Nvidia CEO Jensen Huang declares end of PC interaction era at GTC Taipei

The RTX Spark superchip and conversational AI agents aim to replace keyboards and mice as the primary way humans talk to their computers.

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Add us on Google by Editorial Team Jun. 1, 2026

Jensen Huang wants you to stop clicking. During his keynote at GTC Taipei on June 1, the Nvidia CEO declared that the decades-old paradigm of interacting with computers through keyboards, mice, and screens is effectively over, replaced by AI agents that listen, understand, and act on your behalf.

The centerpiece of the announcement is the RTX Spark superchip, developed in partnership with Microsoft, which Huang positioned as the hardware backbone for turning Windows PCs from passive tools into what he described as proactive teammates. Instead of opening apps and navigating menus, users will simply state their objectives in plain language and let AI agents handle the rest.

From clicking to conversing

The RTX Spark chip is designed to run AI agents locally on a PC, meaning the conversational interface doesn’t rely entirely on cloud processing. Think of it less like asking Siri to set a timer and more like telling a capable assistant to research a topic, draft a report, format it, and email it to your team, all from a single spoken or typed instruction.

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Nvidia also introduced its OpenShell runtime and NemoClaw stack, two pieces of infrastructure specifically built for deploying these agentic AI systems securely, particularly in enterprise environments where data sensitivity matters.

The OpenShell runtime provides the execution layer for AI agents, while NemoClaw handles the orchestration and security guardrails that companies need before letting autonomous software loose on their internal systems.

Vera Rubin and the economics of inference

Huang also confirmed that Nvidia’s Vera Rubin NVL72 rack systems will enter full production by fall 2026. The key number here: a 10x cost reduction in inference compared to previous generations. Inference is the process of running a trained AI model to generate outputs. Reducing that cost by an order of magnitude matters enormously because inference is where the majority of compute spending goes as AI scales from research labs into everyday products.

The Microsoft partnership and what it signals

The RTX Spark chip was developed jointly with Microsoft. Huang’s keynote builds on themes Nvidia has been advancing at multiple GTC events throughout 2026, each pushing the narrative that AI isn’t just a feature bolted onto existing computing.

What this means for investors

The 10x inference cost reduction from the Vera Rubin systems is arguably the more consequential number for markets. Cheaper inference expands the addressable market for AI applications across every industry, meaning more companies can afford to deploy AI at scale, which means more demand for Nvidia’s hardware.

Every major chip designer and cloud provider is racing to cut inference costs. AMD, Intel, Google, and Amazon all have competing silicon programs. If Nvidia’s 10x claim holds up in real-world benchmarks, it widens the company’s lead.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.