In July 2026, two seemingly unrelated news stories hit the trending topics at the same time: one was "90% of AI short drama companies are losing money," and the other was "fried dough stick sellers are starting to bet on robots." The former reflects that the AI startup bubble is accelerating its collapse, while the latter points to a more real trend—enterprise digital transformation is moving from conceptual hype to real implementation, and traditional industries' demand for software development and IoT technology is more urgent than ever.
These two news stories seem contrasting, but in fact they reveal the same underlying logic: the value of AI does not lie in how many new concepts it creates, but in whether it can be embedded into real business processes and solve specific problems. When AI short drama companies have burned through investors' money and still cannot find a profit model, the owner of a street-side breakfast stall doubled efficiency with automated equipment—this is the signal the software development industry should really pay attention to.
According to industry media reports, about 90% of companies in the current AI short drama track are operating at a loss. When AI-generated video technology first emerged in 2025, a large amount of capital poured into this field, and entrepreneurs believed that AI could produce short drama content in batches at extremely low cost and quickly capture the market. However, reality dealt everyone a heavy blow: AI-generated content is still far from passing grade in narrative logic, emotional expression, and audience experience, and viewers are unwilling to pay for shoddy content.
This phenomenon is not isolated. Over the past two years, from AI painting to AI writing, from AI customer service to AI marketing, a large number of products that tried to use AI to directly "replace people" have encountered similar difficulties. On the surface, it is because the technology is not mature enough, but the deeper reason is that these projects ignored the basic laws of software engineering from the very beginning—good products require clear requirement definitions, rigorous system architecture, and continuous user feedback iteration, rather than simply wrapping an AI model around something and delivering it.
Key insight:AI is not a universal silver bullet. Between packaging a large model into a product and truly making a good product lies a completesoftware developmentprocess—requirements analysis, architecture design, front-end and back-end development, testing, deployment, and continuous operations and maintenance. The predicament of AI short drama companies is precisely the price of not respecting this process.
In sharp contrast to the noise of AI short dramas is a news story from a traditional industry—breakfast stall owners are beginning to purchase automated equipment, and the process from frying dough sticks to packaging is being replaced by machines. This seemingly "small" event is actually a microcosm of the digital transformation of thousands of traditional enterprises.
When a breakfast stall owner is willing to spend tens of thousands of yuan on automated equipment, he does not care whether the machine uses the latest large model, nor whether it is connected to an AI Agent. He cares about only three things: whether it can produce food stably, whether it can reduce labor costs, and whether it can support peak-hour capacity. This pragmatic demand is exactly the scenario most familiar to the software development industry—customizing management systems for clients, developing IoT device control platforms, and building smart operations systems that connect online and offline.
From automated equipment at breakfast stalls to MES systems in factories, and then to property management companies'smart community solutions, the digital needs of traditional industries are moving from the stage of "whether to do it" to the stage of "how to do it." This brings two important signals to software development companies:
In July 2026, another industry development worth noting is the continued evolution of Chinese open-source large models such as DeepSeek. The open-source route represented by DeepSeek is gaining global recognition, which means the underlying cost of enterprise-level AI applications is declining, and more small and medium-sized enterprises have the opportunity to access AI capabilities within a reasonable budget.
At the same time, the combination of low-code platforms and AI is redefining the efficiency boundaries ofWeChat developmentandmini-program development. According to industry data, AI-assisted low-code development tools have increased software delivery efficiency by about 300%. In the past, a medium-sized enterprise-level mini program required front-end and back-end collaborative development for three to four weeks; now, with the help of AI code generation tools, the initial build can be completed within a week. This leap in efficiency is changing the competitive landscape of the entire software development industry.
Industry Data at a Glance:
· 2026 IoT + AIoT market size: exceeded one trillion
· Efficiency improvement from AI-assisted low-code development: about 300%
· Growth rate of enterprise-level AI Agent applications: over 150% year-on-year
· Global downloads of DeepSeek open-source models: exceeded tens of millions
Enterprise digital transformation is moving from the ERP era into the AI-native architecture stage. In the past, when enterprises adopted systems, the core demand was to "move offline processes online," with typical scenarios being financial systems, inventory management, and OA approval. Today's enterprise customers, however, require systems not only to record data but also to analyze data, predict trends, and assist decision-making. This means the demand forAPP developmentandmanagement system developmentis upgrading from "informatization" to "intelligence."
A typical change is this: in the past, building a property management system had core functions such as repair requests, payments, and announcement notifications. Now customers require the system to connect to AI access control, monitor energy consumption data in real time, and automatically identify abnormal situations through IoT devices and issue timely alerts. The same logic applies to e-commerce, catering, education, and other industries—customers' digital needs are upgrading from "whether there is a system" to "can the system help me make judgments."
The upgrade of the WeChat mini program ecosystem in 2026 is also a key focus worthy of attention for software development companies. The WeChat platform is opening more AI capabilities to developers, including modular APIs such as intelligent customer service, image recognition, and voice interaction. This meansWeChat mini program developmentIt is no longer just front-end engineering, but an important implementation scenario for AI applications.
For those engaged inWeChat developmentfor teams, this is an important window for capability leap. In the past, the core skills of WeChat development were front-end rendering, API integration, and user experience optimization. Now, developers need to understand how to call AI models, master the basics of Prompt Engineering, and know how to integrate AI capabilities into user interaction flows. This is both a technical challenge for development teams and an opportunity to widen the competitive gap.
According to observations, the directions in the WeChat ecosystem with relatively high AI capability penetration currently include: intelligent customer service bots (replacing more than 70% of routine inquiries handled by human customer service), AI product recommendation (increasing conversion rates for e-commerce mini-programs by 20-30%), and AI content generation (official account assistants, marketing copy tools). These directions are all worthmini-program developmentteams laying out in advance.
Back to today's two trending topics. "90% of AI short drama companies are losing money" does not mean AI has no value; rather, it reminds the entire industry: the path to technology implementation cannot rely on burning money for a sprint, but must respect product principles and business logic. In contrast, although "a fried dough sticks seller betting on robots" sounds less sexy, it represents real market demand - as long as technology can solve specific problems, customers are willing to pay.
For technical service providers in software development and IoT, the current market environment can be summarized in one sentence: the bubble is receding, and demand is upgrading. Enterprise digital transformation has moved from the discussion period of "whether to do it" into the execution period of "how to do it and how well to do it." Those enterprises that can provide reliable, stable technical solutions that truly solve problems will win long-term customers in this round of reshuffling.
Having deeply cultivated the software development field for many years, we observe that the biggest change in 2026 is not a performance breakthrough of some AI model, but an upgrade in customers' understanding of technical services. They are no longer attracted by conceptual packaging, but instead look solidly at delivery capability. For the entire industry, this is a healthy signal after the bubble recedes - the era of truly building products has only just begun.
Note: The industry data involved in this article is compiled from public reports and information released by industry research institutions, for reference only.
— END —
Room 1409, Building C, Shenzhen Century Industrial Center, No. 743 Zhoushi Road, Hezhou Community, Hangcheng Sub-district, Bao'an District, Shenzhen
