The growing challenge of electronic waste, driven by complex devices and lack of accessible repair knowledge, creates a need for solutions that empower users to fix their own electronics. Many people discard devices prematurely due to limited repair options, resulting in higher costs for consumers and environmental harm. Repair cafés and DIY communities show demand for accessible repair guidance, but their impact is constrained by geographic and logistical limitations. A tool that combines AI with repair expertise could bridge this gap by offering personalized, step-by-step assistance to anyone with a broken device.

How an AI Repair Assistant Could Work

One way to tackle this problem is with an AI-driven guide that makes repair knowledge more accessible. Users could interact with the tool via an app or website, describing their issue (e.g., "My phone won't charge" or "Laptop screen is flickering"). The AI would then provide customized instructions—drawing from repair manuals, video tutorials, and crowdsourced tips—and could even analyze photos or live video to help identify parts or diagnose problems. For example, pointing the camera at a disassembled device might prompt the AI to highlight the next screw to remove. If users get stuck, the system could suggest replacement parts, nearby repair cafés, or escalate to a human expert for real-time troubleshooting.

Who Stands to Benefit

• Everyday users: Those looking to save money, reduce waste, or learn repair skills would get an on-demand guide tailored to their device and skill level.
• Repair cafés and small businesses: These could use the tool to assist more customers without requiring additional staff or locations.
• Part suppliers and toolmakers: Integrated recommendations could help them reach customers needing specific components.

Key Considerations for Execution

A lightweight version could start with smartphone repairs, using existing manuals and videos to train the AI. Over time, it could expand to other devices, incorporate crowdsourced fixes, and integrate with repair marketplaces. Open-source communities and "right to repair" advocates could help counter potential resistance from manufacturers. Revenue might come from partnerships with part suppliers, premium support features, or licensing the AI to repair businesses.

Such a tool could shift how people approach broken electronics—from frustration and disposal to empowerment and reuse. By combining existing repair knowledge with adaptive AI assistance, it might reduce waste while making technical skills more accessible to all.