Replacing, Augmenting, or Resisting? Interpretive Repertoires of Human–AI Relations at Work in Reddit Discussions

Abstract

Public discussion of artificial intelligence and employment is often reduced to a binary between technological optimism and pessimism. This qualitative study examines 3275 substantive comments from 22 purposively selected English-language Reddit threads, spanning 18 subreddits and thirteen occupational or institutional domains, collected in early April 2026. Drawing on interpretive-repertoire analysis and using sociotechnical imaginaries as a sensitizing framework, the study identifies nine recurring vocabularies through which participants construct relations between AI and human labor: replacement; displacement and stratification; augmentation and complementarity, including augmentation-as-intensification; surveillance and subordination; algorithmic gatekeeping; selective immunity and embodied resistance; adaptation mandate; institutional redesign; and replacement reversal. The sampled discussions suggest occupationally patterned differences in the salience of these repertoires. Creative work discussions frequently foregrounded replacement and stratification, service-work discussions emphasized intensification and control, skilled-trades discussions mobilized embodied immunity, and legal discussions foregrounded institutional redesign. The findings show that vernacular discussion differentiates among multiple locations, mechanisms, and beneficiaries of AI-related change that cannot be captured adequately through a simple optimism–pessimism distinction.

1. Introduction

Public conversations about artificial intelligence and employment tend to gravitate toward a binary narrative: either AI will eliminate most jobs, or it will create as many as it destroys. This opposition between technological optimism and pessimism structures media coverage across countries [1,2,3], policy documents, and popular commentary. The framing has deep roots. Since Frey and Osborne [4] estimated that 47 percent of US occupations face high risk of automation, a wave of forecasts has debated whether AI systems will replace human workers or merely change the tasks they perform [5]. Successive rounds of commentary, first around robotic process automation, then around large language models, have kept the question alive while shifting its referents [6]. Yet the optimism versus pessimism axis captures only a small part of what people actually say when they talk about AI at work.

Scholarship in science and technology studies, sociology of expectations, and media studies has begun to catalogue the discursive frameworks through which AI enters public understanding. Roberge, Senneville, and Morin [7] trace how AI is translated across social worlds through myths and justificatory narratives. Natale and Ballatore [8] show that recurring cultural myths, such as the thinking machine or the superhuman brain, shape public reception of AI long before concrete applications arrive. Sartori and Bocca [9] find that public perceptions of AI cluster around sociotechnical imaginaries that differ across national contexts, while Wang, Downey, and Yang [10] compare how newspapers in the UK, China, and India construct distinct AI futures. In the sociology of expectations, Vicsek [11] argues that expectations about AI and the future of work operate performatively: they channel investment, justify organizational change, and condition workers’ anticipatory behavior. This body of work establishes that AI discourse is not a mere reflection of technical capacities but an active arena of meaning-making.

A growing literature examines how AI reconfigures the workplace itself. Kellogg, Valentine, and Christin [12] identify algorithms as a new terrain of labor control, operating through restriction, recommendation, and evaluation. Joyce et al. [13] call for moving beyond technological determinism to examine the social relations that shape how digital technologies are adopted. Heinlein and Huchler [14] show that AI may not simply standardize work, but can also serve to maintain the situated knowledge that workers already hold. Nguyen and Elbanna [15] review the literature on human and AI augmentation in the workplace and find that the field remains fragmented, with little consensus on what augmentation means in practice. Woodruff et al. [16] report that knowledge workers expect AI to transform some industries more than others, and that their expectations are shaped less by technical assessments than by occupational identity. What these studies share is an attention to the gap between predictions about what AI will do and the concrete, situated ways in which people make sense of it within their own domains of work.

Less examined is how ordinary people articulate the relationship between AI and their labor. Vernacular online discussion offers a distinct site for such inquiry. Reddit, in particular, hosts large-scale conversation across occupational communities [17]. Its subreddit structure organizes talk by profession, sector, and topic, creating spaces where software engineers, teachers, graphic designers, lawyers, and electricians develop arguments about AI in terms calibrated to their own work. The platform’s pseudonymous norms and upvote mechanisms further shape what positions gain visibility [17]. Reddit discussions about AI and work are thus neither opinion polls nor expert assessments; they are collective, ongoing, and embedded in specific occupational contexts.

This paper asks: what recurring vocabularies do Reddit users mobilize to define the relation between AI and human labor? Drawing on the concept of interpretive repertoires [18], understood as relatively coherent ways of talking about a topic that function as shared resources within a community, we identify nine distinct vocabularies through which posters construct the human–AI relationship at work. These range from full replacement and gradual displacement to augmentation, partnership, and outright resistance. The concept of sociotechnical imaginaries [19,20] provides a complementary lens, directing attention to the collectively held visions of technological futures that sustain and legitimate particular vocabularies.

Our analysis draws on 3275 substantive comments from 22 purposively selected English-language Reddit threads spanning thirteen occupational or institutional domains, collected in early April 2026. The threads were chosen to maximize variation across occupational settings, subreddit ecologies, and initiating formats. Through qualitative thematic coding informed by discursive analysis, we construct a typology of the relational vocabularies available in these sampled discussions about AI and work.

The contribution is threefold. First, the paper moves beyond the optimism-versus-pessimism perspective by showing that participants in the sampled discussions draw on a differentiated set of relational vocabularies. Second, it grounds the study of AI discourse in occupationally situated Reddit discussion rather than in media frames or expert pronouncements. Third, the typology provides an analytical tool for subsequent comparison across platforms, periods, languages, and occupational contexts. The paper also contributes to an emerging sociology of generative AI [21] by treating vernacular discussion of AI’s labor-market effects as a topic amenable to systematic discursive analysis.

2. Literature Review

2.1. Framing AI and Work: From Binary Sentiment to Relational Vocabularies

Research on how AI enters public awareness has concentrated on media coverage. Cools, Van Gorp, and Opgenhaffen [1] show that US newspaper reporting on AI and automation oscillates between utopian and dystopian frames, with the balance varying by publication type and topic domain: labor automation, for instance, attracts more dystopian framing than consumer-facing applications. Ittefaq et al. [3], analyzing over 4000 articles from twelve countries, report that negative sentiment toward AI increased after 2020 and accelerated following the launch of ChatGPT in late 2022, with job displacement ranking among the most frequent thematic frames. Sanguinetti and Palomo [2] add a regional dimension, finding that AI anxiety in newspapers increased after ChatGPT’s release in regional outlets but not in national ones, indicating that institutional position and audience shape how AI is reported. This is consistent with wider media research showing that coverage actively constructs, rather than simply transmits, public understanding of contested socio-technical topics [22]. Beyond media framing, Gámez-Guadix and Mateos-Pérez [23] found that higher levels of academic anxiety in universities were linked to more frequent use of generative AI among students overall. Across the media studies discussed above, public discourse about AI and work is mapped through sentiment polarity and thematic categories. The most prominent are positive versus negative and opportunity versus threat, which are useful for detecting broad trends but poorly suited to capture the specificity of what people say about AI in relation to their own work.

The limitation becomes apparent when one moves from media to vernacular discourse. Reddit users do not simply express optimism or pessimism; they articulate particular models of how AI relates to labor, including full substitution, gradual displacement, productivity enhancement under employer control, and selective immunity of certain trades. To capture this specificity, we draw on the concept of interpretive repertoires as developed in discursive psychology by Potter and Wetherell [18]. Interpretive repertoires are recurrent systems of terms, metaphors, and images that speakers use to construct versions of events and actions. They function as shared linguistic resources available within a community, and different repertoires can be mobilized by the same speaker in different contexts. The concept directs analysis away from measuring attitudes and toward identifying the vocabularies through which people reason about a topic, an approach already applied to the vocabularies of competence through which publics position generative AI [24] and to the ordinary folk theories through which users account for automated agents online [25]. In the domain of AI in the workplace, this means asking what relational models they construct and what assumptions each model carries about tasks, agency, institutions, and the future. Kelan [26], reviewing popular books about the future of work, identifies a related gap: the automation versus augmentation perspective conceals implicit assumptions about gender, class, and race that structure who is expected to lose and who is expected to gain. A repertoire-based approach can make such assumptions visible.

2.2. Sociotechnical Imaginaries and the Performativity of Future Talk

When people discuss what AI will do to their jobs, they are not only reporting perceptions but constructing visions of the future that carry institutional and moral weight. The concept of sociotechnical imaginaries, introduced by Jasanoff and Kim [19] and developed further in their edited volume [20], designates collectively held visions of desirable or feared futures that are sustained by shared understandings of social order and animated by technological possibility. Imaginaries are less explicit than policy agendas; they operate in the space between discourse and decision, providing the cultural resources from which actors build preferences and justify choices [19]. Sartori and Bocca [9] apply the concept to public perceptions of AI, showing that survey respondents cluster around distinct sociotechnical visions that vary by gender, education, and prior familiarity with AI; comparable social variability appears in European survey data on perceptions of AI [27]. Wang, Downey, and Yang [10] demonstrate that newspapers in the UK, China, and India construct different national imaginaries of AI, with UK coverage emphasizing surveillance and employment risk, Chinese coverage foregrounding economic development, and Indian coverage centering inequality and professional displacement.

The sociology of expectations offers a complementary perspective. Vicsek [11] argues that expectations about AI and the future of work operate performatively. They channel investment, legitimate organizational restructuring, and condition how workers anticipate their own trajectories. Drawing on Beckert’s notion of fictional expectations, she shows that projections about AI carry force because they coordinate action under conditions of uncertainty. Powerful narratives can foreclose alternatives, marginalizing discourses that do not fit the dominant expectation [11,28]. Roberge, Senneville, and Morin [7] extend this line of argument by analyzing how AI is “translated” across social worlds through myths and justificatory narratives. They show that a small number of authoritative translators, such as tech corporations, state institutions, or renowned scientists, set the terms of debate, while criticism and justification circulate in an asymmetric relationship in which justificatory claims tend to absorb their critics. Natale and Ballatore [8] trace the longer history of the “thinking machine” myth, demonstrating that technological myths about AI are sustained not despite controversy but through it. As such, skeptics and proponents jointly maintain the myth’s public salience. These accounts establish that AI discourse is a site of active meaning-making in which vocabularies are not neutral descriptions but performative interventions [29].

2.3. From Replacement to Relational Models: Algorithmic Management and Labor Process Perspectives

If vernacular AI discourse is performative rather than merely descriptive, then what people call “replacement” may refer to a range of distinct processes. The algorithmic management literature helps disaggregate the category. Kellogg, Valentine, and Christin [12] identify algorithms as a new terrain of labor control, operating through six mechanisms: restricting, recommending, recording, rating, replacing, and rewarding. Their framework shows that algorithmic systems reshape work by directing, evaluating, and disciplining workers in real time. A distinctive feature of algorithmic control, they note, is its comprehensiveness: it can cover aspects of work that were previously beyond direct managerial oversight. Wood [30], reviewing the consequences of algorithmic management for work organization, similarly emphasizes that algorithmic systems operate simultaneously as tools of direction, surveillance, and evaluation, making it misleading to treat “automation” as a unitary phenomenon.

Joyce et al. [13] call for moving beyond technological determinism in research on digital technology and work. They argue that the future of work is shaped by social relations such as power asymmetries, institutional rules, and collective bargaining arrangements within which those systems are adopted. This future may also involve new forms of social structuring through relations in the labor market, education, and employment. In their study of the demand for new AI-related educational programs among managers and private-sector actors, Pisica, Giurca, and Zaharia [31] find that employers expect at least a basic familiarity with AI concepts and use, suggesting that discussion of AI and the labor market extends across multiple areas of labor-related discourse; the same pattern appears in the skills and epistemic expectations articulated in AI-related job postings [32].

This position has direct implications for how we read vernacular discourse: when a Reddit user says “AI will replace copywriters,” the claim may encode a prediction about technical capability, a judgment about employer behavior, or a complaint about market restructuring. Heinlein and Huchler [14], drawing on German workplace case studies, show that AI can function both as a tool of standardization and as a means of preserving the situated knowledge that workers hold. Rather than flattening tasks, some AI implementations maintain the operational discretion that skilled workers already exercise. Nguyen and Elbanna [15], reviewing the human–AI augmentation literature, find that augmentation itself is an ambiguous term that may mean productivity enhancement for workers, efficiency gains for employers, or the creation of new verification and oversight tasks that did not previously exist. Similea et al. [33] document this ambiguity in practice, showing how professionals who delegate tasks to generative AI reorganize rather than simply reduce their working time, and a comparable ambivalence between assistance and control has been described in everyday uses of conversational AI [34]. Baygi and Huysman [35] argue that generative AI reshapes the social fabric of organizations, including the flows of expertise, trust, and collegiality through which work is coordinated, rather than operating only at the level of individual tasks. In the same organizational landscape, according to Aldossary, Ayad, and Moustafa [36], employees often prioritize data-driven insights over traditional managerial guidance, a shift that effectively diminishes the moderating influence of leadership commitment on performance outcomes. Taken together, these studies caution against treating any single label, for example, “replacement,” “augmentation,” or “automation”, as self-explanatory. Each term covers a family of processes whose meaning is specified in use.

2.4. Reddit as a Site of Public Discourse

Reddit is among the most widely used platforms in social science research, and its structural features make it particularly suited to the study of occupational discourse. Proferes et al. [17], in a systematic review of 727 empirical studies using Reddit data, show that the platform is studied across computer science, communication, psychology, and sociology, with applications ranging from text mining to qualitative ethnography. Reddit’s organization into subreddits creates topically bounded communities with distinct norms, vocabularies, and moderation regimes. Massanari [37] characterizes Reddit as a participatory culture in which community governance, identity construction, and knowledge exchange are co-produced by users, moderators, and platform affordances. Each subreddit stabilizes a particular discursive register, so that the same topic is discussed differently in a programming subreddit than in a skilled-trades forum or a policy-oriented community. Studies of Reddit discussion of AI show how users negotiate moral agency within these registers [38] and how mistrust of AI is thematically organized across threads [39].

The platform’s upvote and downvote system further shapes what discourses become visible. Graham and Rodriguez [40] analyze the sociomaterial effects of Reddit’s voting mechanism, showing that it creates conformity incentives because positions that align with a subreddit’s prevailing norms gain visibility, while dissenting views are suppressed. This means that the discursive landscape of a Reddit thread is not a neutral sample of opinion but a product of collective curation. Litherland and Wood [41], studying climate change discourse on Reddit, find that the platform’s affective discursive register, characterized by irony, geek humor, and dystopian popular culture references, shapes how users articulate their relationship to large-scale threats. Bervar, Pejić Bach, and Bertoncel [42], in a text-mining study of Reddit discussions about generative AI in creative industries, report persistent tensions between enthusiasm and ethical concern, with users emphasizing authenticity, craft, and the risk of homogenization. These findings confirm that Reddit is not a passive container for preformed opinions but an active environment in which platform affordances, community norms, and collective curation co-produce the vocabularies available for public reasoning about technology and work.

3. Data and Method

3.1. Corpus

The archived collection contains 3357 comment records from 22 Reddit threads in which users discuss the effects of AI on employment (see Appendix A). The comments were obtained through automated extraction using the Reddit API and stored in one structured workbook per thread (retained fields: comment identifier, body, username, date, score, and permalink); platform metadata reported 3757 comments for these threads, while 3357 rows were successfully downloaded, with unavailable or otherwise non-returned records accounting for the gap. A deterministic audit of the 22 numbered workbooks found no duplicate comment identifiers. Eighty-two records whose body text was marked [deleted] or [removed] were retained in the archived collection for transparency but excluded from qualitative analysis. The analyzed corpus therefore comprises 3275 substantive (text-bearing) comments. The workbooks were assembled in early April 2026 and contain comments dated from 18 February 2023 through 2 April 2026; the corpus spans eighteen subreddits and thirteen occupational or institutional domains: hiring (T01, T09, T10), software/IT (T02), general AI (T03, T04), customer service (T05, T06, T07, T08), employment systems (T11), writing (T12, T13, T14), design (T15), education (T16, T17), medicine (T18), skilled trades (T19), policy (T20), academia (T21), and law (T22). The threads were originally posted across this window rather than at a single moment, and the corpus was analyzed as a single cross-sectional archive assembled at the collection date.

3.2. Corpus Discovery and Theoretical Sampling

The corpus was assembled through purposive theoretical sampling designed to maximize conceptual variation rather than statistical representativeness. Materials retained during corpus development show that an initial pool of 25 candidate threads was identified through recorded Reddit searches for “AI replacing jobs,” “AI taking over,” “AI and work,” “ChatGPT and jobs,” and “AI layoffs,” followed by targeted browsing of occupation-specific subreddits concerning job replacement, hiring, workplace monitoring, call-center work, copywriting, graphic design, teaching, medicine, skilled trades, academic work, and legal work.

Candidate threads were assessed against five preliminary criteria: substantive focus on AI and work; English-language discussion; public accessibility; sufficient interaction to support qualitative analysis; and contribution to occupational, institutional, or discursive variation. Three candidate threads were not included in the final corpus: two recruiting-related threads with approximately four and zero comments, respectively, and one copywriting thread that substantially duplicated material already represented in the selected copywriting discussions. The resulting corpus contained 22 threads.

A threshold of approximately 30 comments was used as a general indicator of discursive elaboration rather than as an absolute eligibility rule. Two smaller threads were retained as theoretically informative contrasting cases. T08 was retained because it centered on an attempted AI substitution described as unsuccessful, and T18 supplied a profession-specific critique of replacement claims from within medicine. These decisions were made at the level of corpus construction and should not be confused with the analytical repertoire categories developed during coding.

The sample was designed to vary across occupational domain, subreddit ecology, and initiating format. Threads included personal accounts, occupational questions, workplace experiences, and discussions prompted by public claims or news events. These broad contrasts sensitized corpus construction but did not constitute the final codebook. The nine interpretive repertoires reported in the Findings Section were developed subsequently through iterative qualitative analysis.

3.3. Analytical Procedure and Category Development

The analysis combined qualitative thematic coding with the interpretive-repertoire approach of Potter and Wetherell [18]. The individual comment was treated as the principal unit of analysis, while the thread title, initiating post, surrounding discussion, occupational setting, and subreddit context informed interpretation. The objective was not to classify users into mutually exclusive attitude groups but to identify the recurrent discursive resources through which participants constructed relations between AI and labor.

Category development proceeded iteratively. Two authors first conducted a close reading of five contrasting threads—T03, T04, T11, T16, and T19—selected because they differed in occupational setting, initiating format, and apparent relation between AI and work. This generated a provisional set of relational vocabularies. The provisional categories were then compared with material from the remaining threads. Categories were merged when they shared the same relational logic, divided when they attributed substantially different forms of agency or causality, and reformulated when new cases exposed unclear boundaries.

The codebook was stabilized around three recurring analytical questions: what position does AI occupy in relation to human labor; where is causal agency located; and what occupational or institutional future is projected? A second interpretive pass examined attributed mechanisms, including cost reduction, speed, managerial preference, market restructuring, and technological inevitability, together with future modality and occupational context.

The analysis was interpretive and non-exclusive. A comment or thread could mobilize more than one repertoire, and no assumption was made that each comment must receive a single primary code. Two authors conducted the close reading and discussed provisional and borderline cases, resolving them by comparing passages with the operational definitions and boundary rules in the codebook and by returning to the original thread context; the categories were refined iteratively through this process. Because the analysis treats repertoires as overlapping discursive resources rather than mutually exclusive variables, no formal intercoder-reliability coefficient was computed; the aim was analytical differentiation rather than the measurement of population frequencies.

Given the size of the corpus, the authors used large language models (Claude Sonnet 4 and Claude Opus 4.6, Anthropic) in a limited, supplementary capacity after the codebook had been finalized. Their use was confined to flagging candidate passages and locating candidate quotations for the authors to assess against the established categories (see Table 1); they generated no categories, made no coding or interpretive decisions, and produced no frequency estimates. The instruction given to the models is reproduced in Appendix B. Every passage they returned was verified by the authors against the codebook and the original thread context, and only quotations confirmed in the source material were retained. Restricting the models to retrieval reflects evidence that the consistency of large language model coding varies and requires human verification [43].

Table 1. Operational definitions and boundary rules for the nine interpretive repertoires.

Repertoire	Operational Definition	Boundary Rule
Replacement	AI is constructed as occupying the functional position previously held by a human worker, task group, or occupational category.	Unlike displacement, the central claim is substitution rather than redistribution among tiers or roles.
Displacement and stratification	AI reorganizes an occupational market or workflow, reducing some tiers while preserving or advantaging others.	Jobs or occupations continue to exist; the principal issue is unequal redistribution rather than total substitution.
Augmentation and complementarity	AI assists human work while humans retain an active productive or supervisory role. This includes empowerment and intensification variants.	Distinct from surveillance when the central issue is assisted production rather than monitoring or discipline.
Surveillance and subordination	AI or algorithmic systems monitor, measure, evaluate, or discipline workers who remain employed.	Operates within employment; gatekeeping operates at the point of entry into employment.
Algorithmic gatekeeping	AI controls or mediates access to jobs through screening, ranking, filtering, or automated evaluation.	The key relation is between candidate and employment opportunity, not employee and work process.
Selective immunity and embodied resistance	An occupation is constructed as protected because of embodied skill, physical presence, situated judgment, care, liability, or material unpredictability.	Unlike augmentation, the claim emphasizes limits to substitution rather than productive cooperation with AI.
Adaptation mandate	Responsibility for responding to AI is assigned to individuals through demands to upskill, diversify, pivot, or accept displacement.	The defining feature is the moral assignment of responsibility, not merely a prediction of occupational change.
Institutional redesign	AI transforms billing systems, organizational hierarchies, team composition, professional pipelines, or distributive institutions.	The unit of transformation is the institution or occupational structure rather than an isolated job or worker.
Replacement reversal	AI substitution is constructed as failing, being reversed, or proving less capable than replacement narratives predicted.	Unlike general immunity claims, this repertoire centers on a concrete or narrated correction of substitution expectations.

3.4. Ethics

Although the analyzed discussions were publicly accessible, public availability was not treated as sufficient ethical justification by itself. We recognize that Reddit users employ pseudonyms and may not anticipate their contributions being reproduced in academic publications. We also recognize that removing usernames does not eliminate re-identification risk, because verbatim excerpts may be discoverable through search engines [44,45,46]. Verbatim wording nonetheless remains analytically important in interpretive-repertoire research because metaphor, modality, evaluative language, and rhetorical formulation constitute part of the evidence. Quotations retain their original spelling and grammar. To balance analytical fidelity with participant protection, usernames were omitted, excerpts were limited to the minimum wording necessary for the analysis, and quotations containing unnecessary biographical or potentially sensitive detail were avoided. Thread-level occupational and subreddit context was retained only where required for interpretation. The authors acknowledge that residual traceability cannot be eliminated entirely and treat this as a limitation of research using publicly searchable online discourse. The data were collected passively, without interaction with users or modification of threads [47]. No attempt was made to link comments to specific users across threads or platforms. Additional corpus accounting, sampling reconstruction, the operational codebook, the thread-by-repertoire qualitative salience matrix, and quotation-handling details are provided in the Supplementary Materials (Tables S1–S4 and Section S5).

3.5. Limitations

Four limitations should be noted. First, Reddit’s user base is not representative of the general population and is shaped by demographic, linguistic, and platform-participation inequalities [17]. Second, voting, moderation, and ranking mechanisms affect which positions become visible [40], so the visible discourse reflects collective curation as much as individual expression. Third, the corpus is limited to purposively selected English-language threads collected at one point in time; the analysis therefore supports theoretical differentiation rather than estimates of prevalence. Fourth, platform metadata reported more comments than were successfully returned by the Reddit API (3357 of 3757 records), so some posted comments are absent from the archived corpus; this coverage gap reflects deletion, restriction, and non-return at the time of extraction rather than selective inclusion, and it does not affect the provenance of the analytical categories, which were developed and verified by the authors.

4. Findings

The analysis identifies nine interpretive repertoires through which Reddit users construct the relation between AI and human labor. Each repertoire carries distinct assumptions about what AI does to work, where agency lies, and what kind of future is projected. The repertoires are not mutually exclusive: multiple repertoires may appear in the same thread, and individual users sometimes shift between them. What follows presents each repertoire with its defining logic and supporting evidence from the corpus.

4.1. Replacement Repertoires

A prominent vocabulary in the sampled corpus frames AI as a direct substitute for human workers. Replacement claims differ, however, in their scope and attributed mechanism. In some threads, replacement is constructed as total, predicting that AI will eliminate entire occupational categories. In others, it is partial, targeting specific tiers of the workforce, typically entry-level positions. The causal logic also varies. Some users attribute replacement to technical capability, encapsulating the idea that AI can now do what humans did, while others attribute it to employer behavior through claims that corporations prefer cheaper AI regardless of quality.

In T04, where workers report layoffs attributed to ChatGPT, replacement is narrated as an organizational decision driven by cost reduction: “Bro they laid off folks to make headcount for more AI focused folks.” The brevity of this comment is characteristic of replacement framed as a natural fact: the layoff has already happened, and the statement functions less as an argument than as testimony.

Other users in the same thread frame replacement through historical analogy, locating AI within longer automation cycles:

“You seriously think Typesetters survived the rollout of the Personal Computer and graphic design tools? Phone Operators when VoIP and Cellphone took the majority of landlines away? … replacing jobs has always been the name of the game, with some productivity gains on the side.”

In the copywriting threads, replacement is articulated through the language of occupational death. One user in T13, defending the claim that copywriting is “dead (almost),” frames the argument not through AI’s technical capacity but through the market’s indifference to quality. In T12, a veteran copywriter responds to optimistic framings with flat contradiction: “Fucking how? I have been a copywriter since 2011 and the last 5–6 years have been abysmal.”

Entry-level elimination is a recurrent variant. In T02, a CS-career thread, replacement is specifically constructed around age and experience:

“Yeah, if I’m OP and still in school, I’d go back to medicine and healthcare. Speaking as a 40yo working in big tech, I honestly think Claude will replace a lot of new grads position.”

In T15, a graphic designer who graduated in 2022 faces a pipeline that has been eroded from the bottom:

“Normally, graphic designers can start out with easy community posts … but Canva has single handedly destroyed that entry-level role. … eventually there will be no entry-level role candidates, and then no senior-role candidates, and so on.”

Across these threads, the replacement repertoire is not a single claim but a family of related constructions. What unites them is the assertion that AI occupies the same functional position as the human worker. What varies is the mechanism (cost, speed, quality indifference), the scope (total, entry-level, occupation-specific), and the temporal frame (already happened, imminent, gradual).

4.2. Displacement and Stratification Repertoires

A second vocabulary describes AI not as a substitute for workers but as a force that reorganizes markets and workflows, eliminating some tiers while preserving or expanding others. Displacement differs from replacement in that jobs do not disappear outright; instead, the occupational field is restructured so that low-end work erodes while higher-skill or higher-revenue work persists. More broadly, displacement can be understood as a relational process of disconnection and reorganization rather than simple disappearance [48].

In T05, a call-center worker describes stratification by client value:

“I’m in a division of my company that is higher revenue, and they don’t allow the overseas call center workers to handle my clients, so I’m kind of already there. There’s also a system that will kick callers to a live agent if the call gets too complicated so I’m alright for now. It’s the fact that they’re introducing it at all that worries me.”

The phrase “I’m alright for now” captures the conditional quality of this repertoire: protection is temporary, stratified, and subject to revision. In a similar vein, in T13 a copywriter applies the logic to the creative industries, arguing that the market has shifted away from the skills copywriters traditionally offered:

“That’s all outdated thinking. Branding these days is not a slick 3 min film that makes you feel like you can run a marathon if only you had those shoes and that attitude. Branding is now simply user experience. Look at Nike to see whether or not old school cool brand projection matters. It doesn’t. Selection, innovation, delivery and social endorsements are the ONLY currency these days.”

In T04, displacement is framed not as a change in job content but as a change in speed. One user distinguishes AI displacement from earlier automation waves by foregrounding the rate of change:

“It is not about old jobs and new ones. It is about the rate of disappearing jobs that will come. Think of COVID. There was viruses before, but the massive impact of the global pandemic was because it was the rate at which it occurred.”

The displacement repertoire constructs a world in which occupations survive in modified form, but the distribution of work, income, and security within them shifts. The winners are those who occupy the protected tiers—higher-revenue clients, senior roles, specialized niches—while the losers are those at the entry level or in routinized segments.

4.3. Augmentation and Complementarity Repertoires

A third vocabulary positions AI as a tool that enhances human work rather than replacing it. This repertoire takes two forms. In its positive variant, AI is constructed as a productivity amplifier under human control. In its critical variant, augmentation as intensification, AI raises throughput expectations while narrowing worker discretion, so that “augmentation” serves the employer more than the worker.

The positive variant appears most clearly in T12, where a copywriter defends AI as a useful instrument:

“Riding it out is not a thing. It’s improving. To many companies jumped at it thinking it would replace full fledged copywriters. It doesn’t—largely because they can’t recognize sh*t copy from, good copy themselves. It’s a tool. Human in the loop for now. But it’s not getting worse.”

In T13, the argument for human complementarity rests on a distinction between execution and ideation:

“The vast majority of copywriting is about IDEAS and making your audience FEEL something … And AI doesn’t do creativity or original ideas, that’s what humans are for.”

The critical variant, augmentation as intensification, appears in the call-center and teaching threads. In T07, a worker describes how AI changes metrics and expectations rather than reducing labor:

“Well [username omitted], now that I know you can do this type of amazing work… I think it is time to revisit your output metrics to better optimize the standard workload across the board. I will be handing off a couple projects for your activity log and raising your expected productivity benchmark.”

In T17, a teacher frames augmentation as a burden that falls on educators rather than as a benefit:

“Studies are clear that using AI to complete tasks leads to reduced brain activity and, over time, a reduction in critical thinking capacity, logic, creativity and cognitive ability. No thanks. The only catch-up I’m playing is catching up on ways to make sure my students don’t use AI.”

The distinction between augmentation for workers and augmentation for employers is one of the most analytically productive features of the corpus. The same technical arrangement—AI doing part of the task—can be narrated as liberation or as intensified control, depending on who is understood to benefit. Ethnographic research on white-collar workplaces indicates that such escalation of output expectations predates AI and is embedded in managerial time regimes [49].

4.4. Surveillance and Subordination Repertoires

A fourth vocabulary constructs AI not as a tool that performs work but as an infrastructure of control. In this repertoire, the relevant human–AI relation is not replacement or augmentation but subordination: the worker remains employed but is monitored, evaluated, and disciplined through algorithmic systems.

T11, a thread about laptop monitoring, provides the most concentrated instance. One user describes how monitoring was discovered accidentally:

“I didn’t know our company laptops were monitored. My manager came up to me and asked me what I was working on the day before. … He said he has never seen that much activity in one day by a single buyer.”

The anecdote is told as a success story, but the underlying structure—activity that is tracked, anomalies that are flagged, workers who are called in for explanations—describes a surveillance apparatus. Other users in T11 responded with resistance strategies and ironic framings of how monitoring captures activity metrics rather than actual productive thinking. The surveillance repertoire extends the typology beyond production into workplace governance: the question is not whether AI does the work but whether AI controls the worker.

4.5. Algorithmic Gatekeeping Repertoires

A fifth vocabulary positions AI not as a performer of work or monitor of workers, but as a gatekeeper of access to employment. In the hiring threads (T01, T09, T10), AI mediates the boundary between applicants and jobs through automated resume screening, keyword filtering, and application ranking. Comparable gatekeeping configurations have been documented in other institutional settings, where universities and publishers regulate access to knowledge through hybrid human–AI arrangements [50].

In T01, a hiring manager describes the system from the employer side:

“I work for a company that uses AI to filter out candidates … the ‘system’ filters out 99% of applications before we can even see them or set up an interview. … corporate doesn’t give us managers any work around at all.”

The gatekeeping repertoire is distinctive because it positions AI as operating between the worker and the job rather than within the job itself. In T01, another user reports an experiment that exposed the system’s dysfunction:

“There was a story about a manager who got his company to kill their AI screener by submitting his own resume and then showing HR and the higher up that even he couldn’t pass whatever bullshit algorithm they were using.”

In T10, a two-sided gatekeeping dynamic emerges: applicants use AI to generate resumes, employers use AI to screen them, and trust erodes from both directions. This arms-race dynamic distinguishes the gatekeeping repertoire from the replacement repertoire: the issue is not that AI performs the job but that AI controls who gets to perform it.

4.6. Selective Immunity and Embodied Resistance Repertoires

A sixth vocabulary asserts that certain occupations are structurally protected from AI. The claim rests on features of the work that are held to be non-automatable: embodied skill, material variability, site-specific judgment, and physical presence, features that ethnographic research on maintenance and repair work identifies as resistant to standardization [51]. This repertoire was especially salient in T19, where electricians respond to Mark Cuban’s prediction that their trade will be replaced by robots.

“I’m a programmer now and even in this field I think it’ll be more that people are left behind by new technologies, rather than being outright replaced. … AI is simply a new tool, but someone still needs to know how to use it.”

In T16, the selective-immunity repertoire takes a class-stratified form. Teachers argue that replacement will be applied unevenly—with AI for the poor and human teachers for the wealthy:

“Lower income kids will be warehoused with 100 kids to one babysitter and a screen, and kids whose parents can afford it will get to go to real school with a teacher” and “School as we know it today will become a ‘boutique’ experience for the bourgeoisie.”

The selective-immunity repertoire is not simply optimistic. It carries a moral evaluation: protection is distributed along class lines, and the claim to immunity is simultaneously a statement about whose work is valued and whose is not.

4.7. Adaptation Mandate Repertoires

A seventh vocabulary constructs the response to AI as a matter of individual adjustment. Workers must upskill, diversify, or pivot. This repertoire appeared across several sampled threads and often takes a meritocratic form: those who adapt will thrive; those who do not will be displaced. The framing is contested, however: some users treat adaptation as reasonable advice, while others see it as an imposed and class-blind injunction.

In T14, a copywriter offers a diversification strategy:

“Diversify, being in the top 10% in ten areas is better than the top 2.5% in one. For this exact reason, so if a new, superior solution comes along, you’re not fucked.”

In T02, the meritocratic framing is pushed to its limit by a user who casts those already displaced as simply underperforming:

“No offence but if you have been replaced by AI, you just weren’t doing a good job and there’s nothing wrong with that. Not everyone has to be great, but I wouldn’t say the field is dead. It’s just there’s no more room for under achievers which is understandable given the influx of devs during #learntocode and the mirage that was a high paying salary if you could write html.”

This comment blames displaced workers for their displacement, recasting structural change as an individual performance problem. The adaptation mandate is one of the most internally contested repertoires in the corpus: it operates simultaneously as practical advice, as moral judgment, and as a mechanism for redistributing responsibility from institutions to individuals.

4.8. Institutional Redesign Repertoires

An eighth vocabulary reframes AI not as eliminating work but as restructuring the institutional arrangements within which work is organized. In this repertoire, the unit of change is not the job or the task but the billing model, the team structure, the incentive system, or the pipeline of occupational reproduction.

T22, a thread in r/biglaw about Anthropic’s lawyer predicting the end of the billable hour, provides the strongest example. One user describes how AI reshapes team composition without eliminating the work:

“It’s labor-saving, not labor-replacing. … LLMs will eventually mean you can get the same output faster, with a team of 1 partner, 1–2 seniors, and 0–2 juniors.”

Another user argues that AI does not reduce the total volume of work but changes what gets done and by whom:

“It doesn’t reduce the number of team members … It just increases the expectation on quality and quantity of work completed. … There’s always more work that could be done.”

Another poster identifies the structural consequence for occupational reproduction: if junior labor is eliminated, the pipeline that produces future senior lawyers collapses:

“Clients pay for a firm to own the result, not to preserve the labor market for litigation and transactional associates. … The value stays with the partner/firm while the junior labor underneath is killed off.”

The institutional-redesign repertoire is analytically distinct from replacement because the object of transformation is not the worker’s position but the organizational structure within which positions are embedded. What changes is how work is priced, distributed, supervised, and reproduced over time [50].

4.9. Replacement Reversal Repertoires

A ninth and rare vocabulary describes cases in which AI substitution was attempted and failed. This repertoire appears in T08, where a manager reports reversing a customer-service AI deployment, and in T18, where medical professionals frame AI as investment hype rather than genuine clinical capability.

In T18, a user frames the hype cycle around AI in medicine as a financial phenomenon rather than a technological one:

“The second they do this, AI investment (and now a significant portion of American economic growth) collapses. The whole point of all the AI propaganda we’re seeing is that current levels of investment are only justified if LLMs give us massive, jaw-dropping, otherworldly increases in productivity … Acknowledging that the increases in productivity might actually just be moderate and that firms will still have to employ most of their workers would ruin the whole thing for everyone.”

Another user draws a historical parallel that accommodates both the reality of long-term change and the unreality of current hype:

“For sure there’s a bubble. Now I personally think that AI will become something like railroads were in the 1800s: a series of massive bubbles, speculators hyping value up, and frequent crashes. And yet railroads did in the end prove to be massive generators of economic growth. I think the same kind of thing will happen with AI in the long run-but the breathless hype that exists today, skepticism is definitely warranted there.”

The replacement reversal repertoire appeared in a limited number of sampled discussions, but it serves an important analytical function. It demonstrates that the other eight repertoires operate within a discursive field in which failure and reversal are possible, even if they are marginal. Much of the sampled discourse anticipates displacement, but that anticipation is not uncontested.

5. Discussion

5.1. From Sentiment Polarity to Relational Location

The principal empirical contribution of the typology is not that the sampled participants were more pessimistic or optimistic than other publics, but that they located AI at different points in the organization of work. Replacement positions AI in the worker’s functional place; augmentation positions it alongside the worker; surveillance positions it above the worker; gatekeeping positions it between candidates and jobs; and institutional redesign positions it within the rules and structures through which work is priced, distributed, and reproduced. This relational differentiation makes visible mechanisms that broad sentiment categories tend to collapse [1,2,3].

The distinction also clarifies why apparently similar claims can perform different argumentative functions. A prediction that AI will eliminate junior roles invokes replacement or stratification, while a claim that applicants must learn to navigate automated screening invokes gatekeeping and adaptation. The analytical value lies in identifying where agency is located and what social relation is being reorganized, not in placing each speaker on a single positive–negative continuum [52]. In this respect, the analysis operationalizes, in vernacular material, the discursive-psychology insight that repertoires are shared resources speakers draw on and combine rather than fixed individual attitudes [18].

5.2. Control, Benefit, and the Distribution of Risk

The opposition between augmentation-as-empowerment and augmentation-as-intensification demonstrates that technical assistance does not determine who benefits. In the empowerment variant, workers retain discretion and capture at least part of the productivity gain. In the intensification variant, employers raise output expectations, extend monitoring, or reduce staffing while workers absorb the additional burden. This finding complements research showing that AI adoption is shaped by organizational power and the social relations of implementation, and that augmentation is an ambiguous rather than a unitary category [12,13,14,15,30,35]. It thereby provides a vernacular counterpart to the algorithmic-management literature: participants name opacity, metric capture, and the loss of discretion in their own terms rather than in academic vocabulary.

Algorithmic gatekeeping extends this analysis by foregrounding control before the employment relationship begins. The sampled hiring discussions describe opacity, automated exclusion, and adversarial adjustment by applicants and employers alike. Rather than establishing gatekeeping as a universal or quantitatively dominant effect, the analysis identifies the boundary between candidates and jobs as a distinct location at which participants experience and contest algorithmic power—a locus that task-centered accounts of automation tend to overlook.

The adaptation mandate, selective immunity, and institutional redesign repertoires distribute responsibility differently. Adaptation makes the individual worker responsible for surviving technological change; immunity locates protection in the material or relational properties of an occupation; and institutional redesign shifts attention to firms, professions, billing arrangements, and distributive institutions. Their coexistence, sometimes within a single thread, shows that discussion of AI and work is not only predictive but also moral and attributive: it assigns responsibility for disruption and defines who should bear its risks. This is precisely the redistribution of responsibility between individuals and institutions that recent sociological work on generative AI has called for attention to [21,53].

5.3. Occupationally Situated Imaginaries

The repertoires should not be treated as equivalent to fully stabilized sociotechnical imaginaries. They are better understood as discursive resources through which occupationally situated imaginaries are articulated [19,20,54]. Within the sampled threads, creative workers often drew on replacement and stratification, service workers emphasized intensification and control, skilled-trades participants invoked embodied immunity, and legal participants discussed billing systems and professional pipelines. These patterns suggest that expectations about AI are filtered through the material and institutional organization of particular forms of work [55], extending to occupational contexts an argument previously developed for national contexts [9,10] and aligning with evidence that workers’ expectations are shaped more by occupational identity than by technical assessment [16,56]. They are also consistent with the performative account of expectations, in which projections about AI coordinate action under uncertainty rather than merely describing it [11].

Because the sample was purposively constructed and the repertoires were coded non-exclusively, these occupational patterns should be understood as qualitative contrasts rather than prevalence estimates. The contribution is a structured vocabulary for subsequent comparative research, not a representative map of Reddit users or of public opinion. Future studies can test the stability and distribution of the repertoires across platforms, languages, periods, and systematically sampled occupational populations.

6. Conclusions

This study identified nine interpretive repertoires through which participants in 22 selected English-language Reddit threads constructed relations between artificial intelligence and human labor: replacement; displacement and stratification; augmentation and complementarity, including augmentation-as-intensification; surveillance and subordination; algorithmic gatekeeping; selective immunity and embodied resistance; adaptation mandate; institutional redesign; and replacement reversal.

The typology moves beyond optimism and pessimism by differentiating where AI is positioned, how causal agency is assigned, who is expected to benefit, and where responsibility for adaptation is located. It foregrounds algorithmic gatekeeping as a form of control at the boundary of employment and clarifies the internal division between augmentation that supports worker discretion and augmentation that intensifies managerial demands.

The occupational contrasts observed here are bounded by the study design. They concern purposively selected Reddit discussions rather than public discourse as a whole, and they do not establish statistical prevalence. Their value is analytical: they provide a vocabulary for comparing how different communities construct AI’s relationship to work and how those constructions distribute agency, benefit, and risk.

Future research should examine whether these repertoires recur across languages, platforms, periods, and systematically sampled occupational settings. Longitudinal and workplace-based studies could also investigate how these discursive resources influence career decisions, organizational change, collective bargaining, and policy debate.