How the AI Unicorns Will Spark the Next Big Venture Capital Implosion

Venture Capital (VC) firms operate under a unique business model involving funds from limited partners (such as institutional investors and high-net-worth individuals) to invest in high-potential, early-stage companies. VCs earn money from management fees (typically a percentage of the total fund size) and Carried Interest (a share of the profits from the fund's investments).

The VC wants to show high valuations for its existing portfolio to attract more investors and raise larger funds. Higher portfolio valuations can indicate successful investment strategies and strong portfolio company performance, making the VC firm more attractive to potential investors. This, in turn, enables the VC firm to raise larger funds, which increases the management fees it receives.

The gross overvaluing of portfolio companies over the past decade has been an essential factor in the implosion of the VC industry. These inflated return expectations create issues when the companies cannot live up to these high valuations in subsequent funding rounds or exit events. As we’ve witnessed, the result is a breathtaking markdown of fund values.

At this time last year, venture capital firms began to creep up portfolio valuations for the first time in three years, sending predictive signals about what may be lying just over the horizon in the AI investment market.

The principal tool in the three-card Monte game of portfolio valuation is using revenue multiples of comparable public companies, often called “public comps.” This method involves examining the valuation of publicly traded companies within the same industry or sector and applying similar valuation metrics, such as revenue multiples, to privately held startups or growth-stage companies in the VC's portfolio.

Let’s explore how this works.

VCs identify publicly traded companies that operate in the same or similar industries as the portfolio company. The revenue multiple is calculated for the comparables by dividing their market capitalization (or enterprise value) by their revenue. This gives a revenue multiple that indicates how much the market values $1 of the company's revenue.

The derived revenue multiple is then applied to the revenue of the VC's privately held portfolio company to estimate its valuation.

Now, let's consider a theoretical illustration.

Imagine a VC firm has invested in a privately held tech startup, NextGenTech, which specializes in innovative cloud storage solutions.

The first step is identifying publicly traded companies operating in similar sectors and business models. In this case, the VC identifies "CloudCorp" and "StorageSolutions," publicly traded companies providing cloud storage technologies and services.

CloudCorp has a current market capitalization of $10 billion and generated revenue of $2 billion last year, giving it a revenue multiple of 5x ($10 billion / $2 billion = 5).

StorageSolutions has a market capitalization of $8 billion and revenue of $1.6 billion, resulting in a revenue multiple 5x($8 billion / $1.6 billion = 5).

The portfolio company, NextGenTech, reported $100 million in revenue last year. Given the similar business models and sectors, the VC firm used the average revenue multiple of 5x from the comparable companies to estimate NextGenTech's valuation.

To estimate NextGenTech's valuation, the VC firm multiplies the startup's revenue by the average revenue multiple. Thus, based on the revenue multiples of comparable public companies, the VC firm estimates NextGenTech's valuation to be approximately $500 million.

Sleight of Hand? 

This tactic is a sleight of hand because public companies used as comparables are usually at a different stage of growth and have economies of different scales than startups. This makes direct comparisons difficult because the factors driving their valuations (like market share, profitability, and risk profiles) can differ vastly.

Public company valuations are heavily influenced by market sentiment, which can fluctuate widely based on economic conditions, investor behavior, and global events. Applying these valuations to private companies may not accurately reflect their intrinsic value, especially in volatile markets.

Startups and growth-stage companies often have unique business models and revenue streams that do not directly compare to those of more established public companies. This lack of standardization can lead to inaccuracies when applying revenue multiples indiscriminately, especially for earlier-stage companies.

Earlier-stage startups face unique risks, including product market fit, regulatory hurdles, and competitive landscapes, which are not fully reflected in the revenue multiples of public companies. These specific risks significantly impact a startup's actual value.

The Toxic Culture Shallow Tech Investing

Valuing portfolio companies based on the revenue multiples of comparable public companies has created a culture of shallow tech investing, which stagnates innovation investing. This stagnation primarily arises due to the inherent limitations of finding appropriate comparables for innovative or disruptive startups.

Innovation-driven startups often operate at the cutting edge of technology, exploring new markets or creating niche sectors where few to no direct comparables exist. Their business models, technology, or products might be so novel that comparing them to existing public companies becomes challenging or irrelevant. For example, if their technology is unique, a startup developing a new form of renewable energy technology might have a vague comparable among traditional energy companies or even among newer clean tech firms.

When VCs rely heavily on revenue multiples from existing industries, they undervalue startups creating new markets or disrupting old ones. The lack of comparable companies leads to conservative valuations that don’t fully capture the startup's growth and market expansion potential.

This valuation method encourages VCs to look for startups that fit into existing categories with established comparables, leading to a selection bias for less innovative but more easily valued ventures. As a result, capital flows more readily to incremental innovations within well-understood sectors rather than to groundbreaking technologies or business models.

The culture of investing in startups that operate within well-understood and easily comparable sectors reduces the diversity of innovation in VC portfolios. This further limits the ecosystem's capacity for radical innovation and breakthrough advancements.

Startups that are pioneering new fields or technologies, in turn, find it almost impossible to raise funds if their valuation cannot be easily benchmarked against public companies. Consequently, their growth grinds to a halt, preventing them from bringing potentially industry-changing innovations to market.

Emergering founders and entrepreneurs aren’t stupid. They understand the game, forcing them to focus on short-term grifts like cryptocurrencies or faster food delivery services over long-term value creation and innovation. Startups naturally pivot towards more conventional business models or scale back their ambitions to align with the expectations set by comparables, further stifling innovation.

Could AI Be the Bubble to End All Bubbles?

Like thirsty vampires on an island devoid of people to feed on, VCs have become fee-starved in the current market climate. Just when you think they might starve to death, a Carnival Cruise ship full of deliriously unaware passengers has just run aground.

Ring the dinner bell!

The excitement surrounding Artificial Intelligence (AI) offers a familiarly potent allure for VCs, especially those managing funds that may not have performed as expected or are nearing the end of their lifecycle. AI's broad applicability and the transformative potential for virtually every sector make it an attractive focus for investment. In the context of reviving underperforming or "terminally ill" funds and signaling the creation of new, potentially lucrative fund vintages, here's how VCs have begun to leverage the AI hype into a new destructive cycle:

Strategic Rebranding: VCs are pivoting the focus of existing funds towards AI, rebranding them to capture the interest of limited partners (LPs) and the market. BootstrapLabs, for instance, was initially established with a broader tech focus but has pivoted to an AI-first investment strategy. It is now on its third Applied AI seed fund. Peak XV Partners (formerly Sequoia India) had always been involved in tech investments, but they rebranded to Peak XV Partners, reflecting a refined focus on AI. The AI Fund has sharpened its focus to specifically support startups creating solutions with AI, taking a more active development role in the AI space.

Highlighting AI Investments: VCs will attract additional interest and investment by emphasizing any AI components within the portfolio, regardless of their initial focus. This will help improve the portfolio's valuation, which is fueled by AI market momentum.

AI-focused Follow-on Investments: VCs will opt to make follow-on investments in portfolio companies that are pivoting to or incorporating AI technologies, even if only for the marketing buzz.

Launching AI-dedicated Funds: VCs are establishing new funds specifically dedicated to AI investments, and this trend will intensify. These funds will capitalize on the current hysteria about AI, attracting LPs who fear missing out on the next big thing.

Specialization and Expertise: VCs are positioning themselves, almost comically, as specialized investors with deep expertise in AI. This differentiation will be peddled to LPs looking for funds with a strong thematic focus and the potential for high returns.

Marketing Success Stories: Any successes within the AI space, from customer wins to high-profile hirings, will be heavily marketed to demonstrate the fund’s acumen and attract investment into new fund vintages.

Ground Hog Day for Venture Capital

This strategy will create cycles of fund valuations closely tied to the hype around emerging technologies like AI, as companies like Nvidia and Supermicro will be tapped to inflate VC portfolio valuations. 

As if incapable of learning from past mistakes, VCs are steering headlong into amplifying the effects of market corrections when most of the me-too technology companies they back rarely deliver on their promise as quickly as expected.

The early signs are everywhere, but this time, the valuations are out of control, and the money flowing into businesses barely producing revenue is approaching the absurd. 

The Power Law Cartel's use of irrational public comps to inflate portfolio valuations justifies more and bigger funds predicated on an irrational investing strategy for AI companies.

Einstein concluded the definition of insanity is doing the same thing over and over again and expecting different results.

Are we insane?

The scaffolding of portfolio fund valuations was put in place in 2022, and by this time last year, there were indications of an end to the nuclear winter. I expect a sharp uptick in portfolio valuations when 2023 data is fully released.

Could AI Be the Bubble to End All Bubbles?

The dynamics described will, of course, encourage a culture where entrepreneurs and investors replicate existing business models with minor tweaks, hoping to replicate the success of their predecessors. On April 1st, I announced the launch of www.diapers-ai.com (our AI product is the shit!) as an obvious ‘April Fools’ joke. But I was only half kidding. Not surprisingly, the chatbot I deployed to the live website (ok, maybe I took it a bit too far…) received actual inquiries!

Truly innovative startups, especially those creating new markets or technologies, will struggle to find funding because they don't fit the established mold that the diapers-AI.coms of the market will overrun. This situation will lead to potentially groundbreaking ideas, such as the pursuit of computational systems required for AGI, being overlooked or underfunded, delaying or entirely missing the opportunity for significant market impact in the Era of Autonomy.

In the end, unless LPs embrace an alternative version of the future, the gold rush to invest in AI will lead to an overcrowded space, with too many funds chasing a limited number of high-potential startups — meet the new Unicorns, same as the old Unicorns. This will inflate valuations and lead to a correction if growth in AI does not meet expectations.

We have seen this movie before.

While the potential for a disastrous implosion in the AI market looms large, this outcome is not inevitable. By shifting investment strategies towards genuinely transformative and sustainable innovations and applying more rigorous, realistic valuation methods, venture capital firms can mitigate risks and foster a healthier, more robust tech ecosystem. This strategic pivot can ensure that the AI boom leads to genuine progress rather than becoming just another bubble in the history of tech investments.