Deep Chauhan

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• Cloud 3.0 isn't replacing the public cloud—it's changing where AI workloads run. The first wave of cloud adoption focused on centralization. The next wave is focused on workload placement. As AI moves into production, deploying everything to a centralized public cloud is no longer the default architecture. What's driving this shift? ⚡ Inference has different requirements than training. While public cloud is ideal for large-scale model training, production inference prioritizes: Low latency Predictable costs High throughput Data locality 🔒 Data gravity and sovereignty matter. Many organizations can't—or shouldn't—move sensitive or massive datasets to a centralized cloud. Keeping compute closer to data improves performance while helping meet compliance requirements. 🌐 AI infrastructure is becoming distributed. A modern AI stack may use: Public cloud for training Private cloud for sensitive workloads Edge infrastructure for real-time inference Sovereign cloud for regulated environments This isn't a shift away from the cloud. It's a shift toward running each workload where it performs best. That's what many are calling Cloud 3.0.

  Jul 1, 2026

• 🚀 Big AI Update Anthropic's Claude is now integrated into Slack, allowing users to access Claude directly within their workspace to summarize conversations, answer questions, and assist with daily tasks without leaving Slack. The trend is clear: AI is becoming a native part of the tools people already use, rather than something they have to switch to separately.

  Jun 24, 2026

• Apple's AI strategy stands out. While others compete on bigger models, Apple is integrating AI deeper into the OS—making Siri more context-aware, better across apps, and focused on privacy. The goal isn't the smartest chatbot. It's the most useful one.

  Jun 23, 2026