Transcript: - Aseem Datar - Partner at Madrona Ventures - From Intern to General Manager at Azure

Host: Hey Aseem, welcome to the show.

Guest: Hey, thank you Natraj, thanks for having me. Excited to be on and great to talk to you.

Host: Uh, so, you know, I wanted to have you on the podcast, you know, you did a couple of interesting things.

I think you had a, you know, great career at Microsoft and now you're working at Madrona, which is, you know, one of the premier venture firms here in Seattle. Uh, so I want to talk about all those things.

But before that, just to give an introduction to the audience. Uh, can you talk a little bit about your, you know, upbringing and what you did before Microsoft?

Guest: Yeah, you know, I grew up mostly in Mumbai, uh, you know, along with like, you know, millions of my neighbors, a small city, as we all know.

Uh, and I then, you know, got my bachelor's in engineering in a in a institute called VIT, which is based in Pune. It's about, you know, three hours from Mumbai.

And then, you know, post that was looking for a post grad sort of opportunity and was really interested in the world of computer network.

And you know, came over here to the University Washington, you know, to sort of pursue my masters in electrical engineering with a specific focus on networking.

Uh, you know, came here as a grad student, you know, did research, was an RA at the applied physics labs. Uh, believe it or not, I was looking at the application of electrical engineering to, you know, a space like oceanography.

That was kind of my research, so it was kind of fun.

And then interned at Microsoft the following year in the Windows group, you know, writing deep installer level code, and then came back to Microsoft as given I had a full-time offer and just joined the team and sort of stayed there.

So that's kind of my my background in a nutshell.

Host: You had pretty, you know, interesting career at Microsoft itself. So, can you talk a little bit about like and you ended up at some point also, you know, sort of playing a CEO role for Azure, right? Uh, so talk to a little bit about like what are those, uh, early stages of working at Microsoft was, I mean, starting from intern to uh, becoming, you know, I think a GM at Azure.

Guest: Yeah, you know, it was it was really a wild ride to be honest.

Uh, you know, I joined the company as an intern and, you know, I joined the Windows division, which at the time was the biggest division and, you know, my eyes were kind of just glazing over with like the amount of talent and, you know, the expertise that people had already built in the operating system world.

And so I joined them, I interned as a as a developer, here wrote even install level code, but joined back as a PM.

Uh, you know, worked on, you know, migration features where you're migrating settings across the US, you know, was was fortunate enough to have a killer team and work on a killer team and learn so much about, you know, depth experts.

Uh, but then, you know, with any big company, I was always curious on, you know, learning as much as I can and optimizing for breadth.

And, you know, it's when you're when you're in intern when you come back, right, from college, your first job, you don't really know what you want to do in the in the fullness of time. And for me, the biggest focus area was, I just wanted to learn.

I just want to seep it up, I just want to soak it up. And so after being a PM for a few years, you know, I would interact with all these product managers.

I'm like, wow, these these product managers do a really killer job and, you know, they understand what to go build. Like, I want to go learn that.

So pivoted to being a product manager, you know, joined the Windows marketplace team, working with ISVs, you know, spent a few years as a product manager.

Uh, then got working with, you know, a lot of these go to market folks and, you know, they they were looking at doing developer marketing. Got offered a job to sort of run marketing, ran marketing for a bit.

Similar pattern followed, I got interested in sales, went to OEM sales. Prior to that, you know, ran the incubation project for commerce and online payments that then became Office 365 backbone.

So did that for a bit, then went to sales, ran sales for four years.

And and in the blink of an eye, you know, over the past over the first 10 years, I kind of did every role from development to sales and everything in between and that was a phenomenal experience, you know, got a breadth of role.

And right when, you know, Microsoft got into the cloud and, you know, sort of doubled down on the cloud, uh, went and joined the cloud team, uh, and that's kind of where, you know, I was at for the last eight years at Microsoft, grew the Azure business, you know, insane growth, doubling every year.

Just just a fantastic growth engine for the company. So, you know, in retrospect, if I told you that I planned it this way, I'd be lying.

I think it was all my interest towards, you know, having and getting different skill sets that sort of led me into this path.

Host: Because I mean, the truest to think about careers like like some people focus on just doing one thing and like compounded, you know, over the years and you get really better at it.

Like you you see that sort of personas and what you are telling me is like you are interested in more things. So you went sort of uh exploring different uh job roles also, right?

So was that conscious or like did you think about that as a career tactic or Yeah.

Guest: I mean, look, there are really two ways, like you mentioned, right? And there is somewhere in between as well, right?

Like people spend significant amount of time gaining gaining one set of skill sets and domain expertise and then do something else. Look, I always indexed on learning and, you know, I was always a breath guy. I love doing a lot of different things.

Uh, somewhere along the lines, I went and, you know, pursued my business school degree, uh, again from the University of Washington, going to school in the evenings.

And, you know, that really helped me think through the fullness of experiences required to go run a business. That was what I was interested in. Uh, I don't think that there is a silver bullet to say hey, one is better than the other.

I mean, there are hybrid models as well. Uh, and so it was really uh, at that moment decision for me versus sort of something I planned for, right?

Now, when I look back, yes, I've always been a person who collects different experiences, like different different challenges, you know, wants to go different wants to go do different roles. And that theme has sort of stayed with me through my career.

Uh, I don't think like I said, I I didn't plan it. I didn't have a course, I didn't have a path that was sort of linear to a certain extent. I took a fairly non-linear path.

Uh, but there are people who spend, you know, their entire career doing product and that's amazing because you do need these depth experts.

Host: So at at what point, so you were working at Azure and at what point like you decided, uh, I'm assuming like it's pretty interesting role. Uh, you decided, okay, I think this is time and I should go and do something else.

Guest: Yeah, look, I mean, the first eight eight years of Azure were just a rocket ship, right?

Like, you know, we we were growing the business, we were doubling every year, you know, there was a different level of scale, like, you know, the uh, just just to give you an example, we spent billions of dollars every year just building out data centers.

Like, how many companies can do that, right? You were growing sales, you were building the product, you were building a go-to market team, you were expanding product and programs. You know, you were buying and acquiring companies.

I mean, I think to during my time, I think we probably did like north of 10 acquisitions. Uh, so there was a lot of organic as well as inorganic growth that kind of got, you know, uh, done or completed within the Azure team.

And it was Microsoft's fastest growing business at that time and it was just fun to to sort of be a part of that. Uh, and then, you know, post Azure, I think when Azure was a massive business, uh, the Microsoft machine kicked in, right?

Like, you know, Microsoft knows how to execute and run big businesses. And it got to the point where I started on the Azure team when it was a speedboat, to give you an analogy, and then it became a cruise ship, right?

Big dream liner, you know, kind of, uh, I would say, uh, a big vessel that that takes a lot of, you know, inertia to sort of move. I mean, it's still nimble, but you make you make very few fast-paced decisions.

And, you know, I enjoyed the ride tremendously. Uh, but then I was onto, you know, seeking new adventures.

You know, one of the things I realized is I had all these experiences from development to sales and every function and I thought of like, how do I take that and how do I help, you know, create net new businesses?

And, you know, venture was sort of the obvious choice, helping founders and entrepreneurs and sit with them side by side and build these these big businesses.

Host: So, were you before like joining Madrona officially, were you doing Angel investments or like was there any interest before like officially moving into that role?

Guest: Yeah, I mean, look, I always considered Angel investing. Uh, I think one of the challenges that that I had to face was, I was working in a platform business, right?

And so I was pretty careful in not wanting to sort of go full steam on angel and invest in something that could be conflicting or, you know, something that would put me in an awkward position. And so I did not invest a whole lot.

But I always, you know, had this eye of like or willingness to sort of go help startup, smaller companies in whatever shape or way I can, uh, without actually while staying on the right side of the legalities, right? Uh, but it gave me an energy.

I mean, I worked very closely with the Microsoft startups team, the accelerator team, the M12 guys back in the day on the venture fund side of things within Microsoft and kind of help think through like, you know, how do these businesses become successful by partnering with Microsoft?

And so I always had an eye on the startup ecosystem. And so there was there was interest, there was willingness. And so venture seemed to me as a normal like very, very obvious next choice.

Host: And how do you see like making decisions like investing decisions by being part of Madrona versus like, you know, if you were in Azure and you know, you're thinking of a new line of product or business, that's sort of like an investment, but it's a Microsoft scale investment and you're sort of thinking through and I see this at least, uh, myself is like the decision-making framework is slightly different, right?

And the impact is measuring, the impact is different, while early stage startup you pretty much have no data, you're flying blind in a lot of cases, like talk to me a little bit about like that decision framework like and was was it a challenging shift or like how do you think about that, you know, making those bets in general?

Guest: Yeah, in some ways there is similarities and in some ways there is not a lot of commonality, right, in the decision making process.

Uh, from a similarities point of view, yeah, you look at market, you look at customer demand, you know, you look at, you know, what signal you're getting.

Uh, and so all those things tend to be similar in terms of like how you would assess success, right? The areas that are dissimilar are are are are worth calling out, right?

One thing that you want to think of is when you're in a company like Microsoft and you're building products, like often you get the benefit of being in a bigger Microsoft in terms of funding, you get benefit being very close to the customer, co-developing, you get the benefit of people not questioning whether your company is going to exist or not.

And you also get the benefit of, uh, you know, investing from your PNL in a very thoughtful manner. Uh, but you also get, you know, unnatural advantages or asymmetrical advantages, as I like to call it, right?

You don't have to go hire a net new sales team, you don't have to go create net new partner programs. You can go latch on to existing assets that you already have as a big enterprise.

On the Madrona side, if you look at working with startups, like founders don't have that, right? They're building everything from scratch.

They're validating, ideating, staying close to the customer, building pricing models, you know, creating programs, you know, hiring sales people, you know, building great teams.

Uh, you know, those those are the kind of things that are that are different in some senses. You know, another huge differentiator that you think of it is is talent, right?

I mean, it's it's if you if you go back six months, like it was incredibly hard for startups to go hire great talent.

It just because I think the risk appetite is very different for an engineer to join a startup or to join a company like Microsoft, right?

And so you have access to talent, you have access to a lot of these systems being in a company like Microsoft that you otherwise don't have as a founder.

And so those things are something that are that pose real risk to these companies that you have to evaluate through.

But otherwise, if you think of it, like if if you think of it as a CEO or a or an investment officer at at both of these companies, uh, or even as a VC, you are looking at, hey, what is my risk profile to make this successful or not?

And so at the end of the day, all these things kind of go into that hopper of how do you make decisions and you come out with the ultimate outcome of like that that is grounded in how do you manage your risk and how do you make these things successful and what are gates.

You know, one of the things that I learned, I was very fortunate to learn at Microsoft was the stage gating approach of how do you cross gates one time one one step at a time and how do you derisk your next growth engine activity?

So, so there is definitely like I said, richness of knowledge that I got at Microsoft that I then brought to the table here at venture.

And and on the flip side, there is the venture world, which is very different from a scale and a scrappiness perspective early on, that you just have to go and embrace.

Host: So, right now, what are the areas that you are investing and what stage are you investing in?

Guest: Yeah, so Madrona is a unique firm. Uh, you know, we, uh, we call ourselves as investors that work with founders on day one for the long run. So no check is too early for us.

We start sometimes with an idea and a founder, uh, and just work work ourselves, you know, sort of into this thesis and go build a company around it. Uh, there's plenty of examples of companies that have been built from from day zero for us.

Uh, and then more recently, we've also started investing in what we call as acceleration stage where companies that have reached about 5 million in ARR.

And so we tend to sort of go, you know, uh, series B or early C, however you might want to call it, our largest check tends to be about 20 million. So I think we're a unique firm when it comes to investing from zero to let's say the 20 million check.

In terms of areas that I'm passionate about, I mean, look, my background is infrastructure, you know, I did infrastructure at Azure.

Uh, and so I invest in infrastructure, DevOps, security, you know, sort of being beachhead areas within that space, uh, as well as vertical SAS. So I go all the way full stack. I look at like applications that are vertical centric as well.

And then an area that I'm getting very, very excited and interested about in today's world is just this notion around like automation systems and robotics.

And you know, whether you call it low code, no code, whether you call it improvement on productivity, whether you call it robotic systems, that's one one area that I'm pretty bullish on in terms of a thesis.

And so we are we are we're getting there as a firm and building our thesis around what it might mean to sort of go invest in those areas.

Host: Um, what do you think about the entire like the AI hype cycle that's happening right now?

Guest: You know, I I actually think of it in in two ways. One is it's a massive, massive enabler, right? Like if you think of it like and you compare it to some of the earlier technologies that had promise but didn't quite cross the chasm.

AI is slightly different, right? The applications are right in front of you.

Whether it is to, you know, write documents faster or or process images faster or create images or videos, uh, you know, I think I think it's sort of given a boost to those scenarios, right?

If you think of the work that Microsoft is doing and I talked to Thomas recently around the GitHub co-pilot approach, like that is fantastic, right?

I mean, your your productivity is gone up, you know, developers are not spending time on mundane tasks, but they're actually getting knowledge and and a co-pilot like, you know, somebody looking over their shoulders and helping them.

And so I think those scenarios are very real. Those uh, you know, those applications of AI are just massive, right?

I think, you know, Microsoft has done a massive, massive uh, or plays a huge role in this ecosystem, having worked with Open AI to sort of go create this this short in the arm like approach, right?

But ultimately, I think the question you have to ask yourself is where does value get created, right? Like how do companies in the vertical space kind of use AI to their advantage and create a mode around it?

Because otherwise, it's just an API that's accessible to everybody. So I think it'll be it'll be interesting to see how the world evolves in this generative LLMs multimodal kind of way.

Host: So I think you you brought up the I think what I wanted to ask next is uh, the mode, right? Everyone has this question of like where is the mode?

Uh, because a lot of AI companies are I mean, in a way, I mean, for lack of a better word are wrappers around publicly available APIs. Um, and you know, Google is offering, getting partner with Anthropic. I think Amazon also partnered with Anthropic.

Microsoft partnered with Open AI and we'll have stability AI, which is like open source, you know, and we are seeing journey has its own API, which is like 10 people, right? So, I mean, if LLMs are commodity itself, where is the mode for companies?

Uh are we going to look at like you know, copy AI and Jasper AI are going to be like tools for copywriting and you know, writing better.

Uh but at the same time, you could argue that everyone who uses Outlook or Word are actually the distribution, you know, channels where we cannot we're all writing already.

So it makes sense whoever has the distribution access will probably win, right? this you can look at it two ways or probably both can win. I mean, it's not like the market is small.

Uh so I'm curious like what do you think about the more challenges here for startups? Yeah, I mean.

Guest: I mean, that is that is the question that most people are grappling with today, right?

I mean, look, there is there is a reality around what you expressed around, look, there is there's lots of things being done in lots of spaces that could be overlapping.

And like if you take the example of, you know, copywriting or, you know, assisted writing, yes, there is Word, there's Google Docs, there's Google Sheets, there is, you know, code docs, but and everybody is sort of innovating it in in in multiple ways, right?

Uh, you know, one of the things to think about as a differentiator factor is uh, the enterprise data and how do you use that data within the enterprise that is your data, it's proprietary to make these models sharper, contextual and more relevant, right?

That gives you a competitive advantage. I think I think that's principle number one to sort of think about.

Principle number two that you might want to think about is is there a differentiated user experience that makes it so amazing that it's 10x better to sort of use these models or apply these models to you on your day-to-day.

And number three is given the given the proliferation of these models. Look, we believe that all these models are going to coexist, right? There is going to be, you know, value in all of these models, uh, but who is the orchestrator?

Like who is the one sort of layer that is bringing the beauty of all these models together or do I really have to pick and choose, right?

So I think those are those are three questions to sort of think about in terms of like where does where does value get created and how do you sort of win in a particular category.

Now, of course, there's multiple of these scenarios, there's multiple use cases and and then there's going to be tailoring as well, right?

Which is what is a unique experience that, you know, Jasper or a code docs or a Word is going to go build and enable for me that's differentiated than anybody else.

And yes, you can always argue that people catch up, but there is a certain certain thing to be said about even first mover advantages in some of these categories.

Host: Yeah, I think uh, I mean, in all the sort of hype cycles, I think one thing you can always commonly bet on because I mean even I said in last couple of sentence that LLMs are being commoditized, but it's not a straightforward thing that LLMs are commoditized, right?

It's didn't happen yet, right? It's not as commoditized as we think it is. Uh, like there will be still like if you're looking at like AI talent in general, like that's a still non-commoditized thing.

Like the talent itself is not a commodity yet, right? It's not easy to find someone who can like a 2% team who can train the LLM, who can deploy it and to and deploy it or um, optimize it for a certain cost structure, right?

That is still not commoditized. Yeah, I think there is a difference between training and inferencing as well, right? Yeah. I think the training aspect of is is going to be expensive, right?

I mean, imagine the number of GPUs that's, I mean, you probably know better than anybody else being inside of Microsoft as to what's happening in the GPU space and the availability or the lack of availability of those things. Yeah.

And you know, the the second thing that I think we've got to keep in mind is the capital requirements around training these kind of things, right? Yeah.

Uh, you know, as I was talking to someone, uh, you know, who's deep in this space, uh, that gentleman was telling me that the problem with with availability of GPUs today is not anything but power, right?

I mean, there is literally no power available in in the right sort of capital structure to kind of go make this happen and produce more GPUs.

While while that's an interesting posture and I don't know if that's 100% true or not, but these are limits around physics that you will hit.

And who are the companies that, you know, might be in the best position to solve these kind of problems are the ones that have deep pockets.

So it's not that everything, like you said is is immediate commodity, but I think the question that we should be asking ourselves is, what happens in the fullness of time?

Like what happens in the fullness of time when it comes to these things being available for everybody, how do people create unique competitive advantages in the ecosystem so that they're protected as an enterprise.

Host: Yeah, and I think the modalities can also be rethought, right? Like, should we even like copy AI or Jasper AI, should, you know, the editor look like this when everything is, you know, being suggested by AI or like do we have better forms?

And you can like extrapolate that idea across, you know, all products where you want to put AI, right? Uh, so I think you'll see some new ground up ideas, uh, what was not possible before essentially, uh, that we'll see.

But talking about like Open AI and Open AI's collaboration with Microsoft, I'm curious like how do you think about that collaboration, right?

It's part investment, uh, part partnership, uh, and this trend that has resulted because Microsoft partnered with Open AI and Google partnered with some other company, you know, Amazon is partnering with, you know, the other company.

Like how do you see that uh collaboration?

Guest: Yeah, I mean, it's hard to tell from the outside, but I think, you know, Microsoft has done a phenomenal job in sort of bringing AI to the world in partnership with Open AI, right?

I mean, if you look at the Azure Open AI, you know, uh, service that's going to be made available. Like I'm sure enterprise are flocking to Azure and saying, hey, look, how can I retire my credit sense? How can I get early access? How can I use it?

Uh, and I think, you know, kudos to Satya and the team for for an amazing sort of, you know, vision on what this might mean for the world. And and I think it's given Microsoft, I would say a unique advantage to begin with straight out of the gate.

I mean, if you look at the innovations that they showcased working on, uh, you know, being or integrating it with, you know, the productivity suite and office. I think I think it's just like sort of given them a a boost and a short in the arm.

Now the question becomes is how relevant is that going to be in the fullness of time, right? And how sustained that is going to be in terms of driving usage and adoption and and capturing customer value, right?

Uh, but you know, I think I think the the tech is amazing, the innovation is fast paced.

And in the last like six weeks, we've seen so much happen in the ecosystem in the world that the way we interact with technology is going to completely and fundamentally change. And you know, there's no running away from that.

Uh, so I think I think there's an early advantage. And you know, but but you know, there are other companies that are big and huge and massive and they've got this innovative DNA.

I mean, look, I I couldn't imagine, uh, an Amazon or Google not focusing on this. I'm sure they are. I think it's a matter of time as to what they come up with and how they position themselves.

When if you think of it, Google sitting on this this brilliant asset called YouTube, and you know, from a video perspective, like if there's anybody in the best position to sort of go create like even an amazing video model, it's probably Google.

And and I think it's it's worth watching this space closely to see in what announcements get made, you know, over the next six to eight weeks to see how people are integrating these AI capabilities naturally into their products and meeting users where they are.

Host: Do you think, I mean, I think we've seen like a open letter being signed to stop all the AI advancement, uh, for like six months, I think Elon Musk signed it. Uh, what do you think about that whole, um, you know, we should take a step back in terms of developing more powerful models?

Guest: You know, I I am not so sure which way I stand on that, right? Because the innovator in me says, hey, why if if there's innovation happening, what are we trying to solve for uh, when it comes to pause these kind of things, right? Like is it is it going I guess the fear is like, you know, we are creating AGI that's against humanity, right?

Guest: I mean, look, there's always been these movies around like, you know, sci-fi capabilities where the robots turn against you and, you know, they've got enough capability and all that.

Look, I mean, I I I appreciate the thinking and I appreciate, you know, what the what the concern is.

Uh, but at the end of the day, I think any innovation that you work on could be detrimental to society and I think we've got to be careful and we've got to be aware and we've got to sort of think through the hey, what this might mean, but I don't know if if a six month pause really fixes that.

I think the question is like what are the right levels of guard rails that we put in place in order to make sure that we are not sort of using these technologies against the the core premise of humanity.

I don't know if the six month pause is the right answer or if thinking through the guard rails is the right answer. I think it's somewhere in between.

Uh, but I'm not so sure on on like the pause kind of solving it because that because you might get into an analysis paralysis thing, right? And and it's very hard to say, hey, nobody go focus on this thing for the next six months.

I mean that's just not going to happen.

Host: Do you think LLMs are like human brains?

Guest: Uh, I'm not so sure. I mean, I think the human brain aspect of it is slightly different, right? I mean, I I look at it as LLMs are trained on the data that we provide them, right?

So what is the data that that is getting provided to them and does that have the capability of sort of, you know, emulating, like I say, like the the neuron connections in your brain, right?

I'm not a I'm not a scientist in in embedded in deep science to say hey, where does that land? But at the end of the day is it is it powerful tech? Yeah, it's amazing tech. And can it sort of get close to capabilities?

Yes, but then there's also the challenges that we hear around, you know, model hallucination, right? Uh, you know, I think someone said it uh said it really really well like, hey, the an LLM kind of is like a friend you go hang out with, right?

Like you ask the friend a question, the friend gives you a question, but sometimes the friend is wrong. Like what do you do then? Like you apply your own intelligence and and your own best judgment uh to avoid that that hallucination effect.

So, so do we think that we can we can trust it blindly yet? I don't know. I don't think so. I mean, you know, if if I'm writing a blog, I still am going to go proof read it.

I'm still I'm going to go see what the output is and and is it is it contextual relevant. Does it get me 80% there? Sure.

Host: Yeah. I think it's some type of intelligence. I don't think it's human intelligence, it's a different type of intelligence. Uh, but it nonetheless useful one. Um, so we're almost at the end of our conversation.

So I ask pretty much all my guests this question like what your information diet is? Like what are the things that you consume uh on a day-to-day basis uh that sort of inform your worldview?

Guest: You know, I mean, outside of the regular, you know, the tech crunches, the journals, the, you know, the uh, the the research papers coming out of universities that I that I love to read.

I think those are generally the way how I'm trying to keep relevant at least in the spaces that I'm interested in, you know, market maps, like look, you know, looking at different perspectives, you know, point of views, blogs, I mean, the usual stuff.

I am, uh, you know, I I tend to also tap into another resource that I think is incredibly valuable is just humans.

I think when you talk to people who are domain experts, you get a lot more depth and you get a lot more qualitative insight that you otherwise could sometimes miss in a quantitative or a reading a paper kind of thing, right?

When I'm reading a paper or I'm reading an article, if I have questions, who do I ask those questions to, right? I think it just makes it incredibly hard.

But I love the interactiveness of talking to people who are depth experts, but talking to people who spend years and careers building in and researching a space because I can truly get to the five whys of my questioning, right?

And that to me is an incredible resource more than anything else. And so to your point, like do I feel that LLMs can do that? Maybe not. Can humans do that? I think so.

And so I think there is that that level of depth and and that enrichment of dialogue that I really crave for. So, you know, I I tend to spend a lot of my time talking to people and learning from them.

Host: Do you have like a specific approach or like do you like make it a habit of reaching out to people on a consistent basis? Like how do you do that?

Guest: You know, I'm I'm kind of I like to say that I'm an extrovert by nature and so it it's natural to me to sort of reach out and say hello and, you know, maybe even pick somebody's brain on it.

Uh, but you know, there of course like Madrona is a basic and we've got a powerful network of people in our in our circle that we often reach out to. And you know, these are people who who spend their careers doing certain things.

And so talking to them and getting access to them is is amazing.

And you know, I've been in the area for 20 years, 25 plus years, you know, spent 18 at Microsoft and, you know, over a period of time have my own network of of uh, you know, folks that I call upon and, you know, often talk to and connect up with.

And and so it's uh, you know, it's it's pretty natural and and you know, LinkedIn is powerful. I love I love the capability of writing someone a message and saying, hey, I'd love to pick your brain for 30 minutes.

And you know, people are very generous and, you know, I appreciate that by uh, you know, that even when I reach out and and have a question, like they're pretty generous with their time and offering up advice.

Uh, and and that's just an amazing power of the ecosystem.

Host: Awesome. Uh, I think that's a good note to end the conversation. Same, thanks for coming on the show.

Guest: Thank you Natraj.

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