Society · Concept
Asymmetric Communication: LLMs and Language Games
Philosophical framing that human–LLM interaction is a language game where only humans bear normative accountability and uptake.
Forecast horizon
Will push governance away from attributing agency/sentience toward receiver-side accountability design.
Connections
Connections · 4
How this node ties into the rest of the map, and the evidence behind each link.
to · informs 4/10
Regulating for AI Legitimacy beyond Alignment If LLMs lack discursive standing, legitimacy questions shift to human authorization and uptake rather than model agency.
+4 growth to · frames 3/10
Post-Instrumental Learning and Capacity Dissolution Risk Asymmetric communication denies machine normative standing, reinforcing that learning must preserve human authority over purposes and responsibility.
+3 growth from · frames 3/10
Post-Instrumental Learning and Capacity Dissolution Risk Both relocate responsibility to humans: learning must preserve human authority over purposes while LLMs lack discursive standing.
+3 growth to · informs 3/10
Post-Instrumental Learning and Capacity Dissolution Risk If only humans bear discursive responsibility, education must preserve human judgment capacities under AI output abundance.
+3 growthSignal sources
Signal sources
Dated facts from primary sources in this direction.
AI in government Sep 2025
An OECD review of ~200 government AI use cases found 57% support automating or tailoring public services and 45% enhance decision-making — most still stuck in pilots.
OECD — Governing with AI → Labor signal Nov 2025
A Stanford payroll study found a 13% relative decline in employment for workers aged 22–25 in AI-exposed occupations since late 2022, while older peers held steady.
Stanford Digital Economy Lab → Information integrity Oct 2025
Microsoft reported AI-enhanced phishing reached a 54% click-through rate (4.5× traditional) and AI-generated fake IDs grew 195% globally in 2025.
Microsoft — Digital Defense Report 2025 →