# Computing İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Computing" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

The Quantum Computing Threat Approaches, Cryptocurrency May Be Exposed to Risks Before Banks

Quantum computing poses a significant threat to all cryptographic systems, including banks and governments, but decentralized cryptocurrencies with public ledgers like Bitcoin are likely the first practical target. Experts warn that a cryptographically relevant quantum computer (CRQC), capable of running Shor's algorithm to break the elliptic curve cryptography securing most crypto wallets, could emerge around 2029. Recent research shows the required quantum resources for such attacks are shrinking dramatically, potentially enabling key extraction in minutes. The core vulnerability for cryptocurrencies is not the cryptography itself—post-quantum standards are being developed—but the slow, decentralized governance required to implement upgrades. Unlike centralized banks that can swiftly transition, Bitcoin needs near-unanimous consensus among its global network, a historically difficult process as seen in past upgrades. Estimates suggest migrating all vulnerable Bitcoin funds could take at least 76 days of dedicated network time, and it must be completed before a CRQC exists to prevent "now-or-never" attacks on exposed keys. The threat is not binary; it begins when a quantum computer can decrypt data before it loses value, not necessarily in real-time. A significant portion of Bitcoin (estimated at millions of coins) already has public keys permanently exposed on-chain, making them vulnerable to eventual "static attacks." While technical solutions exist, the race is against time for decentralized networks to coordinate a defensive transition, serving as an early warning for the broader financial system.

marsbit10 saat önce

The Quantum Computing Threat Approaches, Cryptocurrency May Be Exposed to Risks Before Banks

marsbit10 saat önce

A Serious Threat is Looming for Bitcoin (BTC) and Cryptocurrencies: They Could Be the First Target!

The rapid advancement of quantum computing poses a serious threat not only to cryptocurrencies but to the entire encryption system, including banking. However, experts warn that the cryptocurrency market might be the first sector to suffer from this transformation. As predictions of a 'Q-Day' approach, the slow governance processes of cryptocurrencies, rather than their cryptography, could become the biggest obstacle to defending against quantum attacks. Quantum Xchange CEO Eddy Zervigon has compared cryptocurrencies to "canaries in the coal mine," stating that the first successful quantum-powered cyberattack is likely to target decentralized blockchain networks. While a cryptographically relevant quantum computer capable of breaking the elliptic curve cryptography underlying Bitcoin's signatures and banking security does not yet exist, work by giants like Microsoft, IBM, and Google suggests the threat is nearer than previously thought. The industry consensus estimates such a computer could emerge around 2029. Supporting this view, recent Google research indicates the number of physical qubits needed to break the cryptographic systems protecting Bitcoin and Ethereum has decreased by about 20 times compared to prior estimates, highlighting the accelerated danger. Zervigon concludes that Bitcoin's greatest risk lies not in its current cryptographic system, but in the slow pace of implementing major network upgrades.

cryptonews.ru21 saat önce

A Serious Threat is Looming for Bitcoin (BTC) and Cryptocurrencies: They Could Be the First Target!

cryptonews.ru21 saat önce

Investors Walked 20,000 Steps in Shanghai

Investors logged over 20,000 steps at the Shanghai event as the 2026 World Artificial Intelligence Conference (WAIC) witnessed unprecedented crowds, a record exhibition space exceeding 100,000 square meters, and over 300 global product debuts. The intense FOMO (fear of missing out) among AI investors was palpable, with tickets being scalped at high prices. The conference highlighted a decisive shift from comparing technical parameters to demonstrating real-world applications and commercial deployment. The vast exhibition, spanning four venues, featured specialized zones for industry applications (H1), foundational computing power (H2), robotics (H3), and startups (H4). The robotics hall (H3) was the most popular, packed with over 200 companies showcasing embodied AI and prototypes, including humanoid robots capable of performing industrial tasks. Investors were omnipresent and exceptionally busy, shuttling between meetings, panels, and networking events organized by leading VC firms. The startup zone hosted over 160 young companies, with many investors hoping to spot the next unicorn. This frenzy mirrors the explosive investment in China's AI sector. In H1 2026 alone, the embodied intelligence segment raised over 90 billion yuan, a fivefold year-on-year increase. The broader AI sector attracted over 300 billion yuan in funding, accounting for half of the total venture capital invested in the period. A wave of companies have reached "unicorn" status with valuations exceeding $10 billion, and at least 20 embodied AI firms are now planning IPOs. Market leaders like Zhipu AI and DeepSeek have reached valuations in the hundreds of billions, with industry insiders predicting the imminent emergence of trillion-yuan AI giants in China. Investors are actively scouting the conference to map out and fund the next decade of technological evolution.

marsbit07/19 07:27

Investors Walked 20,000 Steps in Shanghai

marsbit07/19 07:27

Quantum Computing Approaches "Q-Day": How Encryption Policy, Investment Logic, and Risk Management Are Reshaping the Landscape

Quantum Computing Nears 'Q-Day': Shaping Encryption Policy, Investment Logic, and Risk Management Quantum technology is increasingly intersecting with cryptocurrency policy and cybersecurity discussions as the potential 'Q-Day'—when quantum computers could break current encryption—approaches. While summer brings fast-paced crypto market dynamics, new U.S. legislation, and AI debates, the emerging dimension is how quantum advancements will reshape the digital asset landscape. The next phase of crypto investment is being shaped by two converging forces: clearer regulatory frameworks and cryptographic evolution driven by quantum computing. Investors stand to benefit from reduced uncertainty, but must also recognize that quantum readiness is becoming a core risk factor. Public blockchains rely on cryptography for security, and sufficiently advanced quantum machines could undermine these foundations. This does not mean imminent network collapse, but investors can no longer dismiss the timeline as irrelevant. Key questions now include whether projects have identified their cryptographic dependencies, formulated migration plans to post-quantum cryptography, and established governance for upgrades. For policymakers, the link is clear. Effective crypto policy must look beyond token classification and disclosure to address the underlying infrastructure. As stablecoins, tokenized assets, and blockchain payments integrate deeper into finance, cryptographic resilience becomes a systemic issue. Failure to prepare could lead to investor losses, operational failures, and legal disputes. Policy should encourage risk disclosure, require major intermediaries to maintain upgrade and response plans, and foster coordination across the ecosystem—rather than impose a single technical fix. The sustainability of cryptocurrencies will increasingly depend on their security infrastructure's ability to adapt to these accelerating technological pressures.

Foresight News06/29 07:12

Quantum Computing Approaches "Q-Day": How Encryption Policy, Investment Logic, and Risk Management Are Reshaping the Landscape

Foresight News06/29 07:12

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

AI Booms, But Crypto + AI Remains Sluggish: A Demand-Side Analysis Despite the AI industry's explosive growth and massive investment, the convergence of blockchain and AI (Crypto + AI) has seen limited traction. The core issue is a severe supply-demand mismatch, not a flawed premise. Analyzing four key sub-sectors reveals specific gaps: 1. **Decentralized Compute/Storage:** Offer logical benefits like data sovereignty and cost savings but lack a decisive technical advantage over entrenched cloud giants (AWS, GCP). Enterprises prioritize performance and stability and are unwilling to bear the switching risk and uncertainty of decentralized networks. 2. **Model Verification/Privacy (e.g., ZKML):** Address important long-term issues like auditability and data privacy, but these are not urgent operational pain points for most businesses today. Widespread demand will likely follow regulatory mandates (like the EU AI Act), not precede them. 3. **AI Agent Infrastructure:** Projects are building infrastructure for a future of autonomous, interacting agents. However, the current market focus is on internal process automation within corporate firewalls. The technology is ahead of market readiness. 4. **AI Agent Payments:** This is the only sub-sector where blockchain is on a level playing field with traditional finance. Both are trying to solve the unsolved problem of real-time, micro-transactions for machines, making it the most immediately competitive area. The overarching problem is that the AI industry invests heavily in solutions that solve immediate bottlenecks (e.g., faster memory, more power). Most Crypto + AI solutions target secondary, longer-term concerns (decentralization, transparency) and often come with performance trade-offs. The lack of a flagship, large-scale commercial success case further hinders mainstream capital inflow. The path forward requires either aligning more closely with the current industry's performance demands or patiently building the foundational infrastructure for the next phase of AI.

Foresight News06/29 06:15

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

Foresight News06/29 06:15

The First Large-Scale Generative Model Using Physics as a Computational Primitive, Un-0, is Here: Could AI Energy Consumption Be Reduced by 1000x?

Unconventional AI has unveiled Un-0, a large-scale image generation model that uses the physical dynamics of coupled oscillators as its computational primitive. Described as the "first to use physics as a computation primitive for large-scale generative models," Un-0 aims to demonstrate a path toward dramatically reducing AI's energy consumption—potentially by up to 1000x compared to current GPU-based digital systems. The model operates by training a system of thousands of Kuramoto-like oscillators, where learned coupling strengths and natural frequencies define its behavior. Starting from random phases, the system evolves under its physical dynamics, guided by class-conditioning inputs, and a small traditional decoder then renders the final image. On ImageNet 64x64, Un-0 achieved an FID score of 6.74, performance comparable to early traditional generative models, though not yet state-of-the-art. The project, led by former Databricks AI chief Naveen Rao, represents a foundational step in using physical systems for computation, merging memory and processing in a single dynamic entity to bypass the energy-intensive data movement of von Neumann architectures.

marsbit06/26 10:54

The First Large-Scale Generative Model Using Physics as a Computational Primitive, Un-0, is Here: Could AI Energy Consumption Be Reduced by 1000x?

marsbit06/26 10:54

BitTorrent Launches BTTInferGrid: The Decentralized Infrastructure Layer for Scalable AI Inference

BitTorrent has launched BTTInferGrid, a decentralized GPU computing network designed to meet the surging demand for AI inference workloads. The platform aggregates global idle GPU resources into an open-access, verifiable, and pay-as-you-go infrastructure, aiming to solve the cost, scalability, and supply bottlenecks of traditional centralized cloud providers. BTTInferGrid addresses a key market shift, as industry forecasts indicate over 70% of future AI compute will be for inference—a continuous operational cost. It tackles centralization issues like inflexible resource allocation during volatile demand, prohibitive GPU pricing, and the underutilization of fragmented global compute capacity. The platform establishes a direct corridor between AI developers and idle hardware. On the supply side, it allows providers to monetize underutilized GPUs through tokenized incentives. On the demand side, it offers developers cost-efficient, on-demand inference with on-chain verification. Key differentiators include permissionless access for providers, verifiable service quality through blockchain validation, and a sustainable, demand-driven economic model. Built on BitTorrent's proven DePIN expertise from the BitTorrent File System (BTFS), BTTInferGrid follows a phased roadmap. It begins with network bootstrapping in 2026, focusing on scaling GPU nodes, and aims to evolve into a foundational Web3 AI infrastructure layer by 2028, supporting diverse model architectures and decentralized fine-tuning.

TheNewsCrypto06/18 07:33

BitTorrent Launches BTTInferGrid: The Decentralized Infrastructure Layer for Scalable AI Inference

TheNewsCrypto06/18 07:33

"Nvidia Concept Stock" CoreWeave Co-founder Interview: AI Demand Seems to Intensify Daily

Interview with CoreWeave co-founder Brannin McBee and VP Nick Robbins highlights the ongoing intensification of AI demand, driven by agents, reasoning, and enterprise adoption. They note a structural shift: bottlenecks are expanding beyond GPU availability to powered data center shells, CPU, storage, electrical work, and supply chain execution. CoreWeave, serving top AI labs and cloud giants, emphasizes its customer-driven model to anticipate and build for next-gen needs. The company has redesigned data centers to accommodate more CPU and storage alongside GPUs, anticipating increased demand for Nvidia's Vera CPU and Vera Rubin servers. While GPU procurement remains critical, competition now hinges on comprehensive engineering and delivery capability. CoreWeave leverages its execution track record and performance to compete with hyperscalers and other neocloud providers. Current primary constraint is powered data center shell availability, not components like HBM memory, though cost fluctuations are passed to customers under their contract model. Vera Rubin deployment is expected to ramp significantly through 2027, following a pattern similar to previous Nvidia platforms.

marsbit06/18 03:00

"Nvidia Concept Stock" CoreWeave Co-founder Interview: AI Demand Seems to Intensify Daily

marsbit06/18 03:00

If the AI Bubble Is Already Bursting, Who Will Truly Survive?

If the AI Bubble is Bursting, Who Will Remain? The debate over an AI bubble is intensifying, with figures like Ray Dalio warning of high levels and Jensen Huang seeing immense, early-stage opportunity. Both views hold truth: a speculative bubble in capital markets likely exists, mirroring the dot-com era, but the underlying technological shift is real and transformative. History shows that while bubbles burst—wiping out overvalued companies and speculative capital—they often leave behind critical physical and digital infrastructure. The dot-com bust, for instance, eliminated many firms but left the global fiber optic networks and data centers that enabled the rise of Amazon, Netflix, and cloud computing. Today's massive AI infrastructure investments (projected at trillions by 2030) in data centers, power, cooling, and GPUs may follow a similar path, creating the foundation for future applications. A key divergence from past bubbles is the "Jevons Paradox" effect in AI. As the cost of AI inference has plummeted by over 99.7% since 2023, enterprise spending on AI has skyrocketed. Cheap "tokens" have unlocked vast, previously uneconomical use cases, moving AI from simple chatbots into core business workflows—code generation, legal document review, scientific simulation, and financial analysis. The market is now in a phase of self-correction, weeding out superficial "API-wrapper" startups, but this cleansing process strengthens the ecosystem. The long-term trajectory is clear. The value is gradually shifting from capital expenditure (CapEx) on hardware to operational expenditure (OpEx) on transformative applications. As AI becomes a utility, the winners will be firms that deeply integrate it to solve vertical industry problems in law, healthcare, finance, and manufacturing. The泡沫 will recede, but the foundational shift towards an AI-powered era across all sectors is irreversible. The underlying productive force of AI contains no bubble.

marsbit06/15 04:42

If the AI Bubble Is Already Bursting, Who Will Truly Survive?

marsbit06/15 04:42

Sequoia Dialogue with Jensen Huang: Computing Model Undergoes a 60-Year Transformation; You Won't Be Replaced by AI, But You Will Be Dimensionality-Reduced by 'Those Who Master AI'

NVIDIA founder and CEO Jensen Huang, in a conversation with Sequoia Capital's Konstantine Buhler, argues that we are witnessing the most significant computing shift in 60 years—from retrieval-based to generative computing. Instead of just storing and retrieving data, future systems will generate highly personalized content (text, images, video) on demand, powered by massive "AI factories." Huang envisions a global "intelligence network" that will envelop the planet, following the historical patterns of energy and communication grids. He outlines a five-layer investment framework: 1) Energy, 2) Chips/Computers, 3) Infrastructure (data centers), 4) AI Models, and 5) Applications. He predicts this ecosystem will reach a scale of $20 trillion annually. Crucially, Huang pushes back against fears of AI-driven job loss. He distinguishes between specific "tasks" (e.g., typing, analyzing images) and overall "jobs" (e.g., CEO, radiologist). While AI automates tasks, it increases efficiency and demand for the higher-value problem-solving aspects of professions, thus creating more jobs and "up-leveling" careers. The real risk, he asserts, is not being replaced by AI, but being outperformed by someone who effectively leverages it. He urges everyone to embrace AI as a tool for augmented capability and innovation.

marsbit06/12 02:59

Sequoia Dialogue with Jensen Huang: Computing Model Undergoes a 60-Year Transformation; You Won't Be Replaced by AI, But You Will Be Dimensionality-Reduced by 'Those Who Master AI'

marsbit06/12 02:59

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