Ambient Leverages Solana's High Speed and Efficiency, Introduces Proof of Logits (PoL) Mechanism

As blockchain and artificial intelligence technologies continue to develop, how to effectively combine the two has become one of the goals

As blockchain and artificial intelligence technologies continue to develop, how to effectively combine the two has become one of the goals of many innovative projects. Ambient was born in this context, dedicated to combining decentralized blockchain architecture with ultra-large-scale AI reasoning to explore a new intelligent economic model.

As a complete fork of Solana, Ambient retains Solana's high speed and efficiency, and creates a brand new blockchain ecosystem by introducing the Proof of Logits (PoL) mechanism.

What is Ambient?

Ambient is a Layer-1 blockchain that combines Solana SVM compatibility with a novel proof-of-work mechanism to provide ultra-large-scale verified reasoning. The core concept of the Ambient project is to deeply integrate AI reasoning and blockchain to create a decentralized AI economy.

Unlike traditional proof-of-stake (PoS) systems, Ambient uses an incentive mechanism similar to Bitcoin and provides predictable profits for each node that participates in network reasoning, fine-tuning, or training. This approach avoids reliance on enterprise-level GPUs and ensures sustainable profitability for miners through transaction- and inflation-based compensation. Both miners and users can receive rewards that match their contributions, and the value of the platform continues to increase as the network grows.

Features of Ambient:

Ambient team background and development status

In addition to the background of the founder, Ambient has not yet disclosed information about other members. Ambient's CEO and founder Travis Good has a diverse academic background, and his academic career covers four fields: government, economics, computer science, and machine learning. Travis' leadership style focuses on execution and pragmatism. When promoting technological innovation, he always adheres to practical operations and focuses on executable solutions. In addition, Travis is very active on Twitter and often shares his unique insights on technology, innovation, and industry trends.

On April 1, Ambient completed a $7.2 million seed round of financing, led by a16z CSX, Delphi Digital, and Amber Group. Big Brain Holdings, Superscrypt, Proof Group, Rubik Ventures, Aethir Foundation, Edessa Capital, etc. participated in the investment. Ambient plans to launch a testnet in the second/third quarter.

Logits Proof of Consensus Mechanism

The "Logits Proof" algorithm takes advantage of a key fact: logits (which can be understood as logical units) are both unique fingerprints and can effectively capture the "thinking" state of the model at a specific moment (that is, when the model is "streaming" output) through the hash value generated during the model generation process. Under this mechanism, the hash value of the logits proof is a hash list of each set of logits hash values before each output token. In short, for each token n, until the final token t, the hash value of the logits proof is:

Hash(Hash(n) … Hash(t))

The hash value of the logits progress token proof is the logits hash after generating x tokens, where x is between n and t (inclusive), that is:

Hash(n) … Hash(x) … Hash(t)

Based on this principle, a verification mechanism can be constructed: first, the miner generates text; then, the verifier randomly selects a word in the text and asks the miner to provide the "mental state" at that point (that is, the corresponding logits progress mark proof hash). Then, the verifier performs a reasoning on the word in the same model and context to generate its own "mental state". If the "mental states" of the two (represented by hash values) are consistent, the verification is successful.

This proof-of-work mechanism is consistent with the design principles of Bitcoin: mining (in this case, repeatedly executing the model through inference of 4,000 tokens) is expensive, but the verification process is very cheap (only 1 token is required for inference). This mechanism not only improves efficiency, but also ensures the security and reliability of verification.