Articles

Classical computing is hitting walls for AI: training costs in the billions, energy consumption rivaling small countries, and combinatorial optimization bottlenecks that scale exponentially. Quantum computing offers a fundamentally different computational model for the hardest problems in AI.

RAG has fractured into three architectures: Pipeline, Agentic, and Graph. The convergence into Agentic GraphRAG — where autonomous agents dynamically route between vector search, knowledge graph traversal, and structured queries — is the retrieval architecture that handles what chunk-based search cannot.

AWS Lambda MicroVMs hit GA in June 2026, giving every AI agent its own Firecracker sandbox with a dedicated kernel, filesystem, and network namespace. The industry converged on three isolation tiers: microVMs for untrusted code, gVisor for trusted workloads, V8 isolates for the edge.

85% of generative AI deployments run without observability. When an agent fails at step 10, the root cause is often a tool call at step 3. Here is how to trace, monitor, and debug agentic systems in production.

AI compute provides the raw horsepower. Cloud computing provides the distribution network. Neither works without the other. Here's why global AI depends on both layers — and where the gaps are.