Author: Fairy, ChainCatcher
In today's era of continuous development in blockchain and artificial intelligence technologies, how to effectively combine these two has become a goal for many innovative projects. Ambient was born in this context, dedicated to combining decentralized blockchain architecture with massive AI inference, exploring a new intelligent economic model.
As a complete fork of Solana, Ambient retains Solana's high speed and efficiency, and creates an entirely new blockchain ecosystem by introducing the Logits Proof (PoL) mechanism.
What is Ambient?
Ambient is a Layer-1 blockchain that merges Solana SVM compatibility with a novel proof-of-work mechanism, providing massive verification inference. The core concept of the Ambient project is to deeply integrate AI inference and blockchain, creating a decentralized AI economy.
Unlike traditional Proof-of-Stake (PoS) systems, Ambient adopts a Bitcoin-like incentive mechanism and provides predictable profits for each node participating in network inference, fine-tuning, or training. This approach avoids dependence on enterprise-level GPUs, ensuring sustainable mining profitability through transaction and inflation-based compensation. Both miners and users can receive rewards matching their contributions, while the platform's value continuously increases with network growth.
Ambient's characteristics:
- Efficient Inference and Security: Provides fully verified inference with costs below 1%, while ensuring high security on massive intelligent models (600B+ parameters) and their fine-tuned versions.
- Excellent Training Performance: Improves training performance by 10 times compared to existing methods, enhancing AI model training efficiency.
- High Miner Utilization: Optimizes performance on a single model, improving miner utilization and enhancing inference and verification process efficiency.
- Non-blocking Proof-of-Work Consensus: Adopts a non-blocking proof-of-work mechanism, ensuring economic competition in network core activities (inference, fine-tuning, training) while maintaining high TPS, avoiding traditional blockchain performance bottlenecks.
Ambient Team Background and Current Development
Apart from the founders' background, Ambient has not disclosed information about other team members. Ambient's CEO and founder, Travis Good, has a diverse academic background covering government studies, economics, computer science, and machine learning. Travis's leadership style emphasizes execution and pragmatism, always focusing on practical operations and executable solutions when driving technological innovation. Additionally, Travis is very active on Twitter, frequently sharing his unique insights on technology, innovation, and industry trends.
On April 1st, Ambient completed a $7.2 million seed round, led by a16z CSX, Delphi Digital, and Amber Group. Other participants include Big Brain Holdings, Superscrypt, Proof Group, Rubik Ventures, Aethir Foundation, and Edessa Capital. Ambient plans to launch its testnet in the second or third quarter.
Logits Proof Consensus Mechanism
The "Logits Proof" algorithm leverages a key fact: logits (can be understood as logical units) are both unique fingerprints and can effectively capture the model's "thinking" state at a specific moment (during model's streaming output) through hash values generated during the model production process. In this mechanism, the Logits Proof hash value is a hash list of each group of logits hash values before each output token. In simple terms, for each token n, up to the final token t, the Logits Proof hash value is:
Hash(Hash(n) … Hash(t))
The hash value of the logits progress marker proof is the logits hash after generating x tokens, where x is between n and t (including n and t), which is:
Hash(n) … Hash(x) … Hash(t)
Based on this principle, a verification mechanism can be constructed: First, miners generate text; then, verifiers randomly select a word in the text and request miners to provide the "thinking state" at that point (the corresponding logits progress marker proof hash). Next, verifiers perform an inference on the same model and context for that word, generating their own "thinking state". If the "thinking states" (represented by hash values) are consistent, the verification is successful.
This proof-of-work mechanism is consistent with Bitcoin's design principles: mining (in this case, repeatedly executing the model through 4000 token inferences) is costly, but the verification process is very cheap (requiring inference of only 1 token). This mechanism not only improves efficiency but also ensures verification safety and reliability.
