what ai labs look for in training data

buyer_requirements = {
  relevance:   matches the task the model needs to learn
  rights:      licensed with clear permitted uses
  privacy:     prepared and reviewed before delivery
  scope:       defined, dataset-specific, not "everything we have"
  delivery:    predictable, controlled, auditable
}

what this means in practice

• relevance beats volume. a smaller dataset that matches the task is worth more than a large one that doesn't.
• rights come first. a dataset without clean rights is a liability, not an asset.
• scope has to be specific. labs want a defined slice, not a dump.

which side are you on

if you're a buyer, write requirements before you search for suppliers. if you're a supplier, know which of your records would match those requirements.