Different and profound are the consequences of the further development of artificial intelligence (AI) on society. One of the manifestations of the upcoming changes is the plethora of novel ways in which companies may organize labor and capital. So far, the influence of AI on organizational structure has been mainly studied from either a technological or a broader decision-making perspective. Our paper provides a unique take on the topic, emphasizing the distinctive role of human agency and its function in the upcoming AI-driven organizational changes. Relying on the existing academic literature, we theorized a set of hypotheses concerning best fits to the AI contingency on both macro and meso-organizational levels. To test whether the hypothesized changes might encounter organizational inertia or resistance from jobholders, multiple correspondence analysis (MCA) was applied to the online survey results. Four types of current and potential jobholders were distinguished according to their explicit attitudes towards hypothesized organizational changes: skeptics, doubtful skeptics, optimists, and doubtful optimists, the latter consisting primarily of emerging adults. Finally, we developed a model of intraorganizational response to the AI contingency based on four theoretical groups of individuals, as determined by the analysis. Our findings showed doubtful optimists to be the most important group, able to set organizational trends and positively influence skeptics and doubtful skeptics. Accordingly, promoters of AI-driven organizational changes are advised to design their communication efforts around emerging adults. As the survey was conducted amid the COVID-19 pandemic, its sociological and managerial implications are relevant to the looming reality of the postpandemic world.

In this article, we examine a peculiar issue apropos large language models (LLMs) and generative AI more broadly: the frequently overlooked phenomenon of output homogenization. It describes the tendency of chatbots to structure their outputs in a highly recognizable manner, which often amounts to the aggregation of verbal, visual, and narrative clichés, trivialities, truisms, predictable argumentations, and similar. We argue that the most appropriate conceptual lens through which said phenomenon can be framed is that of Frankfurtian bullshit. In this respect, existing attempts at applying the BS framework to LLMs are insufficient, as those are chiefly presented in opposition to the so-called algorithmic hallucinations. Here, we contend that further conceptual rupture from the original metaphor of Frankfurt (1986) is needed, distinguishing between the what-BS, which manifests in falsehoods and factual inconsistencies of LLMs, and the how-BS, which reifies in the dynamics of output homogenization. We also discuss how issues of algorithmic biases and model collapse can be framed as critical instances of the how-BS. The homogenization problem, then, is more significant than it initially appears, potentially exhibiting a powerful structuring effect on individuals, organizations, institutions, and society at large. We discuss this in the concluding section of the article.