Bob McGrew helped build some of the most influential technologies of the past two decades. Bob was an early engineer at PayPal, an early executive at Palantir, and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4 ,and the o1 reasoning model.During his time at Palantir, he was a pioneer of the Forward Deployed Engineer (FDE) model, a strategy that is at the heart of the AI boom today. On this episode of The Lightcone, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI.
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Anthropic Co-founder: Building Claude Code, Lessons From GPT-3 & LLM System Design
Tom Brown co-founded Anthropic after helping build GPT-3 at OpenAI. A self-taught engineer, he went from getting a B-minus in linear algebra to becoming one of the key people behind AI's scaling breakthroughs. And his work is paying off.Today, Anthropic's Claude is the go-to choice for developers, and his team is overseeing what he calls "humanity's largest infrastructure buildout ever." On this episode of The Lightcone, he discusses his unconventional path from YC founder to AI researcher, the discovery of scaling laws that changed everything, and his advice for young engineers entering AI today.
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How to Spend Your 20s in the AI Era
AI has upended the once "safe" CS career path.New grads are facing unemployment rates twice those of art history majors, and a CS degree is no longer a surefire ticket to wealth. At the same time, small, focused teams are scaling from zero to eight-figure revenue in months.In a special Lightcone Live at AI Startup School, Garry, Diana, Harj, and Jared discuss why it's now more important than ever to focus on building real skills, domain expertise, and agency rather than just chasing credentials.
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Alexandr Wang: Building Scale AI, Transforming Work with Agents & Competing With China
Alexandr Wang started Scale AI to help machine learning teams label data faster.It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that early idea powers a multi-hundred-million-dollar engine behind America's AI infrastructure—fueling everything from Fortune 500 workflows to real-time military planning. Just last week, Meta agreed to invest over $14 billion in Scale AI, valuing the company at $29 billion.Alexandr joined us on the Lightcone to share how Scale AI evolved from a scrappy YC startup into the backbone of some of the world's most advanced AI systems, how he thinks about competition with Chinese AI labs, and what it takes to build infrastructure that shapes the frontier.
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State-Of-The-Art Prompting For AI Agents
At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI.On the Lightcone, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production.They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable.The prompt from Parahelp (S24) discussed in the episode: https://parahelp.com/blog/prompt-design
France