QuantumLight applies Revolut lessons as a venture firm: Dora Zikouli, Investor at Quantumlight VC

0:00Nik Storonsky:Hello and welcome back to the Scaling Europe show. I'm Seb Johnson. Thank you for following along. Today we've got a great conversation from our time at Slush and the conversation is supported by Databricks, whose unified platform power source on everyone's FY2026 target, becoming a truly AI-driven company, and Harmonic. Harmonic is the startup discovery engine used by leading VCs across the world to find their fun returners.

0:22Dora Zikouli:Check them both out. Thank you.

0:24Nik Storonsky:Hello, welcome back. I'm here with Dora from Quantum Light. Quantum Light is one of the most interesting and innovative and different kind of VCs that we've seen, kind of found by Nick from Revolut. So, thank you for joining me. Thank you for having me. Can you give a quick introduction into Quantum Light? You know, like, what is it? Why was it set up? And how is it different to traditional VCs?

0:45Dora Zikouli:Yeah, so, I mean, Quantum Light is the first AI-driven VC, basically, right? So, the background is it was founded a couple of years ago by Nick Storansky, who's the founder of Revolut, with the idea of just building something new and creating a new VC product for both investors and founders. And the way that we do that is we're completely AR driven in our decision making. So we have our own proprietary models that make all investment, sourcing and selection for us. And so we follow basically what the data tells us.

1:15Nik Storonsky:And what data do you look at, do you capture to kind of feed into those models?

1:19Dora Zikouli:So it's all publicly available information. Our model looks at all different data sources that we've put together. We have a big in-house tech team that is working around the clock on improving this and incorporating new data. But it's basically things that an investor would look through their desktop research. It's things around the talent of the company, things around the founder, the competition, the history of fundraising and their investors. but really putting a systematic lens on top of it and making sure it like us leaving the machine to pick up those patterns over a human. And it's generally sort of like not late stage but sort of like breakout stage.

1:58Dora Zikouli:So we focus at Series B+.

2:00Nik Storonsky:Series B +, yeah.

2:02Dora Zikouli:We do pretty much anything tech. Hardware is the one thing that we do. We invest globally. We have portfolio companies in the US and Europe. And really the thing that we're looking for is a good signal from our model. And that's what we do.

2:15Nik Storonsky:And what is the output of the model? Is it like a percentage? Is it like a thumbs up, invest? What does that look like?

2:23Dora Zikouli:So it's a scoring model, right? So it creates a ranking of the companies that it sees out there. So it ranks through thousands of companies. And we focus at the top of that list. And that's what we go with.

2:35Nik Storonsky:So it's constantly ranking, scoring, all of the kind of startups out there at the moment.

2:40Dora Zikouli:any company that is within our scope, it will rank it and it will be assessed by our model based on all the data that is out there.

2:48Nik Storonsky:So I think what's interesting is that this is not like a classic, you know, a startup goes out to raise, then the VCs do their due diligence. This is you, you know, as a firm being like, here's our top 10. Does that give you a really unique advantage, especially when it comes to things like preemptions?

3:03Dora Zikouli:I mean, we go ahead with very high conviction, right? When we speak to a company, we know that we're very interested in them. and so like this gives founders also very different experience we're able to turn around offers much much quicker than the average VC would we don't need to you know take the like multiple weeks and do like doing developing a long thesis around something in order to be able to to go ahead it's it's really just a different process overall so definitely I would say so

3:29Nik Storonsky:you're able to like get there earlier move quicker and from your perspective as an investor how different is your job do you think to an investor at a traditional firm where it is all about finding the startups and building the relationships and then doing the due diligence

3:44Dora Zikouli:you've got this model to do so much for you i think we're like supercharged in a way by our models um it gives us the opportunity to look at so many different things and like things that are under the radar and like be there first as you said i think our role still is about making the connections and making sure that we are also adding value to the founder and like making sure that whatever investment we make, there's something we can bring to the table. So far, AI has not replaced that part. So that's still something that we're mostly focusing on.

4:15Nik Storonsky:And what do you see that value as being from Quantum Light?

4:17Dora Zikouli:I think a lot of our value stems from our DNA and our relation to Revolut. Like the systems that Nick used at Revolut and came up with at Revolut and implemented and the learnings throughout the year, that's something that we're trying to bring into our portfolio companies as well.

4:35Nik Storonsky:And you've written some of the playbooks, right? Yes. Some of the things that Revolut and Nick did phenomenally well, you've documented, you've captured. Can you talk a bit about some of those?

4:45Dora Zikouli:Yeah, so I mean, I think throughout Revolut, what has been really the common denominator is a very systematic approach of doing things. And so what we did was really sit down with Nick and make sure that we capture some of that systems into playbooks that everyone can use and startups can draw inspiration from. So we have actually a very large library of things that we offer to our portfolio companies, but the two ones that we've published and are available for everyone are around performance management and hiring, which are basically, I think, two of the core things that you need to focus when you're scaling.

5:18Nik Storonsky:And these are sort of, I think I read that these are based on, like, interviews that you conducted with Nick, is that right? Exactly, yeah. So this is direct from Nick, you know, the lessons that he learned implementing the policies that he implemented, scaling and growing Revolut.

5:30Dora Zikouli:Exactly.

5:30Nik Storonsky:On both of those points, performance management and hiring, can you give one or two what to do, what not to do tips for companies who are looking to achieve the success that Revolut has achieved?

5:42Dora Zikouli:I was talking about performance management. I think it's a core thing that often is not top of mind for people. I think the top things that you need to keep in mind for that is it needs to be a CEO priority. It needs to be something that is directly handled by the CEO's team, not something that is stuck under HR, because it's really about the core capabilities of your organization. The second thing is it needs to be systematic. You have a system, you have a framework, people know what to expect when they're assessed, people know how to improve, people know how their compensation is affected or how their career path is affected.

6:15Dora Zikouli:And then the third thing is make it as tangible as possible. The way you assess performance should not be based on someone's own criteria or it's not something that's based on vague ideas of what good performance looks like or not. Find these observable behaviors within your organization that is what you want everyone to strive to and make them into a code of performance and a framework that everyone can easily go through.

6:44Nik Storonsky:And what about some things definitely not to do? Is there anything where, you know, more like common practices that are often detrimental to the company?

6:54Dora Zikouli:I mean, I think around performance, what we've seen, like, not work that well is when, first of all, when it's something that's handled by HR, it becomes more of like a sort of admin task that you need to get through, like, once every six months, because it's like a, it's a sort of requirement as you scale. That's, I think, for me, something not to do.

7:12Nik Storonsky:Yeah.

7:13Dora Zikouli:And then I think also that's something that hasn't worked that well, which also we've seen in our portfolio companies. And as you scale, it's 360 degree reviews. So that's something that has not been generally very successful. So instead, our system, what we recommend is like doing one-on-one reviews with the direct report on the manager and like doing also like a self-assessment based on the framework.

7:38Nik Storonsky:And I went to the Revlu kind of people talk this morning, which was great. One of the things that stood out was that Nick has got 40 reports. Was it 20 or 40?

7:49Dora Zikouli:Many, definitely.

7:50Nik Storonsky:It was loads. And I was like, that seems so much to manage and so hard to handle. Do you think that should be the role of the CEO, is to stay as close to each department as possible, each department head, and be the person who is reviewing their performance and being really in the details?

8:08Dora Zikouli:I mean, I think it depends a lot on the type of the CEO, right? I don't think there's a right way or a wrong way. I think as long as you have the right systems in place to support that approach, that's the most important thing. For sure, being on top of things and in our playbooks, we're saying making sure the CEO is on top of things is very important. Having a CEO office is very important as well because you need the right infrastructure and you to be able to do that.

8:35Nik Storonsky:Amazing. I want to get back into Quantum Light. Yes. You must have such an amazing resource of data points. You must be able to track and see the performance of so many startups from around the world and around Jura. Are there any trends or changes that you've seen in the last year or so, a couple of years as we've seen this wave of AI companies appear?

8:57Dora Zikouli:Yeah. I think what we're trying to do is, in a sense, go beyond that. There's always areas that, of course, come up and we see many companies of different types of things over time. But I think what our models allows us is to really capture the core patterns that make a company, that can pick out a company themselves. So there are certain things in the way that they grow or in the certain background of the team or the founders that could be good indicators of success and it doesn't matter if you're building an AI company, it doesn't matter if you're building a cyber security company. I won't say there are any particular trends but rather I think core

9:35Nik Storonsky:characteristics of and what are those things that you you or the model applies particular weight to

9:43Dora Zikouli:many things like we've uh we've included a lot of different features the the the score is a composite of dozens of different features right now um so there are certain things that we carry like a lot of weight in different situations like whether it is the background of the founder whether it is the other investors whether it is like the um the valuation dynamics of a company which is also quite important.

10:06Nik Storonsky:Really interesting. And is the model then also based on the best in class of what's come before? So maybe you're looking at not just companies that you would be interested in investing in, but looking at somebody like Meta or Google. Of course. Or like, you know, these companies, what enabled them to be successful? What was the profile of the founders? And trying to find that almost like the perfect...

10:28Dora Zikouli:Of course. I mean, the model is trained on historical data and looking at what the great companies in the past have been like. So from the point that we're able to retrieve data, we're incorporated into the model.