Concerns over AI-generated content polluting scientific journal submissions have led many to question its role in research, but recent studies from MIT point to a more nuanced reality.
The use of AI in science raises questions on several levels. The most visible concern is "AI slop," the worry that AI-generated work is eroding research quality. A less discussed but equally important question is economic: even if AI is useful, does the time it saves scientists justify what it costs? MIT CSAIL’s paper AI Disruption and Falling Costs in Theory Research tackles exactly this question, and its findings are promising.
To determine the true value of these AI models, the researchers compare how models released from December 2024 to April 2026 solved cutting-edge theory questions in cases that have ground-truth human answers that were entirely unavailable to the AI. The answers came from 254 recent arXiv papers from seven scientific domains spanning mathematics, physics, and economics. The researchers reported that in those sixteen months, the changes in the models were "enormous":
- The share of problems for which models reached the same conclusion as the human authors (got full marks graded against the original research), rose from 3% (o1) to 35% (GPT-5.5) marking a 22 percentage point increase per year.
- These results were achieved at minimal cost, approximately $5 per attempt (or $15 per success) for GPT-5.5.
- Compute spending per full-marks answer fell from roughly $170 to $15, including failed attempts but excluding human labor and independent verification.
- A "back-of-the-envelope" calculation suggests that AI costs are less than 0.3% of what it would cost to have a human expert solve these problems.
Despite AI becoming this much more helpful, it doesn't mean its role in research is as simple as plug and play. The researchers observe that the value driven from the AI depends on the share of research costs and the savings achieved with them, citing an example of an AI model which in order to perform well would need extensive and costly training.
The researchers also draw attention to the fact that when some tasks become cheaper to do, the structure of science will need to evolve to take advantage of it. For instance, even when scientists save seven hours per week using AI, they have a growing backlog of untested hypotheses and substantial demand for output verification.
"Under these conditions, we would expect the field to evolve so that more scientists are focusing on these other complementary tasks," the researchers stated in the paper. "Based on our results, we would expect similar adjustments within the theory community, as some of their historical tasks become much cheaper to do with AI."
Our Deeper View #
Some of the more exciting, useful, and benign use cases of AI involve breakthroughs in research. Yet there seems to be a lot of hesitancy about using it in this domain, because the ability to rapidly create more content with AI has led to watering down the quality of some research submissions. Arxiv wrote about this issue just this month as it has had to update its rate limit policy due to the influx of submissions, fueled by AI, and also, the increase in "dense, AI-written papers" described as "low-value." This MIT research presents the other side, in which AI could help fuel further breakthroughs by performing tasks that net out to be less expensive than the scientist doing the work themselves. The key here is that it doesn't replace the scientist, as they would still be the person checking the results, picking the right questions, and making the larger-picture connections (which they would have more time to do). It's also important to highlight that these models are extremely capable, as highlighted in the report or everyday breakthroughs. Just today, Anthropic published a report explaining how Claude designed protein binders against scorpion neurotoxins, part of a broader experiment to see how Claude can accelerate drug discovery.