Aaref Hilaly

Aaref Hilaly is a Partner at Bain Capital Ventures, where he has invested since 2020 with a focus on seed and Series A companies building AI-powered applications. Originally from Pakistan and raised in England, Hilaly is a first-generation college student who earned scholarships to attend top schools. He read Philosophy, Politics, and Economics (PPE) at the University of Oxford, attended McGill University, and holds an MBA from Harvard Business School. He has described himself as never fully shaking "the feeling of being an outsider," a perspective that informs his empathy-driven approach to working with founders.

Before entering venture capital, Hilaly built a distinguished track record as an entrepreneur. In 2000, he co-founded CenterRun, a pioneer in datacenter automation, which grew to 30 employees and $5 million in revenue before being acquired by Sun Microsystems for $66 million. He then served as CEO of Clearwell Systems, an early SaaS application that automated e-discovery for legal teams. Clearwell grew from near-zero to a $100 million revenue run rate in four years and was acquired by Symantec in 2011 for $410 million.

Following his entrepreneurial career, Hilaly joined Sequoia Capital in 2012, where he spent seven years partnering with dozens of founders from idea stage through IPO and acquisition. His Sequoia portfolio included Guardant Health (NASDAQ: GH), MobileIron (NASDAQ: MOBL), Skyhigh Networks (acquired by McAfee), ThousandEyes (acquired by Cisco), and Lightstep. He joined Bain Capital Ventures in 2020, where he now focuses on early-stage AI companies and works closely with BCV Labs to incubate new ventures.

Hilaly believes that generative AI represents "a once-in-a-generation opportunity, of a similar scale to the Internet." His thesis centers on backing small teams of builders who leverage AI to solve problems previously unaddressed by software. He focuses on application-layer investments rather than infrastructure, arguing that constant shifts in the model layer make infrastructure plays precarious, while application-level companies can adapt to use the best available models.

Key tenets of his investment philosophy include:

On open vs. closed source models, he takes a pragmatic stance: use closed-source models when they work, and post-train open-source alternatives with proprietary data when data control matters, particularly in regulated industries like finance and IP-rich sectors like semiconductors.

• Defensibility is unrealistic at seed stage. He argues that history shows trillion-dollar companies with infinite resources often fail where smaller, faster teams succeed. Instead of moats, he prioritizes team velocity, rapid iteration, and speed to product-market fit.
• Unlocking latent demand. He looks for companies addressing markets that were previously impossible to serve with traditional software but are now accessible through AI capabilities.
• Application-level AI in vertical industries. He identifies healthcare, legal services, insurance, customer support, and code generation as sectors ripe for AI-driven transformation, where large volumes of data processing and repetitive tasks create clear opportunities.
• Post-investment value creation. His primary focus after investing is on "the non-obvious things the company needs to do now, so that it's well-positioned in 6-12 months." He emphasizes the importance of clear, compelling founder narratives and strong go-to-market execution.