BMT Series (49): The Predictive Potential of AI Models on Cluster Patterns
The AI model of Bubblemaps has tremendous potential in predicting cluster patterns, making me feel that InfoFi's future is increasingly intelligent. The default bubble chart displays clusters, but AI can delve deeper to explain why these addresses are connected—whether it’s VC distribution or internal transactions? Holding BMT unlocks this feature, helping you extract insights from the data.
AI is not just descriptive; it can also predict trends. For instance, based on historical transfers, it can simulate future capital flows and provide warnings about potential sell-offs or accumulations. By combining magical nodes, AI uncovers hidden patterns, such as multiple wallets connected through intermediaries, predicting cluster expansions. The V2 version's real-time data feeds AI, making predictions more accurate, and cross-chain analysis can also observe the interactions between Solana and BNB.
In practical cases, I used AI to examine a token's issuance day cluster, and it predicted the likelihood of early holders selling, with astonishing accuracy. The community survey by Intel Desk also relies on AI to prioritize proposals, saving resources. The predictive potential lies in reducing human biases; AI scans for anomalies using algorithms, helping traders capture signals.
Why is the potential great? Because of the explosion of Web3 data, manual analysis cannot keep up, and AI fills the gap. In the future, as it expands to more chains, AI will become even more powerful. BMT holders have priority, incentivizing participation. In summary, AI is not just an aid; it is the core of cluster prediction. If you want to stay ahead of the market, try this feature—it will open new horizons.
