The release of OpenAI GPT-5 marks a significant turning point in AI development, but maybe not the one most enthusiasts had envisioned. The latest version seems to reveal the natural ceiling of current language model capabilities with incremental rather than revolutionary improvements over GPT-4.

Sid and Andrew call back to some of the model-building basics that have led to this point to give their assessment of the early days of the GPT-5 release.

• AI's version of Moore's Law is slowing down dramatically with GPT-5
• OpenAI appears to be experiencing an identity crisis, uncertain whether to target consumers or enterprises
• Running out of human-written data is a fundamental barrier to continued exponential improvement
• Synthetic data cannot provide the same quality as original human content
• Health-related usage of LLMs presents particularly dangerous applications
• Users developing dependencies on specific model behaviors face disruption when models change
• Model outputs are now being verified rather than just inputs, representing a small improvement in safety
• The next phase of AI development may involve revisiting reinforcement learning and expert systems
* Review the GPT-5 system card for further information

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This summary was AI-generated from the original transcript of the podcast that is linked to this episode.

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