Eighteen months ago, Lila Sciences was a quiet startup in Cambridge, Massachusetts. Today, it is becoming a closely watched company in deep tech. The company is now valued at more than $1.3 billion following its $350 million Series A, bringing total funding to $550 million and reflecting growing investor interest in next-generation scientific automation.
Lila is developing an autonomous discovery platform that uses AI, software, and robotics to conduct real-world experiments across life sciences, chemistry, and materials. The system blends simulation, automation, and adaptive learning to generate its own data and identify new molecules and materials faster than traditional labs.
The Story Behind the Round
Lila’s $350 million Series A was co-led by Braidwell and Collective Global, joined by a broad syndicate that includes Altitude Life Science Ventures, Alumni Ventures, ARK Venture Fund, Common Metal, Flagship Pioneering, General Catalyst, March Capital, Modi Ventures, NGS Super, the State of Michigan Retirement System, and ADIA, the sovereign wealth fund of Abu Dhabi.
The round later added participants including NVentures, Nvidia’s investment arm; In-Q-Tel, the CIA’s venture fund; and Dauntless Ventures. The investor group spans biotech specialists, deep-tech builders, global institutions, and national-security funds, all betting that AI will play a larger role in shaping the next era of innovation and discovery.
Proceeds from this round will be used to expand Lila’s physical and organizational footprint through the development of a new 235,500-square-foot facility at Alewife Park in Cambridge and by hiring additional scientific and technical talent as the company begins onboarding its first customers.
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Investor Type
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Representative Firms
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What They See in Lila
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Life Sciences Specialists
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Braidwell, Altitude Life Science Ventures, Common Metal
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A faster, automated model for research and discovery
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Venture and Growth Investors
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Collective Global, General Catalyst, March Capital, Modi Ventures, Alumni Ventures
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Platform potential across industries, not just biotech
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Deep Tech Builders
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Flagship Pioneering, ARK Venture Fund
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A new category at the intersection of biology, computation, and nature
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Strategics
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NVentures (Nvidia), Analog Devices
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Continuous, real-world AI workloads and expanding compute demand
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National Security and Defense
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In-Q-Tel, Dauntless Ventures
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Tools that accelerate discovery in materials, energy, and advanced tech
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Institutional and Sovereign Investors
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NGS Super, Michigan Retirement System, ADIA
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Long-term, infrastructure-style exposure with durable return potential
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Why Nvidia and In-Q-Tel Are Paying Attention
From Nvidia to ADIA, this is not a typical venture syndicate. It represents a strategically aligned group of investors converging around a core thesis that AI is beginning to extend beyond digital environments into physical systems.
The participation of Nvidia and In-Q-Tel illustrates how Lila operates at the intersection of several secular trends, including advancements in AI infrastructure, the automation of scientific research, and the reindustrialization of critical technologies.
For Nvidia, the investment is a logical extension of its compute ecosystem. The company’s growth has historically been driven by demand for large-scale model training, but platforms like Lila introduce a new category of workload in continuous, distributed computation supporting autonomous experimentation. As these networks scale, they could create durable, recurring demand across Nvidia’s GPU, networking, and software layers, diversifying beyond the current dependency on model-training cycles.
For In-Q-Tel, the rationale is strategic and national in scope. Lila’s autonomous R&D infrastructure has potential applications across materials science, energy storage, and semiconductors, which are central to industrial competitiveness and national security priorities. Accelerating experimentation shortens innovation cycles and strengthens the technological base that underpins both economic and defense capabilities.
The Shift from Training to Experimentation
The last wave of AI was built on scale, with bigger models trained on ever-larger datasets. That method is approaching a limit. The internet is finite and increasingly filled with synthetic information.
Lila’s thesis is that the next leap in AI will come not from ingesting more data but from creating it. Its system combines robotics, sensors, and machine learning to design and run physical experiments automatically. Each experiment feeds new insights back into the model, improving the next cycle. The result is a self-reinforcing loop that generates original data which expands in real time.
As Flagship Pioneering’s Molly Gibson and Geoffrey von Maltzahn describe it:
“We believe this scaled learning will enable the emergence of Scientific Superintelligence—AI as the driver of the scientific method, autonomously refining its understanding of the world.”
Lila’s platform is a step toward that vision, a research engine where AI learns directly from the physical world.
A Platform for Discovery
Unlike biotech startups that use AI to develop their own assets, Lila provides its technology as a platform for others. That model allows the company to support hundreds of discovery programs rather than rely on a few high-risk bets.
The model has clear economic appeal. It diversifies customer exposure, builds recurring revenue streams, and continuously improves the underlying AI models through each experiment. Over time, the expanding data loop could develop into a durable competitive moat, reinforcing the platform’s value and performance.
In practical terms, Lila is positioning itself as the AWS of science, a shared infrastructure powering a new generation of research and innovation. Its Cambridge expansion and growing team will serve as the backbone for this scalable infrastructure.
Why It Matters
Lila Sciences reflects an important evolution in artificial intelligence, shifting from analyzing existing data to helping generate new knowledge through automation and experimentation. By connecting AI with real-world scientific processes, Lila points toward a future where discovery becomes faster, more adaptive, and less constrained by traditional research bottlenecks.
For investors and strategics alike, it highlights the growing convergence between AI, infrastructure, and the physical sciences. Platforms like Lila suggest that the next phase of AI may not only enhance digital intelligence but also begin to support the broader systems that drive innovation and competitiveness.
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