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NVIDIA and Zoox Pave the Way for Autonomous Ride-Hailing

‘The world has never seen a robotics company like this before,’ NVIDIA founder and CEO Jensen Huang said in a fireside chat with Zoox CEO Aicha Evans and Zoox cofounder and CTO Jesse Levinson.
by Jessica Soares

In celebration of Zoox’s 10th anniversary, NVIDIA founder and CEO Jensen Huang recently joined the robotaxi company’s CEO, Aicha Evans, and its cofounder and CTO, Jesse Levinson, to discuss the latest in autonomous vehicle (AV) innovation and experience a ride in the Zoox robotaxi.

In a fireside chat at Zoox’s headquarters in Foster City, Calif., the trio reflected on the two companies’ decade of collaboration. Evans and Levinson highlighted how Zoox pioneered the concept of a robotaxi purpose-built for ride-hailing and created groundbreaking innovations along the way, using NVIDIA technology.

“The world has never seen a robotics company like this before,” said Huang. “Zoox started out solely as a sustainable robotics company that delivers robots into the world as a fleet.”

Since 2014, Zoox has been on a mission to create fully autonomous, bidirectional vehicles purpose-built for ride-hailing services. This sets it apart in an industry largely focused on retrofitting existing cars with self-driving technology.

A decade later, the company is operating its robotaxi, powered by NVIDIA GPUs, on public roads.

Computing at the Core

Zoox robotaxis are, at their core, supercomputers on wheels. They’re built on multiple NVIDIA GPUs dedicated to processing the enormous amounts of data generated in real time by their sensors.

The sensor array includes cameras, lidar, radar, long-wave infrared sensors and microphones. The onboard computing system rapidly processes the raw sensor data collected and fuses it to provide a coherent understanding of the vehicle’s surroundings.

The processed data then flows through a perception engine and prediction module to planning and control systems, enabling the vehicle to navigate complex urban environments safely.

NVIDIA GPUs deliver the immense computing power required for the Zoox robotaxis’ autonomous capabilities and continuous learning from new experiences.

Using Simulation as a Virtual Proving Ground

Key to Zoox’s AV development process is its extensive use of simulation. The company uses NVIDIA GPUs and software tools to run a wide array of simulations, testing its autonomous systems in virtual environments before real-world deployment.

These simulations range from synthetic scenarios to replays of real-world scenarios created using data collected from test vehicles. Zoox uses retrofitted Toyota Highlanders equipped with the same sensor and compute packages as its robotaxis to gather driving data and validate its autonomous technology.

This data is then fed back into simulation environments, where it can be used to create countless variations and replays of scenarios and agent interactions.

Zoox also uses what it calls “adversarial simulations,” carefully crafted scenarios designed to test the limits of the autonomous systems and uncover potential edge cases.

The company’s comprehensive approach to simulation allows it to rapidly iterate and improve its autonomous driving software, bolstering AV safety and performance.

“We’ve been using NVIDIA hardware since the very start,” said Levinson. “It’s a huge part of our simulator, and we rely on NVIDIA GPUs in the vehicle to process everything around us in real time.”

A Neat Way to Seat

Zoox’s robotaxi, with its unique bidirectional design and carriage-style seating, is optimized for autonomous operation and passenger comfort, eliminating traditional concepts of a car’s “front” and “back” and providing equal comfort and safety for all occupants.

“I came to visit you when you were zero years old, and the vision was compelling,” Huang said, reflecting on Zoox’s evolution over the years. “The challenge was incredible. The technology, the talent — it is all world-class.”

Using NVIDIA GPUs and tools, Zoox is poised to redefine urban mobility, pioneering a future of safe, efficient and sustainable autonomous transportation for all.

From Testing Miles to Market Projections

As the AV industry gains momentum, recent projections highlight the potential for explosive growth in the robotaxi market. Guidehouse Insights forecasts over 5 million robotaxi deployments by 2030, with numbers expected to surge to almost 34 million by 2035.

The regulatory landscape reflects this progress, with 38 companies currently holding valid permits to test AVs with safety drivers in California. Zoox is currently one of only six companies permitted to test AVs without safety drivers in the state.

As the industry advances, Zoox has created a next-generation robotaxi by combining cutting-edge onboard computing with extensive simulation and development.

In the image at top, NVIDIA founder and CEO Jensen Huang stands with Zoox CEO Aicha Evans and Zoox cofounder and CTO Jesse Levinson in front of a Zoox robotaxi.

NVIDIA GTC 2026: Live Updates on What’s Next in AI

Rolling coverage from San Jose, including NVIDIA CEO Jensen Huang’s keynote, news highlights, live demos and on‑the‑ground color through March 19.
by NVIDIA Writers

How Autonomous AI Agents Become Secure by Design With NVIDIA OpenShell

NVIDIA OpenShell provides tools for controlling autonomous agents in a trusted infrastructure policy layer — adding security in the environment, rather than the model or application layer.
by Ali Golshan

Autonomous agents mark a new inflection point in AI. Systems are no longer limited to generating responses or reasoning through tasks. They can take action: Agents can read files, use tools, write and run code, and execute workflows across enterprise systems, all while expanding their own capabilities. 

Application-layer risk grows exponentially when agents continuously improve and evolve. The NVIDIA OpenShell runtime is being built to address this. 

Part of NVIDIA Agent Toolkit, OpenShell is an open source, secure-by-design runtime for running autonomous agents such as claws. It works by ensuring each agent runs inside its own sandbox, separating application-layer operations from infrastructure-layer policy enforcement.

This means security policies are out of reach of the agent — they’re applied at the system level. Instead of relying on behavioral prompts, OpenShell enforces constraints on the environment the agent runs in — meaning the agent cannot override policies, or leak credentials or private data, even if compromised. 

With OpenShell, enterprises can separate agent behavior, policy definition and policy enforcement. Organizations gain a single, unified policy layer to define and monitor how autonomous systems operate. Coding agents, research assistants and agentic workflows all run under the same runtime policies regardless of host operating system, simplifying compliance and operational oversight.

This is the “browser tab” model applied to agents: Sessions are isolated, resources are controlled and permissions are verified by the runtime before any action takes place.

Securing autonomous systems requires an integrated ecosystem. OpenShell is designed to add privacy and security controls for AI agents. NVIDIA is collaborating with security partners, including Cisco, CrowdStrike, Google Cloud, Microsoft Security and TrendAI, to align runtime policy management and enforcement for agents across the enterprise stack. 

OpenShell Provides an Enterprise-Grade Sandbox for Building Personal AI Assistants

NVIDIA NemoClaw is an open source reference stack that simplifies installing OpenClaw always-on assistants with the OpenShell runtime and NVIDIA Nemotron models in a single command. 

NemoClaw provides enthusiasts with an open reference for building self-evolving personal AI agents, or claws. Since security needs vary, NemoClaw provides a reference example for policy-based privacy and security guardrails to give users more control over their agents’ behavior and data-handling. Users can customize it for their specific use cases — much like adjusting security preferences for applications on a phone. 

NemoClaw includes an example configuration of OpenShell that defines how the agent should interact with systems. NemoClaw uses open source models like NVIDIA Nemotron alongside OpenShell. 

This enables self-evolving claws to run more securely in clouds, on premises or on personal computers, including NVIDIA GeForce RTX PCs and laptops or NVIDIA RTX PRO-powered workstations, as well as NVIDIA DGX Station and NVIDIA DGX Spark AI supercomputers.

Both OpenShell and NemoClaw are in early preview. NVIDIA is building in the open with the community and its partners to enable enterprises to scale self-evolving, long-running autonomous agents safely, confidently and in compliance with global security standards.

Get started with NVIDIA OpenShell and launch a ready‑to‑use environment on NVIDIA Brev, or explore the open source project on GitHub.