Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
foppypoof5195 (Paragraphs please). Humans are extremely complex biological machi…
ytr_UgzEJ937l…
G
from my experience, even original writing can get flagged as AI, which is frustr…
ytc_UgyFq9M41…
G
"b...but think about the potential for great things!"
There's just no value meas…
ytc_UgzixH98O…
G
@khayon4364 Right, but if AI is going to take all the jobs, all I am saying is w…
ytr_UgwGOEyDk…
G
Hi i think there is also another reason for it - soon 50% of our interactions on…
ytc_Ugzk8glKe…
G
For me its confusing, when I try to reduce my dependence on AI tools, others usi…
ytc_UgyGDXcO5…
G
AI can create Genetic specific bio weapons to target anyone or everyone it wants…
ytc_Ugw1wD2Qn…
G
...you can. The fact that ChatGPT can reproduce NYT articles almost verbatim pro…
ytr_UgzMBhQvJ…
Comment
Hello Drew, This "Intelligence Curse" scenario is terrifying because it is mathematically rational for the board. As long as "Efficiency" is the only metric, humans lose every time.
But there is a missing variable in this equation that we need to weaponize: Brand Toxicity.
If a company replaces its workforce with AI, it should not just be a "PR issue"; it should be a Structural Liability.
We need to introduce the concept of "No Fault Redundancy" Protection:
The AI Tax: If a role is automated, the company pays a specific tax that funds the UBI/Retraining for the displaced worker. This removes the "pure profit" incentive of firing them.
The Brand Shield: We need to aggressively support "Human-First" certified companies. If a bank or tech firm purges its staff for bots, the public needs to treat their brand as toxic.
The CEO in your story caved because the cost of keeping humans was higher than the cost of firing them. We need to flip that math. Firing a human for an algorithm should be the most expensive decision a board EVER makes.
youtube
Viral AI Reaction
2025-12-04T14:1…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | company |
| Reasoning | consequentialist |
| Policy | liability |
| Emotion | fear |
| Coded at | 2026-04-26T23:09:12.988011 |
Raw LLM Response
[{"id":"ytc_UgxnVl7qsQHr8Npr31F4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},{"id":"ytc_UgwN3B6O8W6SbRTEy2R4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"unclear","emotion":"fear"},{"id":"ytc_Ugyk806Ac8VgInhfsg54AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},{"id":"ytc_Ugyb5kzUq0c4zirJQel4AaABAg","responsibility":"company","reasoning":"consequentialist","policy":"regulate","emotion":"approval"},{"id":"ytc_UgzklGenAO1pfH021ZN4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"indifference"},{"id":"ytc_Ugy-0T8cU6D3Ot4snJF4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"mixed"},{"id":"ytc_UgxU3hkBff4OqEQQfoZ4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"mixed"},{"id":"ytc_Ugx9_kA1dPyFbhFNW-B4AaABAg","responsibility":"company","reasoning":"consequentialist","policy":"liability","emotion":"fear"},{"id":"ytc_UgxqgySLYyKfcYvRD0B4AaABAg","responsibility":"user","reasoning":"deontological","policy":"none","emotion":"outrage"},{"id":"ytc_Ugyce_mB-E56xg7xIQZ4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"mixed"}]