Scaling and the Road to Human-Level AI | Anthropic Co-founder Jared Kaplan

Brothers Chaz and Arnie Englander started Model ML after building and selling two YC companies. What began as a tool to help them analyze deals has grown into a full AI-powered workspace purpose-built for financial services, empowering firms to create automations and workflows that reflect exactly how their teams operate. And it's already being used by 10% of the world's top investment banks and private equity firms to automate everything from client-ready PowerPoint decks to deep-dive research and due diligence—by orchestrating AI agents that work like expert team members. In this conversation with YC Partner Gustaf Alstromer, they discuss going from internal tool to production platform, the power of perseverance, and their ambition to build a billion-dollar company with just ten people.

Jared Kaplan on June 16th, 2025 at AI Startup School in San Francisco.Jared Kaplan started out as a theoretical physicist chasing questions about the universe. Then he helped uncover one of AI’s most surprising truths: that intelligence scales in a predictable, almost physical way.That insight became foundational to the modern era of large language models—and led him to co-found Anthropic.In this talk, he walks through how that discovery reshaped the path to human-level AI, what it means for future models like Claude, and why even the dumbest questions can lead to the biggest breakthroughs. He reflects on memory, oversight, and what’s left to solve as models grow smarter—and longer-horizon tasks come within reach.

For this episode of Design Review, YC’s Aaron Epstein is joined by Karri Saarinen, co-founder & CEO of Linear, one of the top designer-founders working today. Together, they'll review several sites from the YC community with an eye for how to build and maintain a high-quality brand.Thank you to these companies for volunteering to have their sites reviewed*:Sprites AI (https://www.sprites.ai)GigaML (https://gigaml.com)UnReal Milk (https://www.unrealmilk.com)Confident AI (https://www.confident-ai.com)Dropback (https://www.dropback.com)*Some of the featured websites may be updated between the time we film and publish

Chelsea Finn on June 17th, 2025 at AI Startup School in San Francisco.From MIT through her PhD at Berkeley, where she pioneered meta‑learning methods, and Google Brain, Chelsea Finn has built her career around teaching machines how to learn. Now an Assistant Professor at Stanford and co‑founder of Physical Intelligence, she’s using that foundation to bring learning-driven robotics into messy, real-world environments rather than confined lab setups.In this talk, Chelsea traces the evolution of her team’s work—from early experiments on robotic grasping and vision to today’s ambitious efforts at folding laundry, tidying kitchens, and generalizing across tasks—all without hand-crafted code. Instead, they used scalable foundation models and massive datasets, teaching robots physical common sense as they learn by doing. She shares stories of the rocky setbacks, the surprises hidden in data, and the moment it all clicked: robots equipped with generalizable physical intelligence can indeed adapt and assist in the unpredictable world around us.

Amjad Masad started Replit to make programming accessible to anyone, anywhere. What began as a tool for learning to code has grown into a platform pushing the limits of AI-assisted software creation and recently surpassed $100M in ARR.On The Breakdown with Tom and Dave, Amjad shares the journey from his early days in Jordan, working on open-source projects, to leading Replit through major pivots from "teach a billion people to code" to "let anyone build software." He discusses the evolving nature of programming, the future of work, and the next generation of human-computer collaboration.

John Jumper on June 16, 2025 at AI Startup School in San Francisco.John Jumper is a physicist-turned-computational biologist who led DeepMind’s AlphaFold team—and earned the 2024 Nobel Prize in Chemistry for solving protein folding, a decades-old scientific challenge. In this talk, he shares how a deep learning breakthrough at CASP14 turned into AlphaFold 1 and then AlphaFold 2, delivering atomic accuracy predictions and revolutionizing biology. He explains the scientific puzzle behind protein folding, the key algorithmic breakthroughs, and the impact of making millions of protein structures accessible to researchers worldwide.

Aravind Srinivas on June 16, 2025 at AI Startup School in San Francisco.Aravind Srinivas started Perplexity with one goal: to rethink how we search, browse, and interact with information online. In this conversation, he shares the journey from hacking together a natural-language-to-SQL search tool to building a product used by millions around the world.He talks about the big bet on the AI-powered browser, why agents—not just chatbots—are the next step, and how speed, accuracy, and focus help a startup compete against giants. Along the way, he reflects on co-founder dynamics, early technical challenges, and what it takes to keep building when the biggest players in the world are racing alongside you.

Andrew Ng on June 16, 2025 at AI Startup School in San Francisco.Andrew Ng has helped shape some of the most influential movements in modern AI—from online education to deep learning to AI entrepreneurship. In this talk, he shares what he’s learning now: why execution speed matters more than ever, how agentic workflows are changing what startups can build, and why concreteness beats vagueness when turning ideas into products. He reflects on the rise of AI coding assistants, the shifting bottlenecks in product development, and why, despite faster software, it’s still human judgment and responsibility that will shape what comes next.

François Chollet on June 16, 2025 at AI Startup School in San Francisco.François Chollet is a leading voice in AI. He's the creator of the Keras library, author of Deep Learning with Python, and the founder of the ARC Prize, a global competition aimed at measuring true general intelligence.He's spent years thinking deeply about what intelligence actually is—and why scaling up today’s AI models isn’t enough to reach it.In this talk, he walks through the limits of pretraining and memorized skills, and lays out a path toward true general intelligence—AI that can adapt on the fly, reason in new situations, and invent novel solutions. He explains why abstraction and compositionality matter, how ARC became the benchmark for progress, and what his team at a new research lab called Ndea is building next.

A fireside with Dr. Fei-Fei Li on June 16, 2025 at AI Startup School in San Francisco.Dr. Fei-Fei Li is often called the godmother of AI—and for good reason. Before the world had AI as we know it, she was helping build the foundation.In this fireside, she recounts the creation of ImageNet, a project that helped ignite the deep learning revolution by providing the data backbone modern computer vision needed. She walks through the early belief in data-driven methods, the shock of seeing convolutional networks outperform expectations in 2012, and how those breakthroughs led to captioning, storytelling, and ultimately, generative models.Now, she’s taking on one of AI’s hardest frontiers: spatial intelligence. Fei-Fei shares why modeling the 3D world is essential for AGI—and why it may be even more difficult than language.