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The Download: gig workers training humanoids, and better AI benchmarks

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Why This Matters

The article highlights the growing role of gig workers in training humanoid robots through video data, raising important privacy and ethical concerns, while also emphasizing the need for more realistic AI benchmarks that reflect real-world complexity. These developments are significant as they influence how AI and robotics are integrated into society, impacting industry standards and consumer trust.

Key Takeaways

Zeus is a data recorder for Micro1, which sells the data he collects to robotics firms. As these companies race to build humanoids, videos from workers like Zeus have become the hottest new way to train them.

Micro1 has hired thousands of them in more than 50 countries, including India, Nigeria, and Argentina. The jobs pay well locally, but raise thorny questions around privacy and informed consent. The work can be challenging—and weird. Read the full story.

—Michelle Kim

Our readers recently voted humanoid robots the "11th breakthrough" to add to our 2026 list of 10 Breakthrough Technologies. Check out what else officially made the cut.

AI benchmarks are broken. Here’s what we need instead.

For decades, AI has been evaluated based on whether it can outperform humans on isolated problems. But it’s seldom used this way in the real world.

While AI is assessed in a vacuum, it operates in messy, complex, multi-person environments over time. This misalignment leads us to misunderstand its capabilities, risks, and impacts.

We need new benchmarks that assess AI’s performance over longer horizons within human teams, workflows, and organizations. Here’s a proposal for one such approach: Human–AI, Context-Specific Evaluation.

—Angela Aristidou, professor at University College London and faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute.