Private Markets Data: Data-Rich, But Insights-Poor | Alex Goodwin, Bridge

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Alex Goodwin, welcome to the Wealth Tech podcast.
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I am happy to have you on the show.
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Alex is the co-founder of Bridge.
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Bridge is one of the exciting entrants within the Wealth Tech ecosystem that is doing post trade automation and data analysis for alternative investments.
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The other exciting feat, Alex, is that you are actually my 50th interview for the Wealth Tech podcast.
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So there's there's a milestone for you.
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Congratulations.
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When I started this project, I I I didn't envision that we'd we'd get this far.
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And uh it's been a fun ride since.
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Like I like I say, I get to talk to a lot of people that are smarter than me.
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So it's always been great.
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Um can you do me a favor for the listeners?
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Can you kind of give a uh that might not know Bridge, can you give a kind of a quick update of who you are and what Bridge does for who?
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Yeah, absolutely.
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And thanks for having me on, Mark.
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Uh good to be here.
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So Bridge is an AI native operating system dedicated to private markets.
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Uh and really what that means is we are trying to build the operational infrastructure to support the participation of allocators in alternative investments.
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Uh there's a long roadmap that sits behind all of that, but today, you know, our core focus with our first product in the market that we've been in the market for about a year now with is a post-trade solution, as you noted.
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Right.
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So, really, this is cleaning up the messy post-trade workflows around the collection of information, the standardization of data, and the reporting and insight into that information.
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Tell me a little bit.
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Uh, whenever I get a chance to talk to a founder, I always love to understand their founder's journey a little bit more in terms of what did you see in the marketplace in terms of that unsolved problem that you thought maybe you could build a better mousetrap for?
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And then uh, you know, how has that journey been different than what you thought it was going to be?
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Yeah, good, good question.
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Uh so really the the journey begins probably about three years ago at this point.
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So I had come from a private markets investing background.
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Uh, started my career out of school with a fund called Audax, which is a middle market uh private equity firm, uh, and then did a few more years at Leonard Green, which is more of a large cap focused buyout fund, and then did a few years operating in a venture-backed startup.
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Uh, and that was ultimately my path into business school.
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And business school is what brought me out east, which is where I sit today in Boston, and had the good fortune of meeting a classmate, uh Io Ekator, who then became a co-founder.
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And he was coming from a similar background at Blackstone, where I think we had been seeing a lot of these challenges and problems as it relates to the manual nature of working in private markets.
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And we were on the GP side, right?
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And so I think we were seeing it firsthand, even on that side of the business, where we were saying, wow, this is a lot of manual work to, you know, collect portfolio company information and analyze it and report on it.
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And ultimately, all of that gets synthesized into, you know, a series of reports and documents that are going to get transmitted downstream to the LPs that are the actual investors in these funds.
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And, you know, then we started realizing, wow, it must be even harder for the LPs who are receiving, you know, all of these reports from the Leonard Greens and Audaces and Blackstones and hundreds of others and trying to make sense of all that, right?
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Because they all have their own way that they report, their own cadence on which they report.
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This is not a you know highly regulated space uh today as it relates to the cadence and structure of reporting.
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And so it makes for a very complicated workflow.
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And I think as we looked at that market, um, we did our market work really across two dimensions.
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We said there's GPs and LPs as the bookends of one side of this market, and then there's pre-transaction and post-transaction work to facilitate alts, right?
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So pre-transaction being finding, sourcing, diligencing, you know, and ultimately transacting into a private investment, and then post-trade being everything that happens thereafter.
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And I think as we double-clicked on that set of boxes, we really found the biggest pain point and the most white space in the post-transaction part of the market for LPs.
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And so that's why we started there.
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Uh, and we were just really shocked.
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I mean, this is a $23, $25 trillion asset class today and growing rapidly, most of which flows through one of several B2B allocator profiles who make up the customer base of a bridge today, which is family offices, wealth management firms, institutional allocators across the pension endowment, OCIO, et cetera, buckets.
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Uh, and most of those firms historically have done this work operationally by hand, right?
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Manual ops teams that sit in-house.
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And so I know we'll talk more about the sort of industry landscape today, but that was the opportunity that we saw.
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And uh, you know, we thought that modern tech at the same time was sort of the catalyst in terms of the convergence of trends that were making it a pretty interesting space to go after.
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Yeah, any LP investor that's fallen some sort of endowment uh institutional investment module is going to have a heavy dose of of uh private investments, uh, you know, literally hundreds of uh of LP uh investments that that they have and they have to manage.
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And I like to say that the you know alternative investment industry upgraded to the 90s, converted their document into a PDF and sent it an email and called it a day.
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So it has been great to really see this problem, which was a pervasive problem that that really had no solutions for it.
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Uh, and family offices were all struggling with it to now see multiple options.
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That competitive landscape for Alts is starting to become highly competitive.
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You know, we had a chance, Alex, to talk on the on the prep session that there are a number of areas in the in the family office ecosystem that are becoming more competitive, which I think is good for the industry, certainly good for family offices.
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Uh, talk to me a little bit about how you see the the competitive environment and where is uh bridge finding that that white space?
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Yeah, I I would say the market definitely is becoming more competitive.
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And I think it's because that this problem and and pain point has just become kind of impossible to ignore, right?
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I think all its allocations historically for you know large parts of this investor community were pretty low.
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You know, so I think when it was two, three, five, even eight percent, uh, it was a lot easier to just rationalize that, hey, yeah, we'll just kind of throw a few bodies at this and and we'll get through, right?
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We'll have a band-aid solution.
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Uh, and and that problem didn't feel so acute.
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I think today, as the market has shifted towards a lot of the family office and wealth community emulating more and more the institutional allocation profile, right?
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No more 60-40.
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People talk a lot about the 50, 30, 20 being kind of the new paradigm.
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Um, and obviously that's very bespoke to each firm and what's right for their client base.
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But I would say that we're seeing a lot of folks have that strong appetite to increase allocation.
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And so alongside that, I think has come this burning pain point that naturally folks are trying to enter the market and solve.
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You know, I think the way that we look at the market and a lot of the work that we did uh is sort of putting it into a few waves, right?
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So I think if you go back maybe 15 years, let's say, I think that first wave was really more on the pre-trade side, right?
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It was really around access.
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And, you know, just to give credit where it's due, I think folks like iCapital and and Case were, you know, pioneers in that space, right?
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To start developing solutions that would make the process of actually transacting into a private fund easier, right?
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And more scalable and more accessible.
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And I think that was really wave one.
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And that was call it, you know, in the last 10 to 15 years, I think is when that really got going in a big way.
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Uh and then on the backs of that access and the increased allocation and all the trends that I spoke about, you started seeing this operational complexity and pain start to really resonate with people and they realize, oh, it takes a lot to manage and scale an alt's portfolio.
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Even if you can get the access piece going, arguably much harder is the post-trade.
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Now I have all the operational burden of it, right?
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Right.
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And that scales out you know, linearly at best, maybe more than linearly, right?
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Because of how complex and idiosyncratic the data is across all these uh investment managers.
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So I think that wave two was uh a series of firms that popped up to try to address that problem.
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Right.
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And now I'm talking about maybe eight to ten years ago when you saw the first handful of entrants into you know directly solving these pain points around, hey, can we help firms scale their alts business on the operational side in the back office and the middle office?
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And I think that approach at the time, uh, you know, using the technology available at the time was sort of a hybrid managed services outsourced consulting solution with sort of some sort of tech overlay, right?
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A tech portal, something you can interact with digitally.
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Um and I think just in the last maybe 18 to 24 months now, we're entering what I would think of as a third wave here, right?
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And I think that is, you know, an AI native opportunity in this space, not to belabor the AI point, right?
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It's all anybody talks about these days, but um, you know, I view it really more as a tool than anything, right?
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Much like if you go back early days in technology, you know, very, very early, if you all of a sudden had a computer and your competitors didn't have a computer, that was a pretty big difference in what you were able to do.
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And of course, that became commoditized and it just became a how you do business, right?
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Yeah.
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Much like even the Microsoft Office Suite and Excel being the tool of finance, even today, is still very true.
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Um, you know, early days, that was a big game changer.
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Uh now it's table sticks.
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Everybody has those tools and you have to have them, and it becomes more about how do you use them, what can you build on top of them.
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And so I think in some sense, uh folks that are building today have what I might refer to as a second mover advantage, right?
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And being able to use that tech in a native way.
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And that's the wave that I think we sit in today.
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So that's how I think about, you know, the general competitive landscape here.
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And that's really just even in our lane, right?
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I think as you mentioned, there's all sorts of areas in the broader wealth and investment management space that are being disrupted with new technologies.
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Yeah.
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I mean, there's so many dynamics at play, right?
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There's certainly a lot of the market has moved uh to the private side.
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There's very few IPOs these days.
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Even public companies are going private.
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So to be able to participate in a meaningful way means you need to have access to private markets.
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You're starting to see the RA market, um, the advisory market, where it's maybe uh not ultra-high net worth individuals and families, but high net worth individuals and families at five million to 25 million that that they want to participate in uh private markets.
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So, and RAs have a much lower operational tolerance for uh that burden that comes with alts today.
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And I do think that second mover advantage is real.
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Obviously, at Asseta, we we we believe and subscribe to that.
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We're an AI native platform, we're able to iterate a lot quicker, we have more modern Rails than just a few years ago, and and not the you know, I think AI is so intertwined with the alternative investment story because so much of that investment data that is unstructured, and you really weren't able to decompose and structure that information with OCR and these other technologies that had previously tried to do it with you know an accuracy rate of of 85% is not good enough, right?
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That's it means you're you're just spending too much time checking everything.
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So I think AI has has been a big part of that story.
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Um let's talk about that that operational burden that that uh you know family offices have uh post-transaction.
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What does it look like for for allocators today on that side?
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Yeah, I I think with a growing alt portfolio comes a you know a lot of investment information complexity, right?
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It's not just the actual point of transaction and and how do we choose the right investments.
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But then once you've made those investments, uh, that's when I, you know, we think a lot of the pain really starts.
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And so just to paint a picture of what that looks like, uh, especially in, you know, if you take kind of the multifamily office community as an example, where you've got a handful of different families, each of whom is going to have their various kind of entity structures uh that through which they invest, and all the different allocations they've chosen to take across venture, private equity, real estate, hedge funds, direct investments, right?
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All sorts of different asset classes that fit within the broader private markets bucket, uh, those folks are typically sourcing documents and data from you know multiple dozens, if not even over a hundred unique locations.
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Yeah.
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We see that a lot with our customer base.
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And so what that looks like is a back office team, typically an educated and you know, not underpaid back office team, uh going and doing a lot of menial tasks daily and weekly around the collection of this information.
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And once that information is collected, again, that's really just step one.
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Because now you're sitting there with quarterly reports and investor letters, capital calls, distributions, uh, K1s, uh, you know, all sorts of different information that gets spat out of a private investment.
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And every single one of those is going to look different, right?
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Particularly from one manager to the next.
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But even within managers, we see a high degree of variability in the way that reporting shows up from one fund to the next.
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Yeah.
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From one period to the next, right?
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And I think that's the reason why some of these more uh you know incumbent technologies have struggled is because they don't scale with complexity and variability, right?
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It's one thing to say, let's create a templatized system, right?
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That works really well for reliable data output, right?
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So if you take uh the example I always have to lose, uh love to use is is utilities, right?
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If you have like an electric bill or a phone bill where every month you get the same piece of paper, the usage changes, the date changes, the dollar amount changes, everything's identical, right?
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I would actually argue that things like OCR are better at that than AI, right?
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Because there's a static point on the document, it's easy to pick up, right?
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It's deterministic, right?
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It's it's purely deterministic, unlike the way I AI works.
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But I think that's exactly the point, right?
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This data is while there is a binary right answer in most cases, you can't necessarily apply a templatized deterministic approach to getting that information into a structured and standard view because it's going to come in a different format nearly every single time.
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And so that's the reality of the complexity for these folks, the allocators to these investments, where they are trying to wrangle all that information, make sense of it, take action with it, right?
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And you know, there's the added layer uh on top of this when it comes to cash management that private markets are very unpredictable.
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So, you know, you don't have a daily liquidity capability, right?
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So you have to be tracking cash, you have to be tracking and anticipating cash flows coming in and going out.
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These are really hard things to do.
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And the foundation for doing that well is starting with a clean, structured, and standardized set of data.
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Yeah, that that first mile, uh, you know, low value, just to be able to aggregate that information, normalize it, um, it takes a lot of effort.
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And in like to your point, it is normally in a lot of cases a more senior person that the CFO is getting in involved in integrating documents to be able to interpret it, the results, to be able to convert that into structured information.
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So, you know, you have low-value process and then you have a high high value resource that's being applied to it.
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So it's uh not a great combination there.
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Plus, Mark, uh, what I would say, what I would characterize as a very high cost of failure, right?
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These are the things we're talking about here are non-discretionary mission critical workflows.
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Yeah.
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And there's other things I think that we'll get into today that are, you know, call it nice to have that I think would add a lot of value in orgs, and we're excited about building into more of those things.
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But I think where we've started our focus is on things that are truly non-discretionary.
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You know, you can't miss a capital call.
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You can't not report a total wealth value to the client that you're gonna build them on.
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You can't, right?
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These things have to be done and they have to be correct.
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Yeah, tax consequences.
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I mean, like the K1, just the amount of information that's in the footnotes is is is uh you know overwhelming.
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Um let's talk about let's talk about scale.
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I mean, I I think the scalability alternatives is obviously super low.
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I think you're seeing family offices.
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Like, what is the impact when somebody takes their allocation and they move it, say, from 20% to 30%, that just that additional 10% uh allocation?
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And where does it really become a situation where you can't rely on on human capital and Excel spreadsheets, you know, in terms of number of LP investments or exposure?
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What is that breaking point?
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Yeah, it's a great question, Mark.
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And I I think in some sense it is probably varies from one firm to the next because in some sense it is a function of the headcount that you have on staff, right?
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But I think that's exactly part of the problem, is that historically folks have just linearly scaled headcount with volume to support that growth, right?
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Because to your point, it is a very hard thing to scale.
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So with that allocation two or three X's and the volume of investments that you're tracking two or three X's, uh, that pain gets really painful.
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And I think what happens there is historically a lot of people just throw more bodies at that problem.
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Yeah.
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What's tricky about that, if you take wealth as an example, I think what's tricky about that is a lot of folks in the wealth community, from the advisor perspective, you know, not just want to offer alternatives, but I think it's increasingly becoming a table stakes part of an offering, right?
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I think, especially in the high net worth and ultra-high net worth community, you know, if the wealth advisor can't offer that, uh, it's a big miss, right?
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And it's a great client acquisition and retention tool.
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And so I think it's very important for folks to be able to offer that.
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But what they often miss or don't fully appreciate until they start doing it is the increased cost side of the equation to be able to manage and administer that and charge more for it, right?
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So what that makes for a kind of a negative PL uh proposition, right?
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If you're if you're that firm, scaling that offering, while it may help you attract more clients and retain your clients, it's actually coming in at a much lower profitability than your traditional offering, but you kind of have to offer it.
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And so I think what is happening is people are realizing, yeah, if we're going from 10 to 20 or 30 percent, whether you're a family office or a wealth management firm or anybody else, that operational debt is going to accumulate quickly.
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And so getting the infrastructure layer right so that you can scale it similar to how you've historically scaled a public markets offering, is the key to being able to actually reap the rewards and the benefits that is promised by offering alternatives.
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Yeah, I mean, the technology is available today to be able to put that in place and not to wait until you're already at that breaking point.
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I I don't know.
00:18:57.279 --> 00:19:16.160
I think probably sometimes there isn't necessarily the cause and effect where the front office understands really the full impact the back office is going to have when that increased exposure comes across and they start adding adding more and more funds and they have more LP interests that they they need to track because it it without that infrastructure, right?
00:19:16.160 --> 00:19:18.559
It is very uh very linear.
00:19:19.920 --> 00:19:30.000
And and to that point, right, I think this is partly why I think we're seeing a very wide variety of profiles um, you know, showing interest and adopting solutions, right?
00:19:30.000 --> 00:19:40.319
It's because if you're a small single family office, and I I say small with all respect, I mean, obviously these are very wealthy, uh successful people, but small from an investment count perspective, right?
00:19:40.319 --> 00:19:46.400
30, 50, 80 investments would be relatively low on the scale that that we see out in the market.
00:19:46.400 --> 00:19:58.319
Uh, if you only have a five-person team, you know, two of which are focused on investments and maybe one or two of which are focused on operations and finance, that that already is giving you a lot of problems, right?
00:19:58.319 --> 00:20:09.759
And now take that to the other side of the equation where we see folks on the other end of the spectrum, large RIA aggregators, multifamily offices, institutional firms have in the multiple tens of thousands of holdings that they track, right?
00:20:10.079 --> 00:20:14.480
Yeah, it's uh it's the same problem, just a different side of the coin, right?
00:20:14.480 --> 00:20:19.519
Those family offices are they run really lean and they need to do more with less.
00:20:19.519 --> 00:20:25.359
And then the multifamily offices are they're constantly growing, or at least the good ones are.
00:20:25.359 --> 00:20:27.839
And so they need the they need scale.
00:20:27.839 --> 00:20:38.640
And it's really kind of if you can provide a solution, you're really kind of solving uh both of those problems um that that these whether you're multi or single family offices face.
00:20:38.640 --> 00:20:46.400
I mean, family offices, you you know, a billion-dollar family office is still a very small staff, you know, maybe a dozen people.
00:20:46.400 --> 00:20:56.160
It's a small organization with a ton of complexity, and then they normally have very high exposure to alternative investments, you know, ranging from 50 uh percent plus.
00:20:56.160 --> 00:21:10.319
So then to your point, there's not only the operational burden, but the cash constraints that they then have themselves on the liquidity needs that they have, the unpredictability for capital costs, because they certainly don't want to uh miss that.
00:21:10.319 --> 00:21:20.880
And then they add the problem of who needs the liquidity isn't necessarily the person that has the liquidity and to be able to straighten that out, um, it it really becomes a spaghetti mess.
00:21:20.880 --> 00:21:28.240
Uh I I think to your point, you were you you you talked about the um getting a little bit deeper on the on the valuation.
00:21:28.240 --> 00:21:32.400
Certainly, GPs have to track the operating company uh KPIs.
00:21:32.400 --> 00:21:37.839
How is that information getting through to the LPs?
00:21:37.839 --> 00:21:45.359
And how can LPs got you know do more than just that service level fund analysis and really get deeper into the portfolio?
00:21:45.839 --> 00:21:47.920
Yeah, it's the right question, Mark.
00:21:47.920 --> 00:21:50.240
And what I would say is that you know it's interesting, right?
00:21:50.240 --> 00:21:56.319
I think a lot of people will say things like, oh, you know, alternative investments are so vague, you know, there's no reporting.
00:21:56.319 --> 00:21:57.200
Uh, right.
00:21:57.200 --> 00:21:58.720
I actually would challenge that.
00:21:58.720 --> 00:22:02.799
I think an alternative industry is actually very data rich.
00:22:02.799 --> 00:22:04.640
It's just insights poor, right?
00:22:04.640 --> 00:22:12.480
I think that because the operational accessibility of that data is so challenging and so cumbersome, uh it just actually just doesn't get done.
00:22:12.480 --> 00:22:13.839
But the data is there.
00:22:13.839 --> 00:22:15.039
The data is there, right?
00:22:15.039 --> 00:22:23.680
And I think that that's something that, you know, we have a fairly unique lens into given the nature of our business and the types of information and data that comes into our platform.
00:22:23.680 --> 00:22:37.279
You know, a lot of these reports, somewhere in you know, pages 90 to 105 in the appendix, right, there's going to be some level of really interesting good data on the underlying portfolio holdings that are comprising these funds.
00:22:37.279 --> 00:22:56.240
And being able to understand that alongside your other direct investments and try to build up your portfolio from the bottom to understand exposures and risk management and allocation decision making and all of the things that should go into that process with rigor that today probably go quite underutilized because they're just too cumbersome to wrangle.
00:22:56.240 --> 00:22:56.720
Right.
00:22:56.720 --> 00:23:08.880
And so I think what we think at at Bridge is, you know, we're trying to make it so that we're taking most of the menial administrative work out of the hands of these users to give them more time back to do that deep work.
00:23:08.880 --> 00:23:12.640
And ultimately we are going to build that ourselves as well.
00:23:12.640 --> 00:23:13.039
Right.
00:23:13.039 --> 00:23:14.640
And we've already started working on that.
00:23:14.640 --> 00:23:18.240
And I think the reality is it's it's a very hard thing to build well.
00:23:18.240 --> 00:23:29.599
And part of the reason why is because take everything I've already said about how challenging it is to standardize data across different fund managers, different asset classes, even just at the fund level.
00:23:29.599 --> 00:23:34.000
That already I would say is a hard thing to do nearly 100% well.
00:23:34.000 --> 00:23:37.039
And anybody who says otherwise is is lying to you.
00:23:37.039 --> 00:23:49.279
You know, doing that now one layer deeper and really digging in at the portfolio company level where each of those individual fund marks is going to be comprised of anywhere from probably eight to 20 unique companies.
00:23:49.279 --> 00:23:56.640
And each of those companies is going to have its own metrics that matter, its own industry you know jargon that is relative just to that type of company.
00:23:56.640 --> 00:24:11.519
And so to be able to build a scalable solution that is technology based that can do that repeatably, accurately and across all the variability that we see, that's a really complex build and it's a worthwhile build.
00:24:11.519 --> 00:24:11.920
Right.
00:24:11.920 --> 00:24:16.079
But that's where a lot of the resources are going on kind of call it phase two of our roadmap.
00:24:16.079 --> 00:24:19.599
And so I think it's critical that people understand that.
00:24:19.599 --> 00:24:24.559
And I think that is going to become less of a nice to have and more of a need to have over time.
00:24:24.559 --> 00:24:28.000
I think three to five years from today that's going to be a table stakes thing.
00:24:28.079 --> 00:24:28.240
Yeah.
00:24:28.480 --> 00:24:33.279
I think today it's something very I I don't know a provider that actually does that well yet.
00:24:33.279 --> 00:24:34.720
And we're getting close.
00:24:34.720 --> 00:24:47.759
But whether it's us or others or even just teams, you know, our view is that that is the level of granularity that's going to actually unlock more participation in private markets because that's oftentimes the gap.
00:24:47.759 --> 00:25:03.920
I think the education component and the understanding component can be really challenging where historically folks would say oh yeah why don't we do 5% venture, 5% private equity and 5% real estate well what if all your real estate is landlords that own data centers?
00:25:04.240 --> 00:25:04.400
Yeah.
00:25:04.559 --> 00:25:12.640
What if all your private equity is large service providers to those data centers and all your venture is AI native companies building owners using that capacity.
00:25:12.640 --> 00:25:15.599
Your whole portfolio is is AI data centers, right?
00:25:15.599 --> 00:25:16.960
And you have no idea.
00:25:16.960 --> 00:25:32.880
And I think that is a you know one of many examples where that level of insight and understanding really at a granular level is what's going to unlock people making better investment decisions and ultimately driving the performance they want to get in a broader portfolio from the alternative slice.
00:25:33.359 --> 00:25:34.799
Yeah I think it makes a lot of sense.
00:25:34.799 --> 00:26:08.960
I mean first thing is just free them up you know we we say you know with the setup too with the ability to bring in and automate the the data acquisition point in terms of um you know bank data, transaction data, credit card data so they can pick their head up and be like well why is there so much cash in this account and and how can I better utilize that or you you know the because they struggle to even just understand you know just where that cash is and what it is and they can't take that second level type of analysis and say is this enough is this too much should I be deploying it better.
00:26:08.960 --> 00:26:20.000
The same thing's true with alternative investments obviously if you can free them up to be able to to dive in deeper do that deeper level analysis you can have better decision making better results.
00:26:20.000 --> 00:26:54.000
I I think um you know the other thing that we talked about and I find this this conversation amazing because it it's coming up more and more is is the because of AI is the the DIY type of of product approach and uh you know I'm I'm I'm old enough to remember when they the bike versus build was was a thing and and fortunately that that got put to bed uh versus you know having really quality institutional products and and software versus uh family offices trying to build solutions themselves.
00:26:54.000 --> 00:26:56.160
But it seems to be coming back with AI.
00:26:56.160 --> 00:27:03.279
AI has has brought it back where vibe coding and data warehouses and some of these other tools are available.
00:27:03.359 --> 00:27:44.319
They're amazing but what is the reality check on on DIY with with with AI and and what do what is your point of view in terms of in terms of skepticism what firms should be thinking about uh you know going down that path yeah it's this is a big topic right now and I'm having this conversation frequently um so I've got a lot of thoughts I where I would start is look I think it it's you'd be remiss not to address that there are a lot of narrow use cases, kind of widgets point solutions where you can see homegrown vibe coded things uh really coming into the fold in a real way.
00:27:44.319 --> 00:27:51.440
And I think that's going to be true for a lot of industries and for a lot of sort of specific applications within industries.
00:27:51.440 --> 00:28:08.640
I think where that model falls down and doesn't work so well is in tools that have a lot of uh let's say kind of a circle around the model that is required to actually make the use case and output work within the context of the organization.
00:28:08.640 --> 00:28:09.039
Right.
00:28:09.039 --> 00:28:11.440
And so for a couple examples on that, right?
00:28:11.440 --> 00:28:34.880
So when there's a lot of other software infrastructure and tooling and workflow integration and implementation, uh governance and security regulation, these types of things if if those are critical components to the type of industry or use case that you're talking about, that's where the idea that oh yeah, firms are just going to go out and kind of vibe code their own solution here, I think really falls down in my opinion.
00:28:34.880 --> 00:28:35.200
Right.
00:28:35.200 --> 00:28:38.960
And that's not to say that I'm incredibly bullish on AI, which I am, right?
00:28:38.960 --> 00:28:50.799
I mean I think we have a very big net beneficiary of AI and we use it frequently and you know we uh we have a budding relationship with anthropic uh at the enterprise level and those are all great things, right?
00:28:50.799 --> 00:29:05.200
And what I think from a firm perspective when it comes to homegrown builds is that it sounds a lot easier in theory than it actually is in practice when you're talking about systems where again that cost of failure is incredibly high.
00:29:05.200 --> 00:29:05.599
Right?
00:29:05.599 --> 00:29:07.119
Who's auditing all of that work?
00:29:07.119 --> 00:29:10.000
Who's entrusting the security of that?
00:29:10.000 --> 00:29:32.400
Who is managing the permissions and exceptions around that who is doing the ongoing maintenance of those you know apparatuses that are built because I really would describe it as an apparatus right if if you're just talking about a very simple I drag and drop something into Claude or GPT and say hey can you give me a summary of this yeah I think candidly business that have built their whole you know solution around that might be in trouble.
00:29:32.400 --> 00:29:57.440
But I think when you're talking about businesses where there's a high degree of interoperability you know so the bridges and the asetas of the world can talk to each other and talk to the Adipars and Orions and also to the share points and share files like there's so much actual traditional systems and infrastructure software engineering that actually goes into developing and delivering an integrated workflow solution as opposed to a single kind of point solution or widget.
00:29:57.440 --> 00:30:21.440
And so that's my general view on where I think homegrown will work and will become a disruptor to wrapper type businesses that have really just built on top of a model one-to-one versus businesses where again the AI is just an enabler or a tool that is facilitating the speed accuracy and efficiency of the product but not the product itself I think that makes sense.
00:30:21.519 --> 00:30:31.039
I do think there's a place for it maybe you know reporting last mile the ability to see uh the data the way you want to see that data there's certainly some tools out there.
00:30:31.039 --> 00:31:46.480
I do think you know you're basically getting at points around what it takes around the SDLC process and what it takes to have a professional product right there's um there's data security involved with it there's auditability and permissioning and who can see what data can you track when that data was updated who updated that data why they updated that data I think the you know you you look at a a general ledger software product like Asada you you can't be kind of right you have to be right and that that requires testing that requires a lot of logic that requires a business engine that that processes that data in a very structured and defined way that takes years to be able to create I do think that we will see probably a change in how our product gets used over time that there may be a lot fewer users in our product and that a lot of that data is being used externally that will become a data source to a data lake that's then can be being combined with an IA AI layer on top but you know that AI layer is going to be uh kind of worthless if that data is not right and not structured and not timely, you know so I I completely agree with that.
00:31:46.559 --> 00:31:46.720
Right.
00:31:46.720 --> 00:31:58.960
And I think I maybe a very simple framing of how I think about it is if I think if folks out there are selling a tool right to solve a problem, that is where there's risk.
00:31:58.960 --> 00:32:21.920
But I think if you are selling a comprehensive business outcome that is you know where AI as part of your operation is one input of several that actually delivers that business outcome, that's where there's a lot more of a mope right around building a solution that ultimately is taking real work out of the hands of those users, even if they were to employ AI themselves.
00:32:21.920 --> 00:32:25.680
I mean I always tell folks when they ask us what models we use and what's our accuracy, right?
00:32:25.680 --> 00:32:27.279
We get those kinds of questions all the time.
00:32:27.279 --> 00:32:38.160
If we just let claw vibe code with our prompting and said hey let's just ship that to our customer yeah you know we would churn every single contract that we have right I genuinely mean that.
00:32:38.160 --> 00:32:49.920
And that just goes to show you how much of a build there is around that sort of apparatus to make the outcome achievable for the customer which is ultimately what we're driving towards.
00:32:50.240 --> 00:32:58.559
Yeah and I just you know last point on this I think the other thing that the note is that as you know being in the software business it it never ends.
00:32:58.559 --> 00:33:08.960
You're you have a roadmap and your roadmap is as long as both your arms combine together and and as soon as you click off one item another item is you know three more items are on your roadmap.
00:33:08.960 --> 00:33:16.400
And that the thing that if you're going to do a a DUII and and and it's gonna be like a one and done type of thing it doesn't it doesn't happen that way.
00:33:16.400 --> 00:33:23.519
So that I think that is one of the benefits that you get with working with a a SaaS provider is that they're constantly investing in the product.
00:33:23.519 --> 00:33:44.880
They're constantly developing you know the next capabilities everything is being refined in terms of what the latest tech is security vulnerabilities all that um software is a messy business but a never ending business as I like to say um let's talk about data obviously data and AI very much interrelated.
00:33:44.880 --> 00:34:10.480
What does a firm need to do to be able to prepare for AI and how can they make AI useful in terms of especially taking a look at it it's the impact in terms of how good or how bad their data is yeah so the way I think about AI and data and the relationship there, I think AI is a massive force multiplier from an efficiency gains and productivity standpoint.
00:34:10.639 --> 00:34:12.880
But I think it is not an absolute value.
00:34:12.880 --> 00:34:15.280
It applies positively and negatively.
00:34:15.280 --> 00:34:31.519
And so I think if you have a bad foundational data structure and data model and you know layer that is actually managing all that information against which you now want to apply AI to run fast, you're gonna just compound the cracks that are in your foundational data.
00:34:31.519 --> 00:34:34.320
And you're gonna be worse off than you were before.
00:34:34.320 --> 00:34:34.880
Right.
00:34:34.880 --> 00:34:47.280
And and the reason for that is because as we all know AI can run kind of wild right for the self-iterative beast that just takes what you feed into it and outputs based on that right it's it people always say the phrase garbage in garbage out.
00:34:47.280 --> 00:34:51.679
I think you know this it's probably never been more true than it is for this you get the garbage out quicker.
00:34:51.679 --> 00:35:02.639
Yeah you get it quicker and in in more volume probably more of it yeah and then you and then you're sifting through the garbage trying to find the remnants of the good and so I what I was the problem is that that garbage kind of sounds right.
00:35:02.639 --> 00:35:09.679
Right exactly it's very easy to assume and just look at it and say yeah well it told me it so eloquently it must be right.
00:35:09.679 --> 00:35:23.199
Yeah right right it's like no it's that does not mean that it's right and so I think that you know when it comes to that relationship between AI and data I mean think about it right AI really is the application of a human based uh interpretation of big data.
00:35:23.199 --> 00:35:30.320
That really is all AI is right yeah and so when you think about that what's the first input in that whole equation is the big data.
00:35:30.320 --> 00:35:40.079
And so when it comes to being able to use AI in the way that people want to use AI, I think it really is only going to be as good as the underlying data set that's you know powering it.
00:35:40.079 --> 00:35:46.320
And so that comes back to some of the mission critical workflows, you know, not just in the financial services sector, really across the board.
00:35:46.320 --> 00:35:57.440
And healthcare is a great example there's all sorts of ways where if you want to be able to get the force multiplication and the scale efficiencies by using AI, step one is getting the house in order from a data standpoint.
00:35:57.679 --> 00:35:59.840
Yeah data governance is really important.
00:35:59.840 --> 00:36:05.679
Having real true systems that are are housing and providing that data versus Excel.
00:36:05.760 --> 00:37:14.960
I mean Excel is a people when people use Excel as a system I mean 95% of them have a material error in there you're overwriting some sort of cell there's some sort of corruption that goes on um great reporting tool lousy system uh uh tool so I think that those are really important takes let's talk about I mean the market is moving so fast um that they have a five year outlook I I I I think is is is a bit much but even just taking a look at three years out what is gonna be what is that market landscape looking like what should operators be thinking about today to be able to future proof their business drive those better results and what is the state of tech going to be in three three to five years yeah it's a good one I I I think you know the bet that we're making is that the platforms that will win will be just that they'll be true platforms right they will be platforms that can go end to end within a vertical so that the users of those platforms can manage a you know serviceable set of solutions that can all talk to each other.
00:37:14.960 --> 00:37:15.920
Right?
00:37:15.920 --> 00:37:31.039
I think that will be the tech stack of the near future it will be I don't have 25 point solutions for my business I might have five platforms that you know each serve their their purpose across general functions right.
00:37:31.039 --> 00:37:39.760
So within this space in particular I think you know what you'll see is a platform focus you know that is managing private markets and alternative investments.
00:37:39.760 --> 00:37:59.280
And that's probably going to be everything from the pre-trade workflows around the due diligence and sourcing and evaluation to the point of transaction uh to the post-trade management and reporting of data and insights into that data which really is a virtuous flywheel that feeds right back into the pre-trade decision making process right as I spoke about before.
00:37:59.280 --> 00:38:01.440
So that's what I think will happen there.
00:38:01.440 --> 00:38:09.519
And then you know you'll have your consolidated reporting solution that is taking into account public marketable securities and fixed income and everything else.
00:38:09.519 --> 00:38:14.639
And those two have to talk to each other in a very very embedded way that's going to be table stakes.
00:38:14.639 --> 00:39:05.519
You'll need a cash management you know GL uh accounting solution that's also table stakes non-negotiable and then you know especially in the family office space you're probably gonna have to have some sort of general bill management uh kind of bill pay tool maybe that's integrated into the accounting solution maybe not we'll see I can see those lines blurring uh and then you might have finally a general kind of CRM note taker client management you know tool uh that again can also pull and push data into all of those different places right that is how I think about where this industry will go and so I think as folks are building in the space I think it's important to have an eye towards what is core right what should be on the roadmap and how does it fit into one of a handful of mission critical verticals in terms of how those folks think about their business.
00:39:05.519 --> 00:39:06.320
Right?
00:39:06.320 --> 00:39:08.559
And can you own one of those core verticals?
00:39:08.559 --> 00:39:14.079
I don't think we're gonna get all the way to a bundled solution where you just have a one-stop shop for everything.
00:39:14.079 --> 00:39:40.960
Yeah right I mean we've seen this happen in so many industries look at look at cable and streaming I mean it's just the a never ending up and down of bundling unbundling bundling unbundling right I I think that generally you know you always reach oversaturation on one end of that side where you say ah you know jack of all trades master of none I need a better point solution for these discrete things because it's not working well versus oh I have 25 unique things and they don't talk to each other and this is really painful.
00:39:40.960 --> 00:39:41.280
Right.
00:39:41.280 --> 00:39:45.440
I think we're always going to modulate between those bookends and land somewhere in the middle.
00:39:45.440 --> 00:39:49.920
And uh that middle to me, you know, looks like kind of what I mentioned there.
00:39:49.920 --> 00:39:50.159
Right.
00:39:50.159 --> 00:39:54.960
So that's our take and and that's sort of how we're building with that in mind.
00:39:54.960 --> 00:40:05.760
And I think specifically on alts right what that means is I think you know you're going to have to be able to make that overall experience feel nearly as easy and seamless as Publix.
00:40:06.800 --> 00:40:07.840
Yeah I think that makes sense.
00:40:07.840 --> 00:42:06.639
I think that platform approach is it's you know best best of breed versus all in one but best of breed is not a thousand points alight right it it's it's you're gonna have key platforms and then you're gonna have systems that that hook off the you know the front office you're gonna have a private investment platform a public investment platform that one's going to get custodial data and market prices the other one's gonna hook into uh the the GPs on the we're on the other side the financial platform and we're gonna hook in with a bill payment provider we're gonna hook in with banks for data and credit card data um but you you know you're gonna need to have these key pillars that that you you base your your operations on your your your business on and then those key platforms need to be able to connect in in meaningful ways i i think the other thing that the family offices too often do or are solving yesterday's problem that they'll go get a point solution that to solve an immediate pain point without taking a look at what is the overall strategy where is my business going how do I support my business uh technology you know the future proof it versus you know next thing you know you end up with a dozen point solutions none of them talk to each other there isn't a cohesive strategy because you're solving these kind of one-off problems that uh yeah you know and the other thing I think that's going to become very important uh from an alt standpoint is the idea of real-time data and I think that's becoming possible by building integrated workflow solutions right business outcome focused products um where you necessarily have visibility into the workflows not just the static data points you know typically backward looking by you know a couple months or a quarter but true real-time information that is uh some combination of backward looking reporting and real-time cash flow activity to create a real-time view into things because I think operating on that lagging quarterly data is just something people won't accept anymore as the status quo.
00:42:06.639 --> 00:43:03.840
Yeah right right right that's that's uh people people want more timely information and that they think that the most timely information you get is a is over a quarter old is is cut outdated right so people are even like yeah what do you mean even yesterday I want I want today I want right now is always been the trend has been the trend for a while well Alex has been great I like to end the podcast on a personal note with three questions that have nothing to do with wealth tech you're from Southern California you're now in Boston what has been the biggest cultural shift for you what has got a surprised you the most about moving to Boston that somebody can give you the heads up on let's see there's probably a couple um one I would say is you know I'm a liability to myself being a Lakers fan out here so that's you know that's a bit hard um gotta be gotta be careful there.
00:43:04.079 --> 00:44:11.519
One of the greatest rivals in sports though I will say that oh yeah always uh so you know that's been that's been a tough one so I got to be a little careful you know during playoff season how I how I play that although looks like for the Celtics got them get me going yeah um so you know that's been one another is uh you really just can't get attached to a sunny day here you know I mean California you just have like 15 of these in a row and here you kind of you you have one you really got to cherish it and seize the day because tomorrow's probably gonna rain um so you know so that's been another yeah I think I I I used to work for uh a company in LA and I I think the how maybe somebody from the Northeast and in particular from Boston the communication style can be a little different a little a little bit more forward than people are are used to I I think also if we're starting to take the piss out of you that means we like you so um people can you know not be used to that Look, I will say on the on the positive side, you know, it's got a neighborhood feel, which is very different, right, from Los Angeles where I grew up, which is a conglomerate of suburbs, right?
00:44:11.519 --> 00:44:12.480
It's just sprawling.
00:44:12.480 --> 00:44:15.199
And uh, you know, here it's very walkable.
00:44:15.199 --> 00:44:21.119
Most of our life is walking, um, you know, between adjacent neighborhoods, and that's sort of a nice thing.
00:44:21.119 --> 00:44:27.599
And there's also a very high density, I would say, of kind of the intellectual and financial services community in Boston.
00:44:27.599 --> 00:44:32.239
And uh, you know, so it makes for kind of fun organic interactions with people.
00:44:33.039 --> 00:44:33.440
Love it.
00:44:33.440 --> 00:44:35.199
What's your favorite spot in the city?
00:44:36.320 --> 00:44:48.159
Oh, it's probably I I like doing like a walking loop, uh kind of through the common and then back around up to Charles River and and kind of ending through the back bay, kind of old, you know, district and commonwealth and all around there.
00:44:48.159 --> 00:44:50.000
Very uh very charming walk.
00:44:50.239 --> 00:44:50.559
Nice.
00:44:50.559 --> 00:44:55.920
Yeah, I used to uh I used to live in Watertown and then I worked uh down the seaport.
00:44:55.920 --> 00:45:02.320
That's back when the seaport was all like mud parking lots, and uh worked at the World Trade Center down there for Fidelity.
00:45:02.320 --> 00:45:06.559
And I used to ride my bike uh in the work, probably 100-150 days a year.
00:45:06.559 --> 00:45:10.079
I I commute by bike um along the Charles River.
00:45:10.079 --> 00:45:12.639
And you know, you're watching the sun come up over Boston.
00:45:12.639 --> 00:45:16.320
It was like, you know, not too many people say I really miss my commute.
00:45:16.320 --> 00:45:22.719
I that was like I I really uh enjoyed that and you know, cutting through Chinatown at 7 a.m.
00:45:22.719 --> 00:45:23.599
and stuff like that.
00:45:23.599 --> 00:45:25.280
It was it was always great.
00:45:25.280 --> 00:45:30.639
Um, I will say, probably, you know, somebody better, a good good afternoon.
00:45:30.639 --> 00:45:36.800
You got a Saturday afternoon in Boston, go to Pizzeria, Regina in the north end, the real one.
00:45:36.800 --> 00:45:45.119
Get yourself a couple slices and then head over, you know, walk the bridge over to Charlestown, go to Warren Tavern for a pint or two.
00:45:45.119 --> 00:45:48.400
It's uh it's a classic Boston afternoon.
00:45:48.400 --> 00:45:53.039
Um finally, uh you you what's your Dunkin' Donuts order?
00:45:53.039 --> 00:45:53.760
You go to.
00:45:54.480 --> 00:45:55.119
Oh man.
00:45:55.119 --> 00:45:57.519
Uh it's probably gotta just be an ice coffee.
00:45:57.519 --> 00:46:00.400
I mean, it is really surprisingly good.
00:46:00.400 --> 00:46:02.880
I've never subscribed to the whole Duncan thing when I moved here.
00:46:02.880 --> 00:46:03.519
I just didn't get it.
00:46:03.519 --> 00:46:06.880
I'm like, why do you guys all get a coffee at this you know donuts store?
00:46:06.880 --> 00:46:08.480
But um, I will give them credit.
00:46:08.480 --> 00:46:09.760
It's a good iced coffee.
00:46:09.760 --> 00:46:13.360
Um, but I'm also kind of partial to the local neighborhood thinking cup.
00:46:13.360 --> 00:46:15.760
Uh yeah, I like to support the local coffee shop.
00:46:15.840 --> 00:46:17.039
So uh there you go.
00:46:17.039 --> 00:46:21.119
Uh I'm uh yeah, Dunkin' Donuts ice coffee is is unicorn blood.
00:46:21.119 --> 00:46:22.079
I don't know how they do it.
00:46:22.079 --> 00:46:23.679
I can't make it at home.
00:46:23.679 --> 00:46:27.360
Uh I'm uh I'm Duncan's ice black because it's perfect.
00:46:27.360 --> 00:46:30.239
Like, don't put anything in it all year long, too.
00:46:30.239 --> 00:46:31.920
Uh I love it.
00:46:31.920 --> 00:46:34.159
And then I think their hash browns are a good little treat.
00:46:34.159 --> 00:46:36.880
So that's I'm I'll put in a plug in for their hash browns.
00:46:36.880 --> 00:46:41.039
But Dunkin' Donuts ice coffee is uh it's perfection.
00:46:41.039 --> 00:46:48.639
And it doesn't travel, like you get a Dunkin' Donuts in like New York or something, you're like, ah, it's just not as good, but um in the water.
00:46:48.639 --> 00:46:51.199
It must be that dirty water.
00:46:51.199 --> 00:46:53.440
So all right, Alex, this has been great.
00:46:53.440 --> 00:46:55.599
I really appreciate you coming on the podcast.
00:46:55.840 --> 00:46:56.880
Yeah, no, my pleasure.
00:46:56.880 --> 00:46:58.079
Uh great conversation, Mark.
00:46:58.079 --> 00:46:58.800
Thanks for having me.
00:46:58.800 --> 00:47:00.320
Good stuff.
00:47:03.119 --> 00:47:09.199
Thank you for listening to this episode of the Wealth Tech Podcast, brought to you by Seta AI.
00:47:09.199 --> 00:47:19.039
I really appreciate you listening, and don't forget to subscribe so you don't miss future episodes.
00:47:19.039 --> 00:47:20.639
Talk to you soon.