{"id":"W4313404000","doi":"10.1108/sl-11-2022-0111","title":"Understanding the fundamental economics of AI","year":2022,"lang":"en","type":"article","venue":"Strategy and Leadership","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transformative learning; Originality; Leverage (statistics); Big data; Computer science; Value (mathematics); Set (abstract data type); Task (project management); Management science; Data science; Artificial intelligence; Management; Economics; Sociology; Political science; Creativity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005060737,0.000411381,0.0004751037,0.001791407,0.00168711,0.006763038,0.001140924,0.002360896,0.007920638],"category_scores_gemma":[0.01170894,0.0003274385,0.0004685071,0.001369094,0.0187753,0.008063875,0.001820612,0.003735786,0.001033035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005272911,"about_ca_system_score_gemma":0.003201036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004046178,"about_ca_topic_score_gemma":0.001510136,"domain_scores_codex":[0.9971589,0.001507024,0.00007639635,0.0003157961,0.000691966,0.0002499618],"domain_scores_gemma":[0.989232,0.00821359,0.0004320642,0.0008606714,0.0009564685,0.0003051876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003650331,0.000006500019,0.0001373522,0.00003256472,0.000004412775,0.00001365107,0.0000851624,0.001120116,0.00003040685,0.994782,0.001163422,0.002620741],"study_design_scores_gemma":[0.000002764434,0.000003351365,0.0001509867,0.00004885798,0.000001848816,0.00001300436,0.00009948642,0.001391081,0.0000324193,0.9890389,0.009213485,0.000003929128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02978536,0.03022013,0.1137176,0.2031374,0.001529094,0.0001280513,0.0003150685,0.0001738632,0.6209934],"genre_scores_gemma":[0.9381835,0.01843109,0.01406932,0.007061547,0.002316882,0.0002324241,0.0001108114,0.00009044389,0.019504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007920638,"threshold_uncertainty_score":0.03825784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5339482881966734,"score_gpt":0.2580631101544015,"score_spread":0.2758851780422719,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}