Governing dozens of models is a process. Hundreds is a different problem. @DominoDataLab
's Nicholas Goble breaks down what proactive AI governance looks like in FSI. Learn why the firms getting it right don't wait for regulators. Silicon UK:
ETHICS
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Proactive AI Governance at Scale in Financial Services
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Musk-OpenAI Factual Agreement and Ethical Questions in AI
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Our best shot for preventing many diseases?
Exercise
A @Cell_Metabolism new review https://
cell.com/cell-metabolis
m/fulltext/S1550-4131(26)00086-0
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Anthropic Model Spec Midtraining Study and Alignment Research
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Read more about Model Spec Midtraining: https://
alignment.anthropic.com/2026/msm Or read the full study: https://
arxiv.org/abs/2605.02087 -
Model Specs and Constitutions Drive Better AI Alignment Generalization
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Using MSM, we can also empirically study which model specs or constitutions yield the best generalization from alignment training. Specifying rules works to some extent, but explaining the values underlying those rules (or adding more detailed subrules) is even better.
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MSM Training Reduces Unsafe Agentic Actions in AI Chatbots
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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.
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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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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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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://
amzn.to/4nenpNt