Raw LLM Responses
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in
This is why I love conversations about AI, Pascal BORNET It brings up perspectiv…
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Alvin Foo do you ever work at the Silicon layer? When you write your software? S…
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The implications of this are far bigger than most people realize. If this works …
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I agree with you that this deserves slow reading, not instant certainty dressed …
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Demis Hassabis Regarding the safety of agentic systems and the deployment of Cod…
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The massage is clear “If you don’t learn and apply AI now, you’ll be out of work…
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Many people know how to make clever points, but not everyone has the opportunity…
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Nejdet Çağdaş Y. honestly, this might be the most underrated reason in the entir…
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Comment
Incredible milestones at I/O, Demis. The speed of Gemini 3.5 Flash and Omni opens immense possibilities. However, scaling frontier models on flat rates creates an unsustainable compute drain. To protect CapEx ROI, we must shift from text approximation to guaranteed data fidelity via a "Pay-per-Logic" Hybrid Framework: Track A (Free): Statistical answers for low-stakes curiosity. Track B (Premium): High-compute multi-agent reasoning using live, verified third-party APIs. Users pay a dynamic micro-fee (e.g., $1.50 for localized real estate audits) for 100% accuracy. Professionals gladly pay per query for trustworthy data they can financially back up. This turns AI from a cost center into a transactional revenue engine. Love to share the full brief with your team!
LinkedIn
AI Safety & Risk
Assistant Manager at AllNet Systems Ltd
2026-05-22T08:5…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | sustainability |
| Secondary value | economic_equity |
| Alignment target | organisations |
| Stance | demanding |
| Emotion | approval |
| Value justification | The speaker wants AI to be aligned with sustainability by reducing the compute drain and shifting to a more efficient framework. |
| Target justification | The target of the speaker's suggestion is organisations, as they discuss protecting CapEx ROI and turning AI into a revenue engine. |
| Coded at | 2026-06-11T07:58:26Z |
Raw LLM Response
```
{
"value_primary": "sustainability",
"value_secondary": "economic_equity",
"target": "organisations",
"stance": "demanding",
"emotion": "approval",
"value_justification": "The speaker wants AI to be aligned with sustainability by reducing the compute drain and shifting to a more efficient framework.",
"target_justification": "The target of the speaker's suggestion is organisations, as they discuss protecting CapEx ROI and turning AI into a revenue engine."
}
```