The durable moat in applied AI is not the model. It is the eval suite: the one asset competitors cannot rent, and the one due diligence should price.
A five-level maturity model for enterprise AI agent readiness, with deterministic entry gates and a self-assessment matrix executives can score in minutes.
Model distillation trains a small student on a frontier teacher's outputs. When it wins, when it fails, the provider-terms question, and the break-even math.
Prompting is a commoditized layer now. The real leverage moved to context engineering: assembling the right information into the window at the right time.
A practitioner reference architecture for production AI: the eight layers of the enterprise AI stack and a deterministic way to choose at each one.
Guardrails are the control layer between users, your model, and your systems. What they catch, where they sit in the request path, and how to build them.
Model lock-in is the new cloud lock-in. Here is where switching cost actually accumulates, how to measure your exit price, and how to keep leverage.
Most teams ship LLM features with no real evals, then find failures in production. A practical framework for an evaluation harness that scales.
AI coding assistants raise output, but net productivity depends on task type and codebase age. Where they help, where they add cost, and what to measure.
Frontier models do not win every task. A 2026 framework for when small and mid-sized models beat them on cost, latency, privacy, and accuracy.
The premium paid for machine learning engineers is about to compress. Three converging forces explain why, and what stays scarce as the rest gets cheap.
Most enterprise AI agents stall in pilot. A framework for the narrow, tool-constrained, well-evaluated patterns that ship, and the demoware that does not.
Deep analysis across the systems, strategies, and economics that shape modern technology.
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