Tag: Valuation

The role of startup valuation in staged capital deployment

In his article VCs should play bridge, Alex Danco described the Capital Coordination problem in venture capital, created by the staged nature of investments.

I recommend reading the whole piece, but the summary is that investments are de-risked by staging capital over future milestones (with the implied valuation step-ups) rather than investing everything up-front and hoping for the best.

The heart of this strategy is the signalling game where investors offer affirmation of the investment for their capital partners.

“This gives the next investor cover to say, ok, I’ll do the same thing. I’ll invest at $20M, sending a signal: I believe that this price reflects a discount to the next round. This 20 million price, by current convention, means we believe this hand will play out as another X million in GMV run rate”, or whatever it is your signalling for this new round of bidding.”

Danco’s assessment speaks to the oddly myopic perspective of “venture math“: investors are primarily focused on understanding incremental progress (measured with ARR) rather than the ultimate outcome. This has come at a cost to sectors that are slower to start generating revenue but solve much more important problems.

He’s right to frame venture capital as collaborative (Bridge) rather than strictly adversarial (Poker), this also highlights issues like enmeshment and collusion which emerge when so much stress is put on relationship-driven outcomes over objective measurement.

The approach that Danco describes as “convention bidding” is framed as an alternative to the zero-sum attitude of formal valuation — where investors are primarily concerned with securing their own returns rather than participating in affirmative semaphore.

Indeed, this reflects a revealed preference for market signals over conviction which should trouble anyone who still believes venture capital is responsible for risk capital formation.

Discounting the Future

Further to the above, Danco’s article is a good reflection of venture capital’s poor grasp of valuation, characterised by three common misconceptions:

1. Valuation is transient, and at each stage a new valuation would need to be calculated — creating uncertainty that threatens signal-driven coordination.
2. Valuation is the same as price, and entry points are more a function of market norms and comps than they are of fundamental value creation.
3. The practice of valuation is built on too much uncertainty to be practical for early stage companies.

While the first is inaccurate, the second is actually dangerous. The general practice of market-driven pricing has created huge structural fragility in venture capital, exacerbating the already painfully boom-and-bust nature.

The third, however, is just silly. All investments are predicated on valuation, at least in theory (the alternative being mindless momentum investing). The only difference is whether it’s written out with explicit assumptions or a mental calculation with implicit assumptions.

Indeed, valuation is essentially the story of a startup (“What happens if things go right?”) translated into numbers, with some discounting for the cost of capital and risk of failure. You can think of it as the financial source code of a pitch.

Usefully, by calculating a terminal value based on that story and discounting it using the expected rate of return in venture capital, you can also ascertain a healthy entry point for future rounds — assuming the startup remains broadly on track.

Consider the below: the valuation of a Seed round, calculated on Equidam at just over $20M. As a part of this calculation, and the future it’s predicated on, you can also see the valuation trajectory for the forecasted period. If the company raised a Series A at ~30 months, the valuation would be around $85M.

Of course, all of the usual caveats: the future is uncertain, startups pivot, markets shift. However, it’s fairly easy to built those adjustments into a model and see how they change both today’s valuation and the future trajectory.

Assessing valuation with a higher resolution view of performance enables better judgements: Perhaps top-line revenue is growing on track, but costs are surging. Maybe revenue is lagging, but margins are way better than expected.

The hope is not that forecasts play out precisely, but that they give you a useful relative perspective on performance over time.

If venture capitalists wanted a way to coordinate staged capital in the future, to finance a company efficiently (with minimal time spent agonising over terms) while ensuring a good risk-adjusted return for all investors, this is the logical approach.

Valuation is essentially the process of aligning expectations across market participants by transparently exploring data and assumptions. Typically this occurs between buyer and seller (VC and founder) in a specific transaction, but there’s no reason why downstream capital providers couldn’t benefit from that work.

It also offers the vital benefits of helping investors better understand the value of novel innovation, and limiting exposure to systematic risk of market-based pricing — getting caught in the trap of “money chasing deals“.

(top image: “The Bulls and Bears in the Market” by William Holbrook Beard)

Whether you’re investing in mature companies in the public market, or fast-growing startups in the private market, one question separates good and bad investors:

Do you understand valuation?

Valuation is the rationale by which you determine which opportunities to pursue. To develop your understanding of valuation is to develop your ability to recognise potential.

Despite the central role in investment decisions, valuation is often misconstrued as financial engineering or market-driven pricing exercises.

Valuation is an opinion

Here’s three things valuation is not:

• Based on verifiable inputs
• Provably accurate in output
• A mirror of market sentiment

Instead, valuation is always an opinion based on a set of assumptions about an unknowable future.

“People act like it’s an award for past behavior. It’s not. It’s a hurdle for future behavior.”

Bill Gurley, VC at Benchmark

Whether that valuation is based on a detailed DCF model or napkin-math, it’s an opinion. And it’s no more or less of an opinion as your process gets more or less sophisticated; the only difference is how clearly you outline the assumptions.

When one investor states that a company is overpriced, and another that it is undervalued, neither is right or wrong in the moment — they just have differing opinions.

Valuation can be a simple, implicit part of the process, or it can be an explicit exercise used to better understand an opportunity and check assumptions.

Valuation is a story about the future

In order to form an opinion about a particular future, you must first listen to its story.

“The value of a stock is what people believe it is and could be. A stock is a story.”

Lulu Cheng Meservey, Founder of ROSTRA

All investment decisions are bets on today’s fiction.

• Elon Musk is telling you that we must get to Mars
• Brian Armstrong is explaining the importance of Bitcoin
• Parmita Mishra is describing the future of medicine
• Aaron Slodov is laying out a reindustrialisation roadmap
• Augustus Doricko is talking about rivers in the sky

Do you believe them, when they have nothing but a story?

Does that story involve unleashing a huge amount of economic energy? How much capital does it require?

Based on the credibility of that story, and the potential scale of that economic energy, is it worth paying-up today?

“The scarcest resource isn’t capital or talent. It’s the ability to make people believe in your specific tomorrow strongly enough to fund it today.”

Howard Yu, LEGO® Professor of Management and Innovation

Valuationis the art of using stories to develop opinions about the future

Valuation can be broken down into a few pieces:

• How do you judge the credibility of a story?
• How do you estimate the economic potential of a story?
• How do you estimate the risk associated with a story?

You can think about this via two extremes:

1. You’re looking at a company you’re familiar with. You’ve got a good mental model of the industry, the technology, the market forces, trajectory, and risks. It’s relatively simple for you to make a rough judgement on value in your head. This reflects Kahneman’s “System 1” thinking.
2. You’re looking at a company you have no familiarity with. The technology is novel, the market is emerging, and there’s no real precedent. In order to make a good judgement, you have to submerge yourself in details and scenarios. This reflects Kahneman’s “System 2” thinking.

In the latter case, a more sophisticated valuation process can help you understand the credibility and economic potential of a story. It provides a framework for ingesting information that can control biases, while allowing you to recognise and scrutinise the main drivers of value.

Valuation is essentially the act of running simulations of the future described in a story, focused on the numbers rather than the narrative, to explore the potential.

Pricing is not valuation / Trading is not investing / The present is not the future

Venture capitalists often choose to focus on relative pricing, instead of valuation, as an attempt to proxy experience through crowdsourced activity — allowing for speedy “System 1” style investment decisions.

This means analysing industry activity, which biases investment towards categories with low information friction like B2B SaaS, at a cost to sectors with more idiosyncracy, like deeptech.

Unfortunately that focus on market data also means these investors are not developing their ability to make judgements about the future, only to pattern match today. This compounds into a ‘knowledge worker atrophy‘ problem, weakening venture capital’s institutional competence at funding creative endeavors and novel solutions.

That would appear to be a critically weak link in the value proposition of venture capital, and the premise that it funds important solutions to humanity’s problems.

While GenAI can improve worker efficiency, it can inhibit critical engagement with work and can potentially lead to long-term overreliance on the tool and diminished skill for independent problem-solving. Higher confidence in GenAI’s ability to perform a task is related to less critical thinking effort.

source: The Impact of Generative AI on Critical Thinking

This article is broken down into five segments:

1. Priorities in venture capital
2. The importance of cycles
3. Forcing a reset
4. The ‘knowledge work’ problem
5. Operating from strength

Priorities in Venture Capital

In colder markets, founders just need capital on reasonable terms, and it doesn’t really matter where it comes from. Value-add propositions and brand strength are less important; access to hot companies doesn’t move the needle as much for LPs. Instead they care more about differentiation through strategy.

In hotter markets, the opposite is true. Investors will be chasing the fastest growing companies in the most attractive categories, out on the thin ice of excess risk. LPs, sold the same dream, care only about how GPs can parlay their way into those deals. How you invest is irrelevant, what matters is your network and your brand.

Strangely, at the peak things begin to come full circle. In 2021, when there the incredible amount of capital was spread across a record 1,594 firms, there was a horseshoe effect: with such abundant opportunity for investment, LPs and VCs once again saw the opportunity in strategy-driven alpha.

In normal circumstances, the next stage of this cycle is the crash. The firms that leaned the hardest into chasing heat would be the most exposed, with portfolios that are the most obviously out of alignment with value. What’s left are the firms who chose to focus on solid strategy, who can begin harvesting deals in the down market.

In 2022, this shift was derailed by the emergence of venture banks, designed to escape the typical cycles of venture. The largest firms raised the most capital in subsequent years. Access remained an important part of the story for LPs, especially with the convenient rise of AI.

In fact, you could argue that this was the second time that cycle was disrupted, as many experienced investors called the top in 2016-2018 only to be thwarted by COVID. Two years of intense, irrational enthusiasm for digital only exacerbated the problem.

The Importance of Cycles

Consider how much of the natural world has evolved alongside fire. Wildfires serve an important purpose in preventing ecosystems from choking themselves to death on redundant biomass, and there are even species that have evolved to use fire as a mechanism to spread their seeds.

Humanities view of fire as a threat, and the goal of suppressing it entirely in the natural environment, has had disastrous consequences. We have seen the emergence of ‘mega-fires‘, where biomass accumulates to the point where spread is fierce and inevitable.

There are clear parallels here in venture. The extent to which the market is suffering today is proportional to the amount of time it took to hit a correction. What’s worse, for reasons described above we haven’t yet really allowed the full cycle to complete.

Forcing a Reset

While venture banks steam off into the distance, and venture capital tries to figure out how to navigate this environment, there are three signs of change.

• Increasingly, there’s talk of smaller LPs like family offices looking to pursue direct investing strategies. In theory, this affords them a similar level of risk with better economics, but questions remain about their bandwidth to do this properly.
• There’s been a surprising number of high profile GP departures, both launching their own funds and not. In many cases, this means partners are giving up wharever carry incentive they had. This suggests some discomfort in the status quo.
• An increasing number of founders talking about bootstrapping or ‘seedstrapping’ (one and done fundrasing), or other strategies to avoid the problems assocaited with getting on the venture capital treadmill and the expectations involved.

For the GPs that remain, it’s time to consider what the world would look like if the cycle had completed. How would they be forced to act in a truly ‘down market’ environment. Indeed, if you consider that many smaller firms have been priced out of AI, that may already feel like their reallity.

It is clear that the bar for performance is significantly higher in a cash constrained environment with higher interest rates. While that may not change the reality for venture banks, it is existential for traditional venture capital.

The ‘Knowledge Work’ Problem

In hot markets, where investors take a prescriptive approach to investment, there is a huge problem with atrophy. Completely separate to the poor investments that come out of these periods, it’s also worth examining the practices they establish.

Investors that spend all of their time chasing hot deals based on a number of set criteria have the same basic problem as knowledge workers that rely on Generative AI solutions: they are not using their critical thinking muscles. Executing orders, not problem solving.

Consider how little actual thinking you have to do about an investment if your process is focused on second-order factors. Is it on an a16z market map, is it on YC’s Request for Startups, are other “tier 1” investors are in the round? Will downstream investors will give you the markups you need, and will LPs will be excited about it?

This behavior, geared towards capital velocity, is focused on second order information and pattern matching. It is a prescriptive approach that informs what gets investment, displacing the first-order considerations about things like team, opportunity, valuation, market and strategy.

This dissertation focuses exclusively on moral hazard, which refers to a venture capitalist’s propensity to exert less effort and shirk their fiduciary duties to the investors to maximize their self-interest; specifically, a VC’s propensity to choose subjective selection criteria over more cognitively taxing objective criteria when faced with multiple options and fewer resource restrictions.

“Venture Capitalists’ Decision-Making Under Changing Resource Availability”, by Noah John Pettit

While this approach might broadly work for venture banks, with an army of low-impact investors looking to index across new technology trends, it will not deliver the returns required by the traditional venture model.

Operating from Strength

The contrary to prescriptive investing is quite literally the hunt for outliers. Backing illegible companies. Being consensus averse. Resisting what have been the loudest models.

This might sound like cowboy investing. A Rick Rubin-esque vibes based approach to venture capital. It certainly can be, and if you happen to be Rick Rubin it might just work — but why take the risk?

The way for these investors to operate from a position of strength is to build process alpha. That is, do everything you can to prevent being wrong for predictable reasons (controlling for bias), and to manage the risk of being completely wrong a lot of the time (portfolio construction). Not to overintellectualise investment decisions, but to give yourself the strongest foundation to embrace the risk of uncertainty.

To take the analogy a bit further, for all that Rick Rubin is a total eccentric, guided by his own taste without the need for external validation, he is not cavalier about it. He pays immense attention to environment and routine in order to help him get the best return on his time.

There’s never a need for investors to stray from this disciplined mode of operation, it just so happens that most are prey to the cycles of venture capital and the temptation to inflate fees when opportunity arises.

Discpline is easy when opportunity is limited.