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.
COMMERCIAL OPEN SOURCE COMPANIES
- 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.
SPATIAL COMPUTING
- 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.
NEW ENTERPRISE RESOURCE PLANNING SOFTWARE
- 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.
DEVELOPER TOOLS INSPIRED BY EXISTING INTERNAL TOOLS
- 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.
EXPLAINABLE A.I.
- Diana Hu and Nicolas Dessaigne

Would you trust an AI to diagnose you? Would you swear that a model is unbiased? Or more simply, how can we be sure that a model doesn’t hallucinate an answer?
Understanding model behavior is very challenging, but we believe that in contexts where trust is paramount it is essential for an AI model to be interpretable. Its responses need to be explainable.
For society to reap the full benefits of AI, more work needs to be done on explainable AI. We are interested in funding people building new interpretable models or tools to explain the output of existing models.
L.L.MS FOR MANUAL BACK OFFICE PROCESSES IN LEGACY ENTERPRISES
- 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.
A.I. TO BUILD ENTERPRISE SOFTWARE
- 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.
STABLECOIN FINANCE
- Brad Flora
Stablecoins are digital currencies that peg their value to some external reference. This is typically the U.S. dollar, but it can be other fiat currencies, assets, or even other digital currencies. Their transactions are recorded on a digital ledger, usually a blockchain. This means they can be traded at any time of day between any two wallets on the same network, transactions settle in seconds, and fees are a fraction of what you see in traditional finance.

There’s been much debate about the utility of blockchain technology, but it seems clear that stablecoins will be a big part of the future of money. We know this because YC companies have been effectively incorporating stablecoins into their operations for years now – for cross-border payments, to reduce transaction fees and fraud, to help users protect savings from hyperinflation. This utility is so straightforward it seems inevitable traditional finance will follow suit.
In fact we’re seeing signs of this. PayPal recently issued its own stablecoin. Major banks have started offering custody services and making noise about issuing their own.
It all looks a bit like digital music’s transition from the realm of outlaw file sharing in the early 2000s to becoming the norm as players like Apple entered the market. Importantly, those major players were all outmatched in the end by Spotify, a startup founded during that same transition moment.
$136b worth of stablecoins have been issued to date but the opportunity seems much more immense still. Only about seven million people have transacted with stablecoins to date, while more than half a billion live in countries with 30%+ inflation. U.S. banks hold $17T in customer deposits which are all up for grabs as well. And yet the major stablecoin issuers can be counted on one hand and the major liquidity providers with just a few fingers.
We would like to fund great teams building B2B and consumer products on top of stablecoins, tools and platforms that enable stablecoin finance and more stablecoin protocols themselves.
A WAY TO END CANCER
- Surbhi Sarna
The technology to diagnose cancer at an early stage already exists. Since most cancers are now treatable if caught early enough, this technology would dramatically reduce cancer deaths if rolled out widely and affordably.
The technology we’re talking about is an MRI. Modern MRIs are sensitive enough to detect cancer masses as small as a millimeter.
Some companies are already having success on a small scale offering MRIs to patients for a high cash price. However, there is backlash from the medical community as MRIs also create incidental findings (or false positives), that cost our healthcare system valuable time and money to investigate.
For this to work, the world would need to scale up the number of MRI scans it does by at least 100x. Doing that will require innovations in the MRI hardware, the AI algorithms to interpret scans and reduce false positives, and the business models and consumer marketing to make it a viable business. We’re interested in funding companies looking to tackle this multifaceted problem.
While much exciting progress is being made on cancer therapeutics, finding cancer early enough for our existing therapeutics to be curative might be the opportunity with the greatest potential impact.
FOUNDATION MODELS FOR BIOLOGICAL SYSTEMS
- Surbhi Sarna
The vast majority of scientific innovation fails – either on the bench during early experimentation or while in clinical trials.
Foundation models built around the vast amount of data we now have will not only enable scientists to know what path to pursue much quicker than before, but have the potential to unlock new scientific approaches to disease. Foundation models built around text and images are enabling the next-generation of consumer products; we believe foundation models built around biological systems will do the same for healthcare.
We are interested in funding highly technical founders building foundational models from scratch in any part of biology or medicine.
THE MANAGED SERVICE ORGANIZATION MODEL FOR HEALTHCARE
- Surbhi Sarna
Private equity is consuming small and large private clinics all over the country. By the time more junior healthcare workers are paid, they only make a fraction of what they are billing. This causes them to be overworked but underpaid, as much of the revenue goes to overhead and the private equity owner of the clinic.
A new startup model has emerged as an alternative to PE ownership: the MSO (Managed Service Organizations) model.

The MSO model enables doctors to run their own clinics by (1) providing them software that can handle back office tasks such as billing and scheduling and (2) channeling patients to them.
These functions are largely what PE ownership provides. Doctors who are part of an MSO model can continue to run small, physician-owned practices while competing successfully with large, PE-owned conglomerates.
YC has funded several companies doing this in different verticals: Nourish (nutritionists), LunaJoy (mental health for women), Finni Health (autism care for children), and others.
We are interested in investing in this MSO (Managed Service Organizations) model across every vertical in healthcare.
ELIMINATING MIDDLEMEN IN HEALTHCARE
- Surbhi Sarna
The US spends more money per person on healthcare than any other developed nation, yet our patient outcomes are no better. Much of our spend goes to paying middlemen — which in our view includes everyone not directly providing care to patients.
A recent report on medicare spending on drugs found that 70% of spend went to middlemen (primarily PBMs, wholesalers, and pharmacies) and only 30% to the pharmaceutical companies who make the drugs. Similar dynamics exist in every other vertical — hospital care, medical equipment, insurance, etc.
There are many ways startups could attack these inefficiencies, from using AI to automate repetitive human jobs to exploring new and better business models for providing care. In the spirit of Jeff Bezos’ “your margin is my opportunity”, we believe it’s possible to build a highly profitable business and make the system more efficient at the same time.
BETTER ENTERPRISE GLUE
- 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.
SMALL FINE-TUNED MODELS AS AN ALTERNATIVE TO GIANT GENERIC ONES
- Nicolas Dessaigne
Giant generic models with a lot of parameters are very impressive. But they are also very costly and often come with latency and privacy challenges. Fortunately, smaller open-source models like Llama2 and Mistral have already demonstrated that, when finely tuned with appropriate data, they can yield comparable results at a fraction of the cost.
Moreover, as new hardware continues to be integrated into our phones and laptops, the prospect of running these models at the edge becomes increasingly feasible, unlocking a multitude of new use cases.
We are eager to support companies engaged in developing or fine-tuning such specialized models or creating tools to facilitate their construction.

MSFT seems to have increased Azure revenue by $5B or so a year via AI.

This makes its $10B investment in OpenAI seem small.

Most of the funding of LLMs by $ actually comes from big tech.

How long does this continue?

What are the new foundation model architectures? How do we think about this in an agentic world? Maybe there is more to learn from AlphaGo than chat?

The answer goes beyond the company’s claim that it will soon be able to furnish its customers with fully automated coding “coworkers,” not just an semi-automated assistant that finishes fragments of code-writing as GitHub Copilot does. The startup has created a new type of large language model that can process a huge amount of data, known as an input or context window.

Magic claims to be able to process 3.5 million words worth of text input, five times as much information as Google’s latest Gemini LLM, which in turn was a big advance on OpenAI’s GPT-4. In other words, Magic’s model essentially has an unlimited context window—perhaps bringing it closer to the way humans process information.

That could be especially helpful in a field like software development, where such a model would be able to ingest and remember a company’s entire codebase to generate new code in the company’s style.

Just as important, Magic also privately claimed to have made a technical breakthrough that could enable “active reasoning” capabilities similar to the Q* model developed by OpenAI last year, according to a person familiar with its technology. That could help solve one of the major gripes with LLMs, which is that they mimic what they’ve seen in their training data rather than using logic to solve new problems. (As for how Magic developed its LLM, this person said it took some elements of transformers, a type of AI model that powers consumer products like ChatGPT and coding assistants like Copilot, and fused them with other kinds of deep learning models.)

https://www.theinformation.com/articles/the-magic-breakthrough-that-got-friedman-and-gross-to-bet-100-million-on-a-coding-startup?rc=ocojsj

How do you discern who you hire?

“Hunger”

Michael Moritz