Raw LLM Responses
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in
I agree that we need to examine not only what AI can do, but what organizational…
7466125104671…
in
This is a good practical breakdown. The real value is moving from “using Claude”…
7464633718218…
in
Pascal BORNET As automation expands, human value will shift toward judgment, cre…
7463929950031…
in
All the more reason to run your own AI, privately.⠀ Datacenters (plantations) ar…
7463616969431…
in
Most founders frame this as an AI capability discussion. It's actually a system …
7464989847120…
in
The decline in Stack Overflow usage does not necessarily mean the end of learnin…
7466866541276…
in
The issue isn’t what AI can do. The issue is that governance keeps reacting inst…
7468381507133…
in
Demis. Stochastic weights won't solve AGI alignment or the megawatt energy wall.…
7463323625832…
Comment
This is a helpful way to explain the AI stack. LLMs think, RAG retrieves, Agents act, and MCP connects. But one layer is still missing: structural state. AI cannot make reliable enterprise decisions from files alone. A file stores content, but it does not carry state, permission, responsibility, history, risk, or execution conditions. Humans judge situations through relationships and context, not data alone. The same document can mean different things depending on who approved it, what state it is in, and whether action is allowed. So the next step is turning documents and data from static files into objects. Only then can AI move from retrieval and automation to responsible decision support. Enterprise AI will not mature only by connecting more tools. It will mature when data itself becomes structurally intelligent.
LinkedIn
Workplace & Jobs
Designing Structural Closure and Two-Mode Execu…
2026-05-25T13:4…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | accountability |
| Secondary value | none |
| Alignment target | organisations |
| Stance | demanding |
| Emotion | approval |
| Value justification | The speaker emphasizes the need for AI to consider structural state, permission, responsibility, and history to make reliable decisions, which is related to accountability. |
| Target justification | The comment focuses on enterprise decisions and the maturity of enterprise AI, indicating that the target is organisations. |
| Coded at | 2026-06-11T08:07:58Z |
Raw LLM Response
```
{
"value_primary": "accountability",
"value_secondary": "none",
"target": "organisations",
"stance": "demanding",
"emotion": "approval",
"value_justification": "The speaker emphasizes the need for AI to consider structural state, permission, responsibility, and history to make reliable decisions, which is related to accountability.",
"target_justification": "The comment focuses on enterprise decisions and the maturity of enterprise AI, indicating that the target is organisations."
}
```