"While viral videos of robots performing parkour and backflips dominate social media feeds, industry insiders suggest these acrobatic feats are misleading indicators of progress. Industry executives at the Fortune Brainstorm AI conference, held in early December in San Francisco, argued that the true revolution in robotics is not physical agility, but the ability for robots to "think" for themselves-a capability that is finally bringing them closer to conquering the mundane, yet deceptively difficult, task of, say, opening a door or climbing a set of stairs."
"For the past 70 years, robotics relied on a specific paradigm: intelligent humans pre-programming machines with complex mathematics to execute specific tasks. This approach is now obsolete, argued Sequoia Capital partner Stephanie Zhan and Skild AI CEO Deepak Pathak, in conversation with Fortune's Allie Garfinkle. The industry is undergoing a massive shift where robots, much like the Large Language Models (LLMs) behind tools like ChatGPT, are learning directly from data and experience rather than following rigid code."
Robotics is shifting from a human-programmed, mathematics-driven paradigm toward data-driven models that learn from experience. Robots increasingly use computer vision and deep learning to build foundation models that generalize across tasks rather than follow rigid code. This transition parallels the rise of Large Language Models, applying large-scale datasets and learning to physical agents. Practical progress focuses on enabling robots to perform everyday, deceptively difficult tasks such as opening doors and climbing stairs, rather than achieving spectacular acrobatics. Foundational models trained on diverse data aim to reduce task-specific engineering and improve robots' ability to adapt to varied real-world scenarios.
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