In June 2026, a piece of news set the AI world ablaze: DeepSeek was reported to have completed its first round of financing of approximately $7 billion. At the same time, Google's released Gemini Spark was called "the most amazing and also the most terrifying AI experience." And in the domestic market, low-code platforms + AI boosted software development efficiency by 300%, and WeChat Mini Programs opened AI capabilities to developers. These seemingly independent events actually point to the same trend—AI Agents are accelerating their implementation,enterprise digital transformationis entering truly deep waters.
According to industry news, DeepSeek's first financing round reached as high as approximately $7 billion, making it one of the startups with the highest financing amount in the global AI field. Compared with international players such as OpenAI and Anthropic, DeepSeek has taken a completely different route—open source.
Since 2025, DeepSeek has continuously released multiple open-source large models, gaining widespread attention in the global developer community. Its performance in reasoning capabilities has led many industry insiders to believe that China's AI technology route has gained international recognition. The $7 billion financing means that capital providers have cast a huge vote of confidence in this "open source + high performance" route.
From thesoftware developmentperspective, the open-source ecosystem of foundational models like DeepSeek is enabling AI capabilities to reach every developer at a lower cost. In the past, the cost of calling top-tier large model APIs was prohibitively high; now, open-source models can be deployed on private servers, and enterprises can build their own AI applications more flexibly.
Google's Gemini Spark was evaluated by The Verge as "the most amazing and also the most terrifying AI experience to date." This statement precisely summarizes the current state of the AI industry: on the one hand, AI capabilities are indeed evolving rapidly; on the other hand, this evolution has also triggered widespread discussion about safety, ethics, and employment.
Gemini Spark is not just another large model update. It has achieved obvious breakthroughs in multimodal understanding, real-time interaction, and Agent autonomous decision-making capabilities. This meansAI Agentis no longer just a tool that "can chat," but has begun to possess the ability to "do work"—it can understand complex tasks, break down steps, call tools, and execute autonomously.
For the software development field, this change is particularly significant. The traditional software development process—requirements analysis, design, coding, testing, deployment—requires a large amount of manual work at every stage. Now, AI Agents are penetrating every stage. From automatic code generation to test case writing, from architecture suggestions to deployment automation, AI is transforming from an "auxiliary tool" into a "collaborative partner."
Industry data shows that after low-code platforms are combined with AI capabilities, software development efficiency has increased by about 300%. This number is not empty talk.
TakingWeChat Mini Program developmentas an example, in the past, developing a Mini Program with complete functionality required front-end development, back-end development, and UI designers—at least 2-3 people coordinating for about a month. Now, with AI-assisted low-code development platforms, the same workload can be compressed to about a week. For companies engaged inMini Program developmentandAPP development, this is a substantial efficiency breakthrough.
Specifically, AI has the most significant effects in the following stages:
This does not mean developers will lose their jobs. On the contrary, developers are liberated from repetitive labor and can focus more on high-value work such as business logic, architecture design, and user experience. AI has not replaced developers, but has redefined the content of developers' work.
WeChat recently opened AI capabilities to developers, which means Mini Programs can more conveniently integrate functions such as natural language processing, image recognition, and intelligent recommendations. For the Mini Program ecosystem serving hundreds of millions of users, this is an important capability upgrade.
For companies choosingWeChat development, the opening of AI capabilities brings several new opportunities:
From the perspective of technical implementation, the opening of AI capabilities has lowered the threshold for WeChat development. AShenzhen software developmentcompany that in the past might have needed a dedicated AI team to do these things can now simply call open interfaces.
In 2026,Internet of Things+AIoT market size will break through one trillion. Behind this number is continuous industry transformation: traditional IoT devices are evolving from "data collection" to "intelligent decision-making."
Takingsmart community solutionsas an example, AI access control systems not only support facial recognition and remote door opening, but can also achieve anomaly alerts through behavior analysis. IoT management platforms can uniformly schedule subsystems such as security, lighting, and energy within a community, achieving true intelligent management.
For enterprises, this means a huge market opportunity. Whether it is the digital transformation of traditional enterprises or the intelligent upgrade of new projects, professional development teams are needed to complete system construction and integration. This is precisely the core battlefield for software development companies.
Industry observations show that IoT projects with AI capabilities have 40%-60% higher operational efficiency than traditional solutions, while the overall cost of ownership has instead decreased. This is the true commercial value of AI+IoT—not showing off technology, but real cost reduction and efficiency improvement.
A more fundamental trend is taking place: enterprise software is migrating from traditional ERP architecture to AI-native architecture.
Traditional ERP is process-driven, and users need to operate according to preset processes; AI-native architecture is data-driven, and the system can proactively predict needs and recommend action plans. According to observations by Xiangming Technology, since the second half of 2025, more than 30% of newly built enterprise systems have adopted AI-native architecture design.
What does this mean? For the software development industry, this is a new round of "wardrobe change"—just like the transition from the desktop software era to the mobile internet era, a large number of enterprise systems need to be redesigned and developed.
In the past, when a manufacturing enterprise implemented an ERP system, it required a large amount of custom development, and once the processes were fixed, it was difficult to adjust them. But under AI-native architecture, the system can adapt to changes in business processes through machine learning.Management system developmentis no longer "fixing processes," but "training models."
This paradigm shift has also changed the capability requirements for software development companies. In the past, the core competitiveness was "writing business logic clearly"; in the future, the core competitiveness will be "training good models with data." This requires software development companies to understand both traditional business and have AI engineering capabilities.
Based on the above trends, here are several recommendations for enterprises that are advancing digitalization:
First, do not wait, start running first. The pace of AI technology development far exceeds expectations. What is still being discussed as feasible this year may already have become standard next year. Enterprises should launch AI pilot projects as soon as possible within the feasible scope and accumulate data and experience.
Second, pay attention to the open-source ecosystem. The success of DeepSeek proves the vitality of the open-source route. The solution of open-source models + private deployment can both ensure data security and allow flexible customization. When choosing a software development partner, priority can be given to teams with experience in applying open-source AI technologies.
Third, AI Agent is the next wave of focus. Judging from the performance of Gemini Spark, AI Agent capabilities are rapidly maturing. Enterprises can consider embedding AI Agents into core business processes, rather than merely using them as Q&A robots.
Fourth, choose development partners with industry experience. Technology itself is not scarce; what is scarce is understanding of the business. Whether it is smart communities, e-commerce platforms, or management system development, choosing the right software development partner is more important than simply pursuing technological advancement.
This article is originally published by Xiangming Technology · Reproduction requires attribution
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