My intent was to put a crosshair on the functional uses for AI, but I got a totally new and profound view. More on that in a minute. The first thing on the list was Cognitive Augmentation, which covers things like research, ideation, writing, etc. There are no moving parts so far.  However, the list went on with fluid uses like surveillance, mechanical and procedural automation, and narrative control.

In all, one function was static, and the next six were dynamic based on data flows. This prompted me to ask ChatGPT:

Would you say the prime directive for AI is live-flow data analysis?

Then ChatGPT spilled its guts:

At its most powerful and strategic, AI exists not to archive knowledge but to act upon the world in real time. This aligns it more with control systems than mere analytics. In technocratic logic, knowledge is only as valuable as its immediate utility in managing systems—whether ecological, social, economic, or mechanical.

Think motion. Why live-flow dominates, according to ChatGPT:

1. Cybernetic Control:

AI inherits from cybernetics a commitment to feedback loops—processing input, adjusting outputs, and minimizing system entropy through continuous real-time correction.

2. System Optimization:

Smart grids, autonomous vehicles, financial trading bots, and social platforms rely on AI only insofar as it acts continuously to optimize flow, minimize error, or redirect behavior.

3. Governance and Enforcement:

Technocratic systems—whether in energy, healthcare, or speech regulation—favor AI not as a tool of deliberation but as a mechanism of enforced compliance via perpetual data scanning and instant decision-making.

4. Surveillance as Governance:

The logic of surveillance capitalism and state bio-monitoring depends on AI's ability to observe and react faster than humans, preempting decisions rather than responding to crises after the fact.

Then it noted that static analysis is secondary, but still strategic.

While live data flow is the directive for operational control, static analysis remains crucial for strategic foresight:

Training models require historical datasets.

Forecasting systems depend on archival input.

Ideological programming of AI (e.g., what to nudge, censor, or promote) is built from static corpora.

But all of this culminates in live action—whether in real-time feed recommendations, facial recognition alerts, or smart-city power allocation.