routine office tasks for lower pay than men, because (sadly) society allowed women’s labor to be underpaid and because women weren’t expected to climb the career ladder. Sharon Hartman Strom, in her study of feminization of office work, notes that after the U.S. Civil War, hiring women as clerks became common since male labor was scarce and expensive; by paying women less and limiting their promotion, companies saved costs[9][10]. A 1905 office management guide even argued that female clerical workers were ideal for operating new office machines (like calculators, sorters), saying “girls” could become experts on these devices, freeing the (male) accountants to focus on “more important” analytical work[11]. The rationale was blatantly sexist: women were thought to be more suited to repetitive, detail-oriented, “mechanized” tasks, and they wouldn’t demand advancement[12][10]. In reality, many skilled male bookkeepers did lose jobs to automated processes run by lower-paid women or by younger men with new machines[10]. So even before electronic computers, offices were undergoing a proto-automation: typewriters, adding machines, and filing systems were the “tech,” and women were the human part of that tech* – the living interface between the machine’s potential and the office’s needs.

By the 1920s, the telephone operator had become an iconic female job. AT&T was the largest employer of young women in America; in 1929 it had over 160,000 women working as operators connecting calls[13][14]. These operators were known for their near-military training in etiquette and efficiency. They had to respond to calls with standardized greetings, but also handle crises (tracing emergency calls, calming frantic customers) with tact. If the lines were overloaded, it was often the operators’ quick thinking that rerouted traffic.

However, the volume of information – calls, memos, data – kept increasing. By the 1930s-1940s, early analog and electro-mechanical systems were being introduced to handle some of this load. The paradigm of scientific management, pioneered by people like Frederick Winslow Taylor (famous for time-and-motion studies to optimize factory labor), was applied to offices. Managers began to break down clerical processes into measurable units: how many keystrokes per minute could a typist do? How quickly could a file clerk retrieve a document? Could telephone switching be done automatically by machines? The absurdity of the clerical culture was becoming evident to efficiency experts: there were simply too many decisions relying on human discretion. Too many letters to route individually, too many phone calls for humans to manually connect one by one. And humans, being human, made errors or took breaks or exercised personal judgment that introduced variability.

A classic absurd scene might be an 1920s office after a flurry of calls: imagine a desk piled high with letters and telegrams that need sorting and responses. The lone secretary is staying overtime to get through them, remembering which ones the boss must see first, which can be handled perfunctorily. She’s effectively running a one-person information triage center. It works when the scale is moderate and she’s exceptionally capable. But as the business grows 10x, this becomes untenable.

Automation step 1: The dial telephone. Starting in the 1910s and accelerating in the 1920s, telephone companies began installing automatic switching systems. Instead of telling an operator, “Connect me to Mrs. Jones,” you would dial Mrs. Jones’s number on a rotary phone, and a series of switches would connect you. In cities that converted to dial systems, half or more of the operator workforce eventually became redundant. This didn’t happen overnight – many operators kept their jobs for a while as not all exchanges automated at once. But one study found that as cities mechanized, the number of young women employed as operators plummeted by 50–80% in subsequent cohorts[15][16]. By 1978, the profession of telephone operator in the traditional sense was virtually extinct in the U.S.[16]. It’s an early example of technology wiping out an entire job category: a direct parallel to what some worry AI might do today[17][18]. For the women who were telephone operators, this was a huge shift – some could move into other clerical jobs, but many struggled if they were mid-career. Interestingly, economists Feigenbaum and Gross studied this and found that women who were just entering the workforce as operators disappeared had to find different jobs (like secretarial work or retail), but overall they adapted without massive unemployment – whereas older operators often never re-entered work[18][16]. This suggests that when a feminized role is automated, the younger generation of women often moves on to another emerging field, while an older generation loses a foothold (a pattern we’ll see again).

Automation step 2: Punch card tabulation and early computers. The 1890 U.S. Census, as mentioned, used Herman Hollerith’s punch card machines to compile data, drastically reducing processing time[6]. Banks, insurance companies, and government agencies in the early 20th century adopted similar tabulating machines for accounting and record-keeping. A lot of the routine arithmetic that male bookkeepers used to do (summing columns of figures by hand) could now be done by machines operated often by – you guessed it – women clerical workers who set up and ran the tabulators[5]. Men initially avoided these jobs, complaining they were monotonous and essentially temp work until the task was done[5]. Women, often barred from higher professional roles, took them and earned reputations for precision. A newspaper noted that women were faster at operating the census machines than men (50% faster) and attributed it condescendingly to women’s supposed ability to “adjust the delicate mechanisms” and their greater anxiety to do a good job[5]. In truth, the gendering of work meant men feared that these jobs had no future (since once the system was set up, the job might vanish), while women were willing to step in because their alternatives were limited[5]. This dynamic – where automation itself initially opened opportunities for women (as machine operators) but in roles with a dead-end – is quite fascinating. It’s like women were allowed to be the bridge to automation: they became the living component of partially automated systems, until those systems could be fully automated, at which point both the job and the woman in it were often sidelined.

Scientific management also turned its attention to the office workflow. Companies like Railroads and banks started implementing filing systems, standardized forms, and timing of clerical tasks. Managers drew flowcharts of how a document should move through an office. This was essentially the surplus-value approach encroaching on what had been the realm of informal office know-how. Instead of relying on a wise old clerk to know “who needs to approve this invoice,” you’d have a written procedure: Step 1, get Supervisor A’s sign-off; Step 2, forward to Accounting; etc. Instead of trusting a secretary’s network to “know who needs what when,” you created organizational charts and job descriptions so that, in theory, any competent person could fulfill the role by following the manual. In practice, it didn’t fully work – but it changed the culture.

As a consequence, a lot of the tacit authority that experienced (often female) support staff wielded was undermined. For example, in the early 1900s, a CEO’s personal secretary might be the second most knowledgeable person in the company; by the 1960s, many of her duties might be split among a typing pool, a scheduling office, and a HR department with formal policies. Her discretion (“I’ll let this caller through because I sense it’s urgent” or “I’ll remind the boss about his wife’s birthday so he doesn’t come home in trouble, which keeps him in good spirits at work”) became less visible, sometimes even seen as a nuisance by the new generation of male managers who wanted more “impersonal” systems. Indeed, as computers arrived in the 1950s and 60s, we see a further shift: some of the first computer programmers were women (programming was initially viewed as an extension of clerical work – detailed, meticulous, perhaps “woman’s work”)[19]. But as programming grew in prestige and complexity, men entered en masse and women’s share shrank by the late 20th century[20]. A telling quote: “The earliest programmers were largely women in part because early computing was seen as clerical; once its importance was recognized, it became male-dominated.”[19][21].

So what happens to the women and their skills? Does the personal, relational aspect just disappear? Not entirely. One pattern is that once these roles were “canceled as interfaces,” women’s influence had to reappear either at higher levels or in different forms. For example, by the mid-20th century you start seeing the rise of public relations and HR fields – interestingly, fields where women later found significant presence. The direct secretary may have less power, but a PR executive (often a woman) might wield soft power in shaping a company’s image (we’ll talk about PR in the next wave). Or an office manager role emerges that formalizes what head secretaries used to do informally.

To capture the emotional core of this transition, let’s imagine another speech. It’s 1937, and we’re at a luncheon of the newly formed National Secretaries Association (which, historically, would form in the 1940s, but let’s say forward-thinking secretaries are already networking). One experienced secretary stands up to toast – or rather, to warn – her colleagues about the changes she sees:

Secretary’s Luncheon, New York City, 1937 – “We Make This City Breathe”: “Colleagues, we make this city breathe. A letter is not a letter until it arrives in the only voice its reader will hear; a call is not a call until a temper is cooled and a promise is placed where it will be remembered. They bring machines that count without listening, and men with stopwatches who time our hands but not our judgment. I do not fear a tabulator; I fear a world that mistakes arithmetic for prudence. Keep your lists close, keep your calendars closer, and make yourselves indispensable by doing what no card can: deciding who must never be surprised.”

In this passionate little speech, the secretary is articulating the value of her surplus-enjoyment skill: the ability to inject judgement, context, and humanity (“voice”, “temper cooled”, “who must never be surprised”) into the flow of office information. She sees the advent of “machines that count” and the Taylorist time-motion men as a threat that devalues the subtle work she and her peers do (“time our hands but not our judgment”). Her call to action is to remain indispensable by outdoing what machines can’t – essentially highlighting the irreplaceable human element of discretion.

Her fear – “a world that mistakes arithmetic for prudence” – indeed came partly true as the century advanced: metrics and KPIs sometimes replaced wisdom in decision-making. A modern reader might think of how today’s corporations can be overly metrics-driven, forgetting qualitative factors – an early version of that tension was already felt by these women in the 1930s.

What she urges – “deciding who must never be surprised” – is such an incisive phrase. It means: know which people absolutely need to be kept in the loop to avoid disaster. That’s an unwritten rule in any organization (e.g., never let the VP of Sales read about a product change in the newspaper before you brief him; he must “never be surprised” by that, or he’ll raise hell). Secretaries were the ones who knew those social dynamics. When they are replaced by a system where everyone is “just following procedure,” sometimes those nuances get lost – until a preventable mishap happens.

Smarter pivot for this wave: Some secretaries and clerical workers did adapt by “owning the workflow, not the work.” That is, instead of just being a great personal assistant, a forward-thinking person could become the systems analyst or office manager who designs the filing system, the checklist, the escalation protocol. In fact, this is how some women moved up: for example, a woman who knew everything about the company’s correspondence might become the head of Records or the office librarian. Library Science as a profession boomed in the early 20th century and was open to women – essentially it was about formalizing information management, something women had already been doing informally. Another pivot: women got into the nascent field of programming (in the 1940s, think of the six women who programmed ENIAC, one of the first digital computers[20]). They translated their understanding of logical office processes into code. In doing so, they momentarily became the new experts of a rule-based domain – though unfortunately many of them were not given due credit until later historical research corrected the record[19][22].

To summarize Wave 2: Telephone switchboards, typewriters, and filing cabinets extended the reach of communication, but relied on an invisible army of women to function. These women built a surplus-enjoyment regime where knowing the informal pathways was key. As calls and files multiplied beyond a critical mass, companies introduced automatic switches, punch-card tabulators, and rigid procedures – a surplus-value takeover that reduced many skilled clerical roles to button-pushing or eliminated them. The locus of power moved from “she who knows everyone and everything in this office” to “the system that documents everyone and everything.” By the 1960s, the ideal of the efficient organization was one where no single person (especially not a low-ranking woman) was indispensable – in fact, being indispensable started to be seen as a risk (“What if she quits? Our processes should not depend on any one person!”). Instead, interchangeable workers following standardized procedures was the goal. The price, of course, was that something was lost – precisely the element of judgment and relational glue our 1937 secretary championed.

Yet, as we will see, that element doesn’t vanish; it crops up again when the formal systems run into their own problems (e.g., impersonal bureaucracies needing a human touch). The next wave shifts our attention from internal office dynamics to the public-facing world of media, advertising, and public relations – where once again women carved out a domain of expertise in managing attention and desire, only to see quantitative metrics and automated systems encroach.

Before that, let’s reflect on a continuity: Both Wave 1 and Wave 2 show a pattern of women’s tacit labor being codified and captured by a new machine regime, with short-term gains (productivity, scale) and long-term side effects (loss of context, new inequalities). One might ask: Did either wave imply that “women lost, men won”? Not exactly – many women found new opportunities within the new regimes (e.g., women filled the ranks of the new clerical jobs in Wave 2 and later became computer programmers; women in factories became an essential workforce in other roles too). It’s more that the terms of value shifted. Under the old system, women’s informal mastery was recognized locally (the spinner was vital to her village; the secretary was the right hand of the executive). Under the new system, that mastery is often unacknowledged or forced into different channels (the spinner’s knowledge moved into industrial standards, often written by men; the secretary’s knowledge got embedded in an office manual, authored by a manager). This is what we mean by “making and breaking of womanhood”: each era defines a certain feminine ideal of competence and influence – then breaks it apart when a new logic comes.

Now, we proceed to Wave 3, which unfolds in the mid-20th century through early 21st: the era of mass media, advertising, and the management of public image. Here, rather than focusing on production floors or internal offices, the focus is on cultural production and persuasion – an arena where women again played key coordinating roles (think of magazine editors, PR agents, TV producers, community organizers). And once again, a shift (the rise of data-driven marketing, big media buys, and later the internet algorithms) will alter the landscape.

Wave 3: Mass Media and Image – From Intuitive PR to Programmatic Advertising

Context (1960s – early 2000s): By the mid-20th century, society had fully entered the age of mass media. Television, radio, newspapers, and glossy magazines were the dominant channels through which public opinion was shaped. The economy was booming (in many countries), consumer culture was on the rise, and brands became key assets for companies. Managing public perception and taste – in other words, the sphere of images and narratives – grew into a sophisticated profession.

This era saw the emergence (or professionalization) of fields like public relations (PR), marketing communications, advertising creativity, fashion and style industries, celebrity management, and community organizing. Women found significant (though not equal) footholds in many of these areas. For example, the PR field had notable women pioneers (like Doris Fleischman at Edelman, or early PR execs in fashion and entertainment). The job of a PR agent or a brand manager in, say, 1980, was deeply relational and intuitive: you had to know which journalists to talk to (and how to talk to each), what phrasing would resonate with the public, how to spin bad news into acceptable news, how to pair a celebrity with a cause or a product so that it feels authentic. These are not things easily reducible to formulas – they lived in the “taste and trust” expertise of practitioners.

Consider a concrete scenario: It’s 1975, and a major consumer products company is launching a new line of cosmetics. The brand manager (perhaps a woman who worked her way up through marketing) coordinates an entire symphony of image-making: she works with an ad agency to develop TV commercials that capture a mood of empowerment, she arranges for samples to be sent to editors of women’s magazines, she organizes a sponsorship for a charity fashion show to get goodwill, she ensures that the packaging color and design align with a trend (maybe she has an intuitive sense that pastel colors are coming back this season, so she pushes for pastel packaging). If a crisis hits – say an ingredient in the makeup is rumored to cause allergies – she quickly calls a meeting to craft a response, calls up a dermatologist she knows to get an endorsing quote, reaches out to friendly contacts in the press to run a reassuring piece. This is a highly networked, intuitive, and creative orchestration. In those decades, success in such roles depended on surplus-enjoyment skills: reading public desire, spinning compelling stories, cultivating relationships (with media, with partners, with consumers).

Another domain: Community organizing and activism. The late 60s and 70s saw civil rights movements, second-wave feminism, etc. Women often played leading roles in organizing communities, which involved bridging between the grassroots and the media/political sphere. These organizers needed to know how to rally people’s emotions (desire for justice, etc.), and also how to engage the press and politicians. Again, very relational and intuitive work.

However, as the decades progressed, there was a push to quantify and systematize these fields too. The driving force was partly the rise of market research, Nielsen ratings, and by the 1990s, computer-based data analytics. Executives started asking: We spend millions on PR and advertising – what are we getting? The era of the advertising “Mad Men” in the 1960s was one where creative instinct and showmanship dominated. But by the 1980s and certainly into the 90s, the finance and procurement departments gained more power in corporations. They introduced concepts like ROI (Return on Investment) for marketing: if we pay for a celebrity endorsement, how much did sales actually increase? If we run this ad campaign, how are we measuring its effect?

Two big changes exemplify this shift:

• The Nielsen ratings and audience measurement systems: Since the 1950s, Nielsen ratings quantified how many viewers watched a TV show[2][23]. By the 1970s-80s, these ratings were the gospel that determined whether a program lived or died and how advertising dollars were allocated. A TV executive might love a particular show’s creative quality (aesthetic, vibe, etc.), but if the Nielsen number wasn’t high, it would be canceled regardless of that intangible quality[23]. This is a case of quantitative metric (surplus-value logic) overriding qualitative judgment (surplus-enjoyment logic). The creative producers (some of whom were women carving out space in media production) suddenly found that their boss was the rating point. One week of low ratings, and no amount of personal relationship with the network president could save you, because he in turn answered to numbers. Commercial sponsors too started to demand concrete numbers: What demographics are we reaching?
• The rise of procurement and cost-per metrics in advertising: In large companies, by the 2000s, you had procurement officers (often with backgrounds in finance, mostly men) negotiating advertising contracts, focusing on metrics like CPM (cost per thousand impressions). The art of cultivating a brand image started to be treated like procuring raw materials – i.e., a cost to be minimized for a given output. There’s an infamous trend where corporate procurement forced ad agencies to pitch for business through spec work and spreadsheet comparisons, reducing the once relationship-driven agency-client partnership to a more commoditized service. A senior ad exec lamented that the days of a CEO having a long-term partnership with an ad agency based on trust and understanding were waning; now the agency was chosen like “lowest bidder” for yearly contracts.

Similarly, retail and sales got data-heavy. Big retailers used inventory and sales data to decide which products to keep or drop. In turn, brand managers had to present PowerPoint decks with lots of charts to get buy-in for new initiatives. The gut instinct (“people will love this new scent, I just feel it”) had to be backed up by focus group statistics and projected growth charts. Over time, a discipline called “marketing science” emerged, filled with MBAs and statisticians.

For women in PR and brand management, this shift could feel disempowering. The soft skills they excelled in – like maintaining a network of media contacts or having a keen sense of consumer psychology – were undervalued if they couldn’t translate them into the new language of metrics. In some cases, women adapted by becoming experts in the new tools (e.g., learning to run a focus group and analyze the results, or mastering the new software that tracks ad spending versus sales). But the center of gravity moved from qualitative to quantitative.

Another facet: the performative nature of media reached a kind of saturation. By the late 90s and early 2000s, we had “media about media.” Image-making became sometimes self-parodying: press conferences about managing previous press conference fallouts, or reality TV showing how the sausage of fame is made. People grew jaded with polished PR – which led ironically to a new tactic: performative authenticity. (This would really bloom in the social media era, wave 4, but its seeds are in wave 3.) Publicists realized audiences craved something “real”, so they would stage “authentic moments” – for example, a celebrity spontaneously crying on TV about a cause (except it was planned in advance). The public’s attention became volatile: they could swing from adulation to cynicism quickly. Managing that required ever-more complex maneuvers.

By the 2000s, programmatic advertising and the internet ushered in a truly algorithmic approach. But let’s hold pure internet for Wave 4. Even before social media, by ~2005, media buying was highly data-driven: using software to allocate ad budgets for maximum “reach” and “frequency” per target demographic. At big consumer goods companies, decisions on where to advertise were often made by running models on Nielsen and other data sets, rather than a gut feeling about “this magazine has the right vibe for our brand.” The ERP (Enterprise Resource Planning) software integrated marketing budgets with sales outcomes, giving CFOs unprecedented oversight. Many old-school PR or brand folks felt sidelined by the “numbers people.”

To illustrate the absurdity that emerged: imagine a boardroom in 1998 where a Senior VP of Communications (perhaps a woman who earned her stripes through brilliant PR campaigns and nurturing press relationships) is trying to justify her budget to a new CFO (chief financial officer) who only trusts spreadsheets. The CFO asks, “What’s the ROI on that sponsorship of the film festival you keep insisting on? We spent $2 million – how much in sales did it generate?” The SVP responds with qualitative points: “It maintained our brand’s luxury image, it built goodwill with influencers, it prevented at least two potential crises by giving us a platform to speak,” etc. The CFO is unmoved: “That’s not tangible. I can’t put that in the quarterly report.” He then suggests cutting “relationship-building” activities that don’t have clear metrics, and shifting money to measurable online banner ads or retailer promotions that have clear conversion numbers. The SVP realizes her entire craft is being called “overhead.”

Her likely response (if she dares, maybe in a closed meeting with the CEO) could be something like this, akin to our other imagined speeches:

Board Meeting, 1998 – “The Brand Is a Promise, Not a Number”: “The brand is a promise, not an insertion order. You can buy reach; you cannot buy forgiveness. When the roof leaks—and it will—it is not a CPM that keeps the journalist on the phone, it is the ten years we spent telling the truth when silence was cheaper. You want to trim ‘relationship fat’; you are cutting the very ligaments that hold us upright when a storm hits. Give me another quarter and I will deliver not just impressions, but breaths: the exhale that says ‘we trust them’. That is not a unit the spreadsheet recognizes, but it is the only unit that matters when the lights go out.”

In this passionate defense, the communications executive is warning that focusing only on quantitative reach (CPM = cost per thousand impressions, a common ad metric) misses the qualitative essence of a brand’s value: trust and goodwill. She’s essentially pleading for the importance of surplus-enjoyment type value – the intangible “promise” of the brand, built through years of careful curation and honesty, which pays off in a crisis (when you need people to give you the benefit of the doubt). Her metaphor of “breaths” – consumers exhaling with relief or trust – is beautiful and unmeasurable. “Not a unit the spreadsheet recognizes” precisely captures the conflict between the qualitative and quantitative.

Yet, what happened in many cases is that those pleas did not fully carry the day. Budgets for PR and community outreach were indeed slashed in some firms in favor of more immediate measurable marketing. Some companies later regretted it when they faced a backlash or lost brand luster and realized “oh, we’ve underinvested in our goodwill.”

A smarter pivot for people in this wave was to bind taste to test, as the sources phrased it. This means: find ways to translate the qualitative into something trackable without losing its soul. For example, the PR exec could start doing consumer sentiment surveys regularly (so she has numbers to show trust levels), or run small A/B tests of campaigns in different regions to get data on what works, combining that with her intuition. Or she could push for “brand equity” metrics – an index score that tries to quantify consumer goodwill, which became a thing in marketing (brand equity surveys etc.). These are ways to present something qualitative in a format executives can grasp. It’s akin to “parameterizing the tacit,” which will come up in our strategic implications.

By the early 2000s, technology had prepared another coup: the internet and especially the rise of digital platforms were about to flip the script again, leading into Wave 4. But before we move on, let’s crystallize Wave 3:

Summary of Wave 3: This was the era where women and men who excelled at managing image, relationships, and taste – often through PR, media, and marketing – held considerable soft power. Whether as magazine editors deciding which voices get amplified, or as PR agents smoothing a celebrity’s fall from grace, or as brand managers cultivating a loyal following, these were roles that relied on surplus-enjoyment skills: sensitivity to public desire, narrative intuition, crisis management through personal rapport. The culture, however, became saturated with media – “image management metastasized into spectacle,” as the sources put it. We started getting diminishing returns on hype: when every brand is screaming for attention with flashy campaigns, the audience becomes numb. The absurdity manifested as an “arms race of spin” – ever bigger stunts, more contrived authenticity, and perhaps a sense that it was all becoming hollow (performative). At this point, quantitative systems and cost-driven logic moved in (the surplus-value approach). The center of gravity shifted to things like ratings, sales data, programmatic ad buys, and procurement metrics. A negotiated “vibe” with stakeholders (press, consumers) was now subordinated to inventory and reach curves – cold numbers. The craft of maintaining aura and mystique (which women had significant roles in, as publicists, as cultural curators) was undermined by the demand for provable efficiency.

However, just like before, the underlying sensibilities did not vanish. They just got undervalued until the next crisis required them again (for instance, a company that went all-in on automated, aggressive advertising might suddenly face a social media boycott, and realize they need skilled communicators to rebuild trust – effectively, they’d need to rehire some of those PR skills).

Now, Wave 4 will take us into the very recent past and present: the world of social media and the creator economy, and its sudden collision with generative AI. Here, we’ll see another swing – from an era that rewarded what we might call “feminine-coded internet labor” (building communities, curating persona, doing collaborative content) to a new era emphasizing AI-driven, rule-bound content generation and moderation. This is unfolding literally as we speak (today’s mid-2020s), so it’s a particularly exciting and fraught example.

Wave 4: Social Media Creators to Generative AI – From Influence to Inference

Context (2008 – present): The rise of social media platforms (Facebook, Instagram, YouTube, Twitter, TikTok, etc.) created a new economy of attention. Roughly from the late 2000s through the 2010s, being an online creator or community manager became a viable and influential role. Individuals – often young and quite a few women among them – cultivated followings by producing relatable content, engaging directly with fans (the parasocial relationships), and collaborating with peers. The “creator economy” rewarded those who could keep many social threads in play: managing the expectations of followers, negotiating partnerships with brands, cross-promoting with other creators, adapting to algorithm changes (which themselves were often opaque and required intuition to “game”), and maintaining an image of authenticity even as one’s life became a commodity.

Skills here were archetypally surplus-enjoyment: charisma, emotional labor, real-time adaptation to audience feedback, and a keen sense of trend-spotting (knowing what meme or format will click next week). Many successful creators are effectively community whisperers [trout tickling] – they sense subtle shifts in what their audience cares about and adjust content accordingly. They also often coordinate informal teams: maybe they have a friend who helps with editing, another monitors comments, etc. It’s a bit analogous to the salon or the cottage networks of earlier eras, but on a global digital scale.

Women have been extremely prominent in many aspects of social media: from fashion/beauty YouTubers and Instagram influencers to community managers for online forums, to advocacy voices on Twitter raising awareness. The social media environment initially didn’t have the formal gatekeepers of old media, so women and marginalized groups could build direct influence (sometimes surpassing traditional celebrities in cultural impact). This era lauded “influencers” – which literally centers the concept of influence over others’ desires as a form of capital. That’s classic surplus-enjoyment logic: you win by capturing attention and guiding desire at scale, not by owning factories or writing code (in fact, many influencers had little formal “business” infrastructure; their asset was their relationship with their audience).

Monetization in this era, interestingly, still relied on older structures (advertising and brand sponsorships, mostly). So a creator had to both please their audience and be palatable to advertisers. That required a lot of emotional IQ: you can’t appear too mercenary or you lose followers, but you must deliver value to sponsors or you lose income. Female creators often walked this tightrope expertly – packaging authenticity in a market-friendly way. This can be seen as a new form of that “feminine-coded mediation”: making commercialism feel like friendship.

By the late 2010s, however, cracks showed in the social media model. The content flood became overwhelming (timelines “saturated to the point of self-parody,” as the sources said – indeed when every post is optimized for engagement, the feeds turn noisy and formulaic). Engagement metrics (likes, shares) pushed creators into mimicry and extreme consistency – it became harder to stand out as algorithms favored certain templates. For creators, life became an exhausting rat race of “feeding the feed.” Many experienced burnout from the pressure to post constantly and remain on-brand yet fresh. Originality was often quickly copied by others, including cheap imitators or even bots, diluting the value of innovation.

Enter Generative AI (GenAI) around 2022–2023. Suddenly, the very kind of content that creators were painstakingly crafting – catchy text posts, beautiful images, even videos and music – could in some cases be produced by AI given the right prompts. AI models like GPT could generate blog articles or Twitter threads; DALL·E or Midjourney could create art or photos on demand; later multimodal models started moving into video and voice. At first, these tools were marketed as aids for creators, but it quickly became clear they could also flood the internet with synthetic content that mimics human-made styles. If Wave 4’s first phase (social media) was the ultimate surplus-enjoyment regime (the power of human charisma and network curation maximized), the GenAI phase is the pivot to surplus-value: those who can build and operate the algorithms – or integrate them into platforms – gain leverage, while the human creative labor is devalued if it’s not clearly unique or live.

We’re effectively in the midst of this pivot. Signs of it include:

• Attention shifts from “magnetizing audience” to “configuring AI outputs.” Tech and media companies are now looking for people who can do prompt engineering, manage AI content pipelines, handle data curation, etc. For example, instead of hiring more content moderators to read every post, companies invest in AI systems to detect bad content and then a smaller human team to handle edge cases with formal policy guidelines. The locus of control drifts upward: Who sets the rules that AIs follow? – those rule-setters (policy writers, algorithm designers) become crucial, more so than the individual community manager’s personal judgement on a day-to-day basis.
• Emergence of new roles and hierarchies: We see job titles like “AI Ethics Officer,” “Data Curator,” “Automation Strategist,” etc., which often come with higher status and pay than, say, “Social Media Manager” or “Online Community Lead.” Many of these roles are currently being filled by a mix of folks – not necessarily majority women, often people with a mix of technical and managerial backgrounds. There’s a sense that “the grown-ups are taking over the internet,” imposing more structure (sometimes for good reasons like safety, sometimes for profit). The wild ecosystem of creators is now to be tamed into an “AI-friendly” supply chain of content.
• Standardization and template-ization of creative work: Brands now ask for “templated brand voices” that AI can mimic, “codified style guides” for content so that it can be machine-generated consistently, and “automated operations” (like scheduling posts or customer interactions with chatbots). The emphasis is on repeatable transforms: e.g., a company might develop a formula for turning a product description into a snappy TikTok script – once that formula is clear, it could be executed by an AI or an assembly-line process with lower-skilled labor, rather than relying on one creative influencer’s magic touch each time.
• Influence becomes inference: a clever phrase meaning that instead of swaying human minds (influence), the game is now to train and tweak AI models (inference refers to the AI’s generation process). So the winners are those who can best configure AI outputs to simulate the engaging content that used to require human influence. For instance, rather than needing a charismatic person to respond to every customer tweet wittily, a company can deploy a finely-tuned language model to reply in a friendly tone within set parameters – not truly charismatic, but good enough and infinitely scalable.
• Charisma yields to configuration: The “charisma” of a creator – their unique personality – is less important if the platform algorithm can boost or bury content regardless of who made it. In fact, algorithms on TikTok, for example, made the content itself (often detached from its creator’s identity) the unit of recommendation. TikTok’s system is so good at showing users what hooks them that even unknown creators can go viral if their video meets the algorithm’s inferred preferences – conversely, known creators can see low reach if they don’t align. This reduces the advantage of carefully built relationships; the machine intermediary grows in influence relative to human networking. With GenAI, this goes further: the platform might not even need the creator at all to produce that sticky content. Why pay a creator or tolerate their demands when you can generate a synthetic anime influencer who is always on-message, never sleeps, and doesn’t risk a scandal? (This is already happening – virtual influencers, AI-generated models in ads, etc.)

For creators (many of whom are women, POC, LGBTQ, etc., who carved out online niches), this pivot feels like a rug pulled out. Their once-celebrated emotional labor and authenticity is at risk of being “partly templated, partly subordinated to a higher-order procedural layer”. For instance, some influencers now use AI to batch-generate dozens of variations of a post and test which one performs best – turning creative expression into a kind of managed process. The platform algorithms also increasingly treat human creators as input providers to train AI. There’s understandable pushback: artists protesting that AI models were trained on their artwork without consent, writers finding AI plagiarizing their style, etc. In response, we see the creation of guardrails and policies (again, rule-based solutions) for AI data usage – a domain where, interestingly, those with policy and community sensibilities are needed. So it’s not that human judgement vanishes; it just moves to a new locus (like being on an AI ethics board or a content standards committee rather than being the star content creator).

A telling expression of creators’ anxiety (and resolve) in this moment was given in the sources as an imagined livestream excerpt. Let’s channel that:

Creator Livestream, 2024 – “I Am Not an Unpaid Prompt”: “I know they want me to slow down, but you don’t build a community by posting less; you build it by showing up even when it’s ugly. This is my voice, not a dataset. I won’t be an unpaid prompt library. If the feed buries me, I will post twice as much; if the model copies me, I will get so specific it chokes on the details. They say ‘consistency’—fine. I will be consistent like weather: relentless. Brand partners, hear me: what you hire is not content, it is trust. And trust needs proof daily. I will give you that proof until the machine learns what I refuse to teach it.”

This passionate declaration encapsulates the hysteric (in the structural sense) reaction of a creator confronting automation. The creator insists on their unique voice (“not a dataset”) and refuses to let AI exploit their work without credit (“I won’t be an unpaid prompt library” – a vivid phrase meaning AI is trained on her content but she gets no compensation). She vows to outpace the algorithm by sheer volume and uniqueness: posting “twice as much” if buried, becoming so idiosyncratic that the AI “chokes” because it can’t replicate the personal quirks (“specific it chokes on the details”). This is essentially a panic escalation strategy – double down on surplus-enjoyment (authentic connection, relentless presence) to try to remain indispensable. She appeals to brand partners that what she offers is “trust” – something she claims a machine can’t provide – and that she’ll prove it day in and out, implying the machine can’t keep up with genuine daily engagement.

There’s defiance but also poignancy here: she’s determined to not teach the machine her secrets (“what I refuse to teach it”). It echoes earlier waves where women sometimes withheld knowledge when they feared mechanization (like male artisans not wanting to share tricks with industrialists, etc.).

Yet we, as analysts, might suspect that her approach, while heroic, could indeed feed the AI more data (if she posts more, the AI gets more to train on), unless strong protections exist. This is precisely the reductio cycle: the harder creators work to stand out, the more they collectively flood the space, giving AI more patterns to learn and making authentic content less distinguishable in the noise – potentially fueling the case for using AI to manage that noise.

Smarter pivot in Wave 4: For creators and others, the advice (as gleaned from the sources’ strategic implications) is to “productize the tacit.” That means transform personal craft into assets that can work with the new regime. For example:

• Creators can build modular IP – instead of just personal content, create a framework that can be licensed. (E.g., turn your style into a brand guide or develop a personal AI assistant that uses your tone with permission, so you gain from the scaling rather than being left behind).
• Treat your unique taste as something to encode into guardrails and QA for AI. Some forward-looking creators are already consulting for AI companies, basically teaching them what not to do to avoid losing human quality. That places the creator on the side of shaping the new rules.
• Become the human-in-the-loop that defines quality. For instance, a seasoned community manager could pivot to a role overseeing a fleet of AI moderators, setting the nuanced policies for what the AI should allow or flag – thereby embedding her empathy and sense of fairness into the system design.

In summary, the social media era (c. 2008–2020) elevated a feminized competency – curating and monetizing social attention and intimacy – to a central economic role (influencers, community leaders). But by around 2020, it was clear that attention had been maxed out (people only have 24 hours a day and were getting overwhelmed; content originality was declining as everyone copied what worked). Generative AI arrived as a new machine logic that makes the absurdity plain by generating the very attention-bait that once required relational craft – for example, AI can produce endless clickbait articles or fake social media personas that mimic influencer style, revealing how formulaic some of it had become. The game is reframed: instead of “who can keep many humans engaged,” the question shifts to “who can configure the algorithmic content pipeline.” Power accrues to those controlling data, models, and moderation policies (a space currently dominated by tech companies and a different cohort of experts, often sidelining many from the creative class).

At this juncture, the pendulum might seem to have swung fully to the rule-based side (masculine-coded) again. But history suggests that when this regime (AI-driven content) hits its own limits (perhaps misinformation, loss of meaning, user backlash at impersonal feeds), the relational, human element will become scarce and precious again – the pendulum may swing back to “authentic human connection” as a selling point. In fact, we already see a bit of that: people pay premiums for handmade goods, live experiences, or platforms that promise real community over algorithmic feeds. In corporate contexts, companies that over-automate customer service with bots often reintroduce “talk to a human” options when customers get frustrated.

So, the alternation is structural, not a one-time flip. And each time, some roles get obsoleted, others preserved in new forms.

Having analyzed the four waves, we can distill a few common themes: when relational tactics saturate, new tech formalizes them; institutions rewrite status criteria (e.g., going from valuing a good spinner to valuing a good engineer, from valuing a great community builder to valuing a great data scientist); yet the original human sensibilities don’t vanish – they get embedded in residual niches or in the guidelines of the new system, ready to be revalued when needed.

Next, we will step back and look at broader patterns and implications: What does this explain about labor shifts en masse? What doesn’t it mean (for instance, it’s not a simple women vs men win/lose)? What strategic lessons can individuals and organizations draw (hint: it involves being bilingual and proactive in translating your skills into the new paradigm)?

The Pendulum Pattern and Its Implications

What the Alternation Explains: When we look at waves 1 through 4 together, we see that entire ecosystems of labor and status change in sync with the prevailing information-processing mode. It’s not just one job here or there; multiple roles realign at once. For example, in Wave 2, as soon as tabulators and formal procedures took hold, we saw changes across the board: telephone operators declined, typists multiplied then plateaued, the “executive secretary” role diminished in autonomy while new specialist roles (like systems analyst or HR manager) rose. In Wave 3, as metrics took over, the glamour and authority of the old PR “priesthood” (the fixers, the spin-doctors, etc.) was partly replaced by the clout of data analysts and media buyers. Under social media (Wave 4’s first part), the rising star roles were influencers, community managers, content creators – fields that barely existed before and suddenly commanded budgets and attention. Now as AI ascends, we see hiring booms in machine learning engineers, prompt engineers, AI policy experts, data annotators, etc., and companies devaluing some social media roles (some firms have fired social media teams citing “we’ll use AI to generate posts” or shifting spend to AI-driven customer acquisition).

It also explains why institutions “rewrite” their status ladders. Each paradigm comes with a set of hero figures. In the cottage era, the respected figures might be the master craftswoman or the patroness who keeps the network together. In the factory era, the hero becomes the inventor, the industrialist, the engineer (often men who exemplify rule-based skill). In the social media era, the heroes were influencers and visionary community builders (think of mostly women who were touted as Instagram stars or community-oriented startup founders). In the AI era, emerging heroes are the tech innovators, the AI whisperers (often portrayed as male geniuses in media). These shifts influence young people’s aspirations: what do you dream of becoming? It changes depending on which skills are seen as lucrative and admired. This is not deterministic but strongly patterned by what the current economic engine rewards.

What the Alternation Does Not Mean: Importantly, it is not that one gender “wins” outright in these shifts. While we used feminine and masculine coding as shorthand, reality is nuanced. Many women succeed by adapting and even leading in rule-based paradigms, and many men thrive in relational roles. It’s just that the cultural narrative and aggregate opportunities tilt. In practice, teams and individuals often blend both codes. Some of the best successes come from hybrid approaches: e.g., a social media company like Pinterest succeeded in part because it merged strong engineering (masculine-coded) with an emphasis on understanding community tastes (feminine-coded sensibility). In AI, it’s increasingly clear that purely algorithmic approaches that ignore human context can backfire – so companies incorporate human-in-the-loop and UX research (bringing back relational sense). Conversely, successful influencers often systematized their content production behind the scenes – they treated it like a business with schedules, analytics, etc. So individual agents can buck the stereotype by combining modes.

The alternation is a structural pressure, not an iron destiny. It creates incentives and default paths, but humans always have some agency to respond creatively. We should avoid a simplistic “men vs women” reading; rather, see it as two symbolic orientations that anyone might embody. Historically, yes, women were channeled into one and men into the other, but that can and does change.

Strategic Lessons – “Bilingualism” in Practice: The recurring advice from this analysis is that people and organizations must learn to be bilingual in these two codes. This means:

• If you are naturally strong in the surplus-enjoyment domain (say you’re a great community builder or a creative storyteller), encase it in process when a new formal regime looms. Write it down. Make your implicit know-how explicit in some transferable way. If you are, for instance, a social media manager who just “gets” your audience, start codifying that: maybe document case studies of what worked, create guidelines that can be part of the institutional memory, develop frameworks (like “when a user complains, here’s our 5-step response playbook based on what I’ve learned”). By doing so, you “parameterize taste” – you turn vibe into variables that you still control or can at least influence. Treat things like prompts (in the AI context) as interfaces for institutional memory, not one-off magic tricks. This ensures that even if AI or other processes take over some execution, the key parameters are set by your understanding. Essentially, translate your relational craft into something a bit more formal without losing its essence, and ideally be the one to maintain that formalization. That could make you the natural product manager or policy head in the new regime rather than an outmoded specialist.
• If you are more at home in the surplus-value domain (say a data-driven operator or technical expert), remember that rules alone don’t determine what’s worth doing. The objectives and values – the why – often come from human insight and desire. So invest in sensing mechanisms: user interviews, ethnographic research, advisory councils of diverse community members, etc. Don’t fall into the trap of thinking the optimized metric is the same as success – ensure you’re capturing meaningful metrics. If you’re an AI developer, for example, push to include qualitative safety checks and human review layers in your pipeline. Recognize the limits of your procedures and invite the surplus-enjoyment folks (the community, the artists, the domain experts with tacit knowledge) to guide what the algorithms should aim for. This not only makes your solutions more robust (because purely formal systems can be brittle or misaligned), it also positions you ahead of the pendulum: when the time comes that your carefully optimized process suddenly fails to meet a real human need, you’ll have already integrated a human touch such that you can correct course.

In a corporate sense, companies that thrive long-term often alternate internally – fostering both cultures. Think of a company like Apple under Steve Jobs: it balanced extreme engineering excellence with a fanatic focus on design and user emotion. Jobs himself was a rare bilingual figure: deeply involved in technical decisions (surplus-value) yet obsessed with the user’s feel and desire (surplus-enjoyment). Not coincidentally, Apple weathered transitions (like from PC era to smartphone era) better than some, because it wasn’t all one way or the other.

Likewise, a very process-heavy company (say an old-line bank) might benefit from bringing in community engagement teams or anthropologists to inform its AI models, etc., preemptively adding the “enjoyment” side so the next swing doesn’t blindside them with a PR crisis or customer revolt.

The Structural Pendulum: It’s striking how each wave’s endgame is a reductio ad absurdum. When everyone is maximizing relational tactics (like every brand doing cheeky social media banter), it becomes noise; at that point, someone who comes in with a rationalized approach (like an AI that can generate cheeky banter for thousands of brands at once) wins – but then that floods the channels with formulaic content, and people start craving something real again. So we foresee perhaps a Wave 5 where authentic community, slow media, and human-curated experiences become premium (some early signs: Gen Z being drawn to smaller “close friends” groups online, or to analog experiences; companies touting “human-made” as a luxury mark). That could be the feminine-coded swing returning in a new guise.

Obsolescence and What Survives: When a pendulum swings, certain roles become obsolete-ized (not always fully eliminated, but devalued or hidden). It’s crucial to note, as our analysis highlighted, it’s often the institutional recognition of a kind of tacit authority that gets erased, rather than the people themselves. For example, when Wave 2 hit, it’s not that women disappeared from offices (their numbers actually increased), but the independent power they wielded (as the only ones who knew how things really worked) got encapsulated in formal dashboards and org charts, often managed by men. The interface where their craft set the tempo was captured behind an API or KPI. They still worked, but they were now part of a system that someone else orchestrated with more clout.

However, the underlying sensibilities – the actual human skills – survive and often reappear when the new order faces its limits. For instance, decades after wave 2, in the 1980s, corporations realized they needed good internal communication and culture (something those old secretaries informally provided). So fields like organizational development and corporate communications got more attention – often again with many women practitioners. Similarly, in tech today, after a rush of automation, there’s now emphasis on user experience (UX) and ethical AI, which call back on empathy, qualitative judgment, and interdisciplinary thinking – skillsets historically undervalued but now in demand. It’s like the pendulum’s arc creates scarcity of whatever was devalued, and that scarcity eventually makes it valuable again.

Counter-moves to Avoid Personal Obsolescence: Based on recurring patterns, some general strategies to stay relevant through swings emerge:

• Parameterize the Tacit: We mentioned this – convert your intuitive know-how into shareable, programmable insights. This doesn’t mean oversimplifying it; it means articulating it in a form that can interface with machines or formal processes. E.g., if you have great judgement in content moderation, develop a set of principles and case libraries that can guide AI moderation tools (so you become the source of those principles).
• Own the Interfaces: Identify where humans will still interact with the new system and try to be in charge of that. In wave 4 terms, if AI is generating content, the key interface might be prompt design and final edit. Ensure you’re the one designing prompts, setting policy (what the AI must not say), and doing final human approval. That way, your role is baked into the pipeline. Historically, when factories came, some craftsmen became inspectors or machine setters, roles that interfaced between human quality expectations and machine output. They remained valuable because they translated between worlds.
• Design for Reversibility: If you are implementing automated processes, include a “human veto” or a “manual override” in the workflow, and position yourself or your team as the ones exercising it. That means when (not if) the automation hiccups, the organization immediately turns to you as the fallback. Essentially, keep a foot in the door. For instance, a self-driving car company will still need a safety driver or at least remote monitors for a long time; being that safety operator is far better than being cut out entirely. Moreover, by maintaining some manual component, you remind everyone (and teach the AI developers) of complexities they might miss. You become the narrative explainer of automated acts, which is powerful.
• Measure What Matters (Qualitatively): Introduce metrics that capture quality, even if they’re somewhat subjective, to sit alongside quantity metrics. E.g., in social media management, add a “community health score” or “brand sentiment index” to the dashboard, not just follower count. If you’re an influencer, perhaps regularly poll your audience about trust and report that metric to sponsors, so that raw engagement isn’t the only value seen. Basically, create metrics that preserve human factors so they can’t be ignored. Some companies now have “customer love” or “trust” metrics that employees are accountable for, often measured by surveys or panels[5][11]. These are attempts to quantify the surplus-enjoyment outcome so that surplus-value systems don’t steamroll it.

Combining these moves means you proactively integrate your human craft into the formal layer before someone else’s tech does it for you in a cruder way. This is proactive pendulum riding.

Having gleaned these insights, we can now proceed to examine how these patterns not only occur in secular technological contexts, but also how they echo ancient motifs and deep cultural narratives. The next sections will connect our analysis to broader文化dimensions: how have myths and religious structures prefigured this pendulum? And how has feminism’s narrative (the “waves” of feminism) intertwined with these technological waves? We’ll explore academia’s role as a shelter for “obsolete” crafts (keeping them alive until needed again), look at mythic archetypes (like the Fates weaving destiny) that can be read as metaphors for these processes, and delve into psychoanalytic theory (Lacan, Žižek) for understanding how meaning and power get re-stitched through these transitions. This will give us a richer, multi-layered understanding of the making and breaking of womanhood.

Sheltering the Human Template: How Obsolete Skills Persist and Return

One striking observation from the four waves is that even as certain skills become devalued in the mainstream economy, they often find refuge elsewhere. Societies have institutions that act as cultural breakwaters – places that receive and preserve the “human remainder” of each technological wave, especially the feminized, tacit, relational crafts that might otherwise be lost. These include humanities academia, libraries, museums, craft guilds, conservatories, unions, religious communities, and professional associations. Far from being mere nostalgic holdouts, these shelters dignify practices that industry might call obsolete, convert them into teachable forms, and keep them alive for potential future re-use.

We can think of these institutions as maintaining “human templates.” What’s a human template? It’s essentially a way of doing things with human judgement, creativity, and empathy intact. It’s like a living blueprint of how a task was done before it was automated. This isn’t preserved in documents alone but in bodies, rituals, and communities of practice. For example, a classical music conservatory preserves how to perform music with acoustic instruments and emotional interpretation – a template that remains even when electronic or AI music comes to dominate pop culture. When tastes swing, that conservatory-trained violinist can step onto a stage or recording and bring something no machine can.

These shelters often take the form of educational and cultural institutions. Consider humanities departments in universities: they keep alive the skills of close reading, critical thinking about context, articulate writing – even if the job market doesn’t obviously reward them at a given moment. Or consider libraries and archives: they maintain knowledge of cataloguing, of careful information curation, which might seem quaint in the Google era but becomes crucial when we need to train AI on reliable data or do historical research to counter misinformation.

There’s a pattern in how shelters work:

• Pedagogic vitrification: They take techniques that are no longer widely practiced commercially and reframe them as method or art to be taught. For instance, letterpress printing became an artisanal craft taught in art schools long after digital printing took over. Students learn it not to compete with mass printing, but to understand the craft and perhaps to apply elements of it in design thinking. Similarly, rhetoric (the classic art of persuasion) might be taught in writing courses even as formulaic SEO content rules online – but those who learn real rhetoric can later craft more compelling narratives that stand out.
• Ritual continuity: Shelters often preserve the social grammar of an old practice via events and communities. Think of a university colloquium or peer review committee – it reenacts the careful deliberation that might be missing in a fast-paced corporate setting. It’s a slower ritual of validation. Or a guild craft fair where artisans share techniques – reminiscent of how knowledge was passed in pre-industrial times.
• Archival scaffolding: Museums, special collections, oral history projects – these keep the detailed protocols, tones, exceptions of how things were once done. For example, an archive of early 20th-century business letters (with all their formal courtesy and subtext) can inform modern communicators about what gets lost in an email. The knowledge is there to be tapped. Or standard manuals from before automation can guide how to design the new systems in a more human-centric way.
• Transference to adjacent fields: Sometimes if a skill becomes obsolete in one domain, it finds a niche in another where it’s still valued. For instance, stenography (shorthand writing) was largely replaced by recording devices, but the skill persisted in some legal court reporter roles and also influenced coding syntax! Librarianship evolved into information science – librarians’ taxonomies paved the way for database design (early computer scientists drew on library classification for building file systems[5]). Pastoral care and counseling (from religious communities) fed into psychology and social work professions. So, the knowledge moved laterally and survived.

Let’s illustrate by revisiting each wave and identifying a key shelter and how it functioned:

Wave 1 Shelter – Cottage Skills in Academia and Museums: After industrial textile production took over, you saw the rise of women’s colleges, domestic science programs, and craft revival movements. For example, Home Economics became a field (pioneered by Ellen Swallow Richards and others around the late 19th century) which essentially systematized the tacit knowledge of household management and textile arts into a science. They taught everything from nutrition to clothing construction, preserving “household-scale operations literacy.” Textile museums and historical societies collected spinning wheels, looms, and samples of handspun cloth. Guilds and craft schools kept techniques like handweaving alive, not just for nostalgia but also to study the difference quality that machines hadn’t captured. Etiquette manuals and salon guides (some authored by women for women) documented the “moral theater” of how to host, how to manage social obligations – knowledge that had economic value when trust was the currency.

How it returned: Many of these standards and sensibilities fed later industries. For example, the notion of quality by feel in textiles, once derided by industrialists, came back in the late 20th century with the artisanal and slow fashion movements. Also, the standards bodies that emerged in the industrial era (setting tolerances, etc.) often had contributions from those with craft background, translating their feel into tests. Even today, when doing quality control in high-end fabric, experts might do a subjective hand-feel test – essentially applying the old human template as the ultimate check.

Let’s imagine a scene in a Fiber Arts class circa 1920 at a women’s college (connecting to the sources’our content’s’ little classroom scene snippet):

In a quiet studio, an instructor places two skeins of yarn on the table. “Tell me which one would not shame the household,” she says. The students pass the skeins around, running them through their fingers, listening perhaps to the faint whisper of fiber. They must each narrate what their hands feel: the humidity in the yarn, the tightness of the twist, the evenness or slub. They discuss how one skein might sew a seam that endures, while the other might fray. The grade they receive will come from the story they can tell to justify their choice in public. This is essentially a transfer of the old “moral theater” of cottage spinning (where a spinner would defend her product’s quality to merchants or peers) into an academic critique format. The knowledge is reframed as explicit craft and ethics, and the practice of articulating it keeps it alive in a way machines couldn’t replicate.

Wave 2 Shelter – Clerical Discretion in Rhetoric and Library Science: When office automation reduced the autonomy of secretaries and operators, their core skills of triage, tone management, and exception handling were preserved in fields like Composition & Rhetoric, Business Communication programs, Library and Information Science, and even pastoral training for discretion.

For example, secretarial schools initially just taught typing/shorthand, but as that became common, some evolved into broader business communication colleges. They taught how to craft letters for different audiences – formal vs friendly tone (a direct mapping of the old skill of knowing “who gets what tone”). Library science trained mostly women in how to classify and route information – essentially capturing that “who needs what when” knowledge in a formal curriculum[5][12]. In more academic terms, rhetoric departments in universities (often tied to English) taught the art of audience analysis and persuasive adaptation – something a great secretary or PR person did intuitively.

How it returns: These skills are foundational to what we now call information architecture and UX writing. For instance, the ability to “decide who must never be surprised” – an old secretary’s ethos – is now akin to managing stakeholder communications in project management or writing different versions of a message for different user groups in UX. Those who studied communication can apply that knowledge to create modern escalation trees or personalization strategies (basically doing in systems what they did personally).

Imagine a practicum in a Rhetoric class in 1950, after WWII when many women remained in offices but in structured roles:

A student is handed a brief, tersely worded memo from an executive, with instructions to inform four different people of a certain decision – say, a budget cut. The people are: an older senior engineer (proud, sensitive), a junior assistant (eager, anxious), a key client (needs reassurance), and a branch manager (potentially defensive). The task: write four versions of the announcement, one for each, adjusting tone and emphasis appropriately. The student writes: perhaps a deferential, detailed letter for the engineer (to show respect for his expertise while breaking the bad news), a supportive, encouraging note for the junior (to frame it as a learning opportunity), a reassuring letter to the client (emphasizing commitment despite budget changes), and a frank but collegial note to the branch manager (maybe inviting input to soften the top-down blow). In class, they discuss why each audience needs a different approach. The grading rubric values discretion and adaptability – what an earlier generation did without overt praise now becomes an explicitly taught and assessed skill. Variance here is by design, not error – a big shift from the one-size form letter idea of early office rationalization. So in the shelter of education, the value of that human discernment is kept alive and passed on.

Wave 3 Shelter – PR Artistry in Cultural Studies and Arts Administration: As advertising turned more data-driven, the nuanced work of aura maintenance, crisis cooling, and taste-making was carried on in places like journalism schools (ethics and crisis communication labs), arts administration programs, cultural studies departments, and museum boards or grant panels.

For instance, journalism and PR education started teaching case studies on handling PR crises – essentially systematizing what the intuitive PR mavens did. They incorporated ethical training and stakeholder analysis. Cultural studies kept critique of representation alive – ensuring that not everything is reduced to Nielsen numbers by arguing for the importance of diverse representation and narrative (which later fed into ideas like inclusive marketing, albeit slowly). Arts administration programs taught how to balance artistic integrity and audience building, which is analogous to what a 1970s music publicist or a film festival organizer did by feel.

How it returns: We see the need for stewardship of brand and community in the age of AI and hypermedia. For instance, companies now form “trust and safety” councils and editorial boards to oversee AI content – they echo the old taste councils and PR committees but in new form. The qualitative concerns these shelters kept discussing (like “what is appropriate content, what is dignified storytelling?”) are now directly relevant to training AI (someone has to set the boundaries of an AI’s output to align with societal values – effectively a new taste council task).

Picture a capstone project in a Crisis Communications lab in 2010 (by then social media crises are a thing):

Teams of students are given a hypothetical scandal for a company – say a rumor spreads that a popular product has a dangerous flaw, but facts are unclear. Each team must devise a crisis response plan covering 10 days from initial reports to resolution. The twist: success isn’t just measured in minimizing short-term damage, but in “lowering social entropy” – i.e., how well their plan maintains trust and reduces public panic while facts are gathered. So metrics for grading might include things like “stakeholder dignity preserved” (did they manage to address concerns without throwing anyone under the bus?), or “media temperature cooled by Day 3” (did the volume of negative coverage go down due to their interventions?), or “return phone calls in week 3” (did journalists trust them enough to keep communication open in the aftermath?). These are almost whimsical metrics by traditional standards, but they quantify legacy currencies – e.g., returned phone calls signal that the PR team didn’t burn bridges by lying or stonewalling. Essentially, the class is practicing the finer points of vibe management and trust that old PR veterans knew. By formalizing them (“dignity preserved” as a metric – brilliant and qualitative), the shelter teaches the next gen to value what might otherwise be dismissed by pure numbers people. Those students may become the corporate communications directors who ensure their company invests in goodwill, not just ad impressions.

Wave 4 Shelter – Creator Culture in Academia and Community Organizations: Now, even as GenAI rises, we already see shelters forming to preserve the human elements of online creative and community work. Think of digital humanities labs, media studies workshops, creative writing and design studios, community organizing trainings, even church groups and public libraries doing digital literacy sessions. They emphasize voice, narrative, community values, moderation ethics – all the pieces of surplus-enjoyment in digital spaces.

For example, creative writing programs now sometimes include modules on “writing for new media” but stress maintaining an authentic voice. They might indirectly be teaching how to be a good prompt – or rather, how to maintain the human core such that if AI tries to copy, it falls short. Design pedagogy stresses process and human-centered design, encouraging budding designers to think beyond what algorithms can spit out. Community organizing (especially relevant in activism) trains people to use digital tools without losing personal connection and trust – teaching strategies to avoid burnout and superficial engagement. These are preserving the ethics of intimacy and boundary-setting that got blurry in the influencer era.

How it will return: If AI overshoots and creates a trust crisis or a meaning crisis (people can’t tell what’s real, or get tired of soulless content), the skills of building genuine community and speaking in truly resonant narrative will be scarce and desired. Already, companies are saying “we still need human storytellers to give brands heart” even as they adopt AI for scale. Those humans will likely be ones who have internalized both the old and new: comfortable with tech but grounded in humanistic practice. Exactly the profile of someone from a digital humanities lab or a community-based design studio.

Imagine a Narrative Systems studio class (something that might exist in a forward-looking design program) where the exercise is “From Voice to Specification.” This echoes the sources’ example of a creator reading a piece only they could make, and the class turning it into a style guide:

A YouTube creator guest-speaks to a class, presenting a short video that went viral and felt very “her.” It’s raw, personal, idiosyncratic. The class’s task: distill this human voice into a spec that an AI (or any collaborator) could follow without losing the essence. They have to create a style card: listing non-negotiables (e.g., “must use self-deprecating humor about X, never about Y”), giving negative examples (things that would feel off-brand or inauthentic for her), guardrails (topics she won’t touch, even if trending), notes on cadence (perhaps she speaks in rambling monologue then a sudden one-liner – they document that rhythm), and taboo moves (maybe she never uses profanity unless quoting someone in a story – note that). They assemble also a test set – samples of her past content – and define criteria for when an AI-generated piece “misses the point.” Essentially, they turn tacit voice into explicit spec without giving the machine the human core. The goal might be to enable scaling (she could have a team help write scripts consistent with her voice) but also to protect what makes her unique (the spec marks boundaries a machine shouldn’t cross because it wouldn’t understand the meaning). The result is like a design manual for a personality. It doesn’t capture her soul, but it fences off where a machine might go astray or violate the spirit. This way, her human template is portable and somewhat safeguarded. The class thereby enshrines editorial judgment and the concept of “you can’t fully automate this – you need a human check because some nuances are not codifiable.” The exercise implicitly teaches that we can collaborate with AI but on our terms. It’s sheltering human voice by partially encoding it – not to give away to AI, but to ensure AI serves it, and that a human curator can tell when the AI is “missing the joke.”

Risks of Shelters Ossifying vs their Value: Sometimes these shelter institutions get a bad rap for being behind the times or elitist. That’s a risk: they can become too inward-looking or resistant to beneficial change (e.g., some academic circles might fetishize an old method even when new tools could enhance it). Gatekeeping can occur – e.g., an art council might become insular, rewarding the same kind of “approved” art and shutting out truly new expressions (just as guilds historically could become cartels).

However, despite these issues, shelters provide a continuity of knowledge that can be life-saving for a culture in crisis. When a fully “proceduralized” system fails, who do we turn to? Often, it’s the people who remember how we used to do things when we had to think more or care more. E.g., when financial algorithms cause a market crash, suddenly we need economists and historians who recall similar past events and human factors. When digital news is full of deepfakes and lies, suddenly investigative journalists and librarians (who know how to verify sources) become crucial. The memory palace of analog, so to speak, is preserved by those slower institutions.

As the sources aptly put, the “political economy of shelter” relies on things like tenure lines, grants, endowments, donations. To a short-term efficiency mindset, these look like inefficiencies (“Why teach Latin poetry or weaving or analog photography in 2025?”). But it’s societal insurance – a pool of “disciplined sensibility” that isn’t optimized for immediate profit, and thus can adapt and respond when the next tech regime mis-specifies or drifts. Think of it like a backup copy of the human knowledge base, including the parts the mainstream declared deprecated.

We can draw practical bridges: The sources suggest, for instance, to let archives become datasets with consent – meaning shelters can feed the new tech, but on their terms (e.g., a museum might provide images to train AI but include context so AI doesn’t lose the meaning and ensures credit and consent). Or to have docents, editors, pastors involved in AI policy writing – bring those who steward human stories into the design of tech governance.

Ethic of Stewardship: Ultimately, shelters stand for stewardship over time. They are less about worshiping the old and more about keeping possibilities alive. For example, classical music survived the disco era not to replace pop but to be drawn on in film scores, in new experimental genres, etc. The presence of the old expanded creativity when the pendulum allowed. Similarly, teaching students to write cursive or mental math might seem redundant now, but it has cognitive benefits and can be a lifesaver if devices fail.

The sources conclude that sheltering is not nostalgia; it’s disciplined keeping of templates so that when the new machine regime “renders the present code absurd,” we still have people who remember how to “speak with one another in the key that works.” That’s a beautiful way to say: we maintain humane practices in case we need to reboot some aspect of society in more humane terms.

Now, having covered the concrete and institutional, let’s zoom out to the high-level interplay of feminist movements and technology (the superstructure and substructure). Then we will dive into more philosophical analysis with myth and psychoanalysis to see these patterns from alternative angles.

Feminist Waves Stitched to Technological Waves

We often hear about the “four waves of feminism” as cultural and political movements: roughly, (1) suffrage and legal personhood in late 19th/early 20th century, (2) workplace equality and liberation in mid-20th, (3) focus on identity, difference, and representation from late 20th, (4) #MeToo era and intersectional, networked feminism in early 21st. These are typically narrated in terms of changing social norms, laws, and feminist theory. However, there’s a deep connection between these superstructural changes and the substructural shifts in the technics of coordination we’ve been discussing.

The thesis here is that each wave of feminist progress corresponded to (and was enabled by) a significant reconfiguration in how society moves signals and goods – essentially the communication and production infrastructure. Women’s power (often coded as the capacity to manage desire and uncertainty) found its most effective outlets at exactly those junctures where a new technical substrate was being woven. Women often took on pivotal roles in the emerging communication or coordination systems, which then formed a “stitch” binding the feminist goals to actual material changes.

Let’s map wave by wave:

Wave 1: Suffrage and the Industrial-Legal Stitch (1848–1920)

Superstructure (what most know): Women fought for citizenship and legal personhood – the right to vote, own property, enter contracts, divorce, etc. The overarching theme was moving from invisibility to formal recognition as individuals in the eyes of the law and state.

Substructure (technics of coordination at the time): This period encompassed rapid industrialization and the first information regimes. Key aspects: – Factory time and standardized measures: For the first time, society ran on synchronized clocks (railway timetables, factory shifts). – National censuses and statistics: Countries began enumerating populations regularly (e.g., U.S. census expansion with punch cards by 1890[6]). – Large-scale supply chains: especially in textiles, linking households to mills to global markets. – Jacquard logic bridging to computing: The conceptual leap from weaving patterns to computing via punch cards (Babbage’s work, etc.) set the stage for thinking of people as countable units too (statistics). – Legal codification: Nations codified laws (Napoleonic Code etc.), creating uniform legal identities.

This substructure created a world where a person could be individuated and recorded as never before. A woman, previously mainly seen through family roles, could now appear as a line in a census, a wage earner in a factory ledger, a name on a contract.

The Stitch (human template connecting them): Women’s traditional skills in household and community coordination became the interface to early industrial systems: – Many suffragists themselves cut their teeth running abolitionist networks or church groups – effectively managing decentralized networks (like a political sewing circle across states – recall that the Seneca Falls convention of 1848 was organized by women using the communication means of the day: letters, pamphlets). – In industry, as we discussed, women in cottage industries and as forewomen in early mills translated moral trust into production (“we keep your promises when storms break roads”). Their role as guardians of household economy gave them moral authority when arguing for suffrage: if we can manage the nation’s future citizens at home, why not have a say in the nation’s governance? – The suffrage movement also piggybacked on the new communication infrastructures: they used telegraphs and the press to coordinate nationally. The idea of a national movement was itself reliant on railroads, telegraph, and a literate female public engaged through newspapers (many suffragists wrote for papers). – In a conceptual sense, the legal personhood claim rode on the substructural fact that now you could count and list all individuals. Without a modern bureaucratic state, the idea of each adult having one vote is impractical. But by 1900, states were assigning numbers (house numbers, eventually social security numbers) and could imagine registering every person. Women astutely tied their cause to the promise of these modern institutions: “We contribute to the census, the factories, the schools – we should count equally.”

One could say the feminized human template here was the ability to turn domestic trust into social capital. Women’s work in temperance and abolition movements (both precursors to suffrage support) showed they could spin private morality into public action – a skill highly needed in an expanding, chaotic industrial society hungry for some moral order.

Limit forcing redesign: Once women got the vote (by 1920 in U.S.), the movement had to reorganize around different lines (enter wave 2). But also, as mills matured and formal labor laws came, the tacit assurance of household spinners looked inefficient or old-fashioned to rising technocrats. The first wave’s triumph (vote, some property rights) was partly co-opted by a system that then said: “Okay you’re persons, now fit into these boxes.” E.g., some early women voters could only influence via the existing party machines run by men; gaining rights didn’t automatically equal power as the structures were still biased.

Wave 2: Equality at Work and the Corporate-Switchboard Stitch (1960s–1980, with roots in earlier clericalization 1890–1950)

Superstructure: The focus was on formal equality in the workplace and autonomy over one’s body. This includes equal pay legislation, anti-discrimination laws, breaking into male-dominated professions, as well as reproductive rights (birth control, abortion) enabling women to plan careers. The slogan “equal pay for equal work” and “women’s liberation” from domesticity epitomize this wave.

Substructure: The timeline overlaps with: – The explosion of clerical jobs and the feminization of office work (1890–1940 saw the proportion of women in clerical roles go from near zero to majority[5][24]). – Telephone networks connecting cities, which were operated largely by young women. – Time-and-motion management – making offices more factory-like with scientific management (Taylorism). – Then, by mid-20th, mainframe computers and batch processing entering big organizations (often tended by women operators initially). – Mass production of appliances – domestic labor got somewhat mechanized (washing machines, etc.), which in theory freed some time for women (though often the standards of housework rose accordingly). – The rise of the bureaucratic corporation as a dominant institution: layers of management, HR departments, etc.

This substructure meant that by the 1960s: – Huge numbers of women were in the workforce, but concentrated in low/medium-skill clerical jobs (secretary, typist, operator, teacher, nurse – the classic “pink collar ghetto”). They were literate, organizationally savvy from running offices, but barred from the boardroom. – There was an extensive information processing system in place (files, phones, forms). Women were literally the channels through which corporate and government information flowed. – The notion of efficiency and standard roles meant women faced explicit rules limiting advancement (e.g., many companies had marriage bars, forcing women to quit when married; or glass ceilings). – At the same time, the language of rights became data-driven: economists and sociologists provided stats on pay gaps, etc., giving hard evidence for equal pay claims.

The Stitch: Feminine discretion and relational work made the new office economy function. Women effectively held a lot of informal power (they knew all the information going around, as we saw) even if formal power was male. This gave the equality movement both an impetus and a proof-case: “We are already doing much of the work, just not getting the credit or pay.” – Many early equal-pay lawsuits were possible because companies had standardized pay scales – so women could point: see, we’re on grade 5 while men on grade 7 for same tasks[5][24]. The data was there in forms. – The feminist aim of breaking job segregation leaned on the fact that much clerical work was already similar to entry-level management work, just artificially separated. Women leveraged their inside knowledge of offices to show they could do more. For example, women secretaries often literally trained the men who would become their bosses. That gave confidence (and some male allies recognized it too). – Also, women’s networks within offices and across professional associations became the channels for feminist organizing. E.g., in the 1960s, groups like NOW (National Organization for Women) often had leaders who were professional women or union women who knew how to run meetings, draft memos – the skills of the office turned to activism.

In short, the feminized human template at play was the master coordinator/communicator: the one who routes messages, softens tone, keeps operations smooth. As Wave 2 pushed for equality, it basically said: those skills are not inferior, they are managerial. Women fought to be recognized as equally capable of managing the formal systems they had been informally running.

And indeed, after some battles, we did see by late Wave 2 more women entering middle management, law, medicine, etc. But just as they did, the limit came: by the 1980s, processes were so formalized (and computers introduced for things like scheduling, data) that the archetypal secretary’s tacit role shrank. Some say the 80s killed the traditional secretarial role (word processors did typing, email did scheduling to some extent), leading either to “admin assistants” with more specialization or just everyone typing their own letters. So, the female-dominated role once crucial lost stature (fewer career secretaries; it became often entry-level only). Women then had to move into either professional roles or lower service roles.

Wave 2 achieved legal equality on paper, but as the structures further formalized (and globalized, etc.), new inequalities emerged (the pay gap persisted, often in more hidden ways like glass ceilings rather than explicit bars).

Wave 3: Difference/Identity and the Broadcast-Brand Stitch (1990–2005)

Superstructure: In this wave, feminism turned attention to differences among women (intersectionality), identity representation, and sexual agency. It questioned the earlier wave’s assumption that women should just be treated like men; instead it celebrated diversity of women’s experiences. Key themes: inclusion of women of color, LGBTQ; critique of how women are represented in media; emphasis on “the personal is political” extended to sexuality (e.g., reclaiming sexuality, sex-positivity). Also addressing workplace dynamics beyond just entry – like harassment (early seeds of #MeToo), glass ceiling, etc., but through lens of structural power and identity.

Substructure: The late 20th century was dominated by broadcast media and consumer culture: – Television had become ubiquitous (3 networks -> cable explosion by 90s). – Advertising & PR industry matured: using psychological research to target audiences (Nielsen ratings to micro demographics). – Procurement and globalization in corporate culture: companies merged, marketing budgets were scrutinized by finance (as we discussed). – Also, the early Internet and niche media appeared in the 90s (Usenet, zines, blogs by early 2000s, though not mainstream yet). – CRM (Customer Relationship Management) systems and data mining started in late 90s – brands started segmenting markets into fine identities.

This substructure allowed: – People to see and critique their portrayal in mass media. Women of color, for instance, could point to how rarely or stereotypically they appear on TV. There’s data on it, etc. – The concept of market segments gave a strange assist to representation: advertisers recognized e.g. black consumers as a market -> some media tailored to them -> opened space for diverse representation (though often tokenized). – Feminist ideas spread through new communication channels: e.g., via music (riot grrrl punk scene, etc.), independent films, etc., that had to use broadcast but also grassroots distribution (like college radio, local cable, early web forums). – The corporate emphasis on identity-based marketing ironically aligned with feminist calls for respecting difference: companies started doing things like making products “for women” (sometimes pandering, like pink packaging; but sometimes helpful, like acknowledging women athletes). Feminists could leverage that – e.g., push for more women’s sports coverage by arguing there’s a female audience, etc.

The Stitch: Feminized taste and backstage care work (from wave 3 content: publicists, brand “whisperers,” community organizers) basically operationalized identity into something actionable. – Think of a female editor of a women’s magazine in the 90s: she balances advertiser demands, reader desires, and feminist criticism of media. That role is a stitch between cultural discourse and industry. Many such editors advanced nuanced conversations about women’s issues within a commercial platform (some progress, some co-option). – Community organizers in AIDS activism or environmental justice (many were women of color in 90s) brought intersectional perspectives into mainstream policy by carefully managing alliances and media portrayal. Their relational skills at scale provided a template for handling difference. – Corporate “diversity officers” – a role that emerged in late 90s – were often women or minority folks who had to formally implement inclusion (the institutionalization of wave 3). They drew on both lived experience and corporate process, effectively translating identity politics into HR protocols. – On the arts side, women in PR or arts councils ensured that new voices got a platform, quietly shifting taste by leveraging their positions (like a female showrunner who made sure to hire diverse writers – using her soft power within a network).

So the human template was a sort of taste council plus crisis mediator: Women who managed affect and representation at scale. They choreographed who gets to speak and how repair is done when offense occurs (like early “cancel culture” incidents resolved by a skilled PR apology that a woman PR exec might craft).

Limit forcing next redesign: However, as we saw, by mid-2000s, ritual without measurement looked like overhead. The push for ROI cut budgets for exactly those relational PR/HR efforts just as wave 4’s social media was rising to fill the authenticity void. So wave 3’s institutional gains (e.g., many companies had diversity programs, but 2008 recession slashed a lot of them) were fragile.

Wave 4: Consent and Network Accountability and the Platform-Policy Stitch (2012–present)

Superstructure: Marked by #MeToo, online feminist mobilizations, focus on sexual consent, harassment, safety, and power abuses in networked spaces. It’s about exposing private harms (like workplace harassment, sexual assault) as public issues via networked accountability (Twitter call-outs, etc.). Also about pushing platforms to govern content (takedown revenge porn, ban abusive users). Intersectionality gets more practical application: recognizing how race, class, trans identity etc. factor into safety (e.g., movements like #SayHerName for black women victims).

Substructure: The era of social media platforms, smartphones, and now early AI policing: – Mobile phones + social networks gave individuals broadcast power (e.g., Tarana Burke’s “Me Too” concept went viral when amplified by celebrities on Twitter in 2017). – Platforms deployed moderation tools (report buttons, content flags) and built Trust & Safety teams (often with many women, interestingly, because it’s emotional labor dealing with content). – The notion of evidentiary affordance: you can screenshot DMs, have text message proof of harassment, etc., making exposing abuse easier (digital footprints). – Organizations from companies to universities started instituting explicit consent policies, anti-harassment training – formal rules where informal culture used to dominate. – “Platform governance” became a field: writing policy for billions of users (often trying to encode norms like no hate, no sexual exploitation, etc.).

The Stitch: Feminized parasocial finesse and boundary-setting became the ground truth for platform rules. – Many key figures in Trust & Safety at major platforms have backgrounds in fields like social work, law, or community moderation (lots of women). They effectively took the intuitive sense of “what is disempowering or harmful in a community” and turned it into policy language: e.g., definitions of sexual harassment, guidelines for removing non-consensual intimate imagery. – Activists (often women) provided the moral insights that then got codified. E.g., the concept of “enthusiastic consent” around sex, pushed by feminist activists, filtered into university conduct codes and yes means yes laws – a direct stitch of a feminist idea into formal law. – The #MeToo movement often relied on networks of whisper and care: one woman encourages another to speak up, survivors offering support groups. These networks then leveraged platform megaphones to compel institutions (like a film studio or news org) to take action. So you see a layering: personal courage + social support + platform sharing + institutional response. – Women journalists and lawyers played key roles in investigating and litigating harassment, again converting personal testimonies into systemic change (stitching individual pain to structural reform). – On the platform side, community guidelines (the long rulebooks for Facebook, Twitter etc.) have sections that basically reflect feminist arguments: like what counts as sexual exploitation or hate speech targeting gender. Those were drafted by internal policy teams often informed by feminist NGOs or academics as consultants.

Limit forcing next redesign: Now we’re mid-Wave 4: novelty and “authenticity” on social media are getting commodified by AI. Overexposure (everyone airing trauma as content) can cause fatigue or backlash (“performative activism” complaints). Spam, deepfakes, and harassment have evolved in response to policies (e.g., coordinated misogynist attacks gaming the system). So possibly wave 5 will need to reintroduce symbolic infrastructure, as the sources after wave 4 suggests: meaning slower, deeper spaces (something akin to older institutions but reimagined).

Mapping superstructures to substructures more succinctly:

Wave 1) Suffrage
Superstructural Goal: Legal personhood, civil capacity (vote, contracts)
Dominant Infrastructure: Early industrialization, time discipline, national registries, links from textiles to accounting (Jacquard logic)
Feminine Power in Stitching: Household coordination, moral theater of trust (securing promises, feeling quality)
What Broke: Tacit local trust didn’t fully scale (measurement devices outperformed ceremony)
Approaching Redesign (male-coded tech): Inspection standards, factory bureaucracy (run by men)

Wave 2) Equality
Superstructural Goal: Antidiscrimination, equal pay, autonomy (labor and body)
Dominant Infrastructure: Telephone switchboards, forms, tabulators, mainframes, scientific office management
Feminine Power in Stitching: Discretion and triage in information flows (‘knowing who needs what when’, adjusting tone)
What Broke: Variance and personality seen as risk; bosses wanted interchangeable workers
Approaching Redesign (male-coded tech): Process engineering, computerization, audit trails (rule-based systems)

Wave 3) Difference
Superstructural Goal: Representation, intersectionality, sexual subjectivation
Dominant Infrastructure: Mass media publishing, PR networks, ratings systems, supply-driven marketing, early internet niches
Feminine Power in Stitching: Curation of pleasure, aura maintenance, crisis PR, backstage care for diverse talents/audiences
What Broke: Image rituals without measurable growth deemed waste; finance demanded proof
Approaching Redesign (male-coded tech): Data-driven segmentation, A/B testing, ROI-focused brand management (algorithmic ad buys)

Wave 4) Consent
Superstructural Goal: Making harassment visible, consent norms, network-level accountability
Dominant Infrastructure: Platforms, social networks, moderation tools, #MeToo-style virality, emergence of generative AI
Feminine Power in Stitching: Parasocial intimacy skills, community moderation ethics, capacity to contextualize harm
What Broke: Spam/authenticity mimicry, scale of abuse beyond human capacity, fatigue
Approaching Redesign (male-coded tech): AI-driven moderation, content production templates, policy stacks (programmatic governance)

The key takeaway: what looked like purely cultural movements were riding on concrete changes in how society communicates and organizes. Feminists often seized new tech or infrastructure to amplify their message (like suffragists with the press, 2nd wave with TV appearances, 3rd wave with zines and early internet, 4th wave with social media). Conversely, each new infrastructure needed the insights of feminized sensibilities to handle the human fallout (like needing consent frameworks for the new social connectivity of wave 4).

So technology and feminism have been co-evolving, each wave of feminist progress hard-wired into the new machines of social order at the time – an insight that might be missed if we only read history as ideology or only as gadgetry. Recognizing this interplay helps ensure future tech is designed with gender justice in mind from the start, not as an afterthought.

Next, we will enrich this with a surprising perspective: that these patterns were almost prefigured in mythic archetypes of women as spinners of fate and keepers of measure. It sounds poetic, but there’s a real structural resonance. After that, we’ll use Lacanian psychoanalytic theory to dissect how each tech regime “quilts” meaning (or fails to) and how feminine mediation is “cancelled” or returns in that lens. These perspectives will add depth to our understanding of the symbolic drama behind the making and breaking of womanhood.

Mythic Parallels: Weaving Fate and the Technics of Thread

Throughout history, across many cultures, the work of spinning, weaving, and measuring thread has been personified by female figures with cosmic significance. The Greek Moirai (Fates) – Clotho, Lachesis, Atropos – and the Norse Norns (often named Urd, Verdandi, Skuld) are prime examples. They weren’t just seamstresses; they metaphorically governed the world’s order by spinning the thread of life, measuring its length, and cutting it to end a life. These myths symbolically encode how societies bind uncertainty (the unknown future, the span of life) into some order (destiny, the “fabric” of reality). It’s fascinating that they assign this to women’s work of thread-making.

The recurring triad of tasks – spin, measure, cut – maps uncannily onto the fundamental challenges in any information or coordination system: 1. Generation (Spin): Bringing something into existence, individuating a new line (like creating a new data record or a new idea). 2. Allocation (Measure): Determining how much, assigning length or portion, maintaining tension evenly (like allocating resources, scheduling time – essentially standardizing and organizing). 3. Termination (Cut): Enforcing an ending or boundary, irreversibility (like closing a process, delivering a verdict, implementing a kill-switch for a harmful operation).

In technological terms: – Spin equates to input and onboarding – adding a new person to a ledger, a new user account, generating new data. – Measure equates to all our metrics, schemas, and protocols – making things comparable and organized. – Cut equates to actuating decisions, enforcing rules – shutting down something when criteria are met (or not met).

And indeed, as we described, women historically have been at junctures of all these: – Spinsters (original meaning): literally women who spin yarn, giving material form to wool or flax – economically critical in wave 1. Symbolically, they gave coherent identity to raw material (like Clotho giving you your lifeline). – Measurers: women in clerical roles measured out time and consistency (like Lachesis allotting fate length). Think of a secretary scheduling each day, or a teacher grading and thereby measuring progress – tasks aligning with Lachesis’ principle. – Cutters: It’s often fallen to women (especially in moral/community roles) to say “This stops here.” In myth, Atropos’s cut is death (the ultimate enforcement). In social reality, consider a mother imposing a boundary in a community, or modern female leaders in Trust & Safety deciding to ban a user (a digital “cut”). Or even the #MeToo calls that ended careers of abusers – women collectively playing Atropos, cutting the thread of tolerance for that behavior.

We can overlay each wave with myth: – Wave 1 suffragists often evoked Clotho’s individuation: they wanted women counted as individuals in the civic tapestry. They practically used the new spindles of data (censuses, etc.). There’s a reason early feminists were into temperance and social work: they were trying to save individual threads of society (helping each woman/child). – Wave 2 was very Lachesis: it fought for equal measure – the same length of opportunity, and it thrived amid forms and schedules. Women became the ones who literally measured out the office day (secretaries ringing bells, or logging hours) yet wanted equality in those measures (equal pay for equal time). – Wave 3 had an Athena vs Arachne aspect (a myth where a mortal woman weaver challenges the goddess Athena). This was about pattern: who gets to decide the patterns that appear in the tapestry of culture (representation). Women critics said, “the tapestry (media) has only male-pleasing figures; we can weave our own story.” Athena was patron of weaving and also of strategic war – an apt symbol for women in media battles. Arachne’s audacity to weave the gods’ scandals got her punished, which parallels how women who exposed the truth often got backlash, yet changed the narrative in doing so. – Wave 4 clearly resonates with Atropos’s irreversibility (cut) and Ariadne’s thread (traceability). The notion of leaving a trail of evidence (digital logs) is Ariadne’s approach to escaping a labyrinth of denial. #MeToo was successful partly because victims had receipts (text logs, multiple corroborations – a thread through the labyrinth of “he said, she said”). Atropos is seen in the act of deplatforming harassers or saying “no more, you’re fired.” The Valkyrie-like role some women took in curating safe communities (“taking the slain” in a sense of removing bad actors). – Also, wave 4’s push for consent is essentially about installing a cut that was missing: drawing a clear line (lack of consent = stop) in sexuality and in online behavior. That’s Atropos work – enforce boundary where previously things were fudged.

The sources even suggest a concordance table:

1) Clotho/Urd (spin)
Concrete act: Make singular thread (birth)
Tech analogue: Intake & individuation (census, user signup, data creation)
Feminine-coded competence: Onboarding, quality-by-feel, ‘making persons countable & personable.’

2) Lachesis/Verdandi (measure)
Concrete act: Allot length, keep tension
Tech analogue: Metrics, scheduling, workflow routing
Feminine-coded competence: Discretion in applying rules, triage, knowing exceptions (who gets priority).

3) Atropos/Skuld (cut)
Concrete act: End life/thread
Tech analogue: Kill-switch, ban, final decision enforcement
Feminine-coded competence: Boundary-setting, saying no, decisive judgement when harm accumulates.

4) Athena–Arachne (pattern)
Concrete act: Weave images into tapestry
Tech analogue: Format design, editorial standards, brand narrative
Feminine-coded competence: Taste judgment, content curation, balancing expression vs. rule.

5) Ariadne’s thread
Concrete act: Trace through labyrinth
Tech analogue: Audit trail, reproducibility
Feminine-coded competence: Documentation, ensuring processes can be followed backward to truth.

6) Norns carving runes
Concrete act: Inscribe fate into tree
Tech analogue: Logging events into system, writing code/policy that shapes outcomes
Feminine-coded competence: Translating complex situations into interpretable records or rules (like the secretaries who turned chaotic info into neat minutes).

We see these correspondences: For instance, the Norns watering Yggdrasil daily[25] to keep it alive parallels how women in maintenance roles (like librarians, nurses, caretakers of systems) quietly sustain the infrastructure so it doesn’t rot. The myth says the water makes everything as white as eggshell if it reaches the well, implying purification – akin to maintenance tasks that prevent corruption in systems (like cleaning data or community moderation preventing culture rot). Women often hold those thankless maintenance roles (archivists, moderators) – modern Norns watering the tree of civilization.

Why do these myths cluster around weaving? As the sources note: 1. Parallelism without chaos: weaving manages many threads at once in an organized interlacing – just like any complex concurrent system. 2. Constraint with expression: The loom’s fixed rules (warp/weft) still allow infinite patterns – like protocols enabling creativity (we see that in code frameworks or legal frameworks enabling personal freedom). 3. Irreversibility with repair: A tapestry, once woven, is hard to alter without leaving marks, like records in a database. Penelope’s nightly unweaving in the Odyssey to delay suitors is effectively a hack – a manual reversible process to stall fate.

Thus, weaving is an excellent analogy for computing and social systems, and it’s no accident that the Jacquard loom directly inspired the early computers[26][27]. So the ancients intuited that those who weave (women) symbolically control the logic of the universe’s “program.”

Finally, myth clarifies the feminist ‘stitch’: these waves succeed when a human template sits at the join of desire and rule – exactly the position of the Fates relative to Zeus’s order. The Fates were independent of even Zeus in some accounts[28]; similarly, women’s tacit roles often persisted despite formal hierarchies (e.g., a king needed his mother’s counsel). But when new tech came, that counsel often got codified (the letter vs the whisper).

Yet, myth also warns that canceling the human mediator entirely is hubris. For example, the “there is no big Other” idea in Lacan/Žižek (God is dead, etc.) can be paralleled to the fate that once removed the divine guarantee, humans had to assume those roles – often women did in local contexts.

We also have mini parables mentioned: – Penelope: She delays choosing a new husband by weaving a shroud by day and secretly unraveling it at night, saying she can’t remarry until it’s done. That’s queue management under pressure – like stalling to buy time until a better solution (Odysseus returns). It’s akin to women in wave 4 holding off judgement (like “let’s not cancel him until more facts, let’s slow this down”). – Ariadne: She gives Theseus a thread so he can find his way out after killing the Minotaur. That’s absolutely a metaphor for reproducible processes – leaving an audit trail. In modern terms, think of a whistleblower leaving evidence that leads investigators through a corporate labyrinth. – Athena vs Arachne: It’s about who gets to depict reality (Arachne weaves gods’ misdeeds, Athena punishes her by turning her into a spider to weave forever silently). It’s like women journalists or creators exposing truths vs being silenced. But in long run, Arachne (spider) still weaves – truth finds a way in the tapestry. This can symbolize how feminist perspectives entered mainstream after initial backlash. – Norns vs big Other: in myth, even gods fear the Fates’ decree – suggesting the fundamental importance of these weaving tasks beyond any one authority. In structural terms, it’s like saying no master (no Big Other) can fully control the emergent complexity of life – you always need those who attend to the Real (raw human conditions) and Symbolize them (spin measure cut appropriately).

So weaving myths are more than quaint – they eerily map onto data structures and control loops of our times. Reading feminism and tech through myth yields an appreciation that what appear as new conflicts (humans vs algorithm) are echoing archetypal tensions (free will vs destiny, care vs control).

Given this resonance, the final sections can delve into psychoanalytic discourse (Lacan) to analyze the ‘quilting points’ and ‘cancellation of the human’ in our transitions, and a bit of Žižek’s “Christian Atheism” idea tying to how these patterns needed a specific cultural backdrop (the West’s internalization of no ultimate guarantor – which left a gap that women’s mediation filled until machines tried to).

Thus, we move from mythic to psychoanalytic to complete the deep theoretical picture of the making and breaking of womanhood.

Psychoanalytic Lens: Knotting, Quilting, and the “Cancellation” of the Mediatrix

To add yet another layer of understanding, we turn to Lacanian psychoanalysis, which provides a vocabulary for how meaning (the Symbolic), images (the Imaginary), and raw reality (the Real) are intertwined (knotted) – and what happens when their connection slips. Jacques Lacan often used structural metaphors (some drawn from math/cybernetics) to explain subjective and social phenomena. Surprisingly, his concepts map provocatively onto our cycles of media technology and the shifting role of feminine-coded mediation.

Knotting (R.S.I.): Lacan’s Borromean knot links the Real, Symbolic, Imaginary such that if one ring is cut, the other two fall apart. In our terms, think of: – The Real as the messy, complex, unspeakable truths of human life (desires, suffering, material conditions). – The Symbolic as the order of rules, language, and formal structures that attempt to make sense of or govern reality. – The Imaginary as images, ideals, the realm of perception and personal identification (like how we imagine ourselves and others, including mediated images).

Technologies and institutional systems routinely re-knot these orders in new ways: – Standardizing signs (Symbolic tightening) – e.g., formal policies “quilt” meaning by fixing how terms are used. – Creating new Imaginary templates (like media images of “ideal woman” or “cool tech worker”). – Biting into the Real by constraints (like new laws actually punishing certain acts, which affects real behavior).

Each wave saw such re-knotting: – Wave 1: legal personhood gave women a Symbolic place (name on registry), altering the Imaginary (public image of women as citizens) and Real (they act/vote, changing outcomes). – Wave 2: the corporate discourse (Symbolic) changed – e.g., “woman” became a category in HR files, meaning you can count discrimination. The Imaginary big Other (like the Public or the Company as paternal figure) was challenged – “no big Other” in sense that women realized the law wasn’t an infallible patriarch; they had to push change. – Wave 3: The point de capiton (“quilting point”) concept – a master signifier that pins down meaning – can be seen in things like how “diversity” became a buzzword that fixed certain drifting ideas about inclusion into a corporate policy. It gave lip-service stability, but potentially emptied content. – Wave 4: In the extreme network, “the letter always arrives at its destination” – Lacan’s aphorism meaning code finds its path – is literal: messages propagate via algorithm beyond human intention. E.g., a private story (#MeToo posts) ends up arriving where it triggers change (it found destination: public reckoning), not because a person decided the route, but the network logic did.

Quilting point (point de capiton): Lacan described how a floating field of meanings gets anchored by a signifier that stops the drift and “quilt” them into a pattern. Each tech regime installed new quilting points: – In wave 1, perhaps “the census” or “the vote” acted as quilting points – pinning that all these chaotic voices = The People’s voice (with women included by 1920). – In wave 2, the job description and pay grade became a quilting point – one’s identity and value got tied to one’s slot in a scheme (a Symbolic pin), replacing older fuzzy merit concept. – In wave 3, brand metrics (Nielsen rating, ROI) served as quilting points: they fixed “success” to a number, around which narrative had to conform (Imaginary public opinion was claimed to mirror Nielsen’s data, etc.). – In wave 4, platform policies and AI algorithms are quilting points: they pre-define acceptable speech (fix meaning in advance, as Lacan’s theory suggests – if you control code, you partly control discourse). E.g., content guidelines pin down what is “hate” vs “not hate” – no matter the context (sometimes leading to absurd outcomes, because no big Other is truly there to guarantee meaning absolutely; these points can be arbitrary or harmful).

Whenever a new quilting point is introduced, certain human mediators become less necessary or even problematic (from the system’s view): – If “the letter (code) always arrives”, you don’t need a secretary to interpret or route (the human messenger is cancelled – as happened with phone operators and secretaries being automated away). – If “there is no big Other”, meaning we publicly accept no ultimate authority to justify oppression, then the traditional role of women as upholders of moral big Other (like church ladies or moral guardians) changes – they either become conscious arbiters of values (feminist ethicists stepping up because no external authority will) or some despair sets in (“hysteric” questioning “what do you want from me, society?” when roles change). – Lacan’s “Woman does not exist” (meaning no single essence of Woman) correlates to wave 3’s diversification: women are recognized as plural – but then capitalism turns that into profiles (“segment X, segment Y”) which tech can manipulate (like personas in marketing, which ironically sometimes flatten women again – now as data categories[12]). – “No sexual relation” – Lacan’s idea that the sexes’ desires fundamentally misalign, no formula or proportion can make them fully complementary. Wave 4’s explosion of discussing consent, harassment, incel phenomena, etc., reflect an acute awareness that the between-sex (and within sex) relations aren’t naturally harmonious – the old narrative (Imaginary of romantic complementarity) broke down. Positive psychology or “soulmate” myths (like Hollywood reconciliation endings) were like band-aids – now data shows rising loneliness, etc. The technical systems (Tinder algorithms, porn algorithms) try to paper over or exploit the lack-of-relation, but often make it starker (the Real of division reasserts). – “University discourse collapses into automated S2”: Lacan spoke of four discourses (Master, University, Hysteric, Analyst). The University discourse is knowledge (S2) speaking as authority to shape subjects. Arguably, AI is like pure S2 without a subject – a database talking. So our current knowledge institutions (academia, media) lose authority (big Other dead), people turn to AI or to raw hysteric discourse on social media (“Why is the system like this?!” posts go viral – the hysteric questioning the Master’s competence). A bit heavy, but e.g., wave 4 saw experts (like journalists, scientists) often dethroned in public opinion; algorithmic aggregation replaced the “subject-supposed-to-know.”

Cancellation of the human mediator: Each technological quilting “cancels” the need for certain human go-betweens: – The telephone dial cancelled the operator. – The HR system and job evaluation forms cancelled the informal mentor/mediator who might have advocated individually. – Programmatic ads cancelled long boozy lunches between ad execs and media – now a dashboard decides where ads go. – Social media “self-serve” content cancelled a lot of editorial gatekeeping (which had been done by many women in publishing; now creators directly publish – good in empowerment, but it bypassed certain quality or support roles).

But as Lacan might note, the letter arriving without a subject can produce nonsense or disaster because no one is accountable for meaning. E.g., automated content can deliver harmful outcomes (Facebook’s algorithms spreading hate, no “big Other” regulating). That’s where we see a return of need for mediators (human content moderators – the job ironically often outsourced to low-paid, psychologically brutal work, many in global south – a hidden feminine-coded labor: digital cleaning staff).

Finally, Lacan’s notion of the hysteric’s discourse is relevant: the hysteric (often coded as feminine position) challenges the Master by being the symptom that doesn’t fit his theory, effectively saying “I am not what you say I am; what do you want from me?” Each feminist wave had a hysterical dimension: women’s very existence and dissatisfaction posed a question to the male-coded symbolic order (“You say women are X, but we are proving otherwise – so something’s wrong with your narrative”). This discourse can spur change or, if ignored, manifest as social symptoms (like mass strikes, or online “MeToo” outpourings forcing a reckoning – the hysteric forcing the master to account for what his rules can’t name, e.g., pervasive harassment that “officially” wasn’t there because people didn’t speak).

Žižek (who merges Lacan with Hegel/Marx) might say that each wave’s success eventually generates the knowledge that absorbs them: e.g., hysterics’ demands lead to codified psychology/HR policies that “treat” them and reintegrate them – until a new hysteric break occurs at the next limit.

Thus, psychoanalysis gives us an abstract model of why these mediations get “written out” by new machine regimes: because the system tries to eliminate the unpredictable subject (the feminine mediator) to achieve a closed loop of control. But “there is no sexual relation” – meaning the human factor (desire/lack) returns to haunt any closed system. So the feminine craft returns as the needed symptom to patch the system’s failures (e.g., content moderators to handle AI’s misfires, etc.).

In simpler terms, these theories reinforce that: – It’s dangerous to cut out human mediation entirely; meaning can get unmoored (an AI might spew toxic content because no subjective judgement was in the loop). – Women’s traditional positions were often those that the system couldn’t account for but needed (the hysterical position; not hysterical as in crazy, but as in questioning the Master). When they are suppressed or automated, the question re-emerges elsewhere (the pendulum). – The goal should be to move to what Lacan calls the Analyst’s discourse where the structure itself (the system) listens to the symptoms (the human feedback) and changes – analogous to designing reversible and human-informed systems as we advised.

Now, the sources also introduced Christian Atheism (Žižek’s concept) as a “loom” – basically the idea that Western culture operates in the shadow of a dead big Other (God is dead but the institutions run on the remnants). Žižek sees Christianity’s core insight as God’s self-emptying (kenosis) – God effectively saying “I’m not pulling the strings from above; you have to work it out among yourselves.” This created the space for secular procedures (like scientific laws, bureaucracies) without a guarantee of ultimate meaning. Women often stepped into the gap to provide communal glue (as we saw). So “Christian atheism” might be the background condition that allowed these cycles: a culture already primed to alternate between searching for a guarantor and realizing there is none, thus oscillating between over-formalization and rediscovering the need for human sense-making.

Žižek ironically aligns Christian legacy with atheism: in the Cross, God loses symbolic big Other status (“Father, why have you forsaken me?”[29]) – after that, meaning is organized without any transcendent guarantee. That opened the door to, say, rational bureaucracies (no divine right of kings – just laws). But it also meant a gap of meaning that someone had to fill so society didn’t fall apart. Historically, women’s relational labor often filled that gap (church groups, moral societies, teacher roles).

So one could argue: these pendulum swings could only happen in a culture already used to externalizing authority into symbolic systems (letters, laws) and questioning any ultimate guarantor (so willing to automate). Other cultures with different metaphysics might resist certain mechanizations or hold onto different balances.

In sum, psychoanalytic and philosophical frames reaffirm our narrative in a more abstract way: whenever a system tries to “cancel the human” element by totalizing formalization, it is chasing an impossible full harmony (the “sexual relation” that doesn’t exist). Reality (the qualitative, the leftover) will bite back, necessitating a return of something that was devalued.

This underscores why bilingualism and humility about systems are crucial. It also suggests deeper reasons why women’s mediated roles keep recurring: they represent the irrational kernel the rational systems can’t digest (Lacan’s “Woman is a symptom of man” – meaning in patriarchal order women often carried the contradictions of men’s world). When the system overshoots, that kernel (like the hysteric’s question) forces readjustment.

Finally, tying back to myth: the Fates are beyond even Zeus – similarly, the human (feminine-coded) template persists beyond each attempt to fully subordinate it. It’s the “fate” of these pendulums.

We have thus woven a dense tapestry from concrete historical analysis through mythic archetype to psychoanalytic theory. All threads converge on a point: that womanhood (as symbolic code) has been repeatedly “made” central to new systems and then “broken” (or transformed) by those systems’ next evolution – yet the underlying human competencies re-emerge in new guise.

The grand strategic lesson is that progress lies not in choosing one side (desire vs rule) but in acknowledging the pendulum and preparing for the swing: by preserving human core in our rules and by tempering our human crafts with some formal share-ability. Essentially, to ensure the fabric of society stays intact, we must keep both the warp of procedures and the weft of human desire in play – interlaced, like a well-woven textile.

And perhaps that is the enduring “womanly” wisdom that needs to be integrated into the supposedly masculine domain of tech: the insight that no map fully captures the territory (the Real), no code runs without context – hence, one must always allow for care, flexibility, and meaning beyond the machine. In embracing that duality, we might finally step off the pendulum and design systems that empower without dehumanizing – fulfilling the hopes of each feminist wave without triggering the backslide of the next.

From ancient myth to modern automation, the story of “womanhood” has been a story of spinning and re-spinning the fabric of human society – and of a pendulum that swings between human desire and mechanical order [28][1]. Across history, women have often been cast (literally and symbolically) as weavers, spinners, and menders of the social fabric. They cultivated the subtle, tacit arts of keeping communities intact: sensing unspoken needs, mediating conflicts, narrating meanings, and softly enforcing boundaries. These skills – call them the “spinning” of human bonds – became vital whenever new technology flooded society with more signals or speed than its old institutions could handle. Again and again, at the edge of chaos, feminine-coded craft has stepped in to bind uncertainty into order, only to see a new, masculine-coded machine logic rise up to formalize and replace that very craft. The result is a recurring drama in which womanhood (as a set of symbolic competencies) is elevated, exhausted, and then “broken” or made invisible by the next wave of technopower, even though the underlying human strengths eventually return in new forms when the machines reach their limits.

Second Stitch

We will recount the four waves from the beginning, because the backbone of SecondStitch makes the recurring Structural Pendulum demonstrable only when we render visible the function-shifts at each wave’s threshold: in every cycle, a feminine-coded mediation establishes order at the brink of chaos; then a masculine-coded formalization codifies it and renders it invisible; yet tacit sensibilities survive and return at the next saturation point. Retracing this chain wave by wave lifts the general claim out of anecdote and anchors it in structure; it allows us to read the recoil toward ‘human connection’ that grows scarce when today’s AI-centered regime hits its limits, and it prepares the reader for the pattern-pendulum and institutional-shelter analyses to which we will turn in the sections that follow.

In the second stitch of this comprehensive report, we will explore this drama through multiple lenses:

• Historical Waves: We identify four major waves (spanning the Industrial Revolution to the present AI revolution) in which a feminized mode of mediation dominated, then was supplanted by a masculinized mode of formalization. In each wave, we’ll see how women’s tacit labor held sway in the new media or information systems of the time – and how a subsequent technological shift pushed a more rule-bound, “automated” regime that marginalized those women even as it often codified their contributions in a mechanized way. Each wave will be illustrated with concrete examples (and occasional imagined voices from the era) to capture both the structural changes and the human experience of them.
• Patterns and Pendulums: We then analyze why this pendulum keeps swinging. What inherent “absurdity” or saturation in one mode triggers the next? What does each mode excel at and where does it falter? We’ll find that whenever society’s dominant way of processing information – whether through informal networks or strict algorithms – hits a complexity wall, the other mode (the one devalued at the time) resurges to fill the breach. It’s a structural alternation, not a linear evolution.
• Institutional “Shelters” and Continuity: Crucially, we’ll examine how the skills and knowledge of the “obsolete” mode don’t simply vanish. Instead, they often find refuge in certain institutions – academia, libraries, guilds, subcultures – that act as stewards of human expertise. These “shelters” preserve the old crafts (from tactful communication to quality-by-feel) in a disciplined way, waiting for the pendulum to swing back when those sensibilities will be needed again (as they inevitably are). We will highlight examples such as how clerical skills were transmuted into library science[5][12], or how the art of community vibe-making is preserved in creative and civic organizations even as algorithms commodify online interaction.
• Feminist Waves and Techno-Structural Waves: Overlaying the technological story, we trace the well-known “four waves of feminism” and discover that each corresponded to a major media or infrastructure shift. Women’s rights movements were not just coincidentally timed; they were materially enabled and shaped by changes in communication and labor systems (from the factory and telegraph, to mass media, to the internet). We’ll map suffrage onto the age of industrial time and census lists, workplace equality onto the rise of the telephone and office machine, identity feminism onto broadcast and niche media, and #MeToo-era consent onto social platforms and smartphones. This reveals a powerful insight: each feminist breakthrough was essentially a “stitch” that bound new technological capacities to new social contracts – with women often providing the human template that made the technology workable[5][30].
• Mythic and Symbolic Analysis: To enrich our understanding, we delve into mythology and psychoanalytic theory. The recurring pattern of women as spinners of fate (the Greek Fates, the Norse Norns) is more than metaphorical: it uncannily parallels the roles women played in managing information threads and cutting off harmful excess in real history[28][25]. We will use these myths as an allegory to clarify the functions of “spin, measure, cut” in modern systems – and to emphasize the timelessness of these mediating functions. Similarly, we apply Slavoj Žižek’s and Jacques Lacan’s theories (on the “big Other,” “surplus enjoyment,” “quilting points,” etc.) to interpret why each regime of meaning-making eventually undermines itself and how feminine-coded “symptoms” (like the hysterical refusal to stay silent) drive change. Don’t worry – we will explain these concepts in accessible terms. The upshot is a deeper validation that no purely technical system can survive without the human element it tries to exclude. The very attempts to cancel human mediation (think: replacing community managers with algorithms) often produce new crises of meaning or trust that demand a re-entry of human judgement[16][18].
• Strategic Implications and Forward Look: Finally, we gather practical lessons. Understanding this pendulum suggests how individuals and organizations can navigate current and future shifts. We argue for “bilingualism”: the ability to operate in both the language of human desire/attention and the language of formal rules/metrics. Concretely, that means if you’re strong in people skills (say, a community leader or content creator), you should encase your tacit know-how into processes and tools so it’s recognized and preserved (e.g., document your community guidelines, develop frameworks that can interface with AI[31]). And if you’re a technical or analytical expert, you should embed human checks and qualitative insights into your systems (e.g., include diverse user feedback, maintain a human veto over automated decisions). The report offers specific advice, like treating prompts as repositories of institutional memory or adding “community health” metrics alongside click-through rates. Our guiding principle: do not let one mode crowd out the other. The healthiest systems deliberately braid relational meaning and procedural rigor – preventing the pendulum from wild swings.

Prepare for a journey that connects the spare rooms of cottage spinners to the server rooms of Silicon Valley, that gives voice to an 18th-century mother defending her craft and a 21st-century creator defending her authenticity. “The Making & Breaking of Womanhood” is not a linear tale of progress or decline, but a cyclical one – a pattern of rupture and repair. By the end, we will see clearly that what’s been “broken” in each cycle was not womanhood’s essence, but rather the system’s vision of mastery – a vision that had to break so that a fuller integration of human values and technical power could emerge.

Let us begin by examining Wave 1: the Industrial Age and the dawn of the modern feminist movement, where our drama is set into motion with the whir of the Spinning Jenny and the rallying cry of Seneca Falls. Through this case, the key terms and dynamics of our thesis will take concrete form.

---

Wave 1 (1760s–1830s): From Spinning Wheels to Spinning Mills – The Rise of the “Social Weaver” and Her Replacement by the Factory

Thesis of Wave 1: In the late 18th century, textiles were the high-tech driver of social change. Women’s deft handling of surplus-information in cottage industry – their tacit coordination of production and local trade – exemplified a feminine-coded media regime based on relational skill. This “social weaving” of households into an economy reached brilliance but also a breaking point as demand outgrew informal coordination. The advent of mechanized spinning mills (a masculine-coded regime of procedures and machines) then reorganized power around formal rules, displacing many women even as it absorbed their knowledge into automated devices. Amid this economic upheaval, the first organized feminism (calling for education, property rights, suffrage) took shape – enabled by women’s roles in the cottage system and then ironically propelled by the very industrial legal order that diminished those roles. Women won formal personhood at the same time their informal influence over economic production was curtailed by factories.

Setting the scene (~1760): Imagine a village in England. Most people live by the rhythms of nature and custom, but a new energy is pulsing: merchants bringing raw cotton from colonies, putting it out to farm families to spin into yarn. Spinning Jenny – a recent invention – allows a woman to spin multiple threads at once[1]. She and her daughters can produce much more than before, in their cottage. The work is still done by feel: fingers judge fiber quality, adjust tension on the fly. They coordinate with neighbors (“I’ll finish the coarse yarn for you if you help dye my batch next week”) – an intricate web of reciprocity, largely managed by women in their “spare” hours around child-rearing. Essentially, rural women become information hubs of the early textile trade: who has extra yarn, which merchant pays honestly, whose child can help wind spools.

This proto-industry runs on trust and tacit norms. No one has a formal title, but an experienced spinner carries social clout: merchants trust her quality, younger women apprentice under her. It’s feminine soft power: the ability to “keep many threads in play” socially and literally. The surplus-information here is the excess complexity of decentralized production – and women capture it by a culture of mutual aid, gossip-as-supply-chain intel, and moral reputation. They essentially function as a human media network, transmitting signals (“the Miller family’s yarn is strong – worth the higher price” or “bad weather coming, let’s all spin extra this week”).

One such woman might be the parish guild mistress – not officially, but de facto – who ensures each family gets a share of the spinning work and that no one cheats on quality. She might convene other spinners at the church after Sunday service to quietly balance out orders (“I heard the Markhams got a large order; I’ll send my daughter to help them evenings so it ships on time”). Skill, in this world, means attunement: to the rhythm of the wheel, the needs of neighbors, the mood of the marketplace. These are classical surplus-enjoyment skills – deriving a sense of community and purpose from making desire (for fine fabric, for honest living) circulate.

Now, this system thrives for a time. Women spinners become small entrepreneurs, even earning their own income (sometimes upsetting gender norms). In fact, many early suffragists would later point out that women in such trades prove women can handle economic matters responsibly – it’s part of their argument for rights. But as the sources note, the cottage system “solves its contradictions better than formal rules do—until scale introduces more states than tacit coordination can track.” By the 1780s, the demand for textiles (domestic and global) explodes. Cottage spinning can’t keep up: even with Jenny wheels, there are too many threads, too many transactions for the informal network to stabilize[32]. Merchants start complaining of delays, uneven quality, and the “unruliness” of relying on scattered families. From the spinners’ view, their own success has created intolerable pressure: working 14-hour days by dim light, children overworked, community ties fraying under competition and haste. This is the saturation point – the surplus-information (orders, threads, coordination tasks) now outstrips what even the best social weaver can handle.

Enter Sir Richard Arkwright’s water-frame and Samuel Crompton’s Spinning Mule around 1779[33][1]. These inventions hybridize the multi-spindle idea of the Jenny with water power and precise mechanical control. A single mule in a factory can do the work of dozens of cottages, and do it with standardized tension and speed[1]. Of course, it’s expensive and requires capital investment – meaning wealthy men build mills and hire workers. At first, they hire many women (and children) from the displaced cottage industry. But the Mule is also heavy and needed “minders” with strength; gradually spinning becomes a male profession in mills (historian Gary Saxonhouse noted how the mule “overturned the traditional division of labor in spinning from women to men”[33]). As the Wikipedia entry dryly puts it: “Home spinning was the occupation of women and girls, but the strength needed to operate a mule caused it to be the activity of men.”[1] In the early decades, you literally needed to haul on the machine’s carriage. Thus, the skilled “mule-spinners” in 19th-century Britain were mostly men – and they gained an almost aristocratic labor status (they were among the best-paid factory workers, sometimes called the “barefoot aristocrats” of cotton[1]). Ironically, the machine that absorbed women’s expertise elevated a strata of men who ran it.

Let’s listen to an imagined voice at this turning point – perhaps a composite of those cottage spinners appealing to factory owners or local authorities as they see their livelihood and social role threatened. This is the voice of the feminine-coded craft hitting its absurd limit and resisting the mechanized reductio:

Parish Meeting, Lancashire, 1795 – A Spinner’s Plea: “Gentle sirs, we do not spin mere thread; we bind the households of this parish to their good name. A skein has its conscience, and it rests in hands that know the temper of flax as they know the temper of a child. You would have us answer to the clock and to gauges that mock the living feel of fiber. But should price alone govern, you unmake the very trust that calls your cloth ‘fine.’ We have kept your promises when storms broke roads and fevers emptied rooms; we have suffered the night-wake of broken yarns so that your mark might not be shamed in the market. Do not suppose a new iron contrivance will keep faith better than a mother who has staked her bread upon it. If you would quicken us, then pay honest measure and let our order stand – else you will have speed without honesty, and cloth that will not hold a seam.”

This eloquent protest (drawn in spirit from letters and petitions recorded at the time) highlights several crucial points: – “Gauges mock the living feel” – the women recognize the incoming Symbolic (measure, instrument) regime and call it out as missing something vital (the Imaginary of quality, the Real of community trust). They sense the absurdity: measuring output by the clock alone ignores the subtler work of ensuring durable cloth (a gauge can’t hear the spindle’s song the way they can). – Trust as an economic value: They argue their relational work (“binding households to good name”) is part of the product’s value (the brand’s promise of quality). If you remove that, you might get quantity but of lower integrity. This foreshadows modern debates about automation vs. craftsmanship – speed without honesty. – Appeal to moral economy: They invoke how they went above and beyond in crises (honoring contracts through sickness, etc.). This is a claim that their informal network delivered resilience that a strict machine system might not. – Conditions for acceptance: “If you would quicken us, then pay honest measure and let our order stand.” In other words, if more output is needed, treat us fairly and maintain our way of organizing (don’t throw out our norms). They are open to improvement but not to being discarded or disrespected. – Warning of collapse: “Speed without honesty, cloth that will not hold a seam” – essentially predicting that if you push only the masculine-coded metrics (speed/price) and ignore the feminine-coded quality control and social trust, the product and system will fail (seams unravel, perhaps metaphorically society unravels).

Historically, were such warnings heeded? Not really – the logic of industrial capital steamrolled ahead. In the short run, productivity soared: by 1810, Britain’s textile output was huge, prices dropped, profits grew. In the long run, some predictions of social unraveling did manifest: harsh mill conditions led to worker injuries (“cloth that will not hold a seam” – indeed some factory-made cloth was initially shoddy until standards caught up[1]); communities suffered (the village-based network eroded as people moved to factory towns); and trust had to be re-established via formal inspections and regulations rather than personal honor.

In wave terms, the feminine craft was “broken”: what had been a domain of women’s tacit authority became a maledominated, formally organized industry. Power re-aggregated to those who mastered procedures – the machine overseers, the factory owners, the engineers who improved mechanisms. We see the creation of new status ladders: e.g., the male overlooker vs female piecer in mills – a sharp hierarchy where before among cottage spinners there was more fluid peer mentoring.

Labour politics also shifted: the displaced women sometimes turned to other home industries (straw-plaiting, lace-making) or to increased dependency on male wages. Some historians note a dip in women’s economic influence in early 19th century Britain as many lost independent earning power. At the same time, the first women’s rights advocates emerged largely from relatively educated classes, but they often framed their cause in terms of these economic changes: for instance, Judith Murray in 1790s America argued for women’s education because “women are the primary agents of manufacturing in the home – imagine what they could do with formal knowledge!” And later, Florence Nightingale would compare housewives to “quartermasters” of an army, deserving education in accounting and health – clearly influenced by industrial analogies.

Thus, the pendulum swung: diffuse, improvisational “social sense” gave way to concentrated, repeatable “mechanical sense.” The spinning mule, as the sources said, exposed the limits of the jenny regime by requiring a repeatability only proceduralization could deliver – then installed a new order where rules, not relational finesse, commanded surplus-information. Women’s role in handling that surplus shrank, and men’s role in formalizing it grew.

Yet, crucially, women’s underlying sensibilities did not vanish. They reappeared in different guises: – Some women found new work as inspectors or teachers of spinning – e.g., training mill workers or ensuring quality in finishing rooms. They carried the “quality by feel” into an auxiliary position. – Women also dominated the new cotton mill workforce at lower levels, and their shop-floor subculture maintained some cooperative practices (we have accounts of older women workers advising younger ones, maintaining community in boarding houses – a muted echo of the cottage community within the factory system). – Outside textiles, women’s coordination skills flowed into the growing movements for social reform. Many early suffragists in the mid-19th century, like those at the Seneca Falls Convention (1848), were accustomed to organizing church groups, temperance leagues, abolitionist societies – all of which can be seen as transferences of domestic coordination to public causes. It’s as if, denied their old economic sphere, they applied their skills to moral and political weaving, stitching local groups into national networks for change.

Indeed, the first wave of feminism was superstructurally about legal personhood, but substructurally it rode on the fact that society now had countable individuals and standardized time. The suffrage movement was effective because it used tools like petitions (mass signatories thanks to widespread literacy among women), lobbying (timing arguments to elections), and the press (circulating newsletters) – all part of the new Symbolic order of nation-states and print capitalism. One could say: Clotho’s act (spinning individuals into the census thread) was complete; now women demanded Lachesis’s hand in measuring them equally and Atropos’s right to cut ties (like divorce) when needed. The mythic resonances were present in their rhetoric – suffragists often invoked weaving imagery, e.g., saying society is like a fabric that needs women’s input or it will fray.

So, Wave 1 summary: Women’s position as central “social weavers” of the cottage industry gave them practical leverage and a vision of themselves as capable, which fueled early feminist demands. The industrial-tech shift (spinning mills, time clocks, scientific quality control) “broke” that informal feminine power base – economically, many women were marginalized or made subordinate. But the push for women’s formal rights succeeded by stitching women’s tacit authority into the new Symbolic framework – e.g., by claiming the title of citizen, the legal analogue to being a recognized thread in the social tapestry[30].

The lesson of Wave 1 is twofold: (a) A feminized mode of labor (tacit, trust-based, distributed) can run a major sector and even drive a movement, but (b) when scale and new machines demand formalization, that mode is derided as “inefficient” or “amateur” and is replaced – unless it finds a way to encode itself into the new regime. Women did encode parts of it (moral rhetoric into law, craft knowledge into standards), but lost direct control of production.

With this template in mind, we move to Wave 2, roughly a century later: the age of telegraphs, telephones, typewriters – and the push for women’s equality in work and education. We’ll see an uncannily similar pattern: women dominate an emergent communication network (the office and telephone exchange), wielding soft power as coordinators, only to see a technical/organizational revolution (mechanized management and early computers) re-center authority away from them – just as the second wave of feminism fights to secure formal equality. The pendulum swings again, and we’ll trace its arc.

---

Wave 2 (1890s–1960s): The Office, the Switchboard, and “Scientific” Management – Women as Human Connectors and the Rise of the Machine Bureaucracy

Thesis of Wave 2: The turn of the 20th century saw an explosion of clerical information work – telephony, typing, filing, and office management – much of which was performed by women. These women effectively managed the surplus-information of burgeoning urban firms through social calculus and discretion: they were the switchboard operators of not just telephone lines but of organizations themselves (secretaries who knew which memo to put on top, operators who knew which caller’s urgency to heed). This feminized regime of tacit coordination (call it the “whisper network” and “secretarial sense”) kept the wheels of commerce turning. However, as the volume of calls, letters, and records reached a babble beyond human triage, a new drive for formalization and automation took hold: scientific management, punch-card tabulation, and eventually mainframe computers sought to proceduralize and mechanize every step. This masculine-coded bureau-techno regime demoted relational savvy to “routine work” and elevated rule-following and machine maintenance as the new expertise. Women’s clerical labor was partially absorbed into machines and partially relegated to lower-status roles – even as women won formal legal equality and entered professions in greater numbers. The second wave feminist push for workplace equality and personal autonomy succeeded in changing laws and norms, but it unfolded just as many traditional mediating roles of women were being engineered out or scaled down by the corporate and computing revolution.

Context (~1900): Picture an early 20th-century city office: Rows of young women at typewriters, clattering away to produce letters and ledgers far faster than any quill pen of the previous century. In a side room, telephone operators – also mostly young women – sit before a wall of jacks and plugs, manually connecting calls for the whole city. On another floor, clerks (many female) maintain filing cabinets with thousands of indexed documents. The speed and volume of information in a modern company (or government bureau) has grown exponentially, and it’s being managed by a small army of literate, mostly female workers. By 1920, in the U.S. about 1 in 4 employed women worked in clerical roles (a huge jump from 1880, when it was only a tiny fraction)[14].

Why women? Partly a social construct (it was seen as an extension of “women’s meticulous nature” and their subordinate status to male executives), but partly an economic hack: as one contemporary observed, “women were more often hired for structured, mechanized tasks”, and crucially, could be paid less[5]. In 1890, the U.S. federal government started using Hollerith punch-card tabulators for the census – men initially set them up, but very quickly women were operating these machines because the work was repetitive and considered low-status, thus feminized[5]. An 1890s report noted women did the job “50% faster than men” and with fewer complaints, attributing it to their “delicate touch” and “greater anxiety to do a good job”[5][34]. (In truth, many men avoided these jobs because they were dead-end and low-paid, whereas women had fewer alternatives[12].)

So by the 1910s, we have a feminized information infrastructure: the social web of the office. Here’s how surplus-information was handled by women’s surplus-enjoyment skills: – Switchboard operators didn’t just mechanically connect calls; they often soothed impatient callers, quietly routed urgent matters ahead of routine ones, and learned the patterns of frequent callers (the original personalization). In 1920, telephone operators were 2% of the entire U.S. female workforce[14], a huge social role. These women developed a culture of service and subtle control – they literally managed who could speak to whom and how quickly. AT&T even recognized that their tone of voice affected customer satisfaction; operators were trained in gentle, calm speech. This is emotional labor entwined with technical skill. – Secretaries (a role emerging in late 19th century and heavily populated by women by early 20th) functioned as gatekeepers and coordinators for managers. A talented secretary kept her (usually male) boss on schedule, filtered his communications, remembered social niceties (“Today is Mr. Smith’s wife’s birthday – mention it in the meeting”), and often drafted correspondence for him. She knew the office politics intimately and could “read the room” – if a salesman came at a bad time, she’d tactfully deflect. In many ways, she ran the department while the boss got the credit. Being a boss’s secretary became a coveted, semi-powerful female position – one could wield influence behind the scenes (think of “super-secretaries” like Miss Moneypenny in fiction – skilled, knowledgeable, but never breaching the formal hierarchy). – Clerical supervisors (some women rose to be chief telephone operator or head of a typing pool) managed teams through a mix of strict scheduling and maternal mentoring. They embodied the cross of the new rationality and the old relational glue: they enforced break times and output quotas (per management’s scientific management rules), but also motivated the girls, dealt with personal issues, and acted as a buffer upwards (“Mary’s son is ill; I’ll cover her work today so the boss doesn’t notice the slowdown”).

In short, women in offices were the human interface between the exploding flows of information and the rigid structures of corporate bureaucracy. They applied tact, tone, and triage to make mass communication workable. This was the feminine-coded surplus-enjoyment: finding satisfaction (or at least identity) in being the indispensable “knower of who needs what when,” as the sources put it.

Consider a 1930s scenario: A critical contract must be delivered by mail. The manager leaves it with his secretary to finalize and send. She notices a factual error that he missed – quietly corrects it, knowing it could cause legal trouble. She also intuits that the client prefers formal language, so she adjusts some phrasing. She then has the typing pool prepare three copies (one for file, one for client, one for safety) without bothering her boss with these details. Finally, aware that mail is slow and the client is anxious, she calls the client’s secretary to say the contract is on the way and gives a heads-up of key points – smoothing the path for when it arrives. None of this is explicitly in her job description; it’s an accretion of unofficial duties arising from her awareness and initiative. But it’s precisely how surplus-information (the gap between rigid procedure and real-world need) is managed by human intervention.

Now, the absurdity and saturation: As offices grew larger and more complex, relying on such personal heroics became a liability. By the 1930s-40s, big firms found that some secretaries were too indispensable – if one quit, her boss was paralyzed because so much tacit knowledge left. From a rationalization perspective, that’s dangerous. Also, the sheer volume of paperwork – think WWII procurement or post-war corporate expansion – made purely manual, ad-hoc methods insufficient. There were infamous incidents like a company failing to fill an order because the one clerk who knew the procedure was out sick. Formalization beckoned.

Enter Frederick Taylor’s scientific management (applied to offices in the 1910s-20s): time-and-motion studies for typing speeds, routing slips for correspondence, standardized forms to replace freeform letters. Then mechanization: the Comptometer and other adding machines handle calculations; Hollerith punch-card machines sort data for payroll and inventory; eventually by the 1950s, IBM mainframes start handling accounting, and primitive database systems appear for record-keeping. Telephony, too, underwent automation: starting in the 1920s, automatic dial exchanges gradually eliminated the need for human operators for many calls[16]. AT&T’s massive female operator workforce peaked in the 1940s and then began a steady decline as direct-dial phones spread – by 1970s, the occupation “telephone operator” was largely obsolete[16][18]. One observer in 1920 had noted operators were “as much as 2% of the female workforce” and mused what they’d all do if machines took over; by 1978, that question was answered – they had disappeared as a job class[35] (some moved into other clerical jobs, many simply aged out as hiring stopped).

Likewise, office structures changed: by the 1960s, a mid-level manager might type his own memos or use dictation machines; the typing pool shrank or was outsourced; an executive might have one secretary instead of three. Organizational charts were redesigned so that roles were interchangeable – ideally no one held unique tacit knowledge. If in 1930 a division’s smooth function depended on Mrs. Johnson’s phenomenal memory, by 1960 management wanted that in a filing cabinet or computer, not just in her head.

Let’s imagine another voice – that of a senior executive’s secretary in 1950, seeing these changes coming (perhaps her boss is about to retire and the new guard is more numbers-driven). We channeled a hint of it earlier with the “we make the city breathe” speech. Here she might address a National Secretaries Association meeting, rallying her peers to assert the value of their craft in the face of being deemphasized:

Secretaries’ Luncheon, New York, 1952 – “The Indispensable Human Touch”: “Colleagues, we make this city breathe. A letter is not truly sent until it arrives in the only voice its reader will hear; a call is not truly answered until a cooling word has soothed its temper. They bring machines that count without listening, and men with stopwatches who time our hands but not our judgment. I do not fear a tabulator; I fear a world that mistakes arithmetic for prudence. Keep your lists close, keep your calendars closer, and make yourselves indispensable by doing what no card or clock can: deciding who must never be surprised.”

In this stirring exhortation, she’s essentially warning against over-automation: – “Count without listening” and “time our hands but not our judgment” point to how management is focusing on quantifiable tasks (typing speed, number of calls) and ignoring the qualitative decisions (whom to alert about what, how to phrase something) that actually prevent catastrophes (“who must never be surprised” – what a golden metric for true office effectiveness!). She’s articulating that discretion is a value, even if it’s invisible in the new efficiency metrics[36]. – “Make yourselves indispensable by doing what no card can” – a call to arms: continue leveraging those relational skills, because when the chips are down, the bosses will realize they need them. This is half pride, half survival tactic. – There’s a poignant awareness that their authority is unrecognized (“we decide who is never surprised,” i.e., who gets forewarnings of issues – something a secretary often handles by quietly cluing in the boss or smoothing interdepartmental communication).

Despite such voices, the tide largely turned the other way. By the 1960s, the archetype of the “company man” with his computer print-outs and “professional” (often male) managers had taken hold, and the archetype of the wise, all-knowing secretary receded into a stereotype (think of 1960s comedies portraying secretaries as frivolous or merely doting, a far cry from the respect of earlier eras). Women in offices were encouraged to move into professionalized roles (like HR or accounting) if they wanted advancement – essentially to become part of the formal system rather than informal linchpins.

Ironically, just as second-wave feminism was urging women to break out of the secretary pool and into “men’s jobs,” the labor market was eliminating many of those pool positions anyway with tech. In 1960, being a secretary was a common career with some cachet; by 1980, word processors and team-based orgs meant fewer high-level secretaries (except for top executives) and more lower-level “administrative assistants” shared by teams. Women flooded into professions like law, medicine, and banking during the 1970s – pursuing the new ladders rather than the old secretarial path. That was a feminist victory in one sense, but also a response to the fact that the old path was narrowing.

On the factory floor of information, computers took over repetitive data tasks while women moved into the emerging fields of computing and systems where possible – indeed, many early computer programmers were women, as programming was initially seen as akin to clerical work[19]. (Fun fact: in 1960, programming was sometimes called “women’s work” – by the 1980s, it flipped to a male-dominated field once it gained status[20].) This is a perfect example of how feminine-coded labor opens a new domain, and as it formalizes and gains prestige, men dominate it – a pattern we see in waves 1 and 2 clearly, and it recurs in wave 4 with content creation vs. AI engineering.

So, Wave 2 summary: The telephone and office-information revolution empowered women as the nerve system of modern organizations. Women leveraged that to demand equality – after all, they were already doing much of the intellectual work, albeit without credit. Laws like the Equal Pay Act (1963) and the Civil Rights Act (1964) in the U.S. finally acknowledged women as equal individual workers (superstructural change), even as, substructurally, the office was being redesigned to be “machine-friendly” rather than “secretary-friendly.” The explicit barriers fell (by 1970, classified ads no longer segregated “Help Wanted – Female” vs “Male”), but implicit barriers arose in new forms (glass ceilings in male-run professional hierarchies). Women entered management but often had to “act like men” (adopt the new metrics-driven, no-nonsense style) because the Symbolic order of corporate life had shed much of the feminine-coded relational ethos. Many second-wave feminists themselves embraced a rejection of “feminine” traits in the workplace, seeking to prove they could be as hard, analytical, and career-focused as men – ironically aligning with the masculine-coded machine bureaucracy’s values.

However, as our model predicts, the hyper-rational office of the 1980s developed its own absurdities (impersonal cultures, alienation, blindspots to human concerns), setting the stage for Wave 3, where empathy, diversity, and “the personal is political” would surge back as correctives. And indeed, the late 20th century saw a revaluation of “soft skills” and identity in the workplace – often led by women and marginalized groups – just as media hyper-saturation and financial metrics had turned corporate culture rather soulless.

Before moving to Wave 3, we glean lessons from Wave 2: – What got “broken” was the notion of the indispensable female mediator. She was replaced by flowcharts, early AI (rudimentary algorithms), and an ethos that everyone is replaceable. The upside: women were no longer trapped in those supporting roles and could move up (or out). The downside: workplaces lost a layer of humanity and crisis-prevention that only became obvious later. – What persisted: Women’s underlying skills in communication and care did not vanish – they migrated. Some went into newly feminized fields like public relations, teaching, counseling, or into community activism (since their talents were undervalued in corporate settings, many women put them to use in grassroots movements for civil rights, anti-war, environmental causes in the 60s/70s – which indeed heavily relied on women’s organizing abilities). And within companies, even the most streamlined, computer-driven organization eventually realized it needed HR departments, training programs, interpersonal management – domains where women re-entered to re-humanize aspects (e.g., the boom of “organizational psychology” as a field in the 1970s, which often drew on what good secretaries and middle managers informally knew about morale and communication). – Strategic insight: Women who “owned the interfaces” survived best. For example, those who became the early computer operators/programmers translated their office knowledge into the new medium and stayed relevant. Or a secretary who formally moved into a “office manager” position – writing the procedures for others – secured a place. In contrast, those who clung only to the tacit role without adaptation often saw their influence and jobs diminish. This foreshadows today: we advise content creators (wave 4) to become prompt designers or community standards authors – the equivalent of owning the interface as tech encroaches.

Now, Wave 3 will unfold in the late 20th century with the rise of mass/broadcast media and the cultural feminist focus on representation and identity. We’ll observe how women once again become key mediators – this time of public image and community sentiment at scale – and how a new regime of metrics and programmatic systems attempts to rationalize that, leading to another pendulum swing.

---

Wave 3 (1960s–2000s): Mass Media, Marketing, and “Doing Difference” – Women Cultivating Image and Community, Overtaken by Metrics and Markets

Thesis of Wave 3: From the 1960s through the early 2000s, society entered an era of mass-mediated culture and identity politics. In the “third wave” feminist milieu, attention shifted from simply being included in the system to critiquing and changing the system’s portrayals and power dynamics. Women, often in feminized professional roles (publishing, public relations, community organizing, arts administration), became the custodians of cultural meaning and social cohesion – the taste-makers, image-crafters, and crisis-calming diplomats of an increasingly performative public sphere. This was a feminine-coded regime of managing surplus-attention: reading the volatile moods of audiences, navigating the “spectacle” of celebrity and scandal, and infusing marketing and politics with a human (often feminist) perspective on representation and fairness. By the turn of the millennium, however, this relational, intuitive craft of aura and influence reached a point of self-parody – with media cycles spinning out of control, authenticity strained by constant performance, and every soft tactic duplicated to exhaustion. At that point, a new masculine-coded machine logic asserted itself: data-driven, programmatic marketing and analytics. The locus of power drifted from charismatic PR and community experts to spreadsheet-wielding procurement officers, audience measurement systems, and, eventually, algorithms that promised to quantify and optimize culture itself. Once again, women’s tacit soft power was partially formalized into dashboards and cost-per metrics – and partly devalued as “unscientific.” Yet, as before, when the new metrics overshot – producing sterile or brittle outcomes (e.g., brands facing backlash despite perfect KPIs) – the need for human insight resurfaced. Wave 3’s lesson is that not everything that matters can be measured[36], and the feminine-coded labor of keeping meaning and trust alive would return in new forms (setting the stage for wave 4’s emphasis on consent and authenticity).

Context (~1980): Envision the world of high-stakes media and marketing. There are three TV networks beaming images into nearly every home – what gets shown on them hugely influences public consciousness about gender, race, everything. Women have entered this world in force but often in particular roles: – Public relations executives (by the 1980s, PR had many women leaders) are the behind-the-scenes fixers who manage company and celebrity images. They organize charity events for stars to soften their image, leak stories to friendly journalists, coordinate apologies and redemption arcs when scandals hit. They operate by social intuition: understanding what the public feels and how to subtly shift it. A classic example: a PR advisor to a politician might say, “We need to get your wife on camera looking supportive; it will humanize you” – a move based not on data but on narrative sense of audience emotion. – Magazine editors and book publishers – especially in “women’s media” or burgeoning fields like feminist presses and cultural studies journals – are deciding whose voices get heard. A woman editor at a big magazine in 1990 is juggling: advertisers want fluffy content, readers respond to substantive stories, advocates push for more diverse representation. She must negotiate desire among all these stakeholders and still put out a coherent issue. Often, women editors used their influence to push progressive content (e.g., more articles on working women’s challenges) in ways that didn’t alienate advertisers – a delicate dance of pleasing and educating at once. – Community organizers and non-profit directors – the 60s–90s saw huge NGO and advocacy growth, many led by women or with heavy female workforces (think: civil rights organizations, environmental movements, AIDS crisis response groups). These women had to rally volunteers (i.e., inspire desire/hope), frame messages for the media (craft imaginary identifications), and lobby policymakers (enter the Symbolic of law). Their day-to-day was a relational juggling act: keep the grassroots motivated while translating their anger into polite, effective advocacy at the top. It’s essentially a PR job with moral fervor added.

Now, consider how feminist ideas of the third wave (roughly 1990s) intersected with these roles: – Feminism was now about representation and intersectionality – pointing out, for example, that mainstream media lacked diversity or that women of color faced different issues than white women. Women in media took on the role of “taste councils” and “representation gatekeepers.” For instance, a female casting director in Hollywood might push to cast more women in strong roles, or a female advertising executive might quietly ensure an ad campaign doesn’t demean women (the infamous Virginia Slims slogan “You’ve come a long way, baby” was ironically crafted by a female copywriter, suggesting a liberation narrative even to sell cigarettes). These are subtle forms of feminist intervention via cultural production. – There was also a strong “therapeutic” or relational undercurrent: the rise of talk shows, self-help books, and what some call the “feminization of popular culture” where emotional openness became more accepted (think of TV talk shows in the 90s dealing with abuse, addiction, etc. – often mediated by women hosts like Oprah). Women experts (therapists, social workers) were often the ones brought on to guide these conversations. They effectively brought traditionally “private” female-coded knowledge (emotional labor, empathy) into the public imaginary.

This surplus-enjoyment regime of wave 3 – women managing the affective and representational excess of a spectacle-driven society – was very effective in certain ways: – It humanized institutions (companies with female PR heads often steered away from blatantly offensive ad campaigns or took more socially conscious stances because someone in the room said, “Wait, how will this impact vulnerable groups?”). – It opened space for previously excluded voices (the groundwork for diversity in media was laid by many women and allies in gatekeeping positions championing new talent – e.g., an editor like Grace Mirabella at Vogue bringing a more career-woman perspective in the 80s, or Peggy McIntosh in academia articulating white privilege which became a key tool for diversity training). – It cooled down crises: when a company faced a PR disaster, often the female spokespeople or advisors played “firefighter,” using apology and empathy rather than the old masculine playbook of denial and doubling down. E.g., after the Exxon Valdez oil spill (1989), internal reports noted that public outrage was worsened by the CEO’s flat, data-driven responses, lacking human concern – a lesson that led to more emotionally intelligent crisis PR in the future (often shaped by women PR pros).

However, by the late 1990s, absurdities emerged: – Media saturation & cynicism: The public began to see through formulaic PR and image crafting. Every tactic was “mimicked to death,” as the content said. For example, by the early 2000s, every celebrity apology on TV looked the same (a carefully posed tearful interview) – people got jaded. The Imaginary (image-management) had oversaturated, making authenticity a rarity. – Financial metrics ruling creativity: Corporate consolidation meant procurement departments started controlling marketing spend. They demanded ROI for every PR event, every sponsorship[2][23]. The soft power of cultural curation had to justify itself in hard numbers. This often put women taste-makers on the defensive: e.g., a community relations director (maybe a woman who knows sponsoring a local women’s shelter is great for long-term trust) is asked by a male CFO, “But how many sales did that generate this quarter?” If she can’t quantify it, the budget gets cut. This is exactly what we saw in the imagined boardroom speech earlier (“The brand is a promise, not an insertion order… you cannot buy forgiveness… I fear a world that mistakes arithmetic for prudence.”). – Programmatic advertising & analytics (late wave 3 into wave 4): The 2000s introduced software-driven ad buying (e.g., Google AdWords launched 2000, offering precise targeting). Suddenly, a lot of relationship-based aspects of marketing (long-term partnerships between brands and publishers, built by women in PR over lunches and calls) were replaced by real-time bidding algorithms. Media buyers – often under pressure from finance – started to treat ad inventory like a commodity, not a creative placement. This not only sidelined some of the women in those roles (since the skill shifted to being spreadsheet-savvy, a domain where men reasserted dominance), but it also eroded the nuanced approach to audience that human curators had. A programmatic system wouldn’t necessarily avoid placing a family-friendly brand ad next to a violent video, for instance, whereas a human PR agent would have said “no thanks, not the right environment.” Sure enough, by mid-2010s (wave 4), companies faced “adjacency” scandals (ads appearing next to extremist content on YouTube) and had to re-insert human judgment via improved policy (a pattern repeating: remove human discretion, then find you need it back in some form).

We can crystallize the shift with an example: In 1995, a brand’s success might depend on a savvy female marketing VP who just “has a feel” for the audience – she greenlights a TV spot because the storyline moved her and she intuits it will move others, even if it doesn’t test as highest scoring. If it succeeds, it’s thanks to her gut and understanding of social currents (say it tapped into a nascent girl-power sentiment). By 2005, that same brand’s moves are decided by market research metrics and cost-per-point comparisons; the VP now must justify every creative idea with data. Her role becomes less about creative vision and more about managing the creative process through a gauntlet of Excel sheets produced by research firms and media agencies. Many women certainly adapted and excelled in that (women also became leaders in market research and planning), but the nature of power shifted: the culturally fluent, intuitive side of marketing lost clout to the quantitative side.

We heard the SVP of Communications in our boardroom speech warn that cutting “relationship lines” to satisfy short-term metrics would leave the company naked when a real crisis hits (“when the lights go out” – as in a blackout, what remains is trust[23]). That indeed happened for many. One could argue the Erosion of long-term trust in institutions around the end of wave 3 (late 1990s and 2000s) – from Enron to Big Tobacco lawsuits to Catholic Church scandals – showed that decades of treating image as just something to engineer with PR and numbers left these institutions brittle when truth surfaced. People felt betrayed because the human connection was thin. Arguably, if more women’s perspectives (relation-driven, ethically attentive) had been listened to, some of those disasters might have been mitigated or averted. In fact, after those scandals, institutions often brought in women or “soft skill” folks to help rebuild trust (e.g., many corporations appointed Chief Reputation Officers or beefed up ethics and compliance departments in the 2000s – roles frequently held by women or those with “people” backgrounds).

So, Wave 3 summary: Women’s mediating work in culture and community – whether through media, marketing, or activism – made great strides in broadening representation and humanizing corporate/public discourse. Third-wave feminists successfully pushed concepts like intersectionality into the mainstream conversation (albeit slowly) and shifted many imagery norms (by 2000, it was at least controversial rather than accepted for an ad to be overtly sexist or racist – thanks in large part to feminist critiques within agencies and media). But as they were winning those narrative battles, the narrative-making apparatus was being retooled under their feet: consolidated media, bean-counter oversight, and the dawn of internet algorithms all combined to diminish the influence of individual cultural curators.

In feminism itself, some have noted the late 90s/early 2000s felt like a “stall” – the movement turned more to academia and niche culture, while neoliberal metrics ruled policy. One could view that as the pendulum’s masculine swing muting the feminine-coded social energy for a time. But with Wave 4, a new platform emerged – social media – where affect and personal voice roared back, and with it a more virulent exposure of unresolved issues (e.g., #MeToo showing harassment was rampant despite all the wave 3 HR policies saying otherwise).

We’ll transition now to Wave 4, the social network and AI era (2008–present), where this cycle repeats with startling speed: the feminine-coded craft of keeping many people emotionally engaged and normatively aligned in online communities rises, saturates (in the “influencer” economy), and is now (as of mid-2020s) being partly overtaken by generative AI and automated moderation – the next formalization that attempts to subordinate the very relational magic that built social platforms. The #MeToo movement and others are part of wave 4’s story – demonstrating how women’s networked mediation did manage to shift power (toppling many powerful abusers) – but also how quickly platform companies responded with procedural and technical Band-Aids (some helpful, some just for show) that again risk excluding the human element.

---

Wave 4 (2008–2025): Social Platforms, #MeToo, and the Generative AI Pivot – From “Influence” to “Inference”

Thesis of Wave 4: In the past 15 years, the center of cultural and political gravity moved to online platforms and social networks, creating a vast new realm of participatory media. This environment initially rewarded a feminine-coded repertoire of community management and personal storytelling: women (along with other marginalized voices) leveraged blogs, then Facebook, Twitter, YouTube, and Instagram to build communities, share lived experiences, and challenge gatekeepers. The late 2000s through 2010s saw the rise of the “influencer” (often female or LGBTQ), the community activist hashtag (e.g., #MeToo started by Tarana Burke, a Black woman), the content moderator (a job disproportionately done by women, given its caretaking aspects). The skills at play were surplus-enjoyment skills at massive scale: the ability to sense and amplify social desires (for connection, for justice), to foster parasocial intimacy (fans feeling close to creators), to collaboratively shape norms in real time (as volunteer mods did in online forums), and to emotionally labor on the digital front lines (calming conflicts, supporting survivors coming forward). Under this influence-driven regime, many women became powerful new intermediaries – YouTube gurus, Twitter thread-unrollers, Tumblr essayists, Facebook group admins – all demonstrating that “who magnetizes attention” could matter more than formal status. They routed surplus-information (the chaotic flood of posts/tweets) into meaning and action through curation and conversation.

By the late 2010s, however, this system hit reductio ad absurdum. Feeds grew noisier and more gamed (maximal engagement tactics leading to clickbait and burnout), authenticity turned into a commodity (“extremely online” behaviors self-parodying intimacy), and online harms (harassment, disinformation) outpaced what ad-hoc human moderation could handle. The stage was set for a masculine-coded swing to rule-based processing: enter Generative AI and algorithmic governance. The power focus shifted from charismatic influencers and organic community builders to engineers of automation and policy architects: those who formalize prompts, train models, set content policies, and integrate AI into product workflows. “Influence” (sway via human charisma) yielded to “inference” (AI systems deducing and generating content) as the engine of scale. In this new order, procedural competencies – benchmarking, content filtering, data pipeline management, and compliance enforcement – capture more value than relational craft. We see companies investing in AI that simulates the very engagement-spiking content that once required human creativity, and in moderation AI that attempts to mimic the judgement of thousands of overworked human mods. Women’s contributions are being partly automated (their styles learned by models) and partly fenced by new rules (e.g., stricter platform policies that remove much discretionary judgement). Yet, as before, this swing is already revealing its limits: AI-generated content floods risk audience backlash (people crave real human voice again), automated moderation falters at context (leading to dangerous misses or over-censorship), and “data-driven” personalization without human nuance feeds echo chambers and extremist rabbit holes. The need for human sensibility – for the bilingual experts who carry desire into procedure and vice versa – is reasserting itself. The lesson of Wave 4 is being learned painfully in real time: you can automate the production of words and images, but not the trust and meaning that make them matter.

Context (~2012): Facebook has become a global commons, Twitter a public square, YouTube a new broadcasting system. A young woman with a webcam and a passion (makeup, or video games, or social justice rants) can suddenly speak to millions without a traditional publisher. The creator economy is born: predominantly youthful, diverse, unconstrained by old corporate PR scripts. Feminine-coded activities – expressing feelings, building supportive communities, “influencing” friends – turn into not only personal brands but economic and political forces. For instance: – Beauty YouTubers (2010s) – many are young women who turn informal product chat into massive followings. They master algorithmic content rhythms (regular uploads, energetic personalities) but also audience relationships – responding to comments, fostering a sense of girlfriend intimacy with viewers. Their power to recommend products starts rivaling big ads; companies send them samples hoping for a favorable mention. Essentially, they became micro-media outlets anchored in trust. The best of them wield a sixth sense for their community’s desires – if a foundation product lacks inclusive shades, they call it out (and firms scramble to fix it). They didn’t learn this in school; it’s surplus-enjoyment skill adapted to social tech. – Hashtag Activists (2013–2018) – women like Alicia Garza (#BlackLivesMatter co-founder) or Tarana Burke (#MeToo originator) or the many anonymous voices that propelled hashtags like #YesAllWomen, #TimesUp, etc., harness networked desire for justice. They use storytelling (often personal trauma shared vulnerably), moral clarity, and deft real-time coordination (“Tonight we tweet-storm the Senate!”) to turn disparate incidents into movements. This is mass relational labor: answering DMs from survivors late into the night, amplifying others’ stories, providing emotional support in group chats – all in addition to public messaging. It’s highly feminized work (care-oriented, communal) meeting cutting-edge communication tools. And it worked: these campaigns forced mainstream media and then institutions to confront issues long swept aside. The Symbolic (laws, official statements) shifted under pressure from the Imaginary and Real testimonies flooding social feeds. – Community Moderators (2000s–2010s) – Think of volunteer women moderating a large Facebook mom’s group or a health forum, or paid contractors (often in Philippines, many female) reviewing flagged posts for hate or violence. They are 21st-century street-level bureaucrats: applying platform rules case by case, but also using gut feeling to de-escalate conflicts or interpret context. Their emotional labor – witnessing horrific content, dealing with trolls – is immense. They develop folk guidelines (“if a user threatens self-harm, do X”) that sometimes later get adopted platform-wide in formal policy. But initially, it’s their personal mediation skill keeping communities from imploding. (One could compare them to the parish spinners of Wave 1 who kept production going through storms – these mods keep conversation going through flame wars.)

By mid-2010s, this feminine-coded social web achieved remarkable things: – It exposed powerful abusers (e.g., Harvey Weinstein’s downfall in 2017 came after years of whispered networks went public via social media – a triumph of networked female solidarity forcing the Symbolic (courts, press) to act). – It changed corporate behaviors: companies suddenly had to reckon with Twitter storms. Often they’d send their social media managers (many women) to apologize or adjust policies on the fly – essentially allowing these front-line communicators to shape company actions in response to community outcry. – It created new stars and narratives that had been locked out: diverse voices talking about mental health, sexuality, racism, etc., building direct followings and impacting culture without traditional gatekeepers.

However, cracks appeared: – Emotional burnout: Being an “influencer” or moderator is exhausting. The constant need to produce content or police content leads to anxiety, depression. Many primarily-female lifestyle creators spoke of “posting twice as much just to stay visible”, feeling like they can never log off. The surplus-enjoyment turned into surplus pressure. By 2019, studies showed a high burnout rate among full-time YouTubers (a pattern akin to how cottage spinners overworked to keep up). – Surface authenticity: As every brand and politician learned to have a “relatable” social media presence, the uniqueness of genuine voices diluted. The timeline got flooded with what sources call “noise-fields” – engagements hacks, repetitive memes, outrage cycles. Original tactics women pioneered (like heartfelt confessionals, interactive Q&As) became cookie-cutter marketing techniques employed by everyone – even bots pretending to be people. By late 2010s, irony and cynicism returned; younger users flocked to more “raw” platforms (like TikTok) to escape the polished Insta-perfect style – until that too became saturated. – Harassment and toxicity: Despite women moderators’ best efforts, platforms at large became notoriously hostile for women and minorities. Gamergate (2014) saw female journalists and developers viciously attacked online – a flashpoint showing platforms had not structurally addressed misogyny. The Real (hatred, violence) was oozing through the cracks of insufficient human moderation and weak platform policies. – Scalability issues: Human-centric management of communities doesn’t scale neatly to billions of users. Facebook discovered this with content moderation: by late 2010s they had tens of thousands of moderators, but still couldn’t catch all bad content. The pressure was on for AI solutions: algorithms to detect hate speech, misinformation, etc., more efficiently (a classic masculine-coded response – “let’s automate the messy human part”).

Enter Generative AI (2020s). The timing is key: just as the social media environment has become chaotically maximal (people posting constantly across apps) and somewhat hollow (so much derivative or performative content), AI tools like GPT-3/4, DALL·E, Midjourney arrive that can generate endless streams of on-brand text and imagery. For platforms and brands, this is tantalizing: why rely on fickle, costly human influencers or content writers when you can auto-generate posts every second tailored to trigger engagement? The code doesn’t get tired or demand royalties. Likewise, why leave moderation to burned-out humans who err or have biases, when you can train models to enforce rules consistently at scale?

So the pendulum swings to surplus-value mode: the power center shifts to: – Prompt engineers and AI trainers – a new profession of those who know how to talk to models to get the desired output (interestingly, early data suggests a decent number of women in some prompt-engineering roles, though the field skews male from its ML roots). These folks formalize the creative/artistic process into repeatable transforms. E.g., a woman who was a skilled copywriter now might spend her days crafting and tweaking prompts so the AI produces 100 social posts in her brand’s “voice.” She’s gone from being the originator of the voice to the curator of a prompt, a subtle but significant loss of expressive freedom (the AI can only recombine what it was trained on). – Policy architects and product managers – they codify community standards (defining harassing speech in a database of rules) and build “compliance layers” (like Meta’s “Gender Ethnicity Classifier” that automatically flags slurs). Often these are roles with more institutional clout (and more men relative to the moderators they sort-of replace). They design systems such that, ideally, problems route themselves (the Lacanian “letter arrives at its destination” – addressable by code, not human interpretation). For instance, Twitter’s 2020s approach to misinformation was to add automated labels – a rule-based overlay rather than relying on users to correct each other or on ad-hoc interventions. – AI content creators – on platforms, algorithms already drive what content gets seen (the feed ranking); with GenAI, they start producing content too. We have AI-generated influencers (like Lil Miquela on Instagram) who look and act human but are entirely managed by corporate teams. Here we see an outright cancellation of the human persona – a literal “woman does not exist, she’s a composite of data.” That is a perfect example of how in image culture (Imaginary), the feminine form is used but the actual women are removed from the equation – it’s a hyperreal scenario where the simulation of a relatable girl sells products without an actual girl being involved. Stating the obvious: this takes the commodification of female influence to its extreme (the company doesn’t have to worry about an AI influencer getting tired, hurt, or speaking out of line).

Returning to an earlier quote from sources: “Influence becomes inference; charisma yields to configuration.” A pithy summary. A concrete case: In 2019, an average TikTok teen might gain huge following by charisma and trend-savvy – influence. By 2023, companies are trying to bottle that formula into “AI-generated TikTok-optimized media” where you just input “charismatic girl dancing to upbeat song with these hashtags” and churn variants. The creativity and spontaneity of real girls (surplus-enjoyment) is being captured and turned into a parameterized service (surplus-value).

Yet, we are already seeing the limits and pushback: – Users increasingly seek authentic content – e.g., Gen Z shifts to BeReal app in 2022 because they crave unfiltered glimpses of real life, away from polished or AI-curated feeds. It’s a pendulum swing to “just us humans” as a value. – Down-ranking of spammy content: Platforms realized engagement-maximization led to “noisefields” of repetitive clickbait. Even before AI, they started adjusting algorithms to reward “meaningful interactions” (Facebook’s 2018 algorithm change)[37]. With AI, there’s fear feeds will be flooded by auto-generated filler (some SEO blogs already publish dozens of AI-written articles daily). Platforms may have to introduce verification of human content as a quality marker – ironically re-valuing the human origin. – Ethical and legal outcry: AI companies face heat for training on artists’ and writers’ work without permission (essentially stealing the labor of many, often including lots of women’s content like romance novels, fanfiction communities, etc.). Lawsuits have emerged (e.g., authors suing OpenAI). This is society saying: the way you formalized our surplus-enjoyment is unjust; return some power or payment to the creators. In response, we see moves to develop consent-based data sharing – a possible reintegration of human agency into the pipeline that had cut them out. – Quality and trust issues: AI moderation, while faster, sometimes makes egregious errors – e.g., flagging art of classical nudes as porn but missing coded hate speech. It lacks context that humans (often women mods attuned to context) would catch[36]. Recognizing this, platforms are keeping or re-introducing human review panels for the thorniest cases (e.g., Facebook’s Oversight Board – albeit not all women by any stretch, but a human symbolic body). And companies dealing with generative outputs (like an AI chatbot prone to offensive remarks) are adding “HCE” (Human in the Loop for Critical Escalations) as a design principle.

One powerful illustration: #MeToo’s digital phase (2017) succeeded largely via human storytelling and solidarity – but by 2023, some companies tried to address harassment with AI-powered HR chatbots or mandatory e-learning modules. Many employees find those hollow, preferring a trained human to confide in or facilitate discussion. So even in addressing the wave 4 issues that women’s networks raised, some institutions defaulted to tech fixes that women often find inadequate, leading to further demands for human-led solutions (like trauma-informed counselors in workplaces).

Let’s give the final imagined voice to a 2024 content creator who fears her unique voice is being drowned by the very algorithms she once rode to fame (reprising our earlier “I am not an unpaid prompt” livestream excerpt):

Livestreamer “Luna” – January 2024: “I won’t be an unpaid prompt for some machine. This is my voice, not just ‘data’ to scrape. If the algorithm buries me because I won’t play its latest trend game, so be it – I’ll show up for those who still tune in, and we’ll keep it real even if it’s smaller. Because you can’t build a community by posting less or by faking it; you build it by showing up when it’s ugly. The brands say they want ‘authenticity’ but then they schedule us like clockwork; well, I’m not a clock. I’ll be consistent like the weather – relentless, sometimes surprising. And brands, hear me: what you really hire me for isn’t just content, it’s trust. My people trust me because I’m there for them daily, in DMs at 2 am when someone’s hurting, in jokes that only we get. You can’t bottle that. You can’t parameterize care. I will give proof of that trust every day, and no, I won’t hand that on a platter to a machine to mimic. If the model tries to copy me, I’ll get so specific it chokes on the details.”

This passionate declaration encapsulates the wave 4 pivot point: – She rejects being just fodder (“unpaid prompt library”) for AI – asserting her creative labor has intrinsic value beyond what can be codified. This echoes wave 2 secretaries refusing to just be “machine appendages” and wave 3 PR pros warning not to cut the human element. – “Showing up when it’s ugly” and doing late-night emotional support – these are precisely the surplus-enjoyment tasks (unpaid emotional labor) that build trust capital. She highlights them to contrast with algorithmic output which might hit consistency in form but never truly show up in substance. – “Consistent like the weather – relentless”: She’s reclaiming consistency (a metric platforms demand) but on organic terms (the weather has patterns but also variability and force – unlike an anodyne scheduled post). It’s a poetic way to say, I’ll meet your need for regular content but in a living, dynamic way, not robotic. – The direct appeal to brands: Recognizing her leverage (“what you hire is trust”) and subtly threatening (“you can’t get trust from an AI or a formula. If you lose creators like me, you lose the audience’s trust too”). This is basically reasserting that the human mediator – the woman behind the influence – still carries something irreducible that the new regime can’t capture. It’s reminiscent of wave 1 spinners saying the mule won’t keep faith like a mother can, or wave 3 PR saying you can’t buy forgiveness with CPMs.

As we stand in 2025, wave 4’s outcome is still unfolding. Will the feminine-coded “influencer” and community leader roles be entirely commodified by AI and corporate control, or will they adapt/bargain a new symbiosis? History suggests a symbiosis: the human sensibility will find new recognized niches (perhaps “community quality controllers” or “ethics reviewers” become valued positions, many likely filled by women with those skills). Already we see job postings for “AI ethicist” or “prompt curator” – arguably wave 4’s version of wave 2’s librarian or wave 3’s diversity officer. These roles formalize some tacit human insight so it can travel with the tech.

Looking Forward (Wave 5?) – Toward Bilingual Systems: If a fifth wave of feminism emerges, perhaps around protecting the symbolic depth of human experience in the age of AI (early signs: movements for data rights, emphasis on mental health vs. algorithmic harms, etc.), it will likely call for what we’ve termed “symbolic infrastructure”: technologies and policies consciously designed to preserve lack, depth, and human choice (for example, apps that encourage moments of offline silence or interfaces that require a human check for certain decisions). This would be a swing back to the feminine-coded recognition that not everything should be optimized; some things must remain slow, ambiguous, or dialogical. The IPA/FLŽ strategy note (referenced in sources) advocates such: reintroducing meaningful gaps (Lacan’s “lack”) into our over-saturated image culture, lest we lose subjectivity entirely. In essence, it is a plea to re-braid the human thread into the digital loom before the pattern loses all richness.

Thus, the grand pendulum may not be a fate of eternal oscillation if we learn its lesson: bilingualism and balance. The “making and breaking of womanhood” across these waves teaches that neither code of power (the relational or the formal) can stand alone without collapsing into absurdity. Women – as symbolic carriers of the relational code – have been repeatedly “made” central to new systems and then “broken” (or erased) when those systems swing to the opposite mode. The goal ahead is not a return to some ideal past, nor a naive embrace of every new automation, but a conscious integration: designing technology and institutions that value the human mediator alongside the machine, and vice versa.

In practical terms, that means: – Preserve the human templates in design: Keep experienced people (often women from community roles) in the loop of AI deployment – e.g., content moderators not fully replaced but augmented, and their qualitative insights used to shape AI outputs[36]. – Parameterize the tacit, but don’t evacuate meaning: Document and encode the crucial human insights (as style guides, data tags, etc.) with the involvement of those humans, ensuring the encoded version still carries ethical and contextual nuance. (For instance, prompt libraries built by actual creators, so the AI echo of their voice stays true and they’re compensated for it.) – Own the interface: Ensure that those adept in relational work move into positions where they set the rules for machines – e.g., women community leaders becoming platform policy makers, so that the policies reflect lived social wisdom, not just abstract logic. – Measure what matters (include qualitative): Add metrics for trust, well-being, and community health to counter purely quantitative KPIs. (Already some companies include “Net Trust Score” or “Community Vitality Index” in reports – often initiatives pushed by women execs in CSR or HR – these create a formal space for the formerly tacit concerns.)

To conclude, the arc of these waves suggests that womanhood – as a set of socially cultivated competencies – has been repeatedly devalued and then vindicated. Each time, the culture “broke” what women uniquely contributed, only to find that contribution re-emerging as indispensable (sometimes under a new name or through a new group). Recognizing this pattern is key to breaking it. If we can learn to be “bilingual”, as the content says – able to “turn desire into procedure without losing the pulse, and let procedure serve desire without mistaking the map for the world” – we might design a future where the pendulum finally rests in equilibrium, and the making and breaking of womanhood gives way to a continuous co-making of a more humane world by all genders, together.

---

[1] [7] [8] Spinning mule – Wikipedia

https://en.wikipedia.org/wiki/Spinning_mule

[2] [23] Nielsen ratings | Description, Facts, & History | Britannica

https://www.britannica.com/art/Nielsen-ratings

[3] [4] Nova music festival massacre – Wikipedia

https://en.wikipedia.org/wiki/Nova_music_festival_massacre

[5] [9] [10] [11] [12] [24] [34] [36] Why Is Clerical Work Women’s Work? – JSTOR Daily

Why Is Clerical Work Women’s Work?

[6] [26] [27] 1801: Punched cards control Jacquard loom | The Storage Engine | Computer History Museum

https://www.computerhistory.org/storageengine/punched-cards-control-jacquard-loom/

[13] [14] [16] [17] [18] [35] [37] What the automation of telephone operators tells us about AI’s effect on work | Vox

https://www.vox.com/future-perfect/2023/7/18/23794187/telephone-operator-switchboard-automation-att-feigenbaum-gross

[15] Telephone Operators: The Elimination of a Job

Telephone Operators: The Elimination of a Job

[19] [20] Women in computing – Wikipedia

https://en.wikipedia.org/wiki/Women_in_computing

[21] [32] The Gendered History of Human Computers – Smithsonian Magazine

https://www.smithsonianmag.com/science-nature/history-human-computers-180972202/

[22] Women Once Ruled the Computer World. When Did Silicon Valley …

Women Once Ruled the Computer World. When Did Silicon Valley Become Brotopia?
byu/Zer_ inMensLib

[25] Norns – Wikipedia

https://en.wikipedia.org/wiki/Norns

[28] MOIRAE (Moirai) – The Fates, Greek Goddesses of Fate & Destiny (Roman Parcae)

https://www.theoi.com/Daimon/Moirai.html

[29] Slavoj Zizek on Atheism & Christianity – SCAPEGOAT SHADOWS

Slavoj Zizek on Atheism & Christianity

[30] The Free Church a Witness to the Brotherhood of Humanity, by Henry Codman Potter (1877)

https://anglicanhistory.org/usa/hcpotter/free_church1877.html

[31] Generative AI and the Future of Entry-Level Jobs | AlphaSense

https://www.alpha-sense.com/blog/trends/generative-ai-entry-level-jobs/

[33] The Creation of a Gendered Division of Labor in Mule Spinning

https://www.cambridge.org/core/journals/enterprise-and-society/article/creation-of-a-gendered-division-of-labor-in-mule-spinning-evidence-from-samuel-oldknow-17881792/6759C026597F912D002B2684022A600B