Why the Mid‑Platform Will Become the Backbone of the Future Industry Internet

1. Technical Architecture Layers

Traditional technology architecture is usually divided into four layers:

UI interaction layer: Windows UI, PC Web UI, mobile app UI, WeChat mini‑program UI, camera‑based visual HMI, voice interaction HMI.

Logic layer: object‑oriented/component/SOA/micro‑service middleware, AI/NLP, machine learning.

Data layer: SQL/NoSQL databases, big‑data computing platforms, data warehouses, data lakes, visualization.

Infrastructure layer: cloud IaaS (servers, storage, networking, file systems).

While these layers are useful, the mid‑platform concept does not fit neatly into this perspective; it relies on these technologies but serves a different purpose.

2. Origin of the Mid‑Platform

The term "mid‑platform" (中台) first appeared at Alibaba as a response to the need for reusable, stable common functions that could be shared across many fragmented front‑end applications. JD introduced a similar idea called “borderless retail,” where any consumer traffic can embed JD product purchases via an Open API.

In the web era, traffic was concentrated and could be redirected easily. In the mobile‑app era, traffic is fragmented across many apps, creating the need to connect numerous entry points. The mid‑platform isolates unstable scenario‑specific logic from stable common functionality, turning the latter into reusable services.

Thus, a mid‑platform is an application—not a pure technology—designed to be integrated into various business scenarios through Open APIs.

3. Vision of Future Software

The author argues that many still rely on legacy PC/Web UI, single‑instance relational databases, and ETL/data‑warehouse pipelines, while cloud computing’s first wave has already passed. The future software stack is described in two phases:

First half (current cloud era)

UI layer: mobile apps, WeChat mini‑programs.

Logic layer: distributed micro‑services, distributed messaging middleware.

Data layer: distributed relational databases, distributed NoSQL databases.

Data layer (additional): real‑time big‑data computing platforms.

Infrastructure layer: virtualization, containers.

Second half (next wave)

UI layer: IoT sensors, voice interaction microphones, camera‑based visual recognition.

Logic layer: AI‑driven recommendation and optimal resource scheduling.

Data layer: cross‑chain blockchain.

Infrastructure layer: quantum computing.

Future data input will come from intelligent hardware, vision sensors, voice interfaces, web crawlers, and Open APIs rather than manual entry. Business logic will shift from hard‑coded rules to AI‑trained models that trigger actions via messages, making the mid‑platform logic dynamic and data‑driven.

Output will no longer be static UI screens or reports; instead, processes will run autonomously, only notifying humans when exceptions occur, and allowing human intervention through voice or command interfaces.

4. Application Layering

Within the future industry‑internet ecosystem, applications are also layered:

Front‑end fragmented applications (e.g., retail, procurement, recruitment, expense reporting) that appear in many specific scenarios.

Mid‑platform services that provide stable, common functions exposed via Open APIs for front‑end integration.

Back‑office applications that are highly stable, internal, and often workflow‑ or permission‑centric.

Even accounting functions will evolve into a chain of automated services: e‑commerce → e‑payment → e‑invoice → e‑contract → automatic bookkeeping → settlement → reporting → tax filing → bank reconciliation → audit, with each step offered as an Open API.

5. Mid‑Platform vs. Platform

Both provide Open APIs, but the mid‑platform is business‑oriented, dynamic, and continuously trained by data, whereas a generic platform is static and version‑bound, offering the same behavior regardless of when it is invoked.

6. Complete Six‑Layer System Stack

Layer 1: Numerous fragmented front‑end business applications.

Layer 2: Business mid‑platforms (retail, HR, finance, etc.).

Layer 3: Data mid‑platform – master data, data models, AI business algorithms.

Layer 4: Back‑office applications – ultra‑stable, internal use.

Layer 5: Application platform – workflow/message engines, component services (payment, invoicing, contracts, tax automation), development and operation platforms.

Layer 6: Technical platform – micro‑service middleware, SQL/NoSQL databases, big‑data platforms (Hadoop, Spark), AI engines, cloud IaaS.

7. Core Essence of the Mid‑Platform

In one sentence: the mid‑platform’s core essence is business‑centric, network‑connected, and data‑intelligent.