Lila Sciences is in advanced discussions to raise approximately $2 billion in a Series B financing that would peg the company's pre-money valuation at about $8.5 billion, according to people familiar with the matter.
Those people said the planned round is expected to be anchored by the California Public Employees’ Retirement System and NVentures, the investment arm of Nvidia Corp. The potential financing would represent a significant step up from the private-market valuations Lila has held so far.
Over the past year the company's market value has surged on the basis of a central claim: that AI-driven tools can accelerate the pace of scientific discovery. Investors appear to be pricing that promise into Lila’s current fundraising discussions.
Prior to these talks, Lila disclosed a $350 million Series A that closed in October, which the company said lifted its valuation to above $1.3 billion. The business first emerged from stealth following a $200 million seed round.
The company has developed artificial intelligence trained on academic literature spanning materials science, chemistry and the life sciences, and has been building laboratories to test and validate those models. Rather than constructing narrow, domain-specific applications, Lila has built a general platform designed to enable autonomous scientific discovery across multiple technical domains.
At present the financing remains under negotiation; the discussions described by those familiar with the matter have not been confirmed as finalized. The outcome of the talks and any formal closing of the round could affect both Lila’s capitalization and its operational runway.
For investors and markets watching the intersection of AI and scientific research, Lila’s fundraising trajectory is an indicator of investor appetite for platform approaches that aim to combine machine learning with laboratory testing. The company’s prior funding milestones - the $200 million seed round and the $350 million Series A - mark a rapid fundraising cadence, and the new discussions, if completed, would substantially increase available capital to scale both model development and lab infrastructure.