{ "dataset": { "approved_record_count": 4, "author": "Mike Ye", "canonical_url": "https://strategicsignal.ai/ai-data-transactions/", "methodology_version": "1.0.0", "name": "Strategic Signal — Corporate AI Data Transactions", "notes": "4 approved observations; 5 legacy observations remain provisional and excluded from approved benchmarks. Filter publication_review.status=human_reviewed for approved longitudinal measures.", "provisional_record_count": 5, "published_on": "2026-09-15", "publisher": "Strategic Signal", "record_count": 9, "review_standard": "AI-discovered; human approval required. Legacy observations remain provisional until approved.", "updated_on": "2026-09-16", "version": "1.0.6" }, "transactions": [ { "access_structure": "Astex, Bristol Myers Squibb and Takeda joined the existing AbbVie and Johnson & Johnson initiative. Local computation permits model learning without pooling the underlying records.", "analysis_boundary": "March 27, 2025 founding is context, not a second transaction here. September 14, 2026 performance evidence is a later update. Uplift is consortium-reported, not independently replicated; no price is disclosed.", "announcement_date": "2025-10-01", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "AISB Federated OpenFold3 Initiative", "AlQuraishi Lab", "Apheris" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2025-0001/", "confidence": "high", "corpus_description": "Proprietary experimentally determined protein–small molecule structures supplied for federated fine-tuning. This observation records the October 2025 consortium expansion.", "corpus_renewal": "Renewable Corpus", "data_category": [ "Scientific / pharmaceutical", "Protein–small molecule structures" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-16", "decision_outcome_richness": "Analysis: experimental structures directly constrain molecular predictions; they are not a complete history of clinical decisions or drug-development outcomes.", "disclosed_consideration": { "amount": null, "currency": null, "description": "No data-specific consideration disclosed.", "status": "not_disclosed" }, "economics_scope": "not_disclosed", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "event_history": [ { "count_as_new_transaction": false, "date": "2025-03-27", "event": "Founding initiative" }, { "count_as_new_transaction": true, "date": "2025-10-01", "event": "Three new contributors join the existing initiative" }, { "count_as_new_transaction": false, "date": "2026-09-14", "event": "Consortium-reported performance update" } ], "historical_depth": "not_disclosed", "industry": "Pharmaceuticals & biotechnology", "likely_model_use": "Fine-tune OpenFold3 for protein–ligand prediction.", "likely_strategic_value": "Analysis: multi-owner scientific archives can supply differentiated training examples while retaining source-data control.", "m_and_a_implications": "Analysis: rights to learn can be valuable without an asset sale. Reported predictive improvement is not a cash valuation or evidence of clinical benefit.", "model_uplift": { "baseline": "OpenFold3 Preview 2", "evaluation_n": 1056, "evidence_status": "consortium_reported_not_independently_replicated", "limitations": "Private held-out evaluation; consortium-funded work; clinical utility and independent replication not established.", "metrics": [ { "baseline_pct": 35.6, "change_percentage_points": 16.5, "name": "fraction PL-lDDT >= 0.8", "updated_pct": 52.1 }, { "baseline_pct": 28.9, "change_percentage_points": 17.9, "name": "fraction ligand bisyRMSD <= 2 angstrom", "updated_pct": 46.8 } ], "reported_on": "2026-09-14", "training_structures": 20167 }, "primary_transaction_structure": "Federated / Controlled Learning Rights", "refreshability": "continuing_network; contribution schedule not disclosed", "restrictions": { "de_identification": "not_disclosed", "employee_customer_data": "not_applicable", "geographic_sovereignty": "not_disclosed", "governed_access": "Local computation and contributor-defined access policies.", "privacy_pii": "Confidential scientific data; no patient-data entitlement inferred.", "trade_secret": "Raw structures stay under contributor control." }, "rights": { "derived_model": "yes_model_improvement; current network describes member ownership, not full contract terms", "exclusivity": "not_disclosed", "inference_retrieval": "unknown", "ownership_transfer": "no_disclosed_transfer", "post_training": "yes_explicit_fine_tuning", "retention": "raw_data_remains_with_contributors; model retention terms not_disclosed", "sublicensing": "not_disclosed", "training": "yes_explicit" }, "scores": { "asset": { "coverage_pct": 84, "factor_evidence": { "decision_outcome_richness": { "basis": "analysis", "rationale": "A structure records an experimental result, but does not establish downstream commercial or clinical outcomes.", "source_ids": [ "E1" ] }, "decision_value_density": { "basis": "analysis", "rationale": "Better molecular prioritization can influence expensive research choices; a qualitative domain judgment.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "Drug-discovery research makes predictive accuracy economically consequential.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "The learning objective is closely matched to the described scientific observations.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Reproducing experimental observations requires laboratory effort; cost is not quantified.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "Combining otherwise separate private archives offers access unavailable from public corpora alone.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "The described observations originate in physical experiments rather than generated scenarios.", "source_ids": [ "E1" ] }, "rights_usability": { "basis": "analysis", "rationale": "Local learning is explicitly enabled, while undisclosed downstream terms limit certainty.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "Nonpublic experimental observations are hard to substitute with generic text.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": 3.5, "decision_value_density": 4.5, "domain_value": 4.5, "historical_depth": null, "model_learning_usefulness": 4.5, "non_replicability": 4, "proprietary_advantage": 4.5, "real_world_grounding": 5, "refreshability": null, "rights_usability": 4, "uniqueness_scarcity": 4.5 }, "rationale": "Strong experimental grounding and learning fit. Structural results are not full decision histories. Unknown archive age and contribution cadence remain unscored.", "score": 87 }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "No observable price or auction is provided by this announcement.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "No separately quantified data consideration is available.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "The reviewed announcement supplies no monetary amount usable for valuation.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "Fine-tuning is the stated purpose, not an inference from product deployment.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "A multi-owner structure offers a repeatable pattern for otherwise inaccessible scientific corpora.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "The permitted learning method is clear; the full contractual allocation remains unknown.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "Established research participants support execution relevance, not evidence of a market-clearing price.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 0, "competing_bids": null, "data_consideration_separability": 0, "disclosed_economics": 0, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4.5, "rights_clarity": 4, "strategic_buyer_quality": 4 }, "rationale": "Clear learning arrangement, weak price discovery. Consortium-reported uplift is recorded separately and earns no independent-replication points.", "score": 40 } }, "seller_data_owner": [ "AbbVie", "Johnson & Johnson", "Bristol Myers Squibb", "Takeda", "Astex Pharmaceuticals" ], "slug": "openfold-pharma-federated-training-consortium", "source_code_software_asset": "no", "sources": [ { "publisher": "Apheris", "title": "AISB initiative expansion — October 1, 2025", "type": "primary", "url": "https://www.apheris.com/resources/aisb-network-expands-federated-openfold3-initiative-with-three-new-pharma-contributors" }, { "publisher": "Apheris", "title": "Founding initiative — March 27, 2025", "type": "primary", "url": "https://www.apheris.com/resources/alquraishi-lab-s-openfold3-to-be-fine-tuned-with-pharma-industry-data-in-a-secure-ai-collaboration" }, { "publisher": "Apheris", "title": "Consortium performance report — September 14, 2026", "type": "primary", "url": "https://www.apheris.com/resources/federated-training-dramatically-improves-the-accuracy-of-protein-ligand-co-folding-on-private-pharma-structures" }, { "publisher": "Apheris", "title": "AISB network governance — current description", "type": "primary", "url": "https://www.apheris.com/networks/aisb" } ], "transaction_id": "SS-AIDT-2025-0001", "transaction_status": "active", "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-16T21:38:20.361Z", "decision_id": "conversation-20260916-81BF2CAD028B", "packet_sha256": "4e1f58d72f53b3ad5140e1c8b01fbb7347a8af99dc3f26fc29a5d28f52422173", "record_sha256": "0e4eef8e3f50a13a1e520e21f1545515918d9e29722134c414ceab9f6c02dc16" } }, { "access_structure": "Reported transfer of a finite archive; detailed contract terms and buyer identity were not disclosed.", "analysis_boundary": "The corpus and consideration description are attributed to the former CEO through reporting. Buyer, contract rights, and privacy controls remain unknown.", "announcement_date": "2026-04-16", "asset_disposition": "Stranded / Separable", "buyer_ai_developer": [ "Undisclosed AI buyer" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0001/", "confidence": "medium", "corpus_description": "Thirteen years of the shuttered company's Slack messages, internal email, and Jira tickets, reported sold as AI training data.", "corpus_renewal": "Finite Corpus", "data_category": [ "Internal communications", "Product / engineering workflow", "Institutional memory" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "decision_outcome_richness": "Moderate-to-high: communications and tickets can connect plans, execution, and product outcomes, but outcome labeling is not disclosed.", "disclosed_consideration": { "amount": null, "currency": "USD", "description": "Seller described proceeds as hundreds of thousands of dollars; no exact figure was disclosed.", "status": "reported_range_only" }, "economics_scope": "reported_dataset_specific", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "13 years (reported)", "industry": "Media technology", "likely_model_use": "Training AI systems to navigate realistic workplace communication, engineering, and project-management tasks.", "likely_strategic_value": "A coherent company history provides temporal and organizational context that synthetic office tasks lack.", "m_and_a_implications": "Shows that a failed company's collaboration exhaust may remain separately monetizable after operating value disappears.", "primary_transaction_structure": "Data Asset Acquisition", "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Sale reporting is supported by indexed Forbes excerpts, but full text and an independent corroborating source remain to be captured. Detailed contract rights are not verified.", "status": "legacy_review_required" }, "refreshability": "none_company_shuttered", "restrictions": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "not_disclosed", "governed_access": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "not_disclosed" }, "rights": { "derived_model": "not_disclosed", "exclusivity": "not_disclosed", "inference_retrieval": "not_disclosed", "ownership_transfer": "unknown", "post_training": "not_disclosed", "retention": "not_disclosed", "sublicensing": "not_disclosed", "training": "yes_reported" }, "scores": { "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "seller_data_owner": [ "Cielo24" ], "slug": "cielo24-institutional-memory-sale", "source_code_software_asset": "unknown", "sources": [ { "publisher": "Forbes", "title": "AI's New Training Data: Your Old Work Slacks And Emails", "type": "secondary", "url": "https://www.forbes.com/sites/annatong/2026/04/16/ais-new-training-data-your-old-work-slacks-and-emails/" }, { "publisher": "Fast Company", "title": "Shuttered startups are selling old Slack chats and emails to AI companies", "type": "secondary", "url": "https://www.fastcompany.com/91528808/shuttered-startups-are-selling-old-slack-chats-and-emails-to-ai-companies" } ], "transaction_id": "SS-AIDT-2026-0001", "transaction_status": "reported_completed" }, { "access_structure": "Bankruptcy asset auction. Google was selected at $10 million; Mercor was alternate at $7.5 million. micro1 later noticed a $12.5 million competing bid. No sale order had been entered as of the review date.", "analysis_boundary": "Bid amounts, selected/alternate bidders, asset scope, and pending status are sourced facts. Model-use and valuation implications beyond disclosed purpose are Strategic Signal analysis.", "announcement_date": "2026-08-14", "asset_disposition": "Stranded / Separable", "buyer_ai_developer": [ "Google LLC (selected bidder; challenged)" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0002/", "confidence": "high", "corpus_description": "A deidentified archive spanning roughly 34 years, reported to include about 100 million emails, 500 million Teams messages, 30 million lines of source code, operational data, documents, and internally developed software. Customer databases are outside scope.", "corpus_renewal": "Finite Corpus", "data_category": [ "Internal communications", "Source code and software", "Operational records", "Financial and workforce records" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "decision_outcome_richness": "High: communications, code, operations, scheduling, finance, and maintenance-adjacent workflows create a dense cross-functional operating history.", "disclosed_consideration": { "amount": 10000000, "currency": "USD", "description": "Google selected bid; court approval pending. Later micro1 notice proposed $12.5 million.", "status": "disclosed_bid_not_closed" }, "economics_scope": "dataset_and_related_internal_software_specific", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "Approximately 34 years", "industry": "Aviation", "likely_model_use": "Train and improve AI systems and productivity products on real enterprise work, software, communication, and operational sequences.", "likely_strategic_value": "Large, temporally coherent, multi-modal corporate history with explicit training use and measurable auction demand.", "m_and_a_implications": "Provides unusually clean evidence that a defunct company's data and institutional memory can be auctioned separately from its operating business, while exposing privacy and chain-of-title diligence as value-critical.", "primary_transaction_structure": "Data Asset Acquisition", "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Stretto reports an auction and pending approval through September 11. Treat this as secondary reporting. Underlying filing and current status remain to be verified.", "status": "legacy_review_required" }, "refreshability": "none_airline_ceased_operations", "restrictions": { "de_identification": "required_before_transfer", "employee_customer_data": "Customer datasets excluded; treatment of employee and labor data remains contested.", "geographic_sovereignty": "not_disclosed_for_google_bid", "governed_access": "Independent third-party deidentification contemplated before delivery; final restrictions remain subject to court approval.", "privacy_pii": "Customer databases excluded; incidental consumer data to be deidentified. Employee-data objections remain unresolved.", "trade_secret": "Contract-counterparty and labor objections remain on file." }, "rights": { "derived_model": "yes_intended_use_if_sale_closes", "exclusivity": "asset_sale_subject_to_sale_order", "inference_retrieval": "not_disclosed", "ownership_transfer": "yes_if_sale_closes", "post_training": "yes_general_model_improvement", "retention": "not_final_pending_sale_order", "sublicensing": "not_final_pending_sale_order", "training": "yes_explicit" }, "scores": { "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "seller_data_owner": [ "Spirit Aviation Holdings / Spirit Airlines" ], "slug": "spirit-airlines-data-auction-google", "source_code_software_asset": "yes", "sources": [ { "publisher": "Research Suite by Stretto", "title": "Spirit deidentified-data auction results and docket chronology", "type": "secondary", "url": "https://chapter11cases.com/blogs/news/the-spirit-airlines-deidentified-data-sale-auction-results-objections-and-the-september-30-hearing" }, { "publisher": "Reuters", "title": "Google to buy Spirit Airlines business data for $10 million", "type": "secondary", "url": "https://www.reuters.com/legal/litigation/google-buy-spirit-airlines-business-data-10-million-2026-08-17/" }, { "publisher": "WIRED", "title": "Spirit Airlines Wants to Sell Its Data to Google", "type": "secondary", "url": "https://www.wired.com/story/spirit-airlines-wants-to-sell-its-data-to-google-former-flight-attendants-are-freaked-out" } ], "transaction_id": "SS-AIDT-2026-0002", "transaction_status": "pending_court_approval" }, { "access_structure": "Collaborative physical-AI development; detailed data-access boundaries and model-rights allocation were not disclosed.", "analysis_boundary": "The announced combination of operational data and robot foundation models is fact. Training, retention, exclusivity, and derived-model rights are unknown.", "announcement_date": "2026-09-02", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "FieldAI" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0003/", "confidence": "medium_high", "corpus_description": "Caterpillar operational data, engineering capability, and industry expertise combined with FieldAI robot foundation models for complex jobsites and manufacturing environments.", "corpus_renewal": "Renewable Corpus", "data_category": [ "Industrial operational data", "Sensor and robotics data", "Engineering knowledge" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "decision_outcome_richness": "High: autonomous inspections and operations can generate state–action–outcome data in safety-critical environments.", "disclosed_consideration": { "amount": null, "currency": null, "description": "No consideration disclosed.", "status": "not_disclosed" }, "economics_scope": "not_disclosed", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "not_disclosed", "industry": "Industrial equipment & construction", "likely_model_use": "Improve robotic autonomy, inspection, situational awareness, and digital-twin systems for industrial environments.", "likely_strategic_value": "Connects foundation-model capability to hard-to-reproduce industrial environments and continuous field feedback.", "m_and_a_implications": "Industrial incumbents may structure data-bearing co-development instead of selling operating histories, preserving control while giving AI partners domain access.", "primary_transaction_structure": "Strategic Model Co-Development", "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Caterpillar explicitly mentions operational data and FieldAI foundation models; separately material learning rights are not disclosed. Qualification remains unresolved.", "status": "legacy_review_required" }, "refreshability": "live_and_recurring_operational_data_likely", "restrictions": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "not_disclosed", "governed_access": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "Engineering and operational data are described but controls are not disclosed." }, "rights": { "derived_model": "unknown", "exclusivity": "not_disclosed", "inference_retrieval": "yes_operational_application", "ownership_transfer": "no_disclosed_transfer", "post_training": "unknown", "retention": "not_disclosed", "sublicensing": "not_disclosed", "training": "unknown" }, "scores": { "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "seller_data_owner": [ "Caterpillar" ], "slug": "caterpillar-fieldai-industrial-autonomy", "source_code_software_asset": "unknown", "sources": [ { "publisher": "Caterpillar", "title": "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation", "type": "primary", "url": "https://www.caterpillar.com/en/news/corporate-press-releases/h/caterpillar-and-fieldai-advance-ai-powered-industrial-innovation.html" }, { "publisher": "PR Newswire", "title": "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation", "type": "primary_syndicated", "url": "https://www.prnewswire.com/news-releases/caterpillar-and-fieldai-advance-ai-powered-industrial-innovation-302866862.html" } ], "transaction_id": "SS-AIDT-2026-0003", "transaction_status": "announced_active" }, { "access_structure": "A 33-organization consortium is developing cybersecurity foundation models using member operational and security data. Custody and member-level licensing terms are not disclosed.", "analysis_boundary": "The 830 TB planned corpus, contributors and training purpose are company disclosures. Scores are qualitative Strategic Signal analysis, not measured model performance. Federated topology and contract terms are unknown.", "announcement_date": "2026-09-03", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "NAVER Cloud consortium", "NAVER Cloud", "LG AI Research" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0004/", "confidence": "high", "corpus_description": "Approximately 830 TB of high-quality real-world operational and security data from consortium members across power, finance, telecommunications, science, semiconductors, defense, and aerospace.", "corpus_renewal": "Renewable Corpus", "data_category": [ "Cybersecurity records", "Critical-infrastructure operations", "Industrial systems data" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-16", "decision_outcome_richness": "Strategic Signal analysis: security operations and planned field trials may connect events to responses; intervention/outcome labels are not disclosed.", "disclosed_consideration": { "amount": null, "currency": null, "description": "Government GPU support is disclosed, alongside consortium computing resources. Dataset-specific cash consideration is not disclosed.", "status": "in_kind_and_government_compute" }, "economics_scope": "bundled_program_support", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "not_disclosed", "industry": "Cybersecurity & critical infrastructure", "likely_model_use": "Train defensive and offensive cybersecurity foundation models and validate them in seven critical-industry domains.", "likely_strategic_value": "Pools otherwise unobtainable cross-industry operational security data at sovereign scale.", "m_and_a_implications": "Consortium structures can aggregate embedded national-infrastructure data without a conventional acquisition, making governance and model rights central valuation terms.", "primary_transaction_structure": "Strategic Model Co-Development", "publication_review": { "approval_channel": "owner_conversation", "decision_id": "conversation-20260916-2D6377E44A57", "packet_sha256": "0d3511e1d7c9a9ce6cd876440fe1b414e967551ce11a7404834f16cf82849ba7", "record_sha256": "9e66511ee6acde56b1620a2e82c7786e746dd09abcef983c751844558ad77bff", "reviewed_at": "2026-09-16T21:17:28.097Z", "reviewer": "Mike Ye", "status": "human_reviewed" }, "refreshability": "consortium_operational_sources_likely_renewable", "restrictions": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "Korean program; contractual geographic restrictions not_disclosed", "governed_access": "Consortium/government program; detailed member-level controls are not disclosed.", "privacy_pii": "not_disclosed", "trade_secret": "Operational security and critical-infrastructure data; controls not disclosed." }, "rights": { "derived_model": "planned_open_source_models_commercial_use_terms_not_disclosed", "exclusivity": "not_disclosed", "inference_retrieval": "yes_field_demonstrations", "ownership_transfer": "no_disclosed_transfer", "post_training": "unknown", "retention": "not_disclosed", "sublicensing": "not_disclosed", "training": "yes_explicit" }, "scores": { "asset": { "coverage_pct": 88, "factor_evidence": { "decision_outcome_richness": { "basis": "analysis", "rationale": "Analysis: operational context suggests useful decision relationships; missing outcome-label evidence limits the rating.", "source_ids": [ "E1" ] }, "decision_value_density": { "basis": "analysis", "rationale": "Analysis: mistakes in this domain can have major economic consequences. This is domain judgment, not a disclosed corpus valuation.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "Analysis: the disclosed domain supports economically consequential enterprise activities; this is not a model-uplift result.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "Analysis: the announcement directly links this corpus to model development; improvement magnitude is unmeasured.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Analysis: reproducing owner-specific operating history would require comparable activity and time.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "Analysis: member-owned operational or scientific data offers context beyond ordinary public web corpora.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "Analysis: the described corporate operational or scientific records are grounded in real activity rather than synthetic-only tasks.", "source_ids": [ "E1" ] }, "refreshability": { "basis": "analysis", "rationale": "Analysis: planned field trials and model refinement indicate recurring operational input; no perpetual refresh entitlement is claimed.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "Analysis: access to this owner-contributed domain corpus is difficult to reproduce; exclusivity is not established.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": 3, "decision_value_density": 4, "domain_value": 4.5, "historical_depth": null, "model_learning_usefulness": 4.5, "non_replicability": 4, "proprietary_advantage": 4.5, "real_world_grounding": 4.5, "refreshability": 4, "rights_usability": null, "uniqueness_scarcity": 4 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 81 }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "Analysis: no separately priced corporate dataset is identified; the announcement offers little clean data-price discovery.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "No separately quantified consideration for data is disclosed. Zero measures disclosure weakness, not zero data value.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "No dataset-specific cash amount is disclosed. This rating measures public price disclosure, not whether payment exists.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "The company explicitly connects proprietary corporate data to developing the named AI models.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "Analysis: this is a reusable model-co-development structure, but repeatable standalone pricing is not established.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "Analysis: the announced model-development purpose is clear, while detailed transfer, retention and licensing terms remain unknown.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "Analysis: an identified model developer with a specific domain program makes the observation strategically relevant.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 0, "competing_bids": null, "data_consideration_separability": 0, "disclosed_economics": 0, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4, "rights_clarity": 2.5, "strategic_buyer_quality": 4 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 35 } }, "seller_data_owner": [ "NAVER Cloud", "LG CNS", "KEPCO KDN", "Korea Hydro & Nuclear Power", "Financial Security Institute", "KISTI", "LG Uplus", "Other consortium members" ], "slug": "naver-cloud-cybersecurity-consortium", "source_code_software_asset": "unknown", "sources": [ { "publisher": "NAVER", "title": "NAVER Cloud Consortium to Develop AI Models Tailored for Cybersecurity", "type": "primary", "url": "https://navercorp.com/en/media/pressReleasesDetail?seq=10034653" } ], "transaction_id": "SS-AIDT-2026-0004", "transaction_status": "announced_development" }, { "access_structure": "Customized on-premise model development; Samsung says sensitive technology and operational data remain entirely within its boundaries. Samsung also participated in Mistral's financing round.", "analysis_boundary": "On-premise controls, target use cases, and strategic equity relationship are disclosed. Model ownership, exclusivity, sublicensing, and data-specific consideration are not.", "announcement_date": "2026-09-09", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "Mistral AI" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0005/", "confidence": "high", "corpus_description": "Sensitive semiconductor operational and manufacturing data used inside Samsung boundaries to customize on-premise models for defect detection, equipment optimization, and engineering workflows.", "corpus_renewal": "Renewable Corpus", "data_category": [ "Manufacturing history", "Equipment and process data", "Engineering knowledge" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "decision_outcome_richness": "Exceptional: process interventions, yield, defect, and equipment outcomes can encode extremely high-value manufacturing decisions.", "disclosed_consideration": { "amount": null, "currency": null, "description": "Partnership economics and Samsung's individual equity investment were not disclosed separately.", "status": "not_disclosed" }, "economics_scope": "bundled_with_strategic_equity_relationship", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "not_disclosed", "industry": "Semiconductors", "likely_model_use": "Customize models for semiconductor defect detection, equipment optimization, technical support, and engineering decision workflows.", "likely_strategic_value": "Pairs a frontier-model developer with one of the world's most valuable proprietary manufacturing feedback loops while preserving on-premise control.", "m_and_a_implications": "Equity-linked, on-premise arrangements may become a preferred structure where the corpus is too strategic to sell or externalize.", "primary_transaction_structure": "Proprietary Data + Equity Partnership", "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "Samsung announces customized on-premise models and a strategic equity stake. Processing data within Samsung boundaries does not establish what learning rights Mistral receives.", "status": "legacy_review_required" }, "refreshability": "continuous_manufacturing_operations", "restrictions": { "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed", "geographic_sovereignty": "On-premise / sovereign control", "governed_access": "On-premise deployment; sensitive data stays inside Samsung boundaries.", "privacy_pii": "not_disclosed", "trade_secret": "Explicit protection of sensitive semiconductor technology and operational data." }, "rights": { "derived_model": "unknown", "exclusivity": "not_disclosed", "inference_retrieval": "yes_on_prem_use", "ownership_transfer": "no_disclosed_transfer", "post_training": "yes_customization", "retention": "restricted_to_samsung_boundaries", "sublicensing": "not_disclosed", "training": "yes_customized_on_prem_models" }, "scores": { "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "seller_data_owner": [ "Samsung Electronics" ], "slug": "samsung-mistral-semiconductor-partnership", "source_code_software_asset": "unknown", "sources": [ { "publisher": "Samsung", "title": "Samsung and Mistral AI Announce Strategic Partnership for Intelligence-Driven Semiconductor Infrastructure", "type": "primary", "url": "https://news.samsung.com/global/samsung-and-mistral-ai-announce-strategic-partnership-for-intelligence-driven-semiconductor-infrastructure" }, { "publisher": "Reuters", "title": "French AI company Mistral hits $24 billion valuation in funding round", "type": "secondary", "url": "https://www.reuters.com/world/europe/french-ai-company-mistral-hits-24-billion-valuation-funding-round-2026-09-08/" } ], "transaction_id": "SS-AIDT-2026-0005", "transaction_status": "announced_active" }, { "access_structure": "Built-in licensed/indexed data available to the financial-services product for grounded analysis and citations; specific provider contracts are not public.", "analysis_boundary": "Built-in providers and retrieval/citation use are disclosed. Training, post-training, ownership, and pricing are not claimed.", "announcement_date": "2026-09-10", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "OpenAI" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0006/", "confidence": "medium_high", "corpus_description": "Continuously updated proprietary financial datasets integrated as built-in, cited data sources in ChatGPT for Financial Services. Customer-connected subscription sources are excluded from this record.", "corpus_renewal": "Renewable Corpus", "data_category": [ "Financial datasets", "Private-market data", "Fundamental company data", "Earnings and filings" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "decision_outcome_richness": "High for financial research and valuation, though less direct than operational intervention corpora.", "disclosed_consideration": { "amount": null, "currency": null, "description": "No provider-specific consideration disclosed.", "status": "not_disclosed" }, "economics_scope": "not_disclosed", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "Varies by provider; Daloopa reports 14 years of normalized data.", "industry": "Financial information services", "likely_model_use": "Ground inference, financial analysis, retrieval, and citations; no training rights are claimed.", "likely_strategic_value": "Authoritative licensed data reduces hallucination and makes a general model useful in high-value, source-sensitive financial workflows.", "m_and_a_implications": "Shows that renewable data vendors can license access repeatedly without transferring ownership, but undisclosed contracts provide weak standalone price discovery.", "primary_transaction_structure": "Proprietary Corpus License", "publication_review": { "audited_on": "2026-09-16", "human_approval_recorded": false, "notice": "The cited pages could not be retrieved for this audit. Source-specific rights and the boundary between built-in access and customer connectors remain unresolved.", "status": "legacy_review_required" }, "refreshability": "continuous_provider_updates", "restrictions": { "de_identification": "not_applicable_or_not_disclosed", "employee_customer_data": "Customer-connected datasets are explicitly excluded from this record.", "geographic_sovereignty": "not_disclosed", "governed_access": "Product and provider controls apply; contract details are not public.", "privacy_pii": "not_disclosed", "trade_secret": "Provider-license restrictions not disclosed." }, "rights": { "derived_model": "not_disclosed", "exclusivity": "not_disclosed", "inference_retrieval": "yes_explicit", "ownership_transfer": "no_disclosed_transfer", "post_training": "not_disclosed", "retention": "indexed_on_openai_infrastructure_for_some_sources", "sublicensing": "not_disclosed", "training": "not_disclosed" }, "scores": { "asset": { "coverage_pct": 0, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": null, "domain_value": null, "historical_depth": null, "model_learning_usefulness": null, "non_replicability": null, "proprietary_advantage": null, "real_world_grounding": null, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 0, "factor_ratings": { "clean_price_discovery": null, "competing_bids": null, "data_consideration_separability": null, "disclosed_economics": null, "explicit_model_use": null, "independent_model_uplift": null, "precedent_value": null, "rights_clarity": null, "strategic_buyer_quality": null }, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "score": null } }, "seller_data_owner": [ "LSEG", "PitchBook", "Daloopa", "Crunchbase", "Quartr", "LSEG News" ], "slug": "openai-built-in-financial-data-partnerships", "source_code_software_asset": "no", "sources": [ { "publisher": "OpenAI", "title": "ChatGPT for Financial Services", "type": "primary", "url": "https://help.openai.com/en/articles/12608093-chatgpt-for-financial-services" }, { "publisher": "Reuters", "title": "OpenAI launches ChatGPT for financial services industry", "type": "secondary", "url": "https://www.reuters.com/business/openai-launches-chatgpt-financial-services-industry-2026-09-10/" }, { "publisher": "Daloopa", "title": "Daloopa data infrastructure", "type": "primary", "url": "https://daloopa.com/" } ], "transaction_id": "SS-AIDT-2026-0006", "transaction_status": "launched_active" }, { "access_structure": "Three-year joint scientific laboratory to develop frontier models using TotalEnergies geoscience data and expertise. Specific licenses, data custody and allocation of resulting model rights are not disclosed.", "analysis_boundary": "Program duration, investment lower bound, joint lab and geoscience history are disclosed. Scores are qualitative analysis, not measured uplift or a valuation of the dataset. Ownership, retention and derived-model rights are not disclosed.", "announcement_date": "2026-09-15", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "Mistral AI" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0007/", "confidence": "high", "corpus_description": "Nearly a century of TotalEnergies geoscience data, knowledge, and reservoir-engineering expertise used in a joint laboratory to develop frontier and agentic AI models for exploration and reservoir decisions.", "corpus_renewal": "Renewable Corpus", "data_category": [ "Geological / subsurface data", "Reservoir engineering", "Decision histories", "Scientific data" ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-16", "decision_outcome_richness": "Strategic Signal analysis: reservoir decisions are economically consequential, but decision-linked outcome labeling in the corpus has not been demonstrated publicly.", "disclosed_consideration": { "amount": 100000000, "amount_qualifier": "greater_than", "currency": "EUR", "description": "More than €100 million over three years; explicitly a program-wide investment, not a dataset price.", "status": "disclosed_program_commitment" }, "economics_scope": "bundled_research_compute_people_and_implementation", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "Nearly one century", "industry": "Energy & geoscience", "likely_model_use": "Develop frontier and agentic models that generate exploration scenarios, interpret subsurface data, optimize reservoirs, and support expert decisions.", "likely_strategic_value": "A rare, longitudinal state–decision–action–outcome corpus in a domain where individual decisions can move billions of euros of value.", "m_and_a_implications": "Large program economics confirm strategic importance but do not price the corpus separately; transactions for energy data should distinguish data value from scientists, compute, and implementation.", "primary_transaction_structure": "Strategic Model Co-Development", "publication_review": { "approval_channel": "owner_conversation", "decision_id": "conversation-20260916-5768A8330103", "packet_sha256": "656620707ca856653b6dd4b75854d841b3c4106e4e89e51e1088de26891e85e2", "record_sha256": "f40e71b98171603e1825f3e02d1584b2c8e0822e8f4d59cc9e102b8ea534dd6a", "reviewed_at": "2026-09-16T21:17:28.112Z", "reviewer": "Mike Ye", "status": "human_reviewed" }, "refreshability": "continuing_exploration_and_reservoir_operations", "restrictions": { "de_identification": "not_disclosed", "employee_customer_data": "not_applicable_or_not_disclosed", "geographic_sovereignty": "European strategic-technology context; binding geographic terms not disclosed.", "governed_access": "Joint laboratory; technical access controls and contractual rights allocation are not disclosed.", "privacy_pii": "not_applicable_or_not_disclosed", "trade_secret": "Announcement emphasizes protecting intellectual property; specific binding provisions are not disclosed." }, "rights": { "derived_model": "unknown_joint_program", "exclusivity": "not_disclosed", "inference_retrieval": "unknown", "ownership_transfer": "not_disclosed", "post_training": "unknown", "retention": "not_disclosed", "sublicensing": "not_disclosed", "training": "yes_develop_frontier_models_on_corpus" }, "scores": { "asset": { "coverage_pct": 88, "factor_evidence": { "decision_outcome_richness": { "basis": "analysis", "rationale": "Analysis: operational context suggests useful decision relationships; missing outcome-label evidence limits the rating.", "source_ids": [ "E1" ] }, "decision_value_density": { "basis": "analysis", "rationale": "Analysis: mistakes in this domain can have major economic consequences. This is domain judgment, not a disclosed corpus valuation.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "Analysis: the disclosed domain supports economically consequential enterprise activities; this is not a model-uplift result.", "source_ids": [ "E1" ] }, "historical_depth": { "basis": "analysis", "rationale": "Analysis: the announced near-century geoscience history suggests considerable temporal depth; uniform record completeness is not demonstrated.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "Analysis: the announcement directly links this corpus to model development; improvement magnitude is unmeasured.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Analysis: reproducing owner-specific operating history would require comparable activity and time.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "Analysis: member-owned operational or scientific data offers context beyond ordinary public web corpora.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "Analysis: the described corporate operational or scientific records are grounded in real activity rather than synthetic-only tasks.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "Analysis: access to this owner-contributed domain corpus is difficult to reproduce; exclusivity is not established.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": 3.5, "decision_value_density": 4.5, "domain_value": 5, "historical_depth": 4.5, "model_learning_usefulness": 4, "non_replicability": 4, "proprietary_advantage": 4, "real_world_grounding": 4.5, "refreshability": null, "rights_usability": null, "uniqueness_scarcity": 4.5 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 85 }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "Analysis: no separately priced corporate dataset is identified; the announcement offers little clean data-price discovery.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "No separately quantified consideration for data is disclosed. Zero measures disclosure weakness, not zero data value.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "A program investment lower bound is disclosed, but its allocation to data is not disclosed.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "The company explicitly connects proprietary corporate data to developing the named AI models.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "Analysis: this is a reusable model-co-development structure, but repeatable standalone pricing is not established.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "Analysis: the announced model-development purpose is clear, while detailed transfer, retention and licensing terms remain unknown.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "Analysis: an identified model developer with a specific domain program makes the observation strategically relevant.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 1, "competing_bids": null, "data_consideration_separability": 0, "disclosed_economics": 4.5, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4, "rights_clarity": 2.5, "strategic_buyer_quality": 4 }, "rationale": "Qualitative Strategic Signal assessment based on the disclosed corpus and arrangement. Unknown factors remain null; no measured model uplift is claimed.", "score": 56 } }, "seller_data_owner": [ "TotalEnergies" ], "slug": "totalenergies-mistral-reservoir-models", "source_code_software_asset": "unknown", "sources": [ { "publisher": "TotalEnergies", "title": "TotalEnergies Announces a Partnership with Mistral to Develop Frontier AI Models for Reservoir Exploration and Engineering", "type": "primary", "url": "https://totalenergies.com/newsroom/totalenergies-annonce-un-partenariat-avec-mistral-en-vue-de-developper-des-modeles-de-frontiere-dintelligence-artificielle-dedies-a-lexploration-et-a-lingenierie-des-reservoirs-498514" } ], "transaction_id": "SS-AIDT-2026-0007", "transaction_status": "announced_three_year_program" }, { "access_structure": "Purpose-limited data license to Pathos within a three-party arrangement involving AstraZeneca.", "agreement_effective_date": "2025-04-17", "analysis_boundary": "This observation covers the data-license leg only. Payment completion, corpus renewal and many contractual restrictions remain unknown. Related fees and financing are context, not additional data transactions.", "announcement_date": "2025-04-23", "announcement_date_basis": "SEC filing date", "asset_disposition": "Embedded / Continuing", "buyer_ai_developer": [ "Pathos AI" ], "canonical_path": "/ai-data-transactions/deals/ss-aidt-2025-0002/", "confidence": "high", "confidence_boundary": "High on filed historical terms; current payment performance is unknown.", "corpus_description": "A de-identified multimodal oncology dataset licensed for a foundation-model development program.", "corpus_renewal": "unknown", "data_category": [ "Scientific / pharmaceutical", "De-identified multimodal oncology data" ], "date_added": "2026-09-16", "date_last_reviewed": "2026-09-16", "decision_outcome_richness": "unknown; the filing does not define longitudinal outcome fields", "disclosed_consideration": { "amount": 200000000, "currency": "USD", "description": "$200m data-license fees over three years, including $50m upfront payable. Up to 50% may be settled in Pathos preferred shares. Separate $35m flows run from AstraZeneca to Tempus and Tempus to Pathos.", "status": "disclosed_contractual_fees_not_verified_cash_receipts" }, "economics_scope": "contractually_identified_data_license_fees_within_broader_arrangement", "estimated_economics": { "amount": null, "currency": null, "method": null, "status": "not_estimated" }, "historical_depth": "not_disclosed", "industry": "Pharmaceuticals & biotechnology", "likely_model_use": "Develop and train an oncology foundation model.", "likely_strategic_value": "Analysis: a specified training license demonstrates that a proprietary corpus can carry contractual consideration.", "m_and_a_implications": "Analysis: useful license-fee precedent, not a clean cash sale valuation. Comparability depends on rights, payment form, scope and linked obligations; no annual run-rate or per-patient price is inferred.", "primary_transaction_structure": "Training Rights License", "refreshability": "not_disclosed", "restrictions": { "de_identification": "explicit", "employee_customer_data": "Detailed consent and patient-data terms not_disclosed", "geographic_sovereignty": "not_disclosed", "governed_access": "License purpose is development and training of the specified model.", "privacy_pii": "de_identified_dataset", "trade_secret": "not_disclosed" }, "rights": { "derived_model": "Tempus receives model-use license with field restrictions", "exclusivity": "not_disclosed", "inference_retrieval": "not_disclosed", "ownership_transfer": "no_disclosed_transfer", "post_training": "not_disclosed", "retention": "not_disclosed", "sublicensing": "Tempus may sublicense model to AstraZeneca; dataset sublicensing not_disclosed", "training": "yes_explicit_purpose_limited" }, "scores": { "asset": { "coverage_pct": 71, "factor_evidence": { "decision_value_density": { "basis": "analysis", "rationale": "Oncology research choices can be consequential; the rating does not assert actual patient-outcome improvement.", "source_ids": [ "E1" ] }, "domain_value": { "basis": "analysis", "rationale": "The use case lies in economically significant scientific research.", "source_ids": [ "E1" ] }, "model_learning_usefulness": { "basis": "analysis", "rationale": "The agreement explicitly links the dataset to the model training task.", "source_ids": [ "E1" ] }, "non_replicability": { "basis": "analysis", "rationale": "Reassembly may require specialized data collection; no reproduction-cost estimate is available.", "source_ids": [ "E1" ] }, "proprietary_advantage": { "basis": "analysis", "rationale": "The purpose-specific private license offers access beyond generic public material.", "source_ids": [ "E1" ] }, "real_world_grounding": { "basis": "analysis", "rationale": "A de-identified domain dataset provides real-world grounding; field detail is limited.", "source_ids": [ "E1" ] }, "rights_usability": { "basis": "analysis", "rationale": "Specified training permission is useful, but purpose limits and undisclosed retention constrain reuse.", "source_ids": [ "E1" ] }, "uniqueness_scarcity": { "basis": "analysis", "rationale": "The licensed private corpus is differentiated, but exact scarcity cannot be quantified.", "source_ids": [ "E1" ] } }, "factor_ratings": { "decision_outcome_richness": null, "decision_value_density": 4, "domain_value": 4.5, "historical_depth": null, "model_learning_usefulness": 4.5, "non_replicability": 3.5, "proprietary_advantage": 4, "real_world_grounding": 4, "refreshability": null, "rights_usability": 3.5, "uniqueness_scarcity": 4 }, "rationale": "Potentially valuable domain training corpus; uncertainty about contents, age and renewal materially limits coverage. Score is strategic analysis, not measured utility.", "score": 82 }, "methodology_version": "1.0.0", "transaction_signal": { "coverage_pct": 85, "factor_evidence": { "clean_price_discovery": { "basis": "analysis", "rationale": "Linked financing and reciprocal obligations weaken the isolated market-price interpretation.", "source_ids": [ "E1" ] }, "data_consideration_separability": { "basis": "analysis", "rationale": "The filing identifies a data-license payment leg even though it sits within a broader arrangement.", "source_ids": [ "E1" ] }, "disclosed_economics": { "basis": "analysis", "rationale": "Contractual fees and payment form are disclosed, but realized proceeds are not verified.", "source_ids": [ "E1" ] }, "explicit_model_use": { "basis": "analysis", "rationale": "Model development and training are the express permitted purposes.", "source_ids": [ "E1" ] }, "precedent_value": { "basis": "analysis", "rationale": "The contract structure offers a useful reference if future comparisons preserve its restrictions.", "source_ids": [ "E1" ] }, "rights_clarity": { "basis": "analysis", "rationale": "Purpose and some model rights are visible; exclusivity, retention and data sublicensing are not.", "source_ids": [ "E1" ] }, "strategic_buyer_quality": { "basis": "analysis", "rationale": "A specified specialist model developer provides credible use, without implying open-market bidding.", "source_ids": [ "E1" ] } }, "factor_ratings": { "clean_price_discovery": 2, "competing_bids": null, "data_consideration_separability": 4, "disclosed_economics": 4.5, "explicit_model_use": 5, "independent_model_uplift": null, "precedent_value": 4, "rights_clarity": 3.5, "strategic_buyer_quality": 3 }, "rationale": "Explicit fee allocation is valuable pricing evidence. Equity settlement, reciprocal commitments and missing comparable bids prevent treatment as a clean cash market price.", "score": 75 } }, "seller_data_owner": [ "Tempus AI" ], "slug": "tempus-pathos-oncology-data-license", "source_code_software_asset": "no", "sources": [ { "publisher": "Tempus AI / SEC", "title": "Form 8-K filed April 23, 2025, Item 8.01", "type": "primary", "url": "https://www.sec.gov/Archives/edgar/data/1717115/000119312525090062/d944168d8k.htm" } ], "transaction_id": "SS-AIDT-2025-0002", "transaction_status": "agreement_disclosed; current payment performance not verified", "publication_review": { "status": "human_reviewed", "approval_channel": "owner_conversation", "reviewer": "Mike Ye", "reviewed_at": "2026-09-16T21:38:20.346Z", "decision_id": "conversation-20260916-CD20DA31F0CF", "packet_sha256": "401f767bb940538d1a214a8130fd811dfa4275b939fcba6f15377ce87c2ac9b2", "record_sha256": "12bdd18b6c5a16d4e4612db7460712415283174fb14daa888dd8dd3ebb6a945b" } } ] }