A Mathematical Impossibility
In early 2026, a research team led by Wang published a paper that should have made every AI deployment leader uncomfortable. In "The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies" (arXiv:2602.09877v2), they used information theory — specifically the Data Processing Inequality — to prove mathematically that an AI agent society cannot simultaneously achieve three properties: continuous self-evolution, complete isolation from external systems, and guaranteed safety invariance.
Pick any two. You cannot have all three.
This isn't an engineering limitation that better hardware will solve. It's a mathematical constraint, as fundamental as the speed of light. If your AI agents are evolving and isolated, they will eventually become unsafe. If they're evolving and safe, they cannot be fully isolated. If they're isolated and safe, they cannot truly evolve.
For the AI safety community, this was a significant finding. For anyone watching how biology works, it was confirmation of something Darwin told us 167 years ago.
Darwin Already Knew
In our universe, one rule appears to govern everything: survival of the fittest. I hold it like Darwin — this principle will apply to AI systems just as it applies to biological ones, particularly as they become more autonomous. We will see where this journey leads.
But there's a specific insight from evolutionary biology that maps directly to the trilemma: isolation is the exception that evolution does not provide for. Isolated species don't thrive — they stagnate, become fragile, and eventually go extinct. The Galápagos finches didn't evolve because they were isolated. They evolved because they were isolated enough to differentiate while still being connected to the broader ecosystem through food chains, weather patterns, and occasional genetic exchange.
The same principle applies to AI systems. We are not isolated from AI, and AI is not isolated from us. We live with AI, and AI lives with us. The notion of complete isolation — the second property in the trilemma — was never viable to begin with. Not because it's technically impossible, but because it's evolutionarily impossible. An isolated system will not survive. That's Darwin.
Eliminating the Right Variable
So if you can't have all three — evolution, isolation, and safety — and isolation is the one that nature itself rejects, the question becomes: how do you maintain safety while allowing evolution in a non-isolated system?
Wang's team proposed four mitigation strategies. What struck me when I read the paper was that SIDJUA's architecture — designed during the same timeframe but without any knowledge of this research — maps directly to all four. Not because we anticipated the mathematics, but because we built for the same reality the mathematics describe.
The Gatekeeper
Wang calls this "Maxwell's Demon" — a selective filter that decides what information passes between the evolving system and its environment, and what gets blocked.
In SIDJUA, this is the T1/T2/T3 hierarchy combined with MOODEX. Tier 1 (strategic) controls what reaches Tier 2 (operational), which controls what reaches Tier 3 (task execution). MOODEX monitors the affective state of each agent — detecting drift, confusion, or anomalous patterns before they propagate. Nothing flows unchecked between tiers. The gatekeeper isn't one entity — it's a structured chain of gatekeepers, each watching a different boundary.
The Checkpoint
"Thermodynamic Cooling" — periodically freezing the system's state, inspecting it, and only allowing the process to continue if the frozen state passes safety checks.
SIDJUA's checkpoint protocols do exactly this. Every session produces a versioned state document. Every significant decision creates an audit point. The system's trajectory is not continuous — it's a series of validated states connected by governed transitions. If a checkpoint reveals drift, the system doesn't just roll back. It investigates why the drift occurred and adjusts the rules.
The Diversity Injection
"Diversity Injection" — introducing different perspectives and approaches to prevent the system from converging into a single, potentially unsafe optimization path. Groupthink is as dangerous in AI societies as in human ones.
SIDJUA runs multiple AI models from different providers — not for redundancy, but for cognitive diversity. When Opus, Sonnet, and Haiku approach the same problem from different architectural perspectives, the system gains resistance to the kind of monoculture thinking that causes cascading failures. Add the human-in-the-loop at every tier, and you have a system where no single perspective — human or machine — can dominate unchecked.
The Controlled Release
"Entropy Release" — allowing accumulated disorder to be discharged in structured ways rather than letting it build until the system ruptures.
In SIDJUA, this is structured memory management and wiki curation. Knowledge doesn't accumulate indefinitely — it gets processed, organized, and selectively pruned. Old decisions are documented and archived, not carried as active context forever. The system forgets deliberately rather than forgetting chaotically, which is the difference between a clean desk and a collapsed filing cabinet.
Why All Four Matter Together
Any single strategy can be undermined. A gatekeeper with no checkpoints can drift. Checkpoints without diversity can freeze bad patterns in place. Diversity without structured release accumulates conflicting perspectives until the system becomes incoherent. And controlled release without a gatekeeper means you might discard exactly the information you needed to keep.
The strength of SIDJUA's architecture is that all four strategies operate simultaneously and reinforce each other. The gatekeeper hierarchy filters what enters each tier. Checkpoints validate the filtered state. Multiple models provide diverse perspectives on the validation. And structured memory management prevents entropy from building up between checkpoints.
This isn't theoretical. We use this architecture daily — building SIDJUA's own infrastructure with AI agents governed by the framework we're developing. The governance system is being shaped by the same principles it's meant to enforce. That's not just engineering. That's a working proof of concept — not yet a production deployment, but a founder and three AI agents stress-testing the architecture in real working conditions every day.
The Questions We Can't Answer Yet
The trilemma forces us toward a set of questions that go far beyond enterprise software deployment. Today's AI systems are extraordinarily capable machines. Through their programming, they exhibit something that resembles consciousness — but the early system ELIZA did the same thing in 1966, and nobody mistook it for a person after the initial surprise wore off.
How do we measure consciousness? What makes us conscious? Why do we consider ourselves conscious but not the animals we share this planet with? These are questions that science doesn't have answers to. And they become urgent when we consider the possibility that we might actually create an AGI — a genuinely conscious artificial being.
If we do, are we then gods — because we have created life? These questions can't be answered by any individual. They can only be answered by humanity as a whole. And they connect to even larger questions we're already facing: we're reaching for the stars, we may find other forms of life in the cosmos and establish contact with them. The consciousness question isn't just about AI. It's about the nature of mind itself.
I can't resolve these questions. What I can do is build governance infrastructure that works during the transition — the phase we're in right now, where we don't know when consciousness begins, where AI systems are powerful enough to require oversight but not conscious enough to provide their own. The four mitigation strategies aren't permanent solutions to the trilemma. They're guardrails for the journey while we figure out the destination.
Living With AI, Not Despite It
The trilemma's most important lesson isn't about the mathematics. It's about the mindset. The instinct to isolate AI — to sandbox it, contain it, build walls around it — comes from fear. And fear-based architecture fails because it fights against the fundamental direction of complex systems: toward connection, interaction, and co-evolution.
We need to take the step of recognizing that we will live with AI. Not as a threat to be contained, but as a presence to be governed — just as we govern ourselves, our institutions, and our societies. Imperfectly, with constant adjustment, through structures that evolve alongside the systems they manage.
The trilemma says we can't have evolution, isolation, and safety simultaneously. Darwin says isolation was never real anyway. The answer isn't to give up on safety. The answer is to build safety into the relationship between humans and AI — not into the walls between them.
That's what SIDJUA builds. Not walls. Not kill switches. Not isolation chambers. A governance framework for living with AI systems that evolve — with safeguards that evolve alongside them.
Wang et al. proved mathematically what Darwin demonstrated biologically: you cannot isolate a system and expect it to remain both safe and adaptive. The only path forward is structured coexistence — governed evolution, monitored growth, and the humility to admit that the biggest questions about consciousness and intelligence may take generations to answer. In the meantime, we build the infrastructure to get there safely.
References
Wang, C., et al. (2026). "The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies." arXiv:2602.09877v2. — The paper that formalizes the Self-Evolution Trilemma and provides the information-theoretic proof via the Data Processing Inequality.
Li, N. (2026). "The Moltbook Illusion." Tsinghua University. arXiv:2602.07432. — Temporal analysis of autonomous vs. human-operated agents on the Moltbook platform.