2026-05-17 Domingo

Centro de Notícias - Página 13

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

Warsh Takes the Helm at the Fed: A Capital Layout Clearing the Way for AI Productivity

Kevin Warsh's confirmation as the 17th Federal Reserve Chair signals a significant strategic pivot, not merely a political victory. The core narrative, as framed by the author's "Universal Code," is that capital flows towards maximizing intelligence output per unit of energy—currently represented by the AI-driven semiconductor and energy infrastructure boom. Warsh, uniquely among candidates, is a former tech investor who has personally invested in this AI "productivity miracle." His mandate is to enable this transformation by aligning monetary policy to support, not stifle, the capital-intensive AI buildout. His proposed policy framework blends elements of 1950s financial repression with Alan Greenspan's 1990s playbook: tolerating higher headline inflation driven by volatile components (e.g., energy) while relying on AI-driven productivity gains to suppress core inflation and unit labor costs. This allows for a more accommodative stance than conventional models suggest. The strategy's success hinges on a coordinated "Treasury-Fed Accord" with Treasury Secretary Bessant. Bessant's role is international: securing foreign demand for long-term U.S. debt through bilateral agreements (e.g., with China, Japan, Gulf states) that offer access to AI infrastructure in exchange for recycling trade surpluses into Treasuries. A weaker dollar and controlled real yields are essential to make this foreign duration buying viable. Warsh's Fed must avoid overly restrictive policy that would break this flow. The underlying coalition driving this agenda consists of crypto founders, AI infrastructure operators, and energy investors seeking policy stability. While Warsh's initial meetings may not deliver immediate rate cuts, they will signal a shift in focus toward core inflation and greater policy discretion. The critical variable is the bond market. If long-term yields, term premiums, or real yields rise beyond certain thresholds (e.g., 10-year yields above 5.5%), the entire architecture could fail regardless of Fed actions. The next six months will determine whether the bond market grants the new Fed Chair the space to implement this framework. If successful, the cycle extends, benefiting risk assets, cryptocurrencies, and AI capital expenditure stocks. The market's current pricing of a conventional inflation fight creates an asymmetry versus this productivity-led, financially repressive framework, which represents the potential for significant returns.

marsbit05/14 10:07

Warsh Takes the Helm at the Fed: A Capital Layout Clearing the Way for AI Productivity

marsbit05/14 10:07

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

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

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