2026-04-23 Четверг

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If We Gathered the Most Accurate Gold Forecasters in History, Could We Crack the Future Price of Gold?

The article investigates whether assembling the most historically accurate gold price forecasters could unlock future price movements. The author analyzes three groups: top Wall Street institutions (e.g., LBMA, Goldman Sachs, JPMorgan), prominent gold bulls (e.g., Peter Schiff, Jim Rickards), and analysts famed for precise calls (e.g., Nouriel Roubini, Ben McMillan). The findings reveal significant flaws. Institutions consistently exhibit "lagging predictions," adjusting forecasts too slowly and underestimating bull market magnitudes. Pundits perpetually predict extreme price targets (e.g., $35,000) without precise timing, often being early or wrong. Even "prophetic" forecasters have mixed records; Roubini missed the entire 2009-2012 bull market, and Ray Dalio has a history of erroneous crisis predictions. The analysis notes that the current environment mirrors 2011, where extreme predictions clustered near the market top. Today, forecasts from the same experts range wildly from $5,400 to $35,000. The conclusion is that no consistently accurate forecaster exists. Predictions are often right by chance, not skill. The author ultimately rejects seeking a "wealth password" and instead advocates for a Dalio-inspired approach: avoiding precise price predictions, acknowledging uncertainty, and using portfolio allocation (e.g., 5-15% in gold) for long-term risk management.

marsbit04/03 10:26

If We Gathered the Most Accurate Gold Forecasters in History, Could We Crack the Future Price of Gold?

marsbit04/03 10:26

Two Acquisitions in One Day: OpenAI Buys 'Narrative', Anthropic Buys 'Barriers'

On April 2, OpenAI and Anthropic each announced an acquisition, reflecting their divergent strategies as both target an IPO by late 2026. OpenAI acquired tech talk show TBPN to shape public AI discourse and support its revenue base, which is 60% consumer-driven from ChatGPT subscriptions. In contrast, Anthropic purchased AI biotech startup Coefficient Bio for approximately $400 million in stock, continuing its focused strategy of deepening enterprise capabilities, particularly in high-switching-cost sectors like life sciences. Over the past three years, OpenAI completed 15 acquisitions across diverse fields including hardware, media, and healthcare, spending over $7.7 billion on disclosed deals, such as the $6.5 billion purchase of Jony Ive’s AI hardware firm. Anthropic made only three acquisitions, each precisely strengthening its product stack: Bun for coding infrastructure, Vercept for autonomous agents, and now Coefficient Bio for biotech R&D pipelines. Anthropic’s enterprise-focused revenue (80% of total) drives its strategy to lock in clients with vertical integration, as seen in its sequenced moves into life sciences and healthcare. Meanwhile, with a higher reliance on consumer subscriptions, OpenAI is investing in narrative influence—TBPN aims to boost ad revenue and steer public AI conversation. Both companies are on accelerated IPO paths: Anthropic eyeing a $60+ billion offering led by Goldman Sachs and JPMorgan, and OpenAI targeting a ~$1 trillion valuation. Their acquisitions underscore distinct priorities—Anthropic builds industry-specific moats, while OpenAI amplifies its public story.

marsbit04/03 10:07

Two Acquisitions in One Day: OpenAI Buys 'Narrative', Anthropic Buys 'Barriers'

marsbit04/03 10:07

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

The year 2026 is being positioned as the dawn of the "Physical AI" era, marked by major funding rounds and technological breakthroughs. This shift signifies AI's evolution from understanding the digital world to perceiving and acting within the physical world. Key events include Yann LeCun's AMI Labs raising $1.03 billion to develop "world models," Fei-Fei Li's World Labs securing funding, and companies like Tesla deploying humanoid robots (Optimus) in factories. This transition expands the AI model competition into a broader infrastructure battle encompassing hardware, data, simulation, and real-world integration. The core debate is between two AI paths: the established LLM (Large Language Model) approach focused on text prediction and the emerging "world model" approach, which aims to understand physical states for action-oriented tasks. Hardware, particularly dexterous robotic hands, is a critical and expensive challenge. Companies are racing to build capable robotic bodies, with Tesla, Boston Dynamics, and Figure AI making significant progress. NVIDIA is positioning itself as the essential infrastructure provider for this new era, offering a full suite of development tools and platforms. A major bottleneck is the scarcity of high-quality physical world interaction data, with companies exploring solutions through real-world data collection, synthetic data generation, and human teleoperation. Substantial investments in Q1 2026, exceeding $6.4 billion, signal strong belief in Physical AI's potential, moving beyond concept validation into infrastructure building. While challenges like the sim-to-real gap, unproven business models, and safety regulations remain, the tangible engineering progress suggests this is a genuine technological inflection point, not merely a bubble. For the global Chinese community, this shift represents a significant structural opportunity to leverage their strengths in technology, engineering, hardware manufacturing, and cross-border collaboration to become key players in building the foundational layers of the Physical AI ecosystem.

marsbit04/03 09:39

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

marsbit04/03 09:39

Rhythm X Zhihu Hong Kong Event Skills Recruitment, Sign Up Now for a Chance to Showcase On-Site

Six months ago, "how to write good prompts" was the hottest topic in group chats. Now, that question is clearly outdated. It has been replaced by Skills. The shift was largely triggered by the emergence of OpenClaw, which brought the concept of AI agents into the mainstream. Unlike a smart search engine that answers questions in isolated interactions, an agent can plan, remember, and complete entire tasks autonomously, creating the novel feeling that it is genuinely working for you. This has led to the rise of Skills—specialized capabilities that equip agents to handle specific domains efficiently. Without Skills, an agent is like a smart but untrained newcomer; with them, it can execute complex, precision-sensitive workflows without constant guidance. Popular Skills currently spreading within communities focus on areas like workflow automation, domain-specific rule injection (e.g., for law, finance, or medicine), personalization, and even financial operations such as identifying arbitrage opportunities on Polymarket or executing quantitative trading strategies. This shifts the门槛 from requiring programming and financial expertise to simply installing a Skill. The underlying change is that people are starting to view agents as long-term collaborators, not just disposable tools. Now, with vibe coding, turning an idea into a functional Skill no longer requires a technical team, code, or infrastructure—it can be done over a weekend. The gap between a good idea and a working product has dramatically narrowed.

marsbit04/03 09:18

Rhythm X Zhihu Hong Kong Event Skills Recruitment, Sign Up Now for a Chance to Showcase On-Site

marsbit04/03 09:18

Bitcoin Mining Companies Flee for the Nth Time

Since late last year, major publicly traded Bitcoin mining companies have initiated a significant wave of Bitcoin (BTC) sell-offs. Cango sold about 60% of its holdings (4,451 BTC) in February, Bitdeer liquidated its entire Bitcoin inventory in January, Riot Platforms sold 3,778 BTC in the first quarter, and Core Scientific planned to sell approximately 2,500 BTC. Notably, Marathon Digital (MARA) sold 15,133 BTC in just three weeks in March, cashing out over $1 billion, while also cutting 15% of its workforce as part of a strategic shift toward becoming an energy and digital infrastructure company. This collective divestment is driven by three primary motives. First, mining has become unprofitable for many; the average cash cost to mine one BTC is approximately $79,995, while BTC trades around $68,000–70,000, resulting in an average loss of about $19,000 per coin. Second, AI data centers offer a more stable and lucrative alternative, with tech giants like Google, Microsoft, and financial institutions like Morgan Stanley providing substantial backing and contracts. Mining companies are repurposing their existing infrastructure—cheap power contracts, data centers, and cooling systems—toward AI, which promises higher, predictable margins. Third, some firms are using BTC sales to optimize their balance sheets, such as repurchasing convertible debt at a discount to reduce liabilities and avoid equity dilution. The industry is diverging into three paths: some, like CleanSpark and HIVE, are坚守 (holding fast) to mining, betting on a cyclical recovery; others, like MARA and Riot, are pursuing a dual strategy of maintaining BTC holdings while expanding into AI; and a third group, including Core Scientific and TeraWulf, is undergoing a full pivot to AI, where mining may become a secondary operation. The future of these companies heavily depends on Bitcoin’s price trajectory. If BTC surpasses $100,000 by late 2026, mining profitability could recover. If it remains below $80,000, high-cost miners may continue to exit. If it breaks all-time highs, the industry could see another expansion cycle. Ultimately, this shift raises a broader question about Bitcoin’s security budget, as miners redirect resources to AI, the long-term cost of securing the Bitcoin network may become a growing concern. However, historically, the network has emerged stronger after each mining shake-out, though this time the transition is structural and could have lasting implications.

marsbit04/03 09:09

Bitcoin Mining Companies Flee for the Nth Time

marsbit04/03 09:09

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