OpenAI's latest release, GPT-5.1, was specifically designed to be faster than its predecessor for common queries without sacrificing intelligence. This release also included GPT-5.1 Codex, a specialized model for coding that has driven a 70% increase in pull requests among OpenAI's own engineers, signaling a trend towards more domain-specific, optimized models.
Large language models are being increasingly adopted by the scientific community to accelerate research. Use cases range from aggregating literature to testing hypotheses, with one notable example showing GPT-5 Pro reproducing the complex math from a new physics paper in 30 minutes, a task that took the human author weeks.
The cost of using powerful LLMs has plummeted by one to two orders of magnitude in recent years. OpenAI has found that every price cut to its API has been met with a surge in usage that more than compensates for the lower price, indicating massive, untapped demand that is currently gated by cost.
The speaker emphasizes a shift in perspective from 'the model is everything' to recognizing the critical importance of the 'harness' built around it. This includes the data pipelines, tooling, and operational infrastructure, which are essential for getting high-quality results, especially in enterprise settings where critical knowledge is often undocumented.
The speaker predicts 2025 will be 'the year of coding in the enterprise,' with AI tools seeing meaningful adoption. While full job automation via agents is still difficult, strong use cases are emerging in customer support and sales. Future progress in enterprise AI is as dependent on standardizing data infrastructure as it is on model improvements.
Keep pulling the thread on Olivier Goodman.