Wednesday, August 12, 2026

Nvidia joined in the fun with financial giants and created a "computing power financing platform". The essence is to treat AI computing power as an asset and directly allow institutional capital to be credited. For those of us who want to buy GPUs and rent clouds, lower financing rates may occur in the short term, and computing power rental costs may be reduced. But don't forget that the entry of capital will push up the price of computing power. Coupled with the old problem of chip supply in short supply, it will ultimately benefit major manufacturers and speculators. For ordinary developers, there will be no immediate benefits, but they have to make a budget in advance and don't wait until the project is stuck in computing power before complaining.

At the same time, the domestic AI Agent war has entered a white-hot. Kimi K3 directly pushed the parameter to 2.8 trillion yuan, claiming to be an open source "declaration of scale", while Tencent WorkBuddy, Byte, and Ali are also seizing the office Agent market. There is a clear logic behind it: treat LLM as an underlying capability, package business processes into plug-ins, and allow ordinary employees to interact directly. Technically, this "one-stop" solution is not easy to implement-it requires unified identity governance, context management and security auditing, and any omission will leak corporate data. There are plenty of pitfalls: Prompt failure, tool invocation is unstable, and model costs are out of control. For us developers, it is a good opportunity to practice our skills, especially PrimeIntellect's self-improving Agent and OmniRoute's "one-click access to 300+ models" solution, which saves us the pain of writing our own routes. However, if you really want to implement it in the production environment, you still need to do a good job in monitoring and cost control, otherwise you will be overwhelmed by "AI bills."

Let's look at the trend of open source models. The Muse‑Glimmer‑ 30B just launched by Meta focuses on local residency and is combined with a self-improving agent like PrimeIntellect, which is intended to move LLM from the cloud to the edge. In theory, data compliance and latency issues can be solved, but actual deployment has to face hardware thresholds of memory, computing power and model tuning. For small and medium-sized teams, unless they have dedicated GPU resources, they cannot run; for large companies, they can play tricks on low-cost computing power. The risk is that open source models often lack commercial-grade support and security, and repair costs are high if vulnerabilities occur or performance regresses.

Taken together, the three forces of computing power financing, Agent commercialization, and open source models promote each other in the same ecosystem: capital makes computing power more like financial assets, companies seize the Agent market to absorb computing power needs, and open source models Provide a low-cost computing power entry. But every step comes with hidden risks of tight supply chains, uncontrolled costs and safety compliance. If ordinary developers want to seize the opportunity, they should first position themselves as an "AI tool chain integrator" and learn to bridge multiple models and multiple plug-ins, rather than blindly chasing the latest parameter scales. This will not only avoid capital-driven price fluctuations, but also grab the first wave of productivity dividends in the business.

In today's science and technology news, Nvidia's move to cooperate with financial giants to establish a computing power financing platform and the release of Kimi K3 open source 3 trillion model reflect that the AI field is undergoing a profound transformation. This is not only a breakthrough in the technology itself, but also a profound reshaping of the industry ecology and future development trends.

Nvidia's move means the deep integration of capital and AI technology, providing strong financial support for the construction of AI infrastructure. This will not only help accelerate the popularization of AI computing, but will also promote the entire computing industry to develop in the direction of higher performance and higher efficiency. The open source of Kimi K3 marks China's confidence and openness in the field of AI technology and contributes China's wisdom to the development of global AI technology.

These two incidents have far-reaching implications. First of all, they will promote the widespread application of AI technology. From finance and medical care to manufacturing, AI will penetrate into various industries and bring huge economic benefits to society. Secondly, they will promote innovation in AI technology, and the emergence of open source models will inspire more researchers and developers and promote technological iteration. Finally, they will intensify global competition for AI technology, and all countries will compete for the commanding heights in the AI field.

However, this change is also accompanied by problems and risks. On the one hand, the rapid development of AI technology may aggravate social inequality and lead to the widening of the "digital divide." On the other hand, the abuse of AI technology can raise ethical and legal issues. In addition, the security issues of AI technology have become increasingly prominent, and issues such as data leaks and privacy violations need to be solved urgently.

Nvidia's cooperation with financial giants and the open source of Kimi K3 are milestones in the development of China's AI industry. But this change is far from over, and we still need to face challenges together in the future and promote the healthy development of AI technology. In this process, the government, enterprises, scientific research institutions and the public will all play an important role.

First, the government should formulate reasonable policies and regulations to guide the healthy development of AI technology, while strengthening supervision of AI ethics and safety. Secondly, companies should actively assume social responsibilities, ensure that the application of AI technology complies with ethical standards, and pay attention to user privacy and data security. Thirdly, scientific research institutions should continue to promote innovation in AI technology and provide a steady stream of power for industrial development. Finally, the public should improve their understanding of AI technology, learn to use AI correctly, and jointly promote the popularization and development of AI technology.

In short, Nvidia's cooperation with financial giants to establish a computing power financing platform and the Kimi K3 open source 3 trillion level model mark that the AI field is ushering in a profound change. This change will promote the widespread application of AI technology, but it is also accompanied by problems and risks. We need to work together to promote the healthy development of AI technology and make AI a boost to the progress of human society.

Nvidia teamed up with six financial giants to raise US$500 billion in computing power financing. This seems like a carefully planned capital game, and the logic behind it is worth digging deep into. On the surface, this is solving the financial bottleneck of AI infrastructure construction, but in fact it is more like Nvidia is building a financial moat for itself. The US$500 billion is equivalent to the GDP of a medium-sized country, and these funds will eventually flow to Nvidia's GPU orders. This means that Nvidia is not only selling chips, but also selling financial instruments, locking customers in its own ecosystem for a long time through financing rates and the design of capital pools. Historically, this model is not new. At that time, IBM and Cisco bundled hardware sales with financial services through financial leasing, but in the end they both suffered backlash due to the accumulation of risks. The risk for Nvidia is that when the AI bubble bursts, these financing projects may become bad debts, and financial institutions will pass on the risk to Nvidia's shareholders. More importantly, the core of this cooperation is not technological innovation, but the redistribution of capital-who is making money? Obviously it will be Nvidia and financial institutions, and those paying the bill will be companies that are eager to launch AI projects but lack risk assessment capabilities.

At the same time, the release of Kimi K3 and Claude's invisible watermark incident revealed two completely different development directions for the AI industry. Kimi K3 made a high-profile appearance with 2.8 trillion parameters and open source route, claiming that China's AI is moving from following to defining rules, but behind this scale competition is the endless consumption of computing power and data. Although the open source model lowers the threshold for use, it also means that more companies will flock to this track, further intensifying homogeneous competition. Claude's invisible watermark represents another trend-regulation-driven technological evolution. The EU AI bill requires tags to generate content, which may seem to be for transparency, but in fact may turn into a technological monopoly. Because only major manufacturers have the ability to deploy such a globally tracked watermark system, small and medium-sized enterprises and open source projects will be excluded. These two events together point to a question: Will the future of AI be controlled by a few players, and will the cost of technological progress be paid by society as a whole?

Finally, the record amount of IPO funds raised in Hong Kong and the increase in the allocation ratio of new shares on the Beijing Stock Exchange reflect the anxiety of the capital market in looking for new exports. The prosperity of the Hong Kong market depends largely on the wave of mainland companies listing in Hong Kong, while the increase in the allocation ratio of the Beijing Stock Exchange is to hedge the risk of new shares being broken. Behind this lies a bigger dilemma: when real economic growth slows down, capital can only maintain market vitality through financial instruments and policy adjustments. But whether this short-term stimulus can last depends on whether real industrial innovation can keep up. At present, high hopes are placed on AI and new energy, but the former is still in a bubble period, while the latter faces dual uncertainties in technology and market. The prosperity of the capital market is in sharp contrast to the weakness of the real economy, which is reminiscent of the Internet bubble in 2000-the surge in Nasdaq did not bring about a real industrial revolution, but was ultimately repaired by the recovery of the real economy. Whether today's AI boom will repeat itself may take longer to verify.