# Сопутствующие статьи по теме DeepSeek

Новостной центр HTX предлагает последние статьи и углубленный анализ по "DeepSeek", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

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

OpenAI Goes Left, DeepSeek Goes Right

marsbit04/24 07:33

20 Billion Valuation, Alibaba and Tencent Competing to Invest, Whose Money Will Liang Wenfeng Take?

DeepSeek, an AI startup founded by Liang Wenfeng, is reportedly in talks with Alibaba and Tencent for an external funding round that could value the company at over $20 billion. This marks a significant shift, as DeepSeek had previously relied solely on funding from its parent company,幻方量化 (Huanfang Quantitative), and had resisted external investment. The potential valuation would place DeepSeek among the top-tier AI model companies in China, comparable to competitors like MoonDark (valued at ~$18 billion) and ahead of recently listed firms like MiniMax and Zhipu. The funding—which could range from $600 million (for a 3% stake) to $2 billion (for 10%)—is seen as a move to secure resources for model development, retain talent, and support infrastructure needs, particularly as competition in inference models and AI agents intensifies. Both Alibaba and Tencent are eager to invest, not only for financial returns but also to integrate DeepSeek into their broader AI ecosystems. However, DeepSeek’s leadership is cautious about maintaining independence and may prefer financial investors over strategic ones to avoid being locked into a specific tech ecosystem. Alternative options, such as state-backed funds, offer longer-term capital and policy support but may come with slower decision-making and potential constraints on global expansion. With competing AI firms accelerating their IPO plans, DeepSeek’s window for securing optimal terms may be narrowing. The final decision will reflect a trade-off between capital, resources, and strategic independence.

marsbit04/23 09:53

20 Billion Valuation, Alibaba and Tencent Competing to Invest, Whose Money Will Liang Wenfeng Take?

marsbit04/23 09:53

China's Chips, the Hidden Intersection of DeepSeek and Kimi

China's AI startup Kimi has released its latest open-source model, K2.6, featuring significant improvements in coding and agent capabilities. This version enhances long-context processing, supports up to 4,000 lines of code generation, and improves API accuracy and multi-agent collaboration, allowing up to 300 sub-agents to work in parallel. Kimi also introduced architectural innovations like MuonClip, Kimi Linear, and Attention Residuals to optimize scaling and efficiency. A key development is the "Prefill-as-a-Service" (PrfaaS) approach, which decouples prefill and decode tasks across data centers using heterogeneous hardware, significantly reducing latency and cost per token. This strategy not only improves performance but also creates opportunities for integration with domestic AI chips, as reliance on Chinese hardware grows due to export restrictions. Both Kimi and DeepSeek are increasingly aligning with local semiconductor advancements, shaping a new ecosystem for AI development in China.

marsbit04/22 06:58

China's Chips, the Hidden Intersection of DeepSeek and Kimi

marsbit04/22 06:58

To Counter Chinese Models, Silicon Valley's Big Three Even Formed an 'Avengers Alliance'?

In a rare move, Silicon Valley's AI giants—OpenAI, Anthropic, and Google—have formed a coalition called the "Frontier Model Forum" to combat what they term "adversarial distillation." This practice involves companies, particularly Chinese firms like DeepSeek, Moon Dark Side, and MiniMax, allegedly using massive API interactions to extract and replicate the capabilities of advanced models. Anthropic's report claims these firms engaged in over 16 million interactions, copying logic, reasoning chains, and model behaviors to train their own systems at lower costs. While distillation is a known technique, the alliance argues it threatens both commercial interests and safety, as distilled models may bypass critical risk assessments. However, the accusations are met with skepticism, citing hypocrisy, as these giants themselves face lawsuits over unauthorized data scraping. The debate highlights the unresolved ethical and legal gray areas in AI development.

marsbit04/22 03:13

To Counter Chinese Models, Silicon Valley's Big Three Even Formed an 'Avengers Alliance'?

marsbit04/22 03:13

Autonomy or Compatibility: The Choice Facing China's AI Ecosystem Behind the Delay of DeepSeek V4

DeepSeek V4's repeated delay in early 2026 has sparked global discussions on "de-CUDA-ization" in AI. The highly anticipated trillion-parameter open-source model is undergoing deep adaptation to Huawei’s Ascend chips using the CANN framework, representing China’s first systematic attempt to run a core AI model outside the CUDA ecosystem. This shift, however, comes with significant engineering challenges. While the model uses a MoE architecture to reduce computational load, it places extreme demands on memory bandwidth, chip interconnects, and system scheduling—areas where NVIDIA’s mature CUDA ecosystem currently excels. Migrating to Ascend introduces complexities in hardware topology, communication latency, and software optimization due to CANN’s relative immaturity compared to CUDA. The move highlights a broader strategic dilemma: short-term compatibility with CUDA offers practical benefits and faster adoption, as seen in CANN’s efforts to emulate CUDA interfaces. Yet, long-term over-reliance on compatibility risks inheriting CUDA’s limitations and stifling native innovation. If global AI shifts away from transformer-based architectures, strict compatibility could lead to technological obsolescence. Despite these challenges, DeepSeek V4’s eventual release could demonstrate the viability of a full domestic AI stack and accelerate CANN’s ecosystem growth. However, true technological independence will require building an original software-hardware paradigm beyond compatibility—a critical task for China’s AI ambitions in the next 3-5 years.

marsbit04/21 10:16

Autonomy or Compatibility: The Choice Facing China's AI Ecosystem Behind the Delay of DeepSeek V4

marsbit04/21 10:16

DeepSeek Funding: Liang Wenfeng's 'Realist' Pivot

DeepSeek, a leading Chinese AI company, has initiated its first external funding round, aiming to raise at least $300 million at a valuation of no less than $10 billion. This move marks a significant shift from its founder Liang Wenfeng’s previous idealistic stance of rejecting external capital to maintain independence. Despite strong financial backing from its parent company, quantitative trading firm幻方量化 (Huanfang Quant), which provided an estimated $700 million in revenue in 2025 alone, DeepSeek faces mounting challenges. Key issues include a 15-month gap in major model updates, delays in its flagship V4 release, and the loss of several core researchers to competitors offering significantly higher compensation. The company is also undergoing a strategic pivot by migrating its infrastructure from NVIDIA’s CUDA to Huawei’s Ascend platform, a move aligned with China’s push for technological self-reliance amid U.S. export controls. However, DeepSeek lags behind rivals like智谱AI and MiniMax—both now publicly listed—in areas such as product ecosystem, multimodal capabilities, and commercialization. The funding round, though relatively small in scale, is seen as a way to establish a market-validated valuation anchor, making employee stock options more competitive and facilitating talent retention. It also signals DeepSeek’s transition from a pure research-oriented organization to a commercially-driven player in the global AI ecosystem.

marsbit04/20 11:19

DeepSeek Funding: Liang Wenfeng's 'Realist' Pivot

marsbit04/20 11:19

Anthropic's One Trillion vs. DeepSeek's 10 Billion

In April 2026, Anthropic's implied valuation briefly surpassed $1 trillion in secondary markets, despite its last official valuation being $380 billion. Its annualized revenue surged to $30 billion, largely driven by enterprise adoption of Claude Code. However, OpenAI accused Anthropic of inflating revenue by $80 billion through gross accounting. Anthropic is not expected to be profitable until 2027. Meanwhile, DeepSeek, after three years of rejecting external funding, announced its first fundraising round targeting a valuation over $10 billion. Founder Liang Wenfeng had prioritized independence and research over capital, citing chip shortages as a bigger constraint than funding. The move aims to address talent retention challenges and potential shifts toward commercialization. Both companies represent different AI philosophies: Anthropic’s closed-source, enterprise-focused model contrasts with DeepSeek’s open-source, research-driven approach. Each faces core challenges—Anthropic must justify its valuation with eventual profits, while DeepSeek must balance growth with its founding vision amid new investors.

marsbit04/20 05:40

Anthropic's One Trillion vs. DeepSeek's 10 Billion

marsbit04/20 05:40

The First Year of Computing Power Inflation: The Cheaper DeepSeek Gets, the Harder It Is to Stop This Round of Price Hikes

The year 2026 marks the beginning of "computing power inflation." While AI inference costs have dropped by over 80% in 18 months globally, China's three major cloud providers—Alibaba Cloud, Baidu AI Cloud, and Tencent Cloud—simultaneously announced price hikes of 20–30%. This reflects a deeper structural shift driven by Jevons Paradox: as unit costs fall (e.g., via models like DeepSeek-R1), demand explodes, especially with the rise of reasoning models and AI agents that consume 10–50x more tokens per task. Although DeepSeek open-sourced its model weights, it did not release its inference optimization stack, leaving a significant engineering efficiency gap between cloud providers and smaller players. The big three are leveraging this advantage to reposition: Alibaba focuses on high-margin premium clients, Baidu filters out low-value users, and Tencent capitalizes on ecosystem lock-in. Meanwhile, ByteDance’s Volcano Engine adopts a more moderate pricing strategy to capture displaced customers. Unexpectedly, the price surge is pushing large enterprises toward self-built computing solutions once their cloud bills exceed a certain threshold. While cloud providers aim to boost profitability, they risk driving away innovative startups and accelerating competition from GPU leasing and domestic hardware providers like Huawei. The涨价 trend is expected to persist for 2–3 years, fueled by rising token consumption from reasoning models, AI agent adoption, and NVIDIA export restrictions. The inflection point depends on whether domestic chips can match NVIDIA’s efficiency, likely around 2027–2028. Until then, cloud providers will maintain pricing power, and the key for AI companies is to optimize token usage—the real moat in this era.

marsbit04/17 01:16

The First Year of Computing Power Inflation: The Cheaper DeepSeek Gets, the Harder It Is to Stop This Round of Price Hikes

marsbit04/17 01:16

The DeepSeek You've Been Waiting For Has Long Changed

The article discusses the delayed release of DeepSeek V4, a highly anticipated AI model in China, and explores the reasons behind its slowed development. Initially a leader in the global AI race, DeepSeek has fallen behind competitors like OpenAI, Anthropic, and Google, which release major updates every few months. A key factor is DeepSeek's shift in focus due to national strategic priorities. In early 2025, the Chinese government encouraged the company to use Huawei’s Ascend processors instead of NVIDIA’s GPUs, aligning with broader efforts to achieve technological self-reliance. DeepSeek attempted to train its models on Huawei’s Ascend 910C chips but faced technical challenges, including instability and communication issues during distributed training. As a result, the company continued using NVIDIA hardware for training while only using Ascend chips for inference. In 2026, DeepSeek prioritized adapting V4 to Huawei’s new Ascend 950PR and Cambricon chips, aiming for a full migration from NVIDIA’s CUDA to Huawei’s CANN framework. This adaptation process, particularly ensuring precision alignment across hardware, consumed significant time and resources, slowing down model iteration. The delay also reflects DeepSeek’s evolving role from a purely market-driven entity to a "national mission-oriented" company. This shift has come at a cost: the model now lags behind competitors in areas like code generation and multimodal capabilities, and the company has faced talent drain, with key researchers leaving for better-paying opportunities at larger tech firms. Despite these challenges, V4’s release is seen as a potential milestone for China’s AI industry, demonstrating that advanced models can run on domestic hardware ecosystems. While it may not be a groundbreaking model in terms of performance, its success could validate China’s broader strategy for AI independence.

marsbit04/15 10:32

The DeepSeek You've Been Waiting For Has Long Changed

marsbit04/15 10:32

Stanford 423-Page AI Report: US-China Gap Only 2.7%, Tsinghua DeepSeek Breaks into Global Top Ten

The 2026 AI Index Report from Stanford HAI reveals a rapidly closing gap between the U.S. and China in AI model performance, now at just 2.7%. Chinese models like DeepSeek and Tsinghua have entered the global top ten. Over 90% of cutting-edge AI models now come from industry, not academia. AI capabilities are advancing unprecedentedly—models now outperform humans in tasks like coding (SWE-bench), mathematics (IMO), and multimodal reasoning. However, "jagged frontiers" persist, with models excelling in complex tasks but struggling with basics like reading analog clocks (50.1% accuracy). Global corporate AI investment reached $581.7 billion in 2025, doubling year-over-year, with the U.S. leading. Yet, AI researcher immigration to the U.S. has plummeted 89% since 2017. AI adoption is high globally (58% workplace usage), especially in China (over 80%). Concerns include rising AI-related incidents (362 in 2025) and significant job displacement for young developers (20% decline in employment among 22-25-year-olds). The report highlights a disconnect between rapid AI progress and slower adaptation in regulation, education, and public trust.

marsbit04/15 03:10

Stanford 423-Page AI Report: US-China Gap Only 2.7%, Tsinghua DeepSeek Breaks into Global Top Ten

marsbit04/15 03:10

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