BOSTON, November 29, 2025 — Elite scientific labs are quietly adopting artificial intelligence not to make headline-grabbing discoveries, but to untangle the tedious bottlenecks that can stall research for years. Anthropic, the AI company, is partnering with the Allen Institute and the Howard Hughes Medical Institute to deploy AI agents powered by its Claude model to accelerate scientific workflows.
In exclusive interviews, Jonah Cool, head of life sciences partnerships at Anthropic, and Grace Huynh, executive director of AI applications at the Allen Institute, revealed that these institutions are leveraging AI to streamline analysis, annotation, and coordination—tasks that often consume vast amounts of researcher time.
A ‘Compressed 21st Century’
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Cool, a trained cell biologist and geneticist, drew inspiration from a 2024 essay by Anthropic CEO Dario Amodei, titled “Machines of Loving Grace.” Amodei argued that AI could dramatically accelerate scientific progress, potentially compressing 50 to 100 years of biological advancements into just five to ten years.
Amodei envisions a “compressed 21st century” where AI facilitates near-universal prevention of infectious diseases, significant reductions in cancer mortality, and effective treatments for genetic disorders, Alzheimer’s, and other chronic illnesses. He also suggests AI could enable personalized therapies, enhance human control over biology, and extend healthy lifespans.
Cool believes AI agents will play a crucial role in realizing this vision—not by delivering breakthroughs directly, but by automating time-consuming tasks, freeing up researchers to focus on critical discoveries. “What AlphaFold achieved is incredible,” Cool said, acknowledging the AI’s success in solving the protein-folding problem. “But what we’re talking about here is different. It’s about working with teams across the scientific process and embedding AI into their daily work.”
Huynh explained that the Allen Institute, founded in 2003 by Microsoft cofounder Paul Allen, is building on existing tools, particularly Anthropic’s Claude Code, which is popular among computational biologists. The focus, she said, is on applying AI to specific, impactful areas—like data analysis tasks that can take months—to meaningfully speed up scientific work.
No Single Researcher Can See Every Connection
“We’re starting to reach a point where ‘big science’ is the norm,” Huynh said. The sheer volume of data generated by modern scientific techniques—including single-cell genomics, massive imaging datasets, and connectomics—is overwhelming for individual researchers. “Scientists generate so much data today that no single researcher can hold it all in their head or see every connection anymore.”
Cool highlighted the Allen Institute and the Howard Hughes Medical Institute as ideal partners due to their commitment to creating and sharing foundational scientific resources. The Allen Institute has developed widely used biological datasets, such as detailed maps of the mouse brain showing gene activity in tissues. These resources have become standard tools for researchers across various fields, and are now being refined to single-cell resolution, increasing their scientific value but also their complexity.
Researchers at HHMI’s Janelia Research Campus have also developed essential tools, including calcium indicators like GCaMP, which allow scientists to observe neuron firing in real time, and advances in super-resolution microscopy. Cool emphasized that these institutions’ focus on tools and datasets makes them ideal testing grounds for AI agents, as improvements in analysis and coordination will benefit the broader scientific community.
“Science is a fascinating but highly repetitive and often very tedious practice,” Cool explained. “Increasingly, this means a lot of work related to analyzing and transforming datasets. I think we’re approaching a world where that work will still be substantial, but experiments and the next steps will happen much, much faster.”
A Future Where AI Can Help Make Hypotheses
Cool also envisions a future where AI agents assist scientists in formulating hypotheses—prioritizing experiments based on limited resources and even proposing novel DNA designs based on patterns humans might miss. “We’re moving towards the models being able to help make hypotheses,” he said. “We’re starting with, ‘Help me prioritize the hypotheses I have, because I have a limited amount of resources, and I want to do all 100 experiments, but I only have money for 10.’”
