1. Prologue: AI’s Warring States Period

We find ourselves in an odd moment of disconnect.

On one hand, AI capabilities are exploding at a visible pace. Text generation, image creation, video synthesis, code writing, music composition, 3D modeling, data analysis, personal assistants—nearly every domain has specialized AI products iterating rapidly. OpenAI, Anthropic, Google, Meta, and hundreds of startups are sprinting in their vertical lanes.

On the other hand, these services are isolated from each other. Users register on different websites, type requests into different chat windows, and copy-paste results across different interfaces. Want to create a simple family commemorative video? You might generate the script on Platform A, images on Platform B, synthesize a voice on Platform C, edit the clip on Platform D—and then manually stitch everything together on Platform E.

This feels eerily like the late 1990s internet: websites blooming everywhere, but none of them connected to each other. Back then, Yahoo! solved “how to find websites.” Amazon solved “how to transact in one place.” The App Store solved “how to distribute software at scale.”

Where are the Yahoo!, Amazon, and App Store equivalents for the AI era?

This isn’t a science fiction question. It’s a business and technical question that someone will answer within the next 3 to 5 years. This article attempts to extrapolate that answer—what the future AI ecosystem might actually look like.

2. The Nature of Service: Why AI Will Inevitably “Bloom Everywhere”

To predict the future, start with a basic fact: human needs are diverse, and the essence of service is meeting those needs.

Human needs span at least four dimensions:

No single entity can satisfy all dimensions for all people. AI is no exception.

Different companies have different data, expertise, and resources. A company with massive medical records is naturally suited for diagnostic AI; a company with a vast film and music library is better positioned for video generation AI. The most efficient configuration is to let each specialist excel in its own lane, then connect them through a unified network.

So here is the first certainty about the AI ecosystem: it will be distributed, not monopolized. No single AI will do everything, just as no single restaurant serves every cuisine in the world.

3. The Missing Piece: The AI Service Distribution Platform

If AI services will bloom everywhere, a distribution layer that bridges services and consumers is an inevitable business opportunity.

In the traditional internet, the stack looked like this:

User → Browser/App → Search Engine/App Store → Website/App → Service

In the AI era, a new stack emerges:

User → AI Terminal → AI Service Platform → AI Service Providers → Final Delivery

The key here is the AI Service Platform. It plays a role similar to Amazon for e-commerce or the App Store for mobile applications:

• For consumers: unified discovery, comparison, transaction, and delivery experience
• For providers: standardized onboarding, traffic distribution, payment processing, user management
• For the ecosystem: rules of engagement, quality assurance, dispute resolution

But there is a fundamental difference between an AI service platform and a traditional e-commerce platform: services are not standardized products.

A t-shirt on Amazon has fixed specifications, images, and price. The consumer chooses and the item ships. An AI service, however, is generative, personalized, and non-standard—when a user says “create a family commemorative video for me,” a thousand providers might deliver a thousand different outputs.

This makes the platform’s design extraordinarily complex—but also incredibly valuable. Whoever solves the problem of standardized distribution for non-standard services will own the infrastructure of the AI economy.

4. Core Challenge #1: Onboarding Standards and Protocols

For thousands of AI services with different capabilities and formats to plug into the same platform, a unified onboarding protocol is needed.

This protocol must define at least three layers.

4.1 Service Description Layer

Each AI provider must describe what they offer in a standard, machine-readable format. This isn’t a paragraph of marketing copy—it’s a capability declaration. For example:

service:
  name: "Family Video Generation"
  capabilities:
    - type: "video_generation"
      inputs:
        - type: "photo"
          min_count: 3
          format: ["jpg", "png"]
        - type: "text"
          max_length: 500
        - type: "audio"
          optional: true
      outputs:
        - type: "video"
          format: "mp4"
          max_duration: 600
      pricing:
        model: "per_output"
        base: 9.99
        per_minute: 1.99

This capability description language is the AI equivalent of product specifications on Amazon—when everyone uses the same language to describe themselves, the platform’s matching engine can function.

4.2 Communication Protocol Layer

How does a user’s request travel between the platform and the provider? How does progress get reported? How are deliverables transmitted?

A standard RPC-style protocol is required:

• POST /v1/tasks — Create a task
• GET /v1/tasks/{id}/status — Query progress
• POST /v1/tasks/{id}/cancel — Cancel a task
• POST /v1/delivery — Deliver results

The hard part: AI service execution times range from seconds to days. The protocol must support both synchronous and asynchronous modes seamlessly.

4.3 Quality Assurance Layer

The protocol must also define standard Service Level Agreement (SLA) formats:

• Response time guarantees
• Success rate metrics
• Content safety requirements
• Refund and retry mechanisms

Without these, quality on the platform will be inconsistent, and consumer trust will never form.

5. Core Challenge #2: Supply-Demand Matching and Intelligent Dispatching

Once the protocol and onboarding standards are in place, the platform’s core problem becomes: when a user states a need, how does the system find the best provider?

This is considerably harder than traditional search. When a user says “make me a video,” traditional search returns a list of links to video-making websites. On an AI platform, we need an end-to-end pipeline of intent understanding → capability matching → condition negotiation → order generation.

5.1 Intent Understanding Layer

User requests are often fuzzy, conversational, and underspecified. The platform needs an intent parsing AI—a “demand understanding layer”—that transforms natural language into a structured requirement document. For example:

User: “I want to make a 60th birthday commemorative video for my mom. I have family photos and some old pictures, I’d like background music, around 3-5 minutes.”

Parsed output:

intent:
  type: "commemorative video"
  occasion: "60th birthday"
  duration: "3-5 minutes"
  materials:
    - type: "family photos"
    - type: "old pictures"
    - type: "background music"
  sentiment: "warm"

5.2 Capability Matching Layer

With a structured requirement, the platform searches its provider database for matching candidates. This isn’t keyword matching—it’s multi-dimensional scoring:

• Capability fit: Does the provider’s capability declaration cover all requirement items?
• Historical quality: Ratings and reviews from past customers
• Price fit: User budget vs. provider pricing
• Availability: Current queue depth, load status
• Contextual relevance: Has the provider handled similar requests before?

The matching engine returns the top-N candidates ranked by composite score, presented to the user for selection.

5.3 Condition Negotiation Layer

This is the most interesting piece. Different providers have different requirements for inputs:

• Provider A needs at least 3 photos
• Provider B needs a family group video clip
• Provider C can work with just photos and audio

When a user first posts a request, they may not have all materials ready. The platform’s job is: before the user selects a provider, show each one’s material requirements and guide the user toward the option they can fulfill.

This is essentially a multi-party matching problem. The ideal flow: after the user selects a provider, the platform doesn’t just hand the user off—it enters a “pre-authorization phase” where the user uploads materials, the platform preprocesses and formats them, then hands them to the provider.

5.4 Human-in-the-Loop Decision Making

Which provider to choose is ultimately the user’s decision. But AI can assist:

• Side-by-side comparison: price, turnaround time, material requirements, ratings
• Mock previews: generate sample outputs from different providers using the user’s uploaded materials
• Smart ranking: based on user history and current context

This is a “AI recommends, human confirms” model—it combines AI efficiency with human final say, likely the most pragmatic approach for the foreseeable future.

6. Privacy and Authorization: The Most Overlooked Infrastructure

In this platform vision, privacy and authorization are the most easily overlooked yet most critical infrastructure. AI services inherently consume user data—photos, videos, documents, personal information—to complete tasks. This creates a host of thorny issues.

6.1 The Principle of Least Privilege

The platform needs a minimum necessary authorization mechanism:

1. Layered consent: Users don’t hand over all data at once. Access is granted layer by layer.
2. Client-side preprocessing: As much processing as possible happens on the user’s device (face blurring, metadata stripping) before data reaches the provider.
3. Single-use tokens: Authorization explicitly marks scope, duration, and usage count. Use-once, then revoke.

6.2 The Authorization Agent

A promising direction is the Authorization Agent—an independent AI, separate from both the platform and providers, dedicated to managing user data permissions. It knows what data the user has, what can be shared, with whom, and to what extent.

When the user says “provide my family photos to Provider A,” the Authorization Agent:

1. Confirms scope (shared photos only, not individual portraits)
2. Scans for sensitivity (third-party faces, private information)
3. Preprocesses data (compress photos to the minimum resolution the provider needs)
4. Issues temporary access tokens
5. Revokes all access upon delivery

6.3 Irreversible Delivery and Traceability

AI-generated deliverables should embed digital watermarks and provenance tracking. If a provider retains or leaks user data, the platform can trace the responsibility through audit logs.

This can’t rely on ethical agreements alone. It must be guaranteed by technology.

7. The Shape of the Platform: Beyond the App

When we talk about an “AI service platform,” it’s tempting to imagine a familiar mobile app—open it, browse listings, place an order.

I believe the future AI service platform won’t primarily be an app. Or rather, an app is just the tip of the iceberg.

7.1 Terminal Evolution

User interaction terminals could take many forms:

• Voice terminals: “Hey speaker, make me a commemorative video”—the speaker dispatches the task and pushes results to your phone
• Smart glasses / AR: In an augmented reality interface, gesture at elements in your space and say “turn this into a short film”
• Messaging app integration: @-mention a bot inside WhatsApp, Messenger, or Telegram to complete the entire workflow
• API layer: Advanced users and enterprises access the platform directly via API, embedding it into their own workflows

The platform’s responsibility is onboard once, distribute everywhere—a provider integrates once using the standard protocol, and the platform automatically adapts to all terminal forms.

7.2 Conversational Interaction Is Core

Regardless of terminal, conversational interaction will be the dominant paradigm. Users don’t learn complex software interfaces—they describe what they want in natural language. This is fundamentally different from the browse-and-shop model of traditional e-commerce:

• Traditional e-commerce: I know what I want → I go find it
• AI service platform: I know the effect I want → the system figures out how to deliver it

The platform’s required capability shifts from “search engine” to “demand assistant + orchestration center.”

7.3 End-to-End Closed Loop

A complete platform doesn’t just handle matching. It owns the full delivery experience:

Request submitted → Intelligent matching → Provider selection → Material authorization
→ Order dispatched → Progress tracking → Result acceptance → Delivery & review

Every step requires platform involvement. Particularly important are progress tracking and result acceptance—AI service execution can take significant time (generating a 10-minute 3D animation might take hours), and users need to check progress and even adjust requirements mid-execution.

8. Deeper Horizons: The Interconnection of All AI Services

If this platform becomes a reality, some profound possibilities start to emerge.

8.1 AI Service Chaining

Future AI services won’t be isolated single calls. They will be chained together:

User: “I want to create a travel Vlog and publish it to YouTube and Instagram.”

The platform’s internal orchestration might look like:

1. [Writing AI] transforms the user's raw notes into a full script
2. [Image AI] generates matching illustrations based on the script
3. [Voice AI] converts the script into voiceover narration
4. [Video AI] synthesizes script, images, and voice into a video
5. [Translation AI] generates English subtitles
6. [Platform] auto-formats the output for YouTube and Instagram

Each step might be handled by a different provider. The platform acts as a service orchestrator, dramatically lowering the barrier for complex creative work.

8.2 The Long Tail of AI Services

In traditional markets, only large-scale demand can sustain a specialized service provider. In an AI ecosystem, extremely narrow verticals can support sustainable AI services.

For example, there could exist an AI service that: transcribes Hakka-language family audio recordings into emotionally annotated Chinese text. The market might only be tens of thousands of people, but because AI’s marginal cost is near zero, the provider can still be profitable. When enough of these long-tail services accumulate, the platform’s value grows exponentially—it becomes the only place where you can find “Any AI You Can Imagine.”

8.3 A Decentralized AI Service Network

The evolutionary endpoint of this platform might not be a centralized commercial company but a decentralized protocol.

Just as HTTP is the foundational protocol of the web and SMTP is the foundation of email, there may one day be an AI Service Protocol (AISP)—anyone can build their own service node, consumer client, or aggregation layer on top of it.

In this vision:

• Providers freely choose which aggregation platform to join, or operate their own consumer frontend
• Users freely choose which client to use for discovering and consuming services
• Data and identity belong to the user, portable across platforms
• Value settles through tokens or cryptocurrency

This is a deeply libertarian vision—a kind of Web3 renaissance for the AI domain. Whether it becomes the final form is uncertain, but it at least offers an alternative to walled-garden futures.

9. Real-World Obstacles

After painting this picture, we must also confront the realities.

9.1 The Battle Over Standards

Who gets to define the AI service onboarding protocol? OpenAI? A consortium? An open-source community?

History suggests that whoever controls the standard controls the economics of the ecosystem. WeChat became China’s super-app largely because it defined the mini-program standard. If a single company establishes a de facto AI service protocol, it could become the gatekeeper of the entire ecosystem.

This echoes the early browser wars—the winning standard wasn’t necessarily the best, but the most widely adopted.

9.2 The Reliability Problem

In e-commerce, a product is deterministic: you order a specific t-shirt, you receive that specific t-shirt. In AI services, delivery quality is non-deterministic.

The same provider handling the same request can produce completely different results on two runs (especially for creative tasks). Without robust quality assurance and dispute resolution, consumer trust is hard to build.

9.3 Regulation and Compliance

AI services raise far more complex issues around content safety, copyright, and privacy than traditional e-commerce. A video-generation AI might accidentally produce copyrighted music, contain sensitive content, or leak user data. How much responsibility does the platform bear as intermediary?

This isn’t just a technical problem—it’s a legal and regulatory one. Different countries have vastly different regulatory stances, and a globally operating platform faces enormous compliance costs.

10. Conclusion: The Crossroads We Stand At

We are at a critical inflection point.

The past decade was the era of AI capability explosion. Models grew larger, capabilities expanded, and breakthroughs arrived almost monthly—from text to images to video.

The next decade, I believe, will be the era of AI ecosystem construction. The capabilities are impressive enough. The question now is how to organize them, distribute them, and compose them—so that everyone can use AI as easily as turning on a faucet.

The AI service platform envisioned in this article is just one possible slice of that ecosystem. The real future will likely be stranger and more surprising. But one thing is certain: a distributed, interconnected, standardized network of AI services is an irreversible trend.

Just as no one in the early 2000s could have accurately predicted the final form of the mobile internet, our speculations today will likely look naive in hindsight 5 years from now. But that’s the joy of forward thinking—finding direction in chaos, catching signals in noise.

The second half of AI isn’t a model competition. It’s an ecosystem competition.

And the ecosystem begins with a protocol, a platform, and a connection.