Micro(scopic) 10X Funds

young, kids, students

I wrote both a Twitter thread (I know it’s X now, but habits die hard) and a LinkedIn post recently on student and recent graduate funds. A good friend and I have been seeing a number of small sub-$10M funds run by college students and/or recent grads. And even more since the afore-mentioned social posts came out. In a way, it was my flag in the sand moment inviting additional conversations on the topic.

Full LinkedIn post here. Truncated this to make it easier to read.

The TL;DR version of the post, although the post itself is at most a two-minute read, is that these student funds are interesting. Most will die. But a small, small few will deliver insane returns. As such, as LPs, the underwriting for these funds, where sourcing is extremely predictable (i.e. invest in their peers), needs for these funds to be 10X funds, as opposed to 5X net for the typical seed fund or 3X for the typical Series A fund. Also, we know going in that most, if not all, of these funds won’t be enduring. Most likely one and done.

And so what does the underwriting look like?

I actually elaborated on this in response to a comment that asked what percent of unicorns were founded by students, but thought it made sense to expand here in this blogpost as well.

Venture, at the end of the day, is a game driven by the power law. I’m not the first to say that. And I won’t be the last. In other words, in VC, we are applauded not by our batting average (like buyouts or hedge funds), but by the magnitude of our home runs. We can miss on the vast majority, but as long as we strike one Uber or Coupang or Google or Facebook and it returns multiple times of our portfolio, then… we did it.

To quote a Midas list investor (who’ll go nameless for now, until I have his permission to share his name), who at the time was presenting on stage, “The only reason you are listening to me today is because I’m on the Midas list. And the only reason I’m on the Midas list is because of this one investment I made [redacted] years ago.”

Obviously, there was definitely some modesty there. In fact, he’s hit a number of exits in the years since. Nevertheless, when said in broad strokes, his point stands.

So to the comment that started it all. By numbers, a rather small number of unicorns were founded by active students. I don’t know the exact number (writing this on vacation, and I don’t have Pitchbook access on this small device), but I’m willing to bet that only a small percentage of unicorns are founded by students. And even less when you consider realized unicorn exits. Excluding the crazy markups of 2020-2022. It’s why the average age of a startup founder is 42 at the inception of the company.

That said, “Among the top 0.1% of startups based on growth in their first five years, [an HBR study finds] that the founders started their companies, on average, when they were 45 years old.” In fact, in the same study, they found “[r]elative to founders with no relevant experience, those with at least three years of prior work experience in the same narrow industry as their startup were 85% more likely to launch a highly successful startup.” In a separate Endeavor study, it’s also why there’s only a small sliver of founders with no work experience prior to the founding of their unicorn company.

All that to say, from Alexandr Wang to Jeff Bezos to Mark Zuckerberg to Patrick and John Collison, all were in their early twenties (or earlier) when they started their companies. Each, in their own right, an outlier.

To build a hypothetical portfolio — forgive my generalizations, but doing so for nice, even numbers…

Say one allocates a $10M fund of funds portfolio. It’ll write 10 $1M checks into $5M funds. In other words, for a 20% stake at the fund level. In a bad economy, where $200M is the median ARR to go public, and if we assume a 10x multiple on exit, a $2B unicorn exit in that $5M VC fund returns ~$2.2M in the fund of funds portfolio. 0.6% equity valued at $12M. A 2.4X on the $5M fund alone. And a little over $2.2M back to the LP, as the GP takes 20% carry. This assumes $100K checks, 2% ownership on entry and 70% dilution by the time of exit. Naturally, no reserves. needing about 10-11 unicorns to 2x. A lot to expect for a portfolio of student funds. 10 unicorns out of 400 is quite hard even for most seasoned investors.

And so one must believe that these student funds can find true outliers. And before anyone else. Additionally have enough downstream capital relationships to facilitate intros to funds who will lead current and future rounds. Which luckily for them, a lot of GPs of multi-stage funds are individual LPs in these funds. Playing a pure access approach.

And so, if there’s a $10B exit in one of the VC portfolios, under the same fund strategy assumptions as earlier, a single $10B company exit returns the whole fund of funds portfolio. Every other exit will just be cherries on top. So out of a 400 underlying startup portfolio, only one decacorn exit is needed. Instead of multiple unicorns.

Separately, and worth noting, although I’ll be honest, I haven’t had a single conversation with a young GP where any were as deliberate with their sell strategy as this, there are multiple exit paths today outside of M&A and IPO, most notably secondaries (portfolio and fund) (something that the one and only Hunter Walk wrote recently in a blogpost far more eloquently than I could have put it). And so even in a crazy AI hype right now, there are paths to liquidity in these multi billion valuations at the Series B and C, if not earlier. In the increasing availability of such options, my only hope is that these young fund managers have the wherewithal to be disciplined sellers. Perhaps, an additional reason these young VCs should have LPACs.

A blogpost for another day.

Photo by 🇸🇮 Janko Ferlič on Unsplash


Stay up to date with the weekly cup of cognitive adventures inside venture capital and startups, as well as cataloging the history of tomorrow through the bookmarks of yesterday!


The views expressed on this blogpost are for informational purposes only. None of the views expressed herein constitute legal, investment, business, or tax advice. Any allusions or references to funds or companies are for illustrative purposes only, and should not be relied upon as investment recommendations. Consult a professional investment advisor prior to making any investment decisions.

#unfiltered #90 If A Song Took a Lifetime to Play

music, song

Just the other day, I was listening to one of 99% Invisible’s episodes, interestingly titled as “As Slow As Possible,” named after the organization ASLSP, which stands for the same. My knee-jerk reaction was that the abbreviation and the first letters of each word just didn’t match up. Luckily, Roman Mars and Gabe Bullard explained. Although it still left something more to be desired.

“The title is also a reference to a line in James Joyce’s novel Finnegans Wake. The line is: ‘Soft morning, city! Lsp!’ Where lisp is just spelled L S P.”

Nevertheless, the episode itself circles around the concept of taking one song and using the entire lifespan of a pipe organ (639 years) to play that song just once. That even a single note would take two years to play. A fascinating concept! And which led me down a rabbit hole of thought experiments.

What if we took our favorite song and extrapolated that to the human lifespan? Say 90 years. What note would we be on today? Have we gotten to the chorus yet?

So for the sake of this thought experiment, for a brief second, let’s walk down the lane of music theory. Take the average pop song. The average pop song plays for about three minutes. And many at 120 beats per minute. Apparently, 120 bpm is also the golden number you want to get to if you’re working a crowd as a DJ. You never start at that speed, but you work your way up throughout the night. And if you can get people’s heart rate matching the beats per minute, you’ve hit resonance. But I digress.

So, taking round numbers, the average pop song has a total of 360 beats. Most songs are in 4/4 time. In other words, four beats per bar. An average pop song takes about 2-4 bars for the intro. 16 bars for a verse. Possibly, another 4 bars as the pre-chorus. And the first chorus doesn’t really start till bar 25. And usually lasts another 4-8 bars.

Now, if we were to extrapolate a song to the average human lifespan. 90 years. 360 beats across 90 years. Assuming it takes 24 bars to get to the chorus, the chorus doesn’t start until we’re 24 years old. And the full chorus doesn’t end until we’re 32 years old. With each note lasting a full three months. And the second chorus starts around age 48.

Then again, I remember reading somewhere that most pop songs are played in multiples of four or eight. And that most of these songs only have 80 bars. If that’s the case, the first chorus doesn’t kick in till we’re just past 28 years old and ends around 36 years old.

In either case, the first chorus happens around the time when most people would define as their prime. Young enough to take risks; old enough to be dangerous. The second chorus seems to fit as the second wind people have in their careers. Hell, HBR found, the median age of a startup founder when they start is 45. And with that reference point, they’ll be 47 or 48 when they become venture-backed.

Obviously, this is just me playing around with numbers. Correlation does not mean causation, of course. But nevertheless, the parallels… curious and uncanny.

P.S. Jaclyn Hester and my episode together on Superclusters got me thinking about a lot how much music applies to our lives and how we live and think.

Cover Photo at the top by Marius Masalar on Unsplash


#unfiltered is a series where I share my raw thoughts and unfiltered commentary about anything and everything. It’s not designed to go down smoothly like the best cup of cappuccino you’ve ever had (although here‘s where I found mine), more like the lonely coffee bean still struggling to find its identity (which also may one day find its way into a more thesis-driven blogpost). Who knows? The possibilities are endless.


Stay up to date with the weekly cup of cognitive adventures inside venture capital and startups, as well as cataloging the history of tomorrow through the bookmarks of yesterday!


The views expressed on this blogpost are for informational purposes only. None of the views expressed herein constitute legal, investment, business, or tax advice. Any allusions or references to funds or companies are for illustrative purposes only, and should not be relied upon as investment recommendations. Consult a professional investment advisor prior to making any investment decisions.

#unfiltered #13 The Unlikely Marriage of Cuisine and Team-Building – Flavor Maps, Food Pairings and Bridgings, and How it Relates to Systems Thinking

broccoli, flavor mad science, recipes, team building tips

I met a founder (let’s call him Stan) recently who was about to close on his first big executive hire into a team less than 10 strong. Naturally, I asked what the rest of his team thought of that person. Stan replied, “I haven’t asked them yet.”

So, I subsequently followed up, “Were they able to meet him?”

“He’s been by our office, and I’m sure he’s had the chance to chat with them already.”

When he said that, two things stuck out to me:

  1. Stan’s use of “I’m sure…” implied neither that he was sure nor that he took care to verify.
  2. He seemed to have skipped a fundamental step in building a team. And by transitive property, how it would define his team’s culture.

The Culinary Parallel

Synonymously, a day later, my friend asked me, “How do you come up with your ideas for flavor mad science?”

You’re probably here thinking: “What the hell does this have to do with team-building and culture?” But bear with me here. I swear there’s a parallel.

Although, like all of my ideas and insights, I can’t say any of my flavor experiments are truly original, I always start off at the drawing board with flavor maps. And, you guessed it! Not even the concept of flavor maps is original. A few years ago, an amazing chef taught me this very trick of how he concepts new recipes every season at his critically acclaimed restaurant.

So, what’s a flavor map?

The idea of a flavor map is to start with a core ingredient – the star of your dish. And then slowly add other flavors and elements onto your diagram one by one. The catch is that every new flavor you add has to pair well with every single other flavor on that diagram.

Personally, I just try to think of a dish that I enjoyed, or know many other people enjoy, as the basis for a drawing a line between a pair. The reason I do so is that many generations of experts before me have already done the legwork to make these flavors work. And I’m just iterating off of their discoveries.

The more scientific approach is through flavor networks – specifically food-pairing and food-bridging. In summary, food-pairings are when you combine two ingredients with the same flavor molecules, like cheese/bacon or asparagus/butter. The most bizarre one in a 2011 Harvard study is probably blue cheese/chocolate, which share 73 flavors. On the other hand, food-bridging is when you take two ingredients that don’t share any flavors, like apricots/whiskey, and bridge them with an ingredient that shares commonalities with both, like tomatoes.

Yong-yeol Ahn and his colleagues explore the nuances of flavors and recipes in their 2011 research, which you can find here. But if you want the abridged summary, there’s a great one on Frontiers. Yet, as one of the co-owners of a critically-acclaimed molecular gastronomic restaurant told me not too long ago, take the research with a grain of salt. Food science is still extremely nascent and lacks consistent data points, especially across cultures.

Looping Back

Just like a complete flavor map has all of its ingredients working in cohesion with one another, a strong team needs to hold the same level of trust and respect. I’m not advocating that you need to agree with everyone on your team. In fact, disagreement on warranted grounds is better. But to be a well-oiled machine, a team can only be agile if you reduce the unnecessary friction that may exist now or arise in the future.

Although it is important that every team member can ‘food-pair’ with every other member, what I believe is more important is to have a fair mitigation system to ‘food-bridge’ all current and future disagreements. A system to resolve disputes and to prioritize tasks at hand. To have not only trust in each other, but also in the system design.

The cherry on top

Of course, I don’t know if Stan just forgot to set up times for his team to meet with the potential hire between his various tasks of running a business. Or if he had something he wanted to hide from his team. Regardless, his decision, or I guess, lack thereof to do so, would be detrimental to the delicate string of trust that connected his team to him.

To his and every other founders’ credit, there are often matters that seem obvious to an observer, but less so, when one has skin in the game – some degree of emotional attachment. And the deeper one is in the weeds, the harder it may be to follow rational behavior. Loosely analogized to the boiling frog problem. That said, some actions are excusable. These can often be caught by either a mentor or a close friend/family member. But there are a handful that aren’t. The same can be said on a macroscopic perspective as well. Between friendships. Lovers. Coworkers. You name it.

And, luckily for Stan, this falls under the former.

Top Photo by Hessam Hojati on Unsplash


#unfiltered is a series where I share my raw thoughts and unfiltered commentary about anything and everything. It’s not designed to go down smoothly like the best cup of cappuccino you’ve ever had (although here‘s where I found mine), more like the lonely coffee bean still struggling to find its identity (which also may one day find its way into a more thesis-driven blogpost). Who knows? The possibilities are endless.


Stay up to date with the weekly cup of cognitive adventures inside venture capital and startups!