Competition, Market Power, and Artificial Intelligence Ecosystems in Africa
Bibliographic record
Abstract
The expansion of Artificial Intelligence (AI) use across markets globally and in Africa poses exciting opportunities but also presents risks to economic policy making especially competition policy. Because of the integrated nature of an AI ecosystem, a simple exercise of market power, where a market player can exert power in an immediate level of the market, yields significantly different competition outcomes in an integrated ecosystem. To show the integrated nature of the AI markets, we construct a network structure of an AI ecosystem and use centrality measures based on network analysis methodology from the field of computer science to assess ecosystem power and concentration in the different AI input markets. Our findings are particularly significant because the network structure reflects the current limited participation of African firms in the design, fabrication, and assembly of AI chips or in the semiconductor manufacturing space. Coupled with backward and forward integration that can occur in this ecosystem where firms move into adjacent markets potentially enhancing their ecosystem power in African AI ecosystems. African competition authorities and policy makers should be cognisant of the future implications of consolidation in nascent AI markets in Africa and be proactive in adopting a competition lens in viewing AI ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".