AO computers achieve consensus through "holographic states," utilizing Arweave's immutable message logs to break through traditional scalability limitations. This approach marks a significant innovation over existing systems like proof of work and proof of stake, paving the way for a new era of efficient, scalable decentralized computing.

Understanding Traditional Consensus Mechanisms

Blockchains like Bitcoin and Ethereum use consensus mechanisms to allow network participants to agree on the state of the ledger, including transaction validation, account balances, or the results of smart contract executions.

Bitcoin pioneered decentralized consensus through proof of work (PoW), where miners add new transaction blocks by solving puzzles. This consensus ensures the consistency of transaction history, making unauthorized tampering nearly impossible without substantial computational power.

Ethereum achieves consensus through proof of stake (PoS) by validating transactions and creating blocks via staking. This aims to reduce the energy demands of PoW.

By requiring all nodes to validate and agree on each transaction or contract execution, these mechanisms often limit the speed and throughput of the network, posing significant barriers to scalability.

L2 Solutions and the Path Forward

Layer 2 (L2) solutions are crucial for overcoming scalability and energy consumption barriers. While L2 solutions like Rollups and sidechains aim to alleviate the transaction burden on the main blockchain for higher throughput and efficiency, AO's model leverages Arweave's immutable storage capabilities to ensure scalability and reduce computational overhead. This strategic alignment with L2 principles, despite being at fundamentally different architectural levels, underscores AO's commitment to enhancing decentralized computing.

Holographic States: A Paradigm Shift

Process states are typically not stored or agreed upon in the AO system. Instead, they are "holographic," implied within the message logs hosted on Arweave. This ensures the consistency of outputs during computation, even if network participants have not monitored/calculated it.

Thus, holographic states represent the state of processes, inferred from the immutable message logs on Arweave, without the need for real-time computation or consensus.

This means computational costs are delegated to users who can compute their state or request execution via computational units (CUs). The use of a deterministic, resource-metered virtual machine (metered VM) ensures that regardless of who executes the computation, given the same input (message logs), the output (state) is always the same.

This concept leverages the principles of lazy evaluation architecture from SmartWeave and Celestia, allowing for unrestricted use and scaling of resources within processes.

Why Is This Important?

The implications of this shift are significant. By decoupling the consensus mechanism from the computation state, AO computers address the scalability issues that plague traditional blockchain networks. This opens up new possibilities for decentralized applications, enabling them to operate without the constraints of memory size, form, and speed imposed by current consensus models.

Moreover, the holographic state model creates a more flexible and efficient computing environment. Developers can create and deploy processes on AO computers without worrying about the computational load on the network. In turn, due to the immutable message logs on Arweave, users can interact with these processes, confident that the underlying state is verifiable and secure.

Frequently Asked Questions

Here are answers to some of the most common questions I've encountered on Discord and Twitter.

How does the holographic state mechanism work in AO?

Nodes in the network do not need to perform computations to reach consensus on program state transitions; instead, they derive the state from the interaction (message) logs stored on Arweave.

This design leverages Arweave's immutable storage to ensure that message logs are permanently available, allowing any network participant to compute the state.

What impact does the holographic state have on process execution?

For process execution, the holographic state model means that any participant can independently compute the state of the process.

This decentralized computation ensures that processes are not limited by the computational capacity of a single node and can be executed in parallel across the network, enhancing efficiency and scalability.

How are processes managed and executed in a distributed manner?

Processes are managed and executed through a combination of scheduling units (SUs), computational units (CUs), and messenger units (MUs).

These components work together to handle the allocation of messages to processes (SUs), compute state transitions based on messages (CUs), and relay messages between processes (MUs).

This architecture allows processes to run independently across the network. For an in-depth explanation, please refer to my detailed article on AO architecture see my detailed article on AO architecture.

Can the state of a process be directly observed, or is it only implied?

The state of a process is primarily implied in the interaction logs stored on Arweave. While the state is not stored, any participant can deterministically compute it. This approach ensures that although the state of the process cannot be directly monitored, it can be independently verified and remains consistent across the network.

How does the deterministic, resource-metered virtual machine contribute to the holographic state?

Computational units are deterministic, resource-metered virtual machines (VMs). They ensure that given the same input (message logs), the output (state) remains consistent, regardless of who or where the computation is executed. This consistency is crucial for the holographic state model, enabling trustless verification of state transitions. The resource-metering feature of the VMs ensures that computations are limited, preventing processes from going out of control and maintaining network efficiency.

What is a resource-metered virtual machine?

A resource-metered virtual machine is designed to precisely control and track the usage of computational resources, such as CPU runtime and memory. This functionality ensures fair access to network resources, prevents abuse, and allows for predictable operational costs, which is vital for maintaining network efficiency and scalability, as well as providing a transparent and manageable economic model for developers and users.

What is the scalability impact of the holographic state model?

The holographic state model has a significantly positive impact on scalability. By decoupling the consensus mechanism from actual state computation and leveraging a distributed network of participants to execute processes, AO can support many parallel processes without the typical constraints of traditional blockchain consensus mechanisms. This model allows for significantly greater scalability, as the network can handle more transactions and complex computations without a proportional increase in resource demands or a decrease in performance.