Uber has launched a new initiative in India that allows its driver partners to participate in AI data labelling tasks through the company’s app. The program, currently in pilot mode, provides drivers with the option to engage in microtasks that support the development of artificial intelligence systems while supplementing their primary income from rides.

The initiative reflects the increasing demand for high-quality labelled datasets, which form the foundation of machine learning and AI applications. With AI adoption expanding across industries, the need for structured, human-verified data continues to grow. Uber’s move aims to tap into its vast driver network, enabling a distributed workforce that can contribute to AI development in a flexible way.

According to the company, participating drivers can complete tasks during downtime between rides or when demand is lower. These tasks typically involve identifying and tagging objects in images, classifying short text entries, or verifying simple datasets. The tasks are integrated within the Uber driver app, ensuring accessibility without requiring additional infrastructure or training.

Industry experts note that this is one of the first large-scale efforts by a global mobility company to integrate gig workers into AI-related microtasking through its own platform. AI data labelling has traditionally been dominated by outsourcing firms and crowdsourcing platforms, often involving freelancers and contract workers. By embedding the opportunity within its app ecosystem, Uber is providing drivers with a side hustle that could be both scalable and convenient.

The initiative comes at a time when India’s AI services market is expanding rapidly. Nasscom estimates that the Indian AI market will cross $7 billion by 2027, with significant demand for data preparation, model training, and annotation services. Global tech firms frequently rely on Indian talent for AI-related work due to cost advantages and workforce scale. Uber’s approach positions its driver base as a new participant in this economy.

The company has stated that drivers participating in the program can earn additional income based on the volume and accuracy of completed tasks. Early feedback from pilot participants has highlighted the flexibility of being able to earn during off-peak hours. However, analysts caution that the long-term sustainability of such programs depends on fair compensation models and the complexity of tasks assigned.

There are also broader implications for gig economy workers. By offering access to AI-related work, Uber is signalling that the future of gig work may not be limited to mobility and delivery services. Instead, workers could gradually become part of the wider digital economy, taking on distributed, knowledge-based tasks. “This is a step towards redefining gig work, where a ride-hailing app becomes a gateway to AI-driven opportunities,” noted a Bengaluru-based analyst tracking gig economy trends.

The move has drawn attention from both the AI and labour rights communities. While some industry observers view it as an innovative way to democratize access to AI work, others emphasize the need for clarity on pay structures, task evaluation, and data privacy. Critics argue that without transparent safeguards, such initiatives could add to the precariousness of gig work by transferring high-value tasks to workers without ensuring equitable compensation.

Globally, data labelling has been described as the hidden backbone of AI systems. Companies like Amazon, Google, and Microsoft have previously relied on external contract workforces for this function. Uber’s program represents a shift by integrating the function directly into its own platform, which could reduce reliance on third-party vendors.

The company has not disclosed how it plans to scale the initiative or whether it will be introduced in other markets. However, India’s prominence as both a large driver base and an AI services hub makes it a strategic testing ground.

For marketers and technology leaders, this move illustrates a larger trend: the merging of mobility platforms with broader digital ecosystems. As AI reshapes industries, companies are looking for ways to leverage existing networks of gig workers to support data-intensive processes.

If successful, Uber’s pilot could set a precedent for other gig platforms to introduce parallel earning opportunities tied to digital tasks. It may also spark debate on how the value of AI development is distributed among workers contributing at the ground level.

The program represents another step in the ongoing redefinition of gig work in the AI era. Drivers who once only navigated city streets may soon find themselves helping to chart the course of AI systems worldwide.