The primary bottleneck preventing the rapid adoption of novel technologies is the gap between experimental pilots and industrial scale operations. While innovation is abundant, structural hurdles often prevent tools from moving beyond the prototype stage.

The biggest names behind biotech stocks are investing heavily in AI, hoping they can speed up the decades-long process of developing lifesaving drugs, and cut down on the billions of dollars required to do it.

The life sciences industry has spent the better part of three years chasing model performance. Bigger foundation models, more parameters, better fine-tuning pipelines. The implicit assumption has been that if you get the algorithms right, the rest follows. DNAnexus CEO Thomas Laur theorizes that the primary obstacle to AI-driven drug discovery is not model quality or algorithmic sophistication, but rather the inability to get scientific data into a state that AI systems can actually consume. The evidence supports him.

Todd Oakland, DNAnexus SVP & GM of Pharma & Diagnostics, featured in Pharma's Almanac thought leadership roundtable.

By bridging the gap between raw multimodal data and clinical discovery, DNAnexus enables health leaders to operationalize AI across critical workflows with full regulatory readiness.