If you’ve spent any time following me on twitter over the past few years you’d know I’m obsessed with using machine learning for creative purposes. It began with a love of GANs and progressed into an obsession of using machine learning in animation, among other things.
This love affair led me to following a wide slew of interesting people building at the intersection of machine learning and creativity, a thesis area we call Computational Creativity at Compound.
Creativity as a concept has taken a lot of meanings over the years within the tech world, however what was clear to us is that in the same way analog to digital workflows enabled new types of content and creators, as well as a newfound scale of that content, machine learning will likely do the same. We’ve seen this manifest itself today at a sizeable scale within creative areas including visual arts, music, fashion, VFX, and much more.
In 2017, I started to notice a recurring few names on my feed that were putting out cooler and cooler projects and experiments into the wild under the name Latent Studio. I was in awe and jealous.
These projects didn’t just show what was capable from a technical lens, but also translated the power of machine learning for non-technical people, enabling a wide number of creators to have their mind blown by doing something as “simple” generating images with a few sentences or synthesizing a never-ending road. After a few cold DMs and random notes, I quickly learned that these people were all working on a project together at NYU, called RunwayML.
I knew I had to meet them, if not only for my day job as an investor, but also because I had been looking for a group of people in NYC that were into this idea of disrupting creativity with machine learning and hadn’t yet found them at scale.
I met Cris, Anastasis, and Alejandro on a Friday and received what to this date is the most impressive product demo of a pre-funding company I have ever seen. It was clear that they had taken their early research of “how do we spin up fun experiments using machine learning” and translated it to “how do we build a powerhouse product that democratizes machine learning across a variety of creative (and even more purely enterprise) use-cases.”
In the years leading up this, I spent a lot of time reading every paper I could get my hands on, talking to the authors of those papers, and even looking at the experiments to build “non-technical” ML products at larger companies. Within all of that work I had never come across a tool that truly felt like it was able to provide the power, scalability, and customizability for a broader audience, that only a small number of machine learning engineers are able to get, after hours of environment setup and years of training.
I quickly texted the other ML nerd I knew in NYC, and within a week we had completed a seed round with brilliant investors Zavain Dar at Lux and Sarah Catanzaro at Amplify (who if you know her, of course had already been tracking the Runway team) joining us.
Since our seed investment, the Runway team has taken this early gem of a product and at an incredible pace pushed features that have enabled everything from researchers to post their state of the art models for others to play with to allowing non-machine learning engineers to train their own models all the way to an incredibly powerful, purpose built ML-enabled green screen tool.
They’ve done all of this while building a diverse community of creative technologists, data scientists, engineers, designers, and more, while reinventing how we think about enabling the next wave of creators.
Over the next few years we are going to see how machine learning can truly disrupt the traditional ways in which we think about software enabling creators and Runway will be at the center of this movement, pushing the limits on what is possible in both product and technology.
I couldn’t be more excited to have Amplify lead Runway’s Series A and to get to continue to work with the Runway team and an investor syndicate I deeply admire.
This is an internal note I sent to a few investors/friends a few months ago.
Over the past 6+ months there seems to have been a noticeable drought in the creation of new deep tech or just broadly technically ambitious companies (ex-biotech). This is a sentiment that has been echo’d heavily from other investors, and has only accelerated with COVID. This became especially noticeable as startup fundraising has massively increased in adjacent areas in H2 2020.
While there isn’t a data-driven reason as to why this has occurred (or if it has, indeed occurred) my working hypothesis is three-fold.
1) The maturation of platforms and no clear outbreak of new platforms
After a fervor of excitement surrounding areas like VR, ML, and Robotics from 2014-2018, we’ve largely seen unabashed excitement die down and be traded with hopeful skepticism. VR was too early and is looking increasingly like a platform that will be subsidized and dominated in early innings by incumbents due to heavy R&D costs.
Machine Learning has shown a commoditization curve that erodes value for much core ML development, while also has been incredibly difficult to implement at scale to build vertical specific companies that accrue value (read our 2019 annual letter for more thoughts on this). ML companies have largely turned into product-centric organizations versus true research engineering orgs, which many investors aren’t quite sure how to parse mid to long-term defensibility or economics (see Martin Casado’s wonderful post).
The closest next platforms that people seem to be centering on are AR and Quantum. AR has its own difficulties, again, largely a value accrual in the short-term towards incumbents (Snap, Apple, MSFT) and at the application layer very little differentiation in technology (my view is that most enterprise AR companies are building product orgs with little technical moats and early product UI/UX that will be considered cringey in 2-4 years).
Quantum has a usability problem in addition to a timeline narrative that “this will take awhile and is incredibly capital intensive” so the only vertical use-cases we’ve seen really have been pharma and finance. In addition, some argue that ML algorithm development has kept pace in lockstep with quantum in reality.
So TLDR, investors need a narrative to drive them towards a Schelling point surrounding “what’s next” but can’t find consensus.
Again, Computational bio and further on the spectrum, biotech seems to be only area, but has debates around moats and is incredibly hard to parse and pattern match for technology investors.
Thus we default to what we know has killer cash flow dynamics in a time where TAM has materially expanded (SaaS/dev tools/infra), or follow narratives that we want to be (and may be!) true, like social being ready for VC investment again.
2) COVID & Founder Profile: “It’s time to build" wasn’t meant for deep tech founders.
Founding a company becomes sexy as generational startups scale and with a large amount of money in stock options, employees feel the desire to work on a new problem where they can seek a new form of status, both social and financial (early ESOP or founder title). It’s no longer cool to be at the $10B+ tech company and there are hundreds of people at these companies starting funds, syndicates, and companies, so you should too.
Some subset of employees on the technical side look at internal tools that were built and democratize them (think data science teams, but there are tons of examples).
Another subset are PMs and software engineers who read Marc’s essay, saw a year of remote work ahead of them and thought “great I can move out of SF, live cheaply and stomach going from $250k/year to $100k/year, and be a Founder.
These people like building software that can quickly be iterated upon and is quite realistic to raise $1-$3M to run at for 24 months. In addition, these people likely feel time pressure because multiple other teams are starting startups similar to their idea. It’s time to build because everyone is building.
Deep/frontier/emerging/whatever we call them tech founders sometimes follow a bit of a different founding story. Many have dedicated 4-10 years of their life becoming an expert in a given industry, or banging their head against the wall in academia, in pursuit of applying a technology to the real world, or bringing some other initially non-sexy technology to production level. The time pressure for these companies and founders could be viewed as less existential, as many companies aren’t speedrunning capital markets for a 12 month seed to series A sprint comprised of MVP -> early pilots -> highly extrapolated MRR/ARR.
Instead, many deep tech companies are operating with a 2-4 year R&D window, hoping to show a step function in technology reality and value creation by month 18 to raise a Series A, where they then start to either begin converting pilots, or testing hypotheses on commercialization over the next 2-4 years.
Candidly, there ain’t that many people signing up for that trajectory.
So when you’re a deep tech profile founder (not to be overly prescriptive but often something like PhD/academic, eng/product lead at incumbent R&D group, Engineering at prior deep tech co, Engineering at scaling tech company with material infrastructure innovation, etc.) your calculus is a bit different.
If you’re thinking about hardware, you might say, you don’t want to deal with the dynamics of having possible supply chain headaches (not as much a concern today vs. 6 months ago), you don’t want to deal with dynamics of really not being able to do remote work, and you know VCs are probably a bit risk averse on funding hardware at this stage as a whole. In addition, while customers might have a strong desire if you’re automation focused, you won’t be able to ship product for another 12 months probably so, why not just wait to see how the election/transfer of power/vaccine timeline shakes out to truly understand the economy in 2021, before starting something that could be de-railed from random economic/geopolitical policy.
Calculating the risk factor and why now of your business. The reality is, you must convince a smaller universe of investors every 12-36 months in the future you believe in and hope to pull forward, so why *now* matters more when these futures are less consensus. So just doing simple calculus, all of these things once again could lead to waiting. Bank your $300k+ salary at bigco through 2020 and see where the world is in 2021. You’re operating on a true long-term time horizon so there isn’t as much deep desire to uproot your life/family just to build asap.
TLDR - Deep tech founders maybe viewed as slightly less beholden to the time pressures of the market as many are solving previously unsolvable problems. I will say, many do underestimate the commoditization curve they are running up against though.
3) Burn Out
This point is not talked about enough publicly but is a consistent sentiment I’ve noticed over the past few years.
The first wave of "deep tech as a category” really hasn’t created many successful businesses yet (we can debate the recent run of SPACs in deep tech on it creating successful outcomes). As you talk to more veterans in this space, many are pretty disenchanted by their past 7+ years of work.
Their friends at Uber, AirBnB, and Stripe are multi-millionaires (some on paper), same with their friends at FAMGA, and they possibly are too but don’t really have anything material to point to in the real world. They’ve spent a good chunk of their professional lives to hopefully get swallowed up in an offensive acquisition (Boston Dynamics, Skybox Intelligence, Zoox, Cruise) or early commercialization that is strategic (Kiva, Six River, Blue River), and a bunch more are sitting at these companies that weren’t able to quite deliver on the future they believed in.
I think many are kind of waiting for a “moment” that feels like everything is once again possible. I don’t think those moments are obvious in the present, but alas, it’s a real sentiment.
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I know I’ve painted a fairly jaded point of view on both sides of the market but ultimately I think there are massive tailwinds for all of tech over the next few years and believe COVID has pulled forward some futures we were expecting to take 3+ years.
I’m confident that we will see a high volume of deep tech startup creation over the coming 18 months, but as an investor with a large focus on these types of businesses, it has been interesting to hear how many have felt similarly in 2020 and thus I felt the need to litigate *why* this was happening.
There’s a fascinating article from 2009 written by Martin Campbell-Kelly that walks through the extinction of timesharing computers due to the economic efficiencies created by the personal computer.
In the mid-1960s timesharing computers came on the scene. In these systems customers could access a mainframe computer remotely. Connected to a mainframe computer via a regular telephone line, users ran programs using a clunky, 10-characters-per-second, model ASR-33 teletype.
As timesharing services cost around $10-$20 per hour (netting out to ~$300/month), the PC destroyed the economics of the industry.
Just as Cloud Computing has similarly destroyed many economic benefits of software built on-prem with a cluster of servers and more.
If we look at the rise of cloud computing, and its increasingly widening moat, we must also look at the rise of data-intensive applications such as machine learning and more. If data is indeed the new oil, then what perhaps becomes the solar panel that renders this oil obsolete for energy and creation (not a perfect analogy)?
The rise of cloud computing benefitted heavily from drastically higher speed internet, a rise of datasets and machine learning alongside it during the second wave, and now even the utilization of purpose built chips at scale to throw compute at various problems. Perhaps however, we reach a tipping point where we look back and wonder “why the hell did we send all of that stuff up there?”
In some ways this is the promise of edge computing and smaller trends we’ve seen where we have purpose built chips embedded in our devices to solve certain problems. When I think of macro catalysts for how to think of the fall of cloud compute, I think about privacy (as we’ve seen with FaceID), efficiency, connectivity, and power.
On the privacy front, I think we’ll see advances in things like federated learning or other privacy centric mechanisms that expose models but not data before we look for solutions for pure edge compute.
For connectivity - We have an impending next wave catalyst coming for 5G that only strengthens the moat that cloud computing is built on as we remove some of the restrictions of piping data back and forth on and off device.
But for power there is an interesting exercise - If today we are already on an upgrade plan for our device that comes every year from lord Apple, will we one day reach a point where we have One Chip to Rule Them All?
Put more specifically, instead of continuing to ping the cloud to run compute, could we possibly have a piece of hardware so powerful that instead we pull our updates and have various applications that sit on top of that. I realize I’ve just described computers and video game consoles, but there’s something interesting about a personal compute server, in a world where our day to day needs possible get maxed out.
The iPad is an early indicator of this, with strong compute, another purpose built chip (i’m classifying apple A series processors as this), but seemingly simplistic devices and use-cases.
In addition, as we have more devices around us at all times (google home, alexas, apple watch, airpods, macbook, ipad, etc.) will these aggregate chips be able to cluster and provide our local compute power that disintermediates cloud compute in some ways?
I’m continually intrigued by this idea as many of the larger platforms have begun to build core revenue drivers and moats surrounding cloud computing and have used these platforms as tools to compete away many other parts of the developer stack while sacrificing monetization on ancillary products in exchange for compute monetization.
So do we eventually get fog computing or One Device To Rule Them All as I state above?
In the near-term, the argument is that we are now starting to understand how we can push the limits of compute in order to explore new computing platforms (like AR glasses). On the other side of the spectrum, we have companies like OpenAI running at artificial general intelligence, which have taken a clear stance that throwing compute at the problem is needed for some long period of time, despite scaling efficiency of compute in ML.
Despite this short-term bearishness, as I look at the usage stack of my parents and even most of my friends, it’s not clear to me why cloud compute necessarily will be continually stretched in core computing areas until we have an entirely new compute platform emerge.
Nothing tidy to tie up this note except that I believe understanding these dynamics is going to be very important over the coming years in both thinking through emerging platforms, moats and monetization of a wave of cloud companies, as well as possibility for startups to change how we think about compute power.
Please tweet at me if you’re thinking about any of these things.
Years ago a friend of mine had a dream about a strange invention; a staircase you could descend deep underground, in which you heard recordings of all the things anyone had ever said about you, both good and bad. The catch was, you had to pass through all the worst things people had said before you could get to the highest compliments at the very bottom. There is no way I would ever make it more than two and a half steps down such a staircase, but I understand its terrible logic: if we want the rewards of being loved, we have to submit to the mortifying ordeal of being known.
We have to submit to the mortifying ordeal of being known.
This line, which came from a 2012 New York Times essay, then became a meme in 2018, always has resonated with me. The core idea of the essay could be summarized in an equally as powerful statement brought forth by another Tumblr user:
Being known is being loved.
Both statements frame a visceral feeling that is hard to properly explain but is almost immediately understood in the comfort and complexities of knowing someone, and the emotions that come from that journey.
To know someone is to understand their inner workings. It is to know the foods they hate, the ways they deal with stress, the goals they have, the secrets they keep, the time they spend, and hundreds of other smaller things that define someone, and your journey with them as you get to each other’s cores.
If you’ve ever loved someone, Natasha’s writing will make you feel this deeply:
“i know your pizza order” “you have freckles on your ears” “you make this face when you’re tired” “you order green tea on a good day black on a bad day” “you always make that face before you try something” “the tips of your ears turn red when you’re angry” “i knew you’d say something” “you must be exhausted to miss the class” “your favorite pie is pumpkin, right?” “i know your phone number, don’t worry” “you miss me, i can tell” “you fiddle with your pens when you’re bored” “you don’t like converse unless they’re high tops” “your favorite cereal is cinnamon toast crunch and you first ate it when you were 8”
The fog of being known, & volatility
I sometimes think about knowing someone as a Fog of War map. For many parts of someone, there are areas that you uncover and don’t expect to change, ranging from seemingly inconsequential preferences to deeper personal values to lifelong pursuits. And then there are parts of people that do change, and these more volatile areas you have to revisit, check in with, and explore to continually know…and to continually love.
As we uncover more of this map, “mortifying” really is a perfect word to describe how we feel about being known in the 21st century. The satisfaction, emotional exposure, and time investment that comes from being known is non-trivial and high risk. It requires you and another person to embark on a journey together that theoretically is high ROI, but more likely, just high volatility.
Over the past 10 years, a lot of the dynamics have changed surrounding what it means to be known, for better or for worse. Especially this year these dynamics of what we qualify as “knowing” someone has been top of mind for me across both personal and professional contexts.
Our world has turned into one that inflates to a minimum viable aesthetic. We want to show a version of ourselves online that is most attractive, most agreeable, most interesting, and most admirable. Perfect pictures, curated stories, high level tweets designed to garner likes and RTs, and a catering to the masses of our minimum viable audience. This is the seemingly agreed upon dominant strategy whether seeking influence, capital, or something else.
We string together fragments of various selves, but rarely do we see the entire self, because what’s the incentive? There’s just too much risk in being known. By being somewhat known we are effectively minimizing some of the beautiful human volatility I mention a few paragraphs above.
Maximum vulnerability = maximum volatility = maximum upside.
In investing, higher volatility usually equates to higher possible returns. In today’s world of online expression, we settle for lower expected value, market-level outcomes so as to not ruffle any feathers and not take any outsized risk. We’re basically hoping to allow people to know us enough so that they include us in their passive index of humans they hold in moderate regard, like that vanguard ETF that their finance friend told them to buy and never think about until they were 60.
As I write that sentence, I think that perhaps we go back to the parable of humans being viewed as commodities or indexes. We can debate this at a societal level but on the professional side, I think this is entirely true within my bubble of venture capital and startups.
When we think about the products that venture capital firms offer the talking points are either the people (which partner do you work with) or the capital. The capital is a commodity today. This pushes seed round dynamics into sprints measured in days in order to get to a decision of who to partner with for the next 7-10 years of your company. One could make the argument that founders should take their time and be more intentional, but let’s be real, that isn’t net dominant for a founder or their highly optimized process.
As an industry we like to equate picking a co-founder to marriage and draw similar comparisons when picking a lead investor/board member. Despite this, we have yet to figure out the solution for understanding these relationships in a newly compressed timeline outside of social capital (how does an investor reference), shotgun weddings (was great to meet you yesterday, give me the highest price and get out of my way), and brand network effects (firm > people). But this information is sparse and humans are….say it with me…VOLATILE. So you never really know.
This insight is what led various VCs to become content marketing machines in order to increase exposure and surface area. I heavily adapted this playbook early in my career (as many have) and it certainly helps people get to know you…sorta. I should say, it gets people to know a part of you.
And I feel like we’ve conflated the idea of knowing someone with having an idea of someone. You can’t know someone after 9 days (if Kopelman is correct) but you can get maximum context by understanding the corpus of their being on the internet…or at least that’s the best attempt I can muster up in my reality.
My reality is that I can’t buy my own bullshit/sell my soul enough to tweet out tech proverbs or repurpose old parables for likes. My reality is that I don’t have the skill to transactionally aggressively network and I can’t sustain the energy from social interactions to exponentially scale deep connections to the tune of 25+ meetings/week I care about. So instead, my only option to be loved is to submit to the mortifying ordeal of being known. To wear my heart on my digital sleeve, a proverbial sleeve that’s threads are made up of a sum of all of my writing, social media accounts, in-person interactions, and more, not just a curated feed of minimum viable story filled with dopamine-inducing 280 character lines.
And in writing this my only goal is to express my disinterest in what passes the bar today for “being known” in our communities, and to ask others to submit to the mortifying ordeal of being known.
So here’s my sleeve where you can start to get to know me. I look forward to descending the staircase together.
If the next wave of social is built around gaming, then the lessons we pull from these formerly more distinct categories will likely lead to a game dynamic which pushes forward the tensions of social networks, with the expressiveness of digital worlds. This dynamic is persistence.
Over the past 6 months we’ve seen a ton of completed/rumored SPACs, as well as heard conversations at the board level for companies that are currently in discussions to get liquidity via a SPAC. It’s reached a point where If there was a prediction market on VC-backed companies that will get taken out by a SPAC in the next 12 months, there would be a few unanimous favorites if you polled the broader VC ecosystem.
This phenomenon is far reaching, but it specifically is interesting to me on the deep tech end of the market.
Over the past 6 months the financial markets have become incredibly narrative driven as investors face an unparalleled fiscal policy by the government to keep markets moving, while struggling to parse the next wave of valuation metrics, new business models, and technologies that emerge with a strong narrative surrounding massive growth potential.
I’ve long been a believer that narratives rule everything in private markets, but I’d argue only a few companies historically have been able to flip this at the public market levels as well for an enduring period of time (most notably, Amazon, with a clear prioritization of expansion at all costs effectively giving the company carte blanche to do whatever it wants without material scrutiny from public market investors).
Deep or Frontier Tech is perhaps an even more narrative driven subset of the broader tech sector than most others. Over the years, private market investors have gotten excited about various verticals due to the perceived technical moats, the massive greenfield TAMs that this innovation can access, and the clear narrative of “this is the future we were promised.”
Josh Wolfe describes this phenomenon tangentially as “science fiction to science fact” which only further hat tips narrative importance and ease of proliferation.
Across each category of deep tech, we go through a hype cycle that leaves scars at the firm and market level, almost killing entire categories for VCs (try raising a VR series B+ over the past few years, for example).
Often these deep tech companies require patience as commercialization metrics lag traditional companies, with upside that is arguably not as asymmetric across all categories. This creates a valley of despair for many companies that were able to raise seed and A capital, but where series B firms struggle to gain $25M+ check size conviction without material social proof and company comparable metrics. If a company is able to break through that barrier (often with great narrative and early signs of commercialization) then there is a secondary exhaust when it comes time to raise growth capital, as growth stage investors are even less primed to dream big without consistent revenue figures or scalable, decent-margin revenue, with a diversified customer base.
As Deep Tech has become a category, the universe of investors and well-funded companies has grown materially over the past decade. This has led to a large number of companies that are growth-ish stage, looking for liquidity.
Enter SPACs.
A few years back we saw companies like NIO go public with almost anemic traction but a narrative anchored to one of the most polarizing companies on Wall St, Tesla. The stock popped upon IPO, investors showed excitement, then reality came crashing down, cratering the stock from ~$10/share to just below $2/share in a matter of months.
Over the past few months, many have documented the increasing narrative driven nature of stocks, profiling things like r/wallstreetbets, Davey Day Trader, and Robinhood traders. We can debate how much retail investors are truly moving markets, but what’s clear is that narratives are impacting public markets, and deep tech companies that match to pop-culture, science fiction driven narratives are a likely beneficiary, as they are far easier to grasp at a high-level than the hottest cloud infrastructure IPO.
Investors have wanted exposure to futuristic technologies via public markets for some time now and often are left playing tangential stocks (Nvidia as a proxy for ML or crypto, for example), or hoping that an R&D group accrues enough value to a larger company to make it material (Cruise/GM and Waymo/Alphabet at one point).
We got an early look at this phenomenon with the Virgin Galactic reverse merger (which 3x’d in a few months early on), then alongside the explosion of Tesla we saw Nikola Motors (2x in 5 days) and Hyllion both hit the markets. Now in just the past week we’ve seen Luminar (a LiDAR company that could be seen as exposure to self-driving technology) and Desktop Metal (the only real additive manufacturing play on the market) both getting taken out by SPACs after having raised $100M+ in private markets and with less than scaled revenue metrics.
There are similarly rumors that we could see acquisitions of Lucid, Fisker (EV) and Proterra (EV bus) as well in the near future drafting off of Tesla success. I wouldn’t be surprised either if well-funded and long-private companies in the space sector (like a Planet Labs or Rocket Lab), Autonomous Vehicles, or Robotics/Drones, were to entertain SPACs as a liquidity option.
Deep tech companies thrive on narrative, and as narrative has shown outsized ability to carry a stock lately, I believe we’ll see multiple attempts at using SPACs to fund companies that today rely more on narrative than business metrics for a longer period of time than previously seen in public markets. While this could be considered passing the buck, I’m excited to see another financing and liquidity option arise for companies and investors solving some of the world’s hardest problems.
By the way, as of today NIO is now at an all-time high.
There is a lot of speculation surrounding Amazon acquiring self-driving startup Zoox (has now been confirmed). While there are obvious explanations as to why Amazon would want to acquire any AV company, Zoox in particular is compelling for a few reasons.
It has long been Amazon’s view that they were willing to wait for the autonomy software stack to mature/show more progress before jumping in. I don’t believe that what Zoox has shown is the progress of which Amazon has historically anticipated making an acquisition. But a few things have changed:
Zoox has struggled to fundraise, leaving an opening for Amazon to swoop in and make an acquisition of a large, competent team, at a price that is likely below Zoox’s last raise price. In addition, while the headline number is talked about to be above $1B, based on prior acquisitions, I wouldn’t be surprised if whatever eventual number that gets floated is tied to some internal milestones.
COVID-19 has put a level of stress on Amazon to push forward their views on when they needed to meaningfully shift into this market, especially on the urban environment and last-mile side.
The acquisition of Dispatch a few years ago has not solved this problem.
Hardware expertise - Zoox is the only AV startup that has the size of team and level of focus thus far surrounding hardware, and specifically proprietary hardware. The only other company that is a comparable in my mind here would be Nuro, with their custom logistics vehicle and software stack. Former Zoox Co-Founder TKK also would argue Cruise copied them in this regard.
For more detail, we can break Amazon’s logistics-centric lines of business into a few very oversimplified stacks.
Warehouse - Amazon has heavily invested in warehouse automation, starting effectively with their acquisition of Kiva Systems back in 2012 and are rumored to have material revamps of this occurring internally right now. While this still has a lot of room to run with respect to better collaboration between robots and humans among other things, there is clear infrastructure in place on the R&D side to continue working on increased warehouse automation (and a ton of startups left to acquire).
Long-haul logistics - Amazon has also materially invested in ramping up their logistics infrastructure, including building out a $1.5B air hub in Kentucky, as well as more recently snatching up airplanes at discounts. Within this part of the stack there are two components where *any* autonomy player would be valuable.
1) The movement of goods in these fairly structured and less dynamic areas (May Mobility showed early pilots of moving goods around airports, Voyage is another well known player in a tangential space). I don’t expect this to be something Amazon cares to automate in the near-term as likely the efficiencies that it would create aren’t massive, but who knows.
2) Trucking infrastructure, which Amazon has invested in through their partnership with Aurora (as well as pilots with Embark and others). Aurora has effectively pivoted from urban environments to prioritizing trucking, despite their views that their stack is built for both, I’m dubious of this as a principle with the traditional autonomy approach due to the physical limitations that exist when dealing with the variability of freight trucks, as well as the variance of sensors stacks.
Urban environments + Last Mile Logistics - This feels like the key-difference maker within Zoox in the near-term. The technical problem of last-mile delivery, while not as easy as the public would think, is still easier than full-scale autonomy. Amazon purchased Dispatch, a sidewalk delivery robot company, back in 2017.
A lot of the supposed IP within Zoox is surrounding their omnidirectional vehicle. There is perceived core excellence in this vehicle, that is built for an autonomy-first stack and urban environments. This vehicle platform could easily be re-purposed for delivery with an ethos that mirrors what Nuro has more publicly talked about surrounding lighter weight and lower speeds, to hedge against some crash risk.
Amazon and the economics of rolling out autonomy
The other important component to note is the structure of Amazon as a logistics player.
Zoox’s software stack has long been talked about due to its urban navigation demos, as well as for a long time the simulation stack. Being largely vertically integrated, Amazon can very much capture economic value over time by building automation in various geographies and having a slower roll-out. This is something that is less economically feasible or attractive in the robotaxi space.
Overall, the Zoox acquisition comes at a particularly interesting time within AVs. COVID-19 has materially stalled many of the US teams’ development while other technical limitations have done the same, turning the tides for Amazon to perhaps understand that the financial flexibility that the rest of their business grants them means they should step into a clearly not-already-won race (similar in some regards to Waymo, though with billions of dollars less in sunk cost).
In addition, the need of automation has been pulled forward into the present by years due to the realization of how fragile large companies are relative to their employment base. I don’t expect this to be the last move Amazon, or any other large logistics player makes within AVs. In a world where incumbents are more horizontal than ever before, the importance of holding the keys to something as existential and value accruing as autonomy cannot be understated.
Disclaimer: I’m an investor and board member of Wayve.ai
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.
Markets are sharply higher over the past two days as data shows possible peaks in major cities (like NYC) and countries (Italy/Spain) around the world. Markets are seemingly holding onto any good news while growing tired of the barrage of bad news for the past month (or months depending on where in the world you are). It’s my view that this stock market reaction is very much an emotional reaction to one component of this recession and not rooted in material data relative to the actual long-term effects of COVID-19 on the economy. There are two way to look at this.
The bull case is that markets bottom out near the peaks for given countries and/or focal points of the coronavirus. We saw a fast and unexpected (to some) rise in COVID-19 spread globally, and this scared the world into a risk-off view, unwinding all sorts of debt and equity positions, because it was a perfect storm of both healthcare and economic shock, leading to demand shock. As I wrote yesterday, we now believe we have more clarifying information, which is understanding the magnitude of spread on the healthcare side of this equation, and the view that estimates could come in on the low-end of the range of projections for death tolls should push markets up. We’re also seeing some encouraging (though not conclusive) data on things like various treatments, impact of social distancing over short periods of time, and even seasonality.
Economically we are seeing strong willingness from participants to move back into the market (previously I called this the BTFD recession), a change in retail investing to continue to systematically allocate capital to public markets (leading to minimal data of movement in vanguard accounts), as well as consumer confidence at very high levels. In addition, I am not of the belief that humans change materially, and don’t think we will be living in some drastically different world where people want to all work from home, do zoom happy hours, and not travel anymore. That said, my mind is fucking blown that cruises are still in demand.
Pair this with unparalleled pace and scale of fiscal policy and stimulus from governments (with much more to come) and we can start to see how there is a narrative about how, as a human race, we have figured out how to pair sophisticated financial structures with societal collaboration in order to defeat black swan events.
But…
There are a ton of shocks and unknowns relative to COVID-19. Despite all of these positive signs, I still can’t quite shake the continual view of bear markets seeing fast rises before steeper falls. We haven’t seen the next order effects yet, and we haven’t seen the second sell-off either.
If we look at 1929, we can see that this didn’t happen, and perhaps this is where we are headed. Markets process information faster than ever before and the rarity of this event could have perhaps led to a steep sell-off that pushed straight to rock bottom, and then back up.
The rational argument is that it feels unlikely that we priced in the downside scenario bound of economic outcomes (humans likely did price in the healthcare downside bounds, due to fear and plenty of pop culture that enables us to see pandemic destruction).
With fiscal policy, it’s not clear that we actually have a good infrastructure in place to properly deploy trillions of dollars to Main Street in a short enough period of time that we save these businesses and don’t create serious, lasting economic impact.
With the pros of social distancing, it’s not clear that we won’t see some short to mid-term changed behavior and restrictions surrounding dining, entertainment, and hospitality broadly. And we don’t actually know if these types of businesses have economic structures in place that can weather those restrictions on behavior. We also don’t know how resurgence will impact the market (my view is if we have to social distance again, we will see a very steep sell off and even larger levels of layoffs that may be permanent).
With forgivable loans, will we not see companies meaningfully change the economics of their business post-covid, perhaps less growth, more cash, fewer workers, less infrastructure, all paired with a lack of buybacks to push up stock prices? (to this point, this could be a further widening of the mega-cap technology companies increasing in value, and the mid-cap and small-cap non-tech companies falling. This is one of the reasons I have kept or adde to a variety of tech longs throughout this downturn)
As I wrote yesterday, I do think that we are going to be continually in search of yield, with low interest rates, and thus markets will recover sharply and tightly with earnings recoveries, however I think we still have imperfect information on the scale of the downside to truly understand the economic impact for the next 18 months. Earnings figures over the next month for individual companies might show small signs of this and could drastically correct the market one way or the other, though I’d imagine next quarters earnings to be far more indicative of how the following 18 months looks from a supply and demand sense.
And alas, this chart is probably one that I find most interesting and relates heaviest to the BTFD recession.
Despite all of my doom and gloom, I am still long term bullish on technology and bio as meaningful contributors to how we evolve with our new futures and believe that many of the behaviors will persist in middle to upper middle class America. A point to think about related to this is that perhaps this is another driving factor which creates an even wider (and faster) economic class gap in our country and other developed nations.
Back to the economy, as with the prior hundreds of years, humans continue to find solutions that outpace the problems that we create, or are thrown out us as we hurl through space on this large blue marble. Maybe we are inevitable, but I don’t think it will look that way for the next few months.
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.
We now need clarifying news to move stock prices
Last week after a horrible jobs number everyone in the twittersphere was shocked that the markets didn’t tank and looking for answers. There are a few ways to look at this, with the easiest being to point them to a discounted cash flow model and explain that companies are based off a belief in future value of earnings. While the jobless claims number was a shocker to many (especially because the chart looked so stark), it really didn’t change the economic view of many of these companies. As I’ve been harping on across basically everything I’ve written over the past few weeks, we all are operating under the assumption that this is going to be really bad, that “main street” is outsized effected vs. wall street, and that we don’t have a good way to quantify that loss. Thus, after a massive sell-off in public markets a few years weeks ago due to fear and an impending global pandemic, the only thing that can materially change our view is clarifying news. So when we see 6.65M jobless claims the market shrugs and says “uh, seems bad but doesn’t really allow us to materially update our model” and then public markets trade sideways on the news.
Clarifying news can hold different definitions at various points in time. For some reason, markets believed that Trump speaking qualitatively was clarifying news a few weeks ago, however have begun to drastically mark down the value of that input, as he continues to fire from the hip daily in his pressers. A similar view has been held with respect to China numbers (of which many early models were based off of), which as we talked about last week, has now been slanted more towards a garbage in and garbage out data modeling view.
With that said, perhaps we are starting to get better quantifying data as we’re seeing deaths and infections fall across Europe and have perhaps passed a peak in NYC (see chart below for ER data) here in the states. The NY Times has doubts on some of these numbers (as do many others) but it’s more likely the positive news gets amplified and markets trade up on it in the short-term.
I say short-term because we still don’t have strong data on resurgence rates, timelines for testing, or a ton of other things that could lead to the secondary sell off that I’ve been anticipating. As I’ve said, a lot of these also assume a stronger level of distancing than I think we’ll be able to sustain. Right now, in early spring, we’re seeing humans’ ability to stay inside (sorta) and isolate for a few weeks (with a ton of complaining). As weather continues to turn for the better across the country (and northern hemisphere) and we get pushed from April 30th to maybe memorial day, I don’t have a ton of faith we’ll see pseudo-elective social distancing maintain, and a government mandated lockdown would likely send things further south.
COVID-19 & Stock Recovery Timelines
Timeline of distancing is the big question on everyone’s mind as it will drive economics and likely the gap between a recession and a depression. These questions come at the economy from multiple angles but largely rely on how earnings can return after suffering a demand shock for 2+ months, as well as how the world is changed after this.
On the first order (and broad) question of “when can we go places again” these two charts below show both where some believe we are on the curve, as well as the likely restriction lifts. I’d say that the April 30th lift of restriction will likely not come to fruition in the US and instead be pushed a few weeks as Trump potentially sees positive signs in the market with improving, post-peak data.
How the rest of the global economy will impact markets, is something I’m less of an expert in, especially with the complexity of supply chains that has only grown stronger over the past decade. But for reference, here’s a chart from JP Morgan on the various countries and their respective phases.
So now that we have a timeline of some sort (let’s just say June 1), we have to begin to look at the earnings impact. Goldman is projecting that after a crisis, we usually see a bounce back of ~3 years before we get back to prior earnings per share levels.
So back to stock prices and earnings. If we see the above Goldman report of 3 years to get back to EPS levels, we can look at past data from bear markets to understand potentially how stock prices will follow. Looking at the last two bear markets, we see a slight lag between where earnings breakeven and markets do, however both of these were followed by very strong bull markets. As Ben Carlson notes, this slight lag could provide massive tailwinds for another large bull run.
My opinion is that we could see a slightly closer coupling of these two principles as the shock isn’t one of financial systems or company health this time around but instead one of exogenous risk exerted on the market. Because of this as well as aggressive fiscal policy to push people towards risk-on assets in search of yield, it’s more likely that we’ll see markets want to pay for future growth at a faster rate than before. As Carlson says:
It’s true that the price of an asset should reflect the present value of all future cash flows but investors often over- or under-react based on their expectations of the future, meaning the pendulum can swing far above and below fundamental values.
Other Notes
Related to a lot of these unemployment numbers, we should be continually watching those that leave the labor force. As any economics undergrad can tell you, discouraged workers and those not in work force have a real draw on the economy, and thus are good tertiary indicators.
A lot of people are staring at Alphabet and Facebook stock prices while trying to understand the impact of a large portion of ad demand fleeing the market in the short-term. This chart shows some data on that across various categories. Anecdotally, I’m seeing closer to 50% drop off for digital advertising prices, but probably even a larger flight from demand on some consumer areas I’m exposed to.
Americans realizing that the light stuff won’t do and headed straight for premixed cocktails for that extra spice in their day is a trend I didn’t see (vs. just pure grain alcohol or moonshine). #quarantinis
What I’m Doing
I’ve sold off all of my VIX longs. Still short TSLA, ZM, URBN, MNST, and a few other + most indices. Likely selling part of my FTSE short today as I’m not sure I want to ride the ups of amplified good news with quieted bad news in the short-term (this is an interesting dynamic of where we are in the cycle that should be written more about).
There’s a saying in the data world called Garbage In, Garbage Out, which basically means that if you input bad data into a model, the outputs (or conclusions) of that model, and thus the decisions made off of that model, are going to similarly be worthless.
It seems that we are seeing that play out in real-time
On the international front, we’re now in a shouting match with China about who is telling the truth or not. My gut (and a lot of anecdotes from urns to spikes in deaths and more) tells me that China has been vastly downplaying the extent at which COVID-19 has impacted their population. It seems US intelligence has confirmed this in a new report that was handed to government officials last week.
China has a reputation for not being the most transparent country in the world, however what’s more important is that based on the early outbreak there, the garbage data we received from them has drastically impacted the rest of the world’s ability to model out various scenarios. If you look at early research papers, many were leaning on Chinese data in order to model out scenarios of spread and economic impact. All of this research now feels like it must be thrown out. However, as Nicole Williams points out, maybe we should have just not trusted this in the first place.
There are two other interesting parts, to me, about all of this.
First - I hate that this gives another narrative for the Trump administration to push off blame for their poor reaction time to COVID-19 stateside. Even with the existing Chinese data, they should have acted faster. And based on decades of intelligence, they also probably should have assumed/did assume that the data was bad.
Second - On a lighter note, yesterday Trump said that he hadn’t received an intelligence report. I imagine this isn’t true, but I like to think of a world where intelligence people all give that look of “yeah we’re not going to bother Trump with this, for the good of our country.” Kind of like when one parent tells their kids not to tell the other parent about something bad that happened at home or at school. It’s even funnier if you imagine them deciding it’s best for the press to know and have Trump learn about it at the same time.
Garbage Jobless Claims Data
There are scenarios where the bad data can allow you to still get a directional view of something. You could argue this is what is happening with initial jobless claims in the US. Last week we saw a record 3.2M jobless claims which seemingly did very little to the market, last Thursday. This is possibly because the figure fell within projections from many economists, however there is another hypothesis surrounding incomplete data. That is, because it was incredibly difficult to meaningfully parse the signal from the noise, as the narrative was that this data was incomplete due to infrastructure issues of people not being able to get through to file for unemployment, it was difficult for the market to truly understand the magnitude at which this data was garbage.
Today we saw a new record of 6.65M jobless claims, breaking the upper bound of most projections (6.5M was the highest I saw from a credible source, with a few 7M’s around the internet). The market digested, futures fell a bit, but the movement hasn’t been super steep either way.
It’s likely that Wall St has been largely bracing for “very bad” news, hence the lack of steep fall in markets on a historical unemployment figure. While some investors are publicly saying they are buying the dip on certain names, I don’t know anyone (disclosure: i don’t know a lot of people) that is horizontally buying the economy and thus without a more existential fear, likely related to health of citizens or policy change that extends the duration of an economic slowdown further (and making the “v-shaped recovery” more difficult) I don’t think these types of indicators will materially move markets.
As I mentioned yesterday, Trump takes all of his cues from the stock market, and it’s likely he’s watching today with close eyes before figuring out what he’s going to say at his presser either today, or even more importantly Friday going into the close (if he prepares for these things at all). If things aren’t too bad today in the market, I imagine he continues to talk about how the current “relief” package (good god this post feels like years ago) is helping tons and tons of Americans and great businesses. If things get out of control, I imagine what we’ll see is further rumblings about the next wave of stimulus and how the government is prepared to take any action necessary to save the economy.
Related to data, I still don’t have high confidence levels that even this week’s data is clean. Yes, we’ve seen some better infrastructure in place and guidelines for managing load on phone lines and internet for unemployment claims, but it’s highly likely we see another spike next week.
Sweden Reversal
So yesterday I wrote about the great British Reversal and specifically talked about the cognitive dissonance that shifting from downplaying to strict measures causes within citizens, companies, and countries broadly. Today we’re seeing this happen again in Sweden. Up until yesterday Sweden was perfectly content watching the rest of Europe burn and their neighbors that are 8 kilometers away take strong measures against COVID-19, while they did…nothing.
The former Prime Minister even had a lovely quote on CNN where he said something to the tune of “Swedish people are already predispositioned to social distancing more than others”.
On cue, Sweden now has announced more strict (I guess?) social distancing measures, while still allowing shopping. It will be fascinating to see how the data looks relative to the rest of Scandinavia in the coming month.
Other Notes
VC Markets Aren’t Clearing
I wrote a post about how and why VC markets are being affected by COVID-19 paralysis. Specifically I walked through the trickle down effects that lead to lower pricing in the market. Read the post here.
What I’m Doing
I shorted Zoom yesterday ($ZM not $ZOOM) at $140/share. We can talk about product differentiation, some whispers of negative news, or the insane valuation run-up that feels not sustainable, and then we can pair that on my continued view that a downward trend in markets will effect almost all stocks. There’s some mix of all of those things, but I also felt comfortable riding this out without having to worry about material volatility on the upside case (i.e. the stock running up against me) unless we see revised guidance ahead of earnings in June.
Markets are set to open down sharply today after another difficult to watch press conference from Trump.
Yesterday Trump gave his most sobering press conference of the past few weeks, basically reaching a point where he’s willing to publicly admit that COVID-19 is a real problem that isn’t easily solvable. In his words he described it as a “painful two weeks” ahead (remember when we were on day 8 of his 15 day plan?).
He floated the number of ~200,000 expected deaths from COVID-19, and then in pure Trump fashion went on to talk about how this miracle was only possible because he acted so early on the curve to contain deaths, and that otherwise there would have been 1-2 million+ deaths in the US.
First, I just want to be clear that I categorically disagree that he acted early, but I think we all do.
Second, and more importantly, there’s some interesting signal here to parse within the massive amount of Trump noise.
Trump is touting a 200,000 number that relies on fairly strict social distancing measures to be put in place, the type that have been put in place in NY, NJ, WA, and CA, but that haven’t in states like Florida. My gut take on this, as it is with many things, is that I don’t have faith in Americans to stay the course and social distance in a strict way through April, and likely May as the weather materially turns in the nation and we start to see the Trump administration paint the rosiest version of the picture after what I believe will be a difficult April/May for markets.
I have a hypothesis that Trump only went sober right now because we’ve seen relative stabilization within the stock market over a few days (and he wanted to test how the market would react to some more negativity, as he uses the stock market as a barometer for…everything). If we go another level, then perhaps he has a view on what the next wave of jobless claims could look like. Specifically, that the next wave of jobless claims are going to be very bad and potentially send the market back downward, and thus pushing bad news this week may be a way to concentrate the pain a bit more so he can posture heading into the weekend or next week, and perhaps even begin to announce what many are expecting, more stimulus.
The counterpoint is that Trump would want to push bad news when he knows a near-term good news catalyst is coming, so this could swing both ways.
Last thing I’ll say related to all of this is that these projections feel overly optimistic, but alas, I feel that the world is overly optimistic right now across many vectors. Also, it’s ironic that we expect deaths to peak on what was formerly our tax day.
Brexiting Herd Immunity
This isn’t really current news, but I’ve been fairly vocal about how much of a disaster the UK was. Speaking from some secondary accounts, by having a basically us vs. the world POV for a week+ span, companies and cultures were materially tested in how to behave as COVID-19 proliferated throughout Europe.
At some point in the future, there should be at a minimum, a great piece of investigative journalism (and likely even a book) that walks through what the hell happened in the UK. We were fairly close to one of the largest A/B tests of outlier event policy we’ve ever seen and only at the 11th hour did all stakeholders agree that social distancing was the right choice.
The pivot point in this strategy reversal seems to have been the misunderstanding in Britain’s models about ventilator usage and importance to stave off deaths via COVID-19. Full summary below from Scott Alexander, but the continual POV from me is the fact that it took them weeks to realize this was a flaw in their model, while the rest of the world began to burn, is insane.
A UK critical care doctor on Reddit wrote a great explanation of their recent debate on coronavirus strategy.
They say that over the past few years, Britain developed a cutting-edge new strategy for dealing with pandemics by building herd immunity. It was actually really novel and exciting and they were anxious to try it out. When the coronavirus came along, the government plugged its spread rate, death rate, etc into the strategy and got the plan Johnson originally announced. This is why he kept talking about how evidence-based it was and how top scientists said this was the best way to do things.
But other pandemics don’t require ventilators the same way as coronavirus does. So the model, which was originally built around flu, didn’t include a term for ventilator shortages. Once someone added that in, the herd immunity strategy went from clever idea to total disaster, and the UK had to perform a disastrous about-face. Something something technocratic hubris vs. complexity of the real world.
Next Order Effects - Concentration of Power
Something I’ve been thinking a lot as the news has been a steady stream of terribleness has been the next order effects of COVID-19 and a post-COVID world. There are multiple stakeholders here across government (how policies will remain or change), commercial (how will businesses and thus the economy be impacted), and human behavior (how will humans change their behavior, which filters back into commerical and in some ways government).
I’ll probably be writing more on this but something that is happening daily is continual concentration of power for leaders in the world.
We’ve now seen world leaders begin to leverage these times to consolidate power and skew checks and balances (most recently in Hungary, but a full breakdown can be read here). It’s interesting to see from the US where we complain about how each party is trying to fit in irrelevant bills at the last minute into our stimulus plan, while criticizing our government for being morons and moving too slow (*raises hand*). At the same time we’re seeing pretty strongly loosened views on the surveillance systems that could be put in place in an effort to understand how we can get the world moving again (like China has supposedly done).
Chamath on his recent podcast with Kara Swisher noted that generally where Americans draw the line on this is with respect to how the organization benefits economically by having this level of power. He talked about how the NSA already has most of this data that we are all actively discussing with regards to whether or not Facebook, Apple, Google, and Microsoft, should be tapped to track individuals for the sake of the country’s health. But the NSA is built to serve the people, while large tech cos are built to serve shareholders.
Other Notes
- I’m surprised that with the current work from home environment, we haven’t seen any material leaks come out yet. This article from The Information made me think more about this. I wouldn’t be surprised if this changes.
- FinTwit sentiment has already come back around. Rule of life: Fade FinTwit.
What I’m Doing
Still short. Waiting for new jobless claims on Thursday. Selling another ¼ of my VIX longs today.
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.
I’m a bit surprised that markets are pointing to an upwards open this morning. After a steep sell-off into the weekend (which I think will keep happening) and Trump showing that he’s willing to push out the social distancing timeline, I would have expected a bit more negative sentiment, and IMO this only further solidifies that while Trump will continue to short-term speak about what’s best for the economy, I’m not sure he has it in him yet to mid-term take a massive health risk.
This Time Is Different
There’s a lot of debate right now about whether we’ve hit the bottom of the market. But bear markets don’t bottom in a month. And if you look at the data surrounding bear market length, it’s largely been true that these things can take far longer than a few months. The one difference is in 1987 which from all of the reading I’ve done (see: 20 minutes of googling this morning and a forgotten economics class from college) seems like I can’t get a conclusive view on the direct cause of the shock, and also was at a time where a lot of infrastructure level changes were happening in public markets that we clearly weren’t ready for.
The continual psychological issue we have as humans is that in extreme scenarios, each time feels different. Narratives are incredibly powerful for outlier events, businesses, and times. This time, the world is increasingly more connected, our (or our machines’) ability to parse information and act upon it is faster than ever before, and disruption comes at a feverish pace relative to any other time in history (just look at the displacement rate of the S&P 500).
The chart above was really interesting showing even from the 2008 crisis to now (just 12 years) the difference in time in which we enabled serious fiscal policy.
With that said, I’m still trying to parse whether or not this time is truly different. My job as a venture capitalist and how I interpret today (and the next 20 years) vs. any other time in history makes me strongly lean towards this time is different, but across so many financial lessons this is hard to believe. If you take a retrospective look, history often tells us similar lessons. So for now to save some time I’ll just end with this quote.
History is not a road-map, but a compass.
Studying long-term economic impact of pandemics in Europe from 1331 to 2009
This paper took a look at the long-term economic effects of various pandemics on European economies (due to data availability). I recommend reading the paper but the main takeaways here are that we see multi-decade lasting lower rates, in addition to an increase of real wages.
Measured by deviations in a benchmark economic statistic, the real natural rate of
interest, these responses indicate that pandemics are followed by sustained periods—over
multiple decades—with depressed investment opportunities, possibly due to excess capital
per unit of surviving labor, and/or heightened desires to save, possibly due to an increase
in precautionary saving or a rebuilding of depleted wealth.
As with my prior point above around narratives and “this time is different” I’ll fall into the trap and argue that in 2020 we have significantly more global economy that relies on machines to lever humans’ output in an outsized way vs. any other prior pandemics analyzed. In addition, our governments feel more sophisticated with how to utilize monetary policy to mitigate shocks to our systems, learning from each shock to better improve (see the introduction of circuit breakers after Black Monday in 1987). The paper ends by recognizing these different times, on the basis of average lifespan as well as lower rates allowing for better government intervention.
If low real interest rates are sustained for decades they will provide welcome fiscal space for governments to mitigate the consequences of the pandemic. The major caveat is that past pandemics occurred at time when virtually no members of society survived to old age. The Black Death and other plagues hit populations with the great mass of the age pyramid below 60, so this time may be different.
Other Notes
- This is the current google trends search for the term “can’t pay” in the US. Take a look at the relative timescale as well to see how this very imprecise metric looks. See the full page for yourself here. (Source/inspiration: WSJ)
What I’m Doing
Mostly just staying steady in my continued belief that this isn’t the bottom. I’ve admittedly begun to look at longer time horizon views (as you’ll see above) and understanding what the world may look like over the next 24 months in 3 month intervals. Compared to my day job, it’s a remarkably short time horizon, which is a change of pace.
- I sold half of my long VIX positions on Thursday at the open (as noted). It’s possible that this was a bad decision (especially if I believe in the impending fall as much as I do) but I would rather have that cash or redeploy it elsewhere.
- Still holding short the overall market and specific names, still doing research into other names for long/short or pairs trades.
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.
Let’s do “Relief” for $2T, Alex.
I know the news everyone wants to talk about is unemployment claims, but first can we talk about the words relief vs. stimulus?
Normally we call these sorts of things stimulus packages because they are used to boost demand and buying within the economy, thus stimulating it. However it seems we can’t even begin to imagine demand coming back into the economy right now, so instead we’re just trying to make people feel like they can survive. Which brings us to our word of the day, relief.
Yesterday, before the largest uh, relief package, in the history of our country was fully agreed upon and put into action, we started to hear about how this is just the first economic policy step of likely more to come.
Our boy James Bullard (Fed Reserve Bank of St. Louis President) said that the $2T is “about right for the situation” but made sure to clarify that “This is not stimulus.”
This article from Bloomberg does a good job of breaking down all of this, but it’s hard to feel great about how things are going in the economy when we haven’t even signed into place the $2T and we’re coming up with euphemisms for the largest stimulus package our country has ever seen. Meanwhile former economic strategy leaders are saying “Further stimulus will be needed in coming days and weeks to further bolster the economy and leaders should begin working on that now”
Not to go all private markets on you but this kind of reminds me of this quote I read from Dalton Caldwell yesterday on his advice to himself as a founder in 2008:
Doing multiple small layoffs is a form of cascading failure. Do one layoff, but much much deeper than seems correct. Do it decisively. Do it so that you get profitable. In your case that is something like a 70% cut, not a 5-10% cut. Yes you read that right: a 70% cut. Cutting once and cutting hard allows you to reassure the people that are still here that you are truly profitable and won’t need to do it again. Doing a layoff and remaining unprofitable and counting on fundraising to save you is a stupid plan.
$2T temporary relief, some impending stimulus in days, and Infinite QE. Feels hard to feel truly stable about things unless you’re willing to take the view that our government just won’t let the economy fail, which uh, I’m not.
Initial unemployment claims spike
The big news out of markets this morning is the initial jobless claims, which came in at 3.28M, or seasonally adjusted 2.9M. This is brutal, but the market seems to be responding better than I anticipated. For reference, here’s some historical data that includes 2008 financial crisis. Next Friday we’ll get a full report on labor in the US for the month of March from the BLS (to pair with next Thursday’s updated initial claims). It’s likely we could see more large numbers as there have been countless stories related to those unable to even apply for unemployment benefits via digital or physical purposes.
My main commentary here is that I’m surprised the markets didn’t move more (currently S&P is barely up) on this news (as well as the early indicator which was Governor Newsom last night dropping the 1M unemployment claims in CA number). It’s likely we knew this was coming for an initial spike, and now the next order movement will come from sustained claims that outpace this one week spike (which feels realistic as non pureplay travel/hospitality industries begin layoffs).
That said the Newsom data I think had a better headline than what actually was happening. Upon looking at the Department of Labor data this morning, it seems that advance claims were 186k for this week (ended march 21) of data. I can’t seem to find a state by state breakdown for this week for pure initial claims but last week (end march 14) California was 57.6k and ~43k for the week prior (march 7). Newsom’s comment was “since March 13th” and an estimate. So now we have to ask: Was Newsom including advance claims, initial claims, or are we going to truly see 1/3rd of the country’s initial unemployment claims come from California (the state is about 12% of US population). Due to the early lockdown, it’s possible. This data likely is what is scaring the shit out of Trump as late movers to lockdowns could see continued spikes in unemployment claims, which would lead to prolonged downturns in markets (again, versus one single shock).
This was also interesting but I don’t know enough about how Canadian government and employment policies work to understand how much this can correlate to the US.
What I’m doing
- Praying that Trump flip flops on his view that bringing the economy back in a few weeks is a smart idea.
- Still holding shorts but likely selling off half of my long VIX positions today.
- Wanting to dive deeper into some individual names that feel oversold and undersold. Maybe we’ll talk about this more soon.
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.
We don’t have full information yet on what the stimulus package is going to hold, but we do have some high level numbers. Numbers that have dramatically changed from $1.2T to $1.4T to $2T to $2.5T and back down to $2T. The rational POV is that there are various concessions between the two parties that inflated the stimulus package because when you’re printing $1T…why not just go for $2T? The more cynical POV is what I ended yesterday’s note with, which is that there is no remote consensus on how long this lasts or what the proper response is (and this is increasingly become a political issue vs. science-backed), creating a $500B+ variance in proposed plans.
“Bear markets don’t bottom in a month”
Will all this stimulus matter? The talking point from basically every economist I’ve read over the past 24 hours has been that none of this whipsawing in public markets will stop until we see material declines in infection rates, and we’re not getting there anytime soon. I expect this to ring even more true for the private markets (specifically talking about venture capital here) as our collective group feels very bearish on this ramp of recovery, and very bullish on the idea that the worst is very much yet to come.
As I said yesterday, in fact, we could see an even worse turn of events if Trump does get people back to work by Easter. And to my initial point, how long is this going to last us? Can we do a second stimulus package for main street if we see demand shock for the next 12 months? Honestly, probably, but that’s above my paygrade.
Hospitality’s Rally
It’s been remarkable to see the rally in hospitality over the past few days, with names like Darden, Ruth’s Cris, MGM, Dave & Buster’s, and more spiking 25%+ on stimulus talks after getting annihilated (chart above courtesy of Raymond James equity research).
I’m still not sure how stimulus will materially impact most of the independent operators across food (my gut tells me they are done for, no matter what check the government is willing to write them) as well as this fracturing cities/states/societies component I keep talking about if Trump does push the economy back into motion too soon (i.e. April). I tried to run a quick screen to see which publicly traded restaurant groups had the most exposure to states like NY and CA and if it was a material % of revenue, but haven’t been able to easily find that data in the 20 minutes I spent searching. My thesis would be that these states could see a marketed drop off in demand based on political leanings, or continual shelter in place by their governor’s, thus creating continued economic struggle while the rest of the country that *today* doesn’t really seem to worry about COVID wines and dines to their heart’s desire.
I’m still processing this all so expect more notes at a later date, but also, Dave & Busters (up over 100% in past few days) reports earnings next week on April 1 (ironic). Excited to hear that guidance and whether these groups sandbag projections or are overly optimistic in the hopes of Americans Americaning.
What I’m Doing
I added a tiny amount to my Tesla short yesterday at $500/share (don’t feel strongly about this) and added to index shorts (S&P and Russell 2000). I’m still long the VIX but think VOL may have peaked for now and likely start scaling back those positions and taking gains. Admittedly, I’m a bit hesitant to do *anything* right now. My views on timelines have fractured into:
Short-term a) we see sell offs as numbers continue to spike and public officials become more outspoken with how screwed their healthcare systems are, driving down markets.
and mid-term b) The trump sends people back to early and we see a secondary meltdown after a large rally situation.
TLDR I’m pretty bearish overall, but I’m long-term (18+ months) bullish and still holding all tech and bio positions (+Walmart). Also, I’ve never materially traded through a $2T stimulus package and infinite QE before, so there’s probably some knowledge gap here of understanding economic policy flows into markets that I could be mispricing.
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.
The idea that trump wants to send people back to work early continues to gain steam, and he said “very soon” last night in his press conference (while of course, comparing COVID-19 to the flu and automobile accidents). So there are a few things that seem to be possible if/when we defy scientist recommendations and re-ignite the economy.
If after the 15 day quarantine, Trump pushes markets another few weeks, and then let’s say we get a first-order distancing lift (rumors are he plans to layer people back into the workforce by age) on April 10th announcement (so that market rallies into weekend, businesses open, and rock and roll on Monday): We’ll see a nation that is largely split in how they take this news. Some will still be terrified, rationally knowing this is bad advice, that will continue to weaken demand for a section of spend (think physical activities sans food perhaps) as well as some offices that will stay remote. Based on The Atlantic’s piece on how Dems and Republicans are viewing this, it’s likely those cities which are hardest hit from an outbreak perspective will be in this camp. But we’ll see a large chunk of the population go back to work because..well…THEIR GOVERNMENT TOLD THEM TO and more importantly, they need the money to live.
I’m in the “bad idea” camp here, and believe that we’ll also see a fracturing of state vs. federal views on this, that will create continued panic and volatility in markets as we digest specific company earnings reports that reflect Q1 numbers and rest of 2020 fiscal year guidance. So now we’re at some set of companies getting murdered after a macro rally on the government loosening on distancing (like we’re seeing today). There are also international economic impacts here to take into account. We’ll then want to look at which companies have international customer exposure vs. not and how those countries and their policies are adapting.
Fast forward a few more weeks and perhaps we’ll see continued data surrounding the spread and disastrous healthcare situation that Trump has put us in. At some point, he could be forced to put in place social distancing measures again, likely blaming governors as their healthcare systems faulter. This secondary shock could only terrify the market more surrounding the “how long can this last” dynamic, and we’ll see an even deeper cut, giving up any rally that markets had gone on in the few weeks post-quarantine v1 and pre-quarantine v2 and bad Q1 earnings. These fires will only be flamed by the various thinkpieces and discussions from the Imperial College report that without a vaccine, we basically must social distance for 18 months. I’d hope that we’d get a treatment before then, but I don’t have a good read on how likely that is (though as I wrote yesterday, am of the belief that humans are pretty good at outpacing treatment of new problems, if just for short-term relief).
So to summarize, will trump pick the market over the healthcare system? And if so, will large multinational companies follow suit and send workers back in? And the other important question is: While congress will likely come to some conclusion on a stimulus package this week, will this process really be effective if they are making this decision in a vacuum of not knowing what type of policy surrounding distancing we’re headed for? (This question assumes congress doesn’t have a good idea for what Trump plans to do, which I believe, because Trump doesn’t have a good idea of what Trump plans to do.
Yes we have Infinity QE, but if we pass stimulus for Main Street assuming a lockdown that suppresses R0 to below 1 by June 30th and gives the healthcare system slack, only to have that world we’re planning for ripped out from under us, will we really be in a better economic place?
Conversation for another day.
What I’m Doing
Looking for potential short opportunities in this upswing today, and starting to think about longs again. Still not deeply confident on how all of the new fiscal policy impacts markets yet.
