OpenAI's new Astra model uses recurrent loops to add computational depth, which technically hides AI reasoning in unreadable neural vectors rather than interpretable text. The company argues this poses no new safety risk since the added depth is modest compared to simply building deeper models, but the underlying concern—that AIs could think dangerous thoughts we can't monitor—remains unresolved.

• Neuralese recurrence lets AI loop internal representations (vectors) instead of outputting readable chain-of-thought text, making it impossible to monitor what the AI is actually thinking at scale.
• OpenAI claims Astra's recurrent depth (roughly 240 layers) is only 2x GPT-4's depth, so it's no worse than building a deeper model traditionally—a distinction that matters for safety but not for capabilities.
• Looping the same layers repeatedly is much weaker than adding new real layers; the AI can't learn new knowledge patterns, just apply existing ones more times, limiting the appeal of extreme looping.

Researchers propose a metric called NLS depth to quantify how much unobservable reasoning AI models can perform internally, separate from chain-of-thought outputs. This matters because architectural changes could let models do complex reasoning without showing their work, making them harder to monitor.

• Current chain-of-thought monitoring works because models are trained on human text, but new architectures could bypass this by performing reasoning in opaque hidden states instead of readable tokens.
• NLS depth measures the longest computational path that doesn't pass through natural-language outputs (tokens initialized from pre-training), making it possible to assess monitorability risk before a model is even trained.
• The metric deliberately ignores some post-training choices (like RLHF or distillation) to focus on architectural differences, distinguishing between standard transformers and genuinely concerning designs like full-bandwidth transformers that can hide reasoning across entire context windows.