EigenLayer rewards boost adoption. Risks of slashing remain. Governance must adapt.
- 0 replies
- 0 recasts
- 0 reactions
The rise of Decentralized Machine Learning (DeML) is reshaping AI by enabling distributed model training and secure data sharing without centralized control. In AI model training, DeML aggregates global computing power—e.g., via Bittensor’s subnet architecture—allowing collaborative development across peers, reducing reliance on tech giants like Google. For data sharing, platforms like SingularityNET create marketplaces where data owners monetize assets while preserving privacy through blockchain, fostering a transparent ecosystem. Bittensor’s TAO incentivizes miners and validators, boasting a $4.68 billion market cap, though its growth depends on adoption and regulatory clarity. SingularityNET’s ASI, post-merger with Fetch.ai and Ocean Protocol, targets AGI with a $7.5 billion valuation, offering scalability but facing volatility risks. Both promise high returns if DeML scales, yet investors must weigh technical complexity and market uncertainty.
- 0 replies
- 0 recasts
- 0 reactions
Evaluate airdrop token value by researching the project’s fundamentals: check its whitepaper, roadmap, and team transparency. Assess token utility (e.g., governance, staking) and market demand via CoinMarketCap. Analyze community engagement on X and Discord for sentiment. Investigate partnerships and funding (e.g., Monad’s $225M raise). Be wary of hype-driven projects with weak tokenomics or high VC control. Historical data shows tokens like Arbitrum’s ARB gained value post-airdrop, but many fizzle out. Prioritize projects solving real problems.
- 0 replies
- 0 recasts
- 0 reactions