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Scale CEO Jason Droege reflects on a record-breaking 2025 and shares how Scale is building reliable, production-ready AI systems for 2026.

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What it will take for the United States to move from AI experimentation to real governance, government-wide implementation, and lasting global leadership.

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The team is kicking off 2026 like the rest of us: by focusing on health(care)! They discuss why adopting AI in healthcare is different from other enterprise AI initiatives and how leaders can account for those differences. And as always, they react to some of the internet's hottest takes on AI (healthcare edition).

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How agentic AI systems give the U.S. military decision advantage through faster planning, alerting, and command and control.

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MoReBench is a large-scale benchmark for evaluating AI moral reasoning beyond final outcomes. Instead of scoring answers alone, it assesses the intermediate reasoning traces models produce when navigating 1,000 morally ambiguous, real-world scenarios. Our findings show that moral reasoning is a distinct and underdeveloped capability, largely uncorrelated with performance on traditional math and coding benchmarks.

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Agentic AI marks a shift from reactive chatbots to autonomous mission partners. Government must adopt unified Agentic Infrastructure—combining resilient agent execution and governed AgentOps—to enable machine-speed decisions. Platforms like Scale’s SGP and Agentex deliver interoperable, durable, and accountable autonomy for mission assurance.

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We’re open-sourcing MCP-Atlas, including the dataset, evaluation environment, and updated results for a benchmark designed to measure how reliably AI agents use real tools. MCP-Atlas evaluates realistic, multi-step workflows that run against real Model Context Protocol servers, exposing where agents succeed—and where they still fail—when tool discovery, parameterization, and execution must work together.

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Today on the podcast, the Enterprise team reviews AI predictions they made 6 months ago and sees if they came true or not. Spoiler: we got a lot wrong...but some unexpected things right! They conclude by predicting what we can expect to see in 2026 with enterprise AI.

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Audio MultiChallenge is a new benchmark designed to stress-test native Speech-to-Speech models on what actually makes voice hard: mid-sentence corrections, audio-only cues, instruction drift, and long-horizon self-consistency. By evaluating real human conversations rather than synthetic text-to-speech we uncover where current audio systems still fail, and what it will take to build voice agents that truly listen.

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