Hugo Scott-Gall: Today, I'm delighted to welcome to the show our first Forbes 30 Under 30 Europe honoree, Mariam Ahmed, co-founder and CTO of Menza, a Y Combinator-backed AI platform. She's joining us from London to talk about how AI is changing the way organizations make decisions in a world full of messy, fragmented data.
Before founding Menza, Mariam worked at one of the earliest commercial large language model companies and began her career in equity derivatives at Goldman Sachs, giving her a unique perspective at the intersection of finance, technology, and AI. Mariam, welcome to the show.
Mariam Ahmed: Thank you so much, Hugo. It's a pleasure and an honor to be here with you.
Hugo Scott-Gall: So, you left Goldman Sachs two and a half years ago, and you're pretty young and, obviously, a hotshot. But to leave Goldman to move into the startup world, never mind, and we'll talk about this, why you saw an opportunity for the company you founded, but it takes some guts. It takes a bit of chutzpah to leave so early in your career. What did you sort of see in yourself to make yourself think, "Hold on, I can do this?"
Mariam Ahmed: Yeah, that's a great question. Honestly, I still don't know how I was able to get myself to make that decision. I'm an incredibly risk-averse person in general. So, I still can't believe I have a startup even two and a half years into this and that I'm doing what I'm doing every day.
And also, I loved my time at Goldman. I want to start by saying that I genuinely learned so much. It was a brilliant place to start my career. I had amazing colleagues. I was working on hard problems. I loved my clients as well, working in equity derivatives. I had so many great mentors and teachers and just even by osmosis starting my career in a place like that surrounded by so many brilliant people.
But I think it's also the place where I was able to discover my confidence in myself and my career and my ability to get things done because I honed that skill set through Goldman. It was a very demanding place. And so, it actually all started with a dinner that I had with an old colleague of mine.
He was the founder of the company that you mentioned, Shortly. It was one of the first commercial large language model companies. We met while I was in university. I was the kind of kid who I loved my classes, but I also wanted to work all the time and get my hands dirty.
He let me do that. We became really great friends. And while I was at Goldman, he was like, "Hey, let's go out for dinner." I had no idea what he wanted to discuss, but I could tell he wanted to talk about something. And he was like, "How's Goldman going? How are you finding things? That's great. I'm having a lovely time. Would you consider quitting?" Not something I was expecting to hear at that dinner. And he said to me, "I'm thinking of starting another company." And before, I worked as his founding engineer. This time he wanted me to be his business partner because he'd been a solo founder before but wanted to do something bigger and better. He had a great exit back in 2021, but he wanted to build a generational company and wanted me to be the person building it with him.
First of all, that vote of confidence from someone else meant so much. I never thought about being a founder. I loved supporting him. I loved learning about startups. I loved my role, getting to know companies and how they're built. But then, I have this gut instinct to say yes. I knew I could not say yes immediately. I had to play it cool. So, I said, "Let me think about it."
And then, I immediately got home and I called who I call my board, my parents, because I was 25 at the time and very close to my parents. And I said to them, "Hey, I know you're very proud of me." They're both immigrants. They immigrated to America and they were so proud of this job that I got at Goldman and they saw how hard I worked. And they worked very hard to put me in the situation that I could get this opportunity.
And so, I said, "I'm thinking of quitting my job and starting a company with Gossam. I don't actually know what we're working on yet, but what do you think?" And I was expecting them to say no so that I could go to Gossam and say, "Sorry, I can't do it." And I could always blame them in my head and if he goes on to have some big exit or some big company, I can say, "Someone else told me I can't do it."
And that's kind of the validation I needed that I felt like I couldn't do it. But my parents said, "You know what, Mariam, do it. You're 25. You are young. You don't have a mortgage. You don't have kids. You have no attachments to anything. You need to go to America and go start this company. You need to go to China. You need to go wherever you need to go, do the things you need to do. Goldman Sachs will always be there and you can go back." And I knew that because I had a great team who would support me and maybe I didn't know it, but I knew at least when I quit, I had to leave on good terms just in case things didn't work out. So, I very much rationalized it to myself and to have that support of my friends and my family.
I can't pretend that it was a decision that I made on my own and that I found this fire within myself. I have to give them credit for supporting me. And then, my ability to kind of rationalize, "Okay, worst case, I do this for three months, we crash and burn. It's like the cheapest and fastest MBA I could possibly get. So, why not go for it?"
Hugo Scott-Gall: Got it, got it. So, I understand how the opportunity was presented to you. I understand why you jumped. I think jump's probably the right word. So, let's talk about Menza. I don't want to do the standard, "What's the problem you're trying to solve? What's the pain point you're trying to eliminate?" But at the same time, to take an idea and turn it into a company that then generates revenue, you need to have conviction in something. So, what is it you believed in?
Mariam Ahmed: Yeah, I mean, so initially, the jump was inspired by my belief in myself and my co-founder. And I knew that we could solve problems because we had this experience building this company before, Shortly.
So, it was one of the first commercial large language model companies. And we leveraged LLMs to enable people to write stories using AI. So, this was pre-Chat GPT, 2019, 2020. And it was a very crazy idea. We started working on it in 2018. It was really my co-founder who was like, "What if we could get AI to write stories?" And I said, "I'm a computer scientist. I can tell you it's not possible." The confidence that young people have and because I've learned things in the classroom and I thought I knew, but he was like, "Let's go for it." And we did. And to see what that turned into and the way it took off, because we really ended up going to the customer. We were working with consumers at the time and understood their pain point and to see the magic moment that we created for them. And honestly, I just wanted to chase that.
So, I knew we were able to create that magic moment, and I wanted to see that happen. So, we took a step back and we said, "What are challenges that we had in building that company? What are challenges that I had while I was at Goldman Sachs? What were things that were pervasive across both and that we could speak to and resonate with so that when we go to customers, we kind of know roughly where we're thinking of building?" And the big thing was when we were selling the company in 2021 and while I was at Goldman, a big challenge was making better data-driven decisions because data in both places was incredibly fragmented. And there was a skillset gap. Even though I'm a computer scientist, I don't know exactly what I should be looking for. I can execute once I'm told, but I don't know where to start. And then at Goldman, I was sitting in front office. So, I didn't have access to the data tools or the data itself in the same way that I would if I were an engineer, but I had the engineering skillset.
And so, that was a thread that we found. And we just started pulling and pulling at it. And I talked to colleagues at Goldman. I talked to friends. I talked to people. We did Y Combinator, which is an accelerator based in California, invested in Airbnb and Coinbase and Twitch.
And the founder of Twitch was our main partner. And he gave us a challenge, actually. We had this goal of let's talk to 20 people this week and understand their pain points and if they resonate with this pain point. He said, "Make it 100." So, I literally did 100 customer calls in a week. And this is reaching out to, "Hi, I know we haven't spoken since high school, but I see that you have this job. What are you doing with data? How do you make data driven decisions? Tell me about that." And so, through those conversations, and apologies, Hugo, I'm going on and on, but I promise there's a point here, through those decisions and those discussions, we were able to figure out this pain point of not knowing what to do with your data, where your data is, what to even ask in the first place, what to pull on was pervasive across many industries.
It wasn't just a thing that happened at our startup and happened at Goldman which again are two very different experiences. Everywhere in between was dealing with this problem.
So, because we knew the problem was real and we knew that we were the kind of people who can get obsessed with a problem and go head in, that gave me the conviction that we could solve for this. And the past two and a half years, so much has changed. AI has gone in ways that none of us could have predicted. I hope you don't have a question of what I think AI will be in five years or two years, or even next month because I just refuse to make any guesses these days. But we've been able to survive for so long because of that obsession that we have. And the question might change, the problem might change, but we can always find a solution in the customer.
Hugo Scott-Gall: So, the core of Menza, and tell me if I've got it wrong, is that you are able to take disparate pieces of data and generate insights from them?
Mariam Ahmed: Yes.
Hugo Scott-Gall: So, I've got two questions on that. One is, without the current iteration of AI, or the real shifting capability we've seen in recent years, could you do that? As in, could you exist without current AI capability? And the second is, is it a skill deficit that your customers can't do it for themselves? Or is it a skill deficit, a knowledge deficit, or is it just the sort of capability deficit, and then just not either able to bring things together or don't even know how to do it? So, are you telling them, "You can generate a ton of insights just by using these data sets?" And they said, "Oh, we knew that, we just didn't know how to do it?" Or do they say, "We hadn't even thought of doing that?"
Mariam Ahmed: Yeah, great question. I'm actually going to answer your second question first because it's relevant to the first question. So, I think it really is, to a certain extent, all of the above. It's a skills issue, it's a knowledge issue, it's a capabilities issue. And the questions we get from our customers are both, "I have this idea of what I want, I don't know how to execute it," or "I don't even know where to start." And the big challenge is with data, first of all, it is incredibly fragmented. And so, even in a large organization, you're really only skimming the surface because data can sit in all different kinds of places, especially when you're working with consumers.
So, we do work with, generally, consumer brands, so people who sell directly to consumers. And there's so much data in the first place. It can come in so many different forms. And then, on top of that, it's sitting in so many different systems. So, that's an initial problem. And then, it's what do you do with that data? And the way I frame it is you could hire an employee to dedicate their whole job to working on this problem but they would not be able to sleep. And they would have to have nothing else to do because they have to spend all day, every day sifting through the data. With AI, we're able to do that. And this is something that we would have not been able to do before. The reasoning capabilities of AI, and the ability of AI to systematically go through data and to identify significance is incredibly important. And this is something that just humans can't do on our own.
But there's still an element of humans still being very important and relevant. So, the AI can identify based on context, based on the integrations of the data it has in front of it, revenue opportunities, cost leaks, trends, anomalies, etc. But it's still at the point where humans can identify the significance of that and act on the back of it. And we tell our customers, ultimately, what we're doing with our tool, we're already using AI to perform actions off the back of the data. But what we tell our customers is, at the end of the day, they know what they want to do with their business, what their end goal is. And they have to feed that to the AI. And so, they're very critical to the AI working properly. But this otherwise would not have been possible. Even the simple task of making SQL and writing a query accessible to a user. I mean, that's been around for a while.
But to be honest, Hugo, when I was in university, and I had to study SQL, one of my least favorite classes, it was a database class, I found it so dreadfully dry. If I knew there was a way to use English language to query a database, I would have easily chosen that over learning SQL. And so, even that small application of AI does leaps and bounds. And now, we've gotten to a point where AI can reason and it can handle large amounts of context. That's huge.
Hugo Scott-Gall: And so, your customer base is mainly B2C type businesses. Is that right?
Mariam Ahmed: That's correct.
Hugo Scott-Gall: And so, what you're bringing to them is for very low cost, we can do all these things for you because we have a specialization in analytics from disparate data sources. I imagine for the majority of customers, as you're answering the previous question is, they just don't know how to do it all the time spent to do it would be very hard. So, you've got no problem scaling. And I assume also that your product is a lot better today than it was a month ago, six months ago. So, you're getting all the network effects from the more customers you have, the better you get.
Mariam Ahmed: Exactly.
Hugo Scott-Gall: When you meet a new potential customer, are you charging them on time? Are you charging them on a measurable value delivered? If we can uplift your sales by X percent, we should take a clip of that. How do you think about charging now? And would you think about that differently if your revenue base was 100X what it is currently?
Mariam Ahmed: Yeah, that's a great question. Pricing is honestly one of the biggest challenges of starting a company that I've found because it's constantly changing. And it's hard to fully optimize for it. So, right now, we do usage-based pricing. So, we charge a platform subscription fee and included in that is usage. If they go above that usage on a monthly basis, we charge a little bit extra based on that usage. But we really only want to charge if our customers are using the product. As a builder, that's great because if they're using the product, it means it's giving them value. There are instances, as I learn more and more, of customers who get value out of the product that the pricing doesn't necessarily capture.
It's great as a story, and especially as a startup. And we're a venture-backed startup, so I have bandwidth. It doesn't totally hurt me if I hear like, "Oh, they've had a 100X or 30X return on the product." But as we're going into the future, and I know we're not the only company that has this challenge, it's capturing that value-based pricing. How do you say, "Okay, one of our customers just last month was able to get $30,000 back in taxes from HMRC?" How do I capture that? I'm not going to make anything off the back of that. But if it was two percent even, that is a significant addition to our revenue. And that's just one customer, one example, one insight. So, there are more and more things.
We've had customers who've gotten hundreds of thousands in additional revenue, thanks to Menza. It's a great story, and I'm happy to hear those stories. But it's something that's already churning in my head of how do we capture that. One thing, like I mentioned, is if you have the AI take an action, you can help attribute that revenue to that action. And so, it's Menza directly doing it. Right now, I don't actually know how much money someone's making unless they tell me, based off of the Menza action.
Hugo Scott-Gall: And would you say the skill you have is bringing together disparate data sources, or is it the analytics on top? You're probably going to tell me it's both, but do you still have a reason to exist if bringing together disparate data sources becomes standard practice and there's an agent from one of the big, whoever it is, whether it's a software CRM or something, if that is standardized, is the analytics on top or is it the whole system together?
Mariam Ahmed: Yeah, it is the whole system together. I think it's a question we think about all the time. Especially Claude, for example, has had a lot of major developments in the past couple of months. One thing is the integrations that we've built out. We have over 650 integrations and we're constantly building new ones.
We have a massive library and these include things that don't even have an API. So, data sources that otherwise would have been inaccessible. And that's kind of our bread and butter is kind of looking for the data that no one is touching because that data is still incredibly valuable and for some of our customers can be make or break, but they're not able to capture it otherwise. On the analytics side, they work together. We do have some customers who only use one data source and they still get a lot of value from the analytics itself. And it's not just, oh, we're building dashboards and reports. It's having the AI really sit in there and find things that they'd otherwise be missing. That's kind of the critical component here for a lot of them. And it's, honestly, just making AI accessible as well.
We spent a lot of time on our user experience and that's where I see, historically, we've seen a lot of the winners. The reason people love Apple and put up with their product, even though they've increasingly gotten so much worse, and as a consumer, I'm incredibly frustrated with my Apple products, but embarrassingly, I bought a new Apple product last week because the design is amazing. The user experience is amazing. And the challenge with AI is right now, a lot of people want to use AI, but they don't even know where to start. There are so many things AI can do, but it feels so inaccessible. I get questions from people all the time. They're like, "Oh, can I use AI to do this?" And I'm like, "Absolutely." But it's because I spend all day every day digging into AI applications. It's not necessarily apparent. And what we try to do with our product is really take that work away from the users. They can almost be hands-free. The AI tells them what it can do and how it can help them drive their company. At least that's what I see the future being for the way we use AI because the way we got introduced to it, most people, is through ChatGPT.
And you had to type in your answer, and you're very involved, and you have this back and forth. I see AI being, it runs in the background and is doing all this work for you. It's doing all this thinking for you and can enhance whatever you're doing, whether it's in work or also in your day-to-day life.
Hugo Scott-Gall: And so, I think one of the things you've said is the companies use less than two percent of their data. I don't know what the end game is. I mean, you assume that at zero marginal cost, you control all your data. I asked you what could be replaced by agents outside of your mini ecosystem. But if companies are using less than two percent of their data, we're a long way, right? This is still very, very early, isn't it? And so, bigger picture, you must be a believer in the productivity surge that's likely to come from better organization of data, plus potentially new data, plus much better analytics on top that runs 24/7 without human intervention. I've kind of got myself into the kind of is AI overhyped? I'm not going to ask you that question because it's sort of too broad of a slightly silly question. But from where you sit, there are still tremendous inefficiencies. Is that fair?
Mariam Ahmed: Yeah, 100% because I think, even if we know from a research perspective that AI has come a long way, are people using it in their day-to-day? And for example, there are a lot of really big AI legal tech companies or fintech companies. And I have friends who are lawyers or work in finance and their firms I know pay for these tools. You see the headlines. And I ask them, "Are you actually using these tools?" And often, they'll say either, "I didn't even know we had access to that," or, "Not really, I don't find it super helpful." So, there is still a gap in how we actually use these tools. I think things like I've seen more and more people using Claude these days, and that's really exciting. So, that's one element, right, is the actual implementation. Are people really leveraging these tools?
But I also think on the other side, there's still so much in terms of how AI can develop and think. And these models are still getting better. We haven't really gotten to that plateau yet of the models are not improving. And I guess some people would argue AGI, that we haven't fully gotten there yet. But I think even just the way we think about it, in my view, AI is a tool. I really try not to anthropomorphize AI. I know a lot of people do in their day-to-day, or they see it as AI employees, and that's an area we've gotten into. But I do think it's a tool. And even if we're using it to do workflows in the background, and it's acting without our involvement, it's still a tool. And so, we, I would hope as humans, will get better at figuring out how to use those tools. And that's where I think it's really exciting. And yeah, even just how we live our lives, I think will change quite significantly.
Hugo Scott-Gall: For sure. How much do you worry about access to compute power for you as a business right now? It depends who you talk to, but clearly demand is exceeding supply in the whole semi-supply chain and data centers as well. So, is that bringing up your operating costs? I imagine that is a significant operating cost. And, therefore, is it something you think it just is what it is? Do you worry about it? Do you think it could slow down the pace of innovation?
Mariam Ahmed: Yeah, that's a great question. It's something I think about a lot because, as I mentioned, we're a venture-backed company. And I think the role with venture is just don't worry about costs throw everything in growth. But at the end of the day, I am an immigrant daughter of a South Asian family. So, I know how to make use of things as much as possible. And I've learned to be very spend thrifty and try to be as efficient as possible. And that's something that we've applied to our company since Day 1 is we try to use compute and, honestly, even AI as sparingly as possible. So, only when it really leads to it's necessary, or at least to a specific outcome because there's a lot of other technology that we use within our platform and that I think people could be using beyond just large language models because there's, obviously, a cost from a company perspective.
There's an environmental cost that honestly, I don't think startups talk about enough. And I don't think we're the biggest culprit. But the hope is that we become the big companies one day. And so, it's something we need to think about. But yeah, just from a price perspective, I think companies need to be really aware of how they can be using AI more efficiently, how they can be using compute more efficiently. We've put in systems in place. I know, for example, we've had significant cost reductions on our side from our compute perspective because we sat down and made that a project of we need to figure out how to use less compute. And I think it's something that a lot of companies are going to start thinking about. And I see real winners being people who have already thought about it beforehand and aren't stuck scrambling to figure out how they can cut down costs.
Yeah, I mean, that's where we're at. I don't know what will happen or if we're doing it in the best way possible. But it's something we think about day to day quite often.
Hugo Scott-Gall: So, can I ask you a big picture and probably unfair question, which is year to date, or never mind year to date, if you include last year as well, you've seen public market software stocks getting hammered, which is the technical term for going down a lot. You're a disruptor. People say software is dead. I think that's an incorrect statement. It's just old software might be dead, but new software certainly isn't dead. And, again, we don't have to go into individual companies, but do you, as a disruptor, as an innovator, as someone who is generating revenue, which must be coming from somewhere, do you think that's right that actually a lot of legacy software, unless it fails to adapt, does face existential problems and probably deservedly so because the product is inferior versus new products coming along? That's how it's supposed to work. That's what Clay Christensen told us happens. Again, I'm not asking you to sort of take a view on levels of current share prices, but do you view this classic creative disruption cycle that seems to be happening software? Is that how you see it?
Mariam Ahmed: That's a great question. You said it was an unfair question, but I actually think it's a very fair question.
Hugo Scott-Gall: Okay. I don't want to make you responsible for forecasting.
Mariam Ahmed: No, no, you're all good. Actually, we went into what my company does kind of in the day-to-day, but there's a separate product that we have that we've only started developing and kind of selling in the past four months, so since January. We have an enterprise product and we actually sell our analytics and insights engine that sits below our system that we sell to our brands to large enterprises. And I actually spend a lot of time talking to large enterprises, legacy software companies in particular, to understand what they're nervous about and what they're thinking about. And, obviously, we've seen from a public perspective, the hurt that they've taken, but it's been interesting to have those conversations with leadership and understand what are they thinking about. I think a lot of them do know they have to adapt.
And they've seen, even from their perspective, customers come to them and say, "Hey, I can build this myself with Claude code. I don't need you." And companies that people have come to rely on and kind of the Apple analogy, but there is a lot of software that is fundamentally very bad. And as a user, as a consumer, but as someone who's also technical, I look at these products and I'm like, "Why am I using this? I could build something so much better," but they've had such a stronghold and it's been so hard to build something like that. And now, it's way more accessible. We've even replaced some of the tools that we use internally with our own kind of bespoke Claude coded with a little bit of our own kind of fine tuning because it's so much easier to do that now. And why do I need to pay a high subscription cost for it? The companies that I talk to, they want to implement AI or build some sort of differentiation.
They think about defensibility. And I think that is important. Do I know if that's going to work out or not? My hope is yes. And my hope is that we can help them achieve that. But I don't think I'm the only one who would say, "If you just stay there and don't do anything and kind of hope it's going to go away," AI is not going anywhere. And this attitude that people are building of, I can do it myself, or I can think of an alternative, or they're going to be these challengers who now come in and are able to build something at the level of these large legacy software companies but better and a more price efficient way, whatever that is, that's not going to go away either. So, people can't put their heads in the sand. I know it's very hard for these big companies as well to make big change, to really revamp what they're doing. Right now as a startup, if something comes in and it challenges what we do, I can go tomorrow and say, "Hey, guys, my team all sits in one room. Hey, guys, we need to 180 do something else." And we'll do that.
And I won't name any companies. But there are a lot of companies where they absolutely cannot do that. And so, I think it's going to be an interesting challenge to see how that goes.
Hugo Scott-Gall: I think it's that this keeps happening in cycles, usually around innovation. Innovation doesn't have to come from necessary technology. It can just be a new way of doing something. And I'm interested in your view on this, whether the way to respond as an incumbent is to set up a sort of skunk works in the basement and disrupt from within, whether it's just to keep buying external innovation, to freshen yourself up that way or whether it is to kind of just offset the revenue pressure by ripping out costs, which I think can keep people happy, keep your shareholders at least happy in the short term, but isn't necessarily increasing your total value.
Mariam Ahmed: Yeah, that's a great question. I think the way I think about it, and I loved your point, if it's not necessarily a technological change that causes a wave like this, and I do think there is a cultural change that is happening in the way we look at the value of a product and how we assess it and our buying decisions are now informed in a different way. Right now, it's just starting, but this is what I predict is going to be a bigger thing is even from a developer tool perspective as a CTO of my company, I'm looking at a tool. It's always been a build versus buy conversation. But now, there's a stronger narrative around building it myself because I actually feel like I can build a lot of these things myself. And so, obviously, you're right, it can keep shareholders happy to cut down costs.
I mean, this is how Madonna was still performing at Coachella last weekend because she's been able to keep around because she changes her image whenever she needs to. Companies need to take the Madonna method and pick up what they need to, to survive and to be a lasting company. And maybe they've been able to last for 30 years, 20 years, doing the same thing over and over again. But I think this is going to be their point of reckoning is trying to figure out how do you keep up? And do they build internally versus buy companies that are introducing these new capabilities? Maybe as a small business owner, I say, "Yeah, they should go ahead and buy cool tech companies." But I also think companies have an advantage.
There's no excuse anymore. There's so many great tools for building these days and to build quickly and to really try things out and get in front of customers quickly. So, there's really no excuse to not innovate anymore.
Hugo Scott-Gall: Your experience would suggest, and I think your experience to date in terms of your business, but what you learn back from customers or potential customers is the switching costs are maybe not as high as people suppose. High switching costs can change. Just because they were high 10 years ago, 5 years ago doesn't mean that they can't rapidly change. And I guess that's what you're finding. And that would be your ongoing assumption that as long as you keep developing better product services, you can persuade people to leave their existing supply. That's a straight one for one share gain. Obviously, at times, you're just adding on incremental products and services that people don't currently have.
Mariam Ahmed: Yeah, I think it's that but it's also building a product that is incredibly sticky because you don't want the same thing to happen to you. And so, something that we try to do with Menza, especially on the context side, and the relationship with AI and large language models is really build a system that people spend a lot of time on and they dedicate a lot of time into but also that is self-improving so that the product gets better. And so, now if they want to switch, they're going to have to invest so much time. So, it's not about can we build it and is the technology advanced enough and offering something new? That is true. You know, we still need to build out that. Building something that is sticky enough that people are dedicating the time to it. They're willing to spend the time on it.
Some numbers that we're really proud of is our customers spend an average of 90% of working days on our platform. And we're a business tool. So, we do have people using us on the weekends. But the fact that people are using us almost every day when they're in the office is a big deal because then that makes them less likely to switch to something else. And we've actually had 100% brand retention over the past 12 months because I've even spoken to some customers who they're switching all of their tools to in house built tools with Claude. And I kind of want to kick them off the platform, to be completely honest with you, Hugo.
By the way, I know this is public, but I just feel like I'm going to share it all with you, Hugo, that we don't charge them enough. And I kind of want them to get off of our platform. But they refuse to leave because they've invested so much time and so much energy and Menza works exactly the way they need it to. And part of me is, obviously, I'd love to get paid more by that customer. And now we can because we have a compelling way to get them to do that. But it's also good to hear that we're not defeated by Claude yet, there is still value in our product. And so, whether it's Claude or it's another startup, we feel pretty confident that we can stand up to the test time, we just have to keep doing that.
Hugo Scott-Gall: Do you think you have a sense from your customers and what you see for the underlying strength of the economy? Do you think there are, at a meta level, insights you can take from what the data you're seeing in terms of because you're pretty consumer focused, consumer health, and I guess, changing habits? Do you have enough of a data set to sort of infer behavior insights?
Mariam Ahmed: I think we're not quite there yet, but I see that being in the future. And it's so funny because I do have my history in public markets. And I spent a lot of time with alternative data back when I was at Goldman. And I do sometimes say, "Hey, this could be really valuable to an investor, seeing some of the things that we see." Our customers, we actually give them access to a very anonymized version. Actually, this is inspired by my time at Goldman working with our prime brokerage team. So, we have an opt-in system where people can get access to anonymized kind of benchmark data to see how do my numbers compare to other people on the platform who have similar businesses because that's a big thing for our customers. They constantly are thinking about what are other people doing. And so, it's valuable enough to our customers.
Currently, I don't think there's a large enough data set. Even just geographically, we're not located in enough places where it would be significant enough. But the hope is to get us there soon. We're still growing. So, that is something that I've thought about in the back of my mind just because I did have the stint prior. And I can't erase that history. So, I'm always thinking from an investor perspective.
Hugo Scott-Gall: Of course, of course. So, as we sort of finish up here, I want to almost go back to where we started. We talked at the start about why you and Goldman founding a startup. But as you think about you and your friendship groups, your peers, is this going to be very normal, your generation is just going to be very, very innovative in terms of taking risks and starting up because what was scarce is now abundant, access to compute power, speed of networking, deeper private markets, depth of private markets, so capital raising. Is there a cultural shift that smart people in their 20s, for the foreseeable future can be much more likely to do what you're doing? Do you feel there's a cultural change that makes doing what you're doing, risk taking, more likely a more normal thing to do for smart young people?
Mariam Ahmed: I think that's a great question. For sure. And for two reasons, in my mind. It's the opportunity, like you mentioned, it's way more accessible. I know people who are starting companies, and they still have their daytime job. And one of my customers, for example, he was able to maintain his day job, while growing his company to a million in revenue before going on at full time because of the tools that are available out there. And that's an amazing story. And he's a young person like myself. And honestly, I had no idea he had a day job, until I actually got to chat with him. And it was cool to see that using tools like ours, for example, he can do so much. He was someone like me who had a very traditional job and wouldn't have seen himself as a founder. But now it's way more accessible. On the other side, I do see the necessity as well.
I come from a generation where we were very much sold the dream of study computer science and you will have a high paying tech job waiting for you, no problem. I feel very lucky that I graduated kind of at the perfect time where that was still true. And I think it was definitely when I graduated, I had a lot of great opportunities in front of me. But I have a younger brother who is only two and half years younger than me, and I've seen the shift in the opportunities that are available across different industries. It's not just computer scientists, it's also in finance. There are so many firms that are now reducing the number of juniors that they're hiring because of AI tools that are available. So, it's funny how AI kind of plays both sides here. It creates opportunities but also, a lot of really brilliant people cannot get jobs. And so, now they have this option to start their own company and to innovate and think in a different way. And I think that's not necessarily a bad thing. It's just a different way of working. I mean, we even saw the shift of my parents' generation, they would get jobs and stay in that job for 20, 30, 40 years. They have a career.
And my grandfather had the same job from when he graduated until the day that he passed away. But a lot of people my age, they get in a job, and they're there for two years and like, "Okay, well, it's time to move." You know, that's the norm now.
So, I think it is the next step. It's so interesting to see as well. I was born and raised in the United States. Being a founder was very much a path that seemed quite normal. Maybe it was still risky and out there a little bit. When I left my job at Goldman, I actually had people say, "I do not understand what you're doing," because there's less of a conversation about doing something like this. And people just knew fewer people. In the U.S., you have places like Y Combinator that have made it the norm. I know so many kids who they go into university, and they're like, "Yeah, I'm going to drop out to start a company." And that's pretty acceptable because they want to be a Mark Zuckerberg.
I think the cultural attitude in the UK hasn't necessarily lent itself to that attitude. But I'm really seeing that change over the past couple of years. I think it's exciting. But I think it's nice that kids can kind of think about themselves in a different way. I find it incredibly empowering to run my own business. I've learned so much and to have control over my day to day. And I now have the opportunity to really get into a problem with the customer, build it, and then see them enjoy that product, see the value of it, see it change their day-to-day lives. It's that magic that I referred to earlier. That is intoxicating. And I hope more and more people get to experience that.
Hugo Scott-Gall: Brilliant. Well, that is a great point to finish on. So, Mariam, thank you very much for coming on the show. Thank you for telling us all about Menza and what it's doing. It's been a privilege to talk to you. Thank you.
Mariam Ahmed: Thank you, Hugo. It's been great to chat.
Hugo Scott-Gall: Thank you for listening to today's episode of SuiteTalk. If you found this discussion inspiring and informative, be sure to subscribe to our podcast and leave us a review. Your feedback helps us bring you more engaging content and connect with top business leaders. Follow us on social media and visit our website for more episodes and content. Until next time, keep exploring, keep learning, and keep leading. This is Hugo Scott-Gall signing off from SuiteTalk.
This content is for informational and educational purposes only and is not intended as investment advice or a recommendation to buy or sell any security or to adopt any investment strategy. Investment advice and recommendations can be provided only after careful consideration of investors' objectives, guidelines, and restrictions.
As of the date of this recording, William Blair Investment Management did not hold positions in the company referenced in one or more of its investment strategies. References to specific companies are for illustrative purposes only and should not be construed as investment advice or a recommendation to buy or sell any security. The securities identified and described do not represent all of the securities purchased, sold, or recommended for client accounts. It should not be assumed that any investment in the company's referenced was or will be profitable.
The views and opinions expressed are those of the speakers as of the date of this recording and are subject to change without notice as economic and market conditions dictate. It may not reflect the views and opinions of other investment teams within William Blair Investment Management. Factual information has been obtained from sources we believe to be reliable, but its accuracy, completeness, or interpretation cannot be guaranteed.
Any discussion of particular topics is not meant to be comprehensive and may be subject to change. This material may include forecasts, estimates, outlooks, projections, and other forward-looking statements. Due to a variety of factors, actual events may differ significantly from those presented. Past performance is not indicative of future results. Investing involves risk, including the possible loss of principal. Any investment or strategy mentioned herein may be not suitable for every investor.
