Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

Odaily星球日报Published on 2023-11-04Last updated on 2023-11-04

Abstract

下一牛市周期中潜在的核心赛道基本都已经在市场中涌现。‍

原文来源:Zonff Partners

一级市场供给端和需求端活跃度连续三个季度下降

2023 年 Q3 市场总融资额 16.94 亿美金,融资事件 170 起,平均融资规模略有上升(RampBitGo 两个面向传统机构的基金基础设施完成了 4 亿融资额)。2023 年 10 月融资金额为 4.26 亿美元,更是持续创近 4 年来融资金额最低月份。融资金额和融资事件连续下降,市场上整体交投并不活跃,机构依旧以保守型策略为主,主要资金投向为基础设施与强基本面项目资金抱团,例如 Flashbot 等。

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

数据来源:RootData

市场情绪钝化,有望在未来两个季度迎来回暖

在投融资资金持续下降的过程中,跌幅在逐步缩小,DeFi、GameFi、游戏等原本主力赛道已经降至冰点,RWA 与 BTC 项目并没有带来山寨币市场的连续活跃度。

我们认为,当前一级市场的状态已经出现了类似 2019 年 Q4 的情绪钝化,机构只投向强基本面项目,常规赛道的优化项目已经在过去一年半完成了基本框架的布局,对于情绪面或小创新度项目又较为保守,市场短时间内难以用新叙事打破情绪冰点。当前的市场状态需要在基本面增长达到质变后重新带动信心。

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

图表来源:ROOTDATA 2023.09.28 

考虑到当前链上资金的体量,钱包数量和基本设施基本面持续增长,我们认为一级市场正在经历底部,有望在未来两个季度筑底回暖:

1. Zksync、StarknetCelestia、Layerzero、Eigenlayer、Scroll 等项目在未来两个季度均有大的基本面更新和测试网迭代,或是主网上线,能带动潜在的链上行为活跃度和生态进一步布局的机会,大型 Infra 项目在 Q3 没有上线,链上行为和情绪并没主线;

2. 以太坊坎昆升级后带来 L2 生态进一步繁荣,Q3 L2 生态 TVL 稳定在百亿美金,资产体量和交易量进入增长瓶颈期,坎昆升级后带来更低的 Gas 成本,更快的链上体验,并有效提振二级市场价格和情绪,有望给 L2 生态带来新的布局机会;

3. 游戏赛道将迎来大规模上线,有较大概率带动市场;当前游戏的产品的供给完备, 2021 年下半年游戏融资体量过 50 亿美金,经过一年半以上的准备期,新一代链游从制作质量、运营成熟度、可玩性、体验优化上均有大幅度的提升,并即将在未来三个季度上线;

行业赛道分析

综合来看,本个熊市周期已经持续了超过 6 个季度,期间几乎所有的老叙事都有不同程度演进和发展,当中有一部分已经充分被市场证伪,新的叙事也逐一登场试水,产生有效市场反馈。在 6 个季度观察研究中,我们认为市场产生了比较充足的归纳演绎的素材,可以得出更为可靠的研究思路和观察视角。

ETH 将于未来半年内进行坎昆升级,BTC 将于 7 个月后进行下一次减半;我们认为截止到 2023 年 Q3,下一牛市周期中潜在的核心赛道基本都已经在市场中涌现。

依据过往行情经验与归纳总结,我们根据发生的优先顺序,增长爆发的原因,整体供给的规模等因素,把现存的主要赛道分为四个类别

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

以太坊正统性驱动(重要)

以太坊正统性主要是描述区块链当前发展的核心技术路径,这个类别在 6 个季度的熊市中整体募资规模超过百亿美金,基本占据整个市场募资规模的 40% 左右。

由于大型项目空投成为熊市中为数不多的有效资产发行路径和市场热点,这类项目由此持续积累了较高的用户数据和生态资源;同时“以太坊正统性”类别的项目在早期阶段重要的核心能力是生态 BD,通过头部项目的合作新闻、叙事嵌套、空投预期、强资本背书等方式,提升品牌声量和生态关注度;此类项目的基本面发展主要以技术生态和叙事的构建为主,受市场情绪和比特币价格影响不大,配合较好的资金实力和空投效果,我们认为更容易在牛市早期成为市场的主要引领赛道。

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

客观趋势出现拐点或产生新资产类别(重要)

一级市场赛道的快速增长并引领市场通常需要满足以下的标准之一:

  • 原本的赛道或技术环境稳定发展,并产生了新的叙事思路或运营策略,如 GameFi、DeFi;

  • 客观趋势及用户或资产体量稳定增长,达到质变后产生全新的产品类别,如衍生品交易;

  • 新的叙事,产生新的资产类别,大量新资产的分发渠道不受限,如 NFT;

由此我们梳理了当前市场上有那些类别的赛道符合以上的标准之一,并认为市场部分细分领域已经开始逐步临近拐点,有较大可能成为下一轮牛市的发动机。

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

成熟赛道的规模扩展

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

依赖用户场景突破或者运营经验带来大规模流量

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

Zonff Partners:2023年Q3 Web3一级市场回顾与赛道分析

Related Reads

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit5m ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit5m ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit1h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit1h ago

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit1h ago

Token Inefficient, Economy Tokenless

marsbit1h ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbit1h ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbit1h ago

Trading

Spot
Futures
活动图片