**Synthesis of Findings** Algorithmic performance feedback, when rendered continuous and socially comparative, systematically reconfigures the motivational architecture of knowledge work by eroding perceived autonomy and, in turn, suppressing creative initiative. Across the longitudinal window, the observed decline in self-initiated creative tasks is not merely a behavioral fluctuation but a patterned response to a shifting locus of control: employees increasingly orient toward externally defined performance signals at the expense of internally driven exploration. Crucially, this dynamic is not attributable to informational burden, as initially hypothesized, but is fully explained by diminished autonomy, indicating that the psychological meaning of feedback—rather than its volume—governs its downstream effects. The moderating role of feedback framing further clarifies this mechanism: comparative dashboards intensify social evaluation pressures, amplifying autonomy loss, whereas absolute progress indicators attenuate this effect by preserving a sense of self-referential mastery. The reversibility of motivation following reduced feedback frequency underscores the plasticity of these processes, while the stronger adverse responses among senior employees suggest that accumulated professional identity heightens sensitivity to autonomy threats. Together, these findings cohere into a unified account in which algorithmic visibility, when misaligned with core motivational needs, redirects effort away from creative engagement. **Theoretical and Practical Significance** These findings compel a reconceptualization of feedback interventions within organizational behavior, extending self-determination theory by demonstrating that technologically mediated feedback systems can function as autonomy-undermining controls even when they are informationally rich. The evidence establishes that the motivational consequences of feedback are inseparable from its framing and temporal granularity, thereby challenging assumptions in performance management literature that more frequent and data-driven feedback is inherently beneficial. By isolating autonomy loss as the central mediating mechanism, this research refines existing models of intrinsic motivation, shifting attention from cognitive overload to perceived agency as the critical variable in digital work environments. Practically, the implications are immediate and consequential for remote-first organizations. The results demonstrate that the design of AI-driven performance dashboards is not a neutral technical choice but a managerial intervention with measurable effects on creativity and engagement. Organizations gain a clear directive: feedback systems should prioritize autonomy-preserving formats, such as less frequent summaries and non-comparative metrics, to avoid unintended suppression of innovative behavior. At a broader level, this work contributes to an emerging paradigm that interrogates the psychological costs of algorithmic management, positioning itself at the intersection of organizational psychology and digital labor studies by evidencing how subtle design choices reshape the nature of knowledge work. **Limitations** Several limitations delineate the boundary conditions of these findings. First, the study was conducted within remote-first software companies, a context characterized by high digital mediation and knowledge-intensive tasks. This focus was necessary to isolate the effects of algorithmic feedback in environments where such systems are deeply embedded, yet it limits generalizability to more traditional or hybrid organizational settings. Second, the longitudinal design, while capturing temporal dynamics over eight weeks, does not address longer-term adaptation processes; resource constraints and participant retention considerations precluded extended observation. As such, the durability of both the negative effects and subsequent recovery remains an open empirical question. Third, creative output was operationalized through self-initiated task engagement rather than externally validated innovation outcomes, reflecting a trade-off between ecological validity and measurement precision. This choice ensures behavioral relevance but may not capture all dimensions of creativity. These limitations do not undermine the core conclusions but rather define the specific contexts and constructs to which they most directly apply. **Future Directions** Building on these findings, future research should examine how algorithmic feedback interacts with different organizational cultures, particularly those emphasizing autonomy versus control, to determine whether cultural norms buffer or exacerbate the observed effects. Additionally, investigating hybrid or in-person work environments would clarify whether physical co-presence moderates the relationship between feedback systems and perceived autonomy. A second avenue involves unpacking the differential sensitivity of senior versus junior employees by examining identity-based mechanisms, such as professional self-concept and expectations of autonomy, to explain why experience amplifies negative responses. Third, future studies could incorporate objective innovation metrics—such as patent filings, code novelty, or peer-rated creativity—to assess whether the decline in self-initiated tasks translates into diminished organizational innovation outcomes. Finally, experimental manipulation of feedback design features beyond framing and frequency, including personalization and opt-in control mechanisms, would provide actionable insights into how algorithmic systems can be engineered to support rather than undermine intrinsic motivation. **Closing Remarks** As organizations increasingly rely on algorithmic systems to render work visible and measurable, the question is no longer whether feedback shapes behavior, but how it reshapes the underlying motives that sustain meaningful and creative work. This research demonstrates that even well-intentioned, data-driven interventions can inadvertently narrow the space for autonomy if their psychological implications are overlooked. By foregrounding the centrality of perceived agency in digitally mediated environments, it advances a more nuanced understanding of how technology and motivation intersect. The path forward lies not in abandoning algorithmic tools, but in designing them with a deeper appreciation for the human needs they inevitably engage.