Artificial Intelligence and Corporate Investment Decisions
Bibliographic record
Abstract
Using mergers and acquisitions (M&As) as a proxy, and looking at the case of the US, we find that managers from firms that are more able to substitute human workers with artificial intelligence (AI) are likely to adopt aggressive corporate investment policies, and thereby are more prone to engage in M&As. We reveal that internal and external corporate governance moderates the relationship between AI automation susceptibility and M&A investments. The results hold after the consideration of selection bias and endogeneity issues. At a theoretical level, our study builds on earlier work that suggests a linkage between AI and market concentration. The structural intersectional theoretical literature focuses on the impact of AI-using firms on other actors, but intentionally casts a wide net; it is held that the AI has complex and polyvalent effects, and its usage of data and decision making is one that seeks to afford clear advantage to those deploying it. There is also an association between firm usage of AI and the concentration of markets. This study seeks to deepen this understanding by exploring the approach of AI deploying firms towards others in their sphere; they are more prone to acquiring those firms who supply them or who they supply, which in turn, is likely to contribute to the further foreclosure of competition. We draw out the implications for policy and practice.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".