Scale AI is the data-labeling and AI-data incumbent that has extended into RL environments (simulated web apps, macOS/Windows-like desktop VMs and MCP-tool environments with expert rubrics and automated verifiers). It runs a large public evaluation program through Scale Labs (SWE-Bench Pro, MCP Atlas, Humanity's Last Exam, Remote Labor Index). After Meta's ~$14.3B deal in June 2025 (~49% non-voting stake) and Alexandr Wang's move to Meta, several frontier labs reportedly scaled back work. In 2026 Scale grew mainly in government and enterprise: the Pentagon CDAO agreement ceiling rose to $500M, it acquired ICG Solutions, and Francis deSouza (ex-Google Cloud COO) became CEO in August 2026.
89 invalid instances were removed from the original 731. The release adds anti-cheating measures (sanitized git history, network isolation during the agent phase, re-grading on pristine images, verifier hardening). 23 contracted engineers spent 1,897 hours on cleanup. Claude Opus 5 resolved 638/642 public tasks (99.4%) but only 222/272 private tasks (81.6%).
Why it matters: A widely used coding-agent benchmark. The roughly 18-point public/private gap is evidence of training-time contamination, which matters to anyone using SWE-Bench Pro for evaluation or as an RL training signal.
Announced at the Google Cloud Doha Summit. Agents are built and evaluated in SGP, deployed on GKE with GPUs/TPUs inside customer VPCs, and surfaced through Gemini Enterprise, with A2A/MCP interoperability and an Agent Registry.
Why it matters: A cloud and go-to-market partnership with Google after Google reportedly cut data-labeling ties in 2025. It does not show that Google DeepMind is a data customer.
Produces deployment profiles covering reliability, human-oversight rate and cost for human-plus-agent workflows. It starts with healthcare (CliniCARE-Bench, PSEBench), with financial services announced as coming soon. Built on Inspect AI; paper at arXiv 2609.02095.
Why it matters: Shifts agent evaluation from capability scores to deployment-level metrics such as review burden.
The team generated 603 programmatic verifiers with coding models. When the single most critical number in each of 38 deliverables was corrupted, none of the 38 verifiers failed the output: 25 still scored it 1.0, and 13 docked only 0.4-10.7%. The post recommends deterministic checks combined with vision-capable judges and mutation ('corruption') testing of verifiers. Programmatic verifiers agreed with VLM judges on 99.5% of the items the judge passed.
Why it matters: Directly relevant to reward computation in computer-use RL environments, and a view into Scale's own verifier practice.
deSouza was most recently COO and President of Security Products at Google Cloud, and before that President and CEO of Illumina. Alexandr Wang, quoted as 'Founder and Chairman', endorsed the appointment. The announcement names BP and Mayo Clinic as enterprise customers, plus U.S. and international governments.
Why it matters: Ends the interim period that followed the Meta deal. Hiring an enterprise and security operator as CEO is consistent with a focus on enterprise and government growth. That reading is our inference, not a company statement.
Builds on the May MOU. Scale joins working groups on scientific GenAI benchmarks and evaluation, research agents, and computer-vision and robotics data pipelines tied to data from the 17 national labs.
Why it matters: Formalizes Scale's role in federal scientific-AI evaluation.
Focus areas are reducing clinical documentation-review time, automated detection of patient-safety events, and reducing administrative burden. Patient data stays in Mayo's HIPAA-compliant environment. The post includes a quote from Matthew R. Callstrom, M.D., Ph.D., Medical Director of Mayo's AI Program.
Why it matters: A named healthcare customer. The announcement was published by Scale but quotes a named Mayo executive.
The agreement was formalized in April 2026 and announced 2026-07-07. Planned work: a large-scale legal benchmark for Singapore law, public release of a subset of the benchmarks where possible, and exploration of agentic AI, evaluation methods and multilingual AI safety.
Why it matters: Scale's evaluation business is expanding into international government partnerships.
VeRO (Versioning, Rewards, and Observations) lets an optimizer agent edit any part of a target agent. It reported gains of up to 19 points on multi-step benchmarks, mostly from changes to how the agent interacts with its environment rather than from reasoning. The paper is listed on labs.scale.com/papers as 2026-02-25; the blog post is dated 2026-07-06.
Why it matters: Relevant to harness and scaffold optimization and to avoiding overfitting during agent-improvement loops.
82 problems written by drug-discovery experts. Leading agents (OpenAI, Anthropic and Google models) solved about half. Expert step-by-step plans lifted some previously unsolved tasks to near-perfect scores, and harness optimization added up to 17 points for some models.
Why it matters: A domain-expert agent benchmark that shows Scale's push into scientific and pharma evaluation.
Combines Circadence's cyber readiness platform with Scale's agentic AI infrastructure. Initial priorities are AI-enabled cyber mission environments, workflow automation for cyber operations, and analyst decision support for defense, intelligence and critical infrastructure.
Why it matters: Adds cyber mission environments to Scale's defense portfolio, next to its RL-environment work.
A run of papers on rubric rewards for RL post-training (reward hacking, policy-aware rubric quality, distillation without rubric verifiers) and on long-horizon agent environments: ChainWorld composes long desktop workloads from atomic OSWorld tasks, and SWE-INTERACT covers user-driven coding sessions. Spine-Branch Coordination for Multi-agent Computer Use followed on 2026-08-27.
Why it matters: These methods matter for RL environments with partially verifiable tasks, and the papers show how Scale designs rewards.
Available through Carahsoft's GSA MAS (47QSWA18D008F), and also through SEWP V, ITES-SW2, NASPO ValuePoint and OMNIA Partners contracts.
Why it matters: Gives federal, state and local buyers a simpler procurement path.
Anniversary post by interim CEO Jason Droege with company-stated cumulative figures, including coverage of 150 languages and locales.
Why it matters: The latest scale figures from the company. They are self-reported and not audited.