- OpenAI claims 8.4 million weekly messages are sent about advanced science and math
- GPT-5.2 models can follow long chains of reasoning and verify results independently
- AI accelerates routine research tasks such as coding, literature review, and experiment planning.
OpenAI wants users to treat ChatGPT like a research collaborator, with new research claiming that nearly 8.4 million messages are sent each week and focus on advanced science and math topics, generated by around 1.3 million users worldwide.
OpenAI highlights that this usage has increased by almost 50% over the past year, suggesting that the system is moving beyond occasional experiments into regular research workflows.
These users would engage in work comparable to graduate study or active research in the fields of mathematics, physics, chemistry, biology, and engineering.
Scale of use and research integration
Mathematics receives particular attention in the report. GPT-5.2 models are said to support long chains of reasoning, verify their own work, and work with formal proof systems like Lean.
OpenAI claims the models achieved gold-level results at the 2025 International Mathematical Olympiad and demonstrated partial success on the FrontierMath benchmark.
The report also states that the models contributed to solutions related to Erdős’ open problems, with human mathematicians confirming the results.
Although models do not generate entirely new mathematical theories, they recombine known ideas and identify connections between fields, speeding formal verification and discovery of evidence.
Similar trends are emerging in other scientific fields. On benchmark tests such as GPQA, GPT-5.2 would exceed 92% accuracy without external tools.
Physics labs would use AI to integrate simulations, experimental logs, documentation and control systems while also supporting theoretical exploration.
In chemistry and biology, hybrid approaches combine general-purpose language models with specialized tools such as graph neural networks and protein structure predictors.
These combinations aim to improve reliability while keeping human oversight at the heart of decision-making.
The report places these developments in a broader context. Scientific progress supports medicine, energy systems, and public safety, but research often moves slowly and requires considerable work.
A small portion of the world’s population is responsible for the most fundamental discoveries, while projects such as drug development can take more than a decade.
OpenAI argues that researchers are increasingly using AI tools to handle routine and time-consuming tasks, including coding, literature review, data analysis, simulation, and experiment planning.
He cites case studies ranging from faster mathematical proofs to protein design with RetroBioSciences, where AI is said to have shortened timelines from years to months.
Although the report presents remarkable usage figures and benchmark results, independent validation remains limited.
Questions remain about the resistance of these results over time, their general significance and whether the reported gains translate into lasting scientific advances.
These usage numbers and benchmark scores stand out, but independent validation is still limited.
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