Artificial intelligence adoption in a competitive market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Economists have often viewed the adoption of artificial intelligence (AI) as a standard process innovation where we expect that efficiency will drive adoption in competitive markets. This paper models AI based on recent advances in machine learning that allow firms to engage in better prediction. Focusing on prediction of demand, it is demonstrated that AI adoption is a complement to variable inputs whose levels are altered directly by predictions and whose use is economized by them (that is, labour). It is shown that in a competitive market, this increases the short‐run elasticity of supply and may or may not increase average equilibrium prices. Generically, there are externalities in adoption, with this reducing the profits of non‐adoptees when variable inputs are important, and increasing them otherwise. Thus AI does not operate as a standard process innovation, and its adoption may confer positive externalities on non‐adopting firms. In the long run, AI adoption is shown to lower prices generally and raise consumer surplus in competitive markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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 it