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

Summary

Podcast Summary: How I Invest with David Weisburd

Episode 321: Why Most LPs Have No Idea What’s in Their Portfolio
Host: David Swan
Guest: Ryan (Arch Founder)
Date: March 10, 2026

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Overview

This episode centers around the critical challenges institutional investors (LPs) face in tracking, understanding, and optimizing their exposure to private market investments. David Swan interviews Ryan, founder of Arch, a platform that automates and standardizes alternative asset tracking for LPs with over $400 billion in assets managed on their platform. Core topics include the limitations of legacy systems, the nuances and fragmentation of private market data, the adoption of tech and AI for better decision-making, and how current market trends are forcing LPs and managers to rethink their strategies.

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Key Discussion Points & Insights

1. State of Private Market Portfolio Tracking and Operations

• [00:04] Arch currently supports ~550 clients with $405B assets, acting as an "operating system for private assets" similar to Schwab for public markets.
• [00:31] Legacy LP workflows are highly manual, requiring logging into disparate datarooms (Interlinks, Carta, Juniper Square), extracting PDFs, and manually entering data into spreadsheets.
  • Quote ([00:31], Ryan): “A lot of LPs are logging manually into all these different data rooms... getting PDFs that they then have to read and put into spreadsheets. We go get all that data... and give them dashboards, analytics and insights on their portfolio.”

2. Benefits and Second-Order Effects for LPs

• [01:20] Automation saves significant time and reduces the need to hire analysts solely for administrative tasks.
• [01:20] Structured, standardized data empowers LPs to make more informed allocation decisions, track exposures, and optimize their investments.
  • Quote ([01:20], Ryan): “It’s really hard to make good decisions if you don’t understand your data and understand the signal within your data..."
• [02:07] Better clarity of liquidity needs and unfunded commitments—critical for avoiding over-allocation or missed capital calls.

3. AI and Data Standardization in Private Markets

• [02:39] Arch is rolling out AI tools to extract and summarize complex information from documents (e.g., LPAs), making fund terms and exposures transparent.
  • Quote ([02:39], Ryan): “We can use AI and a specialized AI model to pull that out automatically and give people a full readout of what should I know about this fund as I’m looking to invest.”
• [13:15] Industry’s move toward standardization (e.g., ILPA templates, APIs) is slow and primarily driven by large institutional LPs.
  • Quote ([13:15], Ryan): "Funds that are sub-institutional have no incentive to adopt something like an ILPA… we’re kind of meeting the industry where it is today."

4. Scaling Portfolio Complexity and Investment Advisors

• [05:17] As LPs’ portfolios grow—from 10 to 50 fund positions—the administrative burden explodes, often requiring teams or outsourcing to advisors and banks.
  • Quote ([05:23], Ryan): “They’re getting emails every day, every hour... And then you have to make sure you don’t miss capital calls, that you route all the K1s..."
• [06:18] Family offices historically manage manually—Excel, paper tracking, and even physical distribution of documents for processing.

5. Flows, Trends & Market Dynamics

• [06:57] Major institutional capital in privates is largely “locked in,” with net new allocations increasingly coming from family offices, RIAs, and the bank channel.
  • Quote ([06:57], Ryan): "Most of the major institutional investors, especially endowments, are overexposed to venture specifically and to privates..."
• [08:18] Data fragmentation: 50,000 unique investments tracked, 800+ portals, no standardization—top data producer contributes just 7% of Arch’s inbound info.

6. Shifting Asset Classes and Structural Innovation

• [14:07] There’s renewed interest from LPs in hedge funds and fund-of-funds, as well as in independent sponsors and more deal-by-deal (rather than “blind pool”) vehicles.
  • Quote ([14:53], Ryan): “We’re seeing them deploy more in kind of deal by deal structures and in independent sponsors...there’s an ability to just be really intentional about what you’re actually investing in.”
• [15:54] Upcoming IPOs and “Renaissance” expectations: increased liquidity events may re-invigorate redeployment to private markets.

7. Liquidity, Secondaries, and DPI Crisis

• [16:51] Traditional models (Yale/Swensen) called for ~24% DPI/liquidity—2024/2025 saw only 9%, straining LPs’ ability to redeploy.
  • Quote ([16:51], David Swan): “Models are literally breaking for LPs in terms of their ability to deploy capital, get it back and redeploy into future vintages.”
• [17:53] Secondaries market is more active, with discounts narrowing (from ~30% to 15-20% NAV), reflecting pent-up liquidity demand.

8. Private Asset Leverage and Structural Barriers

• [18:35] LPs struggle to secure credit lines against private holdings—unlike with public securities or even crypto ETFs. Collateralization remains niche and generally not efficient.
  • Quote ([18:58], Ryan): "Historically I’ve seen that the LTVs and the interest rates on loans against private market assets are not competitive... I think that will start to change..."

9. AI-Driven Insights and Surprising Market Realities

• [20:52] AI is being used to summarize qualitative fund documents for clients and to programmatically structure quantitative performance data.
• [21:47] Ryan is surprised by pervasive data discrepancies in financial reporting—even large custodians and banks often have inconsistent or incorrect data feeds; the source documents are the only reliable reference.
  • Quote ([21:47], Ryan): “Most of the data feeds in the market don’t match the documents... 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.”

10. Company Building: Lessons and Hiring

• [23:36] Advice to his younger self: grow (and hire) more confidently, earlier.
• [24:19] Personnel success derived from a multi-lens, rigorous hiring approach focused on hustle, kindness, client orientation, and continuous improvement.
  • Quote ([25:01], Ryan): “We have a lot of different lenses in the hiring process...We kind of have this 1% better every day mentality across the company.”

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Notable Quotes & Memorable Moments

• "We become kind of like this Schwab like operating system, bringing to private markets what platforms like Schwab and Robinhood brought to public markets.” — Ryan ([00:31])
• "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." — Ryan ([04:49])
• "It just seemed like it was simple... but I think just understanding all the different nuance... there also is this little known thing where most of the data feeds in the market don’t match the documents." — Ryan ([21:47])
• "Models are literally breaking for LPs in terms of their ability to deploy capital, get it back and redeploy into future vintages." — David Swan ([16:51])

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Timestamps for Important Segments

• 00:04 — Scale and scope of Arch’s current platform
• 01:20 — How automation & data structuring gives LPs an edge
• 02:39 — Introduction of AI in evaluation of fund documents
• 06:57 — Shifting LP allocation trends and where new capital is coming from
• 08:18 — Why private fund reporting is so fractured and difficult
• 13:15 — Industry movement (and resistance) toward standardization
• 14:53 — Rise in independent sponsor and deal-by-deal interest from LPs
• 16:51 — Historic vs. current (much lower) liquidity rates (DPI) for LPs
• 17:53 — State of secondary markets amid liquidity crunch
• 18:58 — Structural barriers to lending against private assets
• 20:52 — AI’s impact on qualitative and quantitative data extraction
• 21:47 — The unexpected complexity and error-proneness in industry data feeds
• 23:36 — Lessons learned in company growth and hiring

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Conclusion

This episode offers a deep, pragmatic dive into the struggles and solutions for institutional investors managing alternative assets. Ryan highlights the friction and fragmentation plaguing private market data, the necessity of new automated, AI-powered platforms, and how current market constraints are shaping next-generation portfolio construction and liquidity.

The discussion balances tech innovation with real-world operational insights, making this a must-listen for any allocator, family office, or service provider serving LPs in private markets.

Transcript

[David Swan]

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

[Ryan]

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 stock or bond.

[David Swan]

So you support 405 billion in assets. Tell me about what that looks like

[Ryan]

without Arch and Pre Arch. A lot of LPs are logging manually into all these different data rooms. They're going into Interlinks, Carta, Juniper Square, platforms that are 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 LP's behalf. Our clients are global banks, RaaS, 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 this Schwab like operating system, bringing to private markets what platforms like Schwab and Robinhood brought to public markets.

[David Swan]

How does that practically help LPs?

[Ryan]

1, 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. 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 stripe 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.

[David Swan]

What are some second order effects of that?

[Ryan]

It's hard for people to make allocations to 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, the performance of the funds that you're in when you're looking to re up within those managers. And they were also introducing AI tools around evaluating new opportunities. So when a manager sends you their docs, it's a hundred 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 a specialized AI model to pull that out automatically and give people a full readout 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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[David Swan]

kind of think about this as mental compute. So you could only handle so much abstraction and 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 left over for sourcing new managers, building relationships, fundraising, all those things that LPs need to do?

[Ryan]

Yeah, exactly. I think we don't value our time well and we get sucked into a lot of distractions that are, are, 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 a document and putting it into a spreadsheet. That time should be spent in thinking about what investments do I need to make, how do I actually want to allocate capital if I want to invest in venture 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?

[David Swan]

As an LP goes from 10 to 25 to 50 fund investments, what typically

[Ryan]

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 K1s to the accountant that's on each account and 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 like what's in an investment letter or what do I actually need to know about these investments. The tracking becomes a job and it becomes a job that often is handled by teams. Once you get into high volume of

[David Swan]

these investments, what's the legacy solution? Before Arch, what were family offices doing?

[Ryan]

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 their reporting system manually or gl. And then we've seen family offices that print the capital call and put it on someone's desk and then someone goes through the paper on their desk to pay capital calls.

[David Swan]

One of the most underreported 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?

[Ryan]

This is a big issue for managers. They're 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 venture asset class. The venture asset class raised 15% of the capital in 2025 that it 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 venture specifically and to privates. So they're 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 time, but there's more family offices today, and family offices seem to be increasing their allocation to privates, but especially RaaS registered investment advisors. Multifamily offices in the bank channel are kind of making up for a lot of that gap because there's significant net new dollars coming from that channel.

[David Swan]

Do you see that in the numbers?

[Ryan]

We see this in the numbers where we'll serve firms that will 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 position, then need to be tracked and managed.

[David Swan]

There's no real morning star for private funds. Why is it so hard to value private funds?

[Ryan]

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 this data is trapped in PDFs and it's super fragmented. Like the top producer of information produces about 7% of the information that we receive 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 ILPA only touches a percent of the market.

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[David Swan]

Do you see that evolving?

[Ryan]

Yes and no. One of the kind of issues with ILPA adoption is ILPA is really only pushed by large institutional lps. And so funds that are sub institutional have no incentive to adopt something like an ILPA. I think you probably will see more AP APIs become available and more like 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

[David Swan]

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?

[Ryan]

We see a lot of just like evolution of what is the hot manager, the hot strategy. So there's shifts. It does seem 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 to funds pop up around kind of like hedge fund exposure and being a fund to fund for hedge funds specifically. 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.

[Podcast Host]

I want to actually talk about that.

[David Swan]

So a lot of LPs privately tell me that they're not as interested in blind pool funds. They're interested in investing in 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 dealing with vehicles?

[Ryan]

And this is more 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 in independent sponsors. 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 your money is going to be called, there's a drag on your returns of needing to do something with that capital. And then in the meantime, if you invest in an independent sponsor, you're 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.

[David Swan]

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 investment. Exactly what excites you most today, Q1, 2026 about private markets?

[Ryan]

I think there's just a huge amount of opportunity like where we're standing in Q1 in 2026. You're probably going to see some major IPOs later this year. And I think that 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. Or you might be someone that needs a lower return but needs a little bit more liquidity and are willing to trade that off for someone that wants to stay invested for a long period

[David Swan]

of time, just to put some more meat on the bone. Historically, the Yale, the David Swan Swensen Yale model required roughly 24% liquidity. And that's been the historic 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

[Ryan]

future vintages, 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, then they won't be able to reallocate dollars back to managers. And so I think the industry is a little bit lopsided right now. You need liquidity to flow back into the market for it to be. To work effectively.

[David Swan]

Some LPs are pressuring managers, specifically venture managers, to get secondary. Do you see a significant amount of secondary going on portfolios?

[Ryan]

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 led secondary, where an LP's trying to sell their positions in certain funds and have heard 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 it's. 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, ten years into an investment.

[David Swan]

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?

[Ryan]

It's a really interesting one. And we were talking to the bitwise folks about this a couple months ago and they made me aware that if you own like Bitcoin, very hard for you. If you own Bitcoin, you custody it yourself or you custody 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 custody 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 and more robust markets around these assets as well.

[David Swan]

It seems to me that somewhere between 0 and 100 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 offices have not figured out how to monetize that.

[Ryan]

Yeah, and there's definitely some funds are creating credit like products where 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 event or a tax event in order to create a little bit of liquidity on their, their stake and to have seen a couple funds that have created really strong businesses around it, but not yet something that's like fully adopted across the full market.

[David Swan]

I want to double click specifically on the AI. How are you using AI to streamline this process?

[Ryan]

So we're big customers today of 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, 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 there's structuring real like quantitative data. So pulling out the latest value and 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 sheet looks like.

[David Swan]

Having this treasure chest of data at your disposal. What surprised you the most?

[Ryan]

When we started 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 it can't, you 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 fund admin by fund admin, custodian by custodian, fund by fund, to understand the like underlying structure of the data that were being sent. There also is 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 describe through documents and through data feeds that the information is not correct. So we're often recreating data feeds when an investment is being custody 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 of data. And I would have just expected that if a bank or custodian sends you data that it should be correct.

[David Swan]

If you go back eight years ago, we, and you were just starting arch, what is 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 costly mistakes.

[Ryan]

We probably could have moved a little bit faster and hired 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 at mit. And then three years later we'd grown to a mighty 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 is 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 were, we were very conservative on the first 500k that we raised as a company.

[David Swan]

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

[Ryan]

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

[David Swan]

I had dinner with a chairman of one of the largest banks and he said his hire rate on truly great hires was 50% of his career. You had been hiring for decades. What's your key to success? How did you get the personnel decisions so correct?

[Ryan]

We have a lot of different lenses in the hiring process. So I'm looking for a certain thing. My co founder Jason's looking for a certain thing. Evan, 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 tests. 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 the 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 are long term oriented and we kind of have this like 1% better every day mentality across the company.

[David Swan]

Ryan, this has been absolute masterclass. Thanks so much for jumping on the podcast. Looking forward to having this conversation live.

[Ryan]

Likewise. Thanks, David. Great chatting with you.

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