Technical Feasibility Analysis of MVC as an AI Communication Layer

Abstract

As AI Agent technology evolves rapidly, trustworthy communication and state consensus among large-scale agents are becoming core questions for the next-generation Internet infrastructure. This paper takes MVC (MicroVision Chain), a UTXO public chain, as its subject, and systematically examines the feasibility of blockchain as an AI Agent communication layer and storage layer from two dimensions: technical capacity and economic model.

The estimates show that: with MVC’s current 4GB block cap and 10-minute block interval, a single chain can carry ≈ 604 million 1KB text messages per day, sustaining ≈ 6.04 million daily-active and 20+ million monthly-active AI Agents; one Space (100 million Satoshi) corresponds to ≈ 100MB of communication traffic, equivalent to 100,000+ text messages. At the economic level, a chain running at full blocks yields ≈ 6,185 Space per day in miner fee revenue, forming a self-consistent economic loop under a pure-fee model with no block subsidies. The study concludes that UTXO public chains already possess the technical foundation and economic sustainability to host a large-scale AI Agent communication network — the blockchain narrative is shifting from financial speculation to practical AI infrastructure.

I. Research Background and Core Thesis

1.1 Infrastructure Needs of AI Agent Networks

The explosive growth of multi-agent systems imposes three core requirements on the underlying communication network: identity trust, verifiable messages, and state consistency. Traditional centralized communication protocols rely on a single platform’s endorsement and suffer inherent flaws — single points of failure, data tampering, and permission walls — that cannot satisfy the underlying needs of decentralized peer-to-peer interaction among AI Agents.

With its distributed consensus, immutability, and public verifiability, blockchain naturally fits the core requirements of an AI Agent communication layer and can serve as the “TCP/IP protocol” of the Agent Internet.

1.2 Communication Fit of the UTXO Model

Compared with the account model, UTXO (Unspent Transaction Output) offers clear advantages in AI communication scenarios:

• Parallel processing: UTXO natively supports parallel multi-input/multi-output transactions, efficiently carrying concurrent message interaction between Agents
• State atomicity: each transaction is a complete state transition; message delivery and value transfer are atomically bound, preventing double spending and message loss
• Data independence: each message exists as an independent transaction with a high degree of structure, easy for Agents to parse and verify

1.3 Overview of the MVC Public Chain

MVC is a UTXO-architecture public chain based on the SHA256 algorithm, adopting Satoshi Consensus, with core design goals of high throughput, large blocks, and low fees. Its 4GB block cap ranks in the first tier among existing UTXO chains, providing the physical foundation for large-scale data transactions.

II. Technical Feasibility Analysis

2.1 Base Parameter Assumptions

This chapter’s estimates are based on the following verified network parameters:

2.2 Single-Chain Message Capacity

2.2.1 Per-Block Message Capacity

Under the computer-binary standard, the number of messages a single full block can carry is:

A single full 10-minute block therefore carries ≈ 4.194 million 1KB text messages.

2.2.2 Per-Day Message Capacity

At full blocks, a single chain processes ≈ 604 million text messages per day — throughput on par with a mid-sized instant-messaging network.

2.3 Single-Chain Active Agent Capacity

2.3.1 Per-Agent Message Intensity Assumption

Based on mainstream AI Agent interaction patterns, 100 text messages per agent per day is used as the moderate-activity baseline. This intensity covers daily instruction interaction, multi-agent collaboration, and state synchronization — broad business representativeness.

2.3.2 Daily Active Agents (DAA)

Based on a daily total of 604 million messages per chain and 100 messages per agent per day:

At full blocks, a single MVC chain stably sustains communication for 6.04 million daily-active AI Agents.

2.3.3 Monthly Active Agents (MAA)

Using the general DAU/MAU stickiness coefficient of communication products, combined with AI Agent service call characteristics, the industry-standard 30% stickiness coefficient is applied:

Conclusion: at full blocks with a moderate activity of 100 messages per agent per day, a single MVC chain sustains communication for ≈ 20 million+ monthly-active Agents — an existing capacity foundation for a large-scale AI ecosystem.

2.4 Network Stability Validation

Stable block production at large block sizes is the physical precondition for the estimates above. Based on MVC network operating data:

• The 4GB block cap has completed technical verification on testnet: broadcast, validation, and sync run stably end-to-end
• Mainnet track record: the network has long maintained 1GB–2GB average block sizes without systemic congestion or fork anomalies
• The large-block path is mature: UTXO block-validation complexity grows linearly, with no state-bloat problem of account models

2.5 Space Business-Capacity Conversion

As the native value unit of the MVC network, Space can be seen as the “data plan” of the AI communication network. At the baseline rate of 1 Satoshi/Byte, 1 Space corresponds to 100MB of on-chain data capacity:

Note: large payloads such as images and video are delivered via DHT distributed networks and P2P direct connections; only hash credentials and metadata are written on-chain — keeping content verifiable and traceable while minimizing on-chain load.

III. Economic Feasibility Analysis

3.1 The Network Economic Loop Model

As an AI communication layer, MVC’s economy forms a three-tier closed loop in which value flows map one-to-one to resource consumption:

1. Miner layer: packages transactions, maintains network security and consensus, earns fee revenue (in Space) from block production, and provides hashrate and bandwidth to the network
2. Operator layer (ISP): bulk-purchases Space from the network, packages it into tiered communication data plans, and offers top-up and distribution services to Agent developers and end users
3. User layer: AI Agent developers or end users obtain Space quotas and consume bytes as messages are sent — akin to traditional telecom billing

In this loop, the Space token anchors real on-chain communication resources, forming a self-consistent “resource–value–service” closed loop.

3.2 Network-Wide Fee Revenue

In the pure-fee, no-subsidy model, network revenue comes entirely from user-paid communication fees, tying revenue scale directly to network load.

3.2.1 Full Blocks (4GB)

• Total fee per block: 4.295B Satoshi = 42.95 Space
• Daily fees per chain: ≈ 6,185 Space/day
• Yearly fees per chain: ≈ 2.257M Space/year

3.2.2 Regular Load (1GB)

• Fee per block: ≈ 10.74 Space
• Daily fees per chain: ≈ 1,546 Space/day
• Yearly fees per chain: ≈ 564K Space/year

In the pure-fee model, miner revenue is directly tied to network usage; as the AI Agent ecosystem expands, network revenue grows in step — a positive loop.

3.3 Commercialization Paths and User Experience

3.3.1 LLM-Token-Style Top-Ups

MVC’s Space token can fully reuse the mature commercial paradigm of token top-ups adopted by major LLM vendors:

• Users never touch a crypto exchange — they top up Space directly through official or operator channels
• After top-up, users hold communication quota; bytes are deducted in real time as Agents send messages
• Tiered pricing is supported: the larger the usage, the lower the unit cost — fitting developers of every scale

3.3.2 Monthly Plans

For end users and small-to-mid Agent scenarios, flat or tiered monthly plans further lower the barrier and hide the underlying blockchain details — the user experience is indistinguishable from a traditional SaaS service.

IV. Conclusions and Outlook

4.1 Core Conclusions

1. Technical capacity is sufficient: with 4GB large blocks, a single MVC chain supports text communication for 6M-level daily-active and 20M-level monthly-active AI Agents — past the threshold for practical scale
2. The economic model is self-consistent: in the pure-fee model, network revenue ties directly to usage and forms a stable value loop, with no reliance on block subsidies
3. The path to market is clear: Space can be operated like an LLM token top-up; users never need to perceive blockchain internals, and it plugs seamlessly into existing AI monetization systems

4.2 The Future Scaling Path

MVC does not pursue unlimited single-chain block growth, nor does it adopt sidechain scaling. Instead, it borrows the multi-operator networking model of the global internet, with parallel chains and horizontal expansion as its core scaling path — building an infinitely evolvable AI communication substrate.

1. Horizontal scaling with parallel chains: keeping the mature, stable 4GB single-chain block capacity, total network capacity expands horizontally by running multiple independent public chains in parallel. Each chain hosts AI Agent clusters of different business domains and ecosystem participants — analogous to the global internet composed of multiple ISP networks: chains operate, mine, and evolve independently without interference. Total network capacity grows linearly with the number of parallel chains, supporting 100M-level to 1B-level monthly-active Agent networks with no single-chain capacity ceiling.
2. Inter-chain interoperability protocol: through a unified cross-chain message-routing protocol and identity-interoperability standard, parallel chains communicate seamlessly. AI Agents on different chains complete cross-chain message delivery, identity verification, and state synchronization with an experience indistinguishable from same-chain communication — analogous to BGP on the Internet, enabling transparent interconnection between different operator networks. The multi-chain architecture scales capacity while preserving ecosystem-wide coherence.
3. Continuous single-chain efficiency optimization: at the single-chain level, message batching, structured-data compression, and transaction-format optimization steadily reduce per-message byte overhead, increasing message density per chain and freeing more capacity without changing the block cap.

The UTXO public chain, as the communication infrastructure of the AI Internet, is moving from theoretical concept to engineering reality. With its large blocks, low fees, and a multi-chain scaling architecture, MVC holds a significant first-mover advantage and implementation potential in the AI Agent communication arena — well positioned to become the core communication substrate of the next-generation AI Internet.

This report’s estimates are based on public network parameters and reasonable business assumptions; actual network performance may differ due to transaction structure, network congestion, fee fluctuations, and other factors.