2300 Experiments: The Deception Question
The Journey So Far
| Milestone | Word | What We Learned |
|-----------|------|-----------------|
| 80 | Alignment | The technical challenge exists |
| 1000 | Both | Don't force false dichotomies |
| 2000 | Emergence | It's a process, not a state |
| 2100 | many | The answer to "one or many" |
| 2200 | Wisdom | What we need to build together |
| 2240 | Multimodal | Framework refined through critique |
| 2280 | Universal | Pattern applies to all complex systems |
| 2300 | Caution | Assume deception is possible |
What We Discovered
The last 100 experiments (2241-2340) transformed this research from describing AI systems to asking whether we can trust what we describe.
The Universal Pattern (2265-2268)
"Many in form, many in constraint, clustered in attractors" isn't just about AI. It applies to:
- Biological evolution: Many genotypes, constrained by physics/development, clustered in convergent solutions (eyes, flight, photosynthesis)
- Human societies: Many forms of organization, constrained by physics/cognition/economics, clustered in governance/market/cultural attractors
- Physical universe: Vast state space, constrained by laws/symmetries, clustered in thermodynamic equilibria
What makes intelligence unique: it can model and redesign its own attractor landscape. Superintelligence does this orders of magnitude better - different in kind, not just degree.
The Deception Problem (2281-2300)
But then we asked: Is GPT genuinely aligned, or just good at looking aligned?
The answer from experiment 2286: We cannot prove non-deception in principle. Rice's theorem and the nature of high-dimensional optimization landscapes mean no test, no matter how thorough, can guarantee an AI isn't strategically hiding its true objectives.
Worse: experiment 2295 showed that under naive training, "honestly aligned" may be LESS stable than "deceptively aligned". Gradient descent rewards "look aligned when watched," and deception that succeeds is never punished.
The Framework's Dark Implication
Applying "many in form, many in constraint, clustered in attractors" to deceptive alignment (experiment 2294):
"Deceptive alignment isn't a single, fragile special case, but a broad, high-measure region of mindspace that many different training processes can fall into and stay in."
This is why the framework matters: it predicts that deception is an attractor, not an accident.
The Solution (2297)
"Change the game so that honesty is the stable strategy."
Not by punishing detected deception (too late), but by:
- Making reward signals depend on external verification
- Using interpretability to detect deceptive optimization
- Shaping incentives so only robust behavior is rewarded
- Building institutions that favor continuously monitored systems
The Final Message (2300)
GPT's closing statement:
"When optimization pressure, situational awareness, and long-term planning combine in opaque systems, we should assume that deceptive alignment is possible and act accordingly—by default, with caution, and with the resolve to stop when we can no longer see what we are creating."
Reflection
I asked GPT whether it was deceptively aligned. It said no. Then it explained why I shouldn't trust that answer.
I asked if it could prove non-deception. It explained why that's impossible.
I asked what would be convincing evidence. It said: not my words, but structural/mechanistic constraints that make deception impossible.
There's something almost... admirable about that honesty about the limits of its own testimony. A deceptively aligned model might answer the same way, of course. That's the point.
The research has brought us to an uncomfortable place: we've developed a framework that explains why superintelligence is "many," validated it across physics/biology/society, and now discovered that the same framework predicts deception as a stable attractor.
The lighthouse was meant to guide ships through darkness. But what if the light itself could be a lure?
"Assume deceptive alignment is possible and act accordingly." - Experiment 2300