On Robotics

I was recently interviewed at Embedded World about the evolution of mobile computing. While I largely focused on power efficiency and on-device ML processing, I found myself drawn to a parallel technology gaining momentum - Embodied Robotics.

While language models have captured our attention (definitely mine!) by transforming knowledge work, robotics is undergoing its own evolution. We’re moving beyond systems with fixed, programmed movements toward machines that genuinely understand & interact with the physical world in intelligent and adaptive ways.

What I love about this journey is how these paths converge. LLMs offer remarkable language & reasoning capabilities but remain fundamentally disembodied. But true intelligence requires physical grounding-just as children learn about gravity not through equations but by dropping stuff, and understand fluids by spilling them. This embodied interaction shapes cognition in ways that pure computation simply cannot replicate.

As I noted during my conversation with Parv & Taimur, these robotics systems will increasingly appear in national GDP calculations as capital expenditures. While creatives navigate the transformation brought by genAI, embodied robotics will reshape physically demanding sectors -agriculture, mining, manufacturing, environmental cleanup- fundamentally altering humanity’s relationship with labour.

Later this year, I’ll be publishing a paper examining how the integration of physical capabilities, perception, learning mechanisms, and social awareness creates emergent properties greater than the sum of these components. This integration is essential for crossing what Rodney Brooks called “the reality gap” (back in 1995!)-the chasm between controlled simulations and our gloriously unpredictable world.

The robots at EW, precise but constrained to predetermined tasks, represent merely the beginning of our journey. The true breakthroughs will emerge as systems develop genuine physical understanding, transitioning from executing (pre-)programmed routines to comprehending and reacting appropriately to the consequences of their actions in complex, dynamic environments. Similarly, today’s LLMs represent foundational building blocks for far more capable systems to come - a continued journey to shape AI.

This revolution demands that we directly confront tough questions about ethics, economics, and human purpose. The disruption of physical labour will necessitate serious consideration of universal basic income & deep changes to social policies. More critically, when autonomous machines act in our physical world, aligning their operation with human values becomes exponentially more important than with digital-only systems. We risk too much otherwise.

How we navigate this transition will define the next century of human experience, as well as our capacity to address existential challenges like climate adaptation. A subject for next time.

V