When AI Truly Starts to Run: How MEMO Reclaims Compute Execution Rights from Tech Giants

Why We Must Talk About “Decentralized Compute” Now

As AI Agents move into large-scale real-world deployment, the focus of industry competition is shifting from model parameters to compute supply and scheduling rights. In the past, the race was about who could train more powerful models. Today, the more practical challenge is how to ensure that AI systems can access sufficient and stable compute resources at runtime.

By 2026, five major technology companies — including AWS, Google Cloud, and Alibaba — will control nearly 80% of global cloud computing capacity. This high level of concentration has raised serious public concern. Excessive centralization not only limits innovation, but also risks placing the future of AI technology in the hands of a small number of corporations.

MEMO’s decentralized compute initiative is designed to address this exact problem. By bringing distributed GPU resources into an open network, MEMO aims to break existing compute monopolies and ensure that compute can be scheduled transparently and efficiently on a global scale — transforming compute from something that merely “runs” into something that runs reliably, is clearly accounted for, and can be verified.

MEMO’s Five-Layer “Compute Execution Stack”

MEMO does not view “decentralized compute” as a simple aggregation of GPUs. Instead, it has built a comprehensive execution framework that spans Resources → Scheduling → Execution → Verification → Settlement, forming a complete and trustworthy compute execution system.

Resource Layer

The resource layer is the foundation of MEMO’s decentralized compute system. Through the ERC-7829 standard, MEMO turns various data and compute resources into programmable assets that can be managed and scheduled in a standardized way. This not only helps ease supply constraints in compute markets, but also enables compute providers to earn rewards through the MEMO network.

Put simply, if you have idle GPUs, renting them out used to be a hassle. With MEMO, you can:

● Package your idle GPUs into ERC-7829 assets with standardized specifications

● List them on the marketplace

● Deliver compute according to the agreed standards when someone purchases access

● Get paid automatically based on the rules once delivery is completed

Scheduling Layer

The scheduling layer is the hub of MEMO’s decentralized compute architecture. It uses smart contracts and algorithmic scheduling to allocate compute resources based on demand. During execution, it takes into account factors such as workload requirements, time sensitivity, and cost constraints, and then dynamically coordinates compute capacity distributed across the globe. This improves overall utilization efficiency and reduces dependence on any single centralized compute provider.

Importantly, the scheduling layer does more than just assign resources — it also monitors the compute status of each node in real time to ensure tasks can run steadily. As a result, MEMO’s compute supply becomes both flexible and reliable, supporting AI workloads of different sizes and types.

If you’re currently relying on centralized compute, you’re often stuck in a passive position: when a provider faces GPU shortages or raises prices, you have little choice but to accept it. With MEMO’s scheduling, however:

● Compute comes from globally distributed nodes

● There is no single “must-use” provider

● The scheduling objective is decentralization and multi-source availability

So from your perspective, even if one node runs into problems, your system won’t come to a halt.

Execution Layer

The execution layer is where real computation happens. At this layer, MEMO can break a workload into multiple sub-tasks and assign them to suitable nodes for processing. MEMO’s execution layer supports a wide range of compute jobs, including AI model training, inference execution, and data processing. By decentralizing execution, MEMO can avoid the single points of failure and privacy risks that are common in centralized compute platforms — improving both the security and availability of the system.

Verification Layer

To make execution transparent and trustworthy, MEMO introduces TEE (Trusted Execution Environment) and ZK (Zero-Knowledge Proofs) at the verification layer:

● TEE (Trusted Execution Environment): TEE uses hardware-based encryption to create an isolated compute environment. Even the node operator cannot view or tamper with the data and results used during computation. This provides the highest level of protection for sensitive workloads.

● ZK (Zero-Knowledge Proofs): ZK enables verification that a task was executed correctly without revealing the original data or the full computation process. With ZK proofs, MEMO can submit verifiable evidence of execution results on-chain — preserving privacy while ensuring correctness and integrity.

You can think of the verification layer as outsourcing exam grading. When you hand over a confidential exam paper to someone else, your biggest fears are (1) the content being leaked, and (2) the grader cutting corners and denying it later. TEE is like putting the grader in a locked glass room — they can do the work, but can’t take anything out. ZK is like a “grading compliance certificate”, proving the paper was graded according to the rules without exposing the paper’s details.

Settlement Layer

The settlement layer is the final part of MEMO’s decentralized compute architecture, responsible for automated payment and settlement after tasks are completed. At this layer, MEMO integrates the x402 protocol, enabling micropayments to run efficiently and automatically. Once a task is finished, the system calculates the cost and settles it through on-chain smart contracts.

● x402 Protocol: It solves the friction of high-frequency micropayments by enabling automated settlement upon task completion. Each execution node receives rewards based on the compute it delivers, and all fund flows are fully transparent.

● Facilitator Protocol: When disputes arise between participants, the Facilitator can arbitrate based on the execution proofs provided by the relevant layers, ensuring that each party’s rights and interests are protected.

Overall, the settlement layer ensures payments are both efficient and transparent, while also reducing manual intervention and operational overhead — making the economics of decentralized compute far more viable.

MEMO’s Vision for Decentralized Compute

MEMO is committed to breaking the monopoly of traditional centralized platforms by integrating global compute resources into an open, decentralized, transparent, and reliable compute network. Whether it’s an individual’s idle GPU or a large data center’s capacity, any node with compute resources can contribute to AI workloads through MEMO’s network.

Realizing this vision will not only mitigate the systemic risks created by concentrated compute power, but also unlock more innovation opportunities and a fairer competitive environment for the AI industry.