How open models change the economics of AI

Companies want to own the intelligence that makes their business valuable. So what will that mean for the AI models they use, and the systems they build around them?

In this episode of The Deep View Conversations, we sit down with Justin Boitano, VP of enterprise AI at Nvidia, to explore why NVIDIA is investing in open models and how enterprises are turning them into specialized intelligence.

Boitano talks about why NVIDIA makes Nemotron's datasets, training techniques and model weights openly available, how proprietary business knowledge can become a competitive advantage, and why lower token costs can unlock much greater demand for AI.

The conversation also examines why the agent harness that manages tools, memory and execution can matter as much as the model itself. Boitano shares how Nvidia worked with CrowdStrike on AI models for cybersecurity, why AI incidents need the equivalent of an aviation flight recorder, and how OpenShell moves agent controls into the infrastructure layer.

Other topics covered:
• Why NVIDIA is building open models alongside its chip business
• What Nemotron needs to compete with China's open models
• Owning institutional knowledge, protecting data and controlling AI costs
• How model development helps NVIDIA improve hardware efficiency
• Why harness engineering is becoming a new frontier for AI
• Moving cybersecurity from finding bugs to verifying and fixing them
• Transparency and learning from AI incidents
• OpenShell, sandboxing and enforceable limits on agent access
• How Justin uses Hermes as an AI chief of staff
• Jensen Huang's approach to priorities and team alignment

If you're deciding how to build enterprise AI, where your company's competitive advantage comes from, or how to give agents useful access without giving them unlimited control, this conversation offers a perspective from Nvidia's work across the ecosystem.

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