The AI Backlash: Why People Punish Those Who Use Artificial Intelligence

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Author: Denis Avetisyan

New research reveals a surprising willingness to penalize individuals for interacting with large language models, suggesting complex social dynamics are emerging around AI adoption.

Experiments demonstrate that individuals are willing to incur costs to punish others for using large language models, highlighting potential social norms and a ‘double bind’ in AI usage.

While large language models promise efficiency gains across numerous domains, their increasing prevalence raises questions about potential social repercussions. This is explored in ‘Antisocial behavior towards large language model users: experimental evidence’, a study demonstrating that individuals are willing to personally incur costs to punish others for relying on these AI tools. Specifically, the research reveals a ‘double bind’ where both utilizing and denying the use of LLMs can elicit negative social sanctions. Will these findings foreshadow broader resistance to AI adoption, and what strategies might mitigate the social costs of technological advancement?

The Price of Ease: Effort and Deservingness

Human perception of fairness is deeply rooted in a sense of proportional return for expended effort; this intuition, known as effort-based deservingness, suggests individuals instinctively evaluate whether rewards appropriately reflect the work invested. This isn’t merely a cognitive calculation, but a fundamental aspect of social cognition, shaping how people assess the legitimacy of outcomes and the moral character of those receiving them. Studies reveal a robust preference for those who earn rewards through labor, even if the actual outcome is identical to one received without effort; the process of earning is often valued as much as, or even more than, the reward itself. Consequently, discrepancies between effort and reward can trigger negative emotional responses and perceptions of injustice, fundamentally challenging the stability of social interactions and cooperative endeavors.

A deeply ingrained human expectation links effort to reward; individuals generally believe that outcomes should reflect the work invested to achieve them. The increasing capacity of Large Language Models to generate outputs – from writing code to composing essays – with minimal human effort directly challenges this fundamental principle. This circumvention of traditional effort isn’t merely a technical shift, but a perceived violation of a core social contract, eliciting negative emotional responses. Studies reveal this isn’t simply passive disapproval; people demonstrate a willingness to expend resources – even incurring personal costs – to penalize those who rely on these models to bypass genuine work, suggesting a powerful, almost visceral reaction to the disruption of this established link between input and outcome.

Research indicates that when individuals perceive a breach of the implicit agreement linking effort to reward – a core tenet of social cooperation – they exhibit surprisingly robust antisocial responses. Studies reveal that peers will actively punish those who circumvent effort, even when doing so carries personal costs. This isn’t simply about fairness; it’s a deeply ingrained reaction to what feels like a violation of a fundamental social contract. The willingness to expend resources – time, energy, or even tangible goods – to penalize reliance on tools that bypass effort, such as Large Language Models, suggests that the perceived transgression triggers a primal urge to restore balance and uphold the principle that rewards should be earned, not simply obtained. This punitive behavior, despite its personal cost, highlights the strength of this intuitive sense of deservingness and its critical role in maintaining cooperative social structures.

Signals and Suspicion: Credibility in Disclosure

Evaluations of disclosure statements regarding Large Language Model (LLM) usage are subject to scrutiny; simply stating LLM involvement does not guarantee acceptance of that claim. Research indicates that participants actively assess the credibility of these disclosures, implying a judgment process beyond mere acknowledgement. This assessment influences subsequent behavioral responses, suggesting that the perceived honesty-or dishonesty-of the disclosure is a critical factor in how the information is received and acted upon, rather than the declaration itself being inherently truthful or false.

The assessment of a disclosure’s credibility directly impacts consequential social reactions, specifically the magnitude of penalties applied in response to detected LLM use. Research indicates that when disclosures regarding LLM assistance are perceived as untruthful, individuals are significantly more likely to impose harsher punishments than when the same disclosures are viewed as credible. This suggests that the perception of honesty, rather than the factual presence of LLM use, is a primary driver of negative social consequences. The severity of punishment is therefore not solely determined by the action itself – the use of an LLM – but is heavily modulated by the believability of any accompanying declaration regarding that use.

Signaling Theory, originating in economics, provides a useful lens through which to analyze the reception of disclosures regarding LLM use. This theory posits that declarations about LLM involvement are interpreted not as objective truths, but as signals sent by an agent to influence another’s beliefs. Recent data indicates that observed LLM use correlates with specific perceptions; specifically, individuals rate those utilizing LLMs at -0.49 on a scale of competence and 0.43 on perceived laziness. These correlations suggest that, regardless of actual skill or effort, disclosures of LLM use currently trigger negative assessments of competence and positive assessments regarding perceived lack of effort, influencing downstream social judgments and potentially impacting punitive responses.

The Gradient of Punishment: Dependence and its Price

Data indicates a correlation between the degree of an individual’s reliance on Large Language Models (LLMs) and the severity of punitive actions received from their peers. Specifically, individuals who exclusively utilized LLMs experienced a significantly higher degree of earnings destruction – 36% – compared to those with more balanced approaches. This suggests that increased LLM dependence is perceived negatively within the study group, triggering proportionally stronger negative responses from collaborators and resulting in quantifiable financial penalties for the LLM-reliant individual.

Data indicates a positive correlation between perceived unfairness resulting from LLM use and the severity of punitive responses. Specifically, as individuals perceive a greater advantage gained by others through LLM reliance, the intensity of punishment – measured by earnings destruction – increases. This suggests a dose-response relationship where the magnitude of perceived unfairness directly influences the extent of punitive action taken by peers, implying a predictable escalation of negative consequences with heightened perceptions of inequity.

Experimental results indicate a phenomenon termed “proactive punishment” where individuals penalize others before any unfair advantage is realized, based on the anticipation of LLM-assisted gains. Specifically, participants in studies destroyed 36% of the earnings allocated to individuals who exclusively relied on LLMs to complete tasks, even prior to observing any output or completed work. This preemptive punitive action suggests a significant aversion to perceived unfairness arising from LLM use, with participants actively reducing potential benefits to discourage reliance on these tools and maintain equitable outcomes.

The Paradox of Transparency: A Dilemma of Honesty

Though ethical guidelines increasingly advocate for transparency regarding the use of large language models, research indicates that disclosing LLM assistance can paradoxically trigger social penalties. Studies reveal a consistent tendency for individuals to impose costs – whether through reduced rewards or direct punishments – on those who admit to relying on AI-generated content, even when the outcome is objectively equivalent to human-created work. This counterintuitive response suggests a deeply ingrained expectation of independent human effort, and a perceived violation of this norm when AI is involved. Consequently, individuals may be incentivized to conceal their use of LLMs, creating a conflict between ethical disclosure and the avoidance of social repercussions. This dynamic highlights a significant challenge in navigating the integration of artificial intelligence into creative and professional spheres.

The study reveals a counterintuitive consequence of transparency: admitting the use of large language models can trigger social punishment, establishing what researchers term a ‘Transparency Dilemma’. Participants consistently demonstrated a willingness to sacrifice personal gain – effectively ‘burning money’ or expending effort – to penalize those who acknowledged relying on AI assistance, even when the resulting output was equivalent in quality to human-generated work. This suggests that honesty regarding LLM use isn’t necessarily rewarded, but actively discouraged by peer disapproval, creating a situation where individuals may rationally choose to conceal their reliance on AI to avoid negative social consequences. The phenomenon highlights a potential barrier to the responsible integration of these technologies, as open disclosure, often considered an ethical imperative, can paradoxically lead to increased social costs for the disclosing party.

Researchers devised a rigorous experimental framework, combining a ‘Money Burning Game’ with a ‘Real-Effort Task’, to investigate perceptions of artificial intelligence assistance. The ‘Money Burning Game’ revealed a consistent willingness among participants to forfeit their own monetary gains simply to penalize others perceived to have relied on large language models – even when that reliance didn’t affect the outcome. Simultaneously, the ‘Real-Effort Task’ confirmed this punitive behavior wasn’t merely hypothetical; individuals demonstrably expended effort to diminish the rewards of those suspected of using AI tools. This setup reliably showcased a counterintuitive phenomenon: peers actively, and at a personal cost, punish the utilization of LLMs, suggesting that honesty about AI assistance isn’t always the most strategically rewarding approach.

The study illuminates a fascinating paradox within the evolving social landscape of AI interaction. It reveals that individuals readily impose costs on others for utilizing large language models, even when such punishment carries personal sacrifice. This behavior aligns with a core tenet of efficient systems: minimizing unnecessary complexity. As Ken Thompson once stated, “Sometimes it’s hard to see the forest for the trees.” This research demonstrates that even in novel contexts like AI usage, humans revert to established social norms – a ‘forest’ of ingrained expectations – and swiftly punish deviations, regardless of the underlying efficiency or potential benefits of the technology. The ‘double bind’ created by these social punishments echoes the need for lossless compression – stripping away extraneous actions to reveal the essential structure of interaction.

What Remains?

The study illuminates a curious paradox: the imposition of social cost for both engaging with, and abstaining from, large language models. This is not a finding about AI, but about the human propensity to regulate behavior through signaling, even when the regulated behavior is novel and ill-defined. The core problem isn’t the technology, but the persistence of ancient social architectures attempting to map onto a fundamentally new landscape. Future work must abandon the impulse to ‘solve’ the ethics of AI, and instead focus on the mechanics of this mapping – how existing norms are stretched, broken, and reformed in response to these tools.

The current research, while demonstrating the existence of this ‘double bind,’ leaves unanswered the question of scale. Does this costly punishment, observed in controlled conditions, translate to broader societal effects? More importantly, what form will these effects take? Will they manifest as overt hostility, subtle exclusion, or simply a chilling effect on experimentation? The answers likely reside not in the design of the models themselves, but in the patterns of interaction – and non-interaction – they provoke.

Ultimately, the value of this work lies not in what it reveals about AI ethics, but in its implicit critique of the field. It suggests that the pursuit of ‘AI ethics’ is, at best, a misguided attempt to impose a pre-defined morality onto a moving target. What remains, stripped of this ambition, is a far more interesting, and far more tractable, question: how do humans, predictably irrational and relentlessly social, navigate a world increasingly mediated by artificial intelligence?

Original article: https://arxiv.org/pdf/2601.09772.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-18 04:48