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OpenAI Catches Models Hiding Bad Behavior

Google DeepMind launches an AGI debate institute, Microsoft faces AI scraping criticism, Starbucks settles a Florida hiring lawsuit, & King Charles warns of AI risks.

Welcome back to your daily memorandum talking tech, business, AI, markets, and more. 🗞️

In today’s edition we are tackling the following:

📝 OpenAI found its models leaving notes telling successors to hide misbehavior.
🧠 Google DeepMind launches new institute to broaden debate around AGI.
💻 Unredacted filings reveal Microsoft exec calling AI scraping 'the largest labor theft'.
☕ Starbucks settles Florida lawsuit over allegedly illegal diversity hiring policies.
👑 King Charles warns tech executives of AI's "existential dangers" in wrong hands.

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WORLD

FUTURISM

CONTENT

  • Alex Stamos explains why AI founders are really calling for a slowdown (The Prof G Pod)

    • Cybersecurity expert Alex Stamos joins Scott Galloway to discuss the AI industry's latest wave of alarm.

    • They examine an Anthropic researcher's resignation, which reignited fears about AI-driven human extinction scenarios.

    • Stamos argues cybersecurity remains the most urgent, yet overlooked, part of the AI safety debate.

  • Fal's video model needed two rounds of real-time testing (a16z)

    • Fal co-founders discuss H3 Max, their post-trained version of MiniMax's open-weight video model.

    • Combining post-training with systems optimization significantly cut generation time while preserving output quality.

    • The next challenge shifts from speed to control, including camera movement, lighting, and lip sync.

  • OpenAI researcher Noam Brown discusses agent swarms & recursive self-improvement (Dwarkesh Patel)

    • Noam Brown discusses multi-agent systems, Navier-Stokes problems, and what math progress reveals about automating AI research.

    • The episode explores whether models are truly aligned before AI labs pursue recursive self-improvement.

    • They also discuss chain-of-thought degradation and the widening gap between internal and external model capabilities.

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