Autonomous Delivery

DoorDash shares plummeted 17% after announcing significant investments in autonomous delivery, a new tech stack, and acquisitions like SevenRooms and Deliveroo. While revenue beat expectations, earnings fell short, and Q4 adjusted EBITDA guidance was below consensus. Investors are wary of the aggressive spending impacting near-term profitability, despite CEO Tony Xu’s confidence in long-term growth. Analysts acknowledge the operational strength and potential long-term investor support, but caution that patience and consistent disclosure are needed.

DoorDash (DASH) Q3 revenue beat estimates, but earnings missed due to planned 2026 spending, causing a 9% stock drop. Revenue grew 27%, with total orders surging to 776 million. Investment focuses on a new global tech platform and automation, including the “Dot” robot. DoorDash projects Q4 Adjusted EBITDA below analysts’ expectations. The recent Deliveroo acquisition for $3.9 billion marks a significant international expansion, with anticipated contribution to EBITDA in Q4 and 2026.

DoorDash has unveiled “Dot,” an autonomous delivery robot designed to revolutionize last-mile logistics. Dot travels up to 20 mph carrying 30-pound payloads. Currently being tested in Phoenix, Arizona, Dot uses eight cameras and LiDAR to navigate complex environments. DoorDash is also introducing a “smart scale” to improve order accuracy. This move signifies DoorDash’s commitment to automation and enhancing customer experience in the competitive delivery market.

A video shows an SF Express self-driving delivery vehicle driving through peanuts spread on a road for drying in China. The vehicle, while issuing a polite automated message, continued its route despite the obstruction and the woman’s frustration. The incident sparked online debate about the limitations of current autonomous vehicle programming in handling unforeseen real-world scenarios. It highlights the need for AI to balance strict adherence to rules with contextual awareness and the complexity of enabling AV’s to differentiate between obstacles and unusual roadside activities.