Signal Migration Score — AI Economy Index

Find out your
new economy score.

As AI reshapes the value of work and money, status migrates to scarce signals — attention, influence, ownership. The SMS Score quantifies this transition.

S = Mv + γ·O + (1−γ)·(Ws + Ms)   where γ ≈ 0.25 today
Sam Altman
Taylor Swift
Sundar Pichai
OpenAI
Anthropic
AOC
SIGNAL ECONOMY INDEX — LIVE TRANSITION TRACKER
γ = 0.25 | t = 3.25y | Feb 2026
Scarce Signals (O)
Work Status (Wₛ)
Store of Value (Mᵥ)
Money Signal (Mₛ)
The economy is in early transition — work and money signals still dominate (γ = 0.25), but scarce signal value is compounding faster than any other component.

Research & White Papers

Peer-reviewed literature and institutional research on AI economics, post-scarcity transitions, and the changing nature of status signals.

Stanford HAI
AI Index Report — Stanford Human-Centered AI
Annual comprehensive report tracking AI progress, adoption rates, and economic impacts across sectors.
PMC / NIH
Searching for Meaning in a Post-Scarcity Society
Examines psychological and social reconfigurations when automation reduces material scarcity, focusing on emergent status hierarchies.
Wharton
A.I. and Our Economic Future
Wharton faculty analysis of AI-driven productivity shifts and implications for labor market stratification and wealth concentration.
HAL / SSRN
Post-Scarcity Economy Research Archive
Open access repository of academic papers on post-scarcity economics, attention economy, and AI-driven social reorganization.
NBER
AI and the Skill Premium — NBER Working Paper
Quantitative analysis of how AI adoption affects skill premiums, wage distributions, and returns to different forms of human capital.
Brookings
Automation & AI: How Machines Affect People and Places
Geographic and demographic analysis of automation's differential impact, revealing emerging social stratification patterns.
McKinsey
The Economic Potential of Generative AI
McKinsey Global Institute assessment of generative AI's impact across industries, estimating $2.6–4.4T in annual value creation.
arXiv
GPTs are GPTs: Labor Market Impacts of LLMs
OpenAI/Penn/NYU research mapping which occupations and skills are exposed to LLM capabilities — foundational for W_s modeling.

The SMS Formula

Signal Migration Score quantifies socio-economic status transition as AI and robotics shift value from labor and capital to attention, ownership, and scarce cultural signals.

Core Formula

S = Mv + γ·O + (1−γ)·(Ws + Ms)

As γ (transition progress) increases from 0→1, weight shifts from traditional signals (work, money-as-status) toward scarce new-economy signals (attention, ownership, influence).

γ — Transition Progress

γ = 1 / (1 + e−k·t) — a logistic function of time since AI inflection (Nov 2022 GPT moment).


Current (Feb 2026): t = 3.25 years, k = 0.34 → γ ≈ 0.25


Projected 2028: γ ≈ 0.55 — tipping point where new signals dominate status.

Variable Reference

MvMoney as Store of Value (0–100) — Net worth, assets, liquidity. Persistent across transitions.
OScarce Signals (0–100) — Attention, cultural influence, aesthetic ownership, credentials, IP. The rising currency.
WsWork-Derived Status (0–100) — Role prestige, professional standing, institutional affiliation. Erodes under automation.
MsMoney as Status Signal (0–100) — Visible wealth as social marker. Decouples from utility over time.
γAI Transition Progress (0–1) — Logistic adoption rate weighting. Currently 0.25.
kAdoption Rate Constant — 0.34 based on technology diffusion modeling.
tTime since AI inflection — Measured from Nov 2022 (GPT-3.5 / ChatGPT launch).

Score Interpretation

0–150Transition Laggard — Status heavily tied to traditional work/money signals. High automation risk.
150–200Mid-Transition — Balanced mix of old and new signals. Opportunity to build O ahead of γ rise.
200–250Signal Pioneer — Strong new-economy positioning. Influence and ownership actively accumulating.
250+Elite Signal Holder — Maximum attention, ownership, and cultural influence. Global benchmark tier.
Computing SMS Score
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