DTAO和BITTENSOR的演变：以市场为导向的激励措施重塑分散的AI

该分析研究了Bittensor的动态TAO（DTAO）升级如何解决分散的AI中固有的挑战，从而将网络定位为该新兴部门的开创性力量。

In the rapidly evolving landscape of artificial intelligence, the focus has shifted from foundational model development to the optimization of existing systems, a trend evident in the contributions of industry leaders such as DeepSeek and OpenAI. This transition is closely tied to the introduction of Dynamic TAO (dTAO) by Bittensor, a move that has far-reaching implications for decentralized AI.

在人工智能的快速发展的景观中，重点已从基础模型开发转变为现有系统的优化，这在诸如DeepSeek和Openai等行业领导者的贡献中很明显。这种过渡与Bittensor引入动态Tao（DTAO）紧密相关，该举动对分散的AI具有深远的影响。

This analysis delves into how dTAO addresses inherent challenges within decentralized AI, positioning the network as a pioneering force in this emerging sector.

该分析研究了DTAO如何应对分散的AI内的固有挑战，将网络定位为该新兴部门的开拓力量。

Bittensor’s Architecture: A Framework for Decentralized AI

Bittensor的体系结构：分散AI的框架

Bittensor’s architecture is composed of three key elements: the Subtensor blockchain, a Polkadot parachain with EVM compatibility; 64 specialized subnets; and a governance-focused Root Subnet. The network employs a dual-key security system, Coldkey-Hotkey, and a subnet UID framework to facilitate secure and open participation for miners and validators.

Bittensor的体系结构由三个关键要素组成：微调区块链，这是具有EVM兼容性的Polkadot Parachain； 64个专业子网；和一个以治理为重点的根子网。该网络采用双键安全系统，ColdKey-Hotkey和子网UID框架，以促进矿工和验证者的安全和开放参与。

At the heart of its operational model is the Yuma Consensus (YC), a dynamic incentive mechanism that diverges from traditional static reward systems. YC assesses validators’ weight vectors, derived from historical performance and stake, to distribute TAO rewards every 12 seconds, establishing a self-regulating “stake → weight → reward” loop. This mechanism aligns contributions with incentives while mitigating malicious activities through continuous adjustments.

其运营模型的核心是YUMA共识（YC），这是一种与传统静态奖励系统不同的动态激励机制。 YC评估验证者的重量向量，该体重向量源自历史表现和股份，以每12秒分发一次陶奖，并建立自我调节的“股份→权重→奖励”环路。这种机制将贡献与激励措施保持一致，同时通过持续调整来减轻恶意活动。

The dTao Upgrade: Shifting to Market-Driven Resources

DTAO升级：转移到市场驱动的资源

The dTao upgrade, implemented on February 13, 2025, introduces liquidity pools for subnet tokens, fundamentally altering Bittensor’s economic framework. Key innovations include:

DTAO升级于2025年2月13日实施，引入了子网令牌的流动性池，从根本上改变了Bittensor的经济框架。关键创新包括：

* Creation of a common liquidity pool for all subnet tokens on Subtensor.

*为子观念上的所有子网令牌创建一个常见的流动性池。

* Adjustment of the YC to factor in subnet token prices in addition to validators’ performance.

*调整YC以外，除了验证者的性能外，要考虑子网令牌价格。

* Introduction of a subnet economic performance ranking system based on metrics like token price and liquidity.

*基于标记价格和流动性等指标的子网经济绩效排名系统的引入。

* Adjustment of TAO emissions to favor subnets with better market performance and higher user engagement.

*调整TAO排放，以偏爱具有更好的市场性能和更高用户参与度的子网。

This upgrade addresses previous systemic limitations, such as validator centralization, resource redundancy, and misaligned incentives. By linking subnet rewards to market performance, dTao fosters competition, encouraging the development of specialized AI solutions, ranging from multimodal content detection to decentralized search engines.

此升级解决了以前的系统限制，例如验证者集中化，资源冗余和未对准激励措施。通过将子网奖励与市场绩效联系起来，DTAO促进了竞争，鼓励了专门的AI解决方案的开发，从多模式内容检测到分散搜索引擎。

Ecosystem Impact: High-Performance Subnets Emerge

生态系统影响：高性能子网出现

The implementation of dTao has led to the emergence of high-performing subnets, operating within a self-reinforcing feedback loop where increasing token prices attract greater TAO emissions, subsequently drawing more users and validators. Examples include:

DTAO的实施导致了高性能子网的出现，该子网在自我增强的反馈回路中运行，在这种反馈循环中，代价上涨的价格上涨吸引了更大的TAO排放，随后吸引了更多的用户和验证者。示例包括：

* **Prado**: Focused on multi-modal content detection, Prado has witnessed significant user growth due to the integration of several AI-powered services, resulting in high levels of on-chain activity and a rising token price.

*** Prado **：专注于多模式内容检测，Prado由于整合了多种AI驱动的服务而见证了用户的显着增长，从而导致高水平的链上活动和代币价格上涨。

* As the primary subnet for decentralized search, Kaito has attracted a large user base, further boosting its token. However, despite technical capabilities, the lack of integration with core product utility has led to limited user engagement and a stagnating token price, highlighting the importance of balancing technical proficiency with market responsiveness.

*作为分散搜索的主要子网，Kaito吸引了大型用户群，进一步提高了其令牌。然而，尽管技术能力，但与核心产品实用程序缺乏集成导致用户参与度有限和代币价格停滞不前，这强调了平衡技术水平与市场响应能力的重要性。

Despite the advancements introduced by dTao, HTX Research also identifies ongoing challenges, including the lack of real-world demand drivers for TAO rewards, the potential for resource redundancy among overlapping subnets, and persistent validator centralization.

尽管DTAO提出了进步，但HTX研究还确定了持续的挑战，包括缺乏TAO奖励的现实世界需求驱动因素，重叠子网中资源冗余的潜力以及持续的验证者集中化。

To ensure sustained growth, HTX Research emphasizes the necessity for on-chain verifiability, standardized subnet performance benchmarking systems, and the integration of subnet token utility, such as governance or service access, to reduce speculative trading.

为了确保持续增长，HTX研究强调了链核能，标准化子网性能基准制度的必要性以及子网令牌效用（例如治理或服务访问）的整合，以减少投机性交易。

Conclusion

结论

Bittensor’s dTao upgrade marks a departure from centralized governance models and introduces a system of market-driven incentives. While challenges remain in achieving optimal resource allocation and sustained user engagement, Bittensor’s architecture and economic model provide a unique framework for decentralized AI.

Bittensor的DTAO升级标志着与集中式治理模型背道而驰，并引入了市场驱动的激励措施。尽管在实现最佳资源分配和持续的用户参与方面仍然存在挑战，但Bittensor的体系结构和经济模型为分散的AI提供了独特的框架。

As subnet tokens evolve into tools with tangible utility, Bittensor is well-positioned to reshape the competitive and collaborative dynamics within AI ecosystems.

随着子网代币发展为具有切实实用程序的工具，Bittensor的位置良好，可以重塑AI生态系统中的竞争和协作动态。

HTX Research will continue to closely examine these developments and offer actionable insights into the intersection of AI and blockchain technology.

HTX研究将继续密切检查这些发展，并为AI和区块链技术的交集提供可行的见解。