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How Scale AI Went from Labeling Data to Powering LLMs at Enterprise Scale

Scale AI started by labeling data for self-driving cars. Today, it's fueling enterprise LLM deployments with synthetic data, RLHF, and evaluation tools. Here's the evolution.

Author

Marco Fazio
April 22, 2025

AI Breakdowns: Scale AI

How Scale AI Went from Labeling Data to Powering LLMs at Enterprise Scale

In 2016, Alexandr Wang founded Scale AI to solve a very specific problem: cleaning and labeling massive datasets for self-driving car companies. Today, Scale is no longer just about data labeling—it has evolved into a critical vendor in the foundation model ecosystem, providing RLHF, fine-tuning, synthetic data, and LLM eval pipelines for Fortune 500s and U.S. defense contracts.

Here’s how a data services startup turned into an enterprise AI infrastructure layer.

Chapter 1: The Original Wedge—Human-in-the-Loop Labeling

Scale’s early customers were autonomous vehicle companies like:

  • Cruise

  • Waymo

  • Nuro

  • Zoox

They had millions of hours of sensor data, but no scalable way to annotate it. Scale offered:

  • A fleet of human annotators

  • Custom annotation tools

  • APIs to ingest, tag, QA, and export structured data for ML pipelines

This wedge gave Scale deep relationships in ML teams before foundation models took off.

Chapter 2: The Expansion Into AI Model Development

As LLMs took center stage in 2022–2023, Scale repositioned from "data labeling" to AI enablement.

Their services expanded into:

  • RLHF (Reinforcement Learning from Human Feedback) for fine-tuning models

  • Model evaluation frameworks using structured human scoring

  • Synthetic data generation to augment training corpora

  • Enterprise red-teaming and alignment testing

  • Custom annotation for text, image, and multimodal datasets

They worked with:

  • OpenAI (early RLHF tasks)

  • Anthropic

  • Meta and open-weight labs

  • The U.S. Department of Defense (classified LLM projects)

Chapter 3: Products, Tools, and Developer Infra

To escape the “services” label, Scale launched:

  • Scale Nucleus – visual dataset management platform

  • Scale Spellbook – agentic evals and red-teaming system

  • Scale Rapid – fast-turnaround labeling for startups

  • LLM Data Engine – unified solution for fine-tuning datasets

  • LLM Evaluation APIs – integrated into dev pipelines for automated model scoring

These tools helped them land big customers in:

  • Finance

  • Government

  • Healthcare

  • Manufacturing

Chapter 4: Growth, Funding, and Strategic Position

Scale AI has raised over $600M, with backers including:

  • Founders Fund

  • Accel

  • Tiger Global

  • Index Ventures

In 2024:

  • Estimated revenue reached $300M+

  • The company was valued at $7.3B

  • Over 700 employees worldwide

  • Defense work made up a growing share of total revenue

  • Strong presence in both Silicon Valley and Washington, D.C.

Their moat isn’t just infra—it’s trust and regulatory readiness.

Chapter 5: Why It Worked

  1. Started with real customer pain: Clean, labeled data

  2. Moved up the value chain: From labeling to LLM tuning to evals

  3. Enterprise-first DNA: SLAs, compliance, data privacy

  4. Deep integration with model labs: Not just API calls, but real RLHF

  5. Dual-market strategy: Commercial + defense

What You Can Learn

  • Start narrow, but build toward the stack

  • Services businesses can evolve into infra if they scale wisely

  • In regulated or high-risk AI use cases, trust is more valuable than speed

  • Evaluation and tuning will define the next generation of AI model performance

Marco Fazio Editor,
Latestly AI,
Forbes 30 Under 30

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