---
title: "Unique Partner | DataCanvas’s Fang Lei: AI Helps Industry Evolve from Software to Thought-ware"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2023-10-08T17:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20231008a079v200"
source: "https://ffcap.cn/en/research/20231008a079v200"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20231008A079V200"
---

# Unique Partner | DataCanvas’s Fang Lei: AI Helps Industry Evolve from Software to Thought-ware

_Unique Research · Insight / Unique Partner welcome KEEP · October 8, 2023 · Full editorial rewrite of ~2,989 CJK — funding, AIFS/DataPilot, Vector Ocean preserved · inventory type insight_

Beijing **DataCanvas (九章云极)**, an AI foundation-software provider, closed a **RMB 300 million Series D1**. Participants: China Electronics’ Zhongdian Wisdom Fund, Huamin Investment, China Taiping’s Taiping Innovation, Zhejiang Orient’s Orient Jiafu, and AI-focused financial investor Zhuoyuan Capital, among other central/SOE-backed vehicles.

Founded 2013, focused on AI foundation software. 2020: Gartner AutoML key-vendor list; four consecutive years in IDC China ML development-platform Top 3 nationally. Unique Research interviewed Chairman **Fang Lei** on foundation-model practice and how AI foundation software drives enterprise innovation.

## Large enterprises need the ability to build proprietary models

On Jun 30 DataCanvas launched **AIFS (AI Foundation Software)** and **DataPilot** as large-model infrastructure for AI/automation journeys.

Fang describes three modes of injecting enterprise data: (1) **Prompt mode** — OpenAI-style black box; (2) **Fine-tune a white-box base** (e.g. LLaMA) into “your” model; (3) **Train from scratch** with proprietary data from day one. AIFS includes DataCanvas’s white-box **Yuanshi (元识)** large model plus a platform for customers to train proprietary models end-to-end.

“Which path depends on data volume. Only the top ~5% need to train new models; the rest fine-tune. Head enterprises have many shifting scenarios — external models can’t cover everything and vendor quality varies. Only a proprietary model supports long-term development.”

## “Cloud-within-cloud” strategy

Fang maps 2017–18 as **AI 1.0** — end-to-end turnkey AI vendors (four little dragons grew fast). ~2019 enterprises found turnkey rarely finished real business; lasting capability had to be owned. With rising tech literacy, teams, and policy on controllable tech, China’s LLM market accelerated; DataCanvas’s fast phase began ~2020.

Central-enterprise cloud, compute build-out, and large models are the “three horses” of China’s AI 2.0. Compute demand may even outgrow selling models themselves. Old IDC competition was bandwidth vs Alibaba Cloud; emerging intelligent-compute centers (GPU clouds) leave room for many sizes.

Two years ago DataCanvas bet **cloud-within-cloud** (AI cloud inside a thousand industry clouds) — partnering BAT clouds plus Tianyi Cloud, Mobile Cloud, and regional GPU clouds; embed DataCanvas software into the cloud SKU so customers train in-house. AIFS upgrades from Foundation _Software_ to Foundation _Service_.

Fang (ex Microsoft Bing data scientist; early Windows Azure) notes the software + compute + rebate model is mature in the U.S. — Snowflake ~$60B; Databricks ~$40B — win-win for training cost. “Hardware homogenizes; software differentiates; data is the deepest moat. Same hardware — shorter train time, smarter models — is the pain.”

Value is capability step-change, not only cost cut: same headcount/budget from 3 models/year to 30, or from simple to complex models.

## “Vector Ocean” as data’s end state

AI and data remain inseparable — now a **Data for AI** architecture era; large models reverse-empower data (New Data). Warehouses/lakes no longer suffice; after years in databases plus vector directions, DataCanvas proposes **Vector Ocean**: all data in vector blocks with unified query (lakes still split structured tables vs unstructured files).

ETL and retrieval will be GPT-ized (natural language for once-complex jobs); storage vectorized — hence Data Pilot. DataPilot: multimodal Vector Ocean architecture; on-demand automated integration, codegen, orchestration, analytics; NL data access, analysis, and ML modeling — lowering the full data→ML chain.

Yuanshi’s edge: multimodal including **structured** data — vector era’s marker. Experimental **TableGPT**: describe needs in NL; auto-pick stats/ML on private data, explain results, suggest next mining — “what you need is what you get.”

Fang: models raised the ceiling of imagination; what’s missing is not apps but proven business landings and industry-leading models — give mid/large enterprises time to adapt and innovate. AI 2.0 will redefine software: from forward process-automation tools to reverse assembly around human thought — industry from **software** to **thought-ware**.

_Author: Qiuping · Review: Siky_

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20231008a079v200
On-site reading page: https://ffcap.cn/en/research/20231008a079v200