As urban dwellers seek out affordable, compact ways to get around cities, they are turning to options like peer-to-peer motorized electric scooters that are even smaller than cars.
Superpedestrian unveiled an industrial-grade e-scooter that will be sold to operators with their own fleets over the next year. By their very nature, scooters can be seen as cheap and toy-like, which makes many models unsuitable for heavy-duty peer-to-peer use. As Superpedestrian CEO Assaf Biderman puts it, "If you look at the business model of the operator, there are major issues there," says Biderman. "Basically, the vehicles that they’re using are ill-equipped for sharing."
Notably, Superpedestrian's industrial e-scooters are built with AI technology, which allows operators to easily detect problems and potentially extend the life of the transportation device.
Shareable AI E-Scooters
Superpedestrian's Motorized Electric Scooters are Built for P2P Use
Trend Themes
1. Compact Urban Transportation - The demand for smaller and more affordable transportation options in cities is driving the trend of compact urban transportation.
2. Peer-to-peer Mobility - The rise of peer-to-peer motorized electric scooters is creating new opportunities for shared personal mobility.
3. AI-integrated Vehicles - The integration of AI technology in vehicles enables advanced features and maintenance capabilities, opening up opportunities for improved user experiences.
Industry Implications
1. Micro-mobility - The micro-mobility industry can leverage the trend of compact urban transportation to develop innovative solutions for efficient and sustainable city commuting.
2. Shared Mobility - Companies in the shared mobility industry can capitalize on the trend of peer-to-peer mobility by providing platforms and services for individuals to share motorized scooters.
3. Artificial Intelligence - The artificial intelligence industry can explore the disruptive innovation opportunities presented by integrating AI technology in vehicles, such as advanced diagnostics and predictive maintenance.