Abstract
The development of the artificial intelligence (AI) landscape has been impressive in virtually all economic sectors in recent years. Our study discusses the over-concentration of AI knowledge (OCAIK) as the origin of dominance over the global AI industry by a small number of companies and universities that deploy the needed resources to develop and use cutting edge, inimitable AI knowledge. Business agents appropriate AI-related scholarly research and absorb research findings that grant them increasingly inimitable competitive advantages over new entrants. Our study verifies the occurrence of OCAIK by processing thousands of papers presented in AI conferences from 2013 to 2022. To analyze our hypotheses, we used classification techniques and inferential statistics. We found a significant difference between clusters of companies that we called ordinary investors and outlier investors. We also observed the influence of universities in the correlation between OCAIK and investments made in both research and development (R&D) and capital goods. Our findings indicate a strong collaboration between AI leading companies and universities in generating firm-specific AI knowledge. We additionally offer novel insights on the resource-based view (RBV) and the knowledge-based view (KBV) research traditions, in that business competition may reach a point of no return if only incremental innovation is devised instead of radical innovation to break the chains of knowledge accumulation and technological implementation by a strict number of agents.
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Appendix. Synthesis of the Procedures for Mining, Collecting, and Processing the Three Datasets
Appendix. Synthesis of the Procedures for Mining, Collecting, and Processing the Three Datasets
Dataset | Link | Procedure |
|---|---|---|
A | Step 1: access the main link Step 2: access specific link for each year Step 3: download the data (in CSV format) Step 4: remove empty columns in each file, as well as descriptive headers and totaling lines Step 5: standardize column/variable names Step 6: data processed through IDE Spider Python v3.9.7 using scipy and sklearn libraries | |
B | Step 1: access the main link Step 2: access specific link for each year Step 3: download the data (in CSV format) Step 4: remove empty columns in each file, as well as descriptive headers and totaling lines Step 5: standardize column/variable names Step 6: data processed through IDE Spider Python v3.9.7 using scipy and sklearn libraries | |
C | Link provided according to each conference. Examples: https://openaccess.thecvf.com/CVPR2021 https://proceedings.neurips.cc/paper/2021 https://openaccess.thecvf.com/ICCV2021 https://aclanthology.org/volumes/2021.acl-long/ | Step 1: web scraping to access each PDF file published by each conference Step 2: extract the paper titles, authors, affiliations, and funding information from each PDF file, composing a new CSV file Step 3: classify each record (in the new CSV file) according to: 3.1: go through CSV, line by line 3.2: check if affiliations have any company 3.3: check if CSV affiliations have any universities 3.4: check if funding has any company 3.5: check if funding has any university 3.6: check if funding has any government agency 3.7: if the paper has affiliations from universities, then C1 = 1 3.8: if the paper has affiliations from companies, then C1 = 2 3.9: if the paper has affiliations from universities and companies, then C1 = 3 3.10: if the paper has funding from universities, then C2 = 1 3.11: if the paper has funding from companies, then C2 = 2 3.12: if the paper has funding from the government, then C2 = 3 3.13: if the paper has funding from universities and companies, then C2 = 4 3.14: if the paper has funding from universities and government, then C2 = 5 3.15: if the paper has funding from companies and government, then C2 = 6 3.16: if the paper has funding from universities, government, and companies, then C2 = 7 Step 4: data processed through IDE Spider Python v3.9.7 using scipy library Step 5: 98.9% inter-rater agreement between two independent developers |
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Jácome de Moura, P., dos Santos Junior, C.D., Porto-Bellini, C.G. et al. The Over-Concentration of Innovation and Firm-Specific Knowledge in the Artificial Intelligence Industry. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01974-1
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DOI: https://doi.org/10.1007/s13132-024-01974-1