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Abridge’s Shiv Rao: Taking On Microsoft To Cure Doctor Burnout With AI
Shiv Rao argues AI can make healthcare cheaper and improve care by handling work around the clinician-patient conversation.
Upstarts · Alex Konrad
- Shiv Rao is cofounder and CEO of Abridge; practicing cardiologist.
The best minutes
- 31:32Abridge challenged Microsoft with a conversation-first vision4 min
- 27:00Clinical notes connect care delivery to healthcare payment3 min
- 43:21A difficult IVF journey shaped Abridge’s mission3 min
Also worth watching
- 12:04Abridge used UPMC funding and data to build its early AI5 min
- 23:20Abridge started with providers to build a three-sided network3 min
- 40:25Abridge routes work between in-house and frontier models3 min
- 17:00Abridge moved from a consumer app to enterprise for scale3 min
- 20:07IBM Watson lost trust by promising more than it could deliver2 min
Skip the extended product-and-patient workflow walkthrough and general founder background, plus the remaining competition discussion.
Full analysis · 6 min read
Real Alpha · Nov 8
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.
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Read the rest of the analysis
7 sections, the 3 other voices it tests Rao against, and what it means for you. Every episode we cut gets one.
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Abridge treats the conversation as its platform
Rao’s central product claim is broader than automated note-taking. He treats the visit conversation as input for documentation, orders, billing, and decision support.
That makes the product boundary coherent, but not yet evidence of every downstream benefit. Rao says the conversation is computable and clerical work distracts doctors.
One conversation could, in our reading, feed the outputs he lists. He does not say clinicians re-enter that context across systems.
For example, one visit can produce a clinical note and a patient-facing summary. That lets one capture serve clinicians and patients, rather than only clearing a doctor’s inbox.
But the promised clinical upside is less evidenced here than the workflow thesis. Rao said some clinicians save two or three hours daily, without giving a sample or measurement method.
Time saved is measurable. Better decisions and outcomes need separate evidence before becoming budget assumptions.
Our read
Abridge’s strongest case is workflow unification, not proven clinical benefit. Buyers should demand separate evidence for saved time, comprehension, and care outcomes.
Enterprise became the route from app to scale
Abridge began with a direct-to-consumer app in 2019, then shifted toward enterprise after generative AI arrived in 2023. Rao called health systems the center of gravity for scale.
That reverses the common software instinct to win individual users, then move upmarket. In healthcare, enterprise approval can enable data connections and place tools inside existing care workflows.
The trade is demanding procurement in exchange for distribution. Rao said provider sales cycles can run around 18 months, historically.
But that reach is a footprint, not proof of adoption, active use, or clinical impact. Founders should separate signed access from repeated use and attributable results.
Alex Lieberman said on Latent Space in October 2026 that vertical agents face enterprise complexity and margin pressure. That is a category warning, not evidence about Abridge’s delivery costs. Rao discusses trust and integrations here, not the cost of enterprise rollout.
Our read
Enterprise-first gave Abridge a route to scale, but its reported footprint is not utilization. The operating burden remains part of the product.
Health systems grant access only after trust
Rao’s answer to procurement friction is trust, backed by privacy, security, compliance, people, and processes. Health systems will not provide sensitive records unless those conditions are met.
That makes enterprise readiness part of the product, not sales polish. Abridge needs medical-record context before a model can prepare clinicians or return a useful note.
UPMC provided early funding and de-identified data, Rao said. That access helped Abridge build an MVP and recruit technical talent, while retaining control of its P&L.
Rao said doctors explain the tool before using it. Patients are meant to receive a useful visit summary, so the workflow is not invisible to them.
But he reported little patient pushback without giving a refusal rate or patient sample. Procurement readiness is visible, while patient acceptance remains unmeasured.
Our read
Trust is both a prerequisite and a distribution advantage. A buyer should examine the safeguards and patient experience, not treat approval as proof of either.
Documentation links clinical work to payment
The economic bridge in Rao’s account is documentation. A note can carry information required by providers and payers, while AI applies rules clinicians cannot memorize.
This creates a buyer case beyond clerical relief. For example, a cardiac MRI order may need documentation showing clinical appropriateness under a patient’s plan.
Take Rao’s Medicare example. He said documentation may help qualify a patient for care management, connecting administrative rules to a clinical service.
The same mechanism can serve reimbursement rules and patient care at once. The episode gives no quantified effect on denials, costs, or patient outcomes.
Karan Singhal, OpenAI’s health AI head, made a related point on a16z Podcast in October 2026. He said lower intelligence costs alone do not cut healthcare spending without valuable workflow adoption.
So that is a parallel to Abridge’s case, not proof that its deployment saves money.
“and that note is a bill.”
Our read
The note-to-payment link gives Abridge a concrete enterprise use case, but it also creates competing incentives. Buyers need evidence that better documentation improves care as well as billing.
Rao says a conversation-first pitch challenged Microsoft
Rao said Abridge chose top-down enterprise sales in 2022 and 2023. That meant confronting Microsoft and its Nuance product. It was a distribution bet, not a narrow feature race.
The pitch framed conversation as the foundation, with notes, orders, and billing as outputs. He said the contrast resonated with clinicians and medical leaders.
Rao said Abridge had to win head-to-head comparisons. He described a 10x standard, but gave no test design or metric on the show.
That missing denominator matters. Tenfold improvement in what, for which specialty, and under what workflow conditions would turn persuasion into evidence.
He also described CIO peer groups as influential, so early pilots could accelerate referrals or close doors. Concentration makes customer experience unusually consequential.
The story shows how a clear position can open a comparison with an incumbent. But buyers cannot verify the performance claim from this episode alone.
“we had to demonstrate that we were 10x better”
Our read
Abridge paired a distinctive vision with a high-stakes enterprise sale. The next proof is measured results against the buyer’s existing workflow.
In-house models may help Abridge control economics
Rao said 40 to 60% of weekly model outputs are probably driven by in-house work. The hedge and denominator matter, since this is output share, not cost share.
He says frontier models remain useful, with routing and evaluations guiding which tasks merit them. In-house work may help economics alongside product quality.
Marjorie Janiewicz said on MTS in October 2026 that task-specific models can lower customer costs. Her point supports specialization, not a proven margin result for Abridge.
Throughput share alone cannot show gross margin or reliability. The episode gives no per-task cost, model comparison, or resulting unit economics.
Buyers should track total cost per completed workflow, including review and rework. Model choice belongs inside that accounting.
Our read
Rao describes a plausible path to better unit economics, not a demonstrated margin improvement. Buyers should measure cost and performance at the task level.
Rao’s IVF experience sets a patient-side standard
Rao’s account of three years of IVF and PGD explains why he treats patients and families as users, not bystanders. After visits, he and his wife struggled to recall advice.
He said visit summaries did not preserve what mattered, including metaphors and moments of empathy. That is a different quality bar from a complete transcript.
The origin story gives the mission emotional credibility, not product proof. It does not establish that Abridge’s summaries improve adherence, comprehension, or outcomes.
A buyer can test the gap by asking patients what they remember, plan, and still find unclear. This also sharpens product prioritization.
If billing automation expands faster than the patient-facing layer, leaders need evidence that the original promise remains present.
Our read
The patient story makes the intended benefit concrete, but not yet measurable. The product should be judged on what patients understand and do after the conversation.
What it means for you
- Baseline clinician time. Measure documentation time before rollout and after adoption. Report the share of clinicians represented, rather than relying on isolated examples.
- Audit note to bill. Sample notes against payer and health-system requirements. Then track corrections, denials, and follow-up time.
- Test patient recall. After visits, ask patients what they understood, what they plan to do, and whether the summary captured it. That tests the experience Rao described.
- Price each workflow. Track cost per completed task, including review and rework, alongside the in-house output share. Rao’s 40 to 60% figure describes outputs, not costs.
What to watch
- Track whether time savings persist after rollout. Rao cited two or three hours daily for some clinicians, but gave no measurement method.
- Measure patient-summary comprehension and follow-through, not only note completion.
- Watch how much model work remains in-house as frontier models and routing change.
Send this to a health-system CIO and clinical operations lead weighing ambient AI.
Watch the minute where Rao sets Abridge’s 10x bar
Written by Real Alpha from the episode's transcript and the other moments it links, then checked against the transcript. Quotes are the speaker's exact words.
Minute by minute
- 2:16Abridge 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.
- 6:33Visit 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.
- 10:35Rao 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.
- 17:00Abridge 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.
- 18:43Healthcare 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.
- 20:07Rao 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.
- 23:20Healthcare 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.
- 29:19Trust 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.
- 31:32Abridge 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.
- 37:37Rao 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.
- 40:19Model 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.
- 43:21Rao'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.
Published Jul 23, 2026.
Transcript · 39 min read
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.
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.
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.
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.
Read the whole transcript
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.
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?
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
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?
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
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
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?
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.
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.
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.
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.
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
“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.”
“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.”
“they just got so far ahead of their skis in terms of what they were saying they could do”
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“But I think to create this three-sided network, to go after this bigger operating system, you have to start provider first.”
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
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
“So behind the scenes, ultimately in service of the patient, the AI understands what the health system wants to get into that note”
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
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?
“It turned out being ourselves was like such a counter-positioning advantage against Microsoft.”
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
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.
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.
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.
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
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 to 60 percent of our model outputs are probably driven by in-house work”
“what did they just say? Like, what are we supposed to do? And I'm a doctor. I can't even remember.”
We'll be right back.
