Fear, Blame, and Deservingness: The Moral Economy of AI-Labor Anxiety on Reddit

Abstract

Public concern about artificial intelligence and labor markets is documented through surveys, media analyses, and workplace research. This article examines how such concern is organized in sampled Reddit discussions through moral evaluations of fairness, responsibility, deservingness, and institutional accountability. The frozen source inventory contains 3357 downloaded rows in 22 threads across 18 subreddits and 13 occupational or institutional domains; excluding 82 deleted or removed placeholders leaves 3275 text-bearing comments. Drawing on moral economy, affective-discursive practice, and emotion-culture scholarship, the analysis identifies four overlapping configurations: dread and fatalism; anger and resentment around violated meritocratic expectations; irony and dark humor as emotional management; and conditional hope tied to contested adaptation. Blame is treated as a cross-cutting moral dimension rather than a fifth emotional pattern. Technological fear and judgments of institutional failure are intertwined: participants in the sampled threads voice fear of AI capabilities while directing responsibility toward employers, technology firms, governments, and educational institutions. The article reframes AI-labor anxiety as a platform-mediated moral discourse in which emotion and moral judgment are mutually constituted, specifies how the configurations were operationalized and bounded, and formulates institutionally relevant questions about the contested terms of workplace adaptation. As a theoretical sample, the corpus supports analytic description, not population estimates.

1. Introduction

Concern about artificial intelligence and employment is documented across public-opinion research, media studies, workplace research, and scholarship on technological change. Surveys report expectations of occupational disruption (European Commission 2017; Pew Research Center 2023); media analyses trace changing frames across national contexts (Wang et al. 2025) and around the public release of ChatGPT in November 2022 (Sanguinetti and Palomo 2024; Ittefaq et al. 2025); and labor and organization studies examine exposure, productivity, professional identity, control, and adaptation (Frey and Osborne 2017; Eloundou et al. 2024; Brynjolfsson et al. 2025b; Autor 2024; Kelan 2023; Kellogg et al. 2020; Mayer et al. 2025; Wolfe and Mitra 2024). These strands document AI-related concern across multiple settings, but leave room for closer analysis of how people connect emotions to judgments about fairness, deservingness, responsibility, and institutional obligation in ordinary online discussion.

Survey and media-framing research are two prominent approaches rather than the whole field. Surveys measure reported concern and its demographic or occupational correlates, while media studies examine recurring frames and tones in journalistic output (Sanguinetti and Palomo 2024; Ittefaq et al. 2025; Wang et al. 2025; Sartori and Bocca 2023; Cools et al. 2024). Workplace and organizational research adds a relational strand by examining how algorithms reorganize tasks, control, status, trust, entry pathways, and professional boundaries (Kellogg et al. 2020; Baygi and Huysman 2026; Mayer et al. 2025; Wolfe and Mitra 2024; Selenko et al. 2022). The present study does not treat Reddit discussions as direct audience reception of particular news reports. It asks how participants in selected threads assemble technological expectations, lived or anticipated work change, moral evaluation, and blame into public stances.

A different analytical tradition offers the tools needed to fill this gap. The sociology of emotions has long argued that emotions are not private psychological states to be measured through self-report but socially organized phenomena governed by shared norms, shaped by institutional positions, and performed in interaction (Hochschild 1979; Thoits 1989; Barbalet 1998). Emotion cultures, seen as the shared vocabularies, norms, and beliefs about appropriate emotional expression within communities, include feeling rules that specify what members should feel in given situations and sanction those who deviate (Hochschild 1979; Guenther 2009). The concept of moral economy, developed in Thompson’s (1971) analysis of how subordinate groups evaluate the justice of economic arrangements, provides a further resource. Applied to AI, the moral economy lens directs attention to the normative expectations about fair employment, earned reward, institutional protection, and dignified treatment, against which people judge whether AI-driven labor change is legitimate or unjust (Sayer 2007; Chen et al. 2025). In this framework, anxiety is bound up with the perception that the normative order governing a livelihood has been violated.

This article analyzes how emotions, moral evaluations, and blame attributions are organized in a theoretically selected set of Reddit discussions about AI and work. The analytic corpus comprises 3275 text-bearing comments from 22 threads in 18 subreddits and 13 occupational or institutional domains. The research question is: how do participants in these sampled discussions organize emotions about AI and work, and how do those emotions connect to moral judgments about fairness, deservingness, responsibility, and institutional accountability? We answer it through abductive codebook thematic analysis (Braun and Clarke 2006, 2021; Timmermans and Tavory 2012) guided by moral economy (Thompson 1971; Chen et al. 2025), affective-discursive practice (Wetherell 2012, 2013), and emotion-culture research (Hochschild 1979; Guenther 2009).

The analysis identifies four affective-discursive configurations: dread and fatalism tied to perceived structural inevitability; anger and resentment directed at employers, tech elites, and a broken meritocratic promise; irony and dark humor as collective emotional management; and conditional hope attached to contested adaptation narratives. Cutting across all four, a logic of blame and institutional distrust structures how moral responsibility is assigned. These emotional registers are embedded in evaluations of who caused the disruption, who benefits, who bears the costs, and whether the demand to adapt is fair or imposed.

The contribution is threefold. The article reframes AI-labor anxiety as a moral-economy phenomenon rather than a sentiment or psychological trait, showing how concern is organized through evaluations of fairness, accountability, and institutional responsibility; it contributes to the sociology of emotions by showing how feeling rules are publicly contested in online exchange; and it identifies the blame logics through which responsibility is distributed among employers, corporations, wealthy actors, educational institutions, markets, and technology itself. Because these judgments concern employment security, occupational standing, and institutional obligation, the analysis is positioned in relation to labor and employment scholarship on displacement, algorithmic management, and the institutional mediation of technological change (Section 2.5).

The corpus is a theoretical sample assembled for conceptual variation. The findings concern configurations within the analyzed discussions; their limits are consolidated in Section 5.6.

2. Literature Review

2.1. AI Anxiety: From Individual Attitude to Cultural Repertoire

Research on public responses to AI and employment has grown rapidly. Two prominent methodological approaches share a common limitation. Survey-based studies measure AI anxiety as an individual-level attitude. Sartori and Bocca (2023) find that public perceptions of AI cluster around distinct sociotechnical visions that vary by gender, generation, and AI competence. Large-scale European surveys report that substantial majorities of respondents expect AI and robotics to eliminate more jobs than they create (European Commission 2017). Secondary analysis of that instrument examines how European attitudes toward digitisation and automation vary across publics and application domains (Rughiniș et al. 2018). In the United States, Pew Research Center (2023) estimates that roughly one in five workers hold jobs with high exposure to AI, yet workers in more exposed industries are more likely to expect personal benefit than harm. These studies document the distribution of concern but treat it as a property of individuals, to be aggregated and correlated with demographic variables.

Media-related studies examine AI anxiety as a framing phenomenon. Cools et al. (2024) show that US newspaper coverage shifted from predominantly dystopian framing in the late twentieth century toward more benefit-oriented framing in the twenty-first century. Ittefaq et al. (2025), analyzing 38,787 articles from twelve countries, report an increase in the number of headlines with negative sentiment, particularly in 2023. Sanguinetti and Palomo (2024) find that an anxiety index built from headline features increased after ChatGPT’s launch in regional newspapers but declined in national newspapers. Wang et al. (2025) show that anxious framings are not universal but vary by national context: UK outlets emphasize employment risk, Chinese outlets economic competitiveness, and Indian outlets education and skills. Such coverage does not simply transmit an external reality: policy and media narratives about AI are themselves performative, enacting the object they describe (Bareis and Katzenbach 2022). Analysis of a different contested socio-technical issue shows the mechanism at work, with coverage selecting which actors are treated as authoritative and which risks are made visible (Marinescu et al. 2021). These studies map the intensity and distribution of anxiety in media output. They do not ask how audiences receive, rework, or contest these frames.

A third strand of research concerns forecasts and workplace transformation. Frey and Osborne (2017) estimated that 47% of US employment was at high risk of computerization; Eloundou et al. (2024) estimated task exposure to large language models; Brynjolfsson et al. (2025b) documented productivity effects in customer support; and Autor (2024) argued that AI could broaden access to expert tasks. Economic accounts caution that displacement and reinstatement proceed together, so aggregate exposure figures are a poor guide to what happens to particular workers (Acemoglu and Restrepo 2019). Early payroll evidence associates AI exposure with reduced hiring of entry-level workers rather than with broad displacement, on evidence its authors describe as suggestive rather than causal (Brynjolfsson et al. 2025a), and a Danish study of early adoption finds substantial workplace reorganization alongside limited average effects on earnings and hours (Humlum and Vestergaard 2025). Organizational research shows that algorithms are contested systems of control rather than neutral task substitutes (Kellogg et al. 2020), that generative AI can reroute flows of expertise, trust, and collegiality (Baygi and Huysman 2026), and that future-of-work narratives carry classed, gendered, and status assumptions (Kelan 2023). Qualitative studies document active adjustment within professional work, from job crafting among entry-level professionals (Mayer et al. 2025) to the negotiated adoption of AI in fact-checking organizations (Wolfe and Mitra 2024). These studies place AI within existing institutions, career ladders, and normative orders.

Automation debates are also historically patterned. Wajcman (2017) argues that successive waves of automation discourse reproduce a similar structure of prediction and alarm while underplaying the social relations that determine outcomes; Spencer (2018) shows that fear and hope operate as paired rhetorical positions within one debate rather than as rival empirical claims; and Fleming (2019) argues that substitution is bounded by organizational power and cost rather than by technical feasibility alone. These arguments identify, in scholarly debate, the same coupling of emotional register and causal attribution that the present analysis examines in lay discussion.

What the studies reviewed above do not directly capture is the internal structure of anxiety as a public stance. When people express fear about AI and work, they assemble claims about what is happening (displacement, deskilling, surveillance), about who is responsible (employers, tech companies, governments), and about whether the situation is fair. Kelan (2023) shows a version of this in a different empirical context: in popular future-of-work books, automation anxiety and augmentation aspiration carry assumptions about gender, class, and race, the threatened figure is male, the expected adaptation is classed, and the register is one of status loss rather than job loss. Work psychology reaches a compatible conclusion from another direction, arguing that AI at work affects occupational identity and social standing and not only income (Selenko et al. 2022).

Sociological approaches to generative AI direct further attention to professional identity, epistemic authority, stratification, expectations, and myth (Baert et al. 2026; Vicsek 2021; Natale and Ballatore 2020; Roberge et al. 2020). Together with relational organization studies, they caution against treating technology and institutions as separate causal domains: AI capabilities, organizational deployment, and public expectations co-constitute one another. This article accordingly treats AI labor anxiety not as an attitude or a media frame but as a cultural repertoire, a publicly available way of assembling emotions, moral judgments, and institutional evaluations into coherent stances. The question is not how much anxiety exists but how it is organized, what moral grammar it carries, and what it demands from institutions.

2.2. Moral Economy and the Legitimation of Technological Change

The concept of moral economy provides the analytical framework for connecting emotions to normative evaluations of fairness. Thompson (1971) used the concept to describe how eighteenth-century English crowds evaluated bread prices not by market logic but by shared understandings of what constituted a just price. The moral economy was not an economic theory. It was a normative order, a set of expectations about what the powerful owed to the vulnerable and what counted as legitimate economic behavior. When those expectations were violated, the response was not only material hardship but moral outrage.

Later work has both broadened and disciplined the concept. Sayer (2007) treats moral economy as a mode of critique attentive to the norms and sentiments that make economic arrangements defensible or indefensible to those subject to them, while Palomera and Vetta (2016) warn against the tendency to oppose a warm moral community to a cold market and argue instead that moral economies are internally contested and shot through with relations of power. Both cautions apply here. The material analyzed below does not display a shared normative consensus violated from outside; it displays disagreement about which expectations are legitimate and who is entitled to invoke them.

Applied to contemporary AI debates, the moral economy lens directs attention to the normative expectations against which people judge whether AI-driven labor change is acceptable. Chen et al. (2025) use the concept to analyze how Chinese state and corporate actors legitimize AI integration in elder care through moral appeals to family duty, national obligation, and virtuous service, showing that the moral economy of AI operates through the circulation of moral values that justify particular approaches to technology and obscure the labor relations sustaining them. Theirs is an account of legitimation from above. Related work on institutionally produced displacement identifies a mechanism that recurs in the material analyzed below: when loss is produced through a sequence of administrative acts, responsibility is distributed across actors and procedures rather than located in a single decision, and those affected must reconstruct an accountable agent (Zamfirescu and Chelcea 2021).

Our application inverts the direction. Where Chen et al. (2025) study how elites construct the moral economy of AI, we examine how participants in ordinary online discussion draw on moral economy logics to evaluate AI from below. Commenters in the sampled threads do not produce policy justifications. They produce moral evaluations: claims that employers are cutting costs at the expense of workers, that the promise of meritocratic reward has been broken, that adaptation demands are unfair because the rules changed after people had already invested in education and credentials, that governments and educational institutions have failed to protect those who followed the expected path. These evaluations are structured by normative expectations about fair employment, earned reward, institutional accountability, and dignified treatment, and when those expectations are treated as violated the emotional response, whether dread, anger, resentment, or bitter irony, is voiced as a moral response rather than as a report of a psychological state. Research on early adoption indicates that some workplace and hiring changes are already under way (Brynjolfsson et al. 2025a, 2025b; Humlum and Vestergaard 2025), which is the context in which these evaluations are voiced; it does not validate any particular commenter’s forecast. A parallel line of analysis treats disputes over AI governance as conflicts between competing orders of worth, in which actors justify positions by appeal to different principles of evaluation (Rughiniș et al. 2025d), and institutional policy documents themselves show how organizations draw and redraw the boundary of legitimate AI use (Rughiniș et al. 2025c).

Deservingness specifies one part of this moral economy. Welfare research has long shown that judgments about who should receive protection are organized by recognizable criteria, including need, control over one’s situation, prior contribution, and reciprocity (van Oorschot 2000). In the sampled discussions, claims to deserve security, recognition, or protection are grounded in prior effort: education, credentialing, occupational experience, care work, or compliance with institutional rules. Such claims mobilize the moral boundaries through which working people distinguish those who have earned standing from those who have not (Lamont 2000). They can therefore reproduce meritocratic assumptions, which are themselves an object of critique for naturalizing inequality (Littler 2018; Mijs 2021), but they can also criticize institutions for changing the terms after people invested in them. Deservingness is treated here as an empirical dimension of moral evaluation rather than as a synonym for fairness.

2.3. Affective-Discursive Practice and the Public Organization of Emotion

The sociology of emotions provides the conceptual tools for analyzing how emotions are socially organized rather than individually experienced. Shott (1979), in a foundational contribution from the symbolic interactionist tradition, argued that emotions are shaped by social definitions, role-taking processes, and normative expectations. Emotions are constructed through social interaction, defined by cultural labels, and regulated by shared understandings of what feelings are appropriate in a given situation.

Hochschild (1979) established that emotions are governed by “feeling rules”: socially shared expectations about what one should feel in a given situation. People engage in emotion work to align feeling with those expectations. Feeling rules are elements of broader emotion cultures, the vocabularies, beliefs, and expectations that characterize communities and institutions (Hochschild 1979; Guenther 2009). Hochschild (2011) later examined the “market frontier” of emotional life, where market forces reshape the conditions under which people manage feeling.

Thoits (1989), reviewing the field, identifies a core sociological claim: emotional norms both reflect and sustain social structures. They vary by social position, they are learned through socialization, and efforts at emotional conformity help maintain social order. Barbalet (1998) develops the complementary point that emotion and evaluation are not sequential but mutually implicated: emotions such as resentment or fear already contain an appraisal of a social relationship, which is why they can be argued about rather than merely reported. Guenther (2009) shows how emotion cultures operate at the organizational level. Comparing two feminist organizations with different relationships to the state, she finds that each developed distinct norms about which emotions were valued, how they should be expressed, and what political work they accomplished: the organization dependent on the state cultivated emotional restraint, while the autonomous organization embraced anger and grief as tools for raising consciousness. Emotion cultures, in this account, are institutional formations that shape what members can feel, say, and do, not merely expressive norms.

Wetherell (2012, 2013) extends this line of analysis through affective-discursive practice. Emotions are not treated as private states subsequently expressed in discourse; they are constituted through public, interactionally organized, discursively patterned practices. The focus shifts from what people feel to what they do with emotion: performing dread, directing anger, calibrating irony, and enforcing or contesting feeling rules. This framework suits online written discourse because it treats text as a site where emotional stances are assembled, and work on digital affect cultures extends the point to networked settings, where repertoires of emotional expression circulate and are normatively policed across platforms (Döveling et al. 2018).

These traditions are complementary here. Hochschild’s emotion culture identifies the normative expectations at stake; Guenther shows that such expectations vary by institutional context; Wetherell provides a framework for examining how emotions are publicly performed and morally evaluated. Kelan’s (2023) analysis of gendered and classed future-of-work subtexts directs attention to how status position, and not only occupational exposure, shapes AI anxiety. Research on the social imaginaries through which women’s work identities are constructed makes the related point that the normative expectations attached to work are gendered and classed before any technology is introduced (Grünberg and Matei 2020).

2.4. Reddit as Affective Public

Reddit provides a distinct setting for studying the public organization of emotion. Papacharissi (2015) describes affective publics as networked formations that assemble and disperse around shared structures of feeling, sustained by storytelling rather than by deliberation, and that account fits the material examined here: participants converge on a topic, calibrate one another’s emotional stances, and disband. Proferes et al. (2021) document the platform’s use in research while emphasizing that community norms, moderation, and voting shape what becomes visible, and Massanari (2017) shows how algorithm, governance, and culture interact to produce distinctive participatory dynamics. Platform effects should not be assumed uniform: echo-chamber dynamics vary substantially across platforms, including Reddit (Cinelli et al. 2021). Studies of Reddit discussion of AI show that participants negotiate moral agency and ethical responsibility within these settings (Obreja et al. 2025) and formulate anticipatory ethical judgments about AI systems before those systems affect them directly (Obreja and Rughiniș 2023). Thematic analysis has also been used to examine how normative positions are argued out within a single forum (Obreja 2026); that design differs from the present one and is not offered as a warrant for the sampling strategy adopted here.

Three affordances are particularly relevant. First, the subreddit structure creates bounded discussion spaces with recognizable but variable communicative norms. The sampled threads show different balances of structural critique, professional boundary defense, pragmatic advice, and humor. Because most subreddits contribute only one thread, these differences are treated as properties of the sampled discussions rather than as evidence of subreddit-wide emotion cultures. Participant occupation and identity also cannot be verified from pseudonymous posts.

Second, voting and ranking shape visibility. Graham and Rodriguez (2021) show that Reddit scores are sociomaterial outcomes influenced by timing, ranking, participation, and community norms; they are not direct measures of agreement. Litherland and Wood (2026) likewise show that ironic detachment and dark humor can become prominent modes for discussing distressing news. In this study, scores are used only as contextual indicators of visibility and resonance at the time of extraction, not as proof of endorsement or representativeness.

Third, pseudonymity can lower barriers to discussing layoffs, insecurity, and occupational frustration, but it does not guarantee authenticity. Online disinhibition can increase both self-disclosure and antagonistic or exaggerated expression (Suler 2004). Participants may strategically present or misrepresent identities, and occupation cannot be independently verified. Comments are therefore analyzed as public discursive performances rather than transparent reports of identity or private feeling.

A fourth consideration concerns what participants believe about the systems they discuss. Users develop folk theories of algorithmic and automated systems that guide how they attribute agency and responsibility, and these theories are shaped by what platforms make visible (Bucher 2017; Obreja 2024b). Where such theories locate causal force in a technical system rather than in the actors deploying it, they alter the grammar of blame available in a discussion (Obreja 2025). This matters for the analysis in Section 4.5, which examines exactly that allocation.

These affordances make Reddit a setting in which situated norms of emotional expression can be examined. The platform hosts and shapes performances of emotion, but a single thread cannot establish a stable community culture or reveal participants’ private feelings.

2.5. AI, Labor Institutions, and the Terms of Adaptation

Research on AI and employment is frequently organized around exposure and capability: which tasks can be automated, and how quickly (Frey and Osborne 2017; Eloundou et al. 2024). Labor and employment scholarship suggests a different emphasis. The employment consequences of a technology are mediated by managerial strategy, work organization, occupational jurisdiction, and labor institutions rather than following automatically from technical capability. That proposition organizes this section, and it matters for the analysis that follows because the commenters examined here reason in a similar register: they treat AI as something deployed by identifiable actors under particular terms, not as an autonomous force.

Joyce et al. (2023) argue that debates about technology and the future of work reproduce technological determinism even where they explicitly reject it, through rigid periodizations, a narrow conceptual repertoire, and reified notions such as algorithmic control. They propose instead a social shaping approach in which what a technology does to employment is not a property of the technology but an outcome of how it is introduced, by whom, and on what terms.

Work on algorithmic management specifies some of those terms. Kellogg et al. (2020) describe algorithms as a contested terrain of control rather than neutral task substitutes. Wood (2021) surveys the consequences for work organization and working conditions, including intensified monitoring, reduced discretion, and heightened insecurity, which follow from managerial design choices rather than from algorithmic capability as such. The distinction bears directly on a corpus in which one discussion concerns workplace monitoring rather than replacement (T11). Ethnographic research on white-collar workplaces makes a parallel point about time: intensified output expectations followed from managerial reorganization rather than from technology itself (Chelcea 2015).

Hiring is a second site where this mediation is visible. Automated screening is frequently presented as a corrective to human bias, but the delegation of selection to software can entrench exclusion while dispersing accountability for it, since no identifiable actor is answerable for a rejection produced by a scoring pipeline (Ajunwa 2020). Three discussions in the present corpus concern precisely this setting (T01, T09, T10), and the accountability vacuum that Ajunwa describes is recognizable in how participants formulate their complaints.

Employment insecurity is a durable condition with documented consequences. Kalleberg (2009) situates precarious employment as a structural feature of contemporary labor markets rather than a transient effect of any single technology, and De Witte et al. (2016), reviewing three decades of longitudinal research, show that job insecurity is associated with health and well-being outcomes independently of whether displacement occurs. That literature does not measure the discourse analyzed here and is not used to validate it; it indicates that anticipated insecurity is an object of established employment research rather than a purely discursive phenomenon.

Occupational jurisdiction supplies a further mediating structure. Abbott (1988) analyzes professions as a system in which groups compete for jurisdiction over tasks and defend boundaries through claims to abstract knowledge. Several discussions in the present corpus are organized by that kind of boundary work: physicians, lawyers, teachers, and electricians each invoke domains of competence presented as resistant to automation. Jurisdictional defense is a recognizable institutional practice rather than an idiosyncratic reaction to a new technology. Ethnographic work on repair occupations shows how such claims rest on embodied, situated skill that resists codification (Jderu 2023).

Research on adaptation shows workers actively reorganizing tasks around generative systems rather than passively absorbing them (Mayer et al. 2025). Woodruff et al. (2024), working with 54 participants across seven industries, report that knowledge workers largely do not anticipate wholesale replacement but do expect generative AI to amplify deskilling, dehumanization, disconnection, and disinformation. That study used a different design, population, and elicitation method, and is not treated here as corroboration. The contrast is analytically useful precisely because its dominant register differs from the dread expressed in the present corpus, which supports treating platform discussion as a distinct discursive setting rather than a proxy for workforce expectation.

Finally, the terms of adoption are themselves institutionally contested. Doellgast et al. (2025) document social dialogue over AI and algorithmic management across national settings, showing that unions, employers, and public authorities negotiate questions of transparency, consultation, monitoring, and transition support. Comparable boundary-setting occurs within organizations that write their own rules for AI use, where institutional policies decide which hybrid human–machine configurations count as legitimate (Rughiniș et al. 2025c). This is the institutional field in which claims about fair treatment during technological change are ordinarily processed, and, as Section 5.5 notes, it is a field to which the sampled discussions rarely refer.

3. Data and Method

3.1. Corpus and Sampling Strategy

The frozen source inventory contains 3357 downloaded comment rows from 22 Reddit threads. Eighty-two rows whose bodies were exactly [deleted] or [removed] were excluded, leaving 3275 text-bearing comments. The threads span 18 subreddits and 13 occupational or institutional domains. Comments in the retained workbooks are dated from 18 February 2023 through 2 April 2026. Thread size is uneven: the three largest threads contribute 1717 of the 3357 downloaded rows (51.1%).

The sampling strategy follows theoretical sampling (Glaser and Strauss 1967; Patton 2015). Cases were chosen for conceptual variation rather than statistical representation. A contemporaneous planning register specified variation in human–AI relation, occupational domain, emotional register, moral framing, and discussion ecology before close coding. These were sensitizing sampling dimensions, not final findings. The final configurations were refined abductively by connecting emotional expression to discursive action, moral evaluation, deservingness, and blame.

Four axes guided thread selection: human–AI relations (replacement, displacement, augmentation, subordination, surveillance, selective immunity, and institutional redesign); occupational or institutional domain; contrasting forms of evaluation and emotional expression; and discussion ecology (worker-rights, career, profession-specific, hiring, and future-oriented forums). These axes were used to diversify the corpus.

The resulting corpus spans varied occupational and institutional contexts in which emotions about AI-related labor change are publicly organized.

3.2. Thread Identification and Data Collection

Candidate threads were 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. A surviving contemporaneous register documents 25 candidate threads. Two recruiting-related threads were excluded because they contained approximately four and zero comments and did not sustain interactional exchange. One copywriting thread with substantial interaction was excluded to reduce topical duplication within an already dense copywriting cluster; it did not add a distinct analytic contrast beyond retained cases. The resulting 22-thread corpus was frozen after it covered the planned occupational and human–AI relation axes without adding a substantively new discussion type. T08 was retained despite its small size because it offered a distinct failed-replacement case. Supplementary Table S2 records all 25 candidates and the three exclusion reasons.

Data were collected through automated extraction using the Reddit API (Reddit, Inc., San Francisco, CA, USA) and stored in one structured workbook per thread. Retained fields were comment identifier, body, username, date, score, and permalink. The final workbooks were assembled in early April 2026 and contain comments dated through 2 April 2026. A deterministic audit found no duplicate comment identifiers across the 22 numbered workbooks. Platform metadata reported 3757 comments, while 3357 rows were downloaded, a difference of 400. Of the downloaded rows, 82 exact deleted/removed placeholders were excluded. The analysis uses only the remaining 3275 text-bearing comments.

3.3. Corpus Composition

Table 1. Composition of the analytic corpus: 3275 text-bearing comments in 22 Reddit threads, 18 subreddits, and 13 occupational or institutional domains. Deleted/removed placeholders are excluded from N.

ID	Subreddit	Thread Topic	N	Date Range	Domain
T01	r/antiwork	AI taking over job applications	63	October 2024–January 2025	Hiring
T02	r/cscareerquestions	AI making me feel like giving up	133	March 2026	Software/IT
T03	r/ChatGPT	I asked ChatGPT What Jobs Can’t be replaced by AI and Why	189	February–June 2023	General AI
T04	r/ChatGPT	ChatGPT slowly taking my job away	640	May–June 2023	General AI
T05	r/CallCenterWorkers	AI coming for call center jobs	71	September 2025–January 2026	Customer service
T06	r/callcentres	Replace call center with AI agent?	41	September–November 2025	Customer service
T07	r/CallCenterWorkers	AI in call center work	61	January–March 2026	Customer service
T08	r/customerexperience	Replaced agents with AI	16	July 2024–September 2025	Customer service
T09	r/recruitinghell	AI resume screening should be illegal	64	November 2024–July 2025	Hiring
T10	r/jobs	Never make it past the resume bots	95	October–December 2025	Hiring
T11	r/InterviewMan	Company monitoring my laptop	155	March 2026	Employment systems
T12	r/copywriting	AI not taking copywriters’ jobs	64	December 2025–March 2026	Writing
T13	r/copywriting	Copywriting is dead (almost)	70	October 2024	Writing
T14	r/copywriting	If copywriting becomes obsolete	58	August 2024	Writing
T15	r/graphic_design	Graphic designer, AI ruined career	59	January–March 2026	Design
T16	r/Teachers	AI going to replace teachers	381	March–September 2025	Education
T17	r/teaching	AI norm for students, teachers catching up	145	December 2025–January 2026	Education
T18	r/medicine	Tech bros should leave it to doctors	23	April 2026	Medicine
T19	r/electricians	Mark Cuban thinks electricians replaced	664	December 2024–April 2025	Skilled trades
T20	r/Futurology	UBI not a solution to automation	116	April 2024–February 2025	Policy
T21	r/AskAcademia	AI messing up peer review	65	October 2025–March 2026	Academia
T22	r/biglaw	AI will kill the billable hour	102	March 2026	Law

presents the 22 retained threads and their downloaded composition. The N column reports text-bearing comments after exact deleted/removed placeholders were excluded. Dates span all retained comments per thread. Domain labels are descriptive thread-level assignments, not mutually exclusive classifications of every comment. T11 is labeled Employment systems because workplace monitoring does not fit the narrower Hiring category. Supplementary Table S3 adds the pre-analysis human–AI relation, emotional register, moral frame, and sampling role preserved in the planning register; these descriptors document case selection.

3.4. Adequacy of the Theoretical Sample

A theoretical sample is evaluated by the variation and analytical distinctions it supports rather than by population coverage (Glaser and Strauss 1967; Patton 2015). This corpus spans a diverse, not exhaustive, range of human–AI relations: direct replacement and blocked career entry in some threads, work intensification, surveillance, gatekeeping, professional boundary defense, or institutional redesign in others. It also spans positions that differ in exposure, institutional protection, and credentialing, which provides contexts for comparing deservingness claims, since call-center workers, teachers, lawyers, designers, software workers, physicians, electricians, academics, and job seekers invoke different histories of training, service, autonomy, and promised reward. Occupational membership is not verified, so these are discursive positions within threads rather than demographic attributes assigned to users.

The threads further differ in communicative setting, since worker-rights, career, profession-specific, and future-oriented forums make different rhetorical resources available, and in size, since large threads widen the range of visible positions while smaller ones permit closer attention to reply sequences. Because most subreddits are represented by one thread, the comparison concerns these discussions and their interactional tendencies rather than stable community cultures.

Platform Composition and Discourse Recruitment

Two features of corpus construction shape the kind of discourse the sample contains. First, thread retrieval was partly structured by threat-oriented search terms (“AI replacing jobs,” “AI taking over,” “AI layoffs”; Section 3.2). The sample was therefore not designed to assess the relative presence of neutral, indifferent, or enthusiastic discourse about AI and work, and no inference about the distribution of such positions is drawn from the corpus. Second, subreddit purpose makes particular evaluative vocabularies more available: a worker-rights forum, a hiring-complaint forum, and a professional forum supply different resources for assigning responsibility, and the thread-level emphases reported in Section 5.4 should be read accordingly.

Platform mechanics also shape which formulations become prominent. Voting and ranking can increase the visibility of concise humor and sharply drawn normative claims relative to extended or ambivalent reasoning (Graham and Rodriguez 2021; Litherland and Wood 2026); subreddit norms and platform affordances jointly condition what is said and how (Park et al. 2023); pseudonymity may support disclosure while also enabling exaggeration (Suler 2004); and platform architecture shapes the vocabulary available for assigning responsibility, since users draw on what they know about ranking systems when they legitimate or contest what they see (Obreja 2024a, 2024b). The three largest threads supply 51.1% of downloaded rows, so large discussions contribute more textual material than small ones.

Available demographic evidence shows higher U.S. Reddit use among younger adults, men, and college graduates (Pew Research Center 2025), but it does not identify participants in this corpus. Participants’ geography, occupation, political orientation, and other identity characteristics cannot be verified; platform-level demographics are therefore not used to characterize the sample or explain the findings.

The corpus is also one construction among possible others. Bervar et al. (2026), analyzing nearly 4000 posts and comments from six creative-industry subreddits, illustrate how a different sampling logic and analytical strategy applied to the same platform yield a different account of public discourse about generative AI. Within the present corpus, published quotations are drawn from 10 of the 22 threads (Supplementary Table S4); that concentration reflects the selection of illustrative passages rather than the composition of the sample, and the thread-level comparison in Section 5.4 draws on all 22.

3.5. Analytical Procedure

The study uses abductive codebook thematic analysis (Braun and Clarke 2006, 2021). Moral economy, affective-discursive practice, emotion culture, and feeling rules served as sensitizing concepts, in the sense set out by Timmermans and Tavory (2012): theoretical commitments that make certain observations surprising and thereby drive the revision of categories, rather than templates applied to the material. Before close coding, the planning codebook named emotional repertoires, moral evaluation, blame attribution, imagined futures, and rhetorical devices. During analysis these were refined into linked dimensions: emotional register; discursive practice; moral evaluation and deservingness; blame attribution; interactional feeling rules; and technology-directed or mixed counterevidence. Comments could receive more than one code. Supplementary Table S1 gives inclusion, exclusion, and overlap rules, and the subsection “Operationalization of Core Constructs and Boundary Decisions” in Section 3.5 sets out how the study’s core constructs were operationalized and how boundary decisions were made.

Operational boundaries were applied as follows. Dread/fatalism required an expression of threatened loss combined with inevitability, irreversibility, or constrained agency; pessimism without an inevitability claim was not sufficient. Anger referred to direct condemnation or hostility, while resentment additionally invoked an unfair comparison, broken promise, or unrewarded prior investment. Conditional hope required a stated condition under which adaptation, quality, regulation, or market correction could preserve agency; practical advice alone was coded as adaptation rather than hope. Blame required assignment of causal or moral responsibility to a named actor, institution, or system; general criticism without responsibility attribution was coded only as evaluation. Irony/dark humor required an incongruous, sarcastic, or comic formulation that managed or redirected threat.

The analytic sequence had three stages. First, the authors used the planning codebook and sensitizing concepts to read across all 22 threads. Second, boundaries were refined abductively through close reading and comparison; blame was recast as cross-cutting rather than a fifth emotion, and final configurations combined emotional register, discursive action, deservingness, and responsibility attribution. Third, candidate passages were checked against source rows and interpreted in context. The planning register therefore structured case variation but did not mechanically determine the final findings. Technology-directed and mixed cases were retained as counterevidence to any absolute institution-versus-technology claim.

The final structure contains four overlapping affective-discursive configurations and one cross-cutting blame dimension. These are interpretive summaries, not mutually exclusive classes. The original workflow did not use independent double-coding, and no retrospective inter-coder coefficient is reported, since it would not constitute an independent reliability test after categories and quotations were finalized. Trustworthiness rests instead on the documented pre-analysis register, explicit code boundaries, restricted source identifiers, retained contradictory cases, the 25-case sampling audit, deterministic corpus and quotation checks, and the bounded post hoc audit described below.

The initially submitted analysis used Claude Sonnet 4 and Claude Opus 4.6 (Anthropic, San Francisco, CA, USA) in the same bounded auxiliary role after authors had developed categories and coded an initial set: the models flagged candidate comments matching author-written descriptions and returned possible quotations for human inspection. Both versions were used for the same candidate-flagging step, with no stage assigned exclusively to either and no analytic distinction or comparison designed between them. Model outputs were not treated as codes or findings; the authors made the original coding, interpretation, and quotation decisions. The models’ output logs and false-negative estimates were not preserved, so no model-accuracy claim is made; the standardized instruction supplied to the models is reproduced in the Supplementary Methods (Section S2). The models were therefore used as candidate-retrieval aids rather than as coders. That restriction follows independent methodological guidance: language-model outputs on interpretive tasks require task-specific evaluation and human adjudication rather than assumed reliability (Tai et al. 2024; Abdurahman et al. 2025). Complementary studies document task-dependent variation across both multidimensional content analysis (Rughiniș et al. 2025a) and Reddit stance classification (Rughiniș et al. 2025b). Because no held-out benchmark or exhaustive gold-standard labeling was created, no estimate of model recall or of a false-negative rate is offered; the authors read all 22 threads and compared model-flagged candidates against codebook-guided manual review. Deterministic scripts reconciled rows, identifiers, dates, duplicates, the 25-case register, and exact quotation matches. No independent human double-coding or reliability coefficient is claimed.

Operationalization of Core Constructs and Boundary Decisions

The three concepts that organize the analysis operate at different analytical levels, and conflating them would misdescribe the procedure. Textual markers are properties of a comment; first-cycle codes are assigned to comments; affective-discursive configurations are patterns assembled across coded material; and moral economy is a theoretical interpretation of those patterns. Deservingness is the only one of the three headline concepts applied as a comment-level code. Moral economy and affective-discursive practice are higher-order interpretations constructed from linked evidence rather than labels attached to individual comments.

Table 2. Operationalization of the three core constructs: analytical level, evidence threshold, first-cycle codes, and exclusion rules. Code names refer to Supplementary Table S1.

Construct	Analytical Level	Evidence Threshold	First-Cycle Codes	Exclusion/ Negative-Case Rule	Worked Example
Moral economy	Theoretical interpretation, across coded material	An invoked normative expectation about fair treatment, obligation, or the allocation of costs and benefits, together with a perceived breach. Responsibility attribution strengthens the inference but is not required.	Fairness/ obligation; Blame attribution; Deservingness /merit where present	Negative affect, complaint, or prediction without an invoked expectation. Deservingness is one possible component, not a universal requirement.	Section 5.1
Deservingness	First-cycle code, comment level	An entitlement claim to protection, recognition, reward, or security grounded in effort, credentials, service, care, craft, or compliance with institutional expectations.	Deservingness /merit	Status assertion without a stated basis for the earned claim.	Section 4.2
Affective-discursive practice	Configuration-level interpretation	An emotional stance performing an identifiable action—diagnosing, condemning, ridiculing, reassuring, prescribing, or contesting—rather than reporting an internal state.	Emotional register with discursive practice and feeling rule	Expression treated as a transparent report of private feeling. Reply uptake is supporting evidence where available, not a requirement.	Section 4.3

states the evidence threshold applied at each level.

The most consequential analytic decision concerned blame. The planning codebook treated blame as one emotional repertoire among others, implying a fifth category alongside dread, anger, irony, and hope. Close reading did not support that structure: blame appeared inside all four registers rather than beside them, and comments assigning responsibility were not separable from the emotional stance in which the assignment was made. Blame was therefore recast as a cross-cutting moral dimension. The change was structural rather than terminological, because it altered what the findings claim: not that participants express five kinds of feeling, but that responsibility attribution gives emotional expression its social meaning.

Narrower boundary decisions recur throughout, and three illustrate the range. The anger/resentment boundary requires an unfair comparison, broken promise, or unrewarded prior investment for resentment; the indictment of corporate decision-making quoted in Section 4.2 condemns executives without invoking such a claim, and was coded as anger with blame attribution rather than resentment. The dread/fatalism boundary requires threatened loss together with inevitability, and a passage may satisfy it without containing an emotion word: the statement in Section 4.1 that graphic design is “one of the first fields that will go” was coded as dread/fatalism on the basis of unhedged inevitability and thread context, against a rival reading as neutral forecast. Registers can also co-occur within a single comment, as in the passage in Section 4.5 that moves from “it’s scary” to “what’s funny is their short term greed,” which was coded as both dread and irony. Supplementary Table S5 documents eight such decisions, recording the boundary-relevant codes considered, the rule applied, the rival interpretation, and the reason it was rejected; it is not an exhaustive inventory of every code applicable to each complete published passage.

Table S5 was compiled during revision by reapplying the frozen codebook to passages already analyzed, and was reviewed and approved collaboratively by the authors responsible for the formal analysis. It is not a contemporaneous coding log and not a reliability test: a coefficient calculated after categories and quotations had been finalized would provide no independent evidence about the original analytic process. The analysis follows a codebook approach, which Braun and Clarke (2022) distinguish from coding-reliability approaches in which agreement statistics serve as the primary warrant. Explicit decision rules, worked boundary cases, retention of counterevidence, and the account of alternative interpretations below are offered in place of a retrospective agreement statistic.

Three alternative readings of the material as a whole were considered. The first is that the four configurations reproduce the conventions of an online complaint genre rather than moral evaluation. Although complaint-genre conventions may well shape expression, the reply sequences contain explicit attempts to validate, correct, or ridicule what another participant ought to feel (Section 4.4 and Section 5.2), which is feeling-rule work that complaint performance alone does not account for. The second is that institutional blame is displaced fear of the technology. The codebook retained technology-directed and mixed cases in order to test this: the passage in Section 4.1 that assigns AI a purpose (“is here to cut costs and maximize profit”) while locating the motive in capitalist deployment, and the reply in Section 4.4 attributing causal force to improving systems, are both retained, and Section 5.4 states the resulting qualification. The third is that moral economy is imposed on ordinary dissatisfaction. The threshold in Table 2 requires an explicit invoked expectation together with a perceived breach; negative affect without an invoked expectation was insufficient for a moral-economy interpretation.

3.6. Ethical Considerations

The study analyzes posts accessible without joining the sampled subreddits, but public accessibility does not eliminate contextual, consent, or reidentification risks (Franzke et al. 2020; Adams 2024; Fiesler et al. 2024; Rocha-Silva et al. 2024). Data were obtained through the Reddit API. Usernames and permalinks are retained only in restricted source workbooks and are not reported. Quotations remain verbatim because exact wording is analytically relevant and was part of the original study design; they are shortened to the minimum contextually adequate form and presented without usernames or public identifiers. A quotation-by-quotation review by the authors read the complete source comment, confirmed interpretive fit, and graded traceability risk; three passages were rated high risk because of career-stage or detailed biographical disclosure and were retained only in the shorter form used in Findings. Verbatim text can nevertheless remain searchable. Deleted/removed bodies are excluded. No interaction or intervention occurred. Under Article 22(1)(a) of the University of Bucharest Regulation on the Organization and Functioning of the Ethics Commission, ethical certification by the University Ethics Commission (S-CEC) is requested for covered research activities only when they involve significant ethical risks; the authors assessed the present study as not involving significant ethical risks, because it analyzed already public, pseudonymous forum material, did not identify or profile individuals, and involved no sensitive personal data or interaction with human participants. The precautionary measures described above—exclusion of deleted/removed bodies, retention of usernames and permalinks only in restricted source workbooks, omission of identifiers from the manuscript, minimization of quoted wording, and non-redistribution of raw data—were applied so that no significant ethical risk arose. Under this regulation, research falling below the significant-risk threshold is not subject to a separate institutional exemption ruling; the assessment therefore rests with the researchers and is disclosed here as a regulation-grounded author judgment rather than an institutional determination. For the same evidentiary reason, quotations were retained verbatim rather than paraphrased, with the residual searchability noted above minimized rather than treated as eliminable.

4. Findings

The analysis identifies four overlapping affective-discursive configurations and a cross-cutting dimension of blame attribution. A configuration links an emotional register, a discursive action, and a moral evaluation; blame identifies where causal or moral responsibility is placed across those configurations. The categories are not mutually exclusive. Quotations are used to show analytic structure, and technology-directed fear, mixed cases, and adaptation claims are treated throughout as limits on any binary reading.

4.1. Dread and Fatalism: The Discourse of Structural Inevitability

The first configuration is characterized by dread tied to the perception that AI-driven displacement is structurally inevitable. In this register, technological change is not a possibility to be debated but a process already underway, governed by forces that individuals and institutions cannot control. The emotional tone is resignation, showing a settled conviction that the outcome is determined.

This fatalism appears most clearly in threads where participants reflect on the macro-level dynamics of AI adoption. In r/ChatGPT, a commenter responds to a post about job loss with a compressed narrative of inevitability: “We’re watching a car crash in slow motion. We all know what’s coming and we all know our governments are not going to be able to manage immense rate of change”.

The metaphor condenses three claims: the outcome is visible, the trajectory cannot be altered, and those who could intervene lack the capacity to do so. Fatalism is offered here as a collective diagnosis rather than an individual mood, and the shift from “I am afraid” to “we all know” positions dread as shared knowledge rather than personal vulnerability.

A similar logic operates in threads about specific occupations. In r/Futurology, a commenter frames permanent unemployment as the inevitable trigger for political transformation:

“When increasingly large segments of humanity are rendered permanently unemployed, radical economic change is inevitable and will be demanded by all democratic governments lest they completely lose any sense of legitimacy.”

What is distinctive about this comment is its temporal framing. Unemployment is not a risk but a certainty; the only question is what follows. The speaker reaches for the vocabulary of institutional and economic transformation, not career advice. The emotional register combines fatalism about displacement with hope for democratic redistribution and a post-scarcity future, alongside a moral demand that institutions respond.

In r/ChatGPT, another comment articulates the fatalist logic in explicitly capitalist terms:

“You are one of many that will be made useless and thrown away to maximize profit. AI is not here to be our friend or a helper to give you more free time. Is here to cut costs and maximize profit no matter what. We are all ducked and the ones thinking they will be smarter and escape simply don’t get how capitalism works!”

The fatalism in this comment is grounded in a causal account in which capitalism, not technology alone, drives the outcome. The speaker dismisses adaptation hope as naive and enforces a feeling rule: realistic pessimism is appropriate, while optimism signals failure to understand the structural logic. Discourse analysis of climate-change skepticism documents a comparable move, in which competing constructions of the future are mobilized to settle disagreement in the present (Vulpe 2024).

In the graphic design subreddit, fatalism is expressed through direct occupational experience:

“Graphic design is one of the worst affected by AI and is one of the first fields that will go”.

The brevity carries the emotional weight. There is no hedging, no “might” or “could”: the conclusion is stated as settled fact. In a thread where a recent graduate describes feeling that AI ruined a career before it started, that flatness of tone functions as emotional realism, a refusal of false comfort.

4.2. Anger, Resentment, and Violated Meritocracy

The second configuration combines anger directed at specific institutional actors with resentment grounded in the perception that meritocratic promises have been broken. Where fatalism treats displacement as a structural process beyond anyone’s control, anger assigns responsibility. The targets are employers, executives, tech billionaires, and hiring systems. The moral claim is not only that workers are losing but that someone is causing them to lose, and that this is unjust.

In r/antiwork, a commenter responds to a thread about AI in job applications with a wide-ranging indictment of corporate decision-making:

“The corporations are only concerned about eliminating labor costs. These college educated morons that run these corporations believe that the world only exists on paper (or computer) and won’t deviate under any circumstances”.

The anger is directed at executives, but the moral claim is about institutional rationality: corporations are said to operate according to an abstracted model of the world that does not correspond to lived reality. The epithet “college educated morons” condenses a class-coded critique in which education has produced managers who are credentialed but incompetent and who treat human labor as a line item rather than a social relationship. The anger is not about AI as such but about the institutional logic that makes AI adoption a vehicle for cost cutting without accountability.

In r/ChatGPT, a comment distills the antagonism between capital and labor into a stark parallel structure: “Bliss for users, hell for those who provide what it provides. Also: bliss for capitalists and management, hell for labor”.

This is not an argument but a moral accounting. The parallel construction positions the distribution of AI’s consequences as zero-sum and class-determined, with no shared benefit and no middle ground. The emotional register is cold anger rather than despair, a refusal of the framing in which AI benefits everyone.

The resentment component of this configuration appears most clearly in comments where speakers describe having followed the prescribed path, having invested in education and career-building, and then finding the terms changed. A commenter in r/cscareerquestions writes:

“Yeah I’m out here trying to work low paying jobs to just get by and getting hit with ‘over qualified’ crap. They think I’ll bolt at a tech opportunity, but little do they know they’re going away and not coming back. I hate starting over at 35”.

The resentment is layered. The speaker is overqualified for available jobs yet locked out of the field for which the qualifications were earned, and “going away and not coming back” marks this as a permanent shift rather than a cyclical downturn. “I hate starting over at 35” is a claim about temporal injustice: years invested in building a career have become a liability rather than an asset, and the meritocratic contract that promised effort and credentials would be rewarded is presented as violated. Comparative research on job searching shows that whether such failure is attributed to a flawed system or to a flawed self is itself institutionally patterned rather than idiosyncratic (Sharone 2013); the passage above locates the failure squarely in the system.

In r/recruitinghell, anger is directed at the hiring infrastructure itself:

“I think there are a lot of shitty human recruiters that just use ‘CTRL + F’ and see if a skill is listed. Unfortunately, since the typical recruiter is a moron, they’ll see a skill and assume expertise in it”.

This comment does something analytically interesting: within a thread about AI screening, the quoted passage locates the immediate failure of meritocratic evaluation not in AI capability or automated decision-making, but in the human recruitment practices that preceded it. The recruiter is already reducing a person to keywords. The surrounding thread frames AI screening as a technological extension of this existing institutional pathology, but the passage itself assigns responsibility to human recruiters. It is therefore coded as anger and blame attribution, not as technology-directed or mixed counterevidence. The anger is directed at a system that is described as never having been meritocratic in the way it claimed to be.

In r/antiwork, another comment articulates the institutional bad faith behind automated hiring:

“Working as intended. They get to look like they’re trying hard to hire, but they don’t actually have to pay the extra labor costs because nobody gets hired. Bonus! They get to complain about how lazy, entitled workers don’t want to work any more!”.

The phrase “working as intended” reframes what appears to be system failure as system design. The hiring process is not broken; on this account it is functioning exactly as employers want it to. The speaker identifies a double benefit for employers: they avoid labor costs while simultaneously constructing a narrative that blames workers for their own exclusion. The sarcastic “Bonus!” marks the transition from diagnosis to moral condemnation. The complaint is recognizable in legal scholarship on automated hiring, where the delegation of screening to software disperses responsibility for exclusion without removing it (Ajunwa 2020).

4.3. Irony and Dark Humor as Collective Emotional Management

The third configuration uses irony, sarcasm, and dark humor to manage the emotional weight of AI-related labor anxiety. On Reddit, humor can provide a recurring mode of collective emotional processing (Litherland and Wood 2026). It lets participants acknowledge loss and threat without requiring direct vulnerability and can enforce a situated feeling rule: maintain composure through wit. Workplace ethnography records a comparable double function, in which joking simultaneously expresses grievance and disciplines those who fail to perform the expected stance (Collinson 1988; Billig 2005).

In r/Teachers, the comment with the highest recorded score in the sampled thread about Bill Gates’ prediction that AI will replace teachers condenses the entire counter-argument into a single wry observation:

“Unless AI can watch people’s kids while they work, then he is grossly out of touch. There is job security in the ‘glorified babysitter’ role”.

The speaker reclaims a pejorative label normally used to diminish teachers’ professional standing and converts it into a source of job security, on the ground that custodial care is not readily automated. The irony is directed at public figures who discuss education as information delivery rather than as a social institution. The high score indicates visibility and positive net voting at extraction, not representative endorsement.

In r/ChatGPT, a comment captures executive short-termism in a single sarcastic line:

“Lol, that’s for someone else to worry about, I gotta get this quarter’s profits up”.

The “Lol” marks the statement as a performance of executive logic rather than the speaker’s own view. The bitter humor advances a claim about motive: decisions about AI adoption are made for quarterly returns rather than for long-term consequences to workers. The brevity is itself rhetorical, a compressed caricature that relies on shared understanding of corporate incentive structures.

In r/copywriting, when a poster asks what career to pursue if copywriting becomes obsolete, one response consists of two words: “Drug dealer”.

The positive score attached to this two-word response is treated as a visibility and resonance cue, not a direct measure of community endorsement. The joke works because it treats the question as absurd: if an entire profession is eliminated, the remaining options are equally absurd. It is a refusal to engage with the premise of orderly career transition. The dark humor functions as resistance to the feeling rule that says one should respond to displacement with practical planning and adaptation.

In r/biglaw, a commenter responds to a report about Anthropic’s top lawyer predicting the end of the billable hour:

“A guy whose paychecks depend on people continuing to use/buy AI says AI is the best thing since sliced bread? Shocking development”.

The sarcasm performs epistemic work by discounting a claim through the speaker’s material interest. “Shocking development” is ironic: the real surprise would be an AI-company executive speaking against AI adoption. The high score indicates visibility and positive net voting within this sampled thread, not a measure of community opinion.

4.4. Conditional Hope and Contested Adaptation

The fourth configuration centers on hope, but hope that is always conditional, contested, and internally divided. Some participants articulate a version of hope grounded in the belief that markets will correct, that AI output quality is insufficient, or that skilled workers will retain value. Others challenge these claims as class-blind, unrealistic, or serving the interests of those who are already advantaged. The structure is close to what Berlant (2011) describes as an attachment to a promise whose conditions of fulfillment have already been withdrawn, and comparable ambivalence has been documented in everyday encounters with AI systems, where users hold benefit and control together rather than resolving them (Creangă 2025).

In r/copywriting, a commenter expresses measured optimism about market correction: “The pendulum will swing back at some point. Once businesses stop seeing results or people get sick of seeing slop it will come back round.”

The hope is grounded in a specific empirical prediction: AI-generated content is low quality (“slop”), and the market will eventually punish it. But within the same thread, another participant directly contests this:

“Pendulum isn’t swinging back, possibly in the short short term, but every time you type a word here on Reddit and many other places you’re all just (unknowingly apparently) training all the AI systems to get better and better to eventually the point of perfection; aka, you’re digging your own employment grave”.

The exchange illustrates the contestation internal to adaptation discourse. The second speaker reframes the optimist’s hope as naivety and adds a recursive twist: participating in online discussion contributes the training data that will make human writers obsolete, so that “digging your own employment grave” converts participation itself into self-destruction.

In r/graphic_design, a commenter offers what appears to be encouragement but embeds it in a structural concession:

“I got the same wave of despair when AI started coming around - and I’d been in the industry a fair while. [...] The reality is, a lot of AI design sucks, and some people just don’t care. For the same reason some people will pay 5 bucks on fiverr for a shit logo instead of investing in an agency or brand strategy and a professionally done logo design - there’s always going to be people who don’t care, who want it as cheap as possible (which is free, now) and they are happy with shit work. AI will take those people now.”

The hope is conditional on market segmentation: AI captures the low end while the high end persists. The speaker validates the original poster’s despair before offering that stratification argument, so that survival depends on occupying the right tier of the market. This is a version of meritocratic hope and, at the same time, a concession that the low-cost segment is already lost.

In r/biglaw, a commenter frames the AI threat as differentiated by career stage:

“It’s a terrifying future for white collar professionals who are still building their careers. I’m in that group too, for the record. Not speaking from a position of power or superiority.”

The clarification (“not speaking from a position of power”) identifies the speaker with the professionals whose careers are threatened. The speaker acknowledges lawyers’ interest in the existing system and positions fear as appropriate for those still building their careers. The fear is explicitly temporal: it belongs to those still building careers, not those already established. The concern is not confined to platform discussion: early payroll evidence associates AI exposure with reduced hiring of younger workers, on evidence its authors describe as suggestive rather than causal (Brynjolfsson et al. 2025a). That literature is not used here to validate the comment, which is analyzed as a discursive stance; it indicates only that the temporal structure of the worry has a referent outside the thread.

4.5. Blame Logics and Institutional Distrust

Blame operates as a cross-cutting moral dimension across the four configurations. Participants assign causal or moral responsibility to employers, technology firms, public figures, governments, educational institutions, or market systems. The emphasis varies by thread, and technology itself can also be an object of fear or criticism. Blame is therefore not a fifth emotional register but one way dread, anger, humor, and hope acquire social meaning.

Employers are one recurring blame target. In r/ChatGPT, participants frame corporate AI adoption as profit extraction without accountability:

“It’s scary when you realize labor and special skills was the only card we had to play in this capitalist society. Take that away and? If this happens on a mass scale then nobody will have money to keep these peoples shareholders sated. What’s funny is their short term greed is going to accelerate the end of capitalism”.

The causal logic is stated precisely. On the speaker’s account, employers replace labor to maximize short-term shareholder returns and thereby destroy the consumer base on which their own profits depend. The passage attributes to corporate short-termism a systemic contradiction in which the pursuit of profit undermines the system that generates it. Blame is directed at that short-termism rather than at AI as a technology.

A second blame target is the tech elite. In threads that respond to public statements by tech billionaires, participants treat predictions about AI and work as self-interested performances rather than credible forecasts. In r/Teachers, in response to Bill Gates’ claim that AI will replace teachers:

“He’s a dumbass. AI can’t motivate students, solve problems, manage behavior, or do hundreds of the other things real people do in real time with young people and children. These billionaires think kids are just robots you can force feed information to and that’s all education is”.

The directness of “he’s a dumbass” refuses the deference conventionally extended to billionaire public speech, and the moral claim is that the account misunderstands teaching as information delivery rather than relational, institutional work. The high score records visibility and positive net voting in the sampled thread, not representative endorsement.

In r/Teachers, a different comment locates the threat not in AI technology but in the political economy of education:

“They want to get rid of public education and privatize it. They want public tax dollars to flow into private schools where they do not have to pay teachers pensions and provide benefits. [...] The aim is to create an entire industry driven by profit off a public sector.”

Blame here is directed not at AI but at an elite project of institutional capture, with AI cast as a tool in a broader strategy to privatize public services and eliminate the labor protections that public-sector employment provides. Another comment extends this into a class-bifurcation argument:

“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”.

The verb “warehoused” does the moral work, describing the predicted arrangement in the vocabulary of logistics rather than care. The two-tiered system, AI instruction for the poor and human teaching for the wealthy, is presented as a prediction, and the emotional register combines anger with a claim about who will bear the costs.

A third target is the political-economic power of the wealthy, invoked to explain why protective regulation is not forthcoming. In r/Futurology, participants frame that absence as a choice rather than a limitation:

“Why do you think the rich are so keen to disarm the working class. They know what the inevitable results of capitalism are. They want to stack the deck to ensure they get to be feudal lords”.

The passage does not name a regulator. It attributes intent to a class of actors: on this account the wealthy act to weaken the bargaining position of workers because they anticipate the consequences of unrestrained accumulation. The language of “feudal lords” positions the present as a replay of pre-democratic social orders, so that the absence of protection is presented as an outcome of elite capture rather than of institutional incapacity.

In r/biglaw, where the discussion responds to an Anthropic executive’s prediction about the legal profession, the blame logic takes a more specific institutional form:

“The class of tech billionaires really cannot stand that there’s a professional class that they need, but are sufficiently wealthy not to be totally subservient to them. They’re desperate to kill off that tier of upper-middle/lower-upper class professionals”.

The target here is a specific class fraction: tech billionaires who, on the speaker’s account, resent the autonomy of credentialed professionals. The passage attributes the promotion of AI not to client benefit or service quality but to the prospect of subordinating a professional class that tech elites cannot otherwise control, so that the grievance concerns status and autonomy rather than income.

Across these passages, emotional responses acquire social meaning through responsibility judgments. Technology is sometimes feared or criticized directly, while the actors who deploy, profit from, or fail to restrain it are recurring targets. The narrower finding is relational: technological capability and institutional accountability are intertwined in the moral grammar of these discussions.

5. Discussion

AI-related concern in the sampled discussions is patterned rather than diffuse. The four configurations set out in Section 4 are not four separate feelings but four ways of linking an emotional register to a moral evaluation and a responsibility judgment, and they overlap within comments and exchanges. This section examines what that linkage implies for moral economy (Section 5.1), for the sociology of emotions (Section 5.2), for the analysis of platform-mediated discourse (Section 5.3), and for the structure of accountability (Section 5.4), before setting out the questions the analysis raises for labor institutions (Section 5.5) and its limitations (Section 5.6).

5.1. AI Anxiety as Moral Economy

Thompson (1971) described the moral economy as the normative framework through which subordinate groups evaluate whether economic arrangements are just. The bread rioters he studied objected not to scarcity as such but to a specific violation: the perception that merchants, millers, and magistrates had broken the customary obligations governing the provisioning of food. A structurally parallel process is visible here. Participants responding to AI-related labor change do not simply report fear; they report violated expectations, that education and credentialing would be rewarded with stable employment, that employers would not discard workers while posting record profits, that disruptive technologies would be regulated before they destroyed livelihoods, and that the rules would not change after people had committed their careers to a given path. Consistent with the caution advanced by Palomera and Vetta (2016), what the corpus displays is not a single shared moral order but competing invocations of obligation, some of which are contested within the same thread.

The moral economy framework also clarifies how this material differs from what survey instruments are designed to capture. Surveys ask whether respondents are worried and how worried they are. In the passages analyzed here, expressions of concern are articulated together with evaluative claims about fairness, responsibility, and institutional obligation. The analysis therefore indicates that emotion and judgment are interwoven in these expressions; it does not establish that judgment is analytically or experientially prior to worry, and the design cannot adjudicate that ordering. Sociological accounts of emotion have in any case treated appraisal and feeling as mutually implicated rather than sequential (Barbalet 1998; Wetherell 2012). This co-articulation is the article’s narrower contribution, and it is visible in each configuration: the dread in Section 4.1 is voiced together with a claim about the perceived inability of democratic governments to manage change; the anger in Section 4.2 with an indictment of employers who cut labor costs while extracting value from worker expertise; the humor in Section 4.3 with the judgment that billionaire predictions are self-interested and that the demand to “adapt” is empty when the terms of adaptation are undefined.

Chen et al. (2025) analyze the moral economy of AI through state and corporate legitimation in elder care. Our analysis complements that account by examining evaluations voiced in sampled online discussion. Commenters articulate claims about fairness and accountability grounded in meritocratic reward, institutional protection, and dignified work. Similar logics appear in several occupational discussions, but their expression differs with thread topic, timing, and institutional context; the theoretical sample does not establish convergence across occupational populations.

Deservingness clarifies why violated meritocracy recurs across otherwise different discussions. Commenters claim protection or recognition not simply because loss is painful, but because education, experience, care, or rule-following is presented as an investment that institutions should honor. The criteria they invoke, chiefly prior contribution and reciprocity, are among those documented in research on how publics allocate deservingness more generally (van Oorschot 2000). Occupational research documents similar claims, in which hardship is framed as intrinsic to a vocation and therefore deserving of recognition (Zamfirache 2020). Competence with AI itself enters these claims: public discourse has begun to treat familiarity with generative systems as a marker of worth, which redistributes standing among workers independently of their existing credentials (Bran et al. 2023). Other comments contest the premise altogether by treating adaptation as an individual obligation. The corpus therefore contains a struggle over who deserves security and who is expected to absorb technological risk.

5.2. Feeling Rules and Contested Emotion Cultures

Hochschild (1979) demonstrated that emotions are governed by feeling rules, normative expectations about what one should feel in a given situation, and that people engage in emotion work to align feeling with those expectations. The patterns identified here are therefore not only emotional responses; they are also bids to establish what the appropriate response to AI and work should be.

This is visible in the fatalism configuration. When a commenter writes that those who express hope “simply don’t get how capitalism works,” the statement prescribes realistic pessimism as the appropriate stance. In Section 4.4, a prediction of a “pendulum swing” is directly challenged by the claim that platform participation is “digging your own employment grave.” The exchange is an explicit conflict over feeling rules: whether hope is warranted or self-deceptive. Such exchanges are recognizable as symmetrical constructions of credibility, in which each side treats the other’s position as a failure of competence rather than as a difference of information (Rughiniș and Flaherty 2022).

Guenther (2009) showed that organizations can maintain different emotion cultures. The sampled Reddit threads display analogous contrasts in rhetorical emphasis, but the evidence is narrower: r/antiwork, r/cscareerquestions, r/Teachers, and r/biglaw each contribute situated discussions shaped by topic and timing. We therefore describe thread-level tendencies rather than stable subreddit cultures. Kelan’s (2023) account of classed and status-laden automation narratives helps interpret those differences without assuming that commenters’ occupational identities are verified.

The threads occupy what Hochschild (2011) called the “market frontier” of emotional life. The visible emotion work, maintaining composure through humor, converting despair into anger, or resisting demands for optimism, is performed under labor-market restructuring that participants cannot individually control and under a precarity that employment research treats as structural rather than episodic (Kalleberg 2009).

5.3. Affective-Discursive Patterns and Platform Affordances

Wetherell (2012, 2013) argues that emotions are constituted through public, discursively patterned practices rather than private states that are merely expressed. The material analyzed here supports that interpretive focus: dread establishes a shared diagnosis; anger identifies responsible actors; humor manages vulnerability; hope bids for agency; and blame allocates responsibility. Reply sequences provide direct evidence of uptake and contestation, while scores supply only contextual visibility cues.

The platform affordances documented by Proferes et al. (2021), Massanari (2017), and Graham and Rodriguez (2021) shape visibility and interaction. High scores attached to concise humor or sharp moral formulations are contextual evidence of visibility and resonance at extraction, not direct proof of community endorsement. Litherland and Wood (2026) show that ironic detachment and dark humor are common platform practices for engaging with distressing news; the sampled AI-work threads contain similar practices. Read through Papacharissi’s (2015) account of affective publics, the threads examined here are better described as temporary formations organized around a structure of feeling than as communities with settled positions.

The methodological implication is that the emotional stances visible in these threads are not raw expressions of individual feeling but platform-mediated, community-filtered performances already shaped by interaction and selection. What is analyzed is not what people feel about AI but what they do with emotion in a specific discursive setting, which reframes the object of study as the moral and emotional resources available for making sense of AI-driven labor change.

5.4. Blame Logics and the Structure of Accountability

Across all four configurations, moral responsibility is assigned to institutions and to actors that deploy, profit from, or fail to govern AI. This does not mean participants do not fear the technology: technical capability, speed, and opacity remain parts of their causal accounts. The narrower claim is that technological fear is articulated through institutional judgments about employers, technology firms, wealthy actors, and educational systems.

Blame emphases vary across the sampled occupational discussions. The r/Teachers thread emphasizes privatization and elite misunderstanding; r/antiwork emphasizes employers and hiring systems; r/biglaw emphasizes professional autonomy; and copywriting/design discussions emphasize market valuation of low-cost output. Because most subreddits contribute one thread, these comparisons describe situated emphases rather than stable community traits.

Sociological work on blame helps specify what such attributions accomplish. Blame is a relational practice that assigns credit and fault in ways that sustain or contest social ties, rather than a neutral report of causation (Tilly 2008), and cultural theory has long noted that the selection of an accountable party is patterned by institutional position rather than by evidence alone (Douglas 1992). The allocation is also conditioned by what participants believe about the systems in question: where folk theories attribute agency to a technical process, responsibility can migrate away from the actors who commissioned it (Obreja 2025). This is one reason the technology-directed and mixed cases retained in the codebook matter analytically rather than merely as counterexamples.

The pattern is consistent with Thompson’s (1971) emphasis on judgments about accountable actors. In these passages, responsibility is directed toward employers, executives, and wealthy actors, while technological capability remains part of the feared causal process. Emotion and responsibility attribution are treated as mutually constituted rather than as a sequence in which one necessarily precedes the other.

5.5. Questions for Labor Institutions, Employers, and Policy Research

The configurations described above bear on how technological change is handled in workplaces and by labor institutions, but this corpus supports questions and hypotheses, not policy prescription or inference about workers’ preferences or intervention effectiveness. The questions below therefore identify the evidence required to address them.

The first concerns the vocabulary of collective representation. The deservingness claims documented in Section 4.2 and Section 5.1 concern the terms on which transition occurs: what is owed to those who invested in training, what notice and consultation are due, and how the costs of reorganization are distributed. This is the domain that social dialogue over AI and algorithmic management addresses (Doellgast et al. 2025), and its vocabulary is scarce in the corpus. A deterministic string check of the 22 frozen workbooks located union-family terms (union, unionize or unionization in either spelling, and collective bargaining) in 13 of the 3275 text-bearing comments, across four threads; works council, worker representation, shop steward, and guild did not occur. This is a count of wording, not of stance: it cannot show that participants reject collective responses, and low lexical frequency is equally compatible with support, with indifference, or with an assumption that such institutions are unavailable. It generates a question rather than an answer. Under what conditions do moral claims about technological change come to be articulated in the institutional vocabulary that would ordinarily process them? Answering it would require research that asks directly about representation preferences rather than inferring them from unprompted discussion.

The second concerns the procedural basis of legitimacy. Several passages attach blame to the manner of adoption rather than to system capability: to cost-first justification, to opacity about how decisions are made, and to the absence of consultation (Section 4.2 and Section 4.5). This suggests a testable hypothesis, namely that the perceived legitimacy of a workplace AI deployment varies with procedural features under employer control, including stated rationale, disclosure, consultation, and avenues of appeal, partly independently of the system’s technical performance. The same logic appears in the accountability problem that legal scholarship identifies in automated hiring, where responsibility is dispersed across a pipeline rather than borne by a decision-maker (Ajunwa 2020). Testing the hypothesis would require designs that vary procedure while holding capability constant, such as vignette experiments or comparative case studies of deployments. This corpus provides none of that evidence.

The third concerns the scope of skills provision as a response. A response limited to skills training addresses a presumed skills mismatch. Broader transition arrangements may also include placement, income support, credential recognition, and employer obligations, and the distinction matters because the claims recorded here are about fairness, transition costs, and accountability rather than about a shortfall of information. Analyses of the skills demanded in AI-related recruitment show that those demands are themselves stratified and unevenly legible to workers (Nastasa et al. 2025), and survey evidence indicates that familiarity with AI is acquiring the character of symbolic capital rather than of a neutral competence (Similea et al. 2025). The material therefore supports a narrow hypothesis: where participants interpret displacement as a breach of reciprocity or distributive fairness, skills-only provision may be received as insufficient even where it improves employability. Evaluating this hypothesis would require outcome research on transition programs together with representative measurement of the claims themselves.

The fourth concerns adaptation as a contested demand. Section 4.4 shows demands to adapt disputed as differentiated by market position and career stage: reasonable for those placed to absorb transition costs and unreasonable for those who are not. Individualized reskilling programs may therefore encounter normative as well as informational objections. Research on job crafting and augmentation (Mayer et al. 2025; Woodruff et al. 2024) suggests that the redistribution of unrecognized work forms part of what is contested, and studies of professionals who delegate tasks to generative AI show that adaptation can reorganize rather than reduce working time (Similea et al. 2026). Whether objections of this kind actually affect participation in, or the outcomes of, reskilling provision is an open empirical question that the present design cannot address.

Finally, the institutional field itself is a site where these terms are set. Organizations that write rules for AI use are engaged in deciding which human–machine configurations count as legitimate, and they do so under competing principles of evaluation (Rughiniș et al. 2025c, 2025d). Whether the moral vocabulary documented here is legible within those rule-making processes, and whether it makes any difference to them, is a further question that comparative institutional research would need to answer.

5.6. Limitations

Several limitations qualify the analysis. U.S. Reddit users are not representative of the U.S. adult population (Pew Research Center 2025; see the subsection “Platform Composition and Discourse Recruitment” in Section 3.4), and this theoretical sample is not representative of occupational or national populations. Participants’ geography, occupation, and other identity characteristics cannot be verified. Most subreddits contribute one thread, so topic, timing, visibility, and participant composition cannot be separated from broader community norms. Scores are shaped by ranking and timing and cannot be read as endorsement. Pseudonymity can support disclosure but also disinhibition, performance, exaggeration, or antagonism (Suler 2004). Platform-specific clustering can shape exposure, but this study does not measure an echo-chamber effect (Cinelli et al. 2021). Verbatim quotations remain searchable despite removing usernames. Three threads supply 51.1% of downloaded rows, and the 2023–2026 window combines early speculative discussion with later workplace accounts. The theoretical sample supports analytic comparison, not frequency estimates, causal sequencing, population generalization, or policy-preference inference. Finally, the original analysis did not include independent double-coding, and the models’ output logs were not preserved; no inter-coder coefficient or model-accuracy statistic is reported. The revision-stage boundary-case review described in the subsection “Operationalization of Core Constructs and Boundary Decisions” in Section 3.5 documents decision rules and adjudications; it does not substitute for independent double-coding.

6. Conclusions

This article analyzed how emotions, moral evaluations, deservingness claims, and blame attributions are organized in sampled Reddit discussions about AI and work. Drawing on 3275 text-bearing comments from 22 threads across 18 subreddits and 13 domains, the analysis identified four overlapping affective-discursive configurations, described in Section 4, with blame and institutional distrust operating across all of them as a cross-cutting moral dimension.

Three conclusions follow. First, AI-labor anxiety in these discussions is not reducible to technological fear; it is organized through judgments that employers, technology firms, wealthy actors, or educational institutions have failed to meet obligations. Second, emotional stances are platform-mediated performances situated in particular threads, not verified inner states; differences across threads in structural critique, professional boundary defense, pragmatic advice, and humor are treated as situated tendencies shaped by topic, timing, ranking, and participant composition. Third, feeling rules are contested and visibly negotiated: hope is challenged as naivety, adaptation is disputed as a demand differentiated by market position and career stage, and humor acknowledges loss without requiring direct vulnerability. Emotion and moral evaluation are therefore treated as mutually constituted rather than placed in a causal sequence.

For research on AI and society, the moral-economy approach complements attitude measurement by asking how concern is linked to fairness, deservingness, and accountability. For labor and organizational research, the analysis shows how technological expectations enter existing career ladders, status relations, and institutional obligations, and it shows that these discussions rarely use the explicit vocabulary of collective representation examined in Section 5.5.

Four directions follow. Comparative work across platforms would clarify how different architectures shape the emotional registers available for discussing AI and work. Longitudinal analysis would track how this moral vocabulary evolves as the technology matures and as regulatory and labor-market responses develop. Cross-national comparison, building on Wang et al. (2025), would test whether the moral grammar identified here varies by institutional context. Ethnographic work could examine how stances performed online relate to offline occupational experience. Whether the sparse use of this institutional vocabulary extends beyond platform discussion is an empirical question for such research, as are the questions set out in Section 5.5.