L2潮流之下,L1为何重获新生?

Odaily星球日报Pubblicato 2023-11-16Pubblicato ultima volta 2023-11-16

Introduzione

L1 不断创新和专业化,而 L2 则成为新的 L1。

原文作者:Ignas

原文编译:Luffy,Foresight News

L2 越多,我就越看好竞争性 L1。

上一轮牛市是非以太坊 L1 的 Beta 阶段:Alt-L1 通过在 AaveUniswap V2 的分叉协议上提供流动性挖矿奖励来争夺渴望收益的 Degen。以太坊之外的应用层几乎没有创新。

甚至非 EVM 链也推出了 EVM 侧链:NEARAuroraPolkadot 的 Moonbeam、CosmosKava。 EOS EVM 和 Solana 的 Neon 未能赶上这次风口。

这些链唯一的区别是:

  • 更低的 Gas 费;

  • 速度;

  • 品牌;

  • 可以提供多少流动性挖矿奖励。

然而,随着熊市的开始,流动性挖矿奖励减少,TVL 回归到更加安全的以太坊。

更糟糕的是,以太坊 L2 的新叙事领域涌现出了 OptimismArbitrum 等,他们承诺在不影响安全性的情况下为以太坊带来可扩展性。此外,他们还用潜在的空投预期来吸引用户。

L1 需要重塑,我很高兴看到他们做到了:

Avalanche:通过子网加倍扩展;专注于资产代币化;引入更多稳定币等。

L2潮流之下,L1为何重获新生?

Polygon:成为用于任何特定应用程序的主权 L2 的中心;与 OKX 合作推出新链。

NEAR:将自己打造为单体式和模块化的区块链;与 Polygon 合作在 DA 层上扩展以太坊;NEAR 还通过统一 UI ( BOS)和 L2 帐户聚合向 L2 提供链抽象。

L2潮流之下,L1为何重获新生?

Solana:引领单体区块链扩容浪潮,可实现快速交易,且无需繁琐的模块化用户体验。我将在后续文章中分享更多有关以太坊与 Solana 辩论的内容。

L2潮流之下,L1为何重获新生?

Fantom:通过 Sonic 升级改进单体式区块链设计,无需分片或 L2 即可实现 2000 TPS,目标是吸引新一代 DApp。

L2潮流之下,L1为何重获新生?

BNB Chain:推出了 opBNB L2 以降低费用,但更重要的升级是 BNB Greenfield,它专注于数据和 IP 货币化的数据金融,以及去中心化 AI(具有隐私保护的 LLM 训练)。

Cosmos: ATOM 本身在其价值主张方面似乎迷失了方向,但随着 OsmosisInjective、Kuji 的进展,Cosmos Hub 正在蓬勃发展。

L1 不断创新和专业化,而 L2 则成为新的 L1。如今的 L2 忙于吸引分叉协议,但缺乏创新和多样化。

不幸的是,许多 L2 代币的代币经济学较差,看看 ARB 备受争议的「质押」提案便能略知一二。

毫不奇怪,老牌 L1 的代币开始流行。与之前的牛市相比,它们现在提供了更具吸引力的价值主张。

或者,这只是短期轮动?我希望不是。

Letture associate

Podcast Notes: Hyperliquid Has Become the Top Interest Point for Traditional Hedge Funds

Empire Podcast hosts Jason Yanowitz and Santiago Santos discuss the surging institutional interest in Hyperliquid, a decentralized perpetual exchange, marking the highest level of engagement from traditional hedge fund managers since Paul Tudor Jones endorsed Bitcoin in 2020. The primary driver is the demand for weekend trading of commodities like oil, especially during geopolitical tensions such as the Iran conflict, as Hyperliquid provides the only active price discovery venue when traditional markets are closed. Trade XYZ, a front-end on Hyperliquid, has seen significant growth, with weekend oil price predictions having a median error of only 50 basis points. Santos predicts commodity trading volume on Hyperliquid will surpass Bitcoin within the year and that its market cap could rise from $25 billion to $100 billion. Other key points include Kraken raising $200 million at a reduced valuation of $13.3 billion, and the SEC clarifying that self-custodied DeFi frontends like MetaMask are not subject to broker-dealer rules, resolving a major regulatory uncertainty. The hosts also note the strong correlation between crypto and macro markets, with the S&P 500 posting one of its best 10-day rallies since 1950. They highlight MicroStrategy's continued Bitcoin acquisitions and the potential of real-world asset (RWA) tokenization as a key trend. The discussion concludes with skepticism towards many L2 projects, predicting a wave of protocols truly going to zero as capital concentrates in proven assets like Bitcoin and Hyperliquid.

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Podcast Notes: Hyperliquid Has Become the Top Interest Point for Traditional Hedge Funds

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a16z: The Next Frontier of AI, The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

a16z presents a comprehensive investment thesis for the next frontier of AI: Physical AI, centered on a synergistic flywheel of robotics, autonomous science, and novel human-computer interfaces (HCIs) like brain-computers. While the current AI paradigm scales on language and code, the most disruptive future capabilities will emerge from three adjacent fields leveraging five core technical primitives: 1) learned representations of physical dynamics (via models like VLA, WAM, and native embodied models), 2) embodied action architectures (e.g., dual-system designs, diffusion-based motion generation, and RL fine-tuning like RECAP), 3) simulation and synthetic data as scaling infrastructure, 4) expanded sensory channels (touch, neural signals, silent speech, olfaction), and 5) closed-loop agent systems for long-horizon tasks. These primitives converge to power three key domains: * **Robotics:** The literal embodiment of AI, requiring all primitives for real-world physical interaction and manipulation. * **Autonomous Science:** Self-driving labs that conduct hypothesis-experiment-analysis loops, generating structured, causally-grounded data to improve physical AI models. * **Novel HCIs:** Devices (AR glasses, EMG wearables, BCIs) that expand human-AI bandwidth and act as massive data-collection networks for real-world human experience. These domains form a mutually reinforcing flywheel: Robotics enable autonomous labs, which in turn generate valuable data for robotics and materials science. New interfaces provide rich human-physical interaction data to train better robots and scientists. Together, they represent a new scaling axis for AI, moving beyond the digital realm to interact with and learn from physical reality, promising significant emergent capabilities and value.

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a16z: The Next Frontier of AI, The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

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