TLDRs;
- Nvidia expands OpenAI Codex company-wide, integrating AI across engineering, legal, sales, and operations teams.
- The GPT-5.5 powered agent runs on Blackwell infrastructure, boosting efficiency and computational performance significantly.
- Codex shifts from coding assistant to full enterprise AI agent handling complex long-duration workflows.
- The rollout highlights strengthening Nvidia-OpenAI collaboration spanning infrastructure, chips, and next-generation AI systems.
Nvidia (NVDA) shares saw a steady uptick as investor sentiment improved following news that the company has fully deployed OpenAI’s Codex across its global workforce. The rollout marks a major step in Nvidia’s internal AI transformation strategy, positioning the company not just as a leading AI hardware provider but also as an active large-scale user of advanced AI systems.
The adoption comes after a successful pilot program involving approximately 10,000 employees. Following the trial phase, Nvidia expanded access to the entire organization, signaling strong confidence in the system’s productivity and reliability.
Codex expands across departments
Codex, powered by OpenAI’s GPT-5.5 model, is designed to go far beyond traditional coding 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.
NVIDIA Corporation, NVDA
Rather than functioning as a simple chatbot, Codex is structured as an AI agent capable of executing multi-step tasks such as planning workflows, generating code, analyzing internal processes, and assisting in operational decision-making.
Nvidia has framed the rollout as part of a broader shift toward agentic AI systems that actively complete tasks rather than simply responding to prompts.
Blackwell infrastructure powers performance
A key element of the deployment is Nvidia’s own hardware ecosystem. Codex is running on the company’s Blackwell architecture, which is designed for large-scale AI workloads.
According to internal data shared in the rollout, the system delivers major efficiency gains. Nvidia reports that running Codex on its GB200 NVL72 systems significantly reduces cost per million tokens while also improving output efficiency per megawatt of power consumption. These improvements highlight how tightly integrated Nvidia’s hardware strategy is with real-world AI deployment.
Nvidia rolls out GPT-5.5-based Codex to 10,000 of its employees, who apparently all think it’s ‘mind-blowing’ and ‘life-changing’ https://t.co/9iSwRy0lGH
— PC Gamer (@pcgamer) April 24, 2026
The company has also established a dedicated Codex Lab to support training and optimization of the system across different teams.
Security and long-running AI workflows
Security and control have been central to the deployment strategy. Nvidia has implemented a zero-data retention policy for Codex interactions and provides employees with controlled cloud-based virtual machines for execution.
The AI agent connects through secure channels such as SSH to approved environments, while maintaining restricted access to production systems. This structure allows Codex to safely assist with sensitive workflows without exposing critical infrastructure.
One of the more notable capabilities is Codex’s ability to handle long-duration tasks that can run for over 24 hours. This is made possible through a technique known as “compaction,” which compresses contextual memory so workflows remain stable over extended periods.
A broader shift in enterprise AI adoption
Nvidia’s rollout is being viewed as a reference point for broader enterprise adoption of AI agents. OpenAI has already begun deploying Codex in other large organizations, including technology services firms where tens of thousands of employees are now using its tools.
The move also reflects a deepening strategic relationship between Nvidia and OpenAI. Beyond software deployment, both companies collaborate on chip design, with OpenAI providing feedback on future architectures while gaining early access to Nvidia’s system roadmaps.
This collaboration is tied to broader infrastructure commitments, including plans for large-scale deployment of Nvidia systems for next-generation AI workloads.
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