Dear Compound Investors,
We’re excited to once again share our thoughts on the past year of building Compound.
At a high level, we feel that time has continued to validate our beliefs in the asymmetry of the technologies and categories we are excited about (most notably AI/ML, Robotics, Bio, and Crypto). Venture is in many ways about survival, so time and progress also continues to validate our mission of building a world class venture firm and portfolio while continuing to improve our ability to manage the complexities of deploying capital amongst cycles of low industry consensus.
Compound Fund Summary
Compound I investors will receive a distribution in May. This will bring the fund to over x DPI and an estimated MOIC of over x. There will be more color in the coming weeks.
Compound II investments have picked up after a slow start to the fund. As a reminder, we started investing Compound II in September 2021 and invested under $10M from then until January 2023. We felt this was very prudent and in retrospect we feel very good about this decision.
In 2023 we invested in x companies and projects, deploying $xM. Thus far in 2024 we have invested in x companies and projects, deploying $xM, bringing us to a total of $xM invested of our $73M fund. While this is directionally where we would like to be, we feel that we perhaps were too disciplined during 2023 as the market marginally corrected.
As we continue to grow as a firm, one of our jobs is to spot areas of asymmetric risk or imbalanced skepticism/hatred and step in with conviction. While we will discuss this in-depth more later, we have felt unnaturally forced to raise our bar due to the dependence on downstream capital in our portfolio. Generally, we anticipate markets will normalize over time, but this dynamic does mean our analysis needs to be even more precise both at the company level as well as how later investors will interpret milestones properly.
The good news is, with a steady clear out of underperforming companies throughout 2023 and likely heavily on the horizon in 2024, mid-stage capital is starting to come back, and we anticipate there will be slightly more willingness to deploy capital than the prior two years.
Some may call this a “resetting” of markets, however certain rounds would perhaps lead us to call it a “forgetting” of prior mistakes, for better or for worse.
AI/ML & Compound’s Strategy
We discussed AI in-depth at our AGM as well as in our annual letter last year. This past year has only further strengthened our position that for a firm like Compound, the most consistent strategy to generate Alpha is to back what we would call early-movers to fundamental breakthroughs as well as perhaps Final Movers, with the middle being less compelling to us and perhaps more well-suited for large, multi-stage firms to deploy dollars into at scale.
Early movers to fundamental breakthroughs are backed by understanding the edges of areas and making non-consensus investments predicated on more precise or contrarian positions on dynamics like the time horizon to technological breakthrough, value capture, market adoption, or more.
You all know these examples well, but in our portfolio this looks like Deepgram (a belief that deep learning could do speech to text at scale), Runway (a belief that creativity would be augmented by AI far ahead of when others thought), and Wayve (a belief that AI, could end-to-end drive a car better than a series of AI models paired with rules and $150k+ in sensors), among others.
Wayve is also perhaps an example of a Final Mover, understanding the technological approaches and limits of existing consensus automation approaches in AVs and taking a tangential but more sophisticated/speculative technology and pushing the limits.
In the middle of these two paradigms, you get novel applications that look to disrupt incumbents with the interjection of AI that is so materially better it displaces distribution advantages, market inertia, or creates a use-case that was previously impossible. These investments are the easiest to reason through and often seem the most obvious. This paired with the fact that we are at a time where corporate uptake of AI-enabled software is faster than any of us have ever seen, makes go-to market look very easy but also leads to many false winners, as displayed by companies seeing immense revenue churn or an inability to monetize users at scale.
Investing in many of these companies at Seed is difficult because they both get bid up to very high prices (call options for large firms or consensus interesting to many firms) and they operate in incredibly crowded spaces with multiple competitors.
That said, that doesn’t mean there aren’t any interesting investments for us to make in this part of the stack and market.
We’ve long said that our core competency at Compound is not necessarily an ability to pick the best company in a crowded, overfunded area, but instead to notice “n-of-1” approaches or company types that are underappreciated or could be special for a variety of reasons.
We enable this with a strong competency in creative thinking and underwriting technical and scientific risk, while helping founders navigate these complex types of companies across team composition, product, commercialization, and narrative building.
This orthogonal approach may mean we will have a high failure rate as markets mature and become more consensus, but it also should mean we will be able to back founders who are uniquely creatively-minded and unafraid to look stupid, something we believe creates asymmetric outcomes.
With all of this said, if we were to sum up our AI framework it would be that AI currently has few unassailable large moats but instead a collection of meta-moats over periods of time. AI startups are participating in marathons to gain market dominance, not sprints.
A more thorough detailed framework is available in our recent post The Shifting Dynamics & Meta-Moats of AI
Robotics
Last year we opined that papers like RT-1+ and Code As Policies (among others) were effective “GPT moments” for robotics. Since then, Wayve has also pushed forward the state of the art in many areas with research like Lingo-2 and more.
While there is some over-optimism perhaps of the ease at which scaling will happen in robotics (many are mapping it almost directly to LLMs) the market seems to have now fully come around to this framing for better or for worse.
Just as the best minds at incumbent AI labs like Deepmind left to start OpenAI and other labs pre and post-Attention is All You Need, a series of top robotics researchers have left their corporate research groups in 2024 to start their own companies, including Karol Hausman, one of the authors of the RT papers at Deepmind who recently announced Physical Intelligence.
In a similar way that OpenAI and Anthropic are aiming to build an AGI/ASI API, many of these companies are looking to build a brain to enable intelligence in robotics companies, effectively taking a scorched earth approach to all vertically built robotic brains.
In theory this makes sense, as has been shown in LLMs, as these models get better “out of task” data they can improve a core task due to transferred understanding or reasoning capabilities (the most talked about example is how adding coding examples greatly increases broader performance of LLMs for non-coding tasks).
As we move to hardware, a subset also have plans to utilize humanoid robots that integrate with this intelligence to build truly horizontal automation due to the world “being built for humans.” This is long-term valuable, but short-term an overly dogmatic stance by many investors and operators who see what has worked with LLMs and are too strongly domain transferring the utility curve to robotics.
Across many technology category cycles, people routinely underestimate the value of distribution. Our view on robotics, which could age horribly if we are underestimating deployment time, cost curve, and functionality of humanoid robots, is that while a subset of high value tasks will be enabled with costly but highly performant humanoid platforms, many others will see very large businesses built in the next 3-5 years by companies that build models, create data flywheels, and can operate with 90%+ autonomy in order to get distribution with a subset of customers on their way to 99.999% autonomy over time.
Key unlocks here could be a more scaled ability for teams to navigate the simulation to real data gap in order to increase training data without meaningfully increasing capex, as well as ideally an open-source foundation model of sorts aimed at robotics that is performant. A relevant example of this today includes Octo from UC Berkeley and it’s likely some of the larger labs will release open-source foundation models as well in the coming year.
Under this premise, companies that can build custom platforms (likely via majority off-the-shelf components) and implement intelligence to do multiple use-case specific tasks will be able to get distribution and build lock-in far faster and at larger scale ahead of the ability to bring humanoids to market in a cost-effective way.
Lastly, while the spinning off of labs enables companies to likely be more cavalier in their approaches, it does create some pitfalls where teams may end up being forced into narrowing scope due to runway, despite being organizations that have already been oriented towards solving a long-term, Manhattan project-style mission of generalizable humanoid robots.
This could mean these companies can pivot into “simple” narrow tasks, however in reality we have not seen this go well in adjacent areas like autonomous vehicles or prior generations of AI, with instead a wasteland of disillusioned engineers death spiraling companies that create minimal value.
Biology & Progressive Progress
We continue to believe that the intersection of computation, robotics, and biology (canonically known as TechBio) is one of the most asymmetrically upside-skewed opportunities in tech.
While bio is influenced by AI, it’s uniquely not as recursive, with materially longer timelines than other deep tech areas.
This knowledge deeply influences our frameworks in a unique field where:
- The same AI architecture could help in the development for a rare disease and a certain cancer.
- The same lab automation could be relevant for biomanufacturing and drug screening.
In short, once there’s a technology unlock, the vast use-cases provide opportunities for many investments, as these vertical-driven companies have the potential to move into horizontal platforms. Because of this, as technologies de-risk, areas become incredibly consensus and round sizes balloon quickly for consensus approaches (AI for drug discovery etc.) and consensus biological innovations (CRISPR).
As in other verticals, this endemic group think requires us to either pre-empt technology trends and company formation clusters which we are perpetually working on or dig deeper to find the n-of-1 use cases that are in some form, underappreciated.
To speak to the first use-case, the most consensus aggregation of funding around AI has been within drug discovery.
Just as we’ve said for years in other areas of AI, the core “model moat” is relatively short-lived so companies need other forms of differentiation that result in bridging computational to biological gaps. This looks like areas such as resultant drug novelty, how well companies can predict clinical outcomes with their system, and speed of executing the path to clinic.
Because of this, we’re biased towards companies which can create truly novel drugs (in new chemical space) and/or companies which have screens that better represent the human environment, paired with those that understand the data packages needed for the next tranche of funding (something that as mentioned above, has become far more existential in the past 2-3 years).
Related to this we’ve seen a mismatch between new papers being able to predict clinical trial success and pharma’s horizontal use of these tools. Specifically, things like matching genomic changes to disease increases the clinical chance of success by 2-3x. This understanding is what drove our investment in _______ in 2023.
While the TechBio investor ecosystem appreciates extrapolation of single candidates into platform companies, we still have witnessed a progression of fundraising narratives moving from platform-centric to asset-centric.
Tactically what this means is founders have gone from deploying capital heavily into things like building the biggest model, with the most data, and the most automation in their experimental pipeline (enabling them to have many “shots on goal”) to instead focusing on having an edge with the chemical space or screens in order to get a single (or small number) of assets further along in order to raise more capital to build out the platform further.
This is part of a broader trend of areas that carry large science or engineering-risk being loved for their promise at the earliest stages, but entering a trough of disillusionment as investors become impatient and start begging for measurable traction. In bio this has looked like public markets effectively value platforms at 0 until they can start successfully bringing drugs to market.
We see this trend continuing until investors see that AI-enabled drug discovery companies have a higher clinical success rate than traditional drug discovery companies, or until a breakthrough comes that enables platform businesses to have an argument as to how they can accelerate the path to clinic versus the path to asset discovery.
Until this point in time, companies built today may need to use a sub-scale/less-optimized platform to build first-in-class assets while showing the ability to scale up the platform to justify a multi-asset company valuation over time.
Material development of these assets in animal studies (at least mouse) before the Series A feels more and more like a requirement vs. nice to have. This has resulted in some founders targeting areas like ultra-rare disease indications to get in-human proof of concept data before Series A/B rounds.
On Lab Automation
In The Dark Forest of R&D and Capital Deployment of AI we wrote:
“With this cycle of AI we see far fewer dogmatic views, as the intricacies of LLMs has caused many to do the thing they always do when they have high conviction in a category but low conviction in specifics...They invest in infrastructure.”
Bio has in some ways followed a similar trend with lab automation.
In the first wave of TechBio, we saw verticalized companies like Ginkgo Bioworks, Zymergen, and others reinvent the wheel repeatedly on the automation side, furthering our point above that many viewed a scaled platform as necessary to sell the narrative of TechBio. We then saw an evolution where highly generalized “cloud labs” emerged that would enable outsourcing, alongside other lab automation-specific companies that would solve the “build vs. buy” decisions many new platforms were going through.
As we’ve seen in robotics for the past few years, the promise of the technology has outpaced the overall distribution and ROI of it. Investors and founders alike believed in scaled automation across different use-cases in biology, along with a commoditization of hardware and software that would enable easy implementation and utility. This was largely incorrect and, in some ways, informed our investment in Spaero Bio which enables scientists to use these robots without engineers.
With further time and a bevy of learnings from the past 5-7 years, we believe that the market maturation and technological barrier lowering has led to more precise understanding of value capture, leading to companies roughly fitting into workflow modernization (a progressive approach) or more drastic upheaval of scientists workflows (a disruptive/enabling approach).
In terms of workflow modernization, we’re interested in cloud labs which use design thinking principles to be capital efficient in the experiments they offer, which leads to labs matching with areas in which scientists spend the most time/money.
This cycle of boil the ocean-> specialize -> generalize is ironically how many of progressions happen in emerging technologies, with the 1st step building highly capital inefficient and often failed companies, and the 2nd and 3rd step providing value capturing businesses (with varying levels of revenue durability depending on how companies navigate steps 2 to 3).
We view Plasmidsaurus and Primordium (next-day DNA sequencing companies, not in the Compound portfolio) at the early stages of the adoption curves in specialization and think other experimental outsourcing opportunities are possible.
On the higher end of the spectrum, we think closed loop experimentation is the eventual end-state of the movement and have seen more innovation in the protein, and eventually cell engineering world.
As we look at the progression of biology to being significantly more tech-enabled and computational, perhaps one would summarize these company building dynamics as the market rewarding high-fidelity and high-signal data over promises of large market TAMs.
Patient-Directed Care
Circling around all of this continues to be a more fundamental shift of how people are paying attention to biology and their own health.
While patient-directed care broadly is an increasingly consensus trend (borne out of the rise of the quantified self of yesterday and a large number of growing podcasters/authors today) the nuances of how this flows into opportunities for venture-scale outcomes hasn’t been navigated by most.
We feel these areas of bio such as biohacking and decentralized science (DeSci), along with adjacent ways to capture network values in the space, are important multi-decade trends.
With biohacking, we see the core principles of patient-directed care are philosophically broadening to healthy individuals changing their lifestyle and medication to push the limits of human longevity and performance. We spoke about this at the 2023 AGM and still skew towards interest in science-driven products over services in this sector for the time being.
DeSci is centered around a similar premise where people are taking knowledge creation and funding into their own hands and open-sourcing previously obscured processes. Venture-scale value capture here is a bit more nebulous as ownership structures for innovations are still in flux, however as we’ve stated multiple times in this letter, forming precise views amidst uncertainty is one of the things we do best at Compound, and we are attempting to do this each day in DeSci.
It’s likely, alongside our investments in _______, Molecule, and _______, we will accelerate capital deployed against these theses over the coming few years.
Enabling Progress
Summarizing our thoughts on biology is a tall task due to the countless ways computation and robotics can impact this massive area. As we’ve seen across all of tech, people want results over fuzzy-but-big markets when they are uncomfortable with the broader market promise. However, once a market has a sliver of progress, they are once again willing to extrapolate the maximally optimistic view of a company.
Where value capture happens (outside of drug discovery) is still uncertain and thus an opportunity for us to make great investments and help enable this progress.
Crypto & Expected Value
Our crypto investment portfolios in Compound I and Compound II will continue to oscillate alongside the markets.
Compound I’s estimated crypto holdings were worth the following (considering discounts for locked tokens):
Jan 2022: ~$xM + $xM sold
Jan 2023: ~$xM + $xM sold
April 2024: ~$xM + $xM sold
Crypto investing is and has always been about understanding cycles. This conversation often dominates the narrative as investors, builders, and speculators try to understand what inning we’re in during a given cycle. Along those lines, it’s become more consensus that these time cycles are compressing and so participants over-index on trying to rotate to whatever the current narrative is, in an attempt to time the market perfectly for their shortish time horizons.
This cyclical dynamic of crypto happens in part because the space is driven by technological milestones and capital flows. Technological milestones are difficult to understand until implementation, making it hard for markets to coalesce around them, while capital flows, is a metric that is easy to see, but hard to quantify the downstream effects of due to the lack of institutional products and fragmentation of tokens. This has become only even more difficult as a wide range of market participants seek to understand whether BTC ETF flows (and future institutional flows) will make their way on-chain.
The past 16 months or so of price appreciation has been a slightly disappointing market dynamic to us despite excitement around the institutionalization of bitcoin.
Prior crypto cycles have seen a logical flow of BTC → ETH → long-tail of crypto assets (where venture and token investments are made) as capital moves out on the risk and speculation curve, and market participants believe in new narratives. Narratives are often around fundamental shifts of what crypto enables, creating a new wave of believers that either are converted for life, or churn out as prices come back down once speculation subsides.
These narratives can be around organizational structure shifts (ICOs in 2017, DAOs in 2021), business model shifts (DeFi in 2020, NFTs in 2021), or enabling technology that will drive new users (L2s/Alt L1s/Scaling ad infinitum).
We wrote last year that as each year goes by, events continue to stack up that enable us to become more convinced of the fundamental properties that underpin crypto as a necessary and inevitable market. While we have seen regulatory clarity/SEC losses as well as incredible building blocks to enable more early adopters to utilize crypto, this current market has only seen green shoots of narratives in areas such as Decentralized AI, Real World Assets, and adding utility to the Bitcoin ecosystem.
While we have investments in each of these areas, we feel there is fairly muted excitement across the board, relative to prior cycles, and no use-case has taken hold at any scale or reached any material level of retail or early majority belief. The one exception to this statement is speculation via memecoins, which we don’t see as durable usage at-scale once divergence of returns and market efficiencies kick in. To be more precise, we believe memecoins could be net negative to crypto, perpetuating the narrative that crypto is only for gambling and churning users out at a deeper rate that is harder to re-engage amongst negative price action.
Despite all the building blocks being in place to enable new applications, crypto investors remain obsessed with funding infrastructure (technological milestone driven companies or projects).
This strategy is short-term quite profitable and the consensus among private market investors. We walked through in detail the many reasons why this happens and why this is net negative in our post Expected Value in Crypto & Building Infinite Blockspace.
As you read this you may think “well, some of Compound’s best investments have been in infrastructure.” This is true, however just as we’ve stated in areas like AI/ML, as markets mature and evolve, value accrual and sustainable long-term opportunity shifts.
Our job is to understand and invest against those shifts ahead of the consensus. With this in mind, we continue to focus mostly on middleware and application layer-centric investments in crypto moving forward.
If we are going to do something we are saying is currently explicitly lower expected value (but higher asymmetrical skew to the upside) we feel it prudent to explain our investment framework more in-depth:
Components of Compound Crypto Investing
Our crypto investment strategy has two key components:
Long Term Conviction in Early-Stage Projects
We invest in early-stage projects that we believe will matter multiple cycles from now. This precision in time horizon is rooted in our belief that truly transformative projects take time to build and often require navigating multiple market cycles, whether from an adoption perspective or a technological development perspective, in order to reach maximum value.
During bull markets, these types of projects and the builders they attract are sometimes underpriced relative to the market, as short-term speculation takes center stage leading to financings structured to front-run near-term narratives or launch tokens into near-term hype with a “get a token out during this bull run” mindset).
Typically, our investments underpin long-duration theses related to the migration of Web2 and real-world use-cases onto blockchains or are applications uniquely enabled by distributed, permissionless systems.
Examples of areas today include Decentralized Physical Infrastructure Networks (DePIN), Decentralized Finance (DeFi), as well as core protocols we believe can anchor newer ecosystems (like our recent seed investment in _____ on Sui, an emerging Layer-1 Blockchain).
Strategic Liquid Token Investments
The cyclical and heavily levered nature of crypto allows for buying opportunities during down-cycles that can create venture-like returns with liquid tokens that are more properly evaluated as venture investments than public equities. With two bear markets now under our firm’s belt, we’ve gained confidence in our ability to analyze these opportunities and generate returns that make sense for our fund due to the uncapped ability to recycle crypto returns.
In Compound II we have bought core assets such as Ethereum, as well as others such as _______, with a longer list of other assets we are exploring entering at proper network valuations. These tokens typically have multi-cycle value but could reach a local maximum of value in the nearer term. Because of this we also make sure to have strong views of FDVs where we would de-risk a position and that we would be happy to hold and add to upon material drawdowns if our core thesis is not invalidated.
What makes a Compound Crypto Investment?
Again, our guiding principle at Compound is to make investments only in protocols and dapps that we are comfortable holding for many years.
Our investment selection revolves around several key factors:
- Focus areas based on market and user adoption: We have a clear understanding of what we believe in and don't based on crypto and blockchain penetration. It makes sense that we have long been proponents of things that can combine speculation and real-world utility (DeFi, DePIN) while also waiting to see shifts that enable newer primitives to emerge (like RWAs). Our investment decisions are guided by the current state of the market, our long-term prescriptive views of how we expect it to evolve, and the potential for user adoption in specific sectors, all while understanding for a sustainable market to emerge, over time the speculation importance must fall while the utility importance must rise.
- Potential for value capture on the token side: Ultimately, we must believe that in a more reasonable regulatory environment, protocols will have the choice to capture value on the token side in some form. Although this is not always the goal today, some market participants value these assets loosely on this principle. We seek out projects that have the potential to create sustainable legal value for token holders in the long run. The current regulatory environment continues to rate limit the design space here, but we believe this is a temporary burden.
- Duopolistic nature of projects: While crypto may have less of an overall power law dynamic compared to other tech sectors, we do believe that projects are still largely duopolistic at the ecosystem level. This means we back projects that can capture and build on their position as the core project across a given use-case or primitive. We believe this comes from novelty in the early cycles of a use-case (i.e., Helium outcompeting Pollen Chain, a second mover, or Compound and Aave outcompeting many other lending markets) while it comes from larger scope of product or technical/structural innovations in the developed cycles of a use-case (Blur outcompeting OpenSea, Jito v Lido on Solana, Friend Tech v BitClout for social speculation, Farcaster v Lens for Crypto-native Social, etc.).
- N-of-1 protocols: Lastly, as you guessed, we love n-of-1 protocols because they have a far easier time establishing a core primitive, owning the narrative of it, building brand moats, and then are positioned perfectly to be durable across the prior two bullet points. These unique protocols have the potential to create entirely new markets or redefine existing ones.
A Note on Selling
In our May 2022 Investor Update, we broke down the different options that fund managers have as crypto prices become parabolic, netting out to a point that:
“Through the life of Compound we have largely operated under assumption B (The cumulative crypto market cap range of $1-$2T in this past cycle is materially lower than where we believe the world is going.), taking small amounts of liquidity when possible to lock in smaller multiples on investment, while holding liquid tokens we believe will become fundamental protocols within a much larger crypto ecosystem by 2026.”
(redacted commentary)
Overall, our crypto strategies and frameworks are designed to capitalize on our unique strengths as investors. By being thesis-driven, maintaining long-term horizons, having the ability to infinitely recycle our crypto profits, and deploying on a longer-timeline than the industry average for new investments, we can construct a portfolio that captures alpha in the crypto space across various market cycles.
Thesis-Driven, Research-Centric
As many of you know, I (Michael) started my career in the hedge fund world with some focus on public equities and delta-neutral strategies that centered around understanding mispriced volatility (alongside doing a fair amount of classic analyst work on private equity style investments).
While I barely knew what I was doing and was hardly responsible at the end of the day, it was an incredibly intellectually stimulating job that forced me to build a muscle of consuming “all of” the day’s information before I went to sleep (I liked this). It also left me feeling like at any given moment I could be mispositioned if I didn’t understand the short-term nuances of companies, categories, and markets (I didn’t like this).
The short-term oriented views were less rewarding to me than the long-term ones and thus, I made my way towards the startup and venture world. This is a core trait amongst the Compound team.
Through most of 2022 and 2023, amidst the consensus excitement around AI software, was a subdued disappointment and fear around venture capital (and in some ways, all non-AI startups).
People decried the end of venture capital, the end of technology, and difficult market dynamics that exist and persist. We presented AGM slides that said venture capital was not broken.
Still, today these conversations continue to pop up in times of uncertainty. The world is as chaotic as I can remember in my very simple and short 33 years of life. We expect that the world will only get weirder, and that seemingly long-tail outcomes are more likely in the long-term than many people appreciate.
What has been most odd to us though, has been the over-attention paid to the short-term by venture capital investors.
The short-termism cascades into the processes of decision making and building conviction in large-scale firms with stable LP bases just as much (and perhaps more) as emerging firms with less stable capital or results. This hysteria is antithetical to what venture capital optimally should be and is not good for founders trying to build the future. This dynamic has resulted in perhaps the least amount of durable conviction we have collectively seen in the industry in our time building Compound, and the most amount of complaining.
Put another way, it feels with minimal structural change, the wind could blow, and a large percentage of our industry could quickly shift their areas of conviction and overall sentiment around innovation.
The main pushback from many GPs we deeply respect on our strategy at Compound has always been that we are too outwardly prescriptive and that we form views that portray us as knowing more than the incredible founders we hope to partner with. The main pushback we’ve gotten from LPs we deeply respect has been we form these views across a wide range of highly difficult to parse and somewhat disparate areas.
The reality is, the work we do, reflected partially in this letter, enables us to be great partners to our founders, while allowing us to focus on the long-term progression of technology and less on the short to mid-term progression of technology markets. This approach grants us the conviction to allow founders the freedom to either align with or shatter our theses, generating sufficient signal for us to proceed with confidence and clarity, even in the face of the inherent uncertainty that comes with the knowledge that 50% to 80% of our investments per fund will ultimately prove unsuccessful.
This process has been tested in echo chambers of worlds filled with absurd optimism (2021) and now amidst a world filled with extreme chaos and uncertainty.
While we continue to work on this, staring at the rest of 2024, we feel as good as ever about our clarity and we hope that translates into partnering with incredible companies with durable conviction about technology progress and our industry of venture capital.
If Not Now, When?
We found ourselves in an incubation conversation recently in which someone said that while doing market research with a variety of academics/industry experts a potential problem we were hoping to solve was “definitely over 10 years away.”
It was at that point that I said, with some combination of intentional naiveté, combativeness, and optimism:
“If we’re looking at the collection of technologies that we all agree are inflecting right now, is it reasonable to believe that anything is definitely over 10 years away?”
We feel privileged and excited to be working on building a firm to take advantage of the large opportunity in front of us.
If not now, when?
Thank you for your continued belief and support,
Compound
