Data labeling startup Handshake nearly doubled its gross annualized revenue to approximately $1B in recent months, up from $550M in January. Competitor Mercor also reached a $1B+ gross annualized revenue pace this year.
The explosive growth reflects AI companies' insatiable demand for labeled training data. Firms developing large language models and other AI systems require massive datasets annotated by human workers—a process that has become a critical bottleneck in AI development.
Handshake's acceleration from $550M to $1B annualized revenue in less than a year demonstrates the scale of this opportunity. The startup connects companies needing data labeling with remote workers globally.
Mercor, another player in the space, has similarly capitalized on demand, reaching $1B+ annualized revenue. Both companies operate in an often-overlooked sector that has become essential infrastructure for the AI boom.
The surge underscores how AI's rapid advancement depends not just on algorithmic innovation but on the unglamorous work of data preparation. As competition for training data intensifies, these labeling platforms are becoming increasingly valuable to the broader AI ecosystem.
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