The situation behind the request
A CIO deciding how to deploy private AI does not need another list of tools. A product leader deciding whether to build a feature needs to understand tradeoffs, experience and operating economics. An engineer adopting AI coding tools needs to know what quality controls still matter.
Where the obvious solution falls short
Much AI coverage repeats announcements, predicts disruption and leaves the reader with no decision rule. That makes it easy to skim and impossible to use. This publication is designed around concrete questions and evidence.
How we would approach the work
Every feature begins with a thesis and the conditions under which it holds. It examines at least one plausible alternative, identifies failure modes and ends with a framework the reader can apply. Sources and review dates are visible, and examples are labeled correctly.
What the engagement may include
- Decisions: build-versus-buy, cost, architecture and governance.
- Engineering: retrieval, agents, evaluations, security and integration.
- Product Systems: discovery, experience, product engineering and AI-native delivery.
- Operations: workflows, modernization and the economics of real systems.
The choices worth making explicitly
The Review is not a substitute for delivery evidence. Technical claims are checked against documentation and dated sources, and speculative perspectives are identified as such.
What we would test before calling it ready
Authors must be real, illustrations original or licensed, source claims accurate and publication dates honest. Drafts remain unpublished until reviewed. No generic syndication or automated content flooding.
A sensible first step
Start with a question that matters to a current decision.