How Family Offices Evaluate AI Venture Capital Funds
Selecting an AI venture capital fund starts with a decision about the family’s capital: what role should this investment play, how long can it remain illiquid, and what evidence would justify trusting this manager?
An attractive technology theme cannot answer those questions. A family office is selecting a team, an investment process and a contractual relationship that may last well beyond the current AI cycle. The diligence process needs to connect the manager’s technical convictions to ownership, portfolio construction and eventual cash distributions.
This guide proposes eight questions for that assessment. It is written from the perspective of an AI venture capital practitioner, with the aim of making the conversation between family offices and fund managers more concrete.
The eight questions at a glance
Does the fund fit the family’s portfolio and liquidity needs?
What AI exposure is the fund actually buying?
Can the manager demonstrate a repeatable investment edge?
How does the team evaluate AI-specific risks?
What does the track record actually prove?
Does portfolio construction support the investment thesis?
Are economics, governance and incentives aligned?
What must be verified before making a commitment?
For each question, record the manager’s answer, the supporting evidence and what remains unresolved. A persuasive presentation should open the diligence process; the underlying documents and reference conversations should determine whether it progresses.
1. Does the fund fit your family’s portfolio?
Start with the family’s existing exposures. A technology business, listed technology holdings and direct startup investments may already create substantial sensitivity to the same customers, financing conditions or technology cycle. Adding an AI fund can deepen that exposure even when the underlying company names are different.
Define the intended role of the allocation before reviewing managers. Is the objective long-term financial return, access to a particular investment segment, or learning that may also inform the family’s operating businesses? Strategic benefits can matter, but they should have their own assessment rather than quietly replacing the financial case.
Ask the manager for an illustrative capital-call schedule, the fund’s contractual term and extension provisions. Model the effect of faster calls and delayed distributions alongside the family’s other obligations. Treat the schedule as a planning assumption: actual calls and exits will differ.
Evidence to request: a cash-flow illustration, investment-period and fund-term provisions, and an explanation of any recycling or recallable distributions.
Warning sign: the commitment only works if early distributions arrive on the manager’s preferred timetable.
2. What AI exposure are you actually buying?
“AI venture capital” can describe very different portfolios. Application software, model infrastructure, robotics and AI-enabled drug discovery have different development paths, capital needs and routes to commercial validation. Geography and investment stage change the risk profile again.
Ask the manager to explain the mandate through three recent investments and two opportunities it declined. Which risks is the fund deliberately taking? Which risks would make an opportunity unsuitable, even if the company had an exceptional team?
At pre-seed, much of the evidence may concern feasibility, customer discovery and the next experiment. Later-stage diligence can examine longer commercial histories. Applying a mature software checklist to a scientific company before its first validation milestone can obscure the actual investment question.
It is also useful to distinguish a manager’s mandate from its marketing language. Ask what limits the fund documents place on stage, geography, concentration and investments outside the core strategy. A thesis becomes more informative when its boundaries are clear.
Evidence to request: a portfolio exposure map and investment examples showing how the stated mandate becomes an actual decision.
Warning sign: every promising technology company can be made to fit the thesis after the fact.
3. Can the manager demonstrate an investment edge?
An investment edge needs a mechanism. A manager might see opportunities earlier, earn allocations other investors cannot obtain, evaluate a technical problem unusually well, or help a company reach a meaningful milestone. Each claim requires different evidence.
For sourcing, ask how opportunities move from introduction to investment. A large pipeline says little without conversion data, the terms obtained and an explanation of why founders chose this investor. Access to a well-known founder program is useful context; it does not establish investment performance.
For selection, discuss both decisions and counterfactuals. Ask for an investment memo prepared before a financing, a declined opportunity and a case where the original thesis changed. What did the team know at the time? What did it miss? How did the experience change its process?
For execution, establish who does the work. Distinguish the partners making investment decisions from advisers whose involvement may be occasional. Check that the proposed fund size and portfolio workload are credible for the team actually in place.
Evidence to request: attributable investment decisions, allocation history and founder references that describe specific contributions.
Warning sign: the explanation depends on prestigious logos, introductions or adviser biographies without showing who made the investment judgment.
4. How does the team underwrite AI risk?
The family office does not need to reproduce every technical diligence exercise. It does need to understand how the manager turns technical uncertainty into an investment decision, and who can challenge that decision.
Five questions are particularly useful.
What customer outcome is being purchased?
Ask what the product changes for the buyer: time spent, errors, throughput, revenue or the ability to do something previously impractical. Separate a successful demonstration from a deployment someone has budgeted to maintain.
Where a company has commercial history, examine usage, renewals and customer references. Where it does not, ask what experiment would establish willingness to pay and who owns the budget. A pilot is evidence of engagement; its terms determine how much it says about demand.
What remains valuable when foundation models improve?
Ask the manager to describe a plausible model upgrade that could erode the startup’s advantage. Would the company benefit, lose differentiation or have to rebuild its product?
An application using third-party models can still create value through proprietary workflows, distribution, integration or legitimately usable data. Conversely, owning a model does not establish customer demand or attractive economics. Diligence should identify the mechanism that could preserve value and how it will be tested.
What does it cost to deliver a reliable result?
Look beyond the headline model bill. Depending on the business, delivery may include inference, human review, implementation, support and the cost of correcting errors. Ask how those costs change with heavier usage and more demanding customers.
Bessemer’s framework for AI-native services considers market structure, delivery economics and defensibility together. Its scope is services; the useful question for a family office is whether a manager applies an equally explicit economic test to the kinds of businesses its fund buys. Read Bessemer’s evaluation framework.
Which dependencies could interrupt the business?
Discuss model providers, cloud infrastructure, data access and customer concentration. What happens if an important provider changes pricing or access? What rights does the company have to use its data? Which failures would prevent a customer from deploying the product?
The depth of review should match the use case. A creative tool and a clinical system require different expertise and evidence. Ask when the investment team brings in outside specialists, how their conflicts are managed and how findings affect the decision.
What would cause the manager to decline?
This question tests whether technical diligence has consequences. Ask for a case where price, weak validation or an unresolved dependency stopped an investment. A process that produces interesting technical observations but never changes ownership targets, terms or investment decisions needs closer examination.
Evidence to request: a redacted diligence case connecting the customer problem, technical tests, economics, unresolved risks and investment decision.
Warning sign: “proprietary AI” substitutes for an explanation of what is defensible, or technical complexity is treated as evidence of commercial value.
5. What does the track record actually prove?
Begin with scope. Are the numbers for this fund, a predecessor fund, a selection of personal investments or deals completed at another firm? Establish who originated, evaluated and managed the investments, and which parts of that experience are relevant to the proposed strategy.
Request a complete investment schedule that includes losses and write-offs. Read net and gross results separately, using the same reporting date and consistent definitions. Compare funds with attention to vintage, strategy, geography and maturity.
DPI, or distributions to paid-in capital, measures value already distributed relative to capital paid in. TVPI, or total value to paid-in capital, adds the remaining portfolio value to distributions. A high TVPI with little DPI therefore depends heavily on unrealized value. IRR incorporates cash-flow timing and should be read alongside the multiples. Carta’s Q1 2026 report illustrates why that distinction matters: improving valuations in its sample coexisted with limited distributions. Its sample is not a benchmark for every AI fund. Read Carta’s fund performance report.
Ask what drives the remaining value. Is it spread across several holdings or concentrated in one financing? What valuation methodology is used, and what changed since the previous report? If subscription financing is used, ask the manager to explain its effect on reported cash flows and returns.
A first-time fund cannot provide a mature fund-level record. Diligence can still assess attributable experience, decision quality, references and operational readiness, while clearly acknowledging the evidence that does not yet exist.
Evidence to request: the complete schedule, valuation policy, available audited accounts and reconciliation of headline performance to the underlying records.
Warning sign: selective winners presented as a portfolio, unexplained attribution or gross figures described as the investor’s return.
6. Does portfolio construction support the strategy?
Ask the manager to connect fund size, initial checks, entry ownership, follow-on reserves and likely dilution. These choices determine how company-level outcomes translate into fund-level results.
Consider a simplified hypothetical example. An investment of €500,000 buys 2% of a company. If subsequent dilution reduces the stake to 1%, a €1 billion equity exit value would imply €10 million of proceeds before preferences, transaction costs or other adjustments. For a hypothetical €75 million fund, that is approximately 0.13 times total commitments, before fund-level fees and carry. This is arithmetic for illustration, not an Atlas investment or forecast.
The example shows why an impressive exit headline is insufficient. Ownership and the size of the fund matter. Ask to see a portfolio model under several scenarios, including lower exit values, more dilution and longer holding periods.
Discuss how follow-on decisions are made. Are reserves allocated according to new evidence, or mainly to support existing positions? What happens if more companies require financing at the same time than the reserve budget can accommodate?
Evidence to request: a portfolio construction model with explicit assumptions and an explanation of reserve allocation.
Warning sign: a return case that requires unusually favorable outcomes across almost every assumption.
7. Are economics, governance and incentives aligned?
Read the economic relationship as a whole. Establish the management-fee rate and calculation base over time, expenses borne by the fund, carried-interest mechanics and any other charges that affect the investor’s outcome. Ask for a worked illustration of the distribution waterfall.
Understand the general partner’s commitment, including how it is funded, and the team’s ownership and incentive arrangements. The relevant question is whether those arrangements support the long-term behavior the family expects.
Governance deserves the same attention as economics. Discuss key-person provisions, conflicts between vehicles, allocation of co-investments, valuation responsibilities and the information LPs receive when something goes wrong. Establish the roles of the administrator, auditor and other service providers.
ILPA’s Principles provide a useful reference for conversations about alignment, governance and transparency. They are industry guidance; the actual rights and obligations depend on the fund’s documents. Read the ILPA Principles.
Evidence to request: fund documents, expense disclosures, a sample LP report and clear responsibility for approvals and investor communication.
Warning sign: material terms are explained differently in the presentation, the conversation and the documents.
8. What should happen before committing?
Turn diligence into a decision record. For every material issue, distinguish what has been verified, what remains an assumption and what could change the recommendation. A numerical score can help organize discussion, but it should not hide a decisive unresolved concern.
Use reference calls to test specific claims. Ask founders how the manager behaved when plans slipped. Ask other investors about communication and judgment. Where appropriate and with permission, include perspectives beyond the manager’s most successful investments.
The ILPA Due Diligence Questionnaire offers a starting structure for manager diligence. ILPA notes that the questionnaire best aligns with established private equity managers, so adapt the level of detail to the stage and operating model of the venture fund being assessed. Explore the ILPA DDQ.
Before signing, reconcile the commercial understanding with the final documents and the family’s own legal, tax and operational requirements. Agree who will monitor the investment and how changes in the team, strategy or portfolio will be escalated.
Evidence to request: a final document set, completed reference notes and written resolution of material outstanding questions.
Warning sign: a fundraising deadline is used to leave a significant diligence issue unanswered.
A checklist for your first manager meeting
Ask the manager to bring or subsequently provide:
A clear mandate and explanation of the risks the fund intends to take.
One investment case and one declined opportunity, with the reasoning at the time.
A complete, attributable track record with consistent reporting definitions.
A portfolio model connecting checks, ownership, reserves and fund size.
An AI diligence example covering customer value, dependencies and delivery economics.
Fund documents, an all-in expense explanation and a sample investor report.
Relevant references and a list of questions requiring follow-up.
The meeting should leave you able to explain why this particular manager could execute this particular strategy, what could go wrong and which evidence would cause you to change your view.
The Atlas perspective
At Atlas AI VB Fund, our public investment thesis focuses on defensible AI at pre-seed and seed, with startups emerging from venture builders and founder programs. We look for advantages rooted in technology, data or distribution. You can explore that thinking in our investment manifesto and see the companies in our portfolio.
For a family office assessing this approach, the same questions apply: how does sourcing translate into investable opportunities, what evidence supports defensibility, and how do individual investments fit the fund’s construction? A focused thesis is a starting point for that conversation, and it should be tested with evidence.
Frequently asked questions
How is an AI venture capital fund different from an AI ETF?
An AI venture fund generally invests in private companies through a manager-led strategy, with capital commitments and limited liquidity. An AI ETF generally provides exchange-traded exposure to a defined basket of securities. Holdings, liquidity, fees and risks differ materially; compare the specific products and their documents.
How should a family office evaluate a first-time fund manager?
Separate the absence of a fund-level history from the evidence available about the team. Examine attributable prior decisions, relevant experience, founder references, investment discipline and operational readiness. Acknowledging the unproven elements is part of the assessment.
What is the difference between TVPI and DPI?
DPI measures distributions relative to paid-in capital. TVPI includes both distributions and the remaining portfolio value. Read both at the same reporting date and on a consistent net or gross basis. Unrealized value can change substantially before an exit.
Which documents should a family office request?
Start with the strategy presentation, fund documents, track record, valuation policy, portfolio construction assumptions, fee disclosures and sample reporting. Request audited accounts where available, information about service providers and evidence supporting material investment claims.
Can a family office assess an AI fund without an in-house technical team?
It can organize a rigorous assessment by testing the manager’s process and using independent specialists where appropriate. Focus on who performs technical reviews, how disagreements are resolved and whether technical findings change investment decisions.
Is the full commitment paid at signing?
Many closed-end venture funds call capital over time, but initial payments, notice periods, subsequent calls and any recallable distributions depend on the fund documents. Plan liquidity around the full obligation and stressed timing assumptions.
Discuss it with Vitantonio Santoro
If you represent a family office assessing AI venture capital, get in touch to discuss the Atlas investment approach and the questions relevant to your diligence process.
Email vitantonio.santoro@atlassgr.com or connect with Vitantonio Santoro on LinkedIn.
About the author: Vitantonio Santoro is a General Partner at Atlas AI VB Fund. This article reflects a fund manager’s perspective on the diligence questions family offices can use when evaluating an AI venture capital strategy.
This article is for general information and does not constitute investment advice or an offer to invest. Venture capital investments are illiquid and involve the risk of losing capital. Any investment decision should be based on the relevant fund documents and the investor’s own assessment.
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