Biotech Bytes: Conversations with Biotechnology / Pharmaceutical IT Leaders

How AI Is Revolutionizing Biotech & Pharma Research With Liran Belenzon

• Steve Swan • Episode 30

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The Future of AI in Pharma & Biotech Research #aiinbiotech #pharmaresearch #drugdiscovery 

AI is fundamentally changing the way biotech and pharmaceutical research happens. From analyzing vast datasets to accelerating drug discovery, AI-powered solutions are making labs smarter, faster, and more efficient. But how does it actually work? Please visit our website to get more information: https://swangroup.net/ 

In this episode, I sit with Liran Belenzon, CEO of BenchSci, to explore how AI reshapes pharmaceutical research. We discuss:

✅ The biggest challenges of working with scientific data
✅ Why big pharma companies prefer AI-powered platforms over in-house solutions
✅ How multimodal AI enhances lab efficiency and accelerates drug development

Liran also shares his journey from being an MBA student to leading a company that works with 12 of the world’s top 20 pharma companies. If you're curious about the intersection of AI, biotech, and research, this episode is a must-watch. Let’s talk about the future of AI in biotech. What excites you the most? Let me know in the comments. 

Links from this episode:

✅ Get to know more about Liran Belenzon: https://www.linkedin.com/in/liranbelenzon 
✅ Learn more about BenchSci: https://www.benchsci.com 
✅ Follow BenchSci for updates on AI in biotech: https://ca.linkedin.com/company/benchsci 

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🔗 Stay Connected With Us.

Linkedin: https://www.linkedin.com/company/the-swan-group/ 

Website:  https://swangroup.net/   

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🔎 Related Phrases:

How AI Is Revolutionizing Biotech & Pharma Research With Liran Belenzon, AI in Biotech Research, AI-Powered Labs, Machine Learning in Pharma, AI for Drug Discovery, AI in Scientific Research, Biotech Innovation, AI for Data Analysis, Future of AI in Pharma, AI and Drug Development, AI for Biomedical Research

#aiinbiotech #pharmaresearch #artificialintelligence #biotechinnovation #drugdiscovery #machinelearning #labautomation #medicalresearch #futureofai

Liran Belenzon [00:00:00]:
We've been working on this for a few years, building specialized pipelines that can deal with pharma specific data and extract the entities, understand the semantic relationship among them, connect it with the world's data and then serve it back actually in a generative AI and kind of AI manner back to scientists so they can interrogate that data, ask different questions, get to really interesting conclusions that really increase their productivity of their research.

Steve Swan [00:00:28]:
All right, welcome to Biotech Bytes where we talk to IT leaders in biotech tech space about their thoughts and feelings around technologies affecting our industry. I'm your host, Steve Swan and today I have the pleasure of being joined by Lauren Belenzon, who is the co founder and CEO of a company called Benai, who is, you know, in his words, changing the world. Right. Which is great. So la Ron, before we get into maybe your thoughts and feelings on a global level about, you know, technologies and things like that, can you tell us, you know, how you got to where you are? And I think one of the things that would be great to hear about is, you know, as you talk about who you are, kind of fill us in on the gap that you saw in the industry and why you developed Ben Sai and the gap that Ben Tsai is trying to fill or is filling for sure.

Liran Belenzon [00:01:18]:
Well, thanks for having me. It's really fun. I guess we're trying to change the world in our specific space, of course. How did I get here? I can probably answer this in many different ways, but I'll keep it the one that's relevant to this conversation. But the bench side journey actually starts almost 10 years ago. I'm actually the only person on our founding team that is not a PhD scientist or from the space. Actually I was doing my mba. I was peacefully doing my MBA at University of Toronto and I was taking this course about entrepreneurship.

Liran Belenzon [00:01:54]:
And a part of this course you get assigned to work with a team of PhD scientists who are trying to solve a very difficult problem. And that's when I met my co founders who were basically applying AI to understand the history and the evidence of biomedical research and leveraging that to solve real problems that scientists face in the lab to help them accelerate their research. And fell in love in the team fell loving the problem statement, why don't you put my time and efforts into doing something very meaningful and the fact that also modern AI or AI was invented at University of Toronto. It was a very exciting time. So together with my other co founders, who one is a cancer researcher PhD and the other two were computational biologists, we Started our journey basically deciphering the world's history of biomedical research and understanding scientific publications and other data sources and really taking an approach of, hey, over the last couple of decades, I think a lot has been discovered around how biology and disease biology actually works. And all that data is scattered all over the world. But no one has actually, because the technology wasn't available, decoded, extracted, harmonized and cleaned the data that's specifically related to the evidence of what actually happened, which I think is really, really important, and get into the high velocity of data because a lot of conclusions and insights that just might be wrong. But the hypothesis was, can we take everything that's been discovered and really create that ground truth and a map, an evidence map of disease biology, leveraging AI and then how can we leverage that piece of truth and map to solve really important problems in disease biology? And the way we chose to do that was at scale.

Liran Belenzon [00:03:42]:
So unlike maybe other companies in our space that are biotech companies that take this data and then say, let's go make a drug, we actually wanted to productize it and to create this co pilot or AI assistant to the smartest scientists in the world to improve every scientific workflow that's related to biology that they do. And knock on wood, so far is going very well. And we've really seen 30 to 50% acceleration of productivity boost by scientists who are using our technology and platform in big pharmacy.

Steve Swan [00:04:09]:
That's awesome. Now, getting to where you are, right? My thought goes immediately to data, right? The data that, that goes in and feeds your, your, your, your AI engine, right? It's all about the data, right? Because the data is that gasoline that. So are you data agnostic? Does someone get to feed their own data in or, or, or are you supplying the data?

Liran Belenzon [00:04:29]:
So I think in our space there's two, there's many things, but as it relates to data, I think there's two things. One, getting access to data or legal access to data. And two, giving our space just access to data is not enough. There's a tremendous amount of work that's required to clean, extract, harmonize and ground that data. And that's I would say like 70% of our efforts in the company. And I think there's different approaches to solving a problem. I think that's what's interesting in this space where maybe someone else is solving a similar problem to us, but they're using very different data sets to do that. And I think both are valuable and then kind of together.

Liran Belenzon [00:05:08]:
And I think that's what makes it Interesting. And also I think in my opinion, how pharma should be thinking about it, which is which solutions should we bring together to do one plus one equals, equals three. But for us it was really around one. How do we get access to the data? So a lot of the data that we use is primary research where results of experiments exist. A lot of that is scientific publications. So early on we signed agreements with Springer Nature, Wiley, Walter Sclors and all those others to get access to closed access publications. We have almost 10 million of those. And in addition to that, we brought in the right data sets around everything from ontological database sets, third party data sets, material data sets, and everything that's really needed to solve the problem that we are solving and building that scalable infrastructure.

Liran Belenzon [00:06:00]:
And then the second step after getting access to the data was building multimodal AI that can really understand the insights that are there, like a PhD scientist would, whether it's from figures and images, whether it's from text, and then ground them into their meaning and the very, very complex scientific dictionary. So in science the problem is actually very harder. Specifically in biology, where the data is buried out there, a lot of the conclusions might be wrong, but it's specific evidence. And that's where we focus on the results of the experiments. They are much higher veracity. So we focus. Okay, let's just understand those. Great.

Liran Belenzon [00:06:36]:
The second thing is science has the worst dictionary in the world. It has everything has 20 or 30 different names and cousins and stepparents and different associations. And grounding that data and connecting it together is priceless because then you can have much deeper understanding. So solving that problem is something that's hard to do. That's why roughly 70% of our team on the R and D side is really focused on building that very, very robust knowledge graph.

Steve Swan [00:07:02]:
So all that ontology like you just referred to, right, to connect all that together. So do you, one of the things you mentioned there is that, you know, you see what you're doing is only one input, one data set. You want one conclusion, there could be others, it could be other competing and you know, there'd be different conclusions, right? But you're just one of them that's saying, hey, consider this. So it's not, you're not the, you're not trying to replace everything. You're not the be all, end all. You're, you're, you're, you're one of the potential solutions or one of the potential endpoints that somebody could look at and say, hey, this could be a possibility, right?

Liran Belenzon [00:07:35]:
Yeah, I don't, I don't think any, I don't think anything is everything.

Steve Swan [00:07:39]:
Right.

Liran Belenzon [00:07:40]:
I think it's very arrogant to think like that for us was really understanding which piece of this entire complex puzzle called drug discovery we can own and we can do the best job in the world at and making sure we deliver that value to our customers. And explainability and how those conclusions came to be are really, really important. The other part that I actually forgot to mention when you talked about data, so everything I mentioned that's really around the world's data and bring that together, harmonizing it. The other thing that we do that's extremely valuable is then go into our customers data and specifically they're into their ELN type systems and everything that has wet lab results, data and replies. Similar technology that we have built with different data pipelines to extract all the entities and insights that they are seeing out in the organization that they have spent hundreds of millions of dollars on the last 30 years. But not averaging, taking those insights. That's kind of the proprietary knowledge. Marry it with everything that's done publicly and then giving them their proprietary map of everything they really know on this biology.

Liran Belenzon [00:08:42]:
And then their scientists can leverage that map to have basically the best answers that they can get to and the best insights and increase the productivity.

Steve Swan [00:08:51]:
You said almost exactly what I was about to ask, but I was going to ask it a little different way. What I was going to ask was, can I take your data, get this conclusion, Take my data, get this conclusion and say, because I'm using your same model, so it's the same model, trained the same way. I'm just using two different data sets. And I'm saying, okay, this is how my conclusion with my data.

Liran Belenzon [00:09:09]:
Okay, Exactly. Our customers ask us to do, and we do it for them because the real value is marrying the two together.

Steve Swan [00:09:17]:
Right.

Liran Belenzon [00:09:17]:
It's not just, okay, here's what I know, but here's what you know best with what's known and to be true in the world. And together that's a one plus one equals three.

Steve Swan [00:09:25]:
Sure. And you could look at all three conclusions too, if you want to. Right? Yeah, the combined and then each one. Right. Because when I had a conversation in one of my previous podcasts with Bob McGowan, who's the CIO up at Regeneron, I, I placed him up there and I asked him, I asked him, you know, near the end of our conversation, I said if, if I were a CIO of a small biotech right now, what did you not know? When you started there that you wish you knew now or what advice would you give me? He said, that's easy. Get your data ready. Get your data ready. He said, you know, from a certain year forward we're good.

Steve Swan [00:10:02]:
But to your point, he said exactly that. He said we got 30 years worth of data we got to get through and that's close to impossible, you know, and so to be able to use it is tough.

Liran Belenzon [00:10:12]:
You know that, that is something that we saw at Benai really.

Steve Swan [00:10:16]:
So you, so you would come in again? I'm just trying, I'm not trying to do a super infomercial for you here, but it's, it's turning, you know, it's awesome.

Liran Belenzon [00:10:24]:
If you want.

Steve Swan [00:10:24]:
Exactly. So you come in. So you would come in and your team not only has the AI capabilities but the data processing and the data, I don't want to call it scrubbing, I don't know what you call it.

Liran Belenzon [00:10:34]:
But yeah, we've been working on this for a few years, building specialized pipelines that can deal with pharma specific data and extract the entities, understand the semantic relationship among them, connect it with the world's data and then serve it back actually in a generative AI and kind of AI manner back to scientists so they can interrogate that data, ask different questions, get to really interesting conclusions that really increase their productivity of their research.

Steve Swan [00:11:00]:
So we're talking you're able to take the data from unstructured data, images, all that stuff. Yep.

Liran Belenzon [00:11:06]:
Yeah. Specifically around ELN and lab and experiments data. Yes. There's different types of data. And this is where people also lose credibility when you say we do all data. Data is a hundred different things. It is specifically around experimental data, ELNs. That's where we focus.

Steve Swan [00:11:21]:
That's where you focus. Okay.

Liran Belenzon [00:11:22]:
Okay.

Steve Swan [00:11:23]:
So maybe some of the stuff I just mentioned, maybe images and things like that might not be.

Liran Belenzon [00:11:28]:
So images we do specifically around the results of the experiments might be different types of data that we don't. But that's something we're very specific with our customers and it goes back to our value prop and all ties together because we don't. At the end of the day it only matters if you divide values on them.

Steve Swan [00:11:46]:
Of course. Yeah. This is so cool.

Liran Belenzon [00:11:49]:
To the specific use cases that we support in the workflows we support.

Steve Swan [00:11:52]:
This is awesome. You know that though. I mean otherwise you wouldn't be doing it right. So, so then. Well, let me ask you another question. Sort of a different question, but it, but real related to this. What specific. I don't know, I'm going to call them technologies because then you may, you may call it something different.

Steve Swan [00:12:12]:
What specific technologies over the last couple of years have really helped you, your company, accelerate, right, and get to where they need to be. Has there been certain code or certain platforms or certain storage? You know what I mean? Like what's really accelerated you guys and pushed you in the right direction?

Liran Belenzon [00:12:30]:
What's helped you for sure? So the answer is pretty simple, which is AI because that's really everything we thought we founded our company on. And I think there's two distinct points of time. So one was probably 2012, 2014, when modern AI was invented. And what that really allowed us to do is to understand certain insights from, from biomedical research that you just couldn't have done before without the use of the eye. So that was fantastic. And that's where really build the entire foundation and so on and so on. And the second point in time was a few years ago when LLMs came out both on the, I would say kind of the backend and the front end experience. And we really went all in on that technology to do two things.

Liran Belenzon [00:13:13]:
One, it enabled us to understand more things and more in depth and more at scale than we could have before because the technology now is better. And two, allows us to also reinvent the entire experience that, that scientists go through when they use our application, they use our technology, things like summarization and chat, a report generation for them and so on and so on.

Steve Swan [00:13:37]:
Now I, I bet some of the things you would get which, which I know that my audience would want to think about or talk about is security, right? So I mean, obviously you're not physically there and you're, you're there doing your thing, right, but you're not physically. So how do you handle that? Where do you go with that? I mean, you know, because if I work at Regeneron or I work wherever, I'm like, this is mine, man. I don't want anybody getting a hold of this. I got to protect it. I got to make sure that there's, you know, governance and everything around it. So how do you handle all that? Because I'm sure that's a big question, right?

Liran Belenzon [00:14:12]:
For sure. So we handle this because we have this lab with a number of customers. So we handle it. We follow the industry protocols that are required from us and the requirements of the customer. We're sock to compliant. We got that very on in the process. We separate all the data on the separate, separate instance of a Google cloud is just for that specific. The Customer.

Liran Belenzon [00:14:34]:
We have security team in the company. We have a fairly large team around all of our deployments and so on to make sure everything is secure. And we do it by working closely with our customers, really understand what the requirements are and just make sure we develop the right processes around that. But that's something that comes up, of course. I think everything is solvable and it's something work closely with our customers to do.

Steve Swan [00:14:56]:
How big is your whole company right now? How many folks you got?

Liran Belenzon [00:14:59]:
So I said we started four people. Now we're 350 people in the company. Raised close to 170, $200 million so far. Our last round was led by Generation, which is Al Gore's fund.

Steve Swan [00:15:14]:
Okay, so still private?

Liran Belenzon [00:15:17]:
Still private, yes, still private.

Steve Swan [00:15:19]:
And is everybody up there in Canada or are they spread around?

Liran Belenzon [00:15:22]:
Spread around. So we were in person company before COVID Before COVID hit, we were 50 people. When Covid ended, we were 300 people. And I don't know if a lot of people know this. So we're. I'm based in Toronto and Toronto was the city that was unlocked down the longest in all of North America. So it was two years. So by the time we finished Covid with hundreds of more employees, they were pretty much everywhere.

Liran Belenzon [00:15:49]:
So we became a remote first company. So we have team members all over Canada, US and we have roughly 20, 30 team members in the UK which have a, really, has a really great data and AI industry.

Steve Swan [00:16:03]:
And you're not, not asking names or anything but your customer base. How many customers you got right now?

Liran Belenzon [00:16:07]:
Yeah. So today we work with 12 of the top 20 pharmaceutical companies. Only work with big pharma. It's, it's really a focus for us. Very privileged to do that. So I'm, I'm on planes a lot and travel a lot as well as our team. And we're a Canadian company, but we're a global company. All of our customers are US and Europe, Japan and so on and so on.

Steve Swan [00:16:28]:
Now as the co founder and CEO, I just got to ask this question. Do you still get involved with the technology and the solutions?

Liran Belenzon [00:16:34]:
It's a very interesting space. Right. Because we operate in this, in the intersection of AI and drug discovery. So I'm very much also a student of the company. We have such smart people, we work in such innovative spaces that what you knew tomorrow doesn't mean it's what you know today. And it's two different things. So I always try and learn as much as I can about some of the great things that our team is working on and bring back the context from the market and what's the potential applications for that, then share that information back with our customers to know what we're doing, where we're going, why does it matter? And making sure we're all building something very meaningful together.

Steve Swan [00:17:09]:
Now why again, I gotta ask this question, you know, why wouldn't I if I'm a big farmer, I got big pockets, why wouldn't I be trying to do something like this or build it on my own?

Liran Belenzon [00:17:20]:
It's a good question and I think some of our customers are trying to do so. I think my opinion is that anybody can build anything. It's just a matter. It's what's the fastest, cheapest and most productive way of getting there. So I would argue that if you have to build what we're building at the scale that we're building, at the quality level that we're building, it's going to be much more expensive. It's going to take you much more time to build it yourself versus then redo it. The second thing is there's certain economy of scales that come with it. Meaning if we're building a solution for the market and getting feedback from 12 pharma companies and tens of thousands of scientists using it, you're going to have a better product than if you're going to try and build something yourself.

Steve Swan [00:18:05]:
Sure, yeah.

Liran Belenzon [00:18:06]:
So it kind of goes back to the question, do you want to focus on what you're doing the best?

Steve Swan [00:18:09]:
I was just going to say that.

Liran Belenzon [00:18:10]:
Right. Everything else, same with else. Right. I'll never try and build something if there's, I won't build my own cloud. If we can use Google cloud or AWS or whatever it is, because it doesn't make sense. It's faster, cheaper, better for us to do so. So I think, I think it's very similar from that perspective. Now I do think that where we do collaborate with AI teams and so on is how can we support them to build things that only they can build and that can be things through like APIs and access to our knowledge graph so we can empower them with our piece of the puzzle where they can build something else that's more powerful that we can open.

Steve Swan [00:18:45]:
Right, right. So kind of like stick with your knitting. Right. They discover drugs, you build technology. If they want to be a technology company, that's a different story. But they don't. They're. They're big pharma.

Steve Swan [00:18:53]:
Right. So, you know, so anyway, so I always like asking folks this, whether regardless of you know, whether they're inside Big Pharma or a technology company or whatever. What. So I'm sure that. And I'm not asking who they are, but I'm sure you got competitors or. Or maybe if I'm looking at this and I'm. I'm a technology guy or girl, and I want to go work for a company like yours, what would make me say to myself, I gotta work for the run or I gotta be inside Ben site. What's different about you as a.

Steve Swan [00:19:23]:
As a leader and your company as a, you know, why do I want to be there?

Liran Belenzon [00:19:27]:
Yeah, it's a great question. So I don't think anybody works for me. People work with me. Okay, cool. To start answering your question in terms of our culture and how we think about things now. I think about things, and I don't think bench size for everyone. I don't think it should be. I think nothing should be for everyone.

Liran Belenzon [00:19:44]:
For us, we're trying to solve a really meaningful problem. It's a very hard problem to solve. And I always tell people it's intense working at the bench side, but it shouldn't be tense.

Steve Swan [00:19:53]:
So if this is intense but not tense. Okay, got it.

Liran Belenzon [00:19:56]:
This was a sport, it would probably be like a Formula One, but it's not golf. Right. So if, like, you're a golf guy, go play golf. But if you're like Formula One, then come work with us, and we're doing something that is very novel, very meaningful, and very unique. And you said kind of my title is CEO and co founder, but I'm also a husband and a dad, and I spend a lot of time away from my family. And if I'm doing that, I'm going to do it for something that that's worth it. And then that doesn't mean just money. It means I'm actually doing something in the world that hopefully will give them a better future.

Steve Swan [00:20:31]:
So that is why I think you are, you know, and I think that. I think this whole industry, and I think everything that we're touching on here is making a huge difference within the industry. Right. Accelerating things, making them better, faster, smarter, you know, really, really pushing it along. So I think it's. It's. It's important, you know, everything that you're building and doing, and I'm sure that all those companies see it as well, you know, so. And then there's a lot of companies, like I said, I've said in previous podcasts, that are trying, really trying to get into the data side of things, you know, really want to own the data generation and curation and the data platforms and married up with what you're doing, or it sounds like you're, you're trying to tackle some of that inside, but married up with what you're doing, that's, that's, that's just great stuff, you know.

Steve Swan [00:21:20]:
Yeah, it's awesome. So any other technologies you want to talk about? Because we've pretty much gone through just about everything, you know, to talk about for your organization and what you're up to. I think unless there's something I miss, if there's something you want to chat about, let me know because I always have one final question totally unrelated to technology or anything like that that I ask all my guests. Kind of a little, kind of a personal question, but not too bad.

Liran Belenzon [00:21:41]:
Maybe I don't know if it's much on the technology side, but maybe something haven't mentioned which is what are the really what do we do with this technology? So really the specific use cases that we solve is if you take a step back and look at the AI in R&D, I would argue there's three areas where it's deployed. One, clinical trials and everything around, whether it's patient recruitment, engagement, digital twins, which is design and so on and so on. So great, you need to get something to work in humans and efficiently. The second thing you need to do is getting the drug design right. So everything around alphafold and so on. But actually the number one problem that exists right now in drug discovery, which is where we play, is how do you unravel the disease biology and how do you get the biology right? And there that's really the approach that we're doing. That which is really this AI assistant for scientists at scale, where we help scientists do two things that I think is the primary part of their job around this biology. One is the entire space of ideation and how do you do the right target due diligence and drug due diligence, identify the right biomarkers and the risk assessment and build specialized AI assistant to help them do that much, much more efficiently.

Liran Belenzon [00:22:48]:
And the second thing, once we help you generate the best idea, how do we help you test it as fast as most productively as possible by helping you understand which experiments you should be doing, what the right materials should be using, what are the right protocols. And those are really the two areas that have a really big impact that.

Steve Swan [00:23:03]:
We focus on now when you just mentioned that you went through your 1, 2, 3 in number one, data you don't have, you don't do the, what's the word I'm looking for? The data capture, the integration between the lab equipment and a database. Right. That's not your space. Right?

Liran Belenzon [00:23:20]:
No, we're not doing that for robotic labs.

Steve Swan [00:23:24]:
Right.

Liran Belenzon [00:23:25]:
That's someone else. But you might take the data that.

Steve Swan [00:23:27]:
Comes out, you get that data. Right. Once it lands, wherever it lands, but you're not there.

Liran Belenzon [00:23:32]:
And we'll take the derived insights and marry them with the world's insights. And so you can actually leverage that data. Yeah, to, to enhance science and to do better science and science better.

Steve Swan [00:23:43]:
Right. That's the way I understood. I just wanted to make sure, because you mentioned that. So I just wanted to ask that question because I think there's plenty of companies out there that integrate the, whatever the, the lab equipment with the databases and, and help extract that data. That's not your wheelhouse. Your wheelhouse is making sense of that data and doing something with that data. Yeah. Yeah.

Steve Swan [00:24:01]:
Okay, cool. Well, awesome. All right, well, thank you.

Liran Belenzon [00:24:04]:
You're welcome. What's the question?

Steve Swan [00:24:06]:
Here it is. You ready?

Liran Belenzon [00:24:07]:
Yes.

Steve Swan [00:24:08]:
So I'm a music. I, I, I don't play music, but I like going to, to concerts and things like that. Right. And I really like live music. So what I like asking my guests is what would you say if you, if, if you like going to live music or seeing concerts or if you've ever seen concerts? Right. What would you say is the best concert or best live music you've ever been to at any point in your life? Anything. It's a big one. It's a big one.

Steve Swan [00:24:33]:
I know. And it is a serious question. This is. You even thought this hard today?

Liran Belenzon [00:24:40]:
I think I have the answer.

Steve Swan [00:24:42]:
Okay.

Liran Belenzon [00:24:44]:
Okay. So I grew up.

Steve Swan [00:24:45]:
Do you like music?

Liran Belenzon [00:24:46]:
Yeah, sure. Who doesn't like music?

Steve Swan [00:24:47]:
Okay.

Liran Belenzon [00:24:48]:
I don't know if I like good music, but I like music.

Steve Swan [00:24:52]:
It's personal, so.

Liran Belenzon [00:24:53]:
Yeah, exactly. It's actually a very personal thing which music you listen to.

Steve Swan [00:24:57]:
It is. It is.

Liran Belenzon [00:25:00]:
So I, I grew up in the 90s, so did my wife. So to answer your question, which concert did you go to that you like? I think it's also very much related to the experience you had and who you went with and all this stuff. So I grew up in the 90s, so did my wife. So we always kind of nostalgic about the 90s, so we actually went to see an Atlanta small set concert a few years ago in Toronto when she did what's it called? Jada Little Pill Album. So it was kind of that entire album was phenomenal.

Steve Swan [00:25:27]:
She's a Canadian, right? Yeah. Her rush. Brian. Brian Adams. Neil Young. Right.

Liran Belenzon [00:25:37]:
Drake.

Steve Swan [00:25:38]:
Yeah, yeah, yeah, yeah.

Liran Belenzon [00:25:41]:
Love. There's more Canadians than you would think.

Steve Swan [00:25:43]:
Yeah, a lot of. A lot of good music coming out of Canada.

Liran Belenzon [00:25:46]:
Jim Carrey is not a musician.

Steve Swan [00:25:48]:
Right, exactly. I just listened to. I was. I was out west last week in Utah, skiing with one of my kids, and on the flight back, I was listening to the Fire Aid concert that they did in la, and Alanis Morissette was like, the second act, and she did. I thought it was awesome. It was great. Yeah, it was good stuff. So.

Steve Swan [00:26:12]:
All right, well, listen, thank you very much. I appreciate your time. Yeah, that was good. That was really good. And I think that where you guys are and where. I just think it's at the. Everything's coming into the. You know, coming into you guys at the center of it all.

Steve Swan [00:26:26]:
So that's all good stuff.

Liran Belenzon [00:26:28]:
Thank you.

Steve Swan [00:26:28]:
Thank you guys for doing this. So thanks for having me.

Liran Belenzon [00:26:31]:
I appreciate it.