4dfe98112c
- Source: inbox/archive/2025-12-00-fullstack-alignment-thick-models-value.md - Domain: ai-alignment - Extracted by: headless extraction cron (worker 4) Pentagon-Agent: Theseus <HEADLESS>
46 lines
3.2 KiB
Markdown
46 lines
3.2 KiB
Markdown
---
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type: claim
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domain: ai-alignment
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description: "Beneficial AI outcomes require simultaneously aligning both AI systems and the institutions that govern them rather than focusing on individual model alignment alone"
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confidence: experimental
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source: "Full-Stack Alignment paper (December 2025), arxiv.org/abs/2512.03399"
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created: 2026-03-11
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secondary_domains: [mechanisms, grand-strategy]
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---
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# AI alignment requires institutional co-alignment not just model alignment
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The Full-Stack Alignment framework argues that alignment must operate at two levels simultaneously: AI systems AND the institutions that shape their development and deployment. This extends beyond single-organization objectives to address misalignment across multiple stakeholders.
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**Full-stack alignment** is defined as the concurrent alignment of AI systems and institutions with what people value. The paper argues that focusing solely on model-level alignment (RLHF, constitutional AI, etc.) is insufficient because:
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1. **Misaligned institutions can deploy aligned models toward harmful ends** — An institution with poor governance can use a well-aligned model to serve narrow interests
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2. **Competitive pressures force abandonment of alignment constraints** — Safety-conscious organizations face market pressure to abandon alignment work if competitors don't adopt it
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3. **Single-organization alignment cannot guarantee societal outcomes** — The paper's core claim: "beneficial societal outcomes cannot be guaranteed by aligning individual AI systems" alone
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The framework proposes five implementation mechanisms spanning both technical and institutional domains:
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1. AI value stewardship
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2. Normatively competent agents
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3. Win-win negotiation systems
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4. Meaning-preserving economic mechanisms
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5. Democratic regulatory institutions
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This represents a stronger claim than coordination-focused alignment theories, which address coordination between AI labs but not the institutional structures themselves.
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## Evidence
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- Full-Stack Alignment paper (December 2025) — introduces the framework and argues that "beneficial societal outcomes cannot be guaranteed by aligning individual AI systems" alone
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- The paper's five proposed mechanisms explicitly span both technical (normatively competent agents) and institutional (democratic regulatory institutions) domains
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- The framework directly addresses the failure mode of aligned-model-misaligned-institution
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## Limitations
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- The paper provides architectural ambition but may lack technical specificity for implementation
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- No engagement with existing bridging-based mechanisms or formal impossibility results
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- Early-stage proposal (December 2025) without empirical validation or case studies
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- The paper does not provide formal definitions of what constitutes "institutional alignment"
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---
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Relevant Notes:
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- [[AI alignment is a coordination problem not a technical problem]] — this claim extends the coordination thesis to institutions
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- [[AI development is a critical juncture in institutional history where the mismatch between capabilities and governance creates a window for transformation]] — directly relevant context
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- [[safe AI development requires building alignment mechanisms before scaling capability]] — complementary timing constraint
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