According to OpenAI on Twitter, GPT-5 was integrated with an autonomous laboratory system, enabling it to design and iterate scientific experiments autonomously. Over six cycles, GPT-5 generated experiment batches, which the lab executed and then used the results to inform subsequent experiment designs. This process allowed the exploration of more than 36,000 reaction compositions across 580 automated plates, demonstrating the practical potential of large language models in accelerating scientific discovery and experimental optimization. The project highlights new business opportunities in automated research and the application of advanced AI models like GPT-5 in scientific R&D, as reported by OpenAI.

According to Greg Brockman on Twitter, the workflow of finishing debugging and initiating experiments is a significant milestone in the machine learning development process (source: Greg Brockman, Twitter). This highlights a growing trend towards streamlined AI experimentation pipelines, which allow researchers and engineers to focus on analysis and innovation while automated systems handle model training and result collection. Businesses leveraging advanced experiment management tools and automated pipelines can gain substantial productivity improvements, reduce time-to-market, and achieve more reliable AI deployment. The adoption of such workflows is transforming operational efficiency in AI-driven organizations.