Growing up in the modern world: how does artificial intelligence enhance firm growth?
Bibliographic record
Abstract
Purpose This paper examines the relationship between Artificial Intelligence (AI) technology development and firm growth. Specifically, it aims to explore how the availability of AI influences firm growth and whether larger firms benefit more from AI-driven technological advancements compared to smaller firms. Design/methodology/approach Using a dataset from CRSP-Compustat covering public firms from 1975 to 2023, this study employs price per memory (PPM) as a proxy for AI technology accessibility to assess its impact on firm growth. The analysis focuses on three key growth metrics: total assets, tangible assets and market capitalization. By examining how data processing capacity influences these growth rates, the study compares the performance of large firms to small firms. A panel data regression is conducted, controlling for macroeconomic trends and industry-specific effects on firm growth. Additionally, the study investigates the heterogeneous impacts of AI technology accessibility across firms of different sizes. Findings The findings reveal that PPM, as a proxy for AI technology availability, significantly affects firm growth. Specifically, larger firms experience faster growth, especially in recent years, as AI technology becomes more accessible and cost-effective. These results suggest that large firms gain the most substantial benefits from AI advancements, further widening the growth gap between large and small firms. Originality/value This research extends prior studies on the impact of AI on firm growth by introducing PPM as a novel proxy for AI availability. It provides new insights into how AI technologies disproportionately benefit larger firms and offers important policy implications regarding firm financing and information regulation. This study also highlights areas for future empirical research on the role of AI in the financial industry.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".