Several researchers have taken a broad view of scientific progress over the last 50 years and come to the same troubling conclusion: Scientific productivity is declining. It’s taking more time, more funding, and larger teams to make discoveries that once came faster and cheaper. Although a variety of explanations have been offered for the slowdown, one is that, as research becomes more complex and specialized, scientists must spend more time reviewing publications, designing sophisticated experiments, and analyzing data.

Now, the philanthropically funded research lab FutureHouse is seeking to accelerate scientific research with an AI platform designed to automate many of the critical steps on the path toward scientific progress. The platform is made up of a series of AI agents specialized for tasks including information retrieval, information synthesis, chemical synthesis design, and data analysis.

FutureHouse founders Sam Rodriques PhD ’19 and Andrew White believe that by giving every scientist access to their AI agents, they can break through the biggest bottlenecks in science and help solve some of humanity’s most pressing problems.

“Natural language is the real language of science,” Rodriques says. “Other people are building foundation models for biology, where machine learning models speak the language of DNA or proteins, and that’s powerful. But discoveries aren’t represented in DNA or proteins. The only way we know how to represent discoveries, hypothesize, and reason is with natural language.”

Finding big problems

For his PhD research at MIT, Rodriques sought to understand the inner workings of the brain in the lab of Professor Ed Boyden.

“The entire idea behind FutureHouse was inspired by this impression I got during my PhD at MIT that even if we had all the information we needed to know about how the brain works, we wouldn’t know it because nobody has time to read all the literature,” Rodriques explains. “Even if they could read it all, they wouldn’t be able to assemble it into a comprehensive theory. That was a foundational piece of the FutureHouse puzzle.”

Rodriques wrote about the need for new kinds of large research collaborations as the last chapter of his PhD thesis in 2019, and though he spent some time running a lab at the Francis Crick Institute in London after graduation, he found himself gravitating toward broad problems in science that no single lab could take on.

“I was interested in how to automate or scale up science and what kinds of new organizational structures or technologies would unlock higher scientific productivity,” Rodriques says.

When Chat-GPT 3.5 was released in November 2022, Rodriques saw a path toward more powerful models that could generate scientific insights on their own. Around that time, he also met Andrew White, a computational chemist at the University of Rochester who had been granted early access to Chat-GPT 4. White had built the first large language agent for science, and the researchers joined forces to start FutureHouse.

The founders started out wanting to create distinct AI tools for tasks like literature searches, data analysis, and hypothesis generation. They began with data collection, eventually releasing PaperQA in September 2024, which Rodriques calls the best AI agent in the world for retrieving and summarizing information in scientific literature. Around the same time, they released Has Anyone, a tool that lets scientists determine if anyone has conducted specific experiments or explored specific hypotheses.

“We were just sitting around asking, ‘What are the kinds of questions that we as scientists ask all the time?’” Rodriques recalls.

When FutureHouse officially launched its platform on May 1 of this year, it rebranded some of its tools. Paper QA is now Crow, and Has Anyone is now called Owl. Falcon is an agent capable of compiling and reviewing more sources than Crow. Another new agent, Phoenix, can use specialized tools to help researchers plan chemistry experiments. And Finch is an agent designed to automate data driven discovery in biology.

On May 20, the company demonstrated a multi-agent scientific discovery workflow to automate key steps of the scientific process and identify a new therapeutic candidate for dry age-related macular degeneration (dAMD), a leading cause of irreversible blindness worldwide. In June, FutureHouse released ether0, a 24B open-weights reasoning model for chemistry.

“You really have to think of these agents as part of a larger system,” Rodriques says. “Soon, the literature search agents will be integrated with the data analysis agent, the hypothesis generation agent, an experiment planning agent, and they will all be engineered to work together seamlessly.”

Agents for everyone

Today anyone can access FutureHouse’s agents at platform.futurehouse.org. The company’s platform launch generated excitement in the industry, and stories have started to come in about scientists using the agents to accelerate research.

One of FutureHouse’s scientists used the agents to identify a gene that could be associated with polycystic ovary syndrome and come up with a new treatment hypothesis for the disease. Another researcher at the Lawrence Berkeley National Laboratory used Crow to create an AI assistant capable of searching the PubMed research database for information related to Alzheimer’s disease.

Scientists at another research institution have used the agents to conduct systematic reviews of genes relevant to Parkinson’s disease, finding FutureHouse’s agents performed better than general agents.

Rodriques says scientists who think of the agents less like Google Scholar and more like a smart assistant scientist get the most out of the platform.

“People who are looking for speculation tend to get more mileage out of Chat-GPT o3 deep research, while people who are looking for really faithful literature reviews tend to get more out of our agents,” Rodriques explains.

Rodriques also thinks FutureHouse will soon get to a point where its agents can use the raw data from research papers to test the reproducibility of its results and verify conclusions.

In the longer run, to keep scientific progress marching forward, Rodriques says FutureHouse is working on embedding its agents with tacit knowledge to be able to perform more sophisticated analyses while also giving the agents the ability to use computational tools to explore hypotheses.

“There have been so many advances around foundation models for science and around language models for proteins and DNA, that we now need to give our agents access to those models and all of the other tools people commonly use to do science,” Rodriques says. “Building the infrastructure to allow agents to use more specialized tools for science is going to be critical.”