Over the past period, an anomalous industry phenomenon has increasingly caught the attention of analysts: The performance curve of large language models continues to climb at a nearly steep slope, with breakthroughs that refresh cognition (cognitive understanding) nearly every month, ranging from multi-step reasoning to long-text processing, from code generation to multimodal understanding.

However, in stark contrast, the AI application ecosystem for end-users and vertical scenarios is showing a rare contraction.

According to the 'AI Startup Value Creation White Paper (2025)' released by a research team at the University of Oxford, the total annual global AI investment has exceeded $400 billion, yet only about 33% of companies have successfully scaled their AI projects from pilot to widespread application. This means that for most B2B AI startups, while it is easy to secure proof-of-concept contracts, converting them into long-term, large-scale renewal revenue is difficult.

More intriguingly, the number of standalone applications in app stores labeled as 'AI-powered' continues to grow, but the list of top-tier products with monthly active users exceeding one million is shrinking. A significant number of applications fall into user churn and revenue stagnation within three months of launch. This divergent phenomenon of 'rising waters upstream and retreating boats downstream' is forcing the entire industry to re-examine a fundamental question: As models become increasingly versatile, what irreplaceable value do the applications built around them truly offer?

01. Application Scenarios: Vertical Territories Eroded One by One by General Capabilities

During the early days of ChatGPT's emergence, the market saw a surge of AI applications built on the logic of 'filling model gaps.'

For instance, in the legal tech sector, startups gained paying clients among law firms by integrating professional regulatory databases and constructing custom Retrieval-Augmented Generation (RAG) pipelines, effectively reducing the model's hallucination rate in legal citations. In medical consulting, some applications enhanced the reliability of AI diagnostics to near-primary care physician levels by cross-verifying symptom libraries with model inferences. Marketing copywriting tools relied on carefully designed (well-designed) prompt chains and multi-round dialogue templates to align generated content more closely with specific brand tones.

These applications uniformly seized upon the early weaknesses of large models, erecting temporary support structures around the models' soft spots through engineering means. However, the release of GPT-4 nearly overnight eliminated the precision gap in legal citations. Claude's optimization for complex reasoning tasks reduced medical diagnostic error rates to levels comparable to vertical applications. After the expansion of context windows from thousands to millions of tokens, copywriting tools that once relied on segmented processing and external memory mechanisms found their meticulously maintained long-term memory libraries rendered obsolete. This rapid collapse of scenario moats did not stem from technical regression among vertical teams but rather from the models' general capabilities unpredictably leaping forward to cover territories once deemed 'professional barriers.'

Even more alarming is the homogenization and contraction at the interface level. Every successful application in the mobile internet era boasted a unique interaction language—Tinder's swipe-to-choose, Shazam's voiceprint recognition, Instagram's double-tap like—these interaction designs themselves formed part of the scenario definition, with users' mental models shifting as they switched between applications.

Today's AI-native applications, whether for writing, painting, programming, or data analysis, have nearly all devolved into uniform dialog boxes with input fields, occasionally supplemented with a few quick-command buttons. When users interact with all AI products in the same way, the cost of switching between products approaches zero, and brand loyalty becomes elusive. Industry surveys show that over 70% of AI application users have switched their primary AI tools at least three times in the past year, mostly because 'they heard the new model works better' rather than 'the new application solves what the old one couldn't.' This dissolution of scenario identification means application developers can no longer retain users through 'unique usage experiences' and must passively follow the arms race in model capabilities—a War of Consumption (war of attrition) that small and medium-sized teams cannot sustain.

02. Business Models: Pricing Power Lost in the Cracks and Eroding Customer Loyalty

The erosion of scenario advantages directly undermines the commercial foundations of the AI application layer. Traditional SaaS pricing logic is built on functional scarcity and switching costs.

However, the situation with AI applications is starkly different, as their core capabilities rely entirely on calls to upstream large model APIs, with pricing power firmly held by a few foundational model companies.

Application layer entrepreneurs essentially act as 'intelligence wholesalers': They purchase general intelligence from OpenAI or Anthropic at a price of several dollars per million tokens, then repackage it through prompt engineering, output formatting, and minor domain knowledge injection to retail it to end-users via monthly subscriptions.

The fragility of this business model lies in the Continuously declining (continuously declining) upstream wholesale prices—mainstream API call costs have dropped over the past year, while the retail end faces intense user impulse to 'bypass middlemen.' When users discover that direct subscriptions to large model vendors are not only cheaper but also offer access to many open-source features and even early access to the latest model versions, the paid conversion rate for the middle layer plummets.

Even more concerning to capital markets is the AI application layer's consistent inability to establish the 'data lock-in' sticky effect seen in traditional software. In the past, when using a CRM system, enterprise clients accumulated customer profiles, sales processes, and automation rules within the system, making migration extremely costly.

However, historical articles in AI writing tools, style reference images in AI painting tools, and code snippets in AI programming assistants—these user assets can almost all be exported losslessly or retrieved directly from conversation histories. The relationship between users and specific AI applications remains at a shallow 'temporary invocation' level rather than a deep 'strategic partnership.'

03. Future Forms: Transformation from Explicit Tools to Invisible Infrastructure

However, concluding that AI applications will vanish is overly simplistic. Reviewing computer industry history, operating systems did not eliminate application software but instead spawned a software ecosystem more vast than ever before—albeit with successful applications no longer attempting to do what operating systems excel at but instead venturing into vertical scenarios never anticipated by OS designers.

Today's AI models are playing a similar platform role, continuously absorbing general, templatable intelligent tasks while pushing application developers' creativity toward more niche, context-dependent, and physically interactive corners. Those AI products that remain viable uniformly avoid direct competition with general models in 'intellectual output' and instead establish new competitive dimensions in 'execution closure' and 'experience differentiation.'

In a deeper sense, AI applications are undergoing a transformation from 'explicit tools' to 'invisible infrastructure.' The most promising next-generation applications will no longer be standalone Apps bearing AI labels, chat windows with robot avatars, or disclaimers stating 'generated by artificial intelligence.' They will become increasingly silent and background-embedded, operating continuously at levels imperceptible to users, much like electrical power systems.

For example, next-generation office suites will not require you to first open an AI panel and type 'summarize this document for me' but will instead automatically display summaries in the sidebar the moment you select text. Future ERP systems will not demand that financial personnel converse with large models to generate reports but will inject model reasoning capabilities directly into every node of the data flow, pushing explanations and action recommendations the moment anomalies arise.

04. Conclusion: Future AI Applications May Head in Two Directions

Thus, from a commercial logic perspective, surviving AI applications will evolve into two distinct directions.

The first is 'deep-diving' types, entering professional scenarios difficult for general models to penetrate, such as equipment failure prediction in industrial manufacturing, molecular dynamics simulation in drug discovery, and real-time risk control in high-frequency financial trading. These fields require not just natural language understanding but also deep coupling with proprietary hardware, industry databases, and physical simulation engines to construct composite barriers that model capabilities cannot easily infiltrate.

The second is 'surface-level' types, overlaying a thin but highly intelligent interaction layer atop general models, making users feel not 'I'm talking to AI' but 'I'm just getting work done, and everything suddenly feels smoother.'

The commonality of these two application types lies in abandoning the linear thinking of 'earning money by packaging model APIs' and instead treating models as a continuously depreciating public resource, betting true value on industry insights and interaction design beyond model capabilities. For entrepreneurs, this undoubtedly means higher barriers and longer return cycles, but it is precisely the cruel allure of this round of consolidation—it weeds out speculators who rush to market with a few prompts, leaving long-termists who truly understand scenarios, respect workflows, and know how to build solid bridges between intelligence and reality.

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