The last year has seen incredible progress in model capabilities in certain domains — most notably programming, mathematics, and cybersecurity. Yet the real-world impact of these breakthroughs has been surprisingly modest — especially outside of those professions.

Lawyers, architects, salespeople, college professors, and others are certainly using AI models in their work, but AI models don’t seem close to transforming those professions the way they seem to be transforming mathematics and computer programming. And even in the software industry, we’re not seeing the kind of mass unemployment many people expected we would see once AI models reached human capabilities.

A big question is why — and whether the impact might grow in the next few months or years. Many people expect massive AI-driven disruptions to begin hitting other professions in 2028 or even 2027.

I don’t think that’s going to happen, and a concept from robotics helps to explain why: the sim-to-real gap. I encountered this concept when I was editing my colleague Kai Williams’s excellent September article about the scramble to collect robotics training data.

Suppose you want to train a robot to walk. One option is to create a virtual environment with a simulated robot. Then use reinforcement learning to reward behaviors that help the robot move around. The robotics startup Figure used this strategy last year and it worked! Figure said that it collected “years worth of simulated demonstrations in a few hours.” When they transferred the trained model to a real robot, the robot was able to walk “with no tweaks or changes.”