AI funding surge targets blue-collar work

Tuesday, 3 February 2026 03:08 - - {{hitsCtrl.values.hits}}

AI startups are raising billions of dollars to develop software “brains” for robots designed to operate in real-world environments, a shift that could extend automation risks well beyond white-collar occupations, according to reporting by Axios.

The core proposition is to build AI systems that understand physics and changing physical conditions, allowing robots to adapt to complex settings such as oil rigs, construction sites and logistics hubs. Whether these machines resemble humanoids is largely incidental; proponents argue that once a robot has the physical capability to perform a task, flexible software intelligence can allow it to handle a wide range of activities, from plumbing and welding to vehicle repairs and food preparation.

There is, however, no settled approach to applying AI in robotics. Some large technology firms and startups are collecting vast quantities of real-world data to train their models. Others are relying on so-called “world models”, trained on simulated physical environments that incorporate principles such as gravity, offering a lower-cost alternative. This approach has been publicly backed by former Meta chief AI scientist Yann LeCun, who recently launched a new venture, AMI Labs.

Investor interest has accelerated sharply. Toronto-based Waabi has raised up to $ 1 billion, potentially the largest funding round for a Canadian startup, initially targeting autonomous taxis and self-driving trucks. Its founder and Chief Executive Raquel Urtasun told Axios that “the physical AI moment is here”, arguing that autonomy is likely to be the first application to scale. Pittsburgh-based Skild AI has raised about $ 1.4 billion at a valuation of $ 14 billion, while FieldAI secured nearly $ 400 million to focus on hazardous and labour-intensive sectors such as energy and logistics, including the construction of data centres.

The implications for employment remain uncertain. Even where AI-powered robots outperform human workers, high hardware costs and the expense of transitioning existing operations may limit near-term adoption. Optimists argue that, as with previous technological shifts, new forms of work will emerge to offset displacement. Critics counter that AI represents a more profound break from past innovations, making historical parallels an unreliable guide.