# AI Related Articles

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

Chip Stocks Hit Record Highs Since 2000, SaaS Stocks Fall to Yearly Lows: Two Worlds Under the AI Divide

The article highlights a stark divergence in the tech market driven by AI: semiconductor stocks are surging while SaaS stocks plummet. On April 23, Texas Instruments saw its best single-day performance since 2000, with Q1 revenue up 19% and data center revenue surging 90%. Intel also reported blowout results, with revenue far exceeding expectations and its stock rising over 20% after hours. The semiconductor ETF (SMH) is up nearly 28% this year. In contrast, SaaS companies like ServiceNow and IBM experienced historic declines, with ServiceNow dropping 18% despite beating earnings estimates. The software ETF (IGV) has entered a technical bear market, losing about $2 trillion in market cap. The trend, dubbed "SaaSpocalypse," reflects fears that AI agents could reduce the need for software licenses and enable companies to build internal tools instead of relying on SaaS subscriptions. The market is rotating capital from software to semiconductors, betting on AI infrastructure as a safer play. Chip stocks now trade at high valuations—Texas Instruments at a P/E over 50, Intel at 120 times forward earnings—pricing in years of growth. However, this depends on continued massive AI capital expenditure from tech giants. If spending slows, the trend could reverse. For now, the divide persists: chips celebrate, SaaS struggles.

marsbit04/24 07:59

Chip Stocks Hit Record Highs Since 2000, SaaS Stocks Fall to Yearly Lows: Two Worlds Under the AI Divide

marsbit04/24 07:59

OpenAI Goes Left, DeepSeek Goes Right

On April 24, 2026, DeepSeek released V4, a Chinese large language model offering a free "million-token context window," enabling it to process vast amounts of data like entire books or years of corporate documents in one go. In contrast, OpenAI’s GPT-5.5, released around the same time, is more powerful but significantly more expensive, charging up to $180 per million output tokens. DeepSeek’s strategy represents a shift from a pure AI research firm to a heavy-infrastructure player, building data centers in Inner Mongolia’s Ulanqab to bypass U.S. chip export restrictions. This move, supported by Huawei’s Ascend chips and China’s cheap green electricity, highlights a fundamental divergence in AI development models: U.S. firms focus on high-cost, high-margin services, while Chinese players like DeepSeek prioritize accessibility and affordability. Facing intense talent poaching from tech giants, DeepSeek is seeking a $44 billion valuation funding round to retain researchers and scale infrastructure. Meanwhile, Chinese manufacturers are compressing AI models to run on smartphones, making AI accessible offline and across the Global South. Through open-source models and localized solutions, Chinese AI is empowering non-English speakers and low-income users, driving a form of "digital equality." While Silicon Valley builds walled gardens, DeepSeek and others are turning AI into a public utility—like tap water—flowing freely to those previously left behind.

marsbit04/24 07:33

marsbit04/24 07:33

Bitwise: Bullish on Bitcoin's Performance in the Second Half of the Year, AI and Regulation Will Spark a New Altcoin Season

Bitwise CIO Matt Hougan and Research Lead Ryan Rasmussen express strong bullish sentiment on Bitcoin's long-term prospects, suggesting that its $1 million price target may be too conservative. They argue Bitcoin serves a dual role: as digital gold and a potential global settlement asset, especially amid declining trust in traditional monetary systems. Despite a weak Q1 2026 where nearly all crypto assets and prices saw double-digit declines, the analysts remain optimistic due to strong forward-looking catalysts, including institutional adoption via Bitcoin ETFs from major firms like Morgan Stanley and Goldman Sachs. Geopolitical instability, such as Iran’s mention of using Bitcoin for international payments, increases the value of Bitcoin’s “out-of-the-money call option” as a non-political, global settlement currency. This enhances its appeal beyond a mere store of value. . Additionally, Hougan highlights that a clearer regulatory token framework under current SEC leadership, combined with AI efficiency gains and high-performance blockchains, could fuel a new “altseason” by late 2026. This may lead to a wave of legitimate, value-capturing token projects, unlike the earlier ICO boom. . Bitwise also announced an Avalanche ETF, citing its unique architecture and rapid growth in real-world asset (RWA) tokenization, which has surged 10x to nearly $30 billion in two years. The firm believes Layer 1 blockchains are still early in their growth cycle, with significant potential ahead.

marsbit04/24 05:37

Bitwise: Bullish on Bitcoin's Performance in the Second Half of the Year, AI and Regulation Will Spark a New Altcoin Season

marsbit04/24 05:37

Intel Soars 20%, CPUs Return to Center Stage in the Agent Era

Intel's stock surged 20% after reporting exceptional Q1 2026 results, with revenue of $13.6 billion (up 7% YoY) and non-GAAP EPS of $0.29, beating expectations by 29x. The rebound is driven by the resurgence of CPUs in the AI era, particularly as workloads shift from training to inference and agent-based applications. Intel’s Data Center and AI (DCAI) division hit a record $5.1 billion in revenue, up 22% YoY, marking a U-shaped recovery since mid-2025. The growth is attributed to strong demand for Xeon 6 "Granite Rapids" processors and increased AI infrastructure refresh cycles. While NVIDIA and AMD dominated the AI training phase, Intel is benefiting from the focus on AI agents, where CPU performance becomes critical—accounting for 50-90% of workflow latency in agent orchestration. This shift, coupled with management changes and strategic refocus on CPUs (including canceling the Falcon Shores GPU project), has repositioned Intel. New CEO Lip-Bu Tan emphasized that CPUs are re-establishing themselves as essential infrastructure in the AI era.

marsbit04/24 04:39

Intel Soars 20%, CPUs Return to Center Stage in the Agent Era

marsbit04/24 04:39

DeepSeek V4 Finally Released, Breaking the Strongest Closed-Source Monopoly, Explicitly Partnering with Huawei Chips

DeepSeek AI has officially released DeepSeek-V4, available in two versions: the high-performance **DeepSeek-V4-Pro** (49B activated parameters, 1.6T total) and the more efficient **DeepSeek-V4-Flash** (13B activated parameters, 284B total). Both support a 1M context length, making long-context capability a baseline feature rather than a premium offering. The Pro version rivals top closed-source models in agent capabilities, world knowledge, and reasoning performance. It outperforms Claude Sonnet 4.5 in agentic coding and approaches Claude Opus 4.6 (non-thinking mode) in quality. The Flash version offers competitive performance at a lower cost, though it lags in highly complex tasks. A key technical innovation is a new attention mechanism that reduces computational and memory demands for long contexts. The models are optimized for agent frameworks like Claude Code and OpenClaw. API services are available with support for both OpenAI and Anthropic-style interfaces. DeepSeek also announced upcoming support for Huawei’s computing hardware in the second half of the year. The models are open-sourced on Hugging Face and ModelScope.

marsbit04/24 04:21

DeepSeek V4 Finally Released, Breaking the Strongest Closed-Source Monopoly, Explicitly Partnering with Huawei Chips

marsbit04/24 04:21

More and More 'Model Supermarkets' Are Opening: ByteDance, Alibaba, and Tencent Compete to Integrate

Chinese tech giants like ByteDance, Alibaba, and Tencent are accelerating the rollout of integrated AI model subscription services—dubbed “model supermarkets”—to provide developers with bundled access to multiple leading domestic large language models (LLMs). ByteDance’s Volcengine recently upgraded its "Coding Plan" by adding newer models like GLM-5.1, Minimax M2.7, and Kimi k2.6, allowing subscribers to use various top models under a single monthly fee starting at ¥40. However, user feedback reveals significant issues, including rapid consumption of usage limits (e.g., hitting caps within hours), frequent server errors (like HTTP 429), and slow response times during peak hours. Complaints about misleading deduction rates—where calls to advanced models consume more quota—are also common. The trend is industry-wide: Alibaba, Tencent, and Baidu have all launched similar multi-model coding plans. While these platforms reduce trial costs for developers, they also expose challenges in balancing affordability with service quality and computational stability. Amid this shift, independent AI companies like Zhipu, MiniMax, and Moonlight Face (Kimi) are developing strategies to avoid becoming mere “pipes” in this ecosystem—focusing on vertical applications, autonomous agents, and long-context models to retain competitiveness. Analysts suggest that, while platform aggregation may pressure model firms in the short term, specialized and vertical AI capabilities will remain differentiated in the long run.

marsbit04/24 04:07

More and More 'Model Supermarkets' Are Opening: ByteDance, Alibaba, and Tencent Compete to Integrate

marsbit04/24 04:07

OpenAI Launches Workflow Agent, GPTs Begin Countdown

OpenAI has launched Workspace Agents, an evolution of GPTs designed for teams. This new feature allows users to create reusable, shareable agents that automate repetitive workflows. Driven by Codex, these agents operate within their own workspace, can access files, call tools, and run continuously in the background. Users describe a workflow—such as collecting information, making judgments, generating results, and sending outputs—to ChatGPT, which then builds the agent. The entire team can use and refine the agent via ChatGPT or Slack. Once set up, the agent runs autonomously. Workspace Agents are team-shared, code-free, and manageable with predefined permissions and controls. They are suitable for structured, repeatable tasks involving multiple tools and continuous operation, such as software review, feedback整理, report generation, sales follow-up, and risk assessment. The system operates within clear rules, with sensitive actions requiring human confirmation. It aims to transform workflows from being person-dependent to being documented, executable, and reusable—akin to scientific management principles. Competing solutions from Microsoft and Google are also emerging in this space.

marsbit04/24 00:50

OpenAI Launches Workflow Agent, GPTs Begin Countdown

marsbit04/24 00:50

The Richest Fed Chair in History? Three Challenges Kevin Warsh Is About to Face

Kevin Warsh, nominated by Trump as the next Federal Reserve Chair, faces three major challenges as he prepares to take office. With a personal investment portfolio exceeding $130 million, including holdings in crypto protocols and public chains, he is set to become the wealthiest Fed chair in history. Warsh, known for his historically hawkish stance, has recently shifted his position, arguing that AI is a powerful deflationary force that could boost productivity. However, rising CPI and energy prices pose a risk to this outlook. He strongly defended Fed independence, stating that it is eroded not by political pressure but by the institution’s own missteps, such as blurring the lines between monetary and fiscal policy during the 2021-2022 inflation period. Additionally, Warsh advocates for significant balance sheet reduction while potentially cutting rates—a combination that markets find unsettling. He also proposes modernizing inflation measurement with real-time data, including stablecoins and on-chain pricing, to improve policy responsiveness. His confirmation is currently delayed due to a political investigation into current Chair Powell, adding uncertainty to his appointment.

marsbit04/24 00:36

The Richest Fed Chair in History? Three Challenges Kevin Warsh Is About to Face

marsbit04/24 00:36

Rivaling a Documentary, OpenAI President Recounts the 72-Hour Internal Strife

OpenAI President Greg Brockman details the 72-hour internal crisis following CEO Sam Altman's sudden firing by the board. Both were blindsided by the decision, leading Brockman to resign immediately in solidarity. They began planning a new company at Altman’s home, aiming to take all OpenAI employees with them. Despite intense recruitment efforts by competitors, not a single employee accepted an external offer. The situation shifted when co-founder Ilya Sutskever publicly supported the employees' petition, leading to Altman’s reinstatement. Brockman reflects on OpenAI’s origins, its shift from non-profit to capped-profit for computational scale, key breakthroughs like Dota project and GPT, and the importance of resilience and mission-focused leadership. He emphasizes AI’s potential for global benefit and the need for broad access to compute power, while acknowledging risks and the necessity of iterative deployment for safety and alignment.

marsbit04/24 00:28

Rivaling a Documentary, OpenAI President Recounts the 72-Hour Internal Strife

marsbit04/24 00:28

AI "Transfer Station" Earning Millions Monthly? Five Questions Uncover the Truth of Token Arbitrage

The article "AI 'Transfer Station' Earns Millions Monthly? Five Questions Uncover the Truth of Token Arbitrage" explores the emerging business of API token transfer stations, which profit from global AI service price disparities and access barriers. These intermediaries purchase low-cost tokens from overseas AI providers (e.g., OpenAI, Claude) through grey-market methods—such as exploiting enterprise credits, bulk accounts, or subscription benefits—and resell them to Chinese users at a markup. Key drivers include the high cost of using top AI models (e.g., Claude Code costs ~$5 per million tokens), the performance gap between domestic and foreign models, and mismatches between subscription and API pricing. However, the practice carries significant risks: upstream token sources may be unstable or illegal; user data passing through intermediaries can be harvested or injected with hidden prompts; and models might be downgraded without disclosure. The market is evolving, with some operators now exporting cheaper Chinese models (e.g., Qwen3.5 at ~$0.11 per million tokens) to overseas users, leveraging price gaps. Yet, sustainability is low due to compliance crackdowns, instability, and reputational risks. Users are advised to employ detection methods (e.g., prompt adherence tests) and avoid sensitive data usage. The authors caution that while transfer stations offer short-term arbitrage, they lack long-term reliability and security compared to official APIs.

marsbit04/24 00:26

AI "Transfer Station" Earning Millions Monthly? Five Questions Uncover the Truth of Token Arbitrage