Mind Robotics Hires Doug Field to Make AI Factory Robots Production-Ready
Mind Robotics has appointed Doug Field as president, the company announced on October 13, 2026. The appointment was disclosed on stage at Disrupt 2026. TechCrunch
Mind Robotics is an industrial AI and robotics venture that spun out of Rivian in 2025. It was founded by Rivian CEO RJ Scaringe. Scaringe remains involved with the venture, while Field takes responsibility for day-to-day product, engineering and operations.
Rivian reported owning about a third of Mind Robotics as of June 30, 2026. It reported the same stake in Also, its other spinout. That structure leaves Mind Robotics independent but closely tied to its source company.
The technical plan is narrow and production-oriented. Mind Robotics is building purpose-built robots controlled by foundation models, large AI models trained on broad data that can be adapted to specific jobs, to automate industrial and manufacturing tasks at scale. Rivian is its first customer. Its plants serve as a live, high-volume manufacturing environment for model training and deployment.
That customer relationship shapes the engineering. Foundation-model robotics improves with task diversity, unusual edge cases and fast iteration. A captive, high-volume line provides continuous operational data and a place to test system updates under production constraints such as cycle times and safety interlocks, built-in stops that protect people and equipment. It is a different path from lab demos or limited pilots.
The venture is capitalized to pursue that path. Mind Robotics has amassed more than $1 billion in backing. Its backers include Accel, Andreessen Horowitz, Eclipse, Kleiner Perkins and the venture arms of Volkswagen and Salesforce. TechCrunch
Field arrives with experience across hardware scale-up and platform engineering. He left Ford in April after five years shaping the automaker's electric vehicle and technology strategies. Prior to Ford, he led Apple's Project Titan electric, autonomous car project, which was canceled in 2024. Earlier, he served as senior vice president of engineering at Tesla, where he oversaw development of the Model 3.
Looking at what this hire says about execution, productization is the gating problem. The models, actuators, the motors and joints that move a robot, and sensing stacks, the cameras and sensors it uses to understand its surroundings, get most of the attention. The harder work is integration into existing manufacturing systems, reliability engineering, serviceability and change management on the floor. Field has spent his career at that interface, where prototypes meet tooling, supply chain and throughput targets.
The broader context here is the shift from general-purpose humanoid promises to purpose-built industrial systems. Mind Robotics is not pitching a universal worker. It is targeting defined industrial and manufacturing tasks, with a launch customer that can absorb early iterations. In my view, that constraint is a strength. It bounds the operational design domain, the limited set of conditions the robot must handle, simplifies validation, and lets the team measure success in uptime and yield rather than demo complexity.
Worth flagging for practitioners is the software architecture implied by the description. Purpose-built robots running on foundation models suggests a common model backbone, like a shared operating system with different apps for each job, with task-specific policies, tooling and safety layers deployed across different arms and mobile platforms. If Mind Robotics can maintain that commonality while meeting automotive-grade availability, it has a template that ports to other industrial customers without a full re-engineering effort.
The long arc favors this kind of automation, even if adoption is uneven. Manufacturing has absorbed successive waves of programmable control, vision systems and mobile robots, each time by starting with dull, dirty and ergonomically costly tasks. Foundation models could extend that reach to higher-variability work that was previously too brittle to automate. Having a scaled factory as a proving ground should accelerate that learning curve.
