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Sonya Huang
Parallel’s Parag Agrawal: Building a New Web for AI Agents
Parag Agrawal says Parallel is rebuilding web search for agents, not humans, by treating click data as a bug and optimizing for agent feedback, latency, and token efficiency. He also argues the web’s ad economy will need a new pricing model for AI-era usage, likely built on Shapley-style attribution and differential pricing for content owners.
Why Hardware-Software Co-Design Is AI's Real 100x: Dylan Patel of SemiAnalysis
Dylan Patel argues that AI’s biggest gains come from hardware-software-model co-design, not raw chip speed alone. He also makes the case that inference is becoming a giant market, and that benchmarking, supply chains, and data-center economics will determine who wins.
Memory and Continual Learning: Engram's Dan Biderman and Jessy Lin
Engram’s Dan Biderman and Jessy Lin argue that AI’s real bottleneck is memory and continual learning, not raw intelligence. Their thesis is to bake company-specific knowledge into model weights so teams get faster, cheaper, more context-aware models that improve over time.
Simulating Humans at Scale: Simile's Joon Sung Park
Joon Sung Park argues Simile is building a simulation layer for human society, not a superhuman reasoning model, by training on real behavioral data, interviews, surveys, and RCTs. He says the company can already use these models for concept testing, earnings-call simulation, and broader social-science questions, with a long-term vision of modeling large-scale societal dynamics.
Google DeepMind's Logan Kilpatrick: Why the Model Eats the Harness
Logan Kilpatrick argues Google’s next platform shift is from model-centric AI to agent harnesses that the model will increasingly absorb. He also says coding is already a form of narrow superintelligence, while Google’s multimodal Omni effort aims to collapse many separate media systems into one model.