TAO Bittensor, my revenge chart after my defeat.Project description:
Bittensor ( GETTEX:TAO ) is a decentralized machine learning protocol that incentivizes users to contribute computing power and train AI models on the blockchain. By leveraging a decentralized infrastructure, Bittensor aims to create an open, permissionless network for AI development.
Type of project:
Decentralized AI and machine learning protocol.
Is it under a block?:
Yes, Bittensor operates on its own blockchain, utilizing a decentralized network to facilitate AI model training and computation. GETTEX:TAO is used as the native token to reward participants who contribute to the network.
Latest update or news:
As of July 2024, Bittensor introduced Neuron Staking, allowing participants to stake their GETTEX:TAO tokens on AI models they believe will perform well, further aligning incentives within the network and driving improvements in model quality.
Narrative:
Decentralized AI, machine learning, blockchain infrastructure, and incentive-based computation.
Why is it a good investment?
Institutional Backers and Angel Investors:
1. Multicoin Capital:
Multicoin Capital has backed Bittensor due to its innovative approach to decentralized AI, seeing it as a key player in the future of decentralized machine learning networks.
Outlier Ventures:
Outlier Ventures, a venture capital firm known for supporting Web3 and AI projects, has invested in Bittensor, recognizing the importance of decentralized AI infrastructure.
KR1:
KR1, a European blockchain investment firm, has also supported Bittensor’s vision for open, permissionless AI model training on the blockchain, seeing it as a critical step toward decentralized AI systems.
2. Angel Investors:
Trent McConaghy (Founder of Ocean Protocol):
While not directly invested, McConaghy has expressed strong support for decentralized AI projects like Bittensor that utilize blockchain technology to democratize access to AI resources and data.
Sandeep Nailwal (Co-founder of Polygon):
Nailwal, known for supporting decentralized infrastructure projects, has voiced interest in Bittensor’s approach to incentivizing AI model training through decentralized computation, though no confirmed direct investment.
Futuristic Use Case:
Decentralized machine learning model training:
Bittensor enables the training of AI models in a decentralized manner, allowing participants to contribute computational power and data, which is crucial for building AI models that are not dependent on centralized entities.
Incentivized AI development:
Bittensor’s tokenomics incentivize participants to stake GETTEX:TAO tokens on high-performing AI models, aligning incentives across the network and driving continuous improvements in the quality of AI outputs.
AI model sharing across industries:
Industries such as healthcare, finance, and research can benefit from Bittensor’s decentralized AI models, which allow for the sharing and optimization of AI models across sectors in a transparent and permissionless way.
Secure and transparent AI computation:
By leveraging blockchain’s security and transparency, Bittensor ensures that AI models and computations are performed in a trustless environment, reducing risks associated with centralized AI model training.
Why will it make a significant amount of profits?
Unique competitive edge:
Bittensor stands out from traditional AI platforms by decentralizing the training and development of machine learning models. This creates an open and permissionless ecosystem where anyone can contribute to AI development and be rewarded, giving it a unique position in the AI market.
Growing demand for decentralized AI models:
As the AI industry grows, the need for decentralized infrastructure that allows for transparent, secure, and scalable AI model training will increase. Bittensor’s approach of incentivizing contributors with GETTEX:TAO tokens will attract more participants and developers, driving demand for the token.
Neuron Staking and incentivization model:
Bittensor’s Neuron Staking system allows participants to earn rewards by staking GETTEX:TAO on AI models they believe will succeed. This creates a sustainable revenue model while encouraging continuous innovation in AI development.
Revenue from AI model training and computation:
Bittensor generates revenue by offering decentralized AI model training and computation services. As more businesses and developers use its infrastructure for AI model optimization, the value of GETTEX:TAO tokens will increase due to the growing demand for decentralized computation.
