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

Longer contexts and cheaper retrieval are eating the fine-tuning budget, line by line.
The first wave of AI workforce planning often began with subtraction. Companies identified tasks that models could perform and translated those capabilities into hiring freezes, efficiency targets, or smaller teams.
Artificial intelligence spending is entering the stage where scale alone is no longer sufficient evidence of strategy.

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.

After a decade of best-of-breed sprawl, CIOs are consolidating spend at a pace not seen since the cloud migration — reshaping venture math and go-to-market playbooks.

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.

The burden-of-proof reversal survives. Compliance teams have until Monday to get their documentation in order.
The winners are rewriting for citations and answer engines, not blue links.
A new cohort is trading headcount for tooling — and rewriting the seed pitch in the process.

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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