We invented the wheel thousands of years ago. The circular form is mechanically superior to anything nature has produced for locomotion. Yet, wheels are rare in biology; hardly any animals have evolved wheel-like appendages, despite their obvious advantages in efficiency.

Here’s what’s fascinating: we became so committed to the wheel that we reshaped the entire world around it. When our wheeled vehicles couldn’t reach certain terrains, we didn’t abandon wheels. Instead, we built roads, carved tunnels through mountains, and engineered hairpin curves to make the inaccessible accessible. We transformed the environment to suit our superior mechanical solution.

Nature works the opposite way. Evolution doesn’t create an optimal design and then modify the environment to support it. Instead, it develops solutions that fit seamlessly into existing conditions. Legs navigate rocky terrain, wings exploit air currents, fins move through water—each perfectly adapted to their context, not mechanically superior in isolation but environmentally harmonious.

This fundamental difference in approach matters enormously as we design AI systems and robotics for our world.

We’re at a crossroads. Should we follow the wheel paradigm—create powerful AI systems and then restructure our environment to accommodate them? Or should we follow nature’s approach—build AI that adapts to the messy, imperfect world we already inhabit?

The wheel paradigm in AI is already underway. We digitise documents so machines can read them. We standardise data formats for algorithms to process efficiently. We redesign interfaces specifically for automated systems. Like building roads for wheels, we’re creating digital infrastructure that makes AI more effective.

The adaptive approach would build AI that works within our existing reality. Systems that read handwritten notes, understand natural speech patterns, navigate cluttered spaces—prioritising contextual fit over peak performance.

Both paths have compelling logic. The wheel paradigm often delivers superior results and can be cost-effective at scale. Consider how structured data revolutionised search and recommendations—sometimes reshaping the environment unlocks tremendous value.

But the adaptive approach preserves human-centred environments and reduces implementation friction. Not every context should be restructured for machines. Healthcare, education, creative work—these domains often benefit more from AI that fits into existing workflows than from wholesale environmental redesign.

The tension isn’t just technical—it’s philosophical. Do we want to live in a world optimised for machines, or do we want machines optimised for our world?

In practice, we’ll likely need both approaches thoughtfully applied. Critical infrastructure and high-volume processes may warrant environmental modifications. Human-centred applications should prioritise adaptive AI.

The question isn’t whether the wheel was a good invention—it clearly was. The question is whether every problem requires us to build more roads, or whether sometimes we need solutions that can navigate the terrain as it exists.

This choice will define not just our technology’s capabilities, but the very nature of the world we inhabit.