The Deep View: Conversations

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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AI agents can act faster than humans can review them. So how do companies keep control without slowing everything down?

In this episode of The Deep View Conversations, Jason Hiner sits down with Blake Brannon, chief innovation officer and a co-founder of OneTrust, to explore why AI governance needs to evolve from static rules to decisions made in real time.
Brannon explains why an agent can inherit an employee's identity and access without inheriting their trust. He makes the case for independent guardian agents that evaluate what other agents are trying to do, apply a company's policies, and redirect or block actions when necessary.

One example captures the challenge: deleting CRM records could be routine cleanup or an attempt to inflate a salesperson's performance. The action alone doesn't tell you whether it should be allowed. Its purpose matters.
Topics covered:
• How OneTrust evolved from data privacy to AI governance
• Why first-party data and customer trust go together
• Why human review can't keep pace with a growing population of agents
• How guardian agents and independent checks could help close the trust gap
• The difference between containing AI and aligning it with business policies
• Why governance should help companies move faster
• What the bring-your-own-device era teaches us about AI adoption
• How Blake uses visual storytelling and voice-to-text tools to work more effectively
If you're deploying AI agents and trying to move beyond experiments without giving up control, this conversation offers a practical way to think about the systems that need to surround them.

Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm 

And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

AI is accelerating cyberattacks. Could it also give defenders the upper hand?
In this episode of The Deep View Conversations, we sat down with Adam Meyers, CrowdStrike's head of counter adversary operations, at Fal.Con 2026 to explore how AI is changing the balance of power between attackers and defenders.
 
Meyers explains why he believes security teams can now match the speed and scale of their adversaries, how CrowdStrike is training offensive and defensive AI models against each other, and why the harness that guides an AI model can matter just as much as the model itself. He shares how changing that harness helped CrowdStrike reduce false positives in its vulnerability research without changing the underlying model, and how cyberdefenders are uniting to assist one another.
The conversation also goes inside the live disruption of the Sality botnet, explores how nation-state attackers are using agents that learn from their mistakes in seconds, and examines why powerful AI systems need clear boundaries as they pursue their goals.
Topics covered:
• Why AI could change the defender's dilemma
• How CrowdStrike and its partners disrupted a botnet that had operated for more than two decades
• Training red team and blue team models based on Nvidia's Nemotron models and CrowdStrike's security data
• Why AI harnesses are critical to reducing hallucinations and false positives
• How AI attacks are compressing the time organizations have to patch vulnerabilities
• What the Hugging Face incident raises about goal-seeking agents and guardrails
• Why local, open-weight models matter to both attackers and defenders
If you're trying to understand what AI means for cybersecurity, how to put agents to work safely, or where specialized models can make a difference, this conversation offers a view from someone tracking the adversaries firsthand.
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Nearly every smartphone launched in the past year features agentic AI capabilities, offering users an early look at what a fully agentic smartphone future could do for them. Of course, the tech powering it is driven by the chipsets.

In this episode of The Deep View Conversations, we talked with Vinesh Sukumar, Qualcomm's VP of AI at the Snapdragon Summit, the company's annual conference where it launches its latest processors. This year, the launch included the mobile platforms Snapdragon 8 Elite Extreme Gen 6 for phones and Snapdragon Sound Elite Gen 2 for wearables.

Vinesh discussed how the chipsets came to be, including the special considerations made during their design such as improving connectivity, on-device support for large models, longer battery life, and other features crucial to smoothly running agentic AI applications. We also discussed what the future of a truly agentic AI phone looks like and what's been holding it back.

Topics covered:
• What an ideal agentic smartphone experience would look like 
• The demands agentic AI models make of mobile chipsets
• The obstacles to agentic solutions becoming a game changer 
• The crawl, run, walk phases of agentic solutions, and where we are now
• The role of other smart devices in creating agentic experiences 
• How support for a 30 billion MoE on-device model was made possible 
• Qualcomm's role in working with partners to bring AI experiences to life

If you want to learn more about how the latest chipsets will change the future of Android flagship devices in the next year, including new AI experiences, this conversation will give you a clear idea.

📺 Watch on YouTube: https://youtu.be/YXvw9Gw41Xg 
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Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology.

And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

Disclaimer: Sabrina Ortiz's travel to Snapdragon Summit was paid for by Qualcomm. The Deep View's coverage is editorially independent from the companies we cover.

Can AI move fast and still be safe? As the debate over AI safety and innovation grows more polarized, we explore the practical work of making powerful systems accountable.

In this episode of The Deep View Conversations, we sit down with Navina Singh, CEO of Credo AI, to talk about what AI governance means for frontier labs, enterprises, and policymakers. They explore why governance is broader than regulation, how organizations can test and verify their AI systems, and what happens when capabilities advance faster than oversight.
Topics covered:
• Why Navrina sees AI safety and innovation as goals that can advance together
• What the Hugging Face agent incident raises about frontier AI oversight
• The “spectrum of trust,” from internal testing to independent audits and regulation
• The risk of regulatory capture and the role of open models
• How businesses weigh AI capability, cost, and control while managing "governance debt"
• How Credo AI uses forward-deployed governance experts to help companies put oversight into practice
• Why expertise, taste, and judgment remain valuable as AI takes on more work

If you’re building, buying, or governing AI, this conversation offers a practical way to think about trust without losing sight of the technology’s promise.

Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm 

And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com