Can we trust AI with real-world tasks?

Tech leaders are meeting at the TechCrunch Disrupt 2026 conference in San Francisco from October 13 to 15 to address these challenges. The event will host sessions from industry experts, including representatives from companies like Anthropic, Nvidia, and Hello Robot. The core focus is on the transition from experimental projects to actual, reliable systems. Founders and engineers will discuss the security risks of AI agents, which are automated systems that can take independent actions on a user's behalf, and the difficulty of getting robots to function safely in human-populated spaces.

To understand the difficulty here, consider the difference between a prototype and a product. A prototype is a demo created to show that a machine can complete a specific, controlled task. It is like an intern who can follow instructions perfectly in a classroom setting. However, when you put that same machine into a real-world setting, the environment becomes chaotic and unpredictable. A robot helping someone with disabilities, for instance, cannot just be smart; it must be physically safe and consistent every single time. It needs to perceive the world through sensors and react correctly to unexpected obstacles without requiring a person to step in. Currently, many AI systems struggle with this because they lack enough high-quality data from the physical world—data that shows them how to navigate messy, non-digital environments. Building these systems requires a safety culture where testing is not an afterthought, but the foundation of the entire design process.

The gap between a successful experiment and a reliable, safe product is what defines the next stage of the AI industry. If an AI makes a mistake in a chat app, it might be annoying or wrong. If it makes a mistake while controlling an industrial robot or a piece of infrastructure, the consequences are physical and potentially dangerous. For regular people, this means that the future of AI isn't just about it getting smarter or more talkative; it is about it becoming boringly reliable. Earning the public's trust will ultimately depend on whether developers can prove their machines can operate safely, not just during a perfect, choreographed demonstration, but in the middle of our unpredictable daily lives.