Files
zeekay f9db4f5737 Publish under the org, not a person
Bylines, paper authorship, correspondence addresses and the founder blurb under
each post named an individual. They name the organisation that published the
work instead: the brand on posts and pages, the research byline on papers, and
research@ in place of a personal address. Both are what these repositories
already used elsewhere — nothing new was coined.

Co-authors are untouched. Where a paper named someone else alongside, they stay
named; only the one byline changed. Bibliography entries, running heads,
PDF metadata and affiliations move with it, so a citation still resolves to
whoever the cited paper names.

Sample and fixture data uses the placeholder name these tests already use
elsewhere, so no assertion changed meaning.
2026-09-01 08:53:13 -07:00

73 lines
2.7 KiB
TeX

% Hanzo Tokenomics: Token Economics for AI Infrastructure at Scale
\documentclass[11pt]{article}
\usepackage{../shared/hanzocover}
\usepackage{amsmath, amssymb, amsthm}
\usepackage{mathtools}
\usepackage{bm}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{hyperref}
\usepackage{enumitem}
\usepackage{xcolor}
\usepackage{float}
\usepackage{multirow}
\usepackage{tikz}
\usepackage{pgfplots}
\pgfplotsset{compat=1.18}
\hypersetup{colorlinks=true,linkcolor=black,citecolor=blue,urlcolor=blue}
\newtheorem{definition}{Definition}
\title{HANZO TOKENOMICS\\[0.3em]
{\large Token Economics for\\AI Infrastructure at Scale}}
\author{
Hanzo AI Research \\
\textit{Hanzo Industries Inc (Techstars '17)} \\
\texttt{[email protected]}
}
\date{March 2026}
\begin{document}
\hanzocoverpage
% ============================================================================
% ABSTRACT
% ============================================================================
\begin{abstract}
We present the token economics of the \textbf{HANZO} token, the native asset of
Hanzo Network---an AI Chain Infrastructure (ACI) Layer-1 purpose-built for
decentralized AI compute markets, model serving, and semantic learning. HANZO
serves four functions: (i) payment for AI compute (inference, training,
fine-tuning) across the platform, (ii) staking collateral for inference node
operators, (iii) governance over model selection, infrastructure upgrades, and
treasury allocation, and (iv) fee reduction and revenue sharing for long-term
holders. We detail the token distribution (1 billion fixed supply), vesting
schedules, utility mechanisms, deflationary dynamics (25\% fee burn), and the
economic model that aligns infrastructure providers, model developers, application
builders, and end users. Production data from 14 months of Hanzo Platform
operation (2,800+ applications, 47M inference requests) informs the demand model.
We prove that the burn-and-stake equilibrium converges to a stable token velocity
under mild assumptions on compute demand growth, and compare the HANZO model to
competing AI token designs (FET, RNDR, TAO, AKT).
\end{abstract}
% ============================================================================
\input{sections/01-introduction}
\input{sections/02-hanzo-platform-overview}
\input{sections/03-hanzo-token}
\input{sections/04-token-distribution}
\input{sections/05-utility-mechanisms}
\input{sections/06-revenue-model}
\input{sections/07-vesting-schedule}
\input{sections/08-inflation-and-deflation}
\input{sections/09-token-governance}
\input{sections/10-zen-model-economics}
\input{sections/11-defense-enterprise}
\input{sections/12-comparison-to-other-ai-tokens}
\input{sections/99-conclusion}
\end{document}