Have you noticed that AI application generation platforms are heading down a completely different path than everyone expected? Many people initially thought this would be a bloody zero-sum game, where everyone would fight to the death in a price war, ultimately leaving only one dominant player. But reality is surprising: these platforms not only do not kill each other but begin to seek differentiated positions, coexisting and flourishing in various niche markets. This reminds me of the development trajectory of the large language model market, which is equally surprising and enlightening.
Just yesterday, two partners at a16z, Justine Moore and Anish Acharya, published an analytical article (Batteries Included, Opinions Required: The Specialization of App Gen Platforms), and their observations about the AI application generation platform market inspired me greatly. They pointed out that these platforms are undergoing a differentiation process similar to foundational models, shifting from direct competition to specialization. This observation has made me rethink the developmental patterns of the entire AI tool ecosystem and provided deeper reflections on the myth of the 'universal platform.' I have always believed that 'there is no universal coding platform that can dominate everything.' Now there are too many people using AI to build applications, with use cases extremely diverse: prototyping, personal websites, game development, mobile applications, SaaS platforms, internal tools, and so on. How could one product possibly excel in all these fields?
My judgment is that this market will inevitably trend toward segmentation. Consumer-grade applications designed for attractive landing pages will never be the same product as enterprise-grade internal tool builders. The former needs Spotify integration features and might go viral on TikTok; the latter requires SOC 2 compliance and needs to be sold top-down to CTOs. This market is large enough to support multiple companies valued in the billions. Becoming the clear number one in a specific use case and focusing on the required functionalities, integrations, and market strategies for that scenario could be the winning approach.
PS: I recently started my own business, specifically focused on vertical specialization with Vibe coding products, and have quickly closed a round of pre-seed funding. 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, so interested friends can submit their resumes at the end.
Insights from foundational models: from substitutes to complements
Looking back at the foundational model market in 2022, almost everyone held two mistaken assumptions. The first assumption was that these models are essentially substitutes for each other, like interchangeable cloud storage solutions. Since you've already chosen one, why bother with 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 actual trajectory of development is nothing like that. What we see is explosive growth in different directions. Claude has started to delve into coding and creative writing. Gemini excels in multimodal capabilities, providing high-performance models at low costs. Mistral focuses on privacy protection and local deployment. Meanwhile, ChatGPT doubles down on becoming the 'home base' for anyone wanting the most comprehensive and useful general assistant. The market has not seen a winner-takes-all situation but remains open: more models, more diversity, and more innovation. Prices have not only not fallen but have actually risen. Grok Heavy charges up to $300 a month for its excellent AI coding features and viral text-to-image model, a figure that was unheard of for consumer software just a few years ago.
This model can also be seen in other fields. Looking back at the image generation domain, 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 many others, all thriving and coexisting because each focuses on different styles or workflows. These models are not 'better' or 'worse' but assert their own claims in art and function, catering to different creative tastes and needs.
A close observation will reveal that these models are not competitors but 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 of you paying for 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 optimal application scenario, and I do not feel they are competing for my attention but rather fulfilling my different needs at different times.
The differentiation of AI application generation platforms has already begun.
I believe the same situation is happening in the field of AI application generation platforms. These tools help you build complete applications using AI. It is easy to be drawn in by superficial dramatic conflicts, such as Lovable versus Replit versus Bolt, etc. But the truth is, this is not a winner-takes-all game. The market is huge and still growing, with enough space to accommodate multiple breakthrough companies, each occupying a niche in its own field.
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 are tools specifically for rapidly testing 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 are 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-grade application platforms are prepared for teams or the public. These platforms need to have a built-in set of basic functionalities, including authentication, databases, model hosting, payment integrations, and the ability to scale with one click.
In each category, there will likely also be platforms targeting every user level, from ordinary consumers to semi-technical product managers, all the way to core developers. In other words, there will be a series of solutions for every type of application. From Similarweb's data, while still in its early stages, this trend has already begun to emerge in the cross-browser behavior of core application generation platforms. These platforms include Lovable, Bolt, Replit, Figma Make, v0, and Base44.
According to data, there are two types of users. The first type is users loyal to one platform. For instance, in the past three months, 82% of Replit users and 74% of Lovable users only accessed Replit or Lovable within the aforementioned platform combinations. These users may find that application generation platforms are currently similar in functionality but choose to use one primarily 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, within a span of three months, nearly 21% of Bolt users also browsed Lovable. 15% of Base44 users also viewed Lovable. I speculate these are super users who are very active on these platforms, using them in a complementary manner. This user behavior pattern reminds me of how I use different design tools. When I need to quickly 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 a particular 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 toward specialization. Different types of applications have distinctly different requirements for the underlying platforms:
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 capability and integration complexity, rather than the aesthetic quality of the interface.
Utility tool applications are lightweight applications for specific purposes, addressing highly specific needs such as PDF converters, password managers, or backup tools. Most horizontal platforms have already excelled at generating these applications. These applications are characterized by clear functionalities and relatively simple logic, but they have high demands for reliability and performance.
Content platform applications are built specifically for discovering, streaming, or reading content, such as Twitch or YouTube, requiring specialized infrastructure to support content distribution. The technical challenges of 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, and more. 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 involving 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 of these applications lie in handling social graphs, real-time communication, and content moderation.
I have observed that each category has its unique tech stack, integration needs, and user experience considerations. A platform focused on generating e-commerce applications will have built-in payment processing, inventory management, order tracking, and will deeply optimize these processes. In contrast, a platform focused on generating data dashboards will invest more effort in data visualization, real-time updates, and complex query optimization. This specialization is not just about functional differences, but also about fundamentally different product philosophies and technical architectures.
The deeper logic of market segmentation
From a deeper perspective, this market segmentation reflects the complexity of software development itself. In the past, we were accustomed to viewing software development as a unified field, but in reality, different types of applications present entirely different challenges and constraints. Mobile applications need to consider touch interactions, battery life, and offline capabilities; web applications need to address browser compatibility, SEO, and responsive design; internal enterprise tools need to focus on security compliance, existing system integration, and permissions management.
As AI begins to automate application development, these differences become even more important. An AI system that excels at generating attractive landing pages will optimize its training data, prompt engineering, and output around visual appeal, conversion rate optimization, and marketing effectiveness. In contrast, an AI system that excels at generating enterprise-level internal tools will focus entirely differently: 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. But this approach overlooks a key point: the conflict of optimization goals. When you try to optimize for both aesthetics and enterprise compliance at the same time, you often compromise in both directions. Specialized platforms can avoid this compromise by excelling in specific areas.
This reminds me of the evolution of traditional software development tools. We once had some attempts at 'super IDEs' that aimed to cover all development scenarios, but the market ultimately diverged: there are tools dedicated to web development, tools for mobile development, and tools 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, inventory management, and other features. There will be platforms specifically for generating data dashboards, excelling in connecting various data sources, creating interactive charts, and setting up real-time updates. There will also be platforms specifically for generating mobile applications, familiar with 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 quickly prototype with Lovable, develop complex backend logic with Replit, and use other platforms for specific integration needs.
This usage pattern reminds me of the modern developer 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 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. But 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 tool for specific needs.
Reconsidering business models
This trend toward 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 unmatched by other platforms. Its customer base may be smaller than that of general platforms, but the value of each customer is higher and their loyalty is stronger. Such a specialized platform could even develop industry-specific pricing models, such as revenue sharing based on transaction volume, rather than a simple subscription fee.
Similarly, a platform focused on enterprise internal tools can deeply integrate with the existing IT infrastructure of enterprises, 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-registration.
I believe this diversification of business models will actually create a healthier competitive environment. Each platform can focus on serving its core user base rather than trying to meet everyone's needs. This reduces the intensity of direct competition, giving each platform a chance 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-grade applications may attract investors who value user growth and viral spread. Platforms focused on enterprise-grade 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 aspect, I find that different types of applications have distinctly different requirements for the underlying tech stack, further supporting the necessity for specialization. A platform focusing on real-time applications (such as chat applications and collaboration tools) needs to optimize heavily in areas like WebSocket connectivity, 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 the selection and optimization of AI models. Platforms that generate aesthetically pleasing interfaces may use more image generation models and design-related training data. Platforms generating backend logic may utilize more code generation models and software architecture-related training data. This targeted optimization has significantly improved each platform's performance in its specialized field.
More importantly, different types of applications have completely different standards for judging generation quality. A consumer-grade application may prioritize interface aesthetics and smooth user experience, even accepting less elegant code. In contrast, an enterprise-grade application places more importance on code maintainability, security, and scalability, even if the interface is somewhat plain. This difference in judgment standards determines that different platforms need to adopt different optimization goals and quality control mechanisms.
I have particularly noted that some platforms are beginning to differentiate themselves in deployment and operations. Platforms focused on personal projects may offer simple one-click deployments 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 ecosystems
From a more macro perspective, the trend toward specialization in AI application generation platforms reflects the evolution direction of the entire software development ecosystem. We are witnessing a shift from 'tool-centric' to 'result-centric.' Users no longer care about what tools they use but about what results they can achieve. This shift creates tremendous opportunities for specialized platforms.
I expect that in the coming years, we will see an increasing number of vertical AI application generation platforms emerging. There will be specialized platforms for game development that understand game engines, physics systems, and level design. There will be specialized platforms for educational applications that are built with learning management system integrations, progress tracking, and personalized learning paths. There will also be specialized platforms for medical applications that comply with healthcare data protection regulations like HIPAA.
This vertical trend will not only change product forms but also alter the talent demands across the entire industry. Specialized platforms require hybrid talents who understand both AI technology and specific industries. A platform generating financial applications needs individuals who deeply understand financial compliance, risk management, trading systems, and other relevant knowledge. This change in talent demand will further consolidate the competitive advantage of specialized platforms.
I have also observed a trend of collaboration rather than competition emerging among specialized platforms. A platform focused on front-end generation may establish a partnership with a platform focused on back-end generation to provide users with end-to-end solutions. This collaborative model creates a more open and cooperative ecosystem, allowing each platform to focus on its core strengths.
In the long run, I believe this trend toward specialization will drive the entire AI application development field toward greater maturity. When each niche has dedicated platforms delving deep, the overall level of the industry will rise, providing users with a better experience. This creates a win-win situation: platforms can establish strong moats in specialized fields, users can obtain more targeted solutions, and the entire ecosystem will become richer and more diverse.
My predictions and reflections
Based on the above observations and analyses, 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, we will see the market clearly differentiated into several main categories: consumer-facing rapid prototyping platforms, templated application platforms for small businesses, customized internal tool platforms for large enterprises, and specialized platforms for various vertical industries.
In each category, there will ultimately be 2-3 dominant enterprises that gain competitive advantages through deep specialization and ecosystem building. These platforms will not attempt to replace one another but will continuously deepen within their respective fields, providing specialized value that other platforms cannot match.
I am particularly optimistic about platforms that can establish strong moats in specific vertical fields. For example, a platform focused on the restaurant industry that can deeply integrate ordering systems, inventory management, employee scheduling, financial reporting, and other unique needs of the restaurant industry will be hard to replace by general platforms. This accumulation of industry knowledge and specialized integration is difficult for general platforms to replicate.
I also believe 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 those that excel in specific areas can secure a place in the user's toolbox.
From a technical development perspective, I expect that various specialized platforms will display greater divergences in the training and optimization of AI models. Different fields of applications have different requirements for the quality of AI generation, which will drive platforms to develop more targeted AI models. We may see models specifically optimized for code generation, models optimized for interface design, models optimized for business logic, and so on.
Finally, I believe this trend toward specialization will redefine the standards for 'platform success.' In the past, success often meant having the most users and the widest reach. But in a specialized world, success may mean having the deepest influence in a specific field, the highest customer value, and the strongest expertise. This change in success standards will create more diverse business opportunities and make the entire industry healthier and more sustainable.
Overall, the trend toward 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 hold competitive advantages 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.