2026-05-22 Sexta

Centro de Notícias - Página 43

Obtém notícias cripto em tempo real e tendências de mercado com o Centro de Notícias da HTX.

Why the Establishment of SocialFi Originates from a Misunderstanding of Its Own Medium

"Why SocialFi's Establishment Stems from a Misunderstanding of Its Own Medium" This article critiques the failure of SocialFi projects by applying Marshall McLuhan's theory of "hot" and "cool" media. McLuhan posited that a medium's form—not its content—reshapes user behavior. "Hot" media (e.g., print, radio) deliver high-definition, complete information, promoting passive consumption. "Cool" media (e.g., cartoons, telephone calls) provide low-definition, fragmented signals, requiring active user participation to complete the meaning. Traditional social media platforms (like early Twitter) are quintessentially "cool." A tweet or like is an incomplete fragment; its significance emerges only through replies, shares, and community engagement—it's a participation engine disguised as a content system. SocialFi (e.g., Friend.tech) aimed to monetize social capital by attaching real-time, tradable prices to follows and posts. However, this didn't add an economic layer to a cool medium; it fundamentally transformed the medium itself. The explicit, high-resolution signal of price replaced the ambiguous, low-resolution signal of social interaction. The platform became a financial market dressed as a social network. Once the financial dynamics (speculative profits) faded, the underlying social fabric, which had been suffocated from the start, could not sustain it. The medium overheated and collapsed. This "heat death" pattern isn't unique to crypto. Over time, mainstream platforms often drift from cool to hot by adding features like public metrics, verification badges, and algorithmic feeds that optimize for clarity over participation, leading to user disengagement. The article proposes a viable alternative: the "condensation point." Here, capital is introduced locally and infrequently into a cool medium without saturating it. Examples include Substack (subscriptions), Patreon (memberships), and Bandcamp (music purchases). The core social medium remains cool and participatory, while capital condenses at specific, structurally separate points (e.g., a monthly fee). The key lesson: "Liquidity is heat." Adding it to a cool medium doesn't enhance it but alters its fundamental nature. The NFT boom and bust provides a starker example. Collecting is a classic cool medium, where value is built slowly through stories and community. By making floor prices, rarity scores, and real-time charts omnipresent, NFT platforms rapidly overheated the medium, turning collectors into traders and destroying the participatory culture that gave collections meaning in the first place. The conclusion is that for the next wave to succeed, designers must ask not how to price every social action, but how to let capital condense within a social system without disrupting the cool, participatory mechanics that create its enduring value.

marsbit05/14 09:39

Why the Establishment of SocialFi Originates from a Misunderstanding of Its Own Medium

marsbit05/14 09:39

After Storage, Are Copper and Fiber Optic Cables Facing an AI "Great Famine"?

Following the storage sector, copper and fiber optics are emerging as potentially the next major markets to experience explosive growth due to AI. Demand for copper, described by Goldman Sachs as "the oil of the AI era," is surging. Prices are near record highs, with LME copper up 41% over the past 12 months. This is driven by AI's immense and unique requirements: copper is the essential material for the massive electrical distribution (e.g., a 1GW AI data center requires ~27,000 tons) and advanced liquid cooling systems needed for high-power AI clusters like NVIDIA's GB200. Meanwhile, new large-scale copper mine discoveries have been scarce for a decade, tightening supply. Concurrently, a "fiber famine" is unfolding. AI's need for ultra-high-speed, long-distance interconnects between thousands of GPUs is pushing data transmission beyond the physical limits of copper cables. Demand for fiber optics is experiencing a step-change, with a single AI data center requiring up to 36 times more fiber than a traditional CPU rack. This has caused prices for standard G.652D fiber in China to nearly double in just three months. Supply is critically constrained due to the long (18-24 month) lead times required to expand production of the core preform material. In summary, AI's infrastructure demands are cascading down from semiconductors to foundational materials. Copper faces a structural supply-demand imbalance, while fiber optics is entering a period of severe shortage, positioning both as critical and potentially strained components of the AI build-out.

marsbit05/14 09:25

After Storage, Are Copper and Fiber Optic Cables Facing an AI "Great Famine"?

marsbit05/14 09:25

The Construction of SocialFi Originates from a Misreading of Its Own Medium

This article argues that the fundamental failure of SocialFi projects like Friend.tech stems from a misunderstanding of social media's core nature. It applies Marshall McLuhan's theory of "hot" and "cool" media. "Cool" media (like traditional social networks) rely on low-resolution, incomplete signals (e.g., a tweet) that require user participation to create meaning. "Hot" media (like radio or print) deliver complete, high-resolution information that encourages passive consumption. SocialFi attempted to layer finance onto social media by making actions like follows and posts directly tradable with visible, real-time prices. However, this financial signal is a definitive "hot" signal. By superimposing it onto the inherently "cool" medium of social interaction, it fundamentally transformed the medium. Users stopped participating socially and instead began allocating capital rationally based on prices. The financial layer consumed the social one, leaving no genuine social substrate when speculation faded. The article extends this analysis to broader platform decay (e.g., Twitter's shift from cool participation to hot performance metrics) and NFTs. NFT platforms, by optimizing collections with real-time floor prices and rarity scores, rapidly "heated up" the traditionally "cool," participation-rich medium of collecting, destroying its cultural essence and leaving only speculative trading. The solution proposed is not to abandon capital in social contexts, but to design for "condensation points"—localized, infrequent financial interfaces (like Substack subscriptions or Patreon memberships) that allow capital to gather without saturating and overheating the core cool medium. The key lesson is that "liquidity is heat"; adding it to a cool medium doesn't enhance it but alters it, often destroying what made it valuable. Successful platforms will be those that introduce capital while meticulously preserving the cool, participatory nature of their underlying medium.

链捕手05/14 09:22

The Construction of SocialFi Originates from a Misreading of Its Own Medium

链捕手05/14 09:22

Bitwise: Why Are Top-Tier Capitals Frenziedly Betting on New Public Blockchains? The Answer Lies in These Three Points

Recently, a wave of major funding announcements for new public blockchains like Arc, Canton, and Tempo signals a significant industry shift. This article analyzes the driving forces behind this surge. Firstly, regulatory clarity is a key catalyst. These massive investments, including Circle's Arc ($222M), Digital Asset's Canton ($300M), and Stripe's Tempo ($500M), all followed the US passage of the *Genius Act* in July 2025. This suggests that clear legislation is unlocking institutional capital. The anticipated, broader *Clarity Act* could further accelerate growth, particularly in tokenization and compliant infrastructure. Secondly, built-in privacy is emerging as a critical design feature. Unlike Ethereum or Solana, these new chains natively support confidential transactions. This directly addresses real-world business needs, where public transparency can be a liability for corporate dealings or personal salary data, making privacy a potential killer application. Finally, the entry of traditional giants marks a new competitive phase. These projects are backed by major firms: Arc by Circle, Canton by a consortium including Goldman Sachs and Nasdaq, and Tempo by Stripe with partners like Visa. While crypto-native projects remain strong contenders, this institutional involvement brings substantial capital, execution capability, and operational rigor. In conclusion, the convergence of regulatory progress, demand for privacy, and competition from established financial and tech players is rapidly reshaping the blockchain landscape, pushing innovation and expanding the industry's boundaries.

marsbit05/14 09:20

Bitwise: Why Are Top-Tier Capitals Frenziedly Betting on New Public Blockchains? The Answer Lies in These Three Points

marsbit05/14 09:20

Crypto Superapp Legend Announces Shutdown As Industry Shakeout Continues

Legend, a DeFi mobile app aggregator founded by former Compound executives, is shutting down on July 12 after roughly two years. Despite a $15 million funding round led by Andreessen Horowitz and Coinbase Ventures in 2025, CEO Jayson Hobby stated the product failed to achieve the necessary scale for financial viability. The non-custodial app allowed users to earn, trade, borrow, and swap assets via integrations with major protocols like Aave and Uniswap from a single interface. Hobby noted mainstream crypto users care about benefits like better yield and control, not whether a product uses blockchain. Legend's closure is part of a broader sector shakeout, with over 20 DeFi, NFT, and crypto protocols announcing shutdowns this year amid a bear market and declining total value locked.

bitcoinist05/14 09:01

Crypto Superapp Legend Announces Shutdown As Industry Shakeout Continues

bitcoinist05/14 09:01

When the Bubble Comes, How to Short "Smartly"?

Title: When the Bubble Comes, How to "Smartly" Short? Author: Campbell (Macro Analyst) Summary: Amid the heated debate over whether the current AI-driven market is in a bubble, analysts are divided. While some, like Dan Niles and Paul Tudor Jones, argue that the AI boom has further to run, Michael Burry warns of similarities to the dot-com bubble. The author explores practical strategies for navigating and potentially shorting a bubble without being crushed by its momentum. Key challenges in shorting a bubble include the exponential risk from parabolic price increases and the high cost of options due to extreme volatility. Instead of directly shorting the bubbly asset, the author proposes three approaches: 1. **Find the "Wedge"**: Identify external factors that could pop the bubble, such as rising interest rates. By betting on trends that could undermine the bubble (e.g., inflation or higher rates), investors can hedge without timing the bubble's collapse. 2. **Short the "Victims"**: Target assets adjacent to the bubble that are highly vulnerable to its burst, such as over-leveraged companies or sectors with "negative convexity." These assets may have cheaper options and suffer disproportionately when the bubble stalls. 3. **Wait for Confirmation**: Exercise discipline and wait for clear signals of a breakdown, including deteriorating fundamentals, exhausted buying sentiment, and decisive breaks in trendlines. Only then should investors take substantial short positions. The author shares their recent actions, including shorting SPX and high-yield bonds while buying short-term put spreads, and emphasizes avoiding direct shorts on vertically rising assets. The core takeaway: Hedge, identify wedges, wait for confirmation, and only then commit heavily.

marsbit05/14 08:57

When the Bubble Comes, How to Short "Smartly"?

marsbit05/14 08:57

MuleRun CTO: The Moat of Agents Lies in Data Density and User Memory

In a speech titled "Handing AI's Keys to the On-Chain Controllers," MuleRun CTO Shu Junliang discussed the evolution and security of AI Agents in finance and Web3. He outlined six dimensions for a complete AI assistant: dialogue, data input, agent capability, execution environment, user memory, and continuous learning. MuleRun's product integrates these through features like multi-platform IM bots, real-time multi-asset data, smart model routing, cloud sandboxes, persistent user profiles, and a shared knowledge network. Shu emphasized that while AI Agents are advancing from assisting to autonomously executing decisions—potentially enabling individuals to operate like small funds—safety remains paramount. He detailed MuleRun's security measures, including local key handling, isolated sandboxes, full audit trails, and strict permission controls. However, he acknowledged persistent risks like data exposure, model hallucinations, prompt injection, and the "black box" nature of AI decisions, advising manual confirmation for financial operations. He identified key trends: the shift from human-led to Agent-led on-chain interactions requiring infrastructure redesign; the erosion of information advantages replaced by competition in execution speed and strategy; and the balancing effect of Agents, which democratize access but ultimately advantage those with superior judgment. Shu concluded that an Agent's true moat lies in data density and accumulated user memory, not easily replicable technology, and that while Agents will reshape finance and Web3, human oversight over critical decisions must remain.

marsbit05/14 08:50

MuleRun CTO: The Moat of Agents Lies in Data Density and User Memory

marsbit05/14 08:50

Buying BTC is Not as Good as Buying Nasdaq, and This Statement Has an Expiration Date

The article, titled "Investing in Bitcoin Has an Expiration Date", discusses the recent narrative on social media that investing in U.S. tech stocks (like the Nasdaq 100) has been far superior to investing in Bitcoin. This sentiment is fueled by performance comparisons showing the Nasdaq 100 significantly outperforming Bitcoin over a specific five-year window starting in late 2021. However, the author argues that such comparisons are highly sensitive to the chosen timeframe. By shifting the starting point to other key market moments—like the COVID-19 bottom (March 2020), the FTX collapse bottom (November 2022), or the pre-Bitcoin ETF approval period (January 2024)—Bitcoin's returns often match or dramatically exceed those of the Nasdaq 100. The popular Reddit chart essentially cherry-picks a period that started near a Bitcoin cycle high and just before the massive AI-driven rally in tech stocks. The core difference lies in their asset structures. The Nasdaq 100, backed by corporate earnings, exhibits a steadier long-term upward trend. Bitcoin is a highly cyclical asset with extreme volatility, where returns are drastically different depending on where in its bull/bear cycle an investment is made. The article notes Bitcoin often acts like a leveraged version of the S&P 500, magnifying both gains and losses. Currently, Bitcoin is in a "cyclically undervalued zone," having corrected ~37% from its October 2025 peak while the Nasdaq hits new highs. Historically, peak narratives about stocks beating Bitcoin have often coincided with Bitcoin nearing cyclical lows, as seen before its major rally from late 2022. The conclusion is that declaring one asset permanently superior to another is statistically flawed; performance is entirely dependent on timing. The real challenge for investors is not picking the "better" asset, but mastering entry and exit timing.

marsbit05/14 08:34

Buying BTC is Not as Good as Buying Nasdaq, and This Statement Has an Expiration Date

marsbit05/14 08:34

Bezos, Schmidt, Powell Jobs: The Three AI Investment Philosophies of Silicon Valley's Old Money

Jeff Bezos, Eric Schmidt, and Laurene Powell Jobs, three prominent figures from Silicon Valley's "old money," are deploying massive personal fortunes into AI, but with distinctly different investment philosophies reflecting their visions for the future. Eric Schmidt, the former Google CEO, approaches AI as a geopolitical and infrastructural arms race. Through his family office, Hillspire, he invests heavily in defense AI companies, energy infrastructure (like Bolt Data & Energy to power data centers), and space launch capabilities (Relativity Space). For Schmidt, the ultimate AI advantage lies in physical resources—energy, transport, and military application—framing it as a national competition requiring state-level strategy and endurance. Jeff Bezos is building a vertically integrated, full-stack AI empire. His bets span the model layer (via Amazon's massive investment in Anthropic), the application layer (through investments like Perplexity), and now, the physical execution layer. His new venture, Project Prometheus, with $6.2 billion, aims to inject AI into manufacturing, creating a closed loop from AI chips and cloud compute (AWS) to real-world production, potentially for Amazon's own ventures like the Kuiper satellite network. In contrast, Laurene Powell Jobs adopts a more subtle, human-centric approach through her Emerson Collective. Her AI investments focus on specific, positive-impact applications—such as AI for healthcare (Proximie, Atropos Health), education (Curipod), and European AI sovereignty (Mistral AI). A key, high-profile bet was her early backing of Jony Ive's design firm LoveFrom and its spin-off, io, an AI hardware device company later acquired by OpenAI. Her philosophy prioritizes improving human-machine interaction and addressing societal needs over sheer scale or control. These three strategies—Schmidt's focus on state-level infrastructure and security, Bezos's pursuit of end-to-end industrial integration, and Powell Jobs's emphasis on human-centered design and applied solutions—represent fundamentally different wagers on what will define the next decade of AI. While the eventual winner is unknown, the sheer scale of this capital migration from internet-era giants is already reshaping the industry's trajectory.

marsbit05/14 08:11

Bezos, Schmidt, Powell Jobs: The Three AI Investment Philosophies of Silicon Valley's Old Money

marsbit05/14 08:11

Bitcoin at $80,000: Recovery Clear, But Bullish Conviction Lacking

Bitcoin has reclaimed $80K, supported by renewed US ETF inflows, spot buying activity, and a constructive shift in trader positioning. The relative unrealized loss metric has compressed sharply from its February peak, suggesting the sell-off was a shallow correction rather than a deep bear market. However, the recovery lacks the conviction of prior bull cycles, as evidenced by net capital inflows ($2.8B/month) remaining well below the $10B+ monthly thresholds seen in previous major uptrends. Key technical levels show support near $76.9K and resistance around $86.9K. Derivatives markets signal calmer conditions, with implied volatility compressing and skew normalizing, indicating reduced demand for downside protection. While market structure is improving, sustained movement above the current supply zone will likely require stronger spot demand and deeper capital rotation.

marsbit05/14 07:52

Bitcoin at $80,000: Recovery Clear, But Bullish Conviction Lacking

marsbit05/14 07:52

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