Thursday, July 16, 2026

Today's technological signal is just one sentence: AI has fully penetrated, the landing speed is faster than imagined, and even photography competitions, domestic mobile phones, education, and commercialization have been won. But this wave of "collective filing of large-scale end-side models" is the real big deal. Apple, Huawei, Xiaomi, OPPO, vivo, Samsung, and Nubia are all on the table. The end-side AI of domestic mobile phones is finally no longer a PPT and can be used in real terms. In the past, AI was more of a cloud toy with powerful computing power and functional functions, but it couldn't do anything locally; now that end-side AI is over, it means that privacy, real-time, and offline capabilities have all been improved, and the user experience will be qualitative. change. For developers, this is no longer "do you want to do end-side AI" but "you have to do it, otherwise you will be eliminated." There are also many pitfalls in engineering, such as slimming models, accelerating reasoning, and synchronizing data. Whoever does well will have votes. Commercialization is no longer empty talk. Whoever can build the service ecosystem of end-side AI will be able to seize the next wave of traffic dividends.

Looking at DeepSeek's daily internship salary of 5500 yuan and founder Liang Wenfeng becoming the new richest man in AI, this is not as simple as "high salary". It actually exposes the industry's extreme scarcity of talents and the crazy pursuit of high efficiency. In the era of large models, the iteration speed of R & D far exceeds that of traditional software. Whoever can handle project implementation, model optimization, and data pipelines is the "money printing machine". But this also brings risks. Talent bubbles, projects rushing to the shelves, and lax risk control are all hidden dangers. Don't think that it's "even rain and dew" now, and it will soon be divided: the core team makes a lot of money, and ordinary development can only do peripheral maintenance. Practical problems such as AI eating AI, model illusions, and decision-making rollover have not been solved, and it is useless to pile up resources and wages alone. Product managers and engineers must pay attention to data error correction, rule verification, and link traceability, otherwise large models will overturn all day long.

Finally, let's talk about content creation and AI generation applications. These all point to a trend: ordinary people can use AI to work, but "being able to generate" does not mean "being able to use" or "being able to earn". For example, the AI fraud incident in the Hohhot Photography Competition directly slapped the "generation regardless of quality" faction. In the future, all content and applications must have a traceability mechanism, otherwise you will not be able to tell the difference between real and fake. The platform either has anchors or is verified, otherwise reputation and business will collapse. The new gameplay methods such as AI UGC, AI Agent entrepreneurship, and Mini games creation are very attractive, but commercial closed-loop and sustainable operations are still far away. Now it is only a breakthrough at the tool level. How to realize ecology and traffic, how to find users, and how to operate stably depends entirely on the next round of technological and product innovation.

To sum up, all the signals we see today point to a general trend: AI is no longer a laboratory toy, but has become a must-have option for large-scale implementation. The filing of large end-side models, soaring talent prices, content generation and business innovation have all brought the industry into a new stage of "rolling out and rolling application." It's not about whether AI can be used, but about how you use it, whether you use it steadily, and whether you can make money. The risks are that the project has not kept up, hallucinations and car overturns occur frequently, and the ecological closed loop has not yet been formed. The opportunity is that giants switch tracks, and ordinary developers have a window period, but the window period will not be too long. Now I'm still waiting and watching. When you want to get on the bus, there will be no ticket.

Recent signals are intertwined around the two main lines of "the generation boundary of AI" and "the implementation form of AI evolution", reflecting deep changes in the technology, supervision and industrial landscape. Works from the Hohhot Photo Competition were accused of AI generation, a daily salary of 5500 yuan for the head of DeepSeek, approval of seven mobile phone end-side models, Nvidia and Toyota deepened L2+ cooperation, and Anthropic launched the teacher version of Claude-these seemingly scattered fragments are actually telling the same story: AI has moved from a cloud laboratory to public life and critical infrastructure, but the "empty window" of supervision and security is quickly being forced to close.

The controversy over the authenticity of AI content exposes the crisis of trust in information consumption. The general public still cannot distinguish between true and false in visual media, while the platform's "only forwarding and publicizing" attitude shows the weakness of the regulatory chain. At the same time, seven domestic mobile phone end-side models were approved for filing, marking that China's "compliance" of generative AI has entered the practical stage. Regulatory authorities have shifted from "whether to go online" to "whether to comply", requiring models to run locally and data not leaked. This is a pre-emptive defense against risks such as AI illusions and data leaks. Taken together, the two show that while technology is rapidly penetrating, regulation is shifting from passive tracking to active delineation. The industry must invest resources in model interpretability, copyright identification and data governance, otherwise it will face the cost of loss of trust.

At the industrial level, the cooperation between Nvidia and Toyota extends AI from in-vehicle sensing to robots and factory simulation, indicating that "AI software and hardware integration" will become a symbiotic path between automobile and manufacturing. Anthropic's free release of K12 teachers by Claude directly impacted the core business of education technology company Stride, showing that the penetration of large models in vertical industries has entered a critical node in the value chain. DeepSeek CEO's daily salary is 5500 yuan and assets exceed US$36 billion, which proves that the return of capital on model research and development remains high. This will attract more high-paying talents, further push up talent costs, and form an "AI talent dividend" and "ordinary job compression". The two-way tension.

Taken together, the value of these events is that they have accelerated the speed at which AI moves from concept to implementation, and have also led to the simultaneous upgrading of supervision, talent, and business models. The risks focus on the game between lack of trust and lagging supervision: if the platform does not identify and label AI-generated content in a timely manner, public fear of AI may turn into policy suppression; if supervision is excessively harsh, it may inhibit innovation vitality. Beneficiaries include chip manufacturers with localized computing power and data governance capabilities, model providers, and industry players who can quickly iterate security frameworks; while traditional media, low-tech creative positions, and start-ups lacking compliance systems face marginalization. crisis. In the next six months to a year, the AI regulatory framework will be further refined, the ecological competition of end-side models will shift from "being able to run" to "running well", and modeled services in vertical industries will become the focus of a new round of commercial competition.

The controversy over AI generation at the Hohhot Photography Competition and the news that DeepSeek intern daily salary broke records point to a core contradiction under the rapid development of AI technology: the conflict between authenticity and efficiency, and the resulting chaos in value judgment. When the content generated by AI is fake and even raises doubts in professional fields (such as photo competitions), we can't help but ask, where is the value of AI reflected? Does it accelerate content production, or does it blur the line between creation and reproduction? DeepSeek's high-paying recruitment may seem to be a sign of the booming development of the AI industry, but it may also indicate excessive competition for talents and rapid cost expansion. Whether this "burning money" model can be sustained and ultimately transformed into sustainable business value remains to be seen. Historically, whenever a new technology is over-sought after, it is often accompanied by a bubble. What ultimately survives are those AI applications that truly solve practical problems and create unique value, rather than those that just pursue speed and The "concept" of scale.

Another clue worthy of attention is that the end-side AI services of many mobile phone manufacturers have been registered, including Apple, Huawei, OPPO, vivo, Xiaomi, etc., and Wall Wall Intelligence has reached a cooperation with Samsung mobile phones. This marks that AI is moving from the cloud to the terminal, from "model" to "product". However, the implementation of end-side AI is not achieved overnight. Although it promises better privacy protection and faster response times, its computing power, energy consumption and limitations of the model itself remain huge challenges. We have seen that problems such as AI illusions and model collapse still exist, and product managers are still having headaches about how to "get rid of AI illusions." This means that even if technical capabilities are recognized, how to truly integrate AI into users 'daily lives and how to design smart and reliable products still requires a lot of exploration and trial and error. At the same time, ethical and copyright issues related to AI-generated content (AIGC) have also arisen. The photo competition incident is just the tip of the iceberg, and there may be more similar disputes in the future.

In addition, the expansion of cooperation between Nvidia and Toyota to focus on safer and smarter self-driving cars, and Anthropic's launch of the teacher's version of Claude, which directly impacts the business of education technology company Stride, all show that AI is accelerating its application in vertical industries. But behind these collaborations, there are also hidden risks. The cost of verification and testing for the safety of autonomous driving is astronomical, and any mistake can lead to catastrophic consequences. Although Anthropic's free tools can attract users in the short term, its long-term profit model and its disruptive impact on existing education technology companies will take time to test. We have seen that the decline in Stride's share price is precisely the market's concern about this disruptive competition. These cases remind us that the commercialization of AI is not just a matter of technology, but also requires a deep understanding and prudent response to existing business models, social ethics, and laws and regulations.