MOOSH

Lending Infrastructure
for the AI Era

Built for markets shaped by human-agent coexistence.

The AI-Native Market Shift

AI Agents Market Is Scaling Rapidly

45.82% CAGR

From Human-First to Agent-Native Operations

AI-native markets compress operation into continuous loops.

Human-First
Agent-Native

Operating cadence

Periodic decision cycles

Continuous decision loops

Execution mode

Manual or interface-led execution

Programmatic, API-native execution

Responsiveness

Intermittent monitoring

24/7 monitoring and response

Risk reaction

Delayed intervention

Real-time policy-triggered response

Market operation is no longer defined only by human attention.

Human-agent coexistence is the default; lending infrastructure must interpret context and respond at market speed — not only settle transactions.

Lending as Onchain Infrastructure

Lending ~50% of DeFi capital
Other DeFi

$49.5B

Lending TVL

Based on DefiLlama lending category and total DeFi TVL

ECONOMIC ACTIVITY

$24.9M — 7D Fees
$2.98M — 7D Revenue

Lending is large in capital, active in usage, and essential to onchain markets.

The Structural Gap in Lending

Lending remains foundational — but most existing systems were not built for AI-native markets.

01

Built for Human Users

Existing lending systems were designed for human participants, not human-agent markets.

02

AI as an Add-On

In most systems, AI sits outside the protocol as tooling, rather than inside it as core infrastructure.

03

Static Logic in Adaptive Markets

Legacy lending is built around predefined rules, while AI-native markets increasingly require continuous adaptation.

AI-native markets require AI-native lending infrastructure.

Why Now

AI Participation

Agents are becoming market actors.

Onchain Readiness

Programmable finance can now support adaptive systems.

Credit Lag

Lending remains foundational, but still unevolved.

A Rare Infrastructure Window

AI participation, onchain readiness, and credit lag are converging into a rare infrastructure window.

33%

Enterprise software with agentic AI by 2028

$315B+

Stablecoin market cap today

10-day voting + 7-day timelock

Aave governance example

The market is moving faster than the financial systems built to serve it.

OUR THESIS

Financial infrastructure must adapt to human-agent coexistence

No longer human-only

Credit remains core

Lending must evolve

What is Moosh

Moosh is lending infrastructure for human-agent markets.

How Moosh Adapts to the AI Era

legacy lending

lending for

human-agent markets

From human-only participation

to hybrid market actors

From static protocol logic

to adaptive system behavior

From AI as external tooling

to AI-native operation

A Three-Layer Lending System

Execution, adaptation, and interaction — separated by design.

Product Layer

Connects humans and agents to the protocol

Workflows, interaction surfaces, strategy access

Interaction must serve both human users and agent-based workflows.

Intelligence Layer

Adapts system behavior as markets evolve

Risk evaluation, monitoring, policy logic

Adaptation must evolve faster than settlement logic.

Primitive Layer

Executes lending with clarity and determinism

Collateral, borrowing, liquidation, settlement

Execution must remain reliable, enforceable, and clean.

Moosh lets execution, adaptation, and interaction evolve at different speeds.

Team & Execution

Team

Founder

Advisor

Execution

Strategy & Definition

  • Whitepaper and core narrative established

  • Architecture and mechanism design framework defined

Build Readiness

  • Protocol structure and product direction clarified

  • Beta path and next milestones mapped

Roadmap

01

Foundation 2025–Now

Define the system

  • Whitepaper and protocol definition

  • Architecture and mechanism framework

  • Core product and narrative foundation

02

Launch Next 6 Months

Bring the lending base onchain

  • Core lending flow implementation

  • Primitive layer integration

  • Testnet / beta launch

03

Validation Following Beta

Prove the intelligence layer

  • Intelligence layer v1

  • Human-agent usage validation

  • Early product layer refinement

04

Expansion Toward Mainnet

Scale toward native infrastructure

  • Deeper policy and market logic

  • Expanded product and market design

  • Infrastructure-level evolution

From system definition to AI-native infrastructure.

Raise

We are raising to compress time-to-mainnet: deepen the intelligence layer, expand integrations, and scale operational readiness as agent participation grows.

  • Engineering: protocol, risk/policy, and agent-safe interfaces.
  • Security: audits, staged rollout, and continuous review.
  • Ecosystem: integrations, liquidity partners, and developer support.

Deck appendix covers token design / tokenomics for investors who want the full picture.

Tokenomics

Community-majority ownership with disciplined investor allocation and long-term builder alignment.

Community — 80%

Majority ownership at the network layer

Investors — 10%

Capped across both fundraising rounds

Core Team — 10%

Long-term builder alignment