Introducing the Science Blog

Increasing the pace of scientific progress is a core part of Anthropic’s mission. In Machines of Loving Grace, Dario described the prospect of a “compressed 21st century,” with decades of scientific progress possible over a few years. We’re starting to see what the early stages of that compression look like: AI is helping mathematicians discover new proofs, enabling individual researchers to run computational analyses that once required dedicated teams, and allowing biologists to identify functional gene relationships across datasets of millions of cells.

Just as computers externalized computation, AI is now externalizing parts of cognition. Work that used to require years of specialized training can increasingly be done more quickly and cheaply with AI. The rate of progress raises sociological questions about the practice of science and the role of scientific institutions: What should research apprenticeship look like, how do we maintain trust in the literature when AI becomes more involved in producing it, and what does it even mean to be a scientist when the bottleneck shifts from execution to directing?

Although the pace of improvement is rapid, some of these questions may feel premature today—AI’s scientific capabilities are, in many ways, still in beta. While models already feel superhuman at certain parts of the scientific workflow, they can also hallucinate results, be overly sycophantic, and get stuck on problems a domain practitioner would find trivial. Fields Medalist Timothy Gowers captures this tension well, writing that “[…] it looks as though we have entered the brief but enjoyable era where our research is greatly sped up by AI but AI still needs us.”

We hope this blog can discuss both the upsides and challenges of the current moment for AI and science, while also being practically useful—showing how practitioners are using these systems and how AI is enabling qualitatively new modes of scientific investigation.

Science at Anthropic

Anthropic has several initiatives aimed at accelerating scientific progress. Our AI for Science program provides API credits to researchers working on high-impact projects across biology, physics, chemistry, and other fields. Claude for Life Sciences is a dedicated effort to make Claude useful for life sciences researchers and R&D teams, with partnerships across research institutions, pharma, and biotech. Some case studies downstream of these programs were recently documented. And we’re a core partner in the Genesis Mission, a multi-billion-dollar initiative across industry, academia, and government to accelerate American science with AI.

More generally, many researchers and teams across Anthropic work to improve our models’ core scientific capabilities, with the goal of safely speeding up AI-assisted scientific discovery.

What We’ll Cover

In this blog, we plan to share three main types of posts:

• Features.
• Workflows.
• Field Notes.

We’re publishing two pieces alongside this introduction: Matthew Schwartz’s “Vibe Physics: The AI Grad Student”, a spotlight on supervising Claude through a real theoretical physics calculation, and a tutorial on orchestrating long-running tasks for scientific research.

These pieces, and those that will follow, aim to not only share the astounding ways that AI is being used in scientific research, but also remind us why we ought to be excited in the first place. Much of the bottleneck in scientific research is connective or executive, living in the space between what one field knows and what another needs, or in the sheer labor of testing, analysis, and iteration. As AI helps with closing some of these gaps, the consequences will extend well beyond the lab.