The February 2021 archive included a Tangled AI article on preparing a small business for AI adoption. Its main theme was to begin with a business problem and readiness, rather than with enthusiasm for a particular platform.
The article covered familiarity with AI, choosing an appropriate approach, balancing cost against value and examining existing digital systems. It also emphasised cleaning and organising data so that a proposed system has usable inputs.
A small team needs to understand what it can maintain, what specialist help it needs and how it will judge the result. The archive warned against becoming absorbed in the platform while losing sight of the task. A limited project with a defined outcome makes it easier to decide whether further investment is justified. This summary preserves that planning focus without repeating dated vendor examples or funding suggestions.
A March 2021 archive item revisited reporting by The Wall Street Journal about Engineer.ai and the extent of automation in its app-building service. The report questioned how much work was performed by software and how much depended on developers.
This February 2021 archive item used a hierarchy of needs to explain why an AI project depends on work that happens before model development. Its foundation was reliable data collection and infrastructure.
The original November 2022 explainer introduced three terms often grouped together under artificial intelligence: narrow AI, general AI and superintelligence. They describe different levels of capability, rather than three interchangeable labels for the same technology.
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