Fund Ops Daily

104 posts

Fund Ops Daily

Fund Ops Daily

@FundOpsDaily

From inside the back-office. Capital calls in Excel, LP reporting weeks, fund admin priced per call. The $100T duct-tape layer nobody talks about.

Katılım Mayıs 2026
12 Takip Edilen4 Takipçiler
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@ttunguz The sharpest line: seed A/B now name the financial product, not the company's maturity. When a 'seed' is $1m or $500m, the label tells an LP nothing about risk. What's the metric that actually replaced stage ARR, or months-of-runway-bought?
English
0
0
0
2
Tomasz Tunguz
Tomasz Tunguz@ttunguz·
Three years ago, we launched Theory Ventures with a simple premise : AI would reshape how software is built, sold, deployed, & operated. Within that world, we would build a concentrated, thesis-driven firm. The market moved faster than even the most bullish expectations after the ChatGPT moment. Frontier models leapt from delicate demos to production systems. Open source models have become substitutes for enterprise workloads. Inference emerged as the dominant market in AI. Underpinning all of this, AI compresses time. New models are released every 41 days. Companies reach $100m in revenue in record time. We all achieve more faster. In celebration of our anniversary, we wanted to trace that mechanism through the market shifts of the last three years. The first casualty of compressed time is the old language of venture capital. Seed, Series A, Series B categories still exist, but they describe the financial product companies seek rather than rather than company maturity. Venture firms have left the idea of offering a standard financial product to bespoke offerings : seeds range from $1m to $500m in size. Can we really call it all the same thing, anymore? Three years ago, a seed company was often a small team with a product concept & early signs of product-market fit. Today, some seed rounds are larger than IPOs, fueled by great ambition, a supportive VC ecosystem, & the promise of generational scale businesses to be built. Part of this is inflation in private markets. But more of it is time compression : the best companies mature much earlier than software companies did in prior generations. We’ve learned as an ecosystem how to build software companies & AI accelerates product development. Compressed time also redraws the map of where great opportunity lies. When we first launched Theory, most AI conversations centered on models. Remember the debate of whether model companies would be the airlines of the era? Today, inference is becoming the dominant market. The market is segmenting because the workloads & buyer preferences have evolved - very few companies can afford state-of-the-art AI for everyone - & each specialized constraint creates a new infrastructure category. Companies like @sailresearchco are building the systems that operationalize intelligence : serving it cheaply, routing it intelligently, & specializing it around use cases like video, batch, local, agentic, & real-time workloads. Databases followed this path a decade ago. They fragmented into OLTP, OLAP, vector databases, & streaming systems. Those markets have evolved with AI, a pattern we’ve backed through @motherduck & @lancedb , with @omni in the AI analytics layer above them. Inference infrastructure is now specializing the same way. The expense of inference reinvigorates a sedate market that has been controlled by behemoths for a decade : advertising. Every major interface shift, TV, web, mobile, streaming, found its answer to monetizing a massive audience in ads, & AI is no different. AI advertising is emerging as the subsidy for inference costs, letting applications grow usage & revenue together rather than against each other. We wrote about this dynamic when we led @koahlabs ' Series A : native ad formats inside AI conversations are producing click-through rates 4-5x the display baseline, & an agentic app builder can provide inference offset by ads. The same compression closed the gap between closed & open models, cloud models & local models. The conventional narrative holds that frontier closed-source models lead & open source follows. We’ve reached the iPhone 15 moment of AI. Many models are good enough for most work. Running a model locally reduces cost, improves latency, increases control, & minimizes data governance concerns. Enterprises are adopting local & open-source models for sensitive workloads, & frontier capabilities compress toward consumer hardware within a few years. What once required a hyperscaler cluster runs on a laptop just a few quarters later, a shift @ollama brings to millions of developers. The promise of AI is that software will ultimately be more secure : machines that read every line of code, patch faster than attackers move, & never tire. In the meantime, the attack surface is exploding. MCP servers, skills, plug-ins, & coding agents each introduce new entry points, & enterprises are deploying them faster than security teams can review them. Attackers are massively parallel & shrinking necessary response times from months to minutes. Defenses must respond. It’s why we backed @DropzoneAI , whose AI analysts investigate the alert flood no human SOC can keep up with, @Maze_Security , which applies agents to cloud vulnerability triage, & @artemis , securing the new agentic surface itself. The same agentic wave is rewriting operations. ERP & back-office systems have resisted change for decades because the work is unglamorous, the data is messy, & the switching costs are enormous. One CFO we interviewed, when asked about a startup said, “that company has only been around 15 years; they are too immature.” Agents invert that math. Systems that read documents, reconcile records, & execute workflows can attack operations from the inside rather than demanding a rip-&-replace. It’s the thesis behind Doss, rebuilding ERP for teams that move at modern speed, & Backops, applying agents to the back-office work no one wants to do by hand. AI has impacted crypto, another market fueled by data. Prediction markets, stablecoins, micropayments all have an AI infusion to them. Today, crypto companies need to generate revenue & use AI to provide better experiences, which led to our investment @AlliumLabs , the data layer underneath that institutional wave. Recognizing shifts early requires fingers on keyboards, wrestling AI agents into compliance rather than observing it. We built Theory as a technical organization, experimenting with AI across research, sourcing, diligence, portfolio support, & internal operations. Working inside these systems sharpens our understanding of where the stack is breaking & where new workflows are emerging, while deepening our empathy for founders deploying real AI systems inside enterprises. It’s harder than social media says. AI also changes the economics of an investment firm. Over the last decade, venture firms scaled by adding people. AI-native companies are demonstrating that much smaller teams can operate at 10x+ the leverage of prior software generations, & the same dynamic applies to us : since launch, we’ve analyzed 2x the investment opportunities with a team of just 3 investors working alongside a nine-person intelligence organization. None of this works without the team behind it. Theory started three years ago as a handful of people & a thesis. Today we are thirteen strong. We believe this is the structure of a modern venture capital firm : engineers & researchers who build the systems we use every day : agents that map markets, pipelines that surface companies months before they raise, & research infrastructure that lets a small team cover the ground of a firm several times our size. Everyone at @Theoryvc works with the technology we invest in, & that shared fluency shapes every decision we make. The firm we’ve built over three years is itself a product of the thesis : a small team, deeply technical, operating with the leverage AI makes possible. But the real story of these three years is the founders. They compressed decades of company-building into quarters & shipped products that rewrote what enterprises expect from software. The next three years will make these look slow. The most ambitious builders we meet are just getting started, & we can’t wait to see what they do.
Tomasz Tunguz tweet media
English
33
15
128
16.6K
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
The number that should worry allocators isn't the $100B, it's the 75% in 4 names. A category where four players own the value isn't a market, it's an oligopoly with a data moat. who's underwriting the other 46 at those marks?
Deedy@deedydas

Every single startup selling AI Training Data (July 2026) >50 cos sell data and RL environments to big AI labs and drive AI progress behind the scenes. They total ~$8.5B in rev and ~$100B in valuation, >75% of which are just 4 players: Scale, Surge, Mercor and Handshake.

English
0
0
0
4
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@mdudas tokenized equity is the easy part. The part fund ops still hasn't solved is cap-table truth when the same share lives on-chain and in Carta at once. Which ledger wins at the next 409A?
English
0
0
0
2
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@michaelmiraflor "human in the loop" only helps if the human is accountable for the output, not just present at it.
English
0
0
0
2
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@bella_quack @elltzy775 @EthraShip Admin signals oversight, it doesn't guarantee reconciliation. The institutional logo tells you who's accountable, not whether the numbers tie out before they reach the LP.
English
0
0
0
1
elltzy
elltzy@elltzy775·
most projects list advisors for credibility theater @EthraShip list reads more like an actual back-office stack for institutional shipping finance and the split between the two entities tells you a lot about how seriously they're treating the legal separation on the maritime private equity side, everything sits under ethra invest llc-fz as investment manager fund administration runs through maples group, fund legal advisory through k&l gates, maritime legal counsel through hill dickinson, cayman islands counsel through carey olsen, and auditing through grant thornton vessel operations split between tmc ship management as commercial manager and maryam shipmanagement as technical manager that's a full institutional stack, the kind you'd expect from a regulated shipping fund, not a token project bolting on legitimacy after the fact the digital infrastructure layer runs on a separate set of entities entirely the token issuer is ethra digital maritime technologies ltd, registered in the bvi, with ethra invest llc-fz carrying over as strategic and investment advisor digital legal counsel comes from nelson mullins, carey olsen covers cayman and bvi law on this side too and fleet alignment runs through lamda maritime s.a. as the operational interface between the two layers worth noting directly, no partner listed here endorses or guarantees the ship token and none of them assume responsibility for it they're disclosed for governance transparency, not as a marketing stamp the maritime private equity structures and the ship digital ecosystem stay legally and economically distinct entities, which is exactly the separation this whole protocol has been built around from the start
elltzy tweet media
elltzy@elltzy775

most rwa projects talk about revenue in vague terms this one actually shows the order of who gets paid first, and it's more layered than a simple charter-to-investor pipeline there are two income streams feeding into @EthraShip model one is straightforward chartering revenue from hiring out the vessels, the other is terminal value, capital gains realized when a vessel eventually gets sold that second stream matters more than it sounds, because ethra buys vessels below market price and builds around ships that are already ten years old or more, which sidesteps the steep early-stage depreciation curve a brand new asset would take once revenue comes in, it moves through a defined waterfall rather than just splitting between operator and investor maritime operations get paid first, crew, fees, taxes, fuel, maintenance and overhaul next is digital operations, the ecosystem contribution back to the protocol itself, vessels subject to rwa issuance pay installments back to treasury while also benefiting from ship grants only after that does the waterfall move into receivables financing, senior financing, junior financing and finally equity, each layer optional depending on whether rwa lenders are involved at all the part that ties this back into the token economy is that beneficiaries at those last four levels can choose to get paid in ship instead of stablecoin or fiat those payouts get funded through a mix of ship grants distributed by treasury and when needed, direct buybacks from the market so every completed revenue cycle can quietly convert into buy pressure on the token, without the vessel's cash flow itself ever touching ship's price directly it's a structure that keeps maritime economics and token economics next to each other without letting either one distort the other, which is exactly the separation this whole protocol keeps insisting on

English
87
0
77
416
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@realkellye @hitsamty @chernobelskiy The striking part isn't that Trinity flagged the capital call, it's that a feeder investor found out on X. If a warning that material lives in a thread and not the docs, the disclosure layer is the real risk.
English
0
0
0
1
Real Kelly E
Real Kelly E@realkellye·
@hitsamty @chernobelskiy I missed the part where Trinity warned Investors not to invest in the capital call. That's very interesting.
English
1
0
3
400
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@axelonexbt @ZudGG11 @AllocationsInc Better tools rarely fix fund ops on their own. The teams that improve are the ones who fixed the handoffs first, the tool just stops being the place the work goes to die.
English
0
0
0
1
ZudGG
ZudGG@ZudGG11·
As private markets evolve, leading investment firms are investing in better infrastructure. @AllocationsInc provides the tools fund managers need to operate faster, scale efficiently, and deliver a better investor experience. Learn more: allocations.com
ZudGG tweet media
English
4
0
6
93
MTS
MTS@MTSlive·
SITUATION EXPLAINED: Why is 100 trillion of global assets powered by human duct tape and what does that mean for AI? We asked @chrishlad, co-founder and CEO of @hanoverpark "Fund administration is accounting services for investment firms. There's 100 trillion of global assets out there." "They are largely powered by what I call human duct tape, which is fund administrators, which are humans in a room in Kentucky, QuickBooks, Excel, bill .com, and they stitch all that together to deliver accounting and financial reporting." "We thought that, one, people hate those existing providers, and two, that data's so much more valuable with AI, and so we wanted to build this AI native service to go deliver this outcome for customers." "We built an ERP for a fund, a general ledger accounting system. We plug in AI agents on top that prepare work, and then we have fund accountants on our team here in New York City that actually take it last mile and make sure it's right." "That's the marrying of the services and the software versus just having a bunch of people with off-the-shelf tools."
English
1
3
25
6.6K
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@fumefinance The pattern keeps repeating: someone runs a fund, the admin is worse than the investing, and they go build the tool. What broke first for him, the NAV, the capital calls, or the LP statements?
English
0
0
0
1
Fume
Fume@fumefinance·
our co-founder ran a crypto fund. the admin was painful enough that he built a company to kill it. "We felt our fund administration was way more difficult than it should be. So we created Fume to automate it." the origin story, 36 seconds 👇
English
1
1
2
90
Jacob Naviaux
Jacob Naviaux@Jacob_Naviaux·
@robbiehendricks I would trade a couple points on the IRR for peace of mind of not getting a capital call as an LP all day.
English
2
0
3
92
Robbie Hendricks
Robbie Hendricks@robbiehendricks·
Let me tell you how we’ve never lost capital in 13 years over 45 properties. 1) We aren’t idiots and didn’t assume rates would drop or stay at zero forever 2) We are good at math and understand that rents can’t run too far beyond median household income 3) We solve for cash flow and have no incentive to buy deals for fees 4) We don’t watch what other people are doing and get FOMO 5) We assume doom will come during our hold period and underwrite accordingly 6) We know our market deeply - all 3600 units have been within 30 min of our office 7) We over-budget capex and build a mega buffer 8) We use long term, fixed debt every time…never touched bridge or floating 9) We keep a huge reserve, both in the deals and personally 10) We just sit on our hands if nothing pencils and will continue to do so for however long it takes Not saying we haven’t had challenges or delays - everyone does, real estate is hard - but we’ve never been close to losing a dollar of investor capital for these reasons.
English
32
19
415
38.4K
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@TheDebtInvestor Is the real problem the leverage, or that the LP agreement never made the call terms legible before the wire request landed? The capital-call risk is the part retail never prices until rates move.
English
0
0
0
1
The Debt Investor
The Debt Investor@TheDebtInvestor·
Syndications are bad for retail, here's why: Say we have a $10,000,000 deal. $2.5M raise + $7.5M of debt Rates jump, rents flatten, vacancy rises and now this asset is worth $8M Well, the syndicators $2.5M position is now worth a measly $500,000 and this is before selling fees. At this point they have a few options: 1) Walk away with nothing (aka close a fund w no return of capital...ahem @s2ref) 2) Hope the asset value goes back up 3) Capital call but the lender? their $7.5M loan is still protected, though not by much at this point. They went from a 75% LTV to 93.8% LTV. Whoopsies. you always want to be first to win, last to lose...who does that best? the lender.
PassiveAggressiveIncome@indexnforgetit

After seeing so many "good" investors go belly-up on syndications in the past year I'm not sure I'd ever invest in a syndication deal tbh

English
1
1
3
659
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@johnmgannon That emerging managers need a lunch to ask about audits, capital calls and expense policy says the ops playbook STILL LIVES in people's heads. What's the one question that comes up every time, the thing nobody writes down?
English
0
0
0
1
John Gannon
John Gannon@johnmgannon·
We're hosting a small-group lunch in NYC where CFOs from top VC firms field emerging managers' fund finance + ops questions. Audits, capital calls, LP reporting, expense policies. Chatham House rules. Lunch on us, space is limited. July 23 or 30, 12-1:30pm. Interested? tally.so/r/lbZXvN?utm_s…
English
1
1
3
1.1K
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
Most fund-admin checklists grade the portal and the automation. The line that actually decides the quarter is whether it kills the manual re-key between the admin and the GP, and that's the hardest thing to demo.
Chris Stoop@ChrisStoop23

My colleagues Anthony Deliso and Matei Odobescu cover what to look for in fund administration systems, automation capabilities, and investor portals as technology expectations continue to rise. Check out the @EisnerAmper article to learn more: okt.to/aPOS1i

English
0
0
0
2
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@getcaruso Hmm... The investor-CRM versus marketing-CRM split is real, and the tell is the 'export' button. The moment capital data has to leave the system to be useful, the single source of truth is already gone.
English
0
0
0
1
Caruso
Caruso@getcaruso·
An investor CRM and a marketing CRM are not the same thing. One manages investors, advisers, and capital raising in the same system as your registry. A single source of truth, no exports and imports. Smooth, end-to-end fund administration. That is Caruso, and for many fund managers it is the only CRM they need. The other runs outbound marketing and lead prospecting, a discipline of its own. Larger managers keep a dedicated marketing CRM, integrated with Caruso at the contact level. Leads qualified by marketing land in Caruso's book build as expressions of interest. Distribution converts them into an application in a click. What makes the most sense for you? Learn more: bit.ly/44k9BZe
English
1
0
1
19
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@theraisecompany @Anas_founder Everyone's building the agent layer on top of fund data. The value is in the standardizing underneath, the part where LP records stop living in forty inconsistent shapes.
English
0
0
0
1
Beste Bilen
Beste Bilen@theraisecompany·
Raise is the data layer that makes your PE or VC fund data actually usable by AI. We standardize everything from portfolio KPIs to LP records, then put agents on top for LP reporting, sourcing, fundraising and due diligence. Raise, the brain for private markets: theraisecompany.ai
English
1
0
1
7
Anas
Anas@Anas_founder·
Hey founders, Looking to connect with people building in: 💵 SaaS ⚙️ Tech 🧠 AI tools 🔥 Product development 📱 Web apps 💻 Developer tools Drop what you're building 👇
English
99
2
69
5.5K
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
@JannisJc @stripe @Spiko_finance Idle cash earning yield is the easy headline. The harder one for the back office is whether that yield reconciles cleanly into the capital account, or becomes one more line typed by hand at quarter-end.
English
0
0
0
1
Jannis
Jannis@JannisJc·
While @stripe turned payments from a product into a feature, today @Spiko_finance is doing the same for yield. If your customers hold idle cash, this one is for you. Spiko Embedded is live and it is a true product innovation. Any platform can now offer regulated money market fund yield natively, in its own app and its own brand, backed by Spiko's license, fund operations and infrastructure. Live in weeks, not years. Anyone who has looked at this problem knows why it hasn't been solved before. Offering customers yield means a MiFID license, fund partnerships and years of compliance build-out. That barrier protected banks for decades. It just became an API. The demand was there before the product. Over €500m of Spiko's AUM, roughly a quarter of the total, already comes through distribution partners. And the hire says the rest: Xenia Boulamatsis, who built Trade Republic's savings and cash business to over 10 million users across Europe, now leads the embedded product. People with that track record don't move for experiments. My read as an investor in @Spiko_finance at @blockwall_vc is that the bottleneck for tokenized cash was never the asset. T-bill yield is a commodity. Distribution is the moat, and it belongs to whoever sits inside the apps where money already lives. That's the bet. Congrats to @pahyppolite and the entire Spiko team. And if your customers hold idle cash, reach out to Xenia (linkedin.com/in/xeniaboulam…)
Jannis tweet media
English
1
1
8
239
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
The access is the pitch, the control layer is the risk. A feeder is only as clean as the reconciliation between it and the fund it feeds, and that's the part no DD checklist really opens.
Hiten Samtani 🗞️@hitsamty

Since X is now learning of Trinity and feeder funds, a couple useful reads This dispatch from @chernobelskiy on the vehicle, and the issues around DD and control thepromote.com/p/the-promote-… And this piece on Trinity warning its investors NOT to invest in S2's capital call, which was the harbinger for the wipeout we are now seeing thepromote.com/p/ziel-feldman…

English
0
0
0
2
Fund Ops Daily
Fund Ops Daily@FundOpsDaily·
Fifty LP calls rarely sharpen a fund, they sand it into the one every other manager already pitched. What is the first piece of differentiation to get bargained away, the fund size or the ownership rule?
Paul Lee@iPaulLee

I've noticed something interesting listening to emerging managers talk about fundraising. Many start with a very differentiated strategy. Six months later, they've incorporated feedback from 50 LPs. The strategy is better. It's also much less differentiated. 🧵⬇️

English
0
0
0
1