Pod Insights Summation with Auren Hoffman Atlan CEO Prukalpa Sankar - AI agent workflows and hiring AI native talent About this episode Prukalpa Sankar is the co-founder and CEO of Atlan, a data platform used by organizations like Plaid, NASDAQ and the UN to manage data collaboration at scale. Atlan has raised over $200 million and grown revenue 7x in the last two years. In this episode of World of DaaS, Prukalpa and Auren discuss: AI native company transformationsContext as the foundation for enterprise AIBuilding remote-first cultures globallyThe future of metadata for enterprise Looking for more tech, data and venture...
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:02 Auren Hoffman? Hello fellow data nerds. Welcome to World of DAS. I'm your host, Orin Hoffman, CEO of Incubate and GP of Flex Capital. Discover more episodes, get weekly data as a service news, original content, articles on data and more at worldofdas.com. That's worldofdas, D-A-A-S, dot com. Hello fellow data nerds. My guest today is Prukalpa Sankar. Prukalpa is the co-founder and CEO of Autolon, a data ops platform used by teams at NASDAQ, Elastic and the UN to manage data collaboration at scale. Autolon has raised over $200 million and I'm a small investor,
0:43 Auren Hoffman? very happy investor, and grown revenue 7X in the last two years. Prukalpa, welcome to World of DAS.
0:50 Speaker 01 I'm excited to be here and thank you for being a very early supporter.
0:54 Auren Hoffman? I'm super excited to be here as well. You've got this transformation in your company. So many other companies are transforming their company where they're making sure that all their employees are becoming AI native, using AI tools. What are you doing to get everyone to use those tools?
1:11 Speaker 01 We started a transformation. We put out a mission to the company end of last year, early this year, saying we want to be an AI native company. I think it's important to first just define what does it mean to be AI native. And the way we defined it was if we were building the company from the ground up today, in a world where AI exists, how would we reimagine everything?
1:36 Auren Hoffman? And how would a new company basically kill you, essentially?
1:39 Speaker 01 Exactly. Where most companies are, is you're trying to augment an existing human process with AI, which I think is being AI curious. You're not actually being AI natives. I think the transformation to being AI native was really important to us. Like, we've been adopting AI tools for a while. Somewhere in the last year, I think the models got good enough to say, hey, actually, we can truly reimagine this process to be AI native. That kickstarted this transformation journey. It's perhaps even harder than I expected it to be. Our takeaway was the transformation needs to happen at multiple levels. So the way we did this was we first set up an AI task force,
2:22 Speaker 01 which is this whole separate team whose role it was to ship business first use cases. So it operated as its own team. First, we needed to show people that this was real and it could work. So that was the first couple of things that we did. So we shipped out a couple of real life business use cases with AI.
2:40 Auren Hoffman? Use cases, meaning product enhancements or something?
2:44 Speaker 01 We've been shipping AI stuff into our product for a long time. For us, being AI native was how do we as a company become AI native? Customer support, how do we reimagine like an existing internal workflow? And showcase that this stuff really works. Once we did that, we launched a mission to be AI native. With that, we launched an AI productivity challenge across the company. So it became really bottom up. That worked beautifully because the energy across the company, people helping each other out, we have this Slack channel with 200 people on it. And they're constantly submitting stuff as to what they're doing into their day to day workflows. We made it fun. There were a few people who won iPads.
3:27 Speaker 01 There was just stuff like that that we did that I think created the bottom up momentum across the entire company. So for example, today, I think I would be very surprised if there's anyone at Apple who does not work with AI on a daily basis. And now I think we're on the third level of our execution, which we're calling AI native departments, which is we're changing our org charts. So in our customer experience team, we have basically these agents. It's just like multi-agent workflow. All these agents have names.
3:57 Auren Hoffman? So the agent has a name like Susan type of thing?
4:00 Speaker 01 Yeah, like Susan and Lynn. We might even name some agents after people on the team. And so that's the third workflow that now we're reimagining the departments with essentially a new org chart. And what would that org chart look like? And what does the human agent collaboration workflow look like? We truly build an AI native department.
4:21 Auren Hoffman? What are some of the things that you're doing today that you weren't doing six months ago or a year ago that is improving things?
4:29 Speaker 01 A large part of it is AI does two things you do. AI has the ability to process vast amounts of information. AI has the ability to generate things in a way. That's the core element of generative AI. So one element that we have gone really deep into is we listen to every call. Before, we needed to like look at conversion rates and then against conversion rates, we needed to go and say, okay, here's what we need to investigate because this is dropping. Versus now, actually, we can do a completely different workflow. AI can listen to every call. So it's translated into things like we score every single call. We take those into signals that were driving better product, better customer experience. Here's the gaps that this customer might be facing in that implementation.
5:14 Speaker 01 We send that back as coaching to individual team members. So AI, I think, just the ability to process signals has changed dramatically. The other has been a lot of generative use cases. We can write proposals better than just about anyone. We can scale reporting.
5:31 Auren Hoffman? When you want to write an RFP or something like that, are you guys writing that writer? Or is it just a random software company that has a good RFP writer that you use?
5:43 Speaker 01 We are using a tool for RFPs. And so it's been a mix of a build versus buy strategy, at least internally for us. I think for RFPs, we're using an external tool. For some of the enrichment stuff, we're using tools like Clay. For a lot of the internal agentic stuffs, we're building things internally. That's the show.
6:03 Auren Hoffman? Just that idea of before I go do it myself, maybe I should go search for the vendor, go look at it, go look at a bunch of different things, evaluate some things, look at the different tools. It's a different type of mindset, a different type of employee. How does one push that down?
6:21 Speaker 01 It's hard. At least our strategy actually has been a little bit more geared towards build than buy. And a part of this is because we're also fortunate to operate in the data and AI space. I think the right to win for very small use cases of a business application kind of use cases is low. Today, we care a lot about living in the future for our customers because it helps us build the best product for our customers. A lot of customers use Atlan for their agentic use cases, like a power their agentic use cases, and we want to be ahead of that. We buy a few things at the infrastructure layer. We leverage a bunch of the cloud stuff at the infrastructure layer. And then we build agents on top of that. And it's not that complicated to build an agent.
7:03 Speaker 01 Like the real hard part of all of this is the business context. The operating model and the business context is the hardest part of making AI successful in a company. The difference between a co-pilot and an agent is that the agent understands your context perfectly. Your agent has to be better than any single human being and ideally better than all the human beings around that topic. Like that's the power of the agent. That means iteration and flows because, for example, if you think about just a prompt, a simple first level prompt is not enough. The data context that needs to go behind it is problematic, the workflow context, the business, like that's really hard. What typically happens is that people ship something.
7:47 Speaker 01 It has seventy percent accuracy, so you can use it as a co-pilot, but it's not at a hundred percent. It's a lot of work to go from seventy percent to a hundred percent and people give up in the process. Right now, only twenty-five percent of AI experiments make it to production. Forty-six percent of AI experiments are actually abandoned. That chasm is hard to fill and that chasm unfortunately can't be filled purely through technical talent. It needs this alternative operating model between the business, someone who does the job, who knows the job really well, and technical team. And that operating model, I think, is the hardest part. Building the operating model and building the context is really hard.
8:29 Auren Hoffman? Interesting. When you're hiring people, have you changed a little bit how you think about hiring? Maybe the types of traits you're hiring for in this AI model?
8:40 Speaker 01 One of our pillars is AI native talent density. We've made being AI native the core part of hiring.
8:48 Auren Hoffman? You have to already be AI native to get hired at Alon.
8:52 Speaker 01 You should definitely be able to become AI native. And so we made a few changes. Some of the most basic changes we've made is, for example, we have something called a real life problem statement. We always had this. It's a very core part of how we hire at Atlan people work. We believe in seeing people's work rather than them talking about their work. So one thing we did is we rewrote all those prompts. We rewrote all of those challenges. And now we actually explicitly say that we are an AI native company. We expect you to work with AI to solve this. We get you to show us. We're like, okay, show us how you're writing the prompt. Show us how you're working with AI to solve this problem.
9:25 Auren Hoffman? Is that live or do they record it or send it to you? Or is it an async thing or?
9:31 Speaker 01 The prompt that we give is a homework thing. People go work on it async and then they come back and they present with us. And it's like a real life problem solving exercise together. So the intent is we work through the project. Our entire philosophy around the challenge statement has always been it's a way for you to get to know Atlan really well. So we open up everything to you like you were like a real life Kenyan in the company. We give you access to data. We give you access to tools. We give you access to people and you work through the problem together. Now we just added AI is another co collaborator that we expect you to be natively using as a part of your thought process.
10:09 Speaker 01 That has been phenomenal because the interesting thing about AI is that certain skill sets become much more relevant and certain skill sets go away. For example, clean thinking is a very core ability to describe a problem in a structured, clean way is a much more important skill set. Any past context, experience, any of these things, they diminish so much.
10:38 Auren Hoffman? Experience could potentially be correlated with being able to describe the problem or something.
10:44 Speaker 01 I have done this before. Used to be a very important part of the hiring process. Knowledge is not that important because you can learn knowledge very quickly. The question is, can you ask the right questions and can you iterate? Seeing people iterate with AI has been very helpful. We do a lot of questions around, show us what you do, don't talk to us about AI. Because honestly, like if anyone's interviewing in this market and they say, I don't use AI, that's just stupid. We've been thinking a lot about what are the cultural traits that make someone successful in this new world? I actually think it's similar to what it used to be in the old world. So there's this book called The Growth Mindset. And I actually think a lot of the core, being able to approach a problem with beginner's mindset. I believe that extremely secure people do better with AI.
11:28 Speaker 01 If you have any element of insecurity in you, where it leads to unnatural. AI will not be able to do this better than you. Versus if you are completely secure in yourself, there's an element of, of course, I'm going to work with AI. So there's all these very core personality traits.
11:46 Auren Hoffman? Most people have some insecurity here and there. They've got some sort of thing. So there's a spectrum, I assume you're talking about.
11:54 Speaker 01 Of course, everyone has some insecurity. The question is, do you take a growth mindset to the problem? And do you say, hey, this is this amazing technology and I'm going to learn how to do better with it?
12:05 Auren Hoffman? You can't be worried it's going to take your job. If you are worried, it probably will. It's almost like you have to be using it as a tool so that you can get better.
12:14 Speaker 01 There's this tweet that's making the rounds. AI is unlikely to take your job, but somebody else who uses AI is definitely going to take your job. It's actually become a great retention and a talent attraction tool for us at Atlan. Because people who are at Atlan, we are a company that's investing in being AI native. We're helping our people be the most forward-thinking people, hopefully on the planet. And I truly believe we're in the top one percentile of companies doing that. And it means that talent that's working elsewhere, but their companies are not giving them the ability to do that, they're coming to Atlan. Talent wants to stay. I can fact after our data update agent. Cause now like humans don't need to like update data the same way anymore. AI does it for them.
12:55 Speaker 01 We don't know what the long term future is, but I think there'll be companies that don't use AI and die. I think there'll be companies that use AI and become operationally more efficient. But I think there'll be companies that use AI and raise their ambition 10x. Because that's always the challenge of startups. You never have enough resources to do the things that you wanna do. AI makes that easier. We wanna be the third kind of company. We're gonna use this to become 10x more ambitious. And so we're not seeing this as a feature where we're gonna not hire human beings. We just want our human beings to be 10x better and more productive than a human being anywhere else. And meeting that's a competitive mode.
13:30 Auren Hoffman? Do you see it slowing the rate of hiring? Hiring has a huge cost because of the communication. Every time you hire someone, it becomes harder and the company communicate, you become more bureaucratic, things move slower.
13:44 Speaker 01 If you can make everyone 10x more productive, ideally that human being can do 10x more. Which means you can grow. If you just think about your growth rate versus how many people you need to do it, that shrinks. And then you go hire people to go do the new thing, cause you have earned the right to go do the new thing and the new thing. That's the way we're thinking about it. But absolutely, I definitely think it will slow the pace of hiring. It reduces viscosity inside companies and we're excited about all of those things.
14:11 Auren Hoffman? Part of what you're doing is you guys are in the data world, using data, manipulating data. So many companies are not using data in the right way. What are the big mistake blocks you see companies making and how do you see that moving over time?
14:27 Speaker 01 I think the best way I'll describe it was a customer, large bank told me this recently. She said, our company has gone through three waves of data platform replatforming over the last 20 years. We've gone from the Hadoop era to the cloud data warehouse era. Now we're in a cloud data lake era. Like, you know, there's we've gone through all these eras, but nothing has changed for my end users. My users still can't find the data, they still can't trust it, they still don't use it. Nothing has changed. I'm actually much more optimistic about this than I ever was in the past. I think we have solved a lot of the core foundational architectural problems that data was running into over the last couple of decades. I think we're now at the last mile problem.
15:13 Speaker 01 Of data, that last mile problem is a hot problem. That last mile problem is not just a technology problem. That last mile problem is a human plus technology problem. And that last mile problem is even more valuable.
15:26 Auren Hoffman? There's a UI issue there too?
15:28 Speaker 01 I think there's a UI issue there too. There's a diversity issue. I think the thing about data that most people don't realize, I call it the two fundamental roots of data. The first is that data is the most diverse ecosystem, team, landscape that can exist. And the reason for that is because if you have to make a data project successful, now a data and AI project successful. You need multiple different skill sets to come together. You need analyst, engineer, now you need AI engineers. You have business, business is this one word that we use, but actually it means lots of different things. I like finance business is very different than marketing business is very different than sales business. If you break this down, you're like talking about twelve to fifteen different personas in a single project.
16:13 Speaker 01 And the thing about these people is that they're fundamentally different. An engineer is a fundamentally different person than an analyst. They have completely different tooling preferences, they have completely different skill sets, they have completely different DNA. If you think about a sales team, it's a relatively homogeneous team. A data organization is fundamentally heterogeneous. This leads to, in some ways, the tooling proliferation. That's why you see eighty seven percent of companies use two or more BI tools.
16:40 Auren Hoffman? I'm surprised it's not one hundred percent of companies use five or more tools.
16:45 Speaker 01 It's insane. You would think that these problems could turn homogeneous. The number of customers we have that use Snowflake and Databricks is insane. The reason is for that, I think, is because of this fundamental heterogeneity in the ecosystem. Which leads to different tool preferences, which leads to these complex heterogenous data stacks. And the second problem, actually, in this ecosystem more than anything else, has changed. It changes faster than ever before. Two years ago, vector databases were the rage. No one talked about it. Today, you can argue we have stopped talking about it. It's not a thing anymore. And that chain that is the only set in reality, it's just a very fast evolving ecosystem. And that core problem is what leads to in some way.
17:26 Speaker 01 So when you say something as simple as my business doesn't trust this number, it sounds like a really simple question, but it's a very hard question to answer because you're talking about connecting a data landscape of hundred different tools. You're talking about bringing together contextual, like the reason the number could be off is because Pymann changed the calculation of the revenue metrics. Could also be wrong because the data engineer changed the pipeline. Could also be wrong because the quality check failed. There are so many different people involved in this problem. And that I think leads to this complexity of the last mile not being solved.
17:58 Auren Hoffman? So we just saw Salesforce bought Informatica for eight billion dollars. ServiceNow bought Data World. Walk us through how the market is thinking about all these different metadata systems.
18:10 Speaker 01 So I think that brings us to today where this problem was always a big problem. It's almost become ten X more important because of AI. Because AI can't do anything without context. Context is everywhere, right? Like it's the model context, protocol, it is context windows, like everything about AI is can you reach it context? In fact, my co-founder Varan had this, you know, very profound statement he made recently, which I think we'll write a blog about. Where he was like in nineteen ninety six, Bill Gates showed internet data was just happening. Bill Gates showed this blog post and he said content is king in this era. And I think in twenty twenty five, you can actually say in this new era, context is king. That's what people are realizing. So if you think about any of these application layer companies, the application layer company, Salesforce, ServiceNow and so on.
18:55 Speaker 01 They have always held business data and business context that stayed in these tools. You could argue they're becoming a little less. In a world where humans don't create business context, these tools become more irrelevant. They've all been built on the concept of humans will create business context.
19:11 Auren Hoffman? Humans come in and they type in, they add stuff.
19:14 Speaker 01 Exactly. So now they have to win the Asian game. So they have to become the people that replaces the CRM with the agents, which is what say it's supposed to struggle with agents for us and so on. They've all announced these create AI dreams over the last twelve to eighteen months and they've realized as they take these things to production that there's no way they can do it without context. And the context layer is in the metadata layer. And so one of the things that many of these companies are trying to do is that they're trying to buy these capabilities. They say, hey, I need to create the metadata layer inside of my business applications so that I can go when they enter the game. And so that's one change that's happening in the business application layer where a lot of the business data lives.
19:57 Speaker 01 On the other hand, you're seeing almost this creation of the data and AI clubs, which is where Snowflake and Data Rex traditionally played. And these companies are in an interesting space where they have been built on the premise that you will move your data from the business application layer to this data layer or the central data layer. Now, the business application companies make a play to say, no, you're not going to move your data out of us. You're going to keep your agents on us. These companies are now trying to find capabilities to say, hey, we need to be the place that you build and maintain all your agents of the future. What capabilities do I need to make that happen? I need the capabilities to be able to look at context across your ecosystem.
20:41 Auren Hoffman? You think these business application companies are going to more likely be the losers or houses net out?
20:48 Speaker 01 My sense is that we will have a few key changes that will happen. I think the business applications will open up their data through open table formats. So you're seeing this already happen SAP recently on something called the SAP Business Cloud, where they're opening up their data through an iceberg native function and open table formats. What that means is that you don't need to move data anymore. You can keep your data in SAP and you can bring compute to SAP. So I think that will minimize the amount of storage movement where we just keep migrating data. Oh, my God, it's insane. The last twenty years, the ecosystem has migrated data. Like, the amount of money that's been spent on migrations is insane.
21:30 Speaker 01 You just keep migrating stuff. And so I think migrations go away. I think data becomes open.
21:35 Auren Hoffman? What do you mean by open?
21:36 Speaker 01 It's been catching a lot of attention in the ecosystem. About six hundred of Snowflake's customers today are using Iceberg. That's like ten, twelve percent of large enterprise customers. And what you're seeing is it's fundamentally an open table format, which says that in the old world, you used to take data to compute. So you used to move data to a computation layer, like a data warehouse or a data lake house. With an open table format, you can bring compute to your data. You can read any compute to your data.
22:05 Auren Hoffman? And why is that better? Is it just more efficient because you're not transferring terabytes of data back and forth?
22:12 Speaker 01 It's definitely more efficient first because someone was telling me this stat recently. Forty percent of cost of compute is actually insert operations, migrating data from one place to another. So A, like it's just final efficient. Second, it keeps it open because now there's all these interesting innovations and compute layers that are happening. So I have customers telling me, hey, for this operational workload, I'm using this new performance based engine in the ecosystem. What you can do is you have an open ecosystem. You can have customers that use Snowflake for their analytics workload. You can have customers that use Databricks for something else. You can use the new startup for the third thing and the fourth.
22:52 Auren Hoffman? So basically you call like a data store for whatever, but just sitting there. And then you could just bring the tool to the data rather than bringing the data each time. Every time before you'd had to load into every tool and it took a lot of load.
23:05 Speaker 01 Exactly.
23:06 Auren Hoffman? You'd have a problem before syncing the data because it would go out of date or whatever. So now you can always have the most synced data, the most up to date data in one place.
23:16 Speaker 01 Wherever your source data is getting created, that data will start becoming open through open table formats. We will start bringing compute to that. There will obviously be more compute with the AI agent.
23:27 Auren Hoffman? And do you think applications that are creating proprietary data within the application, let's say a Salesforce or something? Are they going to move to this more open data thing? Or do you think they'll try to put roadblocks to make it harder to move that data?
23:42 Speaker 01 I think it depends on the strategy of companies. I think there'll be two kinds of companies that will get created. In fact, last week, there was all this noise in the ecosystem about Salesforce shutting off the Slack data API access. On the other hand, I think there are companies like SAP that were traditionally slightly more closed ecosystem that are saying, yeah, actually, we're going to open up our data through a business cloud. My sense is that what customers will want is open and interoperable. Because in the world where innovation is rapid, we just have to accept that that's the world that we're going to live in in the next ten to fifteen years. There's going to be a lot of change. Customers will want to innovate rapidly. And so customers will want to have the choice. So my sense is if you believe that ecosystems will be based on customer choice,
24:27 Speaker 01 going after open interoperable formats is the right thing for the customer. If you believe that the right thing for the customer is what the industry will go to, I believe that that is what will happen.
24:36 Auren Hoffman? After I saw that Salesforce announcement, today, I would never implement Salesforce if I could avoid it. If you're already part of that ecosystem, it's not that easy to get off of it. Each of these companies are going to have some sort of complicated matrix as they're making a decision there. But it definitely makes me more reluctant. And even if I was a company like on the margin, should I invest more in Salesforce? Or should I invest more in these other tools? I'm probably going to invest more in these other tools today after that announcement. But clearly Salesforce is smart. They're making a closed bet for a reason. How do you still man their strategy?
25:12 Speaker 01 It's a good move. If Salesforce believes that agents is its future. If you think about the world where the CRM goes away and agency plays the CRM, then how do you retain the right over the next two to three years to become the company that owns the agents? What's your mode? And if your mode is not shipping the best product the fastest, I think companies that have business context today definitely have an advantage. Because that's the core of what helps make agents work. I will tell, I think, on which strategy will play out. At least what I tell customers is I tell customers, you should build your stack for a few layers to be open, a few layers to be interoperable, no matter what. And a few layers, you should expect to use multiple tools.
25:58 Speaker 01 So the way I think about it is you should expect to be multi-cloud platform. You should expect to be multiple business applications. You should expect to be multiple data platforms. You should have a centralized context layer. You should have a centralized governance layer. Both of these should also be open and interoperable. So that when you are experimenting with multiple models and foundational models, you're experimenting with multiple clouds. You're experimenting with all of these. You have some layers that you keep centralized. So that your context doesn't get logged into a single application. Your governance doesn't get logged into a single place. But it has to be open and interoperable for you as a customer.
26:36 Auren Hoffman? So you get your context in one place. In some ways, you almost have your data in one place. But then you could be very promiscuous about using GCP or AWS. You could be very promiscuous or using Anthropic or OpenAI. You try all of them out all the time, constantly just trying to learn about what's the best for you.
26:54 Speaker 01 Yes, absolutely.
26:55 Auren Hoffman? And sometimes it's a cost thing and sometimes it's a performance thing. You may have different things you would be optimizing for.
27:01 Speaker 01 And sometimes it's a use case thing. There's some use cases that Anthropic is better for. There's some use cases that OpenAI is better for.
27:07 Auren Hoffman? Interesting. Even though you're a startup, it's complex because you've got people all over the world, all the different time zones and stuff like that. How do you think about managing it? Somehow you have to like async it out or something? It's hard to have synchronous collaboration when you have people that are 12-hour differences and stuff.
27:26 Speaker 01 So we decided to be a completely global remote-first company. That means that we have team members in over 20 countries.
27:35 Auren Hoffman? And mostly working from home type of thing?
27:37 Speaker 01 Mostly working from home. We have a global WeWork membership. Our colleagues can go into like their nearest WeWork for sync things that they want to do, but mostly working from home. Our high-level strategy on this has been to say, what do you optimize in-person time for? And what do you optimize remote time for? So the way we think about it is it's remote for productivity and execution. It's in-person for problem solving and human connection.
28:05 Auren Hoffman? But you also have random meetings that happen during the week or something. Are you just trying to reduce those as much as possible? So you don't have as many video meetings and stuff?
28:15 Speaker 01 So we have something called Atlan Standard Time, which is seven AM to eleven AM Pacific, which is the time that we expect every Atlan to be online for collaboration hours.
28:26 Auren Hoffman? Seven AM to eleven AM Pacific. If you take a job at the company, you gotta be on those four hours. That's how you get everyone to synchronize us.
28:36 Speaker 01 Yeah, that's the synchronous time. That's for the collaboration hours. That's the huddle time.
28:40 Auren Hoffman? That makes perfect sense. So there's some period of time where no matter where you are in the world, if you live in San Francisco, that means you're getting up a little bit earlier than you would normally do. For other places, it means you're changing your habits a bit.
28:54 Speaker 01 We typically tell teams not to burn both sides of the candle. Don't wake up super early and stay up too late. Our teams typically, like we have teams in Asia and India, they typically start their day much later and they go on so that they can stay up till the suspect wakes up.
29:09 Auren Hoffman? During the recruiting process, they know, okay, this is the hours. They're opting into this.
29:15 Speaker 01 Yeah.
29:15 Auren Hoffman? Doing this remote first has both its advantages and disadvantages. How else have you made it work? What else have you done to make it work well?
29:23 Speaker 01 We really believe there are only two ways you can run a company. Either you're all in office and the whole team is in office. At least the team that's working together is in office or you are fully remote. We don't believe in the middle thing.
29:35 Auren Hoffman? I think the whole team has to be not in an office, but they have to be on the same floor. So they can't even be on different floors. You're basically remote at that point.
29:43 Speaker 01 Exactly. And so one has been just making sure that that is important. The second thing has been being really intentional about human there. That has meant, for example, when we meet in person, we have this, we call it an Atlan special meal. We have this specific question that we ask that gets people to open up and talk.
30:01 Auren Hoffman? What's the questions?
30:02 Speaker 01 For example, there's this one question that if an alien showed up on your doorstep, where would you take it for a meal and why? If you're gonna go to Mars, what is the one person item you will take with you? Or one person in the world you could call and thank that you haven't thanked. You could be on the same floor as a human being for an entire year and you might still not know them. And if you go to one of these things, you could walk away after four hours and you could really know the human being. So it's been our key thing, the in-person time and being really intentional about that. It's been creating a bunch of rituals in the way we run the team. For example, we have a very specific thing called unblock meetings.
30:43 Speaker 01 Unblock meeting is specifically meant to get cross-functional collaborators to say, hey, here's the thing we're gonna unblock and there's a format that on it. So you write up a problem for post-solution document. So it's been creating these rituals in the company of how you work and making it very specific to how you work. We have a very big culture of gratitude and feedback. So every time we do things, we have something called I like, I wish, I body. People do these for each other. We do this for the company. You have like an Atlan version of I like, I wish, I body for Atlan as a company. So just creating more of those. It's been trying to standardize culture. Culture is one of the hardest things, I think, because if you're in office and more than fifty percent of the people like norm to the culture, you just see it and it becomes you.
31:29 Speaker 01 It's just harder remotely. So we've written like a seventy-five page leadership handbook. It's like here's dilemmas that you're going to deal with as a leader at Atlan. It's not about like being a leader, it's just about being an Atlan leader. This is what it means to be a leader at Atlan. How do we operationalize those things? My previous company used to be a fully in-office. We were in-office, we had bar beds in-office. Everyone lived within a one-mile radius of the office. It was a very in-office culture. And as we're into the fully remote culture, you have to be really intentional about being a remote company. You can't just slap on what works in person remotely. I think you have to be really intentional about building a remote company.
32:06 Auren Hoffman? Couple of personal questions. You're the only Prukalpa I know. You had mentioned before, it's kind of like a unique name. Is there like a story behind that?
32:15 Speaker 01 I am the only Prukalpa in the world. But if you Google me, it's like me. So that's my defense. People always ask me, like, how do you know you're the only Prukalpa? My thing is, if you don't have like an online profile right now in the world, there could be a Prukalpa somewhere in the world that's not connected to the internet.
32:30 Auren Hoffman? I'm doing it right now. I'm Googling you. I'm OpenAI-ing you and I can't find another one. It's just you. You're the only one that shows up.
32:38 Speaker 01 Yeah, it's just me. My standing joke is my parents thought about SEO before Google happened.
32:43 Auren Hoffman? Totally, yeah. Is it good to have a unique name or bad?
32:47 Speaker 01 I think it's great. It's so easy for me to get all the domains, right? Because it's just me. So I love it.
32:52 Auren Hoffman? Do you always have the @Prakalpa handle?
32:55 Speaker 01 I always have the @Prakalpa handle.
32:57 Auren Hoffman? That's amazing.
32:58 Speaker 01 I don't even need to like use my last name for anything. There are some downsides. When I was a kid and I was in orchid and everyone has these like really funny weird posts that they make when they're a kid. You can actually find that stuff about me if you research me enough. So there's some embarrassing stuff that I can never get away from, but I love it. Prukalpa means brave and courageous. It's a Sanskrit word. It's actually one of the thousand names of Asompanis in India. It also means something cool.
33:26 Auren Hoffman? I love that. That's amazing. What is a conspiracy theory you believe?
33:30 Speaker 01 I am not much of a conspiracy theory person, but I have a standing joke with my co-founder on this, which is that I genuinely think like upgrades have nothing to do with points and have to do with some version of karma or something like that. I will never get an upgrade. And my co-founder and I, when we travel together, he travels much faster than me, but he always gets an upgrade. And it makes no sense. So I keep telling him, this is karma.
33:54 Auren Hoffman? Do you have some theory as to why he gets it and you don't?
33:58 Speaker 01 How don't?
33:59 Auren Hoffman? Maybe he's super nice to the check-in person or something, or he slips him a 20?
34:04 Speaker 01 He's done something in his life where someone has mocked him in some database or something like that. It makes no sense to me as to why he gets upgrades and I don't.
34:12 Auren Hoffman? Interesting. I had a friend who always used to get upgrades. It was never clear why, but do you know the singer Moby?
34:18 Speaker 01 No, what is it?
34:19 Auren Hoffman? There's a singer Moby who's kind of famous person and my friend looks just like him.
34:25 Speaker 01 Oh, my gosh.
34:26 Auren Hoffman? Our theory was everyone was mistaking him for a famous person and they would constantly get upgraded and good. So maybe your co-founder looks like Brad Pitt or something.
34:35 Speaker 01 Maybe he looks like someone, huh? That could totally be the reason.
34:39 Auren Hoffman? Last question we ask all of our guests. What conventional wisdom or advice do you think is generally bad advice?
34:45 Speaker 01 I have a few on leadership. I think the conventional advice on hiring leaders is typically this. Hire really smart people and let them go figure it out. I recently wrote this post about micromanagement. I personally think that the best way to set up a leader for failure is to just hire them and then they like go figure this out. The reason for that is I think they don't have context. Context is really, really important. If you think about the job of a leader, it's about making great decisions. And to make great decisions, you need context. When you join a new company, you don't have context. You don't have the years of context. And so I think it is your job to make sure that this person gets the context that they need
35:30 Speaker 01 before they can go be awesome and make great decisions. Otherwise, they make suboptimal decisions. I think the worst decision is not a good decision or a bad decision. It's a suboptimal decision. And so something I believe is, for example, we have something called a decision review. So every time I unload a leader, their first 20 decisions, they review with me, that we go through them together. This is the decision I'm making. This is how I'm thinking about it. This is why I'm thinking about it. I personally believe that the way to solve problems is not just to hire smart people and let them go do it. I do think you have to hire smart people and I think you have to hire great people. But I think that there is a much higher responsibility to be extremely involved for the first three to six months of their journey in a new company.
36:11 Auren Hoffman? I love that. Well, thank you, Prukalpa, for joining us at World of Dass. I follow you at Prukalpa because you have, of course, that handle on X. I encourage all of our listeners to engage you there. This has been super interesting and a telethon.
36:25 Speaker 01 Thanks for having me, Arin. This is awesome.
36:29 Speaker 02 Thanks for listening. If you haven't already done so, please subscribe to World of DAS wherever you consume your podcasts, YouTube, Apple, Spotify, and more. And please help us get discovered by leaving a review. And check out worldofdas.com. That's worldofdas, D-A-A-S, dot com. And of course, connect with me on Twitter, at Auren, that's A-U-R-E-N. Would absolutely love to hear from you.
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