Saturday, June 6, 2026

Morgan Stanley is tough enough to open its asset management interface to external AI-directly allowing AI agents to penetrate its own trillion-dollar system to extract data. On the surface, it is a financial technology revolution, but in fact it is an arms race for computing power among Wall Street gamblers. Think about it, in the future, hedge fund strategies may all be generated by AI fighting each other, but the devil lies in the details: How to conduct security audits? How to prevent malicious arbitrage by agents? The legal provisions for financial supervision for those old men are still in the era of fax machines. This wave of operations is expected to force Goldman Sachs to follow up quickly, but ordinary developers should not rush to climax. The most you can come into contact with is a castrated version of the API packaged by securities firms.

It is particularly ironic to read the 70,000-word history of blood and tears together with the TP-LINK contract explosion. When the AI projects of large domestic factories shrink, there is never a shortage of internal network essays, but the management solution to the problem is actually to ask employees to sign shell company contracts. What's even more absurd is that the V2EX hot post "Cut" is next to "AI writes code is mentally retarded"-even AI cannot save developers 'employment anxiety during the economic downturn. Now the self-media of one-person company should read the documentary "I've been busy for half a year and zero income." When the stock picking tool generated by GPT-4 encounters the data source and cuts off the supply, the essence of the so-called OPC (one-person company) is to give the platform a creative leek farm.

But today there are really tough things: NTU's PhysX-Anything and Luyten's robotic 3D printing tower cranes are the technology that truly changes the physical world. Generating a 3D model with physical attributes from a single drawing means a discount on the design cost of the manufacturing industry, and the monster combination of tower crane + concrete printing directly allows migrant workers on the construction site to transform into AI supervision. However, the engineering pit is deeper than the Mariana Trench: if you ask AI to generate a screw, the torque parameters may be all wrong, and the cement ratio difference on the construction site will collapse by 1% and the building will collapse. So despite the crowd at CVPR, it is a miracle that these technologies can enter the Foxconn assembly line within five years. Instead of chasing Gemma's new model, ordinary developers might as well study MLCC capacitors-the surge in AI server usage has increased upstream materials by 200%. This wave of dividends is much more than writing a prompt.

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In today's science and technology news, there are several signals worthy of our in-depth analysis. First, it is about the application and development of AI in various fields, second, it is about enterprise management and innovation, and finally, it is about market fluctuations and financial technology.

In the field of AI, the breakthroughs in 3D annotation technology by Professor Cao Ziang's team at NTU, the integration of robots and computer vision on CVPR2026, and the AI strategy released by the Canadian government all point to a trend: AI technology is gradually moving from theoretical research to practical applications, and significant progress has been made in multiple areas. These advances have not only improved production efficiency, but also promoted social progress. However, the widespread application of AI technology has also brought about issues such as ethics and privacy, which require joint attention and resolution by the whole society.

In terms of enterprise management and innovation, the long article "Being Inside the Nail" on the Nail Intranet reveals problems existing in the internal management and development of the enterprise, and the TP-LINK forced transfer of all employees reflects the enterprise's dilemma in human resource management. These events remind us that corporate innovation and management are not just technical issues, but also issues of culture, systems and talent. Enterprises need to establish a good innovation culture and pay attention to talent training and incentive mechanisms in order to remain invincible in the fierce market competition.

In terms of market volatility and financial technology, Morgan Stanley has opened its asset management business interface to external AI agents, Smart Money has laid out AI upstream links in advance, and the Canadian government has provided funds for AI startups and purchased equity in them all indicate that finance Technology is becoming an important force in promoting economic development. However, the plunge in Japanese and Korean stock markets also reminds us that market fluctuations and financial risks cannot be ignored. Financial institutions need to strengthen risk management to ensure the stability of financial markets.

Overall, today's science and technology news reflects the following trends and problems:

1. AI technology is gradually moving from theoretical research to practical application, but it also brings ethical and privacy issues. 2. enterprise innovation and management are not only technical issues, but also cultural, institutional and talent issues. 3. financial technology is becoming an important force in promoting economic development, but market fluctuations and financial risks cannot be ignored.

These trends and issues are intertwined and together constitute a complex picture of current scientific and technological development. Faced with these challenges, we need to remain vigilant, actively respond, promote science and technology for good, and achieve sustainable development.

In today's science and technology news, the most noteworthy topics include the application of AI in the financial field, upstream material investment in AI technology, and internal management issues. These incidents not only reveal the development trend of AI technology, but also reflect the risks and potential problems behind it.

Morgan Stanley has opened its trillion-dollar asset management business interface to external AI agents, marking the financial industry's further acceptance of AI technology. However, whether this openness can really bring about the expected efficiency improvement and risk reduction deserves further discussion. Historically, the financial industry has tried many times to introduce new technologies, but it has often been accompanied by data security and compliance issues. For example, in 2017, many banks tried to use blockchain technology to improve transaction transparency, but ultimately failed to popularize it on a large scale due to immature technology and unclear supervision. Although Morgan Stanley's move seems technologically advanced, the issues behind it cannot be ignored in terms of data security, privacy protection and decision-making transparency of agents. Once an AI agent makes a wrong decision or is maliciously attacked, the consequences will be unimaginable. Moreover, this openness can lead to unfair competition in the market, as larger institutions have more data and technology resources, while smaller investors can be marginalized.

At the same time, AI computing investment is transmitted to the upstream material end, MLCC becomes the third largest cost item of AI server, which reveals the economic cost behind the development of AI technology. The exponential rise in materials such as MLCC (multilayer ceramic capacitor) and glass fiber means that the manufacturing cost of AI servers is increasing. Whether this cost increase can bring sufficient economic benefits through the application of AI technology still needs time to verify. Historically, many new technologies were initially costly and were eventually eliminated due to inability to find a suitable business model. For example, during the Internet bubble of 2000, many companies invested heavily in building optical fiber networks, but eventually went bankrupt due to lack of sufficient user needs and business models. The increase in AI material costs may indicate that bubble risks in the AI market are accumulating. If the market cannot absorb these high costs, the sustainable development of AI technology will be questioned.

On the other hand, the 75,000-word long article "Being Inside the Nail" on the Dingbao Intranet reflects the management problems and internal contradictions that the company may encounter during its rapid expansion. The process from project establishment to contraction of AI products mentioned in the article not only reveals the uncertainty of technological development, but also reflects the company's internal challenges to resource allocation and project management. This kind of internal contradiction is not uncommon in history. In the process of pursuing technological innovation, many companies often ignore internal management and employee satisfaction, which ultimately leads to project failure or brain drain. For example, in 2015, while Uber was expanding rapidly, internal management was chaotic and employee morale was low, leading to a series of negative incidents. The case of Dinghao may indicate that AI companies need to pay more attention to internal coordination and management in their rapid development, otherwise they may repeat the same mistakes.

What these events have in common is that they all reveal the complexity and uncertainty behind the development of AI technology. Morgan Stanley's opening-up measures, rising AI material costs and fixed internal management issues are all potential signals of technology bubbles and market risks. While pursuing technological innovation, we must be alert to these risks and ensure the sustainable development of technology and the rationality of its application. Otherwise, no matter how advanced the technology is, it may be difficult to implement due to various problems, ultimately leading to waste of resources and market disappointment.