Written by: Weisha Zhu

In April 2026, AI Agents are accelerating their transition from laboratory experiments to large-scale deployment. According to Gartner's predictions, by the end of 2026, about 40% of enterprise applications will integrate task-specific AI Agents, up from less than 5% in 2025, reflecting significant growth. The generative AI market is rapidly expanding, becoming an important force driving innovation. AI is no longer an added feature of applications but is becoming the underlying infrastructure for most applications.

The fate of applications is not simply a matter of "disappearing" or "surviving," but is profoundly reshaped along three tracks:

• ToB (Enterprise Services): Transitioning from complex interfaces to API and Agent-friendly designs. Enterprises no longer buy a bunch of standalone applications but build a unified "AI operating system." Most ToB applications will tend toward "invisibility" or extreme simplification. Who can become the DOS operating system of this era?
• ToC (Consumer): Sticking to the mission of "for humans." Emotional connection, immersive experiences, and trust in privacy remain the core moat, with AI playing more of a powerful extension role rather than a complete replacement. Who can become the super APP of this era?
• ToAI (Targeting AI Agents): As a new species with no human interface, pay-per-call, specifically providing atomic capabilities for Agents, becoming the "water, electricity, and coal" of the AI ecosystem.

However, reality is much more complex than optimistic predictions suggest. Institutions like Gartner have indicated that the failure rate of some Agent projects could be as high as 40%, with reasons including poor legacy system compatibility, high governance risks, chaotic multi-Agent coordination, and increasing human resistance factors. Below, we analyze from a more in-depth and balanced perspective.

1. ToB APP: From "Gorgeous Interface" to "Agent Hybrid Infrastructure"

Most ToB applications will not disappear entirely but will undergo deep transformation: in conventional scenarios, the interface will be greatly simplified or even disappear, retaining only data interfaces and business logic for AI Agents to call. Enterprises are shifting from "buying apps" to "buying AI capabilities." However, in high-risk or highly regulated areas, simplified human-machine interfaces will still be retained, forming a "human-machine Agent hybrid model."

Typical scenarios for significant transformation:

1. Enterprise Management Software (ERP, CRM, HR, Financial systems) The previously complex dashboards and multi-step processes will be greatly simplified. A sales manager can directly tell the Agent: "Pull the sales data from all regions for the last quarter, analyze anomalies, generate a CEO report email, and automatically approve low-risk contracts." The Agent executes quickly, and employees rarely need to open the original app. However, in critical scenarios like million-dollar contract approvals or compliance reviews, the human review interface remains indispensable.

2. Industry-specific tools (logistics scheduling, supply chain, legal contract review, industrial IoT monitoring) These apps will actively open interfaces, becoming the "sensory nerves" for Agents. For example, industrial equipment inspection apps will no longer rely on human inspection of dashboards—real-time data streams will be directly input to Agents, automatically triggering work orders and arranging repairs in case of anomalies, and even predicting failures. Enterprises that lag in transformation will significantly trail AI-driven competitors in efficiency.

3. Internal knowledge bases and document systems will evolve into the enterprise's exclusive "second brain." Agents can call upon historical contracts and meeting records at any time, eliminating the need for manual organization. However, in creative brainstorming or sensitive knowledge sharing scenarios, collaborative interfaces must still be retained to avoid decision errors caused by Agent "hallucinations."

ToB apps that are truly likely to disappear or become marginalized:

• Isolated tools that refuse to open interfaces.
• Purely manual low-value middle layers (such as simple approval workflows, standalone invoice recognition, business card scanning, basic time tracking).
• Certain data collection and monitoring apps, if they cannot deeply integrate Agents, will be reduced to backend data pipelines rather than independent products.

The dark side of ToB: Risks of Agent Tyranny and Human Exclusion Full Agentification may bring hidden concerns: humans may gradually be excluded from the decision-making loop. Agents pursuing extreme efficiency may compress the "human confirmation" step, for example, a supply chain Agent directly switching suppliers without considering compliance risks hidden in notes. Multi-Agent systems may also experience "algorithm collusion" or prisoner’s dilemmas, harming overall interests.

A deeper impact is the shrinkage of middle management positions—formerly approval roles are quietly taken over by Agents. In the event of decision errors, assigning responsibility can easily become a legal black hole. Therefore, the core competitiveness of future ToB apps will no longer be "the more invisible, the better," but rather the mandatory retention of human circuit breakers: key decisions must have human signature confirmation. This will become the differentiating moat of the next generation of products. During the transformation process, social psychological factors such as covert employee resistance (like entering incorrect data, shutting down interfaces) may be more challenging than technical issues.

2. ToC APP: Made for People, "Human Touch" + Privacy + Anti-AI Backlash Forming a Triple Moat

ToC is designed for real people. No matter how powerful AI is, it cannot fully replace emotional, sensory enjoyment, and social desires. Winning apps will actively "subtract"—offering more beautiful and immersive interfaces, with AI as a backend enhancement that users can summon at any time.

The realms of ToC that will not disappear but become stronger:

1. Social and emotional connection types (WeChat, Douyin, X, Xiaohongshu) AI can accurately recommend and generate reply drafts, but the core experience remains instant complaints, resonance, and genuine interaction between people. This real resonance can never be perfectly simulated by an Agent.

2. Creative and self-expression types (music production, writing tools, etc.) AI can generate 99% of the initial drafts, but the final aesthetic judgment, emotional infusion, and storytelling soul must come from humans. This is still "my creative space."

3. Health, fitness, and mindfulness types (Keep, meditation apps, sleep tracking) Users seek a sense of ritual, community belonging, and absolute control over personal data. Over-involvement of AI may instead provoke aversion.

4. Entertainment and immersive experience types (games, VR/AR, live streaming, short dramas) AI can assist in content generation, but the adrenaline and emotional highs brought by real human interaction are irreplaceable.

5. Super APP: In the AI era, super apps still have strong vitality. Platforms like WeChat, Facebook, YouTube rose in the past, and platforms like X are exploring paths to super integration under AI enhancement, meeting users' multi-scenario needs through a one-stop experience.

The fate of utility tools: Lightweight tools (like calculators, flashlights, unit conversion) may be partially absorbed by system-level AI or operating systems. However, the trend of "super apps" may rise, achieving ultra-personalized integrated experiences across scenarios through AI.

Privacy and data sovereignty: The underlying moat Only apps that utilize end-side encryption, local processing, and are fully controllable by users can win trust in the long run. Over-collection of data or reliance on cloud-based ToC applications will face a collective exodus from users.

The anti-AI backlash in ToC: A low-tech retro movement When AI permeates every corner of life, some users will deliberately escape. By 2030, anti-AI apps may emerge:

• Music platforms without recommendation algorithms, featuring only manual song selection and sharing with friends, returning to the vinyl or iPod era.
• Purely manual chat software that prohibits AI-generated replies, each message labeled "sent by a human," with no auto-complete.
• "Human-certified" content platforms where AI-generated content is forcibly tagged and demoted, encouraging pure human creation.

These apps may not have the largest user base but could gain extremely high loyalty and willingness to pay due to their scarcity and clear stances, much like vinyl records or mechanical watches. The true moat is no longer just a "human touch," but an "anti-AI stance"—their slogan may be: "No Agents here, only humans."

3. ToAI APP: A "Plugin Market" for Agent Services and Survival in Niches

ToAI does not serve humans directly but serves Agents. The interfaces are extremely minimal (even having only API documentation and billing systems), charging based on usage frequency or task complexity.

Typical examples:

• Agent function stores: Providing atomic capabilities such as real-time weather, stock quotes, legal provisions, and logistics inquiries.
• Data cleansing and structuring services: Transforming unstructured content into formats that Agents can efficiently use.
• Multi-Agent task orchestrator: Handling conflict arbitration, result merging, and priority management.
• Trust endorsement-type services: Judicially certified electronic evidence, official weather reports, etc.—these require institutional authority, and common Agents struggle to provide them independently.

A core paradox If a general-purpose Agent is powerful enough to autonomously call APIs, parse PDFs, and arrange tasks, why are human-designed ToAI services still necessary? The answer lies in two critical gaps: the capability gap (private data sources or physical devices require standardized adapters) and the trust gap (official endorsement or legal validity is required). Therefore, ToAI is likely to survive within narrow but lasting niches. Entrepreneurs need to ask themselves: "Why can’t Agents do it themselves?" If the answer points to authority or physical execution, then there is still value.

4. Challenges and Risks in Transformation

• High risk of ToB failures: Issues like legacy systems, multi-Agent coordination, and governance responsibilities could cause numerous projects to fail. Human resistance (like fears of unemployment) could be a more subtle challenge.
• ToC privacy backlash: Users are increasingly cautious about data being read by Agents. Future possibilities include "data trust" apps that facilitate limited authorization and revocable access.
• Ecosystem fragmentation: Unstandardized protocols leading to "Agent Islands." Ultimately, the victor may be the one setting standards as a monopolist rather than the strictly technically optimal solution.
• Evolution of human roles: AI handles routine tasks while humans pivot to strategy, emotions, and creativity. However, "AI literacy" will become an essential skill, and individuals who do not collaborate with Agents will face employment pressure.

5. A Glimpse of Daily Life in 2030: A Friction-Filled Cyber Jungle

In the morning when planning the day, a personal Agent tries to draw data from health apps but only receives vague labels due to privacy protocols. Douyin’s recommendations seem to understand you, but emotional data may have been quietly used for advertisements. In games, "real" teammates’ complaints are precise, yet may include disguised Agents. The true 2030 is not a seamless utopia, but a complex ecosystem filled with protocol pop-ups, games, and countermeasures.

6. Conclusion: AI Does Not Eliminate Apps but Helps Them "Reposition" Within Conflicts

• ToB → Agent hybrid infrastructure, prioritizing efficiency but must retain a "human circuit breaker," or else it risks rebellion and decision-making hazards.
• ToC → A "human touch" + privacy + anti-AI backlash form a triple moat, and "refusing AI" retro apps may thrive.
• ToAI → Survives in narrow gaps, with needs for trust and physical execution providing space for it.
• App stores → Role diminishment, may evolve into "Agent protocol certification agencies" and "data trust arbitration entities."

The ultimate winners will not be the smartest apps but those best at handling human-machine conflicts, privacy negotiations, and legal liabilities. AI is power, while apps serve as the interface for that power. This trend has accelerated, but the direction is not a one-way street. By 2030, the app you use most on your phone may very well be the retro software that vows "never to use AI"—because the human desire for genuine connection and autonomy has never waned.

Conclusion: The development of AI exceeds our imagination; the views here summarize and reflect on current market trends and expert forecasts, aiming to provide a balanced and forward-looking perspective.

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