Speaker 1: Pushkin.
Speaker 2: Going to the doctor sucks, and not just because you're sick,
Speaker 2: but because it's hard to make an appointment. Then you
Speaker 2: finally get an appointment, and you go in and you
Speaker 2: fill out all these forms. Then you wait for whatever
Speaker 2: an hour. Then you go in and see the doctor
Speaker 2: and she asks you all the questions you just answered
Speaker 2: on those forms.
Speaker 1: And by the way, it's not great for the doctor either.
Speaker 2: She has to see a million patients and do all
Speaker 2: this paperwork and doesn't have time to keep up with
Speaker 2: what's happening in the medical literature with all the new
Speaker 2: studies and recommendations.
Speaker 1: And everybody knows this is true.
Speaker 2: Nobody's figured out how to fix it yet, but it
Speaker 2: is a problem that people are.
Speaker 1: Trying to solve. I'm Jacob Goldstein and this.
Speaker 2: Is What's Your Problem, the show where I talk to
Speaker 2: people who are trying to make technol logical progress. My
Speaker 2: guest today is a lawn block. Alan is a serial entrepreneur.
Speaker 3: He was the co CEO of Wix, the site for
Speaker 3: building websites. He founded a company called Room that buys
Speaker 3: and sells used cars, and then in twenty sixteen Alan
Speaker 3: founded a company called k Health.
Speaker 1: Today, thousands of patients a month are treated through k Health.
Speaker 2: The company has this AI based patient interface and they
Speaker 2: employ around one hundred.
Speaker 3: And fifty doctors through an affiliate, and k help has
Speaker 3: big growth plans. They just raised another fifty million.
Speaker 1: Dollars to keep growing.
Speaker 2: The problem Alan and k help are trying to solve
Speaker 2: is this, can you use data and technology to make
Speaker 2: going to the doctor better? Better for patients and for doctors.
Speaker 4: We built essentially a machine that can engage with you.
Speaker 4: It's a predictive mention that is trying to figure out
Speaker 4: the next best question to ask Jacob. Jacob comes in
Speaker 4: and he's got a headache, and and and you know,
Speaker 4: if we don't know anything about you, we asked for
Speaker 4: your age and your gender, and we have a series
Speaker 4: of conversations given what we know, you know, you know,
Speaker 4: what is the next best question to us to get
Speaker 4: to the differential diagnosis?
Speaker 3: I will say, uh, this is in fact some some
Speaker 3: part of your product as it exists now is is
Speaker 3: what you're describing. And I know this because I tried
Speaker 3: it out before this interview, and as it happens, true story,
Speaker 3: I have COVID right now. Despite my chipper demeanor, I
Speaker 3: feel like asks. And so yesterday I loved Indo K
Speaker 3: Health And the one thing I didn't tell it, I
Speaker 3: didn't I have had.
Speaker 2: A positive COVID test. But I thought it would be
Speaker 2: too easy to tell it that I had a positive
Speaker 2: COVID test. But I said I have. I'm feeling tired?
Speaker 2: Was my ask you?
Speaker 3: You log in and ask you first, like you know
Speaker 3: you're presenting complain, asked like why are you here today?
Speaker 3: What seems to be the trouble? And I started with tired.
Speaker 3: And it has a specific list of possible answers. Right,
Speaker 3: it's not like it's not like chat GPT, it's not
Speaker 3: free form, right, it's very it's clearly categorized, and it
Speaker 3: puts your answer into keating. And so once I said
Speaker 3: I was tired, it asked me a lot of other
Speaker 3: things that said, when did you start a fatigue? Right,
Speaker 3: it called it fatigue, and then it's how long have
Speaker 3: you been feeling fatigue? Lots of questions about fatigue any,
Speaker 3: did your fatigue start after eating, after sun exposure?
Speaker 2: Are you more or less fatigued at the time of day?
Speaker 3: And then it asked me other symptoms that I said,
Speaker 3: I had a fever and It asked me lots of
Speaker 3: specific questions including have you been in close contact with
Speaker 3: anyone who has COVID? Lots of other symptoms, you know,
Speaker 3: loss of smell, no malaise, yes, lots of general questions,
Speaker 3: and then it said the results I'm about to show
Speaker 3: you are not a diagnosis or medical advice, which I
Speaker 3: get that. And then and then it gave me an
Speaker 3: interesting It gave me a probabilistic answer, right, which was
Speaker 3: quite interesting to me. The sort of final screen, and
Speaker 3: I got a screen of I'll just read it to
Speaker 3: you now.
Speaker 2: It says what your symptoms may mean, and then it says,
Speaker 2: here's how eleven thousand, eight hundred and eighty one people
Speaker 2: with cases like yours were diagnosed sixty percent COVID, nineteen
Speaker 2: forty percent upper respiratory infection. And then it said think
Speaker 2: you needed doctor, says it get better from home today,
Speaker 2: chat with a clinician now, and that's we can talk
Speaker 2: about that part later, but let's just talk about I'm
Speaker 2: really interested in that, the specific quantitative nature the eleven thousand,
Speaker 2: eight hundred and eighty one people with cases like yours,
Speaker 2: and then the probabilistic outcome. That's really interesting to me
Speaker 2: because it's sort of like what would.
Speaker 3: Happen if you go to the doctor, but also sort
Speaker 3: of different. So tell me about that.
Speaker 4: So first of all, that makes me ready happy and
Speaker 4: we should have started there. I'm so happy you used it.
Speaker 4: And by the way, coming in with the fatigue case
Speaker 4: is not trivial. You go to your primary care or
Speaker 4: good care and say I have fatigue. Okay, there's many
Speaker 4: many things that can lead to fatigue. It can be temporary,
Speaker 4: can be long term term. It could it could it
Speaker 4: can be related to a serious health condition, or it
Speaker 4: can be something transitory. So that's great. You came there
Speaker 4: and you acted all down. Apologies, but you didn't say
Speaker 4: I have COVID door I think I have COVID dours effected.
Speaker 4: You gave it was a broad based thing. Fatigue can
Speaker 4: also be related to cancer. It can be related to
Speaker 4: thyroid issues, insomnia. So so what what we're trying to
Speaker 4: do just to tile the pieces together. It builds a
Speaker 4: dynamic cluster of people like you, of this, of this,
Speaker 4: of these people that have the same gender, age, medical
Speaker 4: history and symptoms that are relevant to what you're having,
Speaker 4: and it's trying to figure out what it has it
Speaker 4: gives you a statistical output because it's a probabilistic nature
Speaker 4: of what you might have. And it's and as as
Speaker 4: as we say very clearly we don't diagnose you, but
Speaker 4: compare it to doctor Google or any other system out there,
Speaker 4: you won't get that rigger. And every one of the
Speaker 4: questions we ask in our clinical AI is a is
Speaker 4: a very specific question. Is in medicine, this is not
Speaker 4: a hallucinating let me just try and guess what you have.
Speaker 4: It's not chat ept, it's not trying to predict some
Speaker 4: set of sentences. It's trying to predict your diagnosis. We
Speaker 4: generate a diagnosis in treatment, right, but because you know
Speaker 4: this is not regulated, we tell people this is what
Speaker 4: you might have, and if you want to, you can
Speaker 4: press about it and be in front of a doctor
Speaker 4: twenty for seven and deal if not.
Speaker 2: In fact, it says it is not a diagnosis. You're
Speaker 2: saying it's a diagnosis.
Speaker 4: Trying to make Jesus to be clear, this is not
Speaker 4: a diagnosis. Is trying to mimic a diagnosis. What we're
Speaker 4: trying to generate is trying to mimic a really high
Speaker 4: quality physician by having this medical conversation, this medical chat
Speaker 4: and having showing the differential because maybe if you didn't
Speaker 4: have a positive laptist, maybe it's maybe it's an opera
Speaker 4: respiratory infection, maybe it's something else going on here. And
Speaker 4: our goal was not to say, Okay, this is what
Speaker 4: you have and it's impossible that it's something else, because
Speaker 4: that's not how medicine works. The goal is to consider
Speaker 4: the diagnosis. But that's a tricky part of medicine. There
Speaker 4: is a science and art here about how to do it,
Speaker 4: and we're trying to mimic that capability. But in it
Speaker 4: I think it's quite magical that you can engage with
Speaker 4: a machine, have an understanding what you might have and
Speaker 4: which is not some kind of guess, work on doctor Google,
Speaker 4: and then press a button and was in a matter
Speaker 4: of minutes and be in front of a doctor and
Speaker 4: get medical advice and get prescription, get anything else, anything
Speaker 4: else you need twenty four to seven.
Speaker 2: So to build this thing, this machine that's trying to
Speaker 2: come up with, you know, something like a differential diagnosis,
Speaker 2: obviously you need a ton of data. And I know
Speaker 2: there's this key moment early in the life of the
Speaker 2: company when you got access to this like big beautiful
Speaker 2: data set. Tell me about that, Okay, So.
Speaker 4: People pointed me all over the world to try and
Speaker 4: find data. At the time people spoke to me about Estonia, Denmark.
Speaker 4: Any conversation I try to have with a with a
Speaker 4: large hospital group in America at the time was completely stonewalled.
Speaker 4: I can get anywhere because of hip et cetera. And
Speaker 4: we ended up licensing a data set from an HMO
Speaker 4: in Israel called Maccabi. And I'll explained specifically what we
Speaker 4: have there. At the time, we didn't realize just how
Speaker 4: what do you call it? Big and beautiful? The data
Speaker 4: set is Maccabi is structured similar to Kaiser Permanenta. So
Speaker 4: it's an insurance company that also has its own captive providers,
Speaker 4: but they also have their own drugstore, their own lab,
Speaker 4: a single EMR, their own hospital.
Speaker 2: If providers means doctors who work for the company. Just
Speaker 2: to be right, okay. So it's a complete integrated health system, right.
Speaker 3: People who are in this system, they get their prescriptions there,
Speaker 3: they go see the doctor there, they go to the
Speaker 3: hospital there. So this Macabi, this Israel, is going to
Speaker 3: have all of the data for the patients there.
Speaker 4: And then one more big step. People don't switch HMOs
Speaker 4: in Israel. So we essentially got access to two and
Speaker 4: a quarter million people for a data set over twenty years,
Speaker 4: and we essentially were a fly on the wall for
Speaker 4: all these people. Did they go to a doctor, did
Speaker 4: they get a prescription? We know also if they picked
Speaker 4: it up, we know, if they had labs, we know.
Speaker 4: We could put together a time series of all of that,
Speaker 4: and of course postilizations as procedures, surgery's outcomes. So just
Speaker 4: to give you a sense, we got access to about
Speaker 4: four hundred million medical charts, over a billion labs, half
Speaker 4: a billion prescriptions, two million hospitalizations.
Speaker 2: And I presume it's anonymized.
Speaker 4: We built an anonymizer.
Speaker 3: So you have this very large, very robust, very complete,
Speaker 3: longitudinal data set.
Speaker 2: Great, what do you do with it?
Speaker 4: We wanted to focus on these things are both what
Speaker 4: you go to your doctor, your primary care physician, and
Speaker 4: to urgent care. My head hurts, my stomach hurts, I've
Speaker 4: got a rash, I've got an upper rest with our infection,
Speaker 4: I've got a fever. All these different things.
Speaker 2: And some kind of problem that just started. It's not
Speaker 2: a chronic disease.
Speaker 3: It's not a physical it's something that like, oh, my
Speaker 3: head just started hurting, I better go to the doctor.
Speaker 4: It's that, yes, And now it could be my head
Speaker 4: started hurting and I've got a history of migrains a synoscientists,
Speaker 4: and I think I know what it is, or it
Speaker 4: might be something completely out of the blue.
Speaker 2: What's one thing you tried at k Health over the
Speaker 2: course of the company that didn't work.
Speaker 4: We tried in the beginning as we were building through
Speaker 4: as we're learning healthcare, we're trying to send people. When
Speaker 4: we built the information, we said, okay, here's the information
Speaker 4: and we'll connect you to physical doctors in person in
Speaker 4: Manhattan and Brooklyn because we're basing the arc and then
Speaker 4: work at all.
Speaker 2: Why not? So why not?
Speaker 4: Because people wanted immediate solutions of their problems right now,
Speaker 4: that's a scientist or UTI or migraine. And you know
Speaker 4: the whole beauty was it twenty four to seven service?
Speaker 2: So was that when you realized that you had to
Speaker 2: hire doctors.
Speaker 4: We needed to hire doctors and build our own solution,
Speaker 4: own the resolution.
Speaker 2: Which is a huge that's a huge leap right there.
Speaker 2: You're going from being a software company to being like
Speaker 2: a much more complicated, much more expensive to build company
Speaker 2: at that moment.
Speaker 4: Right, Yeah, but you we didn't go in here, timid.
Speaker 4: We went in here. We want to make real changes.
Speaker 4: We don't want to be some yet another cute digital
Speaker 4: health company that does cute things and sells usself. We
Speaker 4: want to build a big, independent company, and we want
Speaker 4: to build a company that will make a difference in
Speaker 4: care delivery in people's lives, starting was primary care. So yeah,
Speaker 4: you have to you have to pick your battles.
Speaker 2: Yeah.
Speaker 3: Well, so let me ask since that came up, I mean, so,
Speaker 3: I know you're just raised another fifty million dollars, which congratulations.
Speaker 3: Hard to do at this moment from what I've heard
Speaker 3: for a lot of companies, and you've raised what three
Speaker 3: hundred million or so so far? Is there some Well,
Speaker 3: do you have a sense of when you'll be profitable?
Speaker 4: I think we'll be profitable in the second half of
Speaker 4: next year.
Speaker 2: Great, So let's let's talk more more fully about how
Speaker 2: your business works at this point, right, I have I
Speaker 2: tried out this sort of consumer facing first step, I
Speaker 2: didn't click through to talk with a clinician, but what like,
Speaker 2: how does the business work now?
Speaker 4: So our business is wet. We have both a direct
Speaker 4: to consumer relationships and we work with payers, and we
Speaker 4: work with large health system providers.
Speaker 2: And payers means insurance companies. Payers is what people are
Speaker 2: in healthcare call insurance companies.
Speaker 4: So we essentially offer our services and we get paid
Speaker 4: as a provider to take care of patients. So if
Speaker 4: it's direct to consumer, we have an offer for consumers
Speaker 4: and you can just sign up and pay out of pocket.
Speaker 4: We announced that a.
Speaker 2: Few weeks ago that we're.
Speaker 4: Working with Cedar Sinai. We're working with other health systems.
Speaker 2: Theesar sign up a big hospital system in Los Angeles.
Speaker 4: Yeah, the largest hospital system in California. We see thousands
Speaker 4: of patients every day, which would be the size of
Speaker 4: a large health system with multiple hospitals and multiple art
Speaker 4: patient centers.
Speaker 2: So large, and how often do you end up referring
Speaker 2: people to see a doctor in the physical world, or
Speaker 2: get a test in the physical world, to do something
Speaker 2: in real life, to do something in meat space.
Speaker 4: So we refer people to in person physician about eight
Speaker 4: percent of the time. That could be to the er,
Speaker 4: it could be to specialty care or to an person
Speaker 4: primary care. Okay, so I was going to say that,
Speaker 4: Just to be clear, there is many times a physical interaction.
Speaker 4: For example, our doctors will send a patient to do ELEB. Yeah,
Speaker 4: but ninety two percent of the time we can resolve
Speaker 4: it without a bricks and marker doctor reviewing that lab.
Speaker 4: A portion of the time we need to we need
Speaker 4: to refer the patient to an in person visit in
Speaker 4: primary care, or to specialty care or to er. But again,
Speaker 4: the vast majority of stuff we can do online. So
Speaker 4: it completely changes the paradigm on access because if it's
Speaker 4: ten pm, what are your choices?
Speaker 3: In a minute, Alan talks about the problems that k
Speaker 3: Health is still trying to solve. It's interesting to think
Speaker 3: about to compare k help Health versus a traditional doctor.
Speaker 3: Say good, you know, seventieth percentile a good but not
Speaker 3: amazing traditional doctor.
Speaker 2: And I'm curious. I would imagine there are some settings
Speaker 2: where KA health is better and some settings where a
Speaker 2: traditional doctor might be better. Some things that are just
Speaker 2: easier to detect in person, say, and I'm curious, like,
Speaker 2: are there particular kinds of cases that worry you? Are
Speaker 2: there particular kinds of patients who you think KA health
Speaker 2: may not be well well suited for.
Speaker 4: I think the same things that doctors will struggle with
Speaker 4: online are things that they will struggle with in person.
Speaker 4: Patients that are not compliant with their medications or with
Speaker 4: their care protocols. Patients that have are just very fragile,
Speaker 4: they're very old, they have you know, they have pneumonia,
Speaker 4: but they also to have multiple other background diseases. Remember
Speaker 4: that when you go into a primary care doctor or
Speaker 4: clinic and you walk out you're ass it's very difficult
Speaker 4: for the doctor to interact with you and follow up
Speaker 4: with you. So there's multiple advantages actually to build a
Speaker 4: system online. People can come back very easily to our platform.
Speaker 3: I mean, I've asked, is there somewhere where KA health
Speaker 3: is worse? And you've given me somewhere where KA health
Speaker 3: is better. But I am curious within the universe where
Speaker 3: you were treating people, are there places where for whatever
Speaker 3: then maybe there's a smell, maybe there's some you know,
Speaker 3: truly right, like there are things that can be perceived
Speaker 3: in the physical world.
Speaker 2: It's a genuine question.
Speaker 4: No, No, I understand the question. I think the areas
Speaker 4: that we're less strong and now is areas that we've
Speaker 4: seen for your patients. It's stuff a little bit different
Speaker 4: from what your intuition is. Your intuition was similar to mine,
Speaker 4: you know, six seven years ago. But it's stuff around
Speaker 4: or simpedic huh, back pain, extremity, medicine, you know. Uh,
Speaker 4: the trauma. We don't do trauma, you know, so so
Speaker 4: stuff around.
Speaker 2: That trauma trauma like did I break my wrist when
Speaker 2: I fell that?
Speaker 4: Or you've got a pain in your in your in
Speaker 4: your foot for a few days. Maybe it's planter fasciaetus
Speaker 4: if you've ever had it, it's pretty painful and appears
Speaker 4: out of the blue. But you know, maybe maybe you
Speaker 4: broke a bone or maybe you've got you know, you know.
Speaker 3: Is the reason is the reason you don't do that,
Speaker 3: because the doctor needs to touch the patient in short
Speaker 3: to make that diagnosi.
Speaker 4: I think we can build systems for orthopedics as well. Uh,
Speaker 4: it's just not it hasn't been an error focus about
Speaker 4: for us, and and over time, I think the whole
Speaker 4: online versus in person is going to be mute. It's
Speaker 4: kind of like going to the ATM to get out
Speaker 4: cash versus venmo. Both are utilities of of getting cash
Speaker 4: or transferring cash. You will use both interchangeably in different
Speaker 4: reasons for different settings. I think that's how medicine's gonna
Speaker 4: go to. If you can resolve your problem right now
Speaker 4: from home in a high quality, high confidence environment, and
Speaker 4: I can follow up with you wherever you are. Maybe
Speaker 4: you need a prescription, maybe not. It's typically better than
Speaker 4: going in in person. Sometimes you need to go in
Speaker 4: person for various things. Medicine is really tricky. It's really
Speaker 4: tough to I'm still at all of how doctors make
Speaker 4: decisions because they don't always have all the facts, but
Speaker 4: yet they need to act. They need to decide should
Speaker 4: I send the patient to the R do I tell
Speaker 4: them to rest at home? I would just jacb I'm
Speaker 4: jumping into something to me is ready burning and important
Speaker 4: for us. We built k to give people access to
Speaker 4: high quality medicine. I think we have uneven quality medicine. Yes,
Speaker 4: there's Mayo Clinic and Cleveland Clinic and top academic institutions
Speaker 4: that act as lost results or solving complex care Cedar
Speaker 4: sin I other institutions, But most of the time people
Speaker 4: need to manage their diabetes and hyppertension, or even better
Speaker 4: avoid getting diabetes talked to or high pretension. Many people
Speaker 4: who have insurance and have money do not manage their
Speaker 4: diabetes and hard conditions. Well, it's not one or two percent,
Speaker 4: it's double digit percent. Why Because it's tough, it's it's expensive,
Speaker 4: it's messy that you know who wants to deal with it?
Speaker 4: Can we build care delivery systems that are dramatically better,
Speaker 4: that are much more data driven and are tailored to
Speaker 4: you admin that take into account prevention. Everybody talks about
Speaker 4: prevention that are preventative systems are similar to for the
Speaker 4: most part, I'm not talking about oncology here to the
Speaker 4: seventies and the sixties. That's pretty awful.
Speaker 1: So let's this is interesting.
Speaker 2: Let's talk about it at a at a slightly more
Speaker 2: specific level, Like you're making a compelling kind of abstract,
Speaker 2: big picture society wide point. Tell me something specific.
Speaker 3: About what you're doing to do a better verse of
Speaker 3: helping patients manage their diabetes or high blood pressure.
Speaker 4: I'll give you one specific example. We took maoclinic data
Speaker 4: and we're able to look at mayoclinic patients in an
Speaker 4: anonymized manner over long periods of time, and look at
Speaker 4: high pertension patients and figure out the best medication or
Speaker 4: combination of medicines to stabilize high blood pressure given their
Speaker 4: current blood pressure, gender, age, comorbidity, and ethnicity, and be
Speaker 4: able to tell a doctor in that same statistical split
Speaker 4: here is potentially the best way to give this medicine,
Speaker 4: and then math medicine.
Speaker 3: And high blood pressure is presumably a good place to
Speaker 3: do that, because there are lots and lots of good, cheap,
Speaker 3: old high blood pressure medicines, but which ones to use
Speaker 3: or which combination to use for which.
Speaker 2: Patient is hard to figure out. Presumably that's why that's
Speaker 2: a good sort of target problem.
Speaker 4: I would venture to say most doctors do not in
Speaker 4: the in their head that algorithm of gender, age, medical history, ethnicity, culpabidity,
Speaker 4: and you know, the current blood pressure. So this is
Speaker 4: really helpful to our doctors. Eight percent of the time
Speaker 4: they change the patient's regimen or change change their mind
Speaker 4: around what they want to do. And it's based on
Speaker 4: male clinical mail clinics standard of care. So that's that's
Speaker 4: really powerful. Every day that you stabilize in better, you know,
Speaker 4: to reduce the risk of strokes and heart attacks.
Speaker 3: What's something you haven't figured out yet that you're still
Speaker 3: working on. What's what's the sort of frontier problem, a
Speaker 3: frontier problem that you're working on.
Speaker 4: Hmm. The stuff that we're working on integrating right now
Speaker 4: is tying what we're doing to a non hallucinating large
Speaker 4: language model. So how do you take what we're doing
Speaker 4: and we we help the patient understand what they might have,
Speaker 4: We put them in front of a doctor and they
Speaker 4: get diagnosed and tree. But let's assume the patient is
Speaker 4: a diabetic patient who also wants to lose ten pounds
Speaker 4: and doesn't like fish and wants to do a certain diet,
Speaker 4: and it needs to be tailored to what we do.
Speaker 4: How do I enable the patient in a free form,
Speaker 4: free text engage with the system that's non hallucinating, that
Speaker 4: is also tied directly to what we're doing. I think
Speaker 4: that's a direction we go.
Speaker 3: So plainly, what you have right now is very structured.
Speaker 3: I can't just come in and like say a million
Speaker 3: things about myself. I can have a presenting complaint and
Speaker 3: then your system will ask me some questions, and there's
Speaker 3: a sort of set universe of categories of conditions whatever.
Speaker 2: And plainly, large language models like chet GPT, which is
Speaker 2: very different from your system, offer a much more wide
Speaker 2: open playing field. A wide open means of interaction between
Speaker 2: a human and machine, with the notable downside, the profound
Speaker 2: downside in this case, that they are not reliable sources
Speaker 2: of information.
Speaker 3: Right, So what if I understand you correctly, what you
Speaker 3: want is the reliability of the system you have built.
Speaker 2: With the freedom of a large language model like chat chip.
Speaker 4: Yeah. So fundamentally, nobody wants to go to a doctor
Speaker 4: that hallucinates ten percent of the time and you don't
Speaker 4: know when the doctor's almost think that's kind of meaningless.
Speaker 4: It's going back to the same doctor Google WebMD problem.
Speaker 4: We care about the clinical context and that is really important.
Speaker 4: I think that's where I's going, and I think LLLM
Speaker 4: could be a huge ally here. But fundamentally, when you're
Speaker 4: asking people specific questions, when you're asking questions that are
Speaker 4: medical and clinical by nature, there's no guesswork here. It
Speaker 4: might need to be modified and change over time, but
Speaker 4: you're asking very specific questions. So for me, a big
Speaker 4: part of what we're answer is integrating that.
Speaker 2: We'll be back in a minute with the lightning round.
Speaker 2: You were the co founder and CEO of Room, a
Speaker 2: used car retail.
Speaker 4: Retail, and oh yeah, I.
Speaker 3: Definitely have a couple of used car questions for you. One,
Speaker 3: what's the most surprising thing you learned about the used
Speaker 3: car business?
Speaker 4: How predictable it is? Huh, how predictable car pricing at
Speaker 4: a certain period. But I was lucky to work there
Speaker 4: pre COVID, where you know, the supply and demand and
Speaker 4: completely changed. That was a big surprise to me. It's
Speaker 4: much more data driven and predictive than people realized.
Speaker 3: Okay, what's one tip for somebody buying or selling a
Speaker 3: used car?
Speaker 4: So most people are buying a car also trading in.
Speaker 4: They're selling a car, okay, And about eighty percent of
Speaker 4: the time people are taking a loan okay. And most
Speaker 4: people come in and they do reams of research and
Speaker 4: coming with their armed guard to a car dealer, and
Speaker 4: they focus just on the buy. In the meantime, they
Speaker 4: get a shitty price on the trade and they get
Speaker 4: a and they get a you know, a bad financing
Speaker 4: deal on their car, and that tax on you know
Speaker 4: many South of daughters, so the cost, but they got
Speaker 4: a great deal on the bi in their minds.
Speaker 3: I read that you bought a house on Ridyon Avenue,
Speaker 3: which was interesting to me because I used to.
Speaker 2: Live on Ridian Avenue in South Beach. Uh, And I
Speaker 2: couldn't think, like, oh, what's that? We both have lived
Speaker 2: on Bridan Avenue. Question, I don't know. Do you go
Speaker 2: to that Publis? How's South Beach strading you?
Speaker 4: I'm here right now. I spend most of the time, okay,
Speaker 4: most of the time in New York, but I'm here.
Speaker 4: I'm here right now.
Speaker 2: It's a very hot time to be on Ridian Avenue.
Speaker 4: From my experienced, I haven't here this time of the year.
Speaker 4: I have a pool, and I'm okay, and I'm close
Speaker 4: to the beach, and I love Miami and I love
Speaker 4: Miami Beach. So it's a lot of fun. It's never
Speaker 4: at an moment here.
Speaker 2: Well, so you grew up in Israel, you lived in
Speaker 2: New York.
Speaker 3: Right, New York little known fact is on the beach,
Speaker 3: and you have a house now in Miami Beach. You
Speaker 3: live a lot of the time in Miami Beach. Of
Speaker 3: all those places, where are the beach snacks? The best
Speaker 3: best beach snacks?
Speaker 4: Oh, Israel, by far, Israel is much more street food
Speaker 4: and snacks.
Speaker 2: Yeah, so, I mean, what is falafela beach snack? I
Speaker 2: don't think of falafel as a beach snack. What's the
Speaker 2: beach snack or is it falafel?
Speaker 4: Yeah, well you can have falafel and almost anywhere in Israel,
Speaker 4: so you could you can also get it on the beach.
Speaker 4: But people people in Israel a eating all the time everywhere,
Speaker 4: so you know, they also eat on the beach, and
Speaker 4: it's very family oriented and the beaches are crowded and
Speaker 4: people are happy.
Speaker 3: Alon Block is the co founder and CEO of k Health.
Speaker 3: Today's show was produced by Gabriel Hunter Chang and Edith Russlo.
Speaker 3: It was edited by Sarah Nicks and engineered by Amanda
Speaker 3: k Wah. You can email us at problem at Pushkin
Speaker 3: dot fm. You can find me on Twitter at Jacob Goldstein.
Speaker 3: I'm Jacob Goldstein and we'll be back next week with
Speaker 3: another episode of What's Your Pop?
Speaker 2: Don't Myself