“For a long time, the assumption was that pharma is not a great buyer of software, but a really good buyer of services,” Sahir Raoof told TechCrunch. While first-time fund managers face one of the toughest fundraising environments in years, Raoof secured $27.5 million for his debut solo vehicle, Proximal Ventures. The firm is wagering that artificial intelligence can reshape the infrastructure, tools, and service pipelines that sustain therapeutics development.
Closing a debut fund during a venture contraction signals growing institutional willingness to back automated alternatives to expensive life sciences contracts. Pharmaceutical corporations historically contracted external providers across multiple phases of pipeline work, ranging from the chemical synthesis of thousands of molecular compounds to commercial scale manufacturing and clinical trial evaluation. This dynamic established a recurring reliance on outsourced agencies rather than internal enterprise software purchases. The strategic tension centers on whether AI-native service operators can finally displace these legacy contractors and capture substantial enterprise budgets.
The Economics of Service Replacement
Sahir Raoof argues that machine learning creates an opening for venture-backed entrants to redirect billions of dollars in pharmaceutical spending. By deploying AI systems directly into service workflows, automated providers aim to deliver identical analytical and operational outputs at lower operational expense and shortened delivery schedules. As Raoof noted, the most valuable AI use cases so far have emerged in service-driven fields like law and finance, and similar opportunities are opening up across pharmaceutical services.
“For a long time, the assumption was that pharma is not a great buyer of software, but a really good buyer of services,” Raoof told TechCrunch.
This purchasing behavior explains why enterprise software vendors frequently stalled inside life sciences organizations while specialized service vendors captured extensive billings. Proximal plans to deploy checks between $500,000 and $1.5 million into pre-seed and seed-stage startups designed to automate these target workloads. The fund has already deployed capital into seven startups, including ChronicleBio, an enterprise co-founded by former OpenAI executive Fidji Simo that analyzes medical and biological datasets to evaluate chronic diseases and discover prospective therapies.
Operational Track Records and Commercialization Barriers
The central commercial hurdle remains the structural inertia of pharmaceutical procurement, as enterprise buyers have spent decades paying for manual service contracts rather than pure technological solutions. Raoof noted that founding teams selling into life sciences struggle primarily to generate an initial commercial flywheel. Overcoming that friction requires distribution networks capable of navigating enterprise contracting and regulatory scrutiny.
Raoof believes AI could automate much of that work, creating an opportunity for startups to capture billions of dollars in pharmaceutical industry spending.
To bridge this barrier, Proximal assembled capital from institutional firms including SignalFire, Kleiner Perkins, and Bertelsmann Healthcare Investments, alongside individual angel investors Nan Li and Zavain Dar of Dimension Capital. Amy Abernethy, co-founder of Highlander Health and former principal deputy commissioner of the FDA, joined as an LP after working alongside Raoof at Flatiron Health, an oncology data company that Roche acquired for $1.9 billion in 2018. Prior to launching Proximal, Raoof spent two years as a principal at SignalFire, oversaw partnerships at Flatiron Health, and directed commercial development at clinical trial recruitment firm Iterative Health.
Can early-stage ventures writing $500,000 to $1.5 million checks systematically unseat entrenched pharmaceutical contract research organizations, or will enterprise life sciences budgets remain locked inside traditional manual service agreements?
What’s behind it
A $500,000 pre-seed check is a modest weapon against entrenched contract research agencies that have monopolized pharmaceutical budgets for clinical testing and molecule synthesis for decades. Proximal's thesis mimics playbooks from legal services: instead of peddling pure enterprise software that life sciences procurement routinely rejects, founders wrap machine learning into automated agency workflows. Yet this shortcut collides with the industry's brutal sales inertia, where even significant operational savings can stall against regulatory approvals. Proximal’s portfolio will test whether algorithmic workflows can truly divert billions away from entrenched manual outsourcing in life sciences.