According to TechRadar, OpenAI has released a report detailing how ChatGPT is being used as a scientific research tool. The company claims that roughly 1.3 million users worldwide now send about 8.4 million messages per week focused on advanced science and mathematics. This usage has grown nearly 50% over the past year. OpenAI highlights that its GPT-5.2 models are being used for graduate-level work and active research in fields like physics, chemistry, and biology. The report specifically notes the AI’s performance in mathematics, claiming gold-level results at the 2025 International Mathematical Olympiad and contributions to solutions connected to open Erdős problems, which were confirmed by human experts.
The new research workflow
Here’s the thing: this isn’t about AI making Nobel Prize-winning discoveries on its own. Not yet, anyway. The report paints a picture of AI as a hyper-competent, tireless graduate assistant. It’s handling the grunt work. We’re talking about writing and debugging code, reviewing mountains of literature, planning experiments, and running data analysis. For researchers buried in administrative and repetitive tasks, that’s a game-changer. OpenAI cites cases like protein design at RetroBioSciences, where AI reportedly shortened timelines from years to months. That’s the real pitch: acceleration. If you can turn a 10-year drug development pipeline into a 5-year one, that’s monumental. But is it really that simple?
The math angle and the caveats
The mathematics section of the report is the most fascinating—and the one that requires the most scrutiny. OpenAI says GPT-5.2 can follow long reasoning chains, check its own work, and operate within formal proof systems like Lean. Contributing to pathways for open Erdős problems is a serious claim. But look, the fine print is important. The AI isn’t generating new theories; it’s recombining known ideas and finding connections humans might miss, which then speeds up formal verification. It’s a powerful pattern-matching and suggestion engine. The benchmark scores (like 92% on GPQA) are impressive, but as the source notes, independent validation is still limited. How often does the model hallucinate a convincing-but-wrong proof step? How much human oversight is *really* needed? The report, which you can read here, is a compelling argument, but it’s still OpenAI’s own analysis of its own technology.
Strategy and the bigger picture
So what’s OpenAI’s play here? This is a brilliant bit of positioning. They’re moving ChatGPT from a cool chatbot to an essential professional tool, a “research collaborator.” By showcasing hard science and math use, they’re targeting the most credible, skeptical user base imaginable. If theoretical physicists and mathematicians are on board, why shouldn’t your business be? It’s a model that locks in high-value users and enterprises. Think about it: a lab that rebuilds its workflow around ChatGPT isn’t going to switch to a competitor easily. This also helps them in the ongoing debate about AI’s real-world utility. It’s one thing to write a poem, another to help solve a protein-folding problem. They’re building a case that’s harder to dismiss.
The human in the loop
The recurring theme, though, is hybrid approach. In chemistry and biology, they’re pairing the general language model with specialized tools like graph neural networks. The human is still “central to decision-making.” I think that’s the realistic takeaway for now. AI is becoming an incredible force multiplier, handling the computational heavy lifting and routine tasks. This frees up human researchers to do what they do best: ask the right questions, provide creative insight, and apply rigorous judgment. For industries that rely on precision and data—like manufacturing or lab environments where you’d need a rugged industrial panel PC to run operations—this hybrid model is the likely path. The tools are getting smarter, but we’re still a long way from fully autonomous discovery. The collaboration is just beginning.
