YC startup ideas: Y Combinator's current Requests for Startups and what a small builder can make from each
Y Combinator publishes its startup ideas as Requests for Startups, a page of problems it would like founders to attack. The Winter 2027 list has 11 requests, from AI for city permit offices to robots made in the US and AI that works inside air-gapped buildings. Most need a company, not a weekend. Below, each is summarized in our own words with a link to the source, plus a small version one person with an AI coding tool could build first.
What are YC's Requests for Startups?
Requests for Startups, or RFS, is Y Combinator's habit of publishing ideas it would like to see founders work on. The official RFS page groups them by batch, with tabs from Summer 2024 to Winter 2027. Each request is signed by a YC author and says what the problem is and why now. YC adds that the list is only a fraction of what it funds, and that you do not have to work on one of these ideas to apply.
That framing matters for how you read the list. An RFS is a YC author's bet about where demand will appear, not a validated spec. It is a good place to see what investors think the next few years look like. It is a poor place to find a weekend project, because most requests describe a whole company: hardware, regulation, large customers, long sales cycles.
So this page does two jobs. It tells you in plain language what each request is asking for. Then, for each, it suggests the smallest useful thing a single builder could make this month with an AI coding tool, which is how you learn the problem before deciding whether it deserves a company. If you want to turn any of these into a prompt, see how to write an app build prompt.
How do you use an RFS if you are not starting a funded company?
Treat each request as a question: who is stuck, what are they doing instead, and what is the smallest piece of that I can make useful on its own? The answer is almost never the whole request. It is one document, one lookup, one checklist or one comparison inside it.
| Step | What to do | Example |
|---|---|---|
| 1. Find the person | Name one job title who feels the problem daily | A permit reviewer at one city office |
| 2. Find the paper trail | Look for forms, spreadsheets and PDFs they already use | A public checklist of what an application must contain |
| 3. Cut to one step | Pick one step that a tool can do without being trusted blindly | Flag which checklist items an application does not mention |
| 4. Keep a human in the loop | The tool suggests, the person decides | Show the page where each item was found |
| 5. Test with real documents | Use public samples, not made-up ones | Ten real application packets you are allowed to use |
Before you spend a month on any of them, run the idea through how to validate an app idea, or try the idea checker. If you have a small version of one of these and do not plan to build it, post it on the board so someone else can.
Which YC startup ideas are about AI doing real jobs?
Three of the Winter 2027 requests are about putting AI to work on the paperwork economy rather than the model itself: turning ordinary workers into experts, moving government benefits online, and speeding up city permits. These are the most approachable for a solo builder, because the raw material is documents and rules that are often public.
01 The USD 15-an-hour expert: AI that turns a trained generalist into a specialist
YC wants tools that let a capable person without a credential do work that used to need a specialist, in fields like benefits, taxes, insurance claims, medical billing and immigration paperwork.
- Good for
- Builders who know one paperwork-heavy field and can read its rules.
- First version
- A guided checklist that walks a worker through one form type, with each answer tied to the rule it comes from.
- Time to first version
- 1 to 2 weekends for one form and one rule set.
- Hard part
- Being right, and showing the source, in a field where a wrong answer costs someone a claim.
The idea behind the request is that the credential is the bottleneck, not the intelligence. A tool that supplies the missing specialist knowledge, at the moment of the task, lets a smart person do the job. The small version is one form in one field: a medical-billing code lookup with plain-language reasons, an insurance-claim completeness check, or an immigration form checklist.
Build a web app with no login. User: a support worker helping a client fill in one specific form. Screen: a short interview, one question at a time, that ends in a list of what is missing or inconsistent. Data: a markdown file of the official instructions for this form, which I will add. Every finding must quote the sentence of the instructions it comes from. Do not build: accounts, document upload, advice outside the instructions. Done when: for 5 sample cases I provide, the tool flags the missing items I expect and quotes the right sentence each time.
Extension: add a second form from the same field, then a review mode where an experienced person checks the output and corrects it. Those corrections become your test set.
02 Plaid for government benefits: prove it once, apply everywhere
YC describes a shared layer that pulls verified income, tax and residency records, with the applicant's consent, into many benefit programs at once instead of making people re-prove everything per agency.
- Good for
- Builders with civic-tech interest and patience for government data.
- First version
- A renewal packet organizer for one program in one state: which documents, in which format, by which date.
- Time to first version
- 1 weekend for a checklist, much longer for any live data connection.
- Hard part
- Real verification needs agency and data-source access you will not get on your own.
The full request is infrastructure: connections to payroll, tax and residency sources. A solo builder cannot ship that. What you can ship is the layer people touch first, the pile of documents they gather again at every renewal. A tool that tells a person exactly what one program asks for, helps them keep the files together, and reminds them before the deadline, solves the repeated-effort half of the problem without touching sensitive data pipes.
Build a mobile-friendly web app. Keep everything on the device; no server storage of personal documents. User: someone renewing one benefit program in one state. Screen: a checklist of required documents from the program's public instructions, a place to note where each file is, and a renewal date reminder as a downloadable calendar file. Data: a JSON file of required documents and deadlines that I will fill in from the agency website. Do not build: accounts, document upload, eligibility decisions. Done when: I can tick off every item, reload and see my progress, and download one calendar entry.
Extension: support a second program that asks for overlapping documents, and show the overlap. That overlap is the evidence for the bigger idea.
Visit Plaid for government benefits: prove it once, apply everywhere (ycombinator.com) →
03 AI for City Hall: permits, 311, records and inspections
YC wants software that helps local government staff review permit applications against code, answer 311 requests, handle public records and run inspections, because most of it is still manual.
- Good for
- Builders who can read a municipal code and enjoy structured documents.
- First version
- A completeness check for one permit type in one city, against that city's published checklist.
- Time to first version
- 1 to 2 weekends.
- Hard part
- Selling to a city, and trusting a model's reading of drawings and codes.
Start from the applicant's side. Many cities publish a checklist of what an application must include. People submit incomplete packets, staff send them back, and months pass. A tool that reads an application and says which checklist items it does not mention helps the applicant first, and shows the city a use case without any procurement.
Build a web app with no login. User: a homeowner preparing a permit application for one project type in one city. Screen: paste or upload the application text; get a table of checklist items marked found, not found or unclear, each with the quoted passage. Data: the city's public checklist as a markdown file I will add. Do not build: drawing analysis, code-compliance verdicts, accounts. Done when: on 5 real sample applications, every item I know is missing is marked not found, and no item is marked found without a quote.
Extension: add a second permit type, then a staff view that lists the most common missing items across applications. That list tells a city exactly what to fix on its own form.
Visit AI for City Hall: permits, 311, records and inspections (ycombinator.com) →
Which YC requests are about AI models and infrastructure?
Four requests are about the models themselves and where they run: better writing, specialist models for narrow fields, AI inside closed networks, and companies owning their own models. A solo builder will not train a foundation model, but the tooling around these needs, such as evaluation, comparison and offline setups, is small enough to build.
01 LLMs that write well
YC says recent models have become worse at natural-sounding writing as labs optimize for code and agents, and asks for a startup that builds a model people enjoy reading.
- Good for
- Writers and editors who can judge prose and are comfortable with a little code.
- First version
- A blind side-by-side voting page for model outputs on the same writing prompts.
- Time to first version
- 1 weekend.
- Hard part
- Turning taste into a score that other people agree with.
You cannot train the model, but you can build the instrument that shows which one writes better. A blind comparison page, where readers pick the passage they prefer without knowing which model wrote it, produces the data a better model needs. It is also a standalone product for editors choosing a tool.
Build a web app. User: a reader judging writing quality. Screen: two anonymous passages answering the same prompt; buttons for Left, Right, Both bad. After a vote, reveal which model wrote which. Data: a JSON file of prompts and pairs of outputs that I will provide. Randomize left and right. Do not build: accounts, model calls, comments. Done when: 20 votes are stored, a results page shows win rate per model, and the left-right split in the data is within 45 to 55 percent.
Extension: let a user upload their own writing samples and prompts, then compare models against their own voice.
02 Vertical foundation models: specialist models for one field
YC wants research labs to build models for manufacturing, energy or biology that work with little or no task-specific training and run far cheaper than general models.
- Good for
- Domain experts with access to data they have the rights to use.
- First version
- A public benchmark: a dataset, a scoring script and a leaderboard page for one narrow task.
- Time to first version
- 1 to 2 weekends for the benchmark.
- Hard part
- Rights to the data, and an honest comparison against existing specialist methods.
The request is a research agenda, but its first step is measurable: a clear task and a score. If you know a field, you can publish what good looks like before anyone trains toward it. A forecasting benchmark on a public energy dataset is an example of the shape.
Build a small benchmark site. User: a researcher comparing methods on one forecasting task. Data: a public time-series dataset I will add, split into a visible training part and a hidden test part. Screen: task description, a download link, a form to submit predictions as a CSV, and a leaderboard sorted by error. Scoring: mean absolute error, computed on the server against the hidden part. Do not build: accounts, model hosting. Done when: submitting a file of the training mean as the prediction gives the baseline score shown on the board.
Extension: add a second metric for compute cost, since the request cares about cheap inference as much as accuracy.
Visit Vertical foundation models: specialist models for one field (ycombinator.com) →
03 AI for the air-gapped economy
YC points out that banks, hospitals, defense suppliers and nuclear plants cannot send data to an outside AI service, and wants companies that bring AI fully inside those networks.
- Good for
- Developers who like deployment, packaging and security work.
- First version
- A local assistant that answers questions over a folder of documents with networking turned off.
- Time to first version
- 1 to 2 weekends.
- Hard part
- Security accreditation and hardware, which take longer than the software.
The product idea is deployment, not a smarter model. A one-command install that runs an open-weight model on a customer's machine, indexes their documents and never makes an outside request is a real starting point. Prove the no-network claim, because that is what the buyer will test.
Build a command-line tool and a local web page. User: an analyst who may not send documents outside the building. Behavior: index a folder of text and PDF files; answer questions with a quoted source for every answer, using a locally run open-weight model. Constraint: the app must work with all network access disabled. Add a startup check that fails loudly if any outbound connection is attempted. Do not build: accounts, cloud sync, telemetry. Done when: with the network off, 5 questions about 20 sample files return answers with quotes, and the outbound-connection check passes.
Extension: an audit log that lists every file the assistant read to answer each question, which compliance teams ask for.
04 Own your intelligence: companies running their own models
YC argues that open-weight models are now close enough to frontier models that more companies will want to own and improve their own, and asks for firms that help them do it.
- Good for
- Developers who like test suites and data pipelines.
- First version
- A tool that turns a company's real workflow examples into a reusable evaluation set.
- Time to first version
- 1 weekend.
- Hard part
- Getting a company to share real examples, and keeping them private.
The request names the gap between downloading a model and owning one that improves a business: someone has to turn workflows into evaluations and failures into improvements. The smallest useful piece is the evaluation set. A team logs inputs, the correct output and what a model returned, and the tool shows which cases a model passes.
Build a web app for a small team. User: an engineer comparing models on one internal task. Screen: a table of test cases (input, expected output); a Run button that sends each input to a model I configure and marks pass or fail; a page that lists every failure. Data: one SQLite file. API keys stay on the server. Do not build: model training, accounts beyond a shared password. Done when: I import 20 cases, run two models and see a pass rate for each plus the failing cases side by side.
Extension: a button that saves any production failure as a new test case, so the set grows from real mistakes.
Visit Own your intelligence: companies running their own models (ycombinator.com) →
Which YC requests are about hardware, space and financing?
The remaining four requests are about the physical side of AI: rockets, robots, data centers and the loans that pay for them. Nobody builds these alone. Still, each has a research or planning layer that is mostly spreadsheets and public documents, and that layer is where a single builder can add something.
01 Startups in a world of abundant space access
YC asks which business models fail today because launching to orbit is expensive, but would work if launch costs fell by a hundred times, and wants founders to start those companies now.
- Good for
- Builders who enjoy modeling and have read about orbital business cases.
- First version
- A break-even calculator: at what cost per kilogram does an orbital business idea start to make money?
- Time to first version
- 1 weekend.
- Hard part
- The inputs are guesses; the value is showing which guess matters most.
A calculator will not launch anything, but it is how a founder discovers which idea is worth a pitch. Let a user enter payload mass, launch price, lifetime and revenue, and show the launch price at which they break even. Add a sensitivity table so the weakest assumption is obvious. Fill in no default numbers you cannot source.
Build a single-page calculator. No backend. User: someone testing an orbital business idea. Inputs: payload mass in kg, launch cost per kg, years of service, yearly revenue, yearly operating cost. Output: total cost, total revenue, break-even launch cost per kg, and a table that shows the result at half and double each input. Leave all inputs empty by default; show a prompt to enter your own numbers. Done when: payload 100, cost per kg 5000, 5 years, revenue 200000 a year, operating cost 50000 a year shows break-even at 7500 per kg.
Extension: save and share scenarios by link, so founders can compare cases.
Visit Startups in a world of abundant space access (ycombinator.com) →
02 Robots made in America: the domestic supply chain
YC wants founders making robots and the parts inside them, such as magnets, motors, gears, sensors, grippers and batteries, in the US, because imported parts can block a robot from counting as domestic.
- Good for
- Builders with an engineering or sourcing background.
- First version
- A bill-of-materials calculator that shows the share of component cost made domestically.
- Time to first version
- 1 weekend.
- Hard part
- Rules and thresholds change, and you must cite the current source for them.
The request mentions a share-of-component-cost rule. A tool that takes a parts list, lets the user mark each part's origin and cost, and shows the domestic percentage turns a rule into a daily planning aid. Make the threshold an input the user types, with a link to the source, instead of baking in a number that may change.
Build a single-page tool. No backend. User: a hardware founder planning a robot. Screen: an editable table (part, supplier, country of origin, unit cost, quantity). Show total cost, domestic cost, domestic percent, and a field for the target percent that I type myself. Do not build: supplier databases, accounts, any built-in threshold. Done when: three parts costing 100 each, one marked domestic, show 33.3 percent domestic, and the page says whether that is above or below the target I entered.
Extension: a directory of domestic suppliers per part type, maintained by contributors, with the date each entry was last checked.
Visit Robots made in America: the domestic supply chain (ycombinator.com) →
03 Wall Street for the physical buildout: financing for robots and GPUs
YC says banks still price a robot like a forklift and asks for lenders, leasing firms and hedges built for machines and compute that earn money in new ways.
- Good for
- People with a finance or accounting background.
- First version
- A lease-versus-buy comparison for one piece of equipment.
- Time to first version
- 1 weekend.
- Hard part
- Real lending is regulated and needs capital; a calculator is education, not advice.
A small builder is not going to start a lender. A clear lease-versus-buy and payback calculator is still useful to a small business owner weighing a machine, and it shows you what questions the financing actually turns on: utilization, residual value, interest rate. State that it is general information, not financial advice.
Build a single-page calculator. No backend. User: a small business owner deciding between buying and leasing a machine. Inputs: price, interest rate, loan years, lease payment per month, lease months, expected resale value, expected extra revenue per month. Output: total cost of each option, the month in which each option pays back, and a plain-language note on which wins and why. Add a visible note: general information, not financial advice. Done when: price 60000, 8 percent over 5 years gives a monthly payment of about 1217.
Extension: a utilization slider that shows how many hours a month the machine must run to justify each option.
Visit Wall Street for the physical buildout: financing for robots and GPUs (ycombinator.com) →
04 Data center supply chain: transformers, switchgear, cooling
YC points to shortages up and down the chain that builds data centers, including large power transformers, switchgear, circuit breakers, liquid cooling and the special steel used in transformer cores.
- Good for
- Builders with an electrical, construction or procurement background.
- First version
- A lead-time board where buyers report quoted delivery times for a type of equipment.
- Time to first version
- 1 weekend.
- Hard part
- Getting enough honest reports, and moderating them.
If an item has a long lead time, the people who feel it are buyers who compare notes in private chats. A simple board where they post a type, a quoted lead time and a month, with no company names, makes the market visible. It also gives a small builder the first dataset: who is asking, and for what.
Build a small web app. User: a procurement engineer comparing delivery quotes. Screen: a form (equipment type from a fixed list, quoted lead time in weeks, month of the quote, region) and a table with the median and range per type and quarter. Rules: no company names, no free-text field, rate-limit submissions by IP, show a type's numbers only once it has at least 5 reports. Do not build: accounts, comments. Done when: 5 test reports for one type show a median, and 4 reports show 'not enough reports yet'.
Extension: email alerts when the median for a type rises by a set amount.
Visit Data center supply chain: transformers, switchgear, cooling (ycombinator.com) →
Which YC request should you build first?
Pick the one where you can name a real person, get ten real documents and finish a version in two weekends. For most readers that is a paperwork request: the expert-worker idea, the benefits checklist or the permit completeness check. Those have public rules, concrete inputs and an output a person can verify against a source.
| Request | Smallest useful build | Needs special access? |
|---|---|---|
| Expert for USD 15 an hour | One form checklist with quoted rules | No |
| Plaid for benefits | Renewal document organizer | No, for the checklist |
| AI for City Hall | Permit completeness check | No, for one public checklist |
| LLMs that write well | Blind comparison page | A model API key |
| Vertical models | Public benchmark | Data you have rights to |
| Air-gapped AI | Offline document assistant | A machine that runs a local model |
| Own your intelligence | Evaluation-set tool | A model API key |
| Space access | Break-even calculator | No |
| Robots made in America | Domestic-content calculator | No |
| Physical buildout financing | Lease-versus-buy calculator | No |
| Data center supply chain | Lead-time board | Enough reporters |
If none of these fit, the board holds ideas from people with problems closer to your own life. Browse developer tools or all ideas, and every one comes with a generated build prompt. For a wider set of startup directions, see startup ideas for 2026 and AI startup ideas.
Frequently asked questions
What are YC startup ideas?
YC startup ideas are the problems Y Combinator publishes in its Requests for Startups, or RFS, as areas it would like founders to work on. Each request is signed by a YC author and explains the problem and why the timing is right. YC says the list is only part of what it funds and that applicants do not need to work on one.
Where is the official YC Requests for Startups list?
It is at ycombinator.com/rfs. The page has tabs for each batch, from Summer 2024 up to Winter 2027, and each tab lists that batch's requests with its author. Lists change from batch to batch, so check the page directly for the current one before basing a plan on it.
Do you have to build a YC idea to get into Y Combinator?
No. YC states on the RFS page that these ideas represent only a fraction of what it funds. If one excites you, it counts as extra validation to dive in, but you can apply with a different idea. Strong applications usually come from founders who understand a problem better than anyone else does.
Can one person build a YC startup idea alone?
Most requests describe whole companies, so not the full version. A single builder can make a useful slice: a checklist, a calculator, a benchmark or a small tool for one customer type. That slice teaches you the problem and shows whether people care before you take on funding, hardware or a sales team.
Are YC's requests for startups good ideas for beginners?
Mostly no, because they are written for founders with funding in mind. They are good reading to learn how investors frame a market. Beginners get more from the paperwork-focused requests, since those can start as a single checklist built from public rules, or from the simple ideas on our board.
How often does YC update its Requests for Startups?
YC publishes a new list for each batch, and the RFS page keeps older batches behind tabs. The current page shows Winter 2027 first, then Fall 2026, Summer 2026, Spring 2026 and earlier. Some topics return in a new form, and others disappear, so treat each list as a snapshot.
Created with AI assistance and edited for this site. Examples are illustrative and do not represent customer research. About the publisher.