It’s an exciting time in robotics.
In 2025, we’ve seen an accelerating trend of robotics companies spinning up all around the world. But while their hardware is sophisticated, the intelligence software still remains primitive, lagging at least 10 years behind. Intelligence is the bottleneck preventing robots from becoming useful. The leading labs have been forced to pivot their plans from shipping robots with autonomy, to relying on tele-operation while they gather more data.
I believe the problem is not with a lack of data or compute, but with the underlying architecture. Current state-of-the-art robots can only perform narrowly-scoped tasks in a controlled environment — such as folding shirts or operating a coffee machine. And that’s with intense training on millions of data points for each of those tasks. These trade show demos are cool to watch for a robot nerd like me, but the progress is too slow for the industry, and severely limits what today’s hardware is capable of. I think the potential can be much greater.
We know that humans don’t learn the same way as AI. We build on existing knowledge, and adapt quickly to learn new things. By contrast, today’s AI models are static and fully connected — every neuron is trained at once, and every neuron fires for every task. This is fundamentally different from how our brains work. What if machines could learn more like humans?
In 2026, I’m starting an intelligence research lab, called i10e (a numeronym for “intelligence”). Our mission is to build a new architecture for intelligence, more like the brain, which will enable robots to understand the world, and learn from experience.
If successful, we’ll unlock the full potential of robotics and opportunities beyond. Which will lead to an abundance of economic opportunity across industries, and a new age of intelligence.