# Cost Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Cost", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

3 People with 100 AI Programmers, Burning Through $1.3 Million a Month! OpenAI: I'll Foot the Bill

In a striking demonstration of AI-powered development, Peter Steinberger (creator of OpenClaw) shared that his three-person team spent $1.3 million in one month to run approximately 100 AI agents (primarily Codex instances). OpenAI covered the cost. The expenditure consumed 6.03 trillion tokens across 7.6 million requests. Steinberger argues that, with "fast mode" disabled, the cost falls below that of a single engineer while providing significantly greater output. This "cloud programmer army" handles core but tedious software engineering tasks: reviewing pull requests, finding security vulnerabilities, deduplicating issues, fixing bugs, monitoring benchmarks, and even generating PRs after meetings. This shifts AI's role from merely writing code to maintaining the entire collaborative fabric of a project. Steinberger's tool, CodexBar (a macOS menu bar app), tracks usage and costs across various AI coding services, highlighting how token consumption is becoming a key metric—a new "means of production." The experiment poses a profound question: if token cost ceases to be a barrier, how will software development transform? As model prices fall, the capability for small teams to leverage large numbers of AI agents could become commonplace, fundamentally altering the scale and speed of development. The future, Steinberger suggests, is arriving rapidly.

marsbit05/17 06:20

3 People with 100 AI Programmers, Burning Through $1.3 Million a Month! OpenAI: I'll Foot the Bill

marsbit05/17 06:20

What Makes XRP Unique? Ripple CEO Explains

Ripple CEO Brad Garlinghouse explains the unique properties of XRP, focusing on its origin and design for efficient payments rather than as a general-purpose blockchain. He highlights the XRP Ledger's key features: over 4 billion transactions processed, settlement times of 3-5 seconds, and transaction costs of less than a penny, emphasizing speed, low cost, and scalability. Garlinghouse also credits the dedicated long-running community, or "XRP family," as a core component of its identity and endurance. He concludes that this combination of technical performance, longevity, and strong community support positions XRP for future success.

bitcoinist05/14 17:01

What Makes XRP Unique? Ripple CEO Explains

bitcoinist05/14 17:01

The Essence of AI Layoffs: Why More AI Adoption Leads to More Corporate Anxiety?

The author, awaiting potential inclusion on an 8000-person layoff list, analyzes the true nature of recent "AI-driven" layoffs. They argue that while AI use, particularly tools like Claude for code generation, has skyrocketed and boosted developer output (e.g., 2-5x more code commits), this has not translated into proportional business growth or revenue. The core issue is a misalignment between increased "Input" (code) and tangible "Outcomes" (user value, revenue). AI acts as a costly B2B SaaS, inflating operational expenses without guaranteed returns. Two key problems emerge: 1) The friction that once filtered out bad ideas is gone, as AI allows cheap pursuit of even weak concepts. 2) Organizational "alignment tax"—the difficulty of coordinating across teams—becomes crippling when development velocity outpaces consensus-building. Thus, layoffs serve two immediate purposes: 1) To offset ballooning AI costs (Token consumption) and maintain cash flow, as rising input costs without outcome growth destroys unit economics. 2) To reduce organizational bloat and alignment friction by simply removing teams, thereby speeding up execution in the short term. Therefore, these layoffs are fundamentally caused by AI, even if AI doesn't directly replace roles. They represent a painful correction until companies learn to convert AI-driven productivity into real business outcomes and streamline organizational coordination to match the new pace of work. The cycle will continue until this learning curve is mastered.

marsbit05/12 10:23

The Essence of AI Layoffs: Why More AI Adoption Leads to More Corporate Anxiety?

marsbit05/12 10:23

AI Relay Stations: The Hidden Pitfalls Behind Low Costs, How to Screen and Avoid Them?

AI Relay Stations: The Hidden Risks Behind Low Costs and How to Avoid Pitfalls AI relay stations are becoming a popular gateway to various models, offering lower prices, a wider selection, and a unified interface for tools like Claude Code and Cursor. However, their appeal masks significant risks. Users may unknowingly surrender prompts, code, business documents, customer data, and even full project contexts. The demand is driven by genuine needs: cost savings compared to expensive official APIs (e.g., GPT, Claude), easier access amid regional restrictions, and the push from AI-powered development tools. But not everyone needs a relay station. Light users should exhaust free official quotas first. Heavy users, like developers, can adopt a layered approach, using top models for critical tasks and cheaper local models for routine work. If a relay station is necessary, follow a careful selection and usage protocol: 1. **Verify First:** Test model authenticity, latency, and stability before purchasing credits. Check the quality of provided documentation. 2. **Isolate Configuration:** Use unique API keys for each service, manage them via environment variables, and set usage limits to control costs and potential damage from leaks. 3. **Classify Your Data:** Develop a habit of data grading before sending requests. Only send non-sensitive, public information directly. Desensitize semi-sensitive data (e.g., internal documents) by removing names and specifics. Never send highly sensitive data like passwords, private keys, or confidential customer information. 4. **Handle AI Coding Tools Separately:** Tools like Cursor can send extensive project context (file contents, directory structures, error logs). Use relay stations only for independent, non-core code tasks. For sensitive projects, switch back to official APIs or local models. 5. **Monitor and Prepare an Exit:** Regularly check billing statements, follow platform updates and community feedback, and always have a backup provider. Ensure your setup uses standard OpenAI-compatible APIs for easy migration. Ultimately, relay stations are tools, not default solutions. Their value lies in solving access needs at a controlled cost, but maintaining that control requires proactive risk management through verification, isolation, data classification, and continuous monitoring.

marsbit05/09 10:16

AI Relay Stations: The Hidden Pitfalls Behind Low Costs, How to Screen and Avoid Them?

marsbit05/09 10:16

AI Giants Enter the Dark Forest

In the AI industry's "dark forest," major players like Anthropic, OpenAI, and DeepSeek are strategically withholding their most advanced models to avoid becoming targets in a high-stakes competitive landscape. Anthropic released Claude Opus 4.7 but admitted it underperforms compared to their unreleased model Mythos, citing safety concerns. They delayed addressing user complaints about performance regression until OpenAI’s GPT-5.5 launch, highlighting a tactic of controlled disclosure aligned with competitors’ moves. OpenAI’s GPT-5.5, though a full retrain since GPT-4.5, was seen as incremental rather than revolutionary. Leaks revealed internal models like Glacier and Heisenberg, indicating significant unreleased capabilities. OpenAI acknowledges a "capability overhang," where real model power exceeds what users experience, often due to infrastructure-driven throttling. DeepSeek launched V4 Preview, a cost-efficient model, but its full potential (V4 Pro Max) awaits Huawei’s Ascend 950 super-nodes量产 in late 2026. Their strategy focuses on affordability and scalability, aiming to democratize AI access globally, a move noted even by NVIDIA’s CEO as a disruptive threat. Together, these actions reflect a broader trend: leading AI labs are deliberately pacing releases, hiding strengths, and aligning disclosures with competitive dynamics—each avoiding the risk of exposure in a forest where first movers become targets.

marsbit04/25 12:47

AI Giants Enter the Dark Forest

marsbit04/25 12:47

Open-Source Plugin Ignites Underlying AI Model Warfare: Behind Claude-mem's Explosive Popularity Lies Big AI Companies' Best-Kept Profit Secret

The open-source plugin "Claude-mem" has ignited a hidden war in the AI industry by tackling a critical weakness in large language models: their lack of memory. This tool, which exploded in popularity on GitHub, works by locally storing and compressing conversation history, slashing redundant token usage by up to 95%. This directly undercuts the "context tax"—the costly practice of repeatedly sending historical data to the cloud with each new interaction. Its integration with another tool, OpenClaw, enabled users to exploit a pricing loophole, using low-cost personal subscriptions to run high-frequency automated tasks meant for expensive enterprise API plans. In response, Anthropic banned third-party OAuth access, triggering a backlash and even a major service outage. Despite the crackdown, Claude-mem’s founder circumvented traditional monetization by launching a cryptocurrency, $CMEM, on the Solana network. The episode highlights key tensions in the AI industry: the fight over pricing models, the value of local memory control, and the risks of building on proprietary platforms. The battle over AI’s future is being waged in the code.

marsbit04/20 00:20

Open-Source Plugin Ignites Underlying AI Model Warfare: Behind Claude-mem's Explosive Popularity Lies Big AI Companies' Best-Kept Profit Secret

marsbit04/20 00:20

Xianyu, the OpenClaw More Suited for Chinese Users

The article "Xianyu: The OpenClaw More Suited for Chinese Users" argues that while OpenClaw (a hypothetical AI agent) has generated significant buzz for enabling AI to perform digital tasks, the Chinese second-hand marketplace Xianyu serves as a more practical and "localized" alternative for many. The author contrasts OpenClaw's high computational costs (token consumption) and complex setup with Xianyu's extremely low-cost, human-powered services. On Xianyu, users can find people to perform a vast array of tasks—from booking hotels and buying tickets to AI subscriptions, hiring help, and even matchmaking—often at a fraction of the cost of using an AI. The platform is portrayed as a massive, flexible ecosystem where unconventional supply meets niche demand, creating a parallel economy with over 200 million monthly active users. The piece draws parallels between the two platforms: OpenClaw acts as a leverage tool using AI and tokens, while Xianyu leverages cheap and abundant human labor. Both systems facilitate a form of arbitrage—OpenClaw with digital tokens, and Xianyu through bilateral transactions between buyers and sellers. Ultimately, the article suggests that for China, with its specific socio-economic context and different attitude towards privacy, Xianyu's human-network-based model is a more effective and accessible "agent" platform than an AI-driven solution like OpenClaw.

marsbit04/07 06:12

Xianyu, the OpenClaw More Suited for Chinese Users

marsbit04/07 06:12

Xianyu, the OpenClaw More Suited for Chinese Users

This article argues that while OpenClaw (a fictional AI agent) represents a global phenomenon by giving AI "hands and feet" to execute digital and physical tasks, the Chinese second-hand marketplace Xianyu is a more practical and "localized" alternative for most users in China. The author highlights that OpenClaw's operational costs, due to massive token consumption for API calls, are prohibitively high. In contrast, Xianyu offers a vast, low-cost human labor marketplace where services—from booking hotels and buying tickets to AI subscriptions and even matchmaking—are available at a fraction of the cost. The platform, with over 200 million monthly active users, has become a parallel economy filled with unconventional services and creative "sellers," often leveraging information asymmetry and extreme cost efficiency. It's described as a "dark web" for China, fulfilling niche demands with flexible supply. The piece draws a parallel between OpenClaw's token-based "AI leverage" and Xianyu's "human leverage," suggesting that in a market with abundant and cheap labor, a human-powered platform like Xianyu is a more effective and culturally adapted tool for task execution and resource allocation than an AI agent. The conclusion is that Xianyu, not OpenClaw, is the "more suitable OpenClaw for Chinese users."

Odaily星球日报04/07 06:03

Xianyu, the OpenClaw More Suited for Chinese Users

Odaily星球日报04/07 06:03

AI's Cost Dilemma: How Infrastructure Economics Will Reshape the Next Phase of the Market

AI is expanding, but its underlying economic model is fragile. While training cutting-edge models like Claude 3.5 Sonnet costs tens of millions—with future models potentially reaching $1 billion—the real burden is inference costs, which accumulate with each API call and strain startups. Three cloud giants—AWS, Azure, and Google Cloud—control two-thirds of global cloud infrastructure, creating market concentration and supply risks. Top AI labs secure GPU access at near-cost rates (as low as $1.30–$1.90/hour) via strategic partnerships, while smaller players pay retail prices exceeding $14/hour—a 600% premium. Energy consumption is another challenge: data centers already use 1–1.5% of global electricity, and AI’s growth will intensify this demand. Decentralized inference networks like Gonka offer an alternative, aiming to reduce costs (e.g., $0.0009 per million tokens vs. $1.50 for centralized services), increase supply elasticity, and enhance sovereignty by leveraging idle GPUs globally. The AI infrastructure war is just beginning. Centralized providers hold scale advantages, but economic pressures may drive adoption of decentralized models, reshaping value distribution in the AI industry.

marsbit03/26 08:14

AI's Cost Dilemma: How Infrastructure Economics Will Reshape the Next Phase of the Market

marsbit03/26 08:14

The Last Time I'll Talk About Backpack, and Also Discussing My Airdrop Farming Principles

The author outlines two primary approaches to airdrop farming (referred to as "撸毛"): a labor-intensive" method of mass participation in many projects, and their own "sniper" method. The sniper approach relies on a rigorous four-point checklist to filter projects and avoid "industrial garbage." The checklist evaluates: 1. **Team (People):** Founders must be intelligent, have strong execution skills, and be genuinely well-intentioned. This is assessed through their social media content and, if possible, personal interactions. 2. **Product (Product-Market Fit):** The product must have a clear market fit, be delivered competently, and the team must show a responsible attitude towards its quality, avoiding releases full of basic errors. 3. **Narrative (Story):** The project should operate in a promising, unproven narrative within Web3 that also aligns with major investment trends in Web2 (e.g., AI). 4. **Timing & Cost (Market Conditions):** Avoid participating when market sentiment is overly FOMO-driven and participation costs are high. If an opportunity causes hesitation, it's best to skip it, as overcrowded airdrops yield minimal or negative returns. Applying this framework, the author explains why they avoided heavily farming the Backpack exchange airdrop: * **Narrative:** They are skeptical of the "compliant CEX" narrative, questioning its unique selling point against giants like Binance and OKX. * **Product:** They criticize Backpack's frequent technical failures, rollbacks, and what they perceive as a lack of product development rigor, comparing it unfavorably to competitors like Hyperliquid. * **Timing & Cost:** The participation cost was high compared to zero-fee alternatives available at the time. The author concludes that Backpack lacks the technical and operational prowess of a serious exchange and views its token more as a "VC-backed meme coin" for secondary market speculation rather than a worthwhile airdrop target.

比推03/23 20:38

The Last Time I'll Talk About Backpack, and Also Discussing My Airdrop Farming Principles

比推03/23 20:38

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