Requests for Startups | Y Combinator
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- Saved: 2026-02-09
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Requests for Startups | Y Combinator
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Requests for Startups
RFS is our tradition of sharing ideas wed like to see founders tackle. These represent just a fraction of what we fund — if one excites you, take it as extra validation to dive in, but you dont need to work on these ideas to apply to YC.
Spring 2026Fall 2025Summer 2025Spring 2025Winter 2025Summer 2024
Spring 2026
The way startups are built has shifted quickly. AI-native companies can now be built faster, cheaper, and with more ambition than ever. Were excited about a range of startup ideas that span AI-native workflows, new financial primitives, modernized industrial systems, and more. This time around, a few even come directly from YC founders sharing opportunities theyre seeing on the frontier.
Cursor for Product Managers#
By Andrew Miklas
Over the last few years, weve seen an explosion of AI tools for writing code. Cursor and Claude Code are great at helping teams build software once its clear what needs to be built. But writing code is only part of building a product people want. The most important part is figuring out what to build in the first place! Every successful product requires product management: talking to users, understanding markets, synthesizing feedback, and deciding what problems are worth solving and how the product should work. Whether this process is done by founders, engineers, or product managers, the activity is the same. Historically, the output has been product requirements docs, Figma mocks, and Jira tickets — artifacts designed to communicate intent to human engineers. Today, teams use AI in isolated parts of this process, but theres no system that supports the full loop of product discovery. Imagine a tool where you upload customer interviews and product usage data, ask what should we build next?, and get the outline of a new feature complete with an explanation based on customer feedback as to why this is a change worth making. The tool would also propose specific changes to your products UI, data model, and workflows, and would break down the development tasks so they could be handled by your favorite coding agent. We think theres an opportunity to build a Cursor for product management: an AI-native system focused on helping teams figure out what to build, not just how to build it. As agents increasingly take the first pass at implementation, the way we define and communicate what to build needs to change. If youre building in this space, wed love to hear from you.
AI-Native Hedge Funds#
By Charlie Holtz
In the 1980s, a small group of funds started using computers to analyze markets. At the time it seemed silly, but quantitative trading is now obvious. Were at a similar inflection point now, and the next Renaissance, Bridgewater, and D.E. Shaws are going to be built on AI. The biggest funds in the world have been slow to adapt. I worked as a quant researcher at one of these funds, and when I asked compliance to let us use ChatGPT, I didnt even get a response. It made it clear to me that the hedge funds of the future wont just bolt AI onto their existing strategies. Theyll use it to come up with entirely new ones. Thats where the alpha is. Weve already got swarms of Claude agents writing our codebases. Imagine swarms of agents doing what hedge fund traders do now - combing through 10-Ks, earnings calls, and SEC filings, synthesizing analyst ideas and making trades. An AI-native hedge fund will be the first to do this well.
AI-Native Agencies#
By Aaron Epstein
Agencies have always been crazy hard to scale. Low margins, slow manual work, and the only way to grow is to add more people. But AI changes this. Now instead of selling software to customers to help them do the work, you can charge way more by using the software yourself and selling them the finished product at 100x the price. Think of a design firm that uses AI to produce custom design work for clients upfront, to win the business before the contract is even signed. Or an ad agency that uses AI to create stunning video ads without the time and expense of setting up a physical shoot. Or a law firm that uses AI to write legal docs in minutes, rather than weeks. Thats why agencies of the future will look more like software companies, with software margins. And theyll scale far bigger than any agencies that exist in these fragmented markets today. If youre rethinking how agencies and service businesses of the future will be built, wed love to hear from you.
Stablecoin Financial Services#
By Daivik Goel
Stablecoins are rapidly becoming critical infrastructure for global finance, yet much of the financial services layer remains unbuilt. The GENIUS and CLARITY Acts are placing stablecoins in a unique position between DeFi and TradFi, compliant but crypto-native. This creates room for financial services that offer DeFi benefits like better yield or access to tokenized real-world assets while operating under traditional compliance frameworks. Today, businesses and individuals must choose between regulated financial products with limited upside and unregulated crypto with real risk. Stablecoins sitting in the regulatory middle ground can bridge this gap, whether thats yield-bearing accounts, new investment access, or infrastructure that makes money move faster and cheaper across borders. The regulatory window is open. The rails are being laid. Its the perfect time to build something that blurs the line between the two worlds.
AI for Government#
By Tom Blomfield
The first wave of AI companies has helped businesses and normal people fill in forms and complete online applications with unprecedented speed and accuracy. On the flip side, many of these forms will be received by local, state, and federal government, where theyre currently printing them out and processing them by hand. Government desperately needs AI tools to deal with the huge increase thats coming down the line. And the benefit is that it will also make government much more cost-effective and responsive. Weve seen hints of this digital government in places like Estonia, but we need to spread it to the rest of the world. This kind of startup is not for the faint of heart. Selling to government is extremely hard, but once youve figured out how to land your first customer, they tend to be very sticky and can expand to huge contracts.
Modern Metal Mills#
By Zane Hengsperger
When people talk about reindustrializing America, they usually focus on labor costs or geopolitics. But a bigger problem is hiding in plain sight: American metal mills are slow by design. If you buy rolled aluminum or steel tube in the U.S., lead times of 8 to 30 weeks are normal. Most buyers cant even purchase directly from mills. And despite high prices, mills still operate on thin margins. Thats not because demand is weak or workers are unskilled—its because the systems running these mills were designed decades ago. Production planning, scheduling, quoting, and execution are fragmented. Mills optimize for tonnage and utilization, not speed, flexibility, or margin. Short runs and spec changes are treated as disruptions instead of opportunities. Automation has lagged at the exact moment the workforce is shrinking. Material handling, changeovers, inspection, and quality control still rely on tribal knowledge held by a few experienced operators. Automation is mostly used to push more tons through a slow system, not to eliminate setup time or variability. Energy is the other half of the problem. Aluminum and steel are extremely energy-intensive, yet most mills rely on legacy power contracts and inflexible grids. New energy models—on-site generation, smarter power management, even next-generation nuclear—could dramatically reduce costs, but theyre rarely designed into mills from the start. Whats changed is that software and energy technology are finally good enough to rethink the entire system. AI-driven planning, real-time MES, and modern automation can compress lead times and raise margins at the same time. We think this creates an opportunity to build modern, software-defined American mills—especially in aluminum rolling and steel tube—where long lead times and energy costs are most entrenched. Modernizing mills isnt just about going faster. Its about making domestic metal cheaper, more flexible, and more profitable—and rebuilding the industrial foundation of the U.S.
AI Guidance for Physical Work#
By David Lieb
You know that scene in The Matrix, where Neo plugs a cable into the back of his head and wakes up a while later and says I know Kung Fu? Physical work is about to get something similar – not through brain implants, but through real-time AI guidance. Much of the AI conversation focuses on which desk jobs will get replaced. But for physical work—stuff like field services, manufacturing, healthcare—AI cant yet act in the world. What it can do is see, reason, and guide the human who does. Imagine wearing a small camera while an AI sees what you see and talks you through the job: turn off that valve, use the ⅜ inch wrench, that part looks worn, replace it. Instead of needing months or years of training, workers can become effective immediately, with AI coaching them and accessing new skills when needed. Why now? Three things have converged. First, multimodal models can now see and reason about real-world situations reliably. Second, the hardware is already everywhere – phones, AirPods, Smart Glasses. And third, skilled labor shortages make this economically urgent and a high wage job for millions of people. There are a few approaches you could take. The most obvious is to build this system and sell it to companies with existing workforces. Or, you could pick a vertical, like HVAC repair or nursing, and build a full-stack superpowered workforce. Or, you could build a platform that lets anyone sign up and become a skilled worker or start their own business. If youre interested in giving physical workers the same type of AI superpowers that Claude Code gives you, wed love to see you apply.
Large Spatial Models#
By Ryan McLinko
Large language models have driven most of the recent breakthroughs in AI, but their impact has been constrained to domains that can be expressed primarily through language. Unlocking the next wave of AI capability, and enabling artificial general intelligence, will require models that are capable of spatial reasoning. Todays systems can handle limited spatial tasks, such as basic relationships or depth estimation, but they cannot robustly reason about spatial manipulation, 2D and 3D features, their relationships, or operations like mental rotation. This limits AIs ability to understand and interact with the physical world. There is an opportunity to build large-scale spatial reasoning models that treat geometry and physical structure as first-class primitives, not approximations layered on top of language. Such models would enable AI systems to reason about and design real-world objects and environments. A company that succeeds in building this capability could define the next AI foundation model, on the scale of OpenAI or Anthropic.
Infra for Government Fraud Hunters#
By Garry Tan
We want to fund startups that bring government fraud investigation into the modern era. Government is the biggest customer on earth—it spends trillions annually at the federal, state and local levels, and it hemorrhages a commensurate amount in fraud. Medicare alone loses tens of billions a year to improper payments. One of the most effective ways to claw this money back at scale is the qui tam provision under the False Claims Act. This lets private citizens file lawsuits on behalf of the government against companies defrauding it. If the case succeeds, these citizens get to keep a percentage of whatevers recovered. At the moment, this process is painfully slow: An insider tips off a law firm, and then the firm spends months or years manually pulling documents and building the case. This should be accelerated with software. Not dashboards, but intelligent systems that can take an insider tip and organize the evidence around it—parsing messy PDFs, tracing opaque corporate structures, and packaging the findings into complaint-ready files. Some startups are already filing FCA claims themselves, but we think theres a big opportunity to build tools that dramatically speed up whistleblower law firms, state AGs, and inspectors general. Founder profile matters here. Were looking for teams where at least one founder has actually done work like this, whether thats a former FCA counsel, compliance lead or auditor. Now is the time to build this: the AI capabilities are finally here, and theres bipartisan tailwind to act. If you can make fraud recovery 10x faster, youll build a huge business — and return billions to taxpayers.
Make LLMs Easy to Train#
By Gabriel Birnbaum
Training large language models is still surprisingly difficult. My co-founder Eric and I have spent the last three years training diffusion and language models at Can of Soup, and despite all the attention AI has received, the tooling has barely improved. On any given day we may spend significant time dealing with broken SDKs, SSHing into busted GPU instances (that you only find out are busted after spinning them up for half an hour), or discovering major bugs in open-source tooling. Not to mention the work of managing, sourcing, processing, and visualizing terabytes of data. Id love to use products that make LLM training easy. • APIs that abstract training. • Databases to easily manage very large datasets. • Dev environments built with ML research in mind. As post-training and model specialization become more important, I could see these products becoming the foundation of how software is built in the future.
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