Wednesday, May 27, 2026

I shook my head when I watched Xiaomi's model sale. A 50% price reduction will be effective permanently and is packaged as a technological breakthrough? Obviously, I am worried by open source models and vertical mini-models. In the financial report, the gross profit of mobile phones rose to 24%, but the net profit was only 6%. Now, the money earned from hardware is used to fill the bottomless pit of AI. A typical Internet approach-but the burning rate of large models is ten times that of the mobile Internet back then. Those entrepreneurs who fantasize about relying on MiMo to build a "one-person company" should wake up. The 30,000 bankruptcy cases in the WeChat hot article tell you that AI can generate PPT, but it cannot generate customer payments.

The fact that enterprise-level AI is stuck in the data quagmire is more fatal than imagined. The cases complained by four leaders in different industries are too typical: the bank reconciliation system cannot even unify the basic fields, the e-commerce risk control rules are all manually adjusted by old masters, and even breakpoints in the financial system have to be filled in manually. Put an LLM in at this time? It's like putting autopilot on a leaky boat. What's even more ironic is that the developer community is frantically creating the artifact of "knowledge mapping to understand code", and companies can't even gather structured data. The reality is that no matter how strong the Lance model, which ranks first in the HuggingFace trend list, is not enough to cure the company's 20-year-old Excel sheets.

The most alarming thing is that technical ethics debts begin to be settled. The three-body CEO poisoning case has exposed a shady story about equity disputes. The Netherlands directly confiscated 800 cybercrime servers, and California almost forced age verification for Linux-the knife of supervision is getting faster and faster. But the real depth bomb is Sutton's "Age of Experience": The father of intensive learning admitted that "current AI learning without physical feedback is lame walking." Looking at the biomedicine article, the technology of 3D printing intestinal organs to grow the nervous system on their own is ten times more important than the large model. After all, the AI that can repair the human body is just what it needs, rather than generating more little red book hit titles.

Entering AI now is like rushing into the Bitcoin mine in 2020: giants are fighting a price war to clear the situation, small and medium-sized enterprises are stuck in the pit of data infrastructure, and the regulatory sledgehammer will fall at any time. But from another perspective, the technology used in the Netherlands to copy servers, MIT's open source solution to print organs, and even the local methods of small companies to remove the "AI smell" are real-world engineering wisdom. Instead of chasing new model releases, it's better to see how Pang Donglai uses community trust to fight against Sam's algorithm-sometimes humanization is the ultimate technical barrier.

Among today's science and technology signals, there are several important themes worthy of in-depth discussion. The first is Xiaomi's AIoT business and its price strategy, which not only represents the company's continued investment in the field of artificial intelligence, but also reflects the large-scale application stage that the entire industry is going through. The sharp price cut for Xiaomi's mimo-V2.5 series models is ostensibly a market strategy adopted by the company to expand market share. In fact, it is a direct reflection of the maturity of AI technology and cost reduction. By lowering the price threshold, Xiaomi allows more users to access and use AI technology, thereby promoting the rapid development of the entire AI ecosystem. This approach is similar to the previous "100 trillion Token creator incentive plan", which both use incentives and price reductions to accelerate the popularization of technology. However, this large-scale price reduction may also trigger other manufacturers to follow up, leading to price wars within the industry and posing challenges to the survival of small and medium-sized enterprises.

Another theme worthy of attention is the frequent incidence of academic misconduct, which not only exposes regulatory loopholes in scientific research, but also reflects potential problems caused by the application of AI-generated content in academia. Scholars holding the title of "Changjiang Scholars" are reported to have forged papers. The frequent occurrence of such incidents will not only damage the reputation of universities, but may also affect the overall credibility of scientific research. In the context of the increasing popularity of AI-generated content, this kind of fraud is easier to cover up, but it is also easier to be seen through. This reminds us that although AI technology can generate high-quality content, the ethical and regulatory issues behind it cannot be ignored. In the future, academia and regulatory agencies need to strengthen cooperation and formulate more stringent data verification and review mechanisms to ensure the authenticity of scientific research results.

The transformation of the supermarket industry is also an important signal. The transformation from "big and comprehensive" to "small and beautiful" reflects the transformation of consumer demand from a single price orientation to diversification, personalization and experience orientation. The defeat of traditional supermarket companies such as Yonghui and Carrefour is in sharp contrast to the rise of emerging players Sam and Pang Donglai. The "Dream City" built by Pang Donglai with an investment of 6.5 billion yuan is not only a commercial project, but also a new community-based and experiential attempt. This model emphasizes deep interaction and trust building with users, which is in line with current consumers 'higher expectations for products and services. In the future, this user-centered supermarket model may be further popularized. Traditional supermarket companies need to cope with the new competitive situation through transformation and upgrading. At the same time, the government's measures to support residents in improving housing conditions, such as Guangzhou's pilot purchase of second-hand houses with less than 3 million yuan and less than 70 square meters, have also provided certain policy support for this transformation and helped accelerate community-based and personalized business. Implementation of the model.

Overall, these events reflect changes in the market landscape brought about by technological maturity and cost decline, ethical and regulatory challenges facing academia, and business model transformation driven by the diversification of consumer needs. Technological progress has brought new opportunities, but it is also accompanied by a series of problems and risks. Sustainable development can be achieved through the joint efforts of all parties.

Today's most prominent theme is the huge gap between the actual implementation dilemma of AI tools and market expectations. The sharp price cut for Xiaomi's MiMo-V2.5 model is in sharp contrast to the WeChat hot article "30,000, Half Year, Cancellation: The First Batch of 'One-Man Company' Bosses in 2026 Have Retired". The former reflects the anxiety of AI service providers facing commercial pressure, while the latter reveals the cruel reality of the bursting of the AI entrepreneurial bubble. When the price war for AI tools reaches the level of the "100 trillion Token incentive plan," what we see is not technology benefits, but the customer acquisition dilemma after the homogenization of basic services-this is the same as the 2018 cloud computing price war and the 2021 low-code platform scuffle script.

The progress of enterprise-level AI applications is equally worrying. The data silos problem pointed out in the article "Four bosses, four industries, but they dare not join AI for the same reasons" essentially exposes the limitations of AI as a solution. History is always strikingly similar: ERP systems in the 2000s and big data platforms in the 2010s faced the same organizational resistance. While technology suppliers are keen to demonstrate the cool ability to "generate everything," customers are paying for old issues such as data compliance, department collaboration, and KPI assessment. CITIC Construction Investment's research report on beryllium metal confirms this point from the side-what really generates value is still dedicated technologies (such as aerospace materials) to solve specific problems, rather than general-purpose AI.

Another sign worthy of vigilance is AI fatigue in the field of content creation. The popularity of "Eliminating" Criminal Evidence ": An Incomplete Manual to Remove the"AI Taste "from Writing" reflects users 'aesthetic fatigue about AI-generated content. This phenomenon has the same evolutionary path as the proliferation of SEO content farms in 2010 and the homogenization of Short Video in 2016. When "bean bags" become the object of ridicule on social media, it means that the market is shifting from technological novelty to value screening. Interestingly, the update of the 36Kr corporate public opinion tool has verified the value of human analysts-in the era of information overload, the ability to screen and insight is more scarce than content production capabilities. This may imply that the focus of AI's competition in the next stage: it will shift from rough generation quantity to refined quality control and personalized services.

Overall, the current AI industry is repeating the typical trajectory of past technology bubbles: from capital mania to setbacks in implementation, to value revaluation. The difference is that this adjustment may come faster-there is only half a year between the price reduction of the Xiaomi model and the mass death of a one-person company. When the technology dividend period has been shortened to this extent, those entrepreneurial mentors who are still advocating "letting AI work for you" may have time to revisit the historical lessons of the 2000 Internet bubble and the 2016 sharing economy collapse.