Why we invest in a lot of companies
(and built a tool so you can see the math)
A few years ago I was having coffee with an LP who wanted to understand what we did differently. He ran a family office and had looked at plenty of seed funds. Midway through the second cup, he asked the question every LP eventually asks a concentrated-conviction GP. “How many companies are you going to invest in?”
I said 150 over the fund’s three-year deployment window.
He paused, then, polite but pointed, said the thing I have now heard dozens of times. “Isn’t that spray and pray?”
That conversation is why this piece exists. And it is why we spent the last few weeks building an interactive tool that lets anyone test the math themselves. The tool is free. You can open it on your phone (although it is better to use on desktop), drag a slider, and see the argument we have been making to LPs for the last three funds, play out in real time.
Here is the argument in one sentence. Venture outcomes follow an extreme power law, and power laws reward more at-bats with better-quality returns, not fewer at-bats with bigger bets.
That is not a slogan. It is a consequence of how the returns distribute. The shape of the distribution is not a matter of opinion. It comes from the largest publicly available dataset of venture outcomes (Correlation Ventures, 21,000 financings), from Horsley Bridge’s empirical analysis of what separates top-tier VC funds from the rest, and from the Kelly criterion, which is the math gamblers and quantitative investors use to decide how much to bet when the odds are asymmetric. Three independent frames. They all point in the same direction.
Let me show you what the math looks like. Then I will explain why more companies produces more value per dollar, not less.
The quality of returns lifts with volume
Imagine you are running a seed fund with industry-average luck. Fifty-five percent of your companies go to zero. Fifteen percent give you your money back. Another fifteen percent produce a modest 3x. Eight percent hit a solid 10x. Two percent are unicorns, half a percent become decacorns, and one in a thousand becomes something like Stripe or OpenAI, which is a 5,000x (or a lot more) outcome.
Most seed funds hold around 30 companies. At that size, with reserves deployed sensibly into the winners, the math says about 21 percent of funds clear a 5x return. The median outcome is a 2.8x gross MOIC. After fees, that is a fund most LPs would politely call “fine.”
Now invest in 150 companies with the same luck and the same per-company budget.
The probability of clearing 5x climbs from 21 percent to 58 percent. Median MOIC nearly doubles, from 2.80x to 5.55x. The range of likely fund outcomes also tightens dramatically. A 30-position fund can land almost anywhere, from near-zero to a home run, and most LPs have no way to know in advance which version they’re getting. A 150-position fund still contains the same unicorns and decacorns, but individual funds cluster much more tightly around their average return. You lose the extreme downside and a little bit of the extreme upside, and you gain the thing LPs actually want from a portfolio: a return profile they can plan around.
Nothing in the outcome distribution changed. We did not improve at picking. We did not forecast winners. The quality of returns got better because we took more shots at a distribution where almost all the value lives in the tail. At 30 positions, you need to catch a unicorn to look good. At 150, you probably catch one or two unicorns just from the math, and the fund’s upside is less dependent on getting any single bet right.
The intuition is the one every poker player already has. If one suit in the deck pays a million and everything else pays nothing, and the dealer is about to turn over cards, you would rather see more of the deck. The concentrated fund is making a smaller number of draws from a distribution where the payoff is wildly asymmetric. Sometimes it works. More often, the unicorn doesn’t show up in those 30 cards.
Same math, two strategies
The chart below is the inside of the tool. The two panels show 30 positions and 150 positions running against the same simulated outcome distribution with the same random seed, so the draws are mathematically identical. The only thing that differs is how many positions the fund owns.
Read the numbers in pairs. The concentrated side has higher variance, which is the technical way of saying the distribution of possible fund outcomes is wide. Sometimes a 30-position fund catches a unicorn and looks like a genius. More often it doesn’t, and the whole fund underperforms. The distributed side compresses that variance. Fewer funds land in the tails at all, and more of them land somewhere the LP can live with. The probability of a 3x fund rises from 45 percent to 69 percent. The probability of a 5x fund nearly triples.
If you are thinking “the concentrated fund’s upside is still real, so maybe it’s worth the variance,” that is the honest version of the counter-argument. A concentrated fund in 1999 that caught Google returned every dollar you put in several hundred times over. The trouble is that you are not the person who caught Google, and you don’t know who is. The 30-position fund that doesn’t catch Google is the typical outcome, not the exceptional one. For an average GP picking 30 companies, the distribution of fund outcomes is dominated by the versions where the unicorn never comes.
Why this isn’t obvious
If the math is this clear, the reasonable question is why every LP isn’t already buying it. The answer is that they already are. They just take it through more expensive wrappers.
A $500 million commitment to a fund of funds lands in 20 to 40 underlying managers, each of whom is running a portfolio of 30 to 80 companies. The endowment, pension, or sovereign writing that check ends up with effective exposure across 800 to 3,000 startups. Mega funds like Sequoia Capital Global Growth and Tiger Global hold hundreds of positions across their strategies for the same reason. The biggest pools of institutional capital have already decided, with their allocations, that venture exposure wants to be held at hundreds or thousands of underlying positions. That is a settled practice at scale.
What is newer is that a single fund with the right deal flow can produce position counts that rival a fund of funds, without the double layer of fees. That requires being able to see, evaluate, and invest in enough high-quality companies per year to make the math work. Which brings us to the part that took us five years to build.
The binding constraint
Here is what the simulator cannot show you. A 150-position portfolio requires access to 150 investable companies in a three-year window. Most seed funds can’t reach that threshold at quality. Their deal flow runs out. Most concentrated funds are not concentrated by choice. They are concentrated by necessity.
We are not. Y Combinator produces roughly 800 pre-vetted companies per year across four batches. That is the raw input. But raw input isn’t enough on its own. You also need the relationships to get in at the terms you want, the intelligence to tell the signal from the noise, and the operating muscle to support 150 founders without becoming the investor who doesn’t pick up the phone. Volume without the rest of the stack is the spray and pray caricature. Volume with the stack is something else entirely.
The stack is where this gets interesting, and it is the part that compounds.
Every investment we make adds to the network. Every founder who works with us can become a scout, a reference, a customer for another portfolio company, or a future LP. Every VC we co-invest with becomes a co-invest partner for the next deal. Every conversation on the Ignite Podcast becomes a relationship with a founder, VC, or operator who now knows us. Every blog post brings in another intelligent reader who wants to understand private markets better.
Said out loud, that sounds like marketing. On the ground it is a set of very specific loops. More companies means more referrals. More referrals means better deal flow. Better deal flow means better companies invested in. Better companies means better outcomes. Better outcomes means more founders want to work with you. More founders want to work with you means more referrals. Run that loop for half a decade and the deal flow problem that constrains most seed funds stops being the binding constraint.
The same loop runs through the other sides of the business. More than three thousand VC relationships means we can route a founder toward the right Series A lead in the right week. Fifteen thousand community members and counting means when a founder needs ten customer conversations, they get ten. Half a million monthly viewers across social means a portfolio company launch moves when we push it. None of this is free. But the marginal cost of adding one more position, given that the network is already in place, is almost nothing. And the marginal value of that position, both for the company and the network, is real.
That is how volume becomes a strategy instead of a prayer.
The honest caveats
If I only tell you the good part, I have failed you. The math has real limitations and it is worth being explicit about them.
First, the simulation assumes the outcome distribution holds across all 150 positions. In reality, if you invest in every YC batch without discrimination, you are also catching the bottom of the batch. Selection still matters. The simulator has a skill slider that adjusts for this, and at every level of skill the volume argument still holds, but the magnitudes change. A below-average picker investing in 150 companies still does meaningfully better than the same picker investing in 30. A great picker investing in 150 does materially better than a great picker investing in 30. Skill and volume compound.
Second, the follow-on math in the tool assumes threshold-based reserve deployment rather than conviction-based. Real GPs make judgment calls on which winners deserve more capital. The simulator handles this by scaling follow-on outcomes with the skill parameter, but the abstraction is imperfect. This is one of the places I genuinely want feedback. If you have seen reserve deployment fail for a reason the model doesn’t capture, tell me.
Third, the three frameworks in the tool (Horsley Bridge, Kelly, and Monte Carlo) are not independent. They share distributional inputs. Change the assumption about how often a unicorn appears and all three move together. The tool’s Overview tab shows an expected-value breakdown that makes this explicit. Look at it. The rare outcomes do most of the work. If you disagree with the rare-outcome probabilities, your disagreement moves the whole result. The tool lets you edit the distribution yourself.
Fourth, and most importantly, this is our strategy. It is not a universal prescription. A great investor running Benchmark-style conviction at 10 positions can beat us if their picking is good enough and their access is deep enough. Historical concentrated funds that worked produced extraordinary returns. The math in this tool is the defense of our particular approach, not a claim that everyone else is wrong.
Fifth, this is version one. The numbers in this piece come from a specific set of defaults. If you change the defaults, you get different numbers. Some of those differences will challenge my framing. Good. That is the point.
Try it yourself
The tool is free and takes no login. You can move a slider from 30 to 500 positions and watch what happens. You can flip between the industry-average distribution and the YC-historical distribution, which has a higher hit rate and tighter outcomes. You can edit the underlying probabilities if you think our assumptions are too generous or too conservative. You can change the skill parameter. You can compare concentrated and distributed strategies head to head with the same random seed so the comparison is fair.
If you are an LP thinking about venture allocation, I would particularly encourage you to play with the head-to-head tab. Pick a GP pitch you have heard recently, estimate the position count they are running, and see what the math says the distribution of outcomes looks like for their strategy versus ours. You do not have to believe our numbers. Edit them until you believe them. The shape of the answer will probably surprise you.
This is version one. We shipped it fast so we could get feedback early. If something is confusing, wrong, or missing, please say so. I read every response.
We run this strategy because we believe the math. We built the tool because we think most LPs deserve to see the math for themselves, without having to take any GP’s word for it, including ours. Venture capital has too many people telling you what to believe and not enough people showing you why.
Here is the math. Here is the code. Run it. Push back. If you come to a different conclusion, I want to hear what it is.
If you want to go deeper on what we do, our value proposition for LPs lays out the wider platform, from quarterly top-ten watch lists to the operator network we deploy on behalf of portfolio companies. If you want to see the companies we have backed, check out our portfolio here. If you want to listen to founders and fund managers describe how all of this plays out in practice, the Ignite Podcast is nearly three hundred-plus episodes deep at this point.
More than anything, the best thing you can do is open the tool and spend five minutes with it. The numbers make the argument better than I can.
Try it here: https://tools.teamignite.ventures/