If you’ve been following along, you’ll know we’ve been quietly collecting data on one of the most under-discussed but absolutely mission-critical parts of venture capital: fund modelling.

And after processing the first wave of responses, I can confidently say two things:

Table of Contents

1 Why This Matters
2 What This Preliminary Report Is / Isn’t
3 Top Findings (So Far)
4 Who Responded
5 (Paid) Tooling: The Excel Monopoly
6 (Paid) The Real Challenges (In Their Words)
7 (Paid) The Most Difficult Areas
8 (Paid) What VCs Think Is Critical
9 (Paid) Early Patterns
10 (Paid) Insight #1 — The Tooling Maturity Gap
11 (Paid) Insight #2 — The Modelling Literacy Gap
12 (Paid) Insight #3 — The Expectation vs Reality Gap
13 (Paid) Deep Dive: Reserves
14 (Paid) Scenario Analysis
15 (Paid) Assumption Documentation
16 (Paid) Tools & Infrastructure
17 (Paid) Portfolio Construction
18 (Paid) LP Communication & Reporting

Everyone talks about strategy.

Everyone talks about sourcing.

Almost nobody talks about the actual model behind the fund — the thing that determines reserves, DPI timelines, pacing, diversification, exit pressure, and LP conviction.

And when nobody talks about it, silent assumptions creep in.

Silent assumptions become blind spots.

Blind spots become fundraising friction… and sometimes, fundraising failure.

That’s why we ran this study.

This is not a polished, final whitepaper. It is the first directional snapshot of how European GPs, emerging managers, and LPs actually build and use fund models - and where the cracks are.

Here are three datapoints that made me sit up:

(And 73% use nothing but spreadsheets.)

Despite the rise of tools, this industry runs on duct tape and VLOOKUPs.

This is the #1 thing LPs scrutinise.

The mismatch creates friction in fundraising and slows diligence.

50% struggle with assumptions & inputs. Fees, reserves, pacing, loss curves — the foundation of every model — are misunderstood more often than we’d like to admit.

Emerging GPs worry about basics.

Established GPs worry about complexity.

LPs worry about clarity.

They’re looking at the same model and seeing three entirely different things.

This is creating what I call the fund modelling literacy gap — and it’s widening, not shrinking.

One thing I’m proud of:

The split between GPs and emerging managers is almost perfectly even - 38% vs 35%.

This gives us a unique lens:

We can compare experienced modelling practice vs first-time fund building in a way most datasets simply can’t.

LPs represent 15% — small but mighty.

Their responses validate (or explode) assumptions that GPs often hold about allocator expectations. This gives us the rare ability to compare what GPs think LPs expect vs what LPs actually expect, and how those diverge from emerging managers building v1 models from scratch.

The full preliminary report is available to EUVC Academy members.

Despite dozens of tools on the market, fund modelling remains overwhelmingly spreadsheet-driven.

According to our survey, Excel/Sheets dominates usage - with proprietary models, off-the-shelf templates and consultants barely appearing as secondary support.

Why this matters:

The tooling maturity gap is real - and visible in every downstream insight.

The qualitative responses reveal a consistent theme:

Respondents cite:

This is where almost every GP overestimates their model - and where almost every LP notices gaps immediately.

69% struggle with return scenarios & sensitivity analysis.

This is the LP litmus test.

Why?

Because scenario design reveals:

If your scenarios are bolt-ons, LPs can smell it.

Respondents overwhelmingly say:

…are the most critical parts of a model.

Yet these are exactly the sections that are most underdeveloped in real fund models.

This is the expectation → execution gap.

This section of the preliminary report pulls a bunch of threads together into one picture - and it’s not a comfortable one.

We already knew Excel/Sheets was widely used. The deeper pattern is that 73% of respondents use it as their only modelling environment, and 96% have it at the core of their stack.

This isn’t tool preference — it’s infrastructure lock-in. Everyone uses spreadsheets because everyone else does. LPs, admins, auditors, co-GPs. No one wants to be the first to move.

69% say scenarios and sensitivities are the hardest part of modelling.

That’s the thing LPs care most about and the thing most models are worst at. The result: beautiful decks sitting on top of fragile scenario logic that collapses as soon as an LP starts poking.

50% of respondents struggle with assumptions & inputs (fees, reserves, pacing, etc.).

These are the parameters that define the fund’s economic reality — and half the market is unsure they’re getting them right. For emerging managers, this often means copying someone else’s model and hoping the assumptions “look market”.

Across the verbatims and charts, reserves show up everywhere: how much to allocate, when to allocate, how to stress-test adequacy across scenarios. Even experienced GPs admit this is where the model feels most fragile.

Allocator responses point to a clear desire: more comparable models, clearer assumption docs, and cleaner scenario frameworks.

GPs, meanwhile, often treat their model as part of their “secret sauce”. That tension - between comparability and customisation - is at the heart of today’s LP–GP modelling friction.

LPs expect:

GPs deliver:

Emerging managers deliver:

This is the root of so much fundraising friction.

Three groups, three different problems:

Everyone is misaligned.

Everyone is frustrated.

No wonder fundraising takes so long.

This is the killer insight:

Emerging GPs think complexity = sophistication.

GPs know complexity = fragility.

LPs think complexity = opacity.

This single dynamic explains ~40% of the friction in fundraise conversations.

Regardless of fund size or maturity, everyone struggles with reserves modelling.

Why?

Even large funds struggle here.

Emerging managers struggle the most.

LPs use scenarios to understand:

Most GP models fail here because:

This is fixable — but only with better frameworks.

50% struggle with assumptions & inputs.

The problem isn’t the assumption itself - it’s the documentation.

LPs care far more about:

than the number itself.

19% cite tools as a primary challenge.

That number is artificially low - because most GPs don’t realise how much their toolset is holding them back.

Spreadsheet fragility compounds over time.

And LPs feel the pain.

Portfolio construction assumptions are the silent driver of fund returns.

Few model them well.

This is an area where we can materially raise literacy across Europe.

Only 12% of respondents picked LP communication and reporting as a top challenge. On the surface, that sounds reassuring. In reality, it’s probably the most under-reported problem in the entire study (so far).

Most GPs instinctively prioritise: “Does this model work for us?”

LPs are asking a different question: “Does this model make it obvious how this fund really behaves?”

Our preliminary report, illustrates this as a three-step funnel:

Most GPs live in step 1.

Some manage step 2.

Very few consistently get to step 3.

The translation challenge is real:

LPs repeatedly say they value:

In other words: the best models are bilingual — they speak GP and they speak LP.

Right now, most are fluent in neither.

A few big trends:

These are systemic issues - not individual GP flaws.

After analysing this first wave of data, one thing is clear: Venture’s fund modelling gap isn’t a technical problem - it’s a literacy, tooling and communication problem. And unless we close it now, the gap between LP expectations and GP execution will only widen.

Here’s what the preliminary findings point to:

Exit outcomes (65%) and assumptions clarity (62%) top the list of “critical model components,” yet they’re also among the most inconsistently modelled across respondents.

We need common frameworks - not just prettier spreadsheets.

69% struggle with scenario design.

50% struggle with assumptions.

Reserves appear as a pain point across every respondent category.

This isn’t about “junior modellers.” It’s systemic.

LPs consistently ask for clarity, pacing realism, defensible sensitivities and transparent assumptions.

Most models today… don’t deliver that.

Not because GPs don’t know how - but because no one has ever taught this properly.

First respondents gave us a strong early signal, but the deeper insights (benchmarks, strategy-specific patterns, vintage effects, reserve ratios, pacing profiles) require a larger dataset.

This project is a perfect example of why we’re building the EUVC Academy.

There’s no consistent place for European GPs and emerging managers to learn:

So we’re fixing that.