Adaptive Biotech Co-Founder Raises $15M to Rethink How AI Trains on Scientific Data

A new startup co-founded by one of Adaptive Biotech's founders has raised $15 million in seed funding to develop a fundamentally different approach to how AI models are trained on scientific literature and experimental data. The core thesis is that current pretraining pipelines treat scientific data the same as general web text, losing the structured, hypothesis-driven nature of scientific knowledge in the process. The startup aims to build domain-specific training infrastructure and data representations that preserve scientific reasoning structures, potentially enabling AI systems that are more reliable and interpretable in research contexts. For developers building AI tools in biotech, pharma, materials science, or any empirical research domain, this approach could eventually produce models that generalize better within scientific reasoning tasks than general-purpose LLMs fine-tuned on domain data. The $15M raise at the seed stage signals strong investor conviction that the scientific AI training problem is both unsolved and commercially significant.