Regulatory Pressure on Life Insurance: What It Changes for Product Partnerships

When a corporate insurer and an insurtech discuss a product partnership, the conversation usually centers on distribution: which channel, which customer segment, how fast the integration can ship. It is rarely framed around what a supervisor will want to see about that product before a single customer ever buys it.

That framing is becoming a mistake. Two regulatory developments, one from the European supervisor and one from the French one, are converging on the same target: the value a product actually delivers to the customer who buys it, and the paperwork an insurer must produce to prove it. Neither development is aimed at partnerships specifically. Both change how fast a partnership can move.

The Value for Money Question Is Now a Governance Requirement, Not a Slogan

EIOPA has been explicit about what it found when it looked closely at unit-linked and hybrid life insurance products across the European market: roughly 15% of the products it analysed raised value for money concerns, and more than half of those were marketed by fewer than 20 undertakings. The average projected return across the products reviewed, including higher-risk ones, came out at 2.6%.

EIOPA's own conclusion is worth taking at face value: this is not a structural problem with the European life insurance market. It is a concentrated problem among a minority of providers. That is precisely why it matters to everyone else. A concentrated problem invites a supervisory response calibrated to catch outliers, which means every product, including one built in partnership with a new insurtech entrant, now gets tested against the same bar.

What Product Oversight and Governance Actually Demands

The mechanism EIOPA is using is not new in name. Product Oversight and Governance, POG, has existed under the Insurance Distribution Directive for years. What has changed is how seriously it is enforced. EIOPA's supervisory statement on value for money in unit-linked products sets out three things a national supervisor now checks: that the target market for a product is defined with enough granularity to mean something, that the product has actually been tested for value for money rather than assumed to deliver it, and that costs, charges, and performance are reviewed on an ongoing basis rather than signed off once at launch.

None of that is exotic. All of it takes time, and time is the resource a product partnership is usually trying to save. A partnership that assumed POG documentation could be assembled after commercial terms were agreed is now assuming something that a supervisor is actively testing for.

ACPR Is Not Waiting for Brussels

The European layer is not the only one intensifying. The ACPR's 2026 supervisory programme includes 213 planned missions, up from 185 in 2025. That is a French-specific increase in the volume of hands-on supervisory work, layered on top of the EIOPA-level obligations rather than replacing them.

For a corporate insurer operating primarily in France, this means the compliance conversation about a new product partnership has two audiences with two different rhythms, not one. A mandate or partnership agreement written as though satisfying EIOPA's framework were sufficient is missing half the picture.

Why This Slows Down Partnerships Specifically

An insurer developing a product internally already owns its target market data, its cost structure, and its historical performance record. A partnership introduces a party that owns none of that by default. An insurtech's product economics, however well designed, have to be reconstructed into the insurer's own POG documentation before the insurer can distribute it under its own licence and its own name.

That reconstruction is the invisible cost of a partnership, and it rarely appears in the term sheet. It shows up later, as a delay between signing and launch that neither party budgeted for, and that gets blamed on "compliance" as though compliance were an external event rather than a predictable consequence of the model chosen.

The Governance Cost That Doesn't Show Up in the Term Sheet

This is the same dynamic already visible in how corporates structure venture relationships more broadly: the governance cost of a structure is rarely obvious at the moment a deal is signed, and it is almost always higher than expected once the structure has to run. A distribution partnership that must pass POG testing before launch carries a governance cost that behaves the same way.

The practical answer is not to avoid partnerships. It is to price the governance cost into the timeline from the start, the same discipline that already applies to any external investment or mandate structure, and to write it into the agreement rather than discover it during review.

What This Means for How a Mandate Gets Written

A mandate or partnership agreement negotiated today without an explicit clause on POG review timing is negotiating around a gap that a supervisor will not ignore. The boundaries of the thesis, who can say yes and how fast, and what happens if a product fails value for money testing after launch all need to be settled in writing before the first product goes live, not after the first supervisory question arrives.

Where Corporates Still Move Fast

There is one structure that avoids most of this friction entirely, at least at the pilot stage: the venture client model, where an insurer pays for a pilot without taking equity and without distributing the partner's product under its own licence. Because nothing is sold to an end customer yet, POG obligations do not attach to the pilot itself. That is precisely why this model remains the fastest entry point for testing an insurtech relationship before committing to the governance load of a full distribution partnership.

Frequently Asked Questions

Does this apply only to unit-linked products?

EIOPA's most detailed findings concern unit-linked and hybrid life insurance products specifically. The POG obligations behind them apply more broadly across the Insurance Distribution Directive, but the concentrated evidence of a problem, and the corresponding scrutiny, is strongest on this segment.

Is this a French-specific issue or a European one?

Both, and they stack. EIOPA sets the European framework and the value for money expectation. The ACPR's expanded 2026 programme adds a French-specific layer of supervisory intensity on top of it, not a substitute for it.

Does a venture client pilot avoid this entirely?

It avoids it at the pilot stage, because nothing is distributed to an end customer under the insurer's licence yet. The obligations return in full once the relationship moves from pilot to actual product distribution.

What should a partnership agreement now include because of this?

An explicit allowance for POG review time in the launch timeline, and clarity on which party owns the documentation burden if the product's target market or cost structure changes after launch.

Conclusion

Regulatory pressure on life insurance is not a temporary tightening that will ease once EIOPA's current review cycle ends. The ACPR's own numbers point the other way: more missions in 2026 than in 2025, not fewer. What determines whether a corporate insurer can still move quickly on a product partnership is not whether it can avoid this pressure, but whether it picked a structure, and wrote an agreement, that already accounts for it.

Working on this? Mandalore Partners runs corporate venture programmes for insurers and financial institutions, inclu

Founder Factories, Part Two: Turning the Alumni Signal Into a Sourcing Process

The first part of this series looked at what the data says: roughly 250 European unicorns have produced more than 1,400 startups founded by their former employees, according to research from Accel and Dealroom. The pattern is real, it is concentrated in a small number of companies, and it clusters geographically.

Knowing that is not the same as being able to act on it. A corporate investor who reads the alumni data and concludes "we should track unicorn alumni" has identified a signal, not built a capability. This piece is about the distance between the two, because that distance is where most corporate sourcing initiatives quietly fail.

The Signal Has a Shelf Life

The single most important property of the alumni signal is that it decays. A map of who left which company, when, and what they are building now is accurate for roughly a quarter. After that it is a historical document.

This is what separates alumni tracking from most other sourcing inputs. A sector thesis stays broadly valid for a year. A relationship with a fund stays valid for as long as the relationship does. A list of people who recently left a scaling company is perishable in a way that neither of those are, because the entire value of the signal is that it identifies founders before they are visible, and visibility arrives on its own within months.

That property determines everything about how the capability has to be built. A one-off mapping exercise, however thorough, produces something that is wrong within two quarters. The question is never whether to build the map. It is whether the organisation is prepared to fund the maintenance.

What the Map Actually Needs to Contain

Three fields do most of the work, and they are less obvious than the ones most teams start with.

The departure, not the arrival. Most tracking starts when a new company is registered or announces a round. By then the signal has already been consumed by everyone else. The useful entry is the departure of a senior operator from a company that fits the thesis, recorded when it happens, with no assumption about what they will do next.

The seniority and the function. Not every departure carries the same weight. The alumni research shows the effect is driven by people who held operating responsibility, not by headcount generally. A departing head of product from a payments company is a different signal from a departing sales hire.

The gap. The time between a departure and the first visible activity is itself informative. A founder who surfaces within weeks was probably building before leaving. One who surfaces after a year has usually taken a genuine decision, and is more likely to be reachable during that gap, which is precisely when a corporate investor can be useful and nobody else is calling.

Where Corporates Get the Geography Wrong

The research finding that more than half of alumni-founded startups launch in the same city as their originating company is the most operationally useful thing in the dataset, and the most frequently ignored.

It means the map does not need to be global to be valuable. A corporate whose strategic interest is European insurance distribution does not need to track alumni of every unicorn in Europe. It needs to track a small number of companies in a small number of cities, which is a tractable problem for one person, and an intractable one for a team that insists on comprehensive coverage first.

The counterweight is that founders relocate, and the mobility of European deep tech founders is a real pattern of its own. A map built only on the origin city will lose the founders who leave, which is why the map records the person rather than the address.

The Part That Decides Whether It Works

Most corporates that attempt this get through the build and fail at the maintenance, for a reason that has nothing to do with the quality of the initial work.

Building the map is a project. It has a scope, an end date, and someone visibly responsible for it. Maintaining it is a job. It has no end date, produces nothing visible in any given week, and is the first thing dropped when the person holding it has a busier quarter than usual.

Organisations fund projects easily and jobs reluctantly. This is why the useful question, before any of the methodology, is which named person owns the weekly update and what else that person will stop doing. A corporate that cannot answer that question will build a map, use it well for one quarter, and quietly stop.

What This Buys That Intermediated Sourcing Does Not

A pipeline assembled from bankers, demo days and inbound decks can be reproduced by any competitor at the same price. That is not a criticism of it, it is a description of what it is: a subscription to the same information everyone else has, priced accordingly in every contested round.

The alumni map is different in one specific way. It is built from the corporate's own decision about which companies matter in its sector, maintained with its own effort, and therefore not available to a competitor who has not made the same decision and done the same work. Whether that is worth the maintenance cost depends on how many decisions per year the corporate expects to make on the back of it, which is the same variable that governs where capital is concentrating and how any venture structure should be sized.

How to Start Without Building Everything

The version of this that works starts far smaller than most teams expect.

Pick five to eight companies whose alumni would plausibly build something the corporate would want to see first. Record senior departures from those companies as they happen, with the date and the function. Review the list once a month, and reach out to the people who have not yet surfaced publicly. Nothing in that requires a tool, a data licence, or a headcount.

The discipline it does require is the willingness to keep doing it in the months where nothing comes of it, which is most months. The value arrives unevenly, and a programme that judges the effort on any single quarter will conclude, correctly for that quarter and wrongly overall, that it is not working.

Frequently Asked Questions

How large should the tracked company list be?

Small enough that one person can review it monthly without it becoming someone's full-time job. In practice that means single digits, chosen against the thesis rather than by size.

Is this a replacement for working with funds?

No. It answers a different question. Funds give breadth and pricing discipline; the alumni map gives early access to a narrow set of founders in a chosen sector. Most corporates need both, for different reasons.

What if the founders are not raising yet?

That is the point. The window where a corporate is genuinely useful, and genuinely differentiated, is before a round exists. A conversation at that stage costs nothing and is remembered.

Can this be outsourced?

The mapping can. The judgement about which companies belong on the list, and the relationships that follow from it, cannot — those are the parts that make the signal proprietary in the first place.

Conclusion

The alumni data is genuinely predictive, and it is available to anyone who reads the research. What is not available to anyone is a maintained map of a chosen set of companies, built on a corporate's own view of its sector, updated by someone whose job it is.

The gap between the signal and the capability is not analytical. It is organisational, and it comes down to whether a named person owns the update. Everything else in this piece is detail.

Working on this? Mandalore Partners runs corporate venture programmes for insurers and financial institutions, including the sourcing layer described above. Talk to us.

What to Measure in Year One of a Corporate Venture Programme

First-year reviews of corporate venture programmes almost always open with the same number: how many deals were done. It is the easiest figure to produce and the easiest to defend in front of a steering committee. It is also the number that carries the least information about whether the programme will still exist in three years. A programme can close eight investments in twelve months and be structurally dead. Another can close two and be in excellent health.

This article sets out what a first-year review should measure instead. Three indicators, none of them a count of transactions, carry more predictive weight than deal volume: how long a decision takes, whether the internal sponsors renew, and where the deals actually came from.

Why Deal Count Fails as a First-Year Metric

Deal count fails for a structural reason, not a philosophical one. In year one, the number of investments a programme closes is mostly a function of how much capital was allocated and how loose the investment criteria were. Both are decisions taken before the programme started. Counting deals therefore measures a budget line and a mandate, not the performance of the team executing them.

It also creates the wrong incentive at the worst possible moment. A team that knows it will be judged on volume at month twelve will lower its bar in month ten. The investments made in that window are the ones most likely to be written off, and they are made precisely when the programme has the least experience to price risk.

There is a second problem. Deal count is not comparable to anything. A corporate that made four investments cannot tell from that number whether it did well, because it has no internal baseline and the external benchmarks compare organisations with different mandates, different capital and different definitions of what counts as a deal.

Decision Cycle Time: The Metric That Predicts Everything Else

The single most informative first-year measurement is the time between first meaningful contact with a company and a documented decision, positive or negative.

It matters for two reasons. The first is competitive: in a contested round, a corporate investor that needs several months to reach a decision is not really in the round. Founders learn this quickly and stop bringing their better opportunities. The programme then experiences a slow degradation of the quality of what it sees, which is invisible in the deal count and fatal to the thesis.

The second reason is diagnostic. Decision cycle time is the only metric that aggregates every structural weakness of a programme into one number. A committee that meets quarterly, an approval chain that runs through a legal department with no venture experience, a mandate ambiguous enough that nobody is sure who can say yes: all of it shows up as elapsed days. When the number is bad, the cause is almost always in the governance design rather than in the team.

Two figures are worth tracking separately: the time to a negative decision, and the time to a positive one. A programme that says no quickly and yes slowly is behaving rationally under an unclear mandate. A programme that says no slowly is wasting the founder's time and its own credibility, and it will be remembered for it.

Sponsor Renewal: Whether the Programme Still Has Internal Buyers

A corporate venture programme is not funded by its returns in year one. It is funded by the continued belief of a small number of executives who agreed to sponsor it. The relevant measurement is therefore whether those sponsors are still actively engaged twelve months in, and whether any new ones have appeared.

This is measurable without ceremony. Which business units requested an introduction to a portfolio company. Which executives attended an investment committee they were not required to attend. Which internal budget holders volunteered a proof of concept. These are observable events, they can be counted, and they are recorded in a calendar rather than in a reporting system.

Sponsor attrition is the most reliable leading indicator of a programme being paused, and it precedes the formal decision by several quarters. A programme that has lost its internal buyers can still be closing deals right up to the week it is suspended, which is exactly why deal count gives no warning.

The counterpart is that a programme with growing internal demand can survive a poor investment year. Corporate venture programmes are rarely shut down for underperformance in a portfolio that is too young to have performance. They are shut down when nobody senior is left to defend them.

Proprietary Sourcing Share: Where the Deals Actually Came From

The third measurement is the share of the pipeline that reached the programme through a channel it built itself, as opposed to through an intermediary who also sent it to everyone else.

The distinction is not about pride of authorship. A pipeline that comes entirely from bankers, accelerator demo days and inbound decks is a pipeline that any competitor can reproduce at the same price, and it will be priced accordingly. A pipeline that comes from a corporate's own operating relationships, from its customers, from its technical teams and from the alumni networks around its sector is the only part of the sourcing function that a competitor cannot buy.

In year one, the absolute share will be low, and that is expected. What the review should look at is the direction of travel across the four quarters, and whether the proprietary channels that exist are maintained by someone whose job it is, or by whoever happened to have time.

This is also the metric most often reported dishonestly, because the definition is elastic. A useful discipline is to define the categories before the year starts, in the mandate agreement and to make the person who sourced the deal name the channel at the moment the company enters the pipeline rather than at the review.

Three Numbers That Look Useful and Are Not

Paper markups. A revaluation in year one reflects the entry price of the next investor, not the judgment of the programme. Reporting it as performance builds an expectation that the same line will keep rising, and it will not.

Portfolio company count. This is deal count with a different label. It answers the same budget question and carries the same incentive problem.

Pipeline volume. The number of companies reviewed measures how much noise reached the team. Without a qualification step, a larger pipeline is a cost, not an asset. If pipeline is tracked at all, it should be tracked as the share that passed a first qualification, and that ratio is more interesting than the total.

What the Year-One Review Should Actually Decide

A first-year review has one useful purpose: deciding whether the programme continues as designed, continues with a changed design, or stops. Framed that way, the three measurements map directly onto the decision.

Decision cycle time tells you whether the governance design works, and it is the one thing that can be fixed quickly without renegotiating anything upstream. Sponsor renewal tells you whether the programme still has a constituency, which determines whether it has a second year at all. Proprietary sourcing share tells you whether the programme is building an asset or renting one, which determines whether the third year is worth funding.

None of these require a reporting function, a data team or a new tool. They require a definition agreed before the year starts, which is a governance question rather than a measurement question, and which belongs in the mandate agreement alongside the fee and approval terms. Programmes that discover in month eleven that they never defined what they were going to measure will spend the review arguing about definitions rather than making a decision.

Frequently Asked Questions

Should returns be measured at all in year one?

They can be recorded, but they should not be used to decide anything. A portfolio built over twelve months has no realised outcomes and its unrealised marks reflect market conditions rather than selection quality.

What is a reasonable decision cycle time for a corporate investor?

There is no defensible universal figure, and any number presented as a standard should be treated with suspicion. The useful comparison is internal: the time the programme took in the fourth quarter against the time it took in the first, and the gap between its negative and positive decisions.

Who should own these measurements?

The programme itself, with the definitions fixed externally. When the team both defines and reports the metric, the definition drifts toward whatever the team is achieving.

Do these metrics apply to an outsourced programme?

They apply more strictly. When a programme runs through an external manager, decision cycle time and sourcing channel are the two variables the corporate is actually paying for, and both should appear in the mandate rather than in a quarterly slide. The cost comparison between the two models rests on the same variables.

Conclusion

Deal count survives as a first-year metric because it is easy to produce and nobody has to defend the definition. The cost of that convenience is that the review measures a budget decision taken a year earlier and learns nothing about whether the programme works.

Decision cycle time, sponsor renewal and proprietary sourcing share are harder to collect, mostly because they require agreement on what they mean before the year begins. What they offer in exchange is a review that can reach a decision: continue, redesign, or stop. A first-year review that cannot reach one of those three conclusions has measured the wrong things.

The First Three Years of a VCaaS Program: A Practical Roadmap

Corporations evaluating a VCaaS mandate can usually find good material on why the model works and how it compares to building internally. What is harder to find is a realistic timeline: what actually happens in year one versus year three, and what milestones indicate a program is on track versus quietly underperforming. This roadmap sets out a practical, stage-by-stage view of how a well-run VCaaS program typically develops over its first three years.

Year One: Mandate, Governance, and First Deals

The first three to six months are almost entirely structural: finalizing the investment thesis, agreeing governance and reporting mechanics, and — if the mandate is structured as a dedicated vehicle rather than direct balance-sheet investment — completing the legal and, where relevant, regulatory setup. Corporations that rush this phase to show early deal activity typically pay for it later with governance disputes or a thesis that needs to be substantially revised within the first year.

From roughly month six, the program should begin closing its first investments — typically three to eight in the remainder of year one, depending on mandate size and stage focus. The realistic goal for year one is not portfolio scale; it is establishing that the sourcing engine works, that the governance model functions at the speed it was designed for, and that the first few investments reflect the thesis accurately. A year-one program that has closed a handful of well-thesis-aligned deals and established a working reporting rhythm is on track, even if the absolute numbers look modest.

Year Two: Portfolio Building and Commercial Activation

Year two is where deal pace typically accelerates, as the sourcing network matures and the governance process proves itself at speed. It is also when the program's strategic value — as opposed to its financial potential, which takes longer to materialize — should start becoming visible, primarily through commercial activation: pilots, distribution conversations, and technical collaboration between portfolio companies and the corporate's business units.

This is the stage where programs most commonly underperform their potential, not because of poor investment selection but because commercial activation was left informal rather than actively managed. A portfolio company with no assigned business-unit sponsor rarely develops a commercial relationship on its own initiative. Programs that treat commercial facilitation as a resourced function — not an occasional bonus — show a measurably higher rate of active portfolio engagement by the end of year two.

Mid-program benchmarking belongs here as well: comparing deal pace, portfolio composition, and early signal quality against the original thesis and against external industry benchmarks, to catch drift before it compounds into year three.

Year Three: Measurable Strategic Value and Strategic Review

By year three, a well-run program should be able to demonstrate a track record on both dimensions of its mandate: early financial signal (follow-on rounds, markups, in some cases early exits) and strategic value (active commercial relationships, technology or market intelligence generated, talent or partnership pipeline). This is also the natural point for a structured strategic review: is the original thesis still the right one, does the program's scale match the corporation's ambition, and should the operating model evolve — scaling the VCaaS mandate, moving toward a hybrid model, or beginning a transition toward a partially internal team.

Programs that treat this review as a formality rather than a genuine strategic decision point tend to drift for another two to three years before the question gets asked seriously — typically triggered by a leadership change rather than a deliberate assessment.

What the Corporate Side Needs to Resource

A VCaaS mandate reduces the corporate's investment execution burden, but it does not eliminate the need for internal resourcing entirely. Across all three years, a corporate should expect to dedicate a senior sponsor with real authority to sit on the investment committee and make timely decisions, and — from year two onward — assigned business-unit contacts for each active commercial relationship, since these do not manage themselves. Corporations that treat the VCaaS mandate as fully turnkey, requiring no internal time investment, are consistently the ones whose commercial activation underperforms in year two.

Milestones to Track at Each Stage

• End of year one — governance and reporting operating as designed, three to eight thesis-aligned investments closed, and a documented, working sourcing pipeline.

• Mid-year two — deal pace at or above the mandate's target run rate, and at least one portfolio company with an active, assigned commercial relationship in progress.

• End of year two — a measurable majority of the portfolio with some form of active commercial engagement, and a mid-program benchmarking review completed against thesis and external data.

• End of year three — demonstrable financial signal, demonstrable strategic value, and a completed strategic review determining the operating model for the next phase.

Common Timeline Mistakes

• Rushing year-one deployment — to show early activity, at the cost of thesis discipline — this is the single most common source of a portfolio that needs significant correction by year two.

• Treating commercial activation as optional — rather than a resourced function, which leaves genuine strategic value underdeveloped even when the investment selection itself is sound.

• Deferring the year-three strategic review — until it is forced by a leadership change or budget pressure, rather than scheduling it as a deliberate part of the program design from the outset.

Frequently Asked Questions

How many investments should a VCaaS program make in its first year?

There is no fixed number, but three to eight investments in the second half of year one is a realistic range for most mandates, with the emphasis on thesis alignment and sourcing quality rather than absolute volume.

When should a corporation expect to see financial returns from a VCaaS program?

Early signals — follow-on rounds and markups — can appear from year two onward, but meaningful realized financial returns from early-stage venture investing typically take five to seven years or more, consistent with venture capital timelines generally.

What triggers a decision to bring a VCaaS program in-house?

Most commonly, sustained deal volume that would justify the fixed cost of an internal team, a long-term strategic commitment that makes deep internal capability building worthwhile, or dissatisfaction with an external partner's alignment or performance — ideally assessed at the year-three strategic review rather than reactively.

Is commercial activation something the VCaaS partner or the corporate is responsible for?

In practice, it works best as a shared responsibility — the VCaaS partner facilitates and manages the relationship, but it requires an assigned sponsor on the corporate's business-unit side to be genuinely productive.

Conclusion

A VCaaS program's first three years follow a reasonably predictable shape: structural setup and first deals in year one, portfolio building and commercial activation in year two, and measurable strategic value alongside a deliberate strategic review in year three. Programs that respect this sequence — rather than trying to compress it to show early results — consistently outperform those that rush it.

Where Insurers Actually Deploy Venture Capital: The Four Models

Most conversations about insurance and startups begin with the wrong question. They ask whether the group should have a venture arm. That question has no useful answer, because there is no single thing called a venture arm. There are four distinct structures, they solve different problems, and they cost very different amounts of governance to run.

An insurer that picks the wrong one does not usually discover the mistake in the portfolio. It discovers it in the friction: a committee that cannot approve anything on a startup's timescale, or a business unit that never adopts what the venture team bought. This article sets out the four models, what each one is actually for, and what each one costs before any capital is deployed.

Model One: The Venture Client, Buying Before Investing

The first model involves no equity at all. The insurer becomes a startup's customer, pays for a pilot, and measures whether the product solves a real internal problem.

The clearest public example in insurance is the Zurich Innovation Championship. Zurich describes the programme, which has run since 2018 and reports more than 150 projects across more than 30 countries, in terms that leave no ambiguity about the mechanism: "Unlike traditional Venture Capital models, we invest in the adoption and validation of a startup's product, service, or technology, without taking equity." The funding it offers is explicitly equity-free.

What this model buys is speed and evidence. A pilot can be authorised by a business unit budget holder rather than an investment committee, and the outcome is a fact about whether the product works inside the organisation rather than a valuation opinion.

What it does not buy is any claim on the upside. If the startup becomes a category leader, the insurer has a supplier relationship and nothing else. That is a deliberate trade, and it is the right one when the strategic question is "does this work for us" rather than "do we want to own part of this".

Model Two: The Dedicated Investment Unit

The second model is the one most people picture: a distinct entity, its own team, its own mandate, investing the group's balance sheet.

Allianz X is a documented example at scale. It presents itself as the strategic investment arm of the Allianz Group and reports 25 current direct investments, more than 1.7 billion in assets under management, five full acquisitions, more than ten exits, and 40 employees across five continents.

Two things are worth noticing in that description. The first is the size of the team relative to the number of positions. A dedicated unit is not a light structure, and the staffing is the real cost, not the capital. The second is that the stated purpose runs beyond returns: the unit exists to build connections between portfolio companies and the group. That is the defining feature of this model, and also its main point of failure. A dedicated unit that produces good returns but no adoption inside the group will eventually be asked what it is for.

Model Three: The External Mandate

The third model delegates the investment function to an external manager operating under a mandate written by the insurer. The insurer keeps the thesis, the strategic direction and usually the co-investment rights. It does not build the team.

This model is deliberately harder to document publicly than the first two, and the honest position is that no insurer mandate can be cited here with a public source. These arrangements are commercial contracts between two private parties, and neither side has an incentive to publicise the terms. Their existence is not in doubt; their details are simply not public.

What can be said structurally is what the mandate has to settle, because that is where the model succeeds or fails: the boundaries of the thesis, who can say yes and how fast, how fees relate to strategic outcomes rather than deployment volume, and what happens at the end of the term. All of that belongs in writing before the first deal, not after the first disagreement, and it is worth reading alongside what the mandate should specify in detail.

The trade in this model is control against speed and cost. The insurer gives up direct authorship of each decision and gains a functioning team without a multi-year hiring cycle. Whether that is a good trade depends almost entirely on how many deals per year the group realistically expects, which is the same variable that drives the cost of each approach.

Model Four: Investing Through Third-Party Funds

The fourth model puts the insurer in the position of investor in someone else's fund, rather than direct investor in companies.

This is the most common and least discussed of the four, because it looks like an allocation decision rather than an innovation decision. It offers diversification, no operational burden, and access to deal flow the insurer would never see alone. It offers almost nothing in strategic proximity: a position in a fund does not give a business unit a reason to talk to a portfolio company, and the reporting arrives quarterly, long after the moment when a commercial conversation would have been useful.

This model is best understood as a complement rather than a competitor to the other three. It is a reasonable way to learn a sector before committing to a structure, and a poor substitute for one.

What Each Model Really Costs in Governance

Capital is rarely the binding constraint for an insurer. Governance is.

The venture client model is the cheapest to govern because the decision sits with whoever owns the budget for the pilot. The dedicated unit is the most expensive, because it requires a permanent team, an investment process, a committee, and a reporting line that has to survive changes in group leadership. The external mandate moves most of that cost outside, but replaces it with the harder task of writing a mandate precise enough to be enforceable. Investing through third-party funds is the lightest of all, and correspondingly the least strategic.

The pattern worth remembering: the more strategic proximity a model offers, the more internal governance it consumes. There is no structure that delivers both close alignment and low overhead.

How to Choose Without Guessing

Three questions settle most of the decision.

What is the real objective? If the group needs to know whether a technology works in its own operations, the venture client model answers that faster and cheaper than any equity structure. If the objective is a claim on the upside of a sector, it does not answer it at all.

How many decisions per year? A group that expects two or three investments a year cannot justify a permanent team, and should be looking at a mandate or at fund positions. A group expecting a genuine flow of transactions can amortise a dedicated unit.

Who inside the organisation will actually adopt the outcome? This is the question most often skipped, and the one that decides whether the programme survives its third year. A model that produces investments nobody internally uses will be defended by its team and by nobody else.

Frequently Asked Questions

Can an insurer run more than one model at once?

Yes, and many do. The combination that works is a venture client programme feeding evidence into an equity structure, so that investment decisions are informed by whether the product actually performed inside the group. The combination that fails is two equity structures with overlapping mandates competing for the same deals.

Is the venture client model just a pilot programme with a new name?

The mechanism is similar. The difference is intent and follow-through: a venture client programme is designed so that a successful pilot leads to a commercial contract, rather than ending in a report.

Does a dedicated unit need to sit outside the insurer legally?

Not necessarily, but it needs to sit outside the insurer operationally. The recurring failure is a unit that reports into a structure whose approval cycle is slower than the market it is investing in.

What is the fastest model to start?

The venture client model, by a wide margin. It requires no vehicle, no committee and no capital allocation, which is also why it is the most common entry point for insurers testing whether they want a venture strategy at all.

Conclusion

The four models are not stages on a maturity path, and an insurer does not graduate from one to the next. They are answers to different questions: whether a technology works, whether to own part of a company, whether to delegate the function, or whether to buy exposure to a sector.

The decision that matters is not which model is best in the abstract. It is which question the group is actually trying to answer this year, and whether it has the governance to support the answer it chooses. Getting that pairing wrong is what produces venture programmes that look active for two years and quietly disappear in the third, a pattern visible in how the first three years of a programme tend to unfold.

De la lutte contre le piratage à la présidence exécutive : le parcours de Daniel Ek

Introduction

Le 1er janvier 2026, Daniel Ek a cessé d'être directeur général de Spotify, la société qu'il a cofondée vingt ans plus tôt. Il ne quitte pas l'entreprise : il devient président exécutif, cédant la direction opérationnelle à deux dirigeants de longue date, Gustav Söderström et Alex Norström. Cette transition, présentée par Ek comme un passage de « joueur à entraîneur », clôt un cycle rare dans l'industrie technologique — celui d'un fondateur resté à la tête de son entreprise pendant deux décennies, de la lutte contre le piratage musical jusqu'à la construction d'une plateforme de streaming utilisée par plus de 700 millions de personnes.

Un entrepreneur précoce, avant même Spotify

Daniel Ek naît en 1983 à Stockholm. Sa vocation entrepreneuriale se manifeste tôt : dès 13 ans, il construit des sites web pour de petites entreprises. À 18 ans, il gagne déjà plus que son père, mécanicien de profession. Passionné à la fois d'informatique et de musique, il intègre brièvement le prestigieux Institut royal de technologie (KTH) de Stockholm avant de s'en désengager, jugeant la formation trop lente au regard de ce qu'il pouvait apprendre en construisant directement des produits.

Avant Spotify, Ek fonde et revend plusieurs entreprises. La plus notable, Advertigo, une société de publicité en ligne, est rachetée en 2006 par l'entreprise suédoise TradeDoubler — cofondée par Martin Lorentzon, qui deviendra quelques mois plus tard le cofondateur de Spotify. Cette acquisition permet à Ek, alors âgé de 23 ans, une forme de retraite précoce et de confort financier. Elle sera de courte durée.

Le piratage musical comme point de départ

Le déclic à l'origine de Spotify remonte au tout début des années 2000. Adolescent, Ek utilise activement Napster, la plateforme de partage de fichiers musicaux qui bouleverse alors l'industrie du disque. La Suède, dotée d'un haut débit parmi les plus rapides au monde grâce à des investissements publics précoces dans les infrastructures internet, devient l'un des foyers les plus actifs du piratage musical en Europe, avec l'émergence de The Pirate Bay sur son propre sol.

Quand Napster ferme sous la pression judiciaire, Ek comprend qu'il est déjà trop tard pour combattre le piratage par la voie légale seule. L'idée qui deviendra Spotify naît de ce constat : il ne s'agit pas de rendre le piratage plus difficile, mais de construire un service légal suffisamment agréable à utiliser pour rendre le piratage moins intéressant que l'alternative payante — tout en reversant des revenus à une industrie musicale exsangue.

La construction méthodique de Spotify

Ek fonde Spotify avec Martin Lorentzon en 2006. Contrairement à l'image d'un lancement fulgurant que l'on associe souvent aux succès technologiques, la mise sur le marché du produit prend plus de deux ans : l'essentiel du travail des deux fondateurs consiste à négocier, un par un, les accords de licence avec des maisons de disques encore traumatisées par une décennie de piratage et profondément méfiantes envers un nouveau service numérique promettant un accès illimité à leurs catalogues. Spotify finit par se lancer en Suède et dans plusieurs marchés européens en octobre 2008.

L'expansion vers les États-Unis, marché stratégique entre tous pour l'industrie musicale, prend trois années supplémentaires et ne se concrétise qu'en 2011, retardée par les mêmes obstacles de négociation de licences. Pour faciliter cette percée américaine, Ek s'associe à Sean Parker, cofondateur de Napster et premier président de Facebook, qui apporte à Spotify une crédibilité et un réseau relationnel déterminants dans l'écosystème technologique californien — notamment une intégration profonde avec Facebook qui transforme l'écoute musicale en expérience sociale partagée.

De la startup à la bourse, puis à un nouveau modèle de gouvernance

Spotify entre en bourse à New York en avril 2018, via une cotation directe plutôt qu'une introduction en bourse classique — un choix alors inhabituel qui deviendra, dans les années suivantes, une option de plus en plus considérée par d'autres entreprises technologiques. Le titre clôture sa première séance de cotation à une valorisation d'environ 27 milliards de dollars.

Les années suivantes voient Spotify élargir son offre bien au-delà de la musique — podcasts, livres audio — tout en dépassant les 700 millions d'utilisateurs. C'est dans ce contexte de maturité opérationnelle que la transition de janvier 2026 prend tout son sens : Söderström et Norström, tous deux présents chez Spotify depuis plus de quinze ans et co-présidents de l'entreprise depuis 2023, pilotaient déjà l'essentiel de la gestion quotidienne avant même l'officialisation de leurs titres de co-directeurs généraux. Ek a lui-même décrit ce changement comme un ajustement de titres à une réalité opérationnelle déjà en place depuis plusieurs années, plutôt que comme une rupture.

Un engagement personnel qui dépasse Spotify

Parallèlement à la direction de Spotify, Daniel Ek a construit, au fil des années, un profil d'investisseur actif en dehors du secteur musical. Son engagement le plus commenté concerne Helsing, une entreprise allemande spécialisée dans l'intelligence artificielle appliquée à la défense, dans laquelle Ek a investi personnellement et significativement à travers son véhicule d'investissement Prima Materia. Cet engagement a suscité des critiques répétées de la part d'artistes présents sur Spotify, certains ayant retiré leur catalogue de la plateforme en signe de protestation contre ce qu'ils considèrent comme une contradiction entre les valeurs affichées par Spotify et les choix d'investissement personnels de son fondateur. Cette controverse a accompagné, en toile de fond, les mois qui ont précédé l'annonce de sa transition vers la présidence exécutive, sans que Spotify n'établisse de lien officiel entre les deux événements.

Cet épisode illustre une tension propre aux fondateurs devenus suffisamment fortunés pour investir massivement en dehors de leur entreprise d'origine : leurs choix personnels, même juridiquement distincts de l'entreprise qu'ils dirigent, sont scrutés comme s'ils engageaient directement la marque qu'ils ont construite.

Ce que cette transition raconte sur la trajectoire d'un fondateur

L'histoire de Daniel Ek illustre une dimension du parcours entrepreneurial que les statistiques sur la création de licornes documentent rarement : ce qui se passe après la réussite initiale. Rester directeur général pendant vingt ans, puis choisir de transmettre la direction opérationnelle sans quitter l'entreprise ni renoncer à son influence stratégique, est une trajectoire distincte de celle, plus fréquemment documentée, du fondateur qui revend son entreprise ou qui enchaîne les créations successives. Le rôle de président exécutif qu'endosse Ek — modèle plus courant en Europe qu'aux États-Unis — lui permet de conserver un rôle actif sur l'allocation du capital et la stratégie de long terme, tout en confiant l'exécution quotidienne à des dirigeants formés en interne pendant plus d'une décennie.

Questions fréquentes

Daniel Ek quitte-t-il complètement Spotify ? Non. Il reste président exécutif, un rôle dans lequel il continue de superviser l'allocation du capital, la stratégie de long terme et les relations avec les régulateurs, tout en laissant Gustav Söderström et Alex Norström diriger les opérations quotidiennes en tant que co-directeurs généraux.

Pourquoi Spotify a-t-elle choisi une cotation directe plutôt qu'une introduction en bourse classique en 2018 ? Une cotation directe permet à une entreprise de faire coter ses actions existantes sans émettre de nouvelles actions ni lever de capitaux frais, et sans recourir à des banques garantes pour fixer le prix initial. Ce choix reflétait la situation financière de Spotify à l'époque, qui n'avait pas besoin de lever des fonds supplémentaires pour financer sa croissance.

Ce type de transition fondateur-vers-président est-il fréquent dans la tech européenne ? Il reste minoritaire, mais suit une logique de plus en plus observée chez les entreprises technologiques matures dont les fondateurs veulent rester impliqués sans continuer à gérer les opérations quotidiennes. Le cas de Spotify se distingue par l'ancienneté exceptionnelle des deux successeurs choisis, formés en interne pendant plus de quinze ans plutôt que recrutés à l'extérieur.

Les investissements personnels d'un fondateur peuvent-ils affecter l'entreprise qu'il dirige ? Le cas de Daniel Ek et Helsing montre que oui, au moins sur le plan de la réputation et des relations avec les parties prenantes de l'entreprise — même lorsque l'investissement est juridiquement séparé de l'entreprise cotée. Pour une plateforme comme Spotify, dont la valeur repose en grande partie sur sa relation avec les artistes et les ayants droit, ce type de tension reste un risque de gouvernance à part entière.

Why a Corporate Becomes an LP Instead of Investing Directly

Ask a corporate why it invests directly in startups instead of putting the same capital into venture funds, and the answer is almost always some version of control. Direct investing means picking the companies, sitting closer to the cap table, and keeping the relationship in-house.

What that answer skips is what control costs, and what it buys. Fund investing is not the fallback for corporates without the ambition to build a real programme. For many, it is the structurally correct choice, and understanding why requires separating four things that get treated as one decision: sourcing, selection, governance, and speed.

What Direct Investing Actually Requires

A corporate that invests directly is not just writing checks. It is running a sourcing function that has to compete with funds whose entire business is sourcing, a selection process that has to price risk on assets most corporate teams have never underwritten, and a governance chain that has to move at a pace startups can survive.

None of that is impossible to build. It is expensive to build, and it takes years before it produces a track record good enough to attract the deals that made the exercise worthwhile in the first place. A corporate weighing direct investment against fund investment is really weighing whether it wants to build that capability, not whether it wants exposure to venture-backed companies.

What Becoming an LP Actually Buys

An LP position buys diversification a single corporate could never build alone, a manager whose full-time job is sourcing and pricing, and exposure to a category without carrying the operating cost of a team.

It also buys something less obvious: a faster path to a working relationship with the venture ecosystem. A corporate with no venture programme and no track record is a slow yes for founders and a slow yes for co-investors. A corporate that shows up as an LP in a credible fund inherits some of that fund's standing immediately, which is difficult to buy any other way.

What it does not buy is proximity. An LP sees a quarterly report, not a term sheet in progress. If the strategic objective requires shaping a deal as it happens, this structure will consistently disappoint that objective, no matter how well the fund performs financially.

The Real Trade Is Speed Against Proximity

Every other difference between direct investing and fund investing collapses into this one trade. Direct investing buys proximity: the corporate is at the table when the deal is shaped, and it can attach commercial terms, pilot rights, or board input to the check. Fund investing buys speed: capital can be committed in months rather than the year or more it takes to build a team, a mandate, and a first deal.

Corporates that get this trade wrong tend to make the same mistake in one of two directions. Some commit to direct investing before they have the governance to move at venture speed, and the programme spends its first two years losing good deals to slower internal approval rather than to bad judgement. Others commit to a purely financial LP position when what they actually wanted was commercial access, and then wonder why the fund's portfolio companies never call.

When the Trade Favours a Fund

The case for an LP position is strongest when the corporate's real objective is market intelligence rather than deal-by-deal influence, when the expected deal volume is too low to justify a dedicated team, or when the corporate needs venture exposure now and cannot wait through the multi-year ramp that a direct programme requires before it performs. It is also the more defensible choice when the corporate has no internal capability to evaluate venture-stage risk and would otherwise be pricing deals it does not understand.

When the Trade Favours Direct Investment

The case for direct investment is strongest when the strategic objective cannot be delivered by a passive position: a pilot in a specific business unit, a right of first look in a defined category, or board-level input into how a portfolio company grows. It also tends to favour corporates with enough expected deal volume to make a dedicated capability worth its fixed cost, which is the same variable that drives the cost comparison between the two models.

What Most Corporates Actually Need

Most corporates evaluating this question are not choosing between two pure structures. In practice, an LP position and a direct programme answer different questions, and a corporate can run both at once without contradiction: the fund position for market coverage and manager relationships, a small number of direct positions for the handful of situations where proximity is the actual objective.

What decides whether that combination works is the same discipline that determines whether either structure works alone: a clear statement, in writing, of what each pool of capital is for. A corporate that cannot answer that question before committing capital will not answer it any better after, which is exactly what a mandate agreement is supposed to force before the first commitment is made.

Frequently Asked Questions

Can a corporate become an LP and still get access to deal flow?

Some fund structures include co-investment rights for LPs, which partially closes the proximity gap. That right has to be negotiated explicitly; it is not a standard feature of a fund commitment.

Is fund investing cheaper than direct investing?

Usually, once the internal team, governance and multi-year ramp-up of a direct programme are counted. The comparison is detailed in the cost comparison between the two models.

Does becoming an LP first make a later direct programme easier?

Often, because it establishes a track record and a set of relationships that a first-time direct investor has to build from nothing. This is a common path in the first three years of a programme.

What is the most common mistake corporates make in this decision?

Choosing based on which option feels more strategic rather than on how many decisions the corporate actually expects to make per year. A structure built for volume that never arrives, or a passive position when the objective required proximity, both fail for the same reason: the structure did not match the actual number of decisions the corporate needed to make.

Conclusion

Direct investing and fund investing are not a beginner's option and an advanced one. They are answers to different strategic questions, and the corporates that get this decision wrong are usually the ones that never asked which question they were actually answering.

The choice is not between ambition and caution. It is between proximity and speed, and between a capability worth building and one worth renting. Getting that pairing right, before any capital moves, is what determines whether the structure still makes sense in year three.

Du trading à la fintech la plus valorisée d'Europe : le parcours de Nik Storonsky

Introduction

Revolut est aujourd'hui la société technologique privée la plus valorisée d'Europe, avec une valorisation de 75 milliards de dollars atteinte lors d'un tour de financement d'octobre 2025 — devant des entreprises bien plus anciennes et bien plus connues du grand public. Derrière cette réussite se trouve un parcours qui ne ressemble à aucun manuel classique de la fintech : un physicien russe devenu trader à Londres, qui a quitté la finance traditionnelle avec 500 000 dollars d'économies personnelles pour construire, à partir de zéro, l'un des plus grands succès entrepreneuriaux du continent — et qui illustre un profil de fondateur bien différent de celui, plus académique, documenté dans nos précédents articles sur les fondateurs de licornes.

Une formation scientifique, pas commerciale

Nikolay Storonsky naît le 21 juillet 1984 à Dolgoprudny, une ville située à une vingtaine de kilomètres au nord de Moscou. Son père occupe un poste de direction scientifique dans un institut de recherche gazier lié à Gazprom, un environnement qui l'expose tôt aux mathématiques et à l'économie. Storonsky se distingue académiquement en physique et en économie, devient champion universitaire de natation, et intègre l'Institut de physique et de technologie de Moscou (MIPT), l'une des institutions scientifiques les plus sélectives de Russie, où il obtient un master en physique générale et appliquée. Il complète ensuite sa formation par un second master en économie appliquée et finance à la New Economic School de Moscou.

Ce parcours — sélectif, scientifique, sans aucune formation initiale en commerce ou en gestion — rejoint une observation déjà documentée sur les fondateurs de licornes : la discipline étudiée importe moins que la capacité à transposer une méthode rigoureuse à un problème d'entreprise.

Le passage par la finance traditionnelle

À 20 ans, en 2004, Storonsky s'installe au Royaume-Uni. Il entre dans le monde de la finance de marché comme trader en produits dérivés actions chez Lehman Brothers, de 2006 à 2008. Après la faillite retentissante de la banque en 2008, il rejoint Credit Suisse, où il reste jusqu'en 2013. C'est durant ces années que Storonsky se confronte directement aux inefficacités structurelles du système bancaire traditionnel, en particulier sur les opérations impliquant plusieurs devises — une frustration qui deviendra, quelques années plus tard, le point de départ de Revolut.

La fondation de Revolut : un pari financé sur fonds propres

En 2015, Storonsky utilise environ 500 000 dollars de ses économies accumulées comme trader pour lancer, avec le développeur ukrainien Vlad Yatsenko, une startup destinée à concurrencer les grandes institutions bancaires internationales. L'entreprise démarre à Level39, un incubateur spécialisé en technologies financières situé à Canary Wharf, au cœur du quartier financier de Londres — un choix d'implantation qui n'a rien d'anodin pour deux fondateurs venus du monde de la banque.

L'ambition initiale est simple à énoncer et complexe à exécuter : proposer une carte et une application permettant de dépenser et transférer de l'argent à l'international sans les frais de change prohibitifs pratiqués par les banques traditionnelles. Storonsky résume lui-même la logique économique de son modèle par la maîtrise de l'infrastructure : en internalisant l'ensemble de la chaîne technique plutôt qu'en dépendant de prestataires tiers, une entreprise de paiement peut devenir rentable à des volumes que ses concurrents historiques jugeraient insuffisants.

Ce point de départ centré sur le change international s'est ensuite élargi méthodiquement à d'autres services financiers — comptes courants, cryptomonnaies, courtage en actions, assurance voyage — dans une logique de « super application » financière unique, une stratégie que Storonsky revendique explicitement comme la clé de la rentabilité du groupe : plus les services consommés par un même client sont nombreux au sein d'une seule plateforme, plus le coût d'acquisition initial de ce client se rentabilise rapidement.

Une croissance réglementaire méthodique

La trajectoire de Revolut se distingue par une accumulation méthodique d'agréments réglementaires, plutôt que par une croissance purement portée par le marketing. En 2018, l'entreprise obtient une licence de banque "challenger" auprès de la Banque centrale européenne, facilitée par la Banque de Lituanie, l'autorisant à accepter des dépôts et à proposer des crédits à la consommation. En juillet 2024, au terme d'un processus de demande engagé trois ans plus tôt, Revolut obtient enfin sa licence bancaire britannique, lui permettant d'élargir son offre à des produits comme les prêts immobiliers et l'épargne réglementée.

Cette accumulation d'agréments accompagne une croissance opérationnelle rapide : Revolut revendique aujourd'hui plus de 50 millions de clients dans plus de 40 pays, emploie plus de 10 000 personnes, et affiche un profit avant impôts dépassant le milliard de livres sterling. Le tour de financement d'octobre 2025, qui valorise l'entreprise à 75 milliards de dollars, en fait la plus grande société technologique privée d'Europe.

Un second acte : de fondateur à investisseur

En 2023, sans quitter la direction de Revolut, Storonsky fonde QuantumLight, un fonds de capital-risque qui applique des modèles de machine learning propriétaires pour identifier des startups à fort potentiel dans l'intelligence artificielle, la fintech et d'autres secteurs technologiques. Ce mouvement — un fondateur de licorne qui devient, en parallèle, investisseur dans d'autres startups — est loin d'être isolé dans l'écosystème technologique, mais il illustre une dynamique intéressante : l'expérience opérationnelle accumulée à la tête d'une fintech mondiale devient elle-même un actif que Storonsky choisit de redéployer vers le financement d'autres fondateurs.

En 2022, à la suite de l'invasion de l'Ukraine par la Russie, Storonsky condamne publiquement l'action militaire russe et renonce à sa nationalité russe, ne conservant que sa nationalité britannique acquise après son installation au Royaume-Uni.

Une culture d'entreprise qui a aussi suscité la controverse

La trajectoire de Revolut ne s'est pas construite sans friction. Storonsky a bâti une culture d'entreprise reconnue pour son exigence : de longues heures de travail, un rythme d'exécution élevé, et une implication personnelle constante dans le développement produit et le retour client. Cette intensité a également valu à l'entreprise plusieurs controverses, notamment des signalements internes sur des pratiques de conformité au tout début de sa croissance, largement documentés par la presse britannique, et des critiques récurrentes sur les conditions de travail. Ces épisodes ont accompagné, plutôt qu'empêché, la trajectoire de l'entreprise vers les agréments réglementaires les plus exigeants du secteur bancaire européen — un rappel que la croissance rapide d'une fintech s'accompagne rarement d'un parcours sans accroc sur le plan de la gouvernance.

Ce que ce parcours illustre

L'histoire de Nik Storonsky s'inscrit dans un mouvement plus large de fondateurs de fintech venus directement du monde de la finance traditionnelle, plutôt que de la recherche ou de l'ingénierie pure — un profil qui reste solidement représenté parmi les fondateurs de licornes, aux côtés des profils issus de la recherche académique documentés ailleurs. Ce que Storonsky et ses pairs partagent n'est pas un pedigree académique particulier, mais une exposition directe et prolongée aux dysfonctionnements précis du secteur qu'ils allaient ensuite chercher à réinventer.

Questions fréquentes

Pourquoi Revolut a-t-elle mis autant de temps à obtenir sa licence bancaire britannique ? Le processus a duré environ trois ans, un délai qui reflète à la fois la complexité réglementaire du secteur bancaire britannique et le niveau d'examen renforcé appliqué aux fintechs de grande taille, dont les pratiques de gouvernance et de conformité ont fait l'objet de plusieurs enquêtes journalistiques au fil de sa croissance.

Nik Storonsky dirige-t-il toujours Revolut au quotidien ? Oui, il reste fondateur et directeur général de l'entreprise, tout en supervisant également QuantumLight, le fonds de capital-risque qu'il a fondé en 2023.

La valorisation de 75 milliards de dollars est-elle définitive ? Non. Il s'agit de la valorisation obtenue lors du tour de financement d'octobre 2025, sur la base des informations disponibles au moment de la rédaction. Les valorisations de ce type évoluent avec chaque nouveau tour de financement ou, en cas d'introduction en bourse future, avec le cours de l'action.

Qu'est-ce qui différencie QuantumLight des fonds de capital-risque traditionnels ? QuantumLight se distingue par son usage revendiqué de modèles de machine learning propriétaires pour identifier des cibles d'investissement à un stade précoce, plutôt que de s'appuyer uniquement sur le jugement humain et le réseau relationnel des associés du fonds — une approche qui reflète directement la culture data-driven que Storonsky a imposée chez Revolut.

Du laboratoire DeepMind à la décacorne : le parcours d'Arthur Mensch et Mistral AI

Introduction

Dans un article précédent, une étude de Revelio Labs et Accel montrait que la part des licornes européennes fondées par un titulaire de doctorat avait doublé depuis 2023, tandis que la part des profils MBA avait été divisée par deux sur la même période. Arthur Mensch, cofondateur et CEO de Mistral AI, est l'une des illustrations les plus visibles de cette tendance. Docteur en informatique formé dans deux des institutions de recherche les plus exigeantes de France, il a quitté un poste de chercheur chez DeepMind pour fonder, en 2023, l'entreprise devenue en trois ans la plus valorisée du secteur de l'intelligence artificielle en Europe — une trajectoire qui permet de mettre un visage et une chronologie précise sur une statistique par ailleurs assez abstraite.

Un parcours académique construit pour la recherche, pas pour l'entrepreneuriat

Arthur Mensch naît en 1992 à Sèvres et grandit à Ville-d'Avray, dans les Hauts-de-Seine. Son parcours scolaire suit une trajectoire académique dense et sélective : l'École polytechnique, puis Télécom Paris et l'École normale supérieure, avant un doctorat mené conjointement à Inria et à l'université Paris-Saclay, sous la direction de Bertrand Thirion et Gaël Varoquaux, sur des sujets liés aux neurosciences computationnelles.

Rien, dans ce parcours, ne dessine a priori la trajectoire d'un entrepreneur. C'est un profil de chercheur pur, formé à la rigueur méthodologique plutôt qu'à la construction d'une entreprise — précisément le type de profil que la donnée Revelio Labs x Accel identifie comme de plus en plus fréquent parmi les fondateurs de licornes européennes.

Le passage chez DeepMind : une compréhension fine du terrain de jeu

Fin 2020, Arthur Mensch rejoint DeepMind Paris, le laboratoire de recherche en intelligence artificielle du groupe Alphabet, où il passe près de trois ans à travailler sur les grands modèles de langage, les systèmes multimodaux et les architectures à récupération augmentée. Il y est l'un des auteurs principaux d'un article de recherche influent sur l'entraînement optimal des grands modèles de langage en fonction du budget de calcul disponible — un sujet devenu central dans l'industrie à mesure que le coût d'entraînement des modèles est devenu un enjeu stratégique majeur.

Ce passage chez l'un des deux ou trois laboratoires les plus avancés au monde sur le sujet lui donne, au moment de quitter DeepMind en mai 2023, une compréhension fine des arbitrages techniques et économiques qui séparent un modèle de langage compétitif d'un projet de recherche sans débouché commercial.

La création de Mistral AI : une vitesse d'exécution inhabituelle

Le 28 avril 2023, Arthur Mensch cofonde Mistral AI avec Guillaume Lample et Timothée Lacroix, deux anciens chercheurs de Meta AI (FAIR) que Mensch a côtoyés à l'École polytechnique. Lample a notamment co-créé LLaMA, le modèle de langage open source dévoilé par Meta en février 2023, qui a servi de socle à une large partie de l'écosystème open source qui a suivi.

Le trio réunit ainsi une expertise technique rare, acquise directement au sein des deux laboratoires américains les plus avancés du moment sur les grands modèles de langage. Cette crédibilité se traduit immédiatement dans le rythme des levées de fonds : dès juin 2023, à peine deux mois après la création de l'entreprise, Mistral AI lève environ 105 millions d'euros en amorçage — un montant considéré comme un record européen pour une société sans produit ni revenus à ce stade. Parmi les investisseurs figurent notamment Xavier Niel, l'ancien secrétaire d'État au numérique Cédric O, et le fonds Lightspeed.

Une croissance fulgurante, portée par la publication en open source

En septembre 2023, quatre mois seulement après sa création, Mistral AI publie Mistral 7B, un modèle en poids ouverts qui surpasse des modèles bien plus volumineux sur plusieurs bancs d'essai de référence — un choix stratégique de publication en open source qui distingue d'emblée l'entreprise de la plupart de ses concurrents américains.

La suite de la trajectoire financière est tout aussi rapide. En juin 2024, Mistral AI lève environ 600 millions d'euros en série B, menée par General Catalyst, valorisant l'entreprise à 6 milliards de dollars et en faisant, à l'époque, la startup d'intelligence artificielle la plus valorisée d'Europe et la plus valorisée au monde en dehors de la baie de San Francisco. En septembre 2025, l'entrée au capital du groupe néerlandais ASML porte la valorisation à 11,7 milliards d'euros, pour un total de plus de 2,7 milliards d'euros levés entre 2023 et 2025. L'entreprise revendique aujourd'hui environ 400 millions de dollars de revenus récurrents annuels, avec un objectif affiché d'atteindre le milliard de dollars d'ici la fin de l'année 2026.

Le contexte : une ambition de souveraineté technologique affichée

La trajectoire de Mistral AI ne peut se lire indépendamment du contexte dans lequel l'entreprise est née. Au moment de sa création, le débat sur la dépendance technologique de l'Europe aux modèles d'intelligence artificielle américains et, plus récemment, chinois, prenait une place croissante dans le discours public et politique. Mensch et ses cofondateurs se sont positionnés dès le départ comme une réponse européenne crédible à ce constat — non pas en répliquant la stratégie des grands laboratoires américains, mais en misant sur une combinaison d'ouverture (publication de modèles en poids ouverts), d'efficience technique (des modèles plus compacts mais compétitifs) et d'infrastructure européenne.

Ce positionnement a directement influencé la structure de l'actionnariat de l'entreprise. Parmi les investisseurs figurent aussi bien des fonds de capital-risque américains classiques que des acteurs industriels européens comme ASML, dont l'entrée au capital en 2025 dépasse la simple logique financière : elle rattache Mistral AI à une chaîne de valeur technologique européenne plus large, de la conception des puces à l'entraînement des modèles.

Ce que cette trajectoire illustre concrètement

Le parcours d'Arthur Mensch n'est pas qu'une success story supplémentaire dans le paysage de l'intelligence artificielle : il constitue une illustration presque manuelle de la tendance documentée par Revelio Labs et Accel. Un doctorant en neurosciences computationnelles, formé exclusivement dans des institutions de recherche, qui rejoint un laboratoire industriel de pointe avant de fonder une entreprise valorisée à plusieurs milliards d'euros en moins de trois ans — c'est précisément le type de trajectoire que la bascule statistique « moins de MBA, plus de PhD » décrit en pourcentages agrégés.

Ce n'est pas non plus un cas isolé au sein même de l'équipe fondatrice : Guillaume Lample et Timothée Lacroix partagent un profil de chercheur comparable, formé dans les mêmes laboratoires industriels de référence. L'équipe fondatrice de Mistral AI n'a, à aucun moment, cherché à compléter son profil scientifique par un profil commercial ou financier extérieur — un choix qui contredit directement l'ancienne intuition selon laquelle un fondateur technique a besoin d'un cofondateur « business » pour réussir.

Questions fréquentes

Pourquoi Mistral AI a-t-elle choisi de publier des modèles en open source dès ses débuts ? Ce choix stratégique distingue Mistral AI de la plupart de ses concurrents américains et répond à un positionnement affiché de souveraineté technologique européenne : proposer une alternative crédible et auditable aux modèles fermés développés outre-Atlantique, tout en construisant une communauté de développeurs autour de ses modèles.

La rapidité des levées de fonds de Mistral AI est-elle exceptionnelle ? Oui, à l'échelle européenne. Lever 105 millions d'euros deux mois après la création d'une entreprise sans produit commercialisé reste rare, y compris aux États-Unis. Cette rapidité s'explique en grande partie par le pedigree technique déjà établi des trois fondateurs avant même la création de l'entreprise.

Ce type de trajectoire est-il reproductible pour d'autres chercheurs ? Les données disponibles ne permettent pas de l'affirmer avec certitude. Le profil des trois fondateurs — passage par les laboratoires industriels les plus avancés au monde sur le sujet précis de leur startup — constitue un avantage compétitif difficile à répliquer à l'identique. La tendance de fond documentée par Revelio Labs x Accel suggère cependant que ce type de parcours devient structurellement plus fréquent, pas plus exceptionnel.

Faut-il un doctorat pour fonder une entreprise d'intelligence artificielle compétitive ? Non, et Mistral AI elle-même n'en est pas une preuve absolue : Guillaume Lample et Timothée Lacroix, les deux autres cofondateurs, n'ont pas suivi le même parcours doctoral qu'Arthur Mensch, mais un parcours de recherche industrielle chez Meta AI, tout aussi spécialisé. Ce que ces trois trajectoires ont en commun n'est pas le diplôme précis, mais des années d'immersion complète dans la recherche de pointe sur les grands modèles de langage, acquise avant la création de l'entreprise plutôt qu'en cours de route.

Un échec, un pacte, et une entrée en bourse à 18 milliards : l'histoire fondatrice de Bending Spoons

Introduction

Dans un précédent article, une donnée de recherche de Stanford GSB montrait que la plupart des fondateurs de licornes qui avaient déjà entrepris avant leur réussite n'avaient tenté qu'une seule fois auparavant — pas cinq, pas dix : une fois. L'histoire de Bending Spoons, la société italienne qui possède aujourd'hui AOL, Evernote, Vimeo et Meetup, et qui vient d'entrer en bourse au Nasdaq à une valorisation de 18,4 milliards de dollars, en est une illustration presque parfaite. Avant de construire l'un des empires technologiques les plus singuliers d'Europe, ses quatre fondateurs ont d'abord vécu l'échec complet de leur première startup.

Le premier échec : Evertale

L'histoire commence en 2010, à Copenhague. Luca Ferrari, Francesco Patarnello, Matteo Danieli et Luca Querella, quatre étudiants italiens en ingénierie formés en grande partie à l'université de Padoue, se retrouvent dans le cadre d'un double-diplôme à l'Université technique du Danemark (DTU). Comme beaucoup d'étudiants en ingénierie de leur génération, ils rêvent de lancer une startup à l'américaine.

Leur premier projet s'appelle Evertale, une application de partage de photos baptisée Wink. Ils la présentent à Disrupt SF 2011, l'un des concours de startups les plus courus de la Silicon Valley, et parviennent à lever des fonds d'amorçage. Mais le produit ne trouve jamais son marché. Evertale s'effondre peu après.

Ce qui suit relève presque de la légende entrepreneuriale : les cinq fondateurs et employés restants concluent un pacte inhabituel — celui qui décrocherait un emploi rémunéré partagerait son salaire avec les autres, le temps de retomber sur leurs pieds. Luca Ferrari obtient la meilleure offre, un poste chez McKinsey. Pendant environ quinze mois, il mène une double vie : consultant en stratégie le jour, tout en reversant une partie de son salaire à ses anciens associés pour payer leur loyer et leurs repas. Quand Evertale est finalement liquidée, il ne reste que 40 000 dollars — la totalité du capital de départ de ce qui deviendra Bending Spoons.

Le pivot stratégique : ne plus créer, mais racheter

C'est de cet échec que naît l'idée fondatrice de Bending Spoons, créée à Copenhague en juin 2013 avant de rapidement s'installer à Milan. Le nom de l'entreprise est un clin d'œil à une scène du film Matrix.

L'intuition de Ferrari, telle qu'il l'a racontée dans l'un de ses rares entretiens publics, tient en une observation simple : une startup traverse deux phases radicalement différentes. La première, celle du « zéro à un », consiste à trouver quoi construire, pour qui, et comment en faire un produit qui fonctionne. Cette phase repose largement sur la chance, le bon timing, et des facteurs difficiles à contrôler — c'est précisément celle qui avait eu raison d'Evertale. La seconde phase, celle du « un à n », consiste à faire grandir un produit qui a déjà trouvé son marché. Cette phase-là, contrairement à la première, repose sur des compétences que l'on peut systématiser : excellence opérationnelle, discipline d'exécution, ingénierie.

La thèse de Bending Spoons est née de ce constat : plutôt que de prendre le risque du « zéro à un » à chaque nouveau projet, l'entreprise choisit de le sous-traiter entièrement à d'autres fondateurs, en rachetant des produits numériques qui ont déjà démontré leur adéquation au marché, mais dont la croissance a stagné ou dont les propriétaires souhaitent sortir.

De Milan aux marchés financiers : la construction méthodique

Pendant plusieurs années, Bending Spoons reste largement autofinancée, une position atypique dans l'univers du capital-risque. Le premier tour de financement significatif n'intervient qu'en 2022. Le premier rachat d'envergure suit en 2023, avec l'acquisition d'Evernote pour environ 200 millions de dollars — une opération qui donne le ton de la méthode : Bending Spoons reprend une application vieillissante, en réduit les coûts d'infrastructure, nettoie le code existant, et augmente sensiblement les prix. Les utilisateurs d'Evernote, qui payaient auparavant une centaine de dollars par an, se retrouvent avec un abonnement à 249 dollars.

Les rachats s'enchaînent ensuite à un rythme soutenu : WeTransfer, Meetup, Eventbrite, Vimeo, komoot, StreamYard, Brightcove, et, en octobre 2025, AOL — la plus grosse opération à ce jour, financée par une levée de 270 millions de dollars valorisant l'entreprise à 11 milliards de dollars, complétée par 2,8 milliards de dollars de dette. Bending Spoons emploie alors environ 1 000 personnes pour piloter l'ensemble de ce portefeuille de marques parmi les plus reconnaissables d'internet — un ratio qui tranche radicalement avec la taille des équipes que ces mêmes marques employaient avant leur rachat.

L'intelligence artificielle occupe une place croissante dans cette mécanique : selon les chiffres communiqués par l'entreprise, elle génère aujourd'hui environ 90 % du code produit en interne, contre moins de 10 % un an plus tôt — un des leviers mis en avant pour expliquer un chiffre d'affaires par employé revendiqué supérieur à celui d'Apple.

L'entrée en bourse

Le 1er juillet 2026, Bending Spoons entre au Nasdaq sous le symbole BSP, à un prix de 29 dollars par action, valorisant l'entreprise à 18,4 milliards de dollars et levant 1,68 milliard de dollars — l'une des plus importantes introductions en bourse d'une entreprise européenne depuis plusieurs années. Le titre clôture sa première séance en hausse de près de 40 %, portant la valorisation de marché à environ 25,7 milliards de dollars.

Les chiffres financiers publiés à l'occasion de l'introduction en bourse racontent une trajectoire de croissance rare pour une entreprise de cette taille : un chiffre d'affaires de 1,31 milliard de dollars en 2025, en progression d'environ 84 % par an depuis 2023, et déjà 601 millions de dollars sur le seul premier trimestre 2026 — plus du double de la même période un an plus tôt. Le résultat net, lui, est passé d'une perte de 112 millions de dollars à un bénéfice de 27,5 millions de dollars sur la même période.

Pour les quatre fondateurs, cette entrée en bourse marque aussi un basculement personnel : tous quatre deviennent milliardaires sur le papier, treize ans après avoir liquidé leur première startup avec 40 000 dollars en poche.

Ce que cette trajectoire raconte sur la réussite entrepreneuriale

Cette histoire ne prouve rien de statistique — un cas unique n'est jamais une donnée généralisable. Mais elle illustre, de façon concrète, un mécanisme que les données évoquées dans nos précédents articles ne font qu'esquisser en pourcentages : la première tentative entrepreneuriale, même ratée, n'est pas un détour avant la réussite. Elle en est souvent la matière première. Ce que les fondateurs de Bending Spoons ont retiré de l'échec d'Evertale n'est pas une compétence produit ou une idée de marché — c'est une thèse entière sur la manière de faire de l'entrepreneuriat autrement, construite en creux, par ce qui n'avait pas fonctionné la première fois.

C'est aussi un rappel utile pour quiconque évalue des équipes fondatrices : le succès de Bending Spoons ne ressemble à aucun manuel classique de capital-risque. Pas de produit propriétaire révolutionnaire, pas de levée de fonds spectaculaire dès les débuts, pas de croissance virale. Une méthode, appliquée avec discipline, année après année, jusqu'à ce que les marchés financiers la valident.

Questions fréquentes

La stratégie de Bending Spoons est-elle unique ? Le modèle du rachat et de la revitalisation d'entreprises technologiques existantes rappelle celui d'acteurs comme Constellation Software au Canada, souvent cité en comparaison par les observateurs du secteur. Ce qui distingue Bending Spoons est l'application de cette logique de « roll-up » à des marques grand public très reconnaissables, plutôt qu'à des logiciels d'entreprise plus discrets.

Pourquoi les prix des applications rachetées augmentent-ils autant ? Selon la méthode revendiquée par l'entreprise, les produits rachetés sont souvent sous-monétisés par rapport à la valeur perçue par leurs utilisateurs les plus fidèles. Les hausses de prix, parfois très fortes comme dans le cas d'Evernote, s'accompagnent en général d'une réduction des effectifs et d'une refonte technique, ce qui a également suscité des critiques sur l'impact social de ces rachats.

Cette histoire est-elle représentative du parcours des fondateurs de licornes en général ? Non, et il faut se garder d'en tirer une règle universelle. Elle est cohérente avec une tendance statistique déjà documentée — la plupart des fondateurs sériels de licornes n'ont tenté qu'une seule fois avant leur réussite — mais elle reste un cas particulier, avec ses propres spécificités (un secteur de niche, une structure capitalistique atypique, un contexte de marché précis).

Le nouveau profil du fondateur de licorne : ce que révèlent les données

Introduction

Pendant longtemps, le fondateur de licorne « typique » sortait d'une école de commerce, avait fait un passage par la finance ou le conseil, et savait vendre une vision avant même d'avoir un produit. Cette image n'est pas sortie de nulle part : elle correspond assez bien à une génération de fondateurs qui a construit les grandes réussites du SaaS et du e-commerce des années 2010, où la capacité à structurer une organisation commerciale et à lever des tours de financement successifs comptait souvent davantage que la profondeur technique du produit.

Les données les plus récentes disent autre chose. Entre les études sur les parcours académiques des fondateurs et les analyses sur leur expérience entrepreneuriale antérieure, un profil différent émerge — plus scientifique, plus expérimenté, et beaucoup moins homogène qu'on ne le pense. Trois jeux de données publiés en 2025 et 2026, portant sur des échantillons larges et des méthodologies transparentes, permettent de reconstituer ce nouveau profil pièce par pièce.

Moins de MBA, plus de PhD

Une analyse de Revelio Labs et Accel portant sur 290 licornes européennes est sans appel : la part des licornes fondées par un titulaire de doctorat a doublé depuis 2023, tandis que la part des profils MBA a été divisée par deux sur la même période. Le vivier de fondateurs se déplace visiblement de la finance et du conseil vers la Big Tech et la recherche académique.

Ce basculement ne veut pas dire que le MBA a perdu toute valeur pour un fondateur — il veut dire que la barrière à l'entrée pour transformer une expertise scientifique en entreprise s'est réduite. Les chercheurs qui, il y a dix ans, seraient restés en laboratoire ou auraient rejoint un grand groupe, ont aujourd'hui accès aux mêmes outils de financement, de recrutement et de mise sur le marché que n'importe quel fondateur issu du monde des affaires.

Pourquoi ce basculement se produit maintenant

Plusieurs dynamiques concomitantes permettent d'expliquer ce mouvement, sans qu'aucune ne suffise isolément à en rendre compte.

La première tient à la nature même des startups qui dominent le paysage actuel des licornes. L'intelligence artificielle, la deeptech, la biotech et la robotique — des secteurs où la barrière technique est réelle et où l'avantage compétitif se construit souvent sur des années de recherche préalable — occupent une place croissante dans les nouvelles licornes. Dans ces catégories, un doctorat n'est pas un simple signal de sérieux : c'est fréquemment la condition d'accès à la compréhension du problème que l'entreprise cherche à résoudre.

La seconde tient à la baisse du coût de démarrage d'une entreprise technologique. Le cloud, les modèles open source, les outils no-code pour construire un premier produit ou une première interface commerciale ont réduit le besoin d'un profil « business » pour porter la partie non technique du projet. Un chercheur peut aujourd'hui itérer seul beaucoup plus loin dans le cycle de vie de son produit qu'il y a quinze ans, avant même de devoir recruter un profil complémentaire.

La troisième tient à la maturité croissante des dispositifs de transfert de technologie universitaires et des programmes dédiés à accompagner des chercheurs vers l'entrepreneuriat. Ce qui relevait de l'exception institutionnelle il y a une décennie est devenu, dans plusieurs grandes universités et centres de recherche européens, un parcours structuré et documenté.

Ce qu'ils ont réellement étudié

Les données d'Andreessen Horowitz sur les licornes américaines fondées entre 2010 et 2024 apportent une nuance importante à cette lecture « PhD contre MBA ». L'informatique arrive certes en tête des filières d'origine, mais avec seulement 29 % des fondateurs — largement moins de la moitié. Le reste se répartit entre économie et finance (23,8 %), ingénierie hors informatique (11,5 %), sciences naturelles et du vivant (10,9 %), sciences sociales et comportementales (9,5 %), humanités, arts et design (9,4 %), et enfin mathématiques, statistiques et data science (5,9 %).

Autrement dit, la majorité des fondateurs de licornes américaines n'ont pas suivi de formation en informatique. Le dénominateur commun entre eux n'est donc pas la discipline étudiée, mais ce qu'ils en ont fait par la suite. Un fondateur venu des sciences sociales ou des humanités n'est pas un accident statistique : il représente près d'un fondateur sur dix, un ordre de grandeur comparable à celui des mathématiques et de la data science, discipline pourtant considérée comme un passage obligé dans l'imaginaire collectif de la tech.

Cette répartition invite à une lecture plus fine du sujet : ce n'est probablement pas la discipline elle-même qui prédit la réussite d'un fondateur, mais la capacité à transposer une méthode d'analyse rigoureuse — qu'elle vienne de l'ingénierie, de l'économie ou des sciences sociales — à un problème d'entreprise. L'informatique reste surreprésentée par rapport à son poids dans la population générale des diplômés, mais elle est loin d'être une condition nécessaire.

L'expérience compte plus qu'on ne le pense — mais pas de la façon dont on l'imagine

Le troisième axe de données, produit par le chercheur Ilya Strebulaev (Venture Capital Initiative, Stanford GSB), porte sur l'expérience entrepreneuriale préalable des fondateurs. Sur 4 357 fondateurs de licornes américaines analysés entre 1997 et 2019, 43 % avaient déjà créé au moins une entreprise avant leur licorne, et 63 % des licornes comptaient au moins un fondateur « sériel » dans leur équipe fondatrice.

Le détail le plus instructif de cette recherche ne porte pas sur le pourcentage global, mais sur sa distribution. Parmi les 1 876 fondateurs sériels identifiés, 1 173 — près de deux sur trois — n'avaient tenté qu'une seule fois avant leur licorne. Seuls 166 en avaient lancé trois, et seuls 51 en avaient lancé cinq ou plus. Le chemin type n'est donc pas une accumulation de tentatives ni une succession d'échecs surmontés : c'est un premier essai, suivi de la réussite.

Cette distribution a une implication directe pour quiconque évalue une équipe fondatrice sur la base de son historique : le nombre de tentatives précédentes n'est pas en soi un signal fort. Un fondateur qui a créé cinq entreprises n'est statistiquement pas plus proche de la licorne qu'un fondateur qui en a créé une seule et qui construit aujourd'hui sa deuxième. Ce qui semble compter davantage, c'est l'existence même d'une première expérience — l'apprentissage qu'elle a produit — plus que sa répétition.

Ce que ces données changent, concrètement

Prises ensemble, ces trois études dessinent un profil de fondateur de licorne sensiblement différent de l'image reçue : plus susceptible d'avoir une formation scientifique ou de recherche, rarement issu d'un seul type de cursus, et souvent — mais pas systématiquement — passé par une première tentative entrepreneuriale avant la réussite.

Pour quiconque évalue des équipes fondatrices, la conséquence pratique est simple : le pedigree académique ou professionnel classique reste un signal parmi d'autres, mais il explique de moins en moins la variance entre les équipes qui réussissent et celles qui échouent. La discipline étudiée, le nombre de tentatives précédentes, l'origine professionnelle — aucun de ces critères pris isolément ne prédit la réussite. C'est leur combinaison, et surtout ce que l'équipe en a fait, qui compte.

Pour les fondateurs eux-mêmes, en particulier ceux issus du monde académique ou de la recherche qui hésitent encore à franchir le pas, ces données envoient un signal rassurant : l'absence de passage par une école de commerce ou par un poste en finance n'est plus un obstacle structurel. Le marché du financement, du recrutement et de l'accompagnement s'est adapté à cette nouvelle génération de profils, et continue de le faire.

Questions fréquentes

Un MBA est-il un désavantage pour fonder une licorne ? Non. Les données montrent un rééquilibrage du vivier de fondateurs, pas une disqualification des profils MBA. La part des fondateurs MBA a diminué en proportion relative, mais des licornes continuent d'être fondées par des profils issus de la finance et du conseil, notamment dans des secteurs où la vente complexe et la structuration organisationnelle restent déterminantes.

Cette tendance est-elle la même dans tous les secteurs ? Les données disponibles ne permettent pas de trancher ce point avec précision : les études citées portent sur l'ensemble des licornes de leur échantillon, sans détail systématique par secteur. Il est raisonnable de supposer, sans que cela soit démontré ici, que le basculement vers les profils PhD est plus marqué dans les secteurs à forte intensité technique.

Faut-il avoir déjà entrepris pour réussir une licorne ? Non plus. Strebulaev est explicite sur ce point : l'expérience entrepreneuriale préalable aide statistiquement, mais 57 % des fondateurs de licornes américaines de l'échantillon n'avaient jamais créé d'entreprise avant. La première tentative de chacun est, par définition, une première tentative.

Limites et prudence méthodologique

Les trois études citées ne portent pas sur le même périmètre géographique : les données Strebulaev et Andreessen Horowitz couvrent des licornes américaines, tandis que les données Revelio Labs x Accel couvrent des licornes européennes. Les comparaisons faites dans cet article sont thématiques — elles mettent en regard des tendances observées séparément — et non une statistique unique et directement comparable d'un échantillon à l'autre. Un lecteur souhaitant une conclusion strictement européenne ou strictement américaine sur l'ensemble des trois dimensions (discipline, doctorat, expérience préalable) devra encore attendre une étude qui couvre les trois à la fois, sur un même périmètre.

D'où viennent les fondateurs qui construisent les plus grandes entreprises du monde ?

Introduction

On parle beaucoup de fuite des cerveaux. L'idée sous-jacente est simple, presque intuitive : un pays forme des ingénieurs et des chercheurs brillants, ceux-ci partent construire leur carrière — et leur entreprise — ailleurs, et le pays d'origine ne récupère jamais l'investissement consenti dans leur formation. Les données sur les fondateurs de licornes américaines racontent une histoire plus intéressante, et plus nuancée : celle d'une circulation des talents qui profite, au moins en partie, à plusieurs écosystèmes à la fois — pas seulement à celui qui capte la réussite finale.

Un quart des fondateurs formés ailleurs

Selon une analyse d'Ilya Strebulaev (Venture Capital Initiative, Stanford GSB) portant sur les fondateurs de licornes américaines, 1 132 d'entre eux — environ un quart du total — ont obtenu leur diplôme en dehors des États-Unis, dans 66 pays différents. Le reste, 2 845 fondateurs, a été formé sur le sol américain.

Ce chiffre à lui seul mérite d'être posé clairement : dans un pays qui dispose du système universitaire le plus richement doté au monde, un quart des fondateurs qui construisent les entreprises les plus valorisées de la planète ont appris leur métier ailleurs. La machine à licornes américaine ne se nourrit pas uniquement de son propre système éducatif — elle importe, année après année, une part significative de son vivier fondateur.

Le classement par pays réserve des surprises

L'Inde arrive en tête avec 189 fondateurs de licornes américaines formés sur son territoire, suivie d'Israël (166) et du Royaume-Uni (144). Le Canada complète le podium élargi avec 122 fondateurs. Mais c'est la suite du classement qui parle le plus directement à un lecteur européen : la France se classe 5ᵉ avec 66 fondateurs formés sur son sol — devant l'Allemagne (36), et juste derrière le Canada.

D'autres pays européens apparaissent plus loin dans le classement : la Suisse (22), l'Italie (16), le Danemark (14), les Pays-Bas (13), le Portugal (12), l'Espagne (12) et l'Irlande (10) contribuent tous, à des degrés divers, au vivier de fondateurs qui construisent aujourd'hui certaines des plus grandes entreprises américaines.

Ce que ce classement révèle sur chaque écosystème

Chacune des positions en tête de ce classement s'explique par une dynamique différente, propre à l'écosystème considéré — sans qu'on puisse réduire ce phénomène à une seule cause.

La première place de l'Inde reflète avant tout une question d'échelle : le pays forme chaque année un nombre d'ingénieurs supérieur à celui de la plupart des autres pays du classement réunis, et dispose d'un réseau de diaspora technologique aux États-Unis particulièrement dense et ancien, documenté depuis plusieurs décennies dans la recherche sur l'entrepreneuriat migrant en Californie.

La deuxième place d'Israël, en revanche, tient moins à l'échelle qu'à la densité : rapporté à sa population, Israël produit un nombre de fondateurs de startups sans équivalent dans le classement. Ce résultat est généralement rattaché à la combinaison d'un service militaire technologique structurant — les unités de renseignement et de cyberdéfense forment chaque année de jeunes talents à la résolution de problèmes complexes en équipe restreinte — et d'un écosystème de capital-risque local mature qui sert souvent de tremplin vers les États-Unis.

Le Royaume-Uni, en troisième position, bénéficie d'un avantage plus structurel : la langue commune avec les États-Unis, des décennies d'échanges universitaires établis, et un écosystème de capital-risque britannique qui, loin d'être une simple antichambre du marché américain, a lui-même produit plusieurs des plus grandes réussites européennes.

La position du Canada, quatrième, est souvent décrite dans la presse économique nord-américaine comme un cas d'école de proximité géographique et culturelle : la frontière avec les États-Unis, la langue partagée, et un système universitaire technique réputé (Waterloo, Toronto, UBC) facilitent un mouvement de talents perçu comme une évidence par beaucoup de diplômés canadiens en ingénierie et en informatique.

La position de la France : un signal à ne pas sous-estimer

Pour un écosystème comme la France, se classer 5ᵉ mondial sur ce critère — devant des économies bien plus grandes en population ou en PIB comme l'Allemagne — est un signal qui mérite d'être pris au sérieux. Il confirme ce que l'on observe par ailleurs dans le classement des filières les plus sélectives en mathématiques et en ingénierie : le système français, porté par ses grandes écoles et ses formations d'ingénieurs, produit un vivier de talents technique reconnu internationalement, capable de rivaliser avec des puissances technologiques bien plus importantes en taille.

La question qui reste ouverte n'est pas celle de la capacité à former — elle semble déjà établie par ce classement — mais celle de la capacité à retenir ces talents, ou à défaut, à capter une partie de la valeur qu'ils créent, où qu'ils choisissent finalement de construire leur entreprise. C'est précisément l'angle mort du concept de « fuite des cerveaux » : il mesure un départ, jamais un retour possible sous une autre forme.

Circulation plutôt que fuite : ce que dit la recherche en économie

Ce sujet n'est pas nouveau pour la recherche en économie de l'innovation. Depuis les années 2000, plusieurs travaux, notamment ceux menés sur les réseaux d'ingénieurs indiens et chinois de la Silicon Valley, ont proposé de remplacer le concept de « fuite des cerveaux » (brain drain) par celui de « circulation des cerveaux » (brain circulation) : les talents partis à l'étranger continuent, dans de nombreux cas, à réinvestir dans leur pays d'origine — financièrement, en tant qu'investisseurs providentiels ou capital-risqueurs ; humainement, en recrutant ou en mentorant des compatriotes ; et parfois physiquement, en revenant fonder une deuxième entreprise sur leur marché d'origine une fois la première réussite consolidée à l'étranger.

Cette lecture ne nie pas la perte réelle que représente, pour un pays, le départ de ses talents les plus prometteurs. Elle invite simplement à mesurer le phénomène sur un horizon plus long, et à travers des canaux de valeur qui ne se résument pas à la localisation du siège social de l'entreprise finalement créée.

Ce que cela signifie pour les écosystèmes qui investissent dans la formation

Si l'on prend au sérieux l'idée de circulation plutôt que de fuite, la conséquence pour les décideurs publics et les acteurs de l'écosystème n'est pas de chercher à empêcher le départ des talents les plus prometteurs — un objectif à la fois difficile à atteindre et probablement contre-productif — mais de construire les canaux qui permettent de capter une partie de la valeur générée, même quand l'entreprise se construit ailleurs. Cela peut prendre la forme de réseaux d'anciens élèves actifs à l'international, de dispositifs facilitant le retour ou l'essaimage d'une deuxième entreprise sur le marché d'origine, ou simplement d'une meilleure connaissance, par les investisseurs locaux, des trajectoires de leurs diplômés une fois partis.

Pour un investisseur, ce classement invite aussi à regarder au-delà du seul marché domestique lorsqu'il évalue le potentiel d'un vivier de talents : la question pertinente n'est pas seulement combien d'entreprises se créent localement, mais combien de fondateurs formés localement construisent des entreprises d'envergure mondiale, où qu'ils choisissent de le faire.

Questions fréquentes

Ce classement signifie-t-il que la France « perd » ses meilleurs talents au profit des États-Unis ? Le classement montre un point de passage dans une trajectoire — le pays où le diplôme a été obtenu — pas un verdict définitif sur la localisation de la valeur créée. Une partie de ces 66 fondateurs formés en France entretient des liens actifs avec l'écosystème français : investissement, mentorat, réseau, voire retour pour une deuxième entreprise. La donnée disponible ne permet pas de quantifier cette part avec précision, mais la recherche sur la circulation des talents suggère qu'elle est loin d'être négligeable.

Faut-il en conclure que les grandes écoles françaises devraient pousser davantage leurs diplômés vers les États-Unis ? Rien dans ces données ne permet de formuler une recommandation aussi directe. Le classement mesure un résultat observé sur une génération de fondateurs, pas une stratégie optimale à suivre. Il documente une réalité — la France forme des profils capables de réussir au plus haut niveau international — sans dire si cette réussite aurait été plus grande, plus petite, ou simplement différente si ces fondateurs avaient construit leur entreprise sur le marché français.

Ce phénomène de circulation des talents est-il propre aux États-Unis ? Non, mais c'est l'écosystème américain qui dispose aujourd'hui des données les plus complètes et les plus régulièrement mises à jour sur le sujet, notamment grâce aux travaux de chercheurs comme Ilya Strebulaev. Un exercice comparable appliqué aux licornes construites en Chine, en Inde ou en Europe donnerait probablement des enseignements tout aussi riches, mais reste, à ce jour, moins documenté publiquement.

Une limite à garder en tête

Cette donnée porte spécifiquement sur les licornes américaines et sur le pays où le fondateur a obtenu son diplôme — pas sur sa nationalité, ni sur le pays où il a grandi, ni sur les licornes fondées ailleurs dans le monde. Un fondateur formé en France peut très bien être originaire d'un autre pays, ou avoir quitté la France dès l'obtention de son diplôme sans y avoir de racines particulières. La donnée mesure un point de passage dans une trajectoire, pas une appartenance — une nuance importante pour qui voudrait en tirer des conclusions sur l'identité ou la nationalité des fondateurs plutôt que sur leur parcours de formation.

Elle ne dit rien non plus des licornes fondées en dehors des États-Unis : un classement équivalent portant sur les licornes européennes, chinoises ou indiennes donnerait vraisemblablement une tout autre hiérarchie des pays d'origine, et reste à ce jour à documenter avec la même rigueur méthodologique.

Europe's Founder Factories: How Today's Unicorns Are Creating Tomorrow's Startups

Ask where Europe's next generation of unicorn founders will come from, and the most reliable answer is: its existing unicorns. Research from Accel and Dealroom tracking the region's venture-backed unicorns has found that founder alumni networks are one of the strongest, most consistent predictors of where new high-growth startups originate — a pattern with direct implications for how corporate investors think about sourcing. This article looks at what the data shows about Europe's "founder factories," and what it means for a corporate venture sourcing strategy.

The Scale of the Alumni Effect

Of the more than 350 VC-backed unicorns across Europe and Israel, roughly 250 have fuelled over 1,400 new tech-enabled startups founded by their former employees, according to Accel and Dealroom's founder factories research. More than half of these new companies have already secured private funding, and a similar share were founded in the same city as the unicorn that produced their founders — meaning the effect doesn't just create new companies, it reinforces existing tech hubs rather than dispersing talent evenly across the continent.

The pattern is heavily concentrated. A small number of category-defining companies account for a disproportionate share of the spinouts: Spotify, Delivery Hero, Zalando and Criteo alone have collectively fuelled over 150 new startups founded by their alumni. In fintech specifically, Revolut, Klarna, N26 and Wise rank among the top producers, with former employees of those four companies alone going on to found well over 100 new ventures.

Why Alumni Founders Outperform Average Odds

The mechanism behind the founder factory effect is fairly intuitive: employees who join a company during its scale-up phase absorb operational patterns — how to hire past the first hundred people, how to navigate a Series C, how to build product for a European multi-market launch — that are extremely difficult to learn any other way. They also leave with a functioning professional network of former colleagues who become early hires, angel investors, or co-founders for their next venture. This combination of pattern recognition and ready-made network is a meaningful part of why alumni-founded startups raise funding at a notably higher rate than the broader founder population.

Sector Concentration and What It Signals

Fintech's dominance among founder factories is not a coincidence: the sector's unicorns scaled earlier and faster than most other European verticals over the past decade, meaning a larger cohort of alumni has had time to found and grow their own companies. As newer categories — AI infrastructure, defense technology, deep tech — mature through their own scale-up phases, the same alumni dynamic should be expected to repeat, with today's AI-native unicorns becoming tomorrow's founder factories in roughly a five-to-eight-year lag.

This timing lag matters for anyone trying to anticipate the next wave rather than simply observe the current one. A corporate building a multi-year sourcing strategy around AI infrastructure, for instance, should expect the strongest alumni-founder signal from today's AI unicorns to materialize closer to the end of the decade — which argues for building relationships with promising current employees and early leavers now, well before they found their own companies, rather than waiting for the spinout wave to become visible in the data.

Beyond the Biggest Names: A More Distributed Second Wave

While a handful of category-defining companies produce a disproportionate share of alumni founders, the broader base of founder factories is considerably more distributed than the headline names suggest — several hundred distinct unicorns across the region have each produced at least a handful of alumni-founded startups. For a corporate with a narrower sector or geographic thesis than "European tech" broadly, mapping the mid-sized unicorns specific to that niche is often more useful than benchmarking against Spotify or Revolut, since the alumni networks of a sector-specific unicorn tend to produce founders with directly relevant domain expertise rather than general operating experience.

What This Means for Corporate Venture Sourcing

For a corporate venture team, the founder factory pattern is directly actionable as a sourcing signal. Rather than relying solely on inbound deal flow or generic sector scanning, mapping which unicorns in a corporate's sector of interest are entering their own scale-up phase — and tracking senior departures from those companies — offers an earlier, more proprietary read on where the next generation of credible founding teams is likely to emerge. This is a pattern-recognition exercise more common among specialist VCs than corporate investors, and it is one of the areas where a VCaaS partner with dedicated sourcing infrastructure can offer a meaningful edge over an internal team scanning the market part-time.

The geographic concentration finding matters equally for sourcing strategy: because more than half of alumni-founded startups launch in the same city as their originating unicorn, a corporate with a genuine regional thesis is often better served by deepening its presence in two or three proven hub cities than spreading scouting effort thinly across the continent.

Frequently Asked Questions

Which European companies are considered the biggest "founder factories"?

Among generalist tech companies, Spotify, Delivery Hero, Zalando and Criteo have collectively produced the largest number of alumni-founded startups. In fintech specifically, Revolut, Klarna, N26 and Wise lead the category.

Do alumni-founded startups raise funding at a higher rate than average?

Yes — research on European and Israeli unicorn alumni startups found that more than half had already secured private funding, a notably higher rate than the broader early-stage founder population.

Why do so many alumni-founded startups launch in the same city as the original unicorn?

Founders typically build on existing local networks — former colleagues, local investors, and city-specific talent pools — which makes launching in a familiar hub faster and lower-risk than relocating, reinforcing that city's position as a tech cluster over time.

How can a corporate venture team use the founder factory pattern in its sourcing strategy?

By tracking which unicorns in its sector of interest are entering scale-up phase and monitoring senior departures from those companies, a corporate can build a proprietary, earlier-stage view of likely future founding teams rather than relying only on inbound deal flow.

Conclusion

Europe's unicorns are not just building companies — they are training the founders who will build the next wave. For corporate investors, understanding which companies function as founder factories, and where their alumni tend to cluster geographically, is a genuinely proprietary sourcing signal that most generalist scouting approaches miss entirely.

The European Founder Mobility Problem: Why So Many Unicorn Founders Relocate and Whether EU Inc Can Change That

Europe has a startup formation problem it has largely solved, and a startup scaling problem it has not. The region continues to create more new companies per year than the United States, yet by early 2025 the EU counted only around 110 unicorns compared with 687 in the US and 162 in China, according to the European Commission's own figures. Part of the explanation is structural: a meaningful share of Europe's most promising deep tech founders end up building — or at least incorporating — outside the EU altogether. In March 2026, the European Commission proposed its most ambitious response yet: a single, EU-wide corporate legal form known as "EU Inc." This article looks at the scale of the founder mobility problem, what EU Inc is designed to fix, and what it means for corporate investors backing European startups.

The Scale of the Problem

Research on Europe's deep tech ecosystem — now a $690 billion sector capturing roughly a third of all European venture capital — found that 40% of European deep tech unicorn founders are now based in the US, having either relocated after founding their company or moved before starting it in the first place. Separate legal analysis covering founders who relocate found that the large majority who do so choose the United States specifically, rather than distributing across other markets — this is not diffuse brain drain, but a consistent, specific pull toward one destination.

The exit data compounds the picture: European deep tech exits are dominated by M&A rather than public listings, and the majority of that M&A value, by Europe's own analysis, is captured by US acquirers rather than European ones — meaning the companies that do succeed often end up owned outside the region regardless of where they were founded.

Why Founders Leave: The 27-Puddles Problem

The most consistently cited structural cause is legal and regulatory fragmentation. The EU operates 27 separate national legal systems with more than 60 different available company forms, a patchwork that the Draghi and Letta competitiveness reports both diagnosed as functioning like an "invisible tariff" on cross-border growth — the IMF has estimated the equivalent burden of persistent EU single market barriers at roughly a 110% tariff on services. In practical terms, a founder scaling from Paris to Berlin to Milan faces different tax codes, labor laws and filing requirements at every border, friction that a founder scaling from New York to Texas to California simply does not encounter.

This fragmentation shows up directly in financing too: European startups have been found to be only about half as likely as their US counterparts to raise financing rounds above $15 million, a gap that compounds the incentive to incorporate somewhere with deeper, more unified capital markets in the first place.

What EU Inc Is Designed to Fix

On March 18, 2026, the European Commission published its legislative proposal for "EU Inc" — formally the "28th regime" — an optional, digital-first EU-wide corporate legal form operating in parallel with the 27 existing national systems. Under the proposal, a founder anywhere in the world could register a company within roughly 48 hours, choose freely where to incorporate and headquarter within the EU, and operate under a single harmonized rulebook rather than navigating national variation at every cross-border step. The proposal also includes simplified insolvency procedures — explicitly framed as giving founders "the chance to try and test, and to start again" — and harmonized employee equity schemes, addressing a second long-standing complaint about Europe's fragmented approach to startup stock options.

The Commission has set an ambitious target of reaching political agreement by the end of 2026, with early political signals — including a European Council statement issued just one day after the proposal itself — suggesting real institutional urgency behind the timeline, even as the measure still requires approval from both the European Parliament and the Council before it can take effect.

Reasons for Skepticism

EU Inc is not the first attempt at a pan-European corporate form — the Societas Europaea, introduced in 2004, aimed at similar goals and saw limited adoption, largely restricted to larger businesses rather than startups. Critics of the current proposal have also pointed to a specific structural weakness: matters not explicitly covered by the EU Inc regulation or a company's own articles of association fall back to national law, which could reintroduce some of the same fragmentation the framework is meant to eliminate, depending on how narrowly or broadly the final regulation is scoped during negotiation.

What This Means for Corporate Investors

For corporate investors building or expanding a European venture strategy, the founder mobility problem is worth treating as a real, quantifiable risk factor rather than background noise: a portfolio company's country of incorporation and its founders' long-term location intentions are legitimate diligence questions, not formalities. If EU Inc is adopted on anything close to its proposed timeline, it would materially reduce one of the structural reasons promising European founders have historically re-incorporated or relocated outside the region — a shift worth monitoring closely over the next 12 to 18 months rather than assuming away in either direction.

Frequently Asked Questions

What percentage of European deep tech unicorn founders are based outside Europe?

Research on the European deep tech ecosystem found that 40% of deep tech unicorn founders are now US-based, having either relocated after founding or moved before starting their company.

What is EU Inc?

EU Inc, also called the "28th regime," is a European Commission legislative proposal, published in March 2026, for an optional, EU-wide corporate legal form that would let founders register and operate across all 27 EU member states under a single harmonized rulebook, rather than navigating 27 separate national systems.

When could EU Inc take effect?

The European Commission has targeted political agreement between the Parliament and Council by the end of 2026, though the measure still requires formal approval through the EU's ordinary legislative procedure before it can take effect.

Why do European startups struggle to raise large funding rounds domestically?

European startups have been found to be roughly half as likely as US counterparts to raise financing rounds above $15 million, a gap linked to comparatively limited institutional investor participation in venture capital in Europe relative to the US.

Conclusion

Europe's founder mobility problem is well documented, structurally understood, and — for the first time in over two decades — the subject of a genuinely ambitious legislative response. Whether EU Inc closes the gap will depend on how the proposal survives negotiation over the next year, but for corporate investors building a European venture strategy today, the direction of travel is worth factoring into portfolio construction and diligence now, rather than waiting for the outcome.

What Europe's 2026 Unicorn Class Reveals About Where Capital Is Flowing

Every new unicorn cohort is a signal of where investor conviction actually sits, as distinct from where it merely talks about sitting. Europe's 2026 class tells a fairly clear story: despite a funding environment investors themselves describe as challenging, capital has kept flowing — just far more selectively, and overwhelmingly toward three categories. For corporate investors calibrating their own thesis, the pattern in this year's crop of new unicorns is worth reading closely.

Fewer Bets, Clearer Categories

Europe continued minting new unicorns through 2026 even as overall venture activity remained selective, with the new cohort concentrated overwhelmingly in AI, defense technology and infrastructure-led innovation. That concentration is a marked shift from the broader, growth-at-all-costs pattern of previous cycles: as one market analyst put it, capital is becoming more selective without necessarily becoming scarcer, with investors favoring sectors of clear strategic relevance and long-term demand over generalist consumer or growth bets.

2025 had already signaled this shift, with Europe minting its highest number of new unicorns since 2021 and European venture funding rising by more than 20% year-on-year according to Atomico's State of European Tech research — evidence that the 2026 pattern is a continuation and sharpening of a trend already underway, not a one-off.

AI, Defense and Infrastructure: The New Center of Gravity

The clearest throughline in Europe's newest unicorns is a rotation toward companies solving infrastructure-level problems rather than consumer-facing ones. Sovereign and defense-oriented AI has been particularly prominent: Germany's Helsing, focused on AI systems for defense applications, has become one of continental Europe's most highly valued private companies, while France's Mistral AI continues to be positioned domestically and across Europe as a flagship bet on AI sovereignty independent of US and Chinese foundation model providers.

Fintech and financial infrastructure remain a durable core of the European unicorn base even amid the AI-heavy new cohort — Revolut and Germany's Trade Republic continue to rank among the continent's most valuable private companies, underscoring that the rotation toward AI and defense is additive to fintech's established strength rather than a replacement for it.

What 'Strategic Relevance' Means in Practice

The sectors attracting capital in 2026 share a common thread beyond their labels: each sits at the intersection of commercial opportunity and a policy or geopolitical priority a government or large enterprise buyer actively wants to see solved — European AI sovereignty, defense modernization, financial infrastructure resilience. This is a meaningfully different investor calculus than the platform-growth theses that dominated the 2020-2021 cycle, and it is one where corporate investors — who often have their own strategic and even policy-adjacent interests — are natural co-investors alongside financial VCs, rather than passive followers of a purely financial thesis.

Market commentary on the shift has framed it as evidence of a maturing ecosystem: valuations increasingly tied to resilience, revenue potential and geopolitical relevance rather than growth narrative alone, positioning Europe to compete more directly with the US and Asia on the kind of high-value, globally relevant companies it has historically struggled to produce at scale. Whether that maturation proves durable through a full market cycle — rather than simply reflecting where capital happens to be concentrated in a single selective year — is the open question worth revisiting as 2027 cohorts emerge.

Regional Variation Within Europe

The 2026 cohort is not evenly distributed across the continent. Historically strong hubs — the UK, France and Germany — continue to account for the large majority of new unicorn formation, reinforcing rather than redistributing the concentration patterns that have defined European tech for the past decade. For corporate investors weighing where to build sourcing presence, this argues for depth in a small number of proven hubs over breadth across the continent, echoing the same geographic concentration effect visible in Europe's founder alumni networks more broadly.

What This Means for Corporate Investors

• Sector thesis validation — a corporate already exploring AI infrastructure, defense technology or financial infrastructure has strong external validation that capital and talent are concentrating in the same direction — reducing one source of internal debate about where to focus a mandate.

• Competition for allocation is rising in hot categories — the same concentration that validates a thesis also means more capital chasing a narrower set of credible targets, raising the value of proprietary sourcing and founder relationships over generic inbound deal flow.

• Strategic investors have a distinctive edge in exactly these categories — sectors defined by regulatory complexity, government procurement dynamics, or deep technical specialization reward investors who bring more than capital — precisely where corporate strategic value tends to matter most.

Frequently Asked Questions

Which sectors dominated Europe's new unicorns in 2026?

AI, defense technology and infrastructure-led innovation accounted for the clear majority of new unicorn formations in 2026, continuing a rotation away from the broader consumer and growth-stage bets that characterized the 2020-2021 cycle.

Is European venture funding actually declining in 2026?

The picture is one of selectivity rather than scarcity: overall deal count has been more disciplined, but funding directed at strategically prioritized sectors — AI, defense, infrastructure — has continued to grow, building on a more than 20% year-on-year rise in European venture funding already recorded in 2025.

Does fintech still matter in Europe's unicorn landscape?

Yes. Despite the AI-heavy composition of the newest unicorn cohort, established fintech players remain among the continent's most valuable private companies, indicating the newer sectors are additive to fintech's position rather than displacing it.

Why are defense and AI sovereignty themes attracting so much capital right now?

These categories sit at the intersection of clear commercial demand and active policy priority — European governments and large enterprises have explicit strategic interest in reducing dependence on non-European providers, which gives investors in these categories both a commercial and a policy tailwind simultaneously.

Conclusion

Europe's 2026 unicorn cohort tells a consistent story: capital is concentrating around sectors where commercial opportunity and strategic or policy relevance overlap, rewarding investors — corporate and financial alike — who can bring more than a check to the table. For corporate investors already exploring AI infrastructure, defense technology or financial infrastructure, that concentration is a signal worth taking seriously when setting mandate priorities.

The First Three Years of a VCaaS Program: A Practical Roadmap

Corporations evaluating a VCaaS mandate can usually find good material on why the model works and how it compares to building internally. What is harder to find is a realistic timeline: what actually happens in year one versus year three, and what milestones indicate a program is on track versus quietly underperforming. This roadmap sets out a practical, stage-by-stage view of how a well-run VCaaS program typically develops over its first three years.

Year One: Mandate, Governance, and First Deals

The first three to six months are almost entirely structural: finalizing the investment thesis, agreeing governance and reporting mechanics, and — if the mandate is structured as a dedicated vehicle rather than direct balance-sheet investment — completing the legal and, where relevant, regulatory setup. Corporations that rush this phase to show early deal activity typically pay for it later with governance disputes or a thesis that needs to be substantially revised within the first year.

From roughly month six, the program should begin closing its first investments — typically three to eight in the remainder of year one, depending on mandate size and stage focus. The realistic goal for year one is not portfolio scale; it is establishing that the sourcing engine works, that the governance model functions at the speed it was designed for, and that the first few investments reflect the thesis accurately. A year-one program that has closed a handful of well-thesis-aligned deals and established a working reporting rhythm is on track, even if the absolute numbers look modest.

Year Two: Portfolio Building and Commercial Activation

Year two is where deal pace typically accelerates, as the sourcing network matures and the governance process proves itself at speed. It is also when the program's strategic value — as opposed to its financial potential, which takes longer to materialize — should start becoming visible, primarily through commercial activation: pilots, distribution conversations, and technical collaboration between portfolio companies and the corporate's business units.

This is the stage where programs most commonly underperform their potential, not because of poor investment selection but because commercial activation was left informal rather than actively managed. A portfolio company with no assigned business-unit sponsor rarely develops a commercial relationship on its own initiative. Programs that treat commercial facilitation as a resourced function — not an occasional bonus — show a measurably higher rate of active portfolio engagement by the end of year two.

Mid-program benchmarking belongs here as well: comparing deal pace, portfolio composition, and early signal quality against the original thesis and against external industry benchmarks, to catch drift before it compounds into year three.

Year Three: Measurable Strategic Value and Strategic Review

By year three, a well-run program should be able to demonstrate a track record on both dimensions of its mandate: early financial signal (follow-on rounds, markups, in some cases early exits) and strategic value (active commercial relationships, technology or market intelligence generated, talent or partnership pipeline). This is also the natural point for a structured strategic review: is the original thesis still the right one, does the program's scale match the corporation's ambition, and should the operating model evolve — scaling the VCaaS mandate, moving toward a hybrid model, or beginning a transition toward a partially internal team.

Programs that treat this review as a formality rather than a genuine strategic decision point tend to drift for another two to three years before the question gets asked seriously — typically triggered by a leadership change rather than a deliberate assessment.

What the Corporate Side Needs to Resource

A VCaaS mandate reduces the corporate's investment execution burden, but it does not eliminate the need for internal resourcing entirely. Across all three years, a corporate should expect to dedicate a senior sponsor with real authority to sit on the investment committee and make timely decisions, and — from year two onward — assigned business-unit contacts for each active commercial relationship, since these do not manage themselves. Corporations that treat the VCaaS mandate as fully turnkey, requiring no internal time investment, are consistently the ones whose commercial activation underperforms in year two.

Milestones to Track at Each Stage

• End of year one — governance and reporting operating as designed, three to eight thesis-aligned investments closed, and a documented, working sourcing pipeline.

• Mid-year two — deal pace at or above the mandate's target run rate, and at least one portfolio company with an active, assigned commercial relationship in progress.

• End of year two — a measurable majority of the portfolio with some form of active commercial engagement, and a mid-program benchmarking review completed against thesis and external data.

• End of year three — demonstrable financial signal, demonstrable strategic value, and a completed strategic review determining the operating model for the next phase.

Common Timeline Mistakes

• Rushing year-one deployment — to show early activity, at the cost of thesis discipline — this is the single most common source of a portfolio that needs significant correction by year two.

• Treating commercial activation as optional — rather than a resourced function, which leaves genuine strategic value underdeveloped even when the investment selection itself is sound.

• Deferring the year-three strategic review — until it is forced by a leadership change or budget pressure, rather than scheduling it as a deliberate part of the program design from the outset.

Frequently Asked Questions

How many investments should a VCaaS program make in its first year?

There is no fixed number, but three to eight investments in the second half of year one is a realistic range for most mandates, with the emphasis on thesis alignment and sourcing quality rather than absolute volume.

When should a corporation expect to see financial returns from a VCaaS program?

Early signals — follow-on rounds and markups — can appear from year two onward, but meaningful realized financial returns from early-stage venture investing typically take five to seven years or more, consistent with venture capital timelines generally.

What triggers a decision to bring a VCaaS program in-house?

Most commonly, sustained deal volume that would justify the fixed cost of an internal team, a long-term strategic commitment that makes deep internal capability building worthwhile, or dissatisfaction with an external partner's alignment or performance — ideally assessed at the year-three strategic review rather than reactively.

Is commercial activation something the VCaaS partner or the corporate is responsible for?

In practice, it works best as a shared responsibility — the VCaaS partner facilitates and manages the relationship, but it requires an assigned sponsor on the corporate's business-unit side to be genuinely productive.

Conclusion

A VCaaS program's first three years follow a reasonably predictable shape: structural setup and first deals in year one, portfolio building and commercial activation in year two, and measurable strategic value alongside a deliberate strategic review in year three. Programs that respect this sequence — rather than trying to compress it to show early results — consistently outperform those that rush it.

What to Negotiate in a VCaaS Mandate Agreement: A Practical Checklist

A VCaaS relationship is only as good as the mandate agreement that defines it. The concepts that make the model work in theory — aligned incentives, clear governance, defined strategic value — either get written into enforceable contract terms or they get renegotiated deal by deal, which is where most disappointing VCaaS relationships actually go wrong. This is a practical checklist of what to negotiate before signing, organized by the areas that most consistently determine whether a mandate performs.

Investment Thesis and Mandate Scope

The mandate should define sectors, geographies, stage focus and the balance between financial and strategic objectives in specific, testable terms — not aspirational language. "Fintech and adjacent technology" is not a thesis; "Series A-B European payments and embedded finance infrastructure, with a strategic priority on companies with distribution synergy to our commercial banking business" is. Equally important is defining how and how often the thesis can be revised, since market conditions and corporate strategy both shift over a multi-year mandate.

Exclusivity terms deserve specific attention: does the VCaaS partner work exclusively for this corporate in the defined sector, or do they run parallel mandates for other clients — potentially including competitors? Neither answer is automatically wrong, but it should be explicit and priced accordingly. A related, frequently overlooked point is what happens to deal flow the manager sources that falls just outside the defined thesis: a good mandate specifies whether these are shared as informational flow, offered on a right-of-first-refusal basis, or simply passed to the manager's other relationships.

Governance and Approval Rights

The mandate should specify the composition and authority of the investment committee, the threshold above which the corporate must approve individual investments (versus deals the manager can execute independently within the mandate), and the reporting cadence and format. The single most common governance failure is a mandate that is vague enough to allow slow, ad hoc approval processes to develop in practice — even when the contract nominally promises venture-speed decision-making.

Decision-making timelines are worth stating as explicit service levels: for example, a defined number of business days for the corporate to respond to an investment recommendation, after which the manager may proceed or the opportunity is deemed declined. Without this, informal delay becomes the default.

Fee and Economics Terms

Whether the mandate follows management-fee-plus-carry economics, a flat retainer, or a deal-based structure, the fee base should be unambiguous — committed capital, deployed capital, and net asset value produce materially different fee outcomes over the life of a mandate, and the difference should be modeled before signing, not discovered afterward. If carried interest applies, the hurdle rate, the calculation methodology, and whether carry is calculated per-deal or on the portfolio as a whole should all be explicit.

Alignment mechanisms are worth negotiating directly: does the manager co-invest its own capital alongside the corporate, and does fee income step down if performance falls below an agreed threshold? Mandates with genuine alignment mechanisms consistently outperform those relying on trust alone.

Co-Investment and Information Rights

A well-structured mandate gives the corporate explicit co-investment rights — the ability to invest directly, alongside the fund, in specific companies of particular strategic interest — and defines the process and timeline for exercising them. Information rights should cover not just standard financial reporting but visibility into the full pipeline the manager is evaluating, not only the deals ultimately recommended, since pipeline visibility is often where the most valuable strategic intelligence sits.

Commercial Facilitation Clauses

If part of the strategic case for the mandate is facilitating commercial relationships between portfolio companies and the corporate's business units — pilots, distribution agreements, data-sharing arrangements — the mandate should define who is responsible for introducing and managing those relationships, and clarify data, IP and confidentiality boundaries between the portfolio company, the manager, and the corporate's operating businesses. Ambiguity here is a common source of friction later, particularly around what information the corporate's business units can access about a portfolio company versus what remains confidential to the investment relationship.

Exit, Term, and Transition Provisions

Mandate term length, renewal conditions, and — critically — transition provisions if the corporate later wants to bring the function in-house or switch providers, should be agreed at signing rather than negotiated under pressure later. A well-drafted mandate specifies how portfolio positions, board seats, and ongoing relationships transfer in a transition scenario, and over what notice period. Corporates that skip this clause often find that switching providers is far more disruptive than it needed to be.

Red Flags to Watch For

Vague thesis language — that would allow the manager to justify almost any investment as being within scope.

• No defined decision-making service level — leaving informal delay as the default approval process.

• No alignment mechanism — beyond a flat fee, with no co-investment or performance-linked economics from the manager.

• Silence on transition mechanics — leaving portfolio transfer and continuity undefined until the corporate actually wants to exit or restructure the relationship.

How to Run the Negotiation Itself

The most productive mandate negotiations involve the corporate's legal, finance and business-sponsor stakeholders from the outset, rather than treating the agreement as a legal-only exercise finalized after the business terms are agreed informally. Governance and commercial facilitation terms in particular need input from the business units that will actually receive portfolio company introductions — legal counsel alone is rarely positioned to specify what a workable commercial facilitation process looks like in practice.

It is also worth requesting reference conversations with the manager's other corporate mandates, specifically about how disputes or ambiguous mandate scope questions were resolved in practice — not just about investment performance. How a manager handles the inevitable gray areas is a better predictor of relationship quality than the contract language alone.

Frequently Asked Questions

How specific should the investment thesis be in a VCaaS mandate?

Specific enough to be testable against an actual deal: sector, stage, geography, and the relative weight of financial versus strategic objectives, with a defined process for revising it over the life of the mandate.

What decision-making timeline should a corporate expect to negotiate?

There is no universal standard, but mandates that specify an explicit number of business days for corporate response to investment recommendations consistently avoid the informal delay that undermines venture-speed decision-making.

Should a VCaaS manager be required to co-invest its own capital?

It is not mandatory, but mandates with a genuine alignment mechanism — manager co-investment, performance-linked fee adjustments, or both — are generally associated with stronger long-term performance than fee-only structures.

What happens to portfolio companies if a corporate ends a VCaaS mandate?

This should be defined explicitly in the mandate's transition provisions. Without pre-agreed terms, portfolio transfer, board seat continuity, and ongoing relationship management can become a significant source of disruption during a transition.

Conclusion

The difference between a VCaaS relationship that performs and one that disappoints is rarely the underlying concept — it is almost always the specificity of the mandate agreement. Thesis scope, governance service levels, fee alignment, co-investment rights, commercial facilitation boundaries, and transition provisions are the six areas worth spending real negotiating time on before signing.

How Much Does VCaaS Actually Cost? Comparing Venture Capital as a Service to an Internal CVC Team

The decision between building an internal corporate venture team and partnering with a Venture Capital as a Service (VCaaS) provider is usually framed around governance, speed and talent access. It is rarely framed around cost — even though the economics are often the deciding factor once a corporation gets past the strategic case. This article sets out a practical framework for comparing what VCaaS actually costs against the true, fully-loaded cost of building an internal corporate venture capital team.

How VCaaS Providers Typically Charge

VCaaS pricing generally follows one of, or a blend of, three models. The most common mirrors standard venture fund economics: a management fee, typically in the range of 1% to 2.5% of committed or deployed capital annually, covering sourcing, diligence and portfolio management, plus a carried interest share of investment gains — often 15% to 20%, frequently with a hurdle rate that must be cleared before the carry applies. Some providers instead charge a flat retainer for advisory and sourcing services, with deal-by-deal fees for execution. A smaller number charge purely on a deal basis, with no ongoing retainer, which tends to suit corporates that want occasional access rather than a continuous program.

The right structure depends heavily on mandate intensity: a corporate expecting a steady cadence of investments over several years is usually better served by the management-fee-plus-carry model, which aligns the provider's incentives with sustained portfolio performance rather than one-off transactions. A corporate testing the model with a handful of opportunistic deals before committing to a full program is often better served by a deal-based or retainer structure, which avoids paying for capacity that a low, uncertain deal pace will not use.

It is also worth distinguishing management fee base carefully: a fee calculated on committed capital is predictable but can feel expensive in years with low deployment, while a fee calculated on deployed or invested capital scales more naturally with actual activity but is less predictable for budgeting purposes. Neither is inherently better — but the two produce materially different cost profiles over a multi-year mandate, and the difference is worth modeling explicitly before signing rather than discovering later.

The True Cost of Building an Internal CVC Team

The headline cost of an internal team is compensation: a credible corporate venture function typically requires, at minimum, a head of venture with prior institutional VC or CVC experience, one to two investment professionals, and part-time or fractional support from legal, compliance and portfolio operations. Experienced venture investors command venture-market compensation, which is frequently well above standard corporate pay bands for equivalent seniority — a recurring source of both cost and retention difficulty.

The larger, less visible cost is time. Building proprietary deal flow, co-investor relationships and sector pattern recognition from a standing start takes most internal teams several years, during which the program pays full run-rate costs while generating a fraction of the deal quality and pace a mature program would achieve. This ramp-up period is the single largest hidden cost of the build-internally path, and it rarely appears in the initial budget case presented to leadership.

A Side-by-Side Cost Framework

Cost profile — an internal team is largely fixed cost — salaries and overhead are payable regardless of deal pace — while VCaaS fees scale with committed capital and, through carry, with actual performance.

• Time to first deal — a VCaaS partner with existing deal flow can typically close a first investment within months of a finalized mandate; an internal team usually needs a year or more to build a credible pipeline from scratch.

• Cost at low deal volume — for programs targeting fewer than five to eight investments a year, the fixed cost of an internal team is rarely justified by the fee economics of an equivalent VCaaS mandate.

• Cost at high, sustained deal volume — for large, long-horizon programs with double-digit annual deal counts, the cumulative management fee and carry on a VCaaS mandate can, over many years, approach or exceed what a mature internal team would have cost — though it arrives without the multi-year ramp-up risk.

Hidden Costs on Both Sides

Internal programs carry hidden costs beyond compensation: recruiting fees and severance risk in a function with historically high turnover, technology and data tooling that a mature external partner has already amortized across many clients, and the compliance and fund administration infrastructure required if the program is structured as a dedicated vehicle rather than balance-sheet investment.

VCaaS mandates carry their own less visible costs: the corporate typically has less day-to-day control over brand and messaging in portfolio company relationships, reporting is only as good as the infrastructure the provider has built, and switching providers — while possible — involves transition costs of its own, including the risk of losing continuity with existing portfolio companies.

When the Economics Favor Each Model

As a general pattern, VCaaS tends to offer superior unit economics for programs below roughly €20 to €50 million in committed capital, for corporations without existing venture investing expertise, and for programs that need to demonstrate results within the first one to two years. Building internally tends to be justified at larger scale, for corporations with a genuine multi-decade strategic commitment to venture investing as a core capability, or where deep proprietary integration with internal R&D and business units outweighs the cost premium of the ramp-up period.

Many mature programs ultimately land on a hybrid: starting with a VCaaS mandate to establish track record and organizational buy-in, then evaluating a transition to a partially or fully internal model once deal volume and strategic value are proven — a path that a well-structured mandate agreement should explicitly accommodate rather than treat as an afterthought.

Modeling the Break-Even Point

Rather than comparing a fee percentage to a salary line, the more useful exercise is modeling total cost over a realistic multi-year horizon under each model, at the deal volume the strategic thesis actually requires. An internal team's cost curve is front-loaded and roughly flat — full run-rate cost from year one, largely independent of how many deals actually close during the ramp-up period. A VCaaS mandate's cost curve tracks capital deployed and, through carry, actual performance — lower absolute cost in early years when deployment is naturally slower, scaling up only as the program demonstrates activity and results.

Plotted over a five-year horizon, these two curves typically cross at a specific, calculable point that depends on target deal volume, average check size, and expected performance. Corporations that model this explicitly — rather than relying on a general sense that "building internally is more expensive" or "VCaaS fees add up over time" — make a substantially more defensible case to their own finance and leadership stakeholders, and are better positioned to negotiate mandate terms that reflect their actual expected usage rather than a generic fee template.

Frequently Asked Questions

Is VCaaS cheaper than building an internal CVC team?

Usually, in the first several years and for programs below roughly €20-50 million in committed capital, because it avoids the multi-year ramp-up cost of building proprietary deal flow from scratch. At very large scale and over long horizons, the fee and carry economics can eventually approach the cost of a mature internal team, though without the ramp-up risk.

What is a typical management fee for a VCaaS mandate?

Most mandates that follow standard venture fund economics charge in the range of 1% to 2.5% of committed or deployed capital annually, though flat retainer and deal-based structures also exist and suit different mandate intensities.

Does carried interest apply to VCaaS mandates?

Where the mandate follows fund-style economics, yes — typically 15% to 20% of investment gains, often subject to a hurdle rate. Retainer-based and deal-fee structures may not include carry at all, generally in exchange for a higher fixed fee.

Can a corporation switch from VCaaS to an internal team later?

Yes, and it is a common trajectory — but the mandate agreement should specify the transition mechanics (portfolio transfer, continuity of relationships, notice periods) up front, rather than being negotiated for the first time when the corporate wants to make the change.

Conclusion

The cost comparison between VCaaS and an internal CVC team is rarely as simple as comparing a fee percentage to a headcount budget. The fixed-cost, multi-year ramp-up profile of an internal team and the variable, performance-linked profile of a VCaaS mandate serve different strategic situations well — and the right answer depends more on expected deal volume, time horizon and existing internal expertise than on which number looks smaller in the first-year budget.

What Makes a Co-Investment Structure Credible to Professional Investors

Too many co-investment structures look good on paper but fall short when scrutiny hits. You want to know which elements truly matter to professional investors before committing capital. We outline the clear markers that make a co-investment SPV credible, focusing on governance and alignment, minority protections, and disciplined execution. For more insights, you might find this resource helpful.

Key Elements of Credible Co-Investment Structures

Governance and Alignment

Proper governance is the foundation of any credible co-investment structure. It ensures that all parties involved have a clear understanding of their roles and responsibilities. Investors look for structures where decision-making processes are well-defined and transparent. This clarity helps in building trust and confidence among stakeholders.

Governance is not just about rules; it's about creating an environment where everyone knows their place. Alignment, on the other hand, ensures that all parties have their interests aligned towards a common goal. This means that the success of the investment benefits everyone involved, not just a select few. To delve deeper into this topic, consider reading this insight.

Minority Protections and Allocation Policy

Minority protections are crucial in safeguarding the interests of smaller investors. These protections ensure that minority stakeholders are not sidelined in decision-making processes. Allocation policies should be fair and transparent, ensuring that all investors have an equal opportunity to participate in investment decisions.

Having a clear and fair allocation policy is essential. It prevents conflicts and ensures that every investor, regardless of size, has a voice. Professional investors often scrutinize these policies to ensure they are not at a disadvantage. For more on this, check out this white paper.

Transparent Fees and Carry

Understanding how fees and carry are structured is vital. Transparent fee structures ensure that investors know exactly what they are paying and what they are getting in return. This clarity helps in building trust and confidence among investors.

A well-structured fee model is not just about transparency; it's about fairness. Investors should know how fees are calculated and what they cover. This knowledge helps them make informed decisions and ensures they are not blindsided by unexpected costs.

Rigorous Investment Processes

Valuation and Conflict of Interest Policies

Rigorous investment processes are crucial for maintaining credibility. Valuation policies should be clear and consistent to ensure fairness. Conflict of interest policies must be in place to prevent any potential biases or unfair advantages.

Investors need assurance that valuations are conducted objectively and without bias. Clear conflict of interest policies ensure that all parties are treated fairly. This transparency helps in building trust and confidence among investors.

Investment Committee Process and Reporting

An effective investment committee process is essential for ensuring that all investment decisions are made systematically. Regular reporting keeps all stakeholders informed and involved in the decision-making process.

The investment committee must be transparent and inclusive. Regular updates and reports keep stakeholders informed and engaged. This involvement is crucial for maintaining trust and ensuring that all parties are aligned in their goals.

Deal Selection Discipline and Downside Protection

Deal selection discipline ensures that only the most promising investments are pursued. Downside protection strategies are essential for mitigating risks and safeguarding investments.

A disciplined approach to deal selection is crucial for ensuring that investments are sound. Downside protection strategies help mitigate risks and protect investments. Investors value these strategies for their ability to safeguard their interests.

Delivering Post-Close Value

Board-Level Engagement

Active engagement at the board level is crucial for ensuring that all stakeholders are aligned and informed. Regular meetings and updates keep everyone on the same page and involved in the decision-making process.

Board-level engagement ensures that all stakeholders are aligned and informed. Regular meetings and updates keep everyone involved and engaged in the decision-making process. This involvement is crucial for maintaining trust and ensuring that all parties are aligned in their goals.

Portfolio Acceleration Services and Impact KPIs

Portfolio acceleration services are essential for ensuring that investments reach their full potential. Impact KPIs provide clear metrics for measuring success and ensuring that all parties are aligned in their goals.

Acceleration services provide the support needed to maximize investment potential. Impact KPIs offer measurable metrics for success, ensuring that everyone is aligned in their objectives. These tools are invaluable for maintaining trust and ensuring that all parties are working toward a common goal.

Frequently Asked Questions

What are the key elements of a credible co-investment structure?
A credible co-investment structure includes strong governance, minority protections, transparent fees, and rigorous investment processes. These elements build trust and ensure alignment among all parties involved.

Why are transparent fees and carry important in co-investments?
Transparent fees and carry provide clarity for investors, ensuring they understand costs and returns. This transparency builds trust and helps investors make informed decisions.

How do minority protections safeguard investors?
Minority protections ensure smaller investors have a voice in decision-making. Fair allocation policies prevent sidelining and ensure equal opportunities for all stakeholders.

Venture Building vs Venture Capital: A Decision Framework for Founders and Corporates

Many founders and corporate leaders face a tough choice: build ventures from the ground up or back startups through venture capital. Each path offers distinct trade-offs that shape control, speed, and risk. This post breaks down the core differences between venture building and venture capital, then offers a clear decision framework tailored to your goals, whether you lead a fintech startup, an insurtech venture studio, or a corporate innovation team. Understanding these options sharpens your innovation strategy and founder–investor alignment. For more information, visit this guide on choosing the right approach to venture building.

Understanding Venture Building

Venture building offers an alternative to traditional funding models by focusing on the creation and growth of startups from the ground up. Here's how it works and when it shines.

Definition and Key Characteristics

Venture building involves creating new companies with a hands-on approach. These organizations, often known as venture studios, provide resources, expertise, and operational support. Unlike traditional investors, venture builders actively manage startups, helping founders with critical decisions and strategy implementation.

A key characteristic is the shared infrastructure that venture builders offer. This includes access to experienced teams, office space, and technology platforms. The goal is to accelerate growth by providing startups with the tools they need to succeed. Venture builders often take a larger equity stake in these companies, reflecting their deep involvement in day-to-day operations.

Advantages and Challenges

Venture building comes with distinct benefits and hurdles. A significant advantage is the comprehensive support provided to startups. Founders benefit from expert guidance, reducing the risk of failure. This model also allows startups to iterate quickly, as they have access to immediate feedback and resources.

However, the challenges are notable. The venture builder's deep involvement can lead to less autonomy for founders. There's also a high dependency on the venture studio's resources and networks. If these are limited, the startup's growth may be stunted. Founders need to weigh the trade-offs between gaining strategic support and retaining control over their company.

Ideal Use Cases and Scenarios

Venture building suits specific scenarios. It's an excellent fit for sectors like fintech and insurtech, where rapid iteration and regulatory navigation are essential. Startups that benefit from a collaborative environment and shared resources thrive under this model. It's also ideal for founders looking for strategic partners who offer more than just capital. If your goal is to rapidly develop a product with expert guidance, venture building might be the best path forward.

Exploring Venture Capital

Venture capital offers a different route, focusing on financial investment rather than operational involvement. Let's explore how this model functions and when it fits best.

Core Principles and Structure

Venture capital (VC) involves investing in startups in exchange for equity. VCs provide the financial resources needed to scale a business, aiming for significant returns when the company succeeds. This model typically involves less operational involvement compared to venture building.

The structure of a VC firm includes partners who manage the fund and make investment decisions. They often focus on specific industries or stages of business development. The primary goal is to identify high-growth potential companies and support them financially through multiple funding rounds. Learn more about the principles of venture capital.

Benefits and Drawbacks

Venture capital offers substantial benefits, such as access to large amounts of funding that can drive rapid expansion. This model can significantly increase a startup's market presence and valuation. VCs also bring valuable networks of contacts that can open doors for partnerships and additional funding.

However, there are drawbacks. The pressure to deliver quick returns can lead to aggressive growth strategies that may not align with the founder's vision. Additionally, founders might face dilution of ownership and decision-making power. Venture capital works best when the potential for rapid scaling outweighs the need for control and when the market opportunity is significant enough to justify the investment.

Suitable Applications and Contexts

Venture capital is well-suited for startups in high-growth industries like technology and biotechnology, where financial resources can catalyze significant advancements. It's ideal when a business has a scalable model and requires large capital injections to capture market share swiftly. For founders ready to trade some control for rapid growth and who have a clear path to a large market, venture capital can be the right fit. Explore the differences between private equity and venture capital.

Decision Framework for Founders

Choosing between venture building and venture capital involves careful consideration of your business goals and resources. This framework helps guide your decision.

Factors to Consider

When evaluating your options, consider your startup's needs, your desire for control, and your growth objectives. If you value strategic guidance and are in a sector that benefits from shared resources, venture building might be more suitable. On the other hand, if you need significant capital to scale quickly and can handle external pressures, venture capital could be more advantageous.

Assess your readiness to collaborate deeply with partners. Venture building requires openness to external input and a willingness to leverage shared resources. Venture capital demands a focus on rapid growth and scalability, often at the expense of autonomy.

Hybrid Models and Combinations

Some founders find success by combining elements of both models. A hybrid approach allows you to leverage the strengths of venture building's strategic support and venture capital's financial power. This combination can be particularly effective in industries like fintech, where both expertise and funding are critical for success.

By blending these models, you might take initial support from a venture builder to develop the product, then seek venture capital to scale rapidly. This approach requires careful alignment of goals with your partners to ensure that both strategic support and financial backing are optimized.

Strategic Alignment and Outcomes

Ultimately, your choice should align with your long-term vision and the outcomes you value. Consider how each model supports your innovation strategy and founder–investor alignment. With the right model, you can achieve the growth and impact you envision for your startup, whether through the strategic capital from venture building or the financial strength of venture capital. For more insights, read this article on corporate venture builders.

Frequently Asked Questions

What is the main difference between venture building and venture capital?

Venture building involves a hands-on approach to creating startups, offering resources and strategic support, while venture capital focuses on providing financial investment for equity, with less operational involvement.

When should a startup choose venture building over venture capital?

A startup should consider venture building if it benefits from shared resources, strategic guidance, and operates in sectors like fintech or insurtech, where rapid iteration is crucial.

Can a startup use both venture building and venture capital?

Yes, a hybrid approach can be effective, leveraging the strategic support of venture building initially, followed by the financial power of venture capital for scaling.

What are the challenges of using venture building?

Challenges include potential loss of autonomy for founders and a high dependency on the venture studio's resources. The model requires alignment with the venture builder's goals.

What sectors are ideal for venture capital investment?

Sectors like technology and biotechnology, which require significant capital for rapid growth and have large market opportunities, are ideal for venture capital investment.