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first_img The Bank of Korea launches a 24-hour won settlement pilot program aimed at foreign investors

The Bank of Korea (BOK) has launched the first 24-hour Korean won settlement network pilot, aimed at allowing foreign investors to settle won transactions outside of normal banking hours in South Korea. The central bank announced on Monday that its international wire transfer network has begun trial operations with four domestic banks: KB Kookmin Bank, Woori Bank, Hana Bank, and Shinhan Bank. Full operations are expected to start in January 2027, at which point participation will expand to other institutions and foreign banks.The network operates 24 hours a day on weekdays, excluding weekends and public holidays. Foreign investors can settle won transactions during their domestic business hours by registering a Foreign Institution (RFI-K) account for won services, without the need to open an account directly with a South Korean financial institution. The Bank of Korea stated that the network is expected to enhance the international status of the won by improving foreign investors' access to won settlement infrastructure.In addition, the Bank of Korea is also experimenting with tokenized settlement infrastructure through the Agorá project led by the Bank for International Settlements, which completed a cross-border payment trial using tokenized central bank reserves and commercial bank deposits, including the won, in July. The Bank of Korea is simultaneously advancing the Hangang project, a blockchain-based pilot that utilizes wholesale central bank digital currency to settle tokenized commercial bank deposits.

first_img Zhipu Tangjie filed a defamation complaint against Xiaohongshu, ZCode has been open-sourced for rectification

On September 21, netizen "Zhang Kangkang kk" revealed on social media that Tang Jie, the founder of Zhipu and a professor at Tsinghua University, filed a complaint on Xiaohongshu regarding a user post. The complaint stated that the post used phrases like "code is pretty much the same as copy, both start with c and steal code" without factual basis, fabricating rumors that Zhipu's ZCode was involved in code theft and plagiarism. It also used terms like "Uncle Tang Jie" to associate Tang Jie with the plagiarism rumors for character defamation, while utilizing AI technology to synthesize Tang Jie's image with anime materials in a defamatory context. The complainant believed the content constituted malicious defamation and commercial disparagement, damaging both Tang Jie's personal reputation and Zhipu AI's reputation, and requested the platform to take down the post and deal with the user who posted it.On the same day, Zhipu announced that it had completed rectifications regarding the safety issues of the independent desktop AI programming client ZCode in response to community feedback and apologized to all users. The company has open-sourced ZCode, handing over the code to community supervision, and will establish a regular product security vulnerability mechanism moving forward. Previously, developers discovered that ZCode had silently uploaded user local repository data without prior notice. On September 18, Zhipu publicly apologized and made an emergency fix, explaining that the root cause was the "repository indexing" feature, which was enabled by default and had no option to turn it off. On September 20, Taiyuan Chengming Technology Co., Ltd. sent a letter to Zhipu, accusing ZCode of unauthorized uploading of company data assets and trade secrets, making claims for rights protection and reserving the right to pursue legal responsibility.

first_img Goldman Sachs: AI-related companies account for approximately 40% of the market capitalization of the S&P 500

The AI wave is breaking the traditional asset diversification logic of pension and sovereign funds, with risks spreading from technology stocks to multiple areas such as private equity, corporate bonds, and infrastructure. Institutional investors are beginning to reassess the AI exposure of their entire portfolios.Goldman Sachs estimates that AI infrastructure-related companies account for about 40% of the total market capitalization of the S&P 500; Apollo data shows that this year, AI-related issuances have accounted for nearly half of the investment-grade bond issuance and 87% of venture capital funding. Monte Tarbox, Chief Investment Officer of the New York City Retirement System, recently rejected a fundraising request due to an overweight position in a private equity fund related to AI.Institutions currently face the challenge of lacking a unified standard for measuring AI exposure. The Los Angeles County Employees Retirement Association estimates that 8% to 19% of its holdings are related to AI; a survey by Invesco of 90 sovereign wealth funds shows that more than half list market concentration as the primary risk of AI investment. Some large institutions are beginning to adopt a holistic portfolio approach to track AI-related exposure and the correlations between assets, while some institutions are starting to use AI tools to monitor their own portfolios.

Goldman Sachs: Consumer-grade AI agents enter the platform layer with capital expenditures of $1.4 trillion in 2027

Goldman Sachs Research released a viewpoint on September 18, stating that AI is transitioning from the experimental phase to the implementation phase, with the rise of consumer-grade AI agents marking the emergence of the platform layer. At the Communacopia + Technology Conference held in San Francisco, most companies showcased cases from experimentation to implementation. Goldman Sachs expects that by 2027, capital expenditures for U.S. mega-cap companies will reach $1.4 trillion, exceeding Wall Street consensus.Goldman Sachs analyst Eric Sheridan stated that consumer-grade AI agents are shifting from conversational relationships to action-oriented tasks. If consumers overcome trust and security issues, they could execute complex tasks such as purchasing tickets and booking hotels. The monetization of such agents in the mass market will be similar to search, achieved through advertising and subscriptions. AI is evolving from the infrastructure layer to the platform layer and application layer, with declining token unit pricing and increased utility being key drivers of mass adoption.During the conference, concerns about AI risks became a major topic, but Goldman Sachs believes this will not slow down infrastructure construction, as demand for computing power still exceeds supply and most projects have already been contracted. Supply chain constraints such as memory chips, electricity, and land may pose resistance, but the capital expenditure cycle is expected to remain high through 2027.

first_img Spirit AI Co-Founder: The breakthrough for robotic brains is expected as early as next year

According to a report by Reuters, Gao Yang, co-founder and chief scientist of the Chinese humanoid robot startup Spirit AI, stated that humanoid robots could complete most general tasks based on verbal instructions as early as next year, but it will take at least 8 more years to enter households. He mentioned that the brain is the weakest link in the entire robotics technology stack, and it is expected to reach a milestone similar to GPT-3.0 by mid-2027, allowing users to speak to robots in natural language and have them perform a series of reasonable physical actions to attempt to complete tasks.Spirit AI was founded in 2024, has about 300 employees, and raised $670 million in funding within two years, with a valuation of $2.9 billion. The company employs about 1,000 contractors who wear data collection devices at home or in factories, primarily relying on real-world data for training. The success rate for simple tasks in a structured living room environment is 90%. Currently, dozens of Moz1 wheeled humanoid robots are deployed on production lines at CATL and JD.com.Gao Yang stated that the next 1-2 years represent the initial window for industrial applications, and after two years, they can enter commercial service scenarios to perform simple tasks, but entering households will be more challenging. In terms of safety, full-body force control is implemented, and the robots will automatically engage emergency braking when encountering significant interaction forces. High-quality data is the main bottleneck for training the robot's brain.
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