Anthropic has set up a wet lab in the San Francisco Bay Area, advancing the biological analysis of Claude from computer-based to real physical experiments. Eric Kauderer-Abrams, head of life sciences, confirmed to Reuters that the company is conducting experiments in its own facilities while also outsourcing some work to external partners, stating that the ultimate validation of biology remains real laboratory experiments for a considerable time.

A spokesperson emphasized that the lab is not specifically for drug discovery and declined to disclose details about its scale, staffing, activation time, and biosafety level. The company continues to outsource some biological work, with its own lab serving as an addition rather than a replacement. Kauderer-Abrams noted that automating experiments with AI is still in its early stages, and human oversight remains essential for safety.

The next step is to have Claude control equipment such as robotic arms, liquid handling robots, and plate readers. The company launched a model hardware standard research preview last month, allowing unified control of microscopes, liquid handlers, and robotic arms, enabling models to adjust parameters based on results and recover autonomously from certain failures. Genentech has already linked liquid handling, robotic arms, and plate readers for protein quantification using this standard; Carnegie Mellon has increased the speed of dose-response experiments by about three times using it.

Anthropic plans to conduct preclinical research focusing on diseases that traditional pharmaceutical companies consider too difficult or lacking commercial value, but will not enter human clinical trials to avoid direct competition with clients like Roche, Genentech, Bristol Myers Squibb, and Novo Nordisk. The company has added Novartis CEO Vas Narasimhan to its board and acquired biotechnology startup Coefficient Bio for approximately $400 million in stock.

Launched in June this year, Claude Science integrates a repository of papers, code, and computational resources into a single workspace, connecting over sixty genomic, protein structure, and cheminformatics interfaces. Life sciences is now one of the company's largest areas of human and resource investment, with ongoing recruitment for procurement leads and biochemists.

In market mechanisms, this represents a model company extending its moat of computational power into a closed loop of wet lab data: what is sold is pure software analysis subscriptions, while what is bought is a platform capable of controlling instruments and generating proprietary experimental data. Funding is directed towards lab automation, preclinical outsourcing networks, and binding workflows of pharmaceutical companies; beneficiaries are instrument vendors and large pharmaceutical clients that have adopted the model hardware standard, while those under pressure are AI drug startups that only perform computational design without feedback from wet experiments.

The company has not disclosed the lab's budget but has provided funding for up to fifty projects in academia, with a maximum of $30,000 in credits for each, directing external experimental traffic into the same toolchain.

Source: Public Information