Read Outs
In-depth explainers on the topics and companies shaping the AI stack — what's happening, why it matters now and over the next few years, how it actually works, and where the real tensions and open questions lie.
- topicStale · 65d
Storage Gets a Promotion: AI as a Data-Movement Problem
The case that AI infrastructure is shifting from a compute constraint to a data-movement constraint is directionally sound and increasingly reflected in 2026 silicon and cloud roadmaps, but its headline economics rest on undisclosed math and vendor-aligned framing rather than neutral benchmarks. Promising as a reframing, not yet established as a universal law.
Memory & Storage Silicon· NAND / FlashModel Distribution & Serving· Inference PlatformsObject & File Storage· High-Performance Object StoreAug 16, 2026 - topicStale · 81d
Three Planes, One Gateway: The 2026 Multi-Model Routing Stack
By mid-2026 enterprise AI infrastructure has coalesced around a centralized gateway that routes requests across multiple models and providers, separating intent-based allocation, governance policy, and serving-capacity dispatch. The layered separation is analytically useful and widely reflected in vendor architectures, but it is not a standardized taxonomy, and the cost and cache-efficiency figures behind it remain vendor-reported rather than independently audited.
Inference Infrastructure· Model RoutingEnterprise Automation· AI Control PlanesAI Models· Multi-Model SystemsJul 31, 2026 - topicStale · 81d
Weights, Not Walls: A Field Guide to Open, Open-Weight, and Closed AI
Openness in AI now runs on a spectrum from fully open-source to open-weight to closed, with the practical dividing line being whether a model's trained parameters can be downloaded. The definitions are settling but not settled, and the policy and safety implications remain genuinely two-sided.
AI Models· Open Weight ModelsInference Infrastructure· Model StrategyJul 31, 2026