Latest / The Neon Show / Why The Next $100B AI Opportunity Is Context Engine| Prukalpa Sankar, Atlan

Transcript

Why The Next $100B AI Opportunity Is Context Engine| Prukalpa Sankar, Atlan · The Neon Show

• 0:00
  SPEAKER_01: In the dawn of the internet era, it said content is plain that Google is in the AI world, context is point displaying context that any agent, human or AI agent, needs to be able to solve this problem for the customers.
• 0:13
  SPEAKER_01: So if you have to build this in a business, you need to be able to bring together first the map of the system, how the system works and done.
• 0:21
  SPEAKER_01: And then what we do for customers is we now create this shared enterprise brain.
• 0:27
  SPEAKER_01: Product market state is a search for two non-traction.
• 0:30
  SPEAKER_01: And that's a really strange thing to internalize as an entrepreneur.
• 0:35
  SPEAKER_01: We've got our first set of investors.
• 0:37
  SPEAKER_01: Not a single investor entropy was made for our first 10 customers, which is extremely unusual.
• 0:41
  SPEAKER_01: Our reason for it was because we were searching for two non-tractures.
• 0:44
  SPEAKER_00: How did you find your first 10 customers?
• 0:47
  SPEAKER_01: A large bank is going to be very different than a small startup in the Bay Area, right?
• 0:51
  SPEAKER_00: And so how do you uh Prakalpa, uh welcome on the Neon Show.
• 1:00
  SPEAKER_00: Very excited to have you.
• 1:01
  SPEAKER_00: You are one of the very few entrepreneurs globally who has been working on context since 2019.
• 1:06
  SPEAKER_00: Uh, Bill Gates said back in 1996 that uh content is king, but you proved that context is king and queen.
• 1:14
  SPEAKER_01: Yeah, it's uh it actually sometimes feels very surreal because I feel like this problem we've been trying to solve for many, many years is now having this moment.
• 1:23
  SPEAKER_01: Uh, I remember when at Atlan we we um our series A blog post said uh we want to be the context layer for the modern data stack.
• 1:32
  SPEAKER_01: Uh and then in 20 uh 25, we wrote this uh uh blog that said in the dawn of the internet era, uh, you know, content is king, is something Bill Gates said.
• 1:47
  SPEAKER_01: And we believe in the AI world, context is going to be king.
• 1:50
  SPEAKER_01: And we tried we scaled the company from doing context for data science and machine learning and analytics use cases to all kinds of hygentic use cases, which really started becoming a thing after the reasoning model dropped.
• 2:02
  SPEAKER_01: Um, and uh yeah, this it feels very surreal because uh now it feels like the entire world is listening and you know we have this massive opportunity to go solve some of the biggest problems that companies are gonna face over the next decade.
• 2:18
  SPEAKER_00: When was the first time that you used the word context?
• 2:23
  SPEAKER_01: Um 2020.
• 2:26
  SPEAKER_01: Uh and we got to this problem early because uh maybe a little bit about my journey.
• 2:35
  SPEAKER_01: I used to be a data leader.
• 2:37
  SPEAKER_01: So prior to Atlanta I'd found a company called Social Cups that did a lot of work in data science for social good.
• 2:42
  SPEAKER_01: And that meant we had these extremely diverse teams: analysts, engineer, data scientist, machine learning researcher, uh, but also economists and public policy folks.
• 2:52
  SPEAKER_01: Uh, and they all had to collaborate together to make a project successful.
• 2:56
  SPEAKER_01: And as we were scaling these projects, we realized that the biggest gap in this team was the lack of shared context.
• 3:04
  SPEAKER_01: Uh, lack of shared context about the problem, lack of shared context about the data.
• 3:09
  SPEAKER_01: Data is really complex when you start making it productionalized.
• 3:12
  SPEAKER_01: So something as simple as the number on this dashboard doesn't look right is a really complicated question to answer because you're talking about, you know, hundreds of systems, uh, you know, data pipelines, metric might have changed, the definition of something might have changed.
• 3:26
  SPEAKER_01: Uh, it's all these, you know, five different people at every stage of the life cycle.
• 3:30
  SPEAKER_01: Uh and we realized that trust in this team broke down because they didn't have shared context.
• 3:38
  SPEAKER_01: So if a number was wrong, you wouldn't know if it was because the uh data engineer didn't do his job or did the data pipeline really broke?
• 3:46
  SPEAKER_01: Because I don't even know what a data pipeline is.
• 3:48
  SPEAKER_01: I can't even see a data pipeline as an end user.
• 3:51
  SPEAKER_01: Um and that was the early insight that made us realize that if you wanted these teams to work together really well, you needed to create a way for them to have shared context.
• 4:02
  SPEAKER_01: And so when we launched Atlanta, we launched Atlan as the shared context platform for the data organization.
• 4:10
  SPEAKER_01: The interesting thing about that was uh in many ways, we're now debating the same problem with AI.
• 4:18
  SPEAKER_02: Yeah.
• 4:19
  SPEAKER_01: Right?
• 4:19
  SPEAKER_01: So with AI as well, the same problem, end users can't trust the AI.
• 4:23
  SPEAKER_01: Um, it's a very interdisciplinary problem uh at multi-step, multi-people, lots of different domains.
• 4:31
  SPEAKER_01: Uh and that same problem of humans collaborating on a data science project very interestingly moves over to how we start thinking about agentic outcomes and humans and AI collaborate.
• 4:46
  SPEAKER_01: Um, and so it's been really cool to see how the problems become so much more important.
• 4:51
  SPEAKER_01: So um, we scaled super fast from you know 2020 to 2022 because you know the Snowflake had just IPO'd and the modern data stack was starting to become this thing.
• 5:02
  SPEAKER_01: And then a couple of years later, early days of Chat GPT happened.
• 5:06
  SPEAKER_01: And we were very fortunate we had these early customers.
• 5:09
  SPEAKER_01: I remember a very large bank here in the US.
• 5:12
  SPEAKER_01: They told us, you're the only company that uh the humans of today will use, but you've built the platform for my AI of tomorrow.
• 5:21
  SPEAKER_01: And I still remember this because they said AI of tomorrow.
• 5:23
  SPEAKER_01: They didn't say agents, agents was not a thing then.
• 5:26
  SPEAKER_01: Uh, but that was when we realized that context was going to be become much bigger than data science and uh every AI use case, because humans were the glue for context.
• 5:36
  SPEAKER_01: Every AI use case, if you want to go towards agentic outcomes, is going to need context.
• 5:41
  SPEAKER_01: Uh, and that kind of led the second growth spurt for the company where we transformed into being a context infrastructure layer for AI.
• 5:48
  SPEAKER_00: And uh uh what I read is that within one year of launching Action, you had 200 customers across 50 countries.
• 5:56
  SPEAKER_00: What led to that kind of scale?
• 5:58
  SPEAKER_00: It's very unusual for a startup to hit in.
• 6:01
  SPEAKER_01: Yeah, um so there's multiple things that happened.
• 6:05
  SPEAKER_01: One was actually because the tools that we built were so um, they were so hard wrought in pain.
• 6:16
  SPEAKER_01: You know, it just we built it out of pain.
• 6:18
  SPEAKER_01: Like if there was an existing product that existed, uh, then it would have been much easier.
• 6:24
  SPEAKER_01: Like we would have just bought it.
• 6:26
  SPEAKER_01: Like we were not trying to, like, I think a lot of software, sometimes you start with the thought process that you're building software because you want to sell software.
• 6:35
  SPEAKER_01: We were the opposite.
• 6:36
  SPEAKER_01: In fact, we tried to buy software to solve our own problems.
• 6:40
  SPEAKER_01: We didn't want to build software to solve our own problems, and wherever we couldn't use existing software was where we built our own software.
• 6:49
  SPEAKER_01: Um, and so I think just that, and then because of this kind of scale that social cuts had, we just had access to really hard environments and really difficult, you know, situations.
• 7:02
  SPEAKER_01: And, you know, uh when I just say like, how do you deal with this kind of data as a data type, for example?
• 7:09
  SPEAKER_01: And you've done, you know, when at one point we were processing, you know, um satellite imagery for you know, hundreds of millions of you know, pixels of satellite imagery a day.
• 7:18
  SPEAKER_01: So I think that was an unusual background, I think, of getting scale like that and then solving problems for yourself.
• 7:26
  SPEAKER_01: Um it was also a journey because the interesting journey that we had to go from social cops to Atlanta was this product market fit journey.
• 7:36
  SPEAKER_01: And um, product market fit is about repeatability, not novelty.
• 7:41
  SPEAKER_01: And uh we had not built just one internal tool, we had actually built many internal tools.
• 7:48
  SPEAKER_01: And so the actual journey of figuring out which of those tools uh are the right tools to pick up to scale uh as a product market fit journey uh was one of the hardest sort of first couple of years of decisions that we had to make at Atlanta.
• 8:08
  SPEAKER_00: So tell us more about the product market fit journey.
• 8:11
  SPEAKER_00: Very interesting to know that you know when earlier, I I would say it was a data services company, would be the right way to label social crop.
• 8:20
  SPEAKER_01: Yeah.
• 8:20
  SPEAKER_00: Yeah.
• 8:20
  SPEAKER_00: So a data services company to a completely infrastructure company attaining product market is also unheard globally.
• 8:28
  SPEAKER_00: I've not seen infrastructure structure companies usually take three years for build before they can get their first customers.
• 8:35
  SPEAKER_01: Interesting.
• 8:36
  SPEAKER_01: Actually, you'd be surprised, you know, how many companies uh have come that journey.
• 8:40
  SPEAKER_01: So DBT was founded like that.
• 8:42
  SPEAKER_01: Uh it came from a consulting firm called Fishtown.
• 8:45
  SPEAKER_01: Um, Altrix was founded like that.
• 8:47
  SPEAKER_01: So came from a consulting firm.
• 8:49
  SPEAKER_01: Actually, see, we'll be very surprised.
• 8:50
  SPEAKER_01: Uh it's not, they're not very popular stories, but you'd be very surprised how many uh real high-quality software products.
• 8:58
  SPEAKER_01: Of course, the Slack story is very well known, but like high-quality software products are are bits like that.
• 9:02
  SPEAKER_01: But I do think you're right in terms of the speed.
• 9:04
  SPEAKER_01: Yeah, yeah, the speed and the intra.
• 9:06
  SPEAKER_01: There's there's layers like that that that uh we would put so maybe I can tell you how I think about product market fit.
• 9:14
  SPEAKER_01: And I found that uh product market fit is a search for truths, not traction.
• 9:21
  SPEAKER_01: And that's a really strange thing to internalize as an entrepreneur.
• 9:28
  SPEAKER_01: So, for example, um when we decided that we were going to go build tooling that can help every data team in the world, uh, we said, well, this is only gonna be powerful if we can go power the most important and the best and the largest scale iconic teams in the world.
• 9:49
  SPEAKER_01: That meant that we needed to figure out how to build for that market.
• 9:54
  SPEAKER_01: And uh if you study the journey of any of those teams, you know, uh, or any phenomenal data team in the world, like you know, they're you have most of them are in the US, like 70% of them are in the US, and then you go to other markets.
• 10:08
  SPEAKER_01: At that time, that was also where cloud maturity was, data maturity was, etc.
• 10:12
  SPEAKER_01: Um and so we said, well, if we have to build an iconic company, then that's the market you have to serve.
• 10:19
  SPEAKER_01: Uh then the second thing, uh, which was very unusual for us, was we stayed in product market fit longer than a normal company.
• 10:26
  SPEAKER_01: So normally what founders typically do is they call people that they know and who trust them.
• 10:32
  SPEAKER_01: And uh they say, Hey, can you please buy my product?
• 10:35
  SPEAKER_01: And that's how you get your first 10 to 20 customers.
• 10:39
  SPEAKER_01: Um I very specifically did not want to call anyone I know.
• 10:44
  SPEAKER_00: So none of the social cops clients.
• 10:48
  SPEAKER_01: Uh there was no uh, in fact, even when we got our first set of investors, not a single investor intro was made for our first 10 customers, uh, which is extremely unusual for like most companies.
• 11:00
  SPEAKER_01: And uh our reason for it was because we were searching for truth, not traction.
• 11:05
  SPEAKER_01: And we knew we could fake traction.
• 11:07
  SPEAKER_01: You know, like the first 10 because people I believe most people love founding stories.
• 11:15
  SPEAKER_01: Yeah, most people want to help.
• 11:17
  SPEAKER_01: Um, and most people will find a way if you speak with them and tell them this is the problem you're trying to solve, they'll find a way to help you.
• 11:25
  SPEAKER_01: Um, and that doesn't mean that they are going to be the right kind of customer for you in the long term.
• 11:31
  SPEAKER_01: And so we actually ran a very painstaking process of uh completely reaching out to strangers, cold emailing everywhere, getting our first conversion funnel, figuring out which markets to then we were a horizontal platform, which was great, you know, VCs love it.
• 11:50
  SPEAKER_01: It's actually a horrible go-to-market problem because, you know, the where do you start?
• 11:54
  SPEAKER_01: It's about repeatability, right?
• 11:55
  SPEAKER_01: Like uh a large bank is going to be very different than a small startup in the Bay Area, right?
• 12:00
  SPEAKER_01: Uh, and so how do you find repeatability?
• 12:03
  SPEAKER_01: Uh, those were the hard decisions we had to take in the journey to our first 10 customers.
• 12:08
  SPEAKER_00: And can you tell us, like, I think founders would love to hear, even if you have said it before, sure.
• 12:12
  SPEAKER_00: How did you find your first 10 customers and what were you selling to them?
• 12:16
  SPEAKER_01: Yeah, so um we had a pretty strong thesis about what the product was because we had lived.
• 12:23
  SPEAKER_00: And what was that?
• 12:24
  SPEAKER_01: Um, so we were basically building um, we called it the context layer for a data team, the home for a data team.
• 12:30
  SPEAKER_01: So we used to say the same way GitHub is for an engineering team, we want to be for the data team.
• 12:35
  SPEAKER_00: But data team has a data warehouse also, data lake also.
• 12:40
  SPEAKER_00: How would you fit in the data stacks then?
• 12:43
  SPEAKER_01: Yeah, so uh um that was actually the um a question that a lot of people would ask us, right?
• 12:51
  SPEAKER_01: Uh depending on who we speak to.
• 12:53
  SPEAKER_01: Uh and I'd say 2019 to 2021, people were not really clear about what were going to be the components of the data stack uh in the market.
• 13:02
  SPEAKER_01: Uh on the other hand, customers really got it.
• 13:05
  SPEAKER_01: So when you talk to, like the way I used to talk to data people, when I spoke to data people, you know, we'd say these are the problems we've identified.
• 13:12
  SPEAKER_01: We used to have the slide, and it used to have these Slack messages.
• 13:15
  SPEAKER_01: And it used to be, which table should I use for this analysis?
• 13:19
  SPEAKER_01: Number on this dashboard is broken.
• 13:21
  SPEAKER_01: We don't know why.
• 13:22
  SPEAKER_01: Uh and uh, you know, you kind of go and talk about these challenges because of collaboration between the analyst and engineer and the scientists.
• 13:30
  SPEAKER_01: And so I used to we used to have the slide of Slack messages.
• 13:34
  SPEAKER_01: We used to first just validate for them do you also face these problems?
• 13:39
  SPEAKER_01: And uh then we would say, um, well, would you pay personal money to solve this problem for yourself?
• 13:47
  SPEAKER_01: Actually, that was our first user insight because we had to build for users, not buyers.
• 13:51
  SPEAKER_01: Because at that time there was no buying market, there was just user market.
• 13:55
  SPEAKER_01: Uh and we almost consistently found that when we spoke to users, analysts, engineers, data engineers in this case, they would say this was one of the top three problems they would pay personal money for.
• 14:08
  SPEAKER_01: So that gave us enough insight that first, this was not a problem that only our unique team at Social Co-ops faced.
• 14:14
  SPEAKER_01: This was something that all teams, it was a global problem, it was a problem everywhere.
• 14:19
  SPEAKER_01: So then we had to go from there to say, okay, well, how do we build the buying budget for this?
• 14:25
  SPEAKER_01: Um, and so in that, we basically ran an outbound campaign um to chief data officers, chief analytics officers, heads of BI, like this whole gamut.
• 14:37
  SPEAKER_01: And we had to figure out who's going to be ready to buy and what pain will they have to see to buy for their teams.
• 14:44
  SPEAKER_01: Um, and uh, we actually should run this uh uh very structured discovery exercise around pain, value.
• 14:52
  SPEAKER_01: We had a hypothesis testing framework.
• 14:54
  SPEAKER_01: So we actually ran it like a lab.
• 14:56
  SPEAKER_01: Uh, and uh the intent was to figure out which of these pain points do they care about the most and which persona cares about it the most, in which industry?
• 15:06
  SPEAKER_01: Where are we finding repeatability on pain?
• 15:08
  SPEAKER_01: Uh, and then are they agreeing to a next step?
• 15:11
  SPEAKER_01: So, how many people will just show up on a call with you versus you know, commit time to show up to our demo?
• 15:18
  SPEAKER_01: And then how many of those would commit to doing a POC?
• 15:21
  SPEAKER_01: And that was the funnel we measured, and we kept optimizing that funnel, and that became how we discovered our ICP.
• 15:28
  SPEAKER_00: So that was a lot of work within one year.
• 15:30
  SPEAKER_01: It was a lot of work within uh, I mean, there was a time when um uh I uh I remember actually it was Jan, Jan 2020.
• 15:42
  SPEAKER_01: Uh and uh we were doing 14 calls a day.
• 15:48
  SPEAKER_01: And uh I was still in India then, uh, which meant that the calls used to start at 3 p.m.
• 15:56
  SPEAKER_01: 4 p.m.
• 15:57
  SPEAKER_01: IST used to go on till about 2 a.m., 3 a.m.
• 16:02
  SPEAKER_01: Uh and so actually we built this whole system uh for how to even manage those calls because it wasn't like back-to-back calls, how do you even manage those calls?
• 16:12
  SPEAKER_01: Um uh so it was quite some uh it was we we really tried to go very well and in retrospect, honestly, I think if we'd gone even slightly later, because I don't think what people realize is uh March 15th, COVID happened, and actually in those insights we realized we had to rebuild our entire product um uh for the buyer, meaning the users loved it, but for the buyer to enter, we needed to like make some foundational infra changes, and so we had to rebuild the entire back end.
• 16:40
  SPEAKER_01: Uh and uh March 15th, we stopped getting calls because COVID happened.
• 16:45
  SPEAKER_01: So there was a three-month complete blackout.
• 16:48
  SPEAKER_00: Yeah, people were worried about their lives.
• 16:51
  SPEAKER_01: No one was ready to take a call with you.
• 16:53
  SPEAKER_01: If we had been three months late, uh we would not have existed.
• 16:59
  SPEAKER_01: Today we would not be alive.
• 17:00
  SPEAKER_01: So uh it always makes me kind of realize how much every like you know, they always say every day matters, but sometimes every day really matters.
• 17:08
  SPEAKER_00: And and using those insights, you build a product, or the product was already built.
• 17:14
  SPEAKER_01: So the thesis and the so there were two journeys to Atlan.
• 17:21
  SPEAKER_01: The first version of Atlan was built completely user-driven, but in many ways we built a perfect product.
• 17:26
  SPEAKER_01: So we got a lot of user love with some early design partner customers, but it was very hard to um bring in given security requirements and things like that for uh companies.
• 17:39
  SPEAKER_01: And so basically, much so the thesis of the product remained the same, the core flows remained the same.
• 17:45
  SPEAKER_01: We just had to rebuild basically the entire infrastructure at the deployment model.
• 17:49
  SPEAKER_01: So it's for all intents and purposes a new product, but the thesis was uh the thesis and like the core flow stayed the same.
• 17:56
  SPEAKER_01: We just had to rebuild the way we delivered software.
• 17:58
  SPEAKER_00: But you also mentioned that a lot of internal tooling that you build as social corps became part of Hatching.
• 18:03
  SPEAKER_00: So uh so I'm not able to relate how how it evolved building a product from scratch when a lot of the pieces were already built.
• 18:12
  SPEAKER_01: Yeah, I mean, in some ways that was the hard part, right?
• 18:14
  SPEAKER_01: Like, because I think two or three things happen.
• 18:17
  SPEAKER_01: You build internal tooling, and that internal tooling actually over many years, it gives you these core insights and theses of what works and what doesn't work, and then you productize it.
• 18:28
  SPEAKER_01: And then the question is like, which what's the right way to scale this?
• 18:33
  SPEAKER_01: And then you rebuild, you almost rebuild on new infrastructure, new scale.
• 18:38
  SPEAKER_01: I mean, actually, I I mean my co-founder Varun always says this like, you know, I don't think software is the rebuilding technology is actually the easiest part of this problem, right?
• 18:48
  SPEAKER_01: Like software itself is fungible, it has always been fungible.
• 18:52
  SPEAKER_01: Like, I I don't know why people now in AI suddenly are like, oh my god, this it was always fungible.
• 18:56
  SPEAKER_01: It was never that hard to but building software that people use is the hard part.
• 19:03
  SPEAKER_01: Um, and that's a lot of the insights that you generate um alongside it.
• 19:08
  SPEAKER_01: So, of course, there were like back-end services and microservices and things like that that we were able to translate from every version of the product and so on.
• 19:15
  SPEAKER_01: But you know, you kind of have to repackage and rebuild a lot from a from a user flow perspective.
• 19:20
  SPEAKER_01: And in fact, I mean that was not the last time.
• 19:22
  SPEAKER_01: I mean, since then we've rebuilt Atlin three times since um, and even though it's like the same user thesis and the market and all of that, you you have to keep keep foundationally reimagining your product.
• 19:32
  SPEAKER_00: So for the first few customers, what was the again coming back?
• 19:37
  SPEAKER_00: What was the the use case that you were solving for a data engineer?
• 19:40
  SPEAKER_00: Since you mentioned that your first few users were individual data engineers and analysts.
• 19:46
  SPEAKER_01: Yeah.
• 19:46
  SPEAKER_01: So two things.
• 19:47
  SPEAKER_01: So the problem that an analyst and a business person had was that they could not find the data that they could use.
• 19:54
  SPEAKER_01: They could not trust the data.
• 19:55
  SPEAKER_01: So when they looked at the data, they would say, Num, you know, what does this column mean?
• 20:00
  SPEAKER_01: Can I use this column?
• 20:01
  SPEAKER_01: Is this the right column to use?
• 20:03
  SPEAKER_01: Um uh and so the challenge for the analyst was uh they used to spend, in fact, about 30-40% of their time just discovering data and playing around with data before they could get to analysis.
• 20:16
  SPEAKER_01: Um, the problem for the data engineer was debugging.
• 20:20
  SPEAKER_01: So they spent their entire life where people would call and say, This broke, this broke.
• 20:25
  SPEAKER_01: Why did this break?
• 20:26
  SPEAKER_01: In fact, like 80% of time of data engineers was spent on debugging things.
• 20:30
  SPEAKER_01: And the reason they would not, they would have to debug things was because things were not connected.
• 20:34
  SPEAKER_01: So you would actually not know from a database to a data warehouse to a um, you know, um dashboard or a data science application, how is data flowing?
• 20:43
  SPEAKER_01: How is data flowing at a column level?
• 20:44
  SPEAKER_01: What's happening?
• 20:45
  SPEAKER_01: What is changing in the ecosystem?
• 20:46
  SPEAKER_01: Oh, this one pipeline changed because this core system that somebody else is sending me data from, they changed their API.
• 20:53
  SPEAKER_01: Uh, that changed something downstream.
• 20:55
  SPEAKER_01: I don't, I don't even know, I don't even have visibility.
• 20:57
  SPEAKER_01: So the first problems we solved for these users was we first we helped them.
• 21:01
  SPEAKER_01: So what Atlan would do was we would go, we'd connect into your entire estate of systems and tools.
• 21:07
  SPEAKER_01: We would reverse construct what we call lineage, which is basically a connectivity map down to a column level of how data flows inside your business.
• 21:16
  SPEAKER_01: And then we opened this up in a very um uh workspace-like environment.
• 21:22
  SPEAKER_01: So the same way every code profile has a profile on GitHub.
• 21:27
  SPEAKER_01: We said, okay, what does the data profile look like?
• 21:30
  SPEAKER_01: Oh, it should be shareable.
• 21:31
  SPEAKER_01: It should just be a link.
• 21:32
  SPEAKER_01: I should just be able to share that link.
• 21:34
  SPEAKER_01: Uh, it should have, you know, my profile of this ecosystem.
• 21:37
  SPEAKER_01: So I should know frequency and distribution.
• 21:39
  SPEAKER_01: I should know who owns it.
• 21:41
  SPEAKER_01: I should know who's maintaining it.
• 21:43
  SPEAKER_01: I should just be able to message them right there on Slack.
• 21:46
  SPEAKER_01: Uh these were the collaboration interfaces we built around the product.
• 21:53
  SPEAKER_01: That was what our first set of users were buying.
• 21:55
  SPEAKER_00: And you mentioned that the product has completely changed two times.
• 22:00
  SPEAKER_00: So, what does the product look like now and who uses it in organization?
• 22:04
  SPEAKER_01: Yeah.
• 22:04
  SPEAKER_01: So since then, uh actually, our I'd say 50% of our custom of our end users are now agents, and the rest 50% are humans.
• 22:13
  SPEAKER_01: Um the next big evolution for us as a company was a couple of years later when the early days of GPT happened.
• 22:25
  SPEAKER_01: And in fact, that we were one of the first companies back then to launch an AI product ourselves.
• 22:29
  SPEAKER_01: So we launched Atlan AI, which uh, because our product lent itself very well to AI or AI use case, because we could go say, oh, we have all your data estate, we'll go auto-document and auto-figure out everything that you need to know about your data estate.
• 22:42
  SPEAKER_01: Um, and that's when we realized how hard it was going to be to productionalize a true AI system.
• 22:46
  SPEAKER_01: Uh, it was a labor of love, actually.
• 22:49
  SPEAKER_01: I think from that time, uh 2023 at Linai, we were no matter how much we pushed, we couldn't get to more than 70% accuracy because it was, you know, the models were just not there then.
• 23:02
  SPEAKER_01: Um and it's been a labor of love to get to the autonomous system.
• 23:06
  SPEAKER_01: We've rebuilt our harness like three or four times on the journey of getting there.
• 23:10
  SPEAKER_01: But that was also when we realized that context was going to be bigger than just something that data users served.
• 23:16
  SPEAKER_01: It was going to be something that every um AI agent was going to need.
• 23:21
  SPEAKER_01: Uh, and so the types of context that they needed was going to be very different.
• 23:24
  SPEAKER_01: Uh, so for example, uh, let's say I'm a uh customer support agent, and a customer calls me and says, my uh uh order was uh not delivered today, I need a refund.
• 23:38
  SPEAKER_01: Um, and I'm really upset with you.
• 23:40
  SPEAKER_01: I need to go get, you know, um uh I'm gonna go on social media and I'm gonna complain about it.
• 23:47
  SPEAKER_01: At this point, if you want to solve this problem for the customer, first you need to know knowledge.
• 23:52
  SPEAKER_01: You need to know who's the customer, why are they calling, what happened in the order, um, you know, what went wrong.
• 23:59
  SPEAKER_01: Um, but then you also need expertise.
• 24:02
  SPEAKER_01: Like, how do I run a de-escalation playbook?
• 24:04
  SPEAKER_01: The customer told me social media, that's a different de-escalation.
• 24:08
  SPEAKER_01: And then I need norms.
• 24:10
  SPEAKER_01: What am I even allowed to do as an agent here?
• 24:12
  SPEAKER_01: I can I refund more than$50.
• 24:13
  SPEAKER_01: How do who do I go to?
• 24:15
  SPEAKER_01: This is the context that any agent, actually human or uh AI agent, needs to be able to solve this problem for the customer.
• 24:24
  SPEAKER_01: Knowledge, expertise, and norms.
• 24:26
  SPEAKER_01: So if you had to build this in a business, you need to be able to bring together first the map of the business, which is the ontology and how this business works and runs, the tacit knowledge and the expertise in a way that's encoded for AI to be able to take and build and learn from.
• 24:44
  SPEAKER_01: Um, and then guardrails and norms that you're again encoding inside the organization.
• 24:49
  SPEAKER_01: Uh, that is what we call the context layer, uh, and it's a category that's now uh being called AI context platforms.
• 24:56
  SPEAKER_01: Um, and what we do for customers is we now create this almost shared enterprise brain, I would say, uh, where it's the brain, it it reads from all your existing systems, it's pulling from that, it's using a lot of AI to create the first version of your brain.
• 25:14
  SPEAKER_01: And then it's serving all these agents downstream, and then it's taking traces from all of these agents downstream and it's bringing back and improving your core context inside the business.
• 25:24
  SPEAKER_00: So, today, who would be the other companies that that uh you know Atlanta sits along with or Atlan has replaced?
• 25:33
  SPEAKER_01: Yeah, so um we typically sit alongside agent orchestration frameworks or harnesses, right?
• 25:41
  SPEAKER_01: So customers would use, you know, name it.
• 25:44
  SPEAKER_01: They would use Google ADK, or they'd use Langraph, or they'd use um, you know, something from the Frontier Labs and or the hyperscalers.
• 25:52
  SPEAKER_01: So that, you know, the agent orchestration layer is something that we typically will work alongside.
• 25:56
  SPEAKER_01: Uh, we would work alongside the AI uh control plane.
• 25:59
  SPEAKER_01: So this is where, you know, gateway, uh guardrails, or you know, ecosystems like that would fit.
• 26:05
  SPEAKER_01: And then there would be existing data platforms and uh systems of record, right?
• 26:08
  SPEAKER_01: So this would be Salesforce, Snowflake, Databricks, so on.
• 26:11
  SPEAKER_01: Um, so those are the ecosystems that we connect into to create the shared.
• 26:15
  SPEAKER_01: So on one side we pull, on the other side we push, and then we bring back and improve this core context layer for companies.
• 26:22
  SPEAKER_01: Um, what we end up replacing, um, it's an interesting question because it is foundationally a new category.
• 26:30
  SPEAKER_01: So there isn't, you're not necessarily replacing too many things.
• 26:36
  SPEAKER_01: Um, but what we see customers say is we used to have old school data management software or data catalogs or metadata management platforms.
• 26:43
  SPEAKER_01: And these platforms used to uh basically just like they're just built on static data and they're built for, you know, my uh human consumers.
• 26:53
  SPEAKER_01: Um and so typically do I even need that anymore?
• 26:56
  SPEAKER_01: And that's basically where uh where they typically, you know, for TCO purposes, you'll see customers replacing their or or retiring their spend on these old school income and products.
• 27:06
  SPEAKER_00: Gotta and and has any company came in the category of Atlanta in the recent one or two years?
• 27:13
  SPEAKER_01: Well, in the last three months, it's the hottest category in the world because in March uh Gartner went on their uh DDA and Analytics Summit uh this year and they said uh context is king, it is going to be the biggest category um this year.
• 27:28
  SPEAKER_01: It you know, it's a board-level investment.
• 27:30
  SPEAKER_01: Go take it seriously.
• 27:32
  SPEAKER_01: Uh, and so since then, in fact, there is it both it's an amazing moment because I've been speaking to a CIO or a CTO or a CDO on a daily basis on you know how they should think about their strategies.
• 27:45
  SPEAKER_01: But on the other hand, there's a lot of rebranding, right?
• 27:48
  SPEAKER_01: So you'll see, you know, semantic layer platforms are rebranding as context platforms, and uh ontologies and knowledge graphs are rebranding as context platforms, and uh data platforms are rebranding as context platforms.
• 28:00
  SPEAKER_01: Uh, the model layers and the agent platforms are saying they have a context layer.
• 28:05
  SPEAKER_01: So right now there's a lot of confusion in the market.
• 28:09
  SPEAKER_01: Uh, you'll see people starting to approach it from different directions.
• 28:14
  SPEAKER_01: So, on one side, the agent platforms are trying to approach and own parts of it.
• 28:18
  SPEAKER_01: Uh, the, you know, the data side they're trying to own.
• 28:21
  SPEAKER_01: So, like there's a lot of confusion because I think people have realized how critical context is.
• 28:25
  SPEAKER_01: It is going to be company IP, it is going to be the true differentiator or the moat in this new world.
• 28:31
  SPEAKER_01: So, people have realized that.
• 28:32
  SPEAKER_01: So, there's a lot of, we're in the hype cycle uh from a market perspective.
• 28:36
  SPEAKER_01: Uh the next year, year and a half is going to be a lot about execution and which company truly stands out and goes and drives that.
• 28:44
  SPEAKER_01: I think we've been very fortunate to have pioneered this category and uh, you know, been really building this.
• 28:52
  SPEAKER_01: When people see what we've built, they typically go, oh wow, this is two years ahead than anything I've seen out there.
• 28:57
  SPEAKER_01: Uh now, you know, yeah, the the pressure I feel on a daily basis is can we go compound that and can we make sure that we can go really win this at large scale?
• 29:06
  SPEAKER_00: So let's say today, how many of the Fortune 500 would Atlan have as customers?
• 29:12
  SPEAKER_01: Uh about 20% of the Fortune 500 are at 100%.
• 29:17
  SPEAKER_00: And and uh uh I assume uh your your weeks as a CEO are filled with customer calls?
• 29:25
  SPEAKER_01: Um It depends on the week and the quarter.
• 29:29
  SPEAKER_01: Uh what I like to do is every quarter, uh Varuna co-CEO, so it helps.
• 29:35
  SPEAKER_01: So we have just more, we have more bandwidth and the ability to do to do more.
• 29:41
  SPEAKER_01: Um But uh what I like to do is every quarter just say, okay, these are the three biggest priorities um that we need to solve.
• 29:50
  SPEAKER_01: And uh I try to prioritize my time on the basis of that.
• 29:54
  SPEAKER_01: But almost always customer-facing conversations will be a core part of it.
• 29:58
  SPEAKER_01: So I will spend uh at least 20 to 30 percent of my time, if not in some cases much higher, in front of customers.
• 30:06
  SPEAKER_00: So what what what are the time that you are spending on building what these days?
• 30:10
  SPEAKER_01: Uh completely random things, actually.
• 30:12
  SPEAKER_01: Like I like to build with AI.
• 30:15
  SPEAKER_01: So sometimes I would be internally.
• 30:16
  SPEAKER_01: In fact, uh I uh I made some of the first commits to in our internal marketing context layer.
• 30:22
  SPEAKER_01: So we've built something called a marketing OS, which powers all our agents, and we have about 16 agents and all the humans and our marketing team.
• 30:29
  SPEAKER_01: So sometimes it would be, you know, building, building internal tooling for ourselves.
• 30:34
  SPEAKER_01: Um, sometimes it would be experimenting on, you know, how to solve certain problems that I've heard about and so on.
• 30:41
  SPEAKER_01: Um and sometimes it'll be like personal AI projects too, like just personal things that make me like I have a I've now finally gotten to a point where my uh my email is truly autonomous.
• 30:54
  SPEAKER_01: Uh, and that took a lot because it was a lot of context engineering that I had to do because I get lots of different kinds of emails and I needed to go read all this context about the company and the company brain and like come back with like a recommendation.
• 31:06
  SPEAKER_01: So it's a mix of those.
• 31:08
  SPEAKER_00: So why has context become so important for a CIO line item while buying?
• 31:13
  SPEAKER_00: Earlier, context was never in the CIO top 10 priorities or any priority, right?
• 31:20
  SPEAKER_00: If you try to sell enterprise brain or context, but today every CIO wants a context engine inside.
• 31:27
  SPEAKER_00: And you mentioned that you are also having a call every day with a CIO.
• 31:31
  SPEAKER_01: Yeah.
• 31:32
  SPEAKER_01: Um, yeah.
• 31:33
  SPEAKER_01: So I think what's happened over the last couple of years is we've been in the middle of this AI hype cycle.
• 31:37
  SPEAKER_01: And on one side, we all read the model breakthroughs that are happening.
• 31:41
  SPEAKER_01: Um, and by any measure, we have compounded in intelligence, right?
• 31:46
  SPEAKER_01: Like last 10 years, we have compounded in intelligence of a model 1000 X.
• 31:51
  SPEAKER_01: In the last six months, 2x.
• 31:53
  SPEAKER_01: Uh, two years ago it couldn't pass the bar.
• 31:55
  SPEAKER_01: Today, if it took a bar, it would be in the top 1% of the test score.
• 31:58
  SPEAKER_01: So we all read these headlines.
• 32:00
  SPEAKER_01: Um, but at the same time, you look at what's happening inside real companies and you say, well, how much impact has this created?
• 32:07
  SPEAKER_01: I work in the real world, I work with real companies.
• 32:10
  SPEAKER_01: Um, and uh they typically say, uh, you know, we had this one project that went into production.
• 32:16
  SPEAKER_01: Uh, and all the stats support it, like uh, you know, 60% of CEOs say zero financial benefit from AI.
• 32:23
  SPEAKER_01: Uh only one out of five projects that you work on actually make it to production.
• 32:28
  SPEAKER_01: So clearly something's going on, right?
• 32:30
  SPEAKER_01: Like there's like there's cognitive dissonance between what's happening.
• 32:34
  SPEAKER_01: Uh I believe the answer actually rests in understanding how humans work.
• 32:39
  SPEAKER_01: So every time I face an AI problem, I go back to what is going on with humans.
• 32:43
  SPEAKER_01: So let's think about how humans perform on the job.
• 32:45
  SPEAKER_01: Would you say that your best employee is also the person who scored the highest on the SAPs?
• 32:51
  SPEAKER_01: Or would you say that it's the person who uh learns the most and takes feedback the best and works the hardest?
• 32:59
  SPEAKER_01: Uh, right?
• 33:00
  SPEAKER_01: Uh, this is what in the real world we call performance.
• 33:03
  SPEAKER_02: Yeah.
• 33:03
  SPEAKER_01: Uh performance is real world outcomes that you want to create.
• 33:07
  SPEAKER_01: And performance is a function of two things.
• 33:09
  SPEAKER_01: It's a function of intelligence, um, but it's also a function of context.
• 33:14
  SPEAKER_01: Uh, context is actually what they call learning on the job in a business.
• 33:18
  SPEAKER_01: And in fact, less than 10% of human job performance is explained through cognitive intelligence.
• 33:24
  SPEAKER_01: Everything else is context and how you learn on the job and how you act in real life.
• 33:31
  SPEAKER_01: Um so, what has happened now is over the last year and a half, you know, most companies have tried to adopt AI.
• 33:39
  SPEAKER_01: And they've realized that beyond simple things like helping humans write better email, if they want to actually drive outcomes where AI is able to take off tasks that humans do or to really try to push the frontier of what they do, they've realized that they need to go invest in the tacit knowledge and the context infrastructure that they've actually built for humans.
• 34:01
  SPEAKER_01: Like just think about for humans for the last hundred years, we have done this.
• 34:05
  SPEAKER_01: You onboard people, you give them a manager, the manager gives them feedback, they have colleagues, they learn from their colleagues, uh, they make mistakes.
• 34:13
  SPEAKER_00: Um, there's first 90-day playbook.
• 34:15
  SPEAKER_00: How do you make an employee even in the first place?
• 34:17
  SPEAKER_01: Exactly, exactly, right?
• 34:18
  SPEAKER_01: So we have done this for humans.
• 34:20
  SPEAKER_01: Uh now you have to figure out well, how do I actually do it for my AI and for my agents?
• 34:25
  SPEAKER_01: And that's the context layer.
• 34:27
  SPEAKER_01: The context layer is really about taking what makes your company unique and making it something that can make this AI actually useful and accurate in the world.
• 34:38
  SPEAKER_01: The second thing that has happened is that people have realized that in this world where everyone has access to the same models, uh, intelligence is getting commoditized.
• 34:48
  SPEAKER_01: So you and I have access to the same intelligence, my competitor and I have access to the same intelligence.
• 34:54
  SPEAKER_01: So they've also realized that actually the IP of the company is actually their context and how they build the self-learning loop of what makes their company special and unique.
• 35:06
  SPEAKER_01: And then how can they learn both from humans and from AI over time to build a self-improving loop or a compounding company or a frontier company in many ways?
• 35:15
  SPEAKER_01: Uh, and so that's the second thing that's driving this investment, where this investment has gone beyond just an enabling technology for uh AI.
• 35:25
  SPEAKER_01: It's actually become a strategic initiative where CEOs are starting to care about how do I actually manage my context?
• 35:32
  SPEAKER_01: Because this is going to be my IP.
• 35:34
  SPEAKER_01: This is what I don't want to let models train on.
• 35:36
  SPEAKER_01: I don't want this to become something that is external to my company.
• 35:39
  SPEAKER_01: I want to make sure that it's not logged into a single provider.
• 35:42
  SPEAKER_01: I want to manage it like it's a true asset.
• 35:46
  SPEAKER_00: And so so when you used to talk earlier versus now, how has your positioning changed?
• 35:52
  SPEAKER_00: They say earlier you would have to explain, I assume, that having organizational context through Atlin uh would bring X losses down or bring X revenue up.
• 36:04
  SPEAKER_01: Yeah.
• 36:04
  SPEAKER_01: Yeah.
• 36:05
  SPEAKER_01: Yeah.
• 36:05
  SPEAKER_01: So I think what's changed is that uh about a year ago, all the conversations I would have would be context is important.
• 36:13
  SPEAKER_01: You cannot get your AI to be successful.
• 36:15
  SPEAKER_01: In fact, investing in the orchestration layer or the model layer, like that's the easier problem to solve.
• 36:20
  SPEAKER_01: Building an agent is easy.
• 36:22
  SPEAKER_01: How do you actually make it accurate?
• 36:23
  SPEAKER_01: How do you actually manage your context?
• 36:25
  SPEAKER_01: That's going to be the hard problem.
• 36:27
  SPEAKER_01: And so I had to, I think, do a lot of evangelism about the category itself.
• 36:31
  SPEAKER_01: Uh, what has changed from that to now is that uh I don't have to evangelize the category at all.
• 36:36
  SPEAKER_01: People have realized, in fact, you know, Gartner says that uh uh about 75% of accuracy and uh 50% of cost, which is token usage, um, uh is going to be realized gains that are going to come from context for businesses.
• 36:57
  SPEAKER_01: And so I think that has helped for the most most companies to realize how important and strategic this investment is.
• 37:04
  SPEAKER_01: So today my conversations are actually a lot about well, I am convinced, but how do I do this?
• 37:11
  SPEAKER_01: If I really want context to become a discipline, the same way I start thinking about data as a discipline in my company, how do I start building this discipline?
• 37:18
  SPEAKER_01: Uh, what does context actually mean?
• 37:20
  SPEAKER_01: Uh how do I differentiate between different types of context?
• 37:25
  SPEAKER_01: Where do I start?
• 37:26
  SPEAKER_01: Then what to like what's the operating model that I need to go invest in?
• 37:30
  SPEAKER_01: Uh, all technology companies, I think, think that the world revolves around them.
• 37:33
  SPEAKER_01: You know, they're like, well, the world is actually like, no, like for most people, they're not waking up in the morning thinking about your technology, they're thinking about their business and their business problems and how to get things done.
• 37:43
  SPEAKER_01: And today a lot of my conversations with uh CIOs and CTOs and AI leaders has been how do you operationalize context?
• 37:50
  SPEAKER_01: Like it's a true asset in your business.
• 37:53
  SPEAKER_00: So, so probably everything for you is green field, right?
• 37:58
  SPEAKER_01: It's very greenfield, yes.
• 38:00
  SPEAKER_01: It's very greenfield.
• 38:02
  SPEAKER_00: Which is a great place to be.
• 38:03
  SPEAKER_01: It's a great place to be.
• 38:04
  SPEAKER_01: It's a great place to be.
• 38:06
  SPEAKER_01: So I was talking to a sales rep at Atlan the other day, uh, and he said, uh, my competition is market confusion.
• 38:14
  SPEAKER_01: Uh, which is a good thing and a bad thing.
• 38:17
  SPEAKER_01: So it's a good thing because we have been pioneering this category.
• 38:21
  SPEAKER_01: I believe we're the most forward in this category.
• 38:23
  SPEAKER_01: Um uh I think that gives that puts us in this unique position where we can go help customers uh and help customers do great things, right?
• 38:34
  SPEAKER_01: I mean, can we accelerate drug discovery?
• 38:36
  SPEAKER_01: Can we, uh, you know, if we have a customer that's trying to deploy an AI agent for doctors, and can you make sure that that's accurate?
• 38:44
  SPEAKER_01: And there's, you know, it's just the the coolness of the problem.
• 38:47
  SPEAKER_01: And if you have to ground this in real truth, you know, the the impact you can drive is phenomenal.
• 38:53
  SPEAKER_01: We get to be a thought partner, most of our customers tell us we want to be, we we want to partner with you, we want to learn with you, which is a phenomenal place to be.
• 39:00
  SPEAKER_01: Um, it's also a challenge because it's not a commodity.
• 39:05
  SPEAKER_01: And uh uh in fact, the world is trying very hard, like especially in the market we live in.
• 39:10
  SPEAKER_01: I think there are more sellers than there are buyers right now in the market, right?
• 39:14
  SPEAKER_01: It's like we're in the middle of this AI, every everything is AI, everything's trying to sell something AI.
• 39:19
  SPEAKER_01: Uh, and uh that means that you know you have to really make sure truly be an advisor to a customer on the other side.
• 39:27
  SPEAKER_01: Um, and how do you ensure that in a world where you're, you know, the world is changing so fast and how your hardness strategy, context retrieval, retrieval is changing every uh every couple of months.
• 39:39
  SPEAKER_01: Uh so in that world, how do you ensure that every single person in your team is truly able to be that advisor of the customer?
• 39:44
  SPEAKER_01: I think that's the challenge that comes with it.
• 39:46
  SPEAKER_00: And uh today, how much, like let's say compared to one year ago, how much the sales process has become easy?
• 39:56
  SPEAKER_01: Um I suspect that uh it would depend on the person uh that you ask, which is again the same.
• 40:05
  SPEAKER_01: Like it depends on what you call easy, right?
• 40:07
  SPEAKER_01: So it's a much more strategic initiative.
• 40:09
  SPEAKER_01: So we have bigger budgets, more important people, we can get to the CIO or the CTO, uh bigger, most more impactful project, right?
• 40:17
  SPEAKER_01: Like that's like you said, like the value added of being more and more strategic over time.
• 40:23
  SPEAKER_01: And like we're top of mind, like we're like top three initiatives that people want to fund.
• 40:28
  SPEAKER_01: Um, on the other hand, uh you are not selling technology.
• 40:33
  SPEAKER_01: Like, you know, it's not like someone's buying a product and you're selling a product.
• 40:38
  SPEAKER_01: You are selling them, you're helping them figure out how they should think about their context strategy, which is a strategic initiative.
• 40:44
  SPEAKER_01: Uh and so it used to be much like, you know, we uh at Atlin, we this is probably a public number.
• 40:50
  SPEAKER_01: Uh, our competitive win rate is uh 85%.
• 40:53
  SPEAKER_01: So if someone tests Atlin against a compet competing product, we would almost always win.
• 41:00
  SPEAKER_01: Uh, because we were like we invested dramatically in product.
• 41:04
  SPEAKER_01: And so what that meant was that uh those were pretty easy, right?
• 41:08
  SPEAKER_01: Like in meant in like in a different way, in a commodity buying cycle, you can basically say, check, check, check, check, check, check, check, check, check, check.
• 41:15
  SPEAKER_01: I have more checks than you.
• 41:17
  SPEAKER_01: Um, versus saying, okay, well, are you sure you understand what's different between this and this?
• 41:24
  SPEAKER_01: Um uh, and here's how you should operationalize your strategy.
• 41:29
  SPEAKER_01: That's uh in some ways a harder conversation to have.
• 41:32
  SPEAKER_01: So uh I would say it depends on who you ask on whether it's easier or harder.
• 41:36
  SPEAKER_00: So are you helping build RFPs then?
• 41:39
  SPEAKER_01: Uh absolutely.
• 41:40
  SPEAKER_01: We're um we're influencing.
• 41:42
  SPEAKER_01: I mean, I don't really know if there are RFPs per se anymore, because again, like the category is too early.
• 41:48
  SPEAKER_01: But we're we're spending a ton of time evangelizing the category, building the market for the category, shaping what we believe the category should be.
• 41:56
  SPEAKER_01: Um, how should you think about evaluating a category?
• 42:00
  SPEAKER_01: Um, how should you evaluate a POC?
• 42:02
  SPEAKER_01: What does it take to get to success?
• 42:03
  SPEAKER_01: Like all of those are things that we're we're shaping uh today as we work with our customers.
• 42:08
  SPEAKER_00: So uh Atl is truly an AI native product, right?
• 42:13
  SPEAKER_00: But you you mentioned that you have built an AI native organization to to make the customers AI native.
• 42:19
  SPEAKER_00: Like so, first of all, like what do you mean by AI native?
• 42:22
  SPEAKER_00: Like the term is being used and abused a lot.
• 42:25
  SPEAKER_01: Yeah.
• 42:25
  SPEAKER_00: And then discuss about the process of how do you made that?
• 42:29
  SPEAKER_01: Yeah.
• 42:29
  SPEAKER_01: Um yeah, so the way I like to think about this is you're a truly AI native company if you operate like the technology truly existed today.
• 42:40
  SPEAKER_01: Um, and you are ready to reimagine everything.
• 42:43
  SPEAKER_01: I I actually am recently starting to call this a frontier company where you are ready to reimagine the pro like the process, the foundation process.
• 42:54
  SPEAKER_01: Like, why does this even exist?
• 42:56
  SPEAKER_01: Why do we do this this way?
• 42:58
  SPEAKER_01: Uh, human roles, teams, team structures, who reports to who, like you have to like leveling structures.
• 43:05
  SPEAKER_01: Like you have to foundationally reimagine how you operate as a company uh to be truly AI native or on the frontier.
• 43:14
  SPEAKER_01: Uh, versus I think the other option of it is you could say, hey, this is how I do things today.
• 43:20
  SPEAKER_01: Uh, I'm gonna go slap on AI a little bit on top of it.
• 43:23
  SPEAKER_01: Um, and I could do that really well.
• 43:25
  SPEAKER_01: I could get a lot of productivity, just doesn't mean that I have truly imagined what my company could be, um, depending on where the technology exists today.
• 43:33
  SPEAKER_01: And so when we started this journey, um, I remember it was right after the reasoning model dropped, and uh we had this realization that the world was gonna change again.
• 43:44
  SPEAKER_01: So we wrote this memo and we said our uh company needs to be able to be the most AI native company on the planet for us to be able to take customers there.
• 43:55
  SPEAKER_01: Uh and uh we were gonna embark on this journey to become truly AI native.
• 44:00
  SPEAKER_01: And since then, I would say we've gone through broadly three stages of that transformation.
• 44:06
  SPEAKER_01: And the third layer is, I think, where we're starting to enter now, which is this idea of a frontier company or a truly self-improving company, where you can have fleets of agents that are going in doing their job.
• 44:22
  SPEAKER_01: At the same time, they're collaborating with humans.
• 44:24
  SPEAKER_01: Humans are managing these fleets.
• 44:26
  SPEAKER_01: Multi-human collaboration.
• 44:29
  SPEAKER_01: One of our biggest realizations has been that humans will always be the center of AI.
• 44:36
  SPEAKER_01: And how humans expand their creativity and their thinking through this is what is what is going to actually drive the next wave of this.
• 44:54
  SPEAKER_01: This is actually also where the context layer, you know, not to make a plug, but like really comes alive.
• 44:59
  SPEAKER_01: Because, for example, I write a skill in my function.
• 45:02
  SPEAKER_01: Like, for example, I've written a company positioning skill.
• 45:07
  SPEAKER_01: The company positioning skill also feeds into our BDR outbounding skill, which also feeds my customer success emails.
• 45:18
  SPEAKER_01: Now, let's say I have updated my product positioning skill.
• 45:22
  SPEAKER_01: How does it feed the downstream context on these?
• 45:26
  SPEAKER_01: Same, I have a competitive intelligence agent.
• 45:29
  SPEAKER_01: Technically, that should be taking and learning to improve the code positioning, right?
• 45:33
  SPEAKER_01: How do you build those loops?
• 45:35
  SPEAKER_01: And then who's responsible for it?
• 45:37
  SPEAKER_01: Because there'll still be a human who's responsible for product marketing.
• 45:40
  SPEAKER_01: There'll still be a human that's responsible for you.
• 45:42
  SPEAKER_01: So then what's which human is taking accountability on this?
• 45:45
  SPEAKER_00: Who's responsible for the agent?
• 45:47
  SPEAKER_01: Who's responsible for the agent?
• 45:49
  SPEAKER_01: Who's responsible for the context behind the agent?
• 45:51
  SPEAKER_01: Um, those are all the deep problems that need to be solved.
• 45:55
  SPEAKER_01: And this is really what we call the sort of like this utopia of like the self-improving company, right?
• 46:00
  SPEAKER_01: And uh, I think this is the uh actually what people don't realize about AI uh is a lot of people today think about efficiency AI.
• 46:09
  SPEAKER_01: Uh, but I think the most beautiful thing that AI can do is it can break down this weird organizational silos that was created over time because of human inefficiency.
• 46:19
  SPEAKER_01: Like, think about it.
• 46:20
  SPEAKER_01: Uh, you have heard from a customer that from a sales rep uh that something went wrong, or like this is feedback that you've heard about the market.
• 46:30
  SPEAKER_01: But typically, as companies grow, by the time that feedback comes back to product and then it comes back to strategy, you know, you are talking about a year of a decision-making process.
• 46:39
  SPEAKER_02: Yeah.
• 46:39
  SPEAKER_01: Now, then by the time it takes from the strategy to feedback into product and to the customer, you're talking about at least another.
• 46:45
  SPEAKER_01: This is for the fastest company that exists in the world.
• 46:48
  SPEAKER_01: Uh you can flip that, you know, like the most um I had a customer the other day tell me this.
• 46:55
  SPEAKER_01: Uh, we're helping them with their customer support agent with their context layer for it.
• 47:00
  SPEAKER_01: And they said, actually, my vision is that the customer shouldn't even need to call.
• 47:05
  SPEAKER_01: Like today, we are trying to optimize the call center.
• 47:08
  SPEAKER_01: But actually, the long term is that the customer shouldn't have to call.
• 47:12
  SPEAKER_01: I should already know what all is broken.
• 47:15
  SPEAKER_01: I should already be able to go fix the network issue that is happening.
• 47:19
  SPEAKER_01: And I should be able to bring all of these two to actually just completely self-learn and improve the loop.
• 47:24
  SPEAKER_01: Uh, that's what we think of as the third.
• 47:26
  SPEAKER_01: I'd say we're in early stages of entering that in some of our functions today.
• 47:31
  SPEAKER_01: Uh, and we're on the quest of rebuilding the entire company in that way.
• 47:36
  SPEAKER_00: Got so uh have you heard about how the frontier labs are building their teams?
• 47:44
  SPEAKER_01: Absolutely.
• 47:44
  SPEAKER_01: Yeah, we we spend a lot of time learning from how the frontier labs are building their teams, how some of the companies that are operating at the frontier um are building their teams and building their internal tooling and workflows and so on.
• 47:59
  SPEAKER_00: Is this similar to what what they are doing?
• 48:01
  SPEAKER_01: Uh yes, I'd say I I mean I think there's um and you know, it depends on uh uh which part of a frontier lab you're talking about today, because you know, they're also uh uh building in uh in some version of organized chaos today.
• 48:17
  SPEAKER_01: Um uh but I'd say this idea, if you talk to the top 10 AI native companies out there in the world today, um, many of them are starting to think about um, and you'll see this actually in the last two months, you'll see um some things, right?
• 48:33
  SPEAKER_01: Like, you know, Block opened up Buzz, which is a multiplayer game.
• 48:36
  SPEAKER_01: Uh, you know, CrowdStrike recently started talking about multiplayer.
• 48:40
  SPEAKER_01: Um uh uh Ramp talked about GLASS, which is their internal marketplace for how to share skills across the ecosystem.
• 48:49
  SPEAKER_01: So you'll start seeing across the most frontier companies this idea of AI as a colleague, needing a common context layer, needing to build tooling and infrastructure for multiplayer ecosystems.
• 49:00
  SPEAKER_01: Uh, so you'll start seeing those kinds of things operate.
• 49:04
  SPEAKER_01: Um, and then people are reimagining roles.
• 49:07
  SPEAKER_01: For example, we have reimagined roles inside Atlanta.
• 49:10
  SPEAKER_01: Like, for example, we say uh in the old world, they used to say T-shaped human.
• 49:14
  SPEAKER_01: You pick one thing and you go deeper.
• 49:16
  SPEAKER_01: Uh, we believe in this next world, it's you know, it goes from T to E-shaped to you know, M-shaped to, you know, you're almost V-shaped, actually, right?
• 49:26
  SPEAKER_01: Like as in and this is the interesting part we've realized.
• 49:29
  SPEAKER_01: Actually, as um AI nativity increases uh on one side, like if you think about the axis of impact, uh, certain human skills also become much more important.
• 49:40
  SPEAKER_01: So when AI raises the bar, um judgment, taste, being a force of nature, uh, these are all human skills that actually become far more important on the human skill axis.
• 49:52
  SPEAKER_01: Uh, and actually, unfortunately, we've not pushed humans.
• 49:56
  SPEAKER_01: Like, if you think about it, how many people have been given feedback that they need to improve their judgment?
• 50:01
  SPEAKER_00: Yeah, and no, don't hear that.
• 50:03
  SPEAKER_01: Okay.
• 50:03
  SPEAKER_01: Uh, and so for a long time, humans have not been pushed intellectually or taste.
• 50:09
  SPEAKER_01: You you don't have taste, you have to improve taste.
• 50:12
  SPEAKER_01: Uh, never you never heard pre-AI.
• 50:15
  SPEAKER_01: Uh, because it was okay pre-AI.
• 50:17
  SPEAKER_00: Yeah, because everybody was so busy in the busy work.
• 50:19
  SPEAKER_01: Exactly.
• 50:20
  SPEAKER_01: I think now we are being pushed as humans and humanity to be able to go learn.
• 50:25
  SPEAKER_01: And these are all things you can actually, if you look at the best people in the world and you say, okay, why do they have judgment like this?
• 50:30
  SPEAKER_01: You know, you ask them what books they read, uh, you ask them what newsletters they follow, you will be able to reverse construct why they have judgment.
• 50:37
  SPEAKER_01: Uh uh, so there's ways to reverse construct this.
• 50:40
  SPEAKER_01: It's just that, you know, humans have not been pushed for a long time and it's been okay.
• 50:44
  SPEAKER_01: And now those things have to, on one side you have to scale your AI nativity, on the other side, you have to scale your human, your human skills and judgment.
• 50:51
  SPEAKER_01: And those are, I think, the the things we have to figure out, which is, and so we uh you might have seen this.
• 50:56
  SPEAKER_01: We recently launched something called Atlan Frontier um uh or becoming frontier, which is like our we're opening up all our experiments of how we are learning, how we're thinking about orgs.
• 51:06
  SPEAKER_01: And uh, in fact, the the core uh purpose is uh there's right now a lot of AI theater going on.
• 51:13
  SPEAKER_01: So people only share the good things about AI.
• 51:15
  SPEAKER_01: Nobody shares the experiments that went wrong, and I made this mistake, and you know, and so what we're trying to do is we're trying to bring the conversation back to how do you build orgs around AI, uh, and how do you actually build uh uh and learn together, right?
• 51:31
  SPEAKER_01: Like there are learnings we have today.
• 51:32
  SPEAKER_01: We're very open to changing our minds.
• 51:34
  SPEAKER_01: Six months later, maybe something, maybe we tried something and it actually does not work at all.
• 51:38
  SPEAKER_01: Um, and and that's been a core part of who we are trying to be and really push the thinking of how you rebuild orgs in this world.
• 51:46
  SPEAKER_00: Got and uh, you know, uh it will be very interesting to note how at each round you you mentioned that investor always happened inbound.
• 51:53
  SPEAKER_00: How did you meet those investors and what made you choose those investors?
• 51:58
  SPEAKER_01: Yeah, so um so one of the things that we were very fortunate about was that our space and our category became extremely um hot starting 2021, which is uh so a few things happened.
• 52:16
  SPEAKER_01: Snowflake IPO'd, because of that, there was this big explosion of data ecosystem.
• 52:21
  SPEAKER_01: Um, and there was a lot of venture capital money that flew into the ecosystem, which is a good and a bad thing.
• 52:26
  SPEAKER_01: You know, like AI, the same thing's happening actually right now with AI, right?
• 52:29
  SPEAKER_01: Like it's a so there's a there's a good, which is there's capital and there's innovation.
• 52:32
  SPEAKER_01: There's also a bad because a lot of people start founding companies for the wrong reasons.
• 52:36
  SPEAKER_01: Uh uh, like I know, I literally know of people who have been given five million dollars by, you know, BCs and said, like, please go start a company, right?
• 52:45
  SPEAKER_01: Like, why don't you start a company?
• 52:47
  SPEAKER_01: Uh um, but what that did for us was uh it gave us a lot of inbound interest where people would start reaching out from fairly well-reputed firms and it would typically be like high-quality people.
• 53:00
  SPEAKER_01: Because if you searched for anything about our category, we would show up right on top.
• 53:04
  SPEAKER_01: Uh uh and um that became my way of starting to get to know more people in the ecosystem.
• 53:12
  SPEAKER_01: Because one thing that was really unique about our journey was Atlan was not born like a typical Silicon Valley company.
• 53:18
  SPEAKER_01: We were at the time in India, uh, and we were competing against the best and the best of the world.
• 53:25
  SPEAKER_01: And most of the best and the best in the valley were born in the valley.
• 53:28
  SPEAKER_02: Yeah.
• 53:29
  SPEAKER_01: They have lived in big tech in the valley, and we were not that company, which meant that in every moment we had to show up.
• 53:36
  SPEAKER_01: Like we were 2x of what anyone else would do.
• 53:40
  SPEAKER_01: Like we had to earn, like I used to always say, like we had to earn everything by working twice as hard and showing up.
• 53:47
  SPEAKER_00: Earning your seat at the table.
• 53:48
  SPEAKER_00: We could really earn our seat at the table.
• 53:50
  SPEAKER_01: Like it was not given to us.
• 53:51
  SPEAKER_01: We didn't look and feel like the company that was supposed to win in this category.
• 53:55
  SPEAKER_01: Um, uh, so we knew that we would be scrutinized more, like the quality of our product would be scrutinized more, the quality of our website would be scrutinized more, like how we had copy would be like all of these things that make a company were all things that we had to put a lot of heart to to make sure we punched above our weight in every moment.
• 54:13
  SPEAKER_01: And so as I would meet these people, um, actually, I wouldn't, I would just talk to them and I would ask them about the market.
• 54:20
  SPEAKER_01: So I would typically introduce myself, and then after that, I would just say, like, I would just learn.
• 54:24
  SPEAKER_01: Um, and uh then I would ask them for help.
• 54:28
  SPEAKER_01: So my favorite questions was I would say, well, okay, how can I leverage you?
• 54:32
  SPEAKER_01: Um, how can you help me?
• 54:34
  SPEAKER_01: Uh, you know, are there customers you can introduce me to?
• 54:37
  SPEAKER_01: Uh, and then my favorite question used to be uh, who is the smartest people that you know uh that you've had the best conversations with?
• 54:45
  SPEAKER_01: And they would tell you some list of names.
• 54:47
  SPEAKER_01: And then I'd say, Oh, wow, like can you introduce me to this person?
• 54:51
  SPEAKER_01: Uh, someone gave me that tip early on, and it has been one of my best uh questions.
• 54:57
  SPEAKER_01: Uh and uh, you know, the reality is out of everybody that I spoke to, maybe out of the five that I spoke to, one person would make intros.
• 55:06
  SPEAKER_00: Yeah, well, the rest would not.
• 55:07
  SPEAKER_01: The conversion, yeah, the conversion rate in this is not very high.
• 55:10
  SPEAKER_01: Uh but of the five people, one person would make intros.
• 55:16
  SPEAKER_01: And uh that firstly allowed me to expand my network because I would meet from a credible person, I would meet another credible person who would, you know.
• 55:24
  SPEAKER_01: And and so that just allowed me to just meet people in non-fundraising ways.
• 55:30
  SPEAKER_01: Uh, I would just meet them, I would learn, I wanted to learn about these things.
• 55:33
  SPEAKER_01: We were very curious.
• 55:35
  SPEAKER_01: Uh, I was mostly trying to learn about company building, what people saw.
• 55:38
  SPEAKER_01: Uh, I was trying to learn at every of those stages, every one of those stages.
• 55:41
  SPEAKER_01: Um, and what that meant was that every time we were raising, uh, we just already knew who we wanted to raise from.
• 55:49
  SPEAKER_01: We have seen them, some of them have made customer intros, we've won those customers.
• 55:53
  SPEAKER_01: Actually, every round that we closed, um, uh, when we raised from an institution, the institution had helped us close some customers.
• 56:01
  SPEAKER_00: This example, how did how did Waterbridge uh round happen?
• 56:04
  SPEAKER_01: Waterbridge is the only unique one because they were our seed check.
• 56:08
  SPEAKER_01: So they happened before even Atlan existed.
• 56:12
  SPEAKER_00: So, how did they find you?
• 56:14
  SPEAKER_01: They were an intro.
• 56:15
  SPEAKER_01: Uh, so Pankajen from 500 startups was a uh investor at Social Cups, and Pankaj introduced me to Manish from Waterbridge, and uh um we were still social cups, so we we were not actually Atlan then.
• 56:27
  SPEAKER_01: And I remember meeting Manish and uh we showed him some of the work we were doing, and then I basically ended up, and we were not raising, uh in fact, like social cups was profitable, so we were never going to raise.
• 56:38
  SPEAKER_01: Um, and so um what I ended up telling Manish was look, we've built all this internal tooling, and we think that there's possibly a product that we might want to build that is right in this market, and so we're thinking about whether we want to do that.
• 56:56
  SPEAKER_01: And I always joke to Manish because I say, I have no idea why you decided that you wanted to invest in us.
• 57:01
  SPEAKER_01: He has a different version of the story, and he said that he saw just you know a spark and we knew these problems deeply.
• 57:07
  SPEAKER_01: And uh, you know, um, but Manish basically said, Hey, I'd like to put in a seat check.
• 57:12
  SPEAKER_01: Um, and uh what was interesting, because when I spoke to Manish, was I said, Well, I really don't know how this is gonna go.
• 57:20
  SPEAKER_01: And are you still gonna be along with us in this journey?
• 57:23
  SPEAKER_01: Um, and the journey that we take, because that was really important to me.
• 57:27
  SPEAKER_01: Um, and that's basically how Manish came in.
• 57:29
  SPEAKER_00: So the company got formed at that point in time only.
• 57:32
  SPEAKER_00: Yes.
• 57:32
  SPEAKER_00: It was a Singapore rescue company, I assume.
• 57:34
  SPEAKER_01: Yes, yes.
• 57:36
  SPEAKER_00: Amazing.
• 57:37
  SPEAKER_00: And then Manish connected to Peak, how did it?
• 57:40
  SPEAKER_01: Uh how did Peak happen?
• 57:43
  SPEAKER_01: Um no, it was uh the way Peak happened was uh when we launched Atlanta publicly, uh Ashwini from Matstreet Den uh had uh reached out to me and she said uh uh hey, I love what you guys are doing.
• 57:59
  SPEAKER_01: Uh and I'd known her uh before and she said, Listen, there's just one because uh Matchstreet Den had been one of the only other companies in India that had built infrastructure and AI and so on, right?
• 58:09
  SPEAKER_01: And she said there's one investor that you have to speak to, and it's Roy from Peak 15, uh Sequoia back then.
• 58:16
  SPEAKER_01: Uh and so you should definitely meet Troy.
• 58:18
  SPEAKER_01: And I remember meeting Roy, and we had this great conversation.
• 58:21
  SPEAKER_01: Uh, we were not ready then, it was still early.
• 58:23
  SPEAKER_01: So we kept in touch.
• 58:25
  SPEAKER_01: Um, and then about six to nine months later, it felt like we were gonna be ready.
• 58:30
  SPEAKER_01: Um, and then that's when uh I think there were two batches into Surge, and Surge had happened, they were, they were starting to scale Surge, and we said, okay, this might make sense.
• 58:39
  SPEAKER_01: So let's let's go into Surge.
• 58:41
  SPEAKER_00: And then next was Insight, I assume.
• 58:43
  SPEAKER_01: Insight was our series A, yes.
• 58:45
  SPEAKER_00: And that also happened uh by some introduction?
• 58:49
  SPEAKER_01: Um no, Insight was an inbound that had happened uh uh that that was around the time that our space had started to get hot.
• 58:57
  SPEAKER_02: In 2021.
• 58:58
  SPEAKER_01: In 2021.
• 58:59
  SPEAKER_01: So I had ended up speaking to someone at Insight through an inbound, uh, and we were starting to scale and win customers and so on.
• 59:09
  SPEAKER_01: And uh uh we had gotten preempted.
• 59:12
  SPEAKER_01: Uh so actually we were I you know we were very uh I was not actively raising then, and so I'd I had had the standing call with this person at Insight, and uh, you know, I we were kind of negotiating at that time, and I said, you know, hey, like, you know, I don't know, we're possibly gonna raise another round.
• 59:31
  SPEAKER_01: And Insight kind of like came in super fast to um uh to to come in and and uh uh the partner like Teddy, our partner there was, you know, just a just a phenomenal investor.
• 59:44
  SPEAKER_01: He I I always joke about this.
• 59:45
  SPEAKER_01: Uh Teddy, um uh the other series A that Teddy had done at that at that time was Viz.
• 59:55
  SPEAKER_01: No, no pressure.
• 59:57
  SPEAKER_01: Um so uh yeah, but Teddy was phenomenal.
• 1:00:00
  SPEAKER_01: And uh so we decided to battle with Insight.
• 1:00:03
  SPEAKER_00: And similarly, the next round GIC and Meritec happened?
• 1:00:06
  SPEAKER_01: That was a C.
• 1:00:07
  SPEAKER_01: The next round was uh Salesforce Ventures, Peak, and um uh Insight, uh cool at that round, and then the round after that was GIC and Meritech.
• 1:00:18
  SPEAKER_00: So I assume all of them would have been largely inbound because by the time Atlan was already in the category.
• 1:00:24
  SPEAKER_01: Yes.
• 1:00:25
  SPEAKER_00: So um uh founders in in India were listening to this podcast.
• 1:00:30
  SPEAKER_00: No, their questions would would uh also be around the initial 0 to 10 GTM or 0 to 10 milli RR GTM.
• 1:00:39
  SPEAKER_00: What are the things that you can share that worked in, and this is probably the last question that we have on the podcast.
• 1:00:45
  SPEAKER_00: I'll have to conclude with this.
• 1:00:46
  SPEAKER_00: Uh, what would be your learnings on it?
• 1:00:48
  SPEAKER_00: What would be your advice is?
• 1:00:51
  SPEAKER_01: Yeah.
• 1:00:51
  SPEAKER_01: Um I would say firstly breaking the journey between zero to whatever, one, two, three, uh and then the next phase um is important because the first phase is actually truly product market fit and you're in a search for truth, not traction.
• 1:01:10
  SPEAKER_01: I would advise founders to take as long as it takes.
• 1:01:14
  SPEAKER_01: Because product market fit's the only thing in the world you can't force.
• 1:01:17
  SPEAKER_01: Uh it just takes as long as it takes.
• 1:01:22
  SPEAKER_01: Um and holding yourself to the journey of making sure that you are finding truths and not searching traction.
• 1:01:30
  SPEAKER_01: Because otherwise, what happens is so you'll see that two places where companies break, right?
• 1:01:34
  SPEAKER_01: You either never find product market fit or you find product market fit, but it's an average product market fit.
• 1:01:39
  SPEAKER_01: And so you peter out at 5 million or 7 million because you can't scale beyond that.
• 1:01:44
  SPEAKER_01: And trust me, it's really hard.
• 1:01:46
  SPEAKER_01: Like, you know, imagine you get to 7 million, then you're like, I've enabled everything, then it's even worse.
• 1:01:51
  SPEAKER_01: Uh so you'd rather stay and iterate and experiment in the early days and then very quickly go from 1 to 10 and 10 to 100.
• 1:01:58
  SPEAKER_01: Like that's kind of the the second mental model.
• 1:02:01
  SPEAKER_01: Um and then maybe the third one, and this is specifically to founders in India, um play the biggest game that you can play.
• 1:02:13
  SPEAKER_01: Play for the biggest problem that you can go after.
• 1:02:18
  SPEAKER_01: Um play for the right market, don't do easy to go after the hard problems, and then do what it takes.
• 1:02:24
  SPEAKER_01: If that means you have to move countries, move countries.
• 1:02:27
  SPEAKER_01: Um, if that means uh you need to prove through work, sometimes you know you just have to learn to prove through work.
• 1:02:36
  SPEAKER_01: Um and you have to show up, you have to earn your seat on the table.
• 1:02:40
  SPEAKER_01: Well, go earn your seat on the table.
• 1:02:41
  SPEAKER_01: Uh and um everything else tends to work out.
• 1:02:45
  SPEAKER_00: I think specifically today what is true is found, the especially the CEO being technically very robust is a necessity.
• 1:02:55
  SPEAKER_01: Um maybe.
• 1:02:57
  SPEAKER_01: I I mean I don't even know what that means anymore.
• 1:03:00
  SPEAKER_00: For example, earlier, let's say there were the versions when you were selling stars, right?
• 1:03:04
  SPEAKER_00: Maybe till 2020.
• 1:03:06
  SPEAKER_00: The the the core personality was that the CEO was a great GTM girl or a guy, and the CTO used to be, you know, again, extremely nerdy girl or a guy.
• 1:03:19
  SPEAKER_00: But I think at least what my observation in some of our recent best companies is both the folks are uh like technically at par equal.
• 1:03:29
  SPEAKER_00: It does that one decides to take the role of a CEO and other decides to take the role of a you you can't uh be a person who who hasn't built products.
• 1:03:39
  SPEAKER_02: Yeah.
• 1:03:40
  SPEAKER_00: Hard hardly like build a product, not a product matter, to build products and be a CEO of a I mean I yes, but I think that's also true just for anybody.
• 1:03:49
  SPEAKER_01: Yeah, I don't see what role you can do without building anymore.
• 1:03:51
  SPEAKER_01: Like I think everybody is a builder.
• 1:03:53
  SPEAKER_00: Like I don't think that But a sales guy don't need to be a builder, right?
• 1:03:56
  SPEAKER_01: Well, I actually think they do.
• 1:03:58
  SPEAKER_01: I think if you're trying to sell to the frontier, um what how are you gonna have a conversation with a customer who's talking to you about, you know, the difference between model gateways and three harnesses and memory systems?
• 1:04:10
  SPEAKER_01: Like, how are you gonna have that conversation?
• 1:04:11
  SPEAKER_01: Why are they even gonna talk to you?
• 1:04:13
  SPEAKER_00: So, do you look for these kind of sales guys?
• 1:04:15
  SPEAKER_01: I definitely expect them to build.
• 1:04:17
  SPEAKER_01: Um, I don't think it's the first skill.
• 1:04:20
  SPEAKER_01: So I think in sales, for example, force of nature is a really important human skill.
• 1:04:24
  SPEAKER_00: Yeah.
• 1:04:24
  SPEAKER_00: Um what do you mean by force of nature?
• 1:04:26
  SPEAKER_01: Force of nature, like um yeah, managing the humans.
• 1:04:33
  SPEAKER_01: And you know, our best salespeople will just never stop.
• 1:04:37
  SPEAKER_01: You know, like there was this one time when we had to deal with it, it was stuck in procurement, and there was a procurement person, and we weren't getting into that, like that procurement person was not calling, someone was out of office, and this guy just uh, you know, found this person's number on the internet, and just the procurement persons are like they randomly had a cold call online and say, Can you move this?
• 1:04:57
  SPEAKER_01: It's in your queue.
• 1:04:58
  SPEAKER_01: It's like, who are you talking to?
• 1:05:00
  SPEAKER_01: Uh right, and so there's just truly being a force of nature.
• 1:05:02
  SPEAKER_01: I think that's a really important skill.
• 1:05:04
  SPEAKER_01: I think problem discovery, like leading with the problem and understanding the problem is really important.
• 1:05:08
  SPEAKER_01: So, like sales, for example, you're absolutely right.
• 1:05:10
  SPEAKER_01: I think it's one of those careers where some of those human skills are really critical.
• 1:05:14
  SPEAKER_01: And of course, I would think of those human skills differently for if uh someone in product or engineering, right?
• 1:05:20
  SPEAKER_01: Like where judgment or taste will be more important.
• 1:05:23
  SPEAKER_01: But uh, and of course, the expectations of how you build, like how you build with AI will be different there versus here.
• 1:05:31
  SPEAKER_01: Um, but absolutely, I do think everybody's a builder, everybody should be building.
• 1:05:35
  SPEAKER_01: Um, and uh I think the the lines are blurred blurring very, very fast.
• 1:05:41
  SPEAKER_01: Uh, and this is actually not just me.
• 1:05:43
  SPEAKER_01: The best CEOs I speak to of Fortune Hundred companies, uh, they are building.
• 1:05:48
  SPEAKER_01: That other day I actually had like a CIO who told me that this became like the reason context leo became really important for them was because their CEO uh was on Claude Code and public company that that too in you know, not a tech place, not a tech like you would not think very traditional industry.
• 1:06:05
  SPEAKER_01: Uh, and he wrote a skill and he said, You give me my reports like this, and so then the whole team has to go.
• 1:06:12
  SPEAKER_01: Uh, and then the best CIOs I know are coding the uh and they're building.
• 1:06:17
  SPEAKER_01: Um in today's world, you can't be a leader and not be pushing your team.
• 1:06:22
  SPEAKER_01: I truly believe like you have to be in the details, and the bar is so low, there's no excuse.
• 1:06:27
  SPEAKER_01: It's so easy.
• 1:06:29
  SPEAKER_01: Like, there's no excuse to not set up and build yourself uh today.
• 1:06:34
  SPEAKER_01: It was much harder five years ago.
• 1:06:36
  SPEAKER_01: Um, and so I think that's true for everybody, not just for founders.
• 1:06:39
  SPEAKER_01: Of course, for founders it's more important than for others.
• 1:06:42
  SPEAKER_00: So, again, coming back to the question, your first advice for founders trying to build for the global market, specifically for the US market from India, is find your product market fit really, really hard.
• 1:06:53
  SPEAKER_00: Like it's the truth that you cannot escape.
• 1:06:55
  SPEAKER_00: Yeah, you cannot fast track it.
• 1:06:56
  SPEAKER_00: Yeah, if it takes three years, four years, whatever it takes, you know, invest that time.
• 1:07:02
  SPEAKER_01: Take your time.
• 1:07:03
  SPEAKER_00: Uh doing that.
• 1:07:05
  SPEAKER_00: Uh right.
• 1:07:06
  SPEAKER_00: What about the second part, which is the scale?
• 1:07:10
  SPEAKER_01: Um go after repeatability.
• 1:07:12
  SPEAKER_01: So find ch find the bottleneck to repeatability.
• 1:07:16
  SPEAKER_01: Uh so the best advice somebody told me was the bottleneck is not sales, it's demand generation and pipeline.
• 1:07:22
  SPEAKER_01: Uh so find what is the channel, like find find the two things you need to drive for driving repeatability and scale, and find what is repeatable.
• 1:07:30
  SPEAKER_01: Um and the second there would be you have to keep the bar high.
• 1:07:36
  SPEAKER_01: So paste, judgment, quality.
• 1:07:39
  SPEAKER_01: At this time, you're probably it's a competitive world.
• 1:07:41
  SPEAKER_01: Um in many ways, you have to be able to punch above the weight.
• 1:07:46
  SPEAKER_01: Um, and I think just keeping pushing the bar high on that uh personally.
• 1:07:52
  SPEAKER_01: Even though you, I mean, of I think the challenge that India has is that India doesn't have the ecosystem that the Bay Area has, right?
• 1:07:59
  SPEAKER_01: In the Bay Area, you had Adobe that uh passed to Google, who passed to Facebook, who passed to, you know, like there's just this like legacy of learning.
• 1:08:07
  SPEAKER_01: Uh, we have a much shorter legacy of learning in India.
• 1:08:10
  SPEAKER_01: Um, but today learning is the easiest thing in the world, right?
• 1:08:14
  SPEAKER_01: Like you can learn from anyone and anything.
• 1:08:16
  SPEAKER_01: People respond to cold emails, people respond to Twitter DMs.
• 1:08:19
  SPEAKER_01: It's just the most beautiful world to build.
• 1:08:22
  SPEAKER_01: So hold your bar high, and it might not feel like you you're there today.
• 1:08:28
  SPEAKER_01: It might, you know, it might feel like it's too far.
• 1:08:31
  SPEAKER_01: Uh, but if you just hold your bar high, then I think you uh you're already better than 99% of the world.
• 1:08:38
  SPEAKER_00: Well, thank you so much, Prakalpa.
• 1:08:40
  SPEAKER_00: I really enjoyed our conversation, learned a lot from you.
• 1:08:42
  SPEAKER_01: Seeing you, this was amazing.
• 1:08:44
  SPEAKER_00: Thank you so much again for making this happen.
• 1:08:46
  SPEAKER_02: Awesome.