OpenAI has published a profile of JioStar, the Indian media and entertainment company, describing how it has applied AI to content discovery for a user base approaching 500 million. The piece features chief architect Vijay Seshadri, who explains the engineering challenges of running AI-powered discovery at that scale. According to the published description, the discussion centres on three areas: evaluation, guardrails, and moving AI systems into production. These are the stages where consumer-scale deployments face the most scrutiny, because even a small error rate can affect a very large number of people. The description does not include specific models, metrics or results, so readers should consult the full article for those details. For Australian businesses, this is a case study rather than a regulatory change, and it does not alter obligations under the Privacy Act. The practical takeaway is the discipline around evaluation: define how you will measure an AI tool's output quality before rolling it out, and put checks in place before it reaches customers. Most Australian firms will not operate at JioStar's scale, so the direct cost or risk impact is limited. However, testing an evaluation approach on a single internal workflow is inexpensive. If your business sends personal information through AI tools, Privacy Act obligations apply regardless of size.