Peter Smith
⬤ The AI robotics sector is hitting unexpected roadblocks despite impressive hardware advances. While humanoid and industrial robots keep getting better mechanically, the software side tells a different story. Development teams are stuck babysitting machines that overheat, break down, or need constant monitoring. Unlike humans who can bounce back from mistakes, robots risk permanent damage with every error. One researcher put it bluntly: patience was the only thing that actually scaled in daily operations, highlighting just how much hands-on maintenance these systems still demand.
⬤ The industry is also dealing with what some call "an epic disaster" in benchmarking. There's no agreement on which hardware to test, what tasks to measure, how to score results, or whether to use simulations versus real-world setups. This Wild West situation means companies can cherry-pick their best demos without proving consistent performance. Unlike established AI fields that use standardized evaluation methods, robotics lacks the scientific discipline needed as it heads into 2026.
⬤ Current robot training relies heavily on "vision-language-action" models borrowed from AI systems built for answering questions about images. The problem? Most of these models focus on language and general knowledge rather than physics and movement. Their visual processing also throws away the fine details that matter when a robot needs to grip something delicate or manipulate objects precisely. Simply making these models bigger won't automatically improve performance, suggesting the field may need entirely different foundations — possibly video-based systems that better understand how the physical world actually works.
⬤ The robotics market remains experimental despite growing investment and hype around AI integration. Hardware has raced ahead while software struggles to keep up, and genuine commercial success will require better testing standards and training methods that match real-world conditions. How quickly the industry solves these fundamental issues will determine whether autonomous robots move beyond flashy demos into everyday use.
Peter Smith
