YouSaid · the spoken record

Omar A. Khokhar

lines on the record: 70
first: 2026-03-04
most recent: 2026-03-04
sittings or episodes: 1
sources: podcast

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“The decision traces, and so fundamentally, you have to rethink the architecture of how you actually collect this data in the first place to even make intelligence. Be specific to the domain. And so that's another sort of big problem in the data layer, but there's a whole sort of range of data layer problems that you have to solve. Then in the middle, I think we have a really big challenge from a clinical intelligence standpoint around defining quality.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“All that. Yep. 100%. So you're like, even just the problem of across EHR instances being able to pull out the context from systems of record in and of itself was an unsolved problem. We started building ambience that we solved, right? And so the ability to read out of any part of an EHR instance, including the data warehouse underneath it, and then using that and having a groomed layer to be able to then build intelligence on top, that was an unsolved problem. I think another thing that a lot of folks don't fully appreciate is that the most valuable thing for AI companies is decision traces. Most EHRs are built on mutable data structures, which means that you inherently destroy”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“These models to be effective in healthcare. And you can break it up into a couple of different categories. And it's going to start with, do the models even have the right context to begin with? I think you probably have a deep appreciation for this, just how messy it is to even build out the right infrastructure to be able to pull context out of systems of record hidden behind Fire APIs and proprietary APIs, the data models are so messy and inconsistent, you'll get specific standards where it's sort of like a concept of a specification. And so you have to like.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a fascinating question. I might answer it a slightly different abstraction, which is that from our experience, AI clock speed is fundamentally different from product clock speed. And part of the reason I think about it that way is there's several aspects to intelligence that do get better with every generation of foundation model. And in many ways, I think Kind of frightening about building in this world is the capabilities are evolving so quickly that, and we use this word constantly inside of ambience. We're building in a world where the floor is lava. You have to have the kind of organization that can. Being said, I think what we find is that there is still a massive last mile problem.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“It's just not good enough. And even the ones that you use it, they're using it for 20, 30, 40 percent of their visits. On the other hand, I think for us, we work with several large academic medical centers and we're at the scale now where 75 plus percent of all their clinicians use ambience every single day in clinic. They're using it for 80 plus percent of all of their visits, which is a completely different sort of opportunity and scale. And so I think that's sort of how the market has shook out in many ways, which is there's a high complexity part of the market that's really, really hard to serve. But if you can serve it, it's hard for others to compete in.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And so it's much easier to serve. And so, my guess is in general, you'll see a proliferation of lots of players trying to compete over the mid-market. For instance, I think, you know, you've got the EHRs in that space that are trying to reinvent themselves to be AI first companies. You've got AI scribes going after that space. And I think they're quickly finding out that to create enough value, they have to own more and more of the stack for these organizations. And then I think the enterprise segment of the market is starting to shake out now where I think the reality is there's only a couple of players that have even had a right to play in that market. And most of them outside of ambience have really, really struggled to actually meet these organizations at the complexity at which they need to practice. So for instance, we'll have so many organizations come to us. They've rolled out something and only 15, 20% other doctors actually use it.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Will look fundamentally different in the next three to five years. Like today, even if you compare the experience today using something like ambience versus what it was a year or two years ago, it already looks so, so transformatively different. But the challenge is that when you're operating in these really complex clinical settings, the job of a primary carer physician who's primarily trying to manage multiple chronic diseases versus the needs of a subspecialized oncologist, they're so different. They're making different decisions. They have different workflows inside of the EHR. They're looking at different data sources. And so to be able to build the kind of infrastructure that serves the broad range of medicine is really, really hard. On the other hand, the moment you move to sort of the mid-market or the small sort of three, five, ten doc practices, the complexity drops dramatically.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Is there a demand for technology that can do this? The question really was for a long time can technology even serve this need well enough in the first place? And I think as the market sort of evolved, there's almost a bifurcation between what I think are largely the high complexity, high value set of use cases and the low complexity, low value set of use cases. And so for us, we found a home specifically in some of the largest IDNs in the academic medical centers. And part of why we think that's a very interesting place to play is just the vast breadth of medicine that's being practiced in those environments and the depth across that breadth is extremely challenging to go tackle. So you think about if you're trying to build our view is that the practice surface area for the clinician”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe we would take a step back and we think about why this is so compelling to the organizations we work with. The reality is like you look at a doctor's workday today, there's not a lot of joy. The practice of medicine anymore, you can't look your patient in the eye. You're constantly feeling like you're running behind. It feels like you went to school to take care of people and to actually grapple with the clinical medicine in the room, but most of your time is spent doing all sorts of other stuff, right? You're like searching through the electronic medical record to find information. You're writing notes. You're navigating these thousands and thousands of coding and billing rules that are different by type of payer, different by region. They change year over year. You're like, I didn't go to school to do this. And so I think in many ways the organic pull from the market was there for a very, very long time. And it wasn't a question of, hey, can”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think when we started our previous company remedy, we were just extremely sober to recognize that we had very little empathy for what it's like to sit in the shoes of an operator of a health system. And to truly understand the entirety of the context and the job to be done and the opportunities, we felt very strongly that we had to hold the responsibility ourselves first and foremost. And I think that not only did give us the flexibility to be able to have the rapid iteration cycles inside of the company to see what worked and what didn't work. But I think it also set the foundations for if we ever did start a platform company, what would it like feel like to sit in the shoes of the CEO looking at one to three percent profit margins, looking at a workforce and burnout crisis amongst your staff, having to navigate the complexity of an IT stack and having to work with EPIC and thinking about like how do you even make decisions in that world? And so in many ways, I think going through the experience of running a care delivery asset.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And you guys kind of did the reverse thing where a lot of startups start by building technology that gets sold into the healthcare markets in particular. And then realize how hard it is to sell technology to providers, you know, for instance, and then decide to go full stack and say, we just want to eat our own dog food. We want to capture more value associated with the services delivered on top of a technology platform. You guys did the opposite where you started full stack and then decided that you wanted to do a platform company in your next play. Talk to us about how you got to conviction that that was the right move.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“The iteration cycles were insane, right? So today people are like, well, I could pull an LLM out of the box and It kind of works out of the box for certain use cases. And then the moment you move into really deep domain verticals is when you have to really invest in post training. Back then, honestly, you had to rethink how you did pre-training, how you did post training, the iteration cycles like a year, and you're building deep, custom bespoke data sets and all the sort of like ML operations infrastructure that you had to build for self-driving cars you were pulling over. So it was a very different world to get these architectures to work and also very different scales, right? We were talking about”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“In those early days, when you saw the performance of those early transformers, today when GBT3 first came out, everyone was like, oh, it sucked at healthcare, right? There was still a ton of hallucination risk. It wasn't trained on a lot of the proprietary data, sort of not on the internet, so to speak, about medical practice and guidelines and all that. And then obviously it's improved drastically over the last few years. But back then, was it super bad or how did it actually perform in a healthcare setting? And when you were applying it to your first startup, Like, how long was the pull of post hoc training and fine tuning that you have to do to get it to actually work in a clinical setting?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think Mike and I were coming to this conviction that we were on the precipice of some sort of exponential here. And when you see the people you respect the most and some of your closest friends go risk their reputations to raise billions of dollars to scale up these architectures, like something is happening here.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think what's interesting is my co founder Mike and I took a step back and we said we're most excited about thinking about how do we take these technologies and apply them to healthcare. And so we actually did the crazy thing, which is we started a care delivery asset and we started to not only run that practice, work with doctors, implement an EHR, but at the same time we were taking all of the techniques that we were seeing at the research labs and bringing them into the practice, right? So 2017, Transformer came out. We were using the Transformer in production to ingest claims data, predict risk of hospitalization. And I think during this time, sort of post transformer, we just saw the entire research community sort of just collapse on this architecture because it was so clear that it solved many of the challenges around language modeling and reasoning in a way that we didn't quite see the previous architecture's work. And all of a sudden, scaling laws and RLHF, we saw a demo of GPT-2 over the dinner table.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“I think at a certain point got called out to Silicon Valley to hang out with a lot of the early researchers in the space. We were all hanging out in Greg's apartment for a little while and that group became OpenAI. In that time, I think there was a belief that in general unsupervised learning and reinforcement learning was going to give rise to general intelligence and general reasoning models, but no one really had a clear sense of what that was going to look like. You had a group of people working on OpenAI Gym, trying to get RL agents to walk and simulated environments. You had a bunch of people working on variational autoencoders. And I think the hard part of doing any kind of work in that time is every six months something would happen and an entire sort of branch of deep learning, which is sort of like collapse, right? Like we went from thinking deep belief nets were going to be the architecture of choice to no one cares about deep belief nets and like about 12 months.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, the last 12 years in AI have been kind of insane. And there's almost like a few different arcs. The story, but you know, I started my career thinking I was going to be an MD PhD, and then I ended up dropping out mostly because I'd lost a mentor to a medical error. And, you know, thinking about Net benefit to the world for another MD PhD versus thinking about how do you solve these problem systemically. And I grew up here in the Bay Area and sort of was embedded at Stanford. And so you might remember when Andrew Ng was building deep belief nets and scaling up those models of GPUs. This is like 2010. I think after that, I started writing a book on deep learning. And”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“I think this is the first time where there's hope. There is a pathway to doing more with less. There is a pathway for the job of being a clinician, being a nurse, to be fulfilling one. There is a pathway for the experience of a patient not being as confusing and full of despair as sometimes it is When Nikhil Budama was a PhD student at Stanford, he lost a mentor to a medical error.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“When I was deploying my own software back in the day, doctors would grow, and they'd be like, oh, yet another tool? Like, why are you stuffing this down my throat? The delta between the magic of the tool that they're experiencing in their consumer lives and what they do in their work has for the first time narrowed just even a little bit to a point where it's just fun really changed the nature of how they view technology.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“We live in a world where the demand for health care is just rising so quickly. We have 10,000 people aging into Medicare every single day, and we just can't train doctors fast enough to take care of all these people. The practice surface area for the clinician. Will look fundamentally different in the next three to five years.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source