OpenAI says researchers are using AI nonstop to solve advanced math puzzles, physics simulations, and biological experiments worldwide | Daily Reports Online
- OpenAI claims 8.4 million weekly messages are sent about advanced science and mathematics
- GPT-5.2 models can follow long reasoning chains and verify results independently
- AI accelerates routine research tasks like coding, literature review, and experiment planning
OpenAI wants users to treat ChatGPT as a research collaborator, with new research claiming nearly 8.4 million messages are sent every week focus on advanced science and mathematics topics, generated by roughly 1.3 million users worldwide.
OpenAI highlights this usage has grown almost 50% over the past year, suggesting the system is moving beyond occasional experimentation into regular research workflows.
These users reportedly engage in work comparable to graduate-level study or active research across mathematics, physics, chemistry, biology, and engineering.
Usage scale and research integration
Mathematics receives particular attention in the report. GPT-5.2 models are said to sustain long reasoning chains, check their own work, and operate 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 the models contributed to solutions connected to open Erdős problems, with human mathematicians confirming the results.
While the models do not generate entirely new mathematical theories, they recombine known ideas and identify connections across fields, which speeds up formal verification and proof discovery.
Similar patterns appear in other scientific areas. On graduate-level benchmarks such as GPQA, GPT-5.2 reportedly exceeds 92% accuracy without external tools.
Physics laboratories reportedly use AI to integrate simulations, experimental logs, documentation, and control systems while also supporting theoretical exploration.
In chemistry and biology, hybrid approaches pair 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 central to decision-making.
The report places these developments in a broader context. Scientific progress supports medicine, energy systems, and public safety, yet research often advances slowly and requires substantial labor.
A small portion of the global population produces most foundational discoveries, while projects such as drug development can take more than a decade.
OpenAI argues that researchers increasingly use AI tools to handle routine, time-consuming tasks, including coding, literature review, data analysis, simulation support, and experiment planning.
It cites case studies ranging from faster mathematical proofs to protein design with RetroBioSciences, where AI reportedly shortened timelines from years to months.
Although the report presents notable usage figures and benchmark results, independent validation remains limited.
Questions remain about how well these results hold up over time, how broadly they apply, and whether the reported gains translate into lasting scientific advances.
These usage figures and benchmark scores stand out, but independent validation is still limited.
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