{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PRIUVX3VBQDCY3CD77C3GER6RD","short_pith_number":"pith:PRIUVX3V","schema_version":"1.0","canonical_sha256":"7c514adf750c062c6c43ffc5b3123e88d95d9f8b1cd7df0d37b18b19f55c5726","source":{"kind":"arxiv","id":"2503.09637","version":1},"attestation_state":"computed","paper":{"title":"Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.GN","q-fin.EC"],"primary_cat":"cs.CY","authors_text":"Brandon De Bruhl, Gul Nisa G\\\"urb\\\"uz, Michael Overton, William G. Resh, Xinyao Xia, Yi Ming","submitted_at":"2025-03-12T01:19:52Z","abstract_excerpt":"This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2503.09637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-03-12T01:19:52Z","cross_cats_sorted":["econ.GN","q-fin.EC"],"title_canon_sha256":"5bcc8145971910b11a4ad9d6634e146fad9eb080354c07b27d50d63344ec06f5","abstract_canon_sha256":"5637361857ee36cb1d22cd2dd2ca9c3f08ebe49bf7ec6b14410754d26c1640bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:14.194330Z","signature_b64":"LchmlucB43fNLH9O2e0K6Lm7GQHFZ0jTxMaoZMhkRdckWjHFnAsJfs/oJ1SFqZFGDyX7Vfm9Pu7KwqJHto/ZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c514adf750c062c6c43ffc5b3123e88d95d9f8b1cd7df0d37b18b19f55c5726","last_reissued_at":"2026-07-05T12:04:14.193806Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:14.193806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.GN","q-fin.EC"],"primary_cat":"cs.CY","authors_text":"Brandon De Bruhl, Gul Nisa G\\\"urb\\\"uz, Michael Overton, William G. Resh, Xinyao Xia, Yi Ming","submitted_at":"2025-03-12T01:19:52Z","abstract_excerpt":"This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.09637","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.09637/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.09637","created_at":"2026-07-05T12:04:14.193865+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.09637v1","created_at":"2026-07-05T12:04:14.193865+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.09637","created_at":"2026-07-05T12:04:14.193865+00:00"},{"alias_kind":"pith_short_12","alias_value":"PRIUVX3VBQDC","created_at":"2026-07-05T12:04:14.193865+00:00"},{"alias_kind":"pith_short_16","alias_value":"PRIUVX3VBQDCY3CD","created_at":"2026-07-05T12:04:14.193865+00:00"},{"alias_kind":"pith_short_8","alias_value":"PRIUVX3V","created_at":"2026-07-05T12:04:14.193865+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.00829","citing_title":"On the Surprising Efficacy of LLMs for Penetration-Testing","ref_index":90,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD","json":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD.json","graph_json":"https://pith.science/api/pith-number/PRIUVX3VBQDCY3CD77C3GER6RD/graph.json","events_json":"https://pith.science/api/pith-number/PRIUVX3VBQDCY3CD77C3GER6RD/events.json","paper":"https://pith.science/paper/PRIUVX3V"},"agent_actions":{"view_html":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD","download_json":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD.json","view_paper":"https://pith.science/paper/PRIUVX3V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.09637&json=true","fetch_graph":"https://pith.science/api/pith-number/PRIUVX3VBQDCY3CD77C3GER6RD/graph.json","fetch_events":"https://pith.science/api/pith-number/PRIUVX3VBQDCY3CD77C3GER6RD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD/action/storage_attestation","attest_author":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD/action/author_attestation","sign_citation":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD/action/citation_signature","submit_replication":"https://pith.science/pith/PRIUVX3VBQDCY3CD77C3GER6RD/action/replication_record"}},"created_at":"2026-07-05T12:04:14.193865+00:00","updated_at":"2026-07-05T12:04:14.193865+00:00"}