{ "dataset": { "name": "Strategic Signal — Corporate AI Data Transactions", "version": "1.0.1", "methodology_version": "1.0.0", "published_on": "2026-09-15", "updated_on": "2026-09-16", "publisher": "Strategic Signal", "author": "Mike Ye", "review_standard": "AI-discovered; human approval required. Legacy observations remain provisional until approved.", "canonical_url": "https://strategicsignal.ai/ai-data-transactions/", "record_count": 8, "notes": "Eight legacy observations are preserved for transparency but are not approved benchmarks. Scores are withdrawn pending evidence review. Filter publication_review.status=human_reviewed for approved longitudinal measures.", "approved_record_count": 0, "provisional_record_count": 8 }, "transactions": [ { "transaction_id": "SS-AIDT-2025-0001", "slug": "openfold-pharma-federated-training-consortium", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2025-0001/", "announcement_date": "2025-10-01", "buyer_ai_developer": [ "OpenFold Consortium", "AlQuraishi Lab", "Apheris" ], "seller_data_owner": [ "AbbVie", "Johnson & Johnson", "Bristol Myers Squibb", "Takeda", "Astex Pharmaceuticals" ], "transaction_status": "active", "industry": "Pharmaceuticals & biotechnology", "data_category": [ "Scientific / pharmaceutical", "Protein–small molecule structures", "Experimental assay data" ], "corpus_description": "Several thousand experimentally determined, proprietary protein–small molecule structures contributed by pharmaceutical companies for privacy-preserving fine-tuning of OpenFold3.", "primary_transaction_structure": "Federated / Controlled Learning Rights", "access_structure": "Federated fine-tuning through Apheris; source data remains in each contributor's controlled environment.", "asset_disposition": "Embedded / Continuing", "corpus_renewal": "Renewable Corpus", "source_code_software_asset": "no", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "No data-specific consideration disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "not_disclosed", "rights": { "training": "yes_explicit", "post_training": "yes_explicit_fine_tuning", "inference_retrieval": "unknown", "ownership_transfer": "no_disclosed_transfer", "exclusivity": "not_disclosed", "retention": "restricted_federated_access", "derived_model": "yes_openfold3_improvement", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "Data stays in each owner's environment; secure aggregation prevents raw-data pooling.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "Confidentiality-preserving computation; corpus is commercially sensitive structural data.", "trade_secret": "Source records remain under contributor control.", "de_identification": "not_disclosed", "employee_customer_data": "not_applicable" }, "refreshability": "periodic_consortium_contributions", "historical_depth": "not_disclosed", "decision_outcome_richness": "High: experimentally validated molecular structures encode expensive real-world outcomes.", "likely_model_use": "Post-train and validate OpenFold3 for joint protein–ligand structure prediction and drug discovery.", "likely_strategic_value": "Improves a foundation model using structures unavailable in public databases while allowing pharma participants to avoid raw-data disclosure.", "m_and_a_implications": "Demonstrates that federated learning can monetize embedded scientific archives without separating or selling the underlying intellectual property.", "analysis_boundary": "The existence and federated training purpose are disclosed facts. Strategic-value and M&A statements are Strategic Signal analysis.", "confidence": "high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "primary", "publisher": "Apheris", "title": "OpenFold3 to be fine-tuned with pharma industry data in a secure environment", "url": "https://www.apheris.com/resources/blog/alquraishi-lab-s-openfold3-to-be-fine-tuned-with-pharma-industry-data-in-a-secure" }, { "type": "secondary", "publisher": "Reuters", "title": "Bristol Myers, Takeda pool data for AI-based drug discovery", "url": "https://www.reuters.com/business/healthcare-pharmaceuticals/bristol-myers-takeda-pool-data-ai-based-drug-discovery-2025-10-01/" }, { "type": "primary", "publisher": "OpenFold Consortium", "title": "OpenFold Consortium", "url": "https://openfold.io/" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "notice": "March 27 Apheris announcement establishes federated fine-tuning with AbbVie and Johnson & Johnson. October 1 Reuters reporting establishes the expansion; it describes intended improvements, not measured uplift.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0001", "slug": "cielo24-institutional-memory-sale", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0001/", "announcement_date": "2026-04-16", "buyer_ai_developer": [ "Undisclosed AI buyer" ], "seller_data_owner": [ "Cielo24" ], "transaction_status": "reported_completed", "industry": "Media technology", "data_category": [ "Internal communications", "Product / engineering workflow", "Institutional memory" ], "corpus_description": "Thirteen years of the shuttered company's Slack messages, internal email, and Jira tickets, reported sold as AI training data.", "primary_transaction_structure": "Data Asset Acquisition", "access_structure": "Reported transfer of a finite archive; detailed contract terms and buyer identity were not disclosed.", "asset_disposition": "Stranded / Separable", "corpus_renewal": "Finite Corpus", "source_code_software_asset": "unknown", "disclosed_consideration": { "status": "reported_range_only", "amount": null, "currency": "USD", "description": "Seller described proceeds as hundreds of thousands of dollars; no exact figure was disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "reported_dataset_specific", "rights": { "training": "yes_reported", "post_training": "not_disclosed", "inference_retrieval": "not_disclosed", "ownership_transfer": "unknown", "exclusivity": "not_disclosed", "retention": "not_disclosed", "derived_model": "not_disclosed", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "not_disclosed", "geographic_sovereignty": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "not_disclosed", "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed" }, "refreshability": "none_company_shuttered", "historical_depth": "13 years (reported)", "decision_outcome_richness": "Moderate-to-high: communications and tickets can connect plans, execution, and product outcomes, but outcome labeling is not disclosed.", "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.", "analysis_boundary": "The corpus and consideration description are attributed to the former CEO through reporting. Buyer, contract rights, and privacy controls remain unknown.", "confidence": "medium", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "secondary", "publisher": "Forbes", "title": "AI's New Training Data: Your Old Work Slacks And Emails", "url": "https://www.forbes.com/sites/annatong/2026/04/16/ais-new-training-data-your-old-work-slacks-and-emails/" }, { "type": "secondary", "publisher": "Fast Company", "title": "Shuttered startups are selling old Slack chats and emails to AI companies", "url": "https://www.fastcompany.com/91528808/shuttered-startups-are-selling-old-slack-chats-and-emails-to-ai-companies" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "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.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0002", "slug": "spirit-airlines-data-auction-google", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0002/", "announcement_date": "2026-08-14", "buyer_ai_developer": [ "Google LLC (selected bidder; challenged)" ], "seller_data_owner": [ "Spirit Aviation Holdings / Spirit Airlines" ], "transaction_status": "pending_court_approval", "industry": "Aviation", "data_category": [ "Internal communications", "Source code and software", "Operational records", "Financial and workforce records" ], "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.", "primary_transaction_structure": "Data Asset Acquisition", "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.", "asset_disposition": "Stranded / Separable", "corpus_renewal": "Finite Corpus", "source_code_software_asset": "yes", "disclosed_consideration": { "status": "disclosed_bid_not_closed", "amount": 10000000, "currency": "USD", "description": "Google selected bid; court approval pending. Later micro1 notice proposed $12.5 million." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "dataset_and_related_internal_software_specific", "rights": { "training": "yes_explicit", "post_training": "yes_general_model_improvement", "inference_retrieval": "not_disclosed", "ownership_transfer": "yes_if_sale_closes", "exclusivity": "asset_sale_subject_to_sale_order", "retention": "not_final_pending_sale_order", "derived_model": "yes_intended_use_if_sale_closes", "sublicensing": "not_final_pending_sale_order" }, "restrictions": { "governed_access": "Independent third-party deidentification contemplated before delivery; final restrictions remain subject to court approval.", "geographic_sovereignty": "not_disclosed_for_google_bid", "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.", "de_identification": "required_before_transfer", "employee_customer_data": "Customer datasets excluded; treatment of employee and labor data remains contested." }, "refreshability": "none_airline_ceased_operations", "historical_depth": "Approximately 34 years", "decision_outcome_richness": "High: communications, code, operations, scheduling, finance, and maintenance-adjacent workflows create a dense cross-functional operating history.", "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.", "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.", "confidence": "high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "secondary", "publisher": "Research Suite by Stretto", "title": "Spirit deidentified-data auction results and docket chronology", "url": "https://chapter11cases.com/blogs/news/the-spirit-airlines-deidentified-data-sale-auction-results-objections-and-the-september-30-hearing" }, { "type": "secondary", "publisher": "Reuters", "title": "Google to buy Spirit Airlines business data for $10 million", "url": "https://www.reuters.com/legal/litigation/google-buy-spirit-airlines-business-data-10-million-2026-08-17/" }, { "type": "secondary", "publisher": "WIRED", "title": "Spirit Airlines Wants to Sell Its Data to Google", "url": "https://www.wired.com/story/spirit-airlines-wants-to-sell-its-data-to-google-former-flight-attendants-are-freaked-out" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "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.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0003", "slug": "caterpillar-fieldai-industrial-autonomy", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0003/", "announcement_date": "2026-09-02", "buyer_ai_developer": [ "FieldAI" ], "seller_data_owner": [ "Caterpillar" ], "transaction_status": "announced_active", "industry": "Industrial equipment & construction", "data_category": [ "Industrial operational data", "Sensor and robotics data", "Engineering knowledge" ], "corpus_description": "Caterpillar operational data, engineering capability, and industry expertise combined with FieldAI robot foundation models for complex jobsites and manufacturing environments.", "primary_transaction_structure": "Strategic Model Co-Development", "access_structure": "Collaborative physical-AI development; detailed data-access boundaries and model-rights allocation were not disclosed.", "asset_disposition": "Embedded / Continuing", "corpus_renewal": "Renewable Corpus", "source_code_software_asset": "unknown", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "No consideration disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "not_disclosed", "rights": { "training": "unknown", "post_training": "unknown", "inference_retrieval": "yes_operational_application", "ownership_transfer": "no_disclosed_transfer", "exclusivity": "not_disclosed", "retention": "not_disclosed", "derived_model": "unknown", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "not_disclosed", "geographic_sovereignty": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "Engineering and operational data are described but controls are not disclosed.", "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed" }, "refreshability": "live_and_recurring_operational_data_likely", "historical_depth": "not_disclosed", "decision_outcome_richness": "High: autonomous inspections and operations can generate state–action–outcome data in safety-critical environments.", "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.", "analysis_boundary": "The announced combination of operational data and robot foundation models is fact. Training, retention, exclusivity, and derived-model rights are unknown.", "confidence": "medium_high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "primary", "publisher": "Caterpillar", "title": "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation", "url": "https://www.caterpillar.com/en/news/corporate-press-releases/h/caterpillar-and-fieldai-advance-ai-powered-industrial-innovation.html" }, { "type": "primary_syndicated", "publisher": "PR Newswire", "title": "Caterpillar and FieldAI Advance AI-Powered Industrial Innovation", "url": "https://www.prnewswire.com/news-releases/caterpillar-and-fieldai-advance-ai-powered-industrial-innovation-302866862.html" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "notice": "Caterpillar explicitly mentions operational data and FieldAI foundation models; separately material learning rights are not disclosed. Qualification remains unresolved.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0004", "slug": "naver-cloud-cybersecurity-consortium", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0004/", "announcement_date": "2026-09-03", "buyer_ai_developer": [ "NAVER Cloud consortium", "NAVER Cloud", "LG AI Research" ], "seller_data_owner": [ "NAVER Cloud", "LG CNS", "KEPCO KDN", "Korea Hydro & Nuclear Power", "Financial Security Institute", "KISTI", "LG Uplus", "Other consortium members" ], "transaction_status": "announced_development", "industry": "Cybersecurity & critical infrastructure", "data_category": [ "Cybersecurity records", "Critical-infrastructure operations", "Industrial systems data" ], "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.", "primary_transaction_structure": "Federated / Controlled Learning Rights", "access_structure": "Government-selected 33-member consortium developing two sovereign cybersecurity foundation models; detailed custody topology is not disclosed.", "asset_disposition": "Embedded / Continuing", "corpus_renewal": "Renewable Corpus", "source_code_software_asset": "unknown", "disclosed_consideration": { "status": "in_kind_and_government_compute", "amount": null, "currency": null, "description": "Government support includes access to up to 256 Nvidia B200 GPUs; no dataset-specific cash consideration disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "bundled_program_support", "rights": { "training": "yes_explicit", "post_training": "yes_model_specialization", "inference_retrieval": "yes_field_demonstrations", "ownership_transfer": "no_disclosed_transfer", "exclusivity": "not_disclosed", "retention": "not_disclosed", "derived_model": "yes_consortium_models", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "Consortium/government program; detailed member-level controls are not disclosed.", "geographic_sovereignty": "South Korea sovereign-AI program", "privacy_pii": "not_disclosed", "trade_secret": "Operational security and critical-infrastructure data; controls not disclosed.", "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed" }, "refreshability": "consortium_operational_sources_likely_renewable", "historical_depth": "not_disclosed", "decision_outcome_richness": "Exceptional: attack detection, analysis, response, and infrastructure operations can link threats, interventions, and outcomes.", "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.", "analysis_boundary": "Corpus size, contributors, training purpose, and program selection are disclosed. Custody, exclusivity, retention, and commercial model rights are not.", "confidence": "high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "primary", "publisher": "NAVER", "title": "NAVER Cloud Consortium to Develop AI Models Tailored for Cybersecurity", "url": "https://navercorp.com/en/media/pressReleasesDetail?seq=10034653" }, { "type": "secondary", "publisher": "Yonhap News Agency", "title": "Naver Cloud consortium to develop sovereign AI model for cybersecurity", "url": "https://en.yna.co.kr/view/AEN20260903002351320" }, { "type": "secondary", "publisher": "The Korea Times", "title": "Naver Cloud consortium to develop sovereign AI model for cybersecurity", "url": "https://www.koreatimes.co.kr/business/tech-science/20260903/naver-cloud-consortium-to-develop-sovereign-ai-model-for-cybersecurity" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "notice": "NAVER describes 33 organizations, an 830 TB training corpus, field trials and planned open-source commercial models. It does not disclose a federated training topology. The draft is corrected to model co-development.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0005", "slug": "samsung-mistral-semiconductor-partnership", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0005/", "announcement_date": "2026-09-09", "buyer_ai_developer": [ "Mistral AI" ], "seller_data_owner": [ "Samsung Electronics" ], "transaction_status": "announced_active", "industry": "Semiconductors", "data_category": [ "Manufacturing history", "Equipment and process data", "Engineering knowledge" ], "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.", "primary_transaction_structure": "Proprietary Data + Equity Partnership", "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.", "asset_disposition": "Embedded / Continuing", "corpus_renewal": "Renewable Corpus", "source_code_software_asset": "unknown", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "Partnership economics and Samsung's individual equity investment were not disclosed separately." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "bundled_with_strategic_equity_relationship", "rights": { "training": "yes_customized_on_prem_models", "post_training": "yes_customization", "inference_retrieval": "yes_on_prem_use", "ownership_transfer": "no_disclosed_transfer", "exclusivity": "not_disclosed", "retention": "restricted_to_samsung_boundaries", "derived_model": "unknown", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "On-premise deployment; sensitive data stays inside Samsung boundaries.", "geographic_sovereignty": "On-premise / sovereign control", "privacy_pii": "not_disclosed", "trade_secret": "Explicit protection of sensitive semiconductor technology and operational data.", "de_identification": "not_disclosed", "employee_customer_data": "not_disclosed" }, "refreshability": "continuous_manufacturing_operations", "historical_depth": "not_disclosed", "decision_outcome_richness": "Exceptional: process interventions, yield, defect, and equipment outcomes can encode extremely high-value manufacturing decisions.", "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.", "analysis_boundary": "On-premise controls, target use cases, and strategic equity relationship are disclosed. Model ownership, exclusivity, sublicensing, and data-specific consideration are not.", "confidence": "high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "primary", "publisher": "Samsung", "title": "Samsung and Mistral AI Announce Strategic Partnership for Intelligence-Driven Semiconductor Infrastructure", "url": "https://news.samsung.com/global/samsung-and-mistral-ai-announce-strategic-partnership-for-intelligence-driven-semiconductor-infrastructure" }, { "type": "secondary", "publisher": "Reuters", "title": "French AI company Mistral hits $24 billion valuation in funding round", "url": "https://www.reuters.com/world/europe/french-ai-company-mistral-hits-24-billion-valuation-funding-round-2026-09-08/" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "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.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0006", "slug": "openai-built-in-financial-data-partnerships", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0006/", "announcement_date": "2026-09-10", "buyer_ai_developer": [ "OpenAI" ], "seller_data_owner": [ "LSEG", "PitchBook", "Daloopa", "Crunchbase", "Quartr", "LSEG News" ], "transaction_status": "launched_active", "industry": "Financial information services", "data_category": [ "Financial datasets", "Private-market data", "Fundamental company data", "Earnings and filings" ], "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.", "primary_transaction_structure": "Proprietary Corpus License", "access_structure": "Built-in licensed/indexed data available to the financial-services product for grounded analysis and citations; specific provider contracts are not public.", "asset_disposition": "Embedded / Continuing", "corpus_renewal": "Renewable Corpus", "source_code_software_asset": "no", "disclosed_consideration": { "status": "not_disclosed", "amount": null, "currency": null, "description": "No provider-specific consideration disclosed." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "not_disclosed", "rights": { "training": "not_disclosed", "post_training": "not_disclosed", "inference_retrieval": "yes_explicit", "ownership_transfer": "no_disclosed_transfer", "exclusivity": "not_disclosed", "retention": "indexed_on_openai_infrastructure_for_some_sources", "derived_model": "not_disclosed", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "Product and provider controls apply; contract details are not public.", "geographic_sovereignty": "not_disclosed", "privacy_pii": "not_disclosed", "trade_secret": "Provider-license restrictions not disclosed.", "de_identification": "not_applicable_or_not_disclosed", "employee_customer_data": "Customer-connected datasets are explicitly excluded from this record." }, "refreshability": "continuous_provider_updates", "historical_depth": "Varies by provider; Daloopa reports 14 years of normalized data.", "decision_outcome_richness": "High for financial research and valuation, though less direct than operational intervention corpora.", "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.", "analysis_boundary": "Built-in providers and retrieval/citation use are disclosed. Training, post-training, ownership, and pricing are not claimed.", "confidence": "medium_high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "primary", "publisher": "OpenAI", "title": "ChatGPT for Financial Services", "url": "https://help.openai.com/en/articles/12608093-chatgpt-for-financial-services" }, { "type": "secondary", "publisher": "Reuters", "title": "OpenAI launches ChatGPT for financial services industry", "url": "https://www.reuters.com/business/openai-launches-chatgpt-financial-services-industry-2026-09-10/" }, { "type": "primary", "publisher": "Daloopa", "title": "Daloopa data infrastructure", "url": "https://daloopa.com/" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "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.", "human_approval_recorded": false, "audited_on": "2026-09-16" } }, { "transaction_id": "SS-AIDT-2026-0007", "slug": "totalenergies-mistral-reservoir-models", "canonical_path": "/ai-data-transactions/deals/ss-aidt-2026-0007/", "announcement_date": "2026-09-15", "buyer_ai_developer": [ "Mistral AI" ], "seller_data_owner": [ "TotalEnergies" ], "transaction_status": "announced_three_year_program", "industry": "Energy & geoscience", "data_category": [ "Geological / subsurface data", "Reservoir engineering", "Decision histories", "Scientific data" ], "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.", "primary_transaction_structure": "Strategic Model Co-Development", "access_structure": "Three-year joint scientific program developing custom proprietary tools while TotalEnergies retains control of its strategic data and intellectual property.", "asset_disposition": "Embedded / Continuing", "corpus_renewal": "Renewable Corpus", "source_code_software_asset": "unknown", "disclosed_consideration": { "status": "disclosed_program_commitment", "amount": 100000000, "currency": "EUR", "description": "More than €100 million over three years; explicitly a program-wide investment, not a dataset price." }, "estimated_economics": { "status": "not_estimated", "amount": null, "currency": null, "method": null }, "economics_scope": "bundled_research_compute_people_and_implementation", "rights": { "training": "yes_develop_frontier_models_on_corpus", "post_training": "unknown", "inference_retrieval": "yes_custom_tools", "ownership_transfer": "no_totalenergies_retains_control", "exclusivity": "not_disclosed", "retention": "governed_totalenergies_control", "derived_model": "unknown_joint_program", "sublicensing": "not_disclosed" }, "restrictions": { "governed_access": "TotalEnergies retains control of strategic data and IP; detailed technical controls are not disclosed.", "geographic_sovereignty": "European strategic-technology context; binding geographic terms not disclosed.", "privacy_pii": "not_applicable_or_not_disclosed", "trade_secret": "Explicit control of strategic data and intellectual property.", "de_identification": "not_disclosed", "employee_customer_data": "not_applicable_or_not_disclosed" }, "refreshability": "continuing_exploration_and_reservoir_operations", "historical_depth": "Nearly one century", "decision_outcome_richness": "Exceptional: exploration bets, reservoir characterization, development choices, interventions, production, and field-life outcomes carry very high economic consequence.", "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.", "analysis_boundary": "Program duration, >€100 million commitment, use case, joint lab, and corpus history are disclosed. Data-specific economics and derived-model rights are not.", "confidence": "high", "scores": { "methodology_version": "1.0.0", "asset": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "uniqueness_scarcity": null, "historical_depth": null, "decision_outcome_richness": null, "decision_value_density": null, "real_world_grounding": null, "domain_value": null, "refreshability": null, "proprietary_advantage": null, "model_learning_usefulness": null, "non_replicability": null, "rights_usability": null } }, "transaction_signal": { "score": null, "coverage_pct": 0, "rationale": "Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values.", "factor_ratings": { "disclosed_economics": null, "clean_price_discovery": null, "data_consideration_separability": null, "explicit_model_use": null, "rights_clarity": null, "competing_bids": null, "strategic_buyer_quality": null, "precedent_value": null, "independent_model_uplift": null } } }, "sources": [ { "type": "primary", "publisher": "TotalEnergies", "title": "TotalEnergies Announces a Partnership with Mistral to Develop Frontier AI Models for Reservoir Exploration and Engineering", "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" }, { "type": "secondary", "publisher": "Reuters", "title": "TotalEnergies and Mistral to develop AI models for reservoirs", "url": "https://www.boursorama.com/bourse/actualites-amp/totalenergies-et-mistral-vont-collaborer-sur-des-modeles-d-ia-dedies-au-developpement-des-reservoirs-5839a37d68decb3613f524009e778abb" } ], "date_added": "2026-09-15", "date_last_reviewed": "2026-09-15", "publication_review": { "status": "legacy_review_required", "notice": "TotalEnergies discloses a three-year program above EUR 100 million and a joint laboratory using geoscience history. Detailed retention, ownership and contractual rights allocation are not disclosed.", "human_approval_recorded": false, "audited_on": "2026-09-16" } } ] }