When The Verge proclaimed "this is the great AGI rebrand moment," when DeepSeek's open-source models made the global AI community re-examine the value of China's technology路线, when the SaaS industry accelerates its reshuffling under the impact of AI-native applications—in the summer of 2026, AI is no longer a "future concept," but an underlying operating system that is reconstructing every industry.
This article will deeply analyze the real changes in the current AI+software industry from three dimensions: industry landscape, technology路线, and application implementation, and provide an actionable thinking framework for enterprises that are planning digital upgrades.
A recent cover article in The Verge put forward a sharp viewpoint: "It's the great AGI rebrand"—companies are quietly upgrading their products from "AI assistants" to "AGI platforms."
This is not a simple upgrade of marketing rhetoric. What it reflects behind it is a fundamental shift in the entire industry's positioning of AI.
Over the past two years, the AI used by most enterprises was still at the stage of "Q&A interaction": you ask it a question, and it replies with a paragraph. But by 2026, AI has evolved to the stage of "task-based interaction"—you give it a goal, and it autonomously plans, executes, and iterates until completion.
This shift directly drivesenterprise digital transformationinto deep water. In the past, ERP systems were the core engine of enterprise digitalization; now, AI Agents are becoming the new "digital brain," directly orchestrating every link in business processes.
This is also why we say: enterprise digitalization is moving from ERP to AI-native architecture.
In Shenzhen, more and more technology companies are abandoning the traditional digitalization path of "tool stacking" and instead building a new generation of business systems centered on AI Agents. According toXiangming Technologyobservations, for those enterprises that were the first to complete the transformation to AI-native architecture, the business process automation rate jumped from 30% to more than 75%.
OnXiangming Technology official website (xiangmingit.com), we have compiled cases of enterprise digital upgrades we participated in over the past six months, and a clear trend has emerged: whether in manufacturing, retail, or services, "AI + core business flows" is becoming the standard answer for digital transformation.
One of the most talked-about events in the global AI community in 2026 is the rise of DeepSeek's open-source models.
Why has DeepSeek attracted such widespread attention? There are three reasons:
First, cost crushing. DeepSeek's training cost is only about one-tenth that of comparable GPT-level models. This has led many developers to rethink a key question: does building a high-level AI application really require burning hundreds of millions of dollars?
Second, open-source transparency. DeepSeek adopts a fully open-source strategy, with model weights and training code all made public. This means any enterprise can conduct private deployment and fine-tuning based on DeepSeek—for industries with strict data compliance requirements (such as finance, healthcare, and government affairs), this is almost a "gift from heaven."
Third, the technology路线 has gained global recognition. For a long time, China's AI was labeled a "follower." But DeepSeek's innovation in the MoE (Mixture of Experts) architecture has made top AI researchers worldwide take notice. This marks the beginning of China's AI technology路线 moving from "following" to "running in parallel."
Forsoftware developmentindustry, what does DeepSeek's open-source ecosystem mean?
Simply put:AI capabilities are no longer the patent of a few giants.
TakingShenzhen software developmententerprises as an example, in the past, if they wanted to develop AI features for B-end clients, they often needed to call paid APIs from OpenAI or other overseas giants, with high costs, high latency, and high compliance risks. Now, based on domestic open-source large models such as DeepSeek or Qwen, local developers can deploy enterprise-grade AI capabilities on their own servers, truly achieving "data does not leave the domain, AI stays by your side."
OnAPP developmentfield, this change is especially obvious. More and more developers are trying to embed lightweight open-source large models into mobile applications to achieve on-device intelligence—completing functions such as text generation, content summarization, and intelligent search without relying on the network.
Another hot article from The Verge mentioned an interesting phenomenon: "Writers are fleeing the Substack Tax"—creators are fleeing Substack's high commissions.
And another phenomenon is equally striking: "Your feed is overrun with clips"—content on social media is being occupied by an army of "clippers." These clippers use AI tools to quickly break long videos and long articles into fragmented short video clips and short posts, then distribute them across platforms to harvest traffic.
The common driving force behind these two phenomena is: AI tools have greatly reduced the cost of content production and distribution.
Fore-commerce platform development, this trend is changing the underlying logic of "shelf e-commerce." In the past, the core competitiveness of e-commerce platforms was "more products, lower prices"; now, "good content, accurate recommendations" is becoming the new decisive factor. AI-driven product content generation, AI product selection, and intelligent customer service are becoming standard capabilities of e-commerce platforms.
Big data analyticsplays a key connecting role in this. By analyzing users' browsing trajectories, purchasing behavior, and content interaction data, AI can know what users need even before they do—and then push it into their information feeds.
Xiangming Technologyhas served several e-commerce clients that have already integrated AI product selection + intelligent customer service + content generation into one, building a full-chain intelligent system from "product selection to conversion." According to their feedback, after integrating the AI system, average user dwell time increased by 120%, and repurchase rate increased by 45%.
"SaaS industry reshuffling: AI-native applications are redefining software"—this is not a prediction, but a fact in progress.
In 2026, almost all SaaS products are doing the same thing: cramming AI capabilities into their products. But "cramming in" and "native integration" are two different things.
A true AI-native application is not adding an AI chat button to traditional software, but redesigning the underlying logic of the software—making AI the core interface for interaction.
For example: with a traditional CRM, users need to manually enter customer information, manually mark follow-up status, and manually set reminders. With an AI-native CRM, users only need to say "follow up with those interested customers from last week," and the system will automatically identify which customers they are, automatically generate personalized follow-up scripts, and automatically schedule appropriate follow-up times.
This is the essential difference between "SaaS+AI" and "AI-native SaaS."
Onmanagement system developmentfield,Xiangming Technologyhas noticed a noteworthy phenomenon: more and more enterprise clients no longer ask "can you build OA/ERP/CRM," but instead ask "can your system automatically help me handle these business processes."
This is forcing the entiresoftware developmentindustry to transform from "feature delivery" to "intelligent delivery." For traditionalinternet platform development, this means a reconstruction of the entire product design approach—from "user operation" to "AI agent operation."
In the fields of WeChat development and mini-program development, this trend is equally obvious. After the WeChat Open Platform fully opened up AI capabilities in 2026, WeChat mini-program development ushered in a true era of intelligence—mini-programs have transformed from a "lightweight application" into a "lightweight AI entry point." A community group-buying mini-program can have intelligent customer service, intelligent recommendations, and intelligent scheduling—and all of this can be achieved by calling just a few lines of API.
In 2026, theInternet of Thingsindustry is undergoing a profound paradigm shift—from IoT (Internet of Everything) to AIoT (Smart Internet of Everything).
Industry reports show that in 2026, China's AIoT market size will officially exceed 1 trillion yuan. Behind this figure are changes in two key variables:
Variable one: the "commoditization" of AI chips. The price of MCU chips supporting local AI inference has dropped to under $5. This means that smart locks costing tens of yuan and smart cameras costing around a hundred yuan all have on-device AI capabilities—face recognition, voice recognition, and abnormal behavior detection no longer require "data to the cloud."
Variable two: edge AI matures. In past IoT architectures, all data was centralized in the cloud for processing, resulting in high latency, expensive bandwidth, and significant privacy risks. Now, more and more AI inference is completed at the edge, with the cloud only doing data aggregation and model updates. This allowssmart community solutionsto achieve a qualitative leap in response speed and privacy protection.
Xiangming Technology's team delivered severalsmart community solutionsIn projects, the application share of AIoT has increased significantly. A typical case is: the AI access control system not only supports facial recognition to open the door, but can also automatically detect tailgating behavior, coordinate elevator dispatch, and record visitor trajectories—all computation is completed on the built-in AI chip of the access control terminal, without relying on cloud services, with a response time of less than 200 milliseconds.
For enterprises that want to enter the AIoT field,Xiangming Technology's advice is:Start from the scenario, not from the technology. Although the AIoT technology stack is complex (involving hardware, embedded development, cloud architecture, and AI models), as long as you identify a real business pain point—such as "community high-altitude falling object monitoring"—you can quickly validate the value, and then replicate it horizontally to more scenarios.
In May 2026, we are experiencing a subtle turning point.
On the one hand, DeepSeek's open source has proven the "infrastructureization" of high-quality AI capabilities—it is becoming as readily available and low-cost as electricity. On the other hand, the "great AGI brand reshaping" means the industry is moving from "technology display" to "practical delivery." Those AI applications that can truly be implemented and can make money are accelerating to the surface.
For enterprises, this means a key judgment question: what should your business look like in an AI-native world?
There is no need to do it all at once. But at least you can start from one scenario: for example, use AI to transform a business process; or use AI to enhance a customer touchpoint; or upgrade a module of an existing system into an intelligent decision engine.
OnXiangming Technology official website (xiangmingit.com), we have planned step-by-step paths from "AI pilot" to "AI native" for enterprises at different stages. If you are interested in how to implement AI capabilities in your own business, welcome to visitXiangming Technology official websiteto learn more.
Finally, let's close with a sentence from that The Verge article:
"This is not just a rebranding. This is the entire industry finally realizing—AI does not exist to answer questions; it exists to complete tasks."
And those enterprises that are the first to use AI to complete tasks will, in this reshaping, stand ahead at the starting line of the next decade.
— Xiangming Technology · Let the power of AI enter every business scenario —
Official website:www.xiangmingit.com
Room 1409, Building C, Shenzhen Century Industrial Center, No. 743 Zhoushi Road, Hezhou Community, Hangcheng Sub-district, Bao'an District, Shenzhen
