Morality in Markets, Efficient Growth, and AI
One Sunday morning, not too long ago, my wife asked me, “You seem really happy. What happened?” She caught me in the middle of reading about the bizarre twists of yet another formerly-high-flying crypto fiasco and the lack of diligence having gone into investments in the company.1 While I felt awful for the people who had been hurt by the meltdown in crypto markets (some of whom are dear friends), I could not hold back my joy now that clear thinking, efficient growth, and ethics were coming back center stage.
As I reflect on the past two quarters and plan how we position ourselves for success in the future, I’ve been asking myself several questions that I’d also like to share with you:
Can morality maximize growth?
Where do durable cash flows come from?
How do companies achieve efficient growth?
Are there durable cash flows and efficient growth in AI?
Can morality maximize growth?
As one friend and public market investor recently said to me, “capital is a claim on future human productivity.” When we look at how investors and society, as a whole, allocate capital, it is not merely a question of seeking returns, but at the heart of moral philosophy. Capital allocation impacts how billions of people today (and even more in the future) spend their lives.
What a market rewards is where capital will be allocated and, ultimately, where human effort will be spent. Will we incentivize meaningful work that creates broad prosperity, or will we dig ditches only to fill them back in?
Furthermore, this process of human effort directed by capital allocation is recursive: the individuals and institutions who accumulate capital will have their values and morals magnified by their allocation decisions, in turn, impacting what the next turn of the market whispers in peoples’ ears.
Many in Silicon Valley (and increasingly many more technology hubs) have benefitted from a half-century of techno-utopian, evidence-driven, and generally-pro-social iterated capital accumulation and deployment cycles. Founders start companies, they and their investors make money, both, in turn, allocate capital to the next generation of founders starting companies who share their values, those founders and investors make money, and so on…2
This sounds like a reasonably stable loop; however, during two times in recent memory (the dot-com boom and whatever we’ll call the 2020-2021 period of exuberance3), something happened. Instead of “normal” clear-eyed technology company building, the whole market went bananas deploying capital in a fit of speculative frenzy, paying more attention to nominal valuations going up and to the right4 than why they were going up and to the right.5
As long-term investors, our figure of merit is growth in invested capital over extended periods. In seeking growth, we can—generally speaking—invest in assets that appreciate because of improvement in their expected cash flow characteristics (“fundamentals") or because they become more desirable by the market (“capital flows”). For equity investing, prices go up because a company does well, or it gets popular: investors bid up prices and are, therefore, willing to settle for a lower return relative to cash flows.
During boom times, most/all companies become popular, making it difficult from the outside to separate returns attributable to popularity from those attributable to fundamentals. Taken to the extreme, an old joke told by Howard Marks illustrates this perfectly:
Two friends meet in the street, and Joe asks Sam what’s new. “Oh,” he replies, “I just got a case of great sardines.”
Joe: Great, I love sardines. I’ll take some. How much are they?
Sam: $10,000 a tin.
Joe: What! How can a tin of sardines cost $10,000?
Sam: These are the greatest sardines in the world. Each one is a pedigreed purebred, with papers. They were caught by net, not hook; deboned by hand; and packed in the finest extra-virgin olive oil. And the label was painted by a well-known artist. They’re a bargain at $10,000.
Joe: But who would ever eat $10,000 sardines?
Sam: Oh, these aren’t eating sardines. They’re trading sardines.
I come from the school of thought that “everything converges eventually.”6 A company’s popularity does not last forever: either cash flows catch up to valuation or valuation declines. At some point, trading sardines will be priced based on their value as food or as art (i.e. proportional to rarity).
The only way to make money going long popular companies without fundamentals (or the prospect thereof) is to get out at the right time…and market timing is really hard. When a boom market inevitably turns, investors lose money and learn lessons.
The tragedy does not end here. The boom environment that favors companies with the boldest proclamations of riches simultaneously makes it more difficult for the hardworking, emotionally-sober entrepreneurs with strong long-term prospects. They must compete for limited talent while keeping their fiscal sanity despite the siren-song of inefficient growth.
Stepping back further is the abstract, but still real, problem of “opportunity cost”—what we could have spent all that money on instead. Allocating capital to trading sardines provides no “consumer surplus”: the money invested in non-durable companies provides few individuals with a better quality of life and few businesses with greater efficiency. All of that capital instead could have gone to companies providing products and services that people want and need. If you subscribe to the theory that lasting economic growth improves the human condition, then capital misallocation is direct harm to future humans.7
Capital allocation is at the heart of moral philosophy, indeed.
Thankfully the market is, again, whispering sweet songs to companies who have the greatest chance of long-term durable cash flows.
Where do durable cash flows come from?
We might first ask, where do they NOT come from? In this bucket, I put factors that are uncontrollable and market-wide that drive short-term pricing fluctuations but not long-term fundamentals.8 Some examples include:
Sector biases—there are good companies in “bad” sectors and bad companies in “hot” sectors.9
Interest rates—when we’re underwriting for a macro environment 10 years in the future, the current rate environment should be a minor factor relative to long-term performance.10
Overall public market performance—when public markets trend down, this can impact short-term IPO opportunities and soften customer demand; however, if we’re underwriting exits a decade in the future, an improving market is a tailwind for growth rates.
Instead of worrying about the direction of the current, I believe it is more productive to focus on how particular people or companies swim relative to the current. To that end, I believe a durable strategy is to focus on efficiency and long-term happy customers11 because I believe happy customers are the initial source of cash flows. Those cash flows can only be made durable if there is an accumulating unfair advantage.12
This begs the question: where do happy customers come from?13 I believe it’s important to deeply understand customer psychology when underwriting early-stage investments: Who will buy, what will they buy, why, and when?
Who will buy?
First, in order for a startup to make a sale, someone must to go out of their way—either in their capacity as an individual or as an agent of an organization—to purchase something. One of the most important things I learned when first selling B2B is: companies don’t make decisions, people do. In large sales, there may even be multiple stakeholders with different goals who need to be satisfied.
What will they buy?
If a problem is large enough, customers are willing to commit on the promise of a product that solves the problem (common in B2B markets). If, however, a problem is not top of mind or people aren’t aware it can be solved (common in consumer markets, e.g. Ford’s car vs. a faster horse), a company must demonstrate the product before selling it.
As it translates to startup execution strategy, divining which situation a company is in determines whether founders validate a product first with customers or build a product first before attempting to validate. Choosing incorrectly can lead to a false negative (trying to validate with customers who don’t understand the problem) or outright failure (building the wrong product because of insufficient validation).
Why will they buy?
Human psychology and motivation in purchasing decisions is too complex to discuss thoroughly here; however, common patterns include: saving time, saving money, making money, and preventing harm or loss.
Sometimes, particularly with so-called “bottom-up” markets, companies employ the tactic of giving something away for free or inexpensively to a first individual in an organization who is motivated in their individual capacity to make their job easier to save time and then find a different individual who is motivated in an organizational capacity to spend money for features that bring non-individual value (e.g. security, compliance, control) to deploy the product more broadly. In bottom-up go-to-market strategies, I’ve found three requirements to get the initial individual to “buy”:
Individual value—the product must add direct value to the person who adopts it.
Permissionless—the individual must be able to start using the product without requiring approval from their manager, IT, or a complex integration process.
Discoverable—there must be some low-cost way the individuals find the new product.14
In a prior update, I discussed one of my favorite techniques for validating who might buy and why: talking to your prospective customers. Because human psychology is so complex, I’ve found no better way to validate whether customers will adopt a given product. Doing this as early as possible in a startup’s life reduces a tremendous amount of risk: if a startup can’t find those early adopters now, it won’t likely be able to find them after investing millions into building a product.
When will they buy?
Put another way: Will customer psychology change from where it is today?
In some cases, customers want something today but may not want it in the future. We need to be careful about these products, such as those that only sell in boom times or that serve a temporary state of the market (e.g. short-term COVID-induced change).
In other cases, customers don’t want something today but we have strong belief that they will in the future. Often we can find early adopters who can serve as indicators of where the secular trend might be; however, we have to be careful to believe that the product can “cross the chasm” to the broader market.
There’s a product design principle I think encapsulates directional change nicely: MAYA—Most Advanced. Yet Acceptable. Raymond Loewy, a father of modern industrial design, posited this principle of gradual advancement in 1951 after observing that people cannot adapt to radical changes quickly and companies must be careful to pay attention to both the product itself and the human psychology of individuals adopting it if they want to succeed in the market.
One of my favorite situations is where a company satisfies an evergreen need for customers with durable funding sources. Helping individuals or organizations save money, make money, save time, and reduce risk are always popular.
How do companies achieve efficient growth?
For equity investors in startups, it’s not just the cash flows a company generates that matters, but how much equity the company needs to raise to get there: dilution. If a company has a product (what) and customers (who) that are motivated in some way (why) to buy it now (when), we have “Product-Market Fit” (PMF). The question then becomes: can we sell to prospective customers efficiently? I like to break down efficient growth into two stages of readiness: “repeatability” and “scalability.”
The goal of “repeatability” is: can the company build a machine that sells their product rather than relying on the pixie-dust of founder-led sales? Founders running around having individual conversations are unlikely to deliver on long-term goals.15
This is where sales and marketing (collectively, “go-to-market” or GTM) come in. In low-unit-value consumer markets, marketing may be the primary focus, whereas, in high-unit-value B2B markets, sales may be the main focus. At the end of the day, the goalpost of repeatability is whether we can get customers to buy/use our products efficiently.
Efficiency is the key concept above because it’s not much of an achievement to sell $1.00 for $0.80. Unsurprisingly, you’ll have many takers of that bargain, and they’ll all tell their friends! Revenue grows through the roof!
If we spend money on sales and marketing in month one, we should hopefully be able to measure the sales (and associated contribution margin) attributable to that spend in future months. When the cash flow catches up to sales and marketing costs, we now have a “payback period.” As you might imagine, shorter payback periods are better because it means that with a fixed amount of capital, we can turn the crank on this growth machine more quickly and therefore achieve a higher revenue growth rate.
Assuming we achieve repeatability, the next step is to try for “scalability”, where we can invest more and more capital in our GTM engine while generating predictable cash flows.
Here be dragons. Nothing destroys capital faster than prematurely scaling a GTM engine before achieving repeatability. I like to describe this state as a “bonfire of cash” with either consumer marketing or B2B sales teams. The antidote is very clear: make sure your payback on GTM spend is real and fast before putting more money into the machine.
Some key metrics companies look at to determine how their GTM engine is faring include:
Customer Acquisition Cost (CAC)—how much sales and marketing expense it takes to gain a new customer
Lifetime Value (LTV)—there are multiple formulations of this, but typically, it’s the total contribution profit (i.e. profit after all variable costs) attributable to a customer after accounting for churn (i.e. people who decide to stop paying for the product).
CAC to LTV ratio—clearly having a CAC lower than LTV is good.
Quota to On-target Earnings (OTE) ratio—Quota is how much salespeople are expected to sell while OTE is how much you pay them for selling that amount.
As you might expect, there are ways that companies commonly misinterpret these metrics and get lured into premature scaling (or worse, intentionally obscure poor performance).
Blended CAC: Companies often have multiple sources of customers (“channels”). For example, a consumer product company may produce some excellent content that brings customers “organically” while also purchasing ads on a social network. By blending the CAC for these two channels together, the team may think they have achieved repeatability when, in fact, they have a very profitable organic channel and a very unprofitable paid marketing channel. Paid marketing is generally easier to scale, so if the company tries to move to scalability by investing incremental capital there, they will end up in trouble. Founders can spot this by always evaluating their “per-channel CAC”. Blended CAC is a lie.
CAC:LTV ratios with long lifetimes: When estimating lifetime value, one has to choose how far in the future to “count” future cash flows. Inherently this is making a judgement on customer retention. For an early stage company, this may be unknowable and it’s tempting to be optimistic particularly with the small, early-adopter customer bases that typically accompany companies at the seeking-repeatability stage. I prefer to look at CAC payback period (i.e. how long it takes for contribution margin to pay back CAC) and ensure that it’s short enough for us to use validated retention numbers to make sure a company does not scale prematurely. CAC payback period is a better metric than CAC:LTV ratio.
On the other end of the spectrum, overly-conservative companies may attempt to execute to profitability which can allow others to pass them by. I prefer to break companies down differently: instead of evaluating typical profitability measures (e.g. Net Income, EBITDA, Free Cash Flow), I prefer to think about the choices a company can make in capital allocation. For example, a company can choose to hold money in the bank, invest in sales and marketing, invest in R&D, take debt, or sell more equity.16 If a company has a profitable GTM engine (CAC << LTV) with a fast payback period (payback period << avg. customer lifetime), they’re usually best served in terms of maximizing per-share price by raising more money via debt or equity and deploying that into growth vs. constraining efficient growth in order to achieve bottom-line profitability.17 Of course, investors like to fund these sorts of companies where capital will be allocated to efficient growth.
If a company does have an efficient, repeatable, GTM engine and they choose to invest in getting to scalability, there are usually growing pains they must face, such as learning how to scale a sales team, selling into new customer segments, finding new channels after early ones saturate, making sure their technology can scale, and so on.
On achieving scalability (i.e. predictable cashflow from incremental GTM investment) we can most likely declare victory in our quest to achieve efficient growth. Founders and early investors will likely see a strong return on their investment of time and money as the market recognizes a solid and growing business. Of course, for the company’s success to be durable, they must nurture unfair advantages that allow them to outcompete future rivals (a topic for another memo).
With respect to the current environment, you may notice that the activities in the quest for product-market-fit and efficient growth are “controllable”—activities whose success is largely dominated by factors that founders can control. For that reason, I believe a profitable path forward is for founders and the investors that back them to sharpen their pencils to make sure they’re pursuing opportunities with hypothetical product market fit (i.e. validated product and customer psychology) while only growing when the company can do so efficiently.
With discipline in mind, these companies can be very successful, and with the tailwind of an economic recovery, I believe they can be objectively exceptional.
Are there durable cash flows and efficient growth in AI?
Clearly, something interesting is happening in AI; however, as startup founders and investors, how do we know if the timing is right to build great businesses?
First, one lesson many technological visionaries learn the hard way is that “being early is indistinguishable from being wrong.” Artificial General Intelligence (AGI) has long been a hazy prospect, but trying to position a business or portfolio correctly with respect to how it will occur has been difficult. Once a revolutionary new technology is likely going to exist but is still moderately far off, there are too many possibilities to make accurate predictions: uncertainty dominates. As the revolutionary technology gets closer, it enters “the adjacent possible”, and reasonable predictions become possible.
After a period of moderate progress in AI, I believe that the adjacent possible will change dramatically in the next year and will continue for some time to come. Even if it takes decades to exceed human performance on all tasks, progress is now fast enough in AI research that AGI vs. not-quite-AGI may be a distinction without a difference in many sectors: when circa-human performance gets cheaper even without exceeding human experts, the sector changes dramatically. In contrast to bullishness in crypto, AI immediately impacts many existing industries instead of trying to create a new ecosystem out of whole cloth. Vendors can implement AI capabilities in their products and immediately create value for their customers. For example, Github Copilot, an AI assistant for software engineers, has already transformed how most developers work just months after launch.
As an investor, the question then is: where will durable value accrue? There’s one view that AGI will surpass human skill so quickly that we’ll be unable to process (or survive) it as a species: the “singularity” or “fast takeoff” scenario. In this case, investment returns become irrelevant. Instead, I’d like to focus on the “slow takeoff” scenario that I believe is more likely given the immense computing power required for each advancement in AI performance.
First, what does the AI ecosystem look like? Here’s an illustrative selection of companies across various “layers of the stack"18:
Hardware—designing and manufacturing the physical hardware used to train and run AI models (e.g. NVIDIA)
Infrastructure-as-a-service (“IaaS”)— operating AI hardware as a service for others (e.g. AWS, Google, Microsoft)
Models—training and productizing the “brain” of AI systems ( e.g. OpenAI, Anthropic, Google, Stability, Midjourney)
Applications—using AI to power workflows in products (e.g. Github, OpenAI, Midjourney, Jasper, Sourcegraph, Recruitbot, Spiral)
To summarize the discussion above, I believe good startup investment opportunities must:
Delight customers
Grow efficiently
Accumulate an unfair advantage (e.g. network effects, technical defensibility, economies of scale, unique access to talent, switching costs)
Additionally, to be a good seed investment, I believe an investment opportunity must be equity efficient (dilution can wash out growth), founded by a force-of-nature team, and on a clear execution path to revenue and subsequent funding milestones.
I believe Hardware, IaaS, and Models to be currently too capital intensive (i.e. they don’t satisfy our “equity efficient” criteria); however, I’ll continually evaluate disruptive technology changes that might allow a startup to compete in these layers of the stack efficiently.
Before discussing the Application layer, I’ll ask the question: will models be a commodity? In the world where one vendor has a monopoly on effective models (generally or in a particular domain), I believe the Model layer will accumulate value; however, in a world where there are multiple substitutable models for any given use case, there will be downward competitive price pressure. In that world, application vendors can choose from a variety of models and will often choose the lowest price. Given early evidence that multiple vendors can produce high-performing models (e.g. Stable Diffusion vs. DALL-E 2 vs. Midjourney, Claude vs. ChatGPT), I believe we’re likely to enter a world where the Model layer commoditizes.
The ability to conjure code, copywriting, or images out of thin air certainly delights customers, but what makes such applications accrue durable advantages? Thin layers on top of widely available models (absent other switching costs or network effects) are probably not defensible. Novelty may seem like durability early in a platform shift, but value quickly erodes as others copy the first mover. For example, I predict many of the consumer AI photo enhancement products will face this fate.
B2B applications, however, often deeply integrate into workflows and data. As many have observed, once an organization adopts an application with deep integration as a “system of record” the application becomes difficult to remove, or “forever-ware”. If fast-moving startups can offer compelling value via AI (e.g. reducing human labor costs or opening up new opportunities by lowering the cost of decision-making) where legacy vendors are unable to match, those startups may be able to provide so much value to their customers that they overcome switching costs and disrupt entire mature software markets. These “platform shift” opportunities occur very infrequently, and I’m actively evaluating opportunities that can take advantage of this rare moment. Furthermore, we have a number of well-positioned portfolio companies that have the opportunity to use AI to accelerate their growth in large existing sectors (e.g. healthcare, robotics, content creation, cybersecurity, and IT administration).
Despite the opportunities AI brings, we need to temper our expectations with a critical view of the adjacent possible. Some particular market timing risks I’m mindful of:
Offline workflows—Where workflows are already digital or can be made digital, AI can rapidly improve efficiency; however, if workflows are primarily in-person and offline, it may take years to bring interactions online before they are “in scope” for AI.
Reliability—Generative AI models are currently generating a lot of excitement; however, they’re not yet at human-level reliability. For example, Copilot produces working code, but the output often contains bugs and security vulnerabilities. Similarly, ChatGPT, when asked for references supporting a claim, can hallucinate fake research. At the moment, humans are still needed to make generative AI output “reliable”, limiting where such models are usable in mission-critical situations.
Labor switching time—AI generates value by replacing human labor at a lower cost or enabling workflows that would be cost-prohibitive with human labor. When AI replaces a job, it frees humans to perform higher-value work while improving overall standards of living by reducing the cost of everything; however, even if overall prosperity improves, the segment of people who are directly impacted may understandably resist AI if their job is wholly eliminated (e.g. self-driving vehicles) rather than improved (e.g. AI-assisted software engineering).
This last issue—the impact of AI on labor—is rightly a significant concern for individuals, employers, and society as a whole. In the past, markets turned over slowly enough that employable skills could last for one’s entire career. As technology cycles march on, we’ll need to embrace more and more frequent “re-skilling”—helping people develop new skills throughout their careers instead of providing education only at the beginning of careers. To that end, I’m optimistic that the cause of disruption to labor markets may also be the solution: AI-assisted tutors have the opportunity to lower the cost and increase the quality of education.19 Already, large language models (LLMs) can explain complex concepts, generate lesson plans, and even pass tests for elite professions (e.g. a Wharton MBA exam, the Bar for lawyers, and the US Medical Licensing Exam).
Finally, to return to the original question of whether we can find durable cash flows and efficient growth in AI, I believe we’re in a perfect storm: a major platform shift (particularly in B2B applications), a reasonable valuation environment, and less competition for talent all occurring simultaneously. Of course, even in attractive situations such as the present, founders and investors must critically evaluate execution risk, defensibility, and market timing if we want to build durably great businesses.
To be clear, I believe there are and will be many good blockchain/crypto businesses; however, the lack of a moral compass and suspension of critical thinking in the space has rubbed me the wrong way.
I’m certainly an example of this: many of my early advisors and investors were successful because of the previous generation of advisors and investors, etc.
CoBoom has a nice ring to it.
As covered in my last letter, mark ups equal promotions for early-career venture capitalists, driving many to invest in companies with the most momentum, not necessarily the best long term prospects.
Venture capital commonly has cycles of “hot” sectors driven by the promise of a new platform—e.g. social local mobile apps, VR/AR—but in such boom times, all sectors are hot.
At the same time, I believe the related saying on why it’s hard to trade against exuberance to also be true: “markets can stay irrational longer than you can stay solvent.”
Economist Tyler Cowen has an excellent piece, Stubborn Attachments, that I’d recommend if you’d like to dive deeper into the point of view that we have a moral duty to focus on economic growth.
Investing at too high of a price relative to residual risk can indeed decrease long-term returns; however, picking the right company tends to be more important than picking the right time given the difference in returns between “great” (100x+) and “not great” (0x) investments.
Of course, we should “know where the bodies are buried” so we do not repeat the mistakes of others.
There are some notable exceptions here where a business’s likelihood of success depends on the current rate environment, such as those who require extensive debt financing or where cash flow comes from lending (“net interest margin”) and whose customers are rate sensitive, but we can catch these exceptions in understanding customer psychology, which I cover below.
I believe founders who deeply internalize these values, even when they’re out of style, will outperform the pack.
In some cases, durability can occur with unhappy customers, such as the case where a company has a large existing customer base and their product has a high switching cost.
How we underwrite the prospect of unfair advantages is a topic for another day.
One boon for the technical infrastructure/dev tools market is that engineers are *always* searching for new and better ways to do things. Nerds love to try new things.
Founder-led sales can, however, take a startup reasonably far when it has large deal sizes.
Later stage companies also have options of issuing dividends, share buybacks, making acquisitions, etc.
The criticism of tech companies as “never profitable” makes no sense to me if the company can allocate capital to growth at a much higher rate of return than alternative capital allocation decisions.
I’m conspicuously leaving out AI developer infrastructure because it’s unclear to me whether the existing machine learning and data science infrastructure market is distinct or not.
For a potent vision for AI-powered education, read Neal Stephenson’s Diamond Age.