Tax-Free QSBS Gains: The Best Kept Secret in Venture Capital

It used to take millions in funding to build a tech startup. Before cloud computing and open-source software, launching a product required expensive hardware, in-house servers, large engineering teams, and significant capital just to reach early milestones. Because of these high costs, venture capital firms primarily funded startups at the Series A stage, when companies needed large investments to scale.

Over the past two decades, however, the cost of building a startup has plummeted. Cloud infrastructure eliminated the need for expensive servers. Open-source software reduced development expenses. Low-cost distribution channels made it easier than ever for startups to access customers. As a result, early-stage investing has evolved, giving rise to new funding stages—first Seed, then Pre-Seed, and now Pre-VC—each emerging as the capital required to launch a company decreased.

For investors, this shift presents a compelling opportunity. While traditional VCs continue to focus on larger deals, many early-stage companies are raising smaller rounds well below the investment minimums of traditional institutional venture capital. The result is a funding gap—the Pre-VC stage—that remains largely overlooked by institutional investors, creating an open playing field for those who recognize its potential.

How Early-Stage Investing Evolved

From Series A to Seed: The First Shift (2000-2010)

Before the 2000s, Series A was the starting point for venture capital, with round sizes typically ranging from $3 million to $10 million. Startups at this stage were often pre-revenue, and investors focused on market size, the strength of the founding team, and long-term growth potential rather than financial performance. Funding rounds below $3 million were often limited to angel investors, friends & family, and bootstrapping.

As technology became more capital-efficient, startups needed less money to build and launch products. This shift led to the rise of Seed rounds. By the mid-2000s, dedicated Seed-stage VC firms formalized Seed investing, with round sizes typically between $1 million and $3 million, making Seed a critical bridge to Series A.

The Rise of Pre-Seed: The Next Gap (2010-2020)

As costs continued to decline, some startups needed even less capital before raising a Seed round, which led to an explosion of Micro-VC funds and the emergence of Pre-Seed funding, with rounds typically ranging from $750,000 to $3 million, a space that had formerly been the sole domain of angel investors, friends & family, and accelerators. However, as more founders sought early capital, Pre-Seed investing became more structured. Also, the introduction of the SAFE note by Y Combinator in 2013 played a major role in standardizing these early rounds, making it easier for startups to raise funds without the complexities of traditional equity financing.

Much like Seed rounds a decade earlier, Pre-Seed investing grew over time. Traditional VCs were initially hesitant to participate due to the small check sizes and the labor-intensive nature of early-stage investing. But as startup funding continued to evolve, Pre-Seed rounds became more popular, and many institutional investors now actively participate in this stage.

The Emergence of Pre-VC Investing

Just as Seed investing institutionalized in the 2000s and Pre-Seed evolved in the 2010s, a new funding gap has emerged between angel rounds and institutional VC: Pre-VC investing.

Today’s institutional venture capital firms typically avoid participating in rounds below $1 million, leaving many early-stage startups reliant on friends & family, angel investors, or their own resources. If this story sounds familiar, it’s because it is. In the 2000s, Seed investing was considered too early for institutional venture capital—until it wasn’t. In the 2010s, Pre-Seed investing was dismissed as too small—until it wasn’t. Now, Pre-VC faces the same skepticism from traditional investors, even as it quietly grows.

While large VC firms hesitate, this emerging stage presents an opportunity for investors willing to adapt. Just as institutional investors once overlooked Seed and Pre-Seed, they are now bypassing Pre-VC. This stage represents a market inefficiency, one that investors can leverage by building diversified portfolios of high-potential early-stage startups.

Why Pre-VC Hasn’t Caught On with Most Institutional Investors

Traditional venture capital firms aren’t ignoring Pre-VC because it lacks potential. Instead, structural challenges within their investment models make it difficult for them to participate effectively.

One challenge is that early-stage investing is labor-intensive. Most traditional VC firms pride themselves on using their subjective expertise to pick winners. They evaluate thousands of pitches annually and conduct extensive due diligence before making an investment. The hands-on nature of their involvement makes it difficult to justify small investments.

Another challenge is portfolio construction math. A traditional $100 million venture fund might invest in 25 companies, with an average of $4 million per company. Smaller investments don’t make economic sense for most VCs because they require just as much time and effort as larger deals while contributing little to overall fund returns. A $250,000 Pre-VC check, for example, is too small to justify the labor involved and too insignificant to meaningfully impact the fund’s performance.

For large institutional VC firms, Pre-VC investing simply doesn’t fit their model.

Why Pre-VC Is a Significant Opportunity for Investors

The Pre-VC stage is attractive to investors for two key reasons: capital efficiency and competitive valuations.

Startups at this stage tend to be exceptionally capital-efficient, benefitting both founders and investors. Highly capital-efficient startups have less reliance on external funding which means greater resilience during bad funding markets, like what we’ve seen over the last couple of years. This can mean less dilution risk and higher potential return on investment. Capital-efficient companies can also pivot faster and adapt to market changes.

Many companies at this stage reach profitability early, which means Pre-VC isn’t just their first funding round—it could be their only funding round. Investors at this stage have the rare opportunity to buy meaningful ownership in startups that may never need to raise additional capital.

In addition to capital efficiency, valuations at the Pre-VC stage remain highly attractive. While valuations at all VC stages have soared in recent years, Pre-VC valuations have remained relatively flat. As an example, between 2014 and 2024, Seed valuations rose by 183% according to Pitchbook. In contrast, valuations for Pre-VC investments at Right Side Capital Management (RSCM) increased by only 10% during that same period. This is all a function of supply and demand of capital. During the past decade, especially before 2022, thousands of new VC firms were created, and the VC industry raised tremendous amounts of capital, leading to ever-increasing valuations.  But at the Pre-VC stage, demand has risen every year from founders but very few institutions address this demand, keeping valuations depressed.

Since 2012, RSCM has invested in over 2,000 startups, specifically targeting this funding gap. By streamlining the investment process and challenging traditional VC norms, RSCM has been able to exploit the inefficiencies at this stage and invest in promising early-stage companies at significantly discounted valuations.

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Pre-VC Funding: Investing in the Future Before the Herd Arrives

Early-stage venture funding has always evolved. Seed rounds were once an informal and overlooked segment of investing until they became institutionalized. Pre-Seed rounds followed a similar trajectory, initially dismissed as too small before maturing into a widely accepted funding stage. Now, Pre-VC is emerging as the next logical step in the evolution of early-stage investing.

This funding gap exists not because startups don’t need capital, but because traditional investors aren’t structured to provide it. For those who recognize this shift, Pre-VC represents a rare and valuable market inefficiency.

• The cost of building a startup has never been lower.
• Institutional VCs are ignoring this stage.
• Valuations remain competitive.

As the venture capital landscape continues to evolve, investors who recognize this shift now will find themselves ahead of the herd—investing in the future before the rest of the industry catches up.

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Michael Lewis’s Moneyball tells the story of how the Oakland A’s, led by general manager Billy Beane, used statistical analysis to identify undervalued baseball players and compete with far better-funded teams. The core insight was that traditional scouting methods, which relied on gut instinct and conventional wisdom, often overlooked players who could contribute significant value. Instead, the A’s adopted a more analytical approach, using data to challenge biases and make more objective decisions.

This philosophy has clear parallels to early-stage investing, where conventional wisdom often drives decision-making. At RSCM, we take a Moneyball-style approach to identifying promising startups, favoring data and systematic analysis over gut feel and hype.

The Moneyball Principles Applied to Startups

1. Don’t Trust Your Gut Feel

One of the most famous lines from Moneyball comes from Beane himself: “Your gut makes mistakes and makes them all the time.” This applies just as much to investing in startups as it does to scouting baseball players. Research on gut feel (known academically as “expert clinical judgment”) consistently shows that expert intuition alone is unreliable. Statistical models built on substantial datasets outperform human judgment, even in fields like medicine and hiring.

The startup world often relies on unstructured interviews and subjective impressions, but these methods are notoriously poor predictors of long-term success. That’s why we focus on quantifiable factors and structured evaluation processes when assessing early-stage companies.

2. Use a “Player” Rating Algorithm (With Caveats)

In baseball, Moneyball relies on deep statistical analysis, drawing from thousands of recorded plate appearances per player. With startups, the data is far scarcer—most founders have very few “at-bats,” and startup outcomes are highly skewed, with the top 10% generating the vast majority of returns. This means that any attempt to create a founder “rating” algorithm will inherently be more limited.

That said, the Moneyball mindset is still valuable: rather than chasing the same overhyped, high-valuation deals as everyone else, we focus on finding undervalued opportunities. Conventional wisdom often favors founders with elite pedigrees, trendy sectors, and strong “social proof.” But those deals tend to be expensive. Instead, we seek a wide range of founders across diverse sectors and geographies, where valuations are more reasonable and potential upside is greater.

The Future of Moneyball for Startups

Even if you don’t predict massive outliers (“home runs”), a systematic approach can still yield strong returns. Our focus is on building a diversified portfolio of well-valued startups and letting the data work in our favor over time. At RSCM, we’ll keep refining our approach, looking for ways to better identify promising startups before the rest of the market catches on.

In a world where everyone chases the obvious winners, we’ll keep finding value where others aren’t looking. That’s the essence of Moneyball for tech startups.

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This post was originally published on 09/28/2011 and was last updated on 03/01/25.

People ike the idea of revolutionizing angel funding. Among the skeptical minority, there are several common objections. Perhaps the weakest is that individual angels can pick winners at the pre-seed stage.

Now, those who make this objection usually don't state it that bluntly. They might say that investors need technical expertise to evaluate the feasibility of a technology, or industry expertise to evaluate the likelihood of demand materializing, or business expertise to evaluate the evaluate the plausibility of the revenue model. But whatever the detailed form of the assertion, it is predicated upon angels possessing specialized knowledge that allows them to reliably predict the future success of pre-seed-stage companies in which they invest.

It should be no surprise to readers that I find this assertion hard to defend. Given the difficulty in principle of predicting the future state of a complex system given its initial state, one should produce very strong evidence to make such a claim and I haven't seen any from proponents of angels' abilities. Moreover, the general evidence of human's ability to predict these sorts of outcomes makes it unlikely for a person to have a significant degree of forecasting skill in this area.

First, there are simply too many random variables. Remember, startups at this stage typically don't have a finished product, significant customers, or even a well-defined market. It's not a stable institution by any means. Unless a lot of things go right, it will fall apart. Consider just a few of the major hurdles a pre-seed-stage startup must clear to succeed.

1. The team has to be able to work together effectively under difficult conditions for a long period of time. No insurmountable personality conflicts. No major divergences in vision. No adverse life events.
2. The fundamental idea has to work in the future technology ecology. No insurmountable technical barriers. No other startups with obviously superior approaches. No shifts in the landscape that undermine the infrastructure upon which it relies.
3. The first wave of employees must execute the initial plan. They must have the technical skills to follow developments in the technical ecology. They must avoid destructive interpersonal conflicts. They must have the right contacts to reach potential early adopters.
4. Demand must materialize. Early adopters in the near term must be willing to take a risk on an unproven solution. Broader customers in the mid-term must get enough benefit to overcome their tendency towards inaction. A repeatable sales model must emerge.
5. Expansion must occur. The company must close future rounds of funding. The professional executive team must work together effectively. Operations must scale up reasonably smoothly.

As you can see, I listed three example of minor hurdles associated with each major hurdle. This fan out would expand to 5-10 if I made a serious attempt at exhaustive lists. Then there are at least a dozen or so events associated with each minor hurdle, e.g., identifying and closing an individual hire. Moreover, most micro events occur repeatedly. Compound all the instances together and you have an unstable system bombarded by thousands of random events.

Enter Nassim Taleb.  In Chapter 11 of The Black Swan, he summarizes a famous calculation by mathematician Michael Berry: to predict the 56th impact among a set of billiard balls on a pool table, you need to take into account the the position of every single elementary particle in the universe.  Now, the people in a startup have substantially more degrees of freedom than billiard balls on a pool table and, as my list above illustrates, they participate in vastly more than 56 interactions over the early life of a startup. I think it's clear that there is too much uncertainty to make reliable predictions based on knowledge of a pre-seed-stage startup's current state.

"Wait!" you may be thinking, "Perhaps there are some higher level statistical patterns that angels can detect through experience." True. Of course, I've poured over the academic literature and haven't found any predictive models, let alone seen a real live angel use  one to evaluate a pre-seed stage startup. "Not so fast! " you say, "What if they are intuitively identifying the underlying patterns?" I suppose it's possible.  But most angels don't make enough investments to get a representative sample (1 per year on average).  Moreover, none of them that I know systematically track the startups they don't invest in to see if their decision making is biased towards false negatives. Even if there were a few angels who cleared the hundred mark and made a reasonable effort to keep track of successful companies they passed on, I'd still be leery.

You see, there's actually been a lot of research on just how bad human brains are at identifying and applying statistical patterns. Hastie and Dawes summarize the state of knowledge quite well in Sections 3.2-3.6 of Rational Choice in an Uncertain World. In over a hundred comparisons of human judgment to simple statistical models, humans have never won. Moreover, Dawes went one better. He actually generated random linear models that beat humans in all the subject areas he tried. No statistical mojo to determine optimal weights. Just fed in a priori reasonable predictor variables and a random guess at what their weights should be.

Without some sort of hard data amenable to objective analysis, subjective human judgment just isn't very good. And at the pre-seed stage, there is no hard data. The evidence seems clear. You are better off making a simple list of pluses and minuses than relying on a "gut feel".

The final line of defense I commonly encounter from people who think personal evaluations are important in making pre-seed investments goes something like, "Angels don't predict the success of the company, they evaluate the quality of the people. Good people will respond to uncertainty better and that's why the personal touch yields better results." Sorry, but again, the evidence is against it.

This statement is equivalent to saying that angels can tell how good a person will be at the job of being an entrepreneur. As it turns out, there is a mountain of evidence that unstructured interviews have little value in predicting job performance. See for example, "The Validity and Utility of Selection Methods in Personnel Psychology: Practical and Theoretical Implications of 85 Years of Research Findings" Once you have enough data to determine how smart someone is, performance on an unstructured interview explains very little additional variance in job performance. I would argue this finding is especially true for entrepreneurs where the job tasks aren't clearly defined. Moreover, given that there are so many other random factors involved in startup success than how good a job the founders do, I think it's hard to justify making interviews the limiting factor in how many investments you can make.

Why then are some people so insistent that personal evaluation is important?  Could we be missing something? Always a possibility, but I think the explanation here is simply the illusion of control fallacy. People think they can control random events like coin flips and dice rolls. Lest you think this is merely a laboratory curiosity, check out the abstract from this Fenton-O'Creev, et al study of financial traders. The higher their illusion of control scores, the lower their returns.

I'm always open to new evidence that angels have forecasting skill. But given the overwhelming general evidence against the possibility, it better be specific and conclusive.


This blog post originally published on 04/27/2009 by RSCM founder Kevin Dick and was last updated on 02/14/2025.