
Rivian Automotive, Inc. (RIVN) Discusses AI-Centric Approach to Vehicle Autonomy and Technology Roadmap December 11, 2025 12:00 PM EST
Company Participants
Robert Scaringe - Founder, CEO & Chairman of the Board Vidya Rajagopalan - Senior Vice President of Electrical Hardware James Philbin Wassym Bensaid - Chief Software Officer
Presentation
Operator
Please welcome RJ Scaringe, Founder and Chief Executive Officer of Rivian.
Robert Scaringe Founder, CEO & Chairman of the Board
We are incredibly excited to host everybody here today. We are in Palo Alto, which is the hub for Autonomy and our technology development te…

Rivian Automotive, Inc. (RIVN) Discusses AI-Centric Approach to Vehicle Autonomy and Technology Roadmap December 11, 2025 12:00 PM EST
Company Participants
Robert Scaringe - Founder, CEO & Chairman of the Board Vidya Rajagopalan - Senior Vice President of Electrical Hardware James Philbin Wassym Bensaid - Chief Software Officer
Presentation
Operator
Please welcome RJ Scaringe, Founder and Chief Executive Officer of Rivian.
Robert Scaringe Founder, CEO & Chairman of the Board
We are incredibly excited to host everybody here today. We are in Palo Alto, which is the hub for Autonomy and our technology development teams. AI is enabling us to create technology and customer experiences at a rate that is completely different from what we’ve seen in the past.
If we look forward 3 or 4 years into the future, the rate of change is an order of magnitude greater than what we’ve experienced in the last 3 or 4 years. Directly controlling our network architecture and our software platforms in our vehicles has, of course, created an opportunity for us to deliver amazingly rich software. But perhaps even more importantly, this is the foundation of enabling AI across our vehicles and our business.
I’d like to talk about Autonomy first. The field of autonomy really started about 20 years ago. And about, let’s say, up until the early 2020s, the approach was centered on a rules-based environment where a set of perception sensors would identify and classify objects and hand those classified and vector-associated objects to a planner that was built around a human-defined rules-based framework.
A few years ago, it became clear the approach to autonomy needed to shift. With innovations around transformer-based encoding and the design of large parameter models, the approach has moved to building a neural net-like understanding of how to drive instead of following a classical rules-based approach. Recognizing this