This email spawns from this thread.
These thoughts are often poorly edited and researched and usually span areas of venture capital, emerging technology, and other random thoughts.
I’d love for this newsletter to be more of a discussion vs. me shouting to the internet, so please email me back and I promise to respond.
Thoughts
1) Minding The Inequality Gap
A ton of my research recently has been sitting between exciting technological development and a future that pushes the gap further between have/have nots and/or further distances the upper classes of society from middle and lower classes.
We can look at this in its earliest stages today with family planning (the mere cost of IVF boxes out many families) and will continue within that category while extending through traditional healthcare and longevity.
Why I’m writing about this today though is an interesting paper surfaced to me by Gwern (follow them). This paper analyzes predictors of educational success based off of both DNA differences between children identified via polygenic scores, paired with their parents’ socioeconomic status.
The paper concludes that there is a meaningful gap between “high-high” (high socioeconomic status and polygenic scores) and “low-low” equal to A- to C- grades, or 77% to 21% college attendance rate.
What was most relevant to me however was the middle results which show mixed variance. In the conclusion the researchers frame a suggestion that very much fits in line with this have/have nots future:
We suggested, for example, that high GPS partially compensates for the disadvantages of children from low‐SES families, increasing their chances of going to university from 21% to 47%. This raises the possibility of doing more to help this group reach its full potential.
Ultimately the researchers caveat that the accuracy of these predictors at an individual student level are incredibly difficult to truly understand, however the sentence above very much painted a picture in my mind where we figure out what groups of age 6-16 humans are worth investing in, and which aren’t, based off of some predictive factors.
As with all points in this space, somebody could easily say that we are already are doing this (whether in other countries that are more explicit about doing so, or in the US where such a narrative exists, and may be factual, across various demographics of people).
Nothing to tie this up but an area that is definitely top of mind these days, and over the next decade as we continually try to quantify the world around us.
2) Illiquidity Discounts & CIO Job Security
There was a great piece written by AQR surrounding the concept of an illiquidity discount. A year ago, I answered a question surrounding how the venture landscape could change versus a potential pullback in broader macroeconomic conditions.
The idea that investors will want to flee to potentially liquid assets makes a lot of sense for the psychological fortitude/regret components that AQR mentions (basically, if you see a stock falling, but you can’t sell, or you don’t see it falling at all because illiquid markets mark less often, then you may enjoy that in downside volatile times). While I generally believe that the net dominant strategy from a returns/diversification standpoint in a market pullback would be to invest in things like illiquid seed funds (they have longer time diversity, though likely pricing will be impacted less vs. growth) I actually think the vector that could be most compelling is for the investment committee members themselves and how they can keep their jobs.
When a sports team struggles and starts to underperform the historical past few seasons, often the manager is the first person replaced. Similarly, when the world begins to burn, often everyone looks at the CIOs and other heads of these groups and the decade-long bull run we saw was all luck, while the market downturn should have had some semblance of skill to be detected. To not detect that downturn is to not have skill, and public markets and shorter-term investments show you incredibly quickly how little skill one can have.
If you’re a CIO or core investment group searching anywhere for yield, it’s likely that you can actually save a few more years of your job by backing venture funds that are expected to lose money over their first few years (the J Curve), have little meaningful data that is thrown off for the first 5-7 years, and are considered potentially low correlation to public markets. I think there are a large batch of people that the dominant career strategy could be to park it in long-term, illiquid assets with little opacity to returns, and venture is an increasingly institution-grade market for that.
3) “Founder is all the matters at seed” is wrong.
There’s a view within early stage venture that I believe has gotten simplified and bastardized over the past 5-7 years surrounding the idea that “founder is all that matters”. It sounds nice as we think about humans being core atomic units of a startup, however it both doesn’t take into account founder-market fit (which I believe is 100% real) as well as how financing dynamics have changed at the early stage.
I don’t have vast data on this but I believe that due to an abundance of pre-seed, seed, and seed extension capital, as well as a higher bar for Series A, founders are able to either make small iterations from pre-seed through seed extension (over the course of 2-4 years) as they try to iterate towards product market fit (showing small inflection points at each raise before failing again) or have a massive, quick failure, which some of the best founders can engineer towards an acqui-hire or soft landing before restarting.
I anecdotally am seeing and hearing far less hard pivots surrounding companies starting in one market and pivoting entirely into another. Because of this, the idea that all that matters is the founder (built on the premise that great founders will pivot around until they hit PMF) feels wrong and overly simplistic.
4) Escapism vs. Reality in Animated Storytelling
There’s a passage in the book Marvel Comics: The Untold Story surrounding the differences between the Marvel and DC universes and the creative decisions of how and where those universes were set. The key view of Marvel was that telling stories that tied to real places gave them an outsized advantage vs. DC which was based in fictional places. As the author says:
…but every kid knew that they were tethered to their respective Metropolis and Gotham City and that never the twain would meet. Who cared if the Acme Skyscraper fell, or the First National Bank had to give up its cash? Timely's NYC on the other hand, was rife with real stuff to destroy.
When looking at this through the lens of virtual influencers/digital celebrities, it’s become quite clear that people feel the need to blend these characters into our real world both as a storytelling mechanism and as a potential monetization strategy for selling real world goods. I think that a large number of the creators of this IP have misjudged the value of escapism and extending their universe though, as the real world tether only more closely ties its IP to potential holes that can be poked in stories, and narrows the ultimate reasoning for why these characters should build a following.
One of the core components of the animation industry is asking the question as to why a given character or universe should be told via animation vs. live action. Where many have gone wrong is thinking of animation as a pure medium vs. a storytelling tool. While in the mid-1900s, we wanted to see our real world in a more dynamic way because our view of what was happening in the world was so narrow. I’m not sure that over the past 50-80 years, this principal has held true for non-live action IP. Today we see every part of the world in a form of hyper-reality, at our fingertips. In 2020 we not only crave escapism, but we also crave creativity and novel ideas/stories.
It in some ways reminds me of this quote from The Mission of a Lifetime.
The immensity of earth used to inspire awe. Then we went to space.
5) Venture firms either evolve with or strongly mold new employees
I’ve now been in the investing world broadly long enough to see a bit of a generation turn (and mostly, expansion) in the venture capital space, as well as to know people just as they join their firm and to spend time with them over the years as they spend more time in that culture.
I increasingly believe that venture firms either evolve with their humans, or the humans bend to their culture in an outsized way. This feels obvious to say but in reality venture firms, which are not scalable, and where team members operate towards a single goal in a day to day decentralized way, feel this far more than any startup.
For the latter, some firms have shown an ability to take a person and integrate them, showing the power of long term brand network effects proliferating all the way to the behavior, analysis, and hiring process. The person often trends towards a lowest common denominator and/or hyper-specific style that is associated with the firm and its partners.
Alternatively other firms have decided to let the humans play an independent game, functioning on a single vector while having a different narrative structure surrounding how they invest and why. This leads to some fragmentation and probably less strong brand network effects in venture at the fund level, but probably a more defensible long-term moat in terms of recruiting and aggregate partnership ability to win a wide range deals.
As with many things in venture, I don’t have a view on which is better just yet.
Research / Links
Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation - Similar to last newsletter’s thoughts on the desire use ML to do text generation of 3D scenes, I’ve also continued to see tons of research surrounding ability to transfer lower quality environments to higher quality standards. This idea was compelling when transferring various synthetic datasets into real world datasets to be used on largely computer vision tasks (most often in autonomous vehicles). There are various arguments surrounding the necessity for high quality 3D environments in ML training, but either way teams are starting to really crack this idea of building synthetic datasets in scalable manner with potentially real diversity of data.
An algorithm that learns through rewards may show how our brain does too Cool article + paper related to mapping dopamine release in the brain and it’s strong resemblance to how reinforcement learning reward mechanisms work. I’m not going to try to take a stance here but if you’ve ever been stuck working through a problem (this mostly resonates with research or being a bad software engineer for me) where you are trying to accomplish a task and then it goes better than you thought, this premise is quite interesting and intuitive vs. prior views around more binary dopamine release.
Write-A-Video: Computational Video Montage from Themed Text - I previously have written about the continual task fo trying to utilize ML to go text to scene. This paper (with accompanying video) is a bit more realistic and interesting as it focuses on using paired video repositories in order to curate the creation process based off fo text in what they call visual-semantic matching.
Illegible Text to Readable Text: An Image-to-Image Transformation using Conditional Sliced Wasserstein Adversarial Networks...Using ML to make handwriting into printed text. Just a cool use-case.
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