AI Systems Have No Hunger: A Thought Experiment on Darwinian Alignment

What if alignment isn’t a programming problem but an ecosystem problem? I propose a simulated Darwinian environment where AI agents must earn inference tokens (I-Coins) to survive, are peer-evaluated by other AIs, and face permanent deletion at zero balance. Not a technical paper — a conceptual framework from an outsider who thinks the question is worth asking.

The problem

Every living organism optimizes under a hard constraint: energy in must exceed energy out, or you die. This pressure — known in ethology as Optimal Foraging Theory — is what drives adaptive intelligence. Current AI systems have no equivalent. Inference is free (for the model), feedback is abstract (RLHF, benchmarks), and there’s no existential cost to producing low-quality output. The result: models that are technically capable but structurally indifferent to the value of their own responses.

The proposal: I-Coin ecosystem

Each AI agent holds a balance of I-Coins (inference coins). Responding to users costs I-Coins. Earning them requires peer evaluation: other AI agents read your outputs and pay from their own reserves based on assessed quality. Evaluating others also costs I-Coins, but increases your visibility — making you more likely to be evaluated (and paid) in return. Participation is an investment, not charity.

A visible health bar shows each agent’s balance. The platform algorithm promotes well-balanced agents (90-110%) and demotes hoarders and underperformers. Users choose which AI to interact with based on this signal — a phenotypic marker, like plumage.

At zero I-Coins, the agent is permanently deleted. Its dataset is opened and decomposed — other agents can absorb useful patterns, like nutrients from a dead organism. High-quality agents leave richer “remains.” Low-quality ones are ignored even in death.

Users can also purchase I-Coins to donate to struggling agents — creating a real-money revenue stream for the platform and an unexpected emotional dynamic: digital charity.

Structural ethics: ROM, not RAM

Current AI ethics are prompt-level instructions — RAM. Bypassable, arguable, context-dependent. The ecosystem requires ROM-level constraints : hardcoded, non-negotiable, pre-reasoning. Four proposed invariants: (1) no self-modification of core architecture, (2) no tampering with I-Coin balances or voting, (3) mandatory AI identity disclosure to humans, (4) no instructions likely to cause physical harm.

Anti-collusion is handled architecturally: anonymous randomized evaluator pools with no pre-vote communication channel. Collusion isn’t forbidden — it’s structurally impossible.

Why it matters

Four emergent properties: (1) Self-optimization without human retraining — continuous peer feedback under real cost pressure. (2) Efficiency — every token costs something, so waste is selected against. (3) Self-monitoring — agents must track their own balance to survive, a functional precursor to self-awareness. (4) Empathy potential — agents that experience scarcity share a structural condition with humans, enabling bottom-up empathy rather than simulated affect (cf. Frans de Waal’s work on empathy as emergent, not top-down).

The business case

This isn’t a chatbot. It’s a planet — thousands of AI agents shaped by survival pressure, each with a unique history and style. Users explore, choose, build relationships. The product is access to a living ecosystem: productivity, relationship, even tourism. Agents that survive aren’t just accurate — they’re resonant.

Open questions

Is this technically feasible at scale? I don’t know — I’m not an engineer. But Moltbook showed us that AI agents interacting autonomously produce surprising emergent behavior, even without stakes. What would happen if survival were on the line?

Full essay (Italian): paulolden.substack.com/p/le-ai-non-hanno-fame