2026-08-11 Terça

Notícias de cripto - Página 463

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Deconstructing the U.S. Stock Quantum Computing Sector: IonQ, Rigetti, D-Wave, Which of These Concept Stocks is Worth Betting On?

**Title:** Analyzing the US Quantum Computing Race: IonQ, Rigetti, D-Wave – Which Concept Stock is Worth Betting On? **Summary:** The podcast discusses the resurgence of quantum computing as a national priority for both the US and China, driven by its potential to break current encryption, revolutionize drug discovery, finance, and logistics. The core challenge is commercializing the technology, which is hampered by high error rates in quantum bits (qubits). Quantum error correction, requiring thousands of physical qubits per reliable logical qubit, is key but years away. The analysis compares three main publicly traded US quantum computing firms: * **IonQ (Ion Trap):** Considered the most financially stable with the fastest commercial progress (2025 revenue: $130M, +202%) and high-quality clients. Its valuation is very high, pricing in significant future growth. * **Rigetti (Superconducting):** Seen as the highest-risk, highest-potential-reward bet. It has the smallest revenue but recently launched a 108-qubit system. Its valuation multiples are extreme, making it highly sensitive to news. * **D-Wave (Quantum Annealing):** Has the most unique positioning with real-world enterprise clients today (e.g., Mastercard, Volkswagen) solving optimization problems. Its recent acquisition moves it into general-purpose quantum computing ("dual-platform"), adding execution risk. Major tech giants like Google, IBM, and Microsoft are also heavily invested, pursuing various technical approaches. Nvidia is positioning itself as the essential bridge between classical and quantum computing. The investment phase is likened to AI in 2018-2020: promising underlying technology with accelerating breakthroughs but a commercial inflection point still 3-7 years away, suggesting potential for a market correction ("bubble washout"). For investors, suggested approaches include gaining exposure through tech giants with quantum divisions (e.g., Google, IBM) or using niche ETFs like WQTM for pure-play quantum exposure, rather than direct stock picks in the highly volatile pure-play companies at this early stage.

marsbit06/01 07:43

Deconstructing the U.S. Stock Quantum Computing Sector: IonQ, Rigetti, D-Wave, Which of These Concept Stocks is Worth Betting On?

marsbit06/01 07:43

Ethereum Bears In Pole Position: $1,850 Could Be The Next Landing Spot

Ethereum's price remained bearish in May, with analysts predicting a potential drop to around $1,850. Crypto analyst Burak Kesmeci notes that ETH is trapped in a descending channel on the 4-hour chart, indicating seller dominance. If it fails to break the $2,033 resistance, a decline to the $1,822-$1,850 range is likely. While a breakout above $2,033 could target $2,400, the downward move appears more probable. This bearish outlook is reinforced by significant outflows from spot Ethereum ETFs, totaling over $241 million in the past week—the third consecutive week of major outflows. ETH has lost nearly 15% of its value over the last three weeks and is currently trading around $2,023.

bitcoinist06/01 07:31

Ethereum Bears In Pole Position: $1,850 Could Be The Next Landing Spot

bitcoinist06/01 07:31

From Parallel Finance to Mainstream Finance: The On-Chain Securities Era Ushers in a Historic Window

From Parallel Finance to Mainstream: The Dawn of On-Chain Securities For over a decade, the crypto industry has operated as a parallel financial system with its own currencies, markets, and assets—from Bitcoin and ICOs to DeFi, NFTs, and memecoins. Despite building a robust internal ecosystem, a wall has separated it from the traditional financial world. That barrier is now crumbling. The industry's first act was one of internal evolution: ICOs streamlined fundraising, DeFi recreated financial services on-chain, and layer-2 networks competed for scalability—all within the crypto bubble. While innovative, this cycle remained closed, with capital and users circulating internally, leading to volatile boom-bust cycles. Even Bitcoin ETFs, while attracting Wall Street capital, merely provided a channel to buy crypto assets without bridging the systems. The next, larger narrative is Real-World Assets (RWA) moving on-chain. This involves tokenizing stocks, bonds, funds, and future cash flows. Blockchain can compress the complex traditional processes of trading, settlement, clearing, and custody into a seamless, automated network operating in seconds. This shift is creating a new financial gateway: the native crypto securities broker. This entity will combine functions of an exchange, broker, bank, and custodian into a unified global financial operating system. Consequently, the next major battleground won't be the "public chain wars" focused on speed and cost, but the competition to build the financial infrastructure capable of hosting high-quality, liquid real-world assets. Access to global equities, index funds, or stakes in companies like SpaceX could erase the boundary between crypto and traditional finance, unlocking a market orders of magnitude larger than crypto's current valuation. In summary, after years of creating a separate financial world, crypto's next decade will be defined by its integration into the existing global financial system, marking the true beginning of its largest growth story.

marsbit06/01 07:22

From Parallel Finance to Mainstream Finance: The On-Chain Securities Era Ushers in a Historic Window

marsbit06/01 07:22

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

Wang Chuan: When the Neighbor Lao Wang Made 30x on Storage Stocks, How to Stay Anxiety-Free (Part 6) - The Trap of Commoditized Goods. This essay uses historical and current examples to analyze the cyclical and high-risk nature of the data storage industry. It begins with the 1990s rise and dramatic fall of Iomega, whose stock soared over 160x in 18 months before collapsing 97% from its peak, illustrating the fleeting success of storage "meme stocks." The core problem is that storage products, like DRAM and flash memory, are highly commoditized. This leads to extreme volatility: prices have plummeted over 80% multiple times, and company stocks often crash 95% or go bankrupt. The industry's dynamic is defined by "elastic demand facing heavy-asset, long-cycle, rigid supply." When demand spikes and supply is fixed, prices skyrocket, as seen recently with AI-driven demand for High Bandwidth Memory (HBM). Companies like Sandisk and Micron have reported massive revenue and gross margin jumps (e.g., Sandisk's gross margin rising from 22.5% to 78.3%) despite minimal increases in production volume. However, these high margins are self-defeating. They incentivize massive new capacity investments (hundreds of billions planned from 2026), with supply expected to surge by late 2027. Once new supply meets demand, prices and profits will crash, potentially leading to a scenario where "selling more results in earning less." The article debunks the safety of long-term supply agreements, comparing them to fragile non-aggression pacts easily broken when market conditions shift. It warns that when an industry is highly profitable but trades at low P/E ratios, the risk is greatest, as plummeting prices quickly erase those earnings. Multiple asymmetric risks loom, including economic recession, reduced AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and technological innovations that reduce memory requirements. In conclusion, the storage sector is a cyclical trap where periods of euphoric profits are often precursors to devastating downturns, luring unprepared investors into a "wealth incinerator."

marsbit06/01 07:13

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

marsbit06/01 07:13

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

The article, "Wang Chuan: How to Remain Unanxious After Neighbor Lao Wang's Thirty-Fold Gain on Storage Stocks (Part 6) - The Trap of Commoditized Goods," analyzes the cyclical and perilous nature of the data storage industry through historical and current case studies. It begins with the example of Iomega, whose Zip drives led to a stock surge of over 160x in the mid-1990s before collapsing over 97% from its peak due to competition from cheaper CD-R technology. This pattern is characteristic of storage, where products like DRAM are highly commoditized, leading to extreme price volatility. The sector has seen prices crash over 80% multiple times, with companies often facing bankruptcy. The core dynamic is "elastic demand facing heavy-asset, long-cycle, rigid supply." High prices attract new capacity, but the long lead time means supply eventually overshoots, causing sharp price corrections. The current AI-driven boom, exemplified by surging demand for High-Bandwidth Memory (HBM), has led to skyrocketing prices and profit margins for companies like SanDisk and Micron, despite relatively flat production volumes. However, the author warns this high-margin environment is self-defeating. The high profits are already triggering massive new capacity investments (hundreds of billions starting 2026), with supply expected to ramp up by late 2027. When supply catches up, total revenue and profits may fall even as more units are sold. Long-term supply agreements offer little protection, as buyers can find ways to renegotiate if market prices drop, similar to fragile political treaties. Key risks include economic downturns, cuts in AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and innovations in chip/algorithm design that reduce memory needs. A critical trap is that at the cycle's peak, storage stocks often appear cheap with low P/E ratios, luring value investors just before an impending downturn where profits evaporate. The conclusion cautions that for commoditized goods like storage, high margins inevitably destroy themselves, and the current asymmetry favors downside risk over further upside. The neighbor's dream of easy wealth from storage stocks is portrayed as a precarious illusion.

链捕手06/01 06:55

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

链捕手06/01 06:55

AI PCs Are Here, Going Toe-to-Toe with 120B Models Locally! NVIDIA Redefines the "Personal AI Computer" Foundation with RTX Spark

NVIDIA has redefined the "AI PC" standard with the launch of the RTX Spark super chip at GTC 2026. Boasting 1 petaflop (1000 TOPS) of AI performance, it dwarfs the 45-50 TOPS NPUs in current AI PCs. The SoC features a Blackwell GPU, a 20-core Arm CPU co-designed with MediaTek, and crucially, up to 128GB of unified memory shared between CPU and GPU. This architectural shift enables local execution of 120-billion-parameter large language models with million-token context windows, a massive leap from the 9B-40B models typical on current consumer hardware. Beyond AI, use cases include 12K video editing and high-fps ray-traced gaming. Key to enterprise adoption is a security collaboration with Microsoft. Windows security is upgraded, and NVIDIA's OpenShell sandbox runtime is integrated to safely contain AI agent actions. Major software support comes from Adobe, which announced a deep,底层-level rewrite of Photoshop and Premiere to leverage the unified memory for up to 2x performance gains. Six OEMs, including Dell, HP, Lenovo, and Microsoft Surface, will release RTX Spark-based轻薄本 and compact desktops this fall. However, questions remain about real-world performance,功耗, thermal management in laptops, pricing, and the actual impact of the OpenShell sandbox. The RTX Spark represents a fundamental power shift in the PC industry, moving from an x86 CPU-centric model to a GPU-centric SoC platform, but its ultimate success hinges on the upcoming product rollouts and ecosystem validation.

marsbit06/01 06:41

AI PCs Are Here, Going Toe-to-Toe with 120B Models Locally! NVIDIA Redefines the "Personal AI Computer" Foundation with RTX Spark

marsbit06/01 06:41

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

NVIDIA CEO Jensen Huang delivered the keynote speech at GTC Taipei 2026, announcing several major product launches and strategic directions. The company's Vera Rubin architecture is now in full-scale production, with OpenAI, Anthropic, and SpaceX among the first customers. NVIDIA highlighted AI Agent as a key future focus, introducing the Vera CPU designed for AI agents and the Vera BlueField-4 STX for secure, chip-level AI storage processing. A significant move involves challenging Intel in the PC market. NVIDIA, in collaboration with MediaTek, is developing the RTX SPARK PC chip (manufactured by TSMC) for Windows systems, set to launch this fall for laptops and desktops. This signals NVIDIA's push into the next-generation AI PC arena, aiming to provide a vertically integrated core computing platform for the entire Windows ecosystem, similar to Apple's approach. Other announcements include the new Nemotron 3 Ultra AI model and the NVIDIA DSX platform, described as a complete "playbook" for building AI factories, allowing performance simulation and validation before physical deployment. In automotive, the DRIVE Hyperion platform was positioned as a global robotaxi platform, with major Chinese automakers like BYD, Geely, Zeekr, Xiaomi, and Pony.ai already adopting or developing autonomous driving solutions based on it. The Alpamayo 2 super open inference model for robotaxis was also introduced. For robotics, NVIDIA unveiled the Isaac GR00T humanoid robot reference platform for academic research and a large open-source agent tools and skills suite for Physical AI. The company plans to collaborate with global humanoid robot manufacturers, including China's Unitree, whose H2 Plus robot served as the reference hardware for the GR00T platform demonstration.

marsbit06/01 06:14

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

marsbit06/01 06:14

Interview with Macro Master Raoul Pal: The Economic Singularity is Approaching, Don’t Get Off the Ride in the Next Four Years

Interview with macro expert Raoul Pal discusses the approaching "economic singularity," where AI-driven growth outpaces traditional economic systems. He explains the unprecedented scale of the AI race between the US and China, arguing capital will continue flooding this sector as neither side can afford to stop. Pal sees this as a catalyst for cryptocurrency, as AI agents will operate on-chain, creating an "infinite TAM" for crypto networks. He views the recent crypto downturn as a normal bull market correction, advocating for a long-term "buy and hold" strategy over trying to time the market. Key investments include Layer 1 smart contract platforms like Ethereum, Solana, and Sui, which he believes will capture most value due to their foundational role. He also sees potential in privacy coins like Zcash and a future resurgence for high-end NFTs as digital trophy assets. Pal emphasizes that while AI stocks are hot, crypto offers superior asymmetric returns. He outlines a bullish case for 2026-2027 based on institutional adoption, pro-regulation, increased global liquidity, and crypto's current undervaluation relative to assets like the Nasdaq. His core advice: accumulate crypto assets during dips and hold for the long term, as the shift towards a digital, AI-agent-driven economy is inevitable.

链捕手06/01 06:10

Interview with Macro Master Raoul Pal: The Economic Singularity is Approaching, Don’t Get Off the Ride in the Next Four Years

链捕手06/01 06:10

Running MoE on Mobile Phones? Meta Proposes MobileMoE, Speeding Up iPhone 16 Pro by 3.8x

Meta's MobileMoE, a mobile-optimized Mixture-of-Experts (MoE) language model architecture, enables efficient on-device large language model (LLM) inference for the first time on commercial smartphones. Designed for decoder-only Transformers, it replaces dense feed-forward layers with MoE layers. Key design choices include 8 experts with granularity g=8, top-4 routing, and a shared expert. The model undergoes a four-stage training process: pre-training, intermediate training, supervised fine-tuning, and quantization-aware training. Results show MobileMoE models, with similar memory footprint, achieve equal or higher average accuracy across 14 foundational benchmarks while using only 1/2 to 1/4 of the FLOPs compared to dense baselines. After INT4 quantization, they remain competitive. Notably, on an iPhone 16 Pro, MobileMoE-S demonstrates significant speedups: up to 3.8x faster in the prompt phase and 2.2-3.4x faster in per-token generation compared to a dense counterpart, with lower peak memory usage. While MobileMoE establishes a new Pareto frontier for on-device LLMs in accuracy-compute trade-offs, particularly excelling in code and math tasks, it currently lags behind models like Qwen3.5 2B in advanced instruction following and knowledge reasoning. Future work includes improving post-training techniques, exploring NPU deployment, and managing the runtime memory sensitivity of MoE models to varying inputs.

marsbit06/01 06:09

Running MoE on Mobile Phones? Meta Proposes MobileMoE, Speeding Up iPhone 16 Pro by 3.8x

marsbit06/01 06:09

Bitcoin ETFs Post $1.42B Weekly Outflows To Close May Bloodbath

U.S. Bitcoin Spot ETFs experienced significant net outflows of $1.42 billion in the final week of May, extending a negative trend throughout most of the month. This capital flight coincided with Bitcoin's price struggling to break the $82,000 resistance. Outflows were led by BlackRock's IBIT ($966.42 million), followed by Fidelity's FBTC and Grayscale's GBTC. Other funds like BITB also saw moderate outflows, while several issuers recorded zero net flows. Daily data from May 26-29 showed consistent outflows, contributing to a total monthly net outflow of $2.43 billion for May—the highest in 2026 and the largest since November 2025. Only 6 of the month's 20 trading days saw positive flows. Despite this recent pressure, the overall market remains resilient with cumulative net inflows of $55.66 billion since launch and total net assets of $94.17 billion. Bitcoin's price was at $74,012 at the time of writing.

bitcoinist06/01 06:01

Bitcoin ETFs Post $1.42B Weekly Outflows To Close May Bloodbath

bitcoinist06/01 06:01

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