Ethereum surged 29% this week, breaking a 10-week downtrend and catching short sellers off guard with $400M liquidated. 

The S&P 500 extended its rally with a nine-day winning streak, its longest since 2004, as optimism over potential trade deals and upcoming Fed and earnings updates lifted sentiment. Gold rose to $3,245 on trade uncertainty and Fed rate cut hopes. CoinDesk Indices, which tracks the broader crypto market, fell 1.50% in the past 24 hours, while Bitcoin and Ether dropped 0.10% and 0.51%, respectively. Trade these markets and more with up to 500x leverage on Bybit MT5 using this 

Today's top performer is AI16Z, which surged 7.0% after Bonsai's smart media protocol integrated with Eliza to empower agent-driven social content creation.

elizaOS (AI16Z), launched in October 2024, formerly ai16z, is a Solana-based DApp that uses a DAO and an AI agent, "Marc AIndreessen," to manage community-driven investments in tech and crypto projects. 

, lets developers create agent-driven social media scripts and templates using ElizaOS plugins. The Bonsai Client, its first implementation, enables easy deployment of these smart media agents on platforms like Lens. Builders can explore these tools and compete for prizes in the Lens Spring Hackathon.

Check out the latest prices, charts, and data of 

Ethereum surged 29% this week, breaking a 10-week downtrend and catching short sellers off guard with $400M liquidated. Despite muted ETF flows and neutral derivatives sentiment, ETH’s dominance remains unshaken—boasting $64B in TVL, dwarfing Solana, BNB Chain, and Tron combined. With fresh momentum from the recent pectra upgrades and Trump distancing from rival altcoins, all eyes are now on a potential push toward $2,700. 

BTC spot ETFs saw $920.9M in net inflows this week, driven by $1.09B into non-GBTC funds despite $171.5M outflows from GBTC. Since inception, total net inflows stand at $41.1B.

 are now live and claimable as a small bonus for users farming the upcoming Aster (AST) airdrop. By depositing BNB to mint asBNB, users can start earning points toward both the main airdrop and future ecosystem rewards. Optional perp trading can boost points further, but simply holding asBNB is enough to qualify.

Beginner

1.png

Ethereum outperforms with a 20% rally as fresh capital flows in and undervaluation signals flash

Our monthly report delves into insights from options volatility to inspire your next crypto move.

: April 2025 saw BTC's implied volatility surge above 75% due to tariff chaos, but it collapsed to 35% within a week, indicating a surprising short-term drop in volatility expectations despite ongoing macro uncertainty.

: BTC's return above $100K was met with muted enthusiasm in the derivatives market, suggesting that 

traders no longer view this level as a significant psychological barrier, and the rally isn’t likely to be sustained.

: ETH significantly outperformed BTC with a 23% rally, leading to increased demand for short-dated calls and a positive shift in the options market, highlighting renewed bullish sentiment.

: Historical trends suggest a potential summer lull in volatility in store for BTC, raising questions about whether this pattern will repeat in 2025 amidst ongoing macroeconomic challenges.

Bitcoin’s volatility plunges in late April

April 2025 witnessed significant volatility in the cryptocurrency market, particularly influenced by US trade policies. As President Trump announced steep reciprocal tariffs, the crypto landscape experienced sharp fluctuations. Bitcoin (BTC) initially demonstrated resilience, despite widespread sell-offs in equities and the US dollar, only to succumb to a decline from $88K to $75K by early April. This period was characterized by a dramatic rise in implied volatility that peaked above 75% as traders sought protection through short-dated puts. However, as the tariffs faced moderation, implied volatility plummeted to 35% by mid-April, marking the lowest level since early March.

Interestingly, while short-tenor volatility collapsed, 

the back end of BTC's term structure remained elevated, signaling that market participants expect heightened volatility in the mid-term

. Amidst this backdrop, realized volatility fell below the five-year seasonal average, paralleling similar trends in previous years and suggesting a 

. The historical pattern contrasts that BTC often experiences a rebound in volatility heading into May, raising questions about whether this trend will persist.

Subdued derivative response suggests Bitcoin rally might not endure

The market's reaction to BTC's resurgence above the $100K threshold was notably subdued compared to previous instances. While BTC rallied 20% following the initial tariff sell-off, the derivatives market displayed a muted response. The 25-delta put/call skew, a measure of market sentiment, indicated only a slight bias toward out-of-the-money calls, contrasting sharply with sentiment previously when BTC first breached the $100K mark in December 2024. This lack of exuberance suggests that traders no longer view $100K as a significant inflection point, raising concerns about the sustainability of the rally. 

Meanwhile, funding rates and implied volatility — though still bullish — didn’t reach the extremes observed in earlier months, indicating more cautious market sentiment. The relationship between BTC's spot price and implied volatility suggests that although traders are optimistic, they’re waiting for BTC to approach previous all-time highs before adjusting their volatility expectations.

Ethereum's option data points to further rally

In stark contrast to BTC, Ethereum (ETH) displayed remarkable resilience and outperformed major cryptocurrencies, gaining 23% overnight following favorable trade news. This rally catalyzed a surge in demand for short-dated calls, pushing front-end volatility expectations significantly higher. The response from the derivatives market was robust, inverting the term structure and indicating renewed bullish sentiment toward ETH, even as it’s trading well below its all-time high.

The ETH options market exhibited a shift from neutral to slightly negative skew, to a 10% bias favoring out-of-the-money calls, reflecting the market's confidence in ETH's potential to regain lost ground. Despite overall macroeconomic uncertainty, the demand for ETH options suggests that traders are optimistic about Ether’s future price movements.

In conclusion, April 2025 highlighted the complex interplay between macroeconomic factors and cryptocurrency market dynamics. While BTC faced challenges in eliciting strong responses from derivatives markets upon recrossing the $100K threshold, ETH's performance indicated a divergence in market sentiment. As traders navigate the evolving landscape, the potential for seasonal volatility patterns and the influence of macroeconomic developments will remain critical factors in shaping future market expectations.

Advanced

Bybit Volatility Report banner.png

Bybit X Block Scholes options volatility report (April 2025): Data suggests Bitcoin rally might not endure, but Ether sees further upside

Our weekly crypto derivatives analytics report delves into macro events; the current state of crypto and trading signals from spot trading volume; and futures, options and perpetual contracts.

After mostly trading sideways at the end of April 2025, a US-UK trade deal sparked a crypto rally. BTC surged past $100K for the first time since February, but it’s ETH's performance that captured the derivatives market's attention. 

A 23% increase in ETH's spot price led to an inverted term structure and a spike in front-end volatility. The put/call skew for ETH shifted from a bearish bias to a nearly 10% skew toward OTM calls. Other sentiment indicators, such as perp funding rates and futures yield, also supported the rally. In contrast, while BTC's funding rates and skew have increased, its implied volatility remains low at 40%, over 30 points below ETH's 7-day implied volatility.

Please check out the report’s highlights.

ETH’s record performance since 2021 was propelled by positive term structure

ETH has significantly outperformed BTC and the top 10 cryptocurrencies, marking its largest single-day gain since May 2021. The options market has reflected this enthusiasm, with implied volatility for short tenors rising sharply and inverting ETH’s term structure, thus contrasting with BTC's muted, positively sloped structure. The 7-day tenor for ETH is now trading at a 33-point premium over that for BTC. 

While puts dominate BTC's open interest, ETH is seeing the opposite trend. On May 8, 2025, options volumes surged over $120M, more than double the typical monthly volume, with $60M more in calls than puts. ETH now boasts a positive futures curve and funding rates, as well as a strong skew toward OTM calls.

ETH sees exceptionally bullish put/call skew

ETH's substantial outperformance in the current crypto rally has led to bullish sentiment in its derivatives markets. Funding rates have surged, futures yields are strongly positive and the put/call skew has shifted significantly toward OTM calls, now close to 10%. This starkly contrasts with early April, when the skew was 20% toward OTM puts. Although ETH has underperformed (relative to BTC and other "ETH killers"), it has rebounded in this rally, with the ETH/BTC ratio rising after spending most of the year declining.

Open interest for Bitcoin options increased, with implied volatility remaining suppressed

The announcement of the new US administration's first trade deal, nearly 30 days into Trump's 90-day tariff pause, jolted crypto markets, pushing BTC back up above $100K. Surprisingly, implied volatility has remained suppressed, failing to invert BTC's term structure, with front-end volatility trading at around an 18-month low of 35-40%. On May 8, 2025, options volumes surged, with call volume reaching $189M, reversing last week's trend of higher put volumes. Meanwhile, open interest rose steadily to exceed $300M, the highest level this month, but remains significantly larger for puts than for calls despite the price increase.

block-scholes-x-bybit-report.jpeg

Bybit X Block Scholes crypto derivatives analytics report (May 9, 2025): Ether’s put/call skew turns remarkably more bullish than Bitcoin’s

This article explores how Sign works, its flagship products (such as TokenTable, SignPass and EthSign), its native token (SIGN) and how to trade SIGN on Bybit.

Sign is an omnichain attestation infrastructure that empowers users and developers to verify real-world claims on-chain. Built for a more transparent and decentralized future, it eliminates the need to “just trust” and enables everyone to verify with cryptographic proof.

This article explores how Sign works, its flagship products (such as TokenTable and EthSign), its native token (SIGN) and how to trade SIGN on Bybit.

 is an omnichain attestation protocol designed to make it easy for anyone to verify the authenticity of any claim — 

. Whether it’s a document, credential or user identity, Sign transforms traditional trust models into verifiable, cryptographic attestations. 

Sign’s mission is to replace outdated, opaque validation systems with transparent, 

 proof — enabling a future where “verify” replaces “trust.” Through tools like schema registries and digital signatures, Sign empowers users to bring accountability and integrity to digital interactions.

Sign is developed by the team behind EthSign, a 

-native e-signature and agreement platform. Its core team brings deep experience in 

 development and cross-chain integrations.

The project is backed by top-tier investors, including Sequoia Capital China (now known as HongShan Capital Group), Mirana Ventures, HashKey Capital and Hack VC.

This strategic funding positions Sign as a foundational layer in the web3 infrastructure stack.

Sign allows users to create attestations — cryptographically signed statements that prove the validity of claims, such as credentials, achievements or actions. These digital proofs can be verified by anyone at any time, making them tamperproof and transparent.

Sign’s key technical elements include a schema registry that ensures consistent data formatting, digital signatures proving authenticity, and on- and off-chain attestations with support for revocation or updates

This system acts like a programmable layer for web3, bringing a new level of trust to identity, credentials and data exchange.

TokenTable is Sign’s flagship token distribution platform. It helps projects unlock, distribute and manage tokens transparently and securely at scale.

It uses Sign’s attestation engine to verify token unlocks, 

 and vesting schedules, ensuring traceable, auditable token flows.

SignPass is a blockchain-based identity solution that brings real-world credentials on-chain. One notable implementation is the Sierra Leone PR Card, used for the world’s first government-approved on-chain permanent residency program.

SignPass offers encrypted on-chain records for easy 

 verification, physical ID issuance for daily identity use and streamlined digital application systems for cross-border access.

It showcases the way blockchain can modernize public sector services worldwide.

EthSign is a decentralized contract signing platform that lets users create, sign and store legally binding agreements directly on-chain. It removes intermediaries and ensures documents are immutable and accessible.

EthSign reinforces Sign’s mission by bringing traditional documentation into the realm of verifiable web3 attestations.

The SIGN token is the native utility token powering the Sign ecosystem. It’s used for:

The total supply is capped at 10 billion SIGN, with 1.2 billion tokens in circulation. While the fully diluted valuation (FDV) sits above $746 million as of May 7, the circulating market cap is approximately $89.6 million. SIGN continues to gain traction across exchanges and developer networks.

SIGN is available for trading on Bybit via Spot trading and as a USDT Perpetual contract. Bybit offers up to 75x leverage for the SIGNUSDT Perpetual contract and supports secure, high-liquidity spot trading.

Start exploring the future of attestations with SIGN on Bybit today.

Intermediate

sign.png

Sign (SIGN): Web3 protocol for trustless attestation

Web3 Experts

Unlock the evolving market landscape of AI and Crypto with insights from our guest author as they dissect market cycles, categorize projects, and highlight emerging trends in assetization.

2401-T16454_Skinny_Banner_for_Blog_and_Learn_Row_53_728x90.png

Skinny_Banner-1600x400.webp

More than a year since the release of ChatGPT, discussions about AI+Crypto have once again heated up in the market. AI is seen as one of the most important tracks in the bull market of 2024–2025, and Vitalik Buterin himself published an article entitled 

, exploring possible directions for future AI+Crypto exploration.

This article won’t make too many subjective judgments, but will instead simply summarize the entrepreneurial projects combining AI and crypto observed over the past year from a primary market perspective. It will examine the perspectives from which entrepreneurs have entered the market, the achievements made so far, and which areas are still being explored.

Throughout 2023, we’ve talked to dozens of AI+Crypto projects, among which distinct cycles can be observed.

Before the release of ChatGPT at the end of 2022, there were few 

 projects related to AI in the secondary market. The main ones that come to mind are 

 (AGIX) and a few other veteran projects. Similarly, there weren’t many AI-related projects available in the primary market.

From January to May of 2023 could be considered as the first concentrated outbreak period for AI projects. After all, ChatGPT’s impact was significant. Many old projects in the secondary market pivoted to the AI track, and almost every week in the primary market AI+Crypto projects were being discussed. Similarly, during this period, AI projects seemed relatively simple. Many of them were based on a “skin-deep” adaptation of ChatGPT, combined with blockchain modifications, with almost no core technological barriers. Our in-house development team could often replicate a project framework in just a day or two. This also led to numerous meetings with AI projects during this period, but ultimately, no action was taken.

From May through October, the secondary market began to turn bearish. Interestingly, during this time, the number of AI projects in the primary market also decreased significantly. It wasn’t until the last month or two that the quantity started to pick up again, and discussions and articles about AI+Crypto became richer. We once again entered a period where we could encounter AI projects every week. Half a year later, it was evident that a new batch of AI projects had emerged with a better understanding of the AI track, the landing of commercial scenarios and an improved integration of AI+Crypto as compared to the first wave of AI hype. 

Although the technological barriers still weren’t strong, the overall maturity level took a step forward. It was only in 2024 that we finally made our first bet on the AI+Crypto track.

Vitalik Buterin, in his article on “promise and challenges,” provides predictions from several relatively abstract dimensions and perspectives, as follows:

We, on the other hand, will summarize the AI projects currently seen in the primary market from a more specific and direct perspective.

Most AI+Crypto projects are centered on the core of crypto, which we’re defining as “technological (or political) decentralization + commercial assetization.”

Regarding decentralization, there isn’t much to say, as it’s all about 

. Therefore, we can roughly divide the categories of assetization into three main tracks:

This is a relatively dense track, as besides various new projects there are also many old projects pivoting. For example, on the 

 side, there’s Akash Network, and on the 

. After pivoting, the tokens have all experienced crazy surges, which also indirectly reflects the market’s optimism toward the AI track. Although 

 (RNDR) primarily focuses on decentralized rendering, it can also serve AI purposes. 

Therefore, many classifications include RNDR-like computing power-related projects in the AI track.

Computing power assetization can be further subdivided into two directions, based on the use of computing power. One is represented by Gensyn, which is “decentralized computing power used for AI training.” The other is represented by most pivots and new projects, or “decentralized computing power used for AI inference” (the ability of machine learning models to base decisions or predictions on previously learned data or models).

In this track, we can observe an interesting phenomenon, or perhaps a chain of disdain:

Traditional AI → Decentralized Inference → Decentralized Training

The main reason lies in the technical aspect — because AI training (especially for large model AI) involves massive amounts of data. What’s even more exaggerated than the data requirement is the bandwidth demand formed by the high-speed communication of this data. In the current environment of transformer large models, training requires a computational matrix composed of a large number of high-end graphics cards like the 4090 series/H100 professional AI cards with communication channels of the hundred-gigabit level formed by NVLink and professional fiber switches. Can you imagine decentralizing this stuff? Hmm …

The demand for computing power and communication bandwidth in AI inference is far less than in AI training. Naturally, the possibility of decentralized implementation is much greater for inference than for training. That’s why most computing power-related projects focus on inference, while training is primarily left to major players like Gensyn and Together AI, which have raised hundreds of millions in financing. However, from the perspectives of cost-effectiveness and reliability, at least at this stage, centralized computing power for inference is still far superior to decentralized options.

This explains why those focused on decentralized inference look at decentralized training and think, “You can’t make it happen at all,” while traditional AI views decentralized training and inference as “unrealistic in terms of training technology” and “unreliable in terms of inference commercially.”

Some say that when BTC/ETH first appeared, the model of having distributed nodes compute everything seemed relatively illogical (as compared to cloud computing). But in the end, didn’t it succeed? Well, that depends upon the requirements for correctness, immutability, redundancy and other dimensions of AI training and inference in the future. Purely in terms of performance, reliability and price, it’s currently impossible to surpass centralized solutions.

This is also a crowded track for projects, and is relatively easier to understand as compared to computing power assetization because one of the most well-known applications after the popularity of ChatGPT is Character.AI. With it, you can seek wisdom from ancient philosophers like Socrates and Confucius, engage in casual conversations with celebrities like Elon Musk and Sam Altman, or even indulge in romantic talks with virtual idols like Hatsune Miku and Raiden Shogun. All of this showcases the charm of large language models (LLMs). The concept of AI Agents has become deeply ingrained in people’s minds through Character.AI.

What if figures such as Confucius, Elon Musk or Raiden Shogun were all 

So, rather than calling it model assetization, it’s more apt to say it’s the assetization of agents built on top of large models. After all, large models themselves can’t be put on the blockchain. It’s more about mapping agents on top of models into NFTs to create a sense of “model assetization” in the AI+Crypto space.

There are now agents that can teach you English or even engage in romantic relationships with you, among various other types. Additionally, related projects, such as agent search engines and marketplaces, can also be found.

The common issue in this track is first of all that there are no technological barriers. It’s basically just the tokenization of Character.AI. Our in-house tech wizards can create an agent that speaks and sounds like a specific character (such as our co-founder, BMAN) in just one night by using existing open-source tools and frameworks. Secondly, the integration with blockchain is very light. It’s somewhat akin to 

, in which the metadata stored may only be a URL or hash, and the models/agents reside on cloud servers. On-chain transactions only represent ownership.

The assetization of models/agents is still one of the main tracks in AI+Crypto for the foreseeable future. In the future, we hope to see projects with relatively higher technological barriers and closer integration with blockchain that are more native.

Logically speaking, data assetization is the most suitable aspect of AI+Crypto because traditional AI training mostly relies on visible data available on the internet — or, to be more precise, public domain traffic data. This data may only represent a small percentage, around 10–20%, with the majority of data actually lying within private domain traffic (including personal data). If this traffic data can be utilized for training or fine-tuning large models, we can undoubtedly have more professional Agents/bots in various verticals.

Therefore, through AI+Crypto, and under the guidance of decentralized incentives, releasing personal and private domain traffic data and assetizing it to provide better and richer “food” for large models sounds like a logical enough approach. Indeed, there are several teams deeply involved in this field.

However, the biggest challenge in this track is that, unlike computing power, data is difficult to standardize. Using decentralized computing power, the model of your graphics card directly translates into the amount of computing power you have. On the other hand, the quantity, quality and purpose of private data are all difficult to measure. If decentralized computing power is like ERC-20, then assetizing decentralized AI training data is somewhat like ERC-721 — and like having many projects, such as 

 and different NFTs with different traits mixed together. The difficulty in liquidity and market-making is much more challenging than with 

. Therefore, projects focusing on AI data assetization are currently facing significant challenges.

Another aspect in the data track worth mentioning is decentralized labeling. Data assetization operates at the “data collection” step, and the collected data needs to be processed before being fed to AI, which is where data labeling comes in. This step is currently mostly centralized and labor-intensive. With decentralized token incentives, transforming this work into decentralized, labeling-to-earn or (similar to crowdsourcing platforms) distributing work is also a viable approach. A few teams are currently working in this area.

Let’s briefly discuss the currently missing puzzle pieces in this track from our perspective.

You might ask why a VC like us can come up with certain scenarios before entrepreneurs in the market can. That’s because we have seven experts on our in-house AI team, five of whom have PhDs in AI. 

Finally, although from the perspective of the primary market, AI+Crypto is still quite early and immature, this doesn’t prevent us from being optimistic about 2024–2025, when AI+Crypto will become one of the main tracks of this bull market cycle. After all, is there a better way to combine productivity that’s liberated by AI with production relations liberated by blockchain?

Viewing AI+Crypto From a Primary Market Perspective

Compete for 2,500 USDT Every Week

Earn Points by Completing Tasks

Event Rules

Register now and get 10 points

Write/Reply Comments (0/5)

Publish an Article (0/5)

Share an Article on Social Media (0/5)

Like an Article (0/5)

Read an Article (0/5)

Weekly Leaderboard