# Automation Related Articles

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

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.

marsbit15h ago

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

marsbit15h ago

a16z's Latest In-depth Analysis on the AI Market: Is Your Company Still "Working with Blood"?

In a16z's latest analysis, AI companies are experiencing unprecedented growth, with top performers expanding at a 693% YoY rate—2.5x faster than non-AI firms—while spending less on sales and marketing. These companies achieve $500k-$1M ARR per employee, far exceeding the traditional SaaS benchmark of $400k, signaling a fundamental shift in business models. Key drivers include: - **Product-led growth**: High customer demand reduces reliance on traditional sales. - **Efficiency gains**: AI-native tools boost development speed 10-20x, reshaping team structures. - **Business model evolution**: Pricing is shifting from subscription/consumption to outcome-based models (e.g., charging per resolved task). Legacy companies face a critical choice: adapt fully to AI-driven workflows ("using electricity") or risk obsolescence ("using blood"). Despite CEO enthusiasm, enterprise adoption lags due to change management challenges. Early adopters like Chime and Rocket Mortgage report massive cost savings (60% in support, $40M annually). The AI infrastructure build-out, led by hyperscalers (e.g., AWS, Microsoft), requires trillions in capex but is demand-driven with no "dark GPU" surplus. AI revenue growth could soon eclipse the entire software industry, with model companies like OpenAI and Anthropic already capturing nearly half of 2025’s new software revenue. This marks the start of a 10-15 year transformation cycle, where companies embracing AI-native paradigms will define the next era.

marsbit2 days ago 00:43

a16z's Latest In-depth Analysis on the AI Market: Is Your Company Still "Working with Blood"?

marsbit2 days ago 00:43

21 Survival Action List for AI Accelerationists

21 Survival Actions for AI Accelerationists This article presents a radical guide for navigating a future of rapid, AI-driven transformation. It argues that if AI progresses exponentially, the world in 10 years will be unrecognizable, rendering traditional long-term planning obsolete. The author provides 21-point checklist of actions across four key areas: **Investment:** Prioritize direct investment in AI (e.g., tech giants, AI labs) and adjacent exponential-growth fields like robotics, biotech, and crypto. A key recommendation is to stop contributing to long-term retirement accounts (e.g., 401k) if under 50, as their value depends on a stable, predictable world that may not exist. **Finance:** Favor securing cash now by taking on fixed-rate debt (e.g., long mortgages, slow student loan repayment), operating on the premise that future debt may be irrelevant. Avoid long-cycle financial products like annuities that assume slow, linear change. **Career & Skills:** Prepare for the deep automation of most mental and physical labor within 5 years. Avoid long educational cycles (e.g., med/law school) for ROI. Instead, get extremely close to AI systems as a developer or power user, and focus on building leverage (audience, brand) over collecting credentials. **Life & Time Planning:** Abandon 30-40 year timelines. Stop optimizing for distant retirement or long-term health consequences (e.g., worrying about sun exposure or cancer decades away), as technology may solve these issues or the world may change utterly. Complete your bucket list now, buy land near family, and generally avoid making plans that extend beyond a 5-10 year horizon.

marsbit02/13 10:09

21 Survival Action List for AI Accelerationists

marsbit02/13 10:09

High-Frequency Trading, $100K Annual Income: The Most 'Boring' Profit Myth on Polymarket

A user known as planktonXD (0x4ffe49ba2a4cae123536a8af4fda48faeb609f71) has generated over $106,000 in profit on Polymarket within a year by executing more than 61,000 predictions—averaging around 170 trades per day. This high-frequency, automated strategy focuses on exploiting small, certain opportunities rather than betting on high-risk, high-reward outcomes. The approach is characterized by market-making and micro-arbitrage: placing orders on both sides of the order book to capture spreads or profiting from mispriced options in low-liquidity markets. The largest single win was only $2,527, illustrating a disciplined, risk-managed method that avoids large drawdowns. The bot operates across diverse categories—sports, weather, crypto prices, politics—constantly scanning for pricing inefficiencies. Notable examples include buying heavily undervalued options in niche markets, such as esports matches or extreme crypto price movements, where probability is mispriced due to emotional trading or thin order books. For instance, a $16 bet on SOL falling to $130 (priced at 0.7¢, implying <1% chance) returned $1,574 during a volatile period. Key takeaways: The strategy highlights the power of compounding small gains, the necessity of automation and API tools, and the superiority of high-probability opportunities over high-risk bets. In prediction markets, the most advanced approach isn’t forecasting—it’s managing probability and liquidity.

marsbit02/11 13:06

High-Frequency Trading, $100K Annual Income: The Most 'Boring' Profit Myth on Polymarket

marsbit02/11 13:06

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

Chain-based lending, which began as an experimental concept around 2017, has evolved into a market exceeding $100 billion, primarily driven by stablecoin borrowing backed by crypto-native collateral like Ethereum and Bitcoin. This system enables liquidity release, leveraged strategies, and yield arbitrage. The key advantage of on-chain lending lies not in technological novelty but in its elimination of financial inefficiencies, offering lower costs (around 5% for stablecoins) compared to centralized crypto lenders (7-12%) due to open capital aggregation, transparency, and automation. On-chain lending is structurally due to permissionless markets that excel in capital pooling and risk pricing, fostering competition and innovation without intermediaries. This model reduces operational costs, replacing manual processes with code, and benefits both capital providers and borrowers. However, the current limitation is not a lack of capital but a shortage of diverse, borrowable collateral. The future of on-chain lending depends on integrating real-world economic value with crypto-native assets, moving beyond abstract financial strategies to serve broader adoption. Traditional lending remains expensive due to inefficiencies in loan origination, risk assessment, and servicing, where misaligned incentives and manual processes inflate costs. Decentralized finance can disrupt this by automating end-to-end operations, ensuring transparency, and reducing expenses. When on-chain lending becomes significantly cheaper and more efficient than traditional systems, widespread adoption will follow, empowering borrowers with faster, more accessible capital. Aave exemplifies this shift, positioning itself as a foundational layer for a new financial backend.

marsbit02/10 02:17

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

marsbit02/10 02:17

AI Models Are Evolving Rapidly, How Can Workers Overcome 'AI Anxiety'?

AI models and tools are evolving rapidly, creating a sense of anxiety among professionals who feel pressured to keep up. The root of this "AI anxiety" isn't the pace of change itself, but the lack of a filter to distinguish what truly matters for one's work. Three key forces drive this anxiety: the AI content ecosystem thrives on urgency and hype, loss aversion makes people fear missing out, and too many options lead to decision paralysis. The solution is not to consume more information, but to build a personalized filtering system. "Keeping up" doesn't mean testing every new tool on day one; it means having a system to automatically answer: "Is this important for *my* work?" Three practical strategies are proposed: 1. **Build a "Weekly AI Digest" Agent:** Use automation (e.g., n8n) to gather news from trusted sources, then use an AI to filter it based on your specific job role and tasks. This delivers a concise weekly report of only the relevant updates. 2. **Test with *Your* Prompts:** When a new tool seems relevant, test it using your actual work prompts, not the vendor's perfect demos. Compare the results side-by-side with your current tools to see if it's truly better for your workflow. 3. **Distinguish "Benchmark" vs. "Business" Releases:** Most announcements are "benchmark releases" (improvements on standardized tests) that have little real-world impact. Focus only on "business releases" that offer new capabilities you can use immediately. Combining these strategies transforms AI updates from a source of stress into a manageable advantage. The real competitive edge lies not in accessing every new model, but in knowing what to ignore and what to test deeply for your specific work. The key is to stop trying to follow everything and start filtering for what truly matters.

marsbit02/09 12:19

AI Models Are Evolving Rapidly, How Can Workers Overcome 'AI Anxiety'?

marsbit02/09 12:19

活动图片