# Сопутствующие статьи по теме Cost

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Cost", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

After Integrating OpenClaw into Every Aspect of My Life, I Personally Switched It Off

After extensively using OpenClaw (formerly Clawdbot and Moltbot) for over a month as a 24/7 AI assistant integrated with Telegram, email, and calendar, the author decided to shut it down. The primary reasons were its unreliability in long-term memory retention despite claims, high and unpredictable API costs (over $150 monthly), and significant security vulnerabilities, including exposed API keys and unauthorized data transmission. The author realized that a constantly running AI was unnecessary for most valuable tasks, which were better handled through active, intentional work. The core functions of OpenClaw—remembering user context and automating tasks—were effectively replicated using Claude’s ecosystem. By creating a consolidated CLAUDE.md file (replacing OpenClaw’s multiple configuration files), leveraging Claude’s built-in memory features, and integrating with Obsidian via CLI for efficient knowledge management, the author achieved similar functionality with greater reliability. For mobile access, Claude’s Remote Control feature or a Telegram bot solution provided seamless interaction. Scheduled tasks were handled through Claude’s Cowork feature, avoiding the cost of continuous API checks. Ultimately, Claude Pro or Max subscriptions offered a more predictable cost structure ($20–$200/month) and a stable, secure environment. The author concluded that Claude’s ecosystem delivers nearly all of OpenClaw’s promised benefits without the operational headaches, making it a superior choice for practical AI assistance.

marsbit03/02 10:13

After Integrating OpenClaw into Every Aspect of My Life, I Personally Switched It Off

marsbit03/02 10:13

a16z Infographic: AI Costs Halved, Usage Doubled, US 30-Year-Olds Enter the Era of 'Delayed Adulthood'

This a16z report analyzes four key trends through data visualization. First, while the "DExit" narrative suggests companies are leaving Delaware due to legal shifts, the data reveals a more nuanced picture; Delaware's overall share of businesses has actually grown, though specific entities like Wyoming LLCs are gaining traction. Second, AI sector demonstrates the Jevons Paradox: as the cost of AI tokens halved, their usage doubled, and demand for older GPUs remains strong, indicating the AI-driven economy is still in its early stages. Third, AI capital expenditure is massive, comparable to total US bank lending and significantly larger than corporate tax income or the military budgets of other G7 nations. Finally, prediction markets like Kalshi are now outperforming professional forecasters in predicting Federal Funds Rates. The report concludes with a societal trend: 30-year-olds are increasingly delaying traditional life milestones like marriage, homeownership, and having children, with college enrollment being the only metric that has risen sharply, though its value is now being questioned.

marsbit03/02 07:16

a16z Infographic: AI Costs Halved, Usage Doubled, US 30-Year-Olds Enter the Era of 'Delayed Adulthood'

marsbit03/02 07:16

Aave Founder: What is the Secret of the DeFi Lending Market?

On-chain lending, which started as an experimental concept around 2017, has grown into a market exceeding $100 billion, primarily driven by stablecoin borrowing backed by crypto-native collateral. It enables liquidity release, leveraged positions, and yield arbitrage. The key advantage lies not in creativity but in validation through real demand and product-market fit. A major strength of on-chain lending is its significantly lower cost—around 5% for stablecoin loans compared to 7–12% plus fees in centralized crypto lending. This efficiency stems from capital aggregation in open, permissionless systems where transparency, composability, and automation foster competition. Capital moves faster, inefficiencies are exposed, and innovation spreads rapidly without traditional overhead. The system’s resilience is evident during bear markets, where capital continuously reprices itself in a transparent environment. The current limitation is not a lack of capital but a shortage of diverse, productive collateral. The future involves integrating crypto-native assets with tokenized real-world value to expand lending’s reach and efficiency. Traditional lending remains expensive due to structural inefficiencies: bloated origination, misaligned incentives, manual servicing, and defective risk feedback mechanisms. Decentralized finance solves this by breaking cost structures through full automation, transparency, and software-native processes. When on-chain lending becomes end-to-end cheaper than traditional systems, adoption will follow inevitably, empowering broader access to efficient capital deployment.

marsbit02/16 04:11

Aave Founder: What is the Secret of the DeFi Lending Market?

marsbit02/16 04:11

The Next Earthquake in AI: Why the Real Danger Isn't the SaaS Killer, But the Computing Power Revolution?

The next seismic shift in AI isn't about SaaS disruption but a fundamental revolution in computing power. While many focus on AI applications like Claude Cowork replacing traditional software, the real transformation is happening beneath the surface: a dual revolution in algorithms and hardware that threatens NVIDIA’s dominance. First, algorithmic efficiency is advancing through architectures like MoE (Mixture of Experts), which activates only a fraction of a model’s parameters during computation. DeepSeek-V2, for example, uses just 9% of its 236 billion parameters to match GPT-4’s performance, decoupling AI capability from compute consumption and slashing training costs by up to 90%. Second, specialized inference hardware from companies like Cerebras and Groq is replacing GPUs for AI deployment. These chips integrate memory directly onto the processor, eliminating latency and drastically reducing inference costs. OpenAI’s $10 billion deal with Cerebras and NVIDIA’s acquisition of Groq signal this shift. Together, these trends could collapse the total cost of developing and running state-of-the-art AI to 10-15% of current GPU-based approaches. This paradigm shift undermines NVIDIA’s monopoly narrative and its valuation, which relies on the assumption that AI growth depends solely on its hardware. The real black swan event may not be an AI application breakthrough but a quiet technical report confirming the decline of GPU-centric compute.

marsbit02/12 04:38

The Next Earthquake in AI: Why the Real Danger Isn't the SaaS Killer, but the Computing Power Revolution?

The next seismic shift in AI is not the threat of "SaaS killers" but a fundamental revolution in computing power. While many focus on how AI applications like Claude Cowork are disrupting traditional software, the real transformation is happening beneath the surface—in the infrastructure that powers AI. Two converging technological paths are challenging NVIDIA’s GPU dominance: 1. **Algorithmic Efficiency**: DeepSeek’s Mixture-of-Experts (MoE) architecture allows massive models (e.g., DeepSeek-V2 with 236B parameters) to activate only a small fraction of "experts" (9%) during computation, achieving GPT-4-level performance at 10% of the computational cost. This decouples AI capability from sheer compute power. 2. **Specialized Hardware**: Inference-optimized chips from companies like Cerebras and Groq integrate memory directly onto the chip, eliminating data transfer delays. This "zero-latency" design drastically improves speed and efficiency, prompting even OpenAI to sign a $10B deal with Cerebras. Together, these advances could cause a cost collapse: training costs may drop by 90%, and inference costs could fall by an order of magnitude. The total cost of running world-class AI may plummet to 10-15% of current GPU-based solutions. This paradigm shift threatens NVIDIA’s valuation, built on the assumption of perpetual GPU dominance. If the market realizes that GPUs are no longer the only—or best—option, the foundation of NVIDIA’s trillions in market cap could crumble. The real black swan event may not be a new AI application, but a quiet technical breakthrough that reshapes the compute landscape.

marsbit02/11 01:58

Bitcoin slips below miner costs: Will Trump’s 10% credit cap boost demand?

Bitcoin is currently trading below its estimated mining cost of around $101,600, a level that historically acts as a support floor as miners reduce selling pressure. This coincides with a potential market rebound, as outflows likely bottomed in late December. Meanwhile, former President Trump's proposal to cap credit card interest rates at 10% may restrict credit access for some borrowers, potentially driving new demand toward Bitcoin and DeFi as alternative financial systems. While these factors support a short-term price recovery, analysts caution that weak long-term liquidity could challenge any sustained rally.

ambcrypto01/13 03:01

Bitcoin slips below miner costs: Will Trump’s 10% credit cap boost demand?

ambcrypto01/13 03:01

Bitcoin miners face rising stress as price trades below growth cost

Bitcoin is trading below the estimated breakeven cost of approximately $95,000–$96,000 per BTC required for sustainable network expansion, placing miners under financial stress. While this has slowed hashrate growth and compressed margins, it has not triggered widespread capitulation. On-chain data indicates miners are accumulating BTC rather than liquidating reserves, and a minor difficulty adjustment of 1.2% suggests adaptation rather than structural decline. The current phase reflects operational consolidation and internal stress management, not a market breakdown, as efficient miners continue functioning despite price pressures.

ambcrypto01/09 18:06

Bitcoin miners face rising stress as price trades below growth cost

ambcrypto01/09 18:06

From Cheap Customer Service to Billion-Dollar Leaks: The Dual Faces of India's Outsourcing Industry

An investigation into India's massive BPO (Business Process Outsourcing) industry reveals a dual reality of cost efficiency and severe security risks, highlighted by a major data breach at Coinbase. In December 2025, Coinbase’s CEO announced the arrest of a former customer support employee in Hyderabad, India, linked to a $400 million data leak. The employee, working for outsourcing firm TaskUs, allegedly stole and sold user data for substantial personal gain—earning up to 200 times their daily wage per photo of sensitive information. This incident is not isolated. Companies like Amazon and Microsoft have also experienced similar breaches due to insider threats from underpaid Indian外包 employees, who often earn as little as $300–500 per month. Despite these risks, India remains the global leader in BPO, with its market valued at around $50 billion in 2024 and projected to grow significantly. The country’s advantages include low labour costs, English proficiency, and time-zone benefits for Western companies. However, the industry is evolving. Many multinationals are now establishing Global Capability Centers (GCCs) in India for higher-value work like R&D and AI, leveraging local talent for innovation at lower costs. Meanwhile, repetitive low-end tasks face growing competition from automation and AI. While companies like Coinbase continue to rely on Indian outsourcing for economic reasons, the recurring security lapses underscore ongoing management challenges and the human cost behind the industry’s “cheap labour” model.

比推01/06 15:18

From Cheap Customer Service to Billion-Dollar Leaks: The Dual Faces of India's Outsourcing Industry

比推01/06 15:18

Bitcoin Miners Are Abandoning 'Mining' for 'Cloud'

Facing a survival crisis due to declining Bitcoin prices and rising operational costs, Bitcoin miners are increasingly pivoting to AI data center leasing as a sustainable alternative. The core business model of Bitcoin mining has become economically unviable, with the average cost to mine one Bitcoin reaching approximately $74,600—exceeding its current market price of around $91,000, resulting in significant per-unit losses. Miners are leveraging their existing infrastructure—including high-performance GPUs, pre-approved large-scale power capacity, and advanced cooling systems—to meet the explosive demand from tech giants for readily available AI data center space. This shift not only provides a stable revenue stream but also reduces reliance on volatile crypto markets. Companies like Core Scientific have successfully transitioned, now operating about 200 MW of data center capacity with plans to expand further. Beyond data center leasing, some miners are exploring broader digital asset technology (DAT) business models. This industry-wide evolution signals a move toward greater resilience and maturity, as weaker pure-play miners exit and leading firms diversify into integrated service providers.

深潮12/16 03:29

Bitcoin Miners Are Abandoning 'Mining' for 'Cloud'