venture capital

After All, What’s Deep Tech?

“Deep Tech” is one of those terms that gets thrown around a lot in venture capital and startup circles, but defining it precisely is harder than it seems. If you check Wikipedia, you’ll find this:

Deep technology (deep tech) or hard tech is a classification of organization, or more typically a startup company, with the expressed objective of providing technology solutions based on substantial scientific or engineering challenges. They present challenges requiring lengthy research and development and large capital investment before successful commercialization. Their primary risk is technical risk, while market risk is often significantly lower due to the clear potential value of the solution to society. The underlying scientific or engineering problems being solved by deep tech and hard tech companies generate valuable intellectual property and are hard to reproduce.

At a high level, this definition makes sense. Deep tech companies tackle hard scientific and engineering problems, create intellectual property, and take time to commercialize. But what do substantial scientific or engineering challenges actually mean? Specifically, what counts as substantial? “Substantial” is a vague word. A difficult or time-consuming engineering problem isn’t necessarily a deep tech problem. There are plenty of startups that build complex technology but aren’t what I’d call deep tech. It’s about tackling problems where existing knowledge and tools aren’t enough.

In 1964, Supreme Court Justice Potter Stewart famously said, “I know it when I see it” when asked to describe his test for obscenity in Jacobellis v. Ohio. By no means am I comparing deep tech to obscenity—I don’t even want to put these two things in the same sentence. However, there is a parallel between the two: they are both hard to put into a strict formula, but experienced technologists like us recognize deep tech when we see it.

So, at Two Small Fish, we have developed our own simple rule of thumb:

If we see a product and say, “How did they do that?” and upon hearing from the founders how it is supposed to work, we still say, “Team TSF can’t build this ourselves in 6–12 months,” then it’s deep tech.

At TSF, we invest in the next frontier of computing and its applications. We’re not just looking for smart founders. We’re looking for founders who see things others don’t—who work at the edge of what’s possible. And when we find them, we know it when we see it.

This test has been surprisingly effective. Every single investment we’ve made in the past few years has passed it. And I expect it will continue to serve us well.

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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.

AI Has Democratized Everything

This is the picture I used to open our 2024 AGM a few months ago. It highlights how drastically the landscape has changed in just the past couple of years. I told a similar story to our LPs during the 2023 AGM, but now, the pace of change has accelerated even further, and the disruption is crystal clear.

The following outlines the reasons behind one of the biggest shifts we identified as part of our Thesis 2.0 two years ago.

Like many VCs, we evaluate pitches from countless companies daily. What we’ve noticed is a significant rise in startups that are nearly identical to one another in the same category. Once, I quipped, “This is the fourth one this week—and it’s only Tuesday!”

The reason for this explosion is simple: the cost of starting a software company has plummeted. What once required $1–2M of funding to hire a small team can now be achieved by two founders (or even a solo founder) with little more than a laptop or two and a $20/month subscription to ChatGPT Pro (or your favourite AI coding assistant).

With these tools, founders can build, test, and iterate at unprecedented speeds. The product build-iterate-test-repeat cycle is insanely short. If each iteration is a “shot on goal,” the $1–2M of the past bought you a few shots within a 12–18 month runway. Today, that $20/month can buy you a shot every few hours.

This dramatic drop in costs, coupled with exponentially faster iteration speeds, has led to a flood of startups entering the market in each category. Competition has never been fiercer. This relentless pace also means faster failures, and the startup graveyard is now overflowing.

For early-stage investors, picking winners from this influx of startups has become significantly harder. In the past, you might have been able to identify the category winner out of 10 similar companies. Now, it feels like mission impossible when there are hundreds—or even thousands—of startups in each category. Many of them are even invisible, flying under the radar for much longer because they don’t need to fundraise.

Of course, there will still be many new billion-dollar companies. In fact, I am convinced that this AI-driven platform shift will produce more billion-dollar winners than ever—across virtually every established category and entirely new ones that don’t yet exist. But by the law of large numbers, spotting them among thousands of startups in each category is harder than ever.

If you’re using the same lens that worked in the past to spot and fund these future tech giants, good luck.

That’s why, for a long time now, we’ve been using a very different lens to identify great opportunities with highly defensible moats to stay ahead of the curve. For example, we’ve been exclusively focused on deep tech—a space where we know we have a clear edge. From technology to product to operations, we have the experience to cover the full spectrum and support founders through the unique challenges of building deep tech startups. So far, this approach has been working really well for us.

I guess we are taking our own advice. As a VC firm, we also need to be constantly improving and striving to be unrecognizable every two years!

There’s no doubt the rules of early-stage VC have shifted. How we access, assess, and assist startups has evolved dramatically. The great AI democratization is affecting all sectors, and venture capital is no exception.

For investors who can adapt, this is a time of unparalleled opportunity—perhaps the greatest era yet in tech investing. The playing field has been levelled, and massive disruption (and therefore opportunities) lies ahead. Incumbents are vulnerable, and new champions will emerge in each category – including VC!

Investing during this platform shift is both exciting and challenging. And I wouldn’t want it any other way, because those who figure it out will be handsomely rewarded.

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.

Our Secret to Finding 100x Opportunities

In previous blog posts (here and here), I’ve delved into the mathematical model for constructing an early-stage VC portfolio designed to achieve outsized returns. In short, investing early to build a concentrated portfolio of fewer than 20 moonshot companies, each with the potential for 100x returns or more, is the way to go.

The math is straightforward—it doesn’t lie. Not adhering to this model can significantly reduce the likelihood of achieving exceptional returns.

However, simply following this model is not enough to guarantee outsized results. Don’t mistake correlation for causation! The real challenge lies in identifying, evaluating, and supporting these “100x” opportunities to help turn their vision into reality.

At TSF, we use a simple framework to evaluate whether a potential investment can meet the 100x criteria:

10x (early stage) x 10x (transformative behaviour) = 100x conviction

The first “10x” is straightforward: We invest when companies are in their earliest stages. For instance, over the past two years, all but one of TSF’s investments have been pre-revenue. This made financial analysis simple—those spreadsheets were filled with zeros!

Many of these companies are also pre-traction. While having traction isn’t a bad thing, savvy investors shouldn’t rely on it for validation. The reason is simple: traction is visible to everyone. By the time it becomes apparent, the company is often already too expensive and out of reach.

At TSF, we have a unique advantage. Before transitioning to investing, all TSF partners were engineers, product experts, successful entrepreneurs, and operators—including a “recovering CEO”—that’s me! Each partner brings distinct domain expertise, collectively creating a broad and deep perspective. This allows us to invest only when we possess the domain knowledge needed to fully evaluate an opportunity. We “open the hood” to determine whether the technology is genuinely unique, defensible, and disruptive, or whether it is easily replicable. If it’s the latter, we pass quickly. A strong, defensible tech moat is a key criterion for us. This approach means we might pass on some promising “shallow-tech” opportunities, but we’re very comfortable with that. After all, we believe the best days of shallow tech are behind us.

Maintaining a concentrated portfolio allows us to commit only to investments where we have unwavering conviction. In contrast, a large portfolio would require us to find a large number of 100x opportunities and pursue those we might not fully believe in. Frankly, I wouldn’t sleep well if we took that route. This route would also make it difficult to provide the meaningful, tailored support we’ve promised our entrepreneurs (more on that in a future post). 

When evaluating product potential, we look beyond the present. At TSF, we assess how a technology might reshape the landscape over the next decade or more. We start by understanding the intrinsic needs of the user and envision how a product could fundamentally change customer or end-user behaviour. This is crucial: if a product that addresses a massive opportunity has a strong tech moat, first-mover advantages, and the ability to change behaviour while facing few viable alternatives, it can unlock significant new value and create a defensible, category-defining business.

This often translates into substantial commercialization potential. If we can foresee how the product might evolve into adjacent markets (its second, third, or even fourth act) with almost uncapped possibilities, we achieve the “holy trinity” of tech-product-commercialization potential—forming the second 10x of our conviction.

Here’s how we describe it:

Two Small Fish Ventures invests in early-stage products, platforms, and protocols that transform user behaviour and empower businesses and individuals to unlock new, impactful value.

This thesis underpins our investment decisions and ensures that each choice we make aligns with our long-term vision for transformative innovation.

While this framework may sound simple, executing it well is extremely difficult. It requires what I call a “crystal ball” skill set that spans the full spectrum of entrepreneurial, technical, product, and operational backgrounds.

Over the past decade, we’ve built a portfolio of more than 50 companies across three funds. By employing this approach, the entrepreneurs we’ve supported have achieved numerous breakout successes. This post outlines our “secret sauce,” and we will continue to leverage it.

As you can see, early-stage VC is more art than science. To do it well requires thoughtfulness, insight, and the ability to envision the future as a superpower. It’s challenging but incredibly rewarding. I wouldn’t trade it for anything.

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.