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Ark Invest holds $50m stake in AI drug firm Recursion

EUROS Newsroom · 1h ago · 1 min read · 🇺🇸 United States
Ark Invest holds $50m stake in AI drug firm Recursion

Cathie Wood’s Ark Genomic Revolution ETF has built a $50 million position in Recursion Pharmaceuticals, signaling sustained institutional interest in biotechs leveraging artificial intelligence.

Cathie Wood’s Ark Genomic Revolution ETF holds over $50 million in Recursion Pharmaceuticals, a $1.8 billion biotech firm utilizing artificial intelligence for drug discovery. The position sits outside the fund’s top ten holdings, which are currently led by Tempus AI, 10x Genomics, and Twist Bioscience.

Recursion’s core value proposition lies in its ability to generate massive biological datasets. The company uses robotics and computer vision to capture millions of cell experiments every week. To process this information, Recursion partnered with Nvidia to leverage supercomputing capabilities, aiming to identify viable drug candidates faster than traditional pharmaceutical research.

The company's pipeline currently contains seven drugs in various clinical stages. Its most advanced candidate, REC-4881, targets a rare genetic condition that can lead to cancer. The drug has shown positive proof-of-concept data, though additional phase 1b/2 data is not expected until the first half of 2027.

While Recursion has no drugs on the market, it has secured therapeutic partnerships with major European healthcare corporations, including Bayer and Roche Holding. These collaboration agreements currently provide the company’s only revenue.

Financially, Recursion operates with a high-risk profile. Management has guided that its current capital will fund operations through early 2028 without the need for additional financing. This provides a runway to advance clinical trials, but the company remains entirely dependent on its future potential.

The AI drug-discovery space is growing increasingly crowded. For investors and executives tracking the sector, Recursion serves as a prime example of the high-risk, high-reward dynamic in computational biology. Its success will ultimately hinge on whether it can convert AI-generated insights into commercialized drugs ahead of its competitors.