{"paper":{"title":"The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Rational decision-makers adopt AI despite anticipating skill erosion, leaving workers less productive in the long run.","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Michael Caosun, Sinan Aral","submitted_at":"2026-04-03T22:50:32Z","abstract_excerpt":"Experimental evidence confirms that AI tools raise worker productivity, but also that sustained use can erode the expertise on which those gains depend. We develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time, trading immediate productivity against the erosion of worker skill. We decompose the tool's productivity effect into two channels, one independent of worker expertise and one that scales with it. The model produces three main results. First, even a decision-maker who fully anticipates skill erosion rationally adopts AI when front-loaded prod"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Even a decision-maker who fully anticipates skill erosion rationally adopts AI when front-loaded productivity gains outweigh long-run skill costs, producing steady-state loss: the worker ends up less productive than before adoption.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The model assumes a specific functional form for how AI usage intensity directly erodes worker skill over time and that productivity decomposes into expertise-independent and expertise-dependent channels.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A dynamic model shows rational AI adoption can trap workers in lower productivity via skill erosion, with five regimes separating beneficial from harmful deployments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Rational decision-makers adopt AI despite anticipating skill erosion, leaving workers less productive in the long run.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"18a3f0b1aef3cf575c30a8575eb650b4d727003487afab32d943b89ccb1ea266"},"source":{"id":"2604.03501","kind":"arxiv","version":3},"verdict":{"id":"466aa0b6-38b0-4a45-b955-e045c7d8cdc9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T18:02:16.476213Z","strongest_claim":"Even a decision-maker who fully anticipates skill erosion rationally adopts AI when front-loaded productivity gains outweigh long-run skill costs, producing steady-state loss: the worker ends up less productive than before adoption.","one_line_summary":"A dynamic model shows rational AI adoption can trap workers in lower productivity via skill erosion, with five regimes separating beneficial from harmful deployments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The model assumes a specific functional form for how AI usage intensity directly erodes worker skill over time and that productivity decomposes into expertise-independent and expertise-dependent channels.","pith_extraction_headline":"Rational decision-makers adopt AI despite anticipating skill erosion, leaving workers less productive in the long run."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.03501/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}