More than two decades ago, I co-founded my first company, Tira Wireless. The business went through several iterations, and eventually, we landed on building a mobile content delivery product. We raised roughly $30M in funding, which was a significant amount at the time. We even ranked as Canada’s Third Fastest Growing Technology Company in the Deloitte Technology Fast 50.
We had a good run, but eventually, Tira had to shut its doors.
We made numerous strategic mistakes, and I learned a lot—lessons that, quite frankly, helped me make far better decisions when I later started Wattpad.
One of the most important mistakes we made was falling into the “bridge technology” trap.
What is the “bridge technology” trap?
Reflecting on significant “platform shifts” over recent decades reveals a pattern: each shift unleashes waves of innovation. Consider the PC revolution in the late 20th century, the widespread adoption of the internet and cloud computing in the 2000s, and the mobile era in the 2010s. These shifts didn’t just create new opportunities; they also created significant pain points as the world tried to leap from one technology to another. Many companies emerged to solve problems arising from these changes.
Tira started when the world began its transition from web to mobile. Initially, there were countless mobile platforms and operating systems. These idiosyncrasies created a huge pain point, and Tira capitalized on that. But in a few short years, mobile consolidated into just two major players—iOS and Android. The pain point rapidly disappeared, and so did Tira’s business.
Similarly, most of these “bridge technology” companies perform very well during the transition because they solve a critical, short-term pain point. However, as the world completes the transition, their business disappears. For instance, numerous companies focused on converting websites into iPhone apps when the App Store launched. Where are they now?
Some companies try to leverage what they’ve built and pivot into something new. But building something new is challenging enough, and maintaining a soon-to-be-declining bridge business while transitioning into a new one is even harder. This is akin to the innovator’s dilemma: successful companies often struggle with disruptive innovation, torn between innovating (and risking profitable products) or maintaining the status quo (and risking obsolescence).
As an investor, it makes no sense to invest in a “bridge” company that is fully expected to pivot within a few years. A pivot should be a Plan B, not Plan A. It’s extremely rare for bridge technology companies to become great, venture-scale investments. In fact, I can’t think of any off the top of my head.
We are currently in the midst of a tectonic AI platform shift. We’re seeing a huge volume of pitches, which is incredibly exciting. Many of these startups built great technologies and products. However, a significant number of these pitches also represent bridge technologies. As the current AI platform shift matures, these bridge technologies will lose relevance. Sometimes, it’s obvious they’re bridge technologies; other times, it requires significant thought to identify them. This challenge is intellectually stimulating, and I enjoy every moment of it. Each analysis informs us of what the future looks like, and just as importantly, what it will not look like. With each passing day, we gain stronger conviction about where the world is heading. It’s further strengthening our “seeing the future is our superpower” muscle, and that’s the most exciting part.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
#paid was one of the first investments we made at Two Small Fish Ventures. It’s been over a decade since we backed Bryan and Adam, who were still working out of Toronto Metropolitan University’s DMZ at the time. They had a vision to build a platform that connected creators and brands before “creator” was even a term! Back then, influencer and creator marketing campaigns were just tiny experiments.
A decade later, the creator economy has taken off. It’s now a $24 billion market—an order of magnitude larger than just a few years ago, with no signs of slowing down. The next wave of growth is still ahead as ad spending continues to shift away from traditional media. With the global ad market approaching $800 billion, one thing remains true: ad dollars follow the eyeballs—always. And where are those eyeballs today? On creators and influencers.
Today, #paid has become the world’s dominant platform, with over 100,000 creators onboard. It addresses a significant challenge: most creators don’t know how to connect with brands, especially iconic brands like Disney, Sephora, or IKEA. On the other hand, brands struggle to find the right creators amidst a sea of talent. #paid bridges this gap, acting as the marketplace that makes collaboration easy. They use data-driven insights to determine what makes a successful match, ensuring that both creators and brands can find each other effortlessly.
At #paid, brands and creators work with a dedicated team of experts to build creative strategies backed by research, first-party data, and industry benchmarks. This means campaigns run smoothly, allowing creators to focus on doing what they love—creating—without getting bogged down by administrative tasks.
I’m not just speaking as an investor—I’ve actually run a campaign with #paid as an influencer myself, and I can personally vouch for how seamless the experience was.
If you think #paid is all about TikTok, Snap, or Instagram, think again. Brands leverage #paid content across every platform. Want proof? Just check out the Infiniti TV commercial, which came from a #paid campaign.
How about billboards in major cities like NYC, Toronto, and more? #paid has that covered too.
#paid also brings creators and marketers together in real life. I had the privilege of speaking at their Creator Marketing Summit in NYC a few weeks ago, and I was amazed at how far #paid has come. The summit brought together hundreds of creators and top brand marketers—an impressive showcase of the platform’s evolution.
Looking back on this journey, here are my key takeaways:
• Great companies take a decade to build.
• To create a category leader, especially in winner-take-all markets, the idea has to be bold and often misunderstood at first. Bryan and Adam saw something that few others did, and their first-mover advantage has solidified #paid’s leading position today.
• There’s no such thing as “done.” #paid constantly reinvents itself. Generative AI is another exciting opportunity for step-function growth, and I can’t wait to see what’s next.
Bryan and Adam should be incredibly proud of what they’ve accomplished.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
Early-stage venture capital (VC) has always been the oddball in asset management. Unlike other asset classes, it offers the highest potential returns, but it also comes with the highest variance—especially when portfolio construction isn’t done right. On top of that, it has an inherent “default rate” of about 80%.
Tell a traditional fund manager about this 80% default rate, and you’ll likely get a strange look.
A few months ago, I was trying to explain how VC works to a fund manager. After covering the usual points—how VC is essentially a home run derby with many misses—he paused and said, “I get it. VC is like buying call options on startups.”
I hadn’t considered it that way before, but he was absolutely right.
For those unfamiliar, buying a call option gives you the right, but not the obligation, to purchase a stock at a predetermined price (the strike price) before a specified expiration date. Investors use this strategy to profit from an anticipated—but not guaranteed—increase in the stock’s price. If the stock price rises above the strike price (plus the premium paid), the option becomes profitable. The potential profit is theoretically unlimited, while the maximum loss is limited to the premium paid.
Similarly, investing in a startup gives you the chance to acquire equity at an attractive price, with a ~20% chance the startup will take off—though this usually takes about a decade to materialize. VCs use this strategy to profit from a potential—but not guaranteed—rise in the company’s value. If the startup succeeds and its valuation soars beyond the investment (plus associated costs), the return can be massive. The potential profit is virtually unlimited if the company becomes a breakout success, while the maximum loss is limited to the initial investment.
VC and call options are strikingly similar, don’t you think? They’re like twins!
From now on, I’ll tell people: Venture capital is call options on startups.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
TLDR – 20 companies in a VC portfolio is the optimal balance between risk and reward, offering a very high chance of hitting outsized returns without significant risk of losing money. This is exactly the approach we follow at Two Small Fish Ventures, as we keep our per-fund portfolio size limited to roughly 20 companies.
In my previous post, VC is a Home Run Derby with Uncapped Runs, I illustrated mathematically why early-stage venture funds’ success doesn’t hinge on minimizing failures, nor does it come from hitting singles (e.g., the number of “3x” companies). These smaller so-called “wins” are just noise.
As I said:
“Venture funds live or die by one thing: the percentage of the portfolio that becomes breakout successes — those capable of generating returns of 10x, 100x, or even 1000x.”
To drive high expected returns for VCs, finding these breakout successes is key. However, expected value alone doesn’t tell the full story. We also need to consider variance. In simple terms, even if a fund’s expected return is 5x or 10x, it doesn’t necessarily mean it’s a good investment. If the variance is too high—meaning the fund has a low probability of achieving that return and a high probability of losing money—it would still be a poor bet.
For example, imagine an investment opportunity that has a 10% chance of returning 100x and a 90% chance of losing everything. Its expected return is 10x (i.e., 10% x 100x + 90% x 0x = 10x). But despite the attractive expected return, it’s still a terrible investment due to the extremely high risk of total loss.
That said, there’s a time-tested solution to turn this kind of high-risk investment into a great one: diversification. While everyone understands the importance of diversification, the real key lies in how it’s done. By building a properly diversified portfolio, we can reduce variance while maintaining a high expected return. This post will illustrate mathematically how the right portfolio construction allows venture funds to generate outsized returns while ensuring a high probability of success.
Moonshot Capital vs. PlayItSafe Capital: A Quick Recap
Let’s start by revisiting our two hypothetical venture capital firms: Moonshot Capital and PlayItSafe Capital. Moonshot Capital swings for the fences, aiming to find the next 100x company while expecting most of the portfolio to fail. PlayItSafe Capital, on the other hand, protects downside risk (at least that’s what they think), but by avoiding bigger risks, it sacrifices the chance of finding outsized returns.
• Moonshot Capital: Out of 20 companies, 17 resulted in strikeouts (0x returns), 3 companies achieved 10x returns, and 1 company achieved a 100x return.
• PlayItSafe Capital: Out of 20 companies, 7 resulted in strikeouts (0x returns), 7 companies broke even (1x), 5 companies achieved 3x returns, and 1 company achieved a 10x return.
Here’s how their expected returns compare:
• Moonshot Capital has an expected return of 6.5x, thanks to one company yielding 100x and three companies yielding 10x (i.e. (1 x 100 + 3 x 10 +16 x 0) x $1 = $130).
• PlayItSafe Capital has a much lower expected return of 1.6x, with its highest return from one 10x company, five 3x returns, and several breakeven companies (i.e. (1 x 10 + 5 x 3 + 7 x 1 + 7 x 0) x $1 = $32).
Despite these differences in expected returns, what’s surprising is that counterintuitively, the probability of losing money (i.e., achieving an average return of less than 1x at the fund level) is quite similar for both firms.
Let’s dive into the math to see how we calculate these probabilities:
Moonshot Capital: 12.9% Probability of Losing Money
1. Expected Return :
2. Variance :
3. Standard Deviation :
4. Standard Error :
Using a normal approximation, the z-score to calculate P(X < 1) is:
Looking this up in the standard normal distribution table gives us:
P(X < 1) = 0.129 or 12.9%
PlayItSafe Capital: 11.6% Probability of Losing Money
Similarly, looking this up in the standard normal distribution table gives us (sparing you all the equations):
P(X < 1) = 0.116 or 11.6%
Shockingly, these two firms’ probabilities of losing money are essentially the same. The math does not lie!
Here’s a graphical representation of the outcomes (probability density) for Moonshot Capital and PlayItSafe Capital.
Probability Density Graphs: Comparing Moonshot and PlayItSafe
As you can see, Moonshot has higher upside potential, as the density peaks at 6x, while PlayItSafe is more concentrated around lower returns. Since their downside risks are more or less the same while PlayItSafe’s approach significantly limits its upside, counterintuitively PlayItSafe is far riskier from the risk-reward perspective.
Proper Portfolio Construction: How Portfolio Size Affects Returns
To further optimize Moonshot’s strategy, we will explore how different portfolio sizes affect the balance between risk and reward. Below, I’ve analyzed the outcomes (i.e. portfolio size sensitivity) for Moonshot Capital across portfolio sizes of n = 5, n = 10, n = 20, and n = 30.
The graph below shows the probability density curves for Moonshot Capital with varying portfolio sizes:
As you can see, smaller portfolios (n = 5, n = 10) exhibit higher variance, with a greater spread of potential outcomes. Larger portfolios (n = 20, n = 30) reduce the variance but also diminish the likelihood of hitting outsized returns.
Why 20 is the Optimal Portfolio Size
1. Why 20 is Optimal:
At n = 20, Moonshot Capital strikes an ideal balance. The risk of losing money, i.e. P (X < 1), remains manageable at 12.9%, while the probability of outsized returns remains high: 62.1% chance of hitting a return higher than 5x. This suggests that Moonshot’s high-risk, high-reward approach pays off without exposing the fund to unnecessary risk.
2. Why Bigger Isn’t Always Better (n = 30):
When the portfolio size increases to n = 30, we see a significant drop-off in the likelihood of outsized returns. The probability of achieving a return higher than 5x drops significantly from 62.1% at n = 20 to 41.9% at n = 30, and counterintuitively, the risk of losing money starts to increase. This suggests that larger portfolios can dilute the impact of the big wins that drive fund returns. It also mathematically explains why “spray-and-pray” does not work for early-stage investments.
3. The Pitfalls of Small Portfolios (n = 5 and n = 10):
At smaller portfolio sizes, such as n = 5 or n = 10, the variance increases significantly, making the portfolio’s returns more unpredictable. For example, at n = 5, the probability of losing money is significantly higher, and the risk of extreme outcomes becomes more pronounced. At n = 10, the flat-curve suggests that the variance is very high. This high variance means the returns are volatile and difficult to predict, increasing risk.
Conclusion: How to Win the Home Run Derby With Uncapped Runs
The key takeaway here is that Moonshot Capital’s strategy of swinging for the fences doesn’t mean taking on excessive risk. With 20 companies in the portfolio, Moonshot is the optimal between risk and reward, offering a very high chance of hitting outsized returns without significant risk of losing money.
While n=20 is optimal, n=10 is also pretty good, but n=30 is significantly worse. So, a ‘concentrated’ approach – but not ‘n=5 concentrated’ – is far better than ‘spray and pray,’ if you have to pick between the two.
This is exactly the approach we follow at Two Small Fish Ventures. We don’t write a cheque unless we have that magical “100x conviction.” We also keep our per-fund portfolio size limited to roughly 20 companies. This blog post mathematically breaks down one of our many secret sauces for our success.
Don’t tell anyone.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
