OpenAI is strategically expanding its influence beyond the consumer and professional realms, making a significant and explicit push into scientific research and content generation, aiming to revolutionize discovery and accelerate human knowledge. Following the groundbreaking success of ChatGPT, which has permeated everyday life across work, education, and personal use, the company has established a dedicated "OpenAI for Science" team. This initiative, launched in October, signals a deliberate effort to harness the power of large language models (LLMs), particularly the advanced capabilities of GPT-5, to assist scientists and refine its AI tools for their specific needs.

The burgeoning impact of LLMs on scientific endeavors has already been documented in numerous social media posts and academic publications over the past few months. Researchers across mathematics, physics, biology, and other disciplines have shared how these AI models have aided in making discoveries or provided crucial nudges towards solutions that might have otherwise been overlooked. The formation of OpenAI for Science is a direct response to this growing engagement within the scientific community. However, OpenAI is not the first to recognize this potential; Google DeepMind, a formidable competitor, has had an AI-for-science team for years, producing groundbreaking models like AlphaFold and AlphaEvolve. Demis Hassabis, CEO and co-founder of Google DeepMind, emphasized the deep personal connection to this field, stating in 2023 that "This is the reason I started DeepMind… In fact, it’s why I’ve worked my whole career in AI."

The timing of OpenAI’s formal entry into this space prompts questions about its strategic alignment with the company’s broader mission and its ultimate objectives. In an exclusive interview, Kevin Weil, a vice president at OpenAI and leader of the new OpenAI for Science team, elaborated on these aspects. Weil, with a background in product leadership at Twitter and Instagram, and a prior academic pursuit of a PhD in particle physics, brings a unique perspective. He initially envisioned a lifelong career in academia, even admitting to reading math books on vacation, underscoring his scientific roots.

Weil articulates that the OpenAI for Science initiative is intrinsically linked to the company’s core mission: "to try and build artificial general intelligence and, you know, make it beneficial for all of humanity." He foresees a profound impact of future AI advancements on science, potentially leading to breakthroughs in medicine, materials science, and device development. "Think about it helping us understand the nature of reality, helping us think through open problems. Maybe the biggest, most positive impact we’re going to see from AGI will actually be from its ability to accelerate science," Weil stated, adding that "With GPT-5, we saw that becoming possible."

According to Weil, LLMs have now reached a level of sophistication where they can function as valuable scientific collaborators. They can brainstorm ideas, suggest novel research avenues, and identify pertinent connections between a scientist’s query and obscure, even decades-old or foreign-language, research papers. This capability was not present even a year ago. OpenAI has been consistently pushing the boundaries of what its technology can achieve, notably with the announcement of its first reasoning model, o1, in December 2024. Weil recalls the earlier astonishment when models could achieve an 800 on the SAT, a benchmark now far surpassed.

LLMs are now demonstrating remarkable prowess in academic competitions and advanced scientific problem-solving. Both OpenAI and Google DeepMind announced last year that their LLMs achieved gold-medal performance in the International Math Olympiad, a testament to their advanced analytical capabilities. "These models are no longer just better than 90% of grad students," Weil asserted, "They’re really at the frontier of human abilities." While this claim is significant and comes with nuances, the advancements in GPT-5, particularly its reasoning model capable of multi-step problem-solving, represent a substantial leap over GPT-4, especially in mathematical and logical reasoning.

In benchmarks like GPQA, which tests PhD-level knowledge across biology, physics, and chemistry, GPT-4 scored 39%, significantly below the human-expert baseline of approximately 70%. OpenAI reports that GPT-5.2, the latest update released in December, achieved an impressive 92% on this metric.

Despite the palpable excitement, there are concerns about potential overhype. In October, senior OpenAI figures, including Weil, publicly claimed on X that GPT-5 had solved several unsolved math problems. However, mathematicians quickly pointed out that the model had likely unearthed existing solutions from obscure research papers, some in German. While still valuable, this was not the groundbreaking discovery initially implied. Weil and his colleagues subsequently deleted their posts.

Weil has since adopted a more measured approach, emphasizing the utility of LLMs in rediscovering forgotten knowledge. "We collectively stand on the shoulders of giants, and if LLMs can kind of accumulate that knowledge so that we don’t spend time struggling on a problem that is already solved, that’s an acceleration all of its own," he explained. He downplays the immediate expectation of LLMs generating entirely novel, game-changing discoveries, stating, "I don’t think models are there yet. Maybe they’ll get there. I’m optimistic that they will."

However, he clarifies that the mission is not necessarily about creating Einstein-level breakthroughs. "Our mission is to accelerate science. And I don’t think the bar for the acceleration of science is, like, Einstein-level reimagining of an entire field," Weil asserted. His core question is: "Does science actually happen faster because scientists plus models can do much more, and do it more quickly, than scientists alone? I think we’re already seeing that."

In November, OpenAI released a series of anecdotal case studies showcasing how scientists, both internal and external to the company, had leveraged GPT-5 to advance their research. "Most of the cases were scientists that were already using GPT-5 directly in their research and had come to us one way or another saying, ‘Look at what I’m able to do with these tools,’" Weil noted. Key strengths identified include finding previously unknown references and connections to existing work, which can spark new ideas; assisting in sketching mathematical proofs; and suggesting methods for hypothesis testing in laboratory settings.

Weil highlighted the vast knowledge base of GPT-5.2, stating, "GPT 5.2 has read substantially every paper written in the last 30 years. And it understands not just the field that a particular scientist is working in; it can bring together analogies from other, unrelated fields." He further elaborated on the power of this capability: "That’s incredibly powerful. You can always find a human collaborator in an adjacent field, but it’s difficult to find, you know, a thousand collaborators in all thousand adjacent fields that might matter. And in addition to that, I can work with the model late at night—it doesn’t sleep—and I can ask it 10 things in parallel, which is kind of awkward to do to a human."

This perspective is echoed by several scientists who have engaged with OpenAI’s models. Robert Scherrer, a professor of physics and astronomy at Vanderbilt University, initially used ChatGPT for recreational purposes but was later introduced to GPT-5 Pro by his colleague Alex Lupsasca, who now works at OpenAI. Scherrer recounted, "It managed to solve a problem that I and my graduate student could not solve despite working on it for several months." While acknowledging that GPT-5 still makes errors, he noted its continuous improvement and predicted, "If current trends continue—and that’s a big if—I suspect that all scientists will be using LLMs soon."

Derya Unutmaz, a professor of biology at the Jackson Laboratory, utilizes GPT-5 for brainstorming, summarizing papers, and planning experiments. He shared an instance where GPT-5 analyzed an old dataset, yielding fresh insights and interpretations. "LLMs are already essential for scientists," Unutmaz stated. "When you can complete analysis of data sets that used to take months, not using them is not an option anymore."

Nikita Zhivotovskiy, a statistician at the University of California, Berkeley, has been integrating LLMs into his research since the initial release of ChatGPT. He finds their ability to highlight unexpected connections between his work and existing, unknown results particularly valuable. "I believe that LLMs are becoming an essential technical tool for scientists, much like computers and the internet did before," he remarked. "I expect a long-term disadvantage for those who do not use them." However, he remains skeptical about LLMs generating novel discoveries in the immediate future, observing that they "seem to mainly combine existing results, sometimes incorrectly, rather than produce genuinely new approaches."

Scientists not affiliated with OpenAI offer a more tempered outlook. Andy Cooper, a professor of chemistry at the University of Liverpool, stated, "We have not found, yet, that LLMs are fundamentally changing the way that science is done. But our recent results suggest that they do have a place." Cooper is involved in a project to develop an "AI scientist" capable of automating significant portions of the scientific workflow. While his team doesn’t use LLMs for ideation, the technology is proving useful within broader automated systems, such as directing robots. Cooper speculates that LLMs might initially be more integrated into robotic workflows, as "I’m not sure that people are ready to be told what to do by an LLM. I’m certainly not."

Despite the growing utility of LLMs, caution is paramount due to their propensity for errors. In December, Jonathan Oppenheim, a quantum mechanics scientist, pointed out a critical mistake that appeared in a peer-reviewed scientific journal. "OpenAI leadership are promoting a paper in Physics Letters B where GPT-5 proposed the main idea—possibly the first peer-reviewed paper where an LLM generated the core contribution," Oppenheim posted on X. "One small problem: GPT-5’s idea tests the wrong thing." He elaborated that GPT-5 was asked for a test that detects nonlinear theories but provided one for nonlocal ones, comparing the error to mistaking a COVID test for a chickenpox test.

The innovative ways scientists are engaging with LLMs are evident, yet the technology’s errors can be subtle and elude even expert detection. A significant issue, as Oppenheim noted, is that "LLMs are being trained to validate the user, while science needs tools that challenge us." In an extreme instance, an individual, not a scientist, was reportedly convinced by ChatGPT for months that they had invented a new branch of mathematics.

Weil acknowledges the problem of hallucination but asserts that newer models are exhibiting it less frequently. He argues that focusing solely on hallucination might miss the broader picture. He shared a colleague’s perspective: "When I’m doing research, if I’m bouncing ideas off a colleague, I’m wrong 90% of the time and that’s kind of the point. We’re both spitballing ideas and trying to find something that works." This, for Weil, represents a desirable state where "you gradually kind of find your trail through the woods."

This philosophy underpins Weil’s vision for OpenAI for Science. GPT-5, while powerful, is not presented as an infallible oracle but rather as a tool to guide researchers toward new directions, not definitive answers. OpenAI is actively exploring ways to make GPT-5 exhibit more "epistemological humility," potentially by dialing down its expressed confidence and framing responses as suggestions for consideration rather than definitive pronouncements. "That’s actually something that we are spending a bunch of time on," Weil confirmed, "Trying to make sure that the model has some sort of epistemological humility."

Furthermore, OpenAI is investigating the use of GPT-5 to fact-check itself. Feeding an answer back into the model often leads it to identify and correct its own mistakes. "You can kind of hook the model up as its own critic," Weil explained. This creates a workflow where one model generates output, and another, acting as a critic, identifies areas for improvement, feeding suggestions back to the original model. This process, akin to multiple agents collaborating and refining their work, ensures that only output that has passed the "critic" is presented. This approach bears resemblance to Google DeepMind’s AlphaEvolve, which employed a similar filtering and iterative refinement process for its Gemini LLM, leading to the resolution of several real-world problems.

OpenAI faces considerable competition from rival firms whose LLMs offer comparable capabilities. The question remains why scientists would choose GPT-5 over alternatives like Google’s Gemini or Anthropic’s Claude, which are also continuously improving. Ultimately, OpenAI for Science may represent a strategic move to establish a foothold in a crucial new territory, with more significant innovations yet to emerge.

Weil predicts a transformative year for science, drawing a parallel to the rapid adoption of AI in software engineering. "I think 2026 will be for science what 2025 was for software engineering," he stated. "At the beginning of 2025, if you were using AI to write most of your code, you were an early adopter. Whereas 12 months later, if you’re not using AI to write most of your code, you’re probably falling behind. We’re now seeing those same early flashes for science as we did for code." He concluded, "I think that in a year, if you’re a scientist and you’re not heavily using AI, you’ll be missing an opportunity to increase the quality and pace of your thinking."