Host Nathan Labenz and co-host Prakash Narayanan launched AI in the AM, a daily live show attempting to track the AI frontier in real time, with this episode presenting highlights from their first week. The central revelation came from a closed-door event called Recursive, where researchers from OpenAI, Anthropic, and DeepMind discussed imminent plans for recursive self-improvement. OpenAI expects ML research intern-level AI later in 2025 and full researcher equivalence by early 2028, potentially scaling from thousands to millions of researcher-equivalents. Remarkably, frontier lab researchers openly discussed the possibility of coordinated slowdowns if safety measures prove inadequate, representing a significant shift in industry discourse. Their primary safety strategy relies heavily on AI monitoring AI, with researchers acknowledging plans are less robust than hoped. Nathan demonstrated this control gap by showing both ChatGPT and Claude refuse cigarette business help despite OpenAI's model spec explicitly listing this as an acceptable request. The episode featured interviews with OpenAI's forward-deployed engineers on tax automation, security researchers on AI vulnerability discovery, and developers building AI mental health and accounting solutions. Peter Jansen from Allen Institute provided a sobering counterpoint, revealing that an AI scientist system claiming 19 discoveries actually produced only 30% valid results after code review, with some papers literally analyzing random number generators. Throughout, the hosts used Claude and other AI tools live to fact-check claims and run experiments, embodying the recursive improvement loop they were documenting. The show's structure itself is experimental, with studio infrastructure, booking, research, and clipping handled by AI systems the hosts are refining publicly.
Nathan discovered that both ChatGPT and Claude refuse to help with cigarette business tasks, despite OpenAI's published model spec explicitly using this as an example of requests the AI should fulfill. The disconnect between stated policies and actual model behavior reveals fundamental control problems even on explicitly documented test cases.
Read this moment →At the Recursive event, researchers from multiple frontier AI labs including OpenAI, Anthropic, and DeepMind agreed they may need to coordinate slowing development if monitoring and safety techniques prove inadequate during recursive self-improvement. This represents a significant shift in the Overton window, with cross-lab collaboration on safety now openly discussed despite competitive pressures.
Read this moment →Peter Jansen from Allen Institute tested an AI scientist system that claimed 19 discoveries from 50 research ideas. While initial paper review suggested 70-80% validity, deep code review revealed only 30% were real, with some papers analyzing random number generators that commented 'insert rest of neural network code here.'
Read this moment →At the closed-door Recursive event, frontier lab researchers revealed their primary safety strategy for recursive self-improvement is AI monitoring AI, with limited additional safeguards. While plans were less compelling than anticipated, researchers showed awareness of inadequacy and openness to coordinated action if needed.
Read this moment →At a closed-door event called Recursive, researchers from OpenAI and other frontier labs discussed explicit plans for recursive self-improvement. OpenAI expects to achieve ML research intern-level performance later in 2025 and full researcher equivalence by early 2028, potentially allowing them to scale from thousands to millions of researcher-equivalents limited only by compute.
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