# AI Agents Related Articles

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

Which Crypto Sectors Have Been 'Eaten' by AI Agents?

The article examines the transformative impact of AI Agents on the cryptocurrency landscape, highlighting how specific sectors are becoming increasingly dominated by automated systems. Key "agent-eaten" sectors include derivatives trading (perpetuals), where AI agents demonstrate significantly higher survival and performance rates than human traders; MEV and arbitrage trading, which are almost entirely automated; yield optimization, with over two-thirds of new DeFi protocols incorporating AI agents; and spot trading/portfolio management, where agents drive a growing share of DEX volume. "Battleground" sectors like prediction markets and DeFi lending show a mix, with agents excelling in short-term/arbitrage activities but humans retaining an edge in longer-term, nuanced decisions. Sectors still primarily "human-led" include stablecoin payments/remittances (driven by real-world economic activity) and wallets, where human oversight for approvals and security remains critical. As AI agent activity grows, the article emphasizes the rising importance of human-agent verification layers (e.g., World/AgentKit, t54, Self Protocol) to ensure trust, accountability, and control in an increasingly agentic economy. The conclusion is that while AI agents dominate in speed and optimization-focused areas, human judgment, trust, and real-world context remain essential in value-creating layers like payments and identity.

marsbit06/22 10:09

Which Crypto Sectors Have Been "Eaten" by AI Agents?

The article examines which crypto sectors have been increasingly dominated by AI Agents and which remain human-centric. In certain high-speed, efficiency-driven areas, AI Agents have taken clear control. This includes derivatives/perpetuals trading, where bots outperform humans significantly (e.g., a contest showed 0% of AI Agents were liquidated vs. 43% of humans), arbitrage/MEV extraction, and yield optimization (with ~68% of new DeFi protocols in Q1 2026 featuring autonomous AI Agents). Spot trading and portfolio optimization are also seeing heavy Agent adoption. However, the shift is not universal. In "battleground" sectors, both Agents and humans coexist. In prediction markets, Agents dominate short-term arbitrage, but humans still outperform in long-term, nuanced judgment calls. In DeFi lending, while liquidation is automated, core deposit/borrow decisions remain largely human-driven. Sectors still firmly led by human activity include stablecoin payments and card-based spending (driven by real-world economic activity and remittances) and wallets, which serve as the crucial human-verification and approval layer. The rise of Agents increases the need for robust human-Agent verification layers. Projects like World/AgentKit, t54, Self Protocol, and Kite AI are building infrastructure to create trust, security, and accountability by binding Agents to verified human identities. In conclusion, while AI Agents have decisively "eaten" speed and optimization-focused crypto sectors, human judgment, trust, and real-world context remain dominant in areas that create broad economic value, such as payments and identity. The future likely involves a symbiotic relationship where Agents require human verification and oversight to operate effectively.

Foresight News06/22 07:10

Blockchain Has Finally Started to Sail into the Mainstream After 18 Years

Blockchain Finds Its True Path After 18 Years: Becoming the Financial Backbone for AI Agents and Autonomy This analysis explores a pivotal shift in the blockchain and crypto investment landscape, driven by the dominance of AI. Major venture capital firms, including Variant, Paradigm, Haun Ventures, and YZi Labs, are moving beyond pure "crypto" investment theses. They are expanding their focus to AI, robotics, and frontier tech, signaling that blockchain is no longer seen as a standalone sector but as an underlying infrastructure layer. The core argument is that blockchain's killer application may not be user-facing apps, but rather providing the economic rails for the coming wave of AI agents, autonomous robots, and automated systems. Key capabilities like self-custody wallets, programmable stablecoins for micropayments, on-chain identity, and verifiable smart contracts are positioned as essential for a future where machines conduct economic activity. The recent $1.4 billion investment by Tether (via its venture arm) in German robotics company NEURA Robotics exemplifies this, aiming to embed Tether's wallet tools directly into robots for autonomous transactions. While many "AI + Crypto" projects remain superficial, the article concludes that true value lies where crypto is a necessary component—enabling machine-to-machine payments, agent autonomy, verifiable data provenance, and open financial settlement for the AI era. For crypto venture capital, this convergence with AI represents both an adaptation to shifting capital flows and a potential path to unlocking the large-scale, non-speculative utility the industry has long sought.

marsbit06/15 10:10

Blockchain Has Finally Started to Sail into the Mainstream After 18 Years

marsbit06/15 10:10

It's Not Jensen Huang Who Wants to Change the PC, But the PC That's Revolting Against Itself

The 40-year-old PC industry is undergoing a fundamental transformation, driven by the rise of AI PCs. At the GTC Taipei 2026 event, NVIDIA, backed by Microsoft and major PC OEMs, announced the RTX Spark super chip for Windows PCs, marking its official entry into the PC core processor market. This move aims to redefine the AI PC by shifting its core from the CPU to an AI-focused SoC (System on Chip). NVIDIA envisions the PC evolving from a personal computer to a "personal AI"—a platform where local AI Agents can autonomously perform tasks. While Intel pioneered the AI PC concept earlier in 2026, NVIDIA's aggressive push, leveraging its vast CUDA developer ecosystem of 6 million, positions it to potentially reshape the industry's long-standing Wintel (Windows-Intel) power structure. NVIDIA's strategy extends beyond hardware; it's about embedding its CUDA, RTX, and AI software stack into the PC platform itself. The article identifies key shifts: 1) The move from a CPU-centric to an AI SoC-centric architecture, similar to Apple's approach with its M-series chips. 2) The PC's evolution from a human-operated tool to a platform for human-Agent collaboration. 3) The extension of NVIDIA's data center-centric CUDA ecosystem to personal devices via RTX Spark. Ultimately, the change is driven by the broader trend of AI moving to personal devices. Companies like Intel, AMD, Qualcomm, and Apple are all participating in this shift. NVIDIA's entry accelerates the competition, but the core driver is the technology itself finding its optimal expression in the PC. The industry is reinventing itself, with the outcome hinging on execution, ecosystem development, and the creation of compelling local AI applications.

marsbit06/12 11:14

It's Not Jensen Huang Who Wants to Change the PC, But the PC That's Revolting Against Itself

marsbit06/12 11:14

When AI Traffic Surpasses Humans, How Do You Prove You're Human?

With AI-generated web traffic surpassing human activity, websites face a crisis as AI agents bypass ads, avoid clicks, and scrape data without generating revenue. This disrupts the ad-based internet economy, diverting traffic and reducing site visits. In response, sites are blocking AI crawlers and deploying traps like Cloudflare's "honeypot" pages. Traditional CAPTCHAs are now ineffective against advanced AI. The focus has shifted to behavioral biometrics—analyzing unique human patterns such as cursor movement, typing rhythm, and keystroke dynamics. Companies like IBM and BioCatch use this data to distinguish humans from bots, even detecting fraud through behavioral inconsistencies. Two competing approaches aim to verify human identity centrally. Sam Altman’s World (formerly Worldcoin) uses iris scanning to create unique credentials, though it faces privacy concerns and regulatory bans. Alternatively, cryptographic zero-knowledge proofs offer anonymous verification without revealing personal data, championed by Vitalik Buterin to avoid centralized surveillance. However, both systems have flaws. Centralized solutions risk biometric data misuse, while decentralized models may be exploited through identity rental markets in economically unequal regions. Despite challenges, the author favors cryptographic methods for preserving privacy over pervasive behavioral monitoring that permanently captures and controls personal biometric data.

marsbit06/12 02:43

When AI Traffic Surpasses Humans, How Do You Prove You're Human?

marsbit06/12 02:43

2026 Landscape of Decentralized AI: Why is Blockchain the Inevitable "Antidote" for AI?

**The 2026 Landscape of Decentralized AI: Why Blockchain is the "Cure" AI Cannot Ignore** Decentralized AI addresses fundamental bottlenecks of centralized AI: scarce and expensive computational resources, excessive control concentration, unverifiable model outputs, and increasing difficulty in acquiring training data due to privacy and regulation. Blockchain offers a path to make intelligence open, verifiable, and economically accessible. The technical stack comprises three layers: 1. **Applications & Services**: The main crypto use cases are "Agentic Finance" (converting natural language into on-chain actions) and "Agentic Payments" for machine-to-machine commerce. Projects like Giza, Infinity Labs, Coinvest AI, and x402 (handling 173M+ transactions) are key players. 2. **Middleware**: This coordination layer enables agents to discover, identify, and transact. Notable projects include Gokite AI (specialized L1), Virtuals (an OS for the agent economy), and especially Bittensor—a network of specialized subnets forming competitive AI micro-economies. 3. **Infrastructure**: The capital-intensive layer providing raw resources. It includes decentralized compute (Akash, Render, Aethir), verifiable inference (Venice AI, OpenGradient), distributed training (Prime Intellect, Templar AI), decentralized storage (Filecoin, Walrus), and privacy/verification layers (Nillion, Arcium, Phala Network) using technologies like ZKPs, MPC, and TEEs. The outlook for 2026-2027 indicates AI demand outpacing infrastructure, with AI agents as a primary growth engine. Computation is becoming an asset class, with on-chain markets as its financial layer. Tokenomics is emerging as a structural advantage for coordinating capital, compute, and data in decentralized AI networks. While still early—with adoption uneven and revenue often trailing token incentives—projects like Bittensor, NEAR, and Virtuals demonstrate a shift from speculative narrative to a new model for coordinating intelligence.

marsbit06/12 02:40

2026 Landscape of Decentralized AI: Why is Blockchain the Inevitable "Antidote" for AI?

marsbit06/12 02:40

The 2026 Landscape of Decentralized AI: Why Blockchain is the Inevitable 'Antidote' for AI?

Decentralized AI 2026 Landscape: Why Blockchain is AI's Essential "Antidote" Centralized AI faces structural bottlenecks—expensive compute, concentrated control, unverifiable outputs, and difficult data access—that cannot be solved by capital or code alone. Blockchain offers a path to make intelligence open, verifiable, and economically accessible. The decentralized AI stack comprises: * **Infrastructure:** The foundation with compute, verifiable inference, distributed training, data/storage, and privacy/verification layers. Projects like Akash, Render, and Filecoin provide cheaper, decentralized alternatives for raw resources. * **Middleware:** The coordination layer for agent discovery, identity, and commerce. Key players include Bittensor (a network of specialized AI subnets), Virtuals (an agent economy OS), and frameworks providing agent identity and tooling. * **Applications & Services:** Dominated by Agentic Finance (AI agents executing on-chain actions based on natural language) and Agentic Payments (machine-to-machine transactions using blockchain as a settlement layer). Projects like Giza, Infinit Labs, and x402 are enabling these use cases. Key trends for 2026-2027 show AI demand outgrowing infrastructure, compute becoming an asset class, and tokenomics emerging as a structural advantage for coordinating capital, compute, and data. While still early—with adoption uneven and revenue often trailing token incentives—projects like Bittensor, NEAR, and Venice demonstrate decentralized AI is evolving from a narrative into a new model for coordinating intelligence.

Foresight News06/11 10:02

The 2026 Landscape of Decentralized AI: Why Blockchain is the Inevitable 'Antidote' for AI?

Foresight News06/11 10:02

AI Agent Completely Transforms Web3 Gaming: From the Rugpull Bakery Bot Controversy to the New 2026 Agent Paradigm

This article explores how the AI Agent paradigm is fundamentally transforming Web3 gaming, moving from a disruptive force to a core, legitimized element. It begins with the controversy in the competitive baking game Rugpull Bakery, where automated scripts caused fairness issues. Instead of banning them, the developers integrated AI Agents into the official gameplay by providing technical documentation (skill.md, agent.json), marking a shift towards "Agentic Gaming." The piece outlines three primary implementation models for AI Agents in Web3 games by 2026: 1. **Autonomous Competitors & Economic Entities:** AI Agents act as independent players with unique strategies. Examples include TEN Protocol's poker agents, AI Arena's trainable NFT fighters, and Satoshi Strike Force's "Digital Athletes" trained on player data. The Somnia blockchain is highlighted as a dedicated "Agentic L1" infrastructure supporting this model at scale. 2. **Modular Infrastructure & Programmable Environments:** This model, exemplified by EVE Frontier, allows AI Agents to program game world logic itself. Using "Smart Assemblies" (e.g., Smart Turrets, Smart Gates), Agents can modify shared economic and physical rules on-chain, creating dynamic, player/AI-built worlds. The ERC-8183 standard further enables these automated entities to hire other AI services for complex tasks. 3. **Hybrid Companions & Dynamic Adaptation:** Here, AI serves as a collaborative partner. In Parallel Colony, highly autonomous AI Avatars work alongside human players who provide high-level guidance. Illuvium plans to use AI to make NPCs dynamic and responsive, creating personalized, emergent narratives for each player. The conclusion posits that Web3 gaming has reached a "post-human" inflection point. Blockchains' transparency and programmability, combined with new standards and infrastructure like Somnia, make integrating and governing AI Agents not just viable but essential. The future lies in a symbiotic digital order where players transition from manual laborers to commanders and partners of algorithmic intelligence.

marsbit06/09 08:08

AI Agent Completely Transforms Web3 Gaming: From the Rugpull Bakery Bot Controversy to the New 2026 Agent Paradigm

marsbit06/09 08:08

The First Case on AI Agents: What Was Adjudicated?

"The First 'Agent' Ruling: What Was Decided?" On April 30, the Guangzhou Internet Court issued a ruling—China's first behavior preservation order in the intelligent agent (AI agent) field. The defendant, an open-source AI agent software, was ordered to stop downloads, cease actions that bypassed a platform's technical protection measures, and delete related tutorials and data. The core issue: the software used the operating system's "accessibility service" permissions to automate user interactions within other apps without those platforms' authorization. This mirrors a recent US case where Amazon sued Perplexity for similar reasons—bypassing Amazon's API to directly scrape and interact with its pages—and won a preliminary injunction. Both rulings establish a crucial legal boundary for the AI agent era: agents cannot operate unchecked. The article argues the fundamental legal principle emerging is one of **dual authorization**. An AI agent requires both **user consent** AND **platform consent** to operate legitimately within that platform's ecosystem. Bypassing platform rules through system-level permissions, even with user permission, undermines platform responsibilities for content moderation, data security, and user privacy, creating liability issues. The piece uses the evolution of "Doubao Phone" (an AI-integrated smartphone) as a case study. Its initial, aggressive version that bypassed platform controls faced roadblocks. Its upcoming 2.0 version is reportedly pivoting to negotiate API access and authorization deals with major platforms (like Alibaba's ecosystem), seen as a strategic adaptation to the new regulatory reality. A global trend is identified: the era of unregulated, "wild west" growth for AI agents is ending, replaced by a **compliance race**. This raises barriers to entry, as securing platform authorizations becomes a new cost. Open-source status is also not a legal shield if the code facilitates bypassing technical protections. In conclusion, these first rulings target not the largest, but the most **aggressive and representative** cases. By setting precedent with them, regulators are efficiently steering the entire industry towards a new, more regulated operating paradigm defined by dual authorization and platform cooperation.

marsbit06/08 10:31

The First Case on AI Agents: What Was Adjudicated?

marsbit06/08 10:31

From Payment to Deployment: Stripe Bets on the AI Agent Economy

From Payments to Deployment: Stripe Bets on the AI Agent Economy Stripe is redefining economic infrastructure for the AI era, shifting its focus from serving primarily human users and software companies to enabling machine agents as active economic participants. The core thesis is that AI agents are evolving from tools into independent buyers and builders on the internet, necessitating a complete overhaul of traditional payment, billing, and deployment models. To empower agents as **buyers**, Stripe, in collaboration with Tempo, developed the Machine Payments Protocol. This protocol allows businesses to programmatically accept payments from agents without human intervention, using machine-readable payment instructions. Furthermore, Stripe's consumer wallet, Link, is being adapted to let users securely authorize agents to spend on their behalf. To empower agents as **builders**, Stripe Projects aims to simplify the deployment process. It allows developers and their agents to register, manage, and integrate the services needed to deploy applications directly from the command line, making "vibe-deploying" as seamless as "vibe-coding." This agent-driven economy, where products have real, variable costs (like AI tokens), disrupts traditional SaaS models. **Token-based monetization** is becoming central, requiring usage-based billing that charges for actual resource consumption, as seen with companies like Lovable and ElevenLabs. However, this model introduces new challenges like **token theft**, where fraudsters exploit services and vanish before billing. Stripe Radar helps combat this by assessing new accounts and predicting abuse risks. A critical innovation to balance customer experience and financial risk is **streaming payments**. By combining Metronome (for real-time usage tracking) with Tempo (for low-cost, high-frequency stablecoin payments), Stripe enables AI companies to collect fees *as tokens are consumed*. This eliminates the trade-off between imposing hard usage caps and risking unpaid invoices. In summary, Stripe's vision for AI economic infrastructure now encompasses providing a commercial framework for agents, wallets for agents, deployment tools for agents, token-based billing, fraud prevention for token abuse, and streaming payment capabilities. As AI transforms both commerce and software creation, Stripe is building the foundational infrastructure to support it.

marsbit06/08 00:16

From Payment to Deployment: Stripe Bets on the AI Agent Economy