Latest / The Neon Show / Why Even a $100 Billion Company Wouldn't Be Big Enough | Tanay Tandon

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Why Even a $100 Billion Company Wouldn't Be Big Enough | Tanay Tandon · The Neon Show

0:00
SPEAKER_00: Can a healthier experience in the US become like Waymo?
0:04
SPEAKER_01: Yeah, for us, that's the goal.
0:06
SPEAKER_01: It should feel like a Waymo for the provider and for the patient.
0:09
SPEAKER_01: Everything but the most essential bit, which is the car itself, is stripped away and abstracted.
0:16
SPEAKER_01: You're looking for people who are almost like 18 million of pain people that are addicted to solving problems and addicted not just to the successes that come with that, but all the pain and the messiness.
0:29
SPEAKER_01: Our health system has now been under duress for 20, 30 years.
0:32
SPEAKER_01: It was sort of a tragedy of common.
0:35
SPEAKER_01: It was never in someone's total incentive to solve it.
0:38
SPEAKER_00: What will be the software spend in the US on healthcare?
0:40
SPEAKER_01: If$500 billion worth of administrative labor that is sitting on that software, moving their mouse, moving files from one place to another, fixing flames, that's really what we get excited about.
0:55
SPEAKER_00: So I'll I'd love to start by the letter that you wrote to your team that Pat Grady shared on Twitter, like that you're looking for people who are almost like heat-seeking missiles for pain.
1:07
SPEAKER_00: Can you elaborate more about it?
1:08
SPEAKER_01: Yeah, we we got a lot of uh inbound interest on our engineering team, on our sales team after that.
1:15
SPEAKER_01: And you know, the core concept of heat-seeking missile for pain is you're looking for people who work at startups really that are addicted to solving problems and addicted not just to uh uh uh the the successes that come with that, but all of the pain and the uh uh you know the messiness uh of the day because pain is signal.
1:35
SPEAKER_01: At the end of the day, if there's customer pain or if there's internal pain, uh if there's pain that you see uh on you know with certain team members, it's a problem that needs to be solved.
1:45
SPEAKER_01: And we create enterprise value in society by solving problems.
1:48
SPEAKER_01: And and the ability to detect pain early and almost like run after it, like seek and destroy, is actually fairly unique.
1:56
SPEAKER_01: Not everyone has it.
1:57
SPEAKER_01: A lot of people try to put their head in the sand and move away from pain.
2:01
SPEAKER_01: And I think when you find someone who actually is addicted to pain, they tend to perform really well at startups and um uh and they tend to make great leaders, they tend to grow very quickly.
2:10
SPEAKER_01: And our thesis has always been find these people and make them celebrities in the company, continue to grow and cultivate them.
2:18
SPEAKER_00: And uh what are the other aspects that you look for while building your team culturally and skill-wise?
2:23
SPEAKER_01: I I think naturally some people have urgency.
2:27
SPEAKER_01: Some people are addicted to speed in the same way that they're addicted to pain.
2:31
SPEAKER_01: Uh, one of our core company values is speed above all else.
2:35
SPEAKER_01: And there's a certain type of person who can operate with rigor, with very high throughput and horsepower, while also moving very quickly.
2:44
SPEAKER_01: And the the people that are naturally addicted to urgency and want to move fast are again amazing fits for companies.
2:52
SPEAKER_01: Uh, I think that people that are very good at questioning requirements, usually in in healthcare or any industry, there's a long list of steps before you can do anything.
3:03
SPEAKER_01: And with any manufacturing process or really any process out there, step one or step zero in some ways is question all the requirements.
3:11
SPEAKER_01: This is like in the Elon manifesto of how to simplify manufacturing processes and make them very efficient for scale.
3:17
SPEAKER_01: It's the same in software and it's the same in healthcare, which is question the requirements, simplify the problem stack, and then move with urgency and like surge resources in a certain area and move as quickly as possible to make that happen.
3:30
SPEAKER_01: I also think that people that are good natural writers do well because in a chaotic environment, you need to be able to distill your thoughts, present them in written form, and then really disseminate that across the org as quickly as possible.
3:43
SPEAKER_01: Uh, and some people can do that very naturally and some people can't.
3:47
SPEAKER_01: Uh, and the people that can tend to also grow in an org very quickly because their way of thinking, their processes, their SOPs turn into the kind of like the Bible of the org and turn into what turns into the day-to-day operating system.
3:59
SPEAKER_00: So let's talk about the US healthcare market.
4:02
SPEAKER_00: Yeah.
4:03
SPEAKER_00: Like infinitely large market, probably a few trillion dollars.
4:06
SPEAKER_00: Yeah.
4:06
SPEAKER_00: Right.
4:07
SPEAKER_00: Trillions, yeah.
4:07
SPEAKER_00: And and extremely messy.
4:09
SPEAKER_00: Yes.
4:09
SPEAKER_00: And probably dominated on the technical side by one player, Epic, right?
4:14
SPEAKER_00: Yeah.
4:15
SPEAKER_00: And uh Epic has has been guarding the systems for a very long period of time.
4:21
SPEAKER_00: I think they have what their revenue is what, like a few tens tens of billion.
4:25
SPEAKER_01: Approaching 10 billion, yeah.
4:26
SPEAKER_01: Yeah.
4:26
SPEAKER_01: Big business.
4:27
SPEAKER_00: Yeah, approaching 10 billion, completely privately owned, no external investor.
4:33
SPEAKER_01: Yep.
4:33
SPEAKER_00: And it's a rumor that any CIO that gets hired in large hospitals in the US is through a recommendation by Epic.
4:41
SPEAKER_00: Sure.
4:42
SPEAKER_00: Right.
4:42
SPEAKER_00: So what's your view?
4:44
SPEAKER_01: I I think first of all, remarkable journey, right?
4:47
SPEAKER_01: I think there's you look at Judy Faulkner, you look at the way that Epic was built, you in in in the last uh uh you know 30, 40 years of of just healthcare IT in the country, and it's dominated by this one company.
5:00
SPEAKER_01: Uh and the the learnings in some ways are you it is, you know, there's a lot of hate at times for the product, but at the same time, there's also a lot of love.
5:09
SPEAKER_01: It solved a very specific problem.
5:11
SPEAKER_01: It took paper records and it digitized them.
5:14
SPEAKER_01: It made compliance easy for the CIOs, it made record keeping and accounting easy for the CFOs, and it provided all of those solutions in one stack.
5:22
SPEAKER_01: You know, our belief is that there's a lot of parts of Epic, however, that aren't built for the AI age and aren't truly AI native, which is it's not just people that are doing these tasks, it's agents.
5:32
SPEAKER_01: And it's not just uh, you know, traditional labor that's sitting behind a screen, it's software that's reading large amounts of information, making decisions, and then working with health insurance companies, summarizing clinical documentation.
5:44
SPEAKER_01: And you need this orchestration layer that almost lives on top of the EMR that can conduct those tasks.
5:50
SPEAKER_01: Uh, the other point that I make is Epic is massive, but there's so many other companies in the industry that are actually like approaching similar scale.
5:58
SPEAKER_01: Or Cerner is actually does more revenue than Epic.
6:01
SPEAKER_01: Uh, it and you look at like Athena Health does billions in revenue, and then you have ECW, and then you have this long tail of EMRs.
6:08
SPEAKER_01: And so Epic is a is a chunk of the market, probably like 30s to 40%, uh, but it's still not a majority of the market.
6:14
SPEAKER_01: And the the story, I think the real story of healthcare in some ways is is the rest of the market that's not even on Epic, that's on very disjointed software solutions.
6:24
SPEAKER_01: That's uh cobbling together patient engagement, intake, revenue cycle, documentation software.
6:29
SPEAKER_01: And our belief is our belief is that with language models, you can automate a lot of that.
6:34
SPEAKER_01: And you can almost leapfrog the standard digitization process and take something that is truly agentic and take a bunch of labor and turn that into software as well and improve the operating margins of the system.
6:44
SPEAKER_01: And I think that's what's happening right now.
6:46
SPEAKER_01: That's really the story of, you know, if if digitization and Epic and the MRs was the story of the 2010s, the story of the late 2020s in healthcare is definitely one of language models and AI replacing standard labor or augmenting that labor to make it much more efficient.
7:01
SPEAKER_00: So the US healthcare almost spends like a trillion dollars?
7:04
SPEAKER_01: Uh in on labor or on.
7:07
SPEAKER_01: So I would the way that I think about it is probably like total healthcare GDP is approaching 5 trillion.
7:11
SPEAKER_01: Okay.
7:11
SPEAKER_01: And the uh, and that includes the services and the clinical work and the you know hospitalizations and whatnot.
7:18
SPEAKER_01: And then your admin labor on that is probably between a trillion and a trillion and five.
7:22
SPEAKER_01: And then if you just look at what we call work tax or admin bloat and admin expense, probably about half a trillion dollars.
7:28
SPEAKER_01: So there's like$500 billion worth of exhaust that the system creates, that is documentation, that is revenue cycle, that is claim submission, that is denials, appeals, going back and forth with insurance companies that doesn't really exist in other industries in the same way.
7:43
SPEAKER_01: When you swipe a credit card, there's an adjudication process.
7:46
SPEAKER_01: There is a you know an interchange process, there's the a bank at the end of the day that's you know processing that information and instantly releasing the funds.
7:56
SPEAKER_01: That that system runs at you know two to three percent cost of transact.
8:00
SPEAKER_01: Healthcare probably runs at 15 to 20% cost of transact.
8:03
SPEAKER_01: So there's so much room to make it more efficient and look and feel more like a credit card swipe.
8:07
SPEAKER_01: And that really is the work that we do and many other companies in the space do.
8:11
SPEAKER_00: Got it.
8:12
SPEAKER_00: And uh what is the biggest bottleneck that why it has not become efficient till now?
8:19
SPEAKER_01: I I I it's a great question because there's I think so many reasons.
8:23
SPEAKER_01: Um, and one it was sort of a tragedy of commons or almost like a bystander effect, where as the bloat accumulated, it was never in someone's total incentive to solve it.
8:36
SPEAKER_01: Uh, it was also, there were always more urgent problems.
8:40
SPEAKER_01: When you know, when a pandemic happens, it's go, go, go.
8:44
SPEAKER_01: Just use the whatever we got in the system and pile on top and just get the job done.
8:50
SPEAKER_01: And I think our health system has now been under duress for 20, 30 years.
8:56
SPEAKER_01: And there was never really time to stop and think and try to fix these problems.
9:00
SPEAKER_01: And it was just you know, between a pandemic, a chronic disease crisis, you know, the obesity epidemic in some ways, the you know, growth of uh uh an aging population, chronic disease, all the system has just been under stress for so long.
9:12
SPEAKER_01: And COVID, in some ways, was this breaking point where we really burnt out our providers.
9:17
SPEAKER_01: And I would go so far as to say one of the reasons that healthcare has been the fastest adopter or amongst the fastest adopting industries of AI is because of that massive workload that was placed on providers during COVID.
9:30
SPEAKER_01: And now it's just there's no option.
9:32
SPEAKER_01: You either adopt AI or you lose your physicians, you lose your nurses, you lose your admin staff.
9:38
SPEAKER_01: So we're we're excited because it's a it's an amazing point in time where you can actually give relief to the system using a tool that was just created in the form of artificial intelligence and language models.
9:48
SPEAKER_01: Uh, and and in in some ways, everything just lined up perfectly for the healthcare problem to be attacked.
9:53
SPEAKER_00: So let's say, on an average, a healthcare provider, physician, or a nurse usually works to eight to twelve hours a day, right?
10:00
SPEAKER_00: On how much of their time they are spending in these admin staffs, filling out the forms, filling out patient records.
10:07
SPEAKER_01: Yeah, I you know, I think in in in some cases, I would say eight to twelve hours is a very good day for a nurse or a physician.
10:13
SPEAKER_01: We we see, you know, sometimes these 14, 20 hour shifts, and it can get pretty brutal.
10:18
SPEAKER_01: Um and I I in the conversations that we've had and the customers that we've worked with, you're spending almost 20, 30, 40% of your time.
10:26
SPEAKER_01: Uh, so hours every day on documentation, on paperwork, on filing appeals, on responding to an insurance company.
10:34
SPEAKER_01: So it is a huge chunk of time.
10:36
SPEAKER_01: And I would say in the worst case, in in some of the uh highest acuity cases, you're seeing almost 50% of the time being spent on administrative work, which is just unsustainable.
10:46
SPEAKER_01: That's not a good use of.
10:48
SPEAKER_01: You think about clinicians, these are some of the smartest, most trained people in our country.
10:52
SPEAKER_01: They should be spending every minute of their day face to face with a patient, taking care of a patient, doing the thing that they were trained to do and love to do, as opposed to this administrative exhaust.
11:01
SPEAKER_00: Yeah.
11:03
SPEAKER_00: And uh let's say there are so many solutions uh today.
11:06
SPEAKER_00: You said COVID was an acceleration point that accelerated the solution.
11:10
SPEAKER_00: But still, entrepreneurs say that healthcare is the one of the hardest market in the US to get demos in, to get pilots in, to get paid contracts in.
11:20
SPEAKER_01: Right.
11:21
SPEAKER_01: I I think it is it is a hard market.
11:24
SPEAKER_01: And in some ways, it's the only market I've spent time in.
11:27
SPEAKER_01: So maybe, you know, I just don't know any worse or better.
11:30
SPEAKER_01: But um the the learnings, I would say, if you contrast healthcare sales in you know, the late 2010s, like 2017 through 2020, to healthcare sales post that in like 2021 onwards, two completely different markets.
11:45
SPEAKER_01: Uh, one where you have a buyer that is like really just feeling the pain viscerally of the administrative tax and is ready to make changes.
11:54
SPEAKER_01: And I'd say technology capabilities in the form of language models that are also just so magical that there's almost no option but to adopt these new capabilities.
12:04
SPEAKER_01: So it's a very different market than the one that's came before.
12:08
SPEAKER_01: It's still hard.
12:09
SPEAKER_01: And it's hard because it is relationship driven, it is distribution driven, it is very top-down.
12:14
SPEAKER_01: You need if you know the CIO and have cultivated a strong relationship with him or her, very different process than if you don't have that.
12:21
SPEAKER_01: And so I think in some ways, counterintuitively, you know, my my recommendation to early stage founders is unless you have that distribution advantage, healthcare is still actually a very hard market to get into.
12:35
SPEAKER_01: And you're better off either working with a larger company or distributing through a larger company or cultivating that relationship somehow, because if you go into it raw, you will lose to the players, the legacy players that have the trust in the distribution, because healthcare isn't really for experimentation.
12:50
SPEAKER_01: It at the end of the day, they need solutions, but they want them from durable, long-standing providers who've been in the space for a while.
12:57
SPEAKER_00: And overall, what will be the software spend in the US on healthcare?
13:01
SPEAKER_01: That's a good question.
13:01
SPEAKER_01: You know, the it there's many ways to size it.
13:04
SPEAKER_01: And I think about the because you have like clearinghouse software, and then you have like straight software, and then per seat licenses.
13:10
SPEAKER_01: What is clear is that in aggregate, the the spending on software and software-related expenses is probably like somewhere between like 70 to 80 billion bucks.
13:19
SPEAKER_01: That itself is not massive.
13:20
SPEAKER_01: And and you know, if if you're just going after that spin, I actually think the problem is less interesting.
13:25
SPEAKER_01: What makes it interesting is the half a trillion in labor spend that is attached to it.
13:29
SPEAKER_01: You have$500 billion worth of administrative labor that is sitting on that software, moving their mouse, moving full files from one place to another, fixing claims.
13:39
SPEAKER_01: And that's what that's really what we get excited about and what our customers get excited about.
13:44
SPEAKER_01: Because if you solve that, one, you get paid faster, uh, the claims come through faster.
13:49
SPEAKER_01: But two, there's a ton of labor in your system that you can reallocate to clinical problems or revenue generating problems, as opposed to purely back office problems.
13:57
SPEAKER_01: So the the 70, 80 billion dollar pure software number, interesting.
14:01
SPEAKER_01: I would, I would argue that the the half a trillion dollar labor number is just much more compelling.
14:06
SPEAKER_01: And it's also the real problem.
14:07
SPEAKER_01: Like the these health systems are not becoming unprofitable because of their software spend necessarily, um, unless it's a massive line item or a big migration.
14:18
SPEAKER_01: It usually is because of the associated labor spend or the dollars that are just leaking through the funnel when insurance denies.
14:25
SPEAKER_00: So uh uh when an entrepreneur is trying to find a building point solution versus building a platform, what would be your recommendation?
14:35
SPEAKER_01: I it's it's hard because you have to start somewhere.
14:37
SPEAKER_01: And you know, when you look at our journey, we started with what effectively looks like a point solution.
14:42
SPEAKER_01: And to this day, many of our hooks into these systems are specific point solutions like ambient scribing or autonomous coding or intake automation, uh, or the you know, voice agent platform that's one of our fastest growing products ever.
14:55
SPEAKER_01: It's a the the call, it takes over the front office and call center.
14:59
SPEAKER_01: Um the the issue in some ways, though, is if you're a one-trick pony, you're not going to get the biggest customers in the country.
15:07
SPEAKER_01: The biggest customers and most important customers in the country want a platform partner.
15:11
SPEAKER_01: They want someone who can come in, customize a solution to their needs with forward-deployed engineering, and then really tie that solution to a suite of other products to automate more than just one thing.
15:21
SPEAKER_01: Because the implementation time is kind of fixed.
15:23
SPEAKER_01: The relationship building time is kind of fixed, and you don't want to put all of that time in for this one little solution.
15:28
SPEAKER_01: That's the ROI case will never make any sense.
15:31
SPEAKER_01: This, the CIOs and the CEOs and the CCOs that we work with are all thinking about this problem over five years.
15:37
SPEAKER_01: I'm making an investment in a partner, and then over five years, they're gonna take over bigger and bigger parts of the system.
15:42
SPEAKER_01: So often when we're demoing, we'll engage on one or two product lines, but the roadmap is always we're gonna take over the call center, we're gonna replace the revenue cycle, the back office.
15:51
SPEAKER_01: And so the ACV expansion story for us is fascinating.
15:54
SPEAKER_01: And more importantly, the labor ROI story for the customer is, I mean, is remarkable because they put in the fixed cost once, and then the software platform expands and augments more and more labor.
16:04
SPEAKER_00: So I assume today there would be many customers who would be paying you eight or nine figure.
16:10
SPEAKER_01: I I you know we have uh cohorts of you know, we have customers that are paying us tens of millions of dollars.
16:16
SPEAKER_01: We have a few customers that are approaching$100 million a year.
16:19
SPEAKER_01: Um, and then we have you know a lot of customers that are in the low single-digit millions, and then you know, hundreds to thousands of customers that are below that.
16:28
SPEAKER_01: And uh I think the beauty of our platform is that it serves small practices, which are, you know, call it like one to 20 provider groups that are physician-owned.
16:38
SPEAKER_01: Uh, and then those same software solutions scale to large massive health systems.
16:43
SPEAKER_01: Uh and uh there's more customization required and more implementation time required when you work with a large system.
16:49
SPEAKER_01: But our long-term belief is that the best software products, you look at Microsoft Word, you look at Google Docs, you look at you know, many of the, you know, like things like like cursor and code editors, they really do scale across the the this the gamut of organization types.
17:03
SPEAKER_01: And with LLMs, I think there's a once-in-a-generation opportunity to reimagine the operating stack for all healthcare practices, small ones and large ones.
17:12
SPEAKER_01: Uh, and that Microsoft Office suite is is being built now.
17:15
SPEAKER_01: And and you know, we really think that our our suite is is the one that will be that answer.
17:18
SPEAKER_00: But today, how much of the market you would have uh, I would say, captured or taken or serving?
17:25
SPEAKER_01: Yeah, it's it's we have you know these numbers up in in our office.
17:29
SPEAKER_01: And uh the I think about so the first number is what percentage of all US healthcare appointments run our run on a platform or you know, a tooling of ours.
17:39
SPEAKER_01: So uh for ambient and charge entry and charge capture, the it's about 7% of all healthcare visits in the US run on our platform.
17:47
SPEAKER_01: Now, the the the more exciting metric in some ways, but also at times uh the one that my team gets more upset about is the number of dollars in terms of full cycle RCM, or we consider our software solution to be fully penetrated, is like 0.1%.
18:04
SPEAKER_01: And that's exciting because we're a company that's doing hundreds of millions of dollars.
18:08
SPEAKER_01: It's almost unheard of to be doing hundreds of millions of dollars in annual revenue and only be 0.1% penetrated in your in your total TAM.
18:17
SPEAKER_01: Uh, and and so the exciting part of that is like great, well, 7% of US appointments run on Ambient and some of our other tooling, that's a great launching ground to then hook and attach the rest of the revenue cycle platform onto.
18:29
SPEAKER_01: And just in our current customers, you know, if we if we stop selling any net new logos and all we did was continue implementations and continue expanding in those accounts, probably like 10 to 15 billion dollars worth of software revenue for for us to go implement and expand into.
18:44
SPEAKER_01: Um, and that gets me excited.
18:46
SPEAKER_01: And then if you play it out and you acquire more net logos, like we will, of course, do, that number just continues to grow.
18:53
SPEAKER_00: Got it.
18:54
SPEAKER_00: So, so I would love to learn about your journey, right?
18:58
SPEAKER_00: You started as a med tech device, right?
19:01
SPEAKER_00: Went to YC and you know, then you know, got some of the legendary investors uh on your chap table.
19:09
SPEAKER_01: You know, it's it's it's been a very interesting journey.
19:12
SPEAKER_01: So I I grew up in the Bay.
19:14
SPEAKER_01: Um, I was a research intern at Stanford AI in high school, and I worked under uh Richard Sotra, who's a professor at Stanford, and then he spun out and started his own company called MetaMind, which was eventually acquired by Salesforce.
19:27
SPEAKER_01: I was a software engineering intern there, uh, and then a research intern again at Stanford AI in the summers after.
19:33
SPEAKER_01: Um, and I learned so much in the in, you know, from him and from the teams at Stanford, because you know, really I tried to be a spun, just come in and see what was happening.
19:44
SPEAKER_01: And I remember when I was in the intern at the Stanford AI lab, it was really when this like first sequence of GPU explosions was happening, like 2013, 2014, 2015.
19:55
SPEAKER_01: And it was clear that accelerated computing would change the world in terms of self-driving, in terms of image recognition, and even like early examples of of language modeling.
20:05
SPEAKER_01: Um and I, you know, this was this was fascinating.
20:09
SPEAKER_01: And one of the first applications that we saw was computer vision in pathology and diagnostics, because computer vision models actually got really good at tasks and you know, human equivalent or superhuman well before language.
20:21
SPEAKER_01: Language took a lot longer and some algorithmic improvements and some scaling improvements.
20:25
SPEAKER_01: Um, but but imaging got there early.
20:28
SPEAKER_01: And our belief, you know, me and me and Deepka, we used to compete in high school science fairs.
20:33
SPEAKER_01: And our belief was that you could take a blood test, the computer, the complete blood count, which was done through flow cytometry or microscopic pathology, and replace that with computer vision.
20:44
SPEAKER_01: Uh, and so we built the prototype, and it was actually quite simple.
20:47
SPEAKER_01: It was like an imager, micro, like a microfluidic test trip, which was a channel with some with the you know, staining in it, and then you would take hundreds of images, CN conventional neural nets would recognize the cells, classify them, count them, spit out a result.
20:59
SPEAKER_01: And that was the prototype, and that was the device, and we were like, great, we can someone might have use for this.
21:04
SPEAKER_01: Like we're like, you know, it was a blood test.
21:06
SPEAKER_01: Like, why wouldn't you use it?
21:07
SPEAKER_01: Um, and that kind of kicked the journey off.
21:11
SPEAKER_01: We demoed the, we got into YC based off of this prototype.
21:14
SPEAKER_01: Um, and then we demoed the product to Alfred at Sequoia uh and the team there.
21:20
SPEAKER_01: And I remember Alfred said something like, This is fascinating.
21:23
SPEAKER_01: Like, you know, as a team of two, you guys have accomplished a lot.
21:26
SPEAKER_01: Uh, we don't really know that much about the med-device space, and Sequoia isn't traditionally a med-device investor, but we really believe in you guys and the concept of applying artificial intelligence to healthcare.
21:36
SPEAKER_01: Uh, and I remember at the time I was like, sounds like Alfred doesn't believe in my medic, my medical device.
21:41
SPEAKER_01: Like it's it's like it sounds like it sounds like he's telling me that uh we're we might have to pivot or turn into something else.
21:47
SPEAKER_01: And Alfred was right.
21:48
SPEAKER_01: The the company transformed in in many ways over the coming years.
21:51
SPEAKER_01: But that core thesis of build products that take artificial intelligence and automate rote tasks in healthcare was the core of the business.
21:58
SPEAKER_01: Uh, we got the medical.
22:00
SPEAKER_01: Device cleared, Sequoid, you know, invested, let our seed round.
22:04
SPEAKER_01: We got the med device cleared, launched scaled at millions in revenue, and then decided that once we were in these practices, once we were serving these doctors and in these clinics, we saw the real pains of running a healthcare business.
22:17
SPEAKER_01: And yes, diagnostics was part of it, but the clinical side was actually well covered.
22:21
SPEAKER_01: In general, the clinicians had the tools they needed for the average patient to get the job done.
22:27
SPEAKER_01: The real problem was administrative.
22:29
SPEAKER_01: It was in how they ran their telehealth operations and how did in how they did their billing and how they fought insurance companies.
22:36
SPEAKER_01: And I think as a team, we decided, hey, we did something really hard.
22:40
SPEAKER_01: Let's go do something harder.
22:42
SPEAKER_01: Let's actually build the whole suite.
22:43
SPEAKER_01: Let's build the rest of the solution set and bring it to all of the practices in America.
22:47
SPEAKER_01: And that's how the solution expanded from one device to the software platform.
22:52
SPEAKER_01: And then as the journey progressed, Heyman from GC General Catalyst led our series B.
22:58
SPEAKER_01: We became very close with the fund as they introduced us to a lot of customers and partners.
23:02
SPEAKER_01: And then we had this very interesting opportunity to take Camura, which is a GC incubation focused on health system transformation, and merge it with Othellus and turn it into really this larger company focused on bringing AI into all healthcare practices and all hospitals in the country.
23:20
SPEAKER_01: And, you know, we're that that merger is one of the best decisions we've made so far.
23:24
SPEAKER_01: It's it's accelerated both businesses and it's brought our technology to some of the largest hospitals in the country.
23:30
SPEAKER_01: And that's been that's really been the summary of the of the journey so far.
23:34
SPEAKER_00: Yeah.
23:35
SPEAKER_00: What have been some of the highs and lows during the journey?
23:38
SPEAKER_01: So many.
23:38
SPEAKER_01: I, you know, I would say that it's almost daily.
23:42
SPEAKER_01: You're you're just taking punches.
23:43
SPEAKER_01: And that's part of the reason I wrote the Heat Seeking Missile for Pain essay is that you really do have to normalize pain.
23:50
SPEAKER_01: You have to normalize the the what it what it means to be in a state of pain very, very often.
23:55
SPEAKER_01: Because even in your best quarter and when you're putting up the best numbers, there are more bad days than good.
24:00
SPEAKER_01: Yeah.
24:00
SPEAKER_01: And the the good days are these like flashes of brilliance where sign a big deal, and then you realize, well, there's 10 other deals that are stuck, or this deal is going to be really hard to implement.
24:11
SPEAKER_01: Uh, and and I think running a business and building products is such a hard, painful endeavor that it makes sense to codify that as like, well, we have to be heat-seeking missiles for pain.
24:21
SPEAKER_01: Uh, and everybody in the company needs to be a heat-seeking missile for pain.
24:24
SPEAKER_01: So uh some of the lowlights in particular, and it took a long time to get our FDA clearance.
24:29
SPEAKER_01: It, you know, it took almost two years longer than we initially expected.
24:32
SPEAKER_01: In hindsight, that was how long it was going to take from the beginning.
24:36
SPEAKER_01: That's how long the FDA takes to review processes.
24:38
SPEAKER_01: And I just assumed that our first application would be perfect, we'd get an approval, and then we'd be after the races.
24:44
SPEAKER_01: Uh, wasn't the case at all.
24:45
SPEAKER_01: It took uh so many iterations, back and forth, visits to DC, engaging with the regulators, redoing the clinical trials four or five times until they finally approved it.
24:55
SPEAKER_01: So that was every time they rejected it, it was painful.
24:57
SPEAKER_01: The first time they were rejected it was super painful because we got this like 40-page document telling us about why we were done.
25:04
SPEAKER_01: Um later on, I think the the way things got painful or hard were you know, if you you have a tough quarter where like sales just the engine isn't revving and you have to really diagnose the problems.
25:15
SPEAKER_01: Is it the product?
25:16
SPEAKER_01: Is there something wrong with you know our the way that we're marketing and top of funnel?
25:20
SPEAKER_01: Um, and that happens, you know, as recently as like a year ago.
25:23
SPEAKER_01: Like we'll we'll feel those pains.
25:25
SPEAKER_01: Uh and and and and then I think some of the other low lights are just sometimes you ship the product and there are serious like gaps between what the customer needs and what you're what you're doing today.
25:36
SPEAKER_01: And the only way to find that out is to be on the market and and take that punch and then work with the customer to solve the issues.
25:42
SPEAKER_01: And uh, you know, I really credit our work with HCA, which is one of the largest systems in the country, for being willing to work on the hardest problems with us, like co-develop an ambient solution for emergency departments and hospitals and acute settings that are not the easy, you know, low-hanging fruit.
25:57
SPEAKER_01: But because of that, I think that's why we have the best ambient platform in the country and the fastest adopted ambient platform in the country.
26:03
SPEAKER_01: It's it's because of those investments and the hard things.
26:06
SPEAKER_00: But uh uh healthcare in general in the in the US, right, has not been a very attractive space for entrepreneurs to build low number of IPOs, for example, in this space, low number of acquisitions, uh uh, right?
26:23
SPEAKER_00: So so why does uh entrepreneurs and investors keep on investing?
26:28
SPEAKER_01: I I think the one, it is it's important.
26:32
SPEAKER_01: It's one of like the, you know, if you think about one of the persistent problems in the human experience is health and the way that we deliver uh healthcare.
26:41
SPEAKER_01: Uh and it's a massive chunk of the economy.
26:43
SPEAKER_01: And so I think it's a noble place to spend time and invest in because eventually we're gonna nail it and we'll get it right.
26:48
SPEAKER_01: That's just you know the way, the way these things work.
26:51
SPEAKER_01: I I also think that historically, the reason some of the outcomes have been relatively smaller is they were going, they were only able to really go after that$70,$80 billion TAM that we talked about in aggregate, which is the software spend in the segment.
27:04
SPEAKER_01: And now the rest of the TAM has opened up.
27:07
SPEAKER_01: And so it's a very different ball game when you're going after half a trillion dollars to spend to maybe even a trillion dollars in spend versus really being pigeonholed in that$70,$80 billion software segment.
27:19
SPEAKER_01: Um, and and and number number two is it's a low operating margin business historically.
27:25
SPEAKER_01: So the customers, unlike you know, tech companies, don't have a lot of dollars to reinvest in their own operational productivity.
27:34
SPEAKER_01: So that flywheel has never existed, where we have a lot of cash, we're gonna invest it in more tools and technology to make ourselves more efficient, and then we'll grow and grow and grow and then expand.
27:44
SPEAKER_01: And because of that, most of the growth in the healthcare sector has come from PE style plays, which is go build these empires by buying up hospitals and buying up practices, roll them together, and then internalize the cost savings at the operational level.
28:00
SPEAKER_01: Uh, you can now run health systems like a technology business, which is super exciting because the labor, the big chunky labor piece of it can turn into software.
28:08
SPEAKER_01: And when that happens, the flywheel emerges.
28:09
SPEAKER_01: When the flywheel emerges, spending behaviors change, the TAM increases, the quality of life for the patients and the providers improves.
28:17
SPEAKER_01: And I think that will be the trend of the next 10 years, where like health tech companies really become technology businesses as opposed to complex operational uh businesses that they've historically been.
28:27
SPEAKER_00: Yeah, we need few of the companies like Commute to become 100 billion businesses, as Heymant calls it, you know, for the new innovation to come up, for it to become attractive for entrepreneurs and investors to put more effort and money into 100%.
28:40
SPEAKER_01: You need big wins in the space to kick that off and um and also just like show the way and show the path.
28:46
SPEAKER_01: Because what once the other truth is like as we expand in these, you know, in these large health systems, we become an attaching ground for net new software.
28:56
SPEAKER_01: Uh, and uh that's not been the case with the historically on-prem solutions that are very hard to integrate with and play ball with.
29:02
SPEAKER_01: Interop is a core thesis within our product.
29:05
SPEAKER_01: Anyone can build on top of our EMR, anyone can build on top of our ambient products versus the historical tools that these hospitals have used.
29:13
SPEAKER_00: Our portfolio companies have really found it hard to integrate with Epic.
29:17
SPEAKER_00: The APIs are not cleanly available, sure, transparent.
29:21
SPEAKER_00: Why do you think is the case?
29:23
SPEAKER_00: Is there not enough incentive for companies like Epic to open up to collaborate?
29:28
SPEAKER_01: I think it's an it's a question, one of incentives.
29:31
SPEAKER_01: Um, but at the same time, I also think it's it's priorities.
29:35
SPEAKER_01: If you think about where does Epic, where does Epic spend their engineering resources and dollars, where do all EMR spend their engineering resources and dollars?
29:42
SPEAKER_01: They have a hundred requests from every customer that needs something really specific built out, a certain type of report, a certain type of tooling.
29:50
SPEAKER_01: And so that and these are often like life or death, uh either for a patient or for the system itself from a financial standpoint.
29:58
SPEAKER_01: And that's what they're gonna prioritize instead of building out a perfect interop suite.
30:02
SPEAKER_01: And and so you almost can't blame them because they're just serving the needs of their customer.
30:07
SPEAKER_01: Now, there's some good tailwinds in the space.
30:09
SPEAKER_01: One is regulatory, which is there is a lot more regulatory pressure to make EMRs interoperable and a lot more requirements that EMRs have these open accessible APIs.
30:18
SPEAKER_01: And that's good because now it's a kind of standardizing, forcing function.
30:23
SPEAKER_01: Uh, number two is I think the concept of agents, where you don't really need a clean API per se, you can make do with unstructured data and take inputs and shoot out outputs that are that are processable, human readable, but also machine readable.
30:39
SPEAKER_01: And that to me is going to be a big mega trend where you the concept of an API is changing very quickly.
30:45
SPEAKER_01: Um, and so those two things paired together create a very strong forcing function for faster interop adopt uh uh uh uh uh adoption, and then also just a floor, like a flurry of companies in the space that can build on top of VMRs that couldn't do so before.
30:59
SPEAKER_00: Got it.
31:00
SPEAKER_00: So uh you you think uh there is no lack of intent from their side, it's just that uh their bandwidth or their priorities on the engineering side has been dedicated to completely something else.
31:12
SPEAKER_01: I I think so.
31:13
SPEAKER_01: And look, I I like to assume good intent at the at the end of the day from from from the players.
31:17
SPEAKER_01: And I and I do think when you, you know, every interaction we've had with an epic engineer is one of someone trying to solve a problem.
31:24
SPEAKER_01: And every interaction we've had with uh uh you know an engineer working at in the CIO's office in a health system is someone that is burdened with a thousand tickets and is just trying to solve problems but needs to prioritize.
31:34
SPEAKER_01: It has it is a little bit of a question of prioritization.
31:37
SPEAKER_01: And then I think it's also the the the the Epica is very responsive to their customers, and their customers are the health system.
31:46
SPEAKER_01: So when a health system needs something and asks for something, they make it happen.
31:49
SPEAKER_01: Uh, but they're not necessarily historically have not necessarily been accountable or responsible to you know some startup out there.
31:57
SPEAKER_01: Um but with interop standards that that changes the game a little bit, where like now there is a maintained set of APIs that people can submit apps into the garden, get approved, and and go from there.
32:07
SPEAKER_00: Got it.
32:07
SPEAKER_00: So so is is the government regulation trying to create like an app store for each EHR?
32:14
SPEAKER_01: I I think the the we've spent time with the administration on this.
32:17
SPEAKER_01: And what is exciting and promising is as opposed to like forcing the ER itself to say create an app store and regulate how that's set up, they're much more focused on ensuring that the data that lives in the MR itself can be accessed in a smooth way.
32:32
SPEAKER_01: And then what emerges from that emerges, and some EMRs have taken the approach of having sanctioned app stores with you know clear libraries in terms of how you integrate and what you pull.
32:42
SPEAKER_01: Other EMRs have taken the approach of here's the data, you have standard standardized HL7 or Firefeeds you can pull in, read, you know, uh write right back in, uh you can pull out right back in.
32:52
SPEAKER_01: And um the the end result from that is I would say in the last three years, the volume of apps and types of tools you can ship on top of the EMR has increased by tenfold.
33:02
SPEAKER_01: And I think in three years from now, it'll have increased by tenfold again, which is really good for the healthcare economy.
33:07
SPEAKER_00: Got it.
33:08
SPEAKER_00: Um said that unless Comure becomes a hundred billion dollar company, it wouldn't have created any impact.
33:14
SPEAKER_01: Sure.
33:15
SPEAKER_01: Why do you think it's well I think it's true?
33:18
SPEAKER_01: It is, if you think about the scale of problem that we're trying to solve in healthcare, uh, we're I wouldn't consider it a success.
33:26
SPEAKER_01: In we really wouldn't have penetrated much of healthcare at all if we weren't a hundred billion dollar company.
33:31
SPEAKER_01: If you look at the breadth of our tools in the suite, they can create real operating profit and uh improvement and operating margin at these health systems.
33:40
SPEAKER_01: And one, we have to do that at scale.
33:43
SPEAKER_01: Uh, and two, we have to continue to do that well such that the part the partners see that operating cash flow increase and use that to reinvest in their own growth, in more of our tooling.
33:53
SPEAKER_01: And so, you know, a$100 billion company means that call it, you know, there's a company that is approaching$10 billion and run rate or whatever multiple it is that you you you want to use, that's still a fraction of a fraction of total healthcare expenditure and and and total transaction volume and all these numbers.
34:09
SPEAKER_01: And so it still wouldn't be a very penetrative company in healthcare.
34:12
SPEAKER_01: And so it's a good benchmark just from, you know, if we do our job reasonably well and we continue to grow in these customers, there's line of sight to being a hundred billion dollar business.
34:20
SPEAKER_00: And you are now trying to expand also outside the US.
34:23
SPEAKER_00: Which are the focus geographies and why?
34:25
SPEAKER_01: Yeah, it's it's very interesting because uh the one, many of our, we were actually initially brought into these regions through our EMR partner, Meditech, uh, who's been like an amazing partner in the space.
34:37
SPEAKER_01: And between the Middle East, uh Australia, Europe, uh, a couple of regions in Asia as well, there's just been so much interest and pull uh that initially it was more of a uh, you know, we're gonna learn about this and go into these markets and have early meetings, but there were fast acting customers.
34:54
SPEAKER_01: And ultimately, you know, as a as a technology company, where you see fast acting customers who have real pains, that's signal to jump in.
35:01
SPEAKER_01: And the problems were also more standardized than we initially realized, where documentation is a problem, or even parts of the revenue cycle stack, even though the payers are different, they look quite similar.
35:13
SPEAKER_01: And so adapting our tools to the needs of the region was in order of magnitude easier than we initially thought.
35:18
SPEAKER_01: And language models have made localization and workflow optimization for a region much easier because just the volume of code you can write and the volume of, you know, the speed at which you can adapt software has increased as well.
35:30
SPEAKER_01: Um, so that's been an exciting part of the journey.
35:33
SPEAKER_01: We have multiple partners in the Middle East now, and multiple partners in in in the UK and in the rest of Europe.
35:38
SPEAKER_01: And I think that segment will continue to grow.
35:40
SPEAKER_00: What is the hardest resistance that you are getting still now from healthcare providers in the US?
35:46
SPEAKER_01: I I think there continues, and I see it as, you know, we have to do a better job of educating in terms of what, you know, when when you see the like level one of adoption is pretty simple, which is like, great, here's a note taker that integrates to your EMR and can automate a lot of documentation time, improve the quality of your documentation, and as a result, improve the dollars that you get back from insurance companies by reducing denial rates and increasing procedure capture rates.
36:13
SPEAKER_01: And so the the that that that sort of everyone believes.
36:17
SPEAKER_01: I think the thing that many health systems haven't realized is just how agentic some of these models have gotten in terms of solving tasks end-to-end, getting a claim back, reading the content, reading the patient's demographic file and their chart history, looking at previous denials and then formulating the fix on the fly can happen in a matter of seconds.
36:37
SPEAKER_01: And the I think continuing to educate CFOs and CIOs and CMIOs about those capabilities through good demos, through good integrations is part of our job.
36:48
SPEAKER_01: And that's that's really the resistance in that people want to make sure that when they hand something off to an autopilot and an agent that's gonna take on all these tasks, it has good error error bars, good error handling, can escalate when it's confused and not go rogue.
37:02
SPEAKER_01: Uh, and and that that continues to be part of the narrative and part of our work to make sure the product has that built in.
37:08
SPEAKER_00: One of the resistance of healthcare systems in the US of adopting agenti technology has been uh ultimately if something goes wrong, lives are at stake.
37:18
SPEAKER_00: So who takes the ultimate accountability?
37:20
SPEAKER_01: Yep.
37:21
SPEAKER_01: Yep.
37:21
SPEAKER_01: I I think the legal structures today are pretty clear on this.
37:25
SPEAKER_01: And I think the right approach is ultimately if you're, you know, the entity that has the malpractice insurance, the entity that is rendering the care, uh, the entity that's making the final call still needs to be a human.
37:37
SPEAKER_01: And that's why, you know, purely AI-driven care, in my opinion, is not the right approach.
37:41
SPEAKER_01: You need to have a human in the loop.
37:43
SPEAKER_01: And that's what physicians are trained for.
37:44
SPEAKER_01: They're trained for accountability.
37:46
SPEAKER_01: They're trained to look at a set of symptoms, analyze them, consult with a second opinion, third opinion where needed, but ultimately make a call.
37:54
SPEAKER_01: And the and the model can help with that and can help synthesize information.
37:59
SPEAKER_01: But I think it's very important that humans are still in that loop and are making that decision.
38:04
SPEAKER_01: Uh, that's how the healthcare organizations that we work with are set up.
38:07
SPEAKER_01: And that's that is also how most patients want care ultimately rendered.
38:11
SPEAKER_01: So uh the the risk, and part of the risk is also, you know, it it is taken on by us in in as a technology provider.
38:17
SPEAKER_01: We need to render a good output, just the same way that you know, if your car malfunctions, uh there's some maybe amount of blame on the truck driver, but you know, ultimately, like the the the truck and the vehicle automation software or whatever's running needs to deliver.
38:32
SPEAKER_01: And so there's there's some of that that we take on as well, and often on a contract-by-contract basis.
38:37
SPEAKER_01: Um, but the provider is the provider and we want to guide them, but we never want to make a call for them.
38:43
SPEAKER_00: So it's not a fully uh FSD mode right now.
38:46
SPEAKER_01: Not full FSD, particularly in clinical care.
38:48
SPEAKER_01: Now, I think it's a different story in the back office.
38:50
SPEAKER_01: I think the back office where it's claims adjudication and it's financially driven and it's much more focused on completing a series of tasks and clearing uh uh admin tax, that's an area where FSD is very alive and very real.
39:04
SPEAKER_01: And for us, 85% of our claims don't touch a human.
39:07
SPEAKER_01: They're entirely autonomous.
39:09
SPEAKER_01: And everything from the packaging of the claim, the submission, the coding of the claim, and then the back and forth with the pair is done by a model, which is very exciting.
39:17
SPEAKER_00: So for a for a patient, this is still in the back office.
39:20
SPEAKER_00: For a patient, when can a healthcare experience in the US become like Waymo?
39:25
SPEAKER_01: Yeah, I think that's a great question.
39:27
SPEAKER_01: And that for us, that's the goal.
39:29
SPEAKER_01: That that is when when we're going to health systems, that's what we're pitching, which is it should feel like a Waymo for the provider and for the patient, which is the the you everything but the most essential bit, which is the care itself, is stripped away and abstracted into beautiful software.
39:45
SPEAKER_01: And we're we're heading in that direction very much.
39:47
SPEAKER_01: So I think some of our practices today, especially in outpatient settings, uh, were we literally the provider will walk in with a microphone and or maybe you know something running on their Apple Watch, and they will see the patient, they'll converse with the patient, the documentation will automatically be generated.
40:06
SPEAKER_01: They won't even look in their EMR and the claim will be submitted before they walk out of the room.
40:11
SPEAKER_01: And to me, that and the patient will have called and a voice agent will have helped them schedule, reschedule, intake and eligibility information will have been handled, and everything will have been kind of loaded into as context for the model.
40:23
SPEAKER_01: And that seamless experience is getting very exciting where you just render care, you just focus on the care, and everything else is handled.
40:30
SPEAKER_01: Uh and the best software experiences, best product experiences feel like that.
40:34
SPEAKER_00: Uh, you have mentioned uh that uh your sharp learner, you know, that you're looking for to hire, yeah, are inspired by companies like Stripe, uh, Rippling, and Palantir.
40:45
SPEAKER_00: Yeah.
40:45
SPEAKER_00: What among these or from each of these organizations that you have absorbed in your hiring practices?
40:51
SPEAKER_01: I I think the when you look at folks that come from Palantir, they tend to be relentlessly resourceful.
40:57
SPEAKER_01: They're willing to uh and they have a very good instinct for adding value to a customer's life in that they will show up, they'll assess and understand these problems, and then they'll attack uh the problem head on and try to add value very, very quickly.
41:10
SPEAKER_01: Palantir's time to value for an organization of its scale is is so impressive.
41:15
SPEAKER_01: Uh, and that's something that we we try to bring into our, you know, we look for people that have those those same instincts.
41:20
SPEAKER_01: It's a blend of technical and non-technical, being able to really listen to the needs of a customer.
41:25
SPEAKER_01: Um, you know, when you when you look at a company like Rippling and how it was built, they were very intentional about building this multi-product platform business from the get-go, which you know has worked well for them in their segment, but I actually think works even better in healthcare.
41:40
SPEAKER_01: Uh, because the needs in healthcare and the patient story and the insurance story and documentation are so interconnected that you really can't just do one thing.
41:49
SPEAKER_01: It it is you know it's seductive to try to just do one thing, but you have to build the rest of the stack.
41:54
SPEAKER_01: And we've learned a lot from folks at Rippling and really how they've approached building a platform business from the get-go, um, making sure that the you know these things are modular, they listen to each other, they speak to each other.
42:06
SPEAKER_01: Um, I think when you look at the the urgency also at both of these companies, they operate with a you know very high velocity and and and in in just quarterly urgency in terms of shipping product, in terms of getting things out of the door.
42:19
SPEAKER_01: Frank Slootman businesses as well as Snowflake Service now.
42:22
SPEAKER_01: You know, we're big believers in the amp it up approach.
42:26
SPEAKER_01: And uh, you know, it it's it's like required reading for for senior leadership at the company.
42:32
SPEAKER_01: So um again, it he talks about speed, he talks about urgency, he talks about raising the bars and just always trying to find a way to get the thing done faster and questioning the default timelines.
42:42
SPEAKER_01: Um startups are all about that, uh, questioning default timelines with your you know customers, your partners, with your internal teams, um, and and you just have to find a way to make things happen faster.
42:52
SPEAKER_01: Because you stick to defaults, you you will die.
42:54
SPEAKER_01: Like there, there are larger businesses that are equipped to service customers at the defaults.
43:00
SPEAKER_00: And if if you reflect back, what have been the some of the reasons why Commute became a success and one of the leading companies in healthcare?
43:09
SPEAKER_01: We're, you know, I I think we're still on the journey.
43:12
SPEAKER_01: So we're we're it it's you know long way to go before I would I would say that we're one of the enduring businesses in healthcare.
43:17
SPEAKER_01: But I think we're well on on track.
43:19
SPEAKER_01: And what it's come down to is there's a level of customer obsession and urgency.
43:25
SPEAKER_01: When you go to the office at 7 or 8 p.m.
43:28
SPEAKER_01: on any day, you always, there's always a huddle of people that are working on some important customer problem.
43:34
SPEAKER_01: And you know, the scale of thousands of customers, there's always going to be a small percentage of them where they're maybe, maybe there's a red alert where you know something, an integration broke because of their existing EMR, or you know, there's some confusing aspect, their dollars are down because they stopped performing XYZ procedure and they need us to help debug that for them.
43:52
SPEAKER_01: And there's a real care for the outcome of the customer itself as opposed to, I think historically software companies have been, we Sold you the tool, it figure it out.
44:02
SPEAKER_01: Like go, you know, use the tool, figure it out.
44:04
SPEAKER_01: And you can scale fast with that approach.
44:06
SPEAKER_01: There's no doubt.
44:06
SPEAKER_01: Like the beauty of software is it is really cookie cutter.
44:09
SPEAKER_01: Like you get it, and then you know you're required to figure it out and use it.
44:12
SPEAKER_01: I I think with language models and the age that we're in, customers expect and demand more, which is they really want to see, they want to see outcomes.
44:21
SPEAKER_01: They want to see real hard dollar success.
44:23
SPEAKER_01: And we can use LLMs as a weapon to go make that happen.
44:26
SPEAKER_01: So those those things have being super ROI-driven for our customers and not allowing ourselves to turn into a you know per seat salesman has been huge, and I think will continue to play to our advantage in the coming years as well.
44:40
SPEAKER_00: And uh till now the enterprises in the US have complained that with agents and AI, uh they haven't seen the cost come down.
44:48
SPEAKER_00: Obviously, there's an increase in amount of uh productivity gains are there.
44:53
SPEAKER_00: That's why you know hundreds of billions of consumption of AI tokens is there, but the the spend hasn't really come down.
45:01
SPEAKER_00: What's your view in healthcare?
45:02
SPEAKER_00: Has your customer spend come down?
45:05
SPEAKER_01: I I think what we've seen in healthcare very, very tangibly.
45:09
SPEAKER_01: And HCA talked about this in their earnings call.
45:11
SPEAKER_01: Um, I think a couple of our other customers have now talked about this on earnings calls as well.
45:15
SPEAKER_01: They've seen the productivity lift very clearly in terms of the number of appointments that a physician can do and the quality lift in terms of denial rates going down in appointments that are touched by AI.
45:26
SPEAKER_01: And so, because of that, you you see that you know some of our customers are driving to historical, historically high profitability and historically high revenue targets in a very difficult healthcare environment.
45:38
SPEAKER_01: And that's a you know a synthesis of strategies, their own growth, their own operational improvements, but AI is 100% part of that story, and Cameria AI tools are 100% part of that story.
45:49
SPEAKER_01: So we, you know, we see the healthcare story a lot clearer.
45:53
SPEAKER_01: I think the ROI story is much clearer.
45:55
SPEAKER_01: The challenge is that if you're an org and these new AI tools come around and you basically have everybody spending now an extra 10, 20 grand on tokens a year, you can quickly lose many of the benefits.
46:06
SPEAKER_01: And I think that's what's happening in some of these larger companies.
46:09
SPEAKER_01: But if you're a tech company that prints billions a year in revenue, you can kind of afford that experimentation tax.
46:13
SPEAKER_01: You you actually want to overspend for a little bit, understand the limits and capabilities, find the, you know, where the gold is hidden, and then follow that down.
46:22
SPEAKER_01: And then there will be a time, obviously, for austerity and you know, fix reducing these costs, and there will be entire companies built in that category.
46:30
SPEAKER_01: So the the other final thing I'll say about healthcare is there's also such a supply-demand imbalance where there are more patients to care for and more appointments to see than most hospitals can take on today, or most ambulatory settings can take on today.
46:46
SPEAKER_01: And because of that, AI has allowed them to service the needs of even just their existing customers.
46:51
SPEAKER_01: So we're seeing you know outcome scores improve, we're seeing you know, overall patient NPS improve, um, and then the dollars and the top line metrics improve as well in in tandem for these for these for these groups.
47:02
SPEAKER_00: But have you seen uh patient outcomes improve, let's say less wrong number of diagnoses, like uh patient recoveries happen faster, or surgery surgeries taking less amount of time, or surgeries being more than that.
47:17
SPEAKER_01: I think we will start seeing that in the next year or two.
47:20
SPEAKER_01: And the and the reason is is that the the adoption of AI was kind of step one was get these tools into the physician's hands and get the productivity improvements and you know, remove the need for them to be typing all day long.
47:34
SPEAKER_01: One, you have some immediate improvements from that itself, which is you have a higher quality appointment.
47:39
SPEAKER_01: You have the physician that's paying more attention, that's making fewer mistakes.
47:42
SPEAKER_01: And the number of times that we've seen, because the AI is running, you know, if it detects there might be a mistake, it services that as an issue to the physician and as a care cue, and the physician can quickly make a change or an intervention or get back on track.
47:55
SPEAKER_01: And and so the, but but I think as as these tools become more adopted, the concept of a care cue, or we call like a nudge, will become more commonplace as well, where it can actually read clinical guidance, help and guide the physician in terms of identifying the issue or even making a suggestion.
48:10
SPEAKER_01: And then we'll see the bend in the cost curve and the bend in the outcome curve in terms of those improvements.
48:15
SPEAKER_01: Um, it'll take time though, just because one, these are also large populations and these are large cohort studies that need to be run.
48:22
SPEAKER_01: But the anecdotes are there.
48:24
SPEAKER_01: And I think the anecdotes have made it very clear that the care quality does improve, uh in especially in these high acuity settings, because you have a more attentive physician and you have an AI assistant pulling the right clinical information at the right time.
48:37
SPEAKER_00: And uh let's say for India now, they're adopting systems, you know.
48:41
SPEAKER_00: Um, so it's obviously learning from the US, but it wants to avoid the mistakes that US has made.
48:48
SPEAKER_00: What would be your guidance on that?
48:50
SPEAKER_01: I think it's a very exciting opportunity because it there's an opportunity to leapfrog, to your point, many of the mistakes that were made over the last 20, 30 years in that in the US health system.
49:00
SPEAKER_01: And I think the the the biggest I have you know some very specific recommendations.
49:05
SPEAKER_01: And one is you you want to keep and allow care to be as simple as possible, and also at the same time avoid weird supply-demand uh uh mishaps that emerge from you know uh a regulatory uh burden.
49:20
SPEAKER_01: And so in the states, I think like an insane policy that we have today is that a physician licensed in Kentucky can't serve a patient that's in California.
49:29
SPEAKER_01: And so you basically you're not, you don't have liquidity in terms of your supply pool of clinical labor.
49:35
SPEAKER_01: And so, same thing with a nurse, and there's all this documentation and re-credentialing and contracting that needs to happen.
49:41
SPEAKER_01: And that it should basically you want like a very simple federal level licensing and credentialing uh uh process.
49:49
SPEAKER_01: Um, and that should hold true for the payers as well.
49:52
SPEAKER_01: Often what happens is if the payers have their own providers, their own provider groups, they have like these incentives to direct care in certain directions versus other directions.
50:02
SPEAKER_01: And there should just be more transparent pricing.
50:05
SPEAKER_01: So, pricing transparency in the US was a great thing.
50:07
SPEAKER_01: I think pricing transparency should be the default and should be where it, you know, things start in India.
50:11
SPEAKER_01: And good regulation there can make a big impact.
50:14
SPEAKER_01: Uh, because once you have pricing transparency, you have pricing standardization and normalization, and you, you know, care and across the street is going to be roughly the same amount, and you can actually have competitive dynamics emerge.
50:24
SPEAKER_01: So the broad recommendation is allow competitive dynamics to emerge by not overtly regulating licensure and overtly regulating how uh uh credentialing and contracting is done.
50:36
SPEAKER_01: Um, the the other piece is you you really don't um there's this big disparity today in the states in terms of how hospital and inpatient care is paid versus outpatient care.
50:47
SPEAKER_01: And because of that, there's a huge drive now to transition more and more care to outpatient settings because it's so much cheaper.
50:53
SPEAKER_01: And the same procedure in one setting gets paid, you know, three times or four times more than a procedure in another setting.
51:00
SPEAKER_01: You want to normalize that.
51:01
SPEAKER_01: You really want to, one, it creates a competitive playing field for the startups and the smaller upstart uh uh physician practices and allows, you know, like a 20, 30 provider group to emerge and compete with the hospital that's been around for 30 years.
51:15
SPEAKER_01: Uh, but at the same time, it also ensures that you're not like overpaying for the same procedure, which happens with insurance a lot of times.
51:22
SPEAKER_01: Uh, and then I would say like embrace an AI native system from the get-go, which is you like tasks like documentation, uh, claim submission, the appeals denials process, it should be agentic.
51:33
SPEAKER_01: There's no reason for a human to be reviewing standards and saying no.
51:37
SPEAKER_01: An LLM can actually do a better job of that and be more transparent about what needs to be fixed.
51:41
SPEAKER_01: Ultimately, you want the claim to get paid.
51:43
SPEAKER_01: Even as an insurance company, if if it is not a fraudulent claim, you want to get it paid so that the dollars continue to flow through the system.
51:50
SPEAKER_01: And the, I think LLMs really help in that endeavor.
51:54
SPEAKER_01: So that that's that's like the the the high level of you know how I design the system.
51:59
SPEAKER_00: But today, are you only on the on the provider side, or also are you providing solutions to the insurers?
52:05
SPEAKER_01: We we exclusively work for providers.
52:07
SPEAKER_01: So our entire tech stack is directed at providers, is you know, all the engineering work that we do, um, claims processing, ambient documentation is for the providers.
52:17
SPEAKER_01: And I think that lets us keep our incentives very much in line with them.
52:20
SPEAKER_01: And we want them to grow and we want them to flourish.
52:22
SPEAKER_01: And in some ways, it creates a bit of an antagonist with the insurance companies.
52:26
SPEAKER_01: But that's good.
52:27
SPEAKER_01: Like I think every good company needs a needs a great enemy, and insurance companies are a wonderful enemy to fight against.
52:32
SPEAKER_00: Yeah.
52:33
SPEAKER_00: So and then why would you take open battle against insurance companies?
52:38
SPEAKER_01: Well, well, one, I I think the uh it makes it more fun.
52:43
SPEAKER_01: Um, and I think there it's a worthy cause to come battle against, which is we want the the fact that some of the uh the fact that insurance companies have such a they're basically monopolies.
52:55
SPEAKER_01: So which is why when I you know when you look at like US policy around monopolies, the fact that they're coming after like Facebook and and Google makes no sense.
53:03
SPEAKER_01: Like it's so easy to switch between Google and some other search engine if you want.
53:06
SPEAKER_01: It's almost impossible to change the hospital that you visit because it is quite literally a regional monopoly.
53:12
SPEAKER_01: And and it's quite it's much harder to change the insurance company when there's only two options in the entire state.
53:18
SPEAKER_01: So the the is a as a broader point, I think many of these insurance companies have settled into being regional monopolies.
53:24
SPEAKER_01: And we have to break that in some way.
53:26
SPEAKER_01: And technology is one of the approaches that can aid that.
53:30
SPEAKER_01: Um and so they're a worthy opponent because of the fact that they've created so many steps that make it hard to practice medicine.
53:38
SPEAKER_01: And the real value in healthcare is the practice of medicine.
53:42
SPEAKER_01: If we can eliminate all of these other steps, really insurance companies should be profit-minimizing exercises.
53:47
SPEAKER_01: Like you don't want a lot of glutton profit to accumulate there because ultimately, you know, the the the premiums should just be covering the um the aggregate premiums should just be covering the total medical claims.
53:59
SPEAKER_01: And and that that's gotten out of whack in terms of how the system is set up today.
54:04
SPEAKER_00: Yeah, because because for a patient, the cost of surgery, let's say in a US is maybe still 20x expensive than India.
54:12
SPEAKER_01: Yeah.
54:12
SPEAKER_01: And I think a big part of that is the administrative state, is the fact that it takes so many steps to build the patient's insurance, to work on denials, to the back like the back and forth.
54:23
SPEAKER_01: And then also the regulatory costs.
54:25
SPEAKER_01: It's expensive to build a surgery center today.
54:27
SPEAKER_01: And we have to eliminate some regulation to make that easier and easier so more of them can pop up.
54:32
SPEAKER_01: And you know, with any market, if you create competitive dynamics, the pricing will go down and it'll it'll come to its fair market value.
54:38
SPEAKER_00: Yeah, because uh ultimately whatever happened in the healthcare system is coming out of the patient's pocket.
54:45
SPEAKER_01: For sure.
54:45
SPEAKER_01: 100%.
54:46
SPEAKER_01: And it it is it's turning into one of the largest line items for employers as well.
54:51
SPEAKER_01: Um, it is a massive line item for most families.
54:54
SPEAKER_01: And so the problem needs to be fixed.
54:56
SPEAKER_01: That the dollars are becoming unsustainable.
54:58
SPEAKER_00: So why not build another insurance company to solve that?
55:02
SPEAKER_01: I I think it's a fascinating approach.
55:04
SPEAKER_01: Like the to build a, and there are companies in the GC portfolio and you know, in the Sequoia portfolio that are working on the insurance side of the problem.
55:12
SPEAKER_01: I I think the there's there's so much low-hanging fruit on the provider side of the problem in terms of let's just make a practice run more efficiently.
55:22
SPEAKER_01: And if profit emerges there, then you can reinvest that in better care.
55:26
SPEAKER_01: It's closer to the types of problems that excite me personally, which is the actual rendering of care.
55:31
SPEAKER_01: And the insurance side, I think from an incentive standpoint and a regulatory standpoint, there's it it building a good insurance company often comes down to building a very efficient like denials machine, or also you have to get like very large at very large populations, be able to predict, you know, Oscar Health has done an amazing job of this.
55:48
SPEAKER_01: If you can basically like predict medical loss at a, you know, with a very high degree of specificity and have enough patients such that you know the the your your models, your modeling works at scale and actually applies at scale.
56:02
SPEAKER_01: So in my opinion, the LLMs are uniquely situated to create operating margin improvement for providers first and clinical and care improvement.
56:11
SPEAKER_01: And that's where our team enjoys spending its time.
56:14
SPEAKER_00: What are some of the other problems that you are not attacking right now on the provider side that could be opportunities for other entrepreneurs, then probably then you can partner with them?
56:23
SPEAKER_01: Yeah, I mean, I'm fascinated by like when you look at like drug discovery, it is it's not quite a provider problem, but it is like ultimately a care problem.
56:30
SPEAKER_01: And it feels like the moment is now ripe.
56:33
SPEAKER_01: There's been so many companies that have been built over the years and blown up and run out of funding.
56:37
SPEAKER_01: Um and I think now there with the the types of things that we can do with Transformers, the amount of compute that we've scaled up to, the amount of dollars that pharma has generated as well.
56:48
SPEAKER_01: I mean, you have trillion dollar pharma companies now because of GLP ones.
56:52
SPEAKER_01: Um, and I think that's a good thing.
56:53
SPEAKER_01: Like pharma companies produce a real product, and they and that real product helps very real people.
56:59
SPEAKER_01: So uh it's great timing to make these large compute investments to develop interesting simulation and drug discovery companies.
57:07
SPEAKER_01: I also think that uh when it comes to the provider stack itself, the physical parts of the provider stack, the medical devices, the robotics, the surgery instrumentation, it's an entire part of the economy that is run basically by four or five companies that operate again as monopolies.
57:23
SPEAKER_01: And there are opportunities and parts of that to go disrupt with faster engineering timelines, cheaper devices, um, and just reintroduce competitive dynamics into the space.
57:34
SPEAKER_01: And so we're very excited about medical robotics and medical like suppliers, um, some of the largest companies in healthcare, strikers,$150 billion company, Metronic, Abbott,$100 billion companies.
57:44
SPEAKER_01: Uh, and no one really talks about them, but they're big parts of the economy.
57:48
SPEAKER_00: Yeah.
57:48
SPEAKER_00: So you have done 15 aquihires and how many acquisitions?
57:52
SPEAKER_01: We've done, I would say, like five meaningful acquisitions and then 15 aqua hires.
57:57
SPEAKER_01: Ex-founders are some of the best talent in our business.
57:59
SPEAKER_01: They're aggressive, they know what it takes.
58:01
SPEAKER_01: Uh, we we really do believe that we're a great training ground for founders as well with the intensity that we operate at.
58:08
SPEAKER_01: Um, so it's it's a aqua hires have been a great source of talent for us.
58:11
SPEAKER_01: And I always tell our team and you know, people outside of the company, if they're running a company and they ever feel like they want to join a larger ship that has a ton of distribution, we should talk because there is a uh there's a conversation to be had for sure.
58:24
SPEAKER_00: What are the incentives for investors and let's say founders to to to be aqua hired?
58:31
SPEAKER_01: I think for founders, the incentive is, you know, let's say you took a stab at an idea and it kind of worked, kind of didn't.
58:38
SPEAKER_01: Maybe some aspects uh worked out and you got stuck in the distribution rut.
58:42
SPEAKER_01: Maybe you know you had an interesting product, but it just wasn't able to take off, which happens a lot.
58:48
SPEAKER_01: Um it it is useful, whether you know it's Camera or another business, to see what excellence looks like at scale.
58:56
SPEAKER_01: What does great distribution look like?
58:58
SPEAKER_01: What does great implementation look like?
58:59
SPEAKER_01: What are some of the problems in healthcare?
59:01
SPEAKER_01: And you can't see that unless you really work at one of these businesses.
59:04
SPEAKER_01: So come work here for you know four or five years and then go start the next thing.
59:08
SPEAKER_01: And I think for investors, um, we we're often a good soft landing because we're, you know, Kamura will be a big business.
59:15
SPEAKER_01: We're on the path to be a$100 billion plus business, their equity will appreciate.
59:20
SPEAKER_01: And so when we structure these deals, uh, there's a way to make it as a win for the investors where their pref stack converts in a favorable way and the uh the you know the existing founders get uh meaningful compensation where they're incentivized to continue to grind for a couple of years and build product with us.
59:36
SPEAKER_01: Um and and then when they start their next thing, their investors can take another swing or we can take another swing.
59:41
SPEAKER_01: And that makes it makes it exciting.
59:43
SPEAKER_00: Have you incubated other companies inside Commune?
59:45
SPEAKER_01: We've never incubated other companies, but um we've obviously over the years had folks that have gone on to start their own companies.
59:52
SPEAKER_01: We've had folks start companies and then come back and boomerang back to us where we back will hire the you know the those businesses.
59:58
SPEAKER_01: Um and so you know, ultimately I think we're good founder training ground because you every engineer spends so much time with the customer.
1:00:06
SPEAKER_01: Every salesperson spends so much time with the engineers, and we're very aggressive in office culture.
1:00:11
SPEAKER_01: So you get to feel and see that in person all like all the time.
1:00:15
SPEAKER_00: For a fast growing startup uh like Commune, when a founder is considering an opportunity versus let's say some of the larger labs, how do you win that talent?
1:00:26
SPEAKER_01: It's I mean, the talent wars are more aggressive now than ever before.
1:00:30
SPEAKER_01: Um, I do think there's a certain type of talent that we attract that the labs don't and might not even be good fits for the labs, honestly, which is you know, we're we're we're addicted to gritty people, people that want to see problems through, finish hard problems.
1:00:46
SPEAKER_01: And the labs candidly right now are attracting a lot of people with shiny object syndrome, which is one, the compensation packages are wonderful.
1:00:53
SPEAKER_01: And you know, you get one of these crazy compensation packages, you should, you know, it's rational to take it.
1:00:58
SPEAKER_01: Um, but a lot of people are going there for the wrong reasons.
1:01:01
SPEAKER_01: I mean, Dario himself said that it's turned a little bit into like a mercenary culture.
1:01:05
SPEAKER_01: Whereas here, you get direct customer impact every day.
1:01:09
SPEAKER_01: You you are in front of the customers, you're solving problems for the customers.
1:01:12
SPEAKER_01: And I think a lot of value will accrue at our layer, which is like that final application layer where the the pri the last inch or the last mile, whatever you want to call it, is how these models get developed.
1:01:23
SPEAKER_01: And we're in pricing bake-offs with all the models every day.
1:01:26
SPEAKER_01: Like we we pick the best model for the job, and we're extracting every last bit of you know margin from them as we can.
1:01:33
SPEAKER_01: Uh, and you know, the minute one, you know, we detect that there's a cheaper model that can accomplish that task more efficiently for our customer, we'll pass those savings onto our customer and take advantage of that.
1:01:44
SPEAKER_01: And so the foundation model companies are serving a very important role.
1:01:49
SPEAKER_01: Um, and they are some of the most fascinating businesses ever built.
1:01:53
SPEAKER_01: But I think there are more interesting problems being solved when you're a domain expert in the arena working with the customer and actually adapting the software to the needs of uh of that customer.
1:02:04
SPEAKER_00: Thank you so much, Taneh.
1:02:05
SPEAKER_00: Love the conversation, love learning from you.
1:02:08
SPEAKER_01: Thank you for having me.
1:02:09
SPEAKER_01: It was a lot of fun.
1:02:10
SPEAKER_00: And specifically, I come from a background of building a healthcare software company in India.
1:02:15
SPEAKER_00: So I can imagine how hard it is to build a company of this scale.
1:02:19
SPEAKER_01: And and you did it, you did it a while ago, and so kudos because it it sounds like it's scaled to millions of patients, and um it is very hard.
1:02:26
SPEAKER_01: So I a lot of respect for anyone that's ever built a health care.
1:02:29
SPEAKER_00: Thank you so much.
1:02:30
SPEAKER_00: I appreciate it.
1:02:31
SPEAKER_01: Amazing.
1:02:31
SPEAKER_01: Thank you.