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What happens when AI stops being a tool you open… and starts becoming a workforce that never logs off?
That transition is happening faster than most business leaders realize. AI agents are moving into the cloud. They can work continuously, collaborate with other agents, access company data, write code, research, take actions, and increasingly operate without a human sitting in front of a computer.
And while the capabilities are accelerating, the safety debate is getting significantly more serious.
This week, Jensen Huang and Bill Gates presented dramatically different views of AI risk. OpenAI unveiled more than 20 announcements at DevDay. Google returned to the frontier-model race with Gemini 4. Anthropic launched Claude Sonnet 5.5. Meta pushed deeper into enterprise AI. Microsoft reshaped its Copilot strategy. And nearly every major player is converging on the same idea: always-on AI agents running in the cloud.
For business leaders, the takeaway is bigger than another round of model upgrades.
Start thinking beyond chatbots.
The emerging AI operating model is persistent agents, shared workspaces, company knowledge, automated triggers, and humans supervising increasingly autonomous systems.
In this session, you'll discover:
- Why Jensen Huang believes AI safety is fundamentally an engineering problem — and why Bill Gates strongly disagrees.
- The growing concern around AI-enabled cybersecurity attacks and the ability to remove model safeguards.
- Why the debate over AI regulation is becoming much harder to ignore.
- Jensen Huang's argument that AI will create more jobs — and the counterargument that this technological revolution may be fundamentally different from every one before it.
- Bill Gates' proposal to tax AI-generated labor as machines replace human work.
- NVIDIA's new approach to monitoring AI agents with independent hardware-level safeguards.
- What OpenAI's massive DevDay reveals about where the company believes AI work is heading.
- Why OpenAI Dots could make persistent, always-running AI agents mainstream.
- Why cost per token is becoming a misleading way to compare AI models.
- How Gemini 4, GPT-6.1 Sol, Claude Sonnet 5.5, and Meta Muse compare in an increasingly crowded frontier-model market.
- How realistic AI video participants could soon join human meetings.
The AI race is no longer just about who has the smartest model.
It's becoming a race to build the operating system for how humans and AI work together.
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