The Woman Behind Gemini: How AI Is Actually Built and Who It Will Replace | Sneha Shah
00:01What is up the entrepreneur, DNA, AIA.It's all anyone can talk about.This episode is going to blow your mind with the guest I have today.Now, us, us little people, we're just getting into AI.This is a big topic for us.But for the big boys, the Amazon's, the Googles, this has been around for a long time.I mean, I have the Queen Bee who has been running this for these companies.For the last two decades, she is the lead.
00:31AI specialist for Google and Amazon.Sneha Shah is here.How are you?I'm doing very well.Thank you, Justin, for having me and very honored to have that level of introduction.Yeah.I just told you off camera, I was like, you're like the little ninja in the back pocketof all these big, big companies, and no one knows is you, like, behind the scenes being
00:53the magician you are.Now, you're on your own thing, and we're going to get to Hazel and everything that you'reup to today.But I want to take a step back.I think, you know, for most of the common people, the lay people, as we say, we look at Amazon and we say it does this shipping thing.And we look at Google and we say it does this search engine optimization thing.And we do it.I order it.
01:16Well, my wife orders Amazon probably 10 times a day.Right.And, you know, now AI's in the search engine category.But for the most part, Google over the last decade, I go to Google to go search whatever I want.you are literally one of the founding members of how Amazon started Amazon delivery, correct?Absolutely.I was part of the team that made same day delivery happen.This is the magic where you order the previous night and it shows up at your doorstep 7 in the morning.
01:50We scaled it to about 10 countries and super proud of the scale that we've been able to build us at.Yeah. You know, it's funny because I just live here. I wasn't even aware Amazon had that many. Are you, well, not you, but are they 100% in most all countries at this point?Many, many countries at this point. Now, walk us through what it took to build something at that scale, right? Like, it wasn't like, oh, I'm just going to start in San Francisco and I'm just going to do Amazon San Francisco. Like, did you start with a state, a couple states? Do you just go national? And how did you build?build to scale with that.Correct.So a lot of people and a lot of team effort goes into this.I have to say, you know, many years went into building this.It is a combination of software, infrastructure,
02:43as well as the delivery team that powers this.But yes, Amazon has the culture of starting small.You know, they make it into a startup within the larger organization.We started the delivery in Seattle, actually.that's that small. Then you expand into bigger metros just to test the scale and then you expandinto countries. You know, some of the bigger challenges was countries like India. You know,the population is huge. So the volume is immense. You know, is there even the infrastructure to support it.So it is a gradual rule out. It took us many years. But we were pioneers in that. Even a Walmart
03:26took, you know, almost half a decade to follow on and be able to meet that.So huge endeavor.Oh, my gosh.Were you one of those like little pods that started it in Seattle or built it out in Seattleand then let it expand?At what point were when did you exit that?I mean, I can only imagine that was all consuming for how long.Yeah.
03:49I spent about five years building that.My team is the one that got to decide.you know, will FedEx make this promise that we made our customer?Will UPS or USPS or whoever make that promise?Eventually, we realized that Amazon should become the transportation itself,like running the last mile delivery, we call it, like the trucks or the small carsto go and deliver it, right?And, you know, all of these different programs come together over many, many years
04:23to make that promise as what we have.are today. And therefore, you know, yes, I saw it over five years, grew it to, like I said,you know, 10 countries. And then it comes to a point where you're like, you know what, I could dothis for all my life. I could become a transportation expert for all my life. But I decided to pickanother challenge then. Yeah. Now, just quickly to stay on topic, when you make this Amazon specifically,when they think through this idea, like they have to look at the cost of what is,the trucks, the people, the salaries, the lost goods,were you a part of trying to figure out the model?
05:05Like, it just sounds so, if you were to say,hey, we want to go start this today,I would think about all the costs that it takesto lift that vertical up.Was that a losing model for a while,or was it always profitable?It's Amazon delivery has always been a losing model.It is all about the experience that you provide to your customer
05:26that brings them back shopping and that stickiness that it created.Of course, there were micro models that were created as to, you know,let's not ship something over air for tomorrow morning 7 a.m.You know, you know, from the United States to Europe.Please don't do that.But of course, there were ceilings created.But for most part, the whole value of Amazon back then was, you know,make this the world's most, you know, customer-friendly side.
05:58And we lived and breathed by that.So, you know, expect.So do you think today is still a lose?I mean, it's so, it's every, I'm not joking when I think I get 10 deliveries a day.I'm not joking about that.Is it still losing because the cost of operation is so high?And then Amazon monetizes in other verticals, but it keeps such a great stickiness to their client.Correct.
06:20Their margins are pretty low on their retail business.That is, no one thinks about that.I almost guarantee people are mine, like I know I'm blown away that it's that low.But it does make sense to say, okay, you got to offer something amazing essentially for free to be able to give them the thing that you can make some money on.Exactly.Now, I know this wasn't your department, but where does Amazon make the most of its money?Is it the AWP?AWS, 100.
06:46AWS?Yes.Yes.That's where, you know, we got to software margins.that's where the scale came in.We also, you know, Amazon Kihau is number one for many, many years to come.So certainly we made most of the money there.Yeah.
07:02Now, were you over at Google before Amazon or were you at Amazon before Google?I started my career in Amazon straight out of Carnegie Mellon,stayed there for six years, grew this, moved to Google,because the opportunity of, you know, building cloud came in and became real at that point.you know, compute and storage just started off.I got the opportunity to become founding engineer of a pioneer database,like a global scale database.I was like, this sounds amazing.
07:32I did physical delivery at Amazon.Now I will get to do bits and bobs like data delivery across the deal.So super exciting opportunity, and I moved to Google for that.That is great.Now, I would assume if we define AI, were you playing with any areas of more logistics,over at Amazon or was there AI layered into it or did you not really go all in on AI until you got over to Google?There were very early versions of AI or machine learning built into all of these platforms.Yeah.
08:04At the scale and the speed that you want to operate at, some of these decisions are baked in with a lot of machine learning that went in early on.At Amazon.What would be, you know, when was this?What years were these?Because I think, again, I'm so excited about this episode because for the commoner, we're like, oh, AI has been around for a year and a half, two years.It's just not the case, right?Absolutely not.When were you kind of working with some of the fundamental AI components in over on Amazon?
08:39What years were these?This is early, you know, 2012, 2014.Right.Yeah.This was called machine learning back then.You know, machine learning dates even before that.It just became mainstream, you know, in the early 2000s where we started seeing value generated very quickly out of it.Yeah.
09:01And now social media has made it the biggest thing on the planet.Absolutely.I mean, every day you're scrolling on LinkedIn, TikToks of the world is all based on that.AI.Now, you get over to Google and you got the privilege to lead up a pod.And what was your focus there?So I was the founding engineer of our database team.It was called Cloud Spanner.
09:25It is one and the only database that allows you to spread your data across the globeand still be accessible at, you know, millisecond speed.To say it very simply, think of the Amazon's, the Walmarts of the worldthat serve a lot of customers across the globe.Imagine every time you had to buy something.something, the data had to come all the way back to the US to verify that you're a userand then go back, you know, to your country in India or Philippines, you know, which isliterally diagonally across. And what tends to happen is it makes it slow, the experience is bad.
10:04And then, you know, people just wane off eventually. You want to be able to make it fast. You wantto be able to make it easy. You want to be able to, you know, have your customers use more ofwhat they are doing. And so Spanner allows that data to be distributed where your user is.Very simply good. Yeah, but not simple to do.Not simple to do. Yeah.This grew, like, you know, we were four when we started. This grew into maybe a 400-person team.We had the likes of the biggest banks in Europe, the biggest gaming companies, you know,I mean, some of these names are public. So, you know, a Deutsche Bank would use.
10:46Nintendo would use a spanner.These are, you know, think of when Pokemon Go was released at a point there were about10,000 users playing the game at the same second.Wow.You'll be able to quickly move the data around, have them the experience that they have,was very critical and that's what we powered.So in use gaming, and I'm not much of a gamer, but like all the streaming and all thatkind of stuff.
11:13The streaming, is that what we're basically talking about?To be, I have a million people streaming a game all at one time to be able to have allthat data going around at that lightning speed.At that lightning speed.Exactly streaming.You know, things like, you know, the leader board is a classic example.Millions of people playing this together.Nobody likes to be one second delayed to be at the leader board, right?
11:39You want to know your top players.That is powered by.Spanner.That's amazing.And so you took out, now how long were you over at Google?Almost a decade.I moved, I got to scale many products at Google.You know, I took Spanner to over 100 million.
11:59Realize that, you know, this can keep growing.The team had become pretty big and, you know, there were enough people to move the scalebeyond that.And so I got the opportunity to move to vertex AI.I built that.like the flagship AI platform for Google, scaled that into Gemini Enterprise, you know, theiragentic platform.So you had the honor to be able to scale.
12:23What was the name before it was called Gemini?Correct.Gemini Enterprise.But before Gemini, what was it called?Vortex AI.Vortex AI.Vertex VE.R.Yeah, vertex AI.
12:36And now is Gemini, which half the world probably uses at this point, if not more.Yeah, exactly.And you were able to build that out.Yes.What does it take?You know, I'm old enough to know that like coding, right, was like my generation,like the internet launching and coding.When you're learning how to do what you do, are you coding?
12:59What is going on behind the scenes of AI, right?Because effectively you built Vertex AI, which is now Gemini, right?So let's just say Gemini moving forward.You built it.How the hell do you build it?What are you doing?So a day in the life of, you know, a senior architect is what I do.It goes anywhere from understanding what the research landscape is doing, right?
13:27Understanding where the customer finds the value.We need to bridge that gap.That's my role.There's a lot of value that can be creative with, with, you know, LLMs, which is the hype now.but bridging that gap for a layman is important.So going from understanding business use cases,going from actually prototyping some of this,you know, the coding that you mentioned,
13:53showing the value to your leadership, right?There are a lot of bets they can makeand why is your bet important?It was one of my very critical rolesto be able to influence themand show them the strategic value for our customers.And then, of course,reading a very large team to make sure these things work in reality in production at scale
14:15and does provide value to your your end customer you know one of our very early customer is abeloved social media website and they do a lot of search they wanted to scale their search onGemini enterprise for billions of users and to be able to have that get unlocked withinof months is the magic that I create.That's wild. I just, it's just so foreign to me, right? Like, I'll go to Claude or I'll go toJem and I'll use it. How the hell was it built? That's, that's insane. So to build it, what is,what is tactically, like what is actually happening to build it? Is it code? Is that,is that the easiest way of explaining? It's just a bunch of code. Yes, it's a bunch of code.
15:05and a bunch of very smart mindsthat come together to definewhat that code would look like.Define the boundaries,define the constraint,define the usability.So there's actually people behind AI.So everyone's talking about machine learning
15:23and we'll get there.Because we'll get there.But to start, to build a Gemini,it is very smart people essentially coding,building in constraints,building it, and I don't even know the right terminology, like building it.Like when someone asks a question, this is what we wanted to be able to spit out.And this is.
15:44Correct.A lot of data went in.So there are teams that just focused on data.What type of answers can we give?You know, there is a lot of news on, you know, books, you know, models have eaten up allthe public books alive.Now they can collect that data and feed it into the system so that we can answer very, very, very,you know, we can get into the details of some of these books.
16:11There were teams that actually took some of these questions and answersand iteratively test how good are we to answer these questions.It took a very long time for LLMs to say 2 plus 2 is equal to 4.Really?Yes, took them a very long time to get there.Because they are probabilistic models to get them to a deterministic answer was hard.And therefore a lot of teams and effortgo into making some of this technology hardened for real use cases.
16:40Wow.And today, you know, the whole hoopla is around outcomes.Like, how do we make sure that this actually creates business value, right?Not just 2 plus 2, 4, everybody knows that.Not just solving math problems that, you know, kids are solving.The business needs to run million and trillion dollar scale.How do we enable that?Yeah, well, I mean, that's the other thing that, you know, I think chat GPT now is ads, right?
17:10So I, because you go, there's got to be a cost to all this.And so who's covering the cost to all this?Because it's got to be a revenue model.Are ads going to be the revenue model?Is it going to turn into Facebook essentially or Google and just ads become a huge revenue piece of how AI exists?Or is there more than that?There'll certainly be on the consumer side.it is much harder to put a value for this and therefore ads or, you know, targeted tools
17:40while you are doing your task will be suggested.But in the enterprise world where I primarily focus on, we definitely believe that modelswill get highly commoditized.You've seen the recent news by Open AI just because open source models are one-tenthsthe cost they are doing as well.AI has conceded lowered their price as well.Right?And now, how can enterprise use this to generate real world outcomes is the question.
18:13Yeah.Yeah, that's, well, let's move into what you're doing now, right?I could ask you a million questions about AI and I think everyone could, right?I mean, you're one to, I don't know, maybe a founding queen of this whole thing.Who knows where you sit into this bigger raise?But I want to talk about.Hazel, but I also want to talk the machine learning part about this. You mentioned it kind of briefly.As it's being used, are we at a place where a lot of this is going to be able to, like,
18:42you already said they've consumed all the public books, right? So they already have the wisdom andknowledge that comes from, you know, 2,000 years of book writing or whenever books started beingwritten. But wouldn't that have all been housed in Google? So this is maybe,my naivete, but where did AI even go find it?Wouldn't have come from Google in the first place?There are two parts to that answer.I think the first part that you're asking is this information that was publicly availableon Google or the Internet is what has been consumed already by the models to be able to give
19:23you an answer now.Google search was about, I don't understand your intent exactly, but I can give youchoices from which you can find your answer.Models are a little moreare one step forward where they are like, okay, you know what?We can understand your intent behind this.And I'll present you with an answerinstead of just giving you suggestions or links
19:47where you'll find your answers.The other part is that, you know,model providers spent a lot of timeactually physically looking into things that were not on the internet.So they took physical books.and fed it into the system.There's a lot of talk arounda very niche models from India
20:08who took scriptures that were literally physical.They've never been put on the internet.Languages that are not very prolific on the internet.Regional content that is not prolificwas all fed into it to make that information available.But the second part that you said isis very true.It has the knowledge,
20:32but I sort of disagree with the wisdom.There's no judgment in an other.Yeah, sure.It's very black and white.It's very, I mean, yes, it has, it is,if search was black and white,this is just multiple shades of gray,but a human judgment or a wisdom,
20:50it completely lacks.Like, it does not understand the environment.It does not know why you're asking,what you're asking,how you're asking.So, but it's good enough to be able to at least give you the direction in which you want to go.Yeah.And I use it for a lot of, you know, thinking through my thoughts and reiterating my thoughts and using it for content as, you know, obviously my podcast done well.
21:14So for that practice, it's great.I mean, I like Claude probably the best in that practice of just, you know, ideating and coming up with ideas and refining the idea and understanding how to explain the idea.but there's so much more to it, right?Whether it's be GROC or, again,Chad is the first, you know,I feel like that's the starter level.And then there's all these other onesthat we're aware of Gemini obviously is there.
21:38Hazel AI, you are now,so you left the Titans,you left the big boys,and you say, I have so much experience,I'm going to go do this better.Talk to me about Hazel AI.So Hazel AI becomes the engine,behind the world's largest economy of making technology.
22:01So today's system integrators, you know, the Accenture, Infoces, TCSs of the world,are the largest technology economy there is.They serve all the global 2000s to become tech native, cloud native.And now they will make them AI native.And Hazel becomes the power engine or the technology engine right behind them.And the way I did this is all the learnings and the wisdom that I had over the years building some of these products from zero to, you know, millions of dollars, I encoded them into the Hazel platform.So now I have a suite of agents with that same level of wisdom, the judgment that it takes so that the technology providers can go faster, have higher throughput and ROI.and leverage, you know, models or AI to generate real value.
22:58Now, so who's Hazel AI's ideal client?Like, who would use Hazel AI?Let's work backwards of how this economy works, right?I'm going to take an example of a very large enterprise today,like a Coca-Cola or an Amtrak.These run on technology or ERPs.Let's take one example as an SAB.Coca-Cola must run a very large ERP system across their finance, procurement, inventory, pricing.
23:31And what's ERP?ERP becomes the software that runs all of these modules of functions.Let's say Coca-Cola's factory needs to produce a million bottles to be shipped to Chicago.All of this work is getting tracked in an ERP.and that work, that becomes a software layer for all of their teams to look into.So their procurement team is saying, I need, you know, Chicago's procurement team will say,I need a million bottles.The factory on the other side is saying, I need to produce these million bottles.
24:07I'm going to create this order over here.I need to, the shipment team is saying, I need to box this and send this to Chicago.So they are tracking this work in the ERPA.So SAP is one of the largest ERP is, you know, many billion dollar term.And what tends to happen is these are extremely complex systems that need to be wired together.Now, a TCS or an infuses or an Accenture comes in and helps Coca-Cola set this up to do the upgrades, to maintain this, to make sure that, you know, this becomes AI native now.Right. And what even an Accenture or DCS would put about 100 to 200 people to maintain this on a regular basis just for Coca-Cola.And all those different, there would be 100 to 200 people doing all those different things.
25:01And what I'm hearing you say is Hazel can come in, consolidate all that work in one place.Maybe there's 10 per, I don't know how many people would work on Hazel, but you can consolidate all those departments, all those different things.things in one place and it's doing it all for for Coca-Cola.It will do it all for the TCS and enforces today.So that the expertise of a TCS person is no longer around, you know, how do I movethe needle from X to Y?Like the coding part is taken care by Hazel agents.Yeah.
25:33What they are bringing to the table is expertise, right?So all the repetitive task of, can I, can I write this as a design, you know, just likehave you ideaed? Can I, you know, write the code for it? Can I go and, you know, test it? Can I go and,you know, deploy it? Can I scale this? All of the engineering goodness is done by Hazel agents,while the folks from TCS and info, the architects on that team now become the layer that justvalidates and pushes it. This not only brings them the speed, but it also brings them the efficiency.Like now they can take not only a Coca-Cola, but the same.The same architect can now work on Epsico, on Frito layers, on everything.
26:19Right?He's not just constrained by the time it takes to do this, his work.So is it easily said, you know, you hear a lot of these experts who kind of try to debunk this, I don't want to say myth, but this idea that like AI is just going to take everyone's job.I do believe there's a component of that, right?I mean, you are, Hazel AI is reducing 200 people into how many?10, 15 people doing the same thing.I don't know.I don't want to make up numbers for you, but you're taking 200 jobs and you're saying,
26:54really, you'll need 10 people to do what Hazel can do, right?So that is a reality, but you still need those 10 people to come in with what you considerwisdom or to come in with expertise in a way, like what,What would I want?I'm hearing it and how I want to say it is,is if the weather's changing,the AI won't be able to understand the weather's changing,which may change certain data points.
27:24That has to have the human behind it, say,based around the weather changing,it's going to change how many bottles we orderbecause we're not going to ship to whatever, right?You still need that human to run that part.Yes.So there are two points to the,the myth that you call.
27:41right? AI is not going to dis, AI is definitely displacing jobs. That means that it's not going tojust take away the jobs from the economy. They're going to become new jobs in the economy.A developer no longer needs to just do his task sitting in a, in a, you know, in a small cubiclein a chair, but now they can get to influencing and decision making. Instead of them justtyping on their keyboard, now they can go into meetings and change the,course of the things that they are building.So is it what but is it still fair and you know and I'm not trying to be combativewould it be fair to say it will remove the lower level jobs that you know do you really need 200
28:27people right? I feel like that's a fair statement to say the lower level jobs that you don'tneed the cost of the human anymore I think that will go away.So entry level jobs who were just sitting anddoing tiny repetitive tasks will no longer be required.Yeah.Right.But that said, those same people will now start at a ladder above, which will be that.If they're smart enough to.
28:54If they're smart enough to.Yes.I mean, that, that, that happened when, you know, cloud came out.There were a lot of database administrative people.Yeah.You just, you know, like kept the lights on on a database.When I came in in Spanner, almost all the jobs went away because Spannerprovided that out of the box.
29:14What did they become?They became SQL experts.Yes, it takes some time to ramp up and, you know, get into that level.But none the less, it wasn't a fancy job to anyways keep the lights on on a database.It's not like they were the happiest people doing that.Yeah.It's, it has been the same way.Like, farmers were the happiest people being in the sun, you know, removing
29:37have gone out of the maze, right?But, you know, the tractors made it easier.Now can they expand into other fields?Yes, 100%.Can they walk to the market and sell at a higher price?Yes, 100%.That's what everybody needs to do.That's fair.
29:56That's fair.Well, listen, I'm with you on that, girl.Like, everyone needs to level up.Like, they've got to find their lane.If AI is coming to take your job, find where you fit.Like, you know, I'm definitely not a victim.at all. It's, hey, it's here. It's not going anywhere. Right. Like, so where do you fit in? Figure thatout. You know, I'm contemplating, you know, I myself, I'm always trying to better myself. I believe in the
30:19law of the lid, meaning my businesses, my income, it can only go as high as I can go. Exactly.Right. And so if you're not improving yourself, there's a lid. You can't earn more. You're notdeserving of it. And when you try, you will likely fail. And I've done that, by the way, where I,was way outside my lid and it didn't go so well for me.So now he's...Go ahead.The things that I wanted to bring back is that we also need to know that the opportunitiesare not finite.
30:54With AI, the size of opportunity has increased.So now, even where, like just in my world of just development,you see a lot of coding has picked up.Like the number of apps have exploded, you know, 10x, 100X.Yeah.Enterprises are taking far more Mets just because AI has made it cheap and dirty.Sure.Right?
31:18And so there are a ton more opportunities than there were previously.And so the level to go up or sideways has also increased.And most people need to get into that discomfort zone, as I call it.Yeah.Be able to find their own lane.Yeah.I love that. I think that if no one understood any of what we're talking about, that is probablyyour biggest takeaway, right? Is that? The opportunities are there. Now, where do you see, you know,
31:48where we're going? Now, Hazel specifically, like, who do you work with? Are you able to say,like, who you work with and where you're integrated or is that you can't mention those names?Very confidential. We'll have a lot of news coming out and we are around it.Let's call. Yes. Congratulations.I'm excited to make a ton of announcements this summer.Good.And some really big names.We work with five of the top 10 GSIs.
32:14You know, we serve over $10 billion in business for some of these companies.The end customers are anywhere from retail giants down to manufacturing to agriculture.So super proud where the impact I can make.You should be very proud of yourself, very proud of yourself.and I'm very honored to be able to have your phone number and be able to call you because I don't know how to work any of it.So I want to bring something to light here.You spent 20 years at two of the largest companies in the whole planet, Amazon and Google.And you're talking about now building a company that is serving, what do you say, $5 billion of enterprise value or $10?
33:03billion dollars in it and it's your company it's yours you are the CEOsnehah is the CEO and I want to highlight something hereand I don't know what that means in revenue and all that kind of stuff because when youtalk about five billion in enterprise value and 10 billion like I'm not I don'tknow how that trickles down to hazel AI but what I'll tell you is what I heard here isyou put in the repetition to deserve to be in the spot you're at today and a lotof people aren't they aren't they wantto be Snehaha because they say, oh, I don't need to go to college. I'm going to go build my own
33:39AI. Well, how I'm going to ask you, this is the question. This is where the question is coming.How did you get into the doors of these very large companies? Did you maybe reference whereyou worked for 20 years? It's definitely my credibility, me building Gemini Enterprise,me being able to scale a lot of these businesses from, you know, zero.I'm going to ask you to pause respectfully.I want everyone to hear what you just said.And I don't know how old you are.I'm 45 and I look at younger.
34:20And when I say younger, not a lot younger, like in the 30s.And they just think like, oh, I'm going to start this AI company.And I'm going to be the new hottest AI company.And they don't even have a quarter of.of the resume you do.Nothing.But they believe they are going to start the biggest new AI thing.And I just, I want to applaud you and I hope that these listeners and viewers understand
34:44what her and I are, the question I just asked was sometimes to build greatness, you need to golearn from people who did it first or do it on someone else's budget, right?Initially, your budget was zero because Google paid for it or Amazon paid for it and you gotto learn off of their budget and you got paid to learn it.And I just wanted to pause you there respectfully because that's so valuable.Oh, absolutely.I mean, there are two types of people.One, who don't mind slogging it out to learn at their jobs.
35:20And a lot of part that I do at Hazel today, which is sales, marketing, even this podcast is way beyond my comfort zone.So I'm learning all the job.And you should be.I mean, if you, if you reach a job.if you don't learn that, you know, it's not nice of time.But I definitely believe that learning from the very smart people at Google and Amazonhas served me really well.The opportunities that I got are nearly, you know, to the top 0.1% if at all.
35:54And I got them.And that goes a long way for my career.Yeah, I appreciate you highlighting that.in Hazel AI, obviously.Now, just for purposes of understanding, the normal everyday-to-day consumer, myself or others,we wouldn't be utilizing Hazel AI.Maybe it's embedded in something else we're using, but there's no, like, consumer value forHazel AI, correct?
36:20Same thing.Okay.Yeah, so I just want to make sure everyone's not like, oh, I got to go use Hazel AI.It's not, you know, Claude.My understanding is very enterprise level.it's embedded within these very large companies,and that's where you'll be able to use it.Any, you know, listen, I could talk about AI.
36:39It's here to stay, obviously.Do you have any, you know, if you could be no stratonisand kind of predict where we're going to be going, right?I think everyone has their opinion.But when someone like yourself that's literally been in the trenches of it for two decades,I tend to believe you more than the influencer says,whatever is going to happen, right? Where is this going? I mean, medically, you know, I was listening to afriend of mine Ed Milet and he says in the next two years, the medical advancements are going to be
37:09unreal, right? I know that that's not your wheelhouse per se, but give us a thought of where this wholecohesive AI movement's going to go. Yeah. I've seen a lot of these generational technologyadvancements and I do believe that we'll definitely make one giant leap for all of us.We'll definitely reinvent ourselves all over again, be it in the medical field, be it intransportation, be it in how consumers now interact with some of these applications.There are pros and cons even with cloud, even with, you know, mobile, even with apps.All of these transformations happen.Some were good, you know, some were forgotten.
37:58And very similar that we happen with AI.But that said, what I do believe in is, if we don't reinvent ourselves now, we will be forgotten ourselves.And that's the biggest takeaway.Sneha, you are amazing.I love your thought on this.I mean, you're very AI forward, but you're a human.And I think that goes a lot to why I think your AI has been so successful.beautiful. Hazel AI, I can't wait to hear the news. Thank you so much for joining us here on
38:29Entrepreneur DNA. Absolutely. I really appreciate you having me and I hope your listeners enjoyed this.Yes, I'm sure they did. All right. Well, if this was pretty cool and you think some peoplewho are interested in AI and technology need to hear this, please share this with at least two of yourfriends and drop us a five-star review. Sneha, thank you so much for joining us.Thank you, Justin. Very nice to meet you.
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