Y Combinator
Y Combinator is a startup accelerator and investor whose channel features startup advice, founder stories, and material about its program for founders.
Stripe Data Shows AI Is Improving Early Startup Outcomes
Patrick Collison argues that founders should scrutinize success as seriously as failure: before raising money, they should ask whether they want to spend the next decade or more running the company they hope to build. Drawing on Stripe’s nearly two-year path to public launch, he says the essential discipline is not launching quickly but reaching production users early and letting their needs shape the product. In the AI era, Collison sees more opportunity rather than a closing window, though he says founders may need to pursue less crowded starting positions while staying anchored to real customer demand.
The 1% Rule for Finding Durable AI Opportunities
Google chief scientist Jeff Dean argues that AI founders should test frontier models on the workflows they hope to build around: if a general model already succeeds meaningfully, rapid model improvement may erase the opportunity. More durable openings, he says, are tasks where the model succeeds only 0% or 1% of the time and where a small team can add private context, distinct data, specialized models or better evaluation loops. The larger question is whether that advantage will persist long enough—and whether solving the problem matters.
Mach Cut-Off Evidence Opened a Path for Overland Supersonic Flight
Boom Supersonic founder Blake Scholl argues that startups can take on industries long dominated by governments and large contractors by turning their constraints into testable problems. He points to Boom’s XB-1 demonstrator, whose supersonic flights were used to reframe US overland-flight rules, and to the company’s effort to own more of its design, manufacturing and testing loop. The broader case is that ambitious hardware requires repeated proof points, not a single leap of faith.
AI’s Startup Boom Depends on Keeping Power Widely Distributed
OpenAI chief executive Sam Altman argues that AI gives startups an unusually large opportunity to take on work once reserved for far bigger organizations, but that their role is also political: keeping economic and technological power from concentrating in a few institutions. In a conversation with Y Combinator’s Garry Tan, Altman says founders should use agents and cheaper compute to pursue more ambitious ideas while maintaining safeguards against serious loss-of-control risks.
Stronger Models Require Smaller Agent Harnesses
Claude Code creator Boris Cherny argues that as models such as Opus 5 become more capable, AI products should remove inherited prompts, tools and workflow constraints rather than accumulate them. He says builders should test models on problems beyond their assumed limits, supply clear guardrails and ways to verify results, and use observed failures—not old model workarounds—to decide what to add back.
NVIDIA Built Its Strategy Around Algorithmic Domains, Not Chips
Jensen Huang argues that NVIDIA’s strategy has never been simply to build better chips, but to identify algorithmic domains where new computing architectures can change what is feasible and then build the stack around them. Recounting NVIDIA’s early failure in 3D graphics, its interpretation of deep learning after AlexNet, and its push into agents and robotics, Huang makes the case that technical leadership depends on confronting wrong assumptions quickly, learning the underlying workload, and organizing close to the work.
World Models Aim to Replace Robotic Trial and Error
Ankit Gupta and François Chaubard argue that AI needs world models—systems that predict how an environment will change after an action—to escape the poor sample efficiency of trial-and-error learning. They contend that passive video could provide broad knowledge of physical change, then be paired with smaller action-labeled datasets to train robots in simulated rollouts. But Chaubard says the approach still faces hard limits in large action spaces, rare safety-critical events, real-time planning, and adaptation when physical conditions differ from the model’s predictions.
Gusto Cofounder Automates Recurring Small-Business Work Through SMS and Slack
Gusto co-founder and head of technology Eddie Kim argues that AI for small businesses should automate recurring work, not present owners with another blank chat box. In a conversation with YC’s Harj Taggar, Kim explains how a missed-flight prototype evolved into Gusto Cofounder, an AI product that uses Gusto’s business context to run tasks such as payroll prep, approvals, reminders, and customer communications through SMS or Slack. He also uses the project to make a broader case for AI-assisted product development: smaller teams can build faster by testing working implementations instead of debating abstractions, but need more discipline as the cost of trying ideas falls.
First Customers Come From Founder-Led Work, Not Sales Automation
YC visiting partner Max Kolysh argues that a startup’s first 10 customers usually come from founder-led, manual work rather than cold-email tooling or automated sales systems. In a Startup School talk based on founder examples, he says early acquisition starts with understanding where buyers actually spend time, exhausting warm networks, showing up in person, and doing enough specific work to earn attention before asking for a meeting.
Juggling Startup Ideas Produces Bad Data for Founders
YC General Partner Jon Xu argues that aspiring founders learn less by testing several startup ideas in parallel than by committing to one and going deep. In a Startup School talk, Xu says shallow exploration creates bad data: founders cannot tell whether an idea is weak or whether they simply failed to understand the customer, the market, or the execution required. His prescription is to pick a direction, close off alternatives, learn the customer’s business in detail, and let sustained contact with reality either build conviction or reveal the better company underneath.
Groww Deferred Monetization After Organic Growth Validated Customer Pull
Groww co-founder and CEO Lalit Keshre argues that the Indian investment platform’s early advantage came from following customer pull even when it made monetization uncertain. In a Startup School India conversation with YC’s Jon Xu, Keshre says Groww abandoned its robo-advisor idea after users demanded more choice and transparency, then spent years prioritizing organic growth, retention and product intensity over revenue. His broader case is that consumer fintech founders should reduce ambiguity where they can, but stay close enough to customers to know which unresolved risks are worth carrying.
Emergent Says AI App Builder Reached $100M ARR in Nine Months
At Startup School India, Emergent co-founder and CEO Mukund Jha argues that AI can move software creation beyond programmers, letting non-technical users build, ship and monetize working products rather than demos. In a conversation with YC managing partner Jared Friedman, Jha says the company’s rapid growth came from betting on autonomous software-engineering agents before the models were fully ready, then rebuilding its architecture as those models improved. He also frames Emergent as a test of whether a global, technology-first company can be built from Bangalore.
Legora Says Legal AI Is Moving From Task Assistance to Matter-Level Agents
Legora CEO Max Junestrand argues that the company’s rise in legal AI came less from a single technical wedge than from moving quickly into law firms’ workflows, selling with unusual conviction, and building toward agents that can handle matter-level legal work. In a YC fireside with Gustaf Alströmer, he describes Legora’s shift from document and task assistance toward enterprise agents embedded in legal data, tools, and user behavior — the areas he sees as defensible as foundation models improve.
Coding Agents Are Becoming a Managed Workforce Inside Conductor
Conductor CEO and co-founder Charlie Holtz argues that AI coding tools should be managed more like a team of workers than used as autocomplete inside an IDE. In a demo of how he uses Conductor to build Conductor, Holtz shows a workflow built around starting multiple agent workspaces, reviewing their pull requests, and merging only the work that passes human judgment. He says the shift makes prompts, architecture, review discipline, and “slop-free” parts of the codebase more important as hand-written code becomes less central.
AI-Native Services Firms Can Turn Labor Markets Into Software-Margin Businesses
YC’s Charlie Warren argues that AI-native services companies are not copilots for existing firms but services businesses rebuilt so AI performs much of the work and customers buy the outcome directly. In his Startup School talk, Warren says the venture-scale opportunity is in outsourced, outcome-oriented markets such as legal services, tax, insurance, audit, regulatory support and healthcare, where AI operating leverage could push services margins toward software-like levels. His test is whether founders can control variance, reduce COGS, price on value and design the process itself as the product.
Giga Says Product Velocity Beat a 400-Person Rival at DoorDash
Giga co-founder Varun Vummadi argues that enterprise AI companies win less by selling a vision than by proving, in paid deployments, that their product can move a customer’s operating metrics. In a Startup School India interview with YC general partner Ankit Gupta, Vummadi traces how Giga abandoned its original edtech idea, followed customer demand into support automation, and used a small engineering team to win accounts including DoorDash. His broader case is that AI startups should charge early, iterate against real business KPIs, and treat product performance as their strongest sales tool.
YC Says Internal Agents Need Shared Context, Tools, and Trust
YC’s Pete Koomen argues that building “superintelligence” inside a company requires more than adding AI features to existing software: agents need access to the organization’s shared context, tools and accumulated work. In a Lightcone discussion with Garry Tan, Jared Friedman, Diana Hu and Harj Taggar, Koomen describes how YC’s internal agent system became useful once it could query a unified company database, reuse hundreds of internal tools and turn repeated judgment into improving skills. The broader claim is that AI-native organizations will depend as much on trust, transparency and broad access as on model capability.
Mission-Controlled Governance Can Keep Successful Companies From Turning Extractive
Eric Ries, author of The Lean Startup, argues in his new book Incorruptible that companies often lose the qualities that made them valuable because standard governance treats them as instruments for shareholder returns rather than institutions with a purpose. In a conversation with Garry Tan, Ries says founder control, aligned investors and dual-class shares are too fragile to protect a mission once a company becomes valuable enough to attack. His answer is legal and governance design—public benefit corporations, mission-controlled boards, trusts or industrial foundations—that gives a company’s purpose authority beyond any founder, investor or executive.
Startups Should Build Recorded, Queryable Operations That AI Can Improve
YC general partner Tom Blomfield argues that startups should not treat AI as a copilot bolted onto existing org charts, but as the basis for a company that records its work, exposes its tools, and improves through recursive loops. In his batch talk, he says founders should make company knowledge legible to AI, spend more on tokens rather than headcount, and rebuild operations around systems that can detect failures, update themselves, and reduce the need for human coordination.
Zepto Is Building India’s Urban Grocery Supply Chain Around Quick Commerce
Zepto co-founder and CEO Aadit Palicha argues that the company is not mainly a quick-commerce app but a grocery infrastructure business built around dark stores, supply-chain control and the promise of 10-minute delivery. In a Startup School India conversation with Jared Friedman, Palicha traces Zepto’s path from a COVID-era WhatsApp grocery group in Mumbai to a platform handling millions of daily deliveries, saying the decisive moves came from staying close to dissatisfied customers and working backward from speed, quality, selection and price.
Swedish Founders Should Leave for Silicon Valley, Then Return
Paul Graham told Swedish founders in Stockholm that the way to strengthen the city’s startup ecosystem may begin with leaving it. The Y Combinator founder argued that Silicon Valley remains the startup world’s dominant center because it concentrates ambitious peers, fast-moving investors, chance encounters and a culture of help; Stockholm’s opportunity, he said, is for founders to absorb those advantages and return with stronger companies, capital and habits that can compound locally.
Personal AI Lets One Builder Do the Work of Teams
Y Combinator CEO Garry Tan argues that personal AI is reaching a stage comparable to the early personal computer: powerful enough to let one person build software that once required a team, but still brittle enough to demand technical ownership. Drawing on his work with Claude Code, OpenClaw and his GStack workflow, Tan makes the case for heavy token use, Markdown-encoded “skills” and multiple coding agents under one accountable human operator. The larger question, he says, is whether users will control their own AI tools, data and prompts, or work inside opaque systems controlled by others.
Razorpay Turned India’s Payments Friction Into a $180 Billion Platform
In a Startup School India fireside with YC’s Jon Xu, Razorpay co-founder and CEO Harshil Mathur argues that the company’s rise in Indian payments came less from an initial fintech thesis than from staying with a painful customer problem through regulation, bank failures and market skepticism. Mathur says Razorpay turned delays into a moat, customer trust into an operating principle, and early bets such as UPI into openings incumbents missed. His broader case is that founders must keep direct ownership of the decisions that define the company, especially as AI lowers the cost of building and raises the cost of slow judgment.