Jensen Huang pushes Nvidia workers to increase AI use

Nvidia CEO Jensen Huang urged employees to increase their use of AI, addressing concerns that automation could threaten jobs.

During an all-hands meeting last week, Huang responded to reports that some managers advised teams to limit AI use, calling the approach misguided.

He said every task that can be automated with AI should be, and assured staff that automation would not reduce their workload.

Nvidia, a leading US-based chipmaker, has recently hired several thousand employees and is expanding offices in the US and Asia.

Huang noted that the company’s engineers use AI coding tools like Cursor, and encouraged further adoption, even if the technology is not yet perfect.

The remarks come as other major tech firms, including Microsoft, Meta, Google, and Amazon, also push internal AI adoption.

Huang acknowledged ongoing debates about the sustainability of the AI boom, and investor skepticism about Nvidia’s continued growth.

🔗 Source: Fortune

🧠 Food for thought

Implications, context, and why it matters.

Huang called for automating every task. He gave no details on Nvidia’s AI policy, approved tools other than Cursor (an AI coding assistant), or safeguards for safe rollout.
Nvidia shared no data on adoption, productivity gains, or Return on investment (ROI) from internal AI use. 72% of enterprises track Generative AI (Gen AI) metrics tied to profitability and productivity 1.
Deloitte, a consulting firm, says in its 2025 survey that most organizations need 2 to 4 years for AI ROI. Only 6% see payback in under a year 2. That raises the need for clear success metrics.

Consultancies and systems integrators (firms that build and connect complex IT systems) have a clear chance to package secure AI adoption. They can deliver governance frameworks and on-premises code assistants (tools deployed inside a company’s own infrastructure). They can add training and ROI analytics for companies swayed by Nvidia’s stance on automation.
88% of companies plan to raise AI budgets, and 62% expect growth above 10% 3. 49% struggle to hire advanced AI talent, and 46% lack effective training 3.
Security vendors can pitch AI safeguards (tools that enforce safety, privacy, and compliance policies). Deloitte says siloed platforms (disconnected tools and data stores) and data quality issues hinder impact tracking 2. Leaders treat AI deployment as enterprise change and use ROI frameworks beyond finance-only metrics 2.