# Abridge’s Shiv Rao: Taking On Microsoft To Cure Doctor Burnout With AI

Markdown copy of https://realalpha.app/v/W4_aRWsHogM/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Shiv Rao argues AI can make healthcare cheaper and improve care by handling work around the clinician-patient conversation.

- Show: Upstarts
- Hosts: [Alex Konrad](https://realalpha.app/people/alex-konrad/)
- Guest: [Shiv Rao](https://realalpha.app/builders/shiv-rao/) is cofounder and CEO of Abridge; practicing cardiologist.
- Published: Jul 23, 2026
- Length: 47 min

## The best minutes

- [31:32](https://realalpha.app/v/W4_aRWsHogM/?t=1892) [Abridge challenged Microsoft with a conversation-first vision](https://realalpha.app/s/abridge-challenged-microsoft-with-a-conversation-first/) (4 min)
- [27:00](https://realalpha.app/v/W4_aRWsHogM/?t=1620) [Clinical notes connect care delivery to healthcare payment](https://realalpha.app/s/clinical-notes-connect-care-delivery-to-healthcare-payment/) (3 min)
- [43:21](https://realalpha.app/v/W4_aRWsHogM/?t=2601) [A difficult IVF journey shaped Abridge’s mission](https://realalpha.app/s/a-difficult-ivf-journey-shaped-abridges-mission/) (3 min)

## Also worth watching

- [12:04](https://realalpha.app/v/W4_aRWsHogM/?t=724) [Abridge used UPMC funding and data to build its early AI](https://realalpha.app/s/abridge-used-upmc-funding-and-data-to-build-its-early-ai/) (5 min)
- [23:20](https://realalpha.app/v/W4_aRWsHogM/?t=1400) [Abridge started with providers to build a three-sided network](https://realalpha.app/s/abridge-started-with-providers-to-build-a-three-sided/) (3 min)
- [40:25](https://realalpha.app/v/W4_aRWsHogM/?t=2425) [Abridge routes work between in-house and frontier models](https://realalpha.app/s/abridge-routes-work-between-in-house-and-frontier-models/) (3 min)
- [17:00](https://realalpha.app/v/W4_aRWsHogM/?t=1020) [Abridge moved from a consumer app to enterprise for scale](https://realalpha.app/s/abridge-moved-from-a-consumer-app-to-enterprise-for-scale/) (3 min)
- [20:07](https://realalpha.app/v/W4_aRWsHogM/?t=1207) [IBM Watson lost trust by promising more than it could deliver](https://realalpha.app/s/ibm-watson-lost-trust-by-promising-more-than-it-could/) (2 min)

Skip the extended product-and-patient workflow walkthrough and general founder background, plus the remaining competition discussion.

## Full analysis: Shiv Rao says Abridge had to be 10x better in head-to-heads

- Rao said Abridge had to demonstrate it was 10x better in head-to-heads.
- The episode gives no test design or metric, leaving that performance bar unverified.
- Health-system buyers should test clinician time, note quality, and patient understanding.

Its 7 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/W4_aRWsHogM/#analysis), free with an account:

- [3:02](https://realalpha.app/v/W4_aRWsHogM/?t=182) Abridge treats the conversation as its platform
- [18:15](https://realalpha.app/v/W4_aRWsHogM/?t=1095) Enterprise became the route from app to scale
- [5:39](https://realalpha.app/v/W4_aRWsHogM/?t=339) Health systems grant access only after trust
- [27:16](https://realalpha.app/v/W4_aRWsHogM/?t=1636) Documentation links clinical work to payment
- [35:01](https://realalpha.app/v/W4_aRWsHogM/?t=2101) Rao says a conversation-first pitch challenged Microsoft
- [41:04](https://realalpha.app/v/W4_aRWsHogM/?t=2464) In-house models may help Abridge control economics
- [45:27](https://realalpha.app/v/W4_aRWsHogM/?t=2727) Rao’s IVF experience sets a patient-side standard

## Summary

Rao is a practicing cardiologist who co-founded Abridge in Pittsburgh in 2018. Before starting the company, he worked as a corporate VC for a large health system and split his time between cardiology and innovation at UPMC.

Shiv Rao says Abridge uses AI to handle administrative work around doctor-patient conversations, helping clinicians focus on patients while making visit information more useful to patients. He describes building trust with health systems as essential to getting access to data and reaching scale, and says Abridge moved from a consumer app to an enterprise strategy as generative AI advanced. Rao argues that its focus on the conversation gives the company a consistent purpose across clinical documentation, billing, and decision support. He also discusses competing with Microsoft, working with frontier AI companies, and how his family’s IVF experience shaped Abridge’s mission.

### Minute by minute

- [2:16](https://realalpha.app/v/W4_aRWsHogM/?t=136) **Abridge puts AI behind care.** Rao says Abridge uses AI to take on work behind the scenes so doctors and patients can focus on each other. The company started with notes, orders, and billing, and is expanding into clinical decision support; Rao says some clinicians save two or three hours a day.
- [6:33](https://realalpha.app/v/W4_aRWsHogM/?t=393) **Visit summaries extend the conversation.** Rao says Abridge can give patients summaries at an appropriate reading level and help them understand their care plans. Before visits, it can use health-system data to help clinicians understand a patient’s history and concerns. He says both patients and clinicians need to know the system is involved, and that health systems must trust Abridge with sensitive data.
- [10:35](https://realalpha.app/v/W4_aRWsHogM/?t=635) **Rao combined medicine and AI.** Before Abridge, Rao practiced cardiology and worked in corporate venture at UPMC, where he learned from founders and Carnegie Mellon researchers. He says Abridge started three months after the Attention Is All You Need paper and initially built with Transformer models, including BERT and BioBERT.
- [17:00](https://realalpha.app/v/W4_aRWsHogM/?t=1020) **Abridge moved from consumer to enterprise.** Rao says Abridge first released a direct-to-consumer app in 2019 to help people capture doctor conversations with permission and understand their care. After generative AI advanced in 2023, the company shifted its focus to enterprise health systems, where clinicians could extend the service to patients at scale.
- [18:43](https://realalpha.app/v/W4_aRWsHogM/?t=1123) **Healthcare distribution took years.** Rao says Abridge spent years testing business models and go-to-market approaches, with a working pre-LLM product by 2022. After ChatGPT arrived, health systems that had received earlier pitches called back; pilots and demonstrated results helped turn that groundwork into customer traction.
- [20:07](https://realalpha.app/v/W4_aRWsHogM/?t=1207) **Rao says Watson overpromised.** Rao says IBM Watson recognized healthcare’s large opportunity and labor needs, but got too far ahead of what its products could deliver. He argues that unmet promises eroded trust across the industry, so Abridge emphasizes that the products it demonstrates will be ready soon rather than selling only a vision.
- [23:20](https://realalpha.app/v/W4_aRWsHogM/?t=1400) **Healthcare sales require several buyers.** Rao says Abridge must address different needs across health systems, insurers, and pharmaceutical companies, while starting provider-first because that is where care is delivered. He describes long sales cycles and says health-system buyers weigh care quality, security and integration, and affordability. He also says the conversation produces a note that functions as a bill, and AI can help document care according to requirements from providers and payers.
- [29:19](https://realalpha.app/v/W4_aRWsHogM/?t=1759) **Trust sets Abridge's product boundaries.** Rao says Abridge’s North Star is to improve experience and clinical outcomes while saving time and money; he says the company will not put ads into workflows if they undermine trust. That focus leads it to integrate with multiple systems of record, including systems used for prior authorization and clinical-trial recruitment.
- [31:32](https://realalpha.app/v/W4_aRWsHogM/?t=1892) **Abridge challenged Microsoft head-on.** Rao says Abridge committed to top-down enterprise sales even though Microsoft was an established competitor and Abridge had about 50 people. He says the company differentiated itself by focusing on the conversation rather than only notes or billing, then had to prove its product in early head-to-head evaluations. Rao says those initial wins helped spread word of mouth among health-system leaders.
- [37:37](https://realalpha.app/v/W4_aRWsHogM/?t=2257) **Rao sees a coherent category emerging.** Rao says Abridge is expanding across health systems, payers, and life sciences, but sees the conversation as the thread connecting its products. He argues that its existing scale and trust let it pursue work that smaller companies could not, including exploring real-time payment with a payer.
- [40:19](https://realalpha.app/v/W4_aRWsHogM/?t=2419) **Model orchestration controls costs.** Rao says Abridge uses its proprietary data and user edits to improve its models, and that its NVIDIA partnership could support a team of 10 agents for every doctor. He says 40 to 60 percent of model outputs in a given week are probably driven by in-house work, with frontier models used selectively. He expects Abridge to partner with AI labs while investing in its own stack and model orchestration.
- [43:21](https://realalpha.app/v/W4_aRWsHogM/?t=2601) **Rao's IVF experience shaped Abridge.** Rao traces Abridge’s mission to his family’s three years of IVF with PGD after they learned of a rare genetic condition. He says he and his wife often left visits unsure what clinicians had told them, and that summaries missed what they wanted to remember. He wants Abridge to help patients, family members, and clinicians do their best.

## Transcript

Upstarts, hosted by Alex Konrad, with Shiv Rao.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[0:00](https://realalpha.app/v/W4_aRWsHogM/?t=0) My wife and I went through, she as the main character, me as the family member, three years of IVF with PGD. I'd go to every single visit with her, be there, and the doctor or the counselor would leave and we'd look at each other and be like, what did they just say? Like, what are we supposed to do? And I'm a doctor. Doctors and clinicians are burned out and their patients don't understand what they're being told, which means whatever it is, they're probably not going to do it. At the same time, the U.S. healthcare system is drowning in paperwork. To the tune of $1.5 trillion in admin spend every single year. Today, we're talking to a founder who's trying to bridge those two problems. His startup, Abridge, is building an AI operating system for healthcare. And in 2025, it was valued at $5.3 billion. More recently, Abridge announced partnerships with Eli Lilly and NVIDIA, and it works with more than 300 healthcare systems.

[0:50](https://realalpha.app/v/W4_aRWsHogM/?t=50) Founder and CEO Shiv Rao is a practicing cardiologist even to this day. And he says a bridge's values help define its positioning. All companies need to have one strong idea at least that they hold tightly to, and that's the North Star. Otherwise, especially in healthcare, you get blown in so many different directions. And like when you're at scale, as we are, there's also so many opportunities. There's a lot of low-hanging dollars. We want to make sure that we're going after the dollars that build trust. We're not going to put ads into that workflow that sacrifice trust. We are always going to be in service of delivering a better experience and a better clinical outcome.

[1:25](https://realalpha.app/v/W4_aRWsHogM/?t=85) We want to be deflationary. We want to save time, save money, save lives. Today on the Upstarts Podcast, we're going to talk about how to position your startup against the Microsoft giant, the three phases of AI companies in health, and how to create a category, then compete as it gets crowded too. Plus, why finding scale in the healthcare system is like eating glass and kissing a bunch of frogs. I'm Alex Konrad, founder and editor of Upstarts Media, and this is the Upstarts Podcast, our weekly show where we talk to startup founders who are punching above their weight to take on the status quo.

[1:57](https://realalpha.app/v/W4_aRWsHogM/?t=117) Shiv, thanks for joining us on the show. Thank you. Super excited to be here. Now, should I say doctor, Shiv Rao, because you are a practicing physician as well, right? Yeah, yeah, I guess so. Weekend a month, not much. This episode is brought to you by Rippling AI, the only AI built to give you full visibility into your startup and the ability to take action across every department. So Shiv, in a nutshell, what is Abridge doing for doctors, patients, you know, your customers? Yeah, we're using AI to bring doctors and patients closer together. That's it in a nutshell. We're trying to do all the work behind the scenes that would allow them to just focus on each other. And it allows us to save time, save money, and really save lives too.

[2:35](https://realalpha.app/v/W4_aRWsHogM/?t=155) Healthcare is a huge industry. It's like 18% of GDP. And we are building the AI layer for healthcare. And where we've started is healthcare delivery. Because when you think about the pain points there, clinicians are burning out. Doctors, nurses don't want to be doctors anymore. Patients can't understand what's going on, much less follow through to be the healthiest versions of themselves. And we're wedging into the moment, the sacrosanct moment between them to help both sides of that room. And where we've started is that conversation. We believe that it's computable and that you can build a platform on top of it. So we started out helping clinicians with clerical work like notes, placing their orders, doing accounting, doing billing, what they call revenue cycle. But now we're also helping with clinical intelligence, with decision support so that they can practice at the top of their license, as they say, so they can not only deliver a better experience, but also hopefully deliver a better clinical outcome. Should we be thinking about Abridge as a software business, as some sort of operating system? I know you resist the idea of being an AI scribe, but how should people think about the output of what Abridge is doing?

[3:44](https://realalpha.app/v/W4_aRWsHogM/?t=224) Yeah, I would think about us as an AI native company for healthcare. All of us have probably seen the doctor or we've had a family member who've seen a doctor at some point in the recent past and you're in that room and sometimes those doctors are stressed out. Sometimes they're not making eye contact. Their back is towards you because they're typing and doing all this other work that they're asked to do that actually distracts them from delivering the best possible care. And so with a bridge now, they're more present. They're making eye contact. They're fully there because they essentially have a team of assistants of agents from a bridge that are doing all of that other work for them behind the scenes. And some of that work is helping them write their notes. Some of that work is helping them with billing and accounting and revenue cycle. But some of that work is actually helping them now make better decisions. So when they're with you, they know that maybe your symptoms represent some rare disease and they should consider that. Or maybe you're a candidate for a clinical trial and they should tell you about that. Or maybe they should consider this therapeutic instead of another. And so again, the idea is

[4:47](https://realalpha.app/v/W4_aRWsHogM/?t=287) save time and we can demonstrate that. Some clinicians are saving two, three hours a day We want to save money for the healthcare system. Like, we have to figure this out as a country. You've seen those bommels cost. disease curves and healthcare is out of control. So we believe that there's no other industry where AI can kind of create the kind of impact that we can create in being deflationary. But also we want to save lives. We want to really, you know, help every single doctor nurse out there feel like a superhero. You work with over 300 healthcare systems, right? Including folks like Kaiser Permanente, Johns Hopkins. Yeah. Would this be something that the doctors are bringing into the system, or it's more top-down where a system is rolling you out across everybody?

[5:30](https://realalpha.app/v/W4_aRWsHogM/?t=330) And then would the patients in these systems be aware that a bridge is operating, or is it more behind the scenes? It's top-down. Everyone's aware. And for both reasons, I think, related to both, it's really all about trust. This industry, I think, in a very special way, moves at the speed of trust. And if healthcare systems don't trust your solution because you can't check off all the security boxes, the compliance boxes, if you don't have the people and the processes, then they're not going to give you that data. This data is really private. It needs to be kept very secure. It's everyone's healthcare data. And that's the context that we have to engineer in order to help in the workflow. The other piece is that we are in workflow. So this isn't something that's working after the fact. It's actually a part of those moments in the emergency room, in the operating room, in the clinic, or, you know, in the hospital. And so if everyone doesn't know that this is taking part, that this is helping everyone involved, it wouldn't really work. What is the experience like that you observe for a patient when a bridge is live versus when a bridge is not present? When a bridge is not present, patients, family members, maybe aren't

[6:45](https://realalpha.app/v/W4_aRWsHogM/?t=405) having a great conversation in the first place. They don't know what's going on with their care plan. They aren't getting a summary at the end that sort of creates a digest of like the most important parts of the conversation they just had and what they're supposed to do, their care plan. So they can't understand and they're like less likely to follow through. They're also having a moment with their clinician where they're stressed out and the clinician is also stressed out. And so that opportunity to really build a relationship isn't there we just did a keynote in new york city um some weeks ago and we told the audience that the most exciting inspiring demo that we could possibly do would just be people talking like this you know like no technology actually getting getting in the middle in the middle because the technology is in the background doing all of the work for them behind the scenes but in real time and in a way where both sides benefit even before the conversation but certainly during and absolutely after and the way patients are benefiting after is they we're creating these summaries that are written at the right reading level that can really help them during the conversation, better interaction. But doctors are benefiting even before they walk in the room

[8:03](https://realalpha.app/v/W4_aRWsHogM/?t=483) because we can help them understand what questions to ask, who this patient is, what their concerns are, why they called the clinic a couple of weeks ago. They can kind of come in and really instantaneously build rapport. How do you know that info? That's the context engineering piece. And that's why I think our go-to-market is so focused on enterprise. There's so many important hoops that we've got to jump through in order to build the trust of the CIO of a large health system who's going to allow us to ingest a bunch of data out of these systems of record, like the EMR, for example, that we can then engineer through these models to help the clinician know who this patient is, you know, what they should talk about. And then we need to create the note the right way on the other side. So I'm a patient, I show up, is this a scheduled visit? Is this a more urgent situation? Could it be both?

[8:52](https://realalpha.app/v/W4_aRWsHogM/?t=532) Yeah, it could be both. It could be both. I think like the beauty of it is like once we get through the enterprise trust sort of gauntlet, then we're in and clinicians can use us as they see fit. You know, they can use us in the urgent care. They can use us in an exam room. I think more than anything, all those clinicians can now feel like technology has their back. There's this team, you know, increasingly, like that's the concept is there's this team of assistants that are working for them 24-7, you know, doing all the work that helps them deliver a better outcome. So the physician knows me better once I come in.

[9:26](https://realalpha.app/v/W4_aRWsHogM/?t=566) Then do you say, hey, I'm going to be taking notes of this visit using a bridge? Yeah, that's what doctors say. So like, hey, do you mind if I use this note-taking solution? It's going to help me focus on you. You're going to get something out of this as well. You're going to get a better summary that really captures what we talked about. And I'm going to get a lot of help with a lot of the work that distracts me. Do patients ever push back that they're just like scared of AI in general? They're like, I'm not comfortable with this. And if so, what is your response? You know, not much. You know, like I'd say the Zika.

[10:00](https://realalpha.app/v/W4_aRWsHogM/?t=600) has shifted so, I think, profoundly over these last years. Like in 2018, for example, the zeitgeist was not meeting notes. It wasn't like capturing every Zoom call. It wasn't, you know, going back to these summaries. And I think GPT hadn't taken off. And like this idea of summarization wasn't as mainstream as it is now. We're in a new world. I think really the sky started to open up in 2023. With humility, our team can take a bunch of credit for normalizing this idea of AI in the conversation in healthcare over these last few years as we've scaled. But now when clinicians tell their patients about it, there aren't many questions. They sort of intuit already what this is all about. Okay. Let's go back in time.

[10:44](https://realalpha.app/v/W4_aRWsHogM/?t=644) Yeah. So you're practicing, you're making the rounds yourself. Then you start to think about sort of more companies and startups in this ecosystem, right? Yeah. Tell me a little bit about what you were doing in that run up to a bridge and what the sort of light bulb moment was if there was one. Yeah, yeah, totally. So prior to this, I was a corporate VC for a large health system. So we're putting a bunch of money into startups, putting a bunch of money into Carnegie Mellon. We started a machine learning and health program. And were you just already interested in startups? Yeah, yeah, yeah, totally. Had a first small little idea that I was pursuing with some MBAs a lifetime ago and got to learn a little bit about what that looks like, what that feels like. We had employees, we're building, like we got to an MVP, angels wanted to give us money. And at the time they told me I couldn't be half pregnant. If I was going to take the money, then I'd have to like give up being a doctor and just fully focus.

[11:33](https://realalpha.app/v/W4_aRWsHogM/?t=693) We saw that that did not work. You, you have stayed a doctor. Yeah. I stayed a doctor. But back then it was, uh, it pivoted you a little bit. Yeah. Yeah. I was like, I think right place, right time. And this large health system, UPMC in Pennsylvania was starting to put a ton of money into innovation and I got to sort of split my time. So it was like halftime cardiologist, halftime on the innovation and corporate venture side, got to sit on boards, learn osmotically from, from founders, from investors, and got to learn from professors at Carnegie Mellon, especially as deep learning was like starting to really take off in the mid 2010s. And, you know, even in terms of our timing of starting this company was three months after attention is all you need. I think that there's like three vintages of AI native company there are post-transformer paper pre-llm companies post-llm pre-agent companies and then now post-agent companies and the name of the game is to be the latest variant as fast as you possibly can. And it's not just in terms of the product that you're building, but also in terms of the way that you operate as a company. So we were post-Transformer.

[12:40](https://realalpha.app/v/W4_aRWsHogM/?t=760) We started with BERT and BioBERT and Longformer and Pegasus and T5. We were fine-tuning all of these Transformer-based models. And then all those skill sets turned out to be incredibly valuable. We just announced our relationship with NVIDIA, and we're building a foundation model at the conversational level for healthcare. But all those skill sets, we still apply to this day. But when LLMs came out, obviously, you refactor very, very quickly what the product can look like, what the technology can look like, how fast you can go, how fast you can scale. And now in this agent moment, it's just an entirely different game as well. And I think the companies that are going to win are going to be the ones that are super agile, because the ground's going to continue to shift on us we've talked to people on the show like may habib who was you know working with the transformer basically as soon as the paper comes out and then i've also you know talking to people like uh we both know the ceo of zoom eric yuan and eric was telling me recently that he actually beats himself up a little bit that if he's honest he didn't jump on llms and sort of the ai ai potential until that chat chpc moment early on were you betting a lot

[13:50](https://realalpha.app/v/W4_aRWsHogM/?t=830) the company on this technology and and i guess how much was a tech moat going to be you know key to a bridge at least in the early days yeah we placed a really big bet that this was going to work that somehow we were living we're at this intersection of moments in technology and also in health care and on the tech side like deep investments and annotations like when we started of the company, we were finding ways, super creative ways, because early stage startup, its rounds weren't what they look like today in those seed rounds. But we absolutely felt like outsiders, like we were trying to do something that in so many ways was going against the grain.

[14:31](https://realalpha.app/v/W4_aRWsHogM/?t=871) Number one, just in terms of the product, recording conversations, not normal in healthcare with HIPAA and all the other privacy issues, really like going against the grain. This thesis about AI and healthcare taking off, not, you know, had not taken off. And so this idea that the conversation could be computable, that you could build a platform on top, definitely something that investors had a hard time sort of grappling with. But then from a technology standpoint, really believing that over time this this this this would unlock and at the time you know pre-llm and and pre this moment that we're in the the name of the game was fine-tuning these models but it involved lots of really expensive annotations so we were getting really creative finding ways to get non-dilutive capital to employ a bunch of doctors and nurses to go through data sets and create annotations that could allow us to even get to the contours of an MVP. So yeah, it was a very different game, but I think it's helped us. Can you give me an example of what a creative way to find capital would look like? Yeah, it was like going to UPMC, for example, and trying to frame the challenge as research and trying to find a way to get them to foot the bill

[15:47](https://realalpha.app/v/W4_aRWsHogM/?t=947) on a data that they could help, that they could use as well, but that we would obviously find a way to leverage. As a corporate VC, I one day just went to my boss at UPMC, a president over there, and just said, like, I quit. I'm going to start this company. I didn't want it to be a spin out. And I just wanted to make sure that we can own and control that P&L so that we could, you know, control our own destiny. And I also said at the moment, like,

[16:14](https://realalpha.app/v/W4_aRWsHogM/?t=974) **Shiv Rao:** hey, like, I'm going to quit. I'm going to start this company. I'm obsessed with this problem with my co-founders and you're going to give us a check and you're also going to give us all your data. And then we're going to annotate as much of that data as possible and we're going

[16:30](https://realalpha.app/v/W4_aRWsHogM/?t=990) to build something. So it was a very YOLO moment, but we got there. And I look back at those emails, I was looking back at them recently and I'm not sure exactly how it all worked out, but it did work out. And they agreed to that? Yeah, they did. They gave the data? They gave us a lot of de-identified data in a responsible way. But without that data, we couldn't be an AI company off the bat. Without that data, we couldn't have attracted these professors or PhDs to work with us. So it gave us something that allowed us, even if we weren't capitalized as such, it made us operate like we were a deep tech company, even off the bat.

[17:05](https://realalpha.app/v/W4_aRWsHogM/?t=1025) When you guys started in 2018, what was the first version of the product? What was the wedge that you guys were providing? Yeah, totally. When we first put our seed deck together, there was one slide where there was a patient on one side of the slide and there was a doctor on the other side. And then there was our application in the middle. And the idea was we can serve both sides. And the barrier to entry on the doctor side, enterprise healthcare, not for the faint of heart. You have to go through a crazy idea maze. It's a labyrinth to sort of figure out how to get out there and actually get distribution and create value. So there was also a whole bunch of R&D sort of risk that we had to navigate as well. And we had the types of PhDs and professors and postdocs, and we had data sets that allowed us to sort of tackle that, but we didn't want to hold our breath on science challenge. And so we put a direct-to-consumer app out first in 2019, and that helped people all of us capture conversations with doctors with permission and the idea was to help them sort of be the healthiest versions of themselves help them understand what they talked about help them you know understand their condition but also

[18:10](https://realalpha.app/v/W4_aRWsHogM/?t=1090) understand their care plan and hopefully follow through on it but then in 2023 obviously the sky

[18:15](https://realalpha.app/v/W4_aRWsHogM/?t=1095) **Shiv Rao:** opened up on generative ai and we were able to rush into that moment and really sort of recognize that the center of gravity to really create scale here is the enterprise. And so now it's finally,

[18:27](https://realalpha.app/v/W4_aRWsHogM/?t=1107) you know, overnight success, I guess, like six or seven years in the making, but we go through the enterprise, we help clinicians, then the clinicians sort of extend to their patients and we can create impact at scale across a big swath of the country right now. What was the capability or the aha moment that was able to then unlock some of those first customers, you know, in the health systems? we were eating glass for many years and while we're eating glass like you just have to kiss a lot of frogs you have to a lot a lot of business models you have to like figure out a lot of go to markets there's that alex rompell quote startups get disruption when they get distribution faster than incumbents get innovation it's like every clayton christensen book and like one line and in health care finding a way to get distribution is is it's hard and so much of is like building trust and so much of it is finding a way to to de-risk this because it's such a risk averse industry so i'd say aha moments for us i don't know if there was one because there was like multiple years of us pre-selling and figuring out how to position ourselves and 2021 2022 especially 2022 we had the contours of a real product that could work that was pre-llm

[19:38](https://realalpha.app/v/W4_aRWsHogM/?t=1178) It was with all that, you know, all those annotations and fine-tuned models. And then GBT comes out and we recognize, like, what's about to happen. We very quickly refactored. And once everybody in healthcare as well had used ChatGPT and sort of understood, hey, there's something here we should take seriously. thankfully they called us back so all those calls that we made in 2020 2021 and 2022 like all that hard work ended up working out ended up paying off because we got to pilot once we piloted and demonstrated results it was like game on i remember in that pre uh chat gbt era um when i was at forbes uh our health editor and i looked a lot at ibm watson we were trying to understand why it had shown so much promise and gotten a lot of exciting kind of announcements to be doing some of this work to digitize and improve the health system.

[20:33](https://realalpha.app/v/W4_aRWsHogM/?t=1233) I even remember seeing a demo where they showed an x-ray and like scans and pre-LLM AI was going to help with a diagnosis. It feels like a lot of that wasn't realized until this most recent era. Why was that looking back? I mean, what they got right is healthcare, huge, huge market. And the market has a labor addiction. One out of five Americans works for healthcare in this country. Health systems can't hire people fast enough to get all the jobs done. There are tons of jobs that nobody wants to do. No human wants to do. So it's the perfect opportunity for AI to assist and to automate as much as possible.

[21:12](https://realalpha.app/v/W4_aRWsHogM/?t=1272) Our concept is like, do it to bring the people closer together. And if you think about it, the people who matter most in healthcare are all of us as patients and then our care teams. And if you can do that, you can refactor how healthcare is experienced, how it's paid for. I don't know that IBM Watson didn't recognize at a high level, huge market, huge opportunity.

[21:32](https://realalpha.app/v/W4_aRWsHogM/?t=1292) **Shiv Rao:** Where they went wrong is like they just got so far ahead of their skis in terms of what they were saying they could do and the products that they could bring to market.

[21:41](https://realalpha.app/v/W4_aRWsHogM/?t=1301) We all ended up in the industry paying for it because it eroded trust. Like you sell these huge stories and then you don't deliver, it really comes back to bite you. So now we're at this moment, though, like even at this keynote that we just had, it was like everything that we're going to demo for you is going to be ready for you in a matter of weeks. We're going to make sure that the industry understands like you don't need to like we're not shipping slides like we're shipping real product. And you're going to be able to on all your clinicians, your patients are going to be able to benefit from it in a way that like, you know, IBM could never promise.

[22:17](https://realalpha.app/v/W4_aRWsHogM/?t=1337) These days you can chat with AI about almost any business problem. rippling ai is built to actually solve them that's because rippling ai is built on your live workforce data that gives you full visibility into your startup and the ability to take action across every department. Say you're planning your next few hires. Just ask Rippling AI, what would it cost to add two more engineers this quarter? You'll instantly see a breakdown of comp ranges by level and location, so you can make the smartest hiring decision based on where your team has gaps and where the talent is. But it doesn't stop there.

[22:49](https://realalpha.app/v/W4_aRWsHogM/?t=1369) Rippling AI can draft the job wrecks, route them for approval, and kick off the hiring workflow. All you have to do is tap confirm and then get back to building. Don't settle for AI that's all talk. head to rippling.ai slash upstarts and get ai that turns insights into action that's r-i-p-p-l-i-n-g dot ai slash upstarts sign up for exclusive access today when you were going in to sign one of these big health systems would you try to talk to both the doctors in the halls and the sort of administrators at the top at the same time and would the message be different based on who the audience was. Yeah, totally. The personas are very different. So healthcare is a hard sell on the enterprise side. When you think about healthcare, I would think about health systems as one stakeholder. Another stakeholder is insurance companies. And then another one is pharmaceutical companies like the life sciences companies. If you can find a way to align across all three, you're transforming things. You can get access to new business models that actually benefit people you can find a way to be cheaper better faster and that's like that's what we're aspiring for and it's all starting to come together for us now finally now that we're we're at lever

[24:02](https://realalpha.app/v/W4_aRWsHogM/?t=1442) like we're at scale and now that we've earned trust we can we're starting to like thread the needle across all three in a health system itself that's the hardest part i'd say to go after this bigger picture there are companies or startups out there fully focused on pharma there are

[24:17](https://realalpha.app/v/W4_aRWsHogM/?t=1457) **Shiv Rao:** companies fully focus on insurance. But I think to create this three-sided network, to go after this bigger operating system, you have to start provider first. Because that's

[24:28](https://realalpha.app/v/W4_aRWsHogM/?t=1468) where the value is actually exchanged. That's where care is delivered. So going into providers really hard, sales cycles can be like 18 months long, historically. So when we were first raising capital, all these VCs would look at this industry and be like, oh my God, this is never going to work like do we have the patience for this and we had to keep telling them look it's going to be different like ai is going to make it different and it has been different these last four years haven't been hockey stick it's been more like telephone pull growth for us we're at scale now across i mean our health system logos represent 250 million Americans. That's how much scale that we've got, the ability to impact really everybody. But on the health system side, how do you do this? CMIO, that's the doctor sort of persona. And they just want the best tools that can help them deliver better care. They don't want to have to worry about what they call pajama time, doing clerical work after the kids are in bed, after they've eaten ants, like they're just sitting in their pajamas and they're writing notes and placing orders and making prior authorization calls or completing paperwork for clinical trials. That stuff just really burns you out over time. They're very aspirational.

[25:36](https://realalpha.app/v/W4_aRWsHogM/?t=1536) They want to save lives too. So clinical decisions board technologies they love. And then there's the CIO. The CIO is thinking, okay, is this another startup in healthcare that's going to die in two years? Like, is this a company that I can actually think about as being a part of our platform? Are they trustworthy? Do they have what it takes to scale? Can they integrate with their existing stack? Whatever that looks like. It's an ERP or a CRM or an EMR. And then there is the CFO. And the CFO is thinking, okay, healthcare looks like grocery stores on the provider side. The margins are that narrow. Are we going to be able to afford this company over time? If they're a platform, how is this going to be? Like, how is this going of impact our top line and our bottom line, you need to be able to sort of channel all those personas to be successful. Yeah. So that was kind of why I was asking is because I can imagine with your doctor peers, you're like, this will help with burnout. This will help you do more with patients. You know, you can resonate with them. I was thinking then at that high level system or insurance provider, is it you're saving money or you're more productive? So you're generating more

[26:44](https://realalpha.app/v/W4_aRWsHogM/?t=1604) revenue because you're seeing more patients like what is sort of the business value prop yeah yeah yeah yeah exactly so the secret that was hiding in plain sight we haven't tried to hide many balls by the way like like over these years we've been very straightforward for example you said you're not about notes and like scribing per se we've always focused on the conversation like this idea that that dialogue that conversation is the most human signal period but certainly in health care and that you can build a platform on top of it. But one of the products of that conversation is a note, and that note is a bill.

[27:18](https://realalpha.app/v/W4_aRWsHogM/?t=1638) And we were always very upfront about that. We told people and made clear for everyone out there that doctors don't get compensated for the care that they deliver. They get compensated. for the care that they documented, that they deliver. So if these notes are essentially bills, then providers care a lot and payers care a lot. They're the ones who are paying, like who are figuring this out with the employers and ultimately with people, with consumers. And so you can use then that as a way to build a bridge, you know, to bring them together. So the way it works now, for example, I see a patient and I don't want to know all the rules related to this patient's insurance plan and how that note should look.

[28:02](https://realalpha.app/v/W4_aRWsHogM/?t=1682) I don't want to know all the rules related to the cardiac MRI I'm going to order and what I need to document in order for the insurance company to know that this patient deserves the cardiac MRI, that it's clinically appropriate. I don't want to know all the rules related to Medicare, if this patient's on Medicare, and how I need to document so that I can get full credit that allows me to actually keep this Medicare patient healthier, like that allows me to send a care manager to their house to help them with their insulin injections. It's impossible to keep

[28:35](https://realalpha.app/v/W4_aRWsHogM/?t=1715) **Shiv Rao:** track with all these rules. AI can keep track of all those rules. So behind the scenes, ultimately in service of the patient, the AI understands what the health system wants to get into that note,

[28:46](https://realalpha.app/v/W4_aRWsHogM/?t=1726) what the payer wants to put into that note, what life sciences companies would want to put into that note. And we can thread the needle across all of them to get all the jobs done, to drive all those different outcomes. Does that make sense? Yeah, I think so. But obviously Epic existed. I wrote 10 years ago about Viva kind of trying to do the HRs. Was there something philosophically unique or different about the way Abridge was approaching this? And as you've created this category where we see some high growth startups now trying to compete with you guys, at least in some of what you do, where would you say a bridge is most differentiated or maybe philosophically different moving forward as well?

[29:23](https://realalpha.app/v/W4_aRWsHogM/?t=1763) I think the philosophy piece is really key. All companies need to have one strong idea at least that they hold tightly to, and that's the North Star. Otherwise, especially in healthcare, you get blown in so many different directions. When you're at scale, as we are, there's also so many opportunities. There's a lot of low-hanging dollars. We want to make sure that we're going after the dollars that build trust so we are in workflow trust is the most important thing we're not going to put like ads into that workflow that sacrifice trust we are always going to be in service of delivering a better experience and a better a better clinical outcome. We want to be deflationary. We want to save time, save money, save lives. That actually starts to make clear then for us what we need to focus on building and how we need to build that in what order. So when we think about how to get after saving time, money, and lives, it becomes clear that actually where we need to live is on top of multiple systems of record. And the EMR, for example, Epic is one of those systems, but there are multiple EMRs out there and we need to live across all of them. And then there's other systems. Like if we want to solve for prior authorization, then, and like helping the patient get the cardiac MRI

[30:38](https://realalpha.app/v/W4_aRWsHogM/?t=1838) faster and helping the clinician not have to go back and forth with an insurance company to make their case, that's another system of record we need to integrate with. If we want to solve for clinical trial recruitment, like help a patient understand that there's a trial that can save their life, then that's a different system of record that we need to integrate with. So it turns out that all these different systems have context that we need to extract. And then we need to make sense of it. And then we need to serve up the insights at the right moment in care delivery that can, you know, bend the trajectory for what happens next. It feels like you guys have, you know, this breakout speed you described as the telephone pole. I see the 300 health systems, the 5 billion plus valuation, the partnership with Eli Lilly recently. When you look back, Was there one sort of strategic door taken or not taken that really stands out as sort of critical to building the momentum today?

[31:36](https://realalpha.app/v/W4_aRWsHogM/?t=1896) I think getting conviction on go-to-market, really in 2022, 2023, we were walking through a one-way door. I think in some ways you could say that we were going to put all of our energy into top-down enterprise. And that meant we had to go after huge bosses. Like the first big boss we had to go after and compete against, I should say, was Microsoft. Microsoft had bought a company called Nuance for like 20 billion plus some years before. They have and had a product that was directly competitive. Microsoft, you know, no one gets fired for using Microsoft. Every CIO, speaking of personas, loves Microsoft.

[32:18](https://realalpha.app/v/W4_aRWsHogM/?t=1938) Everyone's always going to be a customer of Microsoft. so now we're this startup that at the time was like 50 people and we're saying we can go up against microsoft it was a yolo move like i think that's why most VCs tell their startups, go down market first, figure out PMF with a small clinic, build something that the end user loves, and then like swim over time upstream and get to that large system. And for a lot of reasons, including the amount of money we had in the bank, there was no other way. Because like, ultimately, you have to get to those big systems. That's where 75% of the doctors in this country practice.

[32:51](https://realalpha.app/v/W4_aRWsHogM/?t=1971) So if you're going to build a big, impactful, legacy-leaving generational company, you need to get there as fast as you can. If it didn't work out for us with those first couple of systems, we would have been dead because all those CIOs are in WhatsApp groups with each other. And when they see something they don't like, they tell each other the word of mouth is crazy. And then like you probably have to rebrand or recap and come back for your next shot on goal. Like you're just not going to get many shots, probably not more than one shot. Unless you do the second startup, you know, and then you get your second shot.

[33:23](https://realalpha.app/v/W4_aRWsHogM/?t=2003) Yeah, exactly. So on the show, we call something an upstart moment where you're most back against the wall, punching above your weight. It sounds like there might have been an upstart moment when you're trying to get these first couple systems that will be tastemakers on board. Was there a tactic or a strategy that you took that really helped make that happen? I don't know if it was like basically camping out at the hospital or something. What did you do as an upstart there? It sounds like very, maybe a bit too contrived, but like it was kind of being ourselves and being ourselves.

[33:57](https://realalpha.app/v/W4_aRWsHogM/?t=2037) **Shiv Rao:** It turned out being ourselves was like such a counter-positioning advantage against Microsoft.

[34:02](https://realalpha.app/v/W4_aRWsHogM/?t=2042) Just saying, hey, this is what our true north, this is what we're trying to build. We had this one slide in all of our pitch decks at the time that made clear our thesis about the conversation, that it wasn't about notes. It wasn't about orders. It wasn't about billing. It was about the conversation. And on top of that, we could do all these things over time. And that over time, what that meant was that clinicians could just talk. They could just like be creative. They could just focus on all the things they learned in med school and nursing school because we could get technology out of the way.

[34:31](https://realalpha.app/v/W4_aRWsHogM/?t=2071) like that there was something paradoxically profound about ai actually removing all the other technology or pushing it into the background people like resonate with that and microsoft wasn't saying that and i probably still don't believe that and i think that resonance allowed us to figure out how to build relationships with cmios and those doctors because they believed in this because like hey this is this is the story we all want They would go sell to CIOs, convince them, like, hey, we know you love Microsoft, but give this company a shot. Let's just see a head-to-head. And then we had to, like, nail those first few head-to-heads. And we had to demonstrate that we were 10x better. And then those same WhatsApp groups, they worked for us because everyone was like, hey, actually, give this company a shot.

[35:20](https://realalpha.app/v/W4_aRWsHogM/?t=2120) So if we were to boil this down for, you know, other startups to learn from, it's the vision and the audacity to sort of share that bigger vision. Say we're biting off this big, you know, effort. Yeah. And then you have to back it up immediately, basically. Yeah, yeah, yeah. You have to back it off. You have to be ready. And I think some companies might feel like you can play a longer game. And I think on some level you got to play really long games. But the world is moving so quickly right now. that it's hard to triage it's it's it's hard to say like this is tomorrow's problem anymore everything is kind of today's problem and you got to be so forward in terms of making clear why you're different and how you know you'll be a in health care how you'll be a like a 10-year a decades-long partner for them the urgency and the like the emergency like the the level of existential crisis you get to at moments in a company just force you to I think focus and do the thing that you have to do and that was our moment and we had so many moments we had like we had a series a1 and you know whenever there's a number attached to a round you know something went sideways and the series a1 was like you know we're trying to get to the b and we didn't have

[36:39](https://realalpha.app/v/W4_aRWsHogM/?t=2199) like b metrics yet we didn't even have a metrics yet like really venture capital was our business model for for years but we had what we had was a thesis we had great people we had like all of the signs that this could work but you know you can't time a market and so we're telling these investors like please be hope camels with us and we've got agency we can turn this market too but um we had no certainty around when and somehow we pulled a rabbit out our hat our a1 was like a 2x multiple still on the A, but that was definitely an existential moment. I'm sure that would have been really difficult if we couldn't pull that off with that investor.

[37:21](https://realalpha.app/v/W4_aRWsHogM/?t=2241) Does it feel like it's gotten easier now? Because obviously the business is growing fast, but one consequence of your success is that you do you. have a lot of competition. I know a lot of VCs have thrown money. The ones who couldn't get into a bridge have backed a couple other really well-funded startups. And then it looks like Epic might be overlapping with you more in the future. I think that there's more competition because in some ways, what we're building right now, at the same time, in parallel, it looks like the entire healthcare market map of solutions, at least on the health system side.

[37:55](https://realalpha.app/v/W4_aRWsHogM/?t=2275) But now we're connecting to pairs and life sciences. And so we're taking on multiple categories at the same time. But I think what still a lot of folks don't recognize is that we're not opening up a trench coat and like selling you some big like urinary catheter or like big data analytics platform. That's not what healthcare companies always have looked like. By the way, you go to the solutions tab and it's just like random stuff. Like nothing makes sense. There's no coherence. There's no like thread across all of them. There's a thread across everything that we do. And it's still a conversation.

[38:26](https://realalpha.app/v/W4_aRWsHogM/?t=2306) but now that we've earned so much trust i think that like we can start to extend even further we can do bolder things how many companies are out there who are actually figuring out a way to get a health system ceo on the same stage as like a payer system ceo like no one how many companies have gotten like life sciences companies like lily you know to say they're on board with this alongside those pairs and health systems like no one so i think in some ways we are still creating new a new category yet like we are we are still in a category of our own it's i think the most ambitious thing that anyone could go after but we feel like we have to because we've earned this right to scale you couldn't talk about real-time payments for example if you didn't have scale. At our keynote, we had the CIO of Cigna on stage talking about, hey, what if doctors saw a patient and they just got paid by us, like immediately? What if they didn't have to wait weeks or months? Almost every grizzled healthcare veteran out there with PTSD and scar tissue is going to say there's zero way that can work. Like this time is not different. The payer has to worry about flow. The provider has to worry about all sorts of other stuff. There's no way you can get

[39:46](https://realalpha.app/v/W4_aRWsHogM/?t=2386) to be true for sure yeah too good to be true but people have been saying too good to be true about all of our shots these last five years four or five years and and like we have a pretty awesome track record and now we're building this with these pairs and so we've been able to De-risk a lot of the things that we're doing. Yeah, I'd say on that level, like, you know, counter-positioning is a really good way to think about this too. What are things that only a bridge could do that startups couldn't do? Like, well, nobody's at scale, close to the scale that we've got. So the opportunities that we're going after, for example, with pairs has everything to do with that. If you're not in 300 health systems touching 250 million Americans, pairs don't want to build this with you. You mentioned your partnership with NVIDIA for this model. What does that unlock?

[40:29](https://realalpha.app/v/W4_aRWsHogM/?t=2429) We've always been fine-tuning, you know, mid-training now, we're post-training at scale because we have so many users, all those edits and adjustments, that's a proprietary data set that allows us to build a better product, ship a better thing. What the NVIDIA partnership, I think, represents is like, hey, we've got this incredible proprietary data set, what else can we do with it? And it means we can deliver on this promise of 10 agents working for every doctor 24-7. means we can make it economically viable i mentioned this before in another podcast like

[41:04](https://realalpha.app/v/W4_aRWsHogM/?t=2464) **Shiv Rao:** 40 on any given week 40 to 60 percent of our model outputs are probably driven by in-house work

[41:11](https://realalpha.app/v/W4_aRWsHogM/?t=2471) we still use the frontier models but we try to be very careful about which problems deserve those frontier models and so it's orchestration model routing evals are really the operating system for every single ai company out there and if you do a really good job of that you're going to deliver a better product but you're also gonna like be able to compete with your pnl as well so as these companies like open ai and anthropic go public what's going to happen on the token side on the cost side are companies going to be able to afford are they going to be able to pitch like the big story around all the products that they can deliver are they going to be able to concentrate as much value in their core product this is where like the the companies that can reach down lower into the stack the ones that can really own and control their destiny are going to be able to separate do you anticipate you'll be competing more with anthropic and open ai over time obviously they've invested in life sciences i think we're going to be partnering with them more and more and i think the moment that we feel like we're going against the grain of of what is absolutely incredible about this moment with how fast things are changing and how profound this platform shift moment is

[42:20](https://realalpha.app/v/W4_aRWsHogM/?t=2540) like that if that ever happens like we're screwed like whatever they do needs to feel like a tailwind but it does now like when they do new things Oh, awesome. We evaluate. We see where we should point those models. And then we're delivering a better product experience on some level. But we're also getting all of the edits and adjustments. We're getting the feedback loops. We can decide for which tasks we should end up like distilling or fine tuning or, you know, post training. We can be very careful and deliberate about it. So we'll always be riding, I think, with them. And we'll also be always investing in, you know, in our own kind of stack as well. And I think that probably it's going to be that sort of orchestration or portfolio approach to models that's going to win the day.

[43:05](https://realalpha.app/v/W4_aRWsHogM/?t=2585) Lastly, I know you recently raised a bunch of money. You've gotten more into revenue cycle management, these other areas of the stack. For the average person, for a patient who you want to maybe root for a bridge to succeed, how does this stay helping better outcomes, helping people versus maybe helping us get billed? Yeah, yeah, totally. I mean, we want to make healthcare cheaper and we want to improve the experience and we want to help people get better outcomes. And I think we're all aligned on that side too. But like some of the origin stories for the company, I think probably also make clear what our true north is.

[43:43](https://realalpha.app/v/W4_aRWsHogM/?t=2623) So I'm a practicing cardiologist. Nothing crushes my soul more than clerical work for sure. But where this all came from was actually like the patient side of the story too. My wife and I went through, she is the main character me as the family member three years of ivf with pgd we found out about a rare disease in our family so three code on repeat the more the repeats the worse the phenotypic manifestations and we already had a healthy child daughter knock on wood she's great she's awesome she's thriving she's amazing we wanted to have another child and so we went to genetic counselors and counselor after counselor they told us just roll the dice and get pregnant you'll probably be okay. They didn't think that codon repeat would become a problem just yet. They thought that was like generations away. So we get pregnant. We test the fetus. We find out that we have the worst of luck, a huge explosion of these repeats. They tell us that we should probably think hard about terminating the pregnancy. That was medical advice. We do that. Never easy. My wife still has PTSD from that, but it gave her all this grit and determination that we were going to sort of bring that baby back so we went through ivf with pgd where they can select an embryo that doesn't have

[44:57](https://realalpha.app/v/W4_aRWsHogM/?t=2697) that repeat and that's a hard Endeavor. We spent all of my overpaid corporate VC salary traveling the country from Pittsburgh to Santa Monica to New York City, cycle after cycle after cycle. It's a happy ending. RMA New York City, three years into it, put one embryo in and it split. So we have identical twin 10-year-old boys. But through those three years, and we're so lucky and we're so privileged and we could even afford that. And we don't take it for granted, but all those three years, I'd go to every single visit with her. I'd be there. And the doctor, the counselor would leave and we'd look at each other

[45:33](https://realalpha.app/v/W4_aRWsHogM/?t=2733) **Shiv Rao:** and be like, what did they just say? Like, what are we supposed to do? And I'm a doctor. I can't even remember. And then we'd get these visit summaries. They never really represented or

[45:42](https://realalpha.app/v/W4_aRWsHogM/?t=2742) reflected what we really wanted to remember. And maybe it was a metaphor the doctor used. Maybe it was the moment of empathy. Maybe it was like the, hey, just hang in there moment that never ends up in the medical records somehow. And then our parents would call us and we'd be like, we don't know what they said. And so finding a way to help both, you know, the patient and the family member, but also the clinician feel like they can do and be their best is really at the end of the day, what this is all about. And if we do that, we believe we'll end up building a generational company in healthcare. And we think healthcare is the biggest opportunity for AI. Well, Shiv, thanks for explaining your mission and what you're trying to do on the show.

[46:21](https://realalpha.app/v/W4_aRWsHogM/?t=2781) Yeah. Thanks so much, Alex. It's been a privilege. We'll be right back.