Latest Interviews

How To Design In The Agent Era

Stephen Haney, Aaron Epstein

Led by Stephen Haney, Paper is an AI-native design platform currently ranking as the third most-used tool behind Figma and Sketch, utilizing a native HTML and CSS rendering engine to facilitate precise agent collaboration. The application distinguishes itself by treating code as the single source of truth, offering features like direct React component handoff and an MCP server that allows autonomous agents to manipulate local repositories and design canvases. While the team prioritizes human curation to maintain design quality against common AI aesthetic pitfalls, the platform is rapidly evolving into a visual coordination layer for the broader agent ecosystem by integrating tools like Paper Shaders and multi-model image generation.

The Case For Data Centers In Space

Philip Johnston, Mark Mandelmann

Founded by Philip Johnston in 2024 and backed by a $170 million Series A from Benchmark, StarCloud has emerged as YC's fastest-growing unicorn by pioneering orbital data centers to bypass terrestrial energy constraints. The company successfully launched its first satellite in November 2025, deploying custom-modified GPU hardware and thermal management systems to run AI models like Gemini while utilizing automotive-grade components to mitigate radiation risks. With plans to scale to 88,000 satellites by 2028 and deploy high-bandwidth compute for government and hyperscale customers, StarCloud aims to eventually generate terawatts of processing capacity through partnerships with SpaceX and NVIDIA.

Patrick Collison: Is AI Breaking the Lean Startup Playbook?

Patrick Collison, Harj Taggar

Patrick Collison shares his journey from dropping out of college twice to launching Stripe, emphasizing the critical role of retaining "cognitive L1 cache" over reliance on AI tools for deep reasoning. He highlights that while new business formation on Stripe has doubled and AI is accelerating revenue milestones, entrepreneurs should remain wary of decentralization fears and instead focus on solving visceral customer problems like legacy payment friction. Collison concludes by advising founders to build fundamental knowledge of first principles and to assess whether their chosen venture is a cause they are willing to pursue for decades.

Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”

Alexandr Wang, Garry Tan

Alexander Wang discusses his journey from founding Scale AI to leading Meta's Frontier Lab, where he is developing the Muse Spark model to deliver frontier-level AI capabilities at a fraction of the cost. He argues that the immediate future of the industry will be defined by agentic looping and the orchestration of autonomous agents rather than raw model size, predicting a massive expansion in global GDP driven by these technologies. Wang concludes by advising founders to build independent convictions based on first principles while emphasizing that systematic thinking and vision are becoming the scarce resources in an era of abundant computational intelligence.

Sam Altman: "Never a Better Time to Do a Startup"

Sam Altman, Paul Graham, Garry Tan

Sam Altman and Y Combinator highlight an exponential shift in startup capabilities where coding agents reduce development time to mere seconds while hard tech representation in the portfolio surges to 25%. The organization positions itself as a decentralizing force to prevent single-entity control of AI power, explicitly rejecting the "live action role play" mindset in favor of heretical, high-conviction building. Altman warns of safety risks regarding surveillance and model concentration but maintains that widespread founder agency and distributed innovation will ultimately prevent economic collapse or catastrophic safety incidents.

What Actually Makes A Startup Durable

Paul Graham and YC partners outline a strategic framework for navigating the AI era, emphasizing that while AI increases individual productivity by 1,000x, founders must target "hard" problems with durable moats to survive market saturation. The session advises building companies through co-founders who provide essential emotional support rather than just complementary skills, noting that human judgment remains critical for community building and navigating high-stakes pivots. Finally, students are offered over $25,000 in AI credits to neutralize geographical disadvantages, reinforcing the program's focus on rapid execution and deep market engagement over theoretical development.

What Big Tech Missed And How Startups Can Still Win

Alexandre LeBrun, Zuckerberg, Jan, Alex

Ami Labs is raising approximately one billion euros in seed funding to develop foundational world models that train on sensory data rather than text, addressing the limitations of current large language models for robotics applications. Co-founded by Alex K. and Yann LeCun following their shared history at Meta and various startups, the Paris-based company aims to build general-purpose robots capable of operating safely in open environments by solving the compute bottlenecks and latency issues inherent in existing vision-language-action architectures. While the venture tackles the high capital demands of securing thousands of GPUs and top-tier talent, it operates under a strict strategic constraint where failure to deliver visible progress within two years could render the company unsustainable due to the intense external expectations created by the massive funding round.

Why Physical AI Is the Next Platform Shift

Eric Landau, Francois Chollet

Anchored, founded by former quantitative trader Eric Landau, constructs the data layer for physical AI by managing petabytes of multimodal data to power robotics and autonomous systems. After pivoting from healthcare vision to broader industrial applications, the company established a production-ready facility in the Bay Area and refined its sales culture through iterative hiring challenges. With the post-ChatGPT market accelerating demand for specialized physical AI infrastructure, Anchored now leads a crowded sector by offering scalable data collection that moves clients from proof-of-concept to enterprise production.

What Top AI Labs Are Really Doing With Observability

Olivier Pomel, Alexi

Founded by IBM veterans Olivier Pomel and Alexi Laurent, Datadog disrupted the monitoring industry by merging Dev and Ops into a single platform, overcoming initial Y Combinator rejections and regional venture capital biases to achieve a 2019 IPO. Under a leadership philosophy that prioritizes direct customer engagement over written values, the company has expanded its product suite to serve top AI firms while executing a radical shift where engineers will primarily automate code generation within two quarters. This strategic pivot aims to drastically increase development velocity and reorganize the company around smaller, high-autonomy teams, even as it navigates post-lockup valuation volatility and the rapid evolution of AI-driven observability.

He Built the World's #1 Open-Source Coding Agent

Jay V, Gary Tan, Mark Mandelmann, Seth Vargoevic, Frank, Dax, Dylan Patel, Soyal, PG, Jay Poldinger

OpenCode, a sixteen-year-old platform founded by Jay Poldinger and Frank, has grown to 13 million monthly active users and processes 7 trillion daily tokens by serving as a neutral model harness that bypasses vendor lock-in. The company's strategy of supporting over 70 open-source models, which became critical after Anthropic restricted open usage, generated $160,000 in monthly subscription revenue and an estimated $33 million in inference revenue as of June. With significant adoption in China, Brazil, and the United States, OpenCode validates its product-market fit through enterprise organizations that proactively seek security agreements to integrate its agent loops into internal workflows.

How Photoroom Trained Themselves To Dream Bigger

Gary Tan, Elliot

PhotoRoom, the largest Y Combinator company headquartered in Europe, leveraged a strategic pivot to e-commerce photography to achieve 300 million downloads and serve major clients like Amazon and Uber. Its founders attribute this exponential growth to a cultural shift fostered by YC, which replaced European risk aversion with a "failure-safe" mindset that treats a billion-dollar valuation as an attainable target. By enforcing English-only offices, prioritizing deep vertical focus, and utilizing aggressive resource constraints, the company instilled a mandatory ambition across its team to drive rapid scaling and execution speed.

How Supabase Became One Of The Fastest Growing DevTool Companies In The World

Paul Copplestone

Supabase founder Paul Copplestone recently secured a $500 million round at a valuation exceeding $10 billion, propelling the company to decacorn status with a 10 million developer base driven largely by AI agents launching 60-90% of new databases. The fully distributed organization leverages its open-source PostgreSQL strategy and a shift toward Infrastructure as Code to achieve exponential usage growth, reducing time-to-value from minutes to seconds while bypassing traditional marketing channels. Moving beyond standard user interfaces, Supabase is now building self-driving database capabilities and "Infrastructure for Platforms" to support enterprise labs managing millions of ephemeral instances autonomously.

Why Ambitious Startup Ideas Are Actually Easier To Sell

James Hawkins

PostHog is transitioning from an open-source analytics platform to an AI-driven service that autonomously fixes engineering issues and solves customer support tickets by generating production-ready pull requests. Co-founders James and Tim restructured their operations to align with this new "self-driving software" vision, delegating management duties to the latter so the former can focus exclusively on AI product architecture while the company doubles in size. This strategic pivot replaces traditional manual product management with a system where AI configures workflows and implements fixes based on deep user data, aiming to secure massive market valuation through "insane" ambition rather than conservative growth.

World Models, JEPA And The Path To Sample-Efficient RL

Ankit, Francois

This event analyzes the critical bottleneck of sample efficiency in artificial intelligence, contrasting current deep learning models' massive data requirements with the human brain's ability to learn from minimal experience through superior world modeling. The discussion details how advancements in non-differentiable control theories, video diffusion architectures, and Joint Embedding Predictive Architectures are shifting strategies from model-free behavior cloning to synthetic, simulation-based planning for complex robotic and autonomous driving tasks. By addressing scaling challenges in high-dimensional action spaces and architectural limitations like the Transformer's inefficiency in time-domain compression, the presentation outlines a roadmap toward general-purpose robotics and AGI by 2026 through the integration of "awake sleep" mechanisms and physics-informed predictive systems.

YC's Head of Design Shows You How To Design With AI

Eve Bouffard, Aaron Epstein, Ev Bufar

Designer Ev Bufar utilizes a voice-first workflow with tools like Aqua, Conductor, and Paper.design to rapidly produce high-fidelity assets for diverse projects, including the Paxyl transcript analyzer and the Soda Zine. Her recent work at Startup School demonstrates an agent-driven production model that generated visual identities for 6,000 attendees through custom shaders and automated speaker assets, while Paxyl analyzes coding agent interactions to reveal developer patterns. By prioritizing massive context density and disposable internal models, Bufar accelerates the design iteration cycle to handle complex branding and interactive web experiences that explicitly cater to both human users and AI agents.