The end-to-end pipeline of inputs, intermediate artefacts, and downstream applications by which an AI system is built and deployed — typically decomposed as training data → compute → model weights → fine-tuning → deployment → downstream applications.
Definition and scope
The AI supply-chain framing treats AI development as an industrial value chain in which each upstream stage constrains what the downstream stage can do, and each stage raises distinct governance questions. Training data raises copyright, consent, and bias questions (NYT v. OpenAI, GEMA v. OpenAI, Andersen v. Stability AI). Compute raises export-control and concentration questions (US BIS rules on advanced GPUs to China, the CHIPS Act, the 2024 EU Chips Act). Model weights raise open-vs-closed governance questions (Meta Llama, Mistral, DeepSeek vs. closed frontier labs). Fine-tuning raises capability-elicitation questions (Qi et al. 2023 'Fine-tuning Aligned LLMs Compromises Safety'). Deployment raises monitoring and incident-reporting questions. Downstream applications raise sectoral-liability questions (medical-device AI, automated decision-making in employment). Governance treatment is fragmented across the chain. EU AI Act Recital 60 + Art. 25 introduces explicit value-chain obligations: the GPAI provider and the downstream deployer have different obligations, and contracts must allocate them. US EO 14110 §4.2 targeted the compute stage (Defense Production Act reporting for foundation-model training above the threshold). NIST AI RMF GenAI Profile (NIST AI 600-1, 2024) names 'Value Chain and Component Integration' as one of twelve GenAI risk categories. ASEAN AI Guide §3 treats the supply chain as a 'shared responsibility' across actors. The supply-chain framing is increasingly the unit of governance analysis because chokepoints (compute access, training-data legality, weight distribution) determine where policy levers have purchase.
Used by these instruments
Related concepts
- Compute Threshold (AI Governance)— A regulatory trigger expressed as floating-point operations (FLOPs) consumed during model training,
- Training-Data Attribution— Technical methods that identify which training examples most influenced a specific AI model output,
- Model Card— A standardized disclosure document accompanying an AI model that describes its intended use, trainin
- Model Distillation Risk— The risk that a closed-weight frontier model's capabilities can be partially recovered by training a
- Data Poisoning— A training-time attack in which an adversary inserts crafted examples into the training corpus or fi
Appears in topic articles
Editorial note
When citing 'AI supply chain' in policy contexts, name the stage of interest (data / compute / weights / deployment) because governance levers are stage-specific. Confusing stage-level interventions (e.g. export controls on GPUs) with end-to-end claims is one of the most common policy-analysis errors in this domain.
References
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