Why Venture Capital Returns Look Like Nothing Else You Have Invested In — Part 2: The Power Law and The J-Curve

This is the second in a series on venture math for senior lawyers who are (or are considering) investing in legal tech. Part 1 covered portfolio construction and why diversification matters more than most angels appreciate. This piece covers what happens inside a portfolio that's working, and why it defies every financial intuition lawyers tend to bring to investing.

If you have spent a career in finance-adjacent work — M&A, private equity, structured finance — you've internalized a model of investment returns that is, broadly, Gaussian. A portfolio of assets has a distribution of outcomes that clusters around a mean. Some positions outperform, some disappoint, but the average is a reasonable predictor of what you'll actually experience. Outliers pull the distribution, but they do not define it.

Venture capital does not work this way.

The shape of venture returns

Early-stage venture returns follow a power law distribution: a small number of investments produce returns so large that they dominate the outcome of the entire portfolio, while the majority of investments return little or nothing. This is the defining mathematical structure of the asset class.

The empirical data are striking. Across institutional venture portfolios, studies consistently find that roughly 6% of investments generate approximately 60% of total returns. The top decile of investments typically accounts for more than the entire return of the portfolio — meaning that without those outliers, the remaining 90% of investments, in aggregate, would not have covered the losses.

For a lawyer accustomed to thinking about diversification as risk reduction, this is disorienting. In public equities, a single holding that goes to zero is a problem to be managed. In venture, a single holding going to 100x is the plan.

The arithmetic of fund returns — carried by a single name

Consider a $20M fund with 20 portfolio companies — $1M deployed per company (setting aside follow-on reserves for simplicity, even though follow-on strategy plays a very significant role in shaping outcomes). The portfolio can be expected to perform as follows:

• —12 companies fail entirely: $0 returned on $12M invested
• —6 companies return 1–2x: roughly $9M returned on $6M invested
• —1 company returns 15x: $15M returned on $1M invested
• —1 company returns 50x: $50M returned on $1M invested

Total returned: $74M on $20M invested — a 3.7x gross multiple, which is a strong venture fund.

Now observe where the return came from. The two outlier companies — representing 10% of the portfolio by number of investments — generated $65M of the $74M total. The other 18 companies, combined, generated $9M — less than the $12M lost to failures, let alone the opportunity cost.

Remove the 50x company and you have a 1.2x portfolio. Mediocre. Remove both outliers and you have a portfolio which has lost money.

Why this should change how you think about "picking winners"

Angel investors tend to believe that careful selection is the primary value-add. They take concentrated positions in companies they feel confident about (I recall someone saying to me that they would rather invest $500k in a company that he felt strongly about than split that same $500k over 10 or 15 companies), and avoid companies that feel risky or that they understand less well.

The power law suggests this is precisely backwards.

In a power law return environment, the cost of a missed outlier dwarfs the cost of a failed investment. A company that goes to zero costs you 1x your investment. A company that would have returned 100x — and that you passed on because the business model seemed unconventional, or the founder was too young, or the market looked crowded — costs you 100x.

The implication is counterintuitive: in venture, the quality of a portfolio is determined far more by the ceiling of your winners than by the floor of your losers. Getting into the right company matters infinitely more than avoiding the wrong one. And that is one of the key problems with angel investing - very few angel investors have the curated deal flow that gives them access to 100x potential outcomes or the breadth of portfolio that enables them to confidently make unconventional investments. Both of these problems are exacerbated by the fact that an individual angel investor may not have the capital to write twenty $1m checks that would enable him or her to get into the most desirable rounds even if he or she fortuitously learned about these opportunities.

This rewrites the math of concentration entirely. An angel who deploys $500K across three "high-conviction" bets has eliminated his or her exposure to the 90%+ of power law outcomes. A fund that makes 20 investments has preserved optionality on the distribution of outcomes. The portfolio that looks more "reckless" to a trained legal eye may be the one doing venture properly.

The J-curve

There is a third mathematical reality that catches lawyers off guard: the timing of venture returns. Venture funds exhibit what practitioners call a J-curve: early in the fund lifecycle, valuations are marked conservatively, management fees are drawn against committed capital, and the portfolio has not yet had time to develop. The fund looks like it's losing money (parenthetically, that is not always the case, but that is common).

Returns emerge later — typically in years 5-10 of a fund's life — as companies mature, raise subsequent rounds (generating mark-ups), and eventually exit through acquisitions or public offerings. The same portfolio that looked underwater at year 3 may be generating DPI (distributions to paid-in capital) at year 7.

For a senior lawyer used to assets that mark to market daily — or even private equity investments with 3-5 year hold periods and more predictable cash-on-cash returns — the venture J-curve requires a genuine shift in how you assess performance. NAV statements in year 2 are nearly meaningless. Interim IRRs are noisy and often misleading. The question to ask is not "what is this worth today?" but "what is the range of exit outcomes for the companies in this portfolio, and when might those occur?"

Pulling it together: how early stage investing actually works

Across Parts 1 and 2, the mathematical structure of early stage investing has a consistent message:

The asset class rewards breadth over concentration, outlier-hunting over loss-avoidance, and patience over responsiveness. It punishes the intuitions that serve lawyers well everywhere else: careful selection of a small number of high-conviction positions, risk management by exclusion, and evaluation of performance against near-term benchmarks.

None of this means legal expertise is irrelevant. For a serious legal tech investor, domain knowledge is what makes the deal flow curated, the diligence deeper, and the outlier recognition sharper. The edge is real. But it loses its potency when deployed through a structure that the math does not support.

A three-company angel portfolio is a lottery ticket purchased with the confidence of expertise.