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Simulation: the new Scaling Law — Joon Sung Park, Simile AI
Simile AI raised a $2B Series B from GreenOaks and Index Ventures to scale human-behavior simulation for Fortune 100 clients like CVS.
Simile AI, co-founded by Generative Agents researcher Joon Sung Park, announced a $2 billion Series B backed by GreenOaks and Index Ventures, with Fei-Fei Li and Andrej Karpathy among backers. The company runs tens of millions of simulations for Fortune 100 clients including CVS, reporting 85-99% accuracy versus human focus groups and digital twins of 1,000 real people at 85% behavioral accuracy. The long-term ambition is foundation models of human behavior, post-trained on interviews, transaction data, and randomized controlled trials, potentially simulating all 8 billion people.
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How Candidates Could Use AI for Good
Schneier and Sanders argue US candidates should use AI for broad listening and voter engagement, citing Japan's Team Mirai and civic tech tools.
Schneier on Security published an essay arguing candidates in the US midterms could use AI constructively rather than only for deepfakes and one-way messaging. It highlights Japan's Team Mirai party, whose AI interviewer has gathered over 300,000 messages across 16,000 interviews and helped the party win 12 Diet seats. Other cited tools include Scotland-funded CrownShy's Comhairle, deliberation.io, MIT's Cortico, Talk to the City, and Remesh. Higher Ground Labs launched a fund targeting AI-native campaign systems.
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Heart of the Matter: How a Major Children’s Hospital Uses Open Source NVIDIA AI for Cardiac Care
Children's Hospital of Philadelphia uses NVIDIA open-source MONAI, Warp and Newton to build pediatric heart models in seconds for surgical planning.
CHOP's cardiac modeling service uses MONAI, Auto3DSeg and SlicerHeart to turn CT, MRI and 3D ultrasound images into anatomically precise heart models in seconds instead of four hours of manual work. More than 20 US children's hospitals run similar programs, with Boston Children's supporting roughly 500 cardiac surgery cases a year. NVIDIA's Newton physics engine, built on the Warp Python framework, aims to reduce device simulations from hours to near real time in clinical workflows.
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[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over
Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.
Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.
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