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The AI workload presents the ultimate challenge, highlighting the structural limitations of the traditional hyperscaler model. In this segment from a Civo Navigate London 2025 session, Kelsey Hightower explains exactly why AI adoption forces enterprises to confront flawed architecture and rising astronomical costs. When specialized hardware is scarce and rented GPUs sit idle at a premium, it’s clear that traditional cloud providers were not built for this era. Data that didn't move is forcing organizations to move compute back to where it lives.

People worry about AI taking jobs, but what about the new roles AI is creating? James Faure, CEO of Clairo AI, breaks down the three essential non-technical jobs that have emerged in the last two years: Prompt Engineers, Context Architects, and Evaluators. Learn the crucial skills needed to be highly employable in the future of AI.

In this video, our team shares how we work together to move fast, stay aligned, and build impact- across engineering, product, design, marketing, and beyond. You’ll hear honest perspectives on: Whether you're part of Sysdig or just curious how high-performing teams operate, this behind-the-scenes look highlights the mindset and culture that power everything we do.

This 5-minute critique pulls back the curtain on where AI is succeeding and where the biggest challenges remain. Experts expose the gap between market hype and reality: the failure to deploy fully autonomous production agents and the missing human-machine interface for non-developers. It’s a challenge to the entire industry.

The hyperscaler model is built on bait: generous cloud credits for years, especially if you're VC-backed. But there's a serious catch. In this clip, Canopy's James Marks talk about the expected consequence of taking those "free" credits. It's not just about attracting customers; it's about deepening reliance on proprietary platform-native tools. It’s the ultimate vendor lock-in strategy, making it costly and complicated to break away later.