AI Recruiting at NVIDIA, Pika, and Eleven Labs: How to Build Exceptional Teams
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This week, we are talking about hiring trends in the age of generative AI. Hire the right people in the beginning and then let them leave and get out of their way. We found that we had to really set the bar high and hold it there. Filmmakers, artists, they provide a creative lens in the model development process. It doesn't matter if you're an employee, executive, whatever, founder, you have this decision you have to make every day.

I'm Michael McDonough, a partner with Lightspeed, and this is Generative Now. This week's episode is for anyone who's building in AI and is asking themselves how to build the best team. You already know talent matters. Everyone says that. But right now, AI talent is scarce, expensive, and expensive.

and unpredictable. Bain & Company predicts that half of global AI jobs will go unfilled by 2027. 44% of business executives say this talent shortage will make it harder for them to deploy AI. So how do you actually staff an AI team in 2025? I'm sharing the best advice I've heard on this podcast from leaders at PICA, Eleven Labs, and NVIDIA.

We'll check in with Dan Schipper about Every's collaborative approach to content and software development. And finally, we'll pull back the curtain and how we approach talent at Lightspeed with our very own VP of AI talent, Amy Anton. Let's break down what's actually working in AI hiring and what's not.

Let's start at the ground floor. If you're building an AI company, there's infrastructure, product, research, a bunch more, but what's the edge? I asked two impressive founders this. Demi Guo is the co-founder and CEO of idea to video platform Pika.

And Maddy Staniszewski co-founded and runs the audio AI company Eleven Labs. They both said it's all about the people, not just the smart ones, but the people who think differently. For us, I think, first of all, like we try to build a very, the best technical team, both on research engineering side, for example, our founding research team members all came from like the top AI lab, like top,

both from the top AI industry labs such as Google Dynami, Facebook AI, and also the top academic schools like Stanford, MIT. At the same time, I think for us, art is also as important as science when it comes to building a team, but also building a model. So we have a lot of team members are from creative backgrounds who are filmmakers, artists,

on recording, they provide a creative lens in the model development process. Beyond that, of course, we're trying, we're a very hardworking and efficient team. Yeah, I 100% recall the team piece. I mean, it's, I'm lucky to have a co-founder who, who've done a lot of the research before and, and, and has been able to assemble an incredible team of researchers. So 100% that, and in our case, it's also being able to focus that, that,

that team on a very specific set of goals. - Demi and Maddy brought on AI researchers and filmmakers, scientists and storytellers. And it makes sense. When your product's output is expressive, you need people who know what good looks like, not just how to ship it. So why do AI teams need more creative talent right now? Well, as software becomes easier and easier to create, the value on taste

And somebody's judgment on how good something is, is just gonna be that much more valuable. And so I think we'll see more and more talent inside of AI companies helping guide models on things like taste.

But another key question to ask is what kinds of new skills does an AI-driven economy demand? I had the pleasure of chatting about this with Dan Shipper, founder of Every, the first multimodal media company. As the name implies, Every publishes a little bit of everything. Articles, videos, podcasts, software, all with the help of AI, of course. Dan has written extensively about the death of the knowledge economy and the emergence of the allocation economy.

What he means by that is given the many hyper-intelligent AI tools at our disposal, the primary job for future managers will be allocating these resources, choosing the right mix of digital and human labor to get work done faster, better, and cheaper. But an allocation economy changes the attributes you look for in the people you hire.

You'll need fewer subject matter experts and much more multi-talented generalists. At Every, people tend to be a little multimodal themselves, bringing a lot of different skills to the table. Take, for example, how one writer started co-writing with the app builder, Lovable. Here's Dan. A lot of the people that work for Every have that like sort of multidimensional skill set. And particularly for the EIRs, they're like owning the full stack. But like we also have like

For example, we have this with this writer, Katie. When we have EIRs write, sometimes they're good writers, sometimes they're not. And she does a lot of like

co-writing with them to help them like get out their ideas um but she's also like using now using like lovable all the time to like build little tools for herself oh wow um or we have another writer her name is ria who's doing the same thing as like building custom gpts and all these people are like now curious and interested and like they sort of see how powerful it is and once you get them going they're like oh my god this is amazing yeah you know um so i think we have an environment that um stimulates that and values i think in general startups really value generalists um

But this is like incredibly empowering for generalists if you kind of like allow them the space and freedom to play. So like one of the things we do every quarter is we do this thing called Think Week where we don't publish anything new. We don't do any meetings. Every day there's a theme. But the idea of Think Week is to sort of recognize that most of the time in a startup, you're spending time.

time being like sort of very reactionary. Yep. And you're just like under like constantly under fire, like trying to make sure things are not breaking and like whatever. The best creative work comes from a different sort of place where you're not reacting to your circumstances. You are like proactively kind of like playing around and like following like that thing that you just are psyched about. And Think Week is really about getting back in touch with that. So we don't do any meetings. We don't publish anything new to the extent we can. Like we're like

pausing a little bit on some of the product stuff. The idea is pay attention to stuff that inspires you, pay attention to whether or not you're in that sort of reactive mode and sort of get into a play mode. And then like one of the days this time we did like a day where it was like experiment with a new tool that you've been meaning to experiment with but you haven't. And that's how Katie started using Loveable.

And I think that that's actually so fucking important for businesses right now. And we do this with consulting arm. And this is like one of the big recommendations is find space to play. Yeah. It doesn't matter if you're an employee, executive, whatever founder, you have this decision you have to make every day, which is do I do things the way that I know how and get them done? And then, um,

I have so much work to do that. Like if I worked the way I know how for 20 hours a day, it still wouldn't be enough, but like I can just get it done and I can go home and whatever. Or do I spend like two hours like playing around with this new tool that may not work and probably won't. And then I will have to go and do it the old way. I knew how anyway, unless you're someone with like a really, you know, curious early adopter mindset, everyone is going to do the first one and they're just going to get their work done. You just need to like

be given the space to realize that. We do that as a concerted regular practice at Every and it also, I think, works for other types of companies too. Writers who build tools, builders who edit podcasts, everyone experiments.

I think we're going to see a lot more experimentation going on inside of startups as the models just give us more and more capabilities. They're bringing out more creativity in all of us. And I think that creativity is going to translate to newer, faster, and more differentiated products. As Dan says, it's not just about finding people who do a lot of things well. You need people who are always willing to experiment with ways of working. Because in the AI economy, everyone will need to find new ways to work.

So now the question becomes, what happens when you grow? I had a great conversation on that topic with Bill Daley, who grew his research team from 15 to roughly 400 PhD researchers at NVIDIA. As I'm sure you know, NVIDIA provides the core GPU technology powering all of our favorite AI startups.

In a very short amount of time, NVIDIA has become one of the most important companies on the planet, in part due to the incredible depth of scientific talent it has amassed. Here's Bill. I came to NVIDIA in 2009 and inherited a team of, you know, I think it was like about 15 people, most of whom were doing ray tracing, you know, computer graphics. And, you know, from there, you know, created groups, you know, doing, you

you know, architecture and circuits and doing AI. When we first started in any given area, it was very hard because no one wants to come to a place where they're the only one doing something. But by getting some really good people to anchor each place and then hiring really good people, it then becomes easier to recruit talent because people like to join, you know, a team where there are other fun people to talk to and everybody is as smart as you are. We found that we had to really set the bar high and hold it there. If we were to let that bar drop and start hiring people

you know, mediocre people that would be get hiring more mediocre people. So we've had to, had to keep it, um, keep it high. And then we try to create an environment where people like to be. So we have very little turnover. People come and they stay because, you know, they, they, they get to do what they want to do. We have the, they have the resources to do fun, um, fun experiments. They get to work with fun people and they get to have an enormous amount of impact. Um, the one great thing about Nvidia is because we supply the whole industry and

If you develop, you know, whether it's a piece of new hardware for AI or a new type of model, a new training technique, it winds up benefiting everybody, benefiting the whole world. Whereas in some of the people we're competing with for talent, if they develop something, then their company will use that, but it won't be spread as widely as the things that we develop. You hear this sort of thing from a lot of the truly foundational technology companies. Getting top talent early is essential.

A-level players attract other A-level players. So how do you scale headcount without scaling complexity? Well, it starts by acknowledging that as you scale, your company is going to change. There's a general rule of thumb among startup CEOs and founders that at each 50-person interval, the culture breaks down, the process breaks down, and you need to start over from scratch. So that's a good thing to keep in mind. But a lot of founders wait too long to define what great looks like. And by the time they realize it's already embedded in their culture,

Don't let that happen. Be super intentional from the beginning. Let's zoom back in. You're hiring your next few roles, maybe doing outreach yourself. This is where things can go sideways fast. I asked Amy Anton, Lightspeed's new VP of AI talent, how founders can think about hiring without wasting resources and time.

Here's what she had to say. My name's Amy Anton. I'm part of the talent team here at Lightspeed, and my focus is on AI and machine learning talent. And my primary goal is to help make our portfolio companies successful. ♪

What we look for when we're thinking about building strong AI teams is largely similar to what we think about for building strong teams, period. And I think especially in the beginning, you know, there's a special uniqueness to people who not only have AI skills and experience, but who also demonstrate high agency, who can be product thinkers, who may have displayed initiative to push inside the companies where they worked previously. You know, we look for clarity of thought

and vision, you know, what is it that you want to do and why? Have you thought about the dynamics of the industry, you know, or the product or the vision and any kind of competing factors that may be at play in the market? Those are kind of really important aspects of building a successful team. We also look especially for kind of the early,

early founders and senior leadership team, you know, we also look for charisma, right? People who are most successful are able to be talent magnets. They need to be able to recruit. They need to be able to retain top talent as their company grows. And they need to be able to raise money as well. In terms of AI, top AI talent, right? We're in a world right now where AI is such a sexy term and people are

feel like they need to weave AI into their company in some way for people to take them seriously or to...

attract talent or funding or whatever it may be. What I am encouraging people to do is to be really crystal clear about what their needs are, right? I think that there are certainly top AI talent that are, you know, extremely difficult to hire, very, very well paid, doing really meaningful and impactful work. And I frequently get asked the question, you know, how do we hire these people when they're so difficult? And while I would say a lot of those folks aren't the

people who actually need a more junior person or somebody who isn't as kind of well-known or famous or who doesn't have kind of the traditional credentials would be just as effective and potentially even more effective for the work that you're doing.

as a kind of earlier stage company. So I would definitely just encourage people to think about exactly what their needs are, have that conversation with whomever it is that they're working with to staff those teams. I think the answer could vary quite substantially depending on the needs, the specific needs of the company. Wow.

This is the million dollar question I would say, you know, do you staff up quickly or do you hold out for the perfect unicorn? There is always tension in the system around these trade-offs. And I think, you know, my best advice to people is always, you know,

Again, kind of think about the criticality of the role that you're hiring. Of course, a leadership position or if it's, you know, a role that's kind of setting the direction in any way or is potentially a culture carrier. Figure out what you need and what are the non-negotiables and don't negotiate with those.

It's important also to recognize kind of, are you looking for something that doesn't exist? So also hold yourself accountable to, are you looking for, you know, as we sort of say, are you looking for a purple unicorn? And you may be able to find one, but then can you hire them? Can you keep them? Can you compensate them? The

The bigger that you can increase your aperture of what the talent pool looks like, you know, the better. And I think it's really important, depending on where you are in the journey of your company, to not let perfect be the enemy of the good. ♪

It is really important that you get your leadership team in place and right up front because everything sort of trickles from there. They're the culture carriers. They're the people who are setting the research direction. They're the people who are, you know, setting the product vision. They're the people putting in place all of the structures that the company will then kind of implement.

sit on top of. And so being really, really thoughtful about your leadership team in the early stages, I think will just pay dividends over time, really kind of empowering those people to lead and to hire and kind of

you know, relinquish control as kind of a CEO or founder. A lot of people who join early stage companies are really excited to be a part of that growth and to be a really integral part of what happens with that company. Really trying to empower people to own various parts. You have more brains thinking about more problems in different ways, and you probably come up with more creative solutions with more brainpower. Hire the right people in the beginning and then let them lead and get out of their way.

And then hold yourself accountable for kind of looking at the people who you have over time and making sure that they are still the right people, you know, as the company grows, right? Because I think certainly seen, you know, people...

people who are hired for company at 200 people and, you know, they may have wonderful experience to lead or to drive various parts of that company forward at 200 people. When that company gets to be 5,000 people, maybe it's, you know, expanded globally or across different regions and,

you know, with the best of intentions, I think sometimes those people are not the right people to lead the company into the next phase. Just holding yourself accountable for kind of really looking at who is still here and who should kind of continue on in the company over time, I think will help. Because if you have someone who's in the wrong role, the wrong stage of the company, that really can trickle down and have some pretty negative impacts across the organization.

One pitfall I've observed often is companies making up roles just to justify hiring somebody who's super impressive.

What I've seen instead that works better is being really, really specific and intentional about the roles you need to hire for and then going and finding the exact person that matches that role, not the other way around. To wrap up, here's what the best AI teams have in common. They're not just technically great. They're intentionally built and they're constantly evolving. Hire for range, hold the bar, build your team like you build your product. Because in this space, your team is the product.

That's all for this episode of Generative Now. Thank you so much for joining me. If you liked this episode, please do us a favor and rate and review the show on Spotify and Apple Podcasts. This really does help. And if you want to learn more, follow Lightspeed at Lightspeed VP on YouTube, X, LinkedIn, and everywhere else. Generative Now is produced by Lightspeed in partnership with Pod People. I am Michael McDonough, and we will be back next week with another conversation. See you then.