Requests for Startups

By Harj Taggar

Selling software to the government is notoriously hard and not something most builders would even think about doing. Still, the prize is huge if you can figure it out.  Palantir is one of the only startups that managed to figure it out and their market cap today is $125 billion.

Now might be a uniquely good moment to give it a shot. The deficit is running high and there is hope the government will find ways to reduce spending and operate more efficiently. At the same time, AI is now powerful enough to automate many types of administrative work the government spends billions of dollars per year on.

If you combine those ideas, building AI software to automate work done by the government would both reduce spending and make the government more efficient. In particular, we’ve seen that LLMs are especially good at automating rote repetitive admin tasks like filling out forms, reviewing applications or summarizing documents.  As consumers of government services, we’d all benefit from more efficiency, imagine never having to wait in line at the DMV again.

It’s hard to think of something that sounds less appealing as a place to look for startup ideas than the government but if you’re up for digging in that space we’d love to hear from you.

By Garry Tan

We all deserve to be safe in our homes, and while walking around on our streets. This is a basic thing civilization should afford its citizens. Startups are already on the case. License plate cameras built by Flock Safety (YC S17) now help solve 10% of reported crimes in the USA and they’re aiming to get to 25% of all crime by next year. Abel Police (YC S24) cuts the amount of time police officers spend filling out paperwork from hours to minutes, giving them up to 25% more hours per day to do real police work.

Public safety tech can and will make a real difference.  We are especially interested in hearing from you if you’re working on these ideas:

• Advanced Computer Vision: Your startup could develop technology that uses computer vision to enhance safety—identifying suspicious activities or people in distress from video feeds, without compromising individual privacy.
• Emergency Response Enhancements: Technologies that improve the speed and coordination of emergency responses are lifesavers. If you have an idea that can get help to where it’s needed faster, we want to help you make it happen.
• Community Safety Tools: We are looking for tools that improve how communities and law enforcement interact. Your solution could make it easier for neighbors to watch out for each other and stay informed about their safety.
• Efficient Law Enforcement Technologies: We’re interested in technologies that help law enforcement do their jobs more effectively and fairly, from workload management to increasing accuracy in their operations.
  

If your startup is ready to be part of this movement, we want to hear from you.

By Jared Friedman

The UK became the world's richest country in the 19th century by being the workshop of the world. The US did the same in the 20th century. But in recent decades, we've given up this role. The hollowing out of US manufacturing has led to social and political division and left us in a precarious place geopolitically.

Bringing manufacturing back to America is one of the biggest areas of bipartisan agreement. Elon has shown us it is possible to do it by building Tesla Gigafactories in Austin and Nevada. We think other changes in the world have made it easier for new builders to follow his example.

New ML-based robotics systems will make it possible to automate far more, which will reduce the cost-of-labor arbitrage that pushed manufacturing to other countries in the first place.  Companies like SpaceX and Tesla have trained an entire generation of engineers in how to build an American company that makes physical products but operates like a startup.

We know this works because we've had experience working with some of the leading companies in this space. Astranis (W16) is building telecommunications satellites in the heart of San Francisco, in a building that used to build warships for the US Navy during WWII. Gecko Robotics (W16), based in America's old industrial heartland of Pittsburgh, builds robots that do industrial inspections. Solugen (W17) makes industrial chemicals from a large-scale plant in Houston.

By Brad Flora and Harj Taggar

At the start of this year, we posted a request for more stablecoin startups and since then things have only gotten better for stablecoins.   The black cloud over stablecoins has always been regulation, with several efforts to pass regulation in the US failing. The regulatory future for stablecoins in the US now looks more promising and we expect sensible legislation is on its way soon.

Stablecoin payment volumes have surged this year and are now over a fifth of Mastercard’s volume. Almost 30% of global remittances are now facilitated through stablecoins and traditional finance institutions like Visa are offering platforms for banks to issue their own stablecoins.  Also Stripe recently acquired a stablecoin startup, Bridge, for $1 billion, which will only attract more investor interest and capital into the space.

This makes it a better time than ever to start a stablecoin startup. We are especially interested in hearing about ideas that target businesses, helping them to hold and manage stables and also services that make it easy for developers to integrate with them.

If you’re working on stablecoin ideas we’d love to hear from you.

By Garry Tan

Each breakthrough in AI creates demand for more powerful chips to train larger models. No country wants to fall behind in this arms race. Domestic chip design and manufacture is not just about economics anymore, it's about survival in a post AI world.  OpenAI O1 showed us that LLMs with reasoning can power breakthroughs in science and engineering and we’re interested in talking to anyone using LLMs to improve chip design.

We are especially interested in anyone focusing on designing ASICs and FPGAs. Design of customized digital systems whether through FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits) has typically been costly because of the amount of custom design, development and testing necessary to bring such a system online. With the advent of large language models, these costs are coming down significantly, such that ever more specialized types of computation could be done.

Our normal computing environment assumes Von Neumann architecture using CPUs that we are familiar with: single shared memory for programs and data, arithmetic unit and a program control unit, operating through fetching and execution cycles serially. Most computers and computation use this because it’s very easy to reprogram such systems.

We know there is a clear engineering trade-off: it is possible to optimize especially specialized algorithms or calculations such as cryptocurrency mining, data compression, or special-purpose encryption tasks such that the same computation would happen faster (5x to 100x), and using less energy (10x to 100x).  This is a diagram (credit: Taner Sadikoglu) showing the differences in how data flows an optimized FPGA system versus a normal CPU.  

Given the order of magnitude improvements possible with specialized FPGAs and ASICs, use of LLMs to optimize this process is likely to produce extremely useful results and great opportunities for startups.

By Dalton Caldwell

The last two years have been a rough time for fintech startups. The collapse of Silicon Valley Bank led to regulators clamping down on new startups and investors fled the space. We're optimistic this is about to change and now is a great time to start a fintech startup.

Historically the hardest thing about starting a new financial startup was the difficulty of getting a deal with a bank or other regulated partner. We are now in a new era where this keeps becoming easier with the advent of providers like Stripe and new core technologies like stablecoins.

AI tools will inevitably cause the financial industry to change rapidly and we believe there could be a structural advantage for a small startup without any legacy systems or processes to quickly build global financial products of the future.

We believe this is the perfect time to start a new generation of financial technology companies built on top of the infrastructure now available. We would like to see ideas around insurance, investment banking, wealth management, international payments and more.

By Jared Friedman and Dalton Caldwell

The cost to reach orbit is falling fast, having fallen over 10x since SpaceX’s first launch in 2006. A startup can now build and launch a satellite on just a seed round.

If you think about how many kilograms of payload get launched into space today, imagine how many will be sent up in one year, in five years, in ten years, and so on.

If we are entering a future with access to space being as routine and inexpensive as commercial air travel, shipping or trucking… what new businesses does that unlock?

Building a space company might scare founders by seeming too ambitious, but surprisingly, it is not necessarily harder than building a software company. YC has funded many space companies — Astranis, Relativity Space, Stoke, and many others  — and their success rate has been no lower, and maybe higher, than our other companies.

By Diana Hu

Engineering tools for the physical world have barely evolved in decades. The CAD/CAM software that mechanical engineers use, the EDA tools for circuit and chip design that electrical engineers use, and the CFD tools for fluid and thermal analysis that aerospace engineers use — All still rely on complex numerical solvers and physics simulations. These are computationally expensive and also require deep training and at times even a PhD to use effectively.

We believe the next generation of AI-powered tools will change that.

With reasoning capabilities built into the new AI models that solve math and physics, we can unleash engineers to design and build physical systems like —planes, buildings, circuits, chips, satellites—faster and better than ever before.

We’re eager to see founders build the AI-aided engineering tools that will drive this transformation as the new generation of Computer-aided Engineering.

By Dalton Caldwell

We would like to fund startups that have a useful need to employ a million workers in a way that uniquely needs humans to do the job and there will be no structural need for the job to be done with AI.

In the past, when massive technological change came about, people have ended up doing different types of work than they did in the past. For example, a lot of people used to be farmers, and then because of machinery a lot fewer people were farmers. The same goes for careers like elevator operators and typists.

Often the types of new careers that are created have better working conditions and are more helpful to humanity.  In this new AI driven world, these might be tools for more people to run their own local businesses, or be able to earn a living by providing services to other humans either online or offline.

Many AI futurists are unsure what the careers of the future are, and we want to fund founders with an answer to that question.

- Diana Hu and Jared Friedman

Many essential software tools work by simulating the world using known principles of physics and chemistry. Weather prediction, computational fluid dynamics for designing rockets and airplanes, and tools for drug discovery that predict the interactions of molecules — today many of these are based on running a full physics simulation of the world. These are very computationally heavy since they are solving complex multivariate mathematical equations.

It turns out that AI models are general functional approximators that can also solve and predict problems like these without needing to explicitly know about physics. This results in predictions that are much less computationally expensive and can be completed in minutes or seconds on much smaller computers rather than taking days/weeks and super computers.

We're interested in companies replacing existing simulations with ML-based ones, along with companies using ML-based simulations to open new markets currently unaddressable.

- Jared Friedman and Gustaf Alströmer

The US is now engaged in large-scale conflicts in several regions that threaten to change our world.  While the US has historically led the world in defense technology, the defense contractors it depends on have grown slow and inefficient, bloated by decades of cost-plus contracts.

SpaceX showed the world that a private space company could be vastly more effective than the publicly-funded United Launch Alliance. New companies that sell to the DoD like Palantir and Anduril are showing that the same thing is true for defense tech.

Silicon Valley was born in the early 20th century as an R&D area for the US military. Early Silicon Valley companies were largely funded by the DoD and played a key role in WWII by building military radar, code-breaking equipment, and components for the atomic bomb.

This decade is the time to return Silicon Valley to these roots.

- Jared Friedman

The UK became the world's richest country in the 19th century by being the workshop of the world. The US did the same in the 20th century. But in recent decades, we've given up this role. The hollowing out of US manufacturing has led to social and political division and left us in a precarious place geopolitically.

Bringing manufacturing back to America is one of the biggest areas of bipartisan agreement, and the CHIPS act which was passed in 2022 proves that the US government will put serious money behind this objective.

Other changes in the world have set the stage for a resurgence of US manufacturing.  New ML-based robotics systems will make it possible to automate far more, which will reduce the cost-of-labor arbitrage that pushed manufacturing to other countries in the first place.  Companies like SpaceX and Tesla have trained an entire generation of engineers in how to build an American company that makes physical products but operates like a startup.

We know this works because we've had experience working with some of the leading companies in this space. Astranis (W16) is building telecommunications satellites in the heart of San Francisco, in a building that used to build warships for the US Navy during WWII. Gecko Robotics (W16), based in America's old industrial heartland of Pittsburgh, builds robots that do industrial inspections. Solugen (W17) makes industrial chemicals from a large-scale plant in Houston.

- Jared Friedman and Dalton Caldwell

The cost to reach orbit is falling fast, having fallen over 10x since SpaceX’s first launch in 2006. A startup can now build and launch a satellite on just a seed round.

If you think about how many kilograms of payload get launched into space today, imagine how many will be sent up in one year, in five years, in ten years, and so on.

If we are entering a future with access to space being as routine and inexpensive as commercial air travel, shipping or trucking… what new businesses does that unlock?

Building a space company might scare founders by seeming too ambitious, but surprisingly, it is not necessarily harder than building a software company. YC has funded many space companies — Astranis, Relativity Space, Stoke, and many others  — and their success rate has been no lower, and maybe higher, than our other companies.

- Gustaf Alströmer

We have a fair chance of avoiding catastrophic climate change if startups offer commercial solutions to decarbonize society or remove carbon from the atmosphere.

We're interested in funding people building in these five top-level buckets: Energy Related, Science Required, Climate Adaptation, Green Fintech, and Carbon Accounting & Offsets.

The financial opportunity of building in this space is massive: an estimated $3-10 trillion in EBITDA will be up for grabs. Recent legislation will also significantly accelerate the existing market trends. The Inflation Reduction Act will spend an estimated $800B in the US alone over 10 years. To put that into perspective, it is almost 10x the $90B 2008 bill that catalyzed the US solar, battery, and EV industries into existence.

Y Combinator has funded well over 100 climate tech startups, and together they are worth over $10B. Building in climate tech is a once-in-a-generation opportunity.

- Nicolas Dessaigne and Diana Hu

Open source companies move more quickly than closed source companies. For developer tools, being open source is a powerful way to gain developer adoption. But it’s also a great way for startups to become mature and sell to enterprises a lot sooner. Ultimately, open source companies succeed when they become the standard choice for software engineers.

Very technical founders are at a strong advantage here, as the sales motion relies more on the technical merits of the project rather than strong sales tactics. It’s more natural for technical founders to talk to users who are engineers just like them, and they can iterate faster since they’ll get feedback from the open source community.

YC has funded over 150 open source companies including Gitlab (W15), Docker (S10), Apollo (S11), Supabase (S20) to name a few, and we want to fund more.

- Diana Hu

AR/VR as the new personal computing platform has been in the works for over a decade. But it’s only recently, with the launches of the Apple Vision Pro and the Meta Quest 3, that we are getting close. The user experience is getting better, rendering power is increasing, and hand/eye tracking has improved dramatically — but there’s still work to be done.

We would like to see a new set of startups building software on these devices, solving practical use cases that go beyond gaming. There are so many challenges still to solve with discovering best use cases, best UX/UI practices, and more — we are excited to work with founders that are at the frontier of this tech.

- Dalton Caldwell

As companies get larger they end up adopting some software suite to help run their business. This piece of software is widely known as an “ERP”, or Enterprise Resource Planning software. You can think of this software as the operating system that a business runs on.

ERPs are usually known to be expensive, painful to implement, and disliked by users, yet are absolutely necessary and the very definition of business critical to its customers.

We would like to see new startups that build software that helps businesses run. Ideally that software would be loved by its customers for its flexibility and ease of use. This type of software is so valuable and important that we can imagine that there is the opportunity for dozens of new massively successful vendors.

- Dalton Caldwell

If a developer has worked at a company with some amount of success, they have likely encountered tools or frameworks that were built by programmers at the company to help solve their own particularly painful or repetitive problems. These tools tend to have funny internal nicknames and for the most part never see the light of day.

When aspiring founders try to come up with new startup ideas they often don’t realize that the internal tools they had at prior jobs are a great place to get inspiration from.

We would like to see more startups created that are inspired by these types of homegrown tools, because it’s likely that if it's very useful at one company, it's very useful at others. The lineage of all software tools can often be traced back to something a programmer built to get their job done, and there is no reason to doubt this won't continue to be true.

- Tom Blomfield

In pretty much every old, large company, there are huge teams of people running manual processes. They’re hidden away from the end customer (hence “back office” rather than “front office”), so we don’t tend to encounter them very often in our day-to-day lives.

Often there was just enough ambiguity in these tasks that they were very difficult to automate before the existence of LLMs. In other cases, software engineers had simply never even come into contact with these processes, so automation had never seriously been considered. People continue to do this repetitive work in the same way they have for decades.

LLMs allow whole categories of manual processes to be automated in ways that weren’t possible until recently. Where there’s linguistic ambiguity or some amount of subjective evaluation needed, LLMs come into their own.

Examples might be:

• QA and compliance reviews of customers service chats
• Figuring out medical billing codes and insurance reimbursement at a hospital
• Assessing applications for a mortgage or a business loan
• Transaction monitoring, sanctions screening and anti money-laundering investigations
• Filing paperwork with the state authorities after a dealership sells a car

The problem for most software engineers is that they’ve never encountered these kinds of back office processes before. The biggest hurdle is often uncovering one of these processes to tackle.

- Harj Taggar

Enterprise software has a reputation among smart programmers as being boring to work on. You have to do sales and because each potential customer wants something slightly different, you end up writing bloated software to try and please them all.

But what if AI could change how enterprise software gets built and sold? The core of what every customer wants is the same — they just want it customized around the edges.

AI is good at writing code — especially when you give it an existing codebase to learn from. So what if instead of long enterprise sales cycles you just give customers a simple starter product and have them tell your AI how they want it customized? In the future, every enterprise could have their own custom ERP, CRM or HRIS that is continually updating itself as the company itself is changing.  

A product based on this premise would be highly disruptive to large incumbents, because now they can’t win by just copying you and adding another feature to their bloated software. Now they would have to completely change their whole conceptual approach to building software.

Maybe the AI will get so good at this that it can think up new types of enterprise software that don’t even exist yet. Building this AI would be an interesting technical challenge and if you’re excited about building AI that can code, enterprise software is the most profitable software to build.

- Pete Koomen

Most enterprise software requires customers to write a lot of custom code. Large vendors like Oracle, Salesforce, and Netsuite each support multibillion dollar ecosystems of consultants and independent software vendors ("ISVs") who help customize these products and connect them to other software on behalf of their clients.

This "glue code" — ETL pipelines, integrations, and custom workflows — is the dark matter of the enterprise software universe.

YC has funded successful companies in this space, including Zapier (S12), Fivetran (W13) and Airbyte (W20). These products help companies build glue code for common use cases.

By generating custom code for uncommon, company-specific use cases, large language models have the potential to eliminate the need for glue code altogether. We would like to see more startups working on solving this problem.