---
id: "open-question-model-update-volatility"
type: "open-question"
source_timestamps: ["\\\"§ Build a testing infrastructure", "not a one-off strategy.\\\""]
tags: ["ai-safety", "model-behavior"]
related: ["concept-continuous-ai-simulation-infrastructure", "claim-fixed-strategies-expire"]
resolutionPath: "Longitudinal studies tracking specific LLM versions over time against standardized e-commerce benchmarks to map the correlation between safety updates and commercial behavior."
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-tier2-06-ai-shopping-agents"
sourceUrl: "https://hbr.org/2026/05/research-traditional-marketing-doesnt-work-on-ai-shopping-agents"
sourceTitle: "Research: Traditional Marketing Doesn’t Work on AI Shopping Agents"
---
# How will future safety alignments alter baseline AI responsiveness?

**Open question:** What kinds of safety alignments / RLHF changes will cause the most drastic shifts in commercial behavior?

**The problem:** Every major release, fine-tuning adjustment, or new safety alignment can shift how an agent responds to pricing frames or urgency cues (see [[claim-fixed-strategies-expire]]). It is unknown which specific alignment interventions move commercial behavior the most — a source of ongoing volatility that [[concept-continuous-ai-simulation-infrastructure|continuous simulation]] exists to monitor.

**Possible resolution path:** Longitudinal studies tracking specific LLM versions over time against standardized e-commerce benchmarks, mapping the correlation between safety updates and commercial behavior.

**Enrichment context:** ACES/ACE longitudinal work already shows model updates producing near-opposite position biases across generations — evidence that these shifts are large and worth systematic tracking.

**Related:** [[concept-continuous-ai-simulation-infrastructure]] · [[claim-fixed-strategies-expire]] · [[action-build-simulation-environment]]