Quick update: Market views

Go to my notes to read poorly informed morning writings on markets for now

Just wanted to send an update that I’ve been increasing writing cadence a bit on my Notes surrounding my horrible and poorly researched views on the public markets each morning pre-open (with some takes on private markets that I’m much more willing to defend).

I figured it was better to post there than spam this newsletter, which likely you are following for other hot takes on things I know a bit more about. But if for some reason you care, I’ll spam you once.

You can access my posts here or at this twitter thread. See below for today’s for a general idea.

And don’t worry, I’m still reading lots of research, building out theses, and writing on other views, so this is an update, not a content pivot :).

—-

Market Views 3/23/20 - Infinite QE and the BTFD Recession

Disclaimer: This is more for scalable sending notes and personal accountability. Not a professional public market investor, not investment advice, likely all bad advice from someone spending not nearly enough time researching public market dynamics. Also not really editing these pre-posting.

So as I started writing this the Fed announced “open ended quantitative easing” which also added their ability to purchase corporate bonds (a rarity). I don’t have proper time to digest this right now but it feels like the government has now answered the question of “how much money do we need to feel good about the market” with “UNLIMITED”. Shout out to Bulger for this tweet.

FWIW I don’t think this will materially prop up markets from bad news that is coming this week surrounding unemployment.

That said, this doesn’t come as a total shock as we’ve started to hear rumblings that there was a general feeling that we wouldn’t be able to continue much longer with this shutdown (and resulting domino effects in the economy). This sentiment began proliferating through VCs over the past week, which as I said in my last note, felt very odd and I disagreed with, at this time. The various investment banks and research firms throwing out GDP predictions over the past 4 days certainly didn’t help.

This feeling was only further accentuated by Trump and Pence repeatedly bringing up that we are 7 (or 8?) days into THEIR 15 DAY PLAN! Hear that? 15 DAYS! And if you didn’t, Trump made sure to fire this off before he went to sleep last night.

image

So this is basically saying that the 15 day time is when he’s going to try to keep this going, and if not, welp we’re starting this machine up again because fuck it, the economy AND the American healthcare system can’t suffer (well at least, the economy can’t).

The BTFD Recession

I wanted to write more on this but feel important to get out now due to the various conversations I’ve had surrounding this.

There’s a saying in finance called Buy The Fucking Dip, which speaks to a greedy view of people wanting to buy stocks as prices fall. Normally, during widespread panic, what we’ll see is a total fear in holding any equity and a flee to cash. We’ve seen this and will continue to see this across equity and bond markets as traders unravel their positions (hence the 30%+ sell off). I wasn’t professionally trading in 2008, but my sense is that as the financial sector was experience the meltdown (and for some time after) there wasn’t a ton of deep buying that was happening as institutions unlevered. Instead many were wondering if this was the end of the world and our financial system as we know it.

The interesting thing about the coronavirus meltdown has been the overwhelming sentiment within my various circles of many waiting for a price to enter and hoarding cash to do so. Within the tech bubbles, this makes sense. We generally believe software continues to eat the world, high margin companies with duopolistic like moats should win out over time, and we’re broadly optimists about humans and our ability to outpace impending problems with solutions.

However, this sentiment has proliferated a bit even outside of the tech world due to the lack of data and/or general optimism. But the optimism comes from a general unwillingness or lack of desire to imagine a very inconvenient truth in the most literal sense. The inconvenience of being locked inside for a few months and radically changed behavior for possibly ever. To quote one of Josh Wolfe’s oft-repeated lines “failure comes from a failure to imagine failure.”

And that’s where we are today, IMO. We’ve seen a failure of the government to want to imagine the proliferation of COVID-19 in the US and what a shutdown economy looks like. A failure of the population to imagine being locked down for an extended period of time, regardless of how they’ve been short-term impacted due to layoffs or furloughs. A failure of retail investors to understand the trickle down effects from illiquidity in bond markets to the rest of the world (including me at times, tbh). And a failure of many millennials that were in HS/college during ‘08 to understand the wave of layoffs that are coming in the coming weeks.

What I’m Doing

Up until about an hour ago I was thinking that we’d see a sharp sell off today, and agreed upon deal today that would then push the markets up slightly tomorrow, and then further fear of new employment statistics to come on Thursday cause pause or negative movements on weds/thursday. With infinite QE I’m basically holding my shorts, with the belief that we still have at least one more downward run in the market left, but I can’t say I have a nuanced view right now on short-term timeline.

Other Notes

image

- We published our COVID-19 update to our LPs last week. It summarizes a lot of my views on the market and our fund and portfolio specifically. If you’re interested you can read it here.

image

- Ah Zerohedge and paranoia, like peanut butter and jelly.

On My Mind # 10

Inequality gaps of the future, Illiquidity discounts, Escapism in IP universes in 2020, Founders aren't all that matter.

On my mind - by Michael Dempsey

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

image

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

Image result for j curve venture capital"

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

Image result for marvel vs dc locations"

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.

If you have any thoughts, questions, or feedback feel free to DM me on twitter. All of my other writing can be found here.

On My Mind - #9

Narratives & Pseudosecrets, Serendipity in VC is BS, on the *mint* meme, Mobile ML research

On my mind - by Michael Dempsey

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.

Ask - We’re hiring at Compound. If you’d like to work with me or know someone who would be a good fit, please send them my way!

Thoughts

1) Narratives & Pseudosecrets

I wrote a post discussing the importance of narrative building as well as how companies build their narrative around both TAM expansion and sequencing to an ultimate future via what I call Pseudosecrets. I’ve spent a lot of time explaining this phenomenon or principle to founders over the past few years and finally decided to write out my thoughts in long-form.

2) Serendipity in Venture Capital is BS

I also wrote some thoughts on the history of networks within VC, the current state of venture capital, and ideal investing models at the seed stage.

3) Fortnite x Star Wars + VR UIs

Image result for fortnite star wars event

There will be a lot of hot takes surrounding the Fortnite experience, as always, however while many people were *mindblown* around the in-game experience, if you’ve spent time in VR, it’s likely it felt quite familiar. Watching experiences in a 3D, free-roaming world on a flat screen is sub-par, but has been a well-known UI/UX choice for VR developers and is in some ways considered a killer app today. Unlike Fortnite however, in VR you are immersed via head tracking, hand tracking, and a first person view. Ultimately for me, it felt a bit odd watching my character jump around in front of the flat digital screen, in this large world.

Where Fortnite did innovate on the UI was an ability to “focus” with the right click, which allowed the user to be forced into watching/following what Fortnite deemed most important. This is something VR should adapt more often, especially in storytelling centric experiences.

Related to that, the Fortnite x Star Wars experience was cool because of the IP, live nature. and mechanics surrounding user voting, but it also was the first meaningful in-game experience that didn’t progress the Fortnite story. While the metaverse story (read more about this in my Narratives post above) seems to be the goal, and a profitable one at that, I hope that after a reset of a season in terms of vaulting multiple game mechanics, Fortnite continues to innovate on gameplay, and not just on becoming one of the world’s largest native advertising platforms.

4) On the “f**king mint” meme

Image result for fucking mint

Disclaimer: I’m a millennial snipering into Gen Z trends here so take it with a grain of salt.

I’ve been pretty fascinated by the “f**king mint” meme (shout out to trying to avoid spam filters) that has grown on TikTok. Basically the point of the meme is to go around and say self-deprecating/embarrassing/not-so-great things about you, your life, and/or all of your friends and be somewhat OK with it.

I think this meme speaks to something a little more specific going on within gen Z which is self-comfort, open expression, and re-assurance (notice I didn’t say confidence) that millennials perhaps adopted about a decade later in age than this cohort of teenagers has. It’s a small, potentially overfit signal I’ve noticed as we’ve continued to look at other related thesis areas, but one that I think the meme perfectly embodies and could bleed through to other behaviors and purchasing decisions.

5) Mobile compute ML research is underserved. But does it matter?

There are countless research papers that are continually published popping up pushing the limits of what machine learning can do across a myriad of use-cases. The underlying issue with much of this research however is the required compute to make something possible, let alone reproducible.

One could argue that the job of most research labs is to figure out if something is possible, and compute will eventually catch up to make things production-ready at the commercial layer, however I’m not sure we’ve seen this happen as quickly as we’d like as an industry, largely due to the clout coming not from efficiency but power of algorithms.

One area I’d love to see increased publishing and experimentation on is ML at the edge, specifically within mobile phones. If we eventually believe that these mobile supercomputers will turn from bundled interface + compute to possibly compute-centric devices (think post-AR hardware) then we should also be pushing the boundaries on what type of algorithms we can efficiently run.

Examples of compelling mobile-centric ML research I’ve seen recently include this paper which tackles real-time monitoring of drivers via mobile phones, as well as one on mobile action recognition. I hope we see more in the future.

6) AI generated art novelty decay happened even faster than I thought

Katsuwaka of the Dawn Lagoon, (2019) created by Obvious Art. Courtesy of Sotheby's.

Two months ago I wrote about novelty decay AI generated art and more. Specifically I said:

The novelty of “this was made by AI” or “this is digital” will continue to exist, but at an increasingly decaying premium as time goes on. The novelty premium of our favorite artist’s lives may only compound as we see them grow, change, and we build deeper personal connections to them, their tribes, and their ups and downs.

It looks like that decay could have happened significantly faster than even I anticipated with the recent mediocre performance in AI generated art. I’d wager that this decay in value comes partially from the tiring of the group (Obvious) as well as the way in which this art is generated. Once we see a meaningfully different technical approach to art generation, perhaps we then will see another pop in prices. With that said, I don’t believe we’ll see lasting value unless a larger artist is able to manage both the story surrounding the process and their own personal journey, alongside their pieces.

Research / Links

  • A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes - This is a really interesting paper (and video) that walks through a team using VR to train a mobile, in-home robot on specific tasks. The success rate ends up being around 85% across the board, which is also not good enough (despite a 99.6% success rate due to ability to correct errors) however the other problem is the time to complete which is 20x slower on average (and some tasks were 100x slower than a human!). I imagine the eventual future of an in-home robot is going to draw on some form of imitation learning that maps humans more closely to the robot (or vice versa) so that learned tasks can be scaled by more human teaching. Not sure when that future will come though.

  • When equity factors drop their shorts (article here): On the lack of value short trading positions create.

  • Generating High-Resolution Fashion Model Images Wearing Custom Outfits - Generating fashion images from scratch in high resolution. I've spoken to a few different people about this task of generating stock fashion images with GANs. Over the past 6 months we've seen incredible pace as full-sized human body synthesis has improved drastically and thus we can now combine models of pose understanding, and GANs in order to tie together new types of synthesis. What has been funny is people doubting the ability that pace is going to happen in this field specifically. While in some industries I'm a hardcore skeptic on tech people automating or innovating from the outside, when it comes to fashion and stock imagery (and other non-scalable, image related practices within e-commerce) after spending a little time with fashion industry founders, I'm beginning to believe stronger that this innovation may be one that comes from outside the industry vs. an operator from within.

  • Neural Voice Puppetry: Audio-driven Facial Reenactment - Take audio, push it to deepfake. Pretty cool.

  • Generating Animations from Screenplays - Multiple researchers continue to try to crack this utopian vision of inputting text and outputting realistic 3D scenes. Very few have been able to do it at high -degrees of freedom, high quality of art, and by enabling emotion. The argument to be made is that we can speed up the initial work and go from creation -> tuning, or have a more granular storyboarding pipeline if it’s automatic, but many artists just get annoyed by poor implementations of animation that they are then forced to re-do vs. create.

If you have any thoughts, questions, or feedback feel free to DM me on twitter. All of my other writing can be found here.

On My Mind - # 8

Seeking inspiration, CTRL-Labs & Fund Dynamics, Digital goods of tomorrow

On my mind - by Michael Dempsey

This email spawns from this thread.

These thoughts are 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.

5 Thoughts

1) I’m seeking inspiration. Let’s find it together.

vxtacy:
“[ d e s i g n ]
”

Here are a few areas I’ve spent a lot of time in this year:

  • Adversarial attacks on machine learning models

  • AI friends

  • Animation

  • Avatar-first products

  • Creative ML

  • Gender fluidity and its impact on consumer

  • Human pose estimation research

  • Fashion + ML

  • Full-stack robotics companies

  • Future of family planning

  • Modified humans, animals, and plants.

  • Psychedelics and their impact on healthcare

  • Using ML to decode non-verbal communication across animals and humans

Here are other areas I want to continue to talk to people about that I’ve spent some time in this year:

  • Science-based CPG products

  • The past, present, and future of game engines and large scale simulation engines

  • Social VR

  • Projection mapping + holograms

  • Enabling more angel investors

  • Space (non-earth observation related)

If you’d like to chat about any of these or other post-science project ideas, feel free to respond to this email or directly email mike@compound.vc

2) On CTRL-Labs & Fund Dynamics

The tweet above triggered some conversations over the past 24 hours. Friends at Lux, Spark, Founders Fund, GV, and others were all right in a big way, and right on a difficult space to build conviction on. To his credit, Josh Wolfe was a giddy public cheerleader for this company since before he invested (I remember how much he was excited the first time he told me about them). It is easy to be a cheerleader as a VC when the company is at series C+ and has nailed commercialization, it's a lot harder when it’s a Series A neural compute interface startup, so props to Josh.

The point I was making with my tweet was that creeping fund size and round dynamics in 2019 make it really difficult to nail venture returns and change investing dynamics...and this company is a great example of that.

Large “full-stack” firms that view their entry point as majority series A, now must deploy so much capital into a company over its lifespan, gathering more ownership, and thus trading deal multiple for cash-on-cash returns, as they target their 2.5-3x net fund. The dynamics of "will this return the fund" may no longer necessarily apply to these firms. Instead, with larger funds (and opportunity funds), the question weighs capital deployment over initial ownership in a much stronger way than vintages past.

The question becomes: "Can we get enough capital into this company so that if it has the outlier company that we hope to have 1-2x/fund, we'll return the fund (or maybe 75% of it)?"

But well-regarded founders can command substantial capital ahead of traction, and those founders working on massive upside, possible platform companies can further bend round dynamics in a capital abundant world. Specifically, for opportunities that are as "moonshot", capital intensive, and high upside as something like CTRL-Labs is/was, it then makes sense to bend but not break, get into the early rounds with the hopes that you can deploy not only your venture fund into the company, but also your opportunity fund capital. This is something that could have led to five $400M+ fund size firms splitting (to varying degrees) 3 announced rounds ($11M, and two $28M rounds).

Regardless of these dynamics, this is a great investment and as noted, the IRR has got to be incredible.

Two interesting notes in terms of the investors:

  1. You can make the argument that Spark has now had this happen to them 3 times, as early backers of Oculus (alongside Matrix, who also invested in CTRL-Labs) and Cruise as well. So impressive.

  2. Lux on a different vector (massive moonshot, capital intensive) saw this play out successfully (though over more time) with Auris Surgical (sold for $3.4B-$5B+, raised $733M).

Okay, enough armchair VCing, congrats to my friends who invested, and maybe to NYC which hopefully will get a few more deep tech angel investors in the mix.

3) Will digital goods be what kids today, buy as adults tomorrow?

Image result for fortnite skins collection

I recently read This Is Not a T-Shirt by Bobby Hundreds. In the book he makes a point about Japan's role in sneaker collecting culture. Retro AF1s, Dunks, etc. were really sought after in the 2000s because the kids who wanted these shoes in the 1980s were now young professionals with money and were coming to market to "fulfill their fantasies". What is the version of those goods today? What will today’s children lust after 10-20 years from now once they have some discretionary income? Is it goods that already fetch high prices on thee aftermarket that are modeled from the beginning as scarce (a la Yeezys or Supreme), is it digital goods that they grew up with but couldn’t convince their parents to purchase (Roblox goods/Fortnite skins?) And if it IS digital goods, then how will aftermarkets really exist outside of NFTs (maybe NFTs are the answer) when the platforms these digital goods live on become irrelevant or stop functioning?

4) Expectations vs. reality, VC & parenting edition

In the early days of your venture career you often think about all the amazing things you’re going to do to find people, help companies, invest better, etc. Some of them truly do give alpha, while many of them fail for various reasons. As your portfolio grows you start to see where things break in this business and have to be very mindful about the experiments you run to achieve better returns, founder NPS, scale, and more.

Some of these lessons are ones that others who have come before you can teach you, but you really need to learn for yourself.

I wonder if the same thing happens with parenting. You think you’re going to do all of these amazing things for/with your children, but eventually you realize at each age the diminishing (or negative) returns that these idealistic thoughts have, and/or life continues to get in the way of some of these ambitions, and you end up not being able to actually do all of the things that in your childless state you thought you would be able to do one day.

5) Novelty Decay in AI-generated/synthetic content is taking hold

image

I wrote a quick note on how the novelty of both digital celebrities and AI-generated music is decaying and capping the upside of new entrants. I only expect this to accelerate.

Research / Links

  • Game character generation from a single photo - This paper has really interesting implications surrounding 2D photo → 3D asset generation. In general this is an area of research we’ve seen a bunch of people take aim at over the years (most famously, and early, Hao Li’s lab, and more recently everyone from universities to Facebook). There are novel bits and pieces technically, but the main thing is that this is a future that many people have thought about or wanted for some time (Loic at Morphin has often talked about how his origin story of his company started by wanting to put himself in FIFA and other games). Just always cool to see clear science fiction start to push forward towards reality on fairly “mainstream” ideas.

  • How much real data do we actually need: Analyzing object detection performance using synthetic and real data - This paper takes a look at the value that synthetic data can bring to training models. Specifically it realizes something that I don't feel is intuitive to most which is that the open-sourced synthetic data training sets don't generalize very well, nor do they have great diversity of data (these problems go hand in hand). What I've seen spending time in this space is that diversity of data is incredibly misunderstood and underemphasized. Even when looking at something as seemingly "gold standard" years ago as GTA V, researchers and engineers I spoke to at the time realized that the diversity of textures and more were incredibly underwhelming and wouldn't transfer learnings well to a real world environment. What we've started to see now is data expansion via style transfer or just synthesis, essentially utilizing GANs to increase diversity. My feeling on this paper is that it is quite negative for how many perceive synthetic data, especially in areas where highly generalizable perception models are needed (think self-driving), however it also tells me that companies focusing on specific types of perception/understanding are likely far ahead of where current open-source datasets are. I previously wrote about AV simulation and am an investor in AI Reverie.

  • Making the Invisible Visible: Action Recognition Through Walls and Occlusions - Using a combination of visual and non-visual signals to recognize actions through walls and more. Pretty incredible.

  • Adblockradio - Using ML to remove ads from radio feeds. Pretty funny.

If you have any thoughts, questions, or feedback feel free to DM me on twitter. All of my other writing can be found here.

On My Mind - #7

Here's my investment memo template

On my mind - by Michael Dempsey

This email spawns from this thread. The process for this will evolve but as you'll see, some thoughts are random, and most are unfiltered or poorly edited. Either way, let me know what you like, don't like, or want to talk about more.

1 Thought

Spoiler alert: There was interest so see below for my memo template and high-level thoughts.

1) Thoughts on Investment Memos

I’ve talked a lot about investment memos in the past and how I believe we should be more open/transparent about the overall structure. At times, I’ve even shared the exact internal memo with other friendly investors who were doing diligence for a round I was leading. 

The summary of my thoughts are:

How I Use Memos

I’d say ~70% of the time when I write a memo, I end up offering to make an investment in the company, so sometimes a memo goes nowhere but into my archives.

I start my memo usually after meeting #2 with a company and I’ve found it’s very helpful in figuring out what do I have left to understand about the business, what areas should I push on, and separates the company from the investment (i.e. a good company doesn’t always mean a good investment, often due to price).

At times where I have expertise overlap with my partners on an investment, I also will use the memo to bring them up to speed.

My memos traditionally run long for seed (8–10 pages on average) and have a separate attachment of all of the notes I’ve taken as I’ve been meeting with the company. If we’ve had phone calls/video calls, they are multiple pages and have multiple quotes that the founder said. If our meetings have been in person, I usually distill my notes after the fact, as I don’t love taking handwritten notes, and I don’t want to have electronics out in person.

For deals that move quickly, I almost always still try to write a deeper memo within weeks after we invest, to have that record of thinking.

The Actual Memo - CLICK HERE

Here is my investment memo template in google doc form (note: we don’t use a standardized memo at Compound, each partner has their own version/structure). 

In a future post, if there is interest, I’ll walk through various parts of the memo. Please email or tweet at me all specific questions and I’ll do my best to answer them all in this post.

For now, here are the opening parts broken down:

Summary:

This is straightforward. For information about the “required exit for RTF” section read my post on this equation.

Risk/Mitigations:

I try my best to understand what are they key risks within a given business on both from going Seed to A, but also from becoming a fund-returning investment. The biggest learning as I’ve done more investing is that for many risks, there are no mitigations.

Sometimes founders are first-time founders who have never managed a budget before, others have been at big companies and you worry about their ability to move quickly and efficiently, sometimes they have to build really expensive and high-quality engineering teams. Often for these more amorphous risks, the *honest* mitigation is just a leap of faith, or maybe a small proof point in their past or that came through a reference we did on the founders. In most of these cases I will just write “N/A”.

Other common risks are (but not limited to):

  • Concern on the founding team’s ability to fundraise and the market dynamics within the industry — As an investor often investing in highly-technical businesses that won’t have major proof points at Series A, it’s unfortunately important for us to think about follow-on capital dynamics both in the sense of Have any other VCs made investments in competing companies (essentially boxing them out for follow-on)?, Is there a deep pool of possible strategic investors (these can be helpful for early commercialization or less-valuation sensitive follow-on)? Or even; is there a specific love for this type of technology from international, non-VC investors?

  • Raising enough capital to hit meaningful inflection point (this can be both technical and execution risk roped into one)— I think about investing as giving a company enough money and help to reach an inflection point of value so that they can raise more money (eventually leading to a successful exit). It’s kind of sad when put that way, but that’s the most capitalistic view on what we do as seed investors (along with partner with great founders, help people achieve dreams, whatever else we want to pander about on twitter to make ourselves feel/look better). For some businesses, related to the first bullet, it’s actually unclear *what* the right inflection point will be. My job as a lead seed investor is to help our founders know these metrics at a market level, and at an individual investor level. Unlike in more well known areas like enterprise SaaS where people generally have an idea of growth rate and ARR required to raise a strong A (until a hot company blows those expectations out of the water), the areas I invest in are less clear. For robotics, some Series A investors will want to see lots of pilots and customer data paired with LOIs, for some they just want to see a scalable MVP and an elite team, and others think they want to do series A robotics investing but really don’t and won’t get comfortable unless another firm outbids them.

  • Market Size — I admittedly don’t think too deeply about this nor (despite my hedge fund background) do I care to do a finance-style analysis on TAM, especially not in the businesses that I often am investing in. I have a general view of “can you build a $100M+/year business over the next 4–7 years, or not” and that’s about it. Projecting TAMs for companies that are solving previously unsolved problems is guesswork at best.

  • First-time founders — Again, I worry about this from specific skillset angles like ability to manage capital or inspire people to take less salary to join their company, but there isn’t much mitigation. For a few companies a mitigation has been on the acqui-hire value of the people based on their unique skillsets, but this often decays within industries over time.

More case-specific examples of mitigations include:

  • Simulation is built in-house — In AV-first simulation this was a key risk where I was concerned that behaviors from players like Waymo and Zoox. which had built fully-featured simulation software stacks, could continue to proliferate to any of the long-lasting AV companies over the next few years, as these businesses built up the early revenue before expanding into other verticals. 
    Mitigation:Tech-heavy OEMs like Tesla, GM, Ford may be able to attract software talent but with projected rise of auto OEMs + Tier 1s, many won’t be able to hire software expertise to build. The key factor here is the delta in time between proliferation of AV teams (and thus the business that can be built) vs. when someone solves AV and we see industry contraction.”

  • Slowed market expansion means fast traction will come from large customers early on which may be hesitant to implement at scale. — This was for a company working on a massive, but highly technical industry and selling infrastructure to those building companies within that industry. My concern was that in this industry I had repeatedly seen companies be able to go from proof-of-concept to pilot but very few had expanded into scaled recurring revenue, leading to a bunch of bridge rounds to nowhere as everyone waits for the market to mature.
    Mitigation (anonymized): (Strategic Investor) is investing $xM into this round and plans to pilot and then scale new tech product across multiple business lines. The company has shown an ability already on old product to break through procurement at large Fortune 1000 customers.

Deal Structure/Offer Context

The structure is pretty simple: How much are we investing, what is the valuation, what is the total price, and if there are any additional terms (is another VC fund or studio getting warrants for some reason? Is the liquidation preference non-market? Will the founders hold legal responsibility post an acquisition?)

The context is what matters far more: Who else is competing for this investment? What is it that we feel we must write check size-wise to win? Where in the process are the founders and what do they value most? Will we need to potentially bridge this company so should we hold reserves between seed and A?

There are many variables here that lead to where in our investment range we invest (both on the founder side and our side).

If you have any thoughts, questions, or feedback feel free to DM me on twitter. All of my other writing can be found here.

Loading more posts…