#569 Human-Centered FinTech: Rethinking Credit in an Agentic World with Tamara Laine

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Mehmet: Hello, and welcome back to a new episode of the CO Show with Mehmet today. I'm very pleased joining me from the US Tamara Laine. She's the founder of MPWR. We gonna talk 

Tamara: MPWR 

Mehmet: MPWR. Yeah. Uh, thank you for, for correcting me, Tamara. So, you know, usually the audience knows that I don't steal much from my guest time.

We try to give them as much time as possible. So Tamara I'm gonna. Let you introduce yourself, your background, and you have a very interesting background, by the way. We're gonna talk a little bit about that also as well. Um, you know, explain to us what you're currently doing, what's MPWR, and then we can start discussion from there.

It is for the audience we're gonna talk about, I think it's been a while since I discussed this topic with one of my guests. We're gonna talk about, you know, everything from credit, from, you know, um. A little bit about the AI part of it. Of course, not on the [00:01:00] technical side. And you know, we're gonna talk also about how we can keep it human centered when it comes to FinTech.

So without further ado, Tamara, thank you for joining me today, the Floris. Yeah, 

Tamara: thank you for having me. So, just so that I introduce myself to everybody here who's listening, my name is Tamara Laine and I'm the CEO and founder of MPWR. And at MPWR, we're designing the. Agentic ecosystem for lending.

And why we're doing this is because we know that the financial system, um, really is, uh, exceptionally leaning towards those who have great credit. Those who have credit over 7 30, 7 50 and above. And what we know is globally that most people, over 50% have. Under that score. And not because they aren't credit worthy, but because we don't understand them, because the new economy has a different way of spending, has a different way of, um, paying for their homes that they live in, whether they rent, whether they share, um, whether they're [00:02:00] nomadic.

Um, and they have new ways of interacting with the financial system. Maybe it's smaller loans for shorter times. And so what we're doing at MPWR is we're creating the agentic and AI underlying business intelligence so that we can bank those that are underserved, so that we can lend to those that are underserved, um, and thin filed.

And we're really excited about it because we're AI native and, um, we are able to move quickly. 

Mehmet: Great. And thank you again, Tamara, for being here with me today. Now something which. Immediately kind of attracted, uh, you know, my attention. You walked in journalism before starting this. 

Tamara: Yeah. 

Mehmet: And you shifted to be a founder.

So there must be a moment where you faced a problem or maybe someone you know, faced the problem and then you said, okay, there is an opportunity here. I need to do that. I'm always curious to know about these leaps that [00:03:00] happened to, to, to founders coming from different backgrounds. What was that moment for you, Tamara?

Tamara: Yeah, well this is my second company that I founded on the first, I was part of the founding team that launched a live selling shopping platform. And when I, you know, fell in love with startups and the zero to one process, I'm, you know, I advise other companies on zero to one. And so when I. Thought about what is the next problem that I wanna tackle.

I knew that I wanted it to be a large challenge, one that I could throw myself into that I was incredibly passionate about, that I knew was hard, and that, um, that people shied away from tackling because of how big it was and how maybe intrinsic in the system it was. Um. Because I, I have always been about systems change.

And so one of the, the reasons why I decided to do this was I, um, had been working with a company, talking to banks, talking to small businesses that were, um, facing troubles, trying to access credit. [00:04:00] And I heard them and it really struck a chord with me personally as, um, a gig worker for over half of my career.

You know, as a freelance journalist. As a freelance writer. Um, as a then a fractional CXO in the later half of my career, I understood the hardships of the financial services, you know, that you're trying to get. Um, and so I, I said, there's gotta be a way to solve this. And the journalist hat came on where I, you know, dug in on the research and talking to people and reading everything that I could.

Listening to people's stories and then looking at how the technology could solve this. And I thought this is the perfect moment for technology to step in and reimagine a system that is incredibly, um, you know, it was set up in the 1950s. Really? 

Mehmet: Yeah. That's interesting. And actually, you know. People might underestimate the problem here, and I want to dig more into this.

So [00:05:00] credit scores, and I think, I'm not sure if I can claim this, but I would say majority of the countries, they have them here in the ue, United Army members where I am. Like they have, they implemented them like, uh, immediately after the 2008, 2009 crisis. So everyone is familiar, at least I know about credit scores now.

What we often hear is that, you know, we say, they say these scores are data driven yet. We know for a fact that actually it doesn't have all the pictures. Yeah. So what kind of data execs, so you gave, so you gave some examples from, you know, someone working in the gig economy. What else is missing there, Tamara, from your opinion?

Tamara: Well, I mean there's some things that aren't necessarily new in the, in the idea of bringing in new data to credit, which are like big things are rent history, right? Utilities. Um, behavioral data, uh, cashflow analysis, right, that we can now do. Instantly [00:06:00] with, um, a, you know, attaching bank accounts. So those things for, and they're called alternative data, still not, not primary data, which they should be, because that shows the whole picture of someone.

So kind of the traditional credit scores are really based on, um, you know, your liabilities versus how much, um, credit you have open and if you've had any defaults or, um, how many loans you have active, right? Like there's, it's. It's kind of really based on, um, how much credit you've accrued, right? If you're a young person and you're trying to get your first house, but you've decided that you didn't want credit cards because you wanted to pay everything off, that's a huge, huge.

Problem. And you're not able to get something because you haven't built up that history. So it doesn't mean you're not credit worthy, it means we don't understand you at the moment. Right. And so with alternative data and like the data that we can get from someone's behavior, especially [00:07:00] with banking transactions, um, utility payments, all of these different things that kind of, you know, show that you.

Are able to handle money, um, we're able to get a much, much better picture. And there's some very cool, um, alternative credit data sets that are popping up now that have to do with like, um, community banking. And by that I mean, you know, a lot of times people will do like community banks that are not banks.

Right. They'll pool their money together mm-hmm. And they'll. And they'll do things where they give people loans or they buy certain things for the community. And so there are actually people, and this is a hard thing to, to track that are starting to create technology to track that. And then that data can feed into the alternative sets.

And that's really starting to bank and give loans to the underbanked. So we're on the brink of some really cool ways of gauging people's behavior that were never done before. 

Mehmet: Right. [00:08:00] Tam, I gotta ask you, and sorry if it's gonna be kind of a loaded question. So now, um, 

Tamara: love, love loaded questions. Go for it.

Mehmet: Yeah. So two things here and actually this question, I didn't prepare for it, but based on what you were describing now, one of the biggest problem I hear from people, especially when they change country, and I know a lot of people who really 

Tamara: arrived. 

Mehmet: Migrated from somewhere maybe to the US or maybe they came from the us.

They went to Australia, from Australia to Singapore and so on. So the biggest problem I always hear from people like, Hey, like we don't have credit score and this is why we cannot do anything. Right? Can we imagine this also to be able to solve? This problem because now you have quote unquote, correct me if I'm using the right terms.

It's kind of a your financial identity, same as you carry your passport, so you can carry that with you to any country you go. And then based on it, you can have some assessment [00:09:00] of your financial situation. Now yet, and this is why it's a loaded question. Some, someone might, um, argue that, yeah, I can understand where you're coming from, but we as financial institutions, like we were, you know, majority of the time they spend their times in assessing risks and, sorry, like, I need to do this in, in the legacy way, in the physician way.

This doesn't work for me. So in all this, how, how we can change the mindset here, Tamara, for, for like this. If I might call it inclusivity, especially for people who change places a lot, or maybe they, they, they have, you know, this as you mentioned. The way that they rely on gigs sometime, or maybe advisory work, fractional work, so, so how we can change also the mindset to become really a global movement rather than just, you know, solving a problem or a pain on a [00:10:00] smaller scale.

Tamara: Well, you're right. So that is a loaded question because, so this is on my roadmap is to become, um, I call it credit ip. Mm-hmm. So that you can carry your own credit IP and you can basically tokenize it out instead of vice versa, which is, um, which is now. You've got all these different agencies that own your credit and, and someone pulls it from different places, and you don't really know what information people are getting on you based on what bureau, right?

Like it should be the opposite, which is you're able to really hone, understand, um, nurture your own credit and own your credit ip. And so that's something that we have, um, right now. We're working towards our credit ip. Now what you asked is how do we make it global, which is something that we are looking at, not in our first version because we are launching in the us but as we move forward, that isn't just a, um, an institutional problem of will banks accept it, it's actually a regulatory problem as well.

And so what that is and how you'd have to, you know, start to [00:11:00] approach it, is each country mandates what can happen with financial data and where it can go. Right. And so in order to have that sort of system, you have to be able to either have like bases set up in each country, right? And again, depending on the regulations, whether it's regional, you know, 'cause some places have regional bank financial information regulations.

Some people, some countries have, uh, by country. So you would really have to be like all over the world understanding where data can live and flow. That is really the problem with having some sort of global system like that. I've heard, um, of some very interesting solutions on blockchain. But blockchain, you know, is also very far from being kind of adopted in that way from with financial institutions.

So there is a solution. It is not necessarily technical right now because the [00:12:00] technology, I believe is there. It's a regulatory and infrastructure issue that really is going to be the challenge when tackling that sort of problem. That's quick opinion answer. 

Mehmet: No, no. That, that's, that's fantastic. That's great.

Now let me ask you also something, uh, kind of a follow up question now. You mentioned before that you look at different type of data, which is mainly the behavioral data, right? So, um, rather than the fixed data points that usually the traditional systems would do now. Yeah, 

Tamara: yeah, 

Mehmet: yeah. So, so, and, and also like I, I believe, you know, in the world of ai, so maybe some models are doing the nitty gritty here to, to give you, you know, kind of an idea about, you know.

What kind of credit is this person, you know, we can give them and so on. And maybe the model will come and say, [00:13:00] Hey, like even based on this data that you gave me, I don't think this person is maybe trustworthy or we think there are risks and so on Now. I talk a lot with my guests, and actually I learned this from my guests.

It's not, you know, and I started to search for it about the idea of how we can explain the results of, you know, these models to people, which we call it explainability, right? Mm-hmm. Um. I discussed this with people in hr, people in, you know, different, um, verticals and we talk about the ai. People see it as the black box, and usually people, you know, they are looking at kind of, I would say, skeptical about black boxes because they say, Hey, because by the way, e in, even in a traditional way, we know that they are using.

Kind of machine learning and, but it's, it looks at certain data, say, Hey, we, we think, like we can't give you a loan, we can't give you a credit card, you can't get a mortgage, and so on. So in your case, Tamara, [00:14:00] do you, I'm not sure if you implement it or do you plan to implement kind of explainability? So me, when I come to you and you tell me, Hey my man, you know, yes, we can, you, you, we cannot, we don't think like you can have a credit card or whatever that is because.

Tamara: So actually I think AI makes that easier and better. So if you look at your credit right now. Uh, and you pull your credit report, the explainability is actually, uh, pretty bad, right? It'll be like, uh, I actually just pulled my credit report the other day and it was, and it was like, um, you have multiple accounts open and, um, your, you know, your payments.

Versus your a, um, credit limit is moderate, right? And you're just kinda like, oh, what does that mean? What, like, I don't know, like it doesn't, it doesn't give anything tangible. Right. Um, and so with ai, and actually what we've [00:15:00] set up, which is very cool, is when you. Get your feedback, it gives you the bullet points.

Exactly why, what was the thinking that went into it? So is it that you have three missed car payments or is it that, um, you've paid back your phone bill every, you know, time for two years, perfectly on time. You know, like it will give you five to seven bullet points of why that decision was made. And so then you've got some real traction.

You know, the, like, what can you do? Um, whether, if it's great then. Fantastic. If it's not great, then you're gonna see like, here's the decision that went into it. And so these are what you need to pro proactively work on in order to up your score, um, and get a better rate. So that does, that is given to you almost instantaneously when you apply, because through ai we can do it fast and the decision logic is there, right?

It's so trackable. You can watch AI make the decision based on the parameters that you give it. [00:16:00] 

Mehmet: Right Now, one point about, and, and I know the answer, but this is for the audience, Tamara, when, when we are collecting a lot of data and you know, we, some people especially. I know in, in some geographies, sometime in even some, some areas of the world, they would see it as kind of, Hey, are you doing a surveillance on me and are you watching my behavior?

And we heard a lot of debate about, you know, these, uh, central banks control things, the cbdc and all this and that, you know, the central bank might decide like, Hey, based on your, uh. Um, spendings today, like you can't buy an extra, I don't know, ice cream because you, you consumed your credit, uh, the, your emission score, uh, uh, limit for today and so on.

So people are usually, again, skeptical about sharing this information. [00:17:00] So where is the line between. Keeping this without the surveillance and actually getting the benefit out of it. 

Tamara: I mean, such a great question. And that's also a loaded question, right? Because you're gonna talk about the difference in geographies.

That's a, um, like a public policy question because what you hope, and you know, I look at. Ethical ai. Um, I've done a lot of work in ethical ai and so what the hope is is that, um, engineers and and startup founders that are working in AI are doing so in an ethical way with a code of conduct, with code of ethics, right?

Um, working within regulatory sets, and you hope that your regulatory sets are really good so that people know what lines you can and can't cross. Um, and, and so that you can create technology that's human-centered, that helps people thrive [00:18:00] not to create more of a surveillance state. Now, the thing is with technology, the genie is out of the bottle, right?

Yes. Technology is moving fast, so that's why, um. Good regulatory systems are important so that founders know how to create and within what parameters. Now, with that, what you're kind of talking about is the boundary of crossing that line. Um. You know, when you think of surveillance states, you usually think of government.

It can be corporate surveillance states, right? Like you do have some monopolies that control all the data and then that can really control different things with what you see, how you purchase, how you shop. Because if you're only seeing certain things, then um, your behavior reacts to that. And so the, you know, that sort of monopoly.

So you try and keep monopolies outta the system, but how can we. You know, your question I think is, will people be receptive? Mm-hmm. [00:19:00] And I think right now, um, adoption rate rates for ai, I was just looking at this, if somewhere in like 90% of Americans have used ai, but that's like chat GPT and then I think it's something around 40% are skeptical of ai.

I, I should check these facts again, but I, um. And so you've got this real kind of like curve right now that's happening where like a lot of people are using it, but you still have a large portion that are skeptical, but that that skeptical ness is going down, down, down, down, down, super quick. And so, um, that means the adoption of it is going to radically increase in the next few years.

Um, so people are using it. You need the regulations in place to make sure that it's safe and secure. You need to make sure that there's not monopolies in place to, so that there's kind of freedom of thought, and then you wanna ensure that like, especially with the, the government, that the data [00:20:00] is owned by the people, not by other people.

Mehmet: Right. I, you know, to, to your point, I think, yeah, uh, I repeated this a lot of time. The, the gin is out of the bottle indeed. And, and, and there's no way to get it back anytime. Um, for me, and you know, like just kind of, uh, food for thoughts that they say, like for me, regulations are necessarily but should not be blockers.

Tamara: Yeah. 

Mehmet: Also, as well. And you know when, when such discussions comes up tomorrow with me, what I tell people like, Hey. Sometime people complain, Hey, like for example like this, let me mention like Google, they know a lot of about us, da da da, da, da, and said, yeah, but actually they didn't force you to do that.

Because you know, if you read, you know all the terms and conditions and all the disclaimers, you know, when you use a service. Right. So you [00:21:00] like, yeah, they know you, the history that you're doing, and actually you, no one forced you to do that. And let's be honest, it actually, uh, simplified your life. So if 

Tamara: you, well, it's the whole thing.

Nothing is free, right? If it's exactly free, then you are the product. If you're paying, then you're paying for a product. 

Mehmet: Absolutely. And then I tell people like, look, you know, at the end of the day, it's, it's your choice. If you don't want to be part of this, I don't think someone would go and extract the data and put it in a model unless you accept for it.

And this is why I think, you know, the awareness becomes, you know, critical here. Now this is why my opinion, I'm not sure if you share that with me, Tamara, like regulations. Are not and should not be at any time blocker. Um, for, for AI driven credit systems, let's say this, right? 

Tamara: So I, so I agree and disagree at the same time, and I, I'm in a very, um, regulated space.

[00:22:00] So ambiguity can also kill, uh, progress, right? And so if you have ambiguity, investors are less likely to invest in startups. Because they, um, because they don't, they, you know, they're gonna wait to see what happens, right? Um, investors help drive innovation, and if you are, if you are building something and you don't know the regulatory status of what will be in each state, let's just say, then your ability to scale quickly is hampered.

So. Yes, overbearing Reg, uh, regulations are bad. Ambiguity is also a innovation killer, so you have to have kind of decisive, um, frameworks that startups and companies can work within in order for there to be really good growth. 

Mehmet: Right. And I think this is where. This is like, [00:23:00] you know, I'm trying to relate my, my thoughts and you know, the way you're putting that to us, Tamara, whenever we see, and again, like again, I had an episode couple of weeks back with Net from Global Partners and you know, whenever we keep kind of a human in the loop, we call it this way.

And that's why, you know, here we call it like human centered FinTech. Let's call it this way, right? This might. You know, remove the skepticism from one part. You have the control of a human, that actually nothing got wrong is happening. The question here is any system, like what you're doing, Tamara, like how that.

Works from, from a design perspective, not from technology perspective. How, how do you design it in a way that it let everyone feels that they are, you know, really, of course there is [00:24:00] a, a, an AI working in the background, but at the same time it shows that. You are inclusive. Like you're not just running data by a model and relying a hundred percent on the model.

Why? Because someone might go and say, yeah, but these models, they have the hallucination. They can do mistakes as a machine at the end of the day. So. How, how we make this, you know, human centered, 

Tamara: well, the human in the loop, I think is the important part, what you said. So one human in the loop, um, and two, it's the, I think the outcomes that you're driving towards, right?

With inclusivity and, um, transparency. And so when you're, when you're making it so that it betters someone's life, um, and that's an outcome. That that is better than making it for, um, you know, you think. That's a really hard question. 'cause I was gonna say productivity, but productivity is also, you [00:25:00] know, good for, um, human flourishing and thriving.

Right? If you can take away mundane tasks and allow people to do things that are, uh, more mentally stimulating, um, then that's good. So how do we, the question is how do we create something that's. Good. I, I got sidetracked from the question. 

Mehmet: Yeah. Uh, I like to, to, to, you know, per, you know, see the, the, the way you think about it from someone also, because remember when you said in the introduction, like, you, you were doing things and you, you, you solve the problem and you know, because for me.

Like, even if I'm technology guy, I always have this, you know, sometime they tell me, Hey, it's not related to credit necessarily. Like we, we, we checked in the, in the computer and the computer said this and they said, okay, but the computer is not a holy book. Right? Like, it might go and do something. And I always appreciate, you know, any [00:26:00] system, whatever the, the, the, the goal of it.

You know, whether it's like something financial, something, you know, just a service. If someone can explain to me these things, and at least I can understand like why the machine took this again, we, we, we talked about it before, but I mean, I appreciate if a human can explain that to me. Although, like I know how machines work, I know how the algorithms work, so this is to not feel, you know, because some people might.

Go and say, yeah, like, uh, maybe the model is biased, right? Like maybe based on, I don't know, uh, the state I'm coming from, or the city or whatever, you know, age, uh, race, you know, all these things can come, can come together. And it's a very sensitive topic, you know, right at the, you know, the, the. The feeling that, okay, we, there is always a human who can explain is important, you know, from, from my perspective at least.

Tamara: So let me, let me open this up into a bigger question. So here's, [00:27:00] here's what we wrestle with because I think it's very easy to say, okay, what I said before, which is you really try and make it for humans first, and you keep a human in the loop and you think, um, and, and here's what we want to do. I think what's just as important as thinking about what you'd like to accomplish in the outcomes is trying to figure out what the unintended consequences are.

And so why I say that? So, you know, if we look at, um, businesses that had the best intentions over some of the years have actually done some great harm as well, not intentionally. And so how do you. Uh, how do you try and find those unintentional consequences before they happen? So, for example, great case study.

Tom's the shoe company. You know, Tom's the shoe company, or if you don't, yeah. So Tom's the shoe company. I think their model was buy a pair. They give a pair to someone who doesn't have shoes, right in um 

Mehmet: mm-hmm. 

Tamara: The global south. And so they were giving a lot of shoes in, um, south America. [00:28:00] What happened was they put a lot of shoemakers out of business.

Because there was so many shoes down there, so great intentions, okay? We're giving shoes to people who, you know, weren't able to afford it. But what happened was they put a whole economy, uh, or sorry, a whole industry out of business, and so then they had to adjust their model. So the intentions were great.

What happened was not so great. Great. So how do you try and think ahead about what those unintentional consequences are? So now in ai, a very good example is, let's just say in healthcare, being able to identify the probability of someone's success rate on a medication, right? And so then you say, okay, well.

Let's just say we have so much medication, we've got a limited quantity of education, and we're gonna use AI to, to discover the probability of someone's success. On one hand, that's great [00:29:00] because you're, you're identifying, okay, well, we think that this group of people is going to have a lot of success on this.

The unintended consequences are, you're actually. Limiting the ability for a group of people to try that product, right? And we don't know how the body works. They could be successful, right? Uh, or, I mean, we do know how the body works, but we don't have a hundred percent assurance that there's not the probability that this person wouldn't thrive on that medication.

So there could be all these unintended consequences with really great intentions. And how do we mitigate that? This is a huge, huge question that I hope all the brilliant minds that are creating AI are thinking about as we create them. 

Mehmet: I hope so too. I hope so too. Now, Tamara, part of what you're doing actually, if I want to think about it, um, the way we we work is changing it.

It, it's always has been [00:30:00] changing, but I mean. Because even the leaders in the market, whether from tech or you know, other, other verticals also as well, they're talking about this paradigm shift that is happening now in the workforce. So, and we know like probably more and more people would be either doing like, you know, gig will be in the gig economy, let's call it this way.

Or either they will be solopreneurs, right? Build 

Tamara: dollar businesses that are built by one person. 

Mehmet: Yeah. Now, when that would happen, no one knows. Like it can be this year, it can be in five years. But you know, every single signal at least that I try to follow is telling me, and this is by the way, based on real discussions outside of the podcast and on the podcast, I can see this tendency of people who want this kind of freedom.

And AI is allowing them to do so. [00:31:00] Do you see, uh, what you're doing currently as enabler for how the future of work would look like? And in your opinion, how the future of work will look like? 

Tamara: Yeah, I, I definitely hope so. I mean, that is what I kind of preach, right, is that the world is moving towards, um, a different work culture and that's why we have to do a financial industry that fits the new work culture.

Um, gig workers, you know, and I call them gig workers and we should probably come up with a better name for it. Um, right. 

Mehmet: They, 

Tamara: yeah, they, they, I mean, it's a, it's such a wide, uh, percentage of the workforce and it goes from. Your, your graphic designers to your Uber drivers, to your CXOs, um, to your Etsy, um, shops, right?

Like it, it's huge and it's growing and it's some by choice and some by necessity. Um, [00:32:00] but I don't think that we're going back to the model of you have one job for 20 years, 30 years, and you retire there with a pension. The model now is you have several jobs that you, you know, you choose and you can live the lifestyle that you want to live, hopefully.

Um, and the future of work is moving that direction. You see more companies hiring 10 99 employees, and now there's a, there's a real. Um, push and pull right now in the workforce of do we bring people back into the office or do we have to figure out this remote hybrid work situation? Because since COVID people really do want to have a different working experience, right?

Um, being called back into the office is something that people are doing begrudgingly so. That's gonna [00:33:00] happen for a moment, but employers will have to figure out in order to keep top talent how to do some sort of hybrid, remote nomadic experience. Um, so I see the future of work being something that's much more fluid, um, much more on the employee's terms than than.

The, um, corporations and how they work and where they work. Um, and I, and I see it being, you know, you've got, you have the kind of creative you're gonna need, the high level creative, um, empathetic. People who can run multiple AI employees. And then you're also going to have the, um, the work. I talk about this a lot, which is like the workforce that works with their hands.

The plumbers, the electricians, I mean, we have such a, uh, in the [00:34:00] US there's a, um, lack of those positions right now and those aren't the position positions that AI will take over. So you're gonna have a rise in these hands on positions and jobs and companies, and then arise in leaders who can, um, maintain and lead AI employees.

Mehmet: So I know that it's not related to what you're doing, but 'cause you just mentioned this, Tamara, um, it's related somehow, but I gotta tell you how, because just of what you mentioned. So AI replacing jobs, right, which usually humans do. At the same time, we start to hear from multiple figures in the industry.

Uh. I would not hide. Like Elon Musk is the main guy who talks about it all the time. I heard it from different people also as well, as well as the first time I heard it, it was on my podcast three years ago when [00:35:00] no one was, knew about it. I mean, it was known, but not at the, at the mass scale about universal basic income.

And you know how. Will, will, you know, like humans, of course they will work, but actually it'll be optional, kind of. Or maybe you would go and do the thing that you really, really, really enjoy and you'll be paid for it. So any world, and I'm, I'm, I'm being a little bit futuristic here. Um, any world that, you know, AI is doing our jobs, and actually we are, we are just doing, enjoying this, our.

Behavior when it comes to credit and you know how we consume and the need actually a credit score. How do you envision this in the future in a world where the majority of the workforce might be ai? 

Tamara: Well, so. Let me kind of answer your question in a way. Here's, here's one thing that I know, I know that [00:36:00] people really need and want.

A purpose in life and purpose is what drives us. It drives us in our community. It drives us in our family. It drives us in our relationships, is what is that purpose? What is that connection? What is it that makes me wanna get up in the morning and. Be excited, um, and a lot of that is found in a job. And what you can provide and do and challenge yourself towards.

And so where I ponder the question about universal income is how is it done in relationship to people's purpose? 'cause I don't see a situation where people thrive with universal income if they do not have a purpose. I don't see communities thriving without people having purpose. And so, um, you know, a lot of the work that I've done in, in the [00:37:00] foundations that I work with, um, help with workforce development.

And when someone has a job, their outlooks, their families, everything radically changes. And so that to me is something that I think, unless we have the answer to what. Is someone's purpose and how do they, how do they carry that out? Then I don't see universal income working. 

Mehmet: So the answer is again, in if I understand this right, and I tend to, to, to agree with you here more, Tamara, because I don't think, like people are not like, uh.

Created to just sit on a couch all day and do nothing. Right? That would be very boring. So here comes to what we discussed a couple of minutes ago, which about, you know, there maybe the one man company or like one person company, right? Um, and where you have your AI agents probably doing the repetitive [00:38:00] task, the boring stuff, but you focus on, on, you know, what's more important?

This is, you know, as we are almost coming to an end. You, you mentioned your love to start ups, Tamara, and you mentioned the zero to one, you know, and, and this is a, you know, for people who doesn't still know, it's the very famous, you know, term used Bailey in Silicon Valley. Peter thi he, he came up with it like how to take a company from zero to one, meaning like from nothing.

And you become a monopoly. Um. How we can get, in your opinion, more people interested in this. Because to me, I'm not saying that everyone gonna be an entrepreneur, but somehow people need to understand that entrepreneurship is, is not just about building companies. It's not only about, you know, being the.

In the dom and gloom of the startup world, the whole thing that we see, it's also about purpose. So how we can encourage more people, especially in the space you are in [00:39:00] now, to, to try to, to, to get up with something and try to, to at least, you know, try their luck and see can we build something which fulfill our purpose.

Be useful for, for humanity? 

Tamara: Yeah. I think it's about people changing their perspective on whether they have to be a CEO to start a business. Right? Um, I think the misconception is that in order to solve the problem that you wanna solve and to build a company, you have to be the CEO because not everybody's built to be the CEO.

Right. Not everybody's built for that because the CEO is a, a much different sort of position and people have really incredible, unique skill sets, right? Doing this as like my circle and like the prongs coming off of the, the circle. Um, and if you can understand what your unique skillset is and the problem you wanna solve, then you surround yourself with people who can build a company.

Not everyone has to build a [00:40:00] company. Some people can just have a great idea and if they can, um, convince someone else who has a different skillset that compliments theirs to join together with them, then you have a winning combination. Right? Um, I think what scares people off from starting a company is the idea that you have to build, that you as the expert in something, have to then also build it.

And not everybody is built for that. I am super lucky, and I mean, I, I know that, right? Like my skillset is not building the AI agents. I have a CTO who's brilliant at that and wants to do it. My CTO does not wanna get on calls with investors all day. That is the last thing she wants to do, right? Like, and so our, our skill sets compliment each other and we're able to build a company together.

Mehmet: I love that because first. Like, I can't agree enough with you about, [00:41:00] you don't need to follow the title, the CEO or like whatever you want to call it, founder only. You can still be working with someone and you are an entrepreneur, in my opinion. The second thing, and I think this is a very great combination, Tamara, and you have.

I love startups like you, by the way. Um, I'm a big fan. Uh, I was lucky to, to work with, you know, some startups in the past. They were scale ups, but for me, startups in at least the part of the world where I am, and the best thing that I've seen is when you have multiple founders where they exactly know how to divide responsibilities between themselves and give everyone the, you know, the tasks which are.

Doing perfectly and then rely on the others to do the thing that usually we are not as good as. In doing so, and in your case, you just described it in a perfect way. Like, you know, I know like as technical guys, [00:42:00] they don't like to be sitting hearing about, you know, tell us about your burn burn rate and tell us about your runway and tell us about your business model, financial projections.

So this is the last thing maybe the A CTO would like to hear, but they like to build and maybe from your side, like you want to talk strategy, you want to talk about your vision and how you want to apply this. This is the perfect combination. So this is a great example, Tamara, for any existing founder or to be founder if he or she, you're listening to us today.

This is, I think, one of the main takeaways for you today. Like you don't have to master it all. Try to also do it in a partnership with someone, and you don't have to have the title CEO now. Final question. Tamara, any final thoughts you want to share with us and where people can get in touch and learn more about, um, you and the company?

Tamara: Yeah, no, um, I, I love this conversation. I think that it's really important to [00:43:00] talk about how you build technology and the thought process that goes behind it, not just like the. Not just the pie in the sky vision. Um, if anyone wants to get in touch with me, partner with, if there's a bank or financial institution that wants to partner with us, you can hit me up on LinkedIn.

Um, it's Tamara Laine on LinkedIn. Otherwise, you can go to our, uh, websites, MPWR ai.com. Um, you can book a demo and if you are a gig worker, you can go to MPWR Money and you'll get a special discount on, um, three months of our, of our platform for cashflow analysis. 

Mehmet: Great. Thank you very much, Tamara.

And by the way, for the audience, you don't need to rewind and hear where the links were, so I gotta put everything in the show notes. If you're listening on your favorite podcasting app, if you're watching us on YouTube, you'll find the description again, Tamara, I know how busy things can get. As a founder and CEO.

So thank you very much for giving me the time today. I really appreciate it and this is how I [00:44:00] usually add my episodes. This is for the audience. If you just discovered this podcast, thank you for passing by. I hope you enjoyed it. If you did, so give me a favor. Subscribe, share it with your friends and colleagues and if you are one of the loyal people who keep listening, who keep sending me their messages.

Thank you so much. You did fantastic job 2025 and you keeping doing fantastic job in 2026, keeping the podcast. I think now for the 56th or 57th week straight in the top 200 Apple Podcast charts, of course. But as usual, I'm keep promoting different countries. So just today, we are recording this on the sixties of Jan.

Probably you're listening or watching this by the end of this month. We were in two new countries, so we're in Croatia and we were in Denmark. So thank you very much for people there. I'm still waiting for my North American France in the US and Canada. I want to see the podcast there also as well. So give us a shout, share the podcast with your friends and colleagues, and as I say, always stay [00:45:00] tuned for a new episode very soon.

Thank you. Bye-bye.