Rebuild Biotech for the AI Era (www.benchling.com)

A software-engineer-turned-biotech-builder lays out a manifesto: modern drug discovery is trapped in artisanal workflows—slow, costly (often cited ~$2B and 10 years per drug), and failure-prone—despite revolutionary biology (mRNA, CAR-Ts, GLP‑1s). With cloud adoption having finally digitized many labs, the essay argues AI is the “once-in-a-generation” catalyst to systematize discovery: standardize data, preserve institutional knowledge, and let scientists pursue higher‑risk, higher‑reward programs rather than selling early wins to big pharma. Economic context (≈$250B/yr R&D, ~50 approvals/yr) and examples (Benchling’s role; a Deep Research Agent that cut eight months of animal testing) illustrate the scale of the problem and the payoff for better tooling. Technically, the author identifies two pragmatic AI vectors. First, integrated predictive models and foundation models (structure prediction, property and developability scoring, generative design) must be embedded directly in scientist workflows—no command lines or isolated computational silos—so simulation is as routine as pipetting. Second, autonomous agents should automate data capture, instrument pipelines, robotic execution, analysis, and traceable reporting, freeing researchers for design and hypothesis work. Success requires high‑quality experimental data, seamless access, and trustworthy interfaces; done right, these shifts can reduce late‑stage failures, compress timelines, and transform biotech from craft to composable engineering.