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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.
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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 AI Factory Compute Is Becoming an Investable Asset Class

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We announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time.

This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue.

AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world’s most productive assets. In AI, compute is revenue.

A New Infrastructure Asset

NVIDIA compute is not just a chip. It is a complete AI factory platform including accelerated computing, networking, systems software, AI frameworks and a global developer ecosystem.

NVIDIA DSX AI factories can run the world’s broadest range of AI models, modalities and algorithms — language, vision, speech, biology, physical AI and robotics. One NVIDIA AI factory can serve many customers and many workloads. That makes it flexible and fungible.

It is also built on a globally adopted architecture used across every major cloud, and by systems makers and enterprises around the world. When needs change, the factory can be used by another customer, another cloud or another operator. This broad ecosystem gives NVIDIA compute a deep market of potential users and offtakers, helping protect residual value.

CUDA makes the factory better over time. Every generation of NVIDIA software improves the performance, efficiency and total cost of ownership of already- installed infrastructure. The hardware does not stand still: software innovation allows an AI factory to produce more intelligence at lower cost throughout its life, extending its useful economic value.

NVIDIA A100 is a powerful example. NVIDIA introduced the Ampere-based A100 in 2020, and six years later, it remains in active commercial use for AI training, fine-tuning, inference and high-performance computing. Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.

The market is also demonstrating the durability of NVIDIA compute economics. One-year H100 rental pricing rose from about $1.70 per GPU-hour in October 2025 to about $2.35 per GPU-hour in March 2026. Cross-provider on-demand median pricing rose from roughly $2.00 per GPU-hour in October 2025 to $2.70 in June 2026. Blackwell capacity commands a premium, with reported B200 cloud rates spanning approximately $5.30 to $7.05 per GPU-hour.

That is what makes NVIDIA AI factories different. Their value is not fixed at installation: CUDA continuously improves their output; the installed base remains productive well beyond its initial depreciation period; and the same standard architecture serves a deep, growing global market of AI workloads.

These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed.

Bringing Capital to AI Factories

The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly.

That is why we are partnering with the world’s leading long-term capital providers.

Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world’s leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs.

The platforms are designed to help qualified AI labs, enterprises and AI clouds access AI-factory infrastructure at scale. The more than $500 billion figure represents aggregate third-party capital that these platforms are designed to mobilize over time — the capital is not NVIDIA revenue, a single fund or a commitment to a single customer.

The financial institutions will independently assess each opportunity — the customer, demand, utilization, cash flow and residual value. NVIDIA provides the AI factory platform. The financial institutions provide long-term capital and financing expertise.

The Important Questions

Is this circular financing?

This initiative is designed to address that concern. We are bringing independent, long-term institutional capital into the AI infrastructure market.

The demand is real: it comes from frontier AI labs, AI-native startups, enterprises, cloud providers and countries building AI services. The capital providers independently underwrite each project — including the customer, demand, utilization, cash flow and residual value. NVIDIA provides the platform; the investors make independent financing decisions.

This is the beginning of an open capital market for AI infrastructure.

Why would NVIDIA support financing?

In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.

This is substantially lower than other compute-financing arrangements. NVIDIA can provide support because NVIDIA compute is unique: it is fungible, universally adopted, software-upgradable and redeployable across a large ecosystem of customers.

Our role is to help unlock a very large pool of independent capital while maintaining disciplined risk exposure.

Can the market absorb this capacity?

The question is not whether we are building data centers. The question is whether we are building productive AI factories.

An AI factory turns energy and data into valuable intelligence. Its customers are broad: frontier AI labs, AI clouds, enterprises and nations. They are building AI because it has become useful — doing valuable work across every industry.

There is discipline in the model. Each financing partner will independently evaluate demand, utilization, cash flow and residual value. Capacity will be built around real customer economics.

Where is the return on investment?

The return is in the usefulness of AI.

Companies are using AI to write software, discover drugs, design products, serve customers, automate operations and build new services. AI factories make this possible. More compute creates better AI; better AI creates more usage; more usage creates more revenue; and more revenue drives more compute.

This is the virtuous cycle of the AI industrial revolution.

The Infrastructure of Intelligence

Every industrial revolution has been built on infrastructure: electricity, transportation, communications and computing, with every buildout enabled by external financing.

AI factories are the infrastructure of the intelligence era.

With these partnerships, NVIDIA and the world’s leading financial institutions are creating a new way to finance the infrastructure that will power this industrial revolution. We will make AI factories more accessible to the companies, industries and nations building the future.

The age of AI is here. Together, we will build the infrastructure to power it.

NVIDIA Joins NSF State and Regional AI Hubs Program to Expand AI Research and Education Across the US

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NVIDIA is participating in the U.S. National Science Foundation’s (NSF) State and Regional Artificial Intelligence Infrastructure Hubs program, an effort launching today to expand access to the advanced computing, data, software and expertise needed for AI-enabled research and education.

Consistent with the aims of the Genesis Mission, the program will support state and multistate groups of colleges and universities working together to strengthen America’s AI ecosystem. In partnership with private industry, philanthropic organizations and state and local governments, the program will expand the AI infrastructure, software, educational resources and technical support needed by faculty, students and researchers across the country. 

These regional hubs will help institutions share AI computing resources, accelerate scientific discovery and innovation, and prepare students to participate in the AI economy.

Expanding Access to AI Infrastructure

The State and Regional AI Infrastructure Hubs program will bring shared resources closer to the institutions and communities they serve. 

State or regional consortia can pool expertise, focus on specific local priorities, achieve economies of scale and create pathways for institutions that might otherwise remain outside the frontier of AI-enabled research and education. Flexible approaches — including on-premises infrastructure, cloud computing or a combination — will allow consortia to design resources around their regional needs and economic priorities. 

The hubs will resemble the public-private partnership between NVIDIA, NVIDIA cofounder Chris Malachowsky and the University of Florida (UF) in 2020 to turn UF into the country’s first true AI university and provide AI compute access to all Florida public universities. That initiative now serves as a national model. Since launching its university-wide initiative in 2020, UF has grown to more than 300 AI-focused faculty and embedded AI education and research across all 16 colleges. And since 2017, UF faculty and units have received more than $511 million in AI research awards.

NVIDIA has also expanded academic compute access in other ways, including as a leading contributor to the NSF-led National Artificial Intelligence Research Resource (NAIRR) pilot program on which today’s announcement is built. 

Through NAIRR, NVIDIA partnered with university research teams across the country to turn computing resources into usable scientific capacity — giving researchers the infrastructure, tools and expertise needed to move from idea to experiment to discovery. The resources also facilitated meaningful educational opportunities that gave students critical real-world skills for the AI economy. 

Preparing the AI Workforce

AI infrastructure alone is not enough. A successful national AI strategy must include efforts to build a workforce that can use advanced computing, data resources and AI tools in real scientific and industry settings.

That means pairing infrastructure with clear learning pathways. Universities, community colleges and regional partners can build degree programs, short-form certificates and stackable credentials that help learners move from foundational AI literacy into applied skills. Those pathways will help students, faculty, working adults and technical professionals use AI, including open source models and technologies, in fields like physical AI and automation, healthcare, energy, agriculture, manufacturing, quantum computing and cybersecurity. 

NVIDIA can support this work by providing training resources, educator enablement, applied learning content, technical guidance, partner platforms and access to tools that help institutions move from awareness to hands-on capability. As NVIDIA’s education and training offerings evolve, the goal remains the same: help institutions build repeatable, openly available programs that prepare learners to use AI systems, accelerated computing and data workflows responsibly and effectively.

This is how regional hubs become more than infrastructure projects. Students gain practical experience. Faculty expand their ability to teach and apply AI across disciplines. Working professionals can earn new skills without leaving the workforce. And institutions can connect training to local employer needs, research priorities and the economic opportunities that matter most to their communities.

Connecting Research, Workforce and Regional Growth

For policymakers and leaders, the hubs offer an opportunity to connect regional research and educational infrastructure with regional priorities and broader workforce and economic-development strategies.

Institutions can cultivate talent for local needs, support research connected to regional industries and build stronger relationships among universities, community colleges, employers and government. These connections will help translate AI leadership into scientific progress, new businesses and high-quality jobs.

No single organization can build this capacity alone. Sustained collaboration among government, higher education, philanthropic organizations and private industry is essential to ensure that advanced AI resources are broadly available and effectively used.

Private-sector contributions can complement public investment with technology, implementation expertise and workforce development programs. Public institutions, in turn, can help direct those capabilities toward scientific, educational and economic priorities that advance regional needs and national interest.

Learn more about the NSF State and Regional AI Infrastructure Hubs program and how NVIDIA is helping expand access to AI research and education.