E321: Why Most LPs Have No Idea What’s in Their Portfolio

So for context, give me a sense for how big ARCH is today.

Today we support about 550 clients.

Our clients have 405 billion in assets on the platform.

We're a solution for tracking, managing, and automating all the paperwork around

alternative investments. So the assets we serve are

stakes in private equity, hedge funds, venture,

credit fund, some crypto, anything that's not a stocker bond.

So you support 405 billion in assets.

Tell me about what that looks like.

Without ARCH and PreArch, a lot of LPs are logging manually into all these different

data rooms. They're going into interlinks,

CARDA, Juniper Square platforms. They're not necessarily built for

LPs to understand their investments and they're getting PDFs that they then have to read

and put into spreadsheets. We go get all that data and that

information on the LPs behalf. Our clients are global banks,

RAs, about 200 single family offices, institutional allocators,

and they use us to get all the information from

these different funds and their fund platforms,

structure it, standardize it, and then give them

dashboards, analytics, and insights on their portfolio.

So we become kind of like the Schwab-like operating system bringing to private

markets what platforms like Schwab and Robin are up to public markets.

How does that practically help LPs?

One, it saves them a lot of time. So instead of having to hire analysts who spend

a lot of their day going into portals and platforms,

they can now have that process automated for them.

The second thing is it's really hard to make good decisions if you don't

understand your data and understand the signal within your data,

and so it could be things like has this manager performed or how much

unfunded commitments do I have across these investments or these entities

down to how much SpaceX do I have in my portfolio and how much drive do I have,

and am I a net buyer or seller at this price? So we give our clients

an unfair advantage as they look at the market by helping them understand

their existing data and then also power some of their future facing decisions.

What are some second order effects of that?

What it's hard for people to make allocations new funds if they don't know how much they

owe to pass funds. So a lot of folks don't have good understanding of the liquidity needs

in their portfolio, and do they have $1 million or $5 million or $10 million of

net commitments that are going to be called in the next one, three, five years?

So that's one important consideration. The second is being able to understand the performance

of the funds you're in when you're looking to re-off within those managers,

and they're also introducing AI tools around evaluating new opportunities.

So when a manager sends you their docs, it's 100 page limited partner agreements that have

deeply buried on page 69, the terms and the carry and how expenses are treated within the fund.

We can use AI and especially AI model to pull that out automatically and give people a full

read out of what should I know about this fund as I'm looking to invest. And so those are some

of the newer tools that we're developing around driving better decisions through data.

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I kind of think about this as mental compute. So you could only handle so much abstraction

so much thought in a single day. And if you're spending that compute on figuring out what's my portfolio,

how much compute do you have leftover for sourcing new managers, building relationships, fundraising,

all those things that LPs needs to do? Yeah, exactly. I think we don't value our time well.

And we get sucked into a lot of distractions that are not what we actually need to be working on.

And so it's not a good allocation of someone's time to spend that time taking numbers off of a

document and putting it into a spreadsheet. That time should we spend and thinking about

what investments do I need to make? How do I actually want to allocate capital if I want to invest

in vitro capital or private equity? Who are the best managers and how do I get a warm introduction

to them so that I can invest in their fund? As an LP goes from 10 to 25 to 50 fund investments.

What typically breaks down? They typically at some point need to hire someone. They might already

use an investment advisor. This is why about half our business comes through investment advisors

and banks today where they're doing this work on behalf of their clients. But then those advisors

often have high hundreds or thousands or tens of thousands of investments. And then it is just

utter chaos. They're getting emails every day, every hour of the day that need to be responded to.

And then you have to make sure you don't miss capital calls that you route all the K ones to the

count in this on each account that you receive distributions correctly that you could reconcile

that the distributions arrived and the capital calls went out. And then you're not even thinking

about what's in an investment letter or what do I actually need to know about these investments?

They don't tracking becomes a job and it becomes a job that often is handled by teams once you get

into high volume of these investments. What's the legacy solution before Arch? What were family

offices doing? Mostly manual. So you'll see family offices that have a few analysts,

they're managing this information on Excel spreadsheets. Maybe they're using a reporting system,

but they're updating that reporting system manually or in GL. And then we've seen family offices

that grant the capital call and put it on someone's desk and then someone goes through the paper on

their desk to pay capital calls. One of the most under-appointed things in private markets is that

most of institutional capital has allocated. They've picked their 1525 core managers and they're

continuing to invest with those. Where is the net new capital coming from in terms of from the LPs

investing to GPs? This is a big issue for managers that are starting to raise now or trying to raise.

We were speaking at a conference last week and learned in the presentation that from 2022

peaks to today, especially the Vintrasa class. The Vintrasa class raised 15% of the capital in 2025

that are raised in 2022. So there's just significantly less LP dollars being committed,

especially to venture capital because most of the major institutional investors, especially

endowments, are overexposed to Vintra specifically into privates, so not allocating to new venture

managers, even if the manager has returned well. Luckily, this is coinciding with a big shift of

capital coming from the wealth channel. And so you see family offices that have been investing

in privates for a long long time, but there's more family offices today and family offices seem

to be increasing their allocation to privates, but especially RAA's registered investment advisors,

multifamily offices in the bank channel are kind of making up for a lot of that gap, because

they're significant that your dollar is coming from that channel. Do you see that in the numbers?

We see this in the numbers where we'll serve firms. They'll say this wasn't a problem for me three

years ago, five years ago, because I had a couple hundred alternative investments and most of my

clients weren't investing in this asset class, but now people are asking for these investments,

and then they now have a thousand or three thousand or five thousand positions,

then need to be tracked and managed. There's no real morning star for private funds. Why is

it so hard to value private funds? Everyone reports differently. Today, we collect information from

50,000 unique investments, and they all use a different format, a different portal,

do their accounting slightly differently, and all the status trapped in PDFs. And it's super

fragmented. Like the top producer of information produces about seven percent of the information

that we received today. And so we collect from 800 different portals and platforms. There's

just a lot of work needed to go first, get all the documents and structure the data, then standardize

it, and understand the accounting treatment of every fund and every fund admin, and then make it

useful. So it's a lot of work that we've done over the last eight years to get to this point,

but there just isn't a consistent standard around how data is distributed, even something like

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slash Invest Free today. Do you see that evolving? Yes and no. One of the kind of issues with

the OPA adoption is OPA is really only pushed by large institutional LPs. And so funds that are

sub-institutional have no incentive to adopt something like an OPA. And I think you probably will see

more APIs become available and more push for standardization. But this is an industry that moves

extremely slowly. So we're kind of in the background working on some efforts around standardization

and creating kind of like a standard API that people can push data into and pull data out of.

So this can be kind of like the plat of its category. But we're not waiting for that to happen.

So we're kind of meeting the industry where it is today.

And you see ground truth data on where LPs are allocating today. Are there new asset classes or

new structure where LPs are really deploying capital? We see a lot of just like evolution of what is

the hot manager or the hot strategy. So there's shifts. It has seemed like some of the conversations

that hedge funds are back and people are interested in more hedge fund investing. We're seeing a lot

of fund funds pop up around kind of like hedge fund exposure and being a fund fund for hedge funds

specifically. And so there's always kind of like a little bit of a trend. And then there's also

the like independent sponsor world where we're seeing a lot of dollars go in that direction as well.

I want to actually talk about that. So a lot of LPs privately tell me that they're not as interested

in blind pool funds. They're interested in investing independent sponsors or co-invest.

How much do you see that actually in your data? And are LPs really starting to deploy more into

deal by deal vehicles? And this is more of kind of qualitative from the conversation we're having

with clients with LPs. But they are we're seeing them deploy more in kind of deal by deal structures

and independent sponsors. And I think it's hard. It kind of goes back to some of the pain points

around private markets where if you invest in a drawdown fund and you don't know how over what

period your money is going to be called, there's a drag on your returns of needing to do something

with a capital and then in the meantime, if you invest in an independent sponsor or typically

investing 100% on day one. And we've seen like pretty good results from some of those investments.

And I think there's an ability to just be really intentional about what you're actually investing

in. And I think there's also like a little bit of a different duration on those assets that

sometimes makes it more appealing to the LP. Comes down to not only economics, but also

discretion. Also, you don't have to wait for your capital to be called over two to three years,

you get to deploy it. So there's less drag on the muscle. Exactly. What excites you most today,

Q1, 2026 of private markets? I think there's just a huge amount of opportunity like we're

rescinding in Q1 in 2026. You're probably going to see some major IPOs later this year. And I think

rush of liquidity back to the market is going to be really exciting for the market because people

have been missing liquidity and missing DPI over the last couple years. And so I feel like you'll see

a bit of a renaissance when people feel that there is a return from these investments that they've

made and start to see strong return from some of the private asset classes that they've invested in.

Then you can see a lot more capital be redeployed back into this asset class. And we think that

there's just like a lot of opportunity with private markets becoming a little bit more efficient and

a little bit easier to interact with to be able to get in and out of positions in the right time

horizon that can fit the return profiles and time horizons for different allocators where you might

be someone that needs a lower return, but needs a little bit more liquidity or willing to trade

that off for someone that wants to stay and invest it for a long period of time. Just to put some more

meat on the bone, historically the Yale, the David Swenson Yale model required roughly 24%

liquidity. And that's been the historical liquidity on the DPI basis for many decades.

In 2024, it was 9%, then 2025, it was 9% again. So more than half of DPI versus the traditional

model. So models are literally breaking for LPs in terms of their ability to deploy capital,

get it back and redeploy into future ventures, which makes it really hard.

Then if you're a venture firm and you're trying to go raise money from some of these LPs,

if they haven't seen DPI, and then they won't be able to reallocate dollars back to managers.

So I think the industry is a little bit lopsided right now. You need liquidity to flow back into

the market for it to, you know, to work effectively. Some LPs are pressuring manager specifically

venture managers to get secondary. Do you see a significant amount of secondary going on

portfolios? We're definitely seeing a lot of secondary managers pop up to meet the needs of,

I think, that LP demand. So whether it's an LP lead secondary, where an LP is trying to sell

their positions in certain funds, and never that spreads are tightening on that side. So where

certain funds might have been sold at a 30% discount or 25% discount. Now maybe it's like a 20 or

15% discount to NAV. So that's one thing. But also like going to early employees and early

investors and buying out full stakes. I think there's just so much capital tied up in private

markets that I think it's healthy that you have more ability to sell shares when you're three,

five, 10 years into an investment. One of the things that's always confused me is if I have

single stock exposure in a public company, I could go to my fidelity and borrow at a pretty low rate.

But if I have private exposure, I essentially can't borrow anything against my private exposure.

What needs to change for that to become a real institutional instrument in the market?

It's a really interesting one. And we were talking to the Bitwise folks about this a couple of

months ago. And they made me aware that if you own Bitcoin, very hard for you, if you own Bitcoin,

you cuss you yourself or you cuss you with one of the large custodians to get a loan against

that Bitcoin. But you can trade that Bitcoin for Bitcoin ETF and then the banks will give you a

loan against that Bitcoin. And it's actually like more efficient and allows you to create liquidity

off of those holdings. I think you'll see similar things happen in private markets where it's

really hard to lend against something that you can't cuss you or you can't really understand.

But several large banks and a lot of different funds are now trying to figure out how do we

provide liquidity via a credit type of product to private markets. And so it's something people

are looking to solve. Historically, I've seen that the LTVs and the interest rates on loans against

private market assets are not competitive with loans against public market assets. So if you have both,

you're going to take your loan against your house or your public market assets. But I think that

will start to change as you see concrete data around these assets and how they're being priced

in more robust markets around these assets as well. It seems to me that some way between zero and

a hundred, some basket of private assets should be able to be bundled together to loan against. But

for some reason, institutional investors and or even family office did not figure out how to

monetize that. Yeah. And there's definitely some funds are creating credit-like products where

well, they collateralize large swaths of like a founder's equity and then give them credit,

a credit product today, which is like pretty tax-efficient. So they don't have to have a liquidity

of in our tax event in order to create a little bit of liquidity on their their stake. And to

see a couple of funds that have created really strong businesses around it, but not yet something

that's like fully adopted across the full market. I want to double click specifically on the AI.

How are you using AI to streamline this process? So we're a bit customers today.

I have a few foundational models. There's a few things that are key here. One is taking more

qualitative data. So stuff like financial statements and investor letter places where there's

like interesting insights to glean out of these documents and summarizing it in a client-friendly

consumer-friendly way. So giving you a like here's the five things you need to know about

the latest performance of this venture fund or this real estate manager you're in. Then

they're structuring real like quantitative data. So pulling out the latest value in

unfunded commitment and cash flows out of cash flow statements because then you have structured

data that you can use for reporting or to understand what your current balance he looks like.

Having this treasure chest of data at your disposal, what surprised you the most?

Let me start the company in 2018. We probably thought that like there would be far less nuance

than there actually is in this industry. It just seemed like it was simple. It's like okay great.

We go collect all the K1s and we organize them and then an accountant can do someone's taxes

without bugging the individual or their wealth manager. We just get all the statements. We digitize

them and then you'll know exactly what your investments are worth and how they're doing.

But I think just understanding all the different nuance of like okay some fund admins will tell

you that documents are available but they're actually not available because the system is so

overwhelmed with sending the documents that you can't actually can't receive the documents from

that system but they just don't expect anyone to log in as fast as we're logging in to go get those

documents. So there's a lot of those nuances that we've learned over the last eight years

looking like fun admin by fun admin custodian by custodian fun by fun to understand the like

underlying structure of the data that we're being sent. There are says this little known thing where

most of the data feeds in the market don't match the documents. So documents are really a source of

truth and data feeds are oftentimes wrong. We found this with like some of the biggest banks in

the country and we found this with the largest custodians and also data feeds for like public

markets as well that the information just doesn't reconcile which is crazy when you think about the

amount of money that's being kind of described through documents and through data feeds that the

information is not correct. So we're often recreating data feeds when an investment is being

custied via custodian but the information that's flowing through to the reporting system is not

correct. So we're creating like a higher fidelity level data and I would just expect it that if

a banker or custodians send you data that it should be correct. If you go back eight years ago

when you were just starting arch was one piece of advice you'd give a younger version of yourself

that would have either helped you accelerate your career or helped you avoid costum sticks.

We probably could have moved a little bit faster and hard a little bit faster at the beginning.

We were three co-founders myself and two MIT engineers Jason and Joel who studied

computer science and math and MIT and then three years later we grown to MIT team of five and

passed our first billion in assets on the platform and we were just really methodical in building out

the first versions and really understanding our customers and working side by side with our

customers to figure out what does the experience need to be but we probably could have believed in

ourselves a little bit more in the early days and invested a little bit more quickly but we're

very conservative on the first 500k that we raised as a company.

What were some of the mistakes you made personalized as you were building your organization?

I think we were lucky that we haven't really made that many big mistakes from a personal

perspective like we've been very intentional in a lot of the hiring that we've done like the

fourth person we hired now runs all of operations at arch and is this amazing force of nature

and she has kind of been an amazing partner for the business and kind of pushed us in a lot of ways

and we've been able to find people along the way that have been really strong culture carriers that

have pushed the thinking within sales or product or engineering. I'd dinner with a chairman of one

of the largest banks and he said his higher rate on truly great hires was 50% of his career.

Yeah I'd been hiring for decades. What's your key to success? How did you get the personal

decisions? So correct. We have a lot of different lenses in the hiring process so I'm looking for a

certain thing like Oak Islander Jason's looking for a certain thing about our operations team is

looking for something different so for key roles we're able to kind of all look at the candidate

via different lenses and make sure that they pass our various tasks. We want people to come in

and really want to work hard and hustle understand that like we are a builder culture but also a

kind culture so we generally across entire company everyone's kind if you don't have folks that

don't fit that cultural norm and folks that like really care about customers and our long term

oriented and we kind of have this like 1% better everyday mentality across the company.

Ryan this has been absolute masterclass thanks so much for jumping on the podcast looking forward

to having this conversation live. Likewise thanks David great chatting with you.

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