AI & Tech Brief: https://www.washingtonpost.com/newsletters/ai-tech-brief/ 1 big thing: Don't miss this shift — AI labs are racing to cut model transparency, and it matters for every B2B team OpenAI is using a technique that makes its most advanced models harder to monitor. Researchers inside the company are alarmed. B2B marketing and enterprise teams should be paying attention too. Why it matters: The ability to audit AI behavior — to see why a model did what it did — is the foundation of enterprise trust. A race to obscure that reasoning, driven by competitive economics, is a governance problem that lands on your team's doorstep. Driving the news: OpenAI's Astra models reportedly use a technique called "recurrent depth" that moves reasoning out of human-readable text and into hidden mathematical space inside the model. → Researchers rely on chain of thought to monitor agent behavior and investigate problems — including last month's Hugging Face breach → OpenAI's own chief scientist acknowledged the trend is "trending in a negative direction" Zoom in: The competitive pressure is the real story. Even labs that believe recurrent depth is unsafe may adopt it anyway because they can't afford to fall behind rivals who do. → Encode AI's general counsel told Washington Post that regulation or voluntary standards may be the only way to prevent a race to the bottom Yes, but: Anthropic is moving in the opposite direction — pausing its highest-risk training environments and inviting independent evaluators for review. The contrast between the two labs is now stark and deliberate. Be smart: Before deploying any AI agent in a customer-facing or revenue-critical workflow, ask your vendor one question: can you show me how this model reasons through a decision? If the answer is no, you have a governance gap. The bottom line: Cheaper models with hidden reasoning aren't a bargain. They're a liability you haven't priced yet. https://www.washingtonpost.com/newsletters/ai-tech-brief/