From 36Kr, European Central Station:
Jensen Huang's 10,000-word speech at GTC 2026: In the era of AI factories, 80% of applications will disappear. Why is OpenClaw the next Linux?
Every company now needs to develop an "OpenClaw Strategy".
"Welcome to GTC!"
When Jensen Huang stepped onto the stage in his iconic leather jacket, the entire venue erupted. But this time, he did not just launch a new chip—he painted a vision of an entirely new world: a future built by AI factories, token economy, and intelligent agents. In this future, most traditional applications will disappear, data centers will transform into token production factories, and the open-source project OpenClaw is emerging as the operating system of this new world.
Let's recap this nearly three-hour keynote and break down the technology blueprint Jensen Huang laid out for 2026 and beyond.
1. 20 Years of CUDA: Flywheel Effect Accelerates Growth
At the start of the keynote, Huang looked back at NVIDIA's foundation—CUDA. This year marks the 20th anniversary of CUDA. What began as an architecture few initially believed in now boasts hundreds of millions of installations. From programmable shaders to RTX, to the AI explosion, CUDA's flywheel effect continues to accelerate: a massive user base attracts developers, developers create breakthrough algorithms, algorithms spawn new markets, and new markets expand the user base further.
"Downloads of NVIDIA libraries are growing at an astonishing rate, larger than ever before," Huang emphasized. It is this flywheel effect that gives NVIDIA GPUs an extremely long lifespan and broad applicability, covering the entire AI lifecycle from data processing to scientific computing, from training to inference.2. The Inference Turning Point: AI Begins to Think
"Computing demand has increased 1 million times in the past two years," Huang shared a staggering figure. The reason lies in the leap of AI capability: from ChatGPT opening the era of generative AI, to the O1 model gaining reasoning ability, to Claude Code becoming the first agent model capable of working autonomously. Every advancement means exponential growth in computing volume during the inference stage.
"AI needs to think now." Huang pointed out that thinking requires inference, and inference requires generating a large number of tokens. Compared to training, the computing demand for inference has increased by around 100,000 times. This is exactly the inference turning point—AI has moved from "perception" to "generation", and from "reasoning" to "action".
This turning point has brought staggering market demand: in 2026, NVIDIA's Blackwell and Rubin product lines have already secured $500 billion in orders, and by 2027, this figure will reach at least $1 trillion.3. New Hardware Launch: Vera Rubin and Groq Integration
On the hardware front, Huang launched the new-generation AI supercomputing platform Vera Rubin. The platform includes the Vera CPU, Rubin GPU, NVLink-72 interconnect, and all-new storage and networking systems. Compared to Hopper, Vera Rubin delivers a 35x improvement in token throughput at the same power consumption.
What is even more notable is that NVIDIA announced a deep partnership with the Groq team, integrating Groq's LPU (Language Processing Unit) into the Vera Rubin system. Groq chips use a deterministic data flow architecture and a massive SRAM design, optimized specifically for ultra-low latency inference. The combination delivers another 35x performance improvement for inference at the highest value tier.
"We are building a Kyber rack housing 144 GPUs, connected via copper cables, delivering unprecedented scaling density," Huang demonstrated the Rubin Ultra compute node on site. Its size is so large that it required stage machinery to assist with lifting it into place.4. AI Factories: From Data Centers to Token Factories
Huang put forward a core concept: Future data centers will no longer be places to store and process data—they will be "AI factories", and their product is token. Every AI factory is constrained by power: a 1-gigawatt factory can never become a 2-gigawatt factory, so the number of tokens produced per watt has become the key metric.
"This is your future revenue curve," he said as he presented a 2D chart, with token throughput on the vertical axis and inference speed (interactivity) on the horizontal axis. Different tiers of service correspond to different pricing: free tier, mid-tier service, premium research service. By optimizing co-design of hardware and software, NVIDIA can shift the entire curve upward, allowing customers to generate more than 5x the revenue with the same amount of power.
To this end, NVIDIA launched Dynamo, an operating system designed specifically for AI factories, and the DSX platform, a digital twin blueprint for designing and operating AI factories that integrates a full toolchain from mechanical simulation to power grid optimization.5. OpenClaw: Open-Source Operating System for Agent Systems
During the keynote, Huang dedicated a large portion of his talk to an open-source project: OpenClaw. This personal AI agent, developed by Peter Steinberger, became the most popular open-source project in human history in just a few weeks, surpassing 30 years of growth for Linux.
"What is OpenClaw? It is an agent system that can call large models, access tools and file systems, break down tasks, spawn sub-agents, and interact with you in a variety of ways," Huang explained. He believes OpenClaw is essentially an "operating system for intelligent computers": just as Windows ushered in the PC era, OpenClaw will usher in the era of personal agents.
Every company now needs to develop a "OpenClaw Strategy". To support this, NVIDIA launched the NemoClaw reference design, which integrates enterprise-grade security, privacy protection, and policy engines, allowing enterprises to deploy agent systems securely. At the same time, NVIDIA released multiple cutting-edge open models, including Nemotron, Kosmos, ALPAMIO, GROOT, and more, covering fields including language, vision, physical AI, autonomous driving, and others.
"Every SaaS company will become an Agent-as-a-Service company," Huang predicted, that every engineer will have an annual token budget in the future, using AI to amplify their capabilities.6. Physical AI: Robotics and Autonomous Driving....
....MUCH MORE
And more to come as yours truly attempts to digest Day 1 before Day 2 gets going.
