{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KSBWYULUSBG3YUK2BXUJGC7OWF","short_pith_number":"pith:KSBWYULU","schema_version":"1.0","canonical_sha256":"54836c5174904dbc515a0de8930beeb14863d0bfd708ce266ff80960a77f171a","source":{"kind":"arxiv","id":"2308.03740","version":1},"attestation_state":"computed","paper":{"title":"A Cost Analysis of Generative Language Models and Influence Operations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Micah Musser","submitted_at":"2023-08-07T17:38:41Z","abstract_excerpt":"Despite speculation that recent large language models (LLMs) are likely to be used maliciously to improve the quality or scale of influence operations, uncertainty persists regarding the economic value that LLMs offer propagandists. This research constructs a model of costs facing propagandists for content generation at scale and analyzes (1) the potential savings that LLMs could offer propagandists, (2) the potential deterrent effect of monitoring controls on API-accessible LLMs, and (3) the optimal strategy for propagandists choosing between multiple private and/or open source LLMs when cond"},"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":"2308.03740","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-08-07T17:38:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f3561d05587f81e6f5a9d57a78f7be8009f9f014d1cb6669d2fd4db7ed1e79da","abstract_canon_sha256":"7127ea910f1e473457023a172306033aabe2b9eaa87165a630d381f839ec824f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:38:21.023369Z","signature_b64":"3ROqZPgTCvdPKl57KR8V+TEGhhn/JVISJmzNv3pnOvhaqkgcvq2ldLRVnMTVwsJflWAZHR+dz5bjLd15B//iCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54836c5174904dbc515a0de8930beeb14863d0bfd708ce266ff80960a77f171a","last_reissued_at":"2026-07-05T06:38:21.022892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:38:21.022892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Cost Analysis of Generative Language Models and Influence Operations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Micah Musser","submitted_at":"2023-08-07T17:38:41Z","abstract_excerpt":"Despite speculation that recent large language models (LLMs) are likely to be used maliciously to improve the quality or scale of influence operations, uncertainty persists regarding the economic value that LLMs offer propagandists. This research constructs a model of costs facing propagandists for content generation at scale and analyzes (1) the potential savings that LLMs could offer propagandists, (2) the potential deterrent effect of monitoring controls on API-accessible LLMs, and (3) the optimal strategy for propagandists choosing between multiple private and/or open source LLMs when cond"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.03740","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/2308.03740/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":"2308.03740","created_at":"2026-07-05T06:38:21.022949+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.03740v1","created_at":"2026-07-05T06:38:21.022949+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.03740","created_at":"2026-07-05T06:38:21.022949+00:00"},{"alias_kind":"pith_short_12","alias_value":"KSBWYULUSBG3","created_at":"2026-07-05T06:38:21.022949+00:00"},{"alias_kind":"pith_short_16","alias_value":"KSBWYULUSBG3YUK2","created_at":"2026-07-05T06:38:21.022949+00:00"},{"alias_kind":"pith_short_8","alias_value":"KSBWYULU","created_at":"2026-07-05T06:38:21.022949+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02561","citing_title":"Pruning General Large Language Models into Customized Expert Models","ref_index":37,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF","json":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF.json","graph_json":"https://pith.science/api/pith-number/KSBWYULUSBG3YUK2BXUJGC7OWF/graph.json","events_json":"https://pith.science/api/pith-number/KSBWYULUSBG3YUK2BXUJGC7OWF/events.json","paper":"https://pith.science/paper/KSBWYULU"},"agent_actions":{"view_html":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF","download_json":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF.json","view_paper":"https://pith.science/paper/KSBWYULU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.03740&json=true","fetch_graph":"https://pith.science/api/pith-number/KSBWYULUSBG3YUK2BXUJGC7OWF/graph.json","fetch_events":"https://pith.science/api/pith-number/KSBWYULUSBG3YUK2BXUJGC7OWF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF/action/storage_attestation","attest_author":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF/action/author_attestation","sign_citation":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF/action/citation_signature","submit_replication":"https://pith.science/pith/KSBWYULUSBG3YUK2BXUJGC7OWF/action/replication_record"}},"created_at":"2026-07-05T06:38:21.022949+00:00","updated_at":"2026-07-05T06:38:21.022949+00:00"}