DTAOとビテンサーの進化：市場主導のインセンティブで分散型AIを再構築する

This analysis examines how Bittensor's Dynamic TAO (dTAO) upgrade addresses inherent challenges within decentralized AI, positioning the network as a pioneering force in this emerging sector.

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.

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

Bittensor’s Architecture: A Framework for Decentralized AI

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.

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.

The dTao Upgrade: Shifting to Market-Driven Resources

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

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

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

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

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

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.

Ecosystem Impact: High-Performance Subnets Emerge

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:

* **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.

* 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.

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.

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.

Conclusion

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.

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

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

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