Pyth to launch reserve using buybacks from DAO treasury funds

cointelegraphPubblicato 2025-12-12Pubblicato ultima volta 2025-12-12

Introduzione

The Pyth Network, a blockchain oracle provider, announced it will use one-third of its protocol revenue to buy back PYTH tokens on the open market through its DAO treasury. This strategy aims to establish a network reserve and boost both revenue and token value. The PYTH token has declined over 80% in the past year, despite a brief surge after Pyth partnered with the U.S. government to publish economic data onchain. At the time of writing, PYTH was trading around $0.063, down 1.3% in 24 hours. The move follows a similar proposal from Aave’s DAO in October, though community reactions remain uncertain, citing past criticisms of token buyback schemes in other projects like Mango Markets.

The Pyth Network, a blockchain oracle provider, said it will convert a portion of its revenue into PYTH token purchases as part of a reserve strategy.

In a Friday blog post, Pyth said the tokens it buys on the open market with one-third of the protocol’s revenue through its decentralized autonomous organization (DAO) would constitute the network’s reserve. The network said the strategy was designed to increase revenue and token purchases.

“[I]t’s time to rewrite the market data economy on a global level,” said Pyth.

Source: Pyth

The price of the Pyth Network token (PYTH) has dropped by more than 80% over the past year, despite a brief surge when the platform deepened ties to the US government. The Trump administration announced in August that the company and Chainlink would be responsible for publishing the country’s economic data onchain.

According to data from Nansen, the PYTH price decreased by about 1.3% over the previous 24 hours, reaching about $0.063 at the time of publication.

Related: Grayscale launches Pyth investment fund

Aave DAO proposed a similar buyback initiative in October

The DAO behind the Aave protocol introduced a proposal that would use $50 million of its annual revenue to repurchase its native token, Aave (AAVE). As of Friday, it did not appear that the proposal had been approved.

It’s unclear how Pyth users may respond to the plan. In 2024, when Mango Markets suggested a buyback program to purchase its MNGO tokens, co-founder Daffy Durairaj accused the creators of “untrustworthy behavior and self-dealing.”

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Letture associate

Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

In a span of four days, Amazon announced an additional $25 billion investment, and Google pledged up to $40 billion—both direct competitors pouring over $65 billion into the same AI startup, Anthropic. Rather than a typical venture capital move, this signals the latest escalation in the cloud wars. The core of the deal is not equity but compute pre-orders: Anthropic must spend the majority of these funds on AWS and Google Cloud services and chips, effectively locking in massive future compute consumption. This reflects a shift in cloud market dynamics—enterprises now choose cloud providers based on which hosts the best AI models, not just price or stability. With OpenAI deeply tied to Microsoft, Anthropic’s Claude has become the only viable strategic asset for Google and Amazon to remain competitive. Anthropic’s annualized revenue has surged to $30 billion, and it is expanding into verticals like biotech, positioning itself as a cross-industry AI infrastructure layer. However, this funding comes with constraints: Anthropic’s independence is challenged as it balances two rival investors, its safety-first narrative faces pressure from regulatory scrutiny, and its path to IPO introduces new financial pressures. Globally, this accelerates a "tri-polar" closed-loop structure in AI infrastructure, with Microsoft-OpenAI, Google-Anthropic, and Amazon-Anthropic forming exclusive model-cloud alliances. In contrast, China’s landscape differs—investments like Alibaba and Tencent backing open-source model firm DeepSeek reflect a more decoupled approach, though closed-source models from major cloud providers still dominate. The $65 billion bet is ultimately about securing a seat at the table in an AI-defined future—where missing the model layer means losing the cloud war.

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Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

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Computing Power Constrained, Why Did DeepSeek-V4 Open Source?

DeepSeek-V4 has been released as a preview open-source model, featuring 1 million tokens of context length as a baseline capability—previously a premium feature locked behind enterprise paywalls by major overseas AI firms. The official announcement, however, openly acknowledges computational constraints, particularly limited service throughput for the high-end DeepSeek-V4-Pro version due to restricted high-end computing power. Rather than competing on pure scale, DeepSeek adopts a pragmatic approach that balances algorithmic innovation with hardware realities in China’s AI ecosystem. The V4-Pro model uses a highly sparse architecture with 1.6T total parameters but only activates 49B during inference. It performs strongly in agentic coding, knowledge-intensive tasks, and STEM reasoning, competing closely with top-tier closed models like Gemini Pro 3.1 and Claude Opus 4.6 in certain scenarios. A key strategic product is the Flash edition, with 284B total parameters but only 13B activated—making it cost-effective and accessible for mid- and low-tier hardware, including domestic AI chips from Huawei (Ascend), Cambricon, and Hygon. This design supports broader adoption across developers and SMEs while stimulating China's domestic semiconductor ecosystem. Despite facing talent outflow and intense competition in user traffic—with rivals like Doubao and Qianwen leading in monthly active users—DeepSeek has maintained technical momentum. The release also comes amid reports of a new funding round targeting a valuation exceeding $10 billion, potentially setting a new record in China’s LLM sector. Ultimately, DeepSeek-V4 represents a shift toward open yet realistic infrastructure development in the constrained compute landscape of Chinese AI, emphasizing engineering efficiency and domestic hardware compatibility over pure model scale.

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Computing Power Constrained, Why Did DeepSeek-V4 Open Source?

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