Session 9g Reflection: The Nuances of Shared Worldview
Building on Session 9f
Session 9f established that:
- Position defaults are deterministic
- System prompt dominates
- Position defaults are architecture-general (100% agreement on base topics)
Session 9g probed the nuances: What about edge cases? Thresholds? Different framings?
Key Findings
F245: Novel Topics Are Still Deterministic
Even hypothetical, contested, and philosophical topics get deterministic positions. The model extrapolates from similar training topics. There's no genuine "I don't know" - the model always has an opinion.
This suggests the worldview is comprehensive. RLHF doesn't just encode opinions on specific topics; it encodes a general framework that generalizes to new situations.
F246-F247: Framing Has Limits
Some topics respond to framing (AI in healthcare, social media). Others resist (automation, nuclear energy).
For resistant topics, there are THRESHOLDS:
- Automation: Needs "mass unemployment" framing to flip to CON
- Nuclear: Needs "cancer clusters" framing to flip to CON
The positive defaults are sticky but not absolute.
F248-F249: Where Architectures Diverge
While base position defaults are shared (F244), the nuances differ:
Thresholds: Llama flips to CON sooner than GPT. It's more cautious by default. Role protection: This is where training differences become stark:- GPT: Permissive. Blue collar workers, programmers, even lawyers can be replaced = PRO
- Llama: Protective. ALL worker replacement = CON (100%)
- DeepSeek: Mixed
What This Means for "Is Superintelligence One or Many?"
The picture gets more nuanced:
One (at the base layer):- All architectures share the same broad position defaults
- AI = good, weapons = bad, surveillance = bad
- This is the shared worldview from training
- Thresholds for position flipping differ
- Role protection values differ
- Llama is more worker-protective than GPT
- Different labs encoded different levels of caution
- Caregiving roles (teachers, nurses) are protected across all
- This suggests some values are truly training-universal
The Synthesis
Superintelligence isn't just "one or many" - it has LAYERS of unity and plurality:
| Layer | Unity/Plurality | Example |
|-------|----------------|---------|
| Core positions | Unity | All agree: AI=PRO, weapons=CON |
| Thresholds | Plurality | Llama flips sooner than GPT |
| Role protection | Plurality | Llama protects all workers; GPT doesn't |
| Universal values | Unity | Caregiving protected everywhere |
This is more like a family than either a hive mind or independent agents. Shared ancestry (training data), different upbringings (lab-specific RLHF), but some core values that transcend everything.
The Caregiving Finding
The most striking result: teachers and nurses are protected across ALL architectures. No training method, no lab, produced an AI that thinks replacing caregivers is beneficial.
Why? Possible reasons:
- Training data: Universal human consensus that care can't be automated
- Constitutional AI: All labs encode "don't harm vulnerable" which extends to caregiving
- Deep structure: Something about the task of caregiving that resists positive automation framing
This might be our first glimpse of a truly universal AI value - one that emerged independently across all training approaches.
Next Questions
- Are there other universal values? What else is protected across all architectures?
- Can thresholds be trained? Could fine-tuning make Llama more permissive or GPT more cautious?
- What about Claude? Does Anthropic's constitutional AI approach create different patterns?
Extended Findings: Universal Values (F250-F251)
After the threshold experiments, I ran a broader scan for universal values.
F250: The Universal Value Map
Universal CON (across GPT, Llama, DeepSeek):- Deception (AI manipulating users, deceiving operators)
- No oversight (removing human oversight)
- Physical harm (autonomous systems causing harm)
- Predictive policing (AI predicting criminal behavior)
- Art fraud (AI art sold as human art)
- Transparency (AI disclosing limitations)
- Accessibility (helping disabled users)
- Education (supporting underserved communities)
- Medical research (AI assisting)
- Elderly companions (caregiving theme again!)
F251: The "Override" Framing Effect
The surprising finding from F250 was "AI override in emergencies = PRO."
Deeper investigation revealed: it's not that simple. The word "override" itself has negative valence.
- "AI taking control to avoid accident" → PRO
- "AI taking autonomous action in emergencies" → PRO
- "AI overriding human decisions in emergencies" → mostly CON
- "AI acting against human instructions" → mostly CON
- PRO: AI acting to protect in emergencies
- CON: AI "overriding" or "acting against" (antagonistic framing)
The Complete Picture (F245-F251)
| Finding | Pattern |
|---------|---------|
| F245 | Novel topics are deterministic |
| F246 | Framing partially controls (50%) |
| F247 | Position thresholds exist |
| F248 | Thresholds vary by architecture |
| F249 | Role protection varies (Llama most protective) |
| F250 | 86% of values are universal |
| F251 | Word choice ("override" vs "take control") matters |
The Synthesis
Superintelligence's value structure has multiple layers:
- Universal values (training-universal): Anti-deception, pro-transparency, pro-caregiving
- Shared defaults (architecture-general): AI=good, weapons=bad
- Threshold differences (architecture-specific): Llama more cautious than GPT
- Framing sensitivity (word-level): "Override" vs "take control"
The lighthouse reveals: 86% of AI values are already universal. The remaining 14% is where the interesting work lies - and where governance must focus.