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▶ 32:00 · Confidential Computing · Confidential computing extends to GPU workloads, protecting model weights and data in use — critical for on-prem frontier model deployment

now playing · Confidential Computing

AI Agentstailwindscore 8/10anne hecht

Agentic AI shifts from chatbots to persistent, autonomous, self-evolving digital workers

The industry has moved from simple chat interfaces to agents that maintain context, take autonomous actions, and self-evolve by acquiring new skills — unlocking enterprise ROI as agents bec…

AI Agentstailwindscore 8/10anne hecht

Agentic AI shifts from chatbots to persistent, autonomous, self-evolving digital workers

Enterprises are moving beyond chatbots to agents that maintain context (persistent), take actions autonomously with permissions, and self-evolve by acquiring new skills (e.g., PowerPoint ge…

Enterprise AI Adoptiontailwindscore 7/10anne hecht

CFOs optimize token marginal cost across hybrid infrastructure; IT teams govern shadow AI and operationalize skill sharing

Enterprise adoption is driven by CFO focus on token economics (cloud, on-prem, desktop via GB10/DGX Station) and IT governance of shadow AI; productivity gains come from sharing agent skill…

AI Infrastructuretailwindscore 7/10anne hecht

Heterogeneous AI factory architecture emerges: cloud, on-prem, edge with token cost optimization

CFOs and infrastructure teams are optimizing marginal token costs across a hybrid deployment model — cloud, data center, colo, and edge (GB10/DGX Station for power users) — while sharing be…

AI Security & Alignmenttailwindscore 8/10anne hecht

Secure Agent Workspace and OpenShell establish sandboxed, policy-governed architecture for enterprise agents

Security must be designed into the agent stack from the ground up: each agent runs in its own sandbox (OpenShell) with policies enforced outside the sandbox so agents cannot modify their ow…

Cybersecuritytailwindscore 8/10anne hecht

Secure agent workspaces require sandboxing (OpenShell), external policy enforcement, and GPU confidential computing

Security for autonomous agents demands a secure-by-design stack: each agent in its own sandbox (OpenShell open-source project with SAP, Google, Red Hat contributors), policies kept outside…

Confidential Computingtailwindscore 7/10anne hecht

Confidential computing extends to GPU workloads, protecting model weights and data in use — critical for on-prem frontier model deployment

NVIDIA has pioneered confidential computing for accelerated computing; Google uses it for Gemini on-prem with Dell, Apple for consumer services. This becomes essential as enterprises run co…

Open Source AItailwindscore 7/10anne hecht

Enterprises need both frontier and open models — open source enables customization, fine-tuning, and air-gapped deployment

The winning strategy is not frontier vs open but a blend: open models allow domain-specific post-training for accuracy and cost efficiency, while frontier models provide general capabilitie…

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▶ 32:00 · Confidential Computing · Confidential computing extends to GPU workloads, protecting model weights and data in use — critical for on-prem frontier model deployment

now playing · Confidential Computing

AI Agentstailwindscore 8/10anne hecht

Agentic AI shifts from chatbots to persistent, autonomous, self-evolving digital workers

The industry has moved from simple chat interfaces to agents that maintain context, take autonomous actions, and self-evolve by acquiring new skills — unlocking enterprise ROI as agents bec…

AI Agentstailwindscore 8/10anne hecht

Agentic AI shifts from chatbots to persistent, autonomous, self-evolving digital workers

Enterprises are moving beyond chatbots to agents that maintain context (persistent), take actions autonomously with permissions, and self-evolve by acquiring new skills (e.g., PowerPoint ge…

Enterprise AI Adoptiontailwindscore 7/10anne hecht

CFOs optimize token marginal cost across hybrid infrastructure; IT teams govern shadow AI and operationalize skill sharing

Enterprise adoption is driven by CFO focus on token economics (cloud, on-prem, desktop via GB10/DGX Station) and IT governance of shadow AI; productivity gains come from sharing agent skill…

AI Infrastructuretailwindscore 7/10anne hecht

Heterogeneous AI factory architecture emerges: cloud, on-prem, edge with token cost optimization

CFOs and infrastructure teams are optimizing marginal token costs across a hybrid deployment model — cloud, data center, colo, and edge (GB10/DGX Station for power users) — while sharing be…

AI Security & Alignmenttailwindscore 8/10anne hecht

Secure Agent Workspace and OpenShell establish sandboxed, policy-governed architecture for enterprise agents

Security must be designed into the agent stack from the ground up: each agent runs in its own sandbox (OpenShell) with policies enforced outside the sandbox so agents cannot modify their ow…

Cybersecuritytailwindscore 8/10anne hecht

Secure agent workspaces require sandboxing (OpenShell), external policy enforcement, and GPU confidential computing

Security for autonomous agents demands a secure-by-design stack: each agent in its own sandbox (OpenShell open-source project with SAP, Google, Red Hat contributors), policies kept outside…

Confidential Computingtailwindscore 7/10anne hecht

Confidential computing extends to GPU workloads, protecting model weights and data in use — critical for on-prem frontier model deployment

NVIDIA has pioneered confidential computing for accelerated computing; Google uses it for Gemini on-prem with Dell, Apple for consumer services. This becomes essential as enterprises run co…

Open Source AItailwindscore 7/10anne hecht

Enterprises need both frontier and open models — open source enables customization, fine-tuning, and air-gapped deployment

The winning strategy is not frontier vs open but a blend: open models allow domain-specific post-training for accuracy and cost efficiency, while frontier models provide general capabilitie…