2026-06-08 Понедельник

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Blockchain Capital Partner: Most People Have a Narrow Understanding of the On-Chain Economy

Author Spencer Bogart, a partner at Blockchain Capital, argues that most people have a narrow view of the on-chain economy, seeing it primarily as a faster, cheaper version of existing financial systems. While this represents a significant opportunity, he believes it's only a small part of the story. Bogart compares the current state of crypto to the early internet, where email was the obvious "faster mail" application. The truly transformative categories—like search, social media, and cloud computing—were entirely new and unimaginable beforehand. Similarly, the most profound innovations in crypto will not be incremental improvements but entirely new categories enabled by the core properties of public blockchains: atomic execution, shared global state, programmable custody, and composability. He cites the "flash loan" as a prime example of a "new verb"—a financial action structurally impossible before programmable assets and atomic settlement. It allows for uncollateralized, trustless borrowing of any size, provided repayment occurs within the same transaction, enabling novel strategies like arbitrage and collateral swaps. Bogart admits the difficulty in precisely predicting these future innovations, as human imagination tends to extrapolate from the past. He posits that the most exciting applications in ten years will be things that don't exist today and have no precedent—products only possible in a global, composable, always-on environment with programmable assets. While the exploration of this vast design space will involve many failures, the potential for transformative, category-defining breakthroughs is what makes the next decade so promising.

链捕手05/18 02:26

Blockchain Capital Partner: Most People Have a Narrow Understanding of the On-Chain Economy

链捕手05/18 02:26

Cloud PC Gets a Second Chance, Google/Alibaba/Microsoft Battle for Cloud AI Dominance

Google unexpectedly announced "Android Computer," a new high-end productivity-focused PC series, positioning cloud AI as its core rather than an add-on. This move signals a potential revival for the "cloud computer" concept in the AI era. The article argues that current "AI PCs" are essentially traditional Windows machines with AI features grafted on, heavily reliant on cloud AI for complex tasks due to limited local consumer-grade hardware capabilities. This reliance raises questions about the value of premium local AI hardware. Cloud computers, which struggled with latency-sensitive applications like cloud gaming, are seen as a natural fit for AI PCs due to AI's higher tolerance for response time. Google's Android Computer deeply integrates AI (powered by its Gemini model) into the OS interface, making it contextually available. Its hardware-agnostic approach (supporting both x86 and ARM chips) further underscores the shift towards cloud-centric AI. Other players are adapting: Cloud service providers like Alibaba are enhancing their AI cloud computer offerings; chipmakers (Intel, AMD) are focusing on data center AI chips; traditional PC brands are adding AI software layers; and Apple is leveraging its ecosystem and affordable hardware. Microsoft is defining AI PC standards, embedding Copilot (powered by GPT and Bing) into Windows, and also relying on cloud AI. In conclusion, Android Computer challenges the traditional PC form factor by proposing a "light local, heavy cloud" model. This approach appears promising amid rising hardware costs and local compute bottlenecks. The future PC market will involve a multifaceted competition around cloud integration, OS-level AI, and cross-device ecosystems, potentially redefining the PC as a screen and network conduit to cloud-based AI productivity.

marsbit05/18 02:05

Cloud PC Gets a Second Chance, Google/Alibaba/Microsoft Battle for Cloud AI Dominance

marsbit05/18 02:05

Encrypted ETF Weekly Report | Last Week, US Bitcoin Spot ETF Net Outflow $9.95 Billion; US Ethereum Spot ETF Net Outflow $255 Million

Last week, U.S. Bitcoin spot ETFs saw significant net outflows totaling $995 million over three days, with a major contribution of $317 million from BlackRock's IBIT. Their total net asset value (NAV) stands at $104.2 billion. U.S. Ethereum spot ETFs also experienced net outflows of $255 million over five days, largely from BlackRock's ETHA ($186 million out), bringing their total NAV to $12.93 billion. In Hong Kong, Bitcoin spot ETFs recorded a net outflow of 24.91 BTC, reducing their NAV to $323 million. Hong Kong's Ethereum spot ETFs saw no inflows, with an NAV of $68.13 million. U.S. Bitcoin spot ETF options showed increased activity, with a total nominal trading volume of $797 million and a put/call trading ratio of 1.63, indicating a bullish market sentiment. The total open interest reached $23.08 billion. Key developments include VanEck and Grayscale simultaneously filing amended proposals for BNB ETFs, signaling potential SEC review progress. Grayscale also filed for the first U.S. privacy coin ETF (Zcash). Avenir Group remains Asia's largest institutional holder of Bitcoin ETFs. 21Shares launched an actively managed crypto ETF (TKNS), and Bitwise's Hyperliquid ETF (BHYP) is set to list on the NYSE. Institutional activity varied: JPMorgan dramatically increased its Bitcoin ETF holdings (IBIT up 174%), while Jane Street significantly reduced its exposure (IBIT down 71%). Dartmouth College disclosed holdings of $7.7M in Bitcoin ETF and $3.4M in a Solana ETF.

链捕手05/18 02:01

Encrypted ETF Weekly Report | Last Week, US Bitcoin Spot ETF Net Outflow $9.95 Billion; US Ethereum Spot ETF Net Outflow $255 Million

链捕手05/18 02:01

Vitalik: What We Need to Do Is Not Fight AI, But Create Sanctuaries

Vitalik Buterin, in an a16z podcast, addresses the core challenge of the AI era: not to fight AI, but to build "sanctuary technologies" that protect humans without stripping away privacy and agency. He argues the greatest risk is not super-intelligent AI, but humans becoming passive passengers who outsource decisions to centralized systems and AI, leading to a disempowering safety. He redefines Ethereum/crypto's mission as creating such a sanctuary—a parallel, optional space for free coordination, not a fix for the existing system. This becomes crucial as AI and corporate power centralize. Reflecting on his journey from a 19-year-old on "autopilot" to an active "pilot," Vitalik emphasizes that the world reinvents itself every 5-10 years, demanding proactive adaptation. He stresses that active learning is 10x more effective than passive learning, even with equal time. His key advice is to intentionally maintain "manual mode" amidst powerful AI: do tasks yourself, engage in active learning, and avoid total cognitive outsourcing to prevent mental atrophy. For builders, the focus should be on creating tools that preserve human sovereignty, foster serendipity, and keep strategic control. In summary, the AI era demands greater human initiative. True value lies not in computational power, but in active, sovereign individuals who use technology as a tool for agency, not a replacement for it.

marsbit05/18 01:44

Vitalik: What We Need to Do Is Not Fight AI, But Create Sanctuaries

marsbit05/18 01:44

This Chip Sector Is on Fire

The global AI chip market is undergoing a significant paradigm shift, with ASICs (Application-Specific Integrated Circuits) emerging from a niche to a mainstream force, challenging the long-held dominance of GPUs in AI training. This "golden era" for ASICs is primarily driven by the industry's pivot from training to inference, where the cost and energy efficiency advantages of custom chips become critical for scaling to billions of users. Key signals include Google's TPU capturing 78% of its AI server shipments in Q1 2026, OpenAI's plans for a massive custom ASIC cluster with Broadcom, and cloud providers (CSPs) increasingly favoring in-house or custom designs for supply chain control and cost efficiency. Market forecasts are bullish: AI ASIC revenue is projected to hit $300 billion by 2027, with a 34% CAGR, potentially reaching a 45% share of the AI chip market. The competitive landscape is expanding beyond traditional leaders Broadcom and Marvell. MediaTek is aggressively targeting the data center ASIC market, projecting over $10 billion in 2026 revenue, while Qualcomm, leveraging its AlphaWave acquisition, is launching customized inference chips. These mobile chip giants are leveraging their SoC design expertise for a cloud-side transition. In China, companies like VeriSilicon and ASR Microelectronics are capitalizing on this trend as pivotal "enablers," providing full-stack ASIC design services and experiencing explosive order growth, particularly for cloud-side AI projects. However, challenges remain: high development costs, software ecosystem gaps compared to NVIDIA's CUDA, dependency on advanced packaging capacity (like TSMC's CoWoS), and the fundamental trade-off between customization and flexibility. The future is not a simple replacement of GPUs by ASICs but a more specialized coexistence. The consensus points toward "GPUs for training, ASICs for inference," or hybrid clusters. Ultimately, the rise of ASICs represents a democratization of computing power, shifting definition authority from a single chip giant to a broader ecosystem of cloud providers and end-users, offering the industry more choice in the silicon that powers AI.

marsbit05/18 00:29

This Chip Sector Is on Fire

marsbit05/18 00:29

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