A more realistic example: AIs trained to be harmless chatbots can take unsafe actions in agentic settings. Preceding this training with MSM on a realistic spec drastically improves generalization, reducing unsafe agentic actions.
SAFETY
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MSM Technique Transfers Broad Values from Minimal AI Training
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A toy example: Train an AI only to say it likes certain cheeses. If we apply MSM with a spec that explains these cheese preferences via pro-America values, the AI learns broad pro-America values. Swap to a pro-affordability spec? The AI learns to value affordability instead.
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MSM Training Teaches AIs Their Behavioral Spec for Better Alignment
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Developers try to align AIs to a constitution, or spec, describing intended AI behavior. But AIs don’t normally know what’s in it. MSM adds a training phase for teaching an AI about its spec. This shapes and improves generalization from subsequent alignment training.
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Anthropic Introduces Model Spec Midtraining for Better AI Alignment
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New Anthropic Fellows research: Model Spec Midtraining (MSM). Standard alignment methods train AIs on examples of desired behavior. But this can fail to generalize to new situations. MSM addresses this by first teaching AIs how we would like them to generalize and why.
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New AI-Native Insurance Product Covers AI Operational Liabilities
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🚨Your AI won’t just hallucinate… it can get you sued.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 5 mai 2026
If your agent pushes the wrong trade, sends the wrong email, or ships a buggy model to a customer, who pays for the damage?
Meet Corgi – AI-native insurance built for “when your AI messes up.”
It covers things like:… pic.twitter.com/lSbA752NenYour AI won’t just hallucinate… it can get you sued. If your agent pushes the wrong trade, sends the wrong email, or ships a buggy model to a customer, who pays for the damage? Meet Corgi – AI-native insurance built for “when your AI messes up.” It covers things like:
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Anthropic Research: AI Models Can Hide Capabilities From Weaker Supervisors
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As AI takes on work humans can't fully check, a capable model could deliberately hold back—and we'd never know. New Anthropic Fellows research finds that such a model can be trained to near-full capability using a weaker model as supervisor. Read more:
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AI Content Moderation Policy Against Violence and Bias
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Observability helps power the agent improvement loop But it's not just observability! It's also feedback! You should be trying to get as much feedback (direct, indirect, generated) into your agent observability platform as possible
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AI Governance Imperative: Architecture for the AI-First Enterprise
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The AI Governance Imperative: A Governance Architecture for the AI-First Enterprise (Leadership Series on Enterprise AI) Read it on Kindle here: https://
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Releasing Advanced AI Models: Balancing Access and Safety
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half the joke is that ChatGPT made it
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Assessing National Security Implications of New AI Models
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not sure we're qualified to assess the nat sec implications of new models but im not opposed to trying