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Annelies Gamble
AI has entered its systems phase. Outcomes are no longer driven by model size alone, but by how well models are integrated into context, tools, routing, and feedback loops. A small set of primitives is defining this shift: - Tool use turns models into operators, not just assistants. - Dynamic context (e.g. MCPs) makes context portable, composable, and governed at runtime. - Intelligent routing allocates the right model to the right task under real-world constraints. - Telemetry closes the loop, turning production behavior into learning. On top of these primitives, new alignment strategies – synthetic data, verifiable rewards, and multi-turn objectives – are allowing systems to learn from real outcomes rather than static labels. The implication: As capabilities commoditize, differentiation moves up the stack to system design, domain grounding, and alignment with measurable value. Pricing follows the same path, shifting from labor proxies to outcomes, and in some cases even risk-sharing. For builders: think more about control planes that govern routing, context, and accountability. For buyers: evaluate AI as an operator, one that can be audited, improved, and ultimately trusted with responsibility 🔗 More here: https://lnkd.in/gYfF7cWu
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Rhonda Coleman Albazie
Alliance / Coalition Interop Layer In real coalitions, you’ll never have full data sharing or unified models. Partners have different rules, threat tolerances, legal constraints. Vulnerability: you end up with incompatible decision fabrics that can’t synchronize. Coalition Interop & Release Authority • controlled model sharing • redaction policies • shared scenario vocabularies • cross-force confidence alignment
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