Building High-Performing Teams in the AI Era with Charles Guillemet & Sandra Schwarzer

0:00If you hire a very important key senior person in your team, somebody who will influence culture, who will influence the talent that comes, your product, you better make sure that you slow down.

0:15Big welcome to everyone. Thank you so much for being here for what is a very special live recording of 40 Minute Mental with two of the ultimate goats of the talent ecosystem. consistent friends of mine wonderful supporters and just incredible people Charles and Sandra so thank you very much for being here they're going to share their insights on a topic that is incredibly important which often will make and break the success of a startup which is all around building high-performing teams which is a passion topic for myself and many people in this room and I can't wait to dive into it particularly in this new world this AI era that we're in two people that have seen it up close and personal.

0:55Super excited to dive into everything. I've known you both for a little while now, but our audience may not. So I thought we could start with some intros. We'll go ladies first. Sandra, if you don't mind just introducing yourself and your current role at Index. You will hear the accent as we go through, but it changes depending on who I'm talking to. So I currently lead Index Talent Function here in Europe. Joined Index almost five years ago. I don't think I have to tell you more about this organization, what we do, but we're celebrating 30 years this year. So I find it's like one of those milestones in the VC industry where you really can reflect back on all the things that have been achieved and what we're seeing right now.

1:31I started my career in the wrong place and relatively quickly figured that out and have then shifted into all kinds of different roles that go around people and talent. I've advised over 6 ,000 people on their career while I was at INSEAD. I was a chief people officer, so I know how hard it is when you have the theory and then you actually have to apply it into practice. Went into headhunting and came here and you can see it from the fact that I am prepared. I'm originally German. I have come via France to the US and then the UK. So I also bring like a perspective from different markets and different backgrounds.

2:00Thank you. And Charles, everything. Yeah, so my name is Charles. I'm from France. You can hear my accent. That's very true. So I spent about 17 years in SF in the Bay Area. One of the couple of like highlights there is I built a company called Gusto, which is a payroll provider. It's a YC back company. And when I say built is like I helped to come to hire those folks from 30 to about a thousand by the time I left. Now the company is like much bigger. So that's kind of like what I was doing there. Then I worked for a company at Elon Musk company called Neuralink. It's a brand machine interface.

2:35So if you don't know what a brand machine interface is, it's a brand that's connected to your computer. and basically two years there working directly with like Elon and his CEO office to hire the best talent in engineering in different fields that are needed. And then I worked at Meta at the corporate office in Menlo Park and I was doing exec and leadership recruiting there, mostly on like the analytics side, data engineering and data engineering, staffing for VPs and directors. And then I worked at a VC here in London called Local Globe, which is like an early stage investor of many companies that you're aware of.

3:11And then I recently started at Lovable. So I work in Stockholm, but I'm currently based in London and relocating to Stockholm in a few weeks. And I'm married and I have a son. My wife is American and my son is French-American. Awesome. Thank you both so much. And I'm so glad we were able to get you, Charles, before you relocated. Thank you for making the time. Clearly, those are unbelievably distinguished careers and you've worked with some of the greatest entrepreneurs that have ever graced this planet. We should probably start with Elon Musk, a polarizing figure, of course, but no doubt has an eye for talent.

3:45So I'd be very keen to hear your learnings from working with him and any particular things that have stood out to you from that experience. So I am a fanboy of Elon. My wife probably complains about how much I love him, but I would just say I am a fanboy of Elon as an entrepreneur. And I would leave it at that. I think he's done other things since then, but I really respect him and look up to him when it comes to become an entrepreneur and to be an entrepreneur and some of the highlights working with him and for him I think is one he doesn't really play in the hype game meaning that he goes down to he boils down to the fundamentals with him and the principle and the first principle thinking so I don't think he gets hyped by a logo or company or like what you've done in the past I think he relies very heavily on internal interview processes that we built and those indicators are very important to him.

4:37Second thing is I think he came to the office one day with a hammer and was like you're not asking the question why, why and why you know so like peeling as often as possible why someone would make a career decision answer a question a certain way. He was very pragmatic around this and he wanted to boil it down to first principle thinking. Then he's got a couple of triggers. So this might be kind of like polarizing, but it's Elon, so it's fine. But essentially he's not a big fan, like the way to phrase it, I would say is like, he's very careful about a ratio, like PhD theory versus like actually producing things in real life and real time.

5:20So I think if you think about a ratio for like a product or like an AI thing or like a R &D project, he probably wants to have one PhD hire for like 20 engineers who are actually going to ship and produce something. So he's very careful around this. He's also very careful around MBAs. I'm sorry if anyone has an MBA here, but he just basically boils down to actually people who are doers. He says that people who have an MBA usually make slides, but they're not always like a doer. So like he really wants to vet for this. So this really kind of like stayed with me super, super strong because whenever I go to a new place and I have the place to hire, whether I'm at a VC or like, no, I'd love it.

6:02I sort of have that in the back of my mind that like he really cared about this and that came very strong for me. The last thing that I would say is that he's huge on tenure and he thinks that essentially like anyone who has worked at a company for under two years hasn't had the chance to actually build a legacy and leave a footprint so if you see like a candidate who's gone from like you know one company to another over and over again under two years for him that would be a major flag and it would be like look like if you work at a company and you really want to leave a print you probably have to spend more than two years at this company so these were like sort of the principle that we operated with to hire for him super interesting thanks for sharing sandra to you index best in class founders you'll have seen them all across different stages geographies industries i know this is like asking who your favorite child is but i have to ask who are the founders that stand out to you and why particularly when it comes to hiring exceptional talent i will answer the exact same way that i tell my children i don't have a favorite child but i did think back about like are there any common traits that I see coming back over and over again in those founders that are able to attract like top talent?

7:14And for me, it boils down to a couple of, the first thing I would say is they're intensely creative. They're probably also very intentionally about the creativity and the curiosity that comes with that. So they don't go in saying like, I have all the answers. I know exactly what I want. They really go like, what does best in class look like? Not just today and in the past, but tomorrow, what do we actually need? And they're able to shift around some of them way before they need really senior talent. So I have some very early stage founders who are already talking to people who might only join them when they are at 100 or a million plus in revenue.

7:45And in today's world, who knows how fast that will be. They build those networks. They might rope them in as advisors and they might bring them on board in some ways so that they can pull the trigger when they're ready to hire and they will have made their selection already. So they kind of are winning at identifying top talent way earlier. They intentionally in everything they do, they're so speed. And I'm sure we'll come back to how you hire and how you hire fast, but speed is intentional. How they make decisions and what kind of decisions is really important and how the candidate experience makes you experience the culture so that you can filter through who will actually work with us really well is there.

8:19And then I would say in today's time, they experiment and are not afraid to fail. So a lot of them, you know, playbooks are dead. What somebody else does, I don't think you should look into it anymore. It's like, what's the problem we're trying to solve? How does it work for us? and they iterate really, really fast in the same way they do on a product. Super interesting and really resonates with what we're seeing with our clients at the moment. Charles, Lovable, I mean, it is one of the darlings, if not the darling of the European tech scene, one of the fastest growing companies in the world. I mean, what a cool place to be heading up talent.

8:49What does building AI native teams actually look like at a place like Lovable in 2026? And I'd love to hear your thoughts from someone that's been on the VC side recently, working with founders. what has changed in the past two years and what's that like in practice so much has changed i mean i think a lot of what sarah said to me like resonated speed is the one thing where i'm blown away by lovable is how fast they work and how fast they move i think one of the things that surprised me the most is like the agency that enter on the ceo is willing to give to his employees by making decisions on their own there's no approval layers meaning that like if you want to make a decision you want to hire you should go for it so there's this founder mindset at lovable in fact i think out of 130 employees that we have 43 or 45 are ex-founders which just shows like the dna of this company and like almost operating like a little bit siloed in like making decisions on their own we use quite a bit the probation period which it's like a good thing and a bad thing i would say the good thing is like we can actually make hires a lot faster and then we can let go of folks who are not working out and where Lovable is not the right fit, like very fast as well.

10:01I talk about this quite a bit, but like I've never been in a place where acquisition and acquire matter so much. So technically, like Lovable is like a series A, series B, depends on how you want to look at it. It doesn't really matter, but it's very early on. The company has been around for 18 months and already has an M &A function, which if you think about OpenAI, Entropic, they probably had an M &A function like three or four years after the company was created. So what this means is basically we look at acquisition as a way to hit our hiring target as fast as possible. And we look at it in both like technology that we don't have, like a technology gap that we don't have on the team.

10:39And then how can we hire like 20, 30 engineers as fast as possible so that we can ship? The name of the game at Lovable is shipping as fast as possible. There is nothing else that matters. So we have to ship. Why is because if you don't ship new products, you don't get new customers. If you don't get new customers, you don't capture more market share and then Lovable will stop to exist at some point because competition will take over. So the way that we can stay on top of competition is by building as fast as possible and shipping. And you see the speed in execution and shipping and hiring and everything that we do.

11:12I used to be at a company like Gusto or like other companies at Meta where like literally making a decision like, should we hire an exec? Like we take like three months, you write a memo, you'll be like, this is the kind of profile that we need. I'd love everybody's like, let's hire someone. Like, let's go. Like you open the search. You don't even do a kickoff. You just like, let's go. Let's just hire this exec. And we're talking about an exec where like they might relocate the family. Their comp package are insane. And that's kind of like a decision that you make like on the way, like super fast.

11:42Yes, it's incredible, isn't it? how times have changed. The speed is exhausting and exhilarating and amazing to see. Today's interesting because Olix, who very kindly introduced me to, announced this huge 220 million round. We just placed their COO and that process was less than five weeks, probably four. A relentless intensity to it and a huge amount of rigor went behind it. But the speed was unbelievable. Unlike anything I've seen before. I think actually the exec level, there's something new happening in executive searches, I think, in the age of AI, which is that folks probably expect to either be layered or to know that like things are not going to be the same so i give you an example like you know lovable was at 200 million ar in december i think we just crossed 300 this is public and this is a matter of months and if you think about the exec that was the right person for like 100 million ar might not be the person that's the right one for a company that's doing half a billion, for example.

12:40And everyone in the company should know this, myself included. Will I be the right fit to run talent in like two years at Lovable? Most likely not. You know, I just have to be very honest with this. And I think in the age of AI, given how things are moving so fast, I don't know if you see this, Sandra, as well, but basically there is either like fractional exec where like you kind of hire folks and you know that they are going to be good for six months and they might not be the right fit long term. or like you kind of set the expectations that there might be like a different profile to come later on that could be layering them.

13:13I don't know if you've seen something like this. Yeah, I definitely not only have seen this, but it comes back to this curiosity and ongoing learning from the founders. You need to stop from time to time and just reflect whether what you have is what you need for the future or whether you need to shift and change things. And that with the layering, I think that's a really important thing because we can miss the signals of the past and we don't have those anymore where everybody's inventing it new Everybody is doing things differently. And so you have to figure out when is that moment that we're breaking it and we're going to do it totally different again.

13:43And we're going to try something new, even if you're already at scale. And I don't think we have seen this before. Before it was quite linear and the growth was quite linear. That's really interesting. In this market where the velocity is unlike anything else and the competition for talent is so strong, particularly amongst the fastest growing businesses, what can founders do to stand out from the crowds to win that battle? I must say, personally, I'm very opposed to using all this war terminology that is very prevalent in San Francisco and is kind of coming over from time to time here. But we have been in these situations before where the market was so buoyant and hot that the employee and candidate actually had the upper hand.

14:22And you needed to hire incredibly fast and you needed to find the right people. and they had usually multiple offers and you needed to tell them a story of why they had to come to you. And that hasn't changed. What's changed right now is the speed and the number of companies who are doing it concurrently. And I think in Europe, what has changed, which is actually great, but wasn't the same before. You have a lot of US native AI companies pushing into the market. You have a lot of established players pushing into the market. They're all coming to Zurich and to Stockholm and to London and to other places.

14:53And then you have this wonderful crop of European companies that are going at scale and they're winning globally. So all of a sudden, European brands are being recognized in Silicon Valley way before they would have been previously. So all of this is happening at the same time. The way I think about it and I talk about it for founders, for me, there's like three big things as a founder and the person who's hiring who needs to, it's three areas to tackle. One is you need to understand your USP. You actually need to sit down and not just say, this is what we're building, but this is how we're building it.

15:23And therefore, you need to define what kind of person works incredibly well in my organization. And if I boil it down and then on in our portfolio, at Cursor, it's you don't go to meetings, you do things. And you have the autonomy to do things. So if something bothers you, you just build it yourself and off you go. So the autonomy and people who don't want to waste their time in meetings work incredibly well in the Cursor process. Anthropic doesn't have titles. You're all the same. There's no distinction. And there's engineers do research, do engineering projects. There's a boundary that that gets blended in a way.

15:55So you need to be comfortable with that. One of our portfolio companies, wonderful. If you go on their careers page, there's like a one sentence that distills everything they are looking for in a person that will work well. And I can tell you the majority of people will read this and go like, no, thank you. So instead of saying like, this is why you shouldn't work at Google or this is why you should not go to OpenAI, really think about like, why should you come to us and what is it? So that's the first thing. The second thing is the show don't tell. If you say that it's fast decision-making, it's not a lot of meetings, and then your process takes forever and there's new meetings being added on, the culture gets experienced in these fast processes, right?

16:32So you need to be really intentional about how you're doing that. And then to win, and this is going to get harder, is like you can't win on compensation. You have to win on Mishven. So you need to understand compensation and you need to understand the different levers and you need to be able to explain them, which includes explaining how does this role contribute to our outcome that I'm telling you about and why you should accept and be excited about the equity. But you also need to know for yourself what's the cliff for the vesting period so that you can make a decision on whether they're the right person or not before that cliff hits.

17:01You need to understand that sometimes giving them more equity and backdating it so that the vesting schedule isn't linear is great because it buys you a couple more years to figure out how much they really contribute. And most importantly, you need to understand what motivates the person because it might be totally different things. It might be your mission. We see the kind of people Anthropik attracts are different than the kind of people Open Eye attracts in many ways. And so you need to really talk about what is it that makes you unique in your building and how can they contribute to it. And you need to understand their motivators and relate everything back to that.

17:38And I'm certain that pensions probably isn't a word that you expected to hear at an AI focused event on scaling high performing teams. And what we see is that businesses are spending thousands, if not millions of pounds on their most expensive employee benefits, which is pensions. But it's driving no engagement and no interaction actually from that business. So that's why we built Penfold. Penfold is the modern-day workplace pension platform that actually drives incredibly high engagement rates with employees. We're now trusted by thousands of UK businesses and loads of names in the tech sector.

18:11The likes of Anthropik, Airwallex, Lendable, Deal, Cabson Tap, and Eleven Labs are using us because they know that we're the solution that will improve engagement with their workforce as they scale their business. And luckily, we're free to the employer, no cost, and you can switch to Penfold within a 30-minute phone call So very easy to do that. So what we want to do is we want to ensure that your money that you're spending on this benefit is working as hard as your employees are with a pension benefit that really works.

18:42I guess when it comes to moving at this sort of pace, I'd love to hear your thoughts on how you can avoid making expensive mistakes. Clearly, probation is one aspect. But I think balancing the rigor to make these really key hires, the speed that you need to, that's a really challenging thing to do. And often we've seen historically getting the wrong exec hire, for instance, can be terminal. So what are both your thoughts? I don't know which whoever would like to take this one first, but how do you balance those two things? So for me, quality and speed are actually not opposable forces. You can have incredible quality and go very fast.

19:15It's when it's unstructured speed that I think a lot of things break. So again, it comes back to being intentional. So really thinking about like, what do you want to do? So be disciplined in your process, know who's going to contribute, know how you're doing these kind of things. Front loading the calibration, I think is really important, rather than having a vast amount of CVs. I can't remember the number, but I think Syntesia and 11 Labs recently published how many people apply for each of the roles. It's insane. And you can't really filter through all of the AI applications anymore. and you don't really know.

19:43If you don't know what you're looking for in a person for a specific role, it's going to get a lot harder to sift through it. So if you front load the calibration and really go like, what do we need? What are the characteristics? What are the nice to haves versus the must haves? How are we going to make a decision, including who is giving us an opinion in the interview and who's giving us a decision-making criteria? That's important. Compressing the decision-making process, I would always try and get as fast as possible to the do we interview? and if yes, do we move them to the next stage and communicating that back because they are likely in many, many processes and they're getting approached by many people.

20:19And we were just chatting about this. I haven't seen it in Europe as much, but in Silicon Valley, people are getting job offers without ever meeting the company and being interviewed. They're landing in their inbox. We have scoured the market. We know exactly that it's you we need. We've already taken back channels and references. This is your salary. This is the role. Would you like to come to a conversation? Call me. And it works. It's very different. But it works with some, I would say, not with others. So you need to know that that's what's happening in the market. And then really separate what is recoverable versus what is irrecoverable.

20:50If you hire a very important key senior person in your team, somebody who will influence culture, who will influence the talent that comes, your product, you better make sure that you slow down. That is a really tough mistake to kind of unwind. It happens and it will happen. But that is a hard mistake to unwind. If it's a more junior role or if it's a role where you think like, hey, maybe there's three or four people, I don't quite know which one will figure it out. I think it is much easier to unwind the decision that you've made as well. A lot of things that, Sandra, you mentioned is true. I mean, I would love to say that we do this at Loveable very well, but probably not true.

21:27Other things that I think are very important right now, I know this sounds very kind of like typical, but spikes. I think we're very clear about hiring folks who spike like hardcore into a dimension. And we're almost willing to compromise on other things in their profile if they spike really hard into one dimension. So I'll give you an example. Like I think given the speed of execution being like so important, if you hire and find an engineer who can like ship faster than like most of the candidates that we talk to, it's very likely that we're going to make an offer to this candidate because they spike really into one dimension.

22:02Sandra mentioned reference and back channel. I think I know this is very controversial right now, but like we do so much back channel and we heavily rely on back channel. So much so that like there are some very strong indicators and alleviates the risk that you take. You can do like super thoughtful back channeling. And then in the age of AI, I know it's weird to say this, but human connections matter so much more. So actually we don't really do that many, like we do, but it's maybe not 100 % of the interviews are not the traditional interviews where you have like, hey, cross-functional interview or like, hey, like, how is this product interview or what is your coding skills?

22:40We bring a candidate on a Friday night. We fly them, family included. They spend a day. They do like what we call a work session. It's a VIP experience where you come, you stay at a hotel in Stockholm, and then we basically spend a whole day or sometimes two days working with you. I think this brings a lot better signal. And the chances of closing this candidate are actually much higher than like doing the typical interviews where they go through like one or two or three or four interviews that are like super defined in the box yeah creating the time for that deeper connection you want both sides to be as bought in you want both sides to have the same level of conviction if you don't take the time to do that at a deeper level you're opening the chance for them to go elsewhere i think and i love that i guess we've got an audience here of a number of founders a number of people in operator roles we'll have founders watching this back we always like to talk about the harder parts of the job the mistakes that can get made and how we can try and solve for those so what are the common mistakes you've seen over your career that founders can make when it comes to hiring and hiring decisions and what tips and advice can we give them that we haven't already i mean i can share a few i think doing one mistake that founders often do is they don't do a debrief post hire super important even if you make a good hire or Like if it's a successful hire, you should do a debrief always.

23:57I mean, the more senior, that matters so much more. You always can get better. You always learn a lot. So that's really good. I think being decisive, being an operator and then working for a founder, one thing that you expect from a founder is to be decisive. Whether you're going to agree with them or not, one mistake that you can do is not knowing or not making a decision. Like I think we expect the founder to be super decisive, especially towards the end of a process so that we can move forward. And then if we make a mistake, we live and we learn. But I think these are like two common mistakes that I see when it comes to hiring is not debriefing and then not also be decisive enough.

24:35I totally agree. I have so many more. By the way, I include myself in that. I have made pretty much any mistake you can make on hiring. So even when you know things theoretically, the practice of it, I think it's more an art than a science. I alluded to it a little bit earlier, the confounding scaling and linear growth, that is gone. And I think Traditionally, on average, 200k AR per employee was like the metric that people were kind of going in. That's how they had their forecast and their budget and their headcount. Mid-journey, 11 people to 200 million AR. So that's 18 million per employee.