Tesla Cybercab Improvements Are Already on the Minds of Company Engineers
Tesla sent a detailed rider experience survey to Cybercab passengers only days after paid rides began in Austin.
Tesla is actively soliciting feedback from Cybercab passengers to refine the vehicleβs interior and user experience, just days after the autonomous taxi entered public service in Austin.
What Happened
According to reports, Tesla distributed a rider experience survey to Cybercab passengers shortly after paid rides commenced. The questionnaire gauges overall satisfaction and requests star ratings for specific metrics, including availability, wait times, door functionality, the vehicle touchscreen, the mobile app experience, seat comfort, interior space, ride comfort, cleanliness, and cargo space.
The survey also asks riders to select up to three desired features from a list that includes heated seats, ventilated seats, fully reclining seats, a tray table, a wireless phone charger, a better sound system, and additional storage. Participants can also write in custom suggestions or opt for no changes, before providing a final recommendation score from zero to ten.
Why It Matters
This rapid collection of structured data demonstrates Teslaβs strategy of treating early users as collaborators rather than passive customers. By asking for input immediately after launch, the company signals an openness to cabin revisions and feature additions even after the first vehicles have reached public streets.
Early online responses suggest a preference for comfort upgrades such as reclining seats, tray tables, and wireless charging. Because the Cybercab is designed with software-first principles, these requested amenities could potentially be implemented in later production batches or through cabin revisions more quickly than in traditional automotive development cycles.
The Bottom Line
Teslaβs immediate feedback loop for the Cybercab suggests that near-term improvements to the robotaxiβs interior and user experience are likely as the engineering team iterates based on real-world rider data.



