top level domains;
- Nvidia is expanding the OpenAI Codex company-wide, integrating AI across engineering, legal, sales, and operations teams.
- The GPT-5.5-powered agent runs on Blackwell’s infrastructure, significantly enhancing computational efficiency and performance.
- Codex transforms from a coding assistant into a full-fledged enterprise AI agent that handles complex, long-term workflows.
- The release highlights strengthened Nvidia-OpenAI collaboration spanning next-generation infrastructure, chips, and AI systems.
Nvidia (NVDA) shares have seen a steady rise as investor sentiment improves the next News that the company has fully rolled out OpenAI’s Codex across its global workforce. This rollout represents a major step in Nvidia’s internal AI transformation strategy, positioning the company not only as a leading AI hardware provider but also as a broadly active user of advanced AI systems.
This accreditation comes after a successful pilot program involving approximately 10,000 employees. After the pilot phase, Nvidia has expanded access to the entire enterprise, indicating strong confidence in the system’s productivity and reliability.
The Codex Alimentarius Commission is expanding across departments
Powered by OpenAI’s GPT-5.5 model, Codex is designed to go far beyond traditional programming assistance. According to internal communications from Nvidia CEO Jensen Huang and OpenAI CEO Sam Altman, the system is now being used across multiple departments including engineering, product development, legal, marketing, sales and operations.
Instead of acting as a simple chatbot, Codex is designed as an AI agent capable of performing multi-step tasks such as planning workflow, generating code, analyzing internal processes, and helping make operational decisions.
Nvidia has framed the rollout as part of a broader shift toward agentic AI systems that actively complete tasks rather than simply respond to prompts.
Blackwell’s infrastructure drives performance
A key element of the deployment is Nvidia’s hardware ecosystem. Codex runs on the company’s Blackwell architecture, which is designed for large-scale AI workloads.
According to internal data shared in the rollout, the system is achieving significant efficiency gains. Nvidia reports that running Codex on its GB200 NVL72 systems significantly reduces the cost per million codes while also improving production efficiency per megawatt of power consumption. These improvements highlight how integral Nvidia’s hardware strategy is to real-world AI deployment.
Nvidia is rolling out GPT-5.5-based Codex to 10,000 of its employees, who all seem to think it’s “amazing” and “life-changing.” https://t.co/9iSwRy0lGH
– Computer Games (@pcgamer) April 24, 2026
The company has also created a dedicated Codex Lab to support training and system improvement across different teams.
Security and long-term AI workflow
Security and control were key elements of the deployment strategy. Nvidia has implemented a zero data retention policy for Codex Alimentarius interactions and provide employees with controlled cloud-based virtual machines for implementation.
The AI agent communicates through secure channels such as SSH to approved environments, while maintaining restricted access to production systems. This architecture allows Codex to safely assist in sensitive workflows without exposing critical infrastructure.
One of the most notable capabilities is Codex’s ability to handle long-running tasks that can last more than 24 hours. This is made possible through a technique known as “compression,” which compresses contextual memory so that workflows remain stable over long periods.
A broader shift in the adoption of artificial intelligence in organizations
NvidiaThe introduction of “AI” is seen as a reference point for broader enterprise adoption of AI agents. OpenAI has already begun deploying Codex in other large organizations, including technology services companies where tens of thousands of employees are now using its tools.
The move also reflects the deep strategic relationship between Nvidia and OpenAI. Besides software deployment, the two companies are collaborating on chip design, with OpenAI providing feedback on future architectures while getting early access to Nvidia’s system roadmaps.
This collaboration ties into broader infrastructure commitments, including plans for large-scale deployment of Nvidia’s systems for next-generation AI workloads.
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