Simile AI, described in a podcast about a new scaling law for simulation, presented funding and research aimed at recreating human behavior using large numbers of simulations and digital twins for clients. Simile AI reported a $2B Series B round, with claims of 85% accuracy versus human focus groups. The work shifts evaluation from predicting outputs to simulating how people decide, using post-training on causal mechanisms and scaled synthetic populations for product and policy testing.
Google Research introduced the Biomarker Discovery Framework, a multi-agent system for prioritizing candidate clinical biomarkers from wearable physiological time-series data. It was tested across three cohorts totaling 9,279 participant-observations and produced 41 candidate digital biomarkers for mental health and 25 for metabolic outcomes. The framework adds an iterative, statistically rigorous and adversarial validation loop under human supervision, and it improved downstream prediction when its features were combined with demographic variables (ΔR² = 0.040 for depression, 0.021 for insulin resistance).
The guest post argues that the $16B world model funding push is missing the underlying point about what matters for building useful models. It points to multiple high-profile backers and specifically cites Odyssey, a 55-person world model lab, raising $310M. The takeaway is that attention and resources should shift away from the size of the “world model race” and toward the real requirements for success.
Astromech, an AI startup for predicting biological change, raised $20 million and increased its valuation to $3.8 billion. It has mapped 46 longevity-associated genes on a time-calibrated tree of life. The funding will expand its teams, scale genomic infrastructure, and support pilot projects applying its vulnerability forecasting models in health and biosecurity.
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