Edwin Chen, founder and CEO of Surge AI, discusses his contrarian, bootstrapped approach to building a billion-dollar company, the critical role of high-quality data and 'taste' in training advanced AI models, the pitfalls of current benchmarks, and why Reinforcement Learning environments are the next frontier in AI.
Edwin Chen, founder and CEO of Surge AI, discusses his contrarian approach to building a bootstrapped, billion-dollar company, the critical role of high-quality data and 'taste' in training AI, the flaws in current benchmarks, and why reinforcement learning environments are the next frontier for creating models that truly advance humanity.
Nicole Brichtova and Hansa Srinivasan, the leads behind Google's Nano Banana image model, detail the technical breakthroughs in character consistency. They discuss how a focus on high-quality data, Gemini's multimodal architecture, and rigorous human evaluation enabled the model to realistically represent individuals from a single photo. The conversation covers the future of visual AI, moving beyond text prompts to specialized UIs, and the ultimate goal of a single, powerful model that can transform any modality into another, unlocking new applications in personalized education, professional design, and creative storytelling.
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AI's "dirty secret" is its deep reliance on human intelligence. Phelim Bradley, CEO of Prolific, explains how his platform provides the critical human data infrastructure for training and evaluating frontier models, emphasizing the shift from commoditized labor to a sophisticated marketplace of verified expertise.
A discussion on OpenAI's new browser ChatGPT Atlas, Andrej Karpathy's pessimistic timeline for AI agents, the DeepSeek-OCR paper on visual context compression, and a study suggesting large language models can suffer from "brain rot" when trained on low-quality social media data.
Edwin Chen, founder and CEO of Surge AI, shares the company's origin story, its rapid, bootstrapped growth, and its research-driven philosophy on data. He critiques traditional data labeling, explains why metrics like inter-annotator agreement fail for complex tasks, and offers a sharp analysis of benchmark hacking. Chen also details the future of data, from multimodal and agentic reasoning in rich RL environments to the need for hyper-specialized expertise for scientific discovery.