Handshake Acquires Data Label Auditing Startup Cleanlab in Strategic Acqui-Hire
AI
Data Labeling
Acquisition
Handshake
Cleanlab
M&A
Startups
TechCrunch
Artificial Intelligence
8
Data Quality Takes Center Stage
Media Hype
6/10
Real Impact
8/10
What is the Viqus Verdict?
We evaluate each news story based on its real impact versus its media hype to offer a clear and objective perspective.
AI Analysis:
While the acquisition itself isn't a revolutionary breakthrough, the increasing emphasis on data quality in AI is generating significant hype. Handshake’s strategic move reflects this trend and is likely to have a substantial, long-term impact on the industry’s growth.
Article Summary
Handshake, the rapidly growing AI data labeling platform, has made a strategic acquisition of Cleanlab, a startup focused on data label auditing. This ‘acqui-hire’ is designed to significantly enhance Handshake’s ability to produce higher quality data for its clients, which include leading AI labs like OpenAI. Cleanlab’s core technology, developed by a team of MIT PhDs, focuses on automatically identifying and flagging incorrect data labels without requiring manual review. The acquisition brings nine key Cleanlab employees into Handshake’s research organization, led by CEO Curtis Northcutt, who pioneered Cleanlab’s automated auditing techniques. Notably, Cleanlab’s researchers frequently worked with competitors like Mercor, Surge, and Scale AI, further solidifying Handshake’s position within the broader AI data labeling ecosystem. Handshake was last valued at $3.3 billion in 2022 and is projected to reach ‘high hundreds of millions’ in annualized revenue this year. The deal highlights the increasing importance of data quality in the AI space and Handshake’s commitment to providing a robust solution.Key Points
- Handshake acquired Cleanlab in a strategic ‘acqui-hire’ to bolster its data labeling capabilities.
- The acquisition brings nine Cleanlab employees, including MIT PhDs, into Handshake’s research organization.
- Cleanlab’s technology focuses on automatically auditing data labels, improving data quality for AI model training.