Fractal 的 FSIC 与 ASIC,我们买的是“算力”还是“注意力”?

链捕手Publicado em 2024-09-03Última atualização em 2024-09-03

作者:Cookie,BlockBeats

 

距离 Fractal 主网上线的 9 月 9 日只剩不到一周的时间。从最开始的时候,大家期待着是否可以在 Fractal 上复制去年铭文的「新资产狂潮」,注意力已经转向了另外两点。首当其冲的是「抢头矿」的「挖矿」,其次是与 OP_CAT 相关的新型基建。

由于后者还需要等待主网上线以后一段时间来跑出来,可以说当前与 Fractal 相关的最大、最直接热点就是「挖矿」。而与「挖矿」相关的 NFT 项目中,有着最高关注度的分别是 FSIC 与 ASIC。

这两个项目在发售的一开始进度都不太让人乐观,但最后都突然加速直到打完。让我们先看看 FSIC 吧。

FSIC 的发售价格是 0.0048 比特币,总量 3800 个。8 月 29 日开始铸造以后,一直持续到最后的公售阶段才完成了全部的销售。

在前期这个项目遭受了比较多的质疑,比如团队保持匿名、只有一张带有「FSIC」字样的矿机照片。

以及,FSIC 的售价相比于租用同等算力而言是否过高。

但是小编依然在当时铸造了 2 个 FSIC 的理由恰恰是因为该项目在当时遭受了太多的质疑。在发售前,FSIC 已经针对质疑做出了回应,最重要的是明确了其算力来源是向 Solo Fractal 矿池进行一年的租赁。只要这个项目不是发售完以后就跑路而是真的会围绕算力去做事情,这种「挖矿」NFT 就会存在其优势:

算力租赁对于一般的玩家来说存在一定的理解以及操作门槛,比如怎么买才划算、应该找谁买等等,有的租赁甚至有消费门槛,要租赁到一定数额的算力起跳。

自己去租算力更多是一种「单向做多 $FB」,尤其是「挖矿」热度越高,算力租赁价格也会越贵,但相当于「被锁仓」无法实时变现。而可以在二级市场交易的 NFT,虽然可能看上去单个 NFT 的算力并不高,但是多出来的价格可以视为一种「流动性溢价」,你可以随时把自己的算力卖给其它玩家。

所以,「挖矿」NFT 的本质更像是「注意力投资」,而不是究竟可以挖到多少,重点是团队不要拿钱就跑以及可以随时买卖。

因为 FSIC 在全部销售后一度到达 0.01 比特币的地板价格,相比铸造价格完成了翻倍,ASIC 也在剩余 1000 多个的时候开始加速,最终以 0.0062 比特币的价格完成了全部 3150 个「挖矿」NFT 的销售。这个项目和 FSIC 的不同之处有:

  • $FB 挖矿将在 Fractal 主网正式上线后一周才开始,而 FSIC 将从主网上线第一天就开始。由于他们是实打实自己购买矿机,因此部署新的矿机需要时间。
  • 平均单个 ASIC NFT 的算力将达到 17 TH/s,而 FSIC 是 7 TH/s。
  • ASIC 团队实名,FSIC 团队匿名。

重要的博弈点在前面 2 点。ASIC 的平均单个 NFT 算力虽然是 FSIC 的两倍多,但是挖矿会比 FSIC 迟一周才开始,而且是最关键的第一周。目前,我们还看不到市场会如何从价格上对这两点差异进行反应,因为 ASIC 明天才会在 Magic Eden 上架自己的整个合集。不过,目前 FSIC 在 Magic Eden 上的地板价基本上稳定在了 0.007 比特币附近,依然高于 ASIC 的铸造价格,因此小编比较乐观地认为 ASIC 的铸造者们在开局都能拿到收益,但是算力差异上的优势会不会完全兑现在价格上,还需要观察。

Leituras Relacionadas

BIS Report Compliance Observations: The True Risks of Stablecoins Go Beyond 'De-pegging'

The BIS report, "Anchoring trust in money: innovation beyond stablecoins," highlights that the primary risks of stablecoins extend beyond potential de-pegging. It argues that the core challenge is whether stablecoins can be integrated into a financial system that is identifiable, monitorable, accountable, and regulatable. While acknowledging efficiency gains like faster payments and programmability, BIS emphasizes that money requires an institutional framework—including legal certainty, liquidity support, and financial integrity controls—which many stablecoins currently lack. The report details compliance risks, noting that while blockchain transactions are transparent, address visibility does not equate to identity or purpose clarity. This creates a systemic risk as pseudonymity, non-custodial wallets, and cross-chain bridges can undermine AML/CFT controls. Furthermore, these risks can spill over into the traditional financial system through on- and off-ramps. The future direction, per BIS, is not to prohibit innovation but to embed regulatory rules—such as identity verification and transaction screening—directly into the technological infrastructure of tokenized finance. The key takeaway for compliance is that any new financial instrument must clearly address questions of customer identification, transaction monitoring, accountability, and cross-border rule consistency to be viable as a mainstream payment tool.

marsbitHá 58m

BIS Report Compliance Observations: The True Risks of Stablecoins Go Beyond 'De-pegging'

marsbitHá 58m

When US Giants Collectively "Defect" to Chinese AI Models

When Silicon Valley Giants Turn to Chinese AI Models to Cut Costs A surprising trend is emerging: major U.S. tech companies are significantly reducing AI costs by switching to Chinese models. Coinbase, the largest U.S. cryptocurrency exchange, reportedly halved its AI spending after migrating to China's GLM-5.2 and Kimi 2.7 models, despite increasing usage. They achieved this through a sophisticated three-part strategy: implementing an automatic routing system to select the most cost-effective model per task, boosting cache hit rates from 5% to 60% to reuse computations, and employing "context engineering" to provide AI with more precise, less cluttered information. They are not alone. AI startup Lindy switched from Claude to DeepSeek, saving millions, while Snowflake's tests found GLM-5.2 solved 66% of coding tasks compared to Claude Opus's 67%—but at a fraction of the cost (output pricing is 5-7 times lower). While the top Western models may offer slightly better stability, the massive price differential is leading many businesses to reconsider their value proposition. This shift signals a deeper change in the AI industry, moving beyond pure performance benchmarks to a fierce cost competition. As pressure mounts, even OpenAI and Anthropic have begun slashing prices. For users, this means more choices, lower costs, and a crucial lesson: using multiple models based on task complexity, optimizing with caching, and keeping contexts lean are now key to leveraging AI efficiently and affordably.

marsbitHá 1h

When US Giants Collectively "Defect" to Chinese AI Models

marsbitHá 1h

BIS Report Compliance Watch: The Real Risks of Stablecoins Are Not Just 'De-pegging'

BIS Report Compliance Observations: The real risks of stablecoins go beyond "depegging" The BIS report "Anchoring trust in money: innovation beyond stablecoins" argues that while stablecoins and tokenization offer efficiency gains, their primary risk lies in fitting into an identifiable, monitorable, accountable, and regulatable financial system. Money's trust stems not just from technology but from institutional arrangements: a common unit of account, guaranteed redemption at par, liquidity support, regulatory frameworks, and financial integrity requirements. Stablecoins, operating on permissionless blockchains with pseudo-anonymity and non-custodial wallets, create systemic compliance gaps: unclear customer identity, incomplete fund origins, unexplained transaction purposes, fragmented cross-chain paths, and ambiguous liability. On-chain transparency does not equal compliance transparency. Public addresses don't reveal identity or intent. While blockchain analytics aid law enforcement, they cannot replace routine, large-scale AML/CFT controls. Effective compliance requires a closed-loop process encompassing customer onboarding, transaction monitoring, investigation, reporting, and audit. Stablecoin risks are not confined to the blockchain; they re-enter the traditional financial system via on/off-ramps, exchanges, and payment institutions. This forces banks to monitor client accounts for activity linked to virtual assets. The future direction is not to prohibit innovation but to embed rules into the technology. Tokenized finance should integrate with the existing two-tier monetary system, embedding compliance—like customer identification, pre-transaction screening, and auditable data trails—directly into the transaction flow. For compliance professionals, the key takeaway is that any new financial instrument must answer core questions: Who identifies the customer? Who monitors transactions? Who handles exceptions? Who is liable? Compliance is not the antithesis of innovation but the essential infrastructure for its sustainable growth.

链捕手Há 1h

BIS Report Compliance Watch: The Real Risks of Stablecoins Are Not Just 'De-pegging'

链捕手Há 1h

When American Giants 'Defect' to Chinese AI Models

Summary: The trend of major U.S. technology firms adopting more cost-effective Chinese AI models is gaining momentum. A prime example is Coinbase, the largest U.S. cryptocurrency exchange, which reportedly halved its AI expenditure by switching to Chinese models GLM-5.2 and Kimi 2.7, while its usage volume increased. This was achieved through a sophisticated cost-saving system featuring intelligent model routing (selecting the most suitable model per task), dramatically improving cache hit rates from 5% to 60%, and implementing "Context Engineering" to streamline prompts. This shift is not isolated. Other companies like the AI startup Lindy and data cloud firm Snowflake are making similar moves, drawn by the significant price disparity. For instance, GLM-5.2 costs $1.40/$4.40 per million tokens (input/output), compared to $5/$25 for Claude Opus 4.7. While top Western models may offer slightly higher stability or speed in complex tasks, the performance gap is narrowing, making the price difference harder to justify for many enterprise use cases. The implications are significant for both businesses and individual users. It highlights the importance of a multi-model strategy based on task requirements, the value of caching and reusing outputs, and the effectiveness of providing concise context. Ultimately, this migration signals a potential reshaping of the AI industry's pricing model, moving competition from pure performance benchmarks to practical cost-effectiveness, with increased choice and downward price pressure benefiting end-users.

链捕手Há 1h

When American Giants 'Defect' to Chinese AI Models

链捕手Há 1h

Trading

Spot
活动图片