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GP 40: PropTech’s Origin Story and What Next: with Faisal Butt of Pi Labs

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In this episode, Tom Larsson speaks with Faisal Butt, founder of Pi Labs, about the evolution of PropTech and real estate venture capital. Faisal reflects on his journey from a tech-driven background to pioneering investments in property technology, coining the term “PropTech.” He discusses Pi Labs’ progression across three funds, investing in startups ranging from SaaS to robotics and AI. Highlighting breakthroughs like AI converting architectural drawings and security drones, Faisal emphasises the need for clearer pilot-to-rollout processes in real estate innovation. He advocates for defined ROI and leadership trust to unlock scalable growth and drive industry-wide transformation. 

Speaker 1:

Hello and welcome to the Grow Places podcast, where we explore the virtuous circle of people, growth and place Brought to you by Grow Places and hosted by our founder, Tom Larson.

Speaker 2:

Faisal, thank you very much for joining me today.

Speaker 3:

How are you Likewise Delighted to be here. Great to see you again. Good, good, yeah, good to see you.

Speaker 2:

I'm going to jump in with a question straight away. So for you, real estate venture capital, why PyLabs? Why does that all come together?

Speaker 3:

for you. Actually, sometimes you have to look back at your own journey to understand why this makes so much sense. But just to go back to my childhood, I actually come from a real estate family. A lot of people don't know that, but my dad himself was a developer and a builder. So I kind of grew up listening to, you could say, real estate lingo around the dinner and breakfast table. So I was never interested in going into traditional real estate.

Speaker 3:

But I ended up in California for university and I worked in tech in Los Angeles and so I was just fascinated with tech, innovation and just everything you could do, how you could harness tech to improve business. And when I ended up here in London, I was working closely with James Caan, who was one of the dragons at the time on Dragon's Den, and we were developing a strategy about. It was post GFC around 2009. We were developing a strategy to back real estate entrepreneurs and at that point it all started to make sense to me. So I was in venture capital.

Speaker 3:

I found myself in venture capital following a master's at Oxford. We were looking at real estate as a sector. We were looking at new ideas within the space. I already kind of knew enough about real estate. Through my family roots, I was fascinated with tech, so I kind of just combined, I connected the dots and I combined these different segments of my experience my tech experience from California, my real estate roots and the work I was doing with James Kahn to find and back real estate entrepreneurs and I developed a whole new thesis that nobody was thinking about, which was around tech startups that are going to transform the built world, and became the first investor in the space yeah, yeah, fascinating.

Speaker 2:

Well, um, before we actually knew each other personally, I, I was aware of you exactly through that connection, through watching dragons, then james, and then there was a piece of on the two of you, I remember in the yeah, in the media at one time, and um, and then, yeah, it transpires that we, we connected with each other and um and so so how do you see that? Because you say you were the first in the space I don't think the terms contact and prop tech maybe were even kind of didn't exist well known then.

Speaker 2:

So so how do you see that landscape then? How has it matured, and where do you kind of see it going?

Speaker 3:

yeah, well, at the at the time just to set the context we're talking about around 2013 is when I first start to dabble and look at some of the early tech startups emerging within the space. At that point I had no fund and PyLabs had not been formed yet, so I had no choice but to part with some personal capital as an angel. So I wrote some of the first checks personally into some of the early PropTech startups. At that point the term had not been coined yet. I believe in some piece that I was either interviewed for or that was written about me, I used the term for the first time. It rolled off my tongue because at that point fintech was a big term. Fintech is now obviously a global category, but at that point London was very much seen as a hub for fintech and it was growing in the shadows of the financial sector over in the city, and Old Street was called the Silicon Roundabout, which in the term is not being used anymore. So at that point, with FinTech kind of being front of mind for a lot of people, I just said PropTech and it kind of clicked and more people started to pay attention and the sector has transformed a lot right since then. We've been doing this for now over 10 years and I often say to people there's been a couple of different tech waves.

Speaker 3:

So if I look at some of the early investments I made as an angel before even setting up PyLabs, a lot of what was exciting at the time was tech-enabled could say, brokers and agents, people trying to come up with online estate agents. So I backed one of the first online estate agents that arose in the UK called eMove. I then backed the first digital mortgage broker, a company called Trussell, which then eventually exited to a US company. And I also was the first investor in a company called Hubble, which was trying to create a digital commercial real estate broker. So brokers was where it was at back in the you know I could say, 2013-14, that first era. And then, when I launched PyLabs, fund One, we started to see big data and data analytics as a theme that was emerging. So we backed a company called LandTech. So this is a platform that a lot of real estate developers use to conduct due diligence on development opportunities. So Fund One kind of capitalized on the big data wave and captured it. Just because of the timing and the vintage of that fund, it was a 2015 vintage.

Speaker 3:

By the time our second fund came around, which was 2017, there were a lot of SaaS models emerging. Saas became a big thing across the world and we invested in quite a few software as a service companies where the customer was ultimately a real estate business, be it a developer or a construction company or real estate owner. And by the time we if you then fast forward a few years, when we launched our third fund, which is the fund we're currently investing out of Fund 3, there was a whole range of different themes that were emerging, because, as the tech sector became more sophisticated, you had SaaS startups continue to emerge. So we focused on and invested in quite a few SaaS businesses. But then, a couple of years ago, the metaverse became a thing that people were focusing on. Of course, facebook changed its name to Meta. That drew the attention of a lot of investors, so we made some VR and AR investments out of that fund as well.

Speaker 3:

Robotics has become more sophisticated over the last couple of years. We're back to a couple of robotics businesses. Of course, drone technology emerged. We've invested in some drone businesses. So the breadth of what we're investing now the range of what we're investing now has just become broader, much broader over the years than what we initially saw 10 years ago, which were kind of the first wave of digitalizing brokerage in some way, of the first wave of digitalizing brokerage in some way. And um, and once again, tech trends have changed again, you know post gpt. Uh, the world has changed dramatically and happy to share my thoughts on that as well yeah, yeah, yeah.

Speaker 2:

No, I'd love to get into that as well with you.

Speaker 2:

I don't think we can go a podcast without talking about ai and gpt. So we, we must do that, but but before we do, and you've kind of mapped out there. But something I'd observed or read a little bit about is that, as you say, the kind of tech adoption curve is the sort of the sort of low hanging fruit. Is is where the, the kind of energy tends to go first, and historically that's not been real estate, because the sector itself has been seen, as you know, quite big and multi-faceted and complicated. But then, within the sector, once people have dived in, as you say, this kind of sas models, the kind of software models, is the sort of low-hanging fruit within the sector, moving towards more, um, real world solutions, whether it's robotics or internet things or or other things like that. So, by the sounds of it, as you've moved through your funds, you're kind of you're kind of tackling more of the kind of meatier, complicated real world problems as it goes, how do you see that kind of evolving?

Speaker 3:

yeah. So the our positioning the market as an early stage fund is that we're backing what we say is cutting edge and next generation technology. We're not afraid to go in super early, before the business has um, a, you know, fully fine-tuned product, before we can go in, before the startup has any revenue. So, and we have an accelerator program that's set up for that. We've been running it for 10 years and through that program we we've backed very embryonic businesses in robotics and drones and IoT, and so, yes, we are trying to look at the opportunity set quite holistically.

Speaker 3:

I don't think real estate is just one thing. So, just within real estate, there's all the different food groups there's offices, there's retail, there's industrial and then there's the alternatives, like there's, like you know, medical real estate and so on, now data centers, and that's just the food groups within real estate. But if you take a look at the value chain, you have the investors, you have the investment managers, you have the you know the capital allocators, you have developers, you have construction firms, you have structural engineers, you have architecture firms and then you have, of course, infrastructure, which is a whole adjacent category of real estate, and it's starting to merge with real estate in many ways into a broader category people are calling real assets. So that's actually, even though we're a domain-focused and thematic investor, there's a lot to go after. And then, if you start looking at all the different technologies within it, even AI is just so nuanced.

Speaker 3:

Within AI there's computer vision, there's machine learning, there's now agentic AI, there's AI assistance, what people are calling AI co-pilots. We've backed a lot of those different types of AI startups. And then, if you look at the timeline of AI, there's pre-GPm native startups that are emerging, that really are harnessing the power of one of these, um, these new llm uh, the new llm infrastructure, which is a whole new wave of startup emerging. So there's actually a lot to go after and it's a lot more nuanced and complex than you know.

Speaker 2:

The term real estate or prop tech, which is overly simplifies it yeah, absolutely, and um, so within, within that, um, that world there's, obviously there's a lot of noise and you're trying to kind of pick your way through that. Um, and I know that the, the terminology around kind of like vision statements, problem statements, kind of is, is very kind of popular, isn it within startup decks and your accelerator program. I'm sure you're kind of like trying to define problems quite clearly. Although the landscape is changing and shifting, do you feel like the problems are changing and shifting since when you started to now, or do you feel like some of those big problems are kind of consistent and it's maybe just the ways that people are going about solving them?

Speaker 3:

I think they keep shifting. Like we have this ecosystem where on one side, we have our lps and our lps are usually a real estate owner, developer or construction firm, investment or investment manager and um and on the other side, we have the startups that are, you know, building the tech, that are trying to solve those problems. And what we're doing on an ongoing basis is just having discussions with these partners, these LPs of ours, and understanding their problem statements. And a construction firm will have different problem statements to, let's say, a developer, and a developer will have different problem statements to an investor, and so forth. So we're trying to collate all these problem statements and that's kind of what I would call top down, trying to come up with the investment themes that we should go after, based on what the customer is saying.

Speaker 3:

But then you can't ignore bottom up. The bottom up is basically looking at the 4000 business plans that land on our desks every year, at the 4,000 business plans that land on our desks every year, because that gives us a. You know, it puts our fingertips on the pulse of innovation, in the sense that we get inside the heads of the entrepreneurs and it's really important to think about what's on their minds, what are they building? They sometimes come at things completely laterally and from not having any baggage in terms of any experience with the industry. A lot of the founders are actually not from the industry and they come at it with completely lateral and creative perspectives that may be from other adjacent industries. So it's important to go bottom up and just look at the business plans.

Speaker 3:

And the other way we think about the themes is we have in-house research, where we conduct research and we write papers and we do the horizon scanning and market mapping and to give you examples of papers we've written, our next paper is on data centers and all the technology that surrounds data centers. In fact, we've recently announced our first investment within this space. It's a company called Fluix that's using AI to help data centers reduce their energy consumption, which is hugely topical. So what we're researching we're often investing in. So I often like to say that you know we research what we invest in and we also invest in what we research. So the two go hand in hand. And another paper we have coming up soon, in April, is all focused on this new wave of AI startup, the LLM-powered ones and all the use cases within the real estate sectors. We're going to try to unpiece that because we're very actively investing within that space. I think the research will just kind of complement and strengthen our investment thesis for AI.

Speaker 2:

Yeah, no. Well, I'll ask you about the LLM thesis in a second, but just before we do, I want to. What you were describing there reminded me of a question that I asked James Pellett on the podcast Digital Trees. Obviously, I know he works with you as well, and I asked him a question. I said if you ask the same question to a C-suite CEO in a REIT and to one of your Accelerator founders about, you know what are the problems does each of them face and what's the kind of opportunity, what answer do you think they would give?

Speaker 3:

and so I'm going to ask you the same question I think that, um, I think that you'd get very different answers. I think, uh the well, it depends on the type of founder actually. So there are certain founder teams where one founder is the commercial founder and he or she comes from the industry and they have faced this problem firsthand and they decided to quit their job and set up a startup to tackle it, and their co-founder is the technical founder who's building the tech. It's a classic CEO CTO duo. In those cases, I think you'll get the same answer. In those cases, I think you'll get the same answer.

Speaker 3:

You may not get the same answer because the C-suite real estate C-suite CEO is so high up in the organization that they kind of have an aerial view of what's happening, but they're not granular enough, right, enough, right, and the person that ends up being the founder of these startups is somewhere. It's probably like four runs down, and usually four runs down, and they're closer to the coalface and closer to the challenges and the problems that are being faced. So I'd say I would say you, you are going to get different answers the nature of the founder within the startups that are being faced. So I'd say I would say you, you are going to get different answers. The nature of the founder within the startups that we're seeing is usually somebody who is closer to the problem than a ceo of a c-suite real estate company would be yeah, interesting.

Speaker 2:

um, just for information, james's answer that was slightly different. He said if you asked them both, they would both say that the other isn't listening to me. Yeah, was his kind of nuanced answer on it.

Speaker 3:

Yeah, well listen, james got a lot of experience in interfacing between the real estate owner, decision makers and the startups that are trying to deploy and implement their technology within the real estate firms.

Speaker 2:

Yeah, so let's, so let's, for instance, let's, let's stay within the, the kind of the innovators headspace for the minute then, because that's something that you're, you know, uniquely positioned really to to think about and then, and then let's, maybe, let's maybe sort of broach this. You know, ai llm piece kind of what are what was that mindset, what's that enthusiasm, what's that vision kind of coming out? Obviously, everyone's got a different theme, but yeah, but you know, what's that energy directed towards when it comes to real estate and and technology at the moment for ai, yeah, around the AI piece and what it can do, what I'm finding is that 10 years ago and even five years ago, you had this wave of startups that emerged within this domain call it whatever you want to call it built world tech.

Speaker 3:

But this is pre-GPT and many of the startups have done well. Some haven't done so well. What we're seeing now in the last 12 to 18 months is that there's a whole new generation of startups that are emerging that are built on the new LLMs, the new AI infrastructure, and they're, because of the power of the technology that they're built on, they're gaining commercial traction a lot faster than the generation of startups that came before them. May unseat the incumbents from like five or 10 years ago, because those companies won't be fast enough to adopt LLMs and completely change their tech architecture and the infrastructure they're built on. And in terms of where we're seeing these types of startups, we're actually seeing, uh, a new llm native startup emerge for just about every use case within real estate. To give you an example, uh, we recently announced an investment in a company called genia, and genia is using um llms to turn architectural drawings into structural drawings, and and you obviously, with your own professional training, know that architectural drawings on their own take weeks to create and then you hand it over to a structural engineering firm and they take another couple of weeks and structural drawings are all about calculating the load and the weight and how much steel and how much concrete needs to go in to hold up the building. But it's quite algorithmic and the AI can actually do this for you. So if the AI can get accurate enough, such that the structural engineering phase of the overall works shrinks from three weeks down to a day, that's going to just save a lot of time, for you know new buildings to be built. So that's one use case. That's one that we've already invested.

Speaker 3:

Another use case that you know we're currently diligencing we haven't made an investment is on the investment acquisition side.

Speaker 3:

So when you're buying some real estate, you end up with a data room with all the docs that your analyst needs to evaluate in order for you to decide whether you're going to make the investment and make the bid for the investment team, which will go into the data room, conduct the diligence for you, give you the red flag report, come up with your dd questionnaire and then start writing your investment memo. You know it obviously saves a lot of time for real estate investment teams. Probably eventually, as the technology improves, becomes agentic, can probably replace an analyst, so we're seeing kind of use cases across the value chain, which is super exciting. I see this phase in tech history as like a whole new renaissance of new AI startups emerging to tackle just about every problem that exists, and we're very happy to be set up as an early stage fund because we're perfectly positioned to capture it. You know, a late stage fund can't touch this stuff because it's too embryonic and doesn't have the revenue metrics, whereas you know we can go and write a ground zero, which is super exciting.

Speaker 2:

Yeah, it's super exciting and you know I'm super excited about the whole space. I just think, as you say, is, this is the biggest thing you know since sliced bread, shall we say. It's definitely, you know, bigger than the internet in my view, probably electricity or something you know. It's seismic, what's going on at the moment and, um, with that, it's obviously going to going to be really exciting and a lot of disruption comes with that. But, um, so you know, most of the listeners to this at the moment, um, are what you would class as probably traditional built environment people.

Speaker 2:

Obviously, hopefully, after this we gain some more um listeners from startup space. But so, in that world that you just described, which I completely agree with, what is the what, if anything? Is that is the kind of value, the things to kind of hold on to, to focus on of the traditional companies, whether it's, you know, the in-person interactions or whatever you think that might be, because obviously people everyone's got to get on board with adopting AI or die fully believe in that. But what are those things which are going to kind of remain human in in this?

Speaker 3:

yeah, I think um, probably like commercial judgment will still be valued. However, as ai becomes more agentic and goes beyond just being a co-pilot, with a lot of data, you can probably formulate pretty sound commercial judgment as well. So I think that that then makes me think it's actually the relationship building the trust, the face-to-face interaction. Face-to-face interaction, feeling good about doing a deal with the other party, building partnerships, shaking hands, all that human interaction stuff, the stuff that requires EQ. That's the bit that's going to remain super important. But I think anything that's process-driven and mathematical and can be calculated I'm guessing as the AI gets more and more sophisticated, the AI will probably do better.

Speaker 2:

Yeah, yeah, no, no, I agree. And just maybe then in the real-world tech space, whether it's more the contact or robotics, internet things, those kinds of spaces, are there any kind of examples that really come to mind about products that are amazing?

Speaker 3:

Listen, we we backed. Robotics is interesting because robotics startups have have been around for a while. There's actually an article in the in sifted, which is FT's tech publication, I think from last year, talking about how Europe was particularly well positioned for robotics because of its manufacturing base. Germany, as an example, has a world-class manufacturing base, and then now, with all the AI researchers and AI graduates that we have in Europe, when you combine the two, robotics becomes more interesting, because there was like pre-GPT robotics and then there's post-GPT robotics and it's about to get a lot more sophisticated. So we have backed a couple of robotics businesses. We have an investment in a startup called Okibo, which has built a robot that can paint and plaster walls faster than humans without the health and safety issues, and can work 24-7. It's being piloted on a number of construction sites in the U? S and in Germany and, you know, once these pilots are are delivered successfully and um and and the product becomes more sophisticated, then I could see something like this being completely a disruptive technology.

Speaker 2:

Um yeah, I remember seeing that one when I was on your um, you know, mentorship program, exactly yeah, and the thing is, it takes years.

Speaker 3:

Sometimes. These are for these, for these startups, to get out of that early, uh, r&d phase, yeah, and to finally commercialize. And this, this has been through a long r&d phase and now you, they have a robot that's ready to commercialize and that's. That's super exciting to see, um, yeah. And then you know, we've done that in robotics, we've done a few others in drones. We recently backed a company called Sorare which is using drones and AI, and the AI I'm talking about is computer vision AI for security purposes. So large, you could say, warehouse facilities, ports and critical infrastructure have quite high security costs. You need to have a patrol on site and you need to have a control center where you're looking at what's happening on the estate. You have people patrolling regularly, but actually a lot of that cost can be curtailed through using, you know, military grade um security drones, um, so that's a company we've backed for, you know, critical infrastructure and warehouses awesome, awesome.

Speaker 2:

Um, well, fazel, I could talk to you about this stuff all day, but I'm just gonna sort of end with one question which is, forgive me, like a lot of my questions. They're actually quite big and quite broad, but you can try and put it down into a kind of pithy answer. Um, but if you were to think about some of the barriers to adoption, barriers to innovation within the real estate industry, and you were to then have maybe one call to action to to the industry to try and ease some of that and enable some of this innovation, what would you say?

Speaker 3:

I think innovation is a process and ultimately I'm not going to go through all the 12 steps, but ultimately you need to not be too risk-averse and understand that in order to embrace innovation, you need to embrace a bit of failure. So have a piloting program where you can scan a bunch of startups and then decide which one or two you'd like to pilot with. Establish some clear KPIs as to what success looks like for a successful pilot. Define a timeframe for that. Once you're able to, you give that startup the chance to deliver on those KPIs. You've got a clear kind of rollout plan from there. So nobody's expecting a full portfolio wide rollout, but maybe it starts with a pilot on a building and the rollout across five and five goes to 10. If further KPIs are met and then maybe 10 goes to 50.

Speaker 3:

I think a lot of startups in this space are currently stuck at that early piloting Because either the budgets aren't there or the processes aren't there, or they haven't yet proven the the use case in the ROI.

Speaker 3:

So I think defining ROI up front, having a really good process for turning pilots into rollouts, is needed across the industry. So the technology is there, we're seeing this technology, we're investing in it. And now it's really about and a lot of pilots are also happening it's about now pushing further, and you know what I'm advocating is let's define what success looks like and, if the ROI is delivered, let's push the button on the rollout curve that you want to see in a high growth startup. You then get, of course, the benefit to the real estate owner who's deploying it, and the VCs benefit because you've got a, you've got a winner in the portfolio, and then more capital pours in through our LPs into us VCs, which flows through into the startups. So it's a virtuous cycle really, where things get clogged up and are stuck is taking the pilots through to the rollouts. That's what's going to drive revenue growth, and then that pretty much revenue growth is kind of uh, the trigger for the entire virtuous cycle to then flow, yeah, interesting.

Speaker 2:

And back to the point about the humanity in it, that that kind of brings it back to leadership, trust and kind of openness. Doesn't it between parties to kind of allow some of this stuff to happen and see the benefits? And um, yeah, thank you for your time, fazel. Yeah, it's been awesome. No, delighted to be here.

Speaker 1:

Thank you thanks thank you for listening to the grow places podcast. For more, visit growplacescom and follow us at. We Grow Places across all social channels. See you next time.