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The article "iQiyi Is Too Impatient" discusses the controversy surrounding the Chinese streaming platform IQiyi's recent announcement of an "AI Actor Library" during its 2026 World Conference. IQiyi claimed over 100 actors, including well-known names like Zhang Ruoyun and Yu Hewei, had joined the initiative. CEO Gong Yu suggested AI could enable actors to "star in 14 dramas a year instead of 4" and that "live-action filming might become a world cultural heritage." The announcement quickly sparked backlash. Multiple actors named in the list issued urgent statements denying they had signed any AI-related authorization agreements. This forced IQiyi to clarify that inclusion in the library only indicated a willingness to *consider* AI projects, with separate negotiations required for any specific role. The incident, which trended on social media with hashtags like "IQiyi is crazy," is presented as a sign of the company's growing desperation. Facing intense competition from short-video platforms like Douyin and Kuaishou, as well as Bilibili and Xiaohongshu, IQiyi's financial performance has weakened, with revenues declining for two consecutive years. The author argues that IQiyi is "too impatient" to tell a compelling AI story to reassure the market, especially as it pursues a listing on the Hong Kong stock exchange. The piece concludes by outlining three key "AI questions" IQiyi must answer: defining its role as a tool provider versus a content creator, balancing the "coldness" of AI with the human element audiences desire, and properly managing the interests of platforms, actors, and viewers. The core dilemma is that while AI can reduce costs and increase efficiency, it risks creating homogenized, formulaic content and devaluing human performers.

ENI Announces Strategic Brand Upgrade: Evolving from Underlying Protocol to Global Institutional-Grade Financial Infrastructure At the Hong Kong Web3 Festival on April 20, 2026, ENI founder and CEO Arion Ho announced the completion of a comprehensive brand, website, and visual system upgrade. This marks a significant shift from being an "underlying public chain" to an enterprise-grade Blockchain-as-a-Service (BaaS) platform, positioning ENI as a key infrastructure provider bridging traditional finance (TradFi) and Web3. The rebranding emphasizes precision and professionalism, reflected in a refined visual identity featuring a 25-degree tilt and a 1:4 golden ratio in its design elements. This aesthetic upgrade, led by a top-tier design team with experience from Hermès and ByteDance, underscores ENI’s commitment to institutional-grade trust and global sophistication. ENI now functions as a bridge between technological innovation and real-world business applications. It offers tailored architecture solutions for large institutions and standardized, low-friction BaaS tools for SMEs, enabling seamless integration of Web3 capabilities into existing business models. The announcement in Hong Kong, a global financial hub, signals ENI’s matured, global-ready approach to supporting the commercial adoption of Web3. By providing a stable, standardized platform, ENI aims to facilitate the secure migration of real-world assets and operations into the digital economy.

ElorianAI, co-founded by ex-Google AI expert Andrew Dai and former AI specialist Yinfei Yang, has raised $55 million in early funding to develop next-generation AI systems with advanced visual reasoning capabilities. While current large models excel in text-based tasks like programming and math, they perform poorly in visual reasoning—even top models like Gemini only match a 3-year-old’s ability in basic visual benchmarks. The key limitation lies in the architecture of current vision-language models (VLMs), which first convert visual inputs into text before reasoning, losing critical spatial and structural information. ElorianAI aims to build a native multimodal model that processes and reasons directly in visual space, enabling deeper understanding of physical relationships, constraints, and environments. The company plans to release a state-of-the-art visual reasoning model by 2026, with potential applications in robotics, disaster management, engineering, healthcare, and AI hardware. By using high-quality, diverse, and synthetically generated data, ElorianAI intends to create models that don’t just perceive but truly understand and reason about the physical world—bringing us closer to visual AGI.

NVIDIA's investment strategy operates through a "three-track architecture," not just its NVentures venture arm. Corporate Development handles massive strategic bets (e.g., $30B in OpenAI, $10B in Anthropic, $20B in Synopsys). NVentures, a small team, focuses on early-stage, financial investments across sectors like quantum computing (Alice & Bob, PsiQuantum), AI infrastructure (OpenRouter, Tensormesh), and biotech. The NVIDIA Inception accelerator provides non-monetary support. This system allows NVIDIA to nurture startups and lock in strategic partners, creating a vast AI ecosystem. This aggressive capital deployment has drawn scrutiny. Critics like Michael Burry and EU regulators question potential "circular financing," where NVIDIA's equity investments in companies (e.g., CoreWeave, OpenAI) facilitate those companies' purchases of NVIDIA hardware, potentially inflating revenue. Supporters view it as a necessary "virtuous cycle" to secure supply and demand in a compute-scarce market. While NVentures' smaller deals appear like traditional VC, its role within the larger, controversial investment framework remains a point of debate.

Beyond Private Keys: Securing Web3's Expanding Attack Surface from Wallets to L2s and Supply Chains The crypto space faced a wave of security incidents in June, with over $75 million lost across 40 major attacks. These breaches highlighted risks beyond private key theft, exposing vulnerabilities across the entire user interaction chain. Wallet security was compromised not through stolen seed phrases, but via a critical flaw in the Cardano wallet SecondFi's signing implementation. This bug allowed attackers to potentially derive private keys from publicly visible signature data, emphasizing that wallet security depends on correct cryptographic implementation, ideally in open-source, auditable code. Layer-2 networks also revealed complex trust chain risks. Attacks on legacy Aztec deployments exploited inconsistencies in proof systems, showing that a valid zero-knowledge proof is only as secure as its underlying rules. Another attack on Taiko's SGX-based prover stemmed from a leaked signing key and inadequate verification checks. Furthermore, a technical glitch halted Base's block production, underscoring that L2 security encompasses network availability and reliable user exit paths as much as asset safety. Finally, the Polymarket incident demonstrated that even audited smart contracts are not immune. A compromised third-party supplier led to a malicious script being injected into the platform's frontend, resulting in user fund losses. This "supply chain attack" shows that the security of the entire interaction path—from the webpage to the wallet signature—is critical. The conclusion is clear: Web3 security now involves safeguarding the entire journey from transaction intent to on-chain settlement. Users must adopt layered security habits: isolating long-term holdings, using dedicated wallets for daily interactions, scrutinizing transaction details before signing, and managing authorizations cautiously. Defense must evolve from protecting a single point (the private key) to securing a complete chain of interactions.