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Behind the $TAO Crash: The Bittensor Internal Strife and the 'Impossible Trinity' of DeAI

The decentralized AI (DeAI) sector is facing a major crisis following a public conflict within Bittensor ($TAO), a leading DeAI project. Covenant AI, one of its top development teams, which recently successfully trained a 72-billion-parameter large language model, announced its exit from the Bittensor network. The team accused founder Jacob Steeves of having "absolute and dictatorial" control over the network, alleging he arbitrarily cut off token rewards to their subnet without transparent governance. This triggered a panic sell-off, causing $TAO’s price to drop 15-25% in a single day and wiping out hundreds of millions in market value. The incident has raised serious questions about the viability of decentralized AI, highlighting a fundamental tension—referred to as DeAI’s "impossible trilemma"—between model quality and scale, credible neutrality of decentralization, and Sybil-resistant incentive alignment. Covenant’s departure exposed the centralized reality beneath Bittensor’s decentralized facade: although the network relies on a Yuma consensus mechanism for reward distribution, key validator nodes are controlled by early investors and the founder, allowing unilateral intervention. The event underscores systemic governance risks that may deter high-quality developers and institutional participants, threatening the entire DeAI narrative centered around trustless, incentive-driven AI development.

marsbitAyer 08:59

Behind the $TAO Crash: The Bittensor Internal Strife and the 'Impossible Trinity' of DeAI

marsbitAyer 08:59

Crypto Bear Market Startup Guide Part 2: The Token Relay Station - Exchanging Crypto Tokens for AI Tokens

"Token Relay Station: A Guide to Starting a Crypto Bear Market Business (Part 2) - Exchanging Crypto Tokens for AI Tokens" This article explores the business opportunity of creating an AI token relay station, a service that acts as an API aggregation layer. It allows users to pay with cryptocurrency (Crypto Tokens) to access credits for various AI models (AI Tokens), bypassing traditional payment barriers. The piece highlights a significant, underserved market: using crypto to directly purchase AI API credits and the potential "reverse export" of cheaper, high-performing Chinese models (like Qwen, Kimi, GLM) to overseas users. It uses OpenRouter, co-founded by OpenSea's ex-CTO Alex Atallah, as a key case study of a successful pivot from crypto to AI infrastructure, noting its support for crypto payments. The analysis reveals market challenges, including widespread fraud where users pay for premium models but receive inferior ones, and unstable supply chains reliant on bulk accounts prone to bans. It outlines three business models: global/developer-focused (OpenRouter), multi-modal/China-focused (APIMart.ai), and hyper-localized operations. Substantial risks are also detailed: high capital requirements for API procurement and infrastructure, the necessity of stable supply channels, complex legal and compliance issues around data resale and cross-border regulations, and the critical importance of user trust. Ultimately, the article posits this as a viable, revenue-generating business model for the crypto bear market, built on real API usage-based income rather than speculative token narratives.

Odaily星球日报04/10 03:30

Crypto Bear Market Startup Guide Part 2: The Token Relay Station - Exchanging Crypto Tokens for AI Tokens

Odaily星球日报04/10 03:30

Industry Experts Gather, Reflections and Breakthroughs in the AI Agent Era

Industry experts gathered to discuss the challenges and opportunities in the AI Agent era. The event, co-hosted by several organizations, addressed key questions about model selection, token resource sustainability, and strategies for individuals and businesses to adapt. Conflux's Chief Architect highlighted the current trend of granting AI more autonomy, noting that its limitations in complex scenarios stem from difficulties in capturing and retaining key contextual constraints. Future advancements should focus on enhancing external memory, continuous learning, and domain-specific applications. Speakers from Tencent Cloud and Biteye shared practical insights. Tencent's WorkBuddy leverages multi-agent collaboration for tasks like resume screening and report generation, emphasizing enterprise-grade security. Biteye’s founder discussed mitigating AI hallucinations through rigorous code review processes, managing token consumption, and using platforms like Discord for agent coordination. Legal risks were also addressed, with a partner from Mankun Law advising on liability isolation, intellectual property protection, and mitigating platform dependency risks. Investors noted that AI is still in its early stages, with technology rapidly evolving. They emphasized investing in foundational layers like compute power and exploring AI-Web3 convergence. The discussion concluded that AI should be viewed as a productivity tool rather than a threat. Customizable agents can significantly enhance efficiency, but successful implementation requires careful engineering, security measures, and human oversight to integrate AI into complex workflows effectively.

marsbit04/08 05:51

Industry Experts Gather, Reflections and Breakthroughs in the AI Agent Era

marsbit04/08 05:51

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

The year 2026 is being positioned as the dawn of the "Physical AI" era, marked by major funding rounds and technological breakthroughs. This shift signifies AI's evolution from understanding the digital world to perceiving and acting within the physical world. Key events include Yann LeCun's AMI Labs raising $1.03 billion to develop "world models," Fei-Fei Li's World Labs securing funding, and companies like Tesla deploying humanoid robots (Optimus) in factories. This transition expands the AI model competition into a broader infrastructure battle encompassing hardware, data, simulation, and real-world integration. The core debate is between two AI paths: the established LLM (Large Language Model) approach focused on text prediction and the emerging "world model" approach, which aims to understand physical states for action-oriented tasks. Hardware, particularly dexterous robotic hands, is a critical and expensive challenge. Companies are racing to build capable robotic bodies, with Tesla, Boston Dynamics, and Figure AI making significant progress. NVIDIA is positioning itself as the essential infrastructure provider for this new era, offering a full suite of development tools and platforms. A major bottleneck is the scarcity of high-quality physical world interaction data, with companies exploring solutions through real-world data collection, synthetic data generation, and human teleoperation. Substantial investments in Q1 2026, exceeding $6.4 billion, signal strong belief in Physical AI's potential, moving beyond concept validation into infrastructure building. While challenges like the sim-to-real gap, unproven business models, and safety regulations remain, the tangible engineering progress suggests this is a genuine technological inflection point, not merely a bubble. For the global Chinese community, this shift represents a significant structural opportunity to leverage their strengths in technology, engineering, hardware manufacturing, and cross-border collaboration to become key players in building the foundational layers of the Physical AI ecosystem.

marsbit04/03 09:39

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

marsbit04/03 09:39

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