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Why Not a Data Product?

A common question arises:

If everything in the universe is information, and data is the raw material of information, why should AI not be treated as just another form of Data Product?

The AI Product Specification (AIPS) exists precisely because AI cannot be reduced to just data.
While AI Products consume and produce data, their nature and responsibilities go far beyond what a Data Product covers.

Key Distinctions​

1. Dynamic vs Static​

  • Data Products represent curated, relatively stable datasets or streams.
  • AI Products are dynamic systems that transform, infer, or generate outputs. They change their behavior through training, fine-tuning, or even self-learning.

2. Capability vs Content​

  • Data Products are about what content is made available.
  • AI Products are about what capability is offered (classification, generation, reasoning, autonomy).

3. Risk & Ethics​

  • Data Products must handle privacy, quality, and compliance.
  • AI Products introduce new ethical and societal risks: bias, hallucination, manipulation, unsafe autonomy.
    These require risk classification, prohibited uses, and explainability — elements not found in Data Product specs.

4. Lifecycle & Drift​

  • Data Products evolve through versioning and governance.
  • AI Products face model drift, retraining, continuous monitoring, and feedback loops as first-class lifecycle concerns.

5. Energy & Externalities​

  • Data Products have costs of storage and access.
  • AI Products consume compute, energy, and carbon resources during training and inference — with environmental and societal externalities.

Complementary, Not Substitutes​

  • Data Products can be inputs to AI Products (e.g., training data, feature stores).
  • Data Products can also be outputs of AI Products (e.g., embeddings, generated datasets).
  • But an AI Product cannot be collapsed into “just data” because its value lies in capability, autonomy, and governance, not simply in content.

Framing Principle​

Every AI Product involves data, but not every data product involves AI.
Treating AI Products as Data Products would erase the unique responsibilities of AI — risks, autonomy, explainability, drift, and externalities.

Summary​

  • AI Products inherit the universal characteristics of a Product (via BPS).
  • They extend beyond Data Products by addressing capabilities, risks, and lifecycle dynamics.
  • AIPS ensures these extensions are formalized, so AI can be consumed responsibly in enterprises and society.

In short:
AI ≠ Data.
AI Products deserve their own specification — because they are living, dynamic, and impactful entities, not static datasets.