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Sharplink CEO: Ethereum's Future Is Playing Out Now

This article presents a perspective from Joseph Chalom, CEO of Sharplink and a former BlackRock executive. He argues that current controversies surrounding the Ethereum Foundation (EF) and ETH's price miss the bigger picture for institutional adoption. Chalom asserts that Ethereum is decisively winning in the three key attributes institutions value most: trust, security, and liquidity. He cites its dominance in stablecoin settlement, tokenized real-world assets (RWA), and high-value DeFi as evidence. This success is attributed to the EF's consistent, long-term protocol development over a decade, including major upgrades like The Merge and a robust future roadmap. He defends Ethereum's decentralization as a core strength, not a weakness, stating institutions require a neutral infrastructure not controlled by any single entity. Comparing ETH to Amazon, Chalom suggests critics focusing on short-term price are missing its potential to become the foundational settlement layer for the entire global financial system. The article encourages a contrarian "be greedy when others are fearful" investment approach, drawing parallels to Warren Buffett's strategy and BlackRock's continued investment during crypto winters. Chalom concludes that while the EF correctly focuses on core protocol attributes (CROPS: Censorship Resistance, Capture Resistance, Open Source, Privacy, Security), a leadership gap exists in market-facing narrative and institutional adoption. He calls for ecosystem participants, including his own firm Sharplink, to become more vocal advocates to support Ethereum's impending "supercycle" of institutional adoption.

链捕手23 ч. назад

Sharplink CEO: Ethereum's Future Is Playing Out Now

链捕手23 ч. назад

Deconstructing the Investment Methodology of the 'Stock God Serenity' in One Article

"Serenity's Bottleneck Investment Methodology: A Deep Dive" This article dissects the "bottleneck point investment" strategy of the pseudonymous investor Serenity, known for exceptional returns (YTD 4502.45%). The core methodology involves identifying a major technological trend (e.g., AI compute expansion), mapping its supply chain, and investing early in the most irreplaceable, supply-constrained upstream component before the market fully values it. The framework is broken down into a five-factor model: 1. **Deterministic Demand**: Anchored in a large, validated trend. 2. **Constrained Supply**: The component must be difficult to replicate or scale quickly. 3. **Low Market Attention**: Opportunities exist where coverage is sparse. 4. **Value Capture**: The company must have pricing power, high margins, and customer lock-in. 5. **Catalyst**: A near-term event to trigger price discovery (earnings, customer ramp, etc.). The article provides illustrative examples like $AXTI (InP substrates for photonics), $RPI (edge hardware for AI agents), and $AAOI/$LITE (components for cloud ASICs). To apply this method, a six-step process is outlined: identify the macro trend, map the supply chain, pinpoint the true bottleneck, gather evidence (client wins, certifications), assess risks ("anti-thesis table"), and size the position according to research depth. Crucially, the article notes significant limitations: risk of overfitting inferences from sparse data, valuation challenges for pre-revenue companies, liquidity/reflexivity risks due to Serenity's own market influence, and survivor bias amplified by a strong AI bull market. The key takeaway is to emulate the rigorous research process—finding the trend, the bottleneck, the evidence—rather than blindly copying specific stock picks, emphasizing the discipline of "walking through the narrow gate."

marsbit23 ч. назад

Deconstructing the Investment Methodology of the 'Stock God Serenity' in One Article

marsbit23 ч. назад

One Article Deconstructs the Investment Methodology of 'Stock God Serenity'

This article deconstructs the "bottleneck point" investment methodology of the renowned investor known as "Serenity" (aleabitoreddit). Characterized by a YTD return of over 4500%, the strategy involves identifying a major, confirmed trend (e.g., AI data center expansion), mapping its supply chain, and then pinpointing a critical, hard-to-replace upstream bottleneck that the market has yet to fully price in. The core framework is a five-factor model: 1) **Certain Demand** from a clear megatrend; 2) **Constrained Supply** with high barriers to entry and slow replication; 3) **Low Market Attention**, where the company is overlooked; 4) **Value Capture** potential through pricing power and market share; and 5) a near-term **Catalyst** to trigger re-evaluation. Case studies include **$AXTI** (InP substrates for photonics), **$RPI** (edge hardware for AI agents), and companies like **$AAOI** and **$LITE** tied to hyperscaler-specific ASIC demand (e.g., Microsoft Maia, Amazon Trainium). The article provides a six-step guide for applying this approach: 1) Identify a validated macro trend; 2) Map the entire supply chain; 3) Find the true bottleneck; 4) Gather concrete evidence (e.g., filings, customer contracts); 5) Perform rigorous risk assessment ("anti-thesis"); 6) Match position size to depth of research. Key limitations are also noted: the risk of narrative overfitting, difficulty in valuing early-stage companies, Serenity's own market-moving influence creating reflexivity, and potential survivorship bias due to the AI bull market. The essence of the method is not to copy picks but to adopt the research process: find the trend, locate the bottleneck, verify with evidence, assess valuation, await a catalyst, and then invest with discipline. The philosophy is summarized as "walking through the narrow gate"—seeking non-consensus, structurally vital points within booming industries before they become widely recognized.

链捕手Вчера 06:36

One Article Deconstructs the Investment Methodology of 'Stock God Serenity'

链捕手Вчера 06:36

From Suppliers to Shareholders: The Big Three Memory Chip Giants Jointly Invest in Anthropic, AI Supply Chain Power Structure Undergoing Reshuffle

For the first time, memory chip giants Micron, Samsung, and SK hynix have jointly invested in the same AI company, Anthropic, as part of its massive $65 billion Series H funding round. This strategic move, positioning the three rival HBM suppliers as "strategic infrastructure partners," highlights a fundamental shift in the AI industry's power dynamics. With HBM (High Bandwidth Memory) being a critically scarce resource essential for AI model training and inference, securing a stable supply has become a key competitive differentiator. By making these chipmakers shareholders, Anthropic aims to lock in this vital component for its rapid expansion, which includes securing major compute commitments from Amazon, Google, and others. For the memory trio, this investment represents a strategic bet on defining the future of AI hardware. Each company gains: SK hynix reinforces its dominant position in the NVIDIA supply chain; Samsung diversifies its client base beyond NVIDIA; and Micron leverages its geopolitical significance as the sole US-based HBM maker. Their collective move signals that competition in AI is evolving beyond model capability to encompass control over the entire compute supply chain—from chips and memory to power and networking. This vertical integration trend, where infrastructure providers become direct stakeholders in AI firms, marks the industry's maturation as AI transforms from a research project into essential global infrastructure, setting the stage for a new era of ecosystem competition.

marsbitВчера 04:40

From Suppliers to Shareholders: The Big Three Memory Chip Giants Jointly Invest in Anthropic, AI Supply Chain Power Structure Undergoing Reshuffle

marsbitВчера 04:40

Investment Philosophy of Gavin Baker, an Early Nvidia Investor: Long AI Infrastructure Bottlenecks, Short Overall Market Risk

Gavin Baker, an early investor in Nvidia and founder of Atreides Management, outlines his investment philosophy: going long on AI infrastructure bottlenecks while hedging against broader market risk. He argues AI is not a bubble but a supercycle driven by constraints in power, wafers (semiconductors), and compute efficiency (tokens per watt). True alpha, he believes, lies not in application-layer companies like OpenAI but in "picks and shovels" providers—companies solving physical bottlenecks in GPU connectivity (e.g., Astera Labs), memory (Micron), inference chips (Cerebras, Positron), advanced manufacturing (TSMC, ASML), and energy supply. His portfolio reflects this barbell strategy: concentrated bets on key infrastructure players alongside a significant put position on the QQQ ETF to hedge overall market downside. Baker contends this cycle differs from the dot-com bubble because demand is fueled by the strong balance sheets of hyperscalers (Google, Meta, Amazon, Microsoft), not debt, and physical supply constraints (e.g., chip manufacturing capacity) prevent runaway overinvestment. He highlights the growing importance of inference (vs. pre-training), vertical/small language models, sovereign infrastructure deployment speed, and the convergence of energy and space (e.g., orbital compute). His long-term view is that performance-per-watt and token cost reduction will dictate winners as AI scaling hits fundamental physical limits.

marsbitВчера 03:23

Investment Philosophy of Gavin Baker, an Early Nvidia Investor: Long AI Infrastructure Bottlenecks, Short Overall Market Risk

marsbitВчера 03:23

Apple Re-invented Image Compression with AI: Same Quality, One-Third the File Size

Apple’s PICO: An AI-Powered Image Codec That Cuts File Size by Two-Thirds at Equal Perceived Quality In 2025, JPEG AI became the first international standard for learned image compression. However, it, like most codecs, still prioritizes mathematical metrics like PSNR over true perceptual quality—what the human eye finds pleasing. Apple researchers have introduced PICO (Perceptual Image Codec), a neural codec designed to optimize for human perception. It tackles key practical challenges: 1) Speed: A novel "one-shot context model" accelerates entropy encoding without sacrificing compression efficiency. 2) Artifacts: A dedicated TextFidelity loss preserves text clarity, and a TilingArtifact loss eliminates color seams between image tiles processed in parallel. 3) Control: It avoids the "hallucinations" common in GAN-based perceptual models. In a large-scale human evaluation (74,925 comparisons), PICO achieved the same perceived quality as standards like AV1, VVC, and JPEG AI while using only 30-43% of the bitrate. It also outperforms other learned perceptual codecs by 20-40%. Remarkably, it runs in 230ms (encode) and 150ms (decode) on an iPhone 17 Pro Max. While less efficient on synthetic graphics, PICO represents a significant shift from optimizing mathematical scores to directly targeting human visual experience, making high-quality perceptual compression practical for consumer devices. The work builds on expertise from WaveOne, whose team joined Apple and previously advanced neural video compression.

marsbitВчера 02:47

Apple Re-invented Image Compression with AI: Same Quality, One-Third the File Size

marsbitВчера 02:47

Shanghai's Leading Large Model Company Initiates A-Share Listing

Shanghai-based AI large language model leader MiniMax has initiated the process for an A-share listing in China, having filed a pre-IPO tutoring report with the Shanghai Securities Regulatory Bureau on May 29. This move positions it to compete with Zhipu AI for the title of the first major domestic LLM company to list on the A-share market. Having already completed an IPO in Hong Kong in January 2026, MiniMax's stock price has surged approximately 409% since its debut, with its market capitalization reaching around HK$263.45 billion (approximately RMB 227.55 billion) as of May 29. The company's rapid growth is supported by strong business performance. Its Annual Recurring Revenue (ARR) has grown over 100% in the past two months and now exceeds $300 million. It serves over one million global enterprise and developer clients and has around 300 million users worldwide. For the full year 2025, MiniMax reported revenue of $79.038 million, with a gross margin of 25.4%. While it reported an adjusted net loss of $250 million, the loss rate has narrowed significantly year-over-year. On the product front, MiniMax has released several flagship models this year, including MiniMax-M2.5, M2.6, and M2.7, with the first and last being open-sourced. Its models gained significant traction earlier in the year, briefly becoming the top model provider by usage share on the OpenRouter platform in February. The company has also upgraded its AI agent product, now named Mavis, and is preparing to launch its next-generation MiniMax-M3 model. Technical previews indicate M3 will feature a novel "MiniMax Sparse Attention" mechanism, promising substantial improvements in inference speed. MiniMax's push for an A-share listing reflects a broader trend among China's leading AI firms, including Zhipu AI, Moonshot AI, StepFun, and 01.AI, to seek public listings. This strategy aims to secure broader financing channels to support the immense computational costs and ongoing commercialization efforts inherent in developing advanced large language models.

marsbitВчера 02:45

Shanghai's Leading Large Model Company Initiates A-Share Listing

marsbitВчера 02:45

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