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Ask HN: In-house or outsourced data annotation? (2025)

Jun 07, 2025 - news.ycombinator.com
The article discusses the different approaches to data annotation in AI and robotics development, highlighting that big tech companies often outsource this task to specialized firms like Scale AI, TURING, and Mercor. In contrast, companies such as Tesla and Google prefer to maintain in-house teams for data annotation. The choice between outsourcing and in-house annotation depends on various factors, including control over data quality, cost, and the ability to quickly iterate and refine datasets.

The trend in data annotation is likely to evolve as companies weigh the benefits of outsourcing against the advantages of having dedicated in-house teams. Outsourcing can offer scalability and access to specialized expertise, while in-house teams may provide better integration with company-specific processes and faster feedback loops. The decision ultimately hinges on a company's specific needs, resources, and strategic priorities in AI and robotics development.

Key takeaways:

  • Outsourcing data annotation to specialized firms can provide scalability and access to a diverse workforce.
  • In-house data annotation teams, like those at Tesla and Google, allow for greater control over data quality and security.
  • The choice between outsourcing and in-house annotation depends on a company's specific needs, resources, and priorities.
  • The trend may evolve towards a hybrid model, combining the benefits of both approaches for optimal results in AI and robotics development.
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