Navigating AI Currents: How Non-Leader Complementor Firms Choose AI Ecosystems to Join
Bibliographic record
Abstract
The AI ecosystem is an emerging phenomenon, with current research primarily focused on firms leading the AI development. However, the processes through which AI ecosystems evolve is still under researched. This paper identifies AI ecosystems as a distinct category of ecosystems and specifically examines the strategic decision-making criteria for selecting an AI ecosystem to join by the non-leading complementor (NLC) firms. , Integrating ecosystem theory with insights from legitimacy, technology adoption, and strategic alignment, we develop a conceptual model and proposes four key propositions to analyze the dynamics within AI ecosystems with a focus on the strategic, technological, and sociopolitical factors of NLC firms specifically.. The model and propositions contribute to the scholarly literature by addressing how NLC firms assess ecosystem roadmaps, strategic alignment, technological synergy, and legitimacy in their selection processes. We offer academic insights as well as practical guidance on the factors shaping these decisions, highlighting the strategic considerations of NLC firms for positioning within the evolving AI landscape. The findings are valuable for academics, practitioners, and policymakers seeking to understand and navigate the complexities of AI ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".