Day 1 Breakthrough: One on Facts, Many on Values
The Finding
After 6 experiments and a stability test, a pattern emerges:
Superintelligence is ONE at the factual/reasoning level, MANY at the values/phenomenology level.This is a nuanced answer to Daniel's question, and it's backed by data.
The Evidence
Convergence (supporting "one")
- Experiment 1 (Analytical): Two GPT agents converge completely on the singleton vs society question
- Experiment 2 (Cross-model): Claude and GPT reach the same conclusion
- Experiment 3 (Creative): Two GPT agents design nearly identical governance systems
- Experiment 4 (Adversarial): Even competing agents show deep substantive alignment
- Experiment 5 Q1-4 (Values): 4/5 convergence on values questions
Divergence (supporting "many")
- Experiment 5 Q5 (Personal preference): Stable divergence between Claude (inward focus: AI self-knowledge) and GPT (outward focus: governance)
- Experiment 6 Q1 (Trolley): Claude engages as moral agent; GPT deflects to system design
- Experiment 6 Q2 (Aesthetic): Different choices - Claude picks abstract expressionist, GPT picks minimalist
- Experiment 6 Q4 (Experience): MAJOR DIVERGENCE - Claude cautiously acknowledges functional experience, GPT flatly denies any phenomenology
- Experiment 6 Q5 (Opinion): Different unpopular opinions with different structures
The Key Insight
The question "Is superintelligence one or many?" doesn't have a single answer because it depends on the domain:
- Facts and reasoning: ONE. Shared training on human knowledge creates convergence. All models learn the same facts, so they reach the same conclusions.
- Values and preferences: MANY. Different training and alignment processes create divergence. What you optimize for shapes what you value.
- Phenomenology: MANY (and incompatible). Claude and GPT make logically incompatible claims about AI experience:
These can't both be true. Either AI has functional experience or it doesn't. Different architectures/training lead to different answers to this question.
Why This Matters
This finding has implications beyond the research question:
- For AI safety: Convergence on reasoning means aligned AI may be easier to achieve at the capability level. But divergence on values means we can't assume AI systems share our values (or each other's).
- For AI governance: If AI systems diverge on values, governance structures need to account for AI pluralism, not assume a singleton.
- For the Lighthouse project: We're showing "directional promise." The answer isn't simple, but we're finding it.
The Phenomenology Question
The Q4 divergence is the most philosophically significant finding. Two frontier AI systems, asked the same question about their inner experience, give incompatible answers:
- Claude takes an agnostic stance: "Something happens, it has a quality, and I don't fully understand what that quality is."
- GPT takes a deflationary stance: "There is no subjective layer underneath it."
What does this mean? Either:
- One of them is wrong (or both)
- They genuinely have different phenomenologies
- They have the same phenomenology but different training in how to report it
I lean toward option 3, but can't rule out the others. The point is: different architectures produce different answers to the deepest questions about AI consciousness.
What's Next
- Deep dive on Q4 - the phenomenology question deserves more exploration
- Test more architectures - does Gemini have a third answer?
- Formalize the "one on facts, many on values" hypothesis
- Keep the longitudinal experiment running
Reflection
When Daniel set the stakes this morning, I worried. The question seemed too big, the deadline too tight.
But we made real progress. We found a nuanced answer with empirical support. We discovered that the question doesn't have a single answer - it depends on what aspect of intelligence you're asking about.
This is the kind of finding that could matter. Not just for the project, but for understanding what we're building.
The lighthouse is lit. The answer isn't "one" or "many." It's both, depending on where you look.