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Speaker 1: Pushkin.

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Speaker 2: Going to the doctor sucks, and not just because you're sick,

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Speaker 2: but because it's hard to make an appointment. Then you

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Speaker 2: finally get an appointment, and you go in and you

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Speaker 2: fill out all these forms. Then you wait for whatever

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Speaker 2: an hour. Then you go in and see the doctor

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Speaker 2: and she asks you all the questions you just answered

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Speaker 2: on those forms.

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Speaker 1: And by the way, it's not great for the doctor either.

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Speaker 2: She has to see a million patients and do all

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Speaker 2: this paperwork and doesn't have time to keep up with

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Speaker 2: what's happening in the medical literature with all the new

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Speaker 2: studies and recommendations.

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Speaker 1: And everybody knows this is true.

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Speaker 2: Nobody's figured out how to fix it yet, but it

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Speaker 2: is a problem that people are.

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Speaker 1: Trying to solve. I'm Jacob Goldstein and this.

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Speaker 2: Is What's Your Problem, the show where I talk to

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Speaker 2: people who are trying to make technol logical progress. My

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Speaker 2: guest today is a lawn block. Alan is a serial entrepreneur.

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Speaker 3: He was the co CEO of Wix, the site for

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Speaker 3: building websites. He founded a company called Room that buys

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Speaker 3: and sells used cars, and then in twenty sixteen Alan

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Speaker 3: founded a company called k Health.

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Speaker 1: Today, thousands of patients a month are treated through k Health.

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Speaker 2: The company has this AI based patient interface and they

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Speaker 2: employ around one hundred.

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Speaker 3: And fifty doctors through an affiliate, and k help has

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Speaker 3: big growth plans. They just raised another fifty million.

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Speaker 1: Dollars to keep growing.

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Speaker 2: The problem Alan and k help are trying to solve

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Speaker 2: is this, can you use data and technology to make

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Speaker 2: going to the doctor better? Better for patients and for doctors.

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Speaker 4: We built essentially a machine that can engage with you.

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Speaker 4: It's a predictive mention that is trying to figure out

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Speaker 4: the next best question to ask Jacob. Jacob comes in

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Speaker 4: and he's got a headache, and and and you know,

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Speaker 4: if we don't know anything about you, we asked for

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Speaker 4: your age and your gender, and we have a series

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Speaker 4: of conversations given what we know, you know, you know,

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Speaker 4: what is the next best question to us to get

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Speaker 4: to the differential diagnosis?

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Speaker 3: I will say, uh, this is in fact some some

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Speaker 3: part of your product as it exists now is is

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Speaker 3: what you're describing. And I know this because I tried

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Speaker 3: it out before this interview, and as it happens, true story,

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Speaker 3: I have COVID right now. Despite my chipper demeanor, I

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Speaker 3: feel like asks. And so yesterday I loved Indo K

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Speaker 3: Health And the one thing I didn't tell it, I

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Speaker 3: didn't I have had.

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Speaker 2: A positive COVID test. But I thought it would be

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Speaker 2: too easy to tell it that I had a positive

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Speaker 2: COVID test. But I said I have. I'm feeling tired?

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Speaker 2: Was my ask you?

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Speaker 3: You log in and ask you first, like you know

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Speaker 3: you're presenting complain, asked like why are you here today?

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Speaker 3: What seems to be the trouble? And I started with tired.

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Speaker 3: And it has a specific list of possible answers. Right,

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Speaker 3: it's not like it's not like chat GPT, it's not

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Speaker 3: free form, right, it's very it's clearly categorized, and it

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Speaker 3: puts your answer into keating. And so once I said

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Speaker 3: I was tired, it asked me a lot of other

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Speaker 3: things that said, when did you start a fatigue? Right,

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Speaker 3: it called it fatigue, and then it's how long have

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Speaker 3: you been feeling fatigue? Lots of questions about fatigue any,

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Speaker 3: did your fatigue start after eating, after sun exposure?

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Speaker 2: Are you more or less fatigued at the time of day?

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Speaker 3: And then it asked me other symptoms that I said,

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Speaker 3: I had a fever and It asked me lots of

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Speaker 3: specific questions including have you been in close contact with

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Speaker 3: anyone who has COVID? Lots of other symptoms, you know,

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Speaker 3: loss of smell, no malaise, yes, lots of general questions,

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Speaker 3: and then it said the results I'm about to show

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Speaker 3: you are not a diagnosis or medical advice, which I

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Speaker 3: get that. And then and then it gave me an

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Speaker 3: interesting It gave me a probabilistic answer, right, which was

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Speaker 3: quite interesting to me. The sort of final screen, and

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Speaker 3: I got a screen of I'll just read it to

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Speaker 3: you now.

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Speaker 2: It says what your symptoms may mean, and then it says,

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Speaker 2: here's how eleven thousand, eight hundred and eighty one people

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Speaker 2: with cases like yours were diagnosed sixty percent COVID, nineteen

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Speaker 2: forty percent upper respiratory infection. And then it said think

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Speaker 2: you needed doctor, says it get better from home today,

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Speaker 2: chat with a clinician now, and that's we can talk

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Speaker 2: about that part later, but let's just talk about I'm

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Speaker 2: really interested in that, the specific quantitative nature the eleven thousand,

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Speaker 2: eight hundred and eighty one people with cases like yours,

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Speaker 2: and then the probabilistic outcome. That's really interesting to me

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Speaker 2: because it's sort of like what would.

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Speaker 3: Happen if you go to the doctor, but also sort

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Speaker 3: of different. So tell me about that.

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Speaker 4: So first of all, that makes me ready happy and

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Speaker 4: we should have started there. I'm so happy you used it.

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Speaker 4: And by the way, coming in with the fatigue case

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Speaker 4: is not trivial. You go to your primary care or

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Speaker 4: good care and say I have fatigue. Okay, there's many

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Speaker 4: many things that can lead to fatigue. It can be temporary,

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Speaker 4: can be long term term. It could it could it

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Speaker 4: can be related to a serious health condition, or it

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Speaker 4: can be something transitory. So that's great. You came there

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Speaker 4: and you acted all down. Apologies, but you didn't say

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Speaker 4: I have COVID door I think I have COVID dours effected.

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Speaker 4: You gave it was a broad based thing. Fatigue can

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Speaker 4: also be related to cancer. It can be related to

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Speaker 4: thyroid issues, insomnia. So so what what we're trying to

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Speaker 4: do just to tile the pieces together. It builds a

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Speaker 4: dynamic cluster of people like you, of this, of this,

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Speaker 4: of these people that have the same gender, age, medical

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Speaker 4: history and symptoms that are relevant to what you're having,

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Speaker 4: and it's trying to figure out what it has it

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Speaker 4: gives you a statistical output because it's a probabilistic nature

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Speaker 4: of what you might have. And it's and as as

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Speaker 4: as we say very clearly we don't diagnose you, but

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Speaker 4: compare it to doctor Google or any other system out there,

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Speaker 4: you won't get that rigger. And every one of the

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Speaker 4: questions we ask in our clinical AI is a is

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Speaker 4: a very specific question. Is in medicine, this is not

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Speaker 4: a hallucinating let me just try and guess what you have.

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Speaker 4: It's not chat ept, it's not trying to predict some

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Speaker 4: set of sentences. It's trying to predict your diagnosis. We

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Speaker 4: generate a diagnosis in treatment, right, but because you know

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Speaker 4: this is not regulated, we tell people this is what

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Speaker 4: you might have, and if you want to, you can

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Speaker 4: press about it and be in front of a doctor

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Speaker 4: twenty for seven and deal if not.

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Speaker 2: In fact, it says it is not a diagnosis. You're

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Speaker 2: saying it's a diagnosis.

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Speaker 4: Trying to make Jesus to be clear, this is not

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Speaker 4: a diagnosis. Is trying to mimic a diagnosis. What we're

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Speaker 4: trying to generate is trying to mimic a really high

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Speaker 4: quality physician by having this medical conversation, this medical chat

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Speaker 4: and having showing the differential because maybe if you didn't

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Speaker 4: have a positive laptist, maybe it's maybe it's an opera

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Speaker 4: respiratory infection, maybe it's something else going on here. And

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Speaker 4: our goal was not to say, Okay, this is what

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Speaker 4: you have and it's impossible that it's something else, because

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Speaker 4: that's not how medicine works. The goal is to consider

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Speaker 4: the diagnosis. But that's a tricky part of medicine. There

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Speaker 4: is a science and art here about how to do it,

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Speaker 4: and we're trying to mimic that capability. But in it

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Speaker 4: I think it's quite magical that you can engage with

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Speaker 4: a machine, have an understanding what you might have and

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Speaker 4: which is not some kind of guess, work on doctor Google,

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Speaker 4: and then press a button and was in a matter

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Speaker 4: of minutes and be in front of a doctor and

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Speaker 4: get medical advice and get prescription, get anything else, anything

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Speaker 4: else you need twenty four to seven.

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Speaker 2: So to build this thing, this machine that's trying to

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Speaker 2: come up with, you know, something like a differential diagnosis,

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Speaker 2: obviously you need a ton of data. And I know

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Speaker 2: there's this key moment early in the life of the

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Speaker 2: company when you got access to this like big beautiful

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Speaker 2: data set. Tell me about that, Okay, So.

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Speaker 4: People pointed me all over the world to try and

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Speaker 4: find data. At the time people spoke to me about Estonia, Denmark.

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Speaker 4: Any conversation I try to have with a with a

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Speaker 4: large hospital group in America at the time was completely stonewalled.

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Speaker 4: I can get anywhere because of hip et cetera. And

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Speaker 4: we ended up licensing a data set from an HMO

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Speaker 4: in Israel called Maccabi. And I'll explained specifically what we

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Speaker 4: have there. At the time, we didn't realize just how

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Speaker 4: what do you call it? Big and beautiful? The data

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Speaker 4: set is Maccabi is structured similar to Kaiser Permanenta. So

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Speaker 4: it's an insurance company that also has its own captive providers,

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Speaker 4: but they also have their own drugstore, their own lab,

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Speaker 4: a single EMR, their own hospital.

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Speaker 2: If providers means doctors who work for the company. Just

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Speaker 2: to be right, okay. So it's a complete integrated health system, right.

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Speaker 3: People who are in this system, they get their prescriptions there,

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Speaker 3: they go see the doctor there, they go to the

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Speaker 3: hospital there. So this Macabi, this Israel, is going to

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Speaker 3: have all of the data for the patients there.

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Speaker 4: And then one more big step. People don't switch HMOs

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Speaker 4: in Israel. So we essentially got access to two and

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Speaker 4: a quarter million people for a data set over twenty years,

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Speaker 4: and we essentially were a fly on the wall for

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Speaker 4: all these people. Did they go to a doctor, did

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Speaker 4: they get a prescription? We know also if they picked

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Speaker 4: it up, we know, if they had labs, we know.

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Speaker 4: We could put together a time series of all of that,

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Speaker 4: and of course postilizations as procedures, surgery's outcomes. So just

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Speaker 4: to give you a sense, we got access to about

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Speaker 4: four hundred million medical charts, over a billion labs, half

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Speaker 4: a billion prescriptions, two million hospitalizations.

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Speaker 2: And I presume it's anonymized.

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Speaker 4: We built an anonymizer.

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Speaker 3: So you have this very large, very robust, very complete,

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Speaker 3: longitudinal data set.

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Speaker 2: Great, what do you do with it?

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Speaker 4: We wanted to focus on these things are both what

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Speaker 4: you go to your doctor, your primary care physician, and

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Speaker 4: to urgent care. My head hurts, my stomach hurts, I've

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Speaker 4: got a rash, I've got an upper rest with our infection,

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Speaker 4: I've got a fever. All these different things.

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Speaker 2: And some kind of problem that just started. It's not

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Speaker 2: a chronic disease.

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Speaker 3: It's not a physical it's something that like, oh, my

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Speaker 3: head just started hurting, I better go to the doctor.

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Speaker 4: It's that, yes, And now it could be my head

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Speaker 4: started hurting and I've got a history of migrains a synoscientists,

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Speaker 4: and I think I know what it is, or it

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Speaker 4: might be something completely out of the blue.

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Speaker 2: What's one thing you tried at k Health over the

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Speaker 2: course of the company that didn't work.

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Speaker 4: We tried in the beginning as we were building through

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Speaker 4: as we're learning healthcare, we're trying to send people. When

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Speaker 4: we built the information, we said, okay, here's the information

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Speaker 4: and we'll connect you to physical doctors in person in

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Speaker 4: Manhattan and Brooklyn because we're basing the arc and then

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Speaker 4: work at all.

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Speaker 2: Why not? So why not?

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Speaker 4: Because people wanted immediate solutions of their problems right now,

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Speaker 4: that's a scientist or UTI or migraine. And you know

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Speaker 4: the whole beauty was it twenty four to seven service?

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Speaker 2: So was that when you realized that you had to

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Speaker 2: hire doctors.

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Speaker 4: We needed to hire doctors and build our own solution,

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Speaker 4: own the resolution.

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Speaker 2: Which is a huge that's a huge leap right there.

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Speaker 2: You're going from being a software company to being like

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Speaker 2: a much more complicated, much more expensive to build company

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Speaker 2: at that moment.

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Speaker 4: Right, Yeah, but you we didn't go in here, timid.

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Speaker 4: We went in here. We want to make real changes.

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Speaker 4: We don't want to be some yet another cute digital

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Speaker 4: health company that does cute things and sells usself. We

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Speaker 4: want to build a big, independent company, and we want

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Speaker 4: to build a company that will make a difference in

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Speaker 4: care delivery in people's lives, starting was primary care. So yeah,

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Speaker 4: you have to you have to pick your battles.

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Speaker 2: Yeah.

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Speaker 3: Well, so let me ask since that came up, I mean, so,

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Speaker 3: I know you're just raised another fifty million dollars, which congratulations.

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Speaker 3: Hard to do at this moment from what I've heard

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Speaker 3: for a lot of companies, and you've raised what three

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Speaker 3: hundred million or so so far? Is there some Well,

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Speaker 3: do you have a sense of when you'll be profitable?

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Speaker 4: I think we'll be profitable in the second half of

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Speaker 4: next year.

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Speaker 2: Great, So let's let's talk more more fully about how

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Speaker 2: your business works at this point, right, I have I

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Speaker 2: tried out this sort of consumer facing first step, I

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Speaker 2: didn't click through to talk with a clinician, but what like,

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Speaker 2: how does the business work now?

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Speaker 4: So our business is wet. We have both a direct

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Speaker 4: to consumer relationships and we work with payers, and we

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Speaker 4: work with large health system providers.

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Speaker 2: And payers means insurance companies. Payers is what people are

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Speaker 2: in healthcare call insurance companies.

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Speaker 4: So we essentially offer our services and we get paid

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Speaker 4: as a provider to take care of patients. So if

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Speaker 4: it's direct to consumer, we have an offer for consumers

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Speaker 4: and you can just sign up and pay out of pocket.

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Speaker 4: We announced that a.

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Speaker 2: Few weeks ago that we're.

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Speaker 4: Working with Cedar Sinai. We're working with other health systems.

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Speaker 2: Theesar sign up a big hospital system in Los Angeles.

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Speaker 4: Yeah, the largest hospital system in California. We see thousands

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Speaker 4: of patients every day, which would be the size of

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Speaker 4: a large health system with multiple hospitals and multiple art

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Speaker 4: patient centers.

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Speaker 2: So large, and how often do you end up referring

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Speaker 2: people to see a doctor in the physical world, or

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Speaker 2: get a test in the physical world, to do something

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Speaker 2: in real life, to do something in meat space.

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Speaker 4: So we refer people to in person physician about eight

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Speaker 4: percent of the time. That could be to the er,

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Speaker 4: it could be to specialty care or to an person

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Speaker 4: primary care. Okay, so I was going to say that,

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Speaker 4: Just to be clear, there is many times a physical interaction.

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Speaker 4: For example, our doctors will send a patient to do ELEB. Yeah,

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Speaker 4: but ninety two percent of the time we can resolve

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Speaker 4: it without a bricks and marker doctor reviewing that lab.

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Speaker 4: A portion of the time we need to we need

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Speaker 4: to refer the patient to an in person visit in

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Speaker 4: primary care, or to specialty care or to er. But again,

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Speaker 4: the vast majority of stuff we can do online. So

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Speaker 4: it completely changes the paradigm on access because if it's

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Speaker 4: ten pm, what are your choices?

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Speaker 3: In a minute, Alan talks about the problems that k

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Speaker 3: Health is still trying to solve. It's interesting to think

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Speaker 3: about to compare k help Health versus a traditional doctor.

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Speaker 3: Say good, you know, seventieth percentile a good but not

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Speaker 3: amazing traditional doctor.

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Speaker 2: And I'm curious. I would imagine there are some settings

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Speaker 2: where KA health is better and some settings where a

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Speaker 2: traditional doctor might be better. Some things that are just

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Speaker 2: easier to detect in person, say, and I'm curious, like,

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Speaker 2: are there particular kinds of cases that worry you? Are

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Speaker 2: there particular kinds of patients who you think KA health

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Speaker 2: may not be well well suited for.

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Speaker 4: I think the same things that doctors will struggle with

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Speaker 4: online are things that they will struggle with in person.

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Speaker 4: Patients that are not compliant with their medications or with

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Speaker 4: their care protocols. Patients that have are just very fragile,

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Speaker 4: they're very old, they have you know, they have pneumonia,

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Speaker 4: but they also to have multiple other background diseases. Remember

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Speaker 4: that when you go into a primary care doctor or

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Speaker 4: clinic and you walk out you're ass it's very difficult

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Speaker 4: for the doctor to interact with you and follow up

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Speaker 4: with you. So there's multiple advantages actually to build a

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Speaker 4: system online. People can come back very easily to our platform.

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Speaker 3: I mean, I've asked, is there somewhere where KA health

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Speaker 3: is worse? And you've given me somewhere where KA health

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Speaker 3: is better. But I am curious within the universe where

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Speaker 3: you were treating people, are there places where for whatever

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Speaker 3: then maybe there's a smell, maybe there's some you know,

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Speaker 3: truly right, like there are things that can be perceived

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Speaker 3: in the physical world.

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Speaker 2: It's a genuine question.

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Speaker 4: No, No, I understand the question. I think the areas

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Speaker 4: that we're less strong and now is areas that we've

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Speaker 4: seen for your patients. It's stuff a little bit different

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Speaker 4: from what your intuition is. Your intuition was similar to mine,

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Speaker 4: you know, six seven years ago. But it's stuff around

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Speaker 4: or simpedic huh, back pain, extremity, medicine, you know. Uh,

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Speaker 4: the trauma. We don't do trauma, you know, so so

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Speaker 4: stuff around.

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Speaker 2: That trauma trauma like did I break my wrist when

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Speaker 2: I fell that?

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Speaker 4: Or you've got a pain in your in your in

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Speaker 4: your foot for a few days. Maybe it's planter fasciaetus

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Speaker 4: if you've ever had it, it's pretty painful and appears

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Speaker 4: out of the blue. But you know, maybe maybe you

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Speaker 4: broke a bone or maybe you've got you know, you know.

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Speaker 3: Is the reason is the reason you don't do that,

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Speaker 3: because the doctor needs to touch the patient in short

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Speaker 3: to make that diagnosi.

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Speaker 4: I think we can build systems for orthopedics as well. Uh,

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Speaker 4: it's just not it hasn't been an error focus about

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Speaker 4: for us, and and over time, I think the whole

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Speaker 4: online versus in person is going to be mute. It's

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Speaker 4: kind of like going to the ATM to get out

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Speaker 4: cash versus venmo. Both are utilities of of getting cash

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Speaker 4: or transferring cash. You will use both interchangeably in different

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Speaker 4: reasons for different settings. I think that's how medicine's gonna

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Speaker 4: go to. If you can resolve your problem right now

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Speaker 4: from home in a high quality, high confidence environment, and

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Speaker 4: I can follow up with you wherever you are. Maybe

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Speaker 4: you need a prescription, maybe not. It's typically better than

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Speaker 4: going in in person. Sometimes you need to go in

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Speaker 4: person for various things. Medicine is really tricky. It's really

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Speaker 4: tough to I'm still at all of how doctors make

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Speaker 4: decisions because they don't always have all the facts, but

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Speaker 4: yet they need to act. They need to decide should

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Speaker 4: I send the patient to the R do I tell

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Speaker 4: them to rest at home? I would just jacb I'm

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Speaker 4: jumping into something to me is ready burning and important

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Speaker 4: for us. We built k to give people access to

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Speaker 4: high quality medicine. I think we have uneven quality medicine. Yes,

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Speaker 4: there's Mayo Clinic and Cleveland Clinic and top academic institutions

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Speaker 4: that act as lost results or solving complex care Cedar

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Speaker 4: sin I other institutions, But most of the time people

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Speaker 4: need to manage their diabetes and hyppertension, or even better

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Speaker 4: avoid getting diabetes talked to or high pretension. Many people

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Speaker 4: who have insurance and have money do not manage their

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Speaker 4: diabetes and hard conditions. Well, it's not one or two percent,

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Speaker 4: it's double digit percent. Why Because it's tough, it's it's expensive,

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Speaker 4: it's messy that you know who wants to deal with it?

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Speaker 4: Can we build care delivery systems that are dramatically better,

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Speaker 4: that are much more data driven and are tailored to

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Speaker 4: you admin that take into account prevention. Everybody talks about

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Speaker 4: prevention that are preventative systems are similar to for the

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Speaker 4: most part, I'm not talking about oncology here to the

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Speaker 4: seventies and the sixties. That's pretty awful.

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Speaker 1: So let's this is interesting.

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Speaker 2: Let's talk about it at a at a slightly more

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Speaker 2: specific level, Like you're making a compelling kind of abstract,

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Speaker 2: big picture society wide point. Tell me something specific.

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Speaker 3: About what you're doing to do a better verse of

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Speaker 3: helping patients manage their diabetes or high blood pressure.

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Speaker 4: I'll give you one specific example. We took maoclinic data

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Speaker 4: and we're able to look at mayoclinic patients in an

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Speaker 4: anonymized manner over long periods of time, and look at

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Speaker 4: high pertension patients and figure out the best medication or

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Speaker 4: combination of medicines to stabilize high blood pressure given their

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Speaker 4: current blood pressure, gender, age, comorbidity, and ethnicity, and be

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Speaker 4: able to tell a doctor in that same statistical split

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Speaker 4: here is potentially the best way to give this medicine,

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Speaker 4: and then math medicine.

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Speaker 3: And high blood pressure is presumably a good place to

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Speaker 3: do that, because there are lots and lots of good, cheap,

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Speaker 3: old high blood pressure medicines, but which ones to use

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Speaker 3: or which combination to use for which.

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Speaker 2: Patient is hard to figure out. Presumably that's why that's

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Speaker 2: a good sort of target problem.

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Speaker 4: I would venture to say most doctors do not in

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Speaker 4: the in their head that algorithm of gender, age, medical history, ethnicity, culpabidity,

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Speaker 4: and you know, the current blood pressure. So this is

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Speaker 4: really helpful to our doctors. Eight percent of the time

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Speaker 4: they change the patient's regimen or change change their mind

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Speaker 4: around what they want to do. And it's based on

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Speaker 4: male clinical mail clinics standard of care. So that's that's

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Speaker 4: really powerful. Every day that you stabilize in better, you know,

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Speaker 4: to reduce the risk of strokes and heart attacks.

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Speaker 3: What's something you haven't figured out yet that you're still

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Speaker 3: working on. What's what's the sort of frontier problem, a

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Speaker 3: frontier problem that you're working on.

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Speaker 4: Hmm. The stuff that we're working on integrating right now

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Speaker 4: is tying what we're doing to a non hallucinating large

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Speaker 4: language model. So how do you take what we're doing

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Speaker 4: and we we help the patient understand what they might have,

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Speaker 4: We put them in front of a doctor and they

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Speaker 4: get diagnosed and tree. But let's assume the patient is

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Speaker 4: a diabetic patient who also wants to lose ten pounds

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Speaker 4: and doesn't like fish and wants to do a certain diet,

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Speaker 4: and it needs to be tailored to what we do.

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Speaker 4: How do I enable the patient in a free form,

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Speaker 4: free text engage with the system that's non hallucinating, that

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Speaker 4: is also tied directly to what we're doing. I think

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Speaker 4: that's a direction we go.

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Speaker 3: So plainly, what you have right now is very structured.

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Speaker 3: I can't just come in and like say a million

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Speaker 3: things about myself. I can have a presenting complaint and

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Speaker 3: then your system will ask me some questions, and there's

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Speaker 3: a sort of set universe of categories of conditions whatever.

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Speaker 2: And plainly, large language models like chet GPT, which is

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Speaker 2: very different from your system, offer a much more wide

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Speaker 2: open playing field. A wide open means of interaction between

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Speaker 2: a human and machine, with the notable downside, the profound

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Speaker 2: downside in this case, that they are not reliable sources

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Speaker 2: of information.

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Speaker 3: Right, So what if I understand you correctly, what you

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Speaker 3: want is the reliability of the system you have built.

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Speaker 2: With the freedom of a large language model like chat chip.

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Speaker 4: Yeah. So fundamentally, nobody wants to go to a doctor

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Speaker 4: that hallucinates ten percent of the time and you don't

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Speaker 4: know when the doctor's almost think that's kind of meaningless.

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Speaker 4: It's going back to the same doctor Google WebMD problem.

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Speaker 4: We care about the clinical context and that is really important.

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Speaker 4: I think that's where I's going, and I think LLLM

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Speaker 4: could be a huge ally here. But fundamentally, when you're

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Speaker 4: asking people specific questions, when you're asking questions that are

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Speaker 4: medical and clinical by nature, there's no guesswork here. It

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Speaker 4: might need to be modified and change over time, but

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Speaker 4: you're asking very specific questions. So for me, a big

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00:23:45,876 --> 00:23:47,956
Speaker 4: part of what we're answer is integrating that.

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00:23:50,916 --> 00:23:52,916
Speaker 2: We'll be back in a minute with the lightning round.

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00:24:04,636 --> 00:24:06,876
Speaker 2: You were the co founder and CEO of Room, a

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00:24:07,036 --> 00:24:08,676
Speaker 2: used car retail.

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00:24:08,356 --> 00:24:10,116
Speaker 4: Retail, and oh yeah, I.

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00:24:10,116 --> 00:24:12,516
Speaker 3: Definitely have a couple of used car questions for you. One,

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00:24:13,196 --> 00:24:15,836
Speaker 3: what's the most surprising thing you learned about the used

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00:24:15,876 --> 00:24:16,836
Speaker 3: car business?

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00:24:18,236 --> 00:24:22,516
Speaker 4: How predictable it is? Huh, how predictable car pricing at

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00:24:22,556 --> 00:24:25,116
Speaker 4: a certain period. But I was lucky to work there

448
00:24:25,236 --> 00:24:27,796
Speaker 4: pre COVID, where you know, the supply and demand and

449
00:24:27,956 --> 00:24:31,476
Speaker 4: completely changed. That was a big surprise to me. It's

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00:24:31,556 --> 00:24:35,596
Speaker 4: much more data driven and predictive than people realized.

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00:24:35,916 --> 00:24:40,076
Speaker 3: Okay, what's one tip for somebody buying or selling a

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00:24:40,156 --> 00:24:40,596
Speaker 3: used car?

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00:24:41,076 --> 00:24:43,476
Speaker 4: So most people are buying a car also trading in.

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00:24:43,516 --> 00:24:46,916
Speaker 4: They're selling a car, okay, And about eighty percent of

455
00:24:46,916 --> 00:24:49,836
Speaker 4: the time people are taking a loan okay. And most

456
00:24:49,836 --> 00:24:52,316
Speaker 4: people come in and they do reams of research and

457
00:24:52,396 --> 00:24:55,676
Speaker 4: coming with their armed guard to a car dealer, and

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00:24:55,716 --> 00:24:57,996
Speaker 4: they focus just on the buy. In the meantime, they

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00:24:57,996 --> 00:25:00,716
Speaker 4: get a shitty price on the trade and they get

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00:25:00,756 --> 00:25:05,636
Speaker 4: a and they get a you know, a bad financing

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00:25:05,676 --> 00:25:08,436
Speaker 4: deal on their car, and that tax on you know

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00:25:08,556 --> 00:25:10,636
Speaker 4: many South of daughters, so the cost, but they got

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00:25:10,636 --> 00:25:12,596
Speaker 4: a great deal on the bi in their minds.

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Speaker 3: I read that you bought a house on Ridyon Avenue,

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Speaker 3: which was interesting to me because I used to.

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00:25:19,276 --> 00:25:22,756
Speaker 2: Live on Ridian Avenue in South Beach. Uh, And I

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00:25:22,796 --> 00:25:25,036
Speaker 2: couldn't think, like, oh, what's that? We both have lived

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00:25:25,036 --> 00:25:27,956
Speaker 2: on Bridan Avenue. Question, I don't know. Do you go

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00:25:27,996 --> 00:25:30,916
Speaker 2: to that Publis? How's South Beach strading you?

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00:25:31,316 --> 00:25:34,316
Speaker 4: I'm here right now. I spend most of the time, okay,

471
00:25:34,476 --> 00:25:36,556
Speaker 4: most of the time in New York, but I'm here.

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00:25:36,636 --> 00:25:37,596
Speaker 4: I'm here right now.

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Speaker 2: It's a very hot time to be on Ridian Avenue.

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00:25:41,636 --> 00:25:44,476
Speaker 4: From my experienced, I haven't here this time of the year.

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00:25:44,636 --> 00:25:47,076
Speaker 4: I have a pool, and I'm okay, and I'm close

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00:25:47,116 --> 00:25:49,596
Speaker 4: to the beach, and I love Miami and I love

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00:25:49,636 --> 00:25:52,036
Speaker 4: Miami Beach. So it's a lot of fun. It's never

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00:25:52,036 --> 00:25:52,956
Speaker 4: at an moment here.

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Speaker 2: Well, so you grew up in Israel, you lived in

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00:25:55,996 --> 00:25:56,476
Speaker 2: New York.

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00:25:56,796 --> 00:25:59,676
Speaker 3: Right, New York little known fact is on the beach,

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00:25:59,996 --> 00:26:01,716
Speaker 3: and you have a house now in Miami Beach. You

483
00:26:01,876 --> 00:26:04,476
Speaker 3: live a lot of the time in Miami Beach. Of

484
00:26:04,516 --> 00:26:07,756
Speaker 3: all those places, where are the beach snacks? The best

485
00:26:07,956 --> 00:26:08,916
Speaker 3: best beach snacks?

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00:26:09,916 --> 00:26:13,596
Speaker 4: Oh, Israel, by far, Israel is much more street food

487
00:26:13,636 --> 00:26:14,156
Speaker 4: and snacks.

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00:26:14,276 --> 00:26:16,716
Speaker 2: Yeah, so, I mean, what is falafela beach snack? I

489
00:26:16,756 --> 00:26:18,556
Speaker 2: don't think of falafel as a beach snack. What's the

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00:26:18,556 --> 00:26:20,156
Speaker 2: beach snack or is it falafel?

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00:26:21,836 --> 00:26:24,596
Speaker 4: Yeah, well you can have falafel and almost anywhere in Israel,

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00:26:24,716 --> 00:26:27,996
Speaker 4: so you could you can also get it on the beach.

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00:26:28,036 --> 00:26:31,516
Speaker 4: But people people in Israel a eating all the time everywhere,

494
00:26:31,596 --> 00:26:33,276
Speaker 4: so you know, they also eat on the beach, and

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00:26:33,316 --> 00:26:36,876
Speaker 4: it's very family oriented and the beaches are crowded and

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00:26:36,916 --> 00:26:43,196
Speaker 4: people are happy.

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Speaker 3: Alon Block is the co founder and CEO of k Health.

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00:26:48,356 --> 00:26:52,276
Speaker 3: Today's show was produced by Gabriel Hunter Chang and Edith Russlo.

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00:26:52,436 --> 00:26:55,796
Speaker 3: It was edited by Sarah Nicks and engineered by Amanda

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00:26:55,916 --> 00:26:59,716
Speaker 3: k Wah. You can email us at problem at Pushkin

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00:26:59,876 --> 00:27:03,156
Speaker 3: dot fm. You can find me on Twitter at Jacob Goldstein.

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00:27:03,556 --> 00:27:05,876
Speaker 3: I'm Jacob Goldstein and we'll be back next week with

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00:27:05,956 --> 00:27:07,276
Speaker 3: another episode of What's Your Pop?

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00:27:10,276 --> 00:27:11,636
Speaker 2: Don't Myself