The post Nexchain AI Hits Stage 25 in Crypto Presale With $8.7M Raised: Is This the Next 100x Token? appeared on BitcoinEthereumNews.com.

In a crypto market filled with many investment opportunities, one has stood out as the best among all others. With great return promises, Nexchain AI has secured its position while it has reached Stage 25 in funding campaign. According to the current market data, the platform has raised over $8.6 million from USDT contributions, nearing its $9.27 million target. Built on new infrastructure, Nexchain AI presents an innovative blockchain system, potentially setting the foundation for the next 100x through it NEX token. Nexchain AI Advances With AI-Powered Blockchain Architecture Nexchain AI operates as a Layer 1 blockchain protocol constructed entirely using artificial intelligence systems. Its architecture combines advanced computing with next-gen decentralized security mechanisms. The system employs a hybrid Proof-of-Stake model alongside AI-optimized validation, reducing congestion and enhancing network throughput. The platform integrates Directed Acyclic Graphs (DAGs) to enable parallel transaction processing. DAGs remove block-based limitations by allowing asynchronous confirmations. This results in higher speed, low cost, and minimal obstructions. Combined with sharding, Nexchain processes high transaction volumes efficiently across various industries. Currently, Nexchain AI is in Stage 25 of its crypto presale, priced at $0.10 per NEX token. As of now, USDT contributions have reached $8,7 out of a $9,275,000 goal. This amount represents 32% of the total token supply offered in the public sale. Security remains a top priority during the presale phase. Nexchain has partnered with CERTIK for protocol auditing and threat monitoring. The token model follows an inflationary structure but implements an annual burn to balance supply. Smart Contracts With Built-In Optimization Logic Nexchain’s smart contracts stand apart with AI-enhanced features. These contracts self-optimize based on past data and evolving input conditions. They adapt dynamically to reduce latency, detect anomalies, and enforce compliance automatically. Fraud prevention mechanisms use machine learning to flag unusual activity. These tools…

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Filed under: News - @ August 15, 2025 6:26 pm