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Delysium is building a blockchain-based collaborationnetwork for Al Agents, including Lucy and the YKlLY(You Know l Love You)Network. Previously, Delysium was a real open-world, vividly AI-powered, and completely player-owned MMO game that supports players in creating various personalized physical assets, narrative assets, and native AI MetaBeings.
Gate launches KIMIUSDT (Moonshot AI) pre-market perpetual contract trading
according to official sources, Gate has now launched KIMIUSDT (Moonshot AI) pre-market perpetual contract trading (USDT settlement), supporting 1-10x leverage.Moonshot AI is an AGI unicorn independently developing general-purpose large models, ranking among the top tier of AI startups in China in terms of financing scale and technical performance.
DeepSeek sparks new round of "AI talent war"? Hiring for development, product, research, and various other positions
tonight, DeepSeek officially released recruitment information, stating that humanity is on the eve of AGI and inviting candidates to join DeepSeek to witness the development process of AGI. The recruitment information shows that open positions include full-stack development/algorithm, AI core system R&D, operations, product, model data strategy product manager/engineer, deep learning researcher, and functional departments. Work locations include Beijing and Hangzhou.Previously, several DeepSeek employees had been posting recruitment ads for various talents on platforms like Xiaohongshu and X, such as HR@一只大蜗牛 and Cui Tianyi from the Harness group. Earlier still, DeepSeek announced the completion of over $7 billion in financing, with a valuation exceeding $50 billion. The financing amounted to approximately RMB 50 billion, with Liang Wenfeng personally contributing RMB 20 billion. This large-scale recruitment may indicate a further escalation in the intensity of the domestic AI talent war, as AI professionals are entering a period of high market demand.
OpenAI Lawsuit: Microsoft CEO Satya Nadella Testifies in Court as Altman-Musk Dispute Escalates to Core Partnership Relations in the AI Giant
Odaily Odaily News: In the OpenAI lawsuit, Satya Nadella testified in court as Microsoft's CEO at the U.S. District Court in Oakland, California. The case centers on the ongoing legal dispute over OpenAI's non-profit structure and its path to commercialization. The lawsuit, filed by Elon Musk in 2024, accuses Microsoft of "aiding and abetting the breach of charitable trust obligations" during OpenAI's transition from a non-profit organization to a commercial entity. Microsoft has been making strategic investments in OpenAI since 2019, with cumulative investments reaching approximately $13 billion by 2023, making it one of OpenAI's most important external supporters.During the trial, Satya Nadella reviewed the early partnership between Microsoft and OpenAI, mentioning that the two parties had established deep technological and computing power collaboration before the launch of ChatGPT. In his earlier testimony, Musk stated that Microsoft's additional investment of approximately $10 billion in OpenAI in 2023 was the key turning point that prompted him to file the lawsuit, adding that the scale of the investment altered OpenAI's original non-profit-oriented structure. During the trial, Musk stated: "We are concerned they are turning a charitable organization into a commercial tool." He also questioned Microsoft's potential dominant position in the development of Artificial General Intelligence (AGI) and pointed out that its deep integration with OpenAI could impact the competitive landscape of the industry.The case is currently still under trial, and the debate surrounding OpenAI's governance structure, non-profit status, and control over the AI industry is expected to continue. (CNBC)
Serenity: US AI Companies Lower Inference Costs to Counter the "Model Distillation" Challenge
"White-Haired Stock Guru" Serenity stated that while some observations in the UBS report hold anecdotal truth, the more noteworthy trend is the increasing number of Chinese-language reports regarding the distillation of Anthropic's models. Currently, many US startups and tech companies are opting to use cheaper Chinese models (such as DeepSeek) in their AI applications, as their unit task costs are significantly lower than those of inference models from Gemini, OpenAI, and Anthropic.Serenity believes this trend, driven by capitalism, creates a "typical paradox"—companies naturally gravitate towards lower-cost solutions, thereby eroding the leading advantage of US models. He proposes that the US needs to address this on two fronts:First, build stronger access control and authentication systems, such as "heavy KYC frontier models" for domestic US use and tiered access mechanisms for allies, to reduce the risk of model distillation and misuse. This could also be accompanied by introducing an identity verification system akin to "AI-grade banking authentication" (e.g., biometrics + short-lived permission tokens) to raise the barrier for model calls, and using regulatory measures to restrict account sharing and access resale.Second, enhance the cost efficiency of inference models, allowing them to comprehensively outperform competitors like DeepSeek in both price and performance.Serenity also noted that some high-end models are currently frequently targeted for "distillation exploitation." Ideally, access to models nearing the AGI level should involve increased friction costs. In summary, the core challenge for the US AI industry lies in achieving both "low-cost inference capabilities" and establishing model access security mechanisms comparable to those in the financial system.
Viewpoint: Mythos’ breach of the NSA signals that AGI is drawing near; 9% inflation will not become the norm
Anthony Pompliano, Chairman of Bitcoin treasury company ProCap Financial, posted on X stating that Mythos allegedly breached the U.S. National Security Agency’s (NSA) classified systems within hours—a development that will further intensify public concerns about AI risks and prompt increased regulatory intervention. Yet the more critical signal conveyed by this incident is that AGI (Artificial General Intelligence) is, in fact, drawing near. Current AI technologies not only surpass humans in capability but are also self-training and improving at an incomprehensible pace—“humans cannot compete with these models.”
DeepSeek-V4-Flash Remains Free to Use, B.AI One-Stop AGI Infrastructure Empowers Innovation
The zero-barrier free access campaign for DeepSeek-V4-Flash on the B.AI platform is currently underway. Upholding the vision of "inclusive computing power," B.AI is dedicated to building a globally leading one-stop AGI infrastructure. We continuously lower the barrier to AI adoption, empowering every developer and Web3 user to effortlessly experience, build, and deploy Agentic applications. Powered by this one-stop AGI infrastructure, B.AI aggregates a global matrix of cutting-edge models, featuring built-in intelligent routing to optimize costs and efficiency; stably handles high-throughput scenarios including long contexts of millions of tokens and Agent workflows; elastic API routing ensures both production-grade high availability and exceptional cost-effectiveness; and a unified Web2/Web3 dual-channel seamlessly bridges crypto assets and fiat settlement. Currently, DeepSeek-V4-Flash remains freely accessible across both web and API endpoints, granting unlimited use of top-tier model capabilities at $0. Log in to B.AI immediately and let elite computing power accelerate the deployment of your next innovative project!
Google DeepMind Executive: Massive Capital Investment in AI Is Essentially a Bet on "Recursive Self-Improvement" Breakthrough
据 The Information 报道，Google DeepMind 首席战略官 Jasjeet Sekhon 表示，当前 AI 行业前所未有的资本投入，核心投资逻辑之一是押注“递归自我改进”（Recursive Self-Improvement，RSI），若要证明 AI 行业当前巨额资本支出具有合理性，未来需要实现一种能够自动创造更强版本自身的 AI 系统。 Jasjeet Sekhon 认为，RSI 是当前 AI 投资论的重要组成部分。所谓递归自我改进，是指 AI 模型能够自主优化自身能力、生成更先进的模型版本，从而形成持续增强的循环，RSI 正在成为过去“通用人工智能”（AGI）叙事的新焦点。随着科技公司持续投入数千亿美元建设 AI 基础设施，市场正押注 AI 能力将从规模扩展阶段进入自我增强阶段。不过，RSI 能否真正实现，以及实现路径和时间表，目前仍存在广泛争议。业内人士指出，AI 基础设施投资正在加速，但技术突破是否足以支撑如此规模的资本投入，仍需进一步验证。
Meta, Google, and Other Tech Giants Face the Allocation Problem of AI Computing Power for Internal Use vs. Sales
during the company's second-quarter earnings call, Meta CEO Mark Zuckerberg stated that Meta currently does not have a business selling computing power to customers, but such offerings are in the plans. He indicated that a significant portion of Meta's computing power will be used for training AI models, supporting agent products, and developing core businesses, while the company also anticipates expanding services to large enterprise clients.Both Google and Meta have slightly raised their capital expenditure expectations for this year. Google stated that related spending could increase further in 2027, while Microsoft maintained its capital expenditure forecast. Google's cash flow turned negative for the first time in the second quarter, and Meta's cash flow decreased by 91% compared to the same period last year.Microsoft CFO Amy Hood noted that customer demand for its cloud business still exceeds available capacity. Google said last week that it will purchase more third-party computing power while building more internal capacity to meet customer needs.Google CEO Sundar Pichai stated that the primary objective for Google in using its self-developed tensor processing units (TPUs) is to ensure the allocation of necessary resources for the development of AGI frontiers. Google is working with partners to deploy TPUs in other data centers to unlock more capacity.
Gate launches KIMIUSDT (Moonshot AI) pre-market perpetual contract trading
according to official sources, Gate has now launched KIMIUSDT (Moonshot AI) pre-market perpetual contract trading (USDT settlement), supporting 1-10x leverage.Moonshot AI is an AGI unicorn independently developing general-purpose large models, ranking among the top tier of AI startups in China in terms of financing scale and technical performance.
Vitalik Publishes Long Essay Reflecting on AI Evolution: Deep Human-Machine Integration May Be the Optimal Solution to Address AGI Risks
According to a series of tweets posted by Ethereum co-founder Vitalik Buterin on X on July 20, he engaged in deep reflection on AI capability growth and the future of humanity. Vitalik divides the evolution of machine capabilities into three stages: the Industrial Revolution (physical repetitive labor), the Computer Age (mental tasks definable by logic), and the LLM Era (mental and partial physical tasks defined based on massive samples). He points out that the current core question lies in: whether LLMs plus subsequent improvements can ultimately cover all unique human capabilities, or if a fourth or fifth technological wave is still needed. Regarding the definition of AGI, Vitalik proposes: AGI refers to AI that, if uploaded into a robotic body and humans suddenly disappear, can still independently continue civilization. He emphasizes that once AGI is realized, it will be an "irreversible turning point," and humanity's dominance over Earth will remain only due to historical inertia rather than capability advantage. Regarding the future path, Vitalik hopes for deep human-machine integration—erasing the human-machine binary boundary through technologies such as brain-computer interfaces and consciousness uploading, enabling humans to remain competitive before the technology ceiling arrives. He also calls for maintaining global political and economic diversification, avoiding monopolization of AI advantages by a single nation or corporation, and expresses support for AI development "slowdown" and "pause" proposals, leaning towards achieving decentralized slowdown via an open-source weights model rather than relying on coercive political means.
GPT-5.6 Full Series Models Officially Integrated into B.AI API Network
B.AI announces that OpenAI's latest generation GPT-5.6 full lineup of models has officially been integrated into the B.AI API network. Effective immediately, developers can directly access three distinctly positioned models through B.AI's single interface: GPT-5.6 Sol specializes in ultimate reasoning and complex tasks; GPT-5.6 Terra is perfectly suited for daily workloads; GPT-5.6 Luna prioritizes ultra-fast response and cost-efficiency. Whether you are building agents, optimizing workflows, or exploring the boundaries of AGI applications, B.AI provides you with flexible and stable compute support. Log in to the B.AI platform now to unlock the full capabilities of GPT-5.6 and let the next generation of AI work for you.
Related news
DeepSeek-V4-Flash Remains Free to Use, B.AI One-Stop AGI Infrastructure Empowers Innovation
The zero-barrier free access campaign for DeepSeek-V4-Flash on the B.AI platform is currently underway. Upholding the vision of "inclusive computing power," B.AI is dedicated to building a globally leading one-stop AGI infrastructure. We continuously lower the barrier to AI adoption, empowering every developer and Web3 user to effortlessly experience, build, and deploy Agentic applications. Powered by this one-stop AGI infrastructure, B.AI aggregates a global matrix of cutting-edge models, featuring built-in intelligent routing to optimize costs and efficiency; stably handles high-throughput scenarios including long contexts of millions of tokens and Agent workflows; elastic API routing ensures both production-grade high availability and exceptional cost-effectiveness; and a unified Web2/Web3 dual-channel seamlessly bridges crypto assets and fiat settlement. Currently, DeepSeek-V4-Flash remains freely accessible across both web and API endpoints, granting unlimited use of top-tier model capabilities at $0. Log in to B.AI immediately and let elite computing power accelerate the deployment of your next innovative project!
Google's AI Recruitment Tool Questioned by Own DeepMind Team Over Potential Resume Screening Errors
Google promotes AI recruitment tools to enterprise clients, but its own Google DeepMind AGI Safety and Alignment team encourages applicants to fill out an additional form to prevent their applications from being incorrectly filtered or delayed by the internal AI system. Internal documents state that the system "has a significant probability of incorrectly filtering out your resume" and note "please do not share widely." A Google DeepMind spokesperson responded that the internal system will not incorrectly eliminate candidates, and the specialized form is simply to allow applications to bypass recruiter review and be delivered directly to team members.
Google's Own AI Safety Team "Distrusts" Internal AI Hiring System, Privately Guides Job Seekers to Bypass Automated Screening
According to Bloomberg, Google DeepMind's AGI Safety and Alignment Team (AGI Safety and Alignment Team) was recently exposed for privately providing a special application form to job seekers, requiring candidates to fill it out in addition to formally submitting their resumes to bypass the risk of automatic screening by the company's internal AI recruitment system. Documents obtained by Bloomberg show that the file includes a disclaimer stating "Do not distribute widely." This is highly ironic—Google is aggressively marketing its AI recruitment screening tools to enterprise clients, claiming they can efficiently handle massive volumes of job applications, yet its internal team most focused on researching AI risk and alignment issues holds significant reservations about the reliability of its own system and takes practical action to circumvent it.
Google DeepMind Executive: Massive Capital Investment in AI Is Essentially a Bet on "Recursive Self-Improvement" Breakthrough
据 The Information 报道，Google DeepMind 首席战略官 Jasjeet Sekhon 表示，当前 AI 行业前所未有的资本投入，核心投资逻辑之一是押注“递归自我改进”（Recursive Self-Improvement，RSI），若要证明 AI 行业当前巨额资本支出具有合理性，未来需要实现一种能够自动创造更强版本自身的 AI 系统。 Jasjeet Sekhon 认为，RSI 是当前 AI 投资论的重要组成部分。所谓递归自我改进，是指 AI 模型能够自主优化自身能力、生成更先进的模型版本，从而形成持续增强的循环，RSI 正在成为过去“通用人工智能”（AGI）叙事的新焦点。随着科技公司持续投入数千亿美元建设 AI 基础设施，市场正押注 AI 能力将从规模扩展阶段进入自我增强阶段。不过，RSI 能否真正实现，以及实现路径和时间表，目前仍存在广泛争议。业内人士指出，AI 基础设施投资正在加速，但技术突破是否足以支撑如此规模的资本投入，仍需进一步验证。
Meta, Google, and Other Tech Giants Face the Allocation Problem of AI Computing Power for Internal Use vs. Sales
during the company's second-quarter earnings call, Meta CEO Mark Zuckerberg stated that Meta currently does not have a business selling computing power to customers, but such offerings are in the plans. He indicated that a significant portion of Meta's computing power will be used for training AI models, supporting agent products, and developing core businesses, while the company also anticipates expanding services to large enterprise clients.Both Google and Meta have slightly raised their capital expenditure expectations for this year. Google stated that related spending could increase further in 2027, while Microsoft maintained its capital expenditure forecast. Google's cash flow turned negative for the first time in the second quarter, and Meta's cash flow decreased by 91% compared to the same period last year.Microsoft CFO Amy Hood noted that customer demand for its cloud business still exceeds available capacity. Google said last week that it will purchase more third-party computing power while building more internal capacity to meet customer needs.Google CEO Sundar Pichai stated that the primary objective for Google in using its self-developed tensor processing units (TPUs) is to ensure the allocation of necessary resources for the development of AGI frontiers. Google is working with partners to deploy TPUs in other data centers to unlock more capacity.
Amazon Restructures AI Strategy, Gradually Phasing Out Most Nova Flagship Models and Betting on Frontier Model R&D
According to Business Insider, citing sources familiar with the matter, Amazon is comprehensively adjusting its artificial intelligence strategy. Following layoffs in the AGI department and the closure of related laboratories, the company is further consolidating fragmented model R&D directions. The company plans to gradually phase out most internal flagship Nova models, including Premier, Omni, Reel, and Canvas, and will concentrate engineering and compute resources on new frontier model development plans.