Ken Goldberg's company, Ambi Robotics, successfully uses simple suction cups for logistics. He argues that the industry's focus on human-like hands is misplaced, as simpler grippers are more practical, reliable, and capable of performing immensely complex tasks today.

While consumer robots are flashy, the real robotics revolution will start in manufacturing. Specialized B2B robots offer immediate, massive ROI for companies that can afford them. The winner will be the company that addresses factories first and then adapts that technology for the home, not the other way around.

The current excitement for consumer humanoid robots mirrors the premature hype cycle of VR in the early 2010s. Robotics experts argue that practical, revenue-generating applications are not in the home but in specific industrial settings like warehouses and factories, where the technology is already commercially viable.

The most complex challenge in robotics isn't just hardware or software alone, but the "boring" problem of calibration where they meet. Seemingly minor physical misalignments create cascading, hard-to-diagnose software issues that require deep, cross-functional expertise to solve.

The robotics field has a scalable recipe for AI-driven manipulation (like GPT), but hasn't yet scaled it into a polished, mass-market consumer product (like ChatGPT). The current phase focuses on scaling data and refining systems, not just fundamental algorithm discovery, to bridge this gap.

Despite testing with countless objects, Ambi Robotics discovered their system struggled with a common item they hadn't prioritized: plastic shipping bags. Bags fold and lose suction unpredictably, highlighting how real-world deployment uncovers critical edge cases that extensive lab testing misses.

Self-driving cars, a 20-year journey so far, are relatively simple robots: metal boxes on 2D surfaces designed *not* to touch things. General-purpose robots operate in complex 3D environments with the primary goal of *touching* and manipulating objects. This highlights the immense, often underestimated, physical and algorithmic challenges facing robotics.

General-purpose robotics lacks standardized interfaces between hardware, data, and AI. This makes a full-stack, in-house approach essential because the definition of 'good' for each component is constantly co-evolving. Partnering is difficult when your standard of quality is a moving target.

Classical robots required expensive, rigid, and precise hardware because they were blind. Modern AI perception acts as 'eyes', allowing robots to correct for inaccuracies in real-time. This enables the use of cheaper, compliant, and inherently safer mechanical components, fundamentally changing hardware design philosophy.