
Fine-Tuning Is Dead. Long Live Context Engineering.
Longer contexts and cheaper retrieval are eating the fine-tuning budget, line by line.
11 RESULTS FOR “RETRIEVAL”

Longer contexts and cheaper retrieval are eating the fine-tuning budget, line by line.

Extraordinary AI claims often combine a real technical improvement with an undefined business promise. A system may demonstrate impressive reasoning, generate a polished answer, or complete a controlled task.
Artificial intelligence spending is entering the stage where scale alone is no longer sufficient evidence of strategy.

A due-diligence framework for examining AI vendors across data protection, model performance, contractual risk, integration, pricing, governance, and the operational realities hidden behind polished demonstrations.

A structured way to decide whether an AI capability should be purchased, customized, or built internally based on differentiation, data, risk, integration, and long-term operating cost.

AI agents require more than model access. Companies need clear authority boundaries, approval controls, monitoring, ownership, and escalation rules before autonomous systems can operate safely at scale.

Legal and security teams have quietly rewritten the checklist that kept open models out of production stacks.

Another order-of-magnitude drop in token prices is quietly re-pricing every AI product roadmap — and resetting the build-versus-buy math for everyone downstream.

Public benchmarks have decoupled from production performance. Enterprises are building private evals — and keeping them secret.

Forty vendors, four architectures, one procurement checklist. What actually matters when the demos all look the same.

A practical framework for connecting AI initiatives to operating outcomes, separating genuine economic value from impressive demos, activity metrics, and productivity claims that cannot be verified.
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