Uber Races Into AI Data Labeling as Meta’s $14.8B Scale Deal Sparks Mass Defections – Who’s Next?

Uber is moving deeper into the AI services space, expanding its data labeling business just as some AI firms have begun to distance themselves from Scale AI following Meta’s $14.8 billion investment in the company.

On June 20, Uber announced a major expansion of its AI data services unit, now branded as Uber AI Solutions. The company said it will offer its internal technology platforms to AI labs and enterprises looking to build and test large-scale models.

This includes access to ready-made datasets, clickworker task networks, and tools for training AI agents.

The move comes at a time when the AI labeling market is under pressure. Meta’s 49% stake in Scale AI has reportedly unsettled former partners like OpenAI and Google.

Megha Yethadka, general manager of Uber AI Solutions, said the expansion builds on the company’s decade-long experience handling massive data operations.

“We’re bringing together Uber’s platform, people, and AI systems to help other organizations build smarter AI more quickly,” she told Forbes.

Uber AI Solutions first launched in November 2023 under the name Uber Scaled Solutions. It began by offering data annotation tools to train models for clients. The rebrand reflects the company’s broader focus on AI.

$UBER is scaling up its AI data services with the global launch of Uber AI Solutions. It’s opening its internal tools and global talent network—used to train self-driving cars and Gen AI agents—to AI labs and enterprises in 30+ countries. pic.twitter.com/syA5ybutvG

Now active in more than 30 countries, Uber’s platform connects companies with a global pool of contractors. These clickworkers handle tasks like translation, coding, editing, and dataset labeling.

According to Yethadka, there are “tens of thousands” of workers in the network, including subject matter experts across STEM, law, and finance. The top taskers spend around three to four hours daily on the platform, with pay ranging from $20 to $200 per hour, depending on the complexity of the assignment.

How @Uber used LangGraph to build AI developer agents that generate thousands of daily code fixes and saved 21,000+ hours — serving an organization of 5,000 developers working with hundreds of millions of lines of code.

Watch their full session here: https://t.co/3j6kntbHza pic.twitter.com/QrB7eyNUo6

“We do see an opportunity to build this into a meaningful business line for Uber,” said Yethadka.

The company is also developing a user-facing software interface to simplify project setup.

Clients will be able to describe their data needs in plain language, with the system automatically assigning tasks, setting workflows, and overseeing quality control.

Among the tools now available are services for creating datasets involving video, audio, images, and text. Uber is also offering companies access to the same back-end infrastructure it uses to manage its own AI training efforts.

Clients already working with Uber AI Solutions include autonomous vehicle firm Aurora and Niantic, the maker of Pokémon Go, which recently shifted away from gaming to focus on enterprise AI.

The company did not disclose its total clickworker count, but said the workforce has doubled since the start of the year.

With Meta’s partnership reshaping Scale’s client dynamics, Uber’s move comes at a moment of opportunity. Whether it becomes the next major destination for AI data services is still unclear.

Uber’s entry into the AI data labeling market couldn’t come at a more turbulent time.

Meta’s $14.8 billion deal with Scale AI has sent shockwaves through the industry, with Scale CEO Alex Wang now joining Meta to lead its new Superintelligence Lab, directly challenging OpenAI, Google DeepMind, and Anthropic.

The move has prompted clients to rethink their partnerships, with some, like OpenAI, already cutting ties with Scale, according to Bloomberg.

As a result, the field is wide open. Smaller players like Mercor, Turing, and Invisible Technologies are racing to fill the vacuum, but Uber brings unique advantages such as scale and capital.

Unlike VC-dependent startups, Uber already has a massive global contractor network, logistics infrastructure, and experience managing gig work, traits it now hopes to apply to high-skill data annotation.

“More companies want neutral, independent vendors,” said Uber’s head of new AI initiatives, Yethadka.

That neutrality, paired with Uber’s commitment to data privacy, may give it a shot at winning over companies spooked by Scale’s tighter integration with Meta.

Still, competition will come down to talent. “Data labeling is trending toward more complex, skilled tasks,” said Mercor CEO Brendan Foody. Uber’s success will depend on whether it can build and maintain a reliable network of high-skill clickworkers.

With Big Tech expected to spend over $300 billion on AI in 2025, Uber’s shift into this space is a strategic move and a sign that the battle for AI dominance is expanding far beyond just algorithms.

The post Uber Races Into AI Data Labeling as Meta’s $14.8B Scale Deal Sparks Mass Defections – Who’s Next? appeared first on Cryptonews.