# Crypto AI İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Crypto AI" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

Comprehensive Analysis of the AI Inference Market: How Can Crypto Projects Break Through?

"AI Inference Market: A Strategic Overview and Crypto's Path to Disruption" The AI inference market, where trained models generate responses to user prompts, is now the primary economic driver, surpassing model training in value. This market is fragmented: hyperscalers (AWS, Google, Microsoft) dominate enterprise reliability; specialized providers (Together, Fireworks) optimize performance; and routing platforms like OpenRouter act as critical bottlenecks, dynamically allocating requests based on cost, latency, and privacy. Crypto AI networks are not competing directly on reliability but are carving out distinct niches: permissionless access, lower-cost supply, privacy, verifiable computation, and agent-native payments. Key projects include Chutes (decentralized inference platform), Akash & io.net (GPU marketplaces), Targon (confidential computing), Darkbloom & Venice (private, consumer-focused inference), and NuNet (orchestration for distributed workloads). The core differentiator is that traditional providers sell trust and enterprise workflows, while crypto networks offer new incentive loops, censorship resistance, and programmable access to resources like compute. For crypto projects to succeed, key metrics are paid token volume (not just usage), sustainable GPU provider revenue, integration into routers like OpenRouter, robust verification against fraud, and genuine privacy guarantees. Ultimately, market control will belong to entities that route, verify, and settle demand—not just those supplying raw compute. The inference market is evolving to resemble a financial system, with tokens as units of account, and crypto's unique value propositions position it to capture emerging segments in this expanding landscape.

Foresight News06/25 07:08

Comprehensive Analysis of the AI Inference Market: How Can Crypto Projects Break Through?

Foresight News06/25 07:08

Deep Insight: Decentralized Inference is Not Hype, but a Key Track for AI to Break Through Centralized Monopoly

Decentralized Reasoning: Beyond the Hype, a Key to Breaking AI's Centralized Monopoly A future scenario where a powerful AI model is banned by a major government illustrates the core value proposition of decentralized AI: resistance to censorship. The core bet of decentralized inference networks is mitigating this risk, with other benefits like cost being secondary. The path is extremely difficult, involving four key challenges: 1. **Running Massive Models:** Distributing a single model across a decentralized GPU swarm requires sophisticated techniques like pipeline and speculative decoding to overcome crippling network latency, aiming for usable speeds (e.g., 30-40 tokens/second). 2. **Proving Model Integrity:** Verifying that a node runs the correct model is critical. Solutions range from cryptographically secure but slow ZKML to faster, economically-secure methods like statistical fingerprints, deterministic re-execution, or live-weight proofs, each involving trade-offs between integrity, latency, and cost. 3. **Ensuring Prompt Privacy:** Simply sharding a model does not protect user inputs from nodes. Robust solutions currently require trusted hardware (TEEs) or advanced cryptography (FHE), which are not yet widely deployed in consumer swarms. 4. **Building a Real Market:** Identifying the ideal customer is tough. Beyond speculative AI agents, the viable market currently consists of startups embedding AI and projects needing batch processing (e.g., synthetic data generation), where decentralized aggregation can be an advantage over low-latency needs. The article analyzes several projects tackling these problems, such as Dolphin Network (live-weight proofs), Inference.net (statistical verification), Morpheus (TEE-based), and Darkbloom (Apple Secure Enclave). It provides a framework: decentralization is a "tax" for latency-sensitive applications (e.g., chat) but a potential supply-side advantage for throughput-oriented tasks (e.g., batch processing). The long-term vision is a closed data loop where decentralized inference generates valuable data (traces, preferences) to feed decentralized training networks, which in turn produce better open-weight models for the inference networks. A due diligence checklist advises focusing on projects that: are truly decentralized at specific layers; have a credible integrity method; offer real cost benefits; ensure genuine privacy; handle node reliability; have paying users; and are built by teams with deep AI expertise. The ultimate goal should be products that appeal beyond the crypto-native audience, using crypto mechanisms invisibly to deliver better cost, performance, or privacy.

Foresight News06/23 10:36

Deep Insight: Decentralized Inference is Not Hype, but a Key Track for AI to Break Through Centralized Monopoly

Foresight News06/23 10:36

A Company Once on the Verge of Collapse Now Has a Market Cap Surpassing Bitcoin

"Once nearly bankrupt, SK Hynix now surpasses Bitcoin in market cap." On June 22, SK Hynix's market value reached $1.35 trillion, exceeding Bitcoin's $1.29 trillion, driven by soaring demand for its High-Bandwidth Memory (HBM), a critical component for AI training. As NVIDIA's primary HBM supplier with over 60% market share, the company reported a 72% operating profit margin in Q1. Its remarkable turnaround stems from a 13-year bet on HBM technology, initiated in 2009 and rescued by SK Group's acquisition in 2012. The company plans a Nasdaq listing, potentially attracting more institutional capital. This milestone highlights a market preference for tangible AI infrastructure assets—like HBM with its physical scarcity and verifiable revenue—over narrative-driven crypto-AI projects, which remain in early developmental stages and lack comparable proof of scalable demand or economic models. The capital shift underscores the premium placed on real-world supply constraints and profitability in the current AI boom.

marsbit06/22 12:03

A Company Once on the Verge of Collapse Now Has a Market Cap Surpassing Bitcoin

marsbit06/22 12:03

After Collaborating with 35+ DeFi Projects, Pink Brains Discovers the New 2026 KOL Marketing Rules

After collaborating with over 35 leading DeFi projects on marketing over three years, Pink Brains identifies a key shift for effective marketing in 2026: prioritizing the user journey over traditional campaign tactics. The most effective marketing mirrors how users actually behave—starting with discovery on social platforms like X (formerly Twitter), followed by data-driven verification on sites like DefiLlama, and finally, participation with small test funds. Success hinges on genuine, verifiable mechanisms, not just marketing hype. Current user interest centers on several key themes: new DeFi trends (RWA, perps, crypto x AI), meaningful airdrops requiring real contributions, real yield from protocol revenue, and tokens with value capture mechanisms directly tied to product usage. Case studies like Hyperliquid's HYPE (with its aggressive buyback program) and Venice's VVV (linking demand to AI compute) exemplify how strong tokenomics foster user retention. New trading venues like prediction markets, collectibles platforms, and GambleFi are also gaining traction, driven by verifiable activity. The article outlines common mistakes in DeFi KOL marketing, such as using creators unfamiliar with the product, generic messaging, or relying on a few top-tier KOLs. Instead, effective strategies align with different KOL types—educators, content creators, airdrop hunters, and niche experts—for various stages of the user journey. Ultimately, long-term user retention depends on a combination of a genuinely useful product, responsive support, community-aligned tokenomics, and strategic community building. The core takeaway is that sustainable growth stems from products whose value is validated by data and real-world utility, not just promotional efforts.

marsbit06/03 10:23

After Collaborating with 35+ DeFi Projects, Pink Brains Discovers the New 2026 KOL Marketing Rules

marsbit06/03 10:23

The Age of Decoupling Has Arrived: Bitcoin is No Longer the Sole Compass of Crypto

The era of the cryptocurrency market moving in lockstep with Bitcoin is ending, as the industry splits into two distinct asset categories: endogenous and exogenous. Endogenous assets, like Bitcoin, derive value purely from the crypto market's cycles. Their narratives swing between being "interstellar money" in bull markets and "digital collectibles" in bear markets. Exogenous assets, however, are nominally crypto but operate with independent value drivers. Examples include: * **Venice:** An AI inference service using tokens for payments; its consumer-AI business model is decoupled from crypto price swings. * **Figure:** A fintech lender using blockchain to speed up loan approvals; its core value is in credit, not crypto. * **Stablecoin firms like BVNK:** Acquired by traditional finance giants (Mastercard, Stripe), their growth is tied to payment infrastructure, not market cycles. Hybrid projects like **Hyperliquid** (a decentralized exchange) show a shift, with a growing share of non-crypto trading (e.g., prediction markets). This divergence is fundamental. Endogenous assets remain highly correlated to Bitcoin, similar to gold miners to gold. Exogenous assets are evolving to have their own fundamentals, like the weak correlation between gold and the S&P 500. This changes investment analysis. Evaluating exogenous assets requires traditional fundamental research—assessing user bases, unit economics, and moats—more akin to fintech investing than charting Bitcoin. Promising exogenous sectors include: on-chain exchanges/brokers, AI-crypto fusion, privacy-focused digital banks, lending (institutional/private credit), stablecoins/real-world asset tokenization, payment rails, and non-financial crypto-consumer products. Currently, investing via equity is often safer than via tokens, as token value accrual mechanisms need further regulatory and industry development (e.g., the CLARITY Act). Nonetheless, the core trend is clear: crypto market drivers are diversifying from a single factor (Bitcoin) to multiple fundamentals, ending the era of uniform market moves.

marsbit06/01 13:06

The Age of Decoupling Has Arrived: Bitcoin is No Longer the Sole Compass of Crypto

marsbit06/01 13:06

Rhythm X Zhihu Co-host Web4.0 Theme Event: When AI Agent Takes Over On-Chain Permissions

Most discussions about Web 4.0 miss the point. The real question is not whether it is a marketing trend, but rather: who is gaining control over the underlying permissions of the internet? Historically, each iteration of the web has involved a transfer of authority downward: Web 1.0 was read-only; Web 2.0 allowed users to write but platforms owned the data; Web 3.0 enabled true ownership through on-chain assets and private keys. Web 4.0 continues this trend, but the transfer is not to users—it is to AI Agents. The current infrastructure is human-centric, designed around human limitations like attention span and memory. But AI Agents don’t need intuitive UIs, password resets, or sleep. This creates a core tension: an internet built for humans is now being used by entities without human constraints. Two key shifts are underway: the decline of traditional front-end interfaces (replaced by API-driven machine communication) and the replacement of human-centric identity systems (like passwords) with granular, on-chain permissions. A critical enabler is crypto infrastructure. AI can make rapid decisions but lacks independent payment channels and asset sovereignty. Crypto fills this gap. Platforms like Hyperliquid offer 24/7 markets, ideal for non-stop Agent operation. When Agents control wallets and private keys, they can both decide and execute—forming complete economic entities. The real narrative of Crypto × AI isn’t just buzzword synergy—it’s the convergence of complementary infrastructures. The deeper shift is not which products will succeed, but how the rules of economic systems will change when AI becomes a primary on-chain participant, operating at scale and speed beyond human capability.

marsbit04/01 09:10

Rhythm X Zhihu Co-host Web4.0 Theme Event: When AI Agent Takes Over On-Chain Permissions

marsbit04/01 09:10

2025 Crypto x AI Year in Review: Which Narratives Survived?

The 2025 Crypto x AI landscape saw a shift from speculative narratives to practical, utility-driven applications. Key surviving narratives include DeFAI (Decentralized Finance AI), which evolved through three phases: abstraction layers (simplifying DeFi interactions), autonomous yield agents (managing "set-and-forget" strategies), and AI Vaults (auditable, agent-generated smart contracts). Projects like Giza and Almanak led this space. AI agents, initially overhyped for entertainment, declined due to lack of utility but resurged with new standards like Coinbase's x402 and Ethereum's ERC-8004, enabling verifiable, decentralized agent economies. Decentralized AI (DeAI) emerged as a critical pillar, with Bittensor leading a "Darwinian AI" ecosystem that coordinates global resources for model training, reinforcement learning, and privacy-preserving computation. This infrastructure supports商业化 AI agents and enhances DeFi and prediction markets. Prediction markets integrated AI for forecasting and liquidity provision, though they face challenges like limited liquidity and edge decay. Overall, the trend moved from speculation to infrastructure, with 2026 poised for crypto-native AI products gaining indispensability.

深潮12/15 07:18

2025 Crypto x AI Year in Review: Which Narratives Survived?

深潮12/15 07:18

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