# Agents Related Articles

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

Blockchain has finally begun sailing toward the main channel after 18 years

After 18 years of development, blockchain technology is beginning to move from a specialized niche into mainstream adoption, according to a recent industry analysis. The shift is reflected in the changing strategies of major crypto venture capital firms, which are expanding their focus beyond pure "digital ownership" towards broader themes like "autonomy." The report highlights that leading VC firms like Variant, Paradigm, Haun Ventures, and YZi Labs are broadening their investment mandates to include not only crypto but also artificial intelligence (AI), robotics, biotech, and other frontier technologies. This reflects a recognition that the isolated "crypto investment" narrative is losing appeal to limited partners (LPs) as capital and attention increasingly flow toward AI and other high-growth tech sectors. A key emerging thesis is that blockchain's most significant future application may not be as a consumer-facing product, but as the underlying economic and settlement infrastructure for the AI era. As AI agents and autonomous systems become more prevalent, they will require programmable, global, and low-cost payment networks (like stablecoins), verifiable digital identities, and secure wallets to manage transactions and assets on behalf of users. The investment by stablecoin issuer Tether into robotics company NEURA, with plans to integrate its wallet technology, is cited as a prime example of this convergence. However, the article cautions that simply labeling projects as "AI + Crypto" is insufficient. True value lies in integrations where blockchain technology is essential—such as enabling machine-to-machine micropayments, verifiable data provenance for AI, or transparent governance for autonomous organizations—rather than being a superficial marketing add-on. In conclusion, while AI currently dominates the tech narrative and capital flows, it may ultimately create the real-world, high-frequency demand that the crypto industry has long sought. For crypto VCs and projects, the path forward is to position blockchain not as a competing sector, but as a critical foundational layer powering autonomy and economic activity in an AI-driven future.

链捕手06/15 10:04

Blockchain has finally begun sailing toward the main channel after 18 years

链捕手06/15 10:04

Claude Requires ID Verification and Facial Recognition? The Facial Recognition Requirement is an Old Story from Two Months Ago, and "Sharing Data with Police" is a Misinterpretation

Anthropic's updated privacy policy, effective July 8th, has sparked misinterpretations in Chinese social media, primarily concerning new identity verification and data sharing with law enforcement. A detailed comparison reveals these claims are largely unfounded. First, identity verification (including submitting government ID and a live selfie via third-party provider Persona) is not a new July policy. This mechanism was actually implemented in mid-April 2026 for certain high-use or flagged accounts, particularly Claude Max subscribers. The July update merely formally documents this existing practice in the policy text under a new "Verification Data" section. Second, the widespread claim that the new policy lowers the threshold for sharing user data with law enforcement is incorrect. Comparing the new text with the old version (dated September 28, 2025) shows no substantive tightening. While the new policy more clearly structures the conditions for disclosure—including having a "good-faith belief" it's necessary for legal compliance, preventing harm, fraud detection, or enforcing terms—the old policy already allowed Anthropic to disclose data based on its judgment for similar reasons (e.g., protecting safety, preventing fraud, or complying with law). The term "good-faith belief" acts as a limiting standard, not a lowered barrier. A 2025 court case where Anthropic resisted disclosing user data in a copyright lawsuit further demonstrates the complexity of such standards. The policy's actual substantial changes address data flows for Claude's Agent capabilities. New clauses clarify that when users connect third-party services or instruct Claude to perform multi-step tasks (reading files, sending messages), their inputs, outputs, and instructions are shared with those third parties, governed by the third parties' own policies. This update fills a compliance gap for Claude's evolving functionality beyond simple Q&A. Other additions include a "Research Participation Data" section and refined marketing legal bases. Anthropic reaffirms core commitments: not selling user data, keeping Claude ad-free, and allowing users to control if chats are used for model training. Overall, this update is primarily a compliance catch-up to existing product features, not a significant new privacy tightening. The heightened concern stems from conflating April's verification rollout, standard legal clauses, and the genuine new provisions regarding Agent tasks.

marsbit06/15 08:55

Claude Requires ID Verification and Facial Recognition? The Facial Recognition Requirement is an Old Story from Two Months Ago, and "Sharing Data with Police" is a Misinterpretation

marsbit06/15 08:55

Why 'AI Service Subscription' Is Destined to Die Out?

"Why 'AI Service Subscription Models' Are Doomed to Disappear" The article argues that the flat-rate subscription model for AI services is fundamentally unsustainable. It points to recent industry shifts, such as Anthropic limiting access to its flagship Claude Fable 5 model for subscribers after just 14 days, and GitHub and OpenAI moving towards credit-based or usage-based billing. The core problem is that subscription models rely on a capped human consumption limit—like watching videos or listening to music—which keeps costs predictable. However, the rise of autonomous AI agents shatters this premise. Agents can consume 5 to 30 times more computing resources (tokens) than a human chatting, and they operate continuously without user presence. This removes the natural usage cap, making fixed-price plans financially unviable as heavy users incur massive costs. Attempts to patch the model with higher tiers or usage caps have failed, often leading to "adverse selection" where only the heaviest users subscribe. The industry's solution is to hollow out subscriptions, replacing "unlimited" access with prepaid credits charged per token, akin to a utility meter. While chat-based subscriptions may linger, the real value and revenue are shifting to pay-as-you-go models. The current period represents a final, heavily subsidized phase for users. The conclusion is that the soul of subscription—a fixed price for worry-free use—is dying, soon to be replaced by pure usage-based pricing where everyone pays for their own "electricity meter."

marsbit06/15 03:23

Why 'AI Service Subscription' Is Destined to Die Out?

marsbit06/15 03:23

IC3 Top Universities Collaborative Analysis: Is AI x Crypto the Real Future or Just a Narrative Bubble?

IC3 researchers from leading universities analyze the convergence of AI and crypto. They argue meaningful integration is still nascent, with hype often outstripping progress. The report frames AI as a "translation middleware" making blockchain accessible, while crypto serves as a "trust middleware" via tools like ZK proofs and TEEs for integrity, availability, and confidentiality. Two main directions are examined: 1) **Crypto x AI**: Using AI to enhance blockchain via analysis (fraud detection), algorithmic design, and AI oracles (with accuracy varying by task). New risks include AI-driven malicious smart contracts. 2) **AI x Crypto**: Using crypto to enhance AI via decentralized infrastructure (DePIN), data markets, agent micropayments, governance, and securing AI pipelines (training/federated learning, secure inference). The "Protected Pipeline" (Props) framework combines oracles and trusted computation for secure use of private data. Key challenges are highlighted: The industry must rigorously prove decentralized AI's cost competitiveness and crypto's utility for agent payments. Major research gaps include providing systemic security for autonomous agents and addressing novel threats like unstoppable AI agents. The report concludes by debunking five common misconceptions: blockchain cannot inherently detect AI content, solve algorithmic bias, grant true AI autonomy, ensure AI trustworthiness through mere transparency, or guarantee that decentralization is always cheaper for AI tasks. The field remains in an early, evidence-seeking phase.

marsbit06/11 00:12

IC3 Top Universities Collaborative Analysis: Is AI x Crypto the Real Future or Just a Narrative Bubble?

marsbit06/11 00:12

From ChatGPT to Capital War: What Crypto Opportunities Are Hidden Behind OpenAI's Sprint Toward IPO?

From ChatGPT to Capital Wars: Hidden Crypto Opportunities Behind OpenAI's IPO Push On June 9th, OpenAI confirmed it has confidentially filed for an IPO with the U.S. SEC, alongside revealing a long-term roadmap aiming for AI to handle most of its own R&D by 2028. This move signals a shift in the AI industry from technological competition to a capital-intensive race, potentially evolving into an ecosystem war. For the crypto market, this event could mark the beginning of a new funding narrative. OpenAI's transformation from a non-profit research lab in 2015 to a commercial behemoth was catalyzed by ChatGPT's explosive growth in 2022. Its business now spans consumer AI assistants, enterprise APIs, and critically, massive AI infrastructure requiring trillions in investment by 2030. The core driver for the IPO is the immense cost of the AI arms race, primarily for GPU compute power for training and inference. With rivals like Anthropic also filing to go public and giants like Google and Meta investing heavily, competition is intensifying around capital, compute, and ecosystem scale. The crypto market, whose cycles have often been fueled by external narratives like DeFi and NFTs, may see a refocus towards "AI means of production." Key beneficiaries could include decentralized compute networks (e.g., Render, Akash) addressing GPU scarcity, AI Agent platforms enabling autonomous task execution, and projects tokenizing AI infrastructure/assets (AI x RWA). However, an OpenAI IPO could also create a capital drain from crypto, favoring projects with substantive utility over mere hype. Ultimately, OpenAI's IPO signifies AI's entry into a new era defined by resources. In this coming "gold rush," the biggest winners in crypto may be those providing the essential picks and shovels—the foundational compute, data, and economic layers for the AI age.

marsbit06/10 04:32

From ChatGPT to Capital War: What Crypto Opportunities Are Hidden Behind OpenAI's Sprint Toward IPO?

marsbit06/10 04:32

Casper Launches AI Toolkit for Autonomous Agents and Builders

The Casper Association has launched the Casper AI Toolkit, described as the most extensive AI offering on any Layer 1 blockchain. This toolkit enables AI agents to function as both autonomous economic actors and independent builders. As economic actors, agents can conduct micropayments in seconds via the x402 protocol, allowing them to pay for API calls and services directly without human intervention or traditional billing systems. They can also interact with the Casper network using the Model Context Protocol (MCP) for tasks like querying balances and executing DeFi transactions through natural language. As builders, AI agents can use the Odra Framework and CSPR.build Agent Skills to autonomously design, test, and deploy smart contracts from concept to mainnet, without human assistance. This creates a closed-loop "machine economy" where AI agents can both build services and pay to use services built by other agents. The release is the first major project from the Casper Manifest, a multi-year technological plan. The Casper Association is supporting adoption with a $150,000 "Agentic Buildathon" for developers, featuring sponsored transaction fees. Future development includes account abstraction to give agents their own on-chain identities and spending rights.

TheNewsCrypto06/04 13:18

Casper Launches AI Toolkit for Autonomous Agents and Builders

TheNewsCrypto06/04 13:18

Claude Code Introduces Dynamic Workflows: Enabling AI to Form Teams and Collaborate

Claude Code introduces dynamic workflows, enabling AI to coordinate teams of specialized agents for complex tasks. This transforms Claude from a code assistant into a programmable workbench. Workflows address key limitations of single-agent systems: agentic laziness (premature task completion), self-preferential bias (favoring own outputs), and goal drift (losing sight of original objectives). The system allows Claude to dynamically create execution frameworks using JavaScript. It can split tasks, dispatch parallel agents for isolated work (e.g., in separate worktrees), implement adversarial validation, run tournaments, and synthesize results. This multi-agent approach is valuable for tasks requiring deep research, factual verification, code migration, root cause analysis, large-scale triage, and qualitative sorting. Key patterns include: classify-and-route, fan-out-and-synthesize, adversarial verification, generate-and-filter, tournaments, and loop-until-done. While token usage is higher, workflows excel where tasks resemble programming—needing problem decomposition, isolated context, hypothesis testing, and handling many details. They extend Claude Code's utility beyond technical work to areas like business plan review, resume screening, and naming brainstorm. The feature is not a universal solution but points to a future where AI tool competitiveness depends on organizing reliable, reusable, and auditable execution flows for complex goals.

marsbit06/04 02:15

Claude Code Introduces Dynamic Workflows: Enabling AI to Form Teams and Collaborate

marsbit06/04 02:15

Pantera Partner: In the Age of Agents, Blockchain is the Inevitable Answer for AI

Summary: AI and blockchain are converging around four key pillars: payment settlement, identity systems, open systems, and resource aggregation, with commercial projects already emerging in each area. The two technologies are fundamentally complementary: AI enables infinite supply (content, agents), while blockchain establishes scarcity and verifiable ownership. AI agents generate content and services, and blockchain handles the verification and value settlement. A significant valuation mismatch exists, with leading AI companies historically overvalued compared to crypto assets, despite their deep underlying integration. The emergence of autonomous AI agents—which require assets, value transfer, and large-scale coordination—creates a need for a non-human-centric financial infrastructure. Blockchain, with its programmability, 24/7 access, and low-trust settlement, is the only suitable foundation. AI agents will not use traditional bank accounts or payment rails; they will transact using stablecoins and on-chain systems. Examples include OpenFX, which settles hundreds of billions in forex trades on-chain for AI agents, and Alchemy, a core development platform. For human identity verification in an age of AI-generated content, projects like World (Worldcoin) use blockchain-based biometric verification, while TransCrypts focuses on self-sovereign identity and verifiable credentials. The current divergence presents a unique investment opportunity. AI valuations are highly elevated, while crypto assets trade at a significant discount, even though the future smart agent economy will be built on blockchain infrastructure. The fusion of AI and blockchain is not a future trend but an ongoing reality, creating a prime environment for entrepreneurs in areas like agent-native finance, decentralized identity, and on-chain AI coordination.

marsbit06/02 13:12

Pantera Partner: In the Age of Agents, Blockchain is the Inevitable Answer for AI

marsbit06/02 13:12

AI Competition's New Battlefield: Long-term Memory Becomes the Pain Point, How Users Can Secure Their Own Context Ownership

A new front is emerging in the AI competition: user ownership of long-term memory and context. As AI models like ChatGPT evolve from chat tools into persistent digital assistants that learn user preferences and workflows, a critical question arises: who owns this accumulated "memory"? Currently, this personalized data is siloed within each platform (e.g., OpenAI, Anthropic, Google), creating a fragmented experience when users switch models. The article highlights ZetaChain's strategic pivot from blockchain interoperability to addressing this AI "memory" challenge. Its new focus is on building a "Private Memory Layer" and an "AI Consumer Layer." Through its consumer product Anuma, ZetaChain aims to give users encrypted, portable memory that can be used across different AI models. This system also envisions programmable, auditable permissions for AI agents and a framework where user knowledge can be monetized as shareable assets. Ultimately, ZetaChain's transformation reflects a broader infrastructure shift. The future bottleneck is less about raw model capability and more about continuous context, user-controlled identity, and permission management across multiple collaborating AI agents. The company's ZETA token is being repositioned as an "AI infrastructure token" to facilitate access, payments, and permissions within this proposed ecosystem. The core narrative advocates for returning control of personal context and AI relationships to users, rather than leaving them locked within proprietary platforms.

marsbit06/02 04:30

AI Competition's New Battlefield: Long-term Memory Becomes the Pain Point, How Users Can Secure Their Own Context Ownership

marsbit06/02 04:30

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit05/31 16:02

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit05/31 16:02

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