Balancing AI Expertise and Industry Acumen in Vertical Applications

**Nikhil Buduma** (0:00)
Coincidentally, we somehow found ourselves, while building in healthcare, literally in the middle of this most recent wave of AI progress, we ended up becoming one of the first teams to catch wind of the Transformer architecture. We ended up putting it in production across a wide variety of use cases and learn firsthand all the challenges that come from trying to put Transformers in production. And then from 2017 to 2020, several of our closest friends, some of whom actually were our literal housemates, proceeded to work on some of the most important projects inside of OpenAI and subsequently Anthropic, from PPO to the Scaling Laws paper, to GPT-2, to GPT-3, to RLHF. And across the span of those three years, as you hear your closest friends sort of talk about all the experiments that worked and all the experiments that didn't, we just started to see a lot of the puzzle pieces come together. And a lot of the problems that we previously were trying to solve for our own clinicians that felt like we were hitting hard technology ceilings started to feel like, hey, they might be tractable today, or they might be tractable over the next couple of years.

**Derrick Harris** (1:07)
Hi again, and thanks for listening to the a16z AI Podcast. I'm Derek Harris. And this week, we have a discussion between me and Nikhil Buduma, who's the co-founder and chief scientist of an AI-powered healthcare company called Ambience. Ambience aims to improve the lives of both clinicians and patients by producing detailed reports across a number of medical specialties and tasks. prior to Ambience, Nikhil co-founded another startup called Remedy Health, and also authored the Fundamentals of Deep Learning book for O'Reilly in 2017, as well as a revised version in 2022 In this discussion, we talked through all of this, beginning with his childhood experience with chronic heart conditions that cemented his commitment to working in healthcare early in life. But we also framed the discussion through the lens of advances in AI, to which Nikhil had a first-row seat. Google AI leader Jeff Dean was an early investor in both of Nikhil's companies, and he was roommates with some of the early OpenAI team who led the research on some important advancements in the field. So stick around for an enlightening discussion about how changes in artificial intelligence have reshaped the way companies are built, how teams should think about adopting and applying AI models in their own products, and some best practices for building lasting companies in complex vertical industries. As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures.

**Nikhil Buduma** (2:50)
I actually started off my career falling into healthcare first.
I was a pretty sick kid growing up. My parents immigrated from India. Didn't really understand how the healthcare system here worked. I had several heart defects when I was born. Ended up with a pretty complicated recovery process since then. And so I was constantly inside of the healthcare system as a kid. My parents ended up running into a lot of financial issues, sort of trying to just manage the cost of my care. So for me, it was a foregone conclusion growing up that I was going to be in healthcare in some way, shape, or form. And so I was on this sort of traditional track of becoming an MD, PhD when the first exciting wave of machine learning kind of struck. So I spent about eight years doing research at Sounds of State and Stanford University. And in 2009, across campus, there was a group run by Andrew Ng in the computer science department. They were basically showing how they could use GPU compute to accelerate these large machine learning models. I think they had trained a hundred million parameter deep belief net that would normally take them weeks to train and got it to actually work in a single day. I didn't really fully appreciate it then, but over the next couple of years, we started to see a series of breakthroughs that I think kind of gave rise to what we call deep learning today sort of culminating in 2012, GPU compute being applied to convolutional neural networks and sort of the leapfrog that Alex had on all the ImageNet benchmarks. And so sort of being in biology and in medicine in parallel, I personally just became incredibly interested in what these techniques meant for medicine. I ended up co-founding a company. It's working in a different part of the health care stack now, but back in 2013, we were trying to use ConvNets to power these devices that would do low cost malaria detection in the field. And then I started to write a series of blog posts with the goal of teaching more researchers in biology and medicine how to use these techniques for their own work, and eventually those blog posts got picked up by O'Reilly. It turned into one of the early textbooks on machine learning and deep learning methods. And so that's sort of how I fell into machine learning and AI. I ended up meeting my co-founder Mike at MIT around this time, and he has his own incredible story sort of navigating the health care system. He fractured his back, was told he may never be able to walk again because he was originally misdiagnosed with a sprain when he actually had a fracture. We were just obsessively thinking about sort of the intersection of machine learning methods and health care. I ended up spending a lot of time with some of our close mentors at the time. Jeff Dean has been an investor in both of our companies over the last decade, but back at that time, he was building the early days of Google Brain and Google Health. And then while I was in grad school, I got the call from Sam, and he was at that point in time building the early days of OpenAI. There were still a hodgepodge of researchers building out of Greg's apartment in San Francisco. I think it was obvious to us that the tech at that time was really early. But if you think back to all the major technology trends that were in the zeitgeist at that time, I think both Mike and I just felt incredibly strongly that if we could create highly capable general purpose AI models, that would have the single greatest impact on how we work as a society. And we didn't know if it was going to take five years or 10 years or 20 years for all the pieces to come together. But when they did, based off of our personal experiences with the health care system and what the health care system has meant for a lot of our loved ones, we just felt like health care medicine was going to be one of the most meaningful opportunities for this class of technologies.