Redwood City, USA, Dec. 13, 2025 (GLOBE NEWSWIRE) — HashStaking announced that it is working toward the introduction of an AI-powered contract framework designed to improve how users monitor and understand return-related outcomes within staking activities. The initiative focuses on strengthening oversight, transparency, and interpretability, while ensuring that users retain direct authority over their participation settings. As artificial intelligence continues to influence how complex digital systems are managed, platforms across the blockchain ecosystem are reassessing how automated analysis can be applied responsibly. HashStaking's forthcoming framework aims to use AI as an analytical support layer–providing structured insight without automating decisions or altering user-selected parameters. Focus on Oversight, Not Automation According to the company, the AI-powered contract framework is being developed to help users better interpret how staking outcomes are shaped by network conditions, validator performance, and participation duration. Rather than executing changes on behalf of users, the system is designed to present clear, explainable data that supports informed review. The framework will generate analytical summaries that reflect how different operational factors interact over time. These summaries are intended to help users understand performance trends and variability within defined participation terms, without introducing predictive claims or automated reconfiguration. HashStaking states that this approach aligns with a broader industry movement toward algorithmic accountability–where AI supports clarity and traceability while preserving user-directed control. How the AI Contract Framework Is Expected to Function The company outlined several design principles guiding the development: By separating analysis from action, HashStaking aims to ensure that AI enhances understanding without diminishing user autonomy. Context Within the Broader AI Shift AI adoption has accelerated across multiple sectors due to its capacity to process large datasets and identify patterns in complex environments. Within blockchain-based systems, this capability is increasingly applied to monitoring, reporting, and system health analysis. At the same time, users are requesting greater clarity into how outcomes are formed and how different variables affect participation results. HashStaking notes that the new framework is being developed in response to these expectations, with an emphasis on explainability and user oversight. “Artificial intelligence can help surface insights that are difficult to observe in real time, but it should never replace user judgment,” said Communications Lead at HashStaking. “This framework is being developed to improve visibility into how participation outcomes evolve, while ensuring that users remain fully in control of their contracts and choices.” About HashStaking's Platform Established in 2021, HashStaking supports staking across more than 170 digital assets and reports over $50 million in cumulative distributions based on publicly available information. The platform operates with a focus on analytical transparency, structured participation models, and system integrity. Key aspects of the platform environment include: Looking Ahead HashStaking stated that further technical details and documentation related to the AI-powered contract framework will be released closer to implementation. The company views this initiative as part of a longer-term roadmap focused on responsible AI integration, transparency, and user-centered system design. MEDIA CONTACT Disclaimer: The information provided in this press release does not constitute investment advice, solicitation, or a trading recommendation. Readers are encouraged to conduct independent research and consult professional financial advisors before participating in cryptocurrency or digital asset investments.
AI models will analyse validator behaviour, network activity patterns, and historical participation data to produce readable summaries.
All participation settings remain configurable by users, with AI outputs presented for review rather than execution.
The system will document how analytical insights are derived, supporting transparency around data sources and interpretation logic.
Outputs are intended to be descriptive, avoiding prescriptive guidance or outcome forecasting.
Continuous user support availability across time zones
Customisable participation packages aligned with different network characteristics
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