AI agents are rewriting biopharma’s $140B playbook - ending manual, outsourced drug development

For decades, biopharma has relied on outsourcing as the default solution to complex problems. CROs, CDMOs, and specialized consultants became extensions of the pipeline, offering speed and predictability, but at the cost of moving critical capabilities outside pharma’s walls. That reliance has ballooned into a $140 billion market that grows every budget cycle. CROs alone are projected to nearly double, from $70B in 2025 to $126B by 2034. Today, roughly half of all pharma R&D spending flows to external providers. Outsourcing isn’t just part of drug development; it is drug development. But the cracks are showing. Despite billions invested, outsourced workflows are bloated and fragile. PDFs ping-pong through inboxes. Lab notes are retyped multiple times. Critical insights vanish into spreadsheets and file vaults. The result: slow, error-prone, and unsustainable processes in a world that demands speed and precision more than ever. Meanwhile, pharma is staring down a $236 billion revenue cliff as some patents expire over the next five years. Leaders face mounting pressure to squeeze every ounce of output from their teams while hiring curves stay flat, all during one of the most aggressive waves of layoffs the industry has ever seen. Simply layering on more outsourced services is no longer a viable answer. That’s why pharma leaders are turning to AI, not as a side experiment, but as a fundamental shift in how drugs are developed. For healthcare founders, the message is clear: CRO/CDMO moats weren’t just built on scale, but on bundling dozens of line items into a single RFP, priced at cost, plus 10–20% markup. Even if a startup offered a better solution, unbundling wasn’t worth the friction, until now. With new tech delivering 2x-10x efficiency gains, pharma is now willing to break apart vendor packages to adopt best-in-class tools. The sheer scale, fragmentation, and criticality of this outsourced machine create massive opportunities to inject new tech, rewire workflows, and build AI-native platforms that can slot into—or completely upend—billion-dollar contracts. This thesis explores where those opportunities lie and how the next wave of category leaders will seize them.