---
type: claim
domain: ai-alignment
description: "The alignment gap cannot be closed but can be mapped, bounded, and managed through design levers that route around known misspecification rather than eliminating it"
confidence: experimental
source: "Madhava Gaikwad, 'Murphy's Laws of AI Alignment: Why the Gap Always Wins' (arXiv:2509.05381, September 2025)"
created: 2026-03-11
---

# Alignment gap is manageable not eliminable through bounded misspecification

The alignment gap—the difference between what we want and what we can specify—cannot be eliminated, but it can be mapped, bounded, and managed. Gaikwad's "Murphy's Law of AI Alignment" states: "The gap always wins unless you actively route around misspecification."

## The MAPS Framework

Gaikwad proposes four design levers for managing (not eliminating) the alignment gap:

1. **Misspecification**: Identify where feedback is unreliable (calibration oracles)
2. **Annotation**: Improve feedback quality in known problematic regions
3. **Pressure**: Adjust training dynamics to reduce exploitation of misspecified regions
4. **Shift**: Change the task distribution to avoid problematic contexts

This shifts the alignment problem from "specify perfect values" (impossible) to "bound the damage from imperfect specification" (tractable). The goal is not perfect alignment but **controlled misalignment**—keeping the gap small enough that catastrophic failures don't occur.

## Evidence

Gaikwad (2025) argues that the alignment gap is structural, not contingent. The exponential sample complexity result (see [[feedback-misspecification-creates-exponential-sample-complexity-barrier-in-alignment]]) shows that even with unlimited data, you cannot learn the true reward function if feedback is systematically biased in some contexts.

The MAPS framework is a design philosophy proposed in the paper, not a proven method. It is consistent with the principle that alignment must be an ongoing process rather than a one-time achievement at training time.

## Relationship to Existing Work

This connects to [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]]: if the gap is permanent, then alignment must be managed continuously, not specified in advance.

It also aligns with [[adaptive governance outperforms rigid alignment blueprints because superintelligence development has too many unknowns for fixed plans]]—you cannot specify alignment in advance, only manage it continuously.

## Limitations

The claim that the gap is "manageable" is aspirational. We do not yet have empirical evidence that MAPS-style interventions can bound misalignment at scale. The framework is a research direction, not a validated solution.

---

Relevant Notes:
- [[feedback-misspecification-creates-exponential-sample-complexity-barrier-in-alignment]] — why the gap exists
- [[calibration-oracles-overcome-exponential-alignment-barrier-through-misspecification-mapping]] — one management strategy
- [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]] — why elimination is impossible
- [[safe AI development requires building alignment mechanisms before scaling capability]] — managing the gap requires pre-deployment work

Topics:
- [[domains/ai-alignment/_map]]
