At the offices of startup Vicarious in Union City, where the San Francisco Bay Area’s sprawl abuts rolling hills, 10 robot arms tirelessly place travel-sized beauty products into bins on a conveyor belt. Each gray arm ends in a suction-cup-tipped finger that makes a high-pitched whine as it plucks items such as antiperspirant or hand lotion from crowded boxes.

Vicarious buys standard industrial robots, enhances them with its software, and contracts them out the way a temp agency does workers—charging per task completed or at an hourly rate. In Baltimore, Vicarious robots assemble sampler packs for makeup company Sephora, work previously done exclusively by humans. Vicarious CEO and cofounder D. Scott Phoenix says the deal demonstrates his business model: Create artificial intelligence software that makes industrial robots smart enough to perform jobs previously done only by people.

Vicarious hasn’t previously discussed its customers or robots publicly but has earned itself an air of mystery among AI and robot experts since its founding in 2010. The startup has raised more than $130 million, according to data service PitchBook. Its investors include some of Silicon Valley’s most famous names and deepest pockets—venture firm Founders Fund, cofounded by early Facebook investor Peter Thiel, and billionaire entrepreneurs Mark Zuckerberg, Elon Musk, and Jeff Bezos.

Instead of placing these items into boxes the robots throw them with a firm flick to extend their range. Photograph: Phuc Pham

The startup is pursuing its own path in artificial intelligence, looking beyond the technology driving high-profile projects such as content moderation at Facebook and automated driving at Tesla. Phoenix says only a fresh approach to AI can resolve what he calls a paradox of modern society. Robot arms and grippers have been around for a long time, and components such as motors, sensors, and microcontrollers have never been so cheap or capable. But even inside factories and warehouses, robots are restricted to certain tightly controlled tasks because their software must be specifically programmed for every situation and can’t adapt to unexpected variability.

“We're paying people trillions of dollars a year to do stuff that robots have been physically capable of doing for the last 30 or 40 years,” Phoenix says. Anyone who can make industrial robots more adept—and Vicarious is not the only one trying—could transform the economy by shifting the balance of labor between people and machines.

Deep Learning and Its Limits

When you hear a CEO or politician talk of the growing power of artificial intelligence, they are generally referring, even if they don’t know it, to a technique called deep learning. Since 2012, when researchers showed it could make computers much better at interpreting images and text, the technique has rewired the technology industry. Deep learning powers face-swapping photo filters and self-driving cars; it is why Alphabet CEO Sundar Pichai opined at Davos this year that AI is “more profound than fire or electricity.”

"We're paying people trillions of dollars a year to do stuff that robots have been physically capable of doing for the last 30 or 40 years." D. Scott Phoenix, Vicarious cofounder

Vicarious uses deep learning for some things, such as in its robots’ vision systems, but believes other ideas are needed to make computers truly smart. Phoenix started the company in 2010, before the deep learning era, convinced that infusing AI into robots could transform the economy. His cofounder was Dileep George, a software engineer turned researcher who had recently completed a PhD thesis at Stanford titled “How the Brain Might Work.” It used observations from neuroscience to guide the design of AI algorithms. Since then, deep learning has swept through Silicon Valley, and Vicarious has published a series of papers highlighting its limitations and advocating an alternative approach.