Before diving into the discussion, let’s clarify a conclusion: AI Coding and Vibe Coding are not the same. AI Coding has great potential, but Vibe Coding warrants caution. The former targets professional developers, while the latter is aimed at non-professionals.

Recently, many so-called “Vibe Coding miracles” have emerged.

Whether it’s former AI skeptics like Rust expert Steve Klabnik creating a new programming language called Rue with AI, or Linus Torvalds, the creator of Linux, who once derided AI programming, now engaging in Vibe Coding, the trend is undeniable. Numerous Vibe Coding applications and web games have skyrocketed in popularity, with users eager to pay for solutions that resonate with their needs.

AI programming tools like Claude Code continue to break records, such as recreating a distributed agent orchestrator in just 10 days, which took Jaana Dogan’s team a year to conceptualize.

Antirez, the author of Redis, recently admitted that most projects no longer require writing code unless for fun or interest.

As companies like Anthropic refine their programming toolkits, including tools like Code Simplifier, the difficulty of writing code is expected to decrease.

The rise of powerful AI programming tools has made it increasingly difficult for traditional code data providers to thrive, leading to a dramatic drop in traffic for platforms like Stack Overflow. While AI has increased the usage of TailWind, it has also made it harder for its creator Adam Wathan’s company to profit, forcing significant layoffs.

However, most people focus on the superficial noise without recognizing a crucial point—code complexity.

Behind complex Vibe Coding products, there are professional engineers providing support and guidance. Conversely, simpler yet popular Vibe Coding products are often quickly replicated and suffer from numerous flaws, such as maintainability, scalability, and security risks.

Professional programmers emphasize that writing code has always been the least important step in development; the quality of the code is limited by AI’s lack of deep business understanding and complex architectural design capabilities.

Recently, a project involving millions of lines of code generated by GPT-5.2 over seven days was discovered to be non-functional and unfixable, illustrating the pressure that increased complexity places on AI.

Indeed, the scenarios where AI can validate feasibility are still limited, and the overall programming landscape is relatively optimistic. Replit’s CEO Amjad Masad recently noted that currently, the only two profitable agents are AI customer service and AI programming.

So why is AI programming feasible, and what are its limits? What is the underlying logic for assessing the viability of AI Coding versus Vibe Coding? To answer these questions, Zhiwei engaged with several industry experts.

Overall, experts remain optimistic about AI Coding while expressing skepticism about Vibe Coding’s current state. However, they do not dismiss the long-term rationality of Vibe Coding; it is merely a product of the capital market’s “AGI vision,” similar to the concept of a “general agent,” which carries the risk of being overhyped.

Recognizing the current state and exploring how to rationally progress toward the ideal of Vibe Coding is the goal of this discussion. This applies not only to entrepreneurs in Coding Agent products and software products but also to anxious programmers today.

This article consists of the following nine chapters, which you can view as needed:

• What are AI Coding and Vibe Coding?
• The Essence of Optimism for AI Coding
• The Essence of Pessimism for Vibe Coding
• The Existing Gap Between Domestic and International Markets
• Key Landing Scenarios: Legacy Code Refactoring
• Impact on Traditional SaaS Markets
• The Influence of AI Coding on Programmers
• Collaboration with AI
• Future Prospects

Before we formally enter the discussion, let’s clarify the concepts thoroughly.

Zhang Senseng, head of the technology platform group at Ping An Insurance, explained to Zhiwei, “In essence, AI Coding refers to developers using large model languages to assist in software development, primarily covering coding, debugging, refactoring, and testing processes. The most typical tool currently is GitHub Copilot.” He added, “The entire development process is still primarily led by system architects and leaders. AI plays a role more akin to a ‘role programmer’ from an agile development perspective. The core objective of AI Coding remains focused on improving engineering efficiency.”

“At the level of Vibe Coding, there are new changes. Previously, humans adapted to code, but Vibe Coding advocates ’embracing this exponential growth’ and even forgetting the existence of code altogether. Its fundamental logic is that programmers should adapt to this ‘Vibe,’ driving development through intuition and feelings. In this model, users are mostly non-professional developers, often business personnel, product managers, or practitioners from non-technical backgrounds taking on development roles.”

“Vibe Coding emphasizes completing development through ’natural language descriptions of intent,’ allowing AI to achieve end-to-end code generation, from understanding requirements to UI design, from front-end code generation to back-end database connections, and even including deployment tasks.”

It can be understood that the definitions of “AI Coding” and the concept of Agentic Engineering mentioned by Andrej Karpathy on February 5 are similar in this article.

According to Wang Wei, co-founder of GitMe.ai, the AI Coding direction represented by products like Claude Code and Cursor does not exhibit a bubble. He told Zhiwei, “The reason is that the industry has not yet reached a consensus on the future development of AI, and the final form of software delivery and development empowered by AI technology.”

“Also, while the iteration speed of AI may not be as rapid as in 2022 and 2023, it remains relatively fast in the AI programming track. Whether it’s OpenAI, Anthropic, or Anysphere (the parent company of Cursor), at least one or two market-impacting products are released each month.”

“Since technology continues to iterate, it means user experience remains unstable, indicating that there is still exploration needed in the user workflow with AI. The exploration phase is not a bubble.”

“If capital is paying attention to this track and willing to invest, it might make the track somewhat noisy, which is inevitable.”

“Five years ago, the consensus on software engineering was that ‘DevOps is definitely the future of the industry,’ and it was a clear concept: from organizational collaboration to CI/CD pipelines to specific engineering practices, there was a systematic description. Today, AI Coding lacks such a systematic description, so I don’t believe the market demand is far less than the investment from startups and capital; the overall market space remains vast.”

In contrast, Zhang Senseng is not optimistic about the Vibe Coding direction represented by products like Lovable and Bolt.New, stating, “The end-to-end nature of Vibe Coding indicates that it aims to bypass the technical layer to enhance innovation speed and accelerate the transformation from idea to product. Therefore, Vibe Coding is genuinely promoting universal development, a concept that is quite common abroad, allowing non-professionals to participate in development.”

“However, Vibe Coding faces a core issue in practice; it relies entirely on natural language-driven processes and end-to-end generation, which inevitably leads to high uncertainty in many intermediate generation links. Once the complexity of the program increases and long-term maintenance is required, the drawbacks of this model will become apparent.”

“Users cannot perform very deterministic verification or control over the system, making it extremely fragile and filled with various vulnerabilities, thus unmaintainable. Therefore, I am not particularly optimistic about the Vibe Coding direction; such software is essentially disposable.” The preference for innovation and the disposable nature of output indicate doubts about the demand rigidity and users’ willingness to pay continuously for Vibe Coding products.

In terms of efficiency improvement, the user experience of AI Coding is indeed astonishing. Wang Wei shared specific examples, “The most impressive point is its ability to rapidly kickstart new projects. Previously, launching an interactive prototype required a four-person team two weeks, equivalent to 40 person-days, which was costly.”

“Now, the situation is entirely different; the monthly fee for AI tools might only be $10 or $20, and it may take just 5 minutes or even less to complete this work. The improvement is so significant that we believe it can no longer be termed efficiency enhancement but a complete disruption of the original workflow. This means we need to rethink people, processes, and organizations repeatedly.”

For instance, regarding people and processes, AI programming can also facilitate team collaboration. Chen Yuzhao, head of OneHouse Hudi Flink, told Zhiwei, “For code analysis, especially for newcomers or recent graduates, it used to take a lot of time to explain line by line what the code does to new colleagues when facing complex projects. Now, with AI’s help, new colleagues can quickly integrate into the team and gain a deeper understanding of the code.”

“Additionally, during programming, tools like Cursor can provide code suggestions, helping the team maintain a consistent coding style. If it is continuously informed about the team’s preferred style, the code style will be more uniform.”

“Finally, regarding testing, AI’s capability to write tests is quite strong. Cursor and Claude Code have matured in this area. While complex end-to-end tests may be challenging, basic unit tests with mock contexts are manageable. We even use Alibaba’s Tongyi Qianwen to generate test sets, requiring only minor modifications before submitting a PR.”

“Generating unit test code can indeed save a lot of time on ’tedious, dirty, and tiring’ tasks, allowing everyone to focus on other matters. Previously, unit tests were either neglected or insufficiently written. Now, with AI tools, everyone instinctively generates a version with AI first, resulting in richer test content.”

Claude Code once shared 13 usage tips, one of which is to “provide Claude with a way to validate work, which can improve the final result’s quality by 2-3 times.” This quality enhancement mechanism can now even be completed by the model itself, leading to significant efficiency gains, “in writing tests, it has helped us save about 30% to 40% of the time.”

Professional software development does not settle for creating a feasible prototype or relatively simple test scenarios; the ultimate goal is to refactor the prototype into enterprise-grade, production-level code, where AI Coding has also demonstrated strong execution and collaboration capabilities.

Chen Yuzhao stated, “Code refactoring is primarily aimed at enhancing usability and scalability (e.g., when users grow from 100,000 to 1 million, the system capacity needs to be scaled accordingly). Claude Code is indeed quite adept at code refactoring. However, to do this well, a very good input and interaction process is necessary.”

“For instance, if you provide the team’s accumulated coding style preferences over many years and enough contextual references, it will help you refactor effectively. But this process must go through a review. For example, it will submit a PR on GitHub, which you will review, ensuring that the review granularity is very detailed. Only when you tell it ‘OK, merge it’ will it execute, rather than blindly replacing the entire codebase; it is a controlled process, akin to a programmer communicating and collaborating with you.”

“You can even try having AI write some sample code first, then tell it what meets expectations and what doesn’t.”

“Through this continuous communication and adjustment process to accumulate context, you can gradually train AI to your desired specifications. If trained well, aside from code analysis, code refactoring should be one of Claude Code’s standout abilities.”

As professional developers, they can clearly perceive the limits of AI models in the AI Coding process, such as the complexity limit of tasks executed independently at one time, the understanding of new features, and the broader context comprehension capabilities, which serve as benchmarks for developers to determine when and how to take over.

For example, in code refactoring scenarios, the projects involved are often large in scale; what is the AI model’s current limit for executing complex tasks independently?

Chen Yuzhao stated, “Complexity should not be assessed by the number of lines of code in the entire repository; refactoring should be done on a functional module basis. Even if a project has 1 million lines, it can be divided into ten modules of 100,000 lines or even finer. The larger the project, the more the references and dependencies between code files resemble trees or graphs, and AI tools will analyze which classes and their complexities the refactoring functionality covers.”

“AI excels in refactoring scenarios that include basic logic transformations, such as renaming and code style changes; cross-language refactoring, such as switching from Java to Python or from Scala to Java, is something AI is particularly good at; another technique is progressive refactoring, where you first let it refactor one file, then ’train’ it to meet expectations before letting it handle the remaining files in the same manner.”

“As long as the scope is small enough and the logic is not overly complex, but requires a lot of manual effort to handle, AI performs exceptionally well and can save a lot of time.”

“Refactoring scenarios that are difficult for AI to handle include high-coupling core logic, such as the kernel code of a storage engine, where the logic is intricate and tangled; edge cases with numerous ‘patches’; if the core functionality has many upstream and downstream dependencies and numerous historical edge cases, refactoring must be done very carefully to avoid AI missing or incorrectly refactoring these patches.”

“To describe this more precisely and quantitatively, from the perspective of inter-module dependencies, for code scales covering forty to fifty modules and over two hundred files, especially if the logic itself is very complex with many edge-case logics, such refactoring becomes very challenging and still requires human leadership.”

Based on financial business scenarios, Zhang Senseng provided another layer of description, “Regarding the quantification of code complexity, it can be viewed based on the project’s scale and business depth to assess AI’s competency. Demo-level projects can generally be handled by all AIs, with a success rate of about 95% - 99%. For medium/independent projects (like internal enterprise tools), AI’s performance remains good, with a competency rate of around 70% - 80%. For complex business systems (involving microservices, payments, authentication, and high concurrency systems), AI can basically only perform code completion. Relying on it to understand and generate code is unrealistic, with a maximum competency rate of about 40% - 50%. In extremely high-complexity scenarios (like bank system refactoring), the code is very fragile, and any minor change can lead to unacceptable consequences; refactoring requires ‘surgical precision,’ and AI’s competency rate is very low, estimated at a maximum of 20%.”

In contrast to code refactoring, which mainly deals with legacy code, adding new features requires incorporating a lot of new business logic.

Chen Yuzhao clearly stated, “AI is not good at developing new features. We do not use AI when developing new features.”

“Because the logic for developing new features is more complex. As senior or experienced engineers, we spend a lot of time first establishing an idea, then discussing initial plans in rounds. We need to weigh several options, analyzing the advantages and disadvantages of each. Finally, we decide which plan to adopt, establishing the basic architectural framework and how the interfaces (APIs) will look. Writing code is just the last step. This decision-making and design process is too complex for AI to cover.”

“It cannot complete this process because the context required is not only extensive but also difficult to extract explicitly from an engineer’s thinking. The decision-making process is highly dependent on the engineer’s technical sensitivity and experience; for instance, during technology selection, engineers will have many considerations that AI currently cannot fully replicate or think like humans, nor does it possess the accumulated sensitivity and experience of humans over the years.”

Even if implicit context can be extracted, if the scale is too large, the current models are likely unable to handle it. Zhang Senseng noted, “Cursor currently employs RAG to alleviate this issue, but the industry does not yet have a perfect solution for long context. Although models like Gemini are attempting to address this by continually expanding context length, there is always a limit to length. In the early stages, Cursor’s conversations would start to deviate logically after about 10 rounds, and most domestic AI programming software is currently at this level.”

“However, as Claude or Gemini’s long context capabilities improve, this issue is gradually being resolved. In the future, we can only hope for further advancements in large model technology to fundamentally address the issue of detail forgetting from a foundational technical perspective.”

The outputs of Vibe Coding are generally disposable software, but that does not mean all products in this direction are worthless; Lovable is relatively well-regarded.

Zhang Senseng stated, “Compared to Cursor, Lovable has some innovations, such as its ability to show users the business interface in real-time, allowing users to see immediate effects. After generation, users can also interact with the UI to highlight specific issues and directly teach AI how to modify them.”

Despite these highlights, Lovable cannot escape the inherent issues of Vibe Coding; “its code maintainability is extremely poor, and it essentially produces ‘spaghetti code.’ For example, by the tenth round of generation, it can ruin a foundational logic from the first round, making effective debugging impossible.”

“In product development, while ordinary dashboards can be implemented very quickly, once it involves complex computations, high concurrency handling, special hardware interactions, or very intricate animation logic, web development becomes quite challenging. Currently, no product excels at handling logic with complex transitions and state associations (e.g., transitioning from point A to B, C, D, with D needing to maintain state synchronization with A).”

“While Claude is decent, Gemini’s recent front-end performance has also been surprising. However, relying on Vibe Coding for complex engineering projects is simply unrealistic.”

“Thus, even if Lovable is excellent, it still only generates disposable engineering outputs.”

Despite the significant limitations of Vibe Coding, similar products continue to emerge. More broadly, what is the underlying logic behind the frequent virality of AI products that claim to generate with a single sentence or offer end-to-end solutions, often boasting valuations in the tens of millions or even hundreds of millions?

Zhang Senseng stated, “Regarding the application boundaries of Vibe Coding, my advice is very clear: if you must use Lovable for a complex project, I suggest you ‘stop immediately.’”

“However, the logic of the capital market is entirely different. The capital market values the ’end-to-end’ vision. In the eyes of investors, this is a direction that must be developed in the future. Just as discussions about large models have evolved beyond just the models themselves to directly pointing towards AGI, the capital market’s aspirations for AI have reached a new level, transcending simple code completion to envision ’embodied intelligence’ running everywhere.”

“Therefore, from a capital perspective, the logic behind Lovable (or similar Vibe Coding products) is indeed valid, representing the future.”

“But whether it can survive until capital realizes its grand goals depends entirely on its own fortune.”

“In contrast, Cursor, Windsurf, and some emerging integrated development tools (like Google Antigravity) have a more pragmatic survival logic. They acknowledge that Lovable’s end-to-end logic is a long-term trend, but to ‘survive in the present,’ and to adapt to existing technical practices, they choose a super editor model.”

“In the eyes of professional engineers, those Vibe Coding products seem more like toys, but capital is willing to pay for them.”

“Therefore, I expect Cursor’s current revenue capabilities to far exceed those of Lovable. Cursor targets real developers and adds value to productive processes that can create value. The logic of products like Lovable is entirely different; it primarily harvests capital, shareholders, and inexperienced users looking for shortcuts.”

“Of course, in this capital game, investors may not necessarily be the ‘unlucky’ ones; it largely depends on who is playing this ‘pass the parcel’ game. Investors might not care about whether the product can ultimately land; they just need to be the first to present and clarify this story. As long as they ensure they are not the last one holding the parcel, they can successfully cash out before the bubble bursts.”

“Like entrepreneurs, investors are also betting, betting that the direction they invest in can, with the rapid iteration of technology, eventually transform those stories that sound unbelievable or purely ‘dreamy’ into real productivity.”

“The reason this game can continue is that the speed of AI technology development has indeed surpassed imagination.”

After clarifying the essence of AI Coding and Vibe Coding in engineering and capital terms, it is also essential to recognize that there remains an objective gap between domestic and international AI Coding.

Li Nan (a pseudonym), an AI technology expert at a large fintech company, told Zhiwei, “Currently, the overall performance of Coding Agent products from domestic giants is not very good; everyone is trying to create a ‘substitute’ for foreign products, such as domestic versions of Claude Code or Cursor.”

“Currently, I have not seen any company genuinely propose innovative insights from industry logic or programming paradigms. This is directly related to the understanding and capabilities of underlying models.”

“While domestic AI programming models may perform well in benchmarks, there is a limitation that makes reaching the ceiling very challenging; because most domestic large model companies are primarily distillation models, do they have the capability to create training data? It’s quite difficult.”

“The difficulty does not lie in technology; large models are technically not secretive, but in the lack of hardware, slightly weaker engineering integration capabilities, and the scarcity of high-quality training data compared to abroad. While we have platforms like Maoyun for code management and storage, there is still very little high-quality code compared to GitHub.”

In recent years, domestic giants have launched their own AI Coding products, with structures similar to Cursor and other AI IDEs, targeting global markets and utilizing both domestic and international open-source and closed-source large models. “Domestic giants are aggressively pushing AI Coding products for overseas markets, and the underlying logic is very realistic: willingness to pay. Overseas users (especially in Europe and America) have developed a good SaaS payment habit, and going overseas is a ‘shortcut’ to achieve commercial monetization. Moreover, in overseas markets, these products can seamlessly integrate with top international models like GPT-5 or Gemini.”

“I personally tried a domestic giant’s AI Coding product, and my overall evaluation is ’not bad.’ Currently, this product is still in the free phase, and even if it requires a subscription, it is cheaper than Cursor. I observed in its overseas official Discord community that there are many foreign users, and many foreigners do not want to pay for a Cursor subscription.”

“Even if the models used are the same, from the results, at least the code I wrote with Cursor is of much higher quality than this product. While it is seen as a free alternative to Cursor, the gap between the two is quite obvious.”

“Specifically, Cursor excels at predicting development behaviors; it can roughly foresee what you will do next by reading code. This product is more like an intermediate state between Lovable and Cursor, with a clear gap in context management. Cursor’s indexing management technology is very mature, and combined with RAG-based code library retrieval, it allows developers to follow certain I/O behavior rules, making it much faster when handling large-scale code. In contrast, this product currently does not handle large projects as quickly as Cursor.”

“Overall, this product leans more towards fully automated, end-to-end completion of all tasks, which is actually closer to Lovable’s positioning. It can be said that domestic AI Coding products are essentially targeting the capital side of the future market, leaning towards Vibe Coding rather than AI Coding.”

“But ultimately, the issue of data security cannot be avoided. This is a global issue; for instance, Cursor directly provides privacy options within the application, ensuring that code is not stored in the cloud and not used as training data. However, the situation is different domestically.”

“Why are companies reluctant to switch to domestic giants’ AI Coding products? This is not just a technical issue but a more complex commercial consideration, stemming from concerns about code leakage or these programming product vendors obtaining their code.”

“Many companies are very focused on protecting their intellectual property. Using AI IDEs that require scanning all code makes users feel anxious. Currently, discussions are ongoing about the potential for data to be returned from such products; if it involves financial technology companies, the concerns are even more pronounced.”

“So how to address this risk when using domestic products? There’s a difference between the surface and actual operations. On the surface, companies can sign contracts with model vendors, stating that vendors cannot use user data for their model training; additionally, model vendors need to make commitments regarding so-called ‘memory in read committed’ technical memory clearing. However, will companies feel secure signing with domestic giants? Various commercial flaws and actual scandals render this almost meaningless. Our business environment does not support this level of trust.”

“Therefore, companies negotiating data security commitments with suppliers are ineffective; it ultimately returns to how companies internally address external threats. The solution is to create a gateway within the company. This gateway controls which data can flow out and which cannot. Besides this, there is no real way to constrain these suppliers.”

Not only is there reluctance to use domestic products, but domestic enterprises also appear more conservative in their implementation of rapidly evolving AI Coding technologies. After all, innovative uses are not exclusive to Vibe Coding; efficiency improvements inherently drive innovation growth.

Wang Wei stated, “In the past, because development costs were high, we needed to think through ideas as much as possible to avoid waste before entering the delivery pipeline. Today, if AI Coding brings the delivery costs low enough, we can explore more, and the forms of product delivery or interactions with customers can also be faster. The cost here primarily refers to time costs.”

“This actually provides enterprises with more rapid innovation possibilities, not merely helping companies reduce headcount.”

“However, in many industries today, especially domestically, the environment or competitive landscape may not present many new demands. People are reluctant to innovate.”

“If the focus is merely on saving time and reducing manpower, it does not genuinely promote business growth. No matter how many people are cut, it does not solve whether the company can perform well in the market.”

Even if there is sufficient motivation, leveraging AI Coding is not without thresholds; some enterprises’ contextual environments may not meet the lower limits required for AI to function properly. A significant reason is the failure to extract implicit knowledge within the enterprise while expecting AI to understand directly.

Wang Wei stated, “To build a good context for AI Coding, enterprise knowledge extraction and management must first be done. This direction is not new; since the 1970s and 1980s, many enterprises, including consulting firms and even IBM, have been engaged in enterprise knowledge management, which is a specialized consulting area. There is still a significant market space in this direction, and currently, there are no effective solutions in the industry.”

“The current approach in the industry has some issues; most consulting firms, product companies, and AI companies still hope to use AI to brute-force solutions, akin to achieving miraculous results through sheer effort, to obtain accurate results. I do not view this approach favorably.”

“While AI’s ability to understand context is improving, it still cannot grasp the implicit knowledge behind the code. It can only extract the structure of existing code and explain what the code does in natural language, but it is challenging to understand why the code was originally written that way.”

“Often, some troublesome or complex aspects of the code are written that way for underlying reasons, which are also part of the knowledge.”

“If the underlying reasons are not understood, merely following standard recommendations, such as ’these two pieces of code should not be separated,’ may trigger issues that were already resolved five or six years ago, thereby reproducing them.”

“Knowledge management has a crucial principle: distinguishing between what is a consensus standard and what is merely an incidental situation or a temporary workaround. Some enterprises may have coding standards, but everyone has their preferences when writing code.”

“Enterprises with stronger norms tend to see better results when integrating documentation generation tools like Glean or source code analysis tools like DeepWiki. Such code is easier for AI to understand, leading to more accurate outputs.”

“I estimate that in the entire industry, such normative codebases account for at most 30% to 40%, while domestically, it may only be around 5%.”

“This has always been an old problem. Most code is humorously referred to as ‘spaghetti code’; in the past, we called it legacy code or bad code. Due to time and various pressures, developers cannot write code neatly or take the time to refactor, making it challenging for the code to align with business semantics, necessitating constant translation between business, technology, and code.**”

“In such cases, AI Coding is unlikely to perform well, at least with today’s foundational models.”

“Through our solutions, we have been able to compress this work from a month to 5 to 10 minutes in some cases. However, even so, some enterprises may be limited by the development of their industry or the situation of their upstream and downstream supply chains, lacking the motivation for innovation or change. Even if enterprise knowledge management is valuable to them, its priority may not be high. Of course, as the economy recovers and develops, the priority of such demands should increase, further promoting the implementation of AI Coding.”

From enterprise knowledge management to legacy code refactoring, both can provide a good context for AI Coding. This relationship can even form a closed loop, with discussions this year suggesting that legacy code refactoring is the scenario with the highest return on investment for AI Coding.

Chen Yuzhao stated, “Legacy code refactoring is inherently painful and time-consuming, especially unfriendly to newcomers. The current industry has a high turnover rate; many projects maintained for over a decade face challenges as old employees leave, making it very difficult for new hires to quickly understand the code and perform refactoring.”

“If there is an AI tool that can quickly unify basic styles and eliminate redundant methods, it would be a great thing. Building on this, refactoring complex functionalities would save a lot of time. Even in regular development, when encountering inconsistent legacy code styles or inefficient implementations, handing these small code snippets to AI for refactoring into more efficient implementations would yield clear benefits.”

“If it were up to me, I would be willing to purchase such a service.”

“Ultimately, the core reason for the high ROI in this scenario is that current AI is not that intelligent; what it can do is handle logic that is simple yet extremely time-consuming. And these are precisely the tasks programmers are least willing to perform.”

Zhang Senseng believes that using AI Coding for legacy code refactoring has scenario limitations, stating, “While it logically makes sense that legacy code refactoring is the highest ROI in AI programming, I do not believe that current AI capabilities can fully support the implementation of this task. It essentially addresses the issue of business value judgment and avoiding the ’local optimum trap,’ which only humans can judge where changes can be made quickly and where they cannot.”

“So, how many programmers in the market possess the ability to see through complex logic and lead refactoring? I am skeptical about the availability of such talent.”

The virtuous cycle of generation and refactoring may bring hope. A long-standing problem in the domestic SaaS industry is the lack of unified technical standards and the repeated creation of wheels across companies and even departments. Can using AI as a driver for efficiency promote legacy code refactoring and standardization to solve this old problem?

In response, Chen Yuzhao gave a completely negative answer, “I believe it cannot, as there is no hope in the domestic context due to the industry’s ethos.”

“Not only in the software field but also in business, everyone ultimately does e-commerce. The domestic style is to do whatever makes quick money first, and once they grow strong, they want to do everything and eat others.”

“Even in the tech industry, for example, database development, the trend is to pile on more functions. The domestic style does not follow a ‘vertical’ route but rather aims to stuff everything in: supporting inverted indexes, document functions, AI vector retrieval, while also accommodating traditional OLTP and OLAP scenarios. This ‘hodgepodge’ trend is fundamentally different from abroad.”

“Due to this deeply rooted difference, pushing for technical standards in the domestic market is exceedingly difficult.”

The industry ethos driving technology may also explain why domestic ToB enterprises lack innovation motivation. Of course, AI can indeed stimulate competitive anxiety among enterprises. Zhang Senseng stated, “To avoid falling behind in market and efficiency competition, the use of AI Coding must be pushed forward 100%.”

However, if innovation motivation is lacking or there is no time to focus on it, many traditional SaaS companies will face deeper crises in the wake of the AI Coding wave.

Zhang Senseng stated, “Many SaaS companies are currently living in a state of ’trembling.’ Because many SaaS products have very poor code quality, users can now create a similar product in just a few days with AI, which previously required purchasing their software. The greatest risk for these SaaS companies is that the end-to-end problem-solving capabilities their systems can provide are extremely limited. Once AI lowers the development threshold, their original technical barriers will quickly collapse.”

“Specifically, these companies can be divided into two types: the first type has very complex SaaS products. The logic of such products is not easily replicated by AI, and these companies can consider using AI to optimize code or enhance internal processes. The second type comprises companies that create small tools. For example, a Pomodoro timer that used to be listed on the App Store can now be created by anyone. With AI assistance, tools like Cursor can produce it in a snap. Can such a Pomodoro timer still be sold now?”

While the Pomodoro timer may be too low a threshold, there is a category of SaaS products that, despite having a higher threshold, face the greatest survival crisis due to their positioning being too close to AI Coding.

Wang Wei stated, “The original low-code and no-code platforms have not performed well. Based on our past consulting experience, such low-code platforms are not the best investment strategy for enterprises. Low-code ultimately can only achieve some combinatorial functions, failing to meet truly personalized needs. If you want to create a software product, the core is to understand user needs and logic (what their journey looks like). When you truly understand these, you will find that low-code platforms either encapsulate too broadly and lack flexibility or are too granular, requiring a lot of time for orchestration, making it better to write code yourself.”

“Additionally, the low-code platforms I have seen generally have a common issue: insufficient testability, especially unit tests and integration tests between modules, which increases complexity.”

“Now, with AI, you can generate prototypes very quickly. Just tell AI what kind of app you want, what the user habits are like, and what the interface looks like, and the prototype will be produced. Thus, in the AI era, the advantages of low-code may be replaced by AI’s rapid prototyping and highly customizable capabilities.”

Zhang Senseng’s viewpoint aligns closely, stating, “Low-code platforms are likely to be replaced by AI. The biggest problem with low-code is the same as that of the current agents; it is something created by a group of programmers who are self-satisfied. They hope to create a platform that allows business personnel to drag and drop to generate agents or pages.”

“However, in reality, no business personnel genuinely want to use such tools to drag and drop to achieve an end-to-end result. They only do so out of company necessity or because no one else is available to help. If business personnel can find developers to do the work, they would not do it themselves.”

“In reality, this demand has existed for many years, as this story sounds very smooth: allowing business personnel to generate pages through drag and drop to reduce the need for developers. The capital market recognizes this story, and as long as it is pushed internally within the company, forcing business personnel to use it, eventually, some will use it.”

“But in most cases, it becomes an awkward situation: business personnel genuinely do not want to use it, finding drag-and-drop too absurd and unpleasant. Even if it can help achieve some simple logic, it may not fulfill the actual business objectives, leaving business personnel caught in a dilemma.”

“The most crucial point is that drag-and-drop operations come with a learning cost; why should business personnel learn? For a computer novice, this is akin to learning something entirely new. However, some business personnel might find even learning Excel challenging, and there are not many people proficient in Excel. Drag-and-drop may seem simple to programmers or tech-savvy individuals, but they completely fail to see the problem from the true user’s perspective.”

“Whether large models and AI Coding will replace it depends on whether low-code platforms have the motivation to upgrade their cores; in any case, they can no longer design products in the old ‘drag-and-drop’ way. After all, today, business personnel can simply describe in natural language what they want, and AI can handle the drag-and-drop work and generate the pages. So this logic will still exist, but it fundamentally addresses the pain point of ’effort.’” Wang Wei added, “In enterprise applications or software delivery scenarios, our team has consistently advised against using low-code platforms. This also raises a question: what will it look like in the AI era?”

“Rather than focusing on low-code encapsulation, if today’s low-code platforms merely wrap a foundational model and transform it into an agent, it could be feasible. This might allow the entire software construction process to ultimately become an agent, no longer constrained by the original module granularity.”

Furthermore, in the current landscape where AI Coding rapidly consumes the survival space of low-code platforms, is there still room for more innovation at the software development tools and platform levels?

Wang Wei believes there is, but it must be grounded in the context of AI Coding. “In the entire software development chain, whether it’s requirement analysis, architectural design, code writing, test case design and execution, or configuration management, environment management, DevOps, we should consider: what can AI help me with at each step? How can I involve AI in my daily workflow?”

“Once you clarify ‘how to integrate AI into my daily workflow,’ the next step is to abstract and distill. This means extracting those elements that work particularly well in your work, such as a consistently effective prompt structure, a clear question framework, an efficient workflow, or a set of validated best practices. Transforming these from ’experience’ into ’tools.’”

“Truly valuable innovations come from the front lines, from those that can solve real problems for enterprises. Therefore, when you encapsulate, toolize, and systematize these effective patterns from your work, it can not only generate greater value within the enterprise but may also evolve into a new business or product outside the enterprise.”

“Today, the industry does not yet have a consensus, and there are no unified answers about future forms. Given this, it might be better to take the best tools you have and try to productize and weaponize them.”

On the other hand, for non-large model vendors looking to start a business, focusing solely on models may not be the best choice.

For example, Cursor has launched its self-developed programming model, Composer 1, attempting to upgrade from an AI application vendor to a large model vendor. However, the overall industry feedback has been rather average; some Reddit users have noted that while Composer 1 is very fast, it is only better suited for simple and tedious tasks, with a low intelligence ceiling, and some Reddit users believe it should be compared to smaller models like Grok Code Fast 1, or even that it is inferior to the latter.

Zhang Senseng stated, “I used Composer 1 when it was first released, and my personal experience was ‘particularly difficult to use.’ Cursor’s motivation for this is that a significant portion of its annual revenue goes to large model vendors, and I expect they are losing a lot of money each year. Therefore, they think, rather than paying others, it’s better to create their own model and earn that money, which is their commercial consideration. Moreover, Cursor is also telling a story to capital, claiming that its ultimate goal is to achieve Vibe Coding, but it is still far from truly profitable.”

Compared to traditional enterprises and startups, there is a much larger group being dramatically impacted by AI Coding: programmers. So how can programmers better survive and develop in the era of AI Coding?

First, let’s clarify that programmers currently face some career crises, but they are not universally covered.

Chen Yuzhao believes it needs to be categorized by job type, “Those doing basic testing are more likely to face elimination. Currently, writing basic test code can indeed be accomplished by AI.”

“However, slightly more complex testing work that involves business logic is still challenging for AI to replace human roles.”

Wang Wei holds a similar view, stating, “Some companies might say that because I have AI tools, I can cut 60% or 80% of programmers, but I think it’s currently difficult for any company to actually do that.” He further categorized by experience, stating, “Compared to the highest security level, where experts can easily handle AI, intermediate programmers (with around three to five years of experience) face the greatest crisis.”

“Especially in China, during the internet boom over the past decade, many programmers from outsourcing teams entered the IT industry through fast-track methods due to high demand for IT personnel. These individuals may only know how to code according to client requirements without understanding the client’s business or the underlying technical logic.”

“For such individuals, as they age and gain more experience, they indeed need to think about how to coexist and collaborate better with AI, reflecting on where their competitive edge lies.”

“Whether intermediate or novice programmers, the minimum baseline requirement today is to learn how to collaborate with AI.”

Zhang Senseng believes the key lies in long-term accumulated work and thinking habits, stating, “In the AI Coding era, programmers themselves also need to enhance and transform their qualities. The survival path for future programmers is not just to master a single language (like Java or C), but to transform into ‘full-stack’ or even ‘full-language’ masters. Programmers may not need to delve into every detail of each language but must be able to understand every line of code generated by AI and know its role in the overall program architecture.”

“The future of software development will no longer require ‘code movers.’ If a programmer only knows how to write one language or has a work habit of merely filling in logic within a good framework, this type of programmer will definitely be let go.”

“This work mode no longer aligns with the needs of technological development; in an age where AI can efficiently complete filling and completion tasks, such programmers will no longer be defined as ‘programmers.’”

From another perspective, AI Coding does not necessarily have to be a source of crisis; it can also present new opportunities for self-improvement and growth. Chen Yuzhao stated, “For instance, for personal learning, using AI for source code analysis is very suitable and effective.”

Even for novice programmers, as long as they establish the right mindset, they need not worry about over-relying on AI hindering their growth. Chen Yuzhao stated, “Currently, AI programming does not possess all the skills of a senior engineer; it is akin to a high school student or a fresh graduate.”

“What it can assist you with are those easily quantifiable, modular, and templated repetitive tasks. It can help you organize code more efficiently and interact in a way that is closer to human natural language. It essentially integrates and accelerates existing tools rather than replacing them. If novices can proficiently utilize this new tool, it would be even better.”

“The times are evolving; programmers cannot always rely on text editors for programming, just as IDEs have also evolved. Just as it used to be very complex to process images with Photoshop, now with Google’s Nano Banana Pro, you can handle it by just saying a few words.”

“Of course, if you want to delve deeper into a specific field’s industry experience, development history, and other in-depth content, you still need to engage in thorough communication with professionals in that field, as AI is unlikely to provide these insights.”

Wang Wei shares a similar perspective, stating, “For novice programmers or those just out of school, AI is an opportunity. Today, AI can help newcomers quickly reach the output capabilities of former intermediate programmers.”

“Whether through Prompt Engineer or Context Engineer to build a good collaborative model with AI, they can establish output capabilities similar to those of past intermediate programmers within the first month or even the first two weeks of employment.”

“We often emphasize to clients that they must not lay off young programmers. Because only these young individuals, as their understanding of the business deepens and their time in the company increases, can gradually grow into experts.”

“While theoretically, intermediate programmers can also be cultivated into experts, the most reasonable approach is to enable young individuals to quickly grow into experts with the support of AI. Therefore, many industry experts, both domestically and internationally, have been advocating for companies not to relax their recruitment of graduates. Graduates represent a promising generation, and the layer of experts should not be abandoned. That’s why I say the most dangerous group is the intermediate programmers.”

This is not just a prediction; it is already reflected in the actual changes in recruitment demands of some software companies. “According to some statistical reports, it has indeed shown that the total headcount open in the entire software industry has decreased compared to last year, especially in the last six months. Moreover, they are indeed increasing headcount for slightly more experienced programmers and campus recruitment.”

“But to be more frank, if budgets are limited, we would all recommend that campus recruitment should not stop.”

“From the perspective of future enterprise development, there must always be a reserve of talent. Young people need to cultivate and accumulate experience in real business environments. If companies completely stop hiring newcomers and rely solely on external recruitment of experienced programmers, the critical internal context and knowledge transfer, as well as the talent pipeline, may face gaps, posing a greater risk to enterprises in the long run.”

“This may not be apparent today; some companies might think that hiring ten or even a hundred graduates is less cost-effective than hiring two or three expert programmers, which seems to save money and is more direct. However, when the time horizon extends to five years or longer, significant issues will arise.”