Overview
In a Bernstein analyst report dated October 16, findings from a webinar with Tom Fleming, co‑founder and former CEO of automated laboratory services provider Arctoris, were highlighted. Fleming explained that demand for artificial‑intelligence training datasets has broadened from venture‑backed biotechnology firms to major pharmaceutical groups, which now represent the bulk of Arctoris’s data‑generation business, excluding a few partnership arrangements.
Demand Expansion
The report notes that in certain instances the need for experimental data has surged tenfold and, in extreme cases, up to one hundred‑fold. While such volume growth could boost revenues, the analysts caution that declining costs per data point may partially neutralise the financial upside.
Beneficiary Equipment Suppliers
Fleming identified specific laboratory‑instrument manufacturers poised to benefit. Revvity’s (NYSE) Opera Phenix high‑content imaging platform is singled out, with Arctoris having purchased additional units to satisfy rising demand. He also highlighted opportunities for 10x Genomics (NASDAQ), whose technologies produce detailed cellular data, and for Illumina (NASDAQ), which could see higher sequencing volumes, although falling sequencing prices may constrain revenue expansion.
Reagent Consumption and Experimental Workflows
According to Fleming, AI adoption is likely to increase overall laboratory reagent consumption rather than reduce it. While predictive models may eliminate some routine experiments, the proliferation of AI‑designed molecules and proteins will still require extensive physical testing and validation.
Long‑Term Clinical Impact
Fleming emphasized that the greatest long‑term value of AI will stem from improving clinical‑trial success rates rather than merely increasing the number of drug candidates. However, he warned that demonstrating meaningful improvements could take years, given the inherently lengthy development timelines and historically high failure rates in clinical trials.
Industry‑wide Implications
The findings suggest that AI integration could reshape spending patterns across the life‑sciences sector, favouring suppliers of advanced experimental technologies while exerting pricing pressure on more commoditised data‑generation services.