Monday, May 18, 2026

Today's technology news is like a condensed science and technology blockbuster, with various highlights and troughs intertwined. Among them, several themes are particularly eye-catching.

First of all, we must talk about Tencent's accelerated deployment in the AI field. This former "AI slow" giant is now undergoing a profound organizational change. From the restructuring of underlying technologies to the reengineering of product development processes, Tencent has demonstrated a solid culture and low-ego attitude that makes people wonder: Is Tencent going to become a leader in the AI era? In my opinion, Tencent's move is both helplessness and opportunity. The helplessness is that the AI era has arrived, and not following up means being eliminated; and the opportunity is that Tencent has a huge user base and rich application scenarios, which provides unique conditions for the implementation of AI technology. However, in order to succeed in the AI field, Tencent still needs to solve a key issue: how to deeply integrate AI technology with its own business to create truly competitive products.

Secondly, OpenAI's acquisition is also worthy of attention. This time, it acquired an AI sound cloning tool company, which reminds people of the GPT-3 model previously launched by OpenAI. These actions by OpenAI are undoubtedly sending a signal to the outside world: AI technology is developing rapidly, and OpenAI is actively deploying various fields. In my opinion, these actions by OpenAI are on the one hand to consolidate its leadership position in the AI field, and on the other hand to promote the popularization and application of AI technology. However, this also raises a question: Will these actions by OpenAI intensify the monopoly of AI technology?

Finally, Tesla's autonomous driving system updates are also worthy of attention. This update will increase the top speed of intelligent summons to 13 kilometers per hour, which is undoubtedly an important milestone for the development of autonomous driving technology. However, this also raises a risk: the rapid development of autonomous driving technology may pose safety hazards. After all, autonomous driving systems are not perfect, and if they fail, the consequences will be unimaginable.

In general, today's science and technology news not only allows people to see the rapid development of AI technology, but also allows people to see the risks and challenges involved. In this era full of changes, we need to remain vigilant and actively embrace change. After all, only by continuous progress can we gain a foothold in this era.

Today's technology signals reveal a clear trend: AI agents are evolving from technical concepts to industry-based capabilities. Tencent's organizational changes, OpenAI's acquisition of a sound cloning company, and the case of a three-person team burning $1.3 million with 100 AI programmers all point in the same direction-AI is reshaping the way productivity is organized. It's like in the early days of the Industrial Revolution, when people realized that steam engines could not only pump water, but also drive entire factory systems. The emergence of Tencent's WorkBuddy and those Claude programming skill packs means that AI is changing from an "assistant" to a "colleague" and has begun to have complete closed-loop task capabilities.

The second key trend is that the "hydropower" process of AI infrastructure is accelerating. The establishment of a Token factory in Wuxi, multiple open source projects to simplify the AI usage process, and the emergence of various AI API middleware tools mark that AI computing is becoming a basic service like cloud computing. This reminds people of the popularity of AWS EC2 in 2008, startups no longer needed to build their own server rooms. But today's risk is that just as cloud computing spawned "cloud-native" companies, a large number of "AI-native" companies may emerge in the future that rely entirely on external AI capabilities and lack real technical barriers. When platforms like OpenAI begin to directly fund application-level development (such as the $1.3 million case), the upstream and downstream relationships in the industry chain are being reshaped.

The third signal that cannot be ignored is that the integration of AI and hardware has entered a new stage. The "open AI audio" displayed by Shaoyin headphones, the continuous iteration of Tesla's FSD, and those new AI video tools all show that AI is moving from a purely digital world to physical world interaction. This is not just a technical issue, but also involves complex scenario adaptation and user habit cultivation. Just as it took five years for smartphones to transform from communication tools to life platforms, the maturity of AI hardware requires a similar iteration cycle. However, it is worth noting that most AI hardware innovations are still concentrated in the fields of entertainment and transportation, and progress in areas of more social value such as medical care and education is relatively slow.

Today's technological signals are as always dominated by the word "AI", but in this seemingly unstoppable wave, we need to calm down and examine its deep logic through those dazzling new concepts and high numbers. and potential issues. What is particularly noteworthy is the craze surrounding AI Agents and the tension it exposes between computing costs and actual value.

We have seen from Tencent's "ship-changing" strategy, to everyone's product manager's discussion on the nature of Agent products, to the launch of various new products (Loova Agents, Vivago Video Agent, Fere AI), all point to a concept: AI Agents are moving from single command execution to closed-loop complex tasks, and are even described as "intelligent employees." This craze reminds people of the highly anticipated automation technologies in the past. From early expert systems to later RPA (robotic process automation), every time it promises to liberate manpower, but in the end it often only improves the efficiency of specific links and brings new integration and maintenance costs. Do today's Agent products really achieve breakthroughs in "workflow engines and knowledge systems", or do they just put traditional automated scripts in the gorgeous guise of big models? A signal worthy of vigilance is that when all products begin to claim to be "Agents", this often indicates the proliferation of concepts and the blurring of actual differences, and the composition of a bubble may not be low.

What is even more worrying is the astronomical computing costs brought by AI Agents. In the 36th Krypton report, the OpenClaw case of "three people leading 100 AI programmers burned US$1.3 million a month" is undoubtedly a dazzling alarm. There are 603 billion tokens and 7.6 million requests, and the computing power consumption behind this is staggering. Although OpenAI "pays" for Peter Steinberger, where is the sustainability of this model? If every "smart employee" has to operate at such a high cost, can it really bring about a ten-fold efficiency increase, enough to cover its operating expenses? This is not a simple technical issue, but touches on the economic core of the AI business model. Historically, technologies such as cloud computing and big data faced doubts about high costs in the early days, but in the end, costs were reduced through scale and technical optimization. However, it is still a huge question mark whether the "token" consumption of large models and whether their marginal costs can fall at a rate that can keep up with the rate of application expansion. If cost models cannot be effectively optimized, then most AI Agents that claim to improve efficiency may end up just burning toys rather than real productivity tools.

In addition, the penetration of AI in the vertical field, such as the update of autonomous driving FSD, the ups and downs of the AI financial unicorn Vise, and the emergence of the pet health model, all demonstrate the broad prospects for the implementation of AI technology. However, Vise's experience from a "unicorn" to a "bursting valuation bubble" reminds us that technological innovation is not the only factor for success, but comprehensive factors such as business model, organizational management, and market acceptance are equally critical. Big models and AI Agents are powerful, but when entering traditional industries, they still need to face many challenges such as complex real-world rules, data privacy, ethical responsibilities, and user habits. They can do far more than "what they see", but also answer "how to act after seeing it." The complexity and risk of this "how to act" decision-making chain are far higher than we imagine.