Y Combinator

Where does your interest come from?

Founders of enduring companies like OpenAI and SpaceX often launched ventures driven by personal hunches rather than initial commercial intent, a strategy that successfully attracted top global talent and significant capital. This success stems from a convergence where inherent ability intersects with unconventional interests to create unique, high-potential enterprises. Ultimately, these non-commercial origins serve as a critical catalyst for gathering the resources necessary to scale transformative projects.

Using LLMs Instead of Government Consulting

With the U.S. government spending hundreds of billions annually on consulting, rising political pressure and the maturation of Large Language Models are driving a shift away from reliance on major vendors like Deloitte and Accenture. Investors are now targeting startups that streamline FedRAMP compliance and utilize AI to validate policy legality, creating a strategic avenue to displace traditional consulting work. This market evolution presents a clear opportunity to fund LLM-based solutions that replace outsourced labor with automated, cost-effective government operations.

AI Native Enterprise Software

Top-tier enterprise vendors Salesforce and ServiceNow established dominant market positions by pioneering cloud-native solutions, a disruption pattern now emerging in the generational shift toward AI-native systems. This transition promises to transform enterprise software from passive record-keeping into active assistants for sales, HR, and accounting, creating a structural opening for new startups to challenge incumbent architectures. Founders and builders interested in developing these next-generation AI tools are invited to connect to capitalize on this competitive window.

Infrastructure for Multi-Agent Systems

A new initiative invites experienced builders to address the infrastructure and development challenges of scaling AI from single-threaded loops to distributed, multi-agent workflows capable of parallel human-level judgment. Participants are tasked with solving critical pain points including high-throughput reliability, cost control, prompt engineering for parent-subagent hierarchies, and the security of untrusted context inputs. The ultimate objective is to establish the tooling necessary to make operating fleets of autonomous agents as routine and reliable as deploying standard web services or executing Spark jobs.

The First 10-person, $100B Company

Y Combinator is actively funding small, high-agency teams to replicate the capital efficiency of the early cloud computing era, leveraging new AI tools to build multi-billion dollar companies with as little as $500,000. The fund explicitly targets creating the first 10-person startup valued at $100 billion by prioritizing revenue per employee and exploiting the speed advantages smaller groups have over bloated incumbents. This strategic shift aims to redefine startup growth by proving that ambitious founders can scale rapidly with minimal personnel and overhead.

The Finance Startup Bringing Agentic AI to Wall Street

Arnie Englander, Gustaf Alstromer, Chas Englander

Model ML, founded by siblings Arne and Chas, has transitioned its financial services agentic AI platform from a testing phase to rapid deployment, securing contracts with approximately 10% of the world's largest investment institutions. By replicating human cognitive access within familiar interfaces like Excel, the system automates complex data extraction and document creation for C-suite executives, shifting purchasing cycles from decade-long software licenses to annual multi-year agreements. With a global engineering presence and a strategy prioritizing high-trust in-person demos, the founders leverage their previous exits from Fat Llama and Fancy to drive a market where autonomous tasks increasingly replace traditional user interfaces.

Video Generation as a Primitive

This event addresses the rapid evolution of video generation from high-quality clips to a near-zero cost, ubiquitous computing primitive that enables the on-the-fly creation of any visual content. It outlines transformative applications across media, commerce, gaming, and robotics, while highlighting Y Combinator's call for founders to build infrastructure for this new landscape rather than just content outputs. The discussion emphasizes a fundamental industry shift toward infinite, low-latency video tools that allow for personalized experiences and novel interactions that were previously impossible.

Retraining Workers for the AI Economy

The US government's new AI Action Plan addresses the critical shortage of skilled tradespeople needed for AI infrastructure by directing the Departments of Labour and Commerce to fund rapid retraining programs and startups that develop multimodal AI tutors. These technologies, including voice coaching and AR/VR simulations, aim to compress vocational training timelines from years to months while overcoming the scalability limits of traditional human instruction. By enabling infinite, affordable training at this specific intersection of physical labor and artificial intelligence, the initiative invites startups to build solutions that prepare workers for high-demand roles in semiconductor and data center construction.

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

Jared Kaplan, Diana

This event details how precise scaling laws governing pre-training and reinforcement learning enable predictable AI progress, with task horizons doubling every seven months to support long-term autonomous work. It highlights the launch of Claude 4, which introduces persistent memory and refined coding agents to facilitate complex, multi-day tasks while advocating for a strategic focus on "70% right" applications and greenfield domains like law and finance. The discussion concludes with a roadmap for shifting human-AI collaboration from supervisory oversight to full automation, driven by the expectation that incremental self-correction and hybrid training data will exponentially extend AI capabilities.

Brand Design Tips From Linear Founder Karri Saarinen

Karri Saarinen, Aaron Epstein

Kari Saarinen outlines how early-stage startups should align their brand authenticity with their development stage, advising against mimicking mature companies like Stripe or Linear before product readiness. She critiques five specific landing pages for issues such as distracting visuals, vague messaging, and poor audience segmentation, recommending targeted copy and clearer calls to action to improve conversion. The analysis emphasizes that website design must evolve from abstract mystery for waitlists to detailed utility for enterprise sales, ensuring every visual element serves a functional purpose rather than adding noise.

How Replit Went From $10M to $100M ARR In Just 9 Months

Amjad Masad, Tom, Dave

Replit has strategically pivoted to an AI-driven mission centered on its autonomous Replit Agent, a move supported by a dedicated infrastructure of NixOS and parallel agent sampling that achieved 45% monthly growth following a targeted workforce reduction. CEO Amjad Massad describes this transition as lowering barriers to software creation, enabling product managers and designers to deploy applications directly and shifting the primary bottleneck from engineering capacity to the volume of human ideas. As the technology matures beyond model coherence into complex computer use, the company anticipates a future where generalist knowledge and idea generation replace traditional coding syntax as the critical skills for creators.

Small Teams Will Build the Future

Sam Altman

The speaker forecasts a fundamental decade-long shift in productivity driven by significantly reduced coordination costs that will empower individuals and small groups. This structural change is expected to trigger a step change in output, moving beyond incremental gains to enable single actors to accomplish substantially more work. The anticipated outcome includes higher personal satisfaction from high-impact execution and a marked improvement in the quality of goods and services exchanged globally.

Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery

John Jumper

Google DeepMind leader John Jumper presents AlphaFold 2, an AI system that revolutionizes structural biology by accurately predicting protein 3D structures to bridge the massive gap between sequenced genes and experimentally solved forms. The technology's superior accuracy, validated by a thirty-fold reduction in error rates at CASP14 and the release of over 200 million predictions, has accelerated drug discovery and enabled novel applications like re-engineered molecular syringes for targeted therapy. This shift allows experimentalists to advance the pace of biological research by 5 to 10 percent, transforming AI from a narrow computational tool into a fundamental amplifier for scientific discovery.

Aravind Srinivas: Perplexity's Race to Build Agentic Search

Aravind Srinivas, Sami, Akshat, Ravin, Angela

Perplexity AI founder Aravind Srinivasan addresses the company's rapid infrastructure strain and recurring competitive skepticism while detailing the strategic development of "Comet," an AI-powered browser designed to function as a cognitive operating system for complex agentic tasks. Differentiating itself from incumbent tech giants bound by ad-revenue models, the 200-person team leverages AI-driven engineering workflows to iterate quickly, aiming to capture direct transaction revenue through subscriptions rather than competing on traditional search advertising. Srinivasan projects a future where Perplexity maintains market relevance by prioritizing speed and human oversight in distributed systems against inevitable large-scale replication attempts from major competitors.

Andrew Ng: Building Faster with AI

Andrew Ng

AI Fund accelerates startup velocity by co-founding approximately one venture monthly through direct code writing and feature definition, leveraging agentic AI workflows to address complex tasks in sectors like healthcare and legal compliance. The organization emphasizes concrete product hypotheses and agile prototyping to shift engineering bottlenecks toward product management, thereby altering standard PM-to-engineer ratios to 0.5:1 while empowering non-engineering staff with coding literacy. Strategic guidance further stresses that rapid iteration and ethical filters outweigh speculative narratives, prioritizing application-layer revenue generation and open-source accessibility over fears of existential risk or regulatory gatekeeping.