Comparing: BasedAI vs. BitTensor (TAO)
Guest Author: NinjaKitty
“How does BasedAI compare with TAO?”
We’ve heard this question a lot.
The answer isn’t super straight-forward because we’re not comparing apples-to-apples. They differ significantly in their approaches, technology, and overall vision. This blog post aims to outline these differences and highlight why BasedAI is a more advanced and robust solution compared to TAO.
In prep for this blog article, we read a lot of resources, watched a lot of videos, and we’ve talked to a few TAO maxis too. The result is similarities and differences we feel stand out between the two crypto AI projects. You’ll see these summarized in the infographic attached which we hope you’ll share around the interwebs.
Core Technology and Vision
BasedAI is designed around the concept of Zero-Knowledge Large Language Models (ZK-LLMs), integrating Fully Homomorphic Encryption (FHE) to ensure data privacy without compromising computational performance. This unique combination allows computations on encrypted data, making it possible to handle sensitive information securely across a decentralized network.
BitTensor (TAO), on the other hand, focuses on creating a decentralized, permissionless network for AI, where participants can contribute to and benefit from a shared AI training infrastructure. TAO uses a tokenized incentive system to encourage participation and resource sharing among network participants.
Privacy and Security
BasedAI places a significant emphasis on privacy through its use of FHE and the Cerberus Squeezing technique. This ensures that all data processed by the network remains encrypted, protecting user privacy at all stages of computation.
BitTensor (TAO) also aims to provide a secure environment for AI training but lacks the advanced encryption mechanisms of BasedAI. While it promotes a decentralized approach, the specifics of its privacy measures are less robust compared to BasedAI's comprehensive encryption strategies.
Network Architecture
BasedAI utilizes a peer-to-peer network structure with distinct roles for Brain Owners, Miners, and Validators. Each participant in the network is incentivized through $BASED tokens, promoting efficient and truthful operations. The architecture supports a limited number of Brains, creating a competitive environment that drives performance.
BitTensor (TAO) uses a similar decentralized approach but focuses more on creating an open, scalable network where any participant can contribute to AI model training. TAO’s token economy is designed to reward contributions, but the system's scalability and efficiency can be challenged by the lack of specialized roles and structured incentives.
Tokenomics and Incentives
BasedAI features a well-structured tokenomics model with clear incentives for all participants. $BASED tokens are distributed based on performance, stake volume, and network contributions, ensuring a fair and balanced reward system. The emission schedule includes halving events to control inflation and encourage long-term commitment.
BitTensor (TAO) also uses a token-based incentive system, but the details of its tokenomics are less defined compared to BasedAI. While it rewards participation, the lack of a detailed incentive structure can lead to inefficiencies and potential centralization risks over time.
Advanced Features
BasedAI stands out with its innovative Cerberus Squeezing technique, which optimizes FHE performance, making it feasible to integrate LLMs with encrypted data processing. This feature positions BasedAI at the forefront of secure AI development on a decentralized network.
BitTensor (TAO) offers a robust platform for decentralized AI training but does not match the advanced privacy and optimization features of BasedAI. Its strength lies in creating a broad, permissionless network, but it may struggle with the same level of secure and efficient computations.
In Sum
While both BasedAI and BitTensor (TAO) aim to revolutionize the integration of AI and blockchain, BasedAI's advanced encryption techniques, structured incentives, and optimized network architecture make it a more robust and forward-thinking solution. Investors and participants in the BasedAI community can rest assured that they are part of a cutting-edge project that prioritizes privacy, security, and performance, setting the stage for the future of decentralized AI.
