Four years ago I set out to correct a long-standing misnomer in how founders and investors talk about startup valuations. Turns out, it was a thornier issue than I realized, and a proper fix required solving a few puzzles first. This is the first of two essays building up to that answer.

Here’s the first insight: the population of active Seed-stage startups is shrinking. Not necessarily the funding (in aggregate dollars or the average round size) but rather the number of active Seed companies.

Despite the AI boom, exits now outpace new Seed financings, and no one seems to have noticed.

Missing data

To make that claim, I needed a measure of the total number of seed stage companies that are “alive” at any moment. This is surprisingly difficult to locate, as no one tracks this.

This is understandable – companies announce their initial Seed financings, they might publicly celebrate their Series A funding, but they typically don’t announce shut downs (ironic, given it’s the most common outcome). They mostly just die off (“quiet quitting” as they say). When startups do announce an abrupt exit, databases like PitchBook don’t always record this, and even when they do you can’t extract the data at an aggregate, “macro” level.

This creates a missing data problem. One can get around this, but it requires making assumptions about the way the world works that allow you to plausibly “guess” what the missing data looks like.

In this case, I needed to make assumptions about the graduation and “death” / exit rate of Seed stage companies. Knowing this, I could estimate the count of live Seed startups by adding up all the surviving companies across past and present Seed cohorts, estimated with those graduation and exit rates.

Graduation statistics

Thankfully, there is some data out there on graduation rates that I can leverage in multiple ways.

I’ve previously discussed the shifting winds around the graduation of Seed stage companies to Series A, particularly after the 2021 boom period:

... the numerous startups that raised Seed funding during the boom are now facing a massive bust... It’s safe to say the graduation or survival rate from Seed to A has fallen by more than half. -- The Series A Bust

This has been well-documented by multiple sources:

The percentage of companies who make it from seed to Series A within two years fell by a lot for the 2021 seed cohort.

27.5% of companies that raised a seed round in 2019 made it to Series A within 2 years.

Only 17.6% of companies who raised their seed in 2021 have “graduated” to the next round. – Peter Walker (Carta)

It’s hard to believe today, but at some point in the not-too-distant past over 50% of Seed stage companies “graduated” to Series A. That’s unthinkable these days, as most recent Seed cohorts are trending well below that:

I fit S-curves to various benchmarks of Seed conversion to Series A. That part was easy enough (and yes I realize they don’t start right at zero, this was hard to enforce for various technical reasons):

However, I also needed two more things:

1. something to inform the earlier Seed cohorts that Carta and other sources don’t publish, and

2. some way to ground this in the Series A data, most of which represents Seed companies graduating to the next stage.

In other words, the number of graduations our model estimates should correspond to the quantity of Series A deals getting done.

I assume that all Series As “come from” a Seed company that was part of a prior cohort. This is not exactly true: some companies go straight to Series A as their first round of funding. This was more common in the distant past than it is today. So I’m cheating here slightly to keep things simple, but I think it’s fine.

I embedded this in a larger statistical model that estimates the evolution of Seed graduation curves over time, matching both the benchmarks but also the aggregate data on Series A deals. In effect, I backed into the underlying cohorts at the micro level that are driving aggregate deal counts at the macro level. I constrained neighboring cohorts to be “similar” in their behavior, to avoid overfitting the data and ending up with erratic shifts between cohorts.

It’s not perfect, but I was able to get a pretty good fit to the aggregate data without any obvious overfitting:

Doing this reveals some interesting dynamics in the underlying cohort data that are worth highlighting.

First, the graduation rate. More exactly: the eventual proportion of Seed companies in a given (quarterly) cohort that will graduate to Series A. We can see below how that has trended over time – reasonably stable through early 2020 before suddenly falling off a cliff:

From the model I can also estimate the “half-life” of a cohort – the time it takes for 50% of the eventual graduations to occur. This has been mostly stable over time, hovering in the 6-8 quarter range throughout the past 15 years. Not much action here.

Lastly, I estimate how quickly the graduation inflection and eventual saturation happen, with higher “speed” implying more aggressive curvature of the S-curves. The graduation curves have trended more gradual, moderating over time (don’t bother trying to interpret the units).

Here are my estimated cohort graduation curves:

This helps visualize the dramatic collapse in graduation rates, with a very clear “before and after”. And it only took a quarter or two for the new reality to set in.

Inevitabilities: death and taxable M&A events

As I mentioned earlier, there’s no obvious source of data for the death or exits of Seed stage companies, so I need to make assumptions.

I assume any company that doesn’t graduate to Series A “exits” one way or another – either shutting down or getting acquired. The specific outcome doesn’t matter for the analysis.

I further assume that companies within each cohort graduate or exit on S-curves with matching shapes but different end points:

• For example, if 50% of the companies who will ever graduate have done so by quarter six, I assume half of the eventual exits (either acquisitions or shutdowns) have happened by that date too.

• This is of course not true – strong companies tend to raise capital sooner than weak companies quit. But I think it’s fine to fudge a bit here, and it gives me a nice way to use the data I do have to infer the data I don’t have.

This gives us the last piece we need. We now have all the flows in (new Seed deals) and out (graduations and exits). Assuming some reasonable starting point, we can estimate the active stock of Seed startups from past to present.

Stocks and flows

With all that out of the way, the last thing to do is also the simplest – just add up the “alive” companies (those that haven’t yet graduated or exited) across all cohorts at each point in time.

Stock-flow equation: Active Seed Stock = Prior Active Seed Stock + New Seed Deals − Graduations − Exits

Here’s what I got:

Seeds rose, they peaked, then they fell. The peak is roughly Q3 2022, and the live population has been in free-fall ever since.

What’s extra striking to me about this chart is that no one (to my knowledge) has ever bothered to construct it, despite how naturally it follows from the graduation data. Once you have the graduations and a plausible assumption about the timing of exits, this graph naturally falls out.

The data itself is striking too. Maybe I missed it, but I don’t see anyone talking about the apparent empirical fact that the total stock of Seed stage companies is falling. Despite all the crazy headlines about venture funding of AI startups, that torrent of activity is not enough to displace the lost activity from the post-2021 venture market reset.

Looking at rates (flows divided by the stock), the model estimates that in 2024 exits alone (i.e. excluding graduations) began to exceed new Seed financings. 13% of active seed startups are shutting down or being acquired (I’d assume most are shutting down) each quarter, while new financings are only adding about 11% to the pool:

• The uptick in quarterly exits is notable, a doubling relative to the historical norm of ~6%

• Quarterly graduations have fallen somewhat, but seem to have bottomed out

Taking stock

This analysis challenges the representativeness of the headlines that grab attention in Silicon Valley:

• Yes – there’s a lot of activity right now in AI and there are many companies getting funded to attack that opportunity head-on.

• Meanwhile, there’s a whole set of pre-AI companies that got funded during the last boom that are “not going to make it”, and that process is still very much working its way through the system.

Despite all the present activity, the total number of Seed stage companies is in decline.

This is not surprising when you notice that every company that does get funded has something to do with AI.

• What happened to all the perfectly good, non-AI opportunities? It can’t be the case that the “true” opportunity set is 100% AI related.

• There must be a large set of opportunities that aren’t getting funded right now because investors are locked in on AI and still licking their wounds from all the non-AI, pre-AI companies they funded that didn’t or won’t work out.

Prior startup cohorts are flunking out, and the market is funding a narrower style of business today. This has driven the rate of startups exiting the gene pool above the rate of fresh blood entering the system.

This was not what I originally set out to find, but I’m glad to have stumbled upon it. This phenomenon is badly under-discussed.

Part two − the valuation misnomer that started me down this path − coming soon.

Originally published on whoisnnamdi.com

Open source LLMs are 90% cheaper than closed source models at the same benchmarked intelligence level. Yet they capture less than 30% of token share.

If intelligence were all that users cared about, price competition should have closed this gap by now. It hasn’t.

Open source’s share of token volume on OpenRouter, the leading LLM aggregator, has been stable at 30% for some time, despite all the shifts in the broader market. They even go so far as to call this an “equilibrium.”

Standard economic intuition says that, at the same quality level, the cheaper option should win. The LLM market seems to violate this. Something else must be going on.

This puzzle has a precise analogue in economic theory – latent demand. And rather than AI researchers, it’s economists and finance researchers who have the most to say about it.

Priceless

A group of economists from MIT, Boston University, and Microsoft recently published an empirical study of LLM market dynamics, what they call “The Emerging Market for Intelligence.”

The irony of that title: the paper’s results suggest consumers of LLM APIs care about far more than mere intelligence:

… open-source models are approximately 90% cheaper than closed-source models, conditional on the same level of intelligence… Nonetheless, the share of tokens consumed from open-source models remains consistently below 30%, suggesting meaningful differentiation between open and closed-source models not captured by intelligence measures – The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs

Intelligence only explains so much. You can see it visually here, where open source models at any given level of intelligence are consistently cheaper:

Not just that, but there’s incredibly wide dispersion in prices at every level of intelligence. In fact, prices vary by up to two orders of magnitude, and models that are much more expensive relative to their benchmark performance still see substantial use.

That dispersion of pricing has only gotten more severe over time despite prices falling overall. The price-to-intelligence ratio, which you can think of as representing a measure of quality-adjusted token pricing, has become more dispersed over time:

More price puzzles: the average price actually paid for tokens (as opposed to simply the average or median list price) increased over the course of 2025. This seems to have been completely missed in the discourse last year and is strong evidence that price is not a primary motivating factor for many users:

A reasonable hypothesis is that the observed run up in token-weighted prices for output tokens was driven by the release of the Claude 4 models in May 2025. This occurred during a period where, per the prior chart, intelligence-adjusted prices were falling through the ground. This is a powerful argument for revealed preference of users, who migrated toward premium models to pay for something benchmarks don’t fully capture.

The authors conduct a series of regression analyses across about 150 model-provider combinations to further dig into the drivers of pricing across models, controlling for intelligence, being open source, reasoning capabilities, context length, and other factors. Immediately we can see a few interesting phenomena:

• Intelligence alone doesn’t determine pricing: the of a simple regression of token price on benchmarked intelligence is only 10%

• Age is just a number: adding in controls for the age of a given model doesn’t explain much variation in pricing, only increasing the by ~4% over and above the baseline controls. Surprisingly, there’s no guaranteed obsolescence here

• Brand matters, even more so than intelligence: Adding in controls for the model creator bumps up by 13%, even after including many other controls, explaining more pricing variance than intelligence alone

In total, intelligence clearly matters but it’s far from being the only relevant pricing factor.

Volume control

That’s pricing, what about quantities? Here too we find a complex picture with no simple answers.

Again, intelligence is not destiny – LLM customers are making decisions based on many factors. If you weight intelligence over time by the actual token usage of those models on OpenRouter, what you see is that typical usage concentrates far from the frontier of intelligence. The median user isn’t using anything close to the smartest model, and even the 90% percentile token is coming from a model that is well below the best available:

Some more regressions, this time with token volume as the dependent variable.

• Pricing matters but only so much: Prices have a statistically significant relationship with token demand, but within a given model, prices explain only 18% of the variance in token volumes. The remaining 82% is driven by other factors

• Price competition is zero-sum: The price elasticity of demand is almost exactly one, meaning a 10% price cut generates roughly 10% more volume, leaving revenue (price times quantity) unchanged. To grow revenue, providers must shift the demand curve itself through alternative means

• HODL the model: Within a given model/provider combination (rightmost column), price is again statistically significant but explains almost nothing (within of 5%). Most of the action is in the discrete choice of which provider to use in the first place, which is driven largely by factors other than price

We saw a drastic example of this phenomenon during the release of Grok Code Fast 1, xAI’s extremely fast and cheap coding model, with heavily subsidized pricing (even as low as free). Grok Code Fast 1’s release had zero impact on the demand for other models, suggesting more nuanced user preferences not easily swayed by cost:

The pattern on the quantity side mirrors what we saw with pricing. Intelligence alone explains little price variation; price alone explains little quantity variation. In both cases, the observable characteristics we fixate on (benchmark scores, token prices) leave most of the picture unexplained.

Users don’t always pick the smartest model, and they aren’t purely chasing the lowest price. Something else is guiding their choices.

Latent space demand

The asset pricing literature has developed precise tools for exactly this kind of problem. In “A Demand System Approach to Asset Pricing” Koijen and Yogo construct and estimate a “demand system” for public equities. They decompose demand for a given stock into two components, demand driven by observable characteristics (price, profitability, book equity, dividends, etc.) and demand driven by what they call latent demand -- investor preferences for attributes we cannot observe.

We refer to [this] as latent demand, which captures investor‘s demand for unobserved (by the econometrician) characteristics of asset – A Demand System Approach to Asset Pricing

When they apply this framework to the data on U.S. stock returns, they find something striking -- observable characteristics on both the supply and demand side only explain ~20% of the cross-sectional variance of returns. The remaining 80% is dark matter -- driven by unobservable, latent demand among investors:

Thus, stock returns are mostly explained by demand shocks that are unrelated to changes in observed characteristics. This finding is consistent with the fact that cross-sectional regressions of stock returns on characteristics have low explanatory power

Koijen and Yogo are careful to point out that they cannot directly identify what latent demand contains (by definition); they can only quantify how much it matters. Some of their hypothesized drivers: structural constraints (certain investors cannot invest in certain companies), heterogenous beliefs (a fancy way of saying that investors “agree to disagree”), and herding behavior (e.g. meme stocks).

So in the most liquid, most efficient, most studied market in the world, unmeasured factors dominate measured ones by 4:1. How much of AI demand could we realistically hope to explain?

Not much. A non-exhaustive list of factors that could drive one’s choice of LLM:

• Reliability and uptime guarantees (you know who you are)

• Safety policy and content moderation posture (you also know who you are)

• Tooling and ecosystem integration

• Support quality and SLAs

• Privacy and data retention policies

• Brand and reputational risk

These are dimensions that benchmarks like MMLU and SWE-Bench don’t capture. The ~10x price premium for equally intelligent closed source models hints that these unobserved factors really do matter. It’s the LLM equivalent of Koijen and Yogo’s finding: latent demand is the rule rather than the exception.

The AI economists acknowledge this:

There are two potential explanations for this gap. First, the Intelligence Index may not fully capture important differences in model capabilities. Second, users may systematically discount the value of open-source models—either because of actual or perceived differences such as weaker brand reputation, privacy concerns, or less extensive customer support – The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs

Both explanations point to the same conclusion: latent demand is most of the demand.

In fact, the price regressions we looked at earlier show exactly that: intelligence only explains 10% of price variation across LLM providers. Adding in other characteristics like whether the model is open source, reasoning capabilities, context length, and age only gets you to ~45%.

Only by controlling for the model creator (e.g. Anthropic, OpenAI, DeepSeek, etc) are we able to explain more than half of LLM pricing variation. Further, controlling for whether the model is open source muddies the water a bit, since most model labs are either fully open or fully closed source, meaning some of what open source is “explaining” is actually a brand effect.

In total, this data suggests that the identity of the model creator is the biggest single explanatory factor in LLM pricing.

If it fits, it sticks

One way to explore this brand effect is to observe the degree to which users stubbornly “stick” to a particular model, i.e. the degree to which they maintain strong preferences irrespective of price or evolving alternative options.

Here again we can analogize to the stock market. In the decomposition of stock returns from before, the researchers broke latent demand into two parts: the “extensive margin” (certain investors only buying certain stocks) and the “intensive margin” (how much of each stock investors hold, assuming they hold the stock at all).

Applying this framework to the OpenRouter data, we see substantial evidence for both the extensive and intensive margins playing a role. For example, users concentrating all of their usage on a single model aligns with the extensive margin, while users using many models but varying in how they distribute their use cases between them points to the intensive margin.

The data? Just over 50% of OpenRouter users use only one model. Among those that use multiple models, a very large share of users allocate almost all their usage to only one of those models:

To quote the study:

Our interpretation of these patterns is that, for most firms, multi-homing reflects experimentation rather than intensive dual use of models tailored to specific tasks – The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs

That is to say, to the extent users leverage multiple models, it’s mostly for experimental purposes rather than production use. When it’s time to get serious, most users make a singular choice and stick with it. The extensive margin dominates.

OpenRouter themselves recently published their own analysis which came to a similar conclusion, which they call the “Glass Slipper effect” in a nod to Cinderella:

The hypothesis posits that in a rapidly evolving AI ecosystem, there exists a latent distribution of high-value workloads that remain unsolved across successive model generations. Each new frontier model is effectively “tried on” against these open problems. When a newly released model happens to match a previously unmet technical and economic constraint, it achieves the precise fit — the metaphorical “glass slipper.”

For the developers or organizations whose workloads finally “fit,” this alignment creates strong lock-in effects. Their systems, data pipelines, and user experiences become anchored to the model that solved their problem first – State of AI: An Empirical 100 Trillion Token Study with OpenRouter

In the LLM market, it’s a competition to be someone’s #1 pick, and there’s no prize for 2nd place.

Genius is not homogenous

The LLM market presents a puzzle: massive price dispersion at equivalent measured intelligence, persistent market share gaps between closed and open source models despite premium pricing, concentrated usage patterns at the user level despite a panoply of options.

Old school asset pricing models failed because they assumed homogenous investor preferences. In the famous CAPM model, everyone holds (or at least should hold) the market portfolio. In reality, investors have wildly divergent preferences that show up as latent demand.

Similarly, the obsession with model benchmarks assumes homogenous user preference for intelligence over all else. But in fact users have varying preferences that benchmarks don’t and can’t reflect.

So what are the implications? Here’s a crack at a few:

• Models aren’t commodities (yet): It seems many users are willing to pay a premium for quality, capabilities, or stability. Differentiation is possible, and the inordinate influence of latent demand reflects that

• Moats aren’t about weights: Defensibility may be less about locking down model weights and more about trust, safety posture, and other intangibles

• Benchmarks measure what’s measurable: We’re measuring what’s easy to measure, not always what matters. Most of what drives relative LLM adoption lives in dimensions that are hard to quantify. Benchmark-maxxing isn’t everything.

The benchmark obsession is understandable. Numbers are legible. Rankings are simple.

But the market is telling us something different: tokens aren’t fungible. Despite convergence in capabilities across models, in the eyes of the market, “all models are equal, but some models are more equal than others.”

Originally published on whoisnnamdi.com

Seed valuations don't behave like valuations:

• They are too stable over time for such a speculative asset

• They are impervious to shifting interest rates

• They don’t follow public tech valuations

In short, seed valuations are a bit of an enigma — it’s not at all obvious what drives them. However investors arrive at these numbers, they aren’t doing so based on typical Finance 101 factors like discount rates or comparable company analysis.

Seed companies don’t seem to be priced as businesses with intrinsic value derived from future cash flows. Rather than venture capital, they seem to be a proxy for the human capital of the founders and early team.

Seed valuations aren’t valuations.

Non-standard deviations

First: seed valuations aren’t volatile enough.

This really stood out when constructing my Venture Activity Index – the volatility of the late stage is much higher than the early stage, which is true even if you exclude the pandemic era:

This is strange. If seed stage investments are as speculative as they’re purported to be, we'd expect wild valuation fluctuations over time. Late-stage valuations should be more stable, since they have an existing business model, revenues, and potentially even cash flows (hard to believe, I know).

Imagine if large, blue chip stocks were more volatile than small caps. That wouldn't make much sense. The stability of seed valuations relative to more mature startups makes no sense either.

As I’ve previously covered, capital flows affect valuations, and this force is most extreme at the later stages where the so-called "crossover funds" (funds who've crossed over from public market to private market investing) are most active. Thus, capital flows drive excess volatility at the later stages. However, I don't think that can explain everything going on here.

And yes, there are many more seed deals getting done each quarter than later-stage rounds, so due to some fundamental laws of statistics we’d expect less variability with a larger sample. Even so, seed valuations seem too stable on a relative basis.

Discount rates don’t matter if there’s nothing to discount

Second: interest rates don’t affect seed valuations.

In the standard corporate finance logic, early stage companies should be the most sensitive to interest rates because they have the highest duration. Think of duration as a measure of distance, measured in years, to the average dollar of cash flow for a business. For early stage companies, all cash flow the business will ever produce is far into the future, so they definitionally have high duration. In a discounted cash flow model, seed valuations would be extremely sensitive to movements in the discount rate, similar to a long-dated bond:

Duration measures the sensitivity of the value of a bond to a change in interest rates, which is tied to the lifetime of the bond. Bonds with longer tenure or back-loaded cash flows are more sensitive to changes in interest rates

Due to the multiplicative nature of discounting, the present value of far-away payments is more sensitive to a change in interest rates than the value of soon-to-come payments – High Retention = High Volatility

That's not what we see. Some time ago I investigated the “average” impact of interest rate surprises on venture valuations across all stages. I've since revisited and refined that analysis, adding more granularity to the estimates. I've also stripped out the COVID era, where the impact of fund flows contaminates the estimates.

What I found was fascinating. Here's the impact of a surprise 1% increase in the one-year Treasury yield across seed, Series A / B, and Series C / D, along with a measure of uncertainty around these estimates, going twelve quarters out:

• Interest rates have no effect on seed valuations. Not only is the impact zero, it’s precisely zero. There isn't a ton of uncertainty around these estimates.

• For Series A / B, we start to see some effect, maxing out at a 16% decline seven quarters after impact before recovering.

• For proper growth stage rounds like Series C / D, the impact is even quicker and more severe, peaking at 19% six quarters out.

This is the most striking evidence — the clearest indication that investors value seed stage startups in a totally distinct way to the rest of the venture market. It’s hard to rationalize this evidence within the usual frameworks.

They not like us

Third: public tech valuations don’t influence seed valuations.

When the big tech (“FANG”, the “Magnificent 7, etc) valuations move around, private valuations typically follow, with a lag. This makes intuitive sense given venture investors use comparable public company valuations to decide how much they’re willing to pay for private companies. In a rational market we’d expect some correlation between public and private valuations, even if imperfect. This is “comps analysis” in a nutshell.

And that’s what we see — except for seed companies:

When the Nasdaq rises 1%:

• Series A through D valuations rise, matching the bump in the Nasdaq within about a year.

• Seed valuations don’t move at all, marching to the beat of their own, relatively quiet drummer

While there’s a very clear pass-through effect of public prices on private valuations for most stages, seed startups are the exception, seeing no pass-through at all. It turns out, investors don’t care about public comps when pricing seed stage companies.

It’s clear that seed valuations are not really valuations in the traditional sense. They don't behave like valuations in either their volatility over time or their sensitivity to interest rates. Something else must be going on here.

These prices are sticky too

It struck me that seed valuations were incredibly sticky (an early working title for this essay was "Why are Seed Valuations so Sticky?”). Seed valuations neither rise nor fall dramatically far from trend, whereas other stages see much strong gyrations:

This "stickiness" is unique to seed valuations and doesn't mirror the behavior of free-floating financial assets, which are typically much more volatile and difficult to forecast.

I stared at the seed valuation data for a long time as I contemplated this essay. As my eyes glazed over, I tried to come up with analogues, other phenomena that mimic the behavior of seed valuations.

Then it hit me – wages. Wages are often said to be “sticky”, and seed stage valuations look eerily similar to wages over time, which also tend to be quite stable around a long-term, upward trend.

The simplest thing to do is plot compensation against seed stage valuations. I found a wage series called the “Employment Cost Index” (ECI) that measures compensation growth over time, and I pulled out a version of this that’s specific to “professional, scientific, and technical services,” which I take to be a good proxy for tech workers:

• The first thing that stands out is the obvious difference in growth rates. Seed stage valuations have risen much faster than wages for the typical tech worker. For every 1% increase in tech worker wages, seed stage valuations grow 4-5%.

• We can predict seed stage valuations from the wage data. I regress seed valuations on the ECI using pre-2020 data. The fit is tight pre-2020 (which is frankly easy since they’re both roughly straight lines).

• Then I evaluate its forecasts on post-2020 data, “out of sample”. The model returns to a reasonably close fit once valuations settle down after the 2021-2022 bonanza.

• Notably, seed valuations bottomed out at exactly the level you would have predicted using the employment cost index.

This feels like more than coincidence. Regressions do have a high risk of being spurious, which is to say, total nonsense. I was skeptical the first time I ran these numbers. But after multiple sanity checks, this seems to be the real deal. There is a tight connection between seed valuations and wages for tech workers; the two follow the same trend, one an accelerated version of the other. Thus we have a scaling law between tech wages and seed valuations:

The inverse of stock-based compensation, seed companies appear to be compensation-based stocks, at least in how they’re valued.

Google is my BATNA

This analysis doesn't prove anything, but it does suggest an interesting link between tech wages and seed valuations.

Why would seed valuations be linked to the cost of tech labor? And why would those valuations grow so much faster than wages? I’m not even 100% convinced that it’s a direct, causal connection — perhaps there’s some third variable that drives both. Totally plausible. I plan to explore this in a future piece.

Regardless, it’s quite clear to me after crunching the numbers that seed valuations don’t behave anything like valuations, at least not valuations of companies or speculative assets. Their stability suggests investors have a precise sense of their worth, despite their riskiness. The value investors place on these companies does not fluctuate wildly over time.

This is odd only if your mental model values these fledging enterprises as… enterprises. What if seed stage valuations instead represent the value of the labor and human capital of founders and early employees? The behavior of seed valuations would make a lot more sense if we saw them as proxies for the opportunity cost of talented tech workers.

I’ll explore this hypothesis in a future essay.

Originally published on whoisnnamdi.com

To celebrate Patronus AI’s $17M Series A, I wanted to share some thoughts on the current state of AI benchmarking.

Imagine a standardized test that works like this:

• The questions and answers are freely available on the public internet

• Cheating is not regulated, it’s a 100% honor system

• The exam never changes, it’s the same questions every time

• Scores on the exam seem to be getting better every year

In case you’re wondering, the correct reaction is “this is not a very good exam.” Cheating is rampant. The answers can all be memorized. The test isn’t really much of a “test.” The scores are meaningless.

This is the current state of AI benchmarking. Ironically, we hold AI benchmarks to a very low standard. We allow practices that would never fly in other serious domains. It’s happening in plain sight, with a nod and a wink every time an AI lab releases a new model that tops the leaderboards.

We must do better.

Something’s in the water

The first dirty secret of AI benchmarks is quite literally, dirty: training data contamination.

A common refrain among machine learning researchers and engineers — “don’t train on the test set.” In other words, don’t train a model on the same dataset that you will evaluate it on. If you do, whether on purpose or not, that particular test is useless for evaluating model performance.

Most open benchmarks are available on the public internet. In other words, the entire set of questions and answers exists on the open web, likely in multiple places. It is extremely easy for this data to inadvertently make its way into LLM training pipelines. The big AI labs are constantly hoovering up data from the internet via web crawlers, scraping their way across the web. While diligent researchers are careful about what ends up in the training data, applying all sorts of sophisticated filters to the raw data before feeding it to their models, invariably these filters are imperfect, and data from the benchmarks leaks in. It can happen during pre-training, it can happen during fine-tuning, either way, the bad data gets in. This renders invalid the downstream model performance on the benchmark.

The test data from a particular benchmark can appear in many places. It could be in plaintext on a webpage somewhere. It could be a file in a GitHub repo, alongside code which a model is being trained with. It could be in the comments of a file or script inside of a repo. It could be on Reddit or X. It could really be anywhere.

An increasingly important issue with evals is test set contamination. After the creation of a good eval, examples of the eval tend to get propagated into various places in the internet, like arxiv papers, ChatGPT examples, or reddit — Jason Wei, OpenAI

It’s been shown that a single training example can meaningfully impact the behavior of a model, so it only takes one mishap to contaminate a model. Model builders routinely train on trillions of tokens, so there’s no shortage of ways for accidents to occur.

You might wonder, if the entire benchmark makes its way into the training data, then why don’t the models score perfectly on the benchmarks? Remember, these models are complicated mathematical functions of the data — they are optimizing for all sorts of criteria that we don’t fully understand (though we’re making progress) and have been trained on vast sums of data, so any particular benchmark represents a very small share of the overall training corpus. A model trained on a particular dataset will fail to reproduce that data at times. This complex and non-deterministic mapping of inputs to outputs is arguably the distinguishing feature of LLMs relative to traditional machine learning models. So we shouldn’t necessarily expect to see perfect benchmark performance even in the most blatantly contaminated scenarios.

It’s not what you think

The next time you hear about how a new model crushes some well-established benchmark, take a moment to actually go to the primary source and review the tasks in the benchmark. Pick any of them, it doesn’t really matter which. I would bet that, even as a non-specialist, you will very quickly begin to question whether the benchmark is really testing what it claims to test.

Sometimes this is subtle; sometimes it’s not. Many AI coding benchmarks have fairly trivial solutions that don’t require meaningful code understanding or “intelligence.” Many “reasoning” benchmarks don’t require any reasoning.

Again, don’t take my word for it — I encourage you to, right now, read through a few examples from the many published benchmarks, and see what you think about the concepts they are purportedly testing. You will be less than impressed, and you will leave the exercise with a much reduced opinion of and trust in the performance of the latest and greatest models.

This also relates to my point earlier about contamination, which is especially pernicious in the software development domain. Many “brain twister” coding tasks are well-represented in the training data for these models, in part because software developers spend so much time preparing for such questions as part of job interviews. Coding competitions abound, which can serve as a signal to employers of an engineer’s talent and skills. Their competition code often ends up on the public internet, where it gets picked up by training data crawlers.

At test time, the model has already seen the question, perhaps in slightly modified form, and has memorized the answer. It’s not right to say a model regurgitating some code from some repo is “reasoning” about software development. It’s much more akin to an engineer getting stuck and looking up some code on Stack Overflow, pasting it into their editor, and moving on.

There’s nothing wrong with a “Stack Overflow bot”! I would and do pay for such things. I just think we need to be very careful and specific about what we call it.

Teaching to the test

Critics of standardized tests often complain they encourage teachers to “teach to the test”, focusing exclusively on material that will improve their students’ performance on the test but that perhaps misses other information valuable to their overall edification as human beings and citizens. I think this critique has bite to it in the context of AI models.

Here’s why. Kaggle is the most famous competition platform for machine learning practitioners. They hold frequent competitions where the goal is to build a machine learning model that can generate accurate predictions in a particular context based on provided (or otherwise procured) inputs. A key feature of Kaggle competitions is the public leaderboard — the real-time ranking of submitted models on a “validation” dataset that only Kaggle has access to. Thus, they avoid the problem of data leaking into the training set.

However, as is well-known among Kagglers, this “one weird trick” doesn’t solve all problems. After some time, the competition closes, at which point every model is re-evaluated on a previously closed “test set”, the scores for which make up the “private leaderboard”. It is extremely common for the “best” model from the public leaderboard to fall a few places in the rankings on the private leaderboard and for another model to rise to the top. In fact, this happens so often that being in 1st place going into the final evaluation can often be a source of stress rather than relief, since you know there’s some risk you’ve “overfit to the public leaderboard.”

Many users watch the public leaderboard closely, as breakthroughs in the competition are announced by score gains in the leaderboard. These jumps in turn motivate other teams working on the competition in search of those advancements. But it’s important to keep the public leaderboard in perspective. It’s very easy to overfit a model, creating something that performs very well on the public leaderboard, but very badly on the private one. This is called overfitting.

Even though your model never saw the validation data directly, you did see its score on the data. That score is a noisy proxy for the data itself, so anything you do to improve the score implicitly leverages features of validation data, even if you never saw data directly. Since that score influenced the techniques and tricks you employed during model training, there’s a sense in which the validation data did “leak” into training.

You’ve effectively “taught to the practice test”. And as anyone who’s experimented with the materials of various test prep providers, practice tests can differ meaningfully from the actual test.

The same risk applies to most AI benchmarks, which are the “public leaderboard” of AI evaluation. At any point during the training process, researchers can check their models against the benchmark to see how it’s doing and ensure that performance is moving in the right direction. Benchmark performance isn’t a “surprise” at the end of a training run. It’s the same with Kaggle contests — contestants can always check their performance via the public leaderboard. This creates perverse incentives to only do things that drive direct improvement on the benchmarks, leading to overfitting, the curse of all machine learning practitioners.

Again, if you want some intuition, think about the education context. Imagine if teachers knew in real-time how their students would perform on the test — a score every day of class rather than one a year. Whatever concerns you have about teaching to the test, you should be only more concerned if teachers had a real-time view of their students’ test performance. The dopamine rush of seeing those scores bob up and down every day would drive an even more myopic focus on test performance. Anything a teacher could do to drive up those scores, even if it meant rote memorization of facts, would be extremely tempting. This is the world we live in when it comes to AI benchmarking.

There’s a balance we must strike. To the degree the test actually tests what you think and hope it does, it makes sense to “teach to the test”, assuming it’s actual learning and not just coerced memorization. However, if the test isn’t perfectly correlated with the true objective, these efforts can drive perverse behaviors in both the teachers (trainers) and students (models).

Closed source benchmarks

I’m a big fan of open source when it comes to software. I’m a big fan of closed source when it comes to standardized testing.

Open benchmarks have an important role to play, and I don’t think we think we should dispose of them. A common basis of comparison that is open access, that we can all inspect and contribute to, is certainly valuable. But we also need to round out the ecosystem with closed, private benchmarks, a critical complement.

Just like in the world of standardized testing, private benchmarks would need to establish themselves as standard-bearers for quality, which requires building up trust with the broad community. In a competitive market where end users of these models really do care about quality and performance, benchmark providers will sprout up and compete for their business, jostling for position as the ultimate arbiter of model performance. In the limit, the benchmarks will be benchmarked, in the same way that colleges are ranked for quality in explicit and implicit ways. Trusted “brands” will emerge.

Ideally, these private evaluators would not train and market state-of-the-art foundation models themselves, as there would always be an incentive to either leak the benchmark data to the training pipeline or craft a benchmark that the researchers know their model will perform well on.

In a perfect world, you would have blind benchmarking, which is to say benchmarking that is obfuscated from the folks who trained the model, so that there would be no way for the training pipeline to be contaminated. Model trainers would not know in advance what the benchmark is or what the questions are. They would train models to the best of their abilities, and only know how it performs on the benchmark after training is done. This is analogous to pre-registration of scientific studies, where researchers pre-commit to a certain methodology before conducting a trial or analysis. 

Even better, you would want one-time benchmarks: benchmarks crafted *once* and then only used *once* to test a model or a set of models. At that point, the benchmark would be “used up” and no longer valid. That’s how Kaggle works, and it’s exactly how most standardized tests work — each exam is only given once, on a certain day, and then it’s thrown out or farmed out to test prep providers and tutors who use it as part of their materials. This is fine — as long as the score students report to colleges is from a clean, never-before-seen exam.

Again, a college would never accept a student’s scores from an SAT practice exam as equally valid as the real thing. All I’m asking for here is a similar level of rigor.

Super serious

My point here is not — “this AI stuff is a big conspiracy, these models are just stochastic parrots with no real intellect at all”. I think we’re far enough along and the results of these models are way too compelling to short change them like that. We should take these models very seriously; they really are getting better and better all the time.

My point is exactly that — we should take these models seriously, which means taking their evaluation seriously. We aren’t right now, at least not in the public discourse around model performance.

No exam is perfect — as we know, standardized tests have their flaws. But we’re much better off having them than not. Right now, the broader AI community has no form of standardized testing for AI models. This is untenable.

It’s why I was so excited to lead Patronus AI’s seed round last year, and it’s why I’m even more pumped now that they’ve raised a $17M Series A.

Originally published on whoisnnamdi.com

As a reminder, the VAI is an index measuring current venture investment across stages relative to the long-run trend. It’s a useful and simple barometer of activity and sentiment in the venture market. The methodology is here.

Some small modifications since the last version of the index that I published in Q2 2023:

• Updated data for Q4 2023

• Removed seasonality from the data

• Removed super later stage rounds post-Series D. This was causing excess volatility that wasn’t representative of the overall venture market

With that out of the way, here’s where we’re at:

• Capital deployment remains depressed, sitting about 50% below trend

• We’ve hit the trough but remained there rather than recovered to trend. It's a bit of a dead cat bounce

Another interesting view of the data is to show the various components by stage:

• The later stages spiked the most in 2021, the earliest stages the least. This then reversed, with late stage capital falling somewhat more than early stage capital

• However, all stages are similarly depressed at this point relative to their respective trends

As I caveated in the first version of the index, if we continue to stay below trend I may need to update the definition of trend to recognize that we’re on a new trajectory. For now, I'll continue to use a long-term, constant growth rate to define the trend for each stage:

That's it! I think the data mostly speaks for itself here, so I'll keep my commentary light for this one. Will update this again next quarter.

Originally published on whoisnnamdi.com

It's cold out there in Series A land:

A common explanation for the Series A winter is raised expectations – investors are demanding to see better metrics and traction. Most companies don't meet this new, higher bar, hence fewer deals get done.

While certainly true, this can't be the whole story. Only if we look at Series As in a vacuum does this explanation seem comprehensive. Incorporating what's happening in the rest of the market, however, there's a much richer narrative to explore.

The bigger picture? In addition to raising their expectations for Series A, investors have lost faith in Series A as a sign of product-market fit.

Series A investors expect more but believe less.

Relatively speaking

Valuations for both Seed and Series A stage companies rose dramatically over the last decade or so of venture:

The appreciation in Series A valuations seems extreme relative to that of Seed deals. Of course, the scales here are different, so we'd do better to baseline these to the same starting point and track the growth over time:

While the two grew roughly in line for the first few years, Series A valuations began to accelerate around 2017. That acceleration sustained itself until the recent fall off in venture valuations.

Notably, both Seed and Series A valuations are recovering. That's a small point in favor of the "raised expectations" story – investors are funding higher-quality businesses, deserving of stronger valuations.

Here's where the expectations story starts to lose its luster. Below I plot the valuation gap or "premium" for Series A relative to Seed, along with a flexible trend line to remove noise:

• From 2010 to 2014, the two grew roughly in line, with Series As being priced 200% higher or 3x Seed rounds

• Then, Series A valuations accelerate relative to Seeds to a 325% premium or 4.25x multiple

• In 2019, the Series A premium spikes again, peaking in late 2021 at 500% or 6x

• Things collapse after that, ending in Q3 2023 at 2010 levels

If investor expectations told the full story, Series A valuations relative to Seed deals would be higher today. Since Seed investments remain speculative and standards have risen for Series A investment, we'd expect the typical Series A to be even more valuable relative to Seeds, reflecting higher quality. Instead, the gap has compressed.

It's been quite the rollercoaster ride. Notably, this rollercoaster is unique to Series A / Seed. We don't see remotely the same behavior in the premium between later rounds:

The premium for Series B vs. A and the premium for Series C vs. B are both much more stable over time, with little trend once you remove the noise. The Series C / B premium saw a slight bump during the exuberant 2021 days, but that was short lived:

• The Series B / A premium has been roughly 200% for the entire period

• The Series C / B premium hovers around 125% with few exceptions

For whatever reason, the way investors value Series A companies relative to Seed stage companies has fluctuated substantially over time (slowly rising and then suddenly falling), while their views on relative value in later stages have held steady.

Why?

Series A University

What is different about graduating from Seed to Series A than going from Series A to B, Series B to C, etc?

There are many potential explanations, but the one that jumps out to me is some notion of product-market fit (PMF) and business de-risking. Series A companies tend to have much better PMF than Seed companies, in a way that's distinct from Series B relative to Series A, and so on.

Think about it like higher education – a college degree is a signal of one's skills to the labor market. Employers value them accordingly. Further, a college degree means more relative to not having one than having a graduate degree means relative to having only an undergraduate education. Again, employers value them accordingly:

While the probability of a company achieving PMF is certainly not 100% at the time of Series A (many companies raise Series A before that), it's definitely much higher at the A round than at the Seed round, since it's effectively 0% at that point.

Startup risk reduces materially once a company graduates to Series A. In a way, that's exactly why a startup successfully raises its Series A – the founders have de-risked the business, so much so that an investor is willing to invest substantial capital, take a board seat, etc.

If we posit that the "price" or "value" of PMF is well-proxied by the Series A / Seed premium, we can think of this premium moving over time due to fluctuations in the supply (among startups) and demand (among investors) for PMF:

• A rising price typically indicates either contracting supply – the relative supply of startups with PMF vs those without – or growing demand – the relative demand for post-PMF startups vs pre-PMF startups

• Importantly, these are relative concepts – the number of pre and post-PMF startups could be both growing over time, but if the number of pre-PMF startups is growing faster, that would imply a relative decline in PMF supply

So what explains the pre-2022 run up in the Series A / Seed premium: declining (relative) supply of PMF or rising (relative) demand for PMF?

My argument: rising demand for PMF.

First, as I've argued elsewhere, the growth and development of the venture ecosystem has been a mostly demand-side story. That should strongly bias us toward the demand explanation:

Investors are the primary driver of fluctuations in venture activity and equity prices around their long-run trend – We Don't Have Nearly Enough Startups

The venture ecosystem is supply-constrained – there isn't nearly enough startup equity out there to satisfy investor demand.

Additional capital drives opportunistic company formation at the Seed stage. However, the additional capital doesn't improve survival to the later stages – it simply drives prices up for the remaining companies – It's Valuations (Almost) All the Way Down

Second, the ratio of Series A to Seed transactions has been reasonably stable since about 2014. It's declining somewhat (Seed rounds are growing slightly faster than Series As) but not nearly as rapidly or vigorously as relative prices have moved. In fact, it's remarkably consistent, even through the ups and downs of the last few years:

• Seed activity (the denominator) exploded through 2014, bringing down the ratio (A long time ago, Series As were more common than Seeds)

• The Series A vs Seed ratio stabilized thereafter, only slightly falling over the years

My read: this is evidence of stable relative supply of pre vs. post PMF companies. More often than not, when quantities are steady (as in the chart) and prices are rising, it's due to constrained supply rather than constrained demand.

So, investor demand for de-risked companies rose substantially, driving higher relative prices for Series A companies. Meanwhile, the supply of such opportunities didn't grow to match that, so we didn't see a dramatic change in Series A relative to Seed activity.

Busted

Ok, so we have a plausible story for everything that happened up until the 2021 market peak. But things obviously turned after that. For my theory to be credible, it has to offer some explanation for the recent downturn that fits the data.

Speaking of data – everything we've looked at thus far is from the perspective of the overall market. But individual companies and founders don't experience the aggregate, they live their own particular trajectory, raising subsequent rounds spaced apart rather than at the same time. A founder looking to raise Series A cares about how the market looks 18-24 months after they raise their Seed:

Let's take the founder perspective and compare Series A deal activity to Seed activity 21 months prior, a reasonable (median) estimate for the time it takes to raise that next round. Think about it as a rough proxy for the Seed to Series A "graduation" or "survival" rate. Not every Series A was preceded by a Seed financing, so this is more of an upper bound for the graduation rate:

• From 2014-2020 it's a very similar picture to what we looked at before, a stable "graduation rate"

• Conditions improve substantially in 2020, i.e. more Seed companies survive to the next round

• However, post 2021 the graduation rate collapses to the lowest levels ever seen, ~20%

So, while the ratio of Seed to Series A deals at any particular point in time has been stable, due to the timing discrepancy (founders raise their Series A some time after the Seed), the numerous startups that raised Seed funding during the boom are now facing a massive bust. Ironically, though 2021 was the "best" time to raise a Seed based purely on venture activity and valuations, it was the "worst" time to raise Seed funding once you account for today's tough Series A environment.

This simple analysis doesn't accurately identify the "level" of the graduation rate, but it proxies the "change" over time. It's safe to say the graduation or survival rate from Seed to A has fallen by more than half. Other sources like , , and Carta corroborate this:

Decline in Graduation Rate from Seed to Series A - Historically, we've seen a strong pipeline of companies moving from seed to Series A. Recent numbers, however, indicate a significant decline in this graduation rate. Measured graduation rates will continue to fall for several quarters as companies go out for and fail to raise Series A rounds. Graduation rates from seed to Series A could drop to 25%, or one-third or one-half of what they were at the peak. – The Big Reset in Seed to Series A Graduation Rates is Real and Permanent

Back in 2020, approximately 23% of Seed-stage startups made it to Series-A within two years. Fast forward to 2022, and the market looks different. Over the past 20 months since the start of 2022, the graduation rates have decreased to 5%. – A Deep Dive into Q3 2023's Funding Landscape

The percentage of companies who make it from seed to Series A within two years fell by a lot for the 2021 seed cohort.

27.5% of companies that raised a seed round in 2019 made it to Series A within 2 years.

Only 17.6% of companies who raised their seed in 2021 have "graduated" to the next round. – Peter Walker on LinkedIn

How does my theory explain this data? Put simply, venture investors re-assessed their beliefs about Series A companies. They no longer see Series A as a substantial indicator of product-market fit, given so much risk still remains. That's why their relative price has fallen off.

This is different in subtle ways from the common "Series A investors have raised their expectations" narrative:

• If it was only about loftier expectations, the Series A premium would have risen, since only the best companies would be getting funded, and those companies would fetch a high price.

• That's not what we see – relative prices have fallen, not risen.

Investors haven't only raised their expectations; they just don't see Series A as indicative of PMF to begin with. If they did, the relative price of Series A companies would still be elevated. Instead, they no longer see the point in paying up for them.

Again, return to the education analogy:

• If the college wage premium plunged, we'd say employers no longer see the college-educated as having "labor market fit". In other words, they lost faith in the college degree as a signal of quality in the labor market

• You would not intuitively connect this to rising employer expectations (though that could certainly be true too)

So sure, investors raised their expectations – you need more revenue to raise your Series A, more users, etc. But the bigger factor is that investors' beliefs about Series A changed.

Investors today expect more but believe less.

If the product doesn't fit, you must acquit

I have abused the expression "post-PMF". The reality is most companies do not achieve product-market fit by their Series A, and that's always been true. But that's my entire point – investors have wizened up to the fact that Series A companies still have material risk.

PMF is more fleeting than we all had appreciated. Series A valuations corrected to reflect this.

Balance has returned to the Force. Maybe that's a good thing. But it's dreadfully volatile if you're living it in first-person as a founder. The Series A market got the wind knocked out of it just as the large 2021 Seed cohort came up for air.

While it's a tough time for founders, I think it's important to be clear-eyed about the reality of the situation. The bar has been raised, but further, investors are questioning the bar as a meaningful indicator of success in the first place.

So yes, your first priority is still to raise that next round, but there's still much work to be done thereafter. More so than ever, Series A does not mean you have product-market fit. Investors don't believe it and neither should you.

Originally published on whoisnnamdi.com

It's no secret – unless you're building in AI, the venture market isn't very kind right now.

After a euphoric 2021, funding took a nosedive, falling to the lowest pace we've seen in some time. While economists and politicos debate whether we're in an economic recession, there's no debate in the venture economy – it ain't pretty out there for founders trying to raise capital right now.

But that sentiment is somewhat anecdotal, backed by the gut feeling of venture market participants. It'd be great to have a view of the venture cycle that was backed up by the data, some sort of indicator of the phase of the cycle we're currently living through.

So I came up with a methodology for measuring the state of the venture "business cycle" – how the venture market is performing relative to some underlying notion of "trend". Its construction is actually quite simple, and the result is something I think could serve as a useful barometer for the ecosystem as we chart a course from here.

It's called the Venture Activity Index, or "VAI". Let me walk you through how I got there.

Quick stats

To measure the venture cycle, we need a notion of "trend" – the thing around which the venture market is fluctuating. If we plot funding growth on a logarithmic scale across each stage of venture investment, a clear linear trend emerges. (Note: linear trends in logarithms implies constant percentage growth over time):

I could have come up with a fancier notion of trend, but I'll proceed with the linear one as it's nice and simple.

Venture activity, as defined by aggregate funding, fluctuates up and down around this trend. The slope varies by stage, but it's a fairly consistent story across stages – on average, funding grows ~5% each quarter.

One quick note: I could have focused instead on the number of investments rather than the scale of investment activity measured in dollars. However, working in dollar terms is nice because it implicitly accounts for valuations, which is critical for holistically characterizing the state of the venture market.

Next, let's remove that trend and focus on the gyrations around it:

• The cycles were only moderately correlated across stages before 2020. The average cross-stage correlation hovered around ~0.25 pre-2020.

• However, since 2020, they've moved in lockstep, following one common cycle up and then down.

This is typical in economic data: in volatile times, different parts of the market often become much more tightly correlated. It's interesting to see the same phenomenon in the venture data. Further, it's important that the various stages of venture correlate with one another, as otherwise there'd be no sense in talking about a singular "cycle" for the whole ecosystem.

Overall, the correlations are around ~0.65 for the whole period, with the seed stage being the most dissimilar to the other stages of investment:

The amplitudes of the cycles, however, differ across stages, both historically and more recently:

• The seed stage is relatively steady, never more than 50% off trend, with a quarterly standard deviation across time of about 20 percentage points.

• Series A is a bit more volatile but not significantly so.

• Growth and later stages are the most volatile, ranging from a standard deviation of ~30 p.p. for Series B all the way to ~50 p.p. for Series D+.

In other words, booms and busts are substantially larger at the later stages:

And in case you're wondering, this was true before 2020 as well. It's not just a recent phenomenon: the late-stage market has always required a bit of a strong stomach.

One metric to rule them all

Now let's throw it all together. Again, in the spirit of simplicity and robustness, let's take the simple average of the individual trends to arrive at a blended view of the venture ecosystem.

I present to you… the Venture Activity Index (VAI):

The last three years of the VAI are the obvious anomaly:

• Capital deployment rose to 90% above trend, exploding over only a few quarters.

• The correction was just as vigorous, bottoming out at ~60% below trend and stable for the last two quarters.

We haven't seen anything like this in the prior ten years of activity. Previous peaks and valleys were at most +/-25%. The volatility of the last three years is unheard of in recent memory.

When knocked off trend, venture activity returns to steady state after ~1.5 years. Only in that sense was the exuberance of 2021 relatively normal – it lasted roughly as long as such booms tend to last. We'll see if that behavior continues.

Again, I want to emphasize this is a measure of the venture business cycle, not the trend itself, which we first removed. A rise or decline in the VAI implies funding grew faster or slower than trend, respectively, not that funding rose or fell in absolute terms.

Due to its cyclic nature, the VAI forecasts future changes in venture activity. When it's high, future venture activity slows. When it's low, venture activity accelerates in the next few quarters. This is true across stages, though it's most accurate for the later stages:

Overall, a 10% increase in the VAI forecasts anywhere from a 6.5% decline in seed stage venture funding over the next year to a 21.5% decrease at the Series D+ stage (and vice versa for a decrease in the VAI).

Conclusion

The Venture Activity Index is a simple and informative indicator of the state of the venture "business cycle". In a single number, it quantifies the "gut feelings" we all have about the venture climate today, in the past, and in the future.

To wrap up, a few caveats:

• The backward-looking trend updates every quarter, which can slightly change the historical numbers as new data comes in.

• A linear trend may not make sense in the long-run. For example, if the rate of capital deployment never returns to its prior trend, at some point we'd have to accept that a new trend has been established, making the old one irrelevant. I may later change my calculation of trend to address this.

• My simple average of the various stage-specific trends could be improved upon with some sort of factor analysis technique. Again, I may switch to this at a later date.

I plan to update the VAI quarterly as new data comes in. For the latest data, look here.

Originally published on whoisnnamdi.com

Venture valuations have fallen off a cliff, but they are still too high.

Valuations rise and fall with the volume of dollars invested in startups according to a stable ratio: for every one percent change in funding, valuations move two-thirds of a percent.

But since peaking in late 2021, valuations have only fallen 0.4% for every 1% drop in funding. This pricing "error" has accumulated: today's valuations are 60% higher than you’d expect for the amount of capital invested.

In other words, the "shadow price" of venture capital is a lot lower than what we're seeing in announced transactions these days. Never before have valuations and funding diverged so meaningfully from their long-run equilibrium, for so long.

What does it all mean?

A 3-for-2 special

Valuations and capital invested in venture have both grown substantially since 2010:

Interestingly, they trend together. As one rises, the other does too. Same in the other direction.

It's not a perfect relationship, but there's clearly something tying them together.

In fact, you can predict average valuations from the capital invested each quarter. The below chart plots actual valuations over time against what we'd predict based on capital deployment – a simple regression of valuations on funding:

Capital invested predicts the average valuation of deals done each quarter.

Most of the time, the "errors" of this capital-based prediction are small. Further, these deviations typically correct themselves within a quarter or two.

In this sense, venture valuations are a function of capital flows. More capital drives valuations ever higher, in a fairly predictable fashion. There's an equilibrium, a balance between the two.

They don't grow at the same rate, however. For every 1% increase in funding, valuations rise about 0.66% or two-thirds of a percent. If you like clean whole numbers, the ratio is about 3:2.

Most of the increased funding goes to rising prices rather than more individual investments. This mirrors points I've made previously:

"the valuation inflation we've seen "comes from" the incredible growth in demand and lack of supply of startup equity"
…
"Additional capital drives opportunistic company formation at the Seed stage. However, the additional capital doesn't improve survival to the later stages – it simply drives prices up for the remaining companies" – It's Valuations (Almost) All the Way Down

Price check, please?

Economists have a term I think is relevant – shadow price – or, the price of something that either isn't typically traded in the market or for which accurate pricing is hard to come by. For example:

• The price of an illicit good (drugs, contraband of various sorts, etc)

• In certain economies like Argentina, the shadow or "black market" price for exchanging the local currency for a foreign currency like the U.S. dollar, which differs from the "official" rate.

Why is this relevant to venture capital?

Well, because so many venture transactions don't happen. Most startups fundraise only once in a while. Founders delay fundraising if they can't fetch an attractive price from investors. Down rounds are verboten. Those deals are missing from the quarterly venture activity data:

• Accounting for these phantom fundraises would lower the average venture valuation, since low prices are the whole reason those deals aren't happening. In other words, there's massive selection bias.

• Companies also tend to be more public about their valuations the higher they are. Journalists love reporting on high valuations. As a result, the data we have on valuations is also likely skewed too high due to reporting bias.

The prices we see investments getting done at are misleading. Thus, the concept of a shadow price applies to venture – it's the price that would prevail if companies were forced to transact at current prices and we had perfect data on all fundraises.

I think of the dashed line in the chart above as something akin to the "shadow valuation" of venture capital investment.

Wow, that's a high price!

The reliable relationship between prices and funding has broken down in recent quarters.

While valuations and funding have both declined since late 2021, funding declined by much more. Valuations have not fallen by nearly as much as the 3:2 ratio would imply. Instead, since the market peaked the ratio has been more like 5:2, or, said differently, valuations have only fallen 0.4% for every 1% drop in funding.

In other words, even after collapsing, valuations are still too high relative to their historical relationship with funding.

We can visualize this growing "error" – the percentage difference between actual and predicted valuations – shown below. Above the zero line means valuations are too high, below means valuations are too low:

Current valuations are ~60% too high relative to the volume of capital being deployed. There just aren't enough dollars sloshing around to support these prices, at least based on the 10 years of venture history prior to the go-go days of 2021.

Since 2010 valuations have rarely been "off" by more than 20%. 60% is unprecedented.

Again, notice how in the past any deviation from equilibrium quickly reverts, nearly always within a quarter or two. Something has changed – "error correction" is totally absent in recent quarters.

This is worrying. Never in the last decade-plus of venture activity have valuations and funding flows diverged so meaningfully from their long-run equilibrium, for so long. Could this mean there's a lot more pain ahead?

These deals won't last

Now, it's totally possible we have it all backwards.

Till now I've taken for granted the idea that capital flows drive valuations.

Perhaps valuations have risen for separate, independent reasons and funding volume grew to meet these new prices. Thus, it could be funding that corrects itself, rather than valuations. Perhaps the current gap signals funding is too low rather than warning valuations are too high.

Let's check what happened in the past. That is, how have valuations and funding historically reacted to past deviations from their long-run ratio? That would be a strong clue as to which is the driver and which is the passenger here.

That's straightforward enough – just run a regression of valuation and funding growth on deviations. If valuations react more, then we know valuations correct for past deviations from equilibrium. If invested capital reacts more, then its funding that corrects.

Turns out, valuations correct past deviations (left chart), funding does not (right chart):

When valuations and funding volume drift apart, it's valuations that come running back:

• If valuations are high relative to funding, valuations tend to fall the following quarter

• If valuations are too low relative to funding, valuations rise next quarter

Funding doesn't respond at all.

Deviations reflect unusual valuations rather than abnormal funding. Valuations are out of whack, not funding volume.

Pricing "errors" forecast future valuations, since valuations reliably and quickly respond to those deviations in later periods. The implication? Valuations should fall dramatically from here. That they haven't yet can be chalked up to a combination of market inefficiency and reluctance.

Just like in a Keynesian model of the economy, where sticky, slow to adjust prices exacerbate economic downturns, "sticky valuations" lengthen and worsen venture recessions. Deals that could get done at more reasonable valuations don't happen, as founders and existing investors don't want to take the hit:

Private valuations lag public valuations, often by a substantial amount and for a long time – Old Valuations Die Hard

We should prefer swift, definitive corrections over slow moving train wrecks. Instead, we drag things out. Everyone suffers as a result.

Tourist prices

That venture prices are tied so strongly to capital flows is striking:

• This contradicts standard corporate finance which values companies based on fundamentals. The amount of capital in the market shouldn't impact valuations if the intrinsic value hasn't changed.

• However, that's not what we see – there doesn't seem to be anything "fundamental" anchoring the price of startups. Startups are priced based on the amount of capital in the private markets at the time.

More capital, higher prices. Again, it's not one-for-one, but it's clear most of the capital goes toward higher prices rather than more deals getting done.

In my last essay, I was struck by how sensitive private valuations were to interest rates:

… seven quarters out valuations are up ~25%, falling back to their original level after three years – Don't Discount Interest Rates

As readers pointed out, a pure discounted cash flow (DCF) analysis would never suggest such a large impact. This is a fair point, and one I too pondered.

However, these magnitudes make much more sense in the context of capital flows. Low interest rates attract capital to risky assets like venture capital as investors reach for yield unavailable elsewhere. If capital flowing into startups raises prices, then we really have two simultaneous effects:

• The first is traditional, static, corporate finance, which says interest rates (or rather, the discount rate) influence valuations.

• The second is the dynamic effect of interest rates on venture capital funding, which then impacts valuations via the 3:2 relationship I studied above.

This double whammy is how you end up with such severe valuation swings.

Ironically, it's the tourists who "price" venture investments on the margin – those much-derided investors who, like birds, cyclically flock to and flee from venture investing with the changing economic winds. Us "locals" have no choice but to live with it.

Originally published on whoisnnamdi.com

Interest rates are the Federal Reserve's go-to treatment for an ailing, lethargic economy.

But the Fed's meds have a curious side effect – they're a shot of adrenaline for the venture market.

Low interest rates jolt the heart rate of venture capital, driving a manic frenzy of transactions and funding for startups.

On the other hand, high interest rates kill the vibe, causing deals to dwindle and prices to plummet.

You don't need a PhD to understand that. What's less obvious is exactly how large of an effect we're talking about here.

Turns out – it's huge. For a 25 basis point or 0.25% change in the one-year Treasury yield:

• Deal activity adjusts ~10%

• Valuations move ~25%

• Capital invested shifts by ~30%

These effects are persistent, meaning we're always dealing with the aftermath of past interest rate shocks. It's the (highly oxygenated) air we breathe.

It's Jay Powell's world. We're just living in it.

Interest rates are interesting

The Federal Reserve's zero interest rate policy (ZIRP) has come to an end:

It's no secret interest rates affect tech valuations:

During the last few years low interest rates and money printing led to a funding bubble in private technology – Elad Gil, Startup Decoupling & Reckoning

So the corporate finance logic goes, companies are worth the present value of their cash flows, and the "discount rate" one applies to those cash flows is the key input – lower rates mean higher valuations, and vice versa.

As much as investors like to ignore it, there's an obvious connection between valuations and interest rates:

These charts reference publicly traded companies. However, I've never seen anyone quantify the sensitivity of private tech valuations. In other words, "if interest rates fall by X percentage points, venture valuations rise by Y%," and vice versa:

• Merely knowing Y is a positive number is only marginally helpful; we should really want to know exactly how large Y is!

• Also, to what extent are tech valuations driven by interest rates and to what extent by other factors?

• Oh, and while we're at it – is the effect instantaneous? Or does the effect take time to percolate given the inefficiencies of the venture market?

Meanwhile, interest rates could affect more than just valuations. Recall a few essays ago I introduced the following framework for thinking about the individual "components" of a dollar of venture capital funding – deals, valuation, and dilution:

Interest rates could affect all these variables, and we should want to know how:

• Deals: Low interest rates drive speculation, encouraging "betting" on startups. High rates send the gamblers home.

• Dilution: Low rates create a more founder-friendly environment, reducing the ownership founders give up. Vice versa for high rates.

• Funding: To the degree investors "search for yield," startups attract capital when other investment opportunities are scarce.

So I set out to find some answers.

A quick note before we proceed. It turns out, the Federal Reserve is mostly predictable, so most of its moves are already "priced in" by the market. I instead focus on unexpected and unanticipated shifts in interest rates that market actors haven't yet reacted to. To account for these expectations, I control for other macro variables like U.S. GDP, inflation, and the Nasdaq index.

For example, if the one-year Treasury rate declines by 0.5%, the market may have only expected a 0.25% decline based on current economic conditions, leaving 0.25% unexpected:

OK – that's the most complicated concept you need to understand. With that out of the way, let's jump to the results.

Long and variable lags

The following charts trace the effect of a 0.25 percentage point or 25 basis point (bps) cut in the one year U.S. Treasury yield on various measures of venture activity, up to twelve quarters / three years out:

• 25 bps is the typical increment the Federal Reserve uses.

• I focus solely on the "surprise" component. The "total" change in rates would have been even larger.

The effects are quite strong, especially on deal flow and valuations:

• Deal activity rises for six quarters before receding back down to zero after ten quarters. Peak impact is substantial – about 10% more deals are getting done six quarters out

• Valuations take longer to peak but the size of the effect is more extreme; seven quarters out valuations are up ~25%, falling back to their original level after three years

• Dilution is more muted, remaining largely flat initially but then dipping ~5% after a few quarters without ever recovering with the three year window

Critically, interest rates affect deal flow, not just valuations:

• It's not simply the same set of companies raising money at higher prices. More companies get funded in the first place as well.

• As I found in Old Valuations Die Hard, deal flow reacts quickly, while valuations take longer to adjust but see a larger impact overall.

This has pros and cons:

• More deals means more entrepreneurs get to take a swing.

• Conversely, however, when interest rates rise, founders not only accept lower valuations – some founders don't get funded at all.

Let's drive that point home by visualizing the same analysis but for an increase in interest rates rather than a decrease:

Rising rates squeeze the life out of venture capital. Per the logic I outlined in my last essay, we know this reflects reduced investor demand, since quantities and prices drift together:

Demand can shift… which causes prices and quantities to move in the same direction (up when demand increases, down when demand decreases – We Don't Have Nearly Enough Startups

The (multiplicative) aggregation of these individual sub-effects yields the overall effect on venture funding:

• Funding builds up for almost two years, peaks at nearly 30% above baseline, then falls back to zero by about 11 quarters out.

With rising rates, funding falls just over 20%:

Perhaps it's obvious, but these are extremely large effects!

Thankfully, interest rates don't move up or down enough to regularly generate these sorts of reactions. A 10 bps / 0.1% surprise is much more common than a 25 bps / 0.25% one.

Notably, the effects aren't permanent; As interest rates reset, so does the venture market.

A brief history of time value

The current "era" of venture capital has been deeply influenced by this strange interest rate regime, having evolved entirely within it.

With our previous estimates, we can run a backwards-looking "attribution analysis", explaining the ups and downs of venture in terms of interest rates. In other words, we can break down the recent history of venture activity into the portions influenced by interest rates vs. other factors.

First up: deal activity. In red I plot an index of the overall growth in venture deal activity since early 2014, averaged across funding stages, and in blue I plot the portion attributable to unexpected interest rate shocks:

• Interest rates explain nearly the entire explosion of venture deal activity over the last few years.

Of all the charts, this is the one I probably spent the most time staring at and double-checking the numbers, as it's just so striking.

Let's do the same for valuations (notice the bigger scale here):

• The higher rates of the 2018 era pushed down valuations, but the effect reversed as rates fell heading into 2020. Interest rates on their own doubled valuations.

• However, the valuation inflation was so extreme that interest rates can't explain it all, implying more had to be going on.

This is important: that interest rates explain nearly all deal flow but only part of the rise in valuations implies that demand for startups has outstripped supply of startups. With nowhere else to go, that excess demand spills over into prices. You can't invest in startups that don't yet exist, so you compete for the few that do, bidding up prices. This is the same conclusion I reached in a prior essay:

… the valuation inflation we've seen "comes from" the incredible growth in demand and lack of supply of startup equity – It's Valuations (Almost) All the Way Down

Here's dilution:

• Dilution is noisier, complicating interpretation. The way to read this chart is that interest rates increased dilution through 2019 but afterward exerted downward pressure on dilution.

• From 2019 through 2022, interest rates drove dilution down 20%, which is exactly how much they declined in total, therefore accounting for nearly all of the fall.

Putting it all together, here's what our little attribution methodology has to say about the effect of interest rates on overall VC funding:

• From 2014 through the top of the market in 2021, interest rates accounted for a 200% (!!!) expansion in venture funding.

• As with valuations, the run up in venture funding was too extreme to be completely explained by interest rates.

Don't discount interest rates

Monetary actions affect economic conditions only after a lag that is both long and variable – Milton Friedman

Before concluding, I should mention some caveats to this analysis:

• First, there aren't many examples of big interest rate moves over this period. I may have overfit to the few meaningful changes in rates since the 2008 Financial Crisis.

• Second, it's possible I haven't controlled for all the relevant variables. For example, a lot happened during the pandemic that isn't fully captured by my choice of controls.

• Third, I use the U.S. Treasury yield, i.e. the risk free rate, as my measure of interest rates, which doesn't fit a high-risk asset class like venture capital. Unfortunately, no venture-specific analog exists.

• Fourth, and I'm repeating myself because it's important, these are the effects of an unexpected shift in rates, whereas most interest rate movement is expected by the market. If interest rates change by 50 basis points tomorrow, no more than half of that was in fact a "surprise."

Caveats aside, Friedman's proclamation appears to hold for venture capital – it takes multiple years for interest rate effects to fully play out.

Even in the private markets, we're all Fed watchers now: these interest rate effects are too large to ignore. In fact, interest rates are so impactful that they explain most of the mass hysteria of the last few years, both on the upside and the down.

Importantly, interest rates cannot forever move in one direction. Over the years, they mean revert, making their impact temporary at best. Don't get too accustomed to any particular regime – the new normal will always eventually look like the "old normal."

Don't discount interest rates.

Originally published on whoisnnamdi.com

Where did the explosive growth in venture activity come from?

Two possible drivers – investor exuberance and founder fervor:

• Did investors became substantially more favorable toward private startups, and founders merely reacted to that increased interest?

• Or, did we all become much more entrepreneurial, and investors simply provided the capital in response?

In other words, was it demand, supply, or some combination of the two?

Here's a hint: we don't have nearly enough startups.

Narrative violation

Measured in either financing activity or equity valuations, the venture-backed startup ecosystem has grown a lot:

There's a common narrative out there that the bonanza in venture-backed startups over the last decade reflects growing entrepreneurialism, the ease of starting a company, etc.

I question this story.

Only Seed rounds grew significantly faster than real GDP over the last decade. Series A's also outpaced GDP but not by much. The rest are on or even below trend, especially after the recent slowdown:

The long-run trend of real GDP reflects supply-side factors, i.e. the fundamental productive capacity of the economy. The similar trends at the later stages imply the "fundamental productive capacity" of the venture ecosystem, i.e. its ability to generate real, meaningful businesses, is growing no faster than the rest of the economy.

The U.S. economy isn't growing quickly by the way – 2% real growth year-over-year is a good benchmark. So if the trends line up, the venture world can't be growing much faster than 2% in real terms, that is, excluding the impact of valuation inflation that I discussed a few essays ago.

At the later-stage, valuation inflation explains nearly all growth in venture funding over the last eleven years – It's Valuations (Almost) All the Way Down

An equally if not more plausible story is that demand-side factors drove the explosion in venture activity over the last decade. Growing investor interest and appetite for private startups could explain much of the growth.

However, merely staring at trend lines doesn't yield much insight. It's like a tough marketing attribution exercise – the growth attributable to either demand or supply is ambiguous.

We should resolve this ambiguity:

• As participants in this ecosystem, we should want to see it grow in real terms rather than merely nominal ones

• In other words, we'd be much better off if venture activity reflects growing capacity and ability of entrepreneurs to start successful new businesses

• If in fact the growth in venture activity is merely a reflection of investors needing increasingly obscure and speculative places to park their money, we have a big problem on our hands. The shell game will implode:

Valuations can't rise forever, so over the long-run venture capital can't grow much faster than the number of ventures themselves – It's Valuations (Almost) All the Way Down

Economics 101: Supply and Demand

First, let's quickly review Economics 101.

Here's the classic supply and demand chart. Supply and demand together determine equilibrium prices (Y-axis) and quantities (X-axis). Importantly, demand is downward sloping (you want more stuff the cheaper it is) and supply is upward sloping (you produce more stuff when you can sell it for more):

In this model, two forces can move markets. Demand can shift (for reasons that don't have to do with prices themselves), which causes prices and quantities to move in the same direction (up when demand increases, down when demand decreases):

Or, supply can change (again, for reasons other than price), in which case prices and quantities move in opposite directions (price move against supply, quantities move with supply):

In simple terms, when everyone wants the same thing and wants it really badly, the price tends to go up and more of that item gets bought and sold. When everyone wants to produce and sell the same thing and wants to do so really badly, they compete against one another, driving prices down to accommodate the increased activity.

In venture terms, when investor demand for startups rises, valuations increase and more deals get inked. When entrepreneurial supply expands, valuations decline and more deals get done.

VC is trendy

Venture deals and valuations have both grown over time, so it's all demand-driven right?

Not so fast.

While it's tempting to end the analysis here, for reasons I'll skip over, you can't simply compare two trending metrics and assume they're correlated. The relationship could easily be spurious, and we don't want that. By the magical transitive property, anything we concluded based on that relationship would be spurious as well. Cool math.

The key is to "de-trend" the data first. We need to remove the long-run trend and then compare deviations from that trend.

Ignoring the details, here's what that looks like (data normalized and smoothed slightly to remove noise):

Lo' and behold: deal flow and valuations fluctuate together around their respective trends.

OK, maybe that's not always true, but it's nearly always true. Early stage is the exception to the rule.

Seed financings were negatively correlated with valuations before roughly 2017:

• When deal flow increased, valuations plummeted, and vice versa

• This smells like supply to me – there was only so much investor demand, so when more startups came to market, they competed, and prices tended to fall, benefitting investors. When the supply of startups contracted, prices rose as investor battled over the few remaining deals

Series A rounds used to be positively linked to valuations, but they've de-correlated over the last few years:

• Pre-2019, Series A deal flow and valuations tended to move in the same direction

• Sounds like demand to me – startup supply was constrained, so investor sentiment drove the market, moving prices up as they grew more eager and down as they soured on the venture ecosystem

Across growth and later stages, deal flow and valuations are unambiguously positively related, moving almost in perfect unison for the last decade:

• Demand clearly wins it – late stage supply is badly constrained, so investor demand is the prime mover. Their manic and depressive episodes move the market accordingly

Some of these relationships have shifted over time, so let's visualize that with rolling three-year correlations for each stage:

This is my qualitative narrative in quantitative terms:

• The Seed stage flipped being negatively correlated to positively (supply → demand)

• Series A de-correlated to effectively zero (demand → ❓)

• Series B and later deal flow and valuations have always been strongly positively related (demand all day baby)

Channel attribution

This is the best evidence I've seen to date for the demand-side hypothesis.

The scorecard so far suggests demand reigns:

• Investors are the primary driver of fluctuations in venture activity and equity prices around their long-run trend

• Yes, more deals are getting done (so by definition more startups are getting funded), but that appears to be a function of increasingly desperate investors rather than increasingly bold and enabled founders

In other words, the supply of startup equity is badly constrained:

The venture ecosystem is supply-constrained – there isn't nearly enough startup equity out there to satisfy investor demand.

Additional capital drives opportunistic company formation at the Seed stage. However, the additional capital doesn't improve survival to the later stages – it simply drives prices up for the remaining companies – It's Valuations (Almost) All the Way Down

As a reminder, our evidence for this is the positive link between de-trended deal activity and valuations. That's a nice trick, but it only tells us at each point in time whether movements in demand or supply dominated. I want to explain the entire last decade or so of venture history in terms of supply and demand channels.

Yes, yes, history is not bi-causal, hammer looking for a nail, etc, but how about one more magic trick to close things out?

Warning: armchair econometrics ahead!

Let's stretch our simple supply and demand model to the absolute extreme:

• Remember, a positive relationship between deal flow and valuations suggests a change in demand, while a negative or inverse relationship suggests shifting supply

• So, we could simply attribute each quarter of venture activity to either demand or supply demand based on whether deal flow and valuations move in similar or opposing directions during the quarter

• We can then cumulate the respective contributions of demand and supply to the growth of the venture ecosystem over time

Here's what that looks like for venture deal flow since early 2010. Demand is red, supply is blue, sugar is sweet, and so are you:

That's a lot of red out there:

• The demand channel drove most of the growth in venture activity in nearly every stage other than Seed, where its relative contribution is closer to 50/50

• Early stage supply contributed positively to deal flow from 2010 to about 2015 but then stagnated

• Supply has never been a meaningful contributor at the growth and late stage and even seems to have contracted in certain cases

Et tu, valuations?

The story isn't much different for valuations, except perhaps with some signs flipped:

• Here again, investor demand was the main driver, pushing prices higher in every stage

• The increase in early stage supply in the early 2010s relieved some price pressure, but this eventually receded

• The effect of late stage supply on valuations is somewhat noisy, but by the end of the sample those supply constraints appear to have driven prices higher, on balance

In case I haven't sufficiently caveated already: this is extremely unscientific. No Nobels will be awarded for this work (your subscription is enough reward for me, awwww), but it does serve as coarse, suggestive evidence that demand is, or at least has been, king in venture over the last decade.

There's only so many startups to go around

So, what have we learned?

To the degree founders are starting more venture-backable companies, it's largely driven by the cold, rational calculus that investors are much more eager to buy up equity in private companies today than they used to be.

This explains much of the growth in the venture ecosystem over the last decade: more deals get done and those deals are more expensive because of surging investor demand. This has been great for founders.

However, this will be painful for investors, since companies rarely grow into their valuations:

Most often, companies don't grow fast enough to compensate for rising valuation multiples. Instead, high valuations today imply slower value appreciation in the future, i.e. lower returns.
…
Investors like to think companies will grow into their valuations, but more often than not, the stock simply underperforms – Companies Rarely Grow Into Their Valuations

Meanwhile, the supply of startup equity remains constrained. Rather than potential founders becoming more eager to start companies for "fundamental" reasons, entrepreneurs are reacting to investor sentiment. While there's been some growth in supply at the earliest stages, the fundamentals haven't necessarily improved much, which is why late stage deal flow hasn't grown any faster than U.S. GDP.

I'll repeat what I said earlier – we'll all be much better off if more people start companies for good, wholesome reasons that don't have anything to do with valuations.

There was a time where the notion of handing millions of dollars to an extremely young company sounded crazy, and anyone willing to do so extracted a significant ownership stake for taking on that risk. Founders got diluted, badly.

We're well past that now. Dilution has fallen in every stage since 2010:

We have more startups, but:

• It's largely been a reaction to attractive valuations and reduced dilution that founders must endure to raise capital

• Past the seed stage, it's not at all obvious that founders are fundamentally better equipped to build successful companies today vs. a decade ago

The demand side got a bit ahead of itself. It's time for the supply side to catch up.

We don't have nearly enough startups.

Originally published on whoisnnamdi.com

Public technology valuations have crashed.

A worsening economic outlook and tight monetary policy sent tech stocks through the floor.

However, private valuations are slower to adjust.

Private valuations lag public valuations, often by a substantial amount and for a long time:

As we all lick our wounds during this venture downturn, many are asking: when will it all end?

The answer? About three years.

Public-private partnership

Private valuations rose dramatically over the last decade. But so did the Nasdaq:

It's remarkable how well the two track one another.

In a sense, the growth of public tech valuations seems to pin down the rise of private valuations, defining a trend to which private valuations always tend to return. It's clear that venture investors use public valuations to justify private ones.

One feature immediately stands out – private valuations are much more volatile than public valuations, particularly at the later stages:

• This is to be expected – it's not the same companies that are fundraising each quarter, so we'd naturally expect the prices to jump around a bit

• Meanwhile, the composition of the Nasdaq doesn't change much quarter to quarter, so as an aggregate it's much more stable

If we throw each one on its own axis, the relationship is even more obvious:

Across stages, private valuations move one-for-one with public valuations.

• Seed valuations have grown somewhat slower than the Nasdaq, moving 0.5% for every 1% move in the tech index

• Series A, B, and C valuations have moved one-for-one

• Series D+ valuations have grown 1.3% each point of growth in the Nasdaq

We can replicate the same analysis for private venture activity. The relationship is not as tight:

Deal activity is more or less related to public tech valuations, depending on stage. Here too we see much more volatility in the private realm than the public, again, for understandable reasons.

In general the slopes here are flatter than for valuations:

• Seed deals have grown at roughly the same pace as public valuations

• For other stages, growth has lagged public tech valuations – growing at about half the pace

Level with me

Though intuitive, these comparisons are not rigorous. It's too easy to find spurious relationships among variables that are all trending in the same direction. There's less risk of that in our case since public and private technology markets are closely linked, but we should be wary regardless.

We can do better by focusing less on the relationship between level of the Nasdaq and some other variable and more on the correlation between changes in the respective metrics.

The key question: how does a change (up or down) in public technology valuations affect the prices of private financings, and how quickly?

Some good ol' regression analysis can answer this.

Glossing over a bunch of detail:

• We can forecast future changes in private valuations and financings based on current changes in public tech valuations.

• We'll run one regression for each forecast horizon: concurrent (time zero) impact, one quarter out, two quarters out, etc.

• The coefficients of these regressions trace out the impact of the original movement in the Nasdaq on private prices and volume in later periods.

The path to recovery

Hope you're still with me. Let's run those regressions and see what we get.

We'll focus on downward movements in the Nasdaq. This is how a 1% decline in the Nasdaq affects private valuations and deal flow over future quarters:

Private valuations gradually decline after a drop in the Nasdaq:

• Prices drop continuously for four quarters

• At the trough, private valuations fall ~2.25% lower for every 1% loss in the Nasdaq

• Venture valuations take 10 quarters to recover

Movements in the Nasdaq reliably forecast venture deal activity too, which drops and rebounds faster than prices:

• Deal activity bottoms in the third quarter after impact

• Overall deal count rises back to its original level by the 8th quarter

Down for a down round?

This masks a lot of underlying variance between the different segments of the venture ecosystem. Starting with valuations, let's explore how the shape of the recovery varies by stage:

Valuation dynamics depend on stage: Early stage valuations drop 1-1.5% for every 1% decline in the Nasdaq, while growth and late stage valuations decline 2-2.25%.

Note: these aren't exact estimates. Uncertainty grows as we look further out from initial impact, so don't pay much attention to the numbers at the 12-quarter mark. It's the shape and length of the recovery that matters most.

With some back-of-the-envelope math, we can estimate both the magnitude and the length of the current downturn:

• The peak to trough decline in the Nasdaq this past year was about 25%, implying a ~25-35% haircut to early stage valuations and a ~50-55% cut in growth and later stage valuations

• That might sound extreme, but the sudden explosion of late stage valuations these past few years was itself quite unusual. It's not implausible that prices could fall as dramatically as they rose

In fact, the correction is already well underway:

As predicted, late stage valuations drop much more than the Nasdaq, while early stage valuations move roughly in line. Prices have fallen faster than my forecast, but the magnitudes are on point.

Timing the bottom based on my regression here is a bit tricky, as the market began its decline in Q1 2022 and mostly ran its course by Q3 2022. If we use the midpoint of Q2 2022 as the "start" of the venture recession:

• Private valuations should bottom in Q2 2023

• I'm hesitant to forecast the heydays of 2021 ever returning, but if they did, it'd happen around Q4 2024. I wouldn't hold your breath though

Coming to terms

Again, let's focus on a 1% decline in the Nasdaq:

Financings follow an interesting "sine wave" recovery in the aftermath of a plunge in the Nasdaq. Again we see more severe impact in the later stages: The quarterly volume of Seeds and As drops by 0.75-1% for each 1% decline in the Nasdaq. Late stage deals sink 2%.

One ray of hope – financing activity seems to come back stronger after a venture recession.

Pulling out that scribbled envelope again, these numbers suggest early stage deal activity will drop 20-25%; growth and late stage deal volumes should slide about 50%.

Again, the data bears this out:

Remember, the venture deals should only take about a year to hit their low, so activity should stabilize in or around Q1 2023, notably ahead of valuations.

As I've said, these are rough estimates. All the typical caveats apply: "this time could be different," etc.

Conclusion

The bubble burst, but the pain won't last forever.

The historical relationship between the public and private tech markets lets us trace out the likely path of the venture recovery.

The first year is the hardest. Prices drop precipitously, in many cases, much further than their public tech comparables.

Things get better from there, with deal activity back on track after another year or so. Prices, however, remain depressed a little while longer.

All-in-all, it's a three year odyssey from start to finish.

Originally published at whoisnnamdi.com

Venture funding hasn't grown as much as you think.

Soaring valuations over the last decade inflated financing volumes.

As a result, "real", price-adjusted funding growth looks quite different from unadjusted growth, similar to traditional economic measures like GDP.

Adjusting for rising valuations, real venture funding at the early stage is only growing at half the unadjusted pace.

At the later stages, valuation-adjusted venture funding isn't growing *at all*.

It's valuations (almost) all the way down.

Pre-Algebra

First, a quick math refresher:

• Total funding volume equals the number of financing rounds multiplied by the average round size

• Assuming a standard equity round, average round size further separates into valuation multiplied by dilution (the proportion of shares sold to investors)

That gives us three components – deals, valuations, and dilution:

This framework is simple but powerful.

With it, we can explain the individual sources of growth in the venture funding ecosystem, cumulating into overall funding growth.

Algebra II

Let's walk through each component one-by-one.

I'll plot the growth over time of each component since 2010, leveraging data from Pitchbook for all venture deals completed in Western markets through 2021.

Importantly, I'll plot the cumulative log growth:

• This makes the components additive rather than multiplicative, which will help the analysis later

• This also means you can read compound annual growth rates (CAGRs) off of the y-axis. Just take the value on the y-axis and divide by 11, which is the number of years this analysis covers

Let's start with growth in funding volume by stage:

• Across stages, annual venture funding grew by 2.5 to 3 log points, with CAGRs ranging from 23-27% CAGR (again, just divide the log growth by 11)

• The earliest (Seed) and latest (Series D+) stages grew fastest

Next, let's dive into each of the components.

Deal counts have ballooned across all stages, but the Seed stage stands out:

• Seed deals grew the most by far over this period, 2.1 log points or 19% on an annual basis

• On the other hand, Series C deals and later only grew ~0.75 log points, a 7% CAGR

• As and Bs were somewhere in the middle, growing 10% annually over this time

It should be noted: that Seed deals grew so much faster than the rest implies the "graduation" or "survival" rate of startups fell materially, as they've had more than enough time to mature.

More early-stage startups has not led to many more late-stage startups.

We'll revisit this.

Next up, valuations:

• Unlike deal counts, which grew the most at the early stage, valuations grew the most at the later stages

• Series C valuations grew at a ~20% CAGR, while Series D+ grew at ~24%

• Series As and Bs again formed the middle of the pack, growing 1.75 log points or 16% year-over-year

• Seed valuations "only" grew 10% annually, which is still exceptional if you think about it

Lastly, let's look at dilution. Note again these numbers are in log points (not percentage points):

• Dilution has fallen in every stage since 2010

• Dilution at the Series A and C has fallen the most, while dilution at the Series B has fallen the least

• Dilution evolved similarly across stages through 2019, but for whatever reason, VCs over the last few years were much less desperate to "get their ownership" in Series A, C, and D+ rounds

Note that falling dilution negatively impacts funding volume since less equity gets sold.

Multivariable calculus

And now, the main event.

For every financing stage, let's aggregate the change in deals, valuations, and dilution to explain the cumulative growth of funding:

• At the Seed stage, most funding growth came from more deals getting done. Valuations rose too, but deal count explains most of the overall funding growth

This seems healthy – growth in funding should ideally come from growth in the total number of deals.

• It's a completely different story at every other stage: valuations explain most of the growth in funding at Series A and later.

• At the later-stage, valuation inflation explains nearly *all* growth in venture funding over the last eleven years

In contrast to the Seed stage, this seems unhealthy: growth in the majority of the venture ecosystem (at least as measured in dollars) over the last decade was primarily driven by rising valuations.

Now, if you've spent a lot of time staring at data like I have, you'll know that these sorts of analyses can be very dependent on the starting year (in this case, 2010).

Just to be safe, we can avoid privileging any particular year as the starting point by instead calculating the variance in funding levels over time rather than the growth. Variance decomposes the same way as growth, but this time there won't be a "base year" affecting the results.

Do this, and the conclusion is the same – year-to-year variation in valuations accounts for most of the variance in funding volume over time at the later stages:

• At the seed stage, two-thirds of funding variance over time is accounted for by deal volume and one-third by valuations

• At Series A and B, 65-70% of funding variance is explained by valuations

• At Series C and later, valuations account for more than 80% of the annual variance in venture capital funding

In economics, when quantities and prices rise together, you can be fairly sure it's driven by growth of demand, or equivalently, you know the market in question is supply constrained:

• The supply of startup equity at the later stages is constrained; there's only so much of it to go around

• On the other hand, investor demand for venture assets has exploded, especially at the later stages where companies have been significantly de-risked

In this sense, the valuation inflation we've seen "comes from" the incredible growth in demand and lack of supply of startup equity.

Real analysis

Here's another trick – if we go back to our decomposition and divide both sides by valuation, we get the following:

In other words, we can adjust funding volume by the average valuation at which these financings occurred to get a "valuation-adjusted" funding metric. This is analogous to how economists "deflate" nominal GDP by inflation (e.g. CPI or similar) to arrive at "real" GDP.

It's also the result of multiplying deal count each year by average dilution, i.e. the total equity bought and sold in the venture market each year.

This lets us measure "real" growth in venture funding rather than dollar-based growth, which conflates deal activity with valuation movements.

Compare this "real" funding metric to the "nominal" one I showed earlier:

Wow.

Real venture funding is growing much, much slower than you think:

• At the Seed stage, real funding growth has a 17% CAGR vs. 27% for raw funding dollars

• At the Series A and B, real funding grew at a 7% CAGR vs. 23% for nominal funding, one-third the rate

• Real funding was flat for a decade at the later stages before finally ticking up in the heydays of 2021. Real annual growth is only 2% and 4% at the Series C and D+ respectively, vs. 23 and 27% without adjustment

I want to state this as directly as possible: there was no growth in real late-stage funding activity for a decade until the bonanza of 2021.

Said differently, no more late-stage equity traded hands in 2020 than in 2010, measured in terms of points of cap table ownership.

Now you're ready for Economics 101

This data contradicts the common narratives of the past decade of growth in the venture ecosystem.

Venture capital is about two things, ventures and capital.

Capital has been in excess supply the last decade, but ventures haven't.

Valuations can't rise forever, so over the long-run venture capital can't grow much faster than the number of ventures themselves.

In a way it all makes sense:

• By the time startups graduate to the later stages, they are real companies, typically with meaningful revenue, operations, and headcount

• Accordingly, their total count should grow roughly in line with the real growth rate of modern, industrialized economies. We wouldn't expect an order of magnitude difference, otherwise late-stage startups would overtake the whole economy

In other ways, it's highly concerning:

• The supply of late-stage startup equity hasn't risen to match investor demand

• Perhaps this explains why late-stage funding seems so anemic right now - activity has reverted back to the zero-growth trend, which feels quite slow relative to the flurry of 2021 deal-making

The venture ecosystem is supply-constrained – there isn't nearly enough startup equity out there to satisfy investor demand.

Additional capital drives opportunistic company formation at the Seed stage. However, the additional capital doesn't improve survival to the later stages – it simply drives prices up for the remaining companies.

I've long felt this but never had the data to back it up.

Now I do, and I think it's a big problem.

Originally published at whoisnnamdi.com

Subscription revenue is a double-edged sword:

• It's predictable, making the business much easier to forecast

• It's persistent – over or underperformance today reverberates far into the future

Contrary to popular belief, SaaS companies do not in fact "pull forward revenue." A beat today puts the company on a permanently higher trajectory. Revenue doesn't mean revert later.

Likewise, deals might "slip," but there's no "catch up growth" – a missed quarter doesn't get made up for next quarter. A miss today predicts lower revenue for years to come.

Diamonds, beats, and misses are forever 💎.

Long vs. short

Public market investors are often criticized for being too "short-term focused."

Politicians and CEOs alike love to hate on investors for overreacting to current business results, bidding up the stock or kicking off a fire sale depending on how quarterly earnings play out

A widely-held view among Washington policymakers, corporate executives, the media, and the public is that frenzied, short-term stock market trading has coupled with Wall Street’s unquenchable thirst for immediate results to disrupt US firms and badly hurt the economy – How Big Is the Problem of Stock-Market-Driven Short-Termism?

Baked into this criticism is a view that the long-run is hard to influence – fluctuations in the short-run tend to mean revert over time. One's view of a company should change only slowly and in the face of extraordinary evidence.

In defiance of this heavy-handed finger-wagging, there's an alternate view that short-run performance matters and is informative about the long-run. The future is bound to the present, and we should decisively update our forecasts in light of new information.

In summary:

• Long-termists believe revenue will eventually revert back to some long-run trend based on the fundamentals of the business, its market opportunity, etc. Short-term fluctuations are noise and should be ignored. Quarterly beats and misses should have little, if any, impact on fundamental valuation analysis.

• Short-termists (though they would never refer to themselves as such) believe the present moment matters and says much about the future. Short-term performance is a valuable signal that should factor into valuations. Mean revision dominates mean reversion.

Who is right? And how would we know?

A random or deterministic walk down Wall Street?

There's a simple way to resolve this dispute. We only need to rephrase the positions of the long/short-termists, and the resolution will be clear.

Long-termists think today's revenue fluctuations poorly predict future revenue. They don't often phrase it that way, but that's the core underlying logic behind the belief in a stable, long-run trend.

This has multiple important implications.

For one – revenue can be "pulled forward," but this will tend not to influence the long-run trajectory of the company:

Adobe was downgraded to neutral from buy at UBS... Analyst Karl Keirstead said after speaking with 14 large enterprise IT executives and services partners of Adobe, he's worried that spending was pulled forward in 2020 and 2021, which will pressure its growth rate this year. (Link)

Further, a missed quarter today can be made up for next quarter. Missing the target leads to revenue in the future quarters to be slightly higher, as revenue reverts back to trend. You could call this "catch up growth."

Meanwhile, short-termists think today's revenue movements predict future revenue. One's long-term revenue forecast should be quite sensitive to beats and misses in the present day.

So, pulling forward revenue doesn't come at the cost of future revenue. Similarly, missing your target today doesn't mean you're any more likely to hit tomorrow's target. In fact, you're less likely, since you're now on a lower revenue path.

The clean and simple test?

Simply run a regression of future revenue on current over/underperformance! The coefficient tells us to what extent our estimates of future revenue should shift in response to strong or weak results today:

• A coefficient near or greater than one tells us future revenue rises/falls by at least $1 for a $1 beat/miss today (the short-termists win)

• A coefficient well below one means forward revenue is insensitive to today's performance and tends to mean revert (the long-termists win)

Back to the future

The chart below plots revenue growth over four quarters against revenue growth today for 35 public software companies, demeaned by the respective average rate for each company. Positive values mean revenue came in higher than normal, and vice versa:

Future and current revenue over/underperformance are positively related. The coefficient is 1.16, implying a $1 beat today forecasts a $1.16 beat four quarters from now. The exact reverse is true of a miss – a dollar of missed revenue this quarter lowers our expected revenue in four quarters by $1.16.

Additionally, the R^2 is decently high – 0.49, so about half of the variation in expected revenue four quarters out can be explained by revenue performance in the current quarter.

One quick aside since I know what certain folks are thinking here: I am not merely saying that current revenue predicts future revenue. I'm saying changes in revenue predict changes in future revenue.

So far, the short-termists seem to be winning. But one year is not a long-time – perhaps mean reversion takes longer? Let's check by extending the analysis to eight and twelve quarters out. That's too crowded for a single scatterplot, so I'll summarize the results and only show the coefficients at each horizon:

If anything, the case for short-termism only gets stronger. The two-year revenue forecast shifts by $1.25 for a $1 beat/miss today. The three-year forecast changes by $1.43.

Even three years out, we see no evidence of mean-reversion among the typical public software company. Beats and misses permanently shift the trajectory of the company.

Now, dollars are a nice unit of account, but they're admittedly hard to contextualize. A $1 beat/miss that turns into a $1.43 beat/miss three years out could be more or less meaningful depending on the scale of the company and its growth rate. Many of these companies were likely to be much larger in three years anyway.

Let's do the same analysis with percentages instead. What impact does a 1% beat/miss have on future revenue, also in percentage terms?

Persistence persists, but this tells a slightly different story. Revenue three years from now is still 0.85% higher/lower than it would have otherwise been.

My interpretation: the additional (or lost) revenue from a beat (or miss) grows at a somewhat slower pace than the remaining revenue base, so we see some convergence. In other words, "surprise" revenue doesn't grow as fast as "expected" revenue.

You could frame this as slight mean reversion. Personally, I'd say the short-termists still have it.

Working as intended

Why does this happen? Why is the future so sensitive to the present?

Rather than a surprising phenomenon, I see this as the defining characteristic of subscription business models.

To say revenue "recurs" is merely to say revenue today generates revenue tomorrow. Said differently, a good test of subscription revenue quality is the degree to which it persists and predicts future revenue.

In that light, these results are expected. If a change in revenue today didn't predict a change in revenue tomorrow, it'd be hard to call it recurring.

For some intuition, look at those cohort revenue charts that have become so popular among public software companies (at least among those with good retention dynamics to show off):

Missing a quarter means losing a slice of the cohort stack. Assuming positive net dollar retention, that slice would have grown over time; the opportunity cost of weak performance grows over time.

Ironically, a miss for a company with high retention hurts more than one for a company with low net retention, since the high retention company has more (future revenue) to lose. I examined this phenomenon from a slightly different angle a few years back:

Both in theory and in practice, better retention drives higher volatility – "High Retention = High Volatility"

This is why SaaS beats and misses are so consequential.

If investors think a miss or beat is likely to stick, that will meaningfully impact their valuation views, as it must. On the other hand, the stock shouldn't move much if short-term performance reflects merely temporary dynamics in a company's go-to-market engine.

That SaaS valuations tend to react so strongly implies investors do believe these GTM gyrations are permanent and must be reflected in future projections. It's possible there's some mean reversion happening under the hood, but this effect is totally swamped by the magnitude and persistence of the beat/miss itself.

Revision of the mean

A bad reaction to this analysis would be: your sales leader says the quarter came in light because some deals slipped, you look them in the eyes and confidently tell them "there's no such thing as a deal slipping," and point them to this essay.

No. That's not what I'm saying at all. (Please do send them this essay though!)

At the level of individual sales, deals slip from one quarter to another all the time. But at the level of aggregate revenue that doesn't seem to matter. Revenue won't be higher next quarter simply because revenue came in low this quarter. Every quarter is, for the most part, a blank slate.

Likewise with pulling forward revenue – it happens, but on average you can't find it in the data. COVID is a good exception to this – companies that benefitted from the transition to remote work like Zoom or DocuSign are now slowing, reverting back to their long-run trend line:

While DocuSign beat revenue expectations last quarter, full-year guidance came in far lower than expected. DocuSign… was perceived to be a "COVID winner." There are now fears that the company merely pulled forward years of sales over the course of the past two years, and that its revenue growth trajectory will be lower going forward. (Link)

While Coupa's upside certainly isn't at risk long term, it does appear some of its future growth was pulled forward during the COVID-19 crisis. (Link)

Unfortunately for Twilio, it belongs to the “high beta growth” club, which began a secular decline early last year. The San Francisco-based Cloud Communications company boomed from the work-from-home trend... But, like so many other tech companies, it pulled forward too many gains, setting itself up for a nasty re-pricing. (Link)

Note: this analysis only includes software companies. Businesses with more transactional business models probably see less persistence and more mean reversion. Think about companies like Peloton:

• Despite having a subscription service, the vast majority of revenue was in physical bike sales that are one-time in nature

• Peloton really did pull forward demand, which left little market left to grow into by the time things returned to normal

However, if you have significant recurring revenue, mean reversion is much less relevant.

This analysis continues a line of thinking I touched on in a prior piece exploring COVID's impact on software companies:

… mean reversion is a strong force, but COVID was stronger, especially on the downside. Once knocked down, the typical software business never got back up…

Mean revision is at least as important as mean reversion. Not only are software companies below their pre-COVID trend, the trend itself has changed for the worse – COVID Hurt Most Software Companies

For software companies, every day is Day 1 or, at least, dependent on it.

Originally published at whoisnnamdi.com

Layoffs grab headlines. Hiring freezes, less so.

Sites like layoffs.fyi track layoffs in gory detail, as do journalists and tech writers:

However, it's slow hiring, not layoffs, that most drives unemployment.

Layoffs don't tell the whole story.

Stocking up

Employment and unemployment are both "stocks".

Stocks are quantities measured at a point in time that change over time. For example:

• The volume of water in a lake

• The stock (hint hint) of inventory in a retail store

• Cash on a company's balance sheet

On the other hand, "flows" are rates of change of a particular stock. For example:

• Water from various rivers flowing (hint hint) into a lake

• Shipments of new inventory

• A startup's monthly burn rate

A single stock can have multiple associated flows, and the sum total of these flows determines how the stock changes over time.

In the labor market, workers flow between employment and unemployment (stocks) at various rates (flows):

In equilibrium, the various stocks of a closed system are stable. However, if the individual flows change, the related stocks change too:

• During recessions, flows from employment to unemployment (job separation) increase, while the flows out of unemployment into employment (job finding) decrease

• Vice versa for recoveries – people find jobs more quickly and fewer people get laid off in the first place

In this way, variation in job separation and job finding "explain" changes in unemployment.

As the labor market cools, hiring freezes

We commonly associate unemployment with layoffs. During recessions, we imagine heartless employers suddenly laying off droves of workers:

While this story feels intuitive, the data doesn't bear this out.

The below chart plots the U.S. "employment exit rate" (a euphemism for layoffs or voluntary quitting that ends in unemployment) along with the unemployment rate itself. Think about the exit rate as the share of all employed workers who leave employment each quarter:

Employment exits jiggle up and down at a much higher frequency than unemployment, and the timing of those short term fluctuations doesn't align well with granular movements in unemployment. Over longer periods though, they do seem loosely connected.

Now let's look at the other piece of the puzzle. Here, I've swapped out employment exit for the "job finding rate," i.e. the proportion of unemployed workers who find a job each quarter:

The lines perfectly mirror each other.

The fluctuations closely track the state of the economy. When job finding plummets, unemployment rises at the same time. As the economist Robert Shimer put it:

… a decline in the job finding rate… contributed to every increase in the unemployment rate during the post-war period – Reassessing the Ins and Outs of Unemployment

As the job finding rate slowly recovers, so does unemployment.

So, rather than massive layoffs defining recessions and spikes in unemployment, the more powerful explanatory story is one of hiring freezes – employers simply stop hiring during recessions.

Per our stocks-and-flows model, the stock of unemployed workers grows and shrinks primarily due to variation in the outflow from unemployment to employment rather than the inflow of employment to unemployment.

In other words, hiring freezes are the real story, not layoffs.

Hiring or firing?

What's the relative importance of hiring freezes vs layoffs?

Here's how we can estimate this:

• Let's say we hold variable A constant and allow variable B to move around. If unemployment still moves around a lot then we know most of its variation is driven by variable B

• Similar logic applies in the opposite scenario (allowing A to fluctuate while holding B constant)

This table quantifies the relative contributions of job entry and exit rates to overall unemployment variability by calculating counterfactual scenarios in which either variable is held constant while the other is allowed to fluctuate. The first row holds job separation constant and the second holds job finding constant (Source):

The result?

… since 1987, including the recessions in 1990–1991 and 2001, and 2008–2009, the job finding rate accounted for virtually all fluctuations in the unemployment rate – Reassessing the Ins and Outs of Unemployment

The job finding rate explains the vast majority of variation in unemployment, and the proportion has only increased over time.

From 1987 to 2010, variation in the job finding rate explained 90% of the variance in unemployment, while employment exit only explained 10%.

Here's Shimer again:

The bulk of the reason unemployment rises and stays high throughout the recovery is because the unemployed workers stay unemployed for longer…

This is precise, quantitative evidence for the overwhelming importance of hiring over firing in explaining unemployment.

Hyper-(de)growth

If the prior data seemed counterintuitive, the following example shouldn't.

Imagine you're a high-growth startup with big ambitions to grow the employee base in the coming year:

• You are currently 100 employees

• You want to hire an additional 100 people over the next year

• We ignore voluntary attrition just to keep the numbers simple

Suddenly, the environment changes:

• Economic conditions worsen, capital gets tighter, you start to miss plan, etc

• You need to make cuts, both in the current employee base and the future one

• So, you lay off 20% of the team

This is extremely painful, demoralizing, and controversial, only made easier by the fact that everyone else is doing it too:

You also institute a hiring freeze, which only seems fair after laying off so many team members:

It doesn't take an HR savant to realize you aren't going to hit your original 200 headcount target for next year.

Now do the gap analysis – between layoffs and slowed hiring, which is more responsible for missing the original headcount target?

It's the hiring freeze of course!

• You laid off 100 x 20% = 20 people

• Meanwhile, you cut the hiring plan from 100 to zero

In effect, you "fired" or "laid off" 100 future employees while only laying off 20 current ones:

As one founder friend whose recent layoff was leaked on social media told me:

Sure, the layoff news leaked, but when we modeled this stuff out we realized it's the reduced hiring that would save us the most cash.

A hiring freeze is a much bigger story, even if the media only reports layoffs:

• Internally, management knows the hiring freeze will have a larger impact on team velocity (and, frankly, cash burn) than the layoffs.

• Yet externally, everyone will focus on the layoffs.

This misses the raging forest fire for the merely singed trees.

A new perspective

Recognizing the importance of hiring freezes should change your focus regardless of whether you're a founder or an employee.

As a tech worker:

• The biggest risk you face in an economic downturn is not so much losing your current job but instead being unable to find a new one (due to hiring freezes)

• In addition, as you assess the viability of various startups, pay equal, if not more, attention to slowed hiring at the company than highly visible layoffs

• When interviewing, ask about the hiring plan and how it's changed in recent quarters

If you're a founder:

• Slowed hiring is the real sign of weakness and vulnerability in your competition

• Even with no layoffs, your competitors could be in a dire financial straits, necessitating a complete freeze in hiring

• If you're well-capitalized, your biggest opportunity during a downturn isn't picking up people who were laid off (though you should definitely consider this) but rather hiring the people your competitors would have wanted but now can't due to hiring constraints

Lay off the layoffs

Layoffs are a noisy estimate of company performance:

• Layoffs are tainted with worries about public perception and internal morale, making otherwise troubled companies hesitant to do them

• Companies conduct layoffs during downturns in part because everyone else is doing it, and they know they won’t look as bad if they move with the herd

Hiring freezes are a clean signal of a viability and confidence of management:

• Few companies would cut back on their hiring plans simply to fit in

• If anything, knowing others are pulling back makes companies more aggressive in hiring

That said, hiring freezes are opaque from the outside:

• Outside of large organizations, most never publicly announce hiring freezes

• In many cases, hiring freezes aren't even openly discussed internally outside of the core executive team and the board of directors

Keep in mind both the seen and the unseen. It's easy to focus on the shock and awe of layoffs, but you miss so much in doing so.

Layoffs aren't even half the story – they're 10% of it.

Originally published on whoisnnamdi.com.

COVID hurt most software companies.

While many claim COVID accelerated technology adoption and digital transformation, this didn't show up in the revenue of most software vendors.

In fact, the typical public software company remains 10-12% below its pre-COVID revenue trend, and the gap is only widening.

COVID put most software companies on a permanently lower growth trajectory.

A few case studies

The COVID shock generated divergent outcomes across software companies.

Let's focus on three examples to see what I mean: Zoom, Cloudflare, and Splunk.

The solid lines below represent the (log-transformed) quarterly revenue of each company. The dashed line represents a constant-growth linear trend based on their two-year pre-COVID revenue trajectory. Since the scale is in logs, straight lines imply constant quarter-over-quarter growth:

We see some interesting behavior as we cross the dashed line, which represents Q4 2019, the last quarter before COVID struck:

• Zoom immediately takes off, "zooming" above its pre-COVID trend as millions move to online communication. The trend eventually catches up

• Cloudflare is totally in the clouds, floating along like nothing happened, perfectly matching its pre-COVID trend of constant, exponential growth

• Splunk exhibits strong seasonality, so the initial decline is no surprise. However, it never recovers to trend, even two years later. Splunk got dunked

These symptoms run the gamut from manic to lethargic. Clearly, COVID was not a tide that raised all boats.

Mean reversion, or mean revision?

We can repeat the above analysis for all public software companies with enough data to establish a two-year pre-COVID trend. Given the different revenue scales, for visualization purposes let's normalize by dividing each company's actual performance by its extrapolated trend. This gives us the relative revenue performance of each vs. trend:

Each line represents performance vs. trend for one of fifty different public software companies.

With this we can begin to see the rich variety of post-COVID outcomes. Let's highlight our three case studies once more:

• Notice how dramatic and unrepresentative Zoom is among all software companies. Zoom peaks at ~150% above trend before gliding back down to Earth

• On the other hand, Splunk comes out as one of the worst performers, among others like Alteryx and Benefitfocus

There's a lot going on here, making it difficult to get a sense of the underlying distribution. Let's flip the axes and look at it a different way:

The above chart plots histograms of performance relative to trend across public software companies from Q1 2020 onward:

• Upon impact, performance is normally distributed, centered somewhat below zero

• Over the subsequent quarters, the distribution shifts left and widens, implying worse outcomes and greater dispersion between companies

• By eight quarters out, performance is unambiguously below trend, while variance continues to grow

Notice the skew: a handful of winners among a majority of losers.

Another way to look at the data is to calculate the proportion of software companies that climbed above their pre-COVID trend over time:

• Initially, only a quarter of software vendors were above trend

• One year out, one-third of companies have returned to trend

• The proportion stabilizes thereafter. Mean reversion is over

This is interesting. As dispersion grew in the first year of the pandemic, some companies returned to their pre-COVID trend. After these initial quick recoveries, however, mean reversion completely shuts down. The last lifeboats cast off, and if you aren't onboard, you're ngmi.

A key point I want to drive home: mean reversion is a strong force, but COVID was stronger, especially on the downside. Once knocked down, the typical software business never got back up.

Weakness, in numbers

Finally, let's summarize those performance distributions by their means and medians:

This is what I've been building up to – COVID materially impaired the typical software company, knocking it off trend:

• On average, software companies were down ~5% relative to trend 4 quarters out and down ~10% 8 quarters after impact

• The median dampens the impact of outliers like Zoom. Notice, median performance is even worse, ~7.5% at 4 quarters and ~12.5% at 8 quarters

• Meanwhile, underperformance relative to trend is still increasing. The gap between actual revenue and trend is growing ~4% per year

Thus, COVID's impact was not transitory: COVID permanently reduced the annual growth rate of public software companies by 4 percentage points.

Mean revision is at least as important as mean reversion. Not only are software companies below their pre-COVID trend, the trend itself has changed for the worse.

Conclusion: Conflation

I think there's been a massive conflation between:

1. the equity returns of software companies during the first year and half of COVID (exceptional, but temporary),

2. the financial performance of a select few COVID beneficiaries like Zoom (again, exceptional, but transitory), and

3. the business performance of the broader software market (poor, and durably so)

The temporarily exuberant stock prices of most software companies distracted from the toll COVID took on business performance. While investors got high on the Fed's supply, businesses themselves were silently suffocating.

This corroborates the findings of my last essay, where I explored how the mysterious "GDP factor" explains some the the "dark matter" in software valuations:

Despite some claims to the contrary, investors believe software companies are quite sensitive to the broader macroeconomic environment. In fact, it's the variable that correlates most highly with valuations after accounting for the individual financial performance of each company – The Dark Matter of Software Valuations

Here again, I find that software companies are sensitive to economic conditions, even in a best case scenario that supposedly favored them. "Digital transformation" can't come quickly enough it seems.

So let's set the record straight: COVID left a nasty scar on us all, software companies included.

Originally published on whoisnnamdi.com.

There's something strange about software valuations.

Investors tend to focus on revenue growth and profitability as significant variables for valuing software companies, but those metrics only explain about half of the overall variation in valuations.

This leaves a vast amount of "dark matter" in the software universe – variation in valuations that goes unexplained.

I want to explore this vast darkness.

Who knew there was such skew?

Note: this analysis only includes public software companies that traded continuously from January 2020 to May 2022. Companies that went public or got acquired during that period are excluded.

It's been said before, and it's worth repeating: averages don't tell the whole story.

You can begin to see why by plotting average revenue multiples against median multiples. They aren't the same.

In fact, the distance between them tends to fluctuate over time.

• In the lead up to COVID they were quite close

• Then, they grew apart

• Only recently, they've gravitated closer again

A single line at best summarizes the data and invariably misses important details.

Let's zoom in:

Like software monetization, software valuations are fat-tailed:

• Most companies have unremarkable valuation multiples

• A few, however, skew well to the right, creating a long tail of multiples

This tail skews the distribution of multiples and increases the variance between public software companies. I'd imagine the situation is even more extreme among private software companies.

That variance changes over time, rising and falling:

Note: for statistical reasons I won't explain here, the remainder of this essay will refer to log-transformed software multiples instead of their raw values.

I'd love to explain this dynamic.

It's at this point that any good software investor turns to ol' reliable: regressions of valuation multiples on revenue growth and profitability:

We see a similar, if subdued, pattern in the variance that can be explained by financial performance. But notice how much variance our standard regression leaves on the table, unexplained.

Growth and profitability do not perfectly predict valuations.

I call this unexplained variance, the "dark matter" of software valuations:

Growth and profitability never explain much more than half of the variation in multiples:

What's hiding down in the deep, dark depths of the software universe?

Let's grab our telescope and zoom in once again.

Stranger things (and valuations)

We know that growth and profitability are imperfect predictors of valuation.

Let's remove their effect and focus now on the remaining, residual, variance in valuations. This means that we'll focus on relative rather than absolute valuations – relative to what growth and margins would predict, that is.

Here's how it looks:

The fat tail is less severe but still persists, even after accounting for financial performance.

Many companies hover near zero, which is to say their valuations are perfectly predicted by growth and profitability, but a number are quite "overvalued" on the basis of their financials alone.

This gap between expectations and reality is quite persistent. Over/undervaluation does not meaningfully mean-revert, even over multiple years:

• A 50% overvalued company in January 2020 by May 2022 still traded ~40% above the valuation implied by its growth and profitability

• A 50% undervalued company continued to sag ~35% below its predicted valuation by the end of this period

Some did see major revisions:

• Shopify, Okta, Coupa, DocuSign, and RingCentral all fell back down to Earth after flying high for some time

• Cloudflare, Appian, Alteryx, MongoDB, Atlassian, Anaplan, and Guidewire all ended up much higher than where they were pre-COVID

In addition to individual companies, the overall distribution of unexplained "valuation inequality" held in place the past few years, with some fluctuations:

• Investors value the 90th percentile company ~75% more than you'd expect from its growth and profitability.

• Meanwhile, they peg the 10th percentile company ~50% lower than you'd predict.

• In terms of ratios, 90th percentile software vendors are worth 3-5 times their 10th percentile peers.

Remember, we've already accounted for revenue growth and cash flow, yet we still see massive variability:

• On the revenue side, among companies growing ~50% annually we see valuation gaps as large as ~70% (i.e., same growth, yet one company is worth 70% more).

• It's even worse for software companies growing closer to 20% year-over-year, where the gaps are even bigger: the most valuable company is worth ~2.5 times the least valuable.

• On the free cash flow side, if we look at companies within +/- 10 percentage points of breakeven, we see companies worth five times as much as the least valuable

Needless to say, the variation in software valuations is extreme, and that's after factoring out what we'd already expect based on financial profiles.

Darkness into light

I hope I've driven the point home that growth and profitability aren't everything.

OK, but what is this dark matter stuff?

Clearly the most obvious metrics fail to account for a sizable share of the variation in valuations among software stocks. Some sort of unobserved or latent characteristics drive the remaining differences.

Principal Component Analysis (PCA) is a great way to identify such latent factors. It's a statistical technique for summarizing the key informational content contained within a larger data set.

A full explanation of PCA is beyond the scope of this essay. Just know that, like alchemy, PCA lets us put dark matter in and get some nuggets of gold out.

When I run PCA, here's what I find:

PCA spits out two latent "factors" that highly explain our up-till-now unexplained valuation puzzle.

Unfortunately, these factors don't come out of the box with much explanation. We must interpret them manually.

After staring at "Factor 1" for a while, it hit me – the line looks eerily similar to the path of the U.S. economy over that period!

Folks, we have a winner:

Beautiful.

Our first hidden factor is not a particularly mysterious one.

Despite some claims to the contrary, investors believe software companies are quite sensitive to the broader macroeconomic environment. In fact, it's the variable that correlates most highly with valuations after accounting for the individual financial performance of each company.

The latent factor tracks the COVID recovery quite well, in some ways even better than the official GDP statistic, which is only released quarterly – a significant delay.

The factor provides a daily, real-time estimate of economic performance, at least that portion which is relevant for software companies (also called nowcasting).

I had less success figuring out the second factor. If you have any ideas, please send them my way!

With PCA, it's customary to plot the data against the first two factors, with the X and Y axes representing each company's correlation with the factor:

• Companies on the right side correlative positively with the GDP factor, suggesting they've benefitted disproportionately from the COVID bounce-back

• Conversely, stocks hovering to the left had less to gain from the rebound

I could spend (and may have already spent) hours staring at this plot. Rather than continuing to play "Where's Waldo," here are a few clusters I noticed (Q: What did I miss?):

• Financial engineering: Developer-centric tooling and infrastructure like Datadog, Elastic, MongoDB, Atlassian, PagerDuty

• Easy storage, easy life: Storage companies Dropbox and Box

• Trust, but verify: Zero-trust and identity solutions like Okta, Ping Identity, Zscaler

• Call me maybe: Cloud-based contact center and communications companies Five9, RingCentral, 8x8

A step in the right direction

Now let's come full circle and incorporate the GDP factor into our previous regression while also allowing each stock to have different sensitivity to the factor.

Here's what we get:

The GDP factor helps explain some of the mysterious "dark matter" but not always the same amount:

• In the weeks before COVID turned everything upside-down, the GDP factor didn't explain much variation in valuations

• Then, as the pandemic began, the GDP factor rose in importance, granting significant predictive power

• As the first positive results of the mRNA vaccines come out, it recedes again, and for about three quarters the GDP factor doesn't explain much over and above individual financial performance

• Finally, as the COVID recovery finally stalls out, the GDP factor rises in importance again

Here's how it looks in R-squared terms:

Still a lot of dark matter out there, but we've made great progress:

• At it's highest, the combination of (1) individual financial performance and (2) sensitivity to economic conditions explains ~80% of the cross-sectional variance in software valuations

• That said, there remain times when it explains no more than 50% of valuation variance

I'm fascinated by these dynamics, especially the middle period where, for whatever reason, economic sensitivity temporarily stopped mattering for how software companies were being valued, at least relative to one another.

Conclusion

So we've managed to chip away at some of the dark matter hiding among software valuations.

It's often said that the long-term growth trajectory of cloud companies is relatively secular, which is to say it's independent of general economic fluctuations.

It turns out software companies *are* sensitive to broader economic conditions in the short-run, at least in eyes of public investors.

The relevance of this sensitivity rises and falls over time:

• In periods of economic uncertainty (e.g. early pandemic, Q2 2022, etc), the GDP factor begins to matter quite a bit. Public investors care who gains and loses from a weakening economy.

• Meanwhile, when times are good, investors throw caution to the wind and ignore the economic sensitivity of software companies.

More sophisticated factor modeling could yield even more insights. No one pays me to do that though, so I think I'll move on.

If you do have thoughts on that second mystery factor, shoot me a note!

Originally published on whoisnnamdi.com.

For your seed stage startup to graduate to the next level, it must pass a new test.

Suddenly, a PowerPoint deck isn't enough. Traction and monetization matter.

Your startup might have more of one than the other, and that's OK.

But less of one means you need more of the other. That's just math.

How much more? It turns out – more than you think.

Low monetization requires extraordinary traction, and vice versa.

2 + 2 = 4

In addition to factors like team quality and market size (which also matter at the pre-seed/seed stage), investors evaluate Series A startups on their traction and monetization.

By traction, I mean some rough mix of:

• bottom-up adoption

• free usage / free trials

• organic, inbound interest in the product

• proliferation of an open source project

• downloads

• a vibrant, growing community

• product-led growth

By monetization, I'm loosely referring to:

• revenue (duh!)

• customers

• top-down / direct sales

• production deployments

• pilots / POCs (proofs of concept)

• qualified pipeline

Note: monetization is a catch-all for activities and metrics that tend to correlate with revenue, so a company with zero revenue can still have a decent monetization "score."

If traction and monetization are collectively high enough, you will successfully raise your Series A round.

You can even make a little equation out of this:

There's a threshold, a sort of activation energy, required to successfully raise a round at the early-stage. Hit this bar and you're in the clear. Miss it, and you're not:

Let's make up some numbers.

Say the threshold is "4". You'd need some combination of traction and monetization to get to a sum of four:

• A score of two on both traction and monetization would work. We could call that "moderate traction and moderate monetization", or (2, 2)

• But a (3, 1) combo would also work – perhaps "high traction, low monetization", and so on.

As long as you can get to a total of four, you're good.

2 x 2 = 4

What if our model is wrong though?

Imagine investors evaluate your startup not on the sum of traction and monetization, but rather the product. In other words, traction and monetization multiply, rather than add:

Through this lens, the marginal value of one factor depends on the other. Traction makes monetization more valuable, and vice versa. Monetization is more valuable when you have more traction.

This is a much better model for how venture investors think about startups.

Think about it:

• Strong traction makes additional monetization more valuable, since there's a preexisting, large group of interested, engaged users to sell into.

• Strong monetization increases the value of additional traction, since you know that traction will be monetizable in the future.

The multiplicative model has an important implication however – it disproportionately penalizes low traction and/or low monetization.

To successfully fundraise, low monetization requires extraordinary traction, and vice versa:

Here's an analogy – a stock that drops by 50% (OK, maybe more reality than analogy) must rise by much more than 50% to reach its original price level, since returns are multiplicative (50% x 150% is only 75%). Likewise, low monetization requires more than proportionally higher traction to make up for it.

(2, 2) still works, since 2 x 2 = 4. But the (3, 1) combination is no longer viable, since 3 x 1 only equals 3. If your startup is merely a "1" on the monetization scale, you now need to be a "4" on traction. 0.5 on monetization? You need to be an 8 on traction!

You can see where this is going.

3 x 1 = ☠

Unfortunately, founders fall into the trap of thinking low monetization or traction can be made up for with merely moderate amounts of the other factor. This is wrong.

Many founders think investors will evaluate them with the linear, additive model. While many investors are quite linear in their thinking (sorry, had to do it), the multiplicative perspective is the more accurate one.

This clash of reality vs. expectations creates a "no man's land" between the additive and multiplicative perspectives: founders think they'll be fine when they go out to fundraise, only to be rudely mugged by the valley reality:

I've noticed many startups in the space between the linear and convex curves attempting to raise Series A rounds lately. This invariably goes badly. The founders are surprised by the lack of investor interest and end up taking much longer to raise their round, at a lower valuation than expected.

Some never raise at all.

Quick maths

Avoid this needless suffering. Be honest with yourself – if you're lacking in either traction or monetization, be sure you're killing it on the other dimension.

I've made the point that low monetization requires extraordinary traction (and vice versa), but I haven't defined what "extraordinary" means. Frankly, it's subjective at best, and I'd be lying if I said there was an objective standard or rubric.

When you know, you know:

you can always feel product/market fit when it's happening – Marc Andreessen

… once you have product-market fit… [business] performance accelerates so meaningfully that any ambiguity goes out the window. You get it "in your gut." It becomes obvious, as nearly everything about the business gets better – Product-Market Fit is Lindy

If it is true that product-market fit yields much better business performance, then product-market fit should be relatively obvious, even with little data – Product-Market Fit is Lindy

Further, I've simplified the framework here and taken other factors like team, market, etc as given. Realistically, investors will evaluate those things too, complicating our math a bit:

That's a lot harder to visualize, but the same ideas apply. You need to really spike on one or more factors to make up for lower scores on the others. Get this math wrong, and you'll be in for a nasty surprise.

Rather than cram for the exam, delay your Series A until you pass the simple math test I've outlined here. Until then, keep practicing those multiplication tables!

Abundant capital is not a panacea for founders.

For the best companies, raising large war chests along with the branding effect that comes with them can serve as an "unfair advantage" against competitors, compounding gains and maintaining pole position.

This unfair advantage often comes through the hiring channel, which is high-octane leverage for fast-growing companies:

The “10x” in “10x engineer” conveys leverage just as the “3x” in “3x S&P” does. As an investor, leverage can multiply the returns you receive from a great investment thesis. And similarly, if you’re a founder with a great vision, your superstar employees will help you realize it faster and better – Talent as leverage

For most however, a frothy funding environment means more competition for talent. If everyone is raising tons of cheap capital and plowing those funds into hiring, your job as a founder hasn't necessarily gotten easier, since presumably you'll be working that much harder to recruit against every other startup doing the same.

The capital "bottleneck" hasn't disappeared – it's moved to another part of the production chain. Funding gets easier; hiring gets harder.

Cheap money, expensive people

He deleted the tweet, but Mike Speiser once said:

When money is cheap, people are expensive. When money is expensive, people are cheap. #dilution

The idea that hiring gets harder when funding gets easier (and vice versa) has two foundational components. The first one is easy, so let's get that out of the way first.

When companies are cash rich, they tend to spend more (*shocker*). Startups raise funding in large part to expand their teams. When the entire startup ecosystem simultaneously raises tons of capital, the additional money sloshing around inevitably shows up in the W2s of startup employees and the "voluntary churn" column of board of directors presentations. Recruiters spam the LinkedIn inboxes of every warm body in sight, and the hiring market heats up.

That's fairly intuitive. The second reason is much less so – discount rates.

Go back to the Mike Speiser quote. What does it mean for money to be "cheap?" Financial theory introduced us to the concept of the "discount rate" or equivalently the required rate of return or cost of capital. Money is "cheap" when discount rates are low, implying that investors and lenders require only a small rate of return on their investments in order to be satisfied.

Typically, low discount rates are associated with high valuations, since the present value of a series of cash flows rises as discount rates falls. Thus we get the frequent gyrations of the stock market, which summarize this dynamic in a single number:

the large movements in the value of the stock market arise mainly from changes in discount rates and only secondarily from changes in the dividend or profit flow capitalized in the stock market – High Discounts and High Unemployment

It's quite natural to apply the notion of discount rates to the value of financial assets, like company equity. But why limit our thinking to financial assets?

The present value of talent

Discount rates apply to human capital too.

If you don't believe me, check out the following chart. It plots the unemployment rate over time along with the inverse of the detrended (i.e. accounting for the size of the U.S. economy) S&P 500 index (so down is up and vice versa). Notice the tight relationship between the two:

Figure 2 suggests that the stock market and unemployment respond to the same underlying forces, especially in the past few decades – High Discounts and High Unemployment

You might ascribe this correlation to general economic conditions rather than discount rates, but unemployment and output per worker don't actually track each other very well:

To see how discount rates apply to workers in the same way they do to financial assets, imagine the net value of a worker to a company being their productivity minus their cost, i.e. wage. This "job value" takes into account all future value the worker generates along with their cost of employment, in present value terms:

Job value tends to increase during booms, for two reasons:

1. Discount rates are low, which inflates present value calculations,

2. Productivity is more variable than wages, which tend to be sticky

Thus, the present value of productivity moves up and down by more than wages, driving job value up or down with the market cycle:

A higher discount lowers the job value… The present value of the wage moves in the same direction as productivity, but less than in proportion. In this sense, the wage is sticky. Because the wage falls less than productivity, the job value… falls more than in proportion to the fall in productivity – High Discounts and High Unemployment

Naturally, the higher the "job value," the more eager employers are to hire workers. We get cutthroat competition among employers during good economic times and workers being dropped like hot potatoes during bad times. When the present value of hiring an additional worker is high, employers clamor to grow their ranks.

It's possible to use economic data to estimate job value across the entire U.S. economy. Job value and the stock market move together in lockstep:

The similarity of the job value and the stock market value is remarkable. The figure strongly confirms the hypothesis that similar forces govern the market values of claims on jobs and claims on corporations – High Discounts and High Unemployment

The up and down movement of job value is incredibly consistent across industries too:

The strikingly similar responses in diverse industries strongly supports the hypothesis that an aggregate driving force dominates the movements of the job value at the industry level. This evidence points in the direction of aggregate forces such as rising discounts in recessions – High Discounts and High Unemployment

When hiring falls apart

When interest rates are low, leverage becomes more attractive. Although the venture capital world likes to think of itself as distinct from debt-laden private equity land, in fact technology startups engage in all sorts of leverage, especially during boom times. I talked about one form of it, preferred equity, in a prior essay:

In this way, startups become synthetically levered. No debt appears on the balance sheet, yet the returns of the common equity (any really anyone below the most senior preferred stock) get juiced by the lower cost of capital achieved via preferred equity – Schrodinger's Balance Sheet: When Equity Becomes a Liability

I referred earlier to the notion that exceptional employees are a form of leverage. Just like financial leverage, talent leverage allows a company to do things they wouldn't otherwise be capable of with existing resources.

To attract such special individuals, startups have to promise the world to these ambitious folks, a sort of "debt." These superstar employees wouldn't join otherwise – the cash compensation alone is rarely worth it. They must be promised quickly-appreciating equity, as well as the Silicon Valley cache and prestige associated with a successful startup. This is related to Keith Rabois' idea of "talent arbitrage."

This Silicon Valley "spin" peaks during market highs – frothy periods where slick storytellers paint increasingly fantastical and implausible visions for what their company will achieve. It's a competition to see who can lever up their fragile enterprise the most.

If things start to go south, the bubble bursts, and these 10x employees are often the first to abandon ship:

The leverage you get from hiring really talented people is a huge risk during rough times, because these people have lots of other options and the ambition to pursue them.
…
If any of of the people in this chain stop believing the hype around which their project is organized, then the hype becomes unjustified… And once the bubble starts to wobble, 10x employees will move on to the next compelling tech vision, causing the leveraged death spiral mentioned in the last section. Leveraging your company with talent increases your volatility - either you orchestrate a revolution, or you implode – Talent as leverage

It's never pretty when leverage blows up, and talent leverage is no different. Troubled startups face double jeopardy – preferred equity implodes, as does the talent stack.

Easy funding =/= easy life

I'm increasingly skeptical of the notion that easy funding makes the lives of founders easier. Funding merely shifts the bottleneck.

Difficulty hiring is a recurring theme I've seen among founders during red-hot markets. The financial capital comes easy; the human capital doesn't.

In fact, hiring can be tougher after raising at a big valuation because candidates might think all the upside is gone already, making equity compensation less attractive:

At Retool, we decided to raise the smallest Series C we could, at a valuation substantially lower than most offers we received. Why? Because even though maximizing our valuation and money raised might be optically good (the press loves to report on large financing rounds at large valuations), it hurts the core constituency that got us here: our team… Reaching peak valuations too early results in substantially less upside for employees. – Raising less money at lower valuations

Between the increased effort that hiring requires during boom periods and the prospect of employee exodus after hitting choppy waters, founders should be wary of getting caught up in the irrational exuberance of the broader market.

Rules are meant to be broken, and the Rule of 40 is no exception.

The Rule of 40 is a popular heuristic for evaluating financial performance and predicting valuation multiples.

But it's got 99 3 problems:

• It's too rigid

• It's too short-term

• It's too narrowly focused

In this essay, I break apart the Rule of 40, fix it, and put it back together again.

Laying down the rules

After flying high for some time, software valuation multiples have come back down to Earth (Note: LTM multiples used throughout this article):

While there are all sorts of explanations for the recent tech correction, the Rule of 40 concerns itself not so much with the ebbs and flows of software valuations but rather their determinants at a single point-in-time. It provides a simple rule of thumb for evaluating the performance of software companies and estimating their likely valuation multiples:

The Rule of 40 states that the sum of annual revenue growth and profitability (either EBITDA or free cash flow margin) should equal 40 or more for the best performing companies, as in the following slide from Battery Ventures' Software 2021 deck:

In general though, the "40" isn't particularly important. Higher is better; that's it.

"Houston, we have three problems"

I see three problems with standard Rule of 40 analysis.

Problem #1: Revenue and margin may not be equally important

Because the Rule of 40 adds revenue and margin together with no particular weighting of each (it's analogous to a simple average), in a subtle way it implies they are of equal importance.

This isn't necessarily true of course. An additional point of revenue growth could be worth more, the same, or less than an additional point of profitability.

Fix #1: include revenue and margin separately in our regressions.

Problem #2: The long-run may look quite different

Even if revenue and margin were equally important on average, their relative importance could change over time.

Folks tend to run Rule of 40 regressions at a particular point in time, revealing how the market values growth and margin today, and today alone, saying nothing about how those relationships change over the long-run.

In a bull market, where growth is highly valued, our regressions fool us into thinking growth will be forever important. When the market subsequently turns, growth gets crushed, needlessly surprising many analysts and investors.

Fix #2: pool data from multiple time periods.

Problem #3: Growth and margin aren't the only factors

Simple regressions inflate the importance of the Rule of 40 by misattributing the influence of other factors to growth and margin.

For example: Snowflake is a high flying company, both in terms of it's growth rate and revenue multiple. But Snowflake really is a special snowflake – it has a lot of other things going for it too, like the massive market for data warehouse solutions.

Thus, naively comparing Snowflake and other companies with smaller markets in the same regression will tend to overstate the value of revenue growth, leading to the false impression that, "if only we grow as fast as Snowflake, we'll be valued just as highly." This is wrong.

Fix #3: control for the average influence of unobserved factors.

If it's broken, fix it

Let's implement each of these fixes one-by-one to see how they influence the results.

Here's the impact of revenue growth and free cash flow margin on valuation multiples with each of the three fixes implemented sequentially:

First, let's split up growth and margin to see how they separately impact valuations. To start, we pick a random date, June 30th 2021:

• According to this simple analysis, a single additional percentage point of revenue growth increases valuation multiples by 3%, while an additional percentage point of free cash flow margin only adds 0.5%.

• By this measure, growth is six times as important as margin.

Second, we'll pool together two years of trading data, excluding any companies that went public or got acquired during this time so we maintain a balanced sample throughout. The regression reveals the average importance of growth and profitability over the entire period:

• An additional point of revenue growth leads to a 2.7% higher revenue multiple (down from 3%) while an additional point of free cash flow margin yields 1.1% higher valuation (up from 0.5%).

• Growth matters only ~2.5x as much as margin.

Lastly, the results still suffer from the fact that we're attempting to explain all the complexities of valuation with only two variables (the "Snowflake problem"), giving credit to growth and margin where credit isn't due. The final regression controls for the average impact of these unobserved factors (fixed effects for the econometrics nerds):

• 1 point of either revenue growth or free cash flow margin increases valuation multiples by 1%.

• Growth and margin matter equally.

So it turns out growth and margin are equally important after all!

Note: it may be much easier to increase growth or margin by one percentage point, so we have to be a little careful in saying they "matter equally."

Still, we've much more rigorously confirmed that growth and margin have similar effects on valuation. Very cool.

How the rules have changed

Here's how the market value of growth and profitability have evolved over time:

We can see a number of interesting features:

• The market value of profitability temporarily spiked in the early days of the pandemic but then fell significantly, bottoming out at almost zero in Q1 2021

• Meanwhile, the market's thirst for growth surged from only 0.5% per percentage point of growth to 1.5% at the peak in late 2020

• Beginning in early 2021, margin began a steady comeback, and profitability is now as important as it was in the most uncertain days of the pandemic

• The value of growth fell in early 2021, recovered somewhat, and crashed even more spectacularly later in the year, returning growth to its pre-pandemic value

We can also plot the "spread" between the value of growth vs. margin, an interesting chart in its own right:

• Growth and margin start off fairly close, with a slight market preference toward margin

• After a brief period of uncertainty, the value of software growth relative to profitability roars back, peaking at a ~1% premium at the end of 2020

• Again, we see the first blow to growth in the first half of 2021 and a second blow landing in late 2021

• Relative to profitability, growth is even less valuable than it was pre-COVID

Rewriting the rules

A few conclusions:

• On average, growth and profitability are equally impactful to valuation, but one may win out over the other at various periods

• Growth matters much less than simple cross-sectional regressions imply

• As of early 2022, the market has turned decisively against growth in favor of profitability

In a way, these results affirm the utility of the Rule of 40: growth and margin are similarly important. However it adds a wrinkle: the degree of similarity varies over the market cycle.

Though the days of "growth at all costs" are clearly over, it's still fair to ask: growth at what cost? In other words, how should companies trade off growth and margin?

I'll leave that to a future post. 👀

Preferred equity is synthetic leverage.

It combines an equity-like security with a debt-like security. Which one it is depends on the success of the business:

• When things go well, preferred stock converts to standard common equity.

• When things go poorly, preferred stock turns into debt.

In this way, preferred equity exists in a constant state of quantum superposition. It's neither equity nor debt, until it is.

Schrödinger's balance sheet

Erwin Schrödinger was a renowned physicist and one of the first to study the strange world of quantum theory. Among us mortals, he is most known for his famous thought experiment, eponymously called "Schrödinger's cat." In it, he posited that a cat sitting in a box along with a flask of poison could be considered to be both dead *and* alive until we open the box to find out if the flask has shattered, killing the poor kitty.

Preferred equity is like Schrödinger's cat. Open the box: if a startup is very valuable, preferred stock all becomes normal equity. If the box contains a dead or slow growing startup that hasn't created much value, preferred stock becomes debt instead.

You can think about preferred equity as debt combined with a deeply out of the money call option. "Out of the money" is just a fancy way of saying the investor won't make much of a return unless the company gains significant value. If the company creates no value from that point forward, the option component is worthless, and the equity merely becomes debt. If the company creates significant value, the importance of the debt element wanes and the option component dominates the return profile.

Strangely enough, this is the exact reverse of what you'd want to have happen as a founder.

This point has been made by John Cochrane and others in the context of bank capitalization:

Ensuring that banks are funded with significantly more equity should be a key element of effective bank regulatory reform. Much more equity funding would permit banks to perform all their useful functions and support growth without endangering the financial system by systemic fragility.

Things go very wrong when banks are overly-funded with debt, as debt tends not to "fail gracefully" the way equity does. In financial crises, banks "break" in a very hard way due to the significant debt they carry, which must be paid back, necessitating government bailouts in the worst case scenarios. On the other hand, equity tends to break in a soft way: if the company isn't worth anything, nothing is owed. Thus a sudden decline in the value of the company doesn't cause a severe panic.

Cochrane goes so far as to advocate that in times of financial distress, debt on the balance sheet of struggling banks should simply convert to equity rather than be bailed out by the government. Notice what he's proposing – debt on the upside and equity on the downside.

Likewise, as a founder, you'd want your capital structure to be equity when things go badly, making it a variable cost that declines gracefully as your business success declines, and you'd want it to be debt in the upside case, effectively acting as a fixed cost over which you get leverage.

In startup land, however, we do the opposite – we convert equity to debt in times of distress.

Spin up

Preferred equity is more valuable to investors than common equity, so they're willing to pay more for it, necessitating the issuance of fewer shares and leading to less dilution for the company. Thus, relative to raising common equity, preferred equity has a similar benefit as debt, in that it allows you to raise more capital with less dilution.

In this way, startups become synthetically levered. No debt appears on the balance sheet, yet the returns of the common equity (really anyone below the most senior preferred stock) get juiced by the lower cost of capital achieved via preferred equity.

The value of preferred stock can be inferred from its cost, i.e. its premium relative to common equity. The preferred equity premium is rarely laid out explicitly, but researchers at Stanford and the University of British Columbia estimate that these days preferred equity is 50% more valuable than common equity and was even more valuable in the past:

This paper develops the first option pricing model of venture capital-backed companies and their security values that incorporates the dilutive future financing rounds prevalent in the industry. Applying our model to 19,000 companies raising 37,000 rounds shows that preferred contractual features make the most recently issued preferred shares worth on average 56% more than common shares – A Valuation Model of Venture Capital-Backed Companies with Multiple Financing Rounds

A quick illustration: you're a founder, and you know you need to raise roughly $60M to achieve a meaningful outcome. Because preferred equity is worth 50% more to investors than common, you could either raise $60M in preferred stock with some level of dilution, or raise it all in common stock for 50% more dilution. Let's say the common approach would dilute you by 60%. Then raising the same amount via preferred would only dilute you by 40%:

This obviously makes a massive difference in an upside scenario. Simply put, you get to keep much, much more of your company. When things work out, preferred equity has a higher cost to investors, and a lower cost to startups.

This is obviously great in a bull market. Cheap, bountiful leverage, available oftentimes for a fraction of the painful diligence associated with raising real debt.

The availability of preferred equity drives companies to raise more than they otherwise would. We'll look at such a scenario next.

Spin down

When rates of return are sufficiently low, preferred equity begins to look eerily like debt. When are rates of return lowest? When valuations are highest:

High valuation multiples corresponds to lower returns, and vice versa. After periods of frothy valuations, returns end up lower than expected, bringing lofty valuations multiples back down to reality – Companies Rarely Grow Into Their Valuations

The mountains of preferred equity being layered into private companies follows the same behavioral vein typically seen during the peak of a market cycle, that is, massive overuse and abuse of debt that juices returns. This has predictably disastrous consequences when things go south.

As is common in finance, the problems with debt reveal themselves only once it blows up. In a downside scenario, the returns of common equity holders get crushed by the weight of the liquidation preference attached to preferred equity.

Remember, venture capital investors are willing to pay $1.50 for preferred equity granting the same ownership they could get for $1 of common equity. In an upside scenario, they effectively pay $1.50 for $1's worth of equity. What happens in a downside scenario?

Let's return to our previous example. This time, the company chooses a certain, fixed level of dilution that it's willing to endure, 60%, and raises money up to that point. Since preferred stock can be issued at a premium, the company can raise $90M of it for the same dilution as $60M of common:

So far so good. The preferred approach gives you $30M more to put to work in the business. But let's say you never quite achieve product-market fit (not uncommon, even among companies that have raised tens of millions of dollars) and you woefully underperform expectations. You never become a unicorn, and the business is sold for only $100M:

• If you only raised common equity, the founders and employees walk away with $40M. Not as much as hoped for, but not bad!

• If you raised preferred however, founders and employees walk away with only $10M, since the equity is now debt, and the investors must receive their "1x liquidation preference" (their "principal" effectively). Brutal.

This may offer a clue into why the hedge funds of the world have moved so swiftly into venture. As sophisticated, multi-asset investors, they are quite used to lower-returning, low-risk credit investing combined with potentially high-returning, high-risk derivatives. Rather than "fish out of water", as they are often portrayed by traditional venture investors, could they in fact be quite shrewd (tiger)sharks?

The rabbit hole goes further. Define "overvaluation" as the premium at which preferred stock is valued relative to common equity. Analysis shows overvaluation predicts poor exit outcomes, measured in terms of (1) exit value and (2) the probability of going public or being bought out (note: low R^2 implies exit outcomes are difficult to predict, not that the relationships aren't statistically significant):

… overvaluation predicts low exit outcomes… The extent of this overstatement predicts poor subsequent performance – A Valuation Model of Venture Capital-Backed Companies with Multiple Financing Rounds

Abuse of preferred equity predicts worse outcomes, fewer exits, and lower returns.

Synthetic leverage, as with all forms of financial leverage, makes bad situations even worse AND more likely.

Superposition

Now, let's finally explore the quirky quantum world of superposition.

Preferred equity starts as neither debt nor equity: you have to "open the box" to see if the company is successful. Unfortunately, actions that make your upside better (raising a ton of money and putting it to work in the business) also make your downside worse. Things that look smart in the upside scenario (raising a "war chest") look disastrous in retrospect on the downside. Gain and pain are entangled; success and failure are superimposed on one another.

Perversely, the Silicon Valley ecosystem creates more synthetic debt (i.e. preferred equity) exactly as returns fall:

1. Valuations rise

2. Companies take advantage by issuing more preferred stock

3. Returns fall due to inflated valuations

4. Preferred stock converges toward debt

Let's return to financial crises. The capital stack of a company is very similar to that of a home. From the perspective of the founder, common stock is the "equity" in the home and preferred stock is the debt or mortgage. If value of home falls below the principal on the mortgage, the homeowner is "underwater", destroying the value of their equity.

Likewise, when a startup's valuation falls too low, the value comes out of the common shareholders; they get squeezed first. This is most severe in a down round where anti-dilution provisions come into play. This is the Silicon Valley version of a good ol' Wall Street bailout: the debt (preferred) holders get saved, and the equity holders get wrecked.

What tends to happen when homeowners go underwater? We know this from history – they abandon the home:

Now I ask: what might founders and employees do once they are underwater on the preferred equity they've raised?

Equity value motivates founders and employees. If there's no hope for their equity to be worth anything, could we see discouraged founders and employees abandon ship in droves? Even if they don't leave the company entirely, could they effectively go "delinquent" and "check out," significantly reducing their company-focused effort and output?

In startups, we see acceleration of value impairment: as things get worse, they get even worse. Not a great underwriting scenario for a debt-like instrument huh?

Poor performance alone doesn't create a "zombie startup." Zombification requires the total evisceration of team vitality and morale. Thus, Schrödinger's cat becomes Schrödinger's zombie:

"… it's very easy to get into this 'zombie mode,' where your startup is neither truly succeeding nor dying… This is actually worse than failing." – Drew Houston, Founder of Dropbox

Schrodinger's Pandora's box

Imagine you're selling your home and the buyer offers to pay you 50% above the asking price as long as they can get "preferred equity" in your home. You'd be ecstatic about the richer sale price, but you'd also be really curious to know what in the world "preferred equity" is and why it's so valuable to them!

Likewise, as a founder or startup employee, even if corporate finance isn't your favorite topic, you should wonder why investors value preferred equity so highly.

What could investors be so concerned about that they would willingly overpay by 50%? And if they're so concerned, do you think, maybe, you should be too?