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

과거 인터뷰 중에서 러시아 유학가서 돈이 없어서 학업을 중단했는데 친구들이 학교에 탄원서를 내줘서 학업을 이어갔었음.

좋아하는 일을 하면서 굶어죽은 사람이 있을까라는 생각을 했을 때 없다고 생각했음.

https://youtu.be/q95uYkdpH7k?si=FYgMVmD65V19VQav

20년 가까이 연기 생활을 하고 후배 연기자들을 위해서 장학금도 만들고 교육도 했는데.

연기라는 건 결국 다른 사람이 써놓은 것을 잘 연출한다는 건데 나다움이 뭘까를 고민하다가 철학을 공부함. 러시아에서 만난 친구가 그리워서 그리움에 그림을 그리기 되었는데 그 길로 그림을 10년째 그리고 있음.

그림은 본인이 대본을 쓰고 연기를 한다는 점에서 자유롭다는 부분이 인상깊다.

당나귀는 아무 짐도 지지 않는 것을 상상하지만 짐을 지는 것을 찾아나서더라. 다른사람이 뭐라하던지 상관하지 않더라.

나를 찾기위해서 하루하루 살아나가는 그의 이야기에서 삶이 여행이라는 것, 좋은 일만 있을 수 없다는 것.

일반적으로 정해진 길을 걸어가는 것이 아닌 나를 알아가기 위해서 새로운 길을 찾아나서는 것.

자유로움을 느낄수 있는 일을 찾아나서고 그걸 하는 것.

나다운 삶에 대한 고민과 실행이 느껴져서 좋았음.