How can People Leaders use AI Responsibly with Covey’s Vijay Mani and 776’s Chris Vanzetta

Chris Vanzetta:

Any space where we can use AI to work more efficiently, that would be the space where we wanna use AI, as a tool and as a resource, but we wanna still allow humans to do the strategic work, the empathetic work, the human forward work, the emotional work that people practitioners do.

Katelin Holloway:

You're listening to All Hands, a podcast brought to you by Lattice, where people strategy is business strategy. I'm your host, Katelin Holloway. Of all the hot topics this year, one thing seems to have captivated the attention of investors, founders, employees, college professors, and film executives alike. Can you guess what I'm about to say? Yup, artificial intelligence. Despite this technology being around for decades, AI has taken the world by storm this year with the public debut of ChatGPT. Suddenly, every company has an AI spin, every tech employee is being asked to incorporate new tech into their workflows, and people are seriously questioning the relevance of certain job functions. As an investor and people operator, I've been watching the space closely to see how artificial intelligence is transforming and will continue to transform how we, as people leaders, operate every single day.

And my dear listeners, it appears that you all have been keeping a close eye on this, too. We recently asked Lattice's Resources for Humans Slack group for your questions for AI's impact to the HR function. And today is the day we're going to get some answers. I'm chatting with two incredible guests to share their views. First, we have Vijay Mani. He's the founder and CEO of Covey, an AI tool that takes care of all of the boring parts of recruiting: sending timely followups, sifting through talent, even onboarding. We'll also be joined by Chris Vanzetta. Chris is my colleague at 776, the early-stage investment firm I helped to co-found with Alexis Ohanian. He leads our talent and firm outcomes teams, which includes our people and culture function, and is an investing partner leading deals in, yeah you guessed it, HR tech. Covey is the first deal Chris has led here at the firm with AI being a primary driver for the investment decision. Let's get into it, shall we? Vijay and Chris, welcome to All Hands.

Vijay Mani:

Thank you so much for having me. Such a pleasure to be here. Excited to talk about all things people and HR with you both.

Chris Vanzetta:

Thanks, Katelin, for the invite. Vijay, it's so nice to chat with you and really excited to talk about AI and the people space with you both.

Katelin Holloway:

Today, we are talking about something I find absolutely fascinating. It is the intersection of AI and HR. This is where the robots meet the people to do the most human work in any organization. So if you ask around in our industry, you'll find there are two very clear and distinct camps. Are the robots friend or are they foe? Now, I've had the pleasure of exploring this topic with each of you in our work together over the last year, but I'm really excited that we actually get to bring this discussion here to our All Hands audience.

I want to start with some basics. Vijay, from your perspective, where are the areas that you see artificial intelligence being the most helpful for those of us in the people function?

Vijay Mani:

I think in general, whenever there's semi-structured data, unstructured data, cross referencing a bunch of different data sets, AI is useful. More concretely, thinking about sort of the HR recruiting people stack. Starting with recruiting, we ourselves have a product here called Covey Scout. With Covey Scout, essentially, recruiters, hiring managers describe the strategy, who they're looking for, how they evaluate profiles, what trade-offs they make in their own words, and our AI is trained to then build a bot in our domain-specific language to go out and find those folks. This way, the human controls the strategy, controls the vision for what the role is, who the candidate should be. They control the candidate relationships. They build their relationships, they nurture it, and everything else in between is done by the computer system.

So there's these natural places where whenever you think, "Gosh, I, I don't want to be doing something," you know, eyes bleeding, staring at resumes all day, every day, that sort of stuff; AI can play a part where it can take that work out of your plate where you're now doing the things that are most impactful. So I think there's a very natural place there. Couple of other things we've heard with larger customer's we've talked to. We haven't built anything here, but employee engagement is an area where we hear quite a bit, especially with remote work, especially with a distributed workforce, getting a pulse on how the employees feel become so much more important, so, you know, AI's actually pretty good at [inaudible] and things like that.

The other part where we are spending a little bit of time thinking about is just on the data and analytics side of things. There's so much data. V-0 was let's look at all of the reporting data and have it presented. I think V-1 of that is gonna be more proactive instead of reactive, understand what changes you're seeing ane make some decisions as a result of it.

Katelin Holloway:

That makes a ton of sense. I, and I agree. I think recruiting is the tip of the spear here. This is the most obvious initial application of this technology where work is incredibly repetitive. Chris, do you have anything to, to add here about where AI might be able to play a helpful part in the people function?

Chris Vanzetta:

I mean, I would say any space where we can use AI to work more efficiently, that would be the space where we want to use the AI as a tool and as a resource. But we want to still allow humans to do the strategic work, like Vijay said, the empathetic work, the human-forward work, the emotional work that, that people practitioners do. And I feel like there's probably a trillion examples of this. Is AI ready to sit with a manager and an employee and do a mediated conversation, or sit with a leader and talk through org development? Like, AI is so far away from being able to help with those things, but we can use AI to plug in a bunch of data and help inform a conversation around org development or attrition, and understand kind of data and patterns.

Another great example is, like, everything that we build from a policy or program perspective is so unique to our culture and our organization. And I think could ChatGPT pop me out, like, a really amazing expense policy V-1 right now? Yes. Would I need to tweak the crap out of it to help it fit my culture, my organization, my values, my spending habits as a business? Totally, right? They can give us a rough draft, but then you need that human magic to make it ours, make it our organizations, make it our cultures, and make it for us.

Katelin Holloway:

Absolutely. Understand, again, like, this, this is not an opaque, fly-by-night, just accept any output, garbage in, garbage out. You got to make sure that there is a human educated on the back end of this taking that work in, massaging it, and then applying it to their organization. I think that's gonna, you know, really help make it work. I think Vijay is like, "Oh, wow. I really don't want to sit down and look at a blank screen and, and bang this out right now. Give me something to redline. I'll redline all day." Right?

Chris Vanzetta:

Right.

Katelin Holloway:

There's so many different places that we can plug AI into our everyday to make our lives and our jobs that much better. Chris, I want to go back to your earlier statement that there is some HR work that AI should not take on. What are some of the headaches that artificial intelligence may potentially bring to our field?

Chris Vanzetta:

I think that's a great question, and I think it probably ties back to diversity, inclusion, belonging, biases as, like, the big hot topic for me. And I think that AI technology is, is obviously rapidly evolving. And we're very much still learning and on the precipice of the technology, but any flaw in the model for AI that's being trained on, it's gonna be magnified, right? It's going to be, it's gonna be right in your face. Humans are the people that are programming these tools, and humans are, we're flawed, and we are biased whether we want to believe that we are or not. We are in our own small ways, and so our outputs, outputs could be biased, as well. So I think it's really important that, as we're using these tools and integrating them into HR practices and technology, that we are cognizant of that, aware of that, and that we are building diverse teams and people around us that are building this technology.

Katelin Holloway:

That makes so much sense. Vijay, any other pitfalls that, that you want to point out?

Vijay Mani:

Yeah, no. I, I think that makes a ton of sense. And this may be perhaps more of a surprise for myself building sort of, like, an AI product in this space for our team, 'cause in the design of it with our customers, we learned a lot about this, right? There's, like, at least a few [inaudible] things that we've learned, right, I think? Our own product, the recruiter owns the strategy. They're gonna describe why I evaluate that candidate or file this away. And, and one of the trade-offs that I, as a human being, that's the same stuff that I would share with someone else, I share it in my own natural language. And the AI system's job is to then transform it into a domain-specific language that can just go out and find and execute that strategy. So it's extremely constrained, because ultimately that's what takes a bunch of my time, and I don't want to invest any of my time there.

Alongside this, you get a whole bunch of transparency that you need to expose. The decision making around how to build an organization is led by the humans, right? Like a, we, our leadership is all, you know, men. We need more female leadership. I need to take certain actions to remediate that, uh, as far as building an organization that I'm proud of that, that, that, that represents the values that I care about. It's this inner play of I need to own the strategy as the human, as the driver. I need to have the necessary tools that give me visibility into how best to make this decision. But once I have the strategy, once I have almost the declaration of what I want to do, everything else that's not that has to be taken away. And I think that's how I think we need systems here we design. It's gonna have that much control for the human, and, and, you know, if it's an assistant, than it's like, "Hey, give me the suggestions. Let me decide whether or not I want to accept those suggestions." So I think that's the shape I think of how good systems here will likely be designed, as a partner, as something that enhances rather than replaces the existing team.

Katelin Holloway:

I think that you make some really wonderful points here. And I just want to offer, certainly not an alternative perspective, but a flip-side to the coin, which are candidates are not using AI, as well. So there, there are tools for our teams, and then there are tools that are now made available to the public that they are using, everything from writing their resumes, revising their bios, hitting up their LinkedIns and, and juejing those. I mean, I did it to my bio, I don't know, a few months ago. Again, it takes some work, but you have to learn these tools, right? And Chris, I love that you bring this up. There, there is bias in the tool itself, because oftentimes, there's not a diverse team, a team representing the world in which we live, actually building it. And I just recently saw an example.

There is a woman who said that she was revising and updating her personal brand, because she had been recently laid off. And so she had help from ChatGPT to do her resume, and, and to write her bio on LinkedIn. And then she went to Midjourney to kind of jueje up her head shots. And do you know what Midjourney did? It completely over-sexualized her. It gave the male gaze version of what they thought a female employee look like. Well, the robot overlord didn't pick that. That's data in, right? That's the tool that was being built.

Vijay Mani:

We think about that specific thing quite a bit, right? It is trained on data that is highly biased. Large chunks of the internet, it's gonna be quite nice. Uh, so much so, even, like, the underlying entity in all of this, sort of like a word embedding, has bias encoded in it. Language, language that we have that we're trained on has bias in it. I will say one thing that is nice to see is we're having a conversation about this now at the earliest stages of this technology.

Katelin Holloway:

Yes.

Vijay Mani:

Which is amazing.

Katelin Holloway:

Yes, yes, yes.

Vijay Mani:

Uh, and it's nice to see the companies building the foundation models are actually caring about this, and, uh, it, it's coming to the forefront. But there are algorithms here, right? I think de-biasing is a real thing. So while there are things that you can do, these systems are inherently biased. Uh, and they recognize that. You can give it a, uh, ChatGPT a job description. Say, like, "Extract the bias, or explain the bias to me," and can do a pretty good job of it, too. But I think, like, any solution or any sets of solutions here that HR practitioners, recruiters embrace will have to be something that allows you to essentially have some kind of, like, an override on top of that, somewhere where you can explicitly state, control, define your strategic goals, because without that, these systems are gonna be inherently flawed.

Katelin Holloway:

I want to integrate a question that we actually got from Lattice's Resources for Humans Slack community. So we went to the community to talk about AI, right? We wanted to know what was happening within our own community, what questions folks were having, and this conversation we're having right now reminds me of a question from a user, Blythe, heart, Reese. And she says, "My biggest concerns are how to mitigate the historical data's inherent biases. We're far from a point where we can say that there's no evidence of racism or disparate impact on our data. How can bias be controlled for once a tool goes live?" And so, Vijay, you covered a lot of this in your last commentary, but as a founder who is integrating this technology into your own products and building, uh, your own technology and training your own models, I'd love to get more of your thoughts on this.

Vijay Mani:

Uh, all right. As a founder and as a parent to a boy and a, uh, and a girl.

Katelin Holloway:

Yeah, a brand new baby. Congratulations.

Vijay Mani:

Brand new baby, indeed. Just thinking about them and, you know, like, a world where it's not equitable for our children is just gut-wrenching, but that's the world we live in. A lot of it has to go towards intentional design, right? Our system isn't, "Okay, hey. Here's, you know, five people in the funnel. Go find me exactly people that match that profile." Then you're gonna have very skewed behavior. The system has to be, "No, for our organization, these are the strategic goals. This is what an evaluation of a successful profile here looks like. This is what evaluation of the trade-offs that we make looks like. Now, computer system, AI go, build something in a domain-specific language." We built a custom language that our AI knows how to, right into to, to your question about the choices they're making, such that we can sort of, like, put certain guardrails into how it makes those choices, right, in this domain-specific language, build a bot, execute exactly on this sort of, like, strategic requirement, and go find people that match that profile.

So I think, like, the design of these systems has to incorporate that. And I, I do think, like, that's probably gonna be the shape of solutions, not just in HR but in enterprises in general where you take a lot of these amazing emerging abilities, and you fine-tune it for your specific domain, cross your own data in interesting ways, and you constrain it for the needs of the problem. So the human will have to be the strategic decision maker. The transparency has to be there in terms of why certain decisions are made to be able to explain those in whatever software you build. So we spent a lot of time designing, building around those things.

And obviously, the goal is such that you then can spend all of your time building relationships with candidates and managing annoying hiring managers, and everything else that you need to be doing that no one else absolutely can.

Katelin Holloway:

And that's a great segue going back towards the positive, right? If we understand the inherent challenges, and, and frankly, opportunities that we have to adjust and, and inform and educate while we are on the ground floor here, as this technology is being adopted in the earliest days. But Vijay, I want to talk a little bit more specifically about how AI can impact recruiting teams' outcomes. So as we know, many companies have recently downsized their recruiting teams because of the economy, which means there's less cash, (laughs) frankly, to hire people. Therefore, less people needed in the actual recruiting of function within organizations. But that doesn't mean we aren't hiring. There is natural attrition, meaning people are quitting, being out, managed out for performance reasons. So even if you don't have a robust hiring plan for the rest of the year, you're still gonna need someone to help manage your recruiting function. Thinking about Covey specifically because that's, uh, obviously something you're very knowledgeable about, how can teams of different sizes best use AI to improve their efficiency?

Vijay Mani:

Zero interest rate, low interest rate sort of world, there's a lot of choices that we had made. It's like scale, scale, scale, grow, grow, grow, and it was build this massive recruiting army, count army. You hire these super thoughtful, empathetic, amazing humans, and you're, like, stare at profiles all day. And obviously when the market conditions change, when the interest rates change, so on and so forth, it's like, "Hey, you know, team has laid of 10, 20, 30%. Talent team laid off 80%. HR team, in fact, did 90%," right? It's almost as if there's an unfortunate loss of humanity in some of those duties.

So, so everyone that we talk to, I don't think they want to go back to that world of hire a bunch of humans, have them do sort of, like, low, for lack of better words, low-value things. And we're hearing that from all of the CEOs. But as you said, the market is changing. We are seeing growth. We are seeing sings of, of recovery. A lot of our customers are hiring and hiring more aggressively, as well.

Katelin Holloway:

That's such good news.

Vijay Mani:

And so I think the way forward will be you have these recruiters or leaner recruiting teams that own the strategy, that know how to navigate the hiring manager relationship, that know what the organization needs, that understand that, "Hey, we need to have a greater diversity of perspective in our organization to be a healthier organization," right? Leaders who have points of view and want to accomplish that. And then everything in the task of accomplishing that that, really, computer systems are better at doing should be done by a computer system. And I think that it's great to see folks embracing this, but I think we'll see a ton more of this. We'll see it as both, I think, as products and companies for the things like, you know, scout where it is a whole system trained to do as specific purpose, but also see it as features, right?

I'll give you a good example. So obviously, Scout can go find people based on your descriptions, but we launched a little feature where if you're sending out a, a sequence template as a recruiter, some email recruiting automation, Covey can look at the candidate's profile and automatically write a couple of lines, like, a little bit of personalization that, that's impactful and meaningful. And the amount of joy that that brings recruiters has been awesome to see, as well. So I, I think we'll see all, all shapes and sizes of that from a recruiting perspective. And then alongside that, you're gonna have a lot of data work. I think understanding the data, understanding the funnels.

I think one thing I, I wanted to mention the, in the previous sort of thing about bias, like, man, I stare at funnels all day, every day, helping a a lot of our customers. And you see these applicant funnels, right? You'll see them have, you know, like, you know, let's say for an engineering role, you know, like, 80% male, 20% female applicants, things like that. Where it gets, like, shockingly interesting is, like, the, the hiring manager screened [inaudible] it's like you see a massive dip. It gets even worse, right? Those problems don't instantly go away once you kind of, like, wrangle the AI. Like, it's, it's, these will make your life easier so you can now have the mental space to focus on the things that actually matter. So that's something that we're super, super excited about.

Katelin Holloway:

Oh, that is such music to my ears across the board. Focus on the things that matter. And I think back to the earliest days of HR tech, you know, 15 years ago, that didn't even exist as a vertical. Chris and I both joined this world when things were still being developed, and we were using spreadsheets. We were using Excel for everything. And it was just in the beginning of the transformation of the entire people and culture function from HR to people and culture. And the reason we were allowed to do that was because of technology. Without those tools, our teams would still be relegated to the back room crunching numbers, trying to make payroll happen every two weeks. But because we were able to convince a few brave founders, our friends, to build a software that allowed us to do that high-impact work, and, yes, I will give an explicit shout out to Jack Altman at Lattice for engaging and for listening and actually building tools that real operators and practitioners needed so desperately. Only because of that and people like Jack, and, and the whole team at Lattice and, and other HR tech companies, we were allowed to bring strategy into the conversation in a much different way.

And so I view this moment in time with AI absolutely as no different. This is 100% something we should be leaning into and getting really excited about. So thank you for that perspective. Now, Chris, want to go to you and talk about a slightly different experience, which is the candidate experience or the employee experience of fear, fear of AI coming in and taking our jobs. And so looking, again, at our Resources for Humans Slack community, there is a user. Her name is Tamini Fernandez, and she says, "How do you think AI will change the job market? It seems like it has the potential to eliminate, reduce, or change lots of jobs. What are the consequences, and how can we best prepare for them?" Chris, how real do you think the threat of AI is to the average person's job?

Chris Vanzetta:

That is such a loaded question. I with I had a crystal ball, but I feel like it would be wrong for us to make an assumption that AI is going to replace humans altogether, right? We've talked a lot on this podcast about the need for human strategy, the need for human intervention. AI can do a ton, and it can do a lot of menial work. Is it gonna replace human's jobs altogether? I don't think so. The strategic work, right, the empathetic work that we've been talking a lot about in the podcast, I think that AI is going to come in and help humans work more efficiently and effectively. And ultimately, it might take over some human work, right?

Katelin Holloway:

Hopefully, right?

Chris Vanzetta:

Hope... Right, hopefully. Will it replace human's existence? Maybe in some sectors, it will. I think that's a high possibility. But I think that to your point, Katelin, you know, back in the day, we were doing benefits and payroll in spreadsheets. And I think that now, technology is evolving even quicker than it was 10, 15 years ago. And as it relates to AI, the most important thing that we can do is continue to learn so that we don't get left behind. I think another super meta thing that we could do in this moment is ask AI what they think about replacing jobs themselves. Like, what does AI think about AI replacing the work of humans?

Katelin Holloway:

So meta.

Chris Vanzetta:

So meta. So let's ask ChatGPT here.

Katelin Holloway:

Are you gonna do it live?

Chris Vanzetta:

I want to do it live.

Katelin Holloway:

Yes.

Chris Vanzetta:

Yeah, let's see what Chat-

Katelin Holloway:

Yes.

Chris Vanzetta:

Let's see what ChatGPT says about it replacing the job of humans.

Katelin Holloway:

(laughs)

Chris Vanzetta:

Wait, let me press enter, here. I'll read what ChatGPT says about this. Okay, AI is already starting to replace certain jobs, and this trend is li-

Katelin Holloway:

Ooh, cocky.

Chris Vanzetta:

Very cocky. But they know what they're doing. And this trend is likely to continue in the future as AI technologies advance. Jobs that involve repetitive, routine and predicable tasks are more susceptible to being automated by AI. Some examples of jobs that could be replaced or significantly impacted by AI include manufacturing, data entry, customer support, transportation, retail, data analysis, routine medical diagnoses. So that's what ChatGPT says about it.

Katelin Holloway:

Interesting.

Chris Vanzetta:

Yeah, yeah. We're, we're saying maybe not.

Katelin Holloway:

Yes. Absolutely.

Chris Vanzetta:

ChatGPT is very confident that it will, it will be replacing some human's-

Katelin Holloway:

Yeah.

Chris Vanzetta:

... jobs in the future at some point.

Katelin Holloway:

Thanks for doing that quick demo. Um, how fun. Chris, I like what you said about learning. The biggest disservice any of us can do is to stop learning, stop being curious, right? Don't be scared of this. Get in there, learn the tools. And so I, I heard someone say, "AI is not going to replace human. It's going to replace the humans who refuse to learn."

Vijay Mani:

This AI automation wave isn't necessarily massively different from all of the step functions that we've seen in certain ways, right? Obviously there are things that it does that are just mind-boggling and amazing. But as an automation step function, it isn't, like, vastly different, right? There's this massive fallacy, and you can always call lump of labor fallacy, all right, which is like, "Hey, there's a fix don't want to work. If machines come in and do some of that work, then you have no work there, so you'll be out of a job." We know over the last 100, 150-odd years that that's not the case, right?

Usually what happens is you then get hire orders, strategic better jobs, and increased wages, also. And there's no reason to believe that this is gonna be any different from that. I just wanted to reiterate that just because it does seem like this wave is here to stay, which means embracing it, not shying away from it, not treating it as me versus the system, but how do I get more leverage out of it. And I think, like, people are realizing that, you know, the systems need to be designed to be an assistant and associate to humans.

Katelin Holloway:

Absolutely. I, I think about, going back to the analogy with early HR tech, where I remember a moment in time when payroll specialists were petrified that a new payroll system was rolling out, because they were like, "But this is what I do all week. This, what do I do?" And guess what they did. Their scope increased, right? Suddenly, it went from payroll to total rewards. Now, we're looking at total compensation and other ways in which we can support and compensate our employees, not just through a paycheck, right? It helped and gave this time and space for our payroll specialists to go out and actually do benchmarking exercises and actually look at pay equity. And so really looking at it more as, as a broadening of the scope of your role, making you, therefore, more valuable, right?

Chris Vanzetta:

I started getting familiar with it on a personal level, because I think when we think about learning in the workspace, we're so afraid of making mistakes, and there's a higher degree of pressure. So for folks that are listening that are, like, I haven't even touched things like ChatGPT and want to get familiar and start learning, get in there, but make it less heavy. Like, get in there and have it plan a vacation for you, or recommend a book that you really want to read. Start playing with that technology until you get familiar with it where you're staring to understand how it works. And then you can have it help you analyze data sets and a spreadsheet, right? But start small, and that might help people take the fear out of learning.

Katelin Holloway:

I love that tip so much, Chris. And you remind me of an experience that I had several months ago now where I was chatting with a very important person in the financial world. It was my first time meeting this individual, and the first question out of his mouth was, "What do you think about AI?" And so I, you know, I, I just threw the question back at this guy, and I was like, "Well, what do you think about it? I really think this gives us an opportunity to be more human, but I'd love to hear your thoughts." And what he shared with me was an example. He said, "It's my wife and I's 50th anniversary this coming week. And after 50 years of being married, there isn't a whole lot left to give one another in terms of gifts." And so he turned to ChatGPT and said, "What are 10 questions I can ask my wife to deepen our relationship?" And then, when I said, "Oh, my gosh. That's really beautiful. What were the questions," he said, "Hold on," and he pulled out a printed piece of paper. And then he said, "Here, I made an extra copy." I said, "My husband and I are celebrating our 25th this year," and so he gave me a copy.

These things should improve our life experience, not detract, right? And so, Chris, to your point, go and play and learn. This thing is not just about your specific job, your specific function. This technology is going to hopefully help better connect us as humans.

All right. Rapid fire. You all know the gist of this. You've heard the podcast, so we're gonna jump into rapid fire questions. I'm gonna do my best to direct these questions to both of you, but I'll tell you who to go first. Chris, you're first. On what exact date will we welcome our robot overlords?

Chris Vanzetta:

(laughs)

Katelin Holloway:

No, just kidding.

Chris Vanzetta:

Let's ask, let's ask ChatGPT.

Katelin Holloway:

(laughs) Do it. Oh, my god. Rapid fire for ChatGPT, yes, please.

Chris Vanzetta:

The question, this question for ChatGPT is when will you take over the world.

Katelin Holloway:

On what exact date.

Chris Vanzetta:

Okay, as an AI language model, I do not have intentions, desires, or the capability to take over the world or any form of autonomy. My purpose is to assist and provide information to users like yourself. See, just making our lives better, making our lives better.

Katelin Holloway:

(laughs)

Vijay Mani:

I, I tried the same thing to see if it would give me a different answer, and it's like, "I'm not your overlord. Just watch out." (laughs) These things are so nuanced. It's wild. Like, not what, not what one would have expected.

Chris Vanzetta:

Right.

Katelin Holloway:

I have been incredibly impressed with how polite ChatGPT is. And, and in the event the overlords, the robot overlords come, I have done my best to go out of my way to say please and thank you always to Siri, Alexa, all of the, all of the robots that exist in our lives. I'm very polite just in case.

Chris Vanzetta:

(laughs) Just in case, just in case.

Katelin Holloway:

Okay. Vijay, say we live in a world where AI handles all of the monotonous tasks, and we have our time back. How would you spend your days?

Vijay Mani:

That's an amazing question. I mean, right now, I'm obsessed with a couple of things. I would say, like, Covey, Covey-related things, and then kids. Like, well, you know, that, that's been [inaudible] with my wife and kids. I, I think to the extent, Covey's mission is to empower professional lives, and then if in that path, I can then power, have more time to even empower my personal life with my kids. I have two wonderful children, a amazing wife, so I think, I think that's where probably most of that time goes (laughs).

Katelin Holloway:

That, that makes a lot of sense. I, I thought for sure you were gonna say sleep, because you have such a brand new baby.

Vijay Mani:

Sleep would be nice.

Chris Vanzetta:

What is sleep? Like-

Katelin Holloway:

(laughs)

Vijay Mani:

Like, honestly.

Katelin Holloway:

You forgot it's an option.

Vijay Mani:

Like, sleep is, uh, I think just, I just learned to operate on, on a complete lack of sleep.

Katelin Holloway:

Yeah, fumes.

Vijay Mani:

That's a great one (laughs).

Katelin Holloway:

(laughs) Chris, what about you? What, how would you spend your time if AI's planning your vacations?

Chris Vanzetta:

Um, I think that would, you know, similar to Vijay, just spend more time with the people that I love and doing some of the things that I love, just baking a little bit more, reading a little bit more, writing a little bit more poetry, just doing the things that fill my heart.

Katelin Holloway:

Yeah. All right. Chris, we're gonna stick with you. Last and final question, when was the last time you were deeply proud of something you've accomplished?

Chris Vanzetta:

Oh, wow. Okay. This is a, a wildly corny answer, but it's true. Like, truly, the, probably the last time I was really proud of myself was I've been doing yoga and new to yoga and, like, really getting into, to it. And I really perfected the crow pose, which is a specific pose in the yoga practice. And I did it, and I could ho-... I felt my strength, and I probably held it for all of 20 seconds. And it was the first time I did it with, with true stability, and I was just-

Katelin Holloway:

Whoa.

Chris Vanzetta:

... so proud of myself that I, that I nailed it (laughs).

Katelin Holloway:

Aw, I love that. Well, we are proud of you, too.

Vijay Mani:

That's pretty phenomenal.

Chris Vanzetta:

Thank you, thank you.

Katelin Holloway:

And, and Vijay, same question for you. When was the last time you were deeply proud of something you've accomplished?

Vijay Mani:

I can tell you when the last time I felt deeply proud. I don't know if it's something that I accomplished, I don't know if that counts. Like, it was couple days back. The summer, you know, we've been, we have a little basketball hoop at home, and it's been awesome. Uh, we have a five-year-old, and he's starting to shoot some hoops. He actually duped me and, and made shot. And I, like it's-

Katelin Holloway:

(laughs)

Vijay Mani:

It's so w- it's so weird to feel proud, like, that level of pride-

Katelin Holloway:

Yeah.

Vijay Mani:

... for someone else doing something-

Katelin Holloway:

Yeah.

Vijay Mani:

... but that was, like, a, uh, an amazing experience where, like, wow, we didn't really, like, you know, practice that or, or do it like that. Uh, he just kind of did it. And so I think being able to feel pride for someone else's accomplishments is a pretty tremendous feeling.

Katelin Holloway:

Getting dunked on by your little kid. That's a beautiful moment.

Vijay Mani:

Ah, yes.

Katelin Holloway:

(laughs)

Vijay Mani:

So, so I, I will say I shortly thereafter followed up with a block, so I, I felt good about that.

Katelin Holloway:

Yeah, absolutely.

Vijay Mani:

Ma- maybe not so proud, but-

Katelin Holloway:

Yeah.

Vijay Mani:

... I felt good about it. (laughs)

Chris Vanzetta:

(laughs)

Katelin Holloway:

Well, that's a wrap, gang. There you have it. We've talked about AI, we've talked about kids dunking on each other, we've talked about crow poses, but more importantly, we've talked about humans doing more human work and living better lives because of technology. So Chris and Vijay, thank you both so much for sharing your perspectives with us here today on All Hands. And please, please keep leading authentically.

Chris Vanzetta:

Thanks, Katelin.

Vijay Mani:

Thank you so much.

Katelin Holloway:

And to you, dear listeners, thank you so very much for joining me on this week's episode of All Hands. I'm your host, Katelin Holloway. If you want to join Lattice's Resources for Humans Slack community, head to the show notes to learn more about joining. All Hands is produced by Lattice in partnership with Pod People. Special thanks to our production team, Christine Swor, Annette Cardwell, Rachael King, Aimee Machado, Hannah Pedersen, Danielle Roth, David Zwick, and Carter Wogahn. I'll see you next time on All Hands. Until then, my friends, please keep leading authentically.

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