NVIDIA CEO Jensen Huang has made a definitive statement on the future of software architecture, declaring that "There will be no software in the future that's not agentic." In remarks shared via social media, Huang positioned AI agents not as a feature or an add-on, but as the foundational paradigm for all future software development.
What Happened
During what appears to be a public speaking engagement, Huang stated: "There will be no software in the future that's not agentic. And so, it is absolutely true that every software company will become an agentic company." He concluded with a philosophical note on the shifting role of human developers: "Our job is not to do the job. Our job is to have the job be done."
This statement represents a significant escalation in rhetoric from one of the most influential figures in the AI hardware ecosystem. It moves beyond discussing agents as a product category (like coding or customer service agents) and frames them as the essential, underlying architecture for all computational tasks.
Context: The Push for Agentic Infrastructure
Huang's declaration is not made in a vacuum. It follows NVIDIA's aggressive positioning of its hardware and software stack as the essential infrastructure for the AI era. The company's recent product launches, including the Blackwell GPU architecture and the NIM microservice platform, are explicitly designed to serve the needs of complex, multi-step AI agent workflows that require high memory bandwidth and low-latency inference.
This vision aligns with a broader industry trend where major platforms are betting on agents as the next interface layer. Microsoft has deeply integrated Copilot agents across its ecosystem, from Windows to GitHub. Google is pushing its Gemini-based agents within Workspace and Cloud. OpenAI's GPTs and the Assistants API are frameworks for building custom agents. Huang's statement suggests NVIDIA sees this not as a competition between different agent platforms, but as a total transformation of how software is conceived and built.
What "Agentic" Software Means in Practice
The term "agentic" in this context refers to software that uses AI models to perceive, plan, and execute multi-step tasks with a degree of autonomy, often using tools (like APIs, databases, or search) and learning from feedback. This contrasts with today's dominant paradigm of deterministic, rule-based software.
In Huang's vision, future applications—from word processors and spreadsheets to enterprise resource planning (ERP) systems and video games—would have agentic cores. Instead of a user manually clicking through every function, they would delegate high-level intent to an agent that orchestrates the necessary steps. The developer's role shifts from writing explicit instructions for every edge case to defining goals, constraints, and safety boundaries for the agent.
gentic.news Analysis
Jensen Huang's statement is a strategic masterstroke that serves multiple purposes for NVIDIA. First, it's a market-defining narrative. By declaring agentic software as inevitable, NVIDIA is attempting to shape the ambitions of every software CEO and CTO on the planet. If they believe him, their technology roadmaps must prioritize agent development, which in turn creates massive, sustained demand for NVIDIA's GPUs and AI Enterprise software—the very tools needed to train and run these complex agent systems.
Second, this follows a clear pattern in NVIDIA's recent communications. At GTC 2024, the company introduced Project GR00T, a foundation model for humanoid robots, fundamentally an agentic system for the physical world. The NIM platform is designed to simplify the deployment of ensembles of models, which is precisely the architecture needed for sophisticated agents. Huang is now providing the philosophical underpinning for this entire product suite: the age of passive software is ending.
This vision also creates a natural tension with other tech giants. While Microsoft, Google, and Meta are building their own agent platforms, they all rely on NVIDIA hardware. Huang's framing positions NVIDIA as the indispensable infrastructure layer, regardless of which company's agent models ultimately dominate the application layer. It's a bet on the pick-and-shovel provider in a new gold rush.
However, practitioners should view this as a directional prediction, not an immediate reality. The technical hurdles for creating reliable, safe, and cost-effective agentic software at scale remain significant. Hallucination, unpredictable behavior, high latency, and immense computational costs are still major barriers. Huang's timeline of "the future" is deliberately vague. The transition will be gradual, beginning with domains where the cost of error is low and the value of automation is high, like coding assistants and customer support triage, before moving to more critical systems.
Frequently Asked Questions
What does "agentic software" mean?
Agentic software refers to applications built around AI agents that can autonomously perform multi-step tasks to achieve a user's goal. Instead of following rigid, pre-programmed instructions, an agentic system uses a large language model or other AI model to understand intent, create a plan, use tools (like calculators, APIs, or search), execute steps, and adapt based on outcomes. It shifts software from being a tool you operate to a collaborator you delegate to.
How is NVIDIA involved in the AI agent space?
NVIDIA is building the full-stack infrastructure required to develop and run AI agents. This includes its industry-leading H100 and Blackwell GPUs for training and inference, its CUDA and AI Enterprise software platforms, and new offerings like NIM (NVIDIA Inference Microservices) for deploying model ensembles. Through initiatives like Project GR00T for robotics, it is also investing in foundational models specifically designed for agentic behavior in both digital and physical worlds.
Is all software really going to become agentic?
While Jensen Huang's statement is absolute, the transition will be evolutionary, not instantaneous. Certain software categories, like creative tools, coding environments, and data analysis platforms, are already rapidly adopting agentic features (e.g., GitHub Copilot, Adobe Firefly). Other domains, particularly safety-critical systems like aviation controls or medical device software, will adopt agentic principles much more slowly and cautiously due to requirements for verifiable correctness and safety. The core architecture of many applications will likely become a hybrid, blending traditional deterministic code with agentic modules for specific tasks.
What does "Our job is not to do the job. Our job is to have the job be done" mean?
This phrase encapsulates the shifting role of the human in the development loop. In traditional software, developers write explicit code to perform a job. In an agentic paradigm, developers define the objective, provide the necessary tools and knowledge sources, and set the guardrails and evaluation criteria. The developer's "job" is to architect a system where an AI agent can reliably accomplish the task, not to manually script every possible action. It's a move from imperative programming to goal-oriented system design.
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