Humanoid Robots: The Next Personal Computer Driving Economic and Geopolitical Competition

Key Quotes

"My primary motivation is to be useful. What does it mean to improve the way we live and work and where do you focus? So I was very interested in healthcare, the way we take care of each other, you know, and in particular in the way we age."

Jeff Cardenas explains that his core drive for starting a robotics company stems from a desire to improve human well-being, specifically focusing on the challenges associated with aging and healthcare. This personal motivation highlights a human-centered approach to technological development.

"So the way I see this rolling out is in three stages. You can kind of think of it in some ways as easy to hard, though I would say all of this is relatively hard. But stage one is the industrial base. This is where you can control the environment and where you have experts that can work around these machines. So these are like logistics and manufacturing."

Cardenas outlines a phased approach to robot deployment, beginning with controlled industrial environments like manufacturing and logistics. This strategy acknowledges the current limitations and complexities of robotics, prioritizing environments where safety and operational parameters can be managed.

"The way robots for the age of AI work is we're now having sensors in the feedback loop. So we have cameras, we have force sensing, touch sensing, we have temperature, we have an accelerometer that can sense position and orientation. So we have all sorts of sensors that we're bringing together so the robot can be aware and can be sort of dynamic and respond to the environment that's out there."

Cardenas describes a fundamental shift in robot design, moving from simple position control to AI-driven systems that incorporate a wide array of sensors. This integration allows robots to perceive and react to their surroundings dynamically, enabling more complex and adaptable interactions.

"The simple idea is the robots are learning. Traditionally, what we'd done is basically program the robot to do a particular function. This is how it was done in the past is essentially it was given instructions on how to do a task. What's changed, what we call this foundation model, so this is generative AI applied to robotics, is that the robots can now learn."

Cardenas explains that a key advancement in robotics is the shift from pre-programmed tasks to AI-driven learning capabilities. This transition, enabled by foundation models and generative AI, allows robots to acquire new skills and adapt to different functions more efficiently than traditional programming methods.

"I think of the humanoid as the general purpose platform that can scale. But it's early. This is the the 80s maybe of, if you use that analogy. And I think, yeah, they could also be the 60s, though that's true. I think it depends on, you know, my view spending a lot of time in this and and I sort of openly admit that we were very naive going into this."

Cardenas likens the current state of humanoid robotics to the early days of personal computers in the 1980s, suggesting significant growth potential but acknowledging the nascent stage of the technology. He admits to initial naivete about the complexities involved in developing these advanced robots.

"I think that by 2035 you will see these robots in homes. The question is how quick is that uptake and I think that's a point of debate. But I think we're now in the window and I think you're going to see a very fast acceleration."

Cardenas projects that humanoid robots will become common in homes by 2035, anticipating rapid adoption and acceleration in the field. He believes the technology has reached a critical point where widespread integration into domestic life is imminent, though the exact pace of adoption remains a subject of discussion.