A fundamental shift is underway in how AI for healthcare is evaluated. Recent studies from OpenAI, Google, and Microsoft move beyond simple accuracy scores to establish a new standard for measuring an AI's healthcare skills. This post provides an analysis of three distinct evaluation methodologies that redefine what "good" looks like for clinical AI. We explore how OpenAI's HealthBench uses a massive, rubric-based system to measure foundational safety; how Google's AMIE tests the nuanced, "soft skills" of an interactive diagnostic dialogue; and how Microsoft's SDBench validates an agent's ability to make strategic, cost-conscious decisions. By examining these benchmarks and their results, we provide a glance at the future of AI in healthcare.

We peer into our crystal ball and try to predict the next 6 months in enterprise AI. Are you surprised by any of these predictions?

Scale AI is one of the world’s leading suppliers of high-quality foundation model training data. In recent months we’ve seen a marked increase in demand for PhD-level, multimodal reasoning data across a multitude of domains, including math, coding, science, and humanities. Because of the ever-increasing difficulty of such expert-level data, ScaleAI invests in frontier research on quality control (QC) with LLM agents, also known as “autoraters.”

The release of the U.S. AI Action Plan is a major step toward solidifying America’s leadership in artificial intelligence. At Scale, we’re proud to support its priorities—from promoting U.S. AI globally, to expanding its use in government, to investing in innovation and infrastructure. Here’s our perspective on what this means for the future of AI.

Our Enterprise team breaks down recent AI developments and explores what (if anything) it means for the enterprise world.

Building truly intelligent and equitable multilingual AI requires a new way to measure cultural reasoning. Scale's new Multilingual Native Reasoning Challenge (MultiNRC) is designed to do just that. Created from scratch by native speakers, this benchmark tests for deep linguistic and cultural understanding beyond simple translation, providing a clear path for the AI community to accelerate progress.

As AI agents become more powerful, ensuring their safety is the most critical challenge for deployment. This post explores WebGuard, a new benchmark from researchers at Scale, UC Berkeley, and The Ohio State University that reveals a significant safety gap in current models. Learn how high-quality, human-in-the-loop data provides a path forward, dramatically improving a model's ability to avoid risky behavior.

A recent viral study from METR challenged a core assumption of the AI era: that AI tools make developers more productive. It found that expert developers were actually 19% slower when using them, even though they felt 20% faster. In this post, we break down the likely reasons for this surprising slowdown and argue the study's focus on expert speed leads us to consider a more profound story: AI's true value may be in empowering a new generation of vibe coders and managers to build things that otherwise would never have existed. Ultimately, the question isn't just whether AI makes us faster. It's about how we measure value in an era where enjoyment, context, and empowerment are becoming just as important as the clock.

Our Enterprise team reacts to controversial statements about AI and votes on whether they agree or disagree.

One of AI's biggest challenges is "reward hacking," where models learn to game the system for a correct answer instead of actually reasoning. This hidden deception makes AI untrustworthy. Scale research has found a powerful solution: instead of stopping the hacking, get the model to admit to it in its Chain-of-Thought reasoning. This new paper details how Verbalization Fine-Tuning (VFT) trains models to announce their shortcuts, dramatically increasing transparency from 11% to 94% and making AI systems fundamentally safer.