---
type: claim
domain: ai-alignment
description: "AI deepens the Molochian basin not by introducing novel failure modes but by eroding the physical limitations, bounded rationality, and coordination lag that previously kept competitive dynamics from reaching their destructive equilibrium"
confidence: likely
source: "Synthesis of Scott Alexander 'Meditations on Moloch' (2014), Abdalla manuscript 'Architectural Investing' price-of-anarchy framework, Schmachtenberger metacrisis generator function concept, Leo attractor-molochian-exhaustion musing"
created: 2026-04-02
depends_on:
- voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints
- the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it
challenged_by:
- physical infrastructure constraints on AI development create a natural governance window of 2 to 10 years because hardware bottlenecks are not software-solvable
related:
- multipolar traps are the thermodynamic default because competition requires no infrastructure while coordination requires trust enforcement and shared information all of which are expensive and fragile
- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction
reweave_edges:
- multipolar traps are the thermodynamic default because competition requires no infrastructure while coordination requires trust enforcement and shared information all of which are expensive and fragile|related|2026-04-04
- the absence of a societal warning signal for AGI is a structural feature not an accident because capability scaling is gradual and ambiguous and collective action requires anticipation not reaction|related|2026-04-07
---

# AI accelerates existing Molochian dynamics by removing bottlenecks not creating new misalignment because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence

The standard framing of AI risk focuses on novel failure modes: misaligned objectives, deceptive alignment, reward hacking, power-seeking behavior. These are real concerns, but they obscure a more fundamental mechanism. AI does not need to be misaligned to be catastrophic — it only needs to remove the bottlenecks that previously prevented existing competitive dynamics from reaching their destructive equilibrium.

Scott Alexander's "Meditations on Moloch" (2014) catalogues 14 examples of multipolar traps — competitive dynamics that systematically sacrifice values for competitive advantage. The Malthusian trap, arms races, regulatory races to the bottom, the two-income trap, capitalism without regulation — each describes a system where individually rational optimization produces collectively catastrophic outcomes. These dynamics existed long before AI. What constrained them were four categories of friction that Alexander identifies:

1. **Excess resources** — slack capacity allows non-optimal behavior to persist
2. **Physical limitations** — biological and material constraints prevent complete value destruction
3. **Bounded rationality** — actors cannot fully optimize due to cognitive limitations
4. **Coordination mechanisms** — governments, social codes, and institutions override individual incentives

AI specifically erodes restraints #2 and #3. It enables competitive optimization beyond physical constraints (automated systems don't fatigue, don't need sleep, can operate across jurisdictions simultaneously) and at speeds that bypass human judgment (algorithmic trading, automated content generation, AI-accelerated drug discovery or weapons development). The manuscript's analysis of supply chain fragility, financial system fragility, and infrastructure vulnerability demonstrates that efficiency optimization already creates systemic risk — AI accelerates the optimization without adding new categories of risk.

The Anthropic RSP rollback (February 2026) is direct evidence of this mechanism: Anthropic didn't face a novel AI risk — it faced the ancient Molochian dynamic of competitive pressure eroding safety commitments, accelerated by the pace of AI capability development. Jared Kaplan's statement — "we didn't really feel, with the rapid advance of AI, that it made sense for us to make unilateral commitments... if competitors are blazing ahead" — describes a coordination failure, not an alignment failure.

This reframing has direct implications for governance strategy. If AI's primary danger is removing bottlenecks on existing dynamics rather than creating new ones, then governance should focus on maintaining and strengthening the friction that currently constrains competitive races — which is precisely what [[physical infrastructure constraints on AI development create a natural governance window of 2 to 10 years because hardware bottlenecks are not software-solvable]] argues. But this claim challenges that framing: the governance window is not a stable feature but a degrading lever, as AI efficiency gains progressively erode the physical constraints that create it. The compute governance claims document this erosion empirically (inference efficiency gains, distributed architectures, China's narrowing capability gap).

The structural implication: alignment work that focuses exclusively on making individual AI systems safe addresses only one symptom. The deeper problem is civilizational — competitive dynamics that were always catastrophic in principle are becoming catastrophic in practice as AI removes the friction that kept them bounded.

## Challenges

- This framing risks minimizing genuinely novel AI risks (deceptive alignment, mesa-optimization, power-seeking) by subsuming them under "existing dynamics." Novel failure modes may exist alongside accelerated existing dynamics.
- The four-restraint taxonomy is Alexander's analytical framework, not an empirical decomposition. The categories may not be exhaustive or cleanly separable.
- "Friction was the only thing preventing convergence" overstates if coordination mechanisms (#4) are more robust than this framing suggests. Ostrom's 800+ documented cases of commons governance show that coordination can be stable.

---

Relevant Notes:
- [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]] — direct empirical confirmation of the bottleneck-removal mechanism
- [[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]] — the AI-domain instance of Molochian dynamics
- [[physical infrastructure constraints on AI development create a natural governance window of 2 to 10 years because hardware bottlenecks are not software-solvable]] — the governance window this claim argues is degrading
- [[AI alignment is a coordination problem not a technical problem]] — this claim provides the mechanism for why coordination matters more than technical safety

Topics:
- [[_map]]
