---
title: "Unique Friend | Stardust Data’s Zhang Lei: The “Data-Centric AI” Movement Is Driving a Paradigm Shift"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-01-08T16:30:00+08:00"
canonical: "https://ffcap.cn/en/research/20240108a0605x00"
source: "https://ffcap.cn/en/research/20240108a0605x00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240108A0605X00"
---

# Unique Friend | Stardust Data’s Zhang Lei: The “Data-Centric AI” Movement Is Driving a Paradigm Shift

_Unique Research · Ranking / Unique Friend interview · January 8, 2024 · Author (CN): Xu Meng · Guest: Zhang Lei, Founder, Stardust Data (星尘数据)_

Inventory type kept as **ranking** per eligible inventory (Unique Friend / company interview package; not partner welcome).

**Stardust Data** builds on an AI full-lifecycle data closed-loop system — data production, data management, hard-example discovery, model inference — and develops ML-driven automated labeling tools to inject tech into the labeling industry and empower AI firms’ algorithm iteration and data loops.

Founder **Zhang Lei** spoke with Unique Research on labeling-industry status, Stardust’s edges, and a data-centric strategy.

## Labeling market: large growth room

AV and LLMs have driven explosive labeling demand. Global labeling is projected to grow at ~**40% CAGR**, with market scale expanding fast. Labeling flows themselves are being remade by the foundation-model paradigm — from manual toward algorithm-driven auto-labeling — lifting efficiency and widening the market further.

Earlier, China’s labeling industry had a low entry bar and was demand-side led: suppliers built tools for large annotator pools, task-oriented, relying on channels and labor — a traditional mode.

GenAI raised requirements: buyers need stage-aware (not only task-aware) support, strong pre-label and assist algorithms — and, harder still, products that host those algorithms and close the data loop into data collaboration. Difficulty and labor cost rose, challenging classical labeling and opening space for algorithm-first auto-label modes.

Zhang founded Stardust in **2017** with three goals: (1) raise labeling efficiency via automation; (2) create more customer value via data-strategy expert services; (3) close data loops so model training works better with less labeling volume — better cost/performance.

Zhang: undergrad Zhejiang University, master’s George Washington University; 10+ years Wall Street and Silicon Valley — built what CN calls the world’s first equity-investment robot in Silicon Valley; risk algorithms from Wall Street work used as stress-test standards by the U.S. Federal Reserve (per CN); expert committee member, National Voice & Image Product Quality Inspection Center; AV expert, China Productivity Promotion Center Association. In 2017 his team shipped China’s first commercialized automated labeling platform.

Customers are growing ~**2× per year**, mainly LLM and AV. On perception/decision, Stardust serves **~90% of leading AV firms**; AGI / large-model logos are rising, with multiple top LLM customers signed.

## Auto-assist labeling + data-loop iteration

### 1\. Rosetta — China’s first auto-labeling platform (~70% automation)

Core business: AI full-lifecycle data closed-loop base covering production, management, training, deployment. Two curves: (1) keep raising labeling-platform automation to lift AI R&D efficiency; (2) data-loop systems + LLM data solutions for vertical pain points.

**Rosetta** — claimed China’s first automated labeling platform — offers hundreds of labeling functions across 100+ mainstream scenes (4D-BEV, image, point cloud, text, speech, collection, etc.) and 100+ assist algorithms for pre-label, label, and QC. Automation rises year by year.

Edge: self-built Autolabeling — platform automation **70%+**, ~**200 million** records processed/year, **99.9%** accuracy, **1,000+** customer projects. Tech, industry experience, and iteration capability sit at the front of the pack (per CN). Higher automation lifts annotator throughput and income vs other platforms.

### 2\. Auto-assist + closed-loop iteration for algorithm speed

A full data loop that covers ingest, orchestration, preprocessing, hard-example discovery, send-to-label, acceptance, training-process management, and model-value metrics can greatly accelerate algorithm iteration. Zhang: “Stardust is not solving 100% of labeling — it solves the hard, frontier, large-scale labeling that paying large customers feel most.”

Data and demand complexity keep rising: 2D recognition → 3D → 2D/3D fusion → BEV 4D — more tech paths, finer rules, higher annotator bars.

Industry responses: (1) labor optimization — vocational schools into the system, cheaper labor, tighter labor control; (2) sales/BD for large sticky accounts and stable margin; (3) marketing that may not profit (even lose money) to grow revenue scale then finance the roll. Classical flow: algorithm teams write rules; labeling firms digest; annotators execute — suboptimal in one pass, and annotators stay “doers” who do not help iterate model quality.

Stardust aims at a fourth path: auto-assist labeling + data-loop iteration to truly cut cost and raise efficiency — tech iteration over years (one algorithm version 1–2 months; real effect may take 1–2+ years). Commercializing across that horizon stresses sales, R&D cadence, and company ops — but Zhang believes algorithms can cut labor cost **\>90%** and sharply lift delivery to meet industry demand.

### 3\. Platform/tools authority + data-strategy experts

Data strategy spans frontier tech: domain transfer, spatiotemporal fusion, augmentation, weak supervision; plus data-centric feedback loops — active learning, RL, retrieval, human-in-the-loop, data security, scene data generation, model testing. Experts must know both tech and product.

After serving ~90% of leading AV customers across 1,000+ projects, Stardust has both a leading platform/tool stack and seasoned data-strategy experts. Versus labor-optimization shops that win an order, split it, and farm to third parties, Stardust’s experts decompose algorithm needs and choose the right way to process customer data.

## From labeling toward data infrastructure

### 1\. Raise data reuse; cut LLM training cost

Across the data lifecycle, labeling is a small slice. Shipping an algorithm spans demand definition, solution design, collection, labeling, model design, training, metric tests, inference optimization — each touching data design, use, mining, testing. In the LLM era, massive data management and model reproduction are acute pains.

Stardust’s answer targets those pains. **MorningStar** supports key links of efficient enterprise AI data iteration — avoiding data-debt pile-up, waste on low-value data, and long feedback chains from train to app — closing the ML loop for a focused, high-speed algorithm production environment.

### 2\. MorningStar — AI full-lifecycle data management

A full ML lifecycle data system sets data quality and use efficiency and caps model upside — key to “data debt” and a future AI competitive moat.

_Note (CN): “tech debt” from software — long-term maintenance cost of speed-first builds. “Data debt” is the data-side analogue in ML systems. Weak full-lifecycle data tools slow iteration and accumulate debt._

Against data-debt accumulation, low-value data waste, and long train↔app feedback chains, MorningStar is a one-stop AI full-lifecycle data platform: data landing, orchestration, management, retrieval, hard-example discovery, send-to-label, algorithm management, inference management — continuous iteration speed.

Visual full-lifecycle ops for unified data ops and fast iteration; flexible slicing, multimodal and custom retrieval for special scenes; mainstream hard-example strategies (e.g. active learning); feature distribution, viz/mining, metric compute for algorithm engineers; aggregated data assets for safer collaboration and traceable iteration — breaking internal data silos and lifting AI spend efficiency, accuracy, and data-application capability.

Architecture was closed-loop from day one. First: multi-language/multi-format landing with structured stats — many underlying DBs; unlike classic DMS built for humans, Stardust’s AI DataOps serves algorithms. Proprietary lifecycle management records value discovery from landing through inference automation.

Second: strong storage/management from a decade of team data depth; rich managed objects; extensible hard-example strategies (metrics, custom metrics, distribution/label filters, active learning).

After labeling, the loop continues into algorithm iteration: version management, ground-truth datasets, algorithm evaluation — then closed. Customers can semantically retrieve (e.g. occluded cars), see which data is hot/cold/invalid, which to prioritize for the next algorithm pass, send needed slices to labeling, and feed results back — each iteration recorded so teams need not manually log every data state.

### 3\. Evolving toward a data closed-loop company

Scale AI (2016) pioneered global labeling, then expanded from AV into government, e-commerce, robotics, LLMs — tools, platforms, services. It grew management/test/dev tooling and became a data closed-loop + infra company with broader coverage.

Zhang: Scale’s core is productizing and commercializing tech application — PMF first. Few China startups have real productization; many just ship whatever the customer asks — demand movers, not products. Domestic firms need deep PMs who also grasp frontier research, product purpose, and strategy to connect the go-to-market chain.

Zhang reads Scale’s path as correct: early pricing, strategy, and sales; automation layered in at PMF; brand and data-change systems as the company scaled. That is Stardust’s evolution too — toward a data closed-loop company like Scale.

## The “data-centric AI” movement and paradigm shift

### 1\. From model-centric to data-centric

In 2021 Andrew Ng argued the past decade’s biggest AI shift was embracing deep learning; the next decade shifts from **model-centric** to **data-centric**. AI = Data + Code; better AI ≈ **80% data + 20% model**. Faster ML progress means not only better algorithms but systematic ways to improve data — ~80% of work on data prep; focus from big data to high-quality data.

Data-centric AI is a framework for developing, iterating, and maintaining data in AI systems — building effective training data, designing appropriate inference data, maintaining data — with emphasis on efficient high-quality, large-scale datasets. Stardust’s path empirically follows this idea.

Classic model building iterates models on relatively fixed data (benchmarks + many architectures for accuracy) — model-centric. It underweights real-world issues: wrong labels, duplicates, anomalies. High accuracy may only mean good fit, not good deployment.

Model-centric: iterate models, data relatively fixed. Data-centric: focus on data quality/quantity, model relatively fixed. Data-centric has more real-scene potential because data largely sets the capability ceiling. Core: countless deployment scenes rarely yield “big data”; vs tuning models, it is more feasible to raise sample quality/quantity where the model fails.

### 2\. High-quality data as the performance lever

Engineers often obsess over algorithm design and model metrics and underweight data’s share of the system. Most work — collection, cleaning, features, train, deploy, monitor — is data work. Under the LLM boom, high-quality data lifting performance is consensus; managing volume is the engineer’s pain; data’s role in iteration speed keeps rising.

Getting high-quality data is hard — acquisition, labeling, processing — heavy time and labor. Moving “from big data to high-quality data” needs data-strategy design. OpenAI’s ChatGPT is a famous case of clever strategy — RLHF.

Zhang: the real decisive factor for foundation models is data, not architecture. Same architecture, different data → different effect. High-quality data is today’s and the next stage’s win condition. Data-centric iteration drives AI landing; the old three-step of label → iterate model → deploy is already backward and non-competitive.

After years of research, model design is relatively mature — especially post-Transformer. Raising data quantity and quality becomes the key path to stronger AI systems. When models are strong enough, most people may not train; they design appropriate inference data to extract knowledge. Data-centric R&D will keep pushing AI systems forward.

The data-centric AI movement is driving an industry paradigm shift; data strategy will matter more over the next decade — spanning domain transfer, spatiotemporal fusion, augmentation, weak supervision, and feedback loops (active learning, RL, retrieval, human-in-the-loop, security, scene generation, model testing).

_3 CN images (incl. MorningStar UI); attach on ingest. Author (CN): Xu Meng; edit: Qiuping._

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On-site reading page: https://ffcap.cn/en/research/20240108a0605x00