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.
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.
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.
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.
Continuous Learning_Startup & Investment
https://twitter.com/eladgil/status/1760314361544163824
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?
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?
Continuous Learning_Startup & Investment
https://twitter.com/eladgil/status/1760314361544163824
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
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
The Information
The ‘Magic’ Breakthrough That Got Friedman and Gross to Bet $100 Million on a Coding Startup
Former GitHub CEO Nat Friedman and his investment partner, Daniel Gross, raised eyebrows last week by writing a $100 million check to Magic, the developer of an artificial intelligence coding assistant. There are loads of coding assistants already, and the…
How do you discern who you hire?
“Hunger”
Michael Moritz
“Hunger”
Michael Moritz