Model Context Protocol went GA at Microsoft Build in May. Six months in, I have enough production experience with it to give you a more honest take than the launch-day excitement warranted. The short version: MCP is the right abstraction and the ecosystem is growing fast, but there are friction
There's a category of risk in AI tooling that doesn't get discussed much in technical blogs because it feels abstract until it isn't: the risk that a model you've built on becomes unavailable for reasons that have nothing to do with the
Mistral dropped Small 4 this month and it continues a pattern that's been building across the open-weight ecosystem: small models are getting much better, much faster than large models are getting marginally better. Small 4 handles code, structured extraction, and classification tasks at a quality level that would
Local inference has been on the periphery of the AI tooling conversation for a while — interesting to enthusiasts, impractical for most use cases, always "getting better" but never quite ready for production work. That narrative has shifted. Running capable models locally on commodity hardware is now a reasonable
Llama 4, DeepSeek V3.2, Mistral Large 3 — the open-weight model landscape at the end of 2025 is genuinely competitive with closed frontier models for a wide range of tasks. That's the good news. The deployment reality has some friction that's worth understanding before you commit
The economics of AI model access shifted faster in early 2026 than anyone I spoke to had anticipated. Price per token changes, tier restructuring, capability gating behind higher subscription tiers, and model deprecations on compressed timelines — these arrived in close succession and in ways that exposed an uncomfortable dependency for
It's been a year of a lot of announcements. Stepping back from the release cadence, here's what I've seen actually prove out in production environments — not in demos, not in conference keynotes, but in the client work and my own infrastructure. 1. The Agent-Augmented
Two years changes the question. In the first year, the question was "does this work?" The answer was yes, with significant caveats. In the second year, the question became "is this sustainable?" — and the answer to that required being honest about what the system had cost
There's a framing I keep coming back to when talking to clients about their AI strategy: the question isn't "should we build an AI platform" — the question is "do we have the data infrastructure to support AI that actually works?" The models
Provider agnosticism is one of those design principles that sounds like obvious good practice until you're in the middle of a feature and the easiest path is to use a provider-specific API. That's when the principle either holds or it doesn't. I've
Microsoft Ignite landed this week and the message was cleaner than I expected: agents are the new application layer, Fabric is the data substrate, and Copilot is the interface for everyone from developer to executive. The "Frontier Firm" framing they're using positions an AI-first organization not
A year ago, most production pipelines that used LLMs had a single model choice baked in. Today, with the open-weight landscape as competitive as it is and multiple model families offering genuinely different capability-cost tradeoffs, building a routing layer is worth the effort. Here's the framework I actually