It's wrong; vertical specialization is the future.

Have you noticed that AI application generation platforms are heading down a path completely different from what everyone expected? Many people originally thought this would be a bloody zero-sum game, where everyone would fight to the death in a price war, leaving only one dominant player. But reality has been surprising: these platforms not only haven't fought each other, but have instead begun to seek differentiated positioning, coexisting and thriving in different niche markets. This reminds me of the development trajectory of the large language model market, which is equally unexpected and full of inspiration.

Just yesterday, two partners from a16z, Justine Moore and Anish Acharya, jointly published an analysis article titled "Batteries Included, Opinions Required: The Specialization of App Gen Platforms," and their observations on the AI application generation platform market have deeply inspired me. They pointed out that these platforms are undergoing a differentiation process similar to that of foundational models, shifting from direct competition to specialized development. This observation has led me to rethink the development patterns of the entire AI tools ecosystem and has given me deeper reflections on the myth of the "universal platform." I have always believed that "there is no universal code platform that can dominate everything." Now, too many people are using AI to build applications, and their use cases are extremely diverse: prototype design, personal websites, game development, mobile applications, SaaS platforms, internal tools, and so on. How can a single product excel in all these areas?

My judgment is that this market will inevitably move towards segmentation. Consumer-grade applications designed for aesthetically pleasing landing pages will never be the same product as enterprise-grade internal tool builders. The former needs Spotify integration and may go viral on TikTok; the latter requires SOC 2 compliance and needs to be sold to CTOs in a top-down manner. This market is large enough to support multiple companies valued in the billions. Becoming a clear leader in a specific use case, focusing on the necessary features, integrations, and market strategies for that scenario, may be the winning approach.

PS: I recently started my own venture, focusing on a vertically specialized Vibe coding product, and have quickly closed a round of pre-seed financing. If any VC partners are also optimistic about this direction and have some research, feel free to add me on WeChat (MohopeX) to chat. We are also recruiting founding team members; interested friends can check the end of this section to submit their resumes.

Insights from Foundational Models: From Substitutes to Complements

Looking back at the foundational model market in 2022, almost everyone held two incorrect assumptions. The first assumption was that these models were essentially substitutes for each other, like interchangeable cloud storage solutions. Since you've already chosen one, why bother using another? The second assumption was that since these models are substitutes, competition would force prices to the bottom, and the only way to win would be to charge less.

But the trajectory of reality has been completely different. What we see is explosive growth in different directions. Claude has begun to delve into code and creative writing. Gemini stands out with its multimodal capabilities, offering high-performance models at low prices. Mistral focuses on privacy protection and local deployment. Meanwhile, ChatGPT is doubling down on becoming the "base camp" for anyone wanting the broadest and most useful general assistant. The market has not seen a single dominant player; instead, it remains open: more models, more diversity, more innovation. Prices have not only not decreased but have actually increased. Grok Heavy, with its outstanding AI code capabilities and viral text-to-image model, charges up to $300 per month, which was unheard of for consumer software just a few years ago.

This model can also be seen in other fields. Recall the image generation space, where in 2022 people said it was a zero-sum game or "one model to rule them all." But now you see Midjourney, Ideogram, Krea AI, BFL, and others, all thriving and coexisting because each focuses on different styles or workflows. These models are not "better" or "worse"; they each have their own claims in terms of art and functionality, serving different creative tastes and needs.

A closer look reveals that these models are not competitors at all; they are actually complementary. This is in stark contrast to the bottom-line competition of racing to lower prices; this is a positive-sum game: using one tool increases the likelihood that you will pay to use another tool. My own usage experience illustrates this well. When I need to quickly generate code, I use Claude; when I need multimodal analysis, I turn to Gemini; when I need creative writing assistance, I might go back to ChatGPT. Each tool has its best application scenario, and I don't feel they are competing for my attention; rather, they meet my different needs at different times.

The Differentiation of AI Application Generation Platforms Has Begun

I believe the same situation is happening in the field of AI application generation platforms. These tools help you build complete applications with AI. It's easy to be drawn in by the superficial dramatic conflicts, such as Lovable vs. Replit vs. Bolt, etc. But the truth is, this is not a winner-takes-all game. The market is vast and still growing, with enough space to accommodate multiple breakthrough companies, each occupying a niche in its own right.

Justine mentioned in the article that the market has begun to segment in the following ways, with each platform uniquely "standing out" in one of the following areas:

• Prototyping platforms, specifically designed for tools that quickly experiment with ideas. These products need to excel in aesthetics, prompt adherence, and fine visual operations while providing a quick and rough implementation of business logic.

• Personal software platforms, specifically built for you and your workflow. These products may serve the least technically skilled users, requiring "out-of-the-box" functionality and possibly an easy-to-edit comprehensive template library.

• Production apps platforms, prepared for teams or the public. These platforms need to have a built-in set of basic features, including authentication, database, model hosting, payment integrations, and the ability to scale with one click.

In each category, there will likely be platforms targeting every user level, from ordinary consumers to semi-technical product managers to core developers. In other words, for every type of application, there will be a range of solutions. According to data from Similarweb, although still in the early stages, this trend has already manifested in the cross-browsing behavior of core application generation platforms. These platforms include Lovable, Bolt, Replit, Figma Make, v0, and Base44.

Data shows that there are two types of users emerging. The first type is users loyal to one platform. For example, in the past three months, 82% of Replit users and 74% of Lovable users only accessed either Replit or Lovable within the aforementioned platform combinations. These users may find that the application generation platforms are functionally quite similar at the moment but choose to primarily use one, possibly due to marketing, user interface, or specific features they care about. From experience, Lovable seems to be used for aesthetic web applications and prototyping, while Replit appears to be the preferred platform for more complex backend-heavy applications.

The second type is users active on multiple application generation platforms. For example, in the past three months, nearly 21% of Bolt users also browsed Lovable. 15% of Base44 users also checked out Lovable. I speculate that these are super users who are very active on these platforms and use them in a complementary manner. This user behavior pattern reminds me of how I use different design tools. When I need to quickly create a prototype, I might use one tool; when I need more precise design control, I switch to another tool; when I need to collaborate with the development team, I might choose a third tool. Each tool has its unique advantages, and I choose which to use based on specific needs.

Specialization is an Inevitable Trend

I increasingly believe that in the realm of tools that help users build scalable applications, being constrained is better than being unconstrained. Excelling in developing a certain type of product is likely to be much better than being just okay at generating all products. An application generation platform that excels at building internal tools integrated with SAP is unlikely to also be the one that creates the most accurate flight simulator applications.

Let’s further analyze this trend of specialization. Different types of applications have vastly different requirements for the underlying platform:

Data/Service Wrapper Applications need to aggregate, enrich, or present large existing data services or third-party services, such as LexisNexis or Ancestry. The infrastructure must support operations on large datasets. The core challenge for these applications lies in data processing capabilities and the complexity of integration, rather than the aesthetics of the interface.

Utility Applications are lightweight applications for single purposes, addressing highly specific needs, such as PDF converters, password managers, or backup tools. Most horizontal platforms have already done well in generating these applications. These applications are characterized by clear functionality and relatively simple logic, but they have high demands for reliability and performance.

Content Platform Applications are built for discovering, streaming, or reading content, such as Twitch or YouTube, requiring specialized infrastructure to support content distribution. The technical challenges for these applications mainly lie in large-scale content distribution, real-time streaming processing, and personalized recommendation algorithms.

Business Center Applications are platforms that facilitate and monetize transactions, focusing on logistics, trust, reviews, and price discovery. These applications need to support integrations for payments, refunds, discounts, etc. In this field, compliance, security, and the complexity of financial integration are key challenges.

Productivity Tool Applications help users or organizations complete tasks, collaborate, and optimize workflows, often with extensive integrations with other services. These applications require a deep understanding of enterprise workflows and the existing tool ecosystem.

Social/Messaging Applications enable users to connect, communicate, and share content, often forming networks and communities. The infrastructure must support large-scale real-time interactions. The challenges for these applications lie in handling social graphs, real-time communication, and content moderation.

What I observe is that each category has its unique tech stack, integration requirements, and user experience considerations. A platform focused on e-commerce application generation will have built-in features like payment processing, inventory management, and order tracking, and will deeply optimize these processes. In contrast, a platform focused on data dashboards will invest more effort in data visualization, real-time updates, and complex query optimization. This specialization is not just about functional differences; it reflects entirely different product philosophies and technical architectures.

The Deep Logic of Market Segmentation

From a deeper perspective, this market segmentation reflects the complexity of software development itself. In the past, we tended to view software development as a unified field, but in reality, different types of applications face completely different challenges and constraints. Mobile applications need to consider touch interactions, battery life, and offline functionality; web applications need to consider browser compatibility, SEO, and responsive design; internal enterprise tools need to consider security compliance, existing system integration, and permission management.

As AI begins to automate application development, these differences become even more important. An AI system that excels at generating aesthetically pleasing landing pages will have its training data, prompt engineering, and output optimization centered around visual appeal, conversion rate optimization, and marketing effectiveness. Conversely, an AI system that specializes in generating enterprise-level internal tools will focus entirely on data security, system integration, user permission management, audit logs, and so on.

I often see teams trying to build a "universal" AI application generation platform, hoping to meet all users' needs. However, this mindset overlooks a key point: the conflict of optimization goals. When you try to optimize for both aesthetics and enterprise compliance simultaneously, you often end up compromising in both directions. Specialized platforms can avoid such compromises and excel in specific areas.

This reminds me of the evolution of traditional software development tools. We once had some "super IDEs" that attempted to cover all development scenarios, but the market ultimately differentiated: there are tools specifically for web development, tools specifically for mobile development, and tools specifically for data science. Each tool provides an unparalleled experience in its specialized field, which is more valuable than a tool that can do everything but excels at nothing.

In the field of AI application generation, I expect to see similar differentiation. There will be platforms specifically for generating e-commerce websites, with built-in Shopify integration, payment processing, and inventory management features. There will be platforms specifically for generating data dashboards, excelling at connecting various data sources, creating interactive charts, and setting up real-time updates. There will be platforms specifically for generating mobile applications, understanding iOS and Android design guidelines, push notifications, and app store optimization.

Insights from User Behavior

The user behavior data mentioned in Justine's article is particularly enlightening. Those "super users" who switch between multiple platforms actually validate my point: different platforms are suitable for different use cases. A developer might use Lovable to quickly prototype, Replit to develop applications with complex backend logic, and other platforms to handle specific integration needs.

This usage pattern reminds me of the modern developer's toolchain. No one expects a single tool to solve all problems. We use Figma for design, VS Code for coding, GitHub for version control, Vercel for deployment, and Stripe for payment processing. Each tool excels in its specialized field, and the collaborative work of the entire toolchain creates a more powerful development experience than any single "universal tool."

The development of AI application generation platforms is likely to follow a similar path. Users will choose the most suitable platform based on specific needs rather than being forced to use a platform that can do everything but excels at nothing. This freedom of choice will actually enhance the value of the entire ecosystem, as each platform can focus on doing what it does best.

Another interesting phenomenon I have observed is that users' tolerance for "switching costs" is decreasing. In traditional software development, the cost of learning a new tool is high, so developers tend to stick with familiar tools. However, in the AI-driven era, the learning curve for tools has significantly decreased. If a platform can accomplish most operations through natural language, then the barrier for users to try new platforms is very low. This further encourages specialization, as users are more willing to seek the best tools for specific needs.

Reconsidering Business Models

This trend of specialization will also reshape the business models of AI application generation platforms. Traditional SaaS models emphasize economies of scale and network effects, trying to acquire as many users as possible and lock them in. However, in a specialized world, depth is more important than breadth.

A platform focused on e-commerce applications can establish deep integrations with e-commerce platforms like Shopify, WooCommerce, and BigCommerce, providing an e-commerce application generation experience that other platforms cannot match. Its customer base may be smaller than that of a general platform, but each customer's value is higher and their loyalty stronger. Such specialized platforms can even develop industry-specific pricing models, such as revenue sharing, rather than simple subscription fees.

Similarly, a platform focused on enterprise internal tools can deeply integrate with existing IT infrastructure, providing seamless single sign-on, data synchronization, compliance auditing, and other features. Such platforms may adopt an enterprise-level sales model, serving large enterprise clients through direct sales teams rather than relying on self-service registration.

I believe this diversification of business models will actually create a healthier competitive environment. Each platform can focus on serving its core user group rather than trying to meet everyone's needs. This reduces the intensity of direct competition, giving each platform the opportunity to build a strong moat in its specialized field.

From an investment perspective, this also means that different types of investors will be attracted to different platforms. Platforms focused on consumer applications may attract investors who value user growth and viral distribution. Platforms focused on enterprise applications may attract investors who prioritize stable cash flow and long-term customer relationships. This diversity will bring more funding and attention to the entire industry.

Differentiation of Tech Stacks

Delving into the technical level, I find that different types of applications have vastly different requirements for the underlying tech stack, further supporting the necessity of specialization. A platform focused on real-time applications (such as chat applications and collaboration tools) needs to optimize extensively in areas like WebSocket connections, message queues, and state synchronization. In contrast, a platform focused on data-intensive applications needs to invest more effort in database query optimization, caching strategies, and data visualization.

An interesting phenomenon I have observed is that different platforms are also beginning to differentiate in their selection and optimization of AI models. Platforms that generate aesthetically pleasing interfaces may use image generation models and design-related training data more frequently. Platforms that generate backend logic may use code generation models and software architecture-related training data more often. This targeted optimization has led to significant performance improvements for each platform in its specialized field.

More importantly, different types of applications have completely different standards for evaluating generation quality. A consumer application may prioritize the aesthetic appeal of the interface and the smoothness of user experience, even if the code is not elegant. In contrast, an enterprise application places greater emphasis on code maintainability, security, and scalability, even if the interface is somewhat plain. This difference in evaluation standards determines that different platforms need to adopt different optimization goals and quality control mechanisms.

I have particularly noticed that some platforms are beginning to differentiate in deployment and operations. Platforms focused on personal projects may offer simple one-click deployment to static hosting services. In contrast, platforms focused on enterprise applications need to support complex deployment pipelines, multi-environment management, monitoring, and alerting features. These differences may seem subtle, but they have a decisive impact on the end-user experience.

The Evolution Direction of the Ecosystem

From a broader perspective, the trend of specialization in AI application generation platforms actually reflects the evolution direction of the entire software development ecosystem. We are witnessing a shift from "tool-centric" to "result-centric." Users are no longer concerned about what tools they use; they care about what results they can achieve. This shift creates enormous opportunities for specialized platforms.

I expect that in the coming years, we will see more and more vertical AI application generation platforms emerge. There will be platforms specifically for game development, understanding game engines, physics systems, and level design. There will be platforms specifically for educational applications, with built-in learning management system integration, progress tracking, and personalized learning paths. There will be platforms specifically for medical applications, compliant with HIPAA and other medical data protection regulations.

This trend towards verticalization will not only change product forms but also alter the talent demands across the industry. Specialized platforms will require hybrid talents who understand both AI technology and specific industries. A platform generating financial applications will need individuals who deeply understand financial compliance, risk management, and trading systems. This change in talent demand will further solidify the competitive advantage of specialized platforms.

I have also observed that a trend of cooperation rather than competition is beginning to emerge among specialized platforms. A platform focused on frontend generation may establish a partnership with a platform focused on backend generation to jointly provide end-to-end solutions for users. This cooperative model creates a more open and collaborative ecosystem, allowing each platform to focus on its core strengths.

In the long run, I believe this trend of specialization will drive the entire AI application development field towards greater maturity. When each niche area has specialized platforms deeply engaged, the overall level of the industry will improve, and users will receive better experiences. This is a win-win situation: platforms can build deep moats in their specialized fields, users can obtain more targeted solutions, and the entire ecosystem will become richer and more diverse.

My Predictions and Thoughts

Based on the observations and analyses above, I have several predictions for the future development of the AI application generation platform market. I believe that within the next three to five years, the market will clearly differentiate into several main categories: rapid prototyping platforms for consumers, templated application platforms for small businesses, customized internal tool platforms for large enterprises, and various specialized platforms for vertical industries.

In each category, there will ultimately be 2-3 dominant companies that gain competitive advantages through deep specialization and ecosystem building. These platforms will not attempt to replace each other but will continuously deepen their offerings in their respective fields, providing specialized value that other platforms cannot match.

I am particularly optimistic about those platforms that can establish deep moats in specific vertical fields. For example, a platform focused on applications for the restaurant industry, if it can deeply integrate ordering systems, inventory management, employee scheduling, and financial reporting—unique needs of the restaurant industry—will be hard to replace by a general platform. This accumulation of industry knowledge and specialized integration is difficult for general platforms to replicate.

I also believe that user behavior will undergo fundamental changes. As the switching costs between platforms decrease, users will become more "tool rational," choosing the most suitable platform based on specific needs rather than being loyal to a single platform. This change will further drive platform specialization, as only by excelling in specific areas can a platform secure a place in the user's toolbox.

From a technological development perspective, I expect that various specialized platforms will experience greater divergence in the training and optimization of AI models. Applications in different fields have different requirements for AI generation quality, which will drive platforms to develop more targeted AI models. We may see models specifically optimized for code generation, models specifically optimized for interface design, and models specifically optimized for business logic, among others.

Finally, I believe this trend of specialization will redefine the standards of "platform success." In the past, success often meant having the most users and the broadest coverage. However, in a specialized world, success may mean having the deepest influence in a specific field, the highest customer value, and the strongest professional capabilities. This change in success standards will create more diverse business opportunities and make the entire industry healthier and more sustainable.

Overall, the trend of specialization in AI application generation platforms is not only an inevitable result of technological development but also a sign of market maturity. As user demands become more diverse and specialized, the limitations of general solutions will become apparent. Platforms that can deeply understand the needs of specific user groups and provide targeted solutions will gain a competitive advantage in the future. This market is large enough to support multiple successful specialized companies; the key is to find the right positioning and delve deeply into it.

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