GTC 2026 commenced last night at Chinese time, with tech enthusiasts and investors worldwide staying up late to catch Jensen Huang’s keynote speech. As expected, the CEO of NVIDIA once again made waves in the tech and automotive sectors with his groundbreaking announcements.

Today’s GTC is far more than just NVIDIA’s “chip showcase.” Instead, Jensen Huang, in the role of a visionary, painted a grand picture of AI entering its “second act”—evolving from mere digital content generation to fully functional intelligent agents (Agentic AI) and ultimately achieving seamless integration into the physical world, including automobiles and robots.

For professionals in the automotive and intelligent mobility sectors, this is not merely a showcase of cutting-edge technologies but a crucial roadmap that could determine their future success or failure.

While numerous analyses of Jensen Huang’s keynote have emerged from various perspectives, few have delved into the implications of Physical AI in automobiles and embodied intelligent robots. Therefore, this article will dissect NVIDIA’s latest products and technical details from the automotive industry’s vantage point, exploring how they empower the sector and what the future holds for automotive innovation.

1. Background and Trends: AI’s “Inference Inflection Point” Has Arrived, with Computing Demand Skyrocketing

To comprehend NVIDIA’s new offerings, one must first grasp the underlying logic of current AI evolution. Jensen Huang highlighted three major leaps in AI over the past two years:

Generative AI: Led by ChatGPT, AI can now perceive and generate content, shifting computing models from “retrieval-based” to fully “generative.”

Reasoning AI: Represented by the o1 model, AI has begun to reflect, plan, and reason based on factual data.

Agentic AI: exemplified by Claude Code, AI can now read documents, write code, utilize tools, and autonomously execute tasks.

As AI transitions from “perception” to “execution,” it constantly performs inference. Based on this logic, Huang declared: “The inflection point for inference has arrived.” With longer input contexts and a surge in output tokens, AI computing demand has skyrocketed nearly a millionfold in the past two years.

Well, this is certainly a bold marketing move. Jensen Huang stated that by 2027, the global demand for AI computing infrastructure will exceed $1 trillion. The tech industry and investments can revolve around this AI computing infrastructure, with NVIDIA at the epicenter.

2. Game-Changing Arsenal: NVIDIA’s New Generation of “Powerhouse Tools”

Facing massive inference demands, NVIDIA has expanded beyond selling chips to offering a complete suite of “AI factory”-grade hardware and software systems.

At GTC 2026, NVIDIA showcased the following products:

Vera Rubin Supercomputing Platform: Tailored for Intelligent Agents This is a cutting-edge architecture designed specifically for Agentic AI. The Vera CPU, the world’s only data center CPU using LPDDR5, delivers extremely high single-thread performance and perfectly meets the rapid demands of AI agents frequently calling tools (such as browsers and virtual PCs).

Combined with NVLink 72 networking, the Vera Rubin platform achieves astonishing performance leaps, generating 35 times more tokens per watt than the previous generation.

Integration of Groq Technology: Pioneering “Decoupled Inference” To overcome the physical contradiction between high throughput and ultra-low latency, NVIDIA introduced “decoupled inference” through its Dynamo software system. The Vera Rubin supercomputing platform handles massive contexts (KV Cache) and complex attention mechanism calculations.

Another new chip, the LPU (Language Processing Unit), Groq LP30/LP40, leverages massive SRAM for ultra-fast token generation (decoding). This technology comes from the company NVIDIA acquired for $20 billion late last year.

Now, these two distinct chips join forces, delivering unprecedented generation speeds at the core inference level. Could this speed-boosting approach bring hope to true VLA systems desperately needing performance improvements? Perhaps stay tuned for our follow-up analysis at Vehicle.

OpenClaw and NemoClaw: Even a tech giant like NVIDIA follows trends. “Lobster,” the hottest “operating system” for the intelligent agent era, enables AI to autonomously schedule resources, execute code, and call tools. Recently, rumors surfaced about an automaker suffering internal chaos from “lobster farming,” making safe lobster management a corporate necessity. Thus, NVIDIA launched the enterprise-grade secure version, NemoClaw, adding privacy routing and network guardrails to allow agents to securely and compliantly access confidential data within enterprises.

3. Physical AI Explosion: The “ChatGPT Moment” for Autonomous Driving and Embodied Intelligence

While digital intelligent agents on the internet have already captivated audiences, attracting most talent and capital, what truly excites the automotive industry is the full-blown explosion of Physical AI. Jensen Huang officially declared at the conference: “The ChatGPT moment for autonomous driving has arrived!”

Besides the hardware mentioned above, Jensen Huang also prepared various software tools, practically feeding the industry ready-to-use solutions—and of course, reaping the profits:

Alpamayo: The “Thinking and Explaining” Autonomous Driving AI Traditional autonomous driving relies heavily on rules and passive perception. NVIDIA’s Alpamayo large model endows cars with “reasoning” capabilities. For details on Alpamayo’s structure and technology, refer to “Wu Xinzhou Leads NVIDIA’s Charge Toward L4 Autonomous Driving with VLA Large Model Algorithms.” Of course, an updated Alpamayo 1.5 version will be shared later at GTC.

Vehicles equipped with this model can not only drive safely but also explain their decision-making logic in human-like language (e.g., “There’s a double-parked car in my lane; I’m maneuvering around it”) and even directly follow passenger voice commands to accelerate or perform other operations.

Addressing Long-Tail Data: Real-world data is far from sufficient to handle countless “edge cases.” NVIDIA provides the Cosmos world foundation model, Isaac Lab simulation platform, and Newton physics simulation engine to train AI using massive amounts of high-precision synthetic data.

The conference even showcased a robot named Olaf (from Disney), which learned to walk and interact in the physical world entirely within a virtual environment powered by NVIDIA Omniverse and Newton engines.

Finally, another advertising moment: Jensen Huang announced that NVIDIA’s Robotaxi platform has welcomed heavyweight new partners. After Mercedes-Benz, Toyota, and General Motors, BYD, Hyundai, Nissan, and Geely officially joined NVIDIA’s Robotaxi technology alliance. This has set the automotive industry ablaze—after all, we haven’t even mass-produced L3 vehicles yet, and now everyone’s rushing into L4.

Indeed, intelligent assisted driving/autonomous driving represents the most valuable segment in the automotive industry today, both in terms of investment and career prospects, offering high returns. Here’s a plug: For enthusiasts and professionals interested in autonomous driving products and technologies, check out the book mentioned in “Who Should Read ‘Autonomous Driving Product Manager’? What Value Does It Offer?” to quickly understand autonomous driving technology and the entire professional value chain.

4. Industry Predictions and Outlook: What’s the Next Battleground for the Automotive Industry?

Since Jensen Huang covered so much, what implications and insights does this hold for the automotive industry? Based on the major announcements at GTC, we can derive three profound predictions for the automotive industry and the broader tech sector:

All Software and In-Vehicle SaaS Experiences Will Shift Toward “AaaS (Agentic-as-a-Service)” Jensen Huang asserted that all future SaaS (Software-as-a-Service) companies will become AaaS (Agentic-as-a-Service) providers.

For automakers, future vehicles will no longer be mere hardware or traditional smart cockpits but mobile spaces equipped with multiple vertical-domain “expert agents.” The core business model for automakers will shift toward continuously providing users with exclusive AI token services.

If you’ve read Vehicle’s recent financial analyses of automakers, you’ll notice that traditional car manufacturers are largely losing money as they slowly transition and contemplate future profit models for intelligent vehicles.

Indeed, packing smart cars with excessive hardware and software, only to give them away for free while users complain about inferior functionality compared to competitors, ultimately leads to cutthroat competition without profit. After deploying large models, Geely’s Zeekr 8X recently premiered a Tesla Grok-like experience (click “Tesla Grok + FSD = VLA?” to learn about Grok’s in-car experience). This cockpit experience, which doesn’t control the vehicle, might experiment with token-based pricing.

“Annual Token Budgets” Will Become Standard for Enterprises, Requiring Automakers to Establish OpenClaw Strategies In the future, tokens will become a new commodity, like electricity and water. Jensen Huang predicted that software engineers will not only receive base salaries but also “annual token budgets” from companies to exponentially boost productivity.

Just as every company once needed an internet strategy and cloud strategy, future automakers must establish their own OpenClaw strategies, building enterprise-exclusive intelligent agent operating systems to gain an edge in internal R&D efficiency and external autonomous driving services.

This token trend might burden today’s workplace newcomers and interns. While onboarding once involved veteran employees mentoring newcomers, now veterans plus tokens can boost efficiency. Conclusion: GTC 2026 showcased an AI era no longer confined to screens. As AI grows “limbs” and “brains,” entering the physical world through intelligent agents and autonomous vehicles, the automotive industry faces unprecedented transformation.

Whether in terms of workplace dynamics, organizational structures, product forms, or business models, the automotive industry will undergo sweeping changes.

Embracing AI will be the sole ticket for the automotive sector to win future battles. Finally, welcome friends interested in autonomous driving to explore Vehicle’s book, “Who Should Read ‘Autonomous Driving Product Manager’? What Value Does It Offer?”