Ride-hailing giant Uber has made a significant foray into the artificial intelligence (AI) space with the launch of Scaled Solutions, a new division focused on AI training and data labeling. This move marks a departure from Uber's traditional ride-sharing and delivery services, as the company begins to offer its expertise in data annotation, testing, and localization to enterprises across various industries.

Scaled Solutions has already secured its first customers, including Aurora Innovation, a self-driving software company, and Niantic, a game developer building a 3D map of the world. The division is targeting a range of sectors, including retail, automotive, social media, consumer apps, generative AI, manufacturing, and customer support. To support its growth, Uber is hiring gig workers in India, the US, Canada, Poland, and Nicaragua, with plans to expand its corporate team in San Francisco, New York, and Chicago.

The gig workers hired by Scaled Solutions will be tasked with various responsibilities, depending on the specific project requirements. According to Chris Brummitt, Uber's senior director for communications in Asia-Pacific, the skills required may include language skills, programming, or no specific skills at all. However, the company has not disclosed pay rates, stating only that compensation will vary based on the task and may not guarantee the statutory hourly minimum wage for each region.

The data labeling market, in which Scaled Solutions is operating, is a growing space, with companies relying on human annotators to validate data used to train AI models. Scale AI, a prominent vendor in this market, has reportedly reached a valuation of over $13 billion. However, the industry has faced criticism for its treatment of outsourced data annotation workers, highlighting the need for companies like Uber to prioritize fair labor practices.

Irina Sedenko, principal research director at Info-Tech Research Group, believes that Uber's entry into the AI data labeling market will enable the company to leverage its experience working with massive datasets and technology platforms to support data processing and model development. "This move allows Uber to capitalize on the growing demand for AI development services and tap into a new market," she said. "It is also an example of a company launching a new data-driven business capability and defining data as a product."

As Uber expands into this new market, CIOs and organizations considering partnering with Scaled Solutions will need to carefully evaluate the risks and benefits of outsourcing their data labeling needs. Sedenko emphasized the importance of understanding how data will be stored, used, and accessed, particularly when dealing with proprietary or sensitive information. Uber's senior genAI services advisor, Nate Carson, has addressed these concerns, stating that the company has secure and internal networks in place to handle highly sensitive data.

Uber's foray into AI training and data labeling marks a significant shift in the company's business strategy, as it seeks to diversify its revenue streams and capitalize on the growing demand for AI development services. As the company continues to expand its presence in this market, it will be important to monitor its progress and the implications for the broader tech industry.