Software is Dead, Long Live Software

Is software dead?

Today, writing software is no more difficult than pressing a button. You describe what you want. In a few minutes, not a mockup but a fully functional application is ready to use.

I can testify to this personally. In 15 minutes, using AI, I have “written” more software than I did in a full year when I was writing software professionally. Although my old skill is now obsolete, it is wonderful because I can build faster than I ever could. This is the best of times!

So yes, in a narrow sense, the old software opportunity is dead.

The writing has been on the wall for a while. Shallow tech software has been democratized and, in many cases, is not investable. Public markets have finally figured out that a new wave of software is coming. They just do not really know what it is yet, so they sell indiscriminately. Generic business and financial skills do not work during a paradigm shift because disruption does not show up neatly on a spreadsheet full of ARR, EBITDA, and CAGR. Those are the wrong questions to ask when the underlying rules are being rewritten.

At the same time, the early phase of a paradigm shift is often the best time to invest. The people who have new specific knowledge and the courage to build for an AI native world will have a clear edge and, if they are right, capture outsized returns.

Now here is the twist.

When the cost of X collapses, the world does not get less of the thing. It gets flooded with it. That is Jevons Paradox in action. Make something cheaper and easier, and overall demand goes up significantly, often faster than the drop in price. We have seen versions of this before as humanity adopted electricity, personal computers, the internet, and now intelligence.

So software is not dead. We are about to have 10x, 100x, maybe 1000x more software than we have today.

We have seen a similar movie in content. Thanks to the internet and mobile devices, as the cost of content creation and distribution dropped, the amount of content exploded. That created giants that seized the opportunity. Fun fact, I co-founded a business two decades ago on that thesis and rode that wave myself, so yes, I have been there and done that.

Back to software.

The question now is how to capture the opportunity when the world has 1000x more software and the cost of creating software is approaching zero. Inevitably, the business model shifts because we move to a different part of the price elasticity curve when software becomes abundant. When code becomes cheap, value migrates to what stays scarce.

Shallow tech, run-of-the-mill software companies, including a lot of AI wrappers, are generally not investable from a VC perspective because they are so easy to build, copy, and replace. I have been saying this for many years, even before ChatGPT came out. If you still need more evidence, you are already behind. The button is not coming. The button is here.

This does not mean these companies cannot make money. Some will. But “can generate cash when bootstrapping” and “can return a venture fund” are not the same statement.

In contrast, deep tech software is a fantastic opportunity. There is a reason TSF shifted to deep tech investments years ago. That was not an accident. When the cost curve of intelligence collapses, businesses whose primary moat is “we can write this software” or “we spent 100 engineer years building it” need a rethink.

This is why we are unapologetically investing in deep tech.

Deep tech software is a completely different sport. In many cases, the moat is not in the software. The moat is the unique technology embedded in the software, plus the data and the system it connects to. The software is the container. The defensibility sits underneath.

People often ask how to draw the line between deep tech software and everything else. We have a definition, and it is more true than ever in this “software is abundant” era. More importantly, making that call takes specialized skill. That is why deep tech investing is reserved for trained eyes, as it requires engineering judgment, product instinct, operating experience, and recognition of a market gap that comes from building and commercializing disruptive opportunities. We can do deep tech because we are equipped to do so. Been there. Done that.

To be clear, of course, I am not suggesting the only software opportunity is deep tech. There is also a massive opportunity in bespoke software and disposable software.

For decades, companies bought off-the-shelf software because that was the only option that made economic sense, even when the software was not a perfect fit for their workflow. You ended up customizing your workflow around the software. Bespoke-built software was too expensive, too slow, and too hard to maintain.

Now the economics are changing.

We can now build software for problems that were previously too small to matter economically. We can now create personal tools designed for an audience of one. We will ship internal workflows the way we send emails. We can now generate software that lives for a week, does its job, and disappears.

That is a massive opportunity. Much of it will look like a low-tech, large-scale service business. Some of it will become platforms and infrastructure for software generation itself. Some of it will become entirely new categories we do not have names for yet. Some of it will help make deep tech software even more defensible.

But the direction is clear. Software is becoming abundant, and the economics of software will be drastically different.

So, is software dead?

Yes, software as a scarce craft is dying.

Software-as-a-moat because “we spent 100-engineer-years building it” is dying.

But software as leverage is exploding. Software as the fabric of everything is exploding. The world is not losing software. The world is getting more of it than we can possibly imagine.

Back to the movie analogy. It is like the theatre business. The movie is not the only product. The experience is the product. The popcorn is the product. The atmosphere is the product. The movie is what gets you in the door.

The winning recipe has changed forever.

Software is dead. Long live software!

Portfolio Highlight: Zinite. Speed and Energy, Two Birds, One Stone

For most of semiconductor history, progress was a simple loop. Shrink transistors. Fit more into the same area. Get faster compute as a byproduct.

That loop had a name. Moore’s Law. It traces back to Intel co-founder Gordon Moore. He observed in the 1960s that the number of transistors on a chip, and hence its capabilities, tended to double every two years. The industry turned that observation into a roadmap. It was never guaranteed to run forever. Now shrinking is harder because we are starting to hit many limits in physics and economics, and the cost of pushing the frontier keeps rising.

So if the curve is going to keep bending upward, the industry needs new scaling vectors beyond making everything smaller in two dimensions.

This is why Two Small Fish invested in Zinite in 2021 at the company’s inception. The thesis was simple then, and it is still simple now. Scale in the third dimension, using proprietary technology protected by patents to enable true 3D chips.

Zinite stayed deliberately stealth early on, focused on building the core and protecting it properly before saying too much. Five years after we invested, we can finally talk about it more openly.

The company is led by its CEO, Dr. Gem Shoute. Fun fact. Her breakthrough was strong enough that her professors and industry veterans (who helped create fundamental IP used in all chips since 2008) joined her as co-founders, Dr. Doug Barlage and Dr. Ken Cadien.

The Distance Tax

In a recent blog post, I used a factory analogy to explain why speed, latency, and energy are often bottlenecked by movement, not necessarily arithmetic. 

In short, systems don’t lose because they can’t do math. GPUs are already very good at that. Systems lose speed because they can’t feed the math with data fast enough. 

In many systems, moving data costs far more than doing the arithmetic. When movement is expensive, speed and energy efficiency get worse together.

AI inference exacerbates the problem because the computational characteristics of AI inference workloads put a premium on memory behaviour. In many cases, the limiting factor is not arithmetic. It is how efficiently the system can move data. Bringing memory closer to logic matters because it directly reduces that movement.

Sensing fits in the same frame as logic and memory. Sensors generate raw data at high volume. If the system’s first step is to ship raw data far away before anything useful happens, it pays in bandwidth, latency, and power. The more intelligence that can happen closer to where data is produced, the less the system wastes just transporting information.

So the distance tax is one big problem showing up in three places at once. Logic. Memory. Sensing.

Why 3D Matters for Speed and Energy

When people hear 3D chips, they think density. More transistors per area. That matters. The bigger lever is proximity. Current 3D approaches to deliver more performance per area rely on advanced packaging, which is hindered by cost and the distance tax. 

If memory can live closer to logic, the system avoids transfers that dominate both performance and power. If compute and memory can sit closer to sensing, the system avoids hauling raw streams around before doing anything intelligent.

Every avoided transfer is a double win. Speed improves because stalls go down and effective bandwidth goes up. Energy improves because fewer joules are burned moving bits instead of doing work.

That is the two birds, one stone result.

Five years after we invested, Zinite is far from just a concept. The company is doing exceptionally well, and it represents the kind of platform that can extend performance gains into the post-Moore era by reducing the distance tax, not by asking physics for more shrink, but by making data travel less.

100 Years of the Canadian Iron Ring and a Uniquely World Class Vow of Trust

I spent Saturday morning in Hong Kong as a speaker at the Canadian Engineering Asia Pacific Conference, a gathering that felt historic.

Not one, not two, but eight deans of Canadian engineering. In the same room, on the same program, in Asia. The conference materials called it a “historic gathering,” and that’s not an exaggeration.

Hong Kong is the perfect place for this to happen. It has a very large base of Canadian engineering alumni. You could feel it immediately. The electromagnetic pull of hundreds of iron rings in the room. A community that’s stayed connected not just to each other, but to an idea.

And despite the diversity of schools, disciplines, and career paths represented, the conference kept circling back to a single word.

Trust.

Yes, one panel was explicitly about modern engineering ethics and building trust. It was moderated by Dean Kevin Deluzio (Queen’s University) and featured Dean Heather Sheardown (McMaster University), Dean Mary Wells (University of Waterloo), and Dean Caroline Cao (University of Ottawa). What struck me was how the theme showed up everywhere else too. Education, innovation, even the informal hallway conversations. Trust wasn’t a topic. It was the subtext.

This is where Canadian engineering has something uniquely world class to contribute. Why? Because we have a cultural and professional tradition that keeps pulling us back to first principles. What we build touches people. And we take an oath to uphold high ethical standards, safety, and integrity in our professional work. That oath is not performative. It is a commitment the public can hold us to. That is trust.

This conference also marked 100 years since the Calling of an Engineer tradition began in 1925, a uniquely Canadian ritual built around that vow, to uphold high ethical standards, safety, and integrity in our professional work.

That vow is trust.

My panel focused on the future of engineering education, and it was moderated by Dean Chris Yip (University of Toronto). I had the privilege of sharing the stage with Dean Phillip Choi (University of Regina), Dean James Olsen (University of British Columbia), and Dean Viviane Yargeau (McGill University). I shared a view that we are going through a platform shift driven by AI disruption. It is a foundational change that will reshape every sector and touch every aspect of our lives, including university education, where AI can reshape how university students learn and how courses are designed.

That is why I also believe this may be the best time to become an engineer. As an early stage investor in the next frontier of computing and its applications, I get to see this shift firsthand every day. The collapsing cost of intelligence, and hence abundance, is changing what is possible, and it is creating the conditions for entirely new category defining companies.

The most moving part of the day was the re obligation ceremony, hundreds of Canadian engineers forming a human chain to renew our vows.

Standing there, I was reminded of something simple. Canada’s brand, when we earn it, is built on trustworthiness.

Trust becomes a competitive advantage for Canada. But it’s not something you declare. It’s something you practice day in and day out.

That’s what the iron ring symbolizes at its best, not nostalgia, not ceremony, but a commitment to be worthy of trust through ethics, safety, and integrity, in the work we do and the systems we leave behind.

A century in, the ring still does what it was meant to do. And right now, that feels more important than ever.

And on that note, I trust we do not have to wait another 100 years for the next one. Let’s do an Iron Ring 101 next year!

P.S. The group picture is only University of Toronto, so you can tell how big the crowd was. We have eight universities represented!

Portfolio Highlight: ABR’s Funding Round

Edge AI has been a key pillar of our Advanced Computing Hardware investments and a core part of our thesis for a long time. It is the same arc I wrote about in The Next Data Centre: Your Phone a while ago.

We need new architectures to meet the speed, security, and energy demands of the next frontier of computing and its applications, which is the lens I used in The Factory Analogy.

Our portfolio company Applied Brain Research (ABR) just achieved a new milestone: ABR announced the successful closure of its oversubscribed seed funding round, including investment from TSF as a lead investor, with Eva Lau joining the board.

ABR created and patented a new type of AI model, called state space models, to make AI smaller, faster, and more energy efficient than transformer models. State space models deliver real-time voice and time series intelligence without the cloud, built for privacy and efficiency. ABR’s first chip, TSP1, delivers real-time, fully on-device voice AI without the cloud. Full vocabulary speech-to-text and text-to-speech are now possible at under 30mW.

At the edge, every millisecond and every milliwatt count.

For context:

  • 30mW is 100× less than a 3W LED lightbulb.
  • A data-center GPU lives in a different universe: an NVIDIA H200 NVL is up to 600W.

Now connect that to the three constraints that define the edge:

  • Speed: for voice and interaction, half a second is half a second too late. Cloud voice is “a terrible experience,” plagued by delays.
  • Security: shipping voice data to the cloud bakes in privacy risk by default — which is why we keep coming back to intelligence that stays close to the user, as Brandon argued in his post In Favour of Intelligence That Stays Put. ABR calls out “privacy concerns” as a core issue with cloud voice.
  • Energy: edge devices are constrained by battery life and on-device resources. ABR’s on-device voice numbers move this from “interesting” to “deployable.”

This is why ABR enables numerous new use cases that weren’t viable before in categories like AR, robotics, wearables, medical devices, and automotive.

Imagine AR glasses (or other wearables) that respond to your command in real time without draining the battery. Imagine a robot that reacts with no hesitation. Imagine a medical device that can provide insight securely, without exporting sensitive data. Imagine a car that can respond to voice commands even when the network is unreliable. These are just a few examples. The list can go on and on.

Or as Eva put it in ABR’s announcement: sophisticated voice AI doesn’t require the cloud.

The Factory Analogy: Explaining the Next Frontier of Semiconductor Opportunities

The machinery on the assembly line is world-class. On paper, it can produce an enormous volume of goods per hour. And it does.

Yet, the business still misses its targets. Why? Because outcomes are rarely limited by the assembly line itself.

The supply of raw materials to the machinery and the delivery of finished products to customers play equally vital roles. Let me be a bit poetic here:

Raw materials arrive late, the line waits. Finished goods pile high.

In the end, delivery is routes and time. 

And when movement is the game, the bill runs high. 

Each trip costs energy, in money and time.

This is the central lesson: world-class machinery does not guarantee a high-performing, high-throughput factory.

  • Speed is not just how fast the machinery can produce.
  • Latency is the total time from order to delivery.
  • Energy efficiency is about the total cost of keeping the whole operation moving.

They are related, but not the same problem, and they all contribute to overall performance.

The Compute Analogy

This is a perfect analogy for modern computing. The CPU and GPU are the machinery on the assembly line. They are very good at the arithmetic that turns data into answers.

However, many modern workloads are limited by the supply chain and delivery equivalents in chips and semiconductors. Data has to travel from storage to memory, from memory to the processor, and back again. The raw materials—data—spend a surprising amount of time in transfer before they become finished products in customers’ hands: answers.

That transfer time creates a triple threat to performance:

  • It hurts speed because the processor stalls while waiting to be fed.
  • It hurts latency because the system spends time moving data before it can produce an answer, and then spends time delivering that answer to where it is needed.
  • It hurts energy efficiency because moving bits costs power, dissipates waste heat, and repeated transfers compound the cost.

From Graphics to AI: Changing the Bottleneck

Remember, GPU stands for Graphics Processing Unit. It was originally designed and optimized for graphics—first for gaming in the 1990s—and later for other math-heavy tasks. The bottleneck back then was arithmetic, and GPUs were the solution that gave us the most bang for the buck.

But modern workloads—AI inference in particular—have different characteristics, which makes the bottlenecks show up differently. The computational characteristics of inference put immense pressure on memory behaviour and data transfer. In many cases, the limiting factor is not just the math anymore. It is the movement and the waiting. And the delivery problem is getting bigger too.

The Rise of the “Edge” Factory

Sensors are everywhere now. They generate raw data where the action is. If every sensor stream has to be shipped to a distant “factory” (a data center) before anything useful happens, latency and bandwidth also become part of the product.

That is why edge computing is increasingly important. It is the computing version of building smaller factories closer to customers and shipping less raw material—or sometimes no raw material—across the network.

Investing in New Architectures

Of course, this does not mean CPUs or GPUs are obsolete. It means there are many other bottlenecks now. We need:

  • Less distance between memory and compute.
  • Less shuttling of data inside the system.
  • Less distance between sensing and decision.

The Von Neumann architecture used by many modern computers today is about 80 years old. The first CPU is more than half a century old. The first GPU is almost 30 years old. It is time for new architectures.

This is a core part of our investment thesis in the next frontier of computing and its applications, specifically in Advanced Computing Hardware, one of the five areas we invest in. For many years, we have made investments in semiconductor companies, including Zinite, Hepzibah, ABR, and Blumind, that, through architectural innovation, address performance bottlenecks across speed, latency, and energy efficiency in ways faster GPUs alone will not solve.

We are super excited about this massive opportunity and are looking for new investments. If you are a deep tech researcher or founder in this area, please reach out to us at pitch@twosmallfish.vc.

A Day at Ontario Tech University

I spent a full day at Ontario Tech University in Oshawa a few weeks ago. It was my first time on campus, despite it being just over a 40-minute drive from Toronto, where I live. I arrived curious and left with a clearer picture of what they’re building.

Ontario Tech is still a relatively young university, just over two decades old. What’s less well known—and something I didn’t fully appreciate before the visit—is how quickly it has grown in that time, now serving around 14,000 students, and how deliberately it has established itself as a research university rather than simply a teaching-focused institution.

That research orientation shows up not just in output, but in where the university has chosen to build depth—areas that sit close to real systems and real constraints.

This came through clearly in conversations with Prof. Peter Lewis, Canada Research Chair in Trustworthy Artificial Intelligence, whose work focuses on trustworthy and ethical AI. The university has launched Canada’s first School of Ethical AI, alongside the Mindful AI Research Institute, and the work here is grounded in how AI systems behave once deployed—how humans interact with them, and how unintended consequences are identified and managed.

Energy is another area where Ontario Tech has built serious capability. The university is home to Canada’s only accredited undergraduate Nuclear Engineering program, which is ranked third in North America and designated as an IAEA Collaborating Centre. In discussions with Prof. Hossam Gaber, the emphasis was on smart energy systems, where software, sensing, and control systems are developed alongside the physical energy infrastructure they operate within.

I also spent time with Prof. Haoxiang Lang, whose work in robotics, automotive systems, and advanced mobility sits at the intersection of computation and the physical world.

That work is closely tied to the Automotive Centre of Excellence, which includes a climatic wind tunnel described as one of the largest and most sophisticated of its kind in the world. The facility enables full-scale testing under extreme environmental conditions—from arctic cold to desert heat—and supports research that needs to be validated under real operating constraints.

I can’t possibly mention all the conversations I had over the course of the day—it was a full schedule—but I also spent time with Dean Hossam Kishawy and Dr. Osman Hamid, discussing how research, entrepreneurship, and industry engagement fit together at Ontario Tech.

The day also included time at Brilliant Catalyst, the university’s innovation hub, speaking with students and founders about entrepreneurship. I had the opportunity to give a keynote on entrepreneurship, and the visit ended with the pitch competition, where I handed the cheque to the winning team—a small moment that underscored how early many technical journeys begin.

Ontario Tech may be young, but it is already operating with the structure and discipline of a mature research institution, while retaining the adaptability of a newer one.

Thank you to Sunny Chen and the Ontario Tech team for the time, access, and thoughtful conversations throughout the day.

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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.

Dual Use Is the Next Frontier of Deep Tech

I wrote my master’s thesis on Code Division Multiple Access, or CDMA, a wireless communication technology that originated from military needs in World War II. CDMA uses a technique called direct sequence spread spectrum, which spreads a signal across a wide bandwidth so that it appears as random noise. This made it far better at encryption, resisting jamming, and avoiding eavesdropping. Needless to say, it was perfect for military environments long before it found its way into everyday communication.

A startup company called Qualcomm was beginning to commercialize CDMA. I spent countless hours studying their technical papers, which demonstrated how a technology with military grade robustness could also be applied to large scale commercial mobile networks. Working on that thesis in the 90s was also the first time I encountered the idea of dual use, the concept of a technology that can be used in both military and civilian environments, and one that has existed since the post–World War II era.

Geopolitics Has Recentered Dual Use

Fast forward to today. Geopolitics has returned to the foreground. Defence budgets around the world are rising. Countries are rethinking supply chains and rediscovering the importance of technological sovereignty. The focus is no longer only on wartime capability but also on the resilience of civilian systems that society relies on every day.

In this environment, dual use has moved from the background to the forefront of national strategy. In the AI era we are in, governments everywhere are looking for new technologies that strengthen national security and economic competitiveness at the same time. Technologies that once seemed far removed from defence are now recognized as essential.

A Tailwind for Deep Tech

For Two Small Fish Ventures, none of this comes as a surprise. Deep tech has always lived at the intersection of what is scientifically hard and what is societally important. Today, it naturally lends itself to dual use.

Breakthroughs in the five areas that TSF invests in — vertical AI platforms, physical AI, AI infrastructure, advanced computing hardware, and smart energy — were never designed to be solely military. Yet many of these technologies have clear applications in resilience, cybersecurity, automation, sensing, communication, and energy stability.

In other words, dual use does not narrow a company’s mission. It broadens it. It is the rare case where one innovation can truly kill two birds with one stone.

Defence Technology Is Not Only About Weapons

There is a common misconception that defence technology refers only to weapons. That has never been true.

Most technologies are neutral. I am certain our national defence department uses Microsoft Office, for instance. This is a reminder that much of what defence departments buy is not lethal but operational.

To be clear, we do not invest in companies whose sole purpose is military lethal weapons systems.

Our focus remains on building companies in the areas where we believe the next frontier of computing is taking shape. When those technologies also support national resilience, that is not mission drift. It is simply the nature of deep tech.

Deep tech requires scientific and engineering breakthroughs that are difficult to copy. In a dual use environment, this becomes an essential advantage.

A New Frontier for Founders

Founders often think of defence as a separate world. That is changing. Defence is a complicated beast, and anyone who believes they can simply walk in will be disappointed. But for those who understand the landscape and can navigate it, this is a generational opportunity waiting to be captured.

When I first studied CDMA decades ago, I never imagined that a communication technique developed for the battlefield would become the backbone of commercial wireless networks.

Today, many deep tech founders are standing at a similar moment. For founders and investors in deep tech, this is the beginning of an important cycle. And we are excited to support the innovators who will define what comes next.

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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.

Geopolitics Now Matters to Every CEO

In October, at our Two Small Fish Ventures AGM, I had the chance to sit down with Benjamin Bergen for a fireside chat. At the time, he was still leading the Council of Canadian Innovators. None of us knew he would soon become the new CEO of the CVCA. Looking back, the timing could not have been better.

I have known Benjamin for many years. When I was CEO of Wattpad, I worked closely with him through CCI, which played an important role in advocating for Canadian scaleups. That experience gave me a front row view of how policy, talent mobility, capital, and global markets intersect. I did not expect that perspective to become even more useful on the investor side, but today it is proving to be exactly that.

At Two Small Fish, our portfolio founders often hear us talk about our full cycle view of company building. We have built companies, operated them at global scale, navigated regulatory and geopolitical realities, and now invest across deep tech. We have seen the journey from the very first product decision all the way to commercialization. That experience matters today because geopolitics is no longer something happening far away. It is showing up directly in the work of founders.

The World Has Changed Irreversibly

Founders do not necessarily always think about politics, especially geopolitics. I certainly did not in my early days as a founder. But over the past year, the global environment has shifted in ways that affect talent, capital, customers, supply chains, and data. These forces are becoming part of the operating conditions for every innovative company.

At the AGM, Benjamin and I spent time unpacking what this new reality looks like.

  • Talent We spoke about the growing brain drain and how global mobility is changing. The tightening of the H1B program in the United States has created a ripple effect across the entire talent ecosystem. Early stage companies are rethinking where they build teams, and immigration policy is becoming a strategic consideration rather than an afterthought.
  • Capital The rise of protectionism and shifting global alliances are affecting how and where capital can move. The changing dynamics among the United States, China, and Canada raise new questions for both founders and investors. Some are beginning to view geographic diversification as a practical response to political uncertainty.
  • Customers National preference policies such as Buy Canadian and Buy American are becoming more common. These policies may begin as political statements, but they influence real procurement and partnership decisions. For founders, gaining early customers is no longer just about product and timing. There is a political dimension that needs to be understood.
  • Infrastructure and Defense We also talked about how export controls and security requirements are expanding. Technologies that once seemed purely commercial are now viewed through a strategic lens. Even young companies are discovering that they may be operating in areas that governments consider sensitive.
  • Supply Chains Global supply chains have shown their fragility in areas such as semiconductors, rare earth materials, and energy. These vulnerabilities create friction but also open new opportunities for companies building more resilient and regional alternatives.
  • Data Sovereignty Data localization and national data governance rules continue to spread. More countries want their data stored and processed within their borders. For companies operating internationally, this introduces new architectural and operational decisions much earlier in the journey.

Benjamin also shared how CCI’s new advisory group, Signa Strategies, is helping founders navigate exactly these types of challenges. It felt like a natural evolution of the work he has been doing for years.

As our conversation wrapped up, I was reminded how valuable it is to have seen this ecosystem from both sides. As a founder, I saw how talent, markets, and policy could quietly redirect a company’s path. Through CCI, I saw how national priorities and regulation shape the environment innovators work in. These experiences feel especially relevant now. The geopolitical questions that once appeared at the edges are moving closer to the center.

This is the environment founders are building in today. And with our full cycle experience, we hope to help them navigate it with clarity, context, and confidence.

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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.

The AI Bubble That Is Not When Everyone Is All In

At the beginning of this year, I wrote an op-ed for The Globe about what many were already calling the AI bubble. Nearly a year later, almost all of what I said remains true. The piece was always meant to be a largely evergreen, long term view rather than a knee jerk reaction.

The only difference today is that the forces I described back then have only intensified.

We are in a market where Big Tech, venture capital, private equity, and the public markets are all pouring unprecedented capital into AI. But to understand what is actually happening, and how to invest intelligently, we need to separate noise from fundamentals. Here are the five key points:

  1. Why Big Tech Is Going All In while Taking Minimal Risk.
  2. The Demand Side Is Real and Growing.
  3. Not All AI Investments Are Created Equal.
  4. Picking Winners Matters.
  5. Remember, Dot Com Was a Bubble. The Internet Was Not.

1. Why Big Tech Is Going All In while Taking Minimal Risk

The motivations of the large technology companies driving this wave are very different from those of startups and other investors.

For Big Tech, AI is existential. If they underinvest, they risk becoming the next Blockbuster. If they overinvest, they can afford the losses. In practice, they are buying trillions of dollars worth of call options, and very few players in the world can afford to do that.

The asymmetry is obvious. If I were their CEOs, I would do the same.

But being able to absorb risk does not mean they want to absorb all of it. This is why they are using creative financing structures to shift risk off their balance sheets while remaining all in. At the same time, they strengthen their ecosystems by keeping developers, enterprises, and consumers firmly inside their platforms.

This is not classical corporate investing. Their objective is not just profitability. It is long term dominance.

For everyone outside Big Tech, meaning most of us, understanding these incentives is essential. It helps you place your bets intelligently without becoming roadkill when Big Tech transfers risk into the ecosystem.

2. The Demand Side Is Real

AI usage is not slowing. It is accelerating.

The numbers do not lie. Almost every metric, including model inference, GPU utilization, developer adoption, enterprise pilot activity, and startup formation, is rising. You can validate this across numerous public datasets. Directionally, people are using AI more, not less. And unlike previous hype cycles, this wave has real usage, real dollars, and real infrastructure behind it.

Yes, there is froth. But there are also fundamentals.

3. Not All AI Investments Are Created Equal

A common mistake is treating AI investing as a single category.

It is not.

Investing in a public market, commoditized AI business is very different from investing in a frontier technology startup with a decade long horizon. The former may come with thin margins, weak moats, and hidden exposure to Big Tech’s risk shifting. The latter is where transformational returns come from if you know how to evaluate whether a company is truly world class, differentiated, and defensible.

Lumping all AI investments together is as nonsensical as treating all public stocks as the same.

4. Picking Winners Matters

In public markets, you can buy the S&P 500 and call it a day. But that index is not random. Someone selected those 500 winners for you.

In venture, picking winners matters even more. It is a power law business. Spray and pray does not work. Most startups will not survive, and only the strongest will break out, especially in an environment as competitive as today.

Thanks to AI, we are in the middle of a massive platform shift. Venture scale outcomes depend on understanding technology deeply enough to see a decade ahead and identify breakout successes before others do. Long term vision beats short term noise. Daily or quarterly fluctuations are simply noise to be ignored.

5. Dot Com Was a Bubble. The Internet Was Not.

The dot com era had dramatic overvaluation and a painful crash, but the underlying technology still reshaped the world. The problem was not the internet. It was timing, lack of infrastructure, and indiscriminate investing in ideas that were either too early or simply bad.

Looking back, the early internet lacked essential components such as high speed access, mobile connectivity, smartphones, and internet payments. Although some elements of the AI stack may still be evolving, many of the major building blocks, including commercialization, are already in place. AI does not suffer from the same foundational gaps the early internet did.

Calling this a bubble as a blanket statement misses the nuance. AI itself is not a bubble. With a decade long view, it is already reshaping almost every industry at an unprecedented pace. Corrections, consolidations, and failures are normal. The underlying technological shift is as real as the internet was in the 1990s.

There is speculation. There are frothy areas. And yet, there are many areas that are underfunded. That is where the opportunities are.

⸻

History shows that great venture funds invest through cycles. They invest in areas that will be transformative in the next decade, not the next quarter.

For us, the five areas we focus on, including Vertical AI platforms, physical AI, AI infrastructure, advanced computing hardware, and smart energy, are the critical elements of AI. Beyond being our expertise, there is another important reason why these categories matter: Bubble or not, they will thrive.

We are not investing in hype, nor in capital intensive businesses where capital is the only moat, nor in companies where technology defensibility is low. As long as we stay disciplined and visionary, and continue to back founders building a decade ahead, we will do well, bubble or not.

After all, there may be multiple macro cycles across a decade. Embrace the bubble.

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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.

Reflections from the Impact 2025 Summit

I had the opportunity to join a panel at the Impact 2025 Summit in Calgary, moderated by Raissa Espiritu, with Janet Bannister and Paul Godman. Ironically, none of us are labelled as impact investors, and I explained on stage why Two Small Fish Ventures does what we do.

At Two Small Fish Ventures, we’ve never called ourselves an impact fund. That’s not because we’re indifferent to impact; in fact, it’s core to what we do. Our focus is on deep tech, the next frontier of computing, where innovation can create meaningful, long-term change. Specifically, we invest in five key areas: Vertical AI Platforms, Physical AI, AI Infrastructure, Advanced Computing Hardware, and Smart Energy.

We care deeply about scientific advancement, and more importantly, about turning those breakthroughs into real-world impact. That’s how meaningful progress happens.

Eva is our General Partner, and both of us are immigrants. Diversity isn’t a marketing point for us; it’s part of who we are. It naturally shows up in our portfolio: about half of our companies have at least one female founder, and many come from underrepresented backgrounds. That said, uncompromisingly, we back amazing deep tech founders who are turning their creations into world-class companies.

It’s actually rare that we talk about topics like women investing or investing in underrepresented groups in isolation. Not because we don’t care, quite the opposite. The fact that Eva is one of the few female GPs leading a venture fund, and that we’re both immigrants, already says a lot. Our actions speak volumes. We walk the walk and talk the talk.

We need to deliver results. Period. Our competition isn’t other venture funds; it’s every other investment opportunity available in the market. If we can’t perform at the highest level — top decile in everything we do — we can’t sustain our mission. Delivering some of the best results in the industry enables us to do what we love and make an impact.

That’s why I believe impact and performance are not opposites. The most powerful kind of impact happens when companies succeed, when they become world-class companies. Strong returns and meaningful impact can, and should, reinforce each other.

I also talked about the importance of choosing the right vehicle for the right purpose. When we made a 2 million dollar donation to the University of Toronto to establish the Commercialization Catalyst Prize, it wasn’t about investing. It was about supporting a different kind of impact — helping scientists and engineers turn their research into innovations that can reach the world. Not every kind of impact should come from the same tool.

At the end of the day, labels matter less than intent and execution. We don’t need to call ourselves an impact fund to make a difference. Our goal is simple: to back bold deep tech founders using science and technology to build a better future and to do it with excellence.

A big thank you to Raissa, George Damian, Sylvia Wang, and the entire Platform Calgary team for putting together such a thoughtful and well-run event.

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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.

Embrace Risks, Execute with an Edge and Seek the Asymmetrical Upside

On Friday, October 17, 2025, Chancellor Kathleen Taylor at York University conferred on Eva Lau the degree of Doctor of Laws, honoris causa.

I purposely avoided hearing her practice so I could experience it on stage for the first time, just like everyone else in the audience.

It turned out to be the right call. By taking the risk of not previewing it, I gained an asymmetrical upside! Ironically, these are the lessons she shared. Frankly, I wish someone had told me these lessons when I was in my twenties.

Please read her remark when you have a moment:

********

Dean Zwick, faculty, honoured guests, proud families, and—most importantly—the graduating class of 2025: thank you. It is a profound honour to stand before you today.

First, I want to thank the school for recognizing my work in entrepreneurship and innovation and granting me this extraordinary honour.

I also want to thank my mom and my late father, whose leap of faith to immigrate to Canada opened the doors for me and my sister to build our lives here.

To my daughters—you’ve been my biggest motivators. You are the reason I push forward, because I want you to know your potential is uncapped. You can chart your own paths and build your own successes.

And to my husband, Allen: thank you for your love, support, and affirmations. You mean the world to me, and you make me a better person every day.

And to the graduating class—congratulations! Today is a celebration of your hard work, your determination, and your costly Red Bull habits. It is my greatest joy to share a few words with you. Since we are all business school graduates, let’s get straight to business. I want to share with you a strategy—a framework, at least for me—on how to calculate risk, how to execute with an edge, and how to maximize the upside, to a point that it can be transformational. That’s right, I am putting our education to work here. No group project required!

Lesson 1: Embrace Risk—But Know Your Bottom Line

In school, we learned about Risk Management in Finance. Identify the risks. Quantify them. Mitigate them. Protect value. Very neat, very rational.

But in life, risk doesn’t come with a spreadsheet. In life, managing risk means asking: how much am I truly willing to lose? Unless you define that, you can never truly take a risk.

When people read about the success of Wattpad, the story can look deceptively simple. We built the product. Users loved it. The product went viral. And ta-da! It became one of the most iconic internet platforms in the world, serving over 100 million users worldwide, sharing over 1 million new stories in 50 languages every day. We even have TV and movie products around the world. When the company was acquired in 2021, it was one of the most significant tech exits in Canadian history.

But the “ta-da” moment was actually years of sweat, doubt, and very small numbers. At the start in 2006, Wattpad didn’t just have few users—we had so few that our total ad revenue was…two dollars. Not two million. Not two thousand. Not twenty. Just two. And Allen and Ivan, the two cofounders, had to split it. I think one bought a coffee… the other just got the receipt.

Our family’s finances? Let’s just say “tough” doesn’t quite capture it. We were running low on savings and even had to leverage our house to keep everything going. Allen and I had many long kitchen-table talks. In the end, we decided we were willing to risk everything—even selling the house—if that’s what it took. But we drew a firm line: we were willing to go down to zero, but we were not willing to go into the negatives.

That’s what embracing risk looks like for an entrepreneur. It’s not about avoiding loss. It’s about defining your boundaries and then giving it everything you’ve got. Try your hardest to stay clear of the bottom line.

And when mobile computing took off with the launch of iphones and android devices, all that persistence paid off. Wattpad became the world’s number one story-sharing app on all app stores.

We took the same approach when exploring new frontiers at Wattpad—first with AI, then with entertainment. In 2012—long before “ChatGPT” became a household name—we became one of the first companies to deploy AI at scale on a commercial platform. It was a bold move, and yes, a risky one. Then in 2016, we leapt into film and TV production—an entirely different world for us. Both were high-stakes bets, but because we had clearly defined what we were willing to invest and what we were prepared to lose, we could take those risks with confidence.

So my first lesson: embrace risk. Define your bottom line so you can move forward without fear. Knowing your worst-case scenario gives you the freedom to take that leap of faith. In your case, living in your parents’ basement could be the worst-case scenario. But hey, you already know them well enough. I think you will survive.

Lesson 2: Leverage Your Uniqueness

Once you’ve defined your risk boundaries, the next step is execution. And here’s the secret: the best execution comes from knowing what makes us unique and leaning into it.

When I began my journey as a venture capitalist a decade ago, I knew I couldn’t just be another investor. What set me apart was lived experience: I had scaled a product from a few thousand users to tens of millions. I understood the fear, the pivots, and the sleepless nights—not from theory, but firsthand.

Before Wattpad, I worked in a semiconductor company, managing a product line that was competing with a startup at the time, called Nvidia. AMD later acquired the company for US$ 4 billion. That experience gave me the technical and operational lenses very few investors had.

And then there were my learnings from some of the best investors in the world—people who backed Twitter, Coinbase, Google, and even OpenAI. I had the opportunity to learn directly from them since they were also Wattpad investors.

All of that shaped my unique edge as a venture capitalist at Two Small Fish. With a distinct investment thesis, we became one of the few deep-tech investors in Canada, backing founders tackling hard technology problems with novel innovations. Today, I’m proud to say Two Small Fish is not only among the top-performing VC funds globally, but also a firm that founders love working with—because we do things differently.

That’s the second lesson: know our uniqueness and use it. Don’t downplay it. Don’t hide it. It’s our superpower.

Lesson 3: Chase Asymmetrical Upside

The third lesson is about aiming high. Really high. Chase the Asymmetrical Upside.

Entrepreneurship and innovation are not about making something just 10-20% better. They are about creating something 100 times, 1000 times better. Something transformational.

If you only focus on small, incremental gains, you might survive—but you won’t thrive when the next wave of disruption comes. But if you go after opportunities with asymmetrical upside—where the potential payoff is massive compared to the risk—you position yourself for breakthroughs.

Take Wattpad again. If we had only wanted to build a small reading app for a niche audience, that would have been fine. But by dreaming bigger—by imagining an AI-powered global entertainment company—the outcome was transformational.

And this applies to your careers too. You won’t change industries—or the world—by playing it safe. You have to reach for opportunities that feel a little terrifying, a little out of your league.

I like to remind young entrepreneurs: I have never seen a basketball player aim for the bottom of the net. They always aim above it. That’s how slam dunks happen.

So my third lesson: don’t settle for small steps. Chase the opportunities that stretch you, the ones that scare you, the ones that could redefine everything.

So, Class of 2025, to sum these up, I encourage you to:

  • Embrace risk. Define your boundaries. Know how much you’re willing to lose, and let that clarity free you.
  • Leverage your uniqueness. Don’t try to be a knockoff of someone else. Your unique mix of experiences, skills, and quirks is your competitive edge.
  • Chase asymmetrical upside. Don’t aim for incremental change. Aim for the slam dunk.

Your journey will not be a straight line. There will be pauses, setbacks, and zigzags. But each twist is part of the story that prepares you for the next leap forward.

So step into your future with courage. Take the risk! The world doesn’t need another safe bet—it needs bold leaders, innovative thinkers, and dreamers who are willing to take the shot.

Congratulations once again, Class of 2025. The future is yours—go and dunk it.

$2M Donation Fuels U of T’s Eva and Allen Lau Commercialization Catalyst Prize

The Laus have some exciting news to share. We are making a $2 million donation to the University of Toronto, our alma mater, to launch the Eva and Allen Lau Commercialization Catalyst Prize for Computing & Engineering Innovation.

And the best part: U of T’s Faculty of Arts & Science and Faculty of Applied Science & Engineering will match our gift, doubling its impact. This partnership will give even more researchers the resources they need to take their inventions and turn them into impactful companies.

Why This Matters

Canada is full of world class talent. At U of T alone, researchers are pushing the boundaries of semiconductors, AI, robotics, and quantum technologies—fields that will shape the future. The brilliance is already here.

But getting from invention to an impactful company is not easy. What is often missing are the resources that help move ideas out of the lab: mentorship, funding, workspace, and access to prototyping labs. That is where this prize comes in.

Each year, one team from Arts and Science and one from Engineering will receive support to bridge that critical gap and bring their boldest ideas closer to reality.

Why Catalyst

We named this prize Catalyst for a reason. Commercialization does not happen in isolation. It takes a community of mentors, peers, industry partners, and funders to transform research into companies that scale.

Our hope is that the Catalyst Prize sparks more than just a few startups. We want it to inspire others to join in, to create the conditions where many more homegrown tech giants can start, grow, and scale right here in Canada.

U of T

The University of Toronto is already a leader in innovation and entrepreneurship. It is home to one of the world’s top university incubators, has helped launch more than 1,200 venture backed startups, and is ranked among the top ten universities worldwide for powering innovation .

Add to that U of T’s research depth, industry partnerships, and global alumni network, and you have a powerful engine for turning big ideas into global impact. We are thrilled to help fuel that engine.

Coming Full Circle

For us, this is also personal. U of T gave us our start, Allen in electrical engineering and Eva in industrial engineering, and laid the foundation for everything that followed. From building Wattpad to starting Two Small Fish Ventures, we have lived the journey from idea to scale.

Now, through the Catalyst Prize, and with U of T matching to double the impact, we want to give today’s researchers and students even more opportunities than we had.

Innovation is unpredictable and the path is seldom linear. But with the right support at the right time, sparks can turn into something extraordinary.

That is what the Catalyst Prize is all about, helping Canadian innovators move their ideas out of the lab and into the world.

We cannot wait to see what they build.

— Eva and Allen

Quantum: From Sci-Fi to Investable Frontier

When I was studying electrical engineering, out of my curiosity, I chose to take an elective course on quantum physics as part of advanced optics. It sparked my curiosity in quantum. The strange, abstract, counterintuitive rules, for example particles existing in multiple states or being entangled across distance, captivated me.

Error correction, closely related to fault tolerance in quantum systems today, is the backbone of telecommunications, one of the areas I majored in.

Little did I know these domains would converge in such a way that my earlier academic training would become relevant again years later.

For me, computing is not just my profession, it is also my hobby. As a science nerd, I actively enjoy following advances, and I keep going deeper down the rabbit hole of the next frontier of computing. That mix of personal curiosity and professional focus shapes how I approach both the opportunities and risks in the space. Over the past few years, I have gone deeper into the world of quantum. My academic and professional background gave me the footing to evaluate both what is technically possible and what is commercially viable.

From If to How and When

In June, I wrote Quantum Isn’t Next. It’s Now. We have passed the tipping point where the question is no longer if quantum technology will work, it is how and when it will scale.

This momentum is not just visible to those of us deep in the field. As the Globe and Mail recently reported, we at Two Small Fish have been following quantum for years, but did not think it was mature enough for an early-stage fund with a 10-year lifespan to back. This year, we changed our minds. As I shared in that article: “It’s much more investible now.”

The distinction is clear: when quantum was still a science problem, the central question was whether it could work at all. Now that it has become an engineering problem, the questions are how it will work at scale and when it will be ready for commercialization.

This shift matters for investors. Venture capital focuses on engineering breakthroughs, hard, uncertain, but achievable on a commercialization timeline. Fundamental science, which can take many more years to mature, is better supported by governments, universities, and non-dilutive funding sources. I will leave that discussion for another post.

One of Five Frontiers

At Two Small Fish Ventures, we have identified five areas shaping the next frontier of computing. Quantum falls under the area of advanced computing hardware, where the convergence of different areas of science, engineering, and commercialization is accelerating.

Each of these areas is no longer a speculative science experiment but a rapidly advancing field where engineering and commercialization are converging. Within the next ten years, the winners will emerge from lab prototypes and become scaled companies. Quantum is firmly on that trajectory.

How We Invest in Quantum

Our first principle at Two Small Fish is straightforward: we only invest in things we truly understand, from all three technology, product, and commercialization lenses. That discipline forces us to dig deep before committing capital. And after years of study, it is clear to us that quantum has moved into investable territory, but only selectively.

Not every quantum startup fits a venture time horizon. Some promising projects will take too many years to scale. But we are now seeing opportunities that, within a 10-year window, can realistically grow from an early-stage idea to a successful scale-up. That is the standard we apply to every investment, and quantum finally has companies that meet it.

From Sci-Fi to Reality

Canada has played an outsized role in building the foundation of quantum science. Now, it has the chance to lead in quantum commercialization. The next few years will determine which teams turn breakthrough science into enduring companies.

For investors, this is both an opportunity and a responsibility. The quantum era is not a distant possibility, it is here now. What once sounded like science fiction is now an investable reality. And for those willing to put in the work to understand it, the frontier is already here.

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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.

Portfolio Highlight: Axiomatic

Last year we invested in Axiomatic AI. Their mission is to bring verifiable and trustworthy AI into science and engineering, enabling innovation in areas where rigour and reliability are essential. At the core of this is Mission 10×30: achieving a tenfold improvement in scientific and engineering productivity by 2030.

The company was founded by top researchers and professors from MIT, the University of Toronto, and ICFO in Barcelona, bringing deep expertise in physics, computer science, and engineering.

Since our investment, the team has been heads down executing. Now they’ve shared their first public release: Axiomatic Operators.

What They’ve Released

Axiomatic Operators are MCP servers that run directly in your IDE, connecting with systems like Claude Code and Cursor. The suite includes:

  • AxEquationExplorer
  • AxModelFitter
  • AxPhotonicsPreview
  • AxDocumentParser
  • AxPlotToData
  • AxDocumentAnnotator

Why is this important?

Large Language Models (LLMs) excel at languages (as their name suggests) but struggle with logic. That’s why AI can write poetry but often has trouble with math — LLMs mainly rely on pattern matching rather than reasoning.

This is where Axiomatic steps in. Their approach combines advances in reinforcement learning, LLMs, and world models to create AI that is not just fluent but also capable of reasoning with the rigour required in science and engineering.

What’s Next

This first release marks an important step in turning their mission into practical, usable tools. In the coming weeks, the team will share more technical material — including white papers, demo videos, GitHub repositories, and case studies — while continuing to work closely with early access partners.

Find out more on GitHub, including demos, case studies, and everything else you need to make your work days less annoying and more productive: Axiomatic AI GitHub

We’re excited to see their progress. If you’re in science or engineering, we encourage you to give the Axiomatic Operators suite a try: Axiomatic AI.

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Jevons Paradox: Why Efficiency Fuels Transformation

In 1865, William Stanley Jevons, an English economist, observed a curious phenomenon: as steam engines in Britain became more efficient, coal use didn’t fall — it rose. Efficiency lowered the cost of using coal, which made it more attractive, and demand surged.

That insight became known as Jevons Paradox. To put it simply:

  • Technological change increases efficiency or productivity.
  • Efficiency gains lead to lower consumer prices for goods or services.
  • The reduced price creates a substantial increase in quantity demanded (because demand is highly elastic).

Instead of shrinking resource use, efficiency often accelerates it — and with it, broader societal change.

Coal, Then Light

The paradox first appeared in coal: better engines, more coal consumed. Electricity followed a similar path. Consider lighting in Britain:

PeriodTrue price of lighting (per million lumen-hours, £2000)Change vs. startPer-capita consumption (thousand lumen-hours)Change vs. startTotal consumption (billion lumen-hours)Change vs. start
1800£8,000—1.1—18—
1900£250↓ ~30×255↑ ~230×10,500↑ ~500×
2000£2.5↓ ~3,000× (vs. 1800) / ↓ ~100× (vs. 1900)13,000↑ ~13,000× (vs. 1800) / ↑ ~50× (vs. 1900)775,000↑ ~40,000× (vs. 1800) / ↑ ~74× (vs. 1900)

Over two centuries, the price of light fell 3,000×, while per-capita use rose 13,000× and total consumption rose 40,000×. A textbook case of Jevons Paradox — efficiency driving demand to entirely new levels.

Computing: From Millions to Pennies

This pattern carried into computing:

YearCost per GigaflopNotes
1984$18.7 million (~$46M today)Early supercomputing era
2000$640 (~$956 today)Mainstream affordability
2017$0.03Virtually free compute

That’s a 99.99%+ decline. What once required national budgets is now in your pocket.

Storage mirrored the same story: by 2018, 8 TB of hard drive storage cost under $200 — about $0.019 per GB, compared to thousands per GB in the mid-20th century.

Connectivity: Falling Costs, Rising Traffic

Connectivity followed suit:

YearTypical Speed & Cost per Mbps (U.S.)Global Internet Traffic
2000Dial-up / early DSL (<1 Mbps); ~$1,200~84 PB/month
2010~5 Mbps broadband; ~$25~20,000 PB/month
2023100–940 Mbps common; ↓ ~60% since 2015 (real terms)>150,000 PB/month

(PB = petabytes)

As costs collapsed, demand exploded. Streaming, cloud services, social apps, mobile collaboration, IoT — all became possible because bandwidth was no longer scarce.

Intelligence: The New Frontier

Now the same dynamic is unfolding with intelligence:

YearCost per Million TokensNotes
2021~$60Early GPT-3 / GPT-4 era
2023~$0.40–$0.60GPT-3.5 scale models
2024< $0.10GPT-4o and peers

That’s a two-order-of-magnitude drop in just a few years. Unsurprisingly, demand is surging — AI copilots in workflows, large-scale analytics in enterprises, and everyday generative tools for individuals.

As we highlighted in our TSF Thesis 3.0, cheap intelligence doesn’t just optimize existing tasks. It reshapes behaviour at scale.

Why It Matters

The recurring pattern is clear:

  • Coal efficiency fueled the Industrial Revolution.
  • Affordable lighting built electrified cities.
  • Cheap compute and storage enabled the digital economy.
  • Low-cost bandwidth drove streaming and cloud collaboration.
  • Now cheap intelligence is reshaping how we live, work, and innovate.

As we highlighted in Thesis 3.0:

“Reflecting on the internet era… as ‘the cost of connectivity’ steadily declined, productivity and demand surged—creating a virtuous cycle of opportunities. The AI era shows remarkable parallels. AI is the first technology capable of learning, reasoning, creativity… Like connectivity in the internet era, ‘the cost of intelligence’ is now rapidly declining, while the value derived continues to surge, driving even greater demand.”

The lesson is simple: efficiency doesn’t just save costs — it reorders economies and societies. And that’s exactly what is happening now.

If you are building a deep tech early-stage startup in the next frontier of computing, we would like to hear from you. This is a generational opportunity as both traditional businesses and entirely new sectors are being reshaped. White-collar jobs and businesses, in particular, will not be the same. We would love to hear from you.

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Masterclass Series: The Rule of 3 and 10 — Lessons I Wish I Learned Earlier

One of the most powerful frameworks I’ve come across is the Rule of 3 and 10, coined by Hiroshi Mikitani-san, founder and CEO of Rakuten. The idea is simple: every time a company triples in size, everything breaks.

As Rakuten grew from a handful of people into a global business, Mikitani-san noticed a clear pattern. At each stage — 1 to 3 people, 3 to 10, 10 to 30, 30 to 100, 100 to 300, and beyond — what worked before suddenly stopped working. And by everything, it really does mean everything: payroll, meetings, communication, budgeting, sales, even the org chart. The challenge is that many leaders blow right through these milestones without realizing what’s happening until it’s already broken.

What I Wish I Knew

I’ve been part of many really fast-growing companies — first as an employee, and later as a co-founder in two of them. And I can tell you, this rule is 100% true.

At Wattpad, I didn’t fully internalize it until we were approaching 100 people. By then, we had already missed natural breaking points where we could have rebuilt earlier. That lag made scaling harder than it needed to be.

Looking back, the stages feel something like this:

  • At 3 people, you’re a tight-knit unit where everyone knows everything.
  • At 10, you need to change how you communicate just to stay aligned.
  • At 30, the days of everyone reporting to the CEO are long gone — a first layer of leaders emerges.
  • At 100, there are layers of layers of leaders, and even well-designed systems need rethinking.
  • At 300, you’re running a completely different company than the one you started.
  • At 1,000, it feels like a mini-society with its own subcultures, bureaucracy, and politics — alignment becomes the hardest problem of all.

The Employee’s View

Before becoming an entrepreneur, I lived through this as an employee too. The breaking points are just as visible from the inside.

As companies scale, it gets harder to push things through. Meetings multiply, but decisions slow. Bystander problems appear — more people in the room, but fewer actually taking ownership. From the employee’s perspective, it feels frustrating and inefficient. But it’s not about capability; it’s about systems that no longer fit the size of the company.

Why This Matters

In the moment, it can feel like failure. But it isn’t. It’s simply that scale changes everything.

The good news: these challenges are solvable. Every growing company has faced them. The bad news: if you only react after things break, you’ll always be catching up instead of leading.

My Takeaway

If you’re building a fast-growing company, expect everything to break at 3, 10, 30, 100, 300, 1,000… and plan for it.

Don’t see it as failure. See it as evolution. Each breakdown is proof you’ve unlocked a new stage of growth. The chaos is part of the privilege — it means you’re building something worth scaling.

If I could go back and tell my younger CEO self one thing, it would be this: anticipate the breaks before they happen. Build a culture that embraces reinvention at every stage. You’ll save yourself and your team a lot of unnecessary pain — and you’ll enjoy the ride more.

P.S. The banner is using Ideogram Character to generate. It rocks!

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Annoucing Our Investment in FUTURi Power: The Last Dumb Box in Our Home Gets a Brain

For nearly 70 years, the home electrical panel has looked the same. Meanwhile, the home itself is transforming: solar on the roof, batteries in the garage, heat pumps, EVs in the driveway, and smart appliances and devices everywhere.

And yet, the panel? Still the same. It is the last dumb box left, and FUTURi is fixing that with deep tech.

FUTURi’s Energy Processor

FUTURi Power, founded by Dr. Martin Ordonez (UBC Professor, Kaiser Chair at UBC, and recipient of the King Charles III Coronation Medal for leadership in clean energy innovation), reimagines the panel as the Energy Processor, a programmable energy computer that finally gives the home’s electrical system a brain. It is designed as a like-for-like replacement for the traditional panel that is future-proof and intelligently measures and coordinates loads, avoids peaks, and manages energy use at the edge.

Why This Matters

Homes are no longer passive energy consumers. They are dynamic nodes in the grid. By making the panel intelligent, FUTURi enables:

  • For homeowners: Achieve a 100% electric home without costly service upgrades. A smarter, more resilient, and efficient energy ecosystem.
  • For utilities: Demand peaks flattened, demand response (DR) programs and distributed energy resources (DERs) integrated, deferring costly capital expenditures.
  • For builders and communities: Intelligent electrification helps accelerate the deployment of built infrastructure without overloading the grid.

This is why FUTURi and utilities are already collaborating on projects to evaluate how Energy Processors can strengthen the grid and benefit customers.

Our Perspective

As Dr. Martin Ordonez, Founder and CEO of FUTURi Power, puts it: “Panels used to be passive. The Energy Processor is active, safe, and software-defined. It gives homes and grids a common language.”
At TSF, Smart Energy is one of our five focus areas. Our thesis is simple: the cost of intelligence is collapsing, and the biggest opportunities lie where software and hardware come together to reshape behaviour.

FUTURi is exactly that blueprint for intelligent electrification: deep-tech power electronics plus intelligent control. That combination turns a 70-year-old box into the brain of the modern home. Dr. Ordonez and his team are globally recognized experts in electrification who are translating decades of pioneering research into transformative commercial solutions.

And this is just the beginning. There is so much more the company can do to make electricity truly intelligent. FUTURi has a bright future ahead (pun fully intended).

Five Areas Shaping the Next Frontier

The cost of intelligence is dropping at an unprecedented rate. Just as the drop in the cost of computing unlocked the PC era and the drop in the cost of connectivity enabled the internet era, falling costs today are driving explosive demand for AI adoption. That demand creates opportunity on the supply side too, in the infrastructure, energy, and technologies needed to support and scale this shift.

In our Thesis 3.0, we highlighted how this AI-driven platform shift will reshape behaviour at massive scale. But identifying the how also means knowing where to look.

Every era of technology has a set of areas where breakthroughs cluster, where infrastructure, capital, and talent converge to create the conditions for outsized returns. For the age of intelligent systems, we see five such areas, each distinct but deeply interconnected.

1. Vertical AI Platforms

After large language models, the next wave of value creation will come from Vertical AI Platforms that combine proprietary data, hard-to-replicate models, and orchestration layers designed for complex and large-scale needs.

Built on unique datasets, workflows, and algorithms that are difficult to imitate, these platforms create proprietary intelligence layers that are increasingly agentic. They can actively make decisions, initiate actions, and shape workflows. This makes them both defensible and transformative, even when part of the foundation rests on commodity models.

This shift from passive tools to active participants marks a profound change in how entire sectors operate.

2. Physical AI

The past two decades of digital transformation mostly played out behind screens. The next era brings AI into the physical world.

Physical AI spans autonomous devices, robotics, and AI-powered equipment that can perceive, act, and adapt in real environments. From warehouse automation to industrial robotics to autonomous mobility, this is where algorithms leave the lab and step into society.

We are still early in this curve. Just as industrial machinery transformed factories in the nineteenth century, Physical AI will reshape industries that rely on labour-intensive, precision-demanding, or hazardous work.

The companies that succeed will combine world-class AI models with robust hardware integration and build the trust that humans place in systems operating alongside them every day.

3. AI Infrastructure

Every transformative technology wave has required new infrastructure that is robust, reliable, and efficient. For AI, this means going beyond raw compute to ensure systems that are secure, safe, and trustworthy at scale.

We need security, safety, efficiency, and trustworthiness as first-class priorities. That means building the tools, frameworks, and protocols that make AI more energy efficient, explainable, and interoperable.

The infrastructure layer determines not only who can build AI, but who can trust it. And trust is ultimately what drives adoption.

4. Advanced Computing Hardware

Every computing revolution has been powered by a revolution in hardware. Just as the transistor enabled mainframes and the microprocessor ushered in personal computing, the next era will be defined by breakthroughs in semiconductors and specialized architectures.

From custom chips to new communication fabrics, hardware is what makes new classes of AI and computation possible, both in the cloud and on the edge. But it is not only about raw compute power. The winners will also tackle energy efficiency, latency, and connectivity, areas that become bottlenecks as models scale.

As Moore’s Law hits its limit, we are entering an age of architectural innovation with neuromorphic computing, photonics, quantum computing, and other advances. Much like the steam engine once unlocked new industries, these architectures will redefine what is computationally possible. This is deep tech meeting industrial adoption, and those who can scale it will capture immense value.

5. Smart Energy

Every technological leap has demanded a new energy paradigm. The electrification era was powered by the grid. Today, AI and computing are demanding unprecedented amounts of energy, and the grid as it exists cannot sustain this future.

This is why smart energy is not peripheral, but central. From new energy sources to intelligent distribution networks, the way we generate, store, and allocate energy is being reimagined. The idea of programmable energy, where supply and demand adapt dynamically using AI, will become as fundamental to the AI era as packet switching was to the internet.

Here, deep engineering meets societal need. Without resilient and efficient energy, AI progress stalls. With it, the future scales.

Shaping What Comes Next

The drop in the cost of intelligence is driving demand at a scale we have never seen before. That demand creates opportunity on the supply side too, in the platforms, hardware, energy, physical systems, and infrastructure that make this future possible.

The five areas — Vertical AI Platforms, Physical AI, AI Infrastructure, Advanced Computing Hardware, and Smart Energy — represent the biggest opportunities of this era. They are not isolated. They form an interconnected landscape where advances in one accelerate breakthroughs in the others.

We are domain experts in these five areas. The TSF team brings technical, product and commercialization expertise that helps founders build and scale in precisely these spaces. We are uniquely qualified to do so.

At Two Small Fish, this is the canvas for the next generation of 100x companies. We are excited to partner with the founders building in these areas globally, those who not only see the future, but are already shaping it.

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Backing the Scientists Who Helped Invent Blockchain with SureMark Digital

A few years back, Eva met Dr. Scott Stornetta. Later, I did too. Alongside Dr. Stuart Haber, Scott is widely credited as the creator of blockchain. Blockchain is a technology built on a simple but radical idea at the time: decentralization. No single authority, no central point of control, just a trusted system everyone can rely on.

Now, these two scientists are teaming up again to start a new company, SureMark Digital. Their mission is to bring that same decentralized philosophy to identity and authenticity on the internet, enabling anyone to prove who they are, certify their work, and push back against deepfakes and impersonation. No middlemen. No central gatekeepers.

It took us about 3.141592654 seconds to get excited. We are now proud to be the co-lead investor in SureMark’s first institutional round.

At Two Small Fish, we love backing frontier tech that can reshape large-scale behaviour. SureMark checks every box.

Eva has written a deeper dive on what they are building and why it matters. You can read it here.

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Legends of Semiconductors: The Only Dinner Where the Edition Number Matters

At most dinners, introductions start with your name and maybe what you do.

At this one, we began with:
“Second edition.”
“Fourth edition.”

Why? Because this was our “School of Fish – Legends of Semiconductors” dinner, hosted at our home, where your relationship with the Sedra & Smith textbook was the common thread.
(I’m second edition, if you’re wondering.)

We were incredibly honoured to have Dr. Adel Sedra, former Dean of Engineering at the University of Waterloo, join us. Recently appointed to the Order of Canada, Dr. Sedra is a towering figure in the world of electrical engineering. Since 1982, his textbook has taught more than three-quarters of the world’s electrical engineers. It is hard to find someone in the field who has not studied from it. I consider myself extraordinarily fortunate, not just to have learned from his book, but to have been his student more than 30 years ago at the University of Toronto. Few have had the privilege of learning directly from a legend.

We were equally honoured to host Benny Lau, co-founder of ATI Technologies, whose legacy lives on in AMD’s GPUs to this day. AMD acquired ATI for $5.4 billion nearly 20 years ago, still one of the largest tech acquisitions in Canadian history. When Eva worked at ATI, she had the chance to work closely with Benny. His presence brought our conversation full circle, from classroom to commercialization. Adding even more depth to the evening, Benny was also once a student of Dr. Sedra. Two generations of engineers at the same table, both shaped by the same teacher.

From left to right: Benny Lau, Eva Lau, Ljubisa Bajic

This evening was also a chance to reconnect with those who shaped my own journey. Martin Snelgrove and Raymond Chik, my professor and TA respectively, were both there and are now serial entrepreneurs. They are also co-founders of Hepzibah, a Two Small Fish portfolio company. (I still can’t help but sometimes call him Professor Snelgrove.) Xerxes Wania, another one of my TAs from back in the day, went on to build and exit two semiconductor companies and added his voice to the conversation.

From left to right: Xerxes Wania, Dr. Adel Sedra, Allen Lau, Martin Snelgrove, Raymond Chik

We were also joined by Ljubisa Bajic, former CEO of TensTorrent and now CEO of Taalas, who also spent part of his career at ATI, further adding to the thread that connected many of us. Chris Yip, Dean of Engineering at the University of Toronto, and Deepa Kundur, current Chair of U of T’s Department of Electrical & Computer Engineering—continuing the legacy of leadership that Dr. Sedra once held in that position—also attended. Professor Tony Chan Carusone, now also CTO of Alphawave Semi and coauthor of the Sedra & Smith textbook starting with the 8th edition, brought both academic and commercial perspectives to the table.

From the TSF portfolio side, we were thrilled to have Professor Doug Barlage of the University of Alberta and Professor Chris Eliasmith of the University of Waterloo, co-founders of Zinite and ABR, respectively.

And of course, our partner Dr. Albert Chen joined us. He is a graduate of Waterloo Engineering and knows a thing or two about semiconductors himself.

Semiconductors brought us together that night.
Textbook and tapeout were what we talked about, and we all loved them.

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Masterclass Series: The Triathlon Rule of Deep Tech Startups

A swimming world champion, a cycling champion, and a marathon champion each tried their hand at a triathlon.

None of them even came close to the podium. All were easily defeated.

Why?

Because the swimming champion could not bike, nor could he run fast.

The cycling champion did not swim well.

The marathon runner was painfully slow in the water.

The winner?

It was someone who had been humbled by the swimming champion in the pool for years, finishing second in the world championships multiple times. He was an exceptional swimmer, yes. However, he could also bike fast and run hard. Not the best in any single discipline, but strong across all three. And that is what won him the race.

The takeaway:

To win in triathlon, you need to be competitive in all three disciplines.

The winner is often world class in one of them, but they must be very good if not great at the other two.

This is the same mistake many first time deep tech founders make.

They believe that superior technology alone is enough to win.

It is not.

While technology is crucial, and in fact it is table stakes and the foundation of innovation, it must be transformed into a usable product. If it does not solve a real problem in a way people can adopt and benefit from, its brilliance is wasted.

And even if you have built world class technology and a beautifully crafted product, you are still not done. Without effective commercialization, which includes distribution, pricing, sales, positioning, and partnerships, you will not reach the users or customers who need what you have built.

I wrote more about this in The Three Phases of Building a Great Tech Company: Technology, Product, and Commercialization. Each phase demands different skills. Each must be taken seriously.

Neglecting any one of them is like trying to win a triathlon without training for the bike or the run.

Just like a triathlete must train in all three disciplines, a founder must excel across all three pillars:

  • Great and defensible technology
  • An excellent product
  • Execution on commercialization

You need all three.

That is how you win the world championship.

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Computing. Then Connectivity. Then Intelligence. For Half a Century, Cost Collapses Drove Massive Adoption.

In the history of human civilization, there have been several distinct ages: the Agricultural Age, the Industrial Age, and the Information Age, which we are living in now.

Within each age, there are different eras, each marked by a drastic drop in the cost of a fundamental “atomic unit.” These cost collapses triggered enormous increases in demand and reshaped society by changing human behaviour at scale.

From the late 1970s to the 1990s, the invention of the personal computer drastically reduced the cost of computing [1]. A typical CPU in the early 1980s cost hundreds of dollars and ran at just a few MHz. By the 1990s, processors were orders of magnitude faster for roughly the same price, unlocking entirely new possibilities like spreadsheets and graphical user interfaces (GUIs).

Then, from the mid-1990s to the 2010s, came the next wave: the Internet. It brought a dramatic drop in the cost of connectivity [2]. Bandwidth, once prohibitively expensive, fell by several orders of magnitude — from over $1,200 per Mbps per month in the ’90s to less than a penny today. This enabled browsers, smartphones, social networks, e-commerce, and much of the modern digital economy.

From the mid-2010s to today, we’ve entered the era of AI. This wave has rapidly reduced the cost of intelligence [3]. Just two years ago, generating a million tokens using large language models cost over $100. Today, it’s under $1. This massive drop has enabled applications like facial recognition in photo apps, (mostly) self-driving cars, and — most notably — ChatGPT.

These three eras share more than just timing. They follow a strikingly similar pattern:

First, each era is defined by a core capability, i.e. computing, connectivity, and intelligence respectively.

Second, each unfolds in two waves:

  • The initial wave brings a seemingly obvious application (though often only apparent in hindsight), such as spreadsheets, browsers, or facial recognition.
  • Then, typically a decade or so later, a magical invention emerges — one that radically expands access and shifts behaviour at scale. Think GUI (so we no longer needed to use a command line), the iPhone (leapfrogging flip phones), and now, ChatGPT.

Why does this pattern matter?

Because the second-wave inventions are the ones that lower the barrier to entry, democratize access, and reshape large-scale behaviour. The first wave opens the door; the second wave throws it wide open. It’s the amplifier that delivers exponential adoption.

We’ve seen this movie before. Twice already, over the past 50 years.

The cost of computing dropped, and it transformed business, productivity, and software.

Then the cost of connectivity dropped, and it revolutionized how people communicate, consume, and buy.

Now the cost of intelligence is collapsing, and the effects are unfolding even faster.

Each wave builds on the last. The Internet era was evolving faster than the PC era because the former leveraged the latter’s computing infrastructure. AI is moving even faster because it sits atop both computing and the Internet. Acceleration is not happening in isolation. It’s compounding.

If it feels like the pace of change is increasing, it’s because it is.

Just look at the numbers:

  • Windows took over 2 years to reach 1 million users.
  • Facebook got there in 10 months.
  • ChatGPT did it in 5 days.

These aren’t just vanity metrics — they reflect the power of each era’s cost collapse to accelerate mainstream adoption.

That’s why it’s no surprise — in fact, it’s crystal clear — that the current AI platform shift is more massive than any previous technological shift. It will create massive new economic value, shift wealth away from many incumbents, and open up extraordinary investment opportunities.

That’s why the succinct version of our thesis is:

We invest in the next frontier of computing and its applications, reshaping large-scale behaviour, driven by the collapsing cost of intelligence and defensible through tech and data moats.

(Full version here).

The race is already on. We can’t wait to invest in the next great thing in this new era of intelligence.

Super exciting times ahead indeed.

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Footnotes

[1] Cost of Computing

In 1981, the Intel 8088 CPU (used in the first IBM PC) had a clock speed of 4.77 MHz and cost ~$125. By 1995, the Intel Pentium processor ran at 100+ MHz and cost around $250 — a ~20x speed gain at similar cost. Today’s chips are thousands of times faster, and on a per-operation basis, exponentially cheaper.

[2] Cost of Connectivity

In 1998, bandwidth cost over $1,200 per Mbps/month. By 2015, that figure dropped below $1. As of 2024, cloud bandwidth pricing can be less than $0.01 per GB — a near 100,000x drop over 25 years.

[3] Cost of Intelligence

In 2022, generating 1 million tokens via OpenAI’s GPT-3.5 could cost $100+. In 2024, it costs under $1 using GPT-4o or Claude 3.5, with faster performance and higher accuracy — a 100x+ reduction in under two years.

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.

Quantum Isn’t Next. It’s Now.

In the early 2000s, it was a common joke in the tech world that “next year is the year of the smartphones.” People kept saying it over and over for almost a decade. It became a punchline. The industry nearly lost its credibility.

Until the iPhone launched. “Next year is the year of the smartphones” finally became true.

The same joke has followed quantum for the past ten years: next year is the year of quantum.

Except it hasn’t been. Not yet.

And yet, quietly, the foundations have been built. We’re not there, but we’re far from where we started.

We’re getting closer. Much closer. I can smell it. I can hear it. I can sense it.

Right now, without getting into too much technical detail, we’re still at a small scale: fewer than 100 usable qubits. Commercial viability likely requires thousands, if not millions. The systems are still too error-prone, and hosting your own quantum machine is wildly impractical. They’re expensive, fragile, and noisy.

At this stage, quantum is mostly limited to niche or small-scale applications. But step by step, quantum is inching closer to broader utility.

And while these things don’t progress in straight lines, the momentum is real and accelerating.

Large-scale, commercially deployable, fault-tolerant quantum computers accessed through the cloud are no longer science fiction. They’re within reach.

I spent a few of my academic years in signal processing and error correction. I’ve also spent a bit of time studying quantum mechanics. I understand the challenges of cloud-based access to quantum systems, and I’ve been following the field for quite a while, mostly as a curious science nerd.

All of that gives me reason to trust my sixth sense. Quantum is increasingly becoming a reality.

Nobody knows exactly when the iPhone moment or the ChatGPT moment of quantum will happen.
But I’m absolutely sure we won’t still be saying “next year is the year of quantum” a decade from now.

It will happen, and it will happen much sooner than you might think.

At Two Small Fish, our thesis is centred around the next frontier of computing and its applications.

This is an exciting time and the ideal time to take a closer look at quantum, because the best opportunities tend to emerge right before the technology takes off.

How can we not get excited about new quantum investment opportunities?

P.S. I’m excited to attend the QUANTUM NOW conference this week in Montreal. Also thrilled to see Mark Carney name quantum as one of Canada’s official G7 priorities. That short statement may end up being a big milestone.

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Announcing TSF’s Investment in ENVGO

Humans have conquered land, sea, and space.

Yet the ocean remains surprisingly underdeveloped — in fact, it’s the least developed.

Land transportation has been electrified. In space, payload costs have dropped drastically. Now, it’s time for marine to catch up.

Unlike cars, you can’t simply add an electric motor and battery to a boat and make it work. Why? One reason is that water’s viscosity is much higher than air, meaning drag or resistance is an order of magnitude greater. As a result, replacing a gas motor with an electric one would require a gigantic battery, making it impractical and, frankly, unusable. That’s why marine electrification has lagged.

Until now. 

The “iPhone moment” of marine transportation has arrived. ENVGO’s hydrofoiling NV1 tackles these multidisciplinary complications head-on. Led by successful serial entrepreneur Mike Peasgood, the team brings together expertise in AI, robotics, control systems, computer vision, autonomous systems, and more. Leveraging their prior success as drone pioneers at Aeryon, they are now building a flying robot — on water.

It’s day one of a large-scale transformation of marine transportation. Two Small Fish is privileged and super excited to lead this round of funding, alongside our good friends at Garage, who are also participating. We can’t wait to see how ENVGO reimagines the uncharted waters — pun fully intended.

Read our official blog post by our partner Albert here. 

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Wattpad Was My Regular Season. TSF Is My Playoff Hockey

When entrepreneurs exit their companies, it is supposed to be a victory lap. But in reality, many find themselves in an unexpected emotional vacuum. More often than you might think, I hear variations of the same quiet confession:

“It should have been the best time of my life. But I felt lost after the exit. I lost my purpose.”

After running Wattpad for 15 years, I understand this all too well. It is like training for and running a marathon for over a decade, only to stop cold the day after the finish line. No more rhythm. No more momentum. No next mile.

Do I Miss Operating

Unsurprisingly, people often ask me:

“Do you like being a VC?”

“Do you miss operating?”

My honest answer is yes and yes

(but I get my fix without being a CEO — see below).

Being a founder and CEO was deeply challenging and also immensely rewarding. It is a role that demands a decade-long commitment to building one and only one thing. And while I loved my time as CEO, I did not feel the need to do it again. Once in a lifetime was enough. I have started three companies. A fourth would have felt repetitive.

What I missed most was not the title or the responsibility. It was the people. The team. The day-to-day collaboration with nearly 300 passionate employees when I stepped down. That sense of shared mission — of solving hard problems together — was what truly filled my cup.

Back in the Trenches in a Different Role

Now at Two Small Fish Ventures as an operating partner, I work with founders across our portfolio. I am no longer the operator inside the company, but I get to be their sounding board — helping them tackle some of the biggest challenges they face.

Let’s be honest: they call me especially when they believe I am the only one who can help them. Their words, not mine. And there have been plenty of those occasions.

That gives me the same hit of adrenaline I used to get from operating. At my core, I love solving hard problems. That part of me did not go away after my exit. I just found a new arena for it — and it is a perfect replacement.

A Playground for a Science Nerd

What people may not realize is that the deep tech VC job is drastically different from a “normal” VC job. As a deep tech VC, I am constantly stretched and go deep — technically, intellectually, and creatively. It forces me to stay sharp, push my boundaries, and reconnect with my roots as a curious, wide-eyed science nerd.

There is something magical about working with founders at the bleeding edge of innovation. I get to dive into breakthrough technologies, understand how they work, and figure out how to turn them into usable and scalable products. It feels like being a kid in a candy store — except the candy is semiconductors, control systems, power electronics, quantum, and other domains in the next frontier of computing.

How could I not love that?

Ironically, I had less time to indulge this curiosity when I was a CEO. Now I can geek out and help shape the future at the same time. It is a net positive to me.

You Do Not Have to Love It All

Of course, every job — including CEO and VC — has its less glamorous parts. Whether you are a founder or a VC, there will always be administrative tasks and responsibilities you would rather skip.

But I have learned not to resent them. As I often say:

“You do not need to love every task. You just need to be curious enough to find the interesting angles in anything.”

Those tasks are the cost of admission to being a deep tech VC. A small price to pay to do the work I love — supporting incredible entrepreneurs as they bring transformative ideas to life, and finding joy in doing so. And knowing what I know now, I do not think I would enjoy being a “normal” VC. I cannot speak for others, but for me, this is the only kind of venture work that truly energizes and fulfills me.

A New Season. A New Purpose.

So yes, being a VC brings me as much joy — and arguably even more fulfillment (and I am surprised that I am saying this) — than being a CEO. I feel incredibly lucky. And I am all in.

It feels like all my past experience has prepared me for what I do today. I often describe this phase of my life this way:

Wattpad was my regular season. TSF is my playoff hockey.

It is faster. It is grittier. The stakes feel higher. Not because I am building one company, but because I am helping many shape the future.

P.S. Go Oilers!!

A Decade of Fish – Celebrating 10 Years of Two Small Fish Ventures

This year marks a big milestone: Two Small Fish Ventures turns ten!

That’s 10 years, 120 months, and 3,653 days (yes, we counted the leap years). What started as a bold experiment in early-stage investing has become a decade-long journey of backing audacious founders building at the edge of what’s possible.

Over the weekend, we wired funds for our 60th first investment. That’s not including the many follow-on cheques we’ve written along the way—if we counted those, the number would be much higher. We’re not naming the company just yet, but like the 59 before it, this one reflects deep conviction. We think it’ll make a splash!

For years, we’ve said we write 5 to 7 new cheques per year. Not because we aim for a quota, but because this is what a power-law portfolio construction strategy naturally produces. In venture, just a few outlier companies drive the vast majority of returns. The trick is to consistently back companies with 100x potential. That’s the focus—not pacing. And yet, the numbers tell their own story: we’ve averaged exactly six new investments a year. Apparently, clarity of focus brings consistency as a byproduct.

We’re now six months into our tenth year, and we’re right on pace.

To the founders we’ve backed: thank you for trusting us at the earliest, riskiest stage.

To those we haven’t met yet: if you’re building deep tech in the next frontier of computing, we’d love to hear from you. We invest globally. If you’ve got a breakthrough, we can help turn it into a product. If you’ve got a product, we can help turn it into a company.

Sound like you? Reach out.

Here’s to the next 10!

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A Thought for Asian Heritage Month

I recently spent a few weeks in Asia, visiting Tokyo, Hong Kong, Singapore, and Taiwan before returning to Toronto. Eva joined me for the first part of the journey, while I spent time in Singapore on my own, a city I’ve visited numerous times before. In Taiwan, I was accompanied by Albert, who was born there and still has family ties in the region.

This was by far my longest trip in quite some time. These destinations represent some of the world’s most developed economies, with GDP per capita levels comparable to or exceeding those in North America. Singapore, for instance, has a per capita GDP of around 90,000 USD, roughly 50 percent higher than the United States’ 66,000 USD.

Conversations and Perspectives

Coincidentally, my visit aligned with Liberation Day. Needless to say, it sparked many fascinating conversations, including the so-called “penguin tariffs,” whether AI is already smarter than certain politicians, and everything in between. Around that time, I also came across a perspective that stood out. While tariffs were initially expected to threaten Asian economies, many locals believed they had ended up affecting the United States more. Businesses in the Asia Pacific region had begun diversifying away from reliance on the US market years ago. As a result, they now have more leverage, and the direct impact of tariffs has been relatively limited. The broader concern was the possibility of a global recession.

Tech Energy in the Region

Across all four regions, I witnessed growing momentum in tech entrepreneurship. I had the chance to speak at tech conferences, lead masterclasses, take part in fireside chats, and encourage high school students to consider entrepreneurship as a path worth exploring.

Why It’s Happening

Why is this happening? These regions have strong technical capabilities. Taiwan, for example, manufactures about 90 percent of the world’s most advanced chips, and its capabilities are unmatched by any other country. Singapore, on the other hand, excels in semiconductor fabrication and biotechnology, and its presence in AI and computing infrastructure continues to grow.

Understanding the Cultural Landscape

At the same time, the cultural differences between East and West remain clear. The East tends to emphasize social harmony, collective behaviour, and conformity. The West often puts more weight on individual expression and free spirit.

Even small things reflect these differences. Take jaywalking. In Tokyo and Singapore, it is rare. People stare at you if you do it. In contrast, after jaywalking was recently legalized in New York City, I actually felt social pressure to jaywalk. Not doing so made me feel out of place.

Why Culture Matters in Business

For companies working across borders, recognizing these kinds of cultural nuances is not optional. It is essential. A one-size-fits-all approach often leads to missteps.

The Bicultural Perspective

Having been raised in Asia and now living in Canada for decades, I’ve come to appreciate the value of navigating both worlds. That dual perspective has become a quiet but important asset in both my personal and professional life. It is not a liability.

As I often say:

“Bamboo is neutral. If it’s used as a ceiling, it becomes a barrier. But if it’s used as a pole for jumping, one can leap incredibly high. Biculturalism is a powerful asset. If you leverage it in the right context, it can become your unfair advantage.”

Biculturalism, when used with intention, becomes a meaningful advantage. It helps you understand nuance, communicate across different environments, and approach global opportunities with more adaptability.

Looking Ahead

In an increasingly interconnected world, going global is no longer just a choice. During Asian Heritage Month, this feels especially relevant. Let’s celebrate not only our roots but also the advantages that come from navigating multiple worlds.

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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.

TSF Thesis 3.0: The Next Frontier of Computing and Its Applications Reshaping Large-Scale Behaviour

Summary

Driven by rapid advances in AI, the collapse in the cost of intelligence has arrived—bringing massive disruption and generational opportunities.

Building on this platform shift, TSF invests in the next frontier of computing and its applications, backing early-stage products, platforms, and protocols that reshape large-scale behaviour and unlock uncapped, new value through democratization. These opportunities are fueled by the collapsing cost of intelligence and, as a result, the growing demand for access to intelligence as well as its expansion beyond traditional computing devices. What makes them defensible are technology moats and, where fitting, strong data network effects.

Or more succinctly: We invest in the next frontier of computing and its applications, reshaping large-scale behaviour, driven by the collapsing cost of intelligence and defensible through tech and data moats.

Watch this 2-minute video to learn more about our approach:

Our Evolution: From Network Effects to Deep Tech

When we launched TSF in 2015, our initial thesis centred around network effects. Drawing from our experience scaling Wattpad from inception to 100 million users, we became experts in understanding and leveraging exponential value and defensibility created by network effects at scale. This expertise led us to invest—most as the very first cheque—in massively successful companies such as BenchSci, Ada, Printify, and SkipTheDishes.

We achieved world-class success with this thesis, but like all good things, that opportunity diminished over time.

Our thesis evolved as the ground shifted toward the end of 2010s. A couple of years ago, we articulated this evolution by focusing on early-stage products, platforms, and protocols that transform user behaviour and empower businesses and individuals to unlock new value. Within this broad focus, we zoomed in specifically on three sectors: AI, decentralized protocols, and semiconductors. That thesis guided investments in great companies such as Story, Ideogram, Zinite, and Blumind.

But the world doesn’t stand still. In fact, it has never changed so rapidly. This brings us to the next and even more significant shift shaping our thesis.

A New Platform Shift: The Cost of Intelligence is Collapsing

Reflecting on the internet era, the core lesson we learned was that the internet was the first technology in human history that was borderless, connected, ubiquitous, real-time, and free. At its foundation was connectivity, and as “the cost of connectivity” steadily declined, productivity and demand surged, creating a virtuous cycle of opportunities.

The AI era shows remarkable parallels. AI is the first technology capable of learning, reasoning, creativity, cross-domain functionality, and decision-making. Like connectivity in the internet era, “the cost of intelligence” is now rapidly declining, while the value derived from intelligence continues to surge, driving even greater demand.

This shift will create massive economic value, shifting wealth away from many incumbents and opening substantial investment opportunities. However, just like previous platform shifts, the greatest opportunities won’t come from digitizing or automating legacy workflows, but rather from completely reshaping workflows and user behaviour, democratizing access, and unlocking previously impossible value. These disruptive opportunities will expand into adjacent areas, leaving incumbents defenceless as the rules of the game fundamentally change.

Intelligence Beyond Traditional Computing Devices

AI’s influence now extends far beyond pre-programmed software on computing devices. Machines and hardware are becoming intelligent, leveraging collective learning to adapt in real-time, with minimal predefined instruction. As we’ve stated before, software alone once ate the world; now, software and hardware together consume the universe. The intersection of software and hardware is where many of the greatest opportunities lie.

As AI models shrink and hardware improves, complex tasks run locally and effectively at the edge. Your phone and other edge devices are rapidly becoming the new data centres, opening exciting new possibilities.

Democratization and a New Lens on Defensibility

The collapse in the cost of intelligence has democratized everything—including software development—further accelerated by open-source tools. While this democratization unlocks vast opportunities, competition also intensifies. It may be a land grab, but not all opportunities are created equal. The key is knowing which “land” to seize.

Historically, infrastructure initially attracts significant capital, as seen in the early internet boom. Over time, however, much of the economic value tends to shift from infrastructure to applications. Today, the AI infrastructure layer is becoming increasingly commoditized, while the application layer is heavily democratized. That said, there are still plenty of opportunities to be found in both layers—many of them truly transformative. So, where do we find defensible, high-value opportunities?

Our previous thesis identified transformative technologies that achieved mass adoption, changed behaviour, democratized access, and unlocked unprecedented value. This framework remains true and continues to guide our evaluation of “100x” opportunities.

This shift in defensibility brings us to where the next moat lies.

New Defensibility: Deep Tech Meets Data Network Effects

Defensibility has changed significantly. In recent years, the pool of highly defensible early-stage shallow tech opportunities has thinned considerably, with far fewer compelling opportunities available. As a result, we have clearly entered a golden age of deep tech. AI democratization provides capital-efficient access to tools that previously required massive budgets. Our sweet spot is identifying opportunities that remain difficult to build, ensuring they are not easily replicated.

As “full-spectrum specialists,” TSF is uniquely positioned for this new reality. All four TSF partners are engineers and former startup leaders before becoming investors, with hands-on experience spanning artificial intelligence, semiconductors, robotics, photonics, smart energy, blockchain and others. We are not just technical; we are also product people, having built and commercialized cutting-edge innovations ourselves. As a guiding principle, we only invest when our deep domain expertise can help startups scale effectively and rapidly cement their place as future industry-disrupting giants.

Moreover, while traditional network effects have diminished, AI has reinvigorated network effects, making them more potent in new ways. Combining deep tech defensibility with strong data-driven network effects is the new holy grail, and this is precisely our expertise.

What We Don’t Invest In

Although we primarily invest in “bits,” we will also invest in “bits and atoms,” but we won’t invest in “atoms only.” We also have a strong bias towards permissionless innovations, so we usually stay away from highly regulated or bureaucratic verticals with high inertia. Additionally, since one of our guiding principles is to invest only when we have domain expertise in the next frontier of computing, we won’t invest in companies whose core IP falls outside of our computing expertise. We also avoid regional companies, as we focus on backing founders who design for global scale from day one. We invest globally, and almost all our breakout successes such as Printify have users and customers around the world.

Where We’re Heading

Having recalibrated our thesis for this new era, here’s where we’re going next.

We have backed amazing deep tech founders pioneering AI, semiconductors, robotics, photonics, smart energy, and blockchain—companies like Fibra, Blumind, ABR, Axiomatic, Hepzibah, Story, Poppy, and Viggle—across consumer, enterprise, and industrial sectors. With the AI platform shift underway, many new and exciting investment opportunities have emerged. 

The ground has shifted: the old playbook is out, the new playbook is in. It’s challenging, exciting, and we wouldn’t have it any other way.

To recap our core belief, TSF invests in the next frontier of computing and its applications, backing early-stage products, platforms, and protocols that reshape large-scale behaviour and unlock uncapped, new value through democratization. These opportunities are fueled by the collapsing cost of intelligence and, as a result, the growing demand for access to intelligence as well as its expansion beyond traditional computing devices. What makes them defensible are technology moats and, where fitting, strong data network effects.

Or more succinctly: We invest in the next frontier of computing and its applications, reshaping large-scale behaviour, driven by the collapsing cost of intelligence and defensible through tech and data moats.

So, if you’ve built interesting deep tech in the next frontier of computing, we invest globally and can help you turn it into a product. If you have a product, we can help you turn it into a massively successful business. If this sounds like you, reach out. 

Together, we will shape the future.

P.S. Please also read our blog post Five Areas Shaping the Next Frontier.

Eva + Allen + Brandon + Albert + Mikayla

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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.

Gensee AI

A solo musician doesn’t need a conductor. Neither does a jazz trio.

But an orchestra? That’s a different story. You need a conductor to coordinate, to make sure all the parts come together.

Same with AI agents. One or two can operate fine on their own. But in a multi-agent setup, the real bottleneck is orchestration.

Yesterday, we announced our investment in GenseeAI. That’s the layer the company is building—the conductor for AI agents, i.e. the missing intelligent optimization layer for AI agents and workflows. Their first product, Cognify, takes AI workflows built with frameworks like LangChain or DSPy and intelligently rewrites them to be 10× faster, cheaper, and more reliable. It’s a bit like “compilation” for AI. Given a high-level workflow, Cognify produces a tuned, executable version optimized for production. Their second product, currently under development, goes one step further: a serving layer that continuously optimizes AI agents and workflows at runtime. Think of it as an intelligent “virtual machine” for AI, where the execution of agents and workflows is transparently and “automagically” improved while running.

If you’re building AI systems and want to go from prototype to production with confidence, get in touch with the GenseeAI team.

Read Brandon‘s blog post here or in the following for all the details:

At Two Small Fish, we invest in founders building foundational infrastructure for the AI-native world. We believe one of the most important – yet underdeveloped – layers of this stack is orchestration: how generative AI workflows are built, optimized, and deployed at scale.

Today, building a production-grade genAI app involves far more than calling an LLM. Developers must coordinate multiple steps – prompt chains, tool integrations, memory, RAG, agents – across a fragmented and fast-moving ecosystem and a variety of models. Optimizing this complexity for quality, speed, and cost is often a manual, lengthy process that businesses must navigate before a demo can become a product.

GenseeAI is building the missing optimization layer for AI agents and workflows in an intelligent way. Their first product, Cognify, takes AI workflows built with frameworks like LangChain or DSPy and intelligently rewrites them to be faster, cheaper, and better. It’s a bit like “compilation” for AI: given a high-level workflow, Cognify produces a tuned, executable version optimized for production. 

Their second product–currently under development–goes one step further: a serving layer that continuously optimizes AI agents and workflows at runtime. Think of it as an intelligent “virtual machine” for AI: where the execution of agents and workflows is transparently and automatically improved while running.

We believe GenseeAI is a critical unlock for AI’s next phase. Much of today’s genAI development is stuck in prototype purgatory – great demos that fall apart in the real world due to cost overruns, latency, and poor reliability. Gensee helps teams move from “it works” to “it works well, and at scale.”

What drew us to Gensee was not just the elegance of the idea, but the clarity and depth of its execution. The company is led by Yiying Zhang, a UC San Diego professor with a strong track record in systems infrastructure research, and Shengqi Zhu, an engineering leader who has built and scaled AI systems at Google. Together, they bring a rare blend of academic rigor and hands-on experience in deploying large-scale infrastructure. In early benchmarks, Cognify delivered up to 10× cost reductions and 2× quality improvements – all automatically. Their roadmap – including fully automated optimization, enterprise integrations, and a registry of reusable “optimization tricks” – shows ambition to become the default runtime for generative AI.

As the AI stack matures, we believe Gensee will become a foundational layer for organizations deploying intelligent systems. It’s the kind of infrastructure that quietly powers the AI apps we’ll all use – and we’re proud to support them on that journey.
If you’re building AI systems and want to go from prototype to production with confidence, get in touch with the team at GenseeAI.

Written by Brandon

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.

Perhaps My Title Should Be…Yoda?

Yesterday was Star Wars Day — aka “May the Fourth be with you” — and it got me thinking, so I put together this blog post.

You might notice my title is “Operating Partner,” not “General Partner,” “Managing Partner,” or “Board Partner.” That’s intentional because I spend most of my time working directly with portfolio CEOs.

The Operating Partner role has its roots in private equity. Historically, Operating Partners are often former CEOs or COOs who use their experience to guide leadership teams, improve operational execution, and drive results, ultimately increasing the value of portfolio companies.

As far as I know, I’m the only former scale-up CEO in Canada who plays this role in an early-stage VC. At least, ChatGPT and Perplexity couldn’t find anyone else! Even in the U.S., this is very rare.

That said, I’ve always felt the “Operating Partner” title is a bit misleading. Unlike many private equity Operating Partners, I don’t step into full-time or part-time leadership roles within portfolio companies. I don’t give advice or directives either. Instead, I help CEOs solve their own problems rather than solving problems for them.

My single objective is to help portfolio CEOs improve the quality of their decisions by leveraging my experience.

Why? Most CEOs don’t need to be told what to do—they already know. Telling a CEO to grow their KPIs faster or hire great people is useless.

No CEO intentionally grows slower or hires bad people!

The real challenge for CEOs isn’t the what—it’s the how. This is where I come in, helping them navigate the how: strategic thinking, future-proofing, and decision-making that drive tangible progress, while staying alert to blind spots that could undermine success.

Hiring is an example. Many venture firms have talent partners who assist portfolio companies with recruitment. These partners, often from recruitment backgrounds, are excellent at sourcing candidates once roles are defined. However, they usually lack deep business context and may not fully understand the culture of the companies they’re supporting. This can result in untargeted candidates who don’t fit. I experienced this issue firsthand when I was a CEO.

That’s why I strongly favour internal recruiters who have an intimate understanding of the business and culture. Even so, recruiters typically get involved after roles are clearly defined. Before that, to design the organization, we need someone who has visibility into the broader perspective of the business. Only one person truly has it: the CEO. Besides, CEOS usually can’t ask their leaders about organizational design for obvious reasons.

That’s where I step in—well before recruiters are involved. I act as a sounding board for organizational design, considering not just immediate hiring needs but also how roles and teams will evolve over time. What level of talent should they hire now? When will this position need to level up? What downstream implications will these decisions have?

By addressing these questions early, I help ensure hiring decisions are aligned with the company’s long-term strategy and culture.

Of course, hiring is just one area where I provide support. Design future-proof stock option plans? Manage internal and external communication challenges? Interact with strategic conglomerates? Navigate inbound acquisition offers? Resolve leadership dysfunction? Handle unreasonable investors? Make board meetings more effective? Fend off super aggressive competitors or internet giants?

And yes, one of the most frequent requests I get is: “Can you help me with my pitch deck?”

Bring them on!

I’ve faced these challenges firsthand multiple times, and when CEOs bring them to me, I’m ready to share my war scars.

At the minimum, I help narrow the options from “I don’t know how” to a set of multiple choices. I don’t make decisions for CEOs; I help them make better ones. They are ultimately responsible for their decisions, and I see my role as a guide, not a decision-maker.

Being the CEO of a fast-scaling company is an enormous challenge that people should not underestimate—the level of experience, capacity, intensity, and mental strength that one needs to cope with. That’s why it is the loneliest job. Empathy is not enough. The best help I ever got was from a more experienced CEO than me at the time — someone who had walked the road ahead — and now it’s my turn to pay it forward. It is payback time for me.

The more I think about it, the less “Operating Partner” seems to fit. I don’t step into the spotlight or take over operations. My role is more like Yoda—helping Skywalker fight the battles while staying behind the scenes.

So perhaps my title shouldn’t be Operating Partner after all. Maybe it should just be… Yoda.

May the Force be with you!

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AI’s Real Revolution Is Just Beginning

Thank you to The Globe for publishing my op-ed about AI last week. In it, I draw parallels between the dot-com crash and the current AI boom—keeping in mind the old saying, “History doesn’t repeat itself, but it often rhymes.” The piece also explores how the atomic unit of this transformation is the ever-declining “cost of intelligence.” AI is the first technology in human history capable of learning, reasoning, creativity, cross-domain thinking, and decision-making. This fundamental shift will impact every sector, without exception, spurring the rise of new tech giants and inevitable casualties in the process. The key is knowing which land to grab!

The piece is now available below.

In the past month, everyone I spoke to has been talking about DeepSeek and Nvidia. Is Nvidia facing extinction? Have certain tech giants overspent on AI? Are we seeing a bubble about to burst, or just another public market overreaction? And what about traditional sectors, like industrials, that haven’t yet felt AI’s impact?

Let’s step back. We’ll revisit companies that soared or collapsed during the dot-com crash – and the lessons we can learn. As Mark Twain reputedly said, “History doesn’t repeat itself, but it often rhymes.”

The answer is that the reports of Nvidia’s demise are greatly exaggerated, though other companies face greater danger. At the same time, new opportunities are vast because this AI-driven shift could dwarf past tech disruptions.

Before 2000, the dot-com mania hit full speed. High-flying infrastructure players such as Global Crossing – once worth US$47-billion – provided backbone networks. Cisco delivered networking equipment, and Sun Microsystems built servers. However, amid the crash, Global Crossing went bankrupt in January, 2002. Cisco plummeted from more than US$500-billion in market cap to about $100-billion. Sun Microsystems sank from a US$200-billion market cap to under US$10-billion.

They failed or shrank for different reasons. Global Crossing needed huge investments before real revenue arrived. Cisco had decent unit economics but lost pricing power when open networking standards commoditized its gear. Sun Microsystems suffered when cheaper hardware and free, open-source software (such as Linux and Apache) undercut it, and commodity hardware plus cloud computing made its servers irrelevant.

However, these companies did not decline because they were infrastructure providers. They declined because they failed to identify the right business model before their capital ran out or were disrupted by alternatives, including open or free systems, despite having the first-mover advantage.

Meanwhile, other infrastructure players thrived. Amazon, seen mostly as an e-commerce site, earned 70 per cent of its operating profit from Amazon Web Services – hosting startups and big players such as Netflix. AWS eliminated the need to buy hardware and continually cut prices, especially in its earlier years, catalyzing a new wave of businesses and ultimately driving demand while increasing AWS’s revenue.

In hindsight, the dot-com boom was real – it simply took time for usage to catch up to the hype. By the late 2000s, mobile, social and cloud surged. Internet-native giants (Netflix, Google, etc.) grew quickly with products that truly fit the medium. Early front-runners such as Yahoo! and eBay faded. Keep in mind that Facebook was founded in 2004, well after the crash, and Apple shifted from iPods to the revolutionary iPhone in 2007, which further catalyzed the internet explosion. A first-mover advantage might not always pay off.

The first lesson we learned is that open systems disrupt and commoditize infrastructure. At that time, and we are seeing it again, an army of contributors drove open systems for free, allowing them to out-innovate proprietary solutions.

Companies that compete directly against open systems – note that Nvidia does not – are particularly vulnerable at the infrastructure layer when many open and free alternatives (such as those solely building LLMs without any applications) exist. DeepSeek, for example, was inevitable – this is how technology evolves.

Open standards, open source and other open systems dramatically lower costs, reduce barriers to AI adoption and undermine incumbents’ pricing power by offering free, high-quality alternatives. This “creative destruction” drives technological progress.

In other words, OpenAI is in a vulnerable position, as it resembles the software side of Sun Microsystems – competing with free alternatives such as Linux. It also requires significant capital to build out, yet its infrastructure is rapidly becoming commoditized, much like Global Crossing’s situation. On the other hand, Nvidia has a strong portfolio of proprietary technologies with few commoditized alternatives, making its position relatively secure. Nvidia is not the new Sun Microsystems or Cisco.

Most importantly, the disruption and commoditization of infrastructure also democratize AI innovation. Until recently, starting an AI company often required raising millions – if not tens of millions – just to get off the ground. That is already changing, as numerous fast-growing companies have started and scaled with minimal initial capital. This is leading to an explosion of innovative startups and further accelerating the flywheel.

The next lesson we learned is that the internet was the first technology in human history that was borderless, connected, ubiquitous, real-time, and free. Its atomic unit is connectivity. During its rise, “the cost of connectivity” steadily declined, while productivity gains from increased connectivity continued to expand demand. The flywheel turned faster and faster, forming a virtuous cycle.

Similarly, AI is the first technology in human history capable of learning, reasoning, creativity, cross-domain functions and decision-making. Crucially, AI’s influence is no longer confined to preprogrammed software running on computing devices; it now extends into all types of machines. Hardware and software, combined with collective learning, enable autonomous cars and other systems like robots to adapt intelligently in real time with little or no predefined instructions.

These breakthroughs are reaching sectors scarcely touched by the internet revolution, including manufacturing and energy. This goes beyond simple digitization; we are entering an era of autonomous operations and, ultimately, autonomous businesses, allowing humans to focus on higher-value tasks.

As with connectivity costs in the internet era, in this AI era, “the cost of intelligence” has been steadily declining. Meanwhile, the value derived from increased intelligence continues to grow, driving further demand – this mirrors how the internet played out and is already happening again for AI. The parallels between these two platform shifts suggest that massive economic value will be created or shifted from incumbents, opening substantial investment opportunities across early-stage ventures, growth-stage private markets and public investments.

Just as the early internet boom heavily focused on infrastructure, a significant amount of capital has been invested in enabling AI technologies. However, over time, economic value shifts from infrastructure to applications – just as it did with the internet.

This doesn’t mean there are no opportunities in AI infrastructure – far from it. Remember, more than half of Amazon’s profits come from AWS. Services, such as AWS, that provide access to AI, will continue to benefit as demand soars. Similarly, Nvidia will continue to benefit from the rising demand. However, many of today’s most-valuable companies – both public and private – are in the application layer or operate full-stack models.

Despite these advancements, this transformation won’t happen overnight, but it will likely unfold more quickly than the internet disruption – which took more than a decade – because many core technologies for rapid innovation are already in place.

AI revenues might appear modest today and don’t yet show up in the public markets. However, if we look closer, some AI-native startups are already growing at an unprecedented pace. The disruption isn’t a prediction; it’s already happening.

As Bill Gates once said, “Most people overestimate what they can achieve in one year and underestimate what they can achieve in ten years.”

The AI revolution is just beginning. The next decade will bring enormous opportunities – and a new wave of tech giants, alongside inevitable casualties.

It’s a land grab – you just need to know which land to seize!

P.S. If you enjoyed this blog post, please take a minute to like, comment, subscribe and share. Thank you for reading!

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.

Network Effect is Dead. Long Live Network Effect.

When Two Small Fish first started in 2015, we formulated our “Thesis 1.0” to focus on network effects exclusively. We leveraged our hands-on product experience in scaling Wattpad from 0 to 100 million users—essentially a marketplace for readers and writers—and applied a similar lens to other verticals, both in B2C and B2B.

It worked incredibly well for TSF because, at the time, network effects were the holy grail for defensibility, yet they were often misunderstood (for example, going viral is not the same as having network effects, and simply operating a marketplace does not guarantee strong network effects!). Our skill is more transferable than you might think!

So, Eva created the ASSET framework, which helped us identify the best network-effect investment opportunities and, more importantly, helped entrepreneurs understand and increase their network effect coefficient—the measure of true network effects—and ultimately embed strong network effects into their products. In short:

• A stands for “atomic unit”

• S stands for “seed the supply side”

• The other S stands for “scale the demand side”

• E stands for “enlarge the network effect” or “enhance the network coefficient”

• T stands for “track proprietary insights”

This framework provided a simple yet systematic way to judge whether a company truly had network effects or merely the illusion of them.

However, toward the end of the last decade, it became increasingly difficult to find investable network-effect opportunities. Well-established incumbents already had very strong network effects in place, effectively setting the world order. It became exceedingly difficult for emerging disruptors—both in consumer and enterprise spaces—to find a gap to break through.

We began looking for other forms of technology defensibility (for example, semiconductors) and gradually moved away from “shallow tech” network-effect investments, as we found very few investable opportunities. In fact, our last shallow tech investment was made about three years ago.

Then, in late 2022, ChatGPT arrived.

As the world now understands, generative AI is the first technology in human history capable of learning, reasoning, creativity, cross-domain functionality, and decision-making. It’s the most significant platform shift since mobile, social, and cloud computing in the late 2010s—and arguably the biggest one in human history. It also means the playing field has been leveled. Today, there are numerous ways to create new products with powerful network effects that can render incumbents’ offerings obsolete (for example, I haven’t used Google Search regularly for a long time) because newcomers can disrupt incumbents from all three angles: technology, product, and commercialization (e.g., business models). Incumbents are vulnerable!

On the other hand, the ASSET framework also needed a refresh, as we’re no longer dealing with simple, well-understood marketplaces. What if one side of the marketplace is now AI? Even though our original framework was designed to handle data-driven network effects, the speed and scale of data generation have multiplied by orders of magnitude. How does this affect enlarging the network effects and increasing the coefficient?

The good news is that there are now ways to massively increase the network effect coefficient in a remarkably short time. The bad news is that all your competitors—large or small—can do the same. Competition has never been fiercer.

After ChatGPT was released, we quickly revised our ASSET framework to version 2.0. Since then, we’ve been guest-lecturing this masterclass worldwide for well over a year. By fully leveraging AI’s creativity and reasoning capabilities, entrepreneurs can now harness human-machine collaboration to supercharge both the demand and supply sides, blitz-scale, and create new atomic units. Here’s the gist of 2.0:

• A – Atomic Unit of Product

• S – Super Seed the Supply Side (now amplified by Gen AI)

• S – Supercharge the Demand Side (now leveraging Gen AI)

• E – Exponential Engagement (using the human + AI combo)

• T – Transform Business with New AI-powered Atomic Units

Like 1.0, this new framework is easy to understand but difficult to master—and it’s even more complex now because, with Gen AI, it’s non-linear. Our masterclass covers the lecture material, but the real work happens in our private tutoring, where execution matters—and this is how we help our portfolio companies win.

The old network effect is dead. Thanks to the AI platform shift, network effects are roaring back in a different and far more potent way in the new world order. The combination of deep tech defensibility plus network effect defensibility is the new holy grail—and we are specialized in both.

With the AI platform shift, all of a sudden, there are many new investable opportunities that didn’t exist before. At the same time, the ground has shifted: the old playbook is out, and the new playbook is in. It’s exciting; we love the challenge, and we wouldn’t have it any other way.

P.S. If you enjoyed this blog post, please take a minute to like, comment, subscribe and share. Thank you for reading!

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.

Investing in Fibra: Revolutionizing Women’s Health with Smart Underwear

At Two Small Fish Ventures, we love backing founders who are not only transforming user behaviour but also unlocking new and impactful value. That’s why we’re excited to announce our investment in Fibra, a pioneering company redefining wearable technology to improve women’s health. We are proud to be the lead investor in this round, and I will be joining as a board observer. 

The Vision Behind Fibra

Fibra is developing smart underwear embedded with proprietory textile-based sensors for seamless, non-invasive monitoring of previously untapped vital biomarkers. Their innovative technology provides continuous, accurate health insights—all within the comfort of everyday clothing. Learning from user data, it then provides personalized insights, helping women track, plan, and optimize their reproductive health with ease. This AI-driven approach enhances the precision and effectiveness of health monitoring, empowering users with actionable information tailored to their unique needs. 

Fibra has already collected millions of data points with its product, further strengthening its AI capabilities and improving the accuracy of its health insights. While Fibra’s initial focus is female fertility tracking, its platform has the potential to expand into broader areas of women’s health, including pregnancy detection/monitoring, menopause, detection of STDs and cervical cancer and many more, fundamentally transforming how we monitor and understand our bodies.

Perfect Founder-Market Fit

Fibra was founded by Parnian Majd, an exceptional leader in biomedical innovation. She holds a Master of Engineering in Biomedical Engineering from the University of Toronto and a Bachelor’s degree in Biomedical Engineering from TMU. Her achievements have been widely recognized, including being an EY Women in Tech Award recipient, a Rogers Women Empowerment Award finalist for Innovation, and more.

We are thrilled to support Parnian and the Fibra team as they push the boundaries of AI-driven smart textiles and health monitoring. We are entering a golden age of deep-tech innovation and software-hardware convergence—a space we are excited to champion at Two Small Fish Ventures.

Stay tuned as Fibra advances its mission to empower women through cutting-edge health technology.

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.

Announcing Our Investment in Hepzibah AI

The Two Small Fish team is thrilled to announce our investment in Hepzibah AI, a new venture founded by Untether AI’s co-founders, serial entrepreneurs Martin Snelgrove and Raymond Chik, along with David Lynch and Taneem Ahmed. Their mission is to bring next-generation, energy-efficient AI inference technologies to market, transforming how AI compute is integrated into everything from consumer electronics to industrial systems. We are proud to be the lead investor in this round, and I will be joining as a board observer to support Hepzibah AI as they build the future of AI inference.

The Vision Behind Hepzibah AI

Hepzibah AI is built on the breakthrough energy-efficient AI inference compute architecture pioneered at Untether AI—but takes it even further. In addition to pushing performance/power harder, it can handle training loads like distillation, and it provides supercomputer-style networking on-chip. Their business model focuses on providing IP and core designs that chipmakers can incorporate into their system-on-chip designs. Rather than manufacturing AI chips themselves, Hepzibah AI will license its advanced AI inference IP for integration into a wide variety of devices and products.

Hepzibah AI’s tagline, “Extreme Full-stack AI: from models to metals,” perfectly encapsulates their vision. They are tackling AI from the highest levels of software optimization down to the most fundamental aspects of hardware architecture, ensuring that AI inference is not only more powerful but also dramatically more efficient.

Why does this matter? AI is rapidly becoming as indispensable as the CPU has been for the past few decades. Today, many modern chips, especially system-on-chip (SoC) devices, include a CPU or MCU core, and increasingly, those same chips will require AI capabilities to keep up with the growing demand for smarter, more efficient processing.

This approach allows Hepzibah AI to focus on programmability and adaptable hardware configurations, ensuring they stay ahead of the rapidly evolving AI landscape. By providing best-in-class AI inference IP, Hepzibah AI is in a prime position to capture this massive opportunity.

An Exceptional Founding Team

Martin Snelgrove and Raymond Chik are luminaries in this space—I’ve known them for decades. David Lynch and Taneem Ahmed also bring deep industry expertise, having spent years building and commercializing cutting-edge silicon and software products.

Their collective experience in this rapidly expanding, soon-to-be ubiquitous industry makes investing in Hepzibah AI a clear choice. We can’t wait to see what they accomplish next.

P.S. You may notice that the logo is a curled skunk. I’d like to highlight that the skunk’s eyes are zeros from the MNIST dataset. 🙂 

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.

Contrarian Series: Your TAM is Zero? We love it!

Note: One of the most common pieces of feedback we receive from entrepreneurs is that TSF partners don’t think, act, or speak like typical VCs. The Contrarian Series is meant to demystify this, so founders know more about us before pitching.

Just before New Year, I was speaking at the TBDC Venture Day Conference together with BetaKit CEO Siri Agrell and Serial Entrepreneur and former MP Frank Baylis.

When I said “Two Small Fish love Zero TAM businesses,” I said it so matter-of-factly that the crowd was taken aback. I even saw quite a few posts on social media that said, “I can’t believe Allen Lau said it!”

Of course, any business will need to go after a non-zero TAM eventually. But hear me out.

Here’s what I did at Wattpad: I never had a “total addressable market” slide in the early days. I just said, “There are five billion people who can read and write, and I want to capture them all!”

Even when we became a scaleup, I kept the same line. I just said, “There are billions of people who can read, write, or watch our movies, and I want to capture them all!”

Naturally, some VCs tried to box me into the “publishing tool” category or other buckets they deemed appropriate. But Wattpad didn’t really fit into anything that existed at the time. Trust me, I tried to find a box I would fit in too, but none felt natural.

Why? That’s because Wattpad was a category creator. And, of course, that meant our TAM was effectively zero.

In other words, we made our own TAM.

Many of our portfolio companies are also category creators, so their decks often don’t have a TAM slide either.

Yes, any venture-backed company eventually needs a large TAM. And, of course, I don’t mean to suggest that every startup needs to be a category creator.

That said, we’re perfectly fine—in fact, sometimes we even prefer—seeing a pitch deck without a TAM slide. By definition, category creators have first-mover advantages. More importantly, category creators in a large, winner-take-all market—especially those with strong moats—tend to be extremely valuable at scale and, hence, highly investable.

So, founders, if your company is poised to create a large category, skip the TAM slide when pitching to Two Small Fish. We love it!

P.S. Don’t forget, if you have an “exit strategy” slide in your pitch deck, please remove it before pitching to us. TYSM!

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.

Celebrating the Unintended but Obvious Impact of Wattpad on International Women’s Day

It’s been almost three years since I stepped aside from my role as CEO of Wattpad, yet I’m still amazed by the reactions I get when I bump into people who have been part of the Wattpad story. The impact continues to surface in unexpected and inspiring ways frequently.

Wattpad has always been a platform built on storytelling for all ages and genders. That being said, our core demographic—roughly 50% of our users—has been teenage girls. Young women have always played a pivotal role in the Wattpad community.

Next year, Wattpad will turn 20 (!)—a milestone that feels both surreal and deeply rewarding. When we started in 2006, we couldn’t have imagined the journey ahead. But one thing is certain: our early users have grown up, and many of them are now in their 20s and 30s, making their mark on the world in remarkable ways.

A perfect example: at our recent masterclass at the University of Toronto, I ran into Nour. A decade ago, she was pulling all-nighters reading on Wattpad. Today, she’s an Engineering Science student at the University of Toronto, specializing in machine intelligence. Her story is not unique. Over the years, I’ve met countless female Wattpad users who are now scientists, engineers, and entrepreneurs, building startups and pushing boundaries in STEM fields.

This is incredibly fulfilling. Many of them have told me that they looked up to Wattpad and our journey as a source of inspiration. The idea that something we built has played even a small role in shaping their ambitions is humbling.

Now, as an investor at Two Small Fish, I’m excited about the prospect of supporting these entrepreneurs in the next stage of their journey. Some of these Wattpad users will go on to build the next great startups, and it would be incredible to be part of their success, just as they were part of Wattpad’s.

On this International Women’s Day, I want to celebrate this unintended but, in hindsight, obvious outcome: a generation of young women who grew up on Wattpad are now stepping into leadership roles in tech and beyond. They are the next wave of innovators, creators, and entrepreneurs, and I can’t wait to see what they build next.

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.

Celebrating Richard Sutton’s Turing Award

I’d like to extend my heartfelt congratulations to Richard Sutton, co-founder of Openmind Research Institute and a pioneer in Reinforcement Learning, for being honoured with the 2024 Turing Award—often described as the “Nobel Prize of Computing.” This accolade reflects his groundbreaking contributions, which have shaped modern AI across a wide spectrum of applications, from LLMs to robotics and everything in between. His influence resonates throughout classrooms, research, and everyday life worldwide.

As a self-professed science nerd, I’ve had the privilege and honour of working with him through the Openmind board. Rich co-founded Openmind alongside Randy Goebel and Joseph Modayil as a non-profit focused on conducting fundamental AI research to better understand minds. We believe that the greatest breakthroughs in AI are still ahead of us, and that basic research lays the groundwork for future commercial and technological innovations.

A core principle of Openmind—and a guiding philosophy of its co-founders—is a commitment to open research: there are no intellectual property restrictions on its work, ensuring everyone can contribute to and build upon this shared body of knowledge. Rich’s vision and dedication continue to inspire researchers and practitioners around the world to push the boundaries of AI and openly share their insights. This Turing Award is a well-deserved recognition of his transformative impact, and I can’t wait to see the breakthroughs that lie ahead as his work continues to redefine our understanding of intelligence.

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.

Masterclass Series: Use This Framework to Move Fast and Make High-Quality Decisions

In many companies, the bottleneck isn’t necessarily in the execution of decisions. The real bottleneck is the excessive time people waste making decisions.

When I was Wattpad’s CEO, everyone in the company knew I had a simple 2×2 framework to empower the whole team to make fast, high-quality decisions – all by themselves!

The essence of this framework comes down to two questions:

• Is this decision reversible?

• Is this decision consequential?

These two factors create four types of decisions:

1. Reversible and inconsequential

2. Reversible and consequential

3. Irreversible and inconsequential

4. Irreversible and consequential

Examples of Each Type

1. Reversible and Inconsequential

This actually makes up the bulk of decisions in a company:

• Internal Slack messages? Delete them if you don’t like them.

• Marketing team’s benign social media copy? Remove the post if it doesn’t work.

• Small typo like the one in the above image? Yes, I purposely left the typo there. I look sloppy, but I could silently replace it with a better one when I have time.

• Small bugs in the product? If a bug fix causes other problems, revert the changes.

The list goes on. The trick is to empower each person in the company to make these decisions independently. I reinforced the same message to the Wattpad team over and over again:

From the most junior interns to the most senior leaders—you’re empowered to make the call all by yourself.

No boss to ask. No approval process. Just do it!

The company moves fast when most decisions don’t require a meeting!

2. Irreversible and Inconsequential

Here’s an example:

At one point, we ran out of space at Wattpad’s Toronto HQ and needed overflow space. We found a small office—just a few hundred square feet with a couple of meeting rooms—in the building right next door. The location was perfect, but the space itself? Just okay.

The problem was the lease—it was relatively long. Once we signed, we couldn’t back out. That limited our flexibility (irreversible), but we knew that if we needed more room, we could always find another expansion space. The cost was small in the grand scheme of things (inconsequential).

Given our growth, there was little downside to signing the lease. So we moved fast, signed the deal, and moved on to the next item on the to-do list.

For this type of decision, you can still move fast. Just be careful—double-check the lease for any hidden “gotchas.” It’s not about if we sign or not. We will sign, but we just want to make sure the bases are covered before we do.

You’d be surprised how much time people waste on indecision. Just make the call and do the due diligence!

3. Reversible and Consequential

A perfect example? A big product release.

Sonos’ poorly executed product release is a great case study. (See my blog post Masterclass Series: Complete Redesign That Actually Works for all the details.)

When done properly, product releases can be very consequential but still reversible. At Wattpad, we released high-risk software all the time—but always with a way to roll back if things didn’t work.

We knew how to press the undo button!

For these kinds of decisions, move fast and make the call—but monitor the outcome and always be ready to press undo.

Important: How to Increase the Quality of These Decisions

For both Irreversible and Inconsequential decisions and Reversible and Consequential decisions, always ask:

Is there any way to make this decision more reversible or less consequential?

If you can tweak the decision to minimize fallout—no matter how small—do it. It will save time and stress down the road.

4. Irreversible and Consequential

Many of these are leadership-team-level or CEO-level decisions.

They’re rare but also the hardest to make. They require a lot of context, consideration, and, sometimes, choosing between two bad options. Occasionally, you get a good one and choose between a few great choices.

The ultimate example for me?

Whether to take the company public, maintain the status quo and keep going, or accept an acquisition offer.

You all know the decision I made.

Sometimes, knowing which quadrant a decision falls into is an art. But imagine if we didn’t have this framework—slow decision-making would have ground the company to a halt.

The key to moving fast isn’t just execution—it’s deciding fast, too.

P.S. If you enjoyed this blog post, please take a minute to like, comment, subscribe and share. Thank you for reading!

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.


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.

P.S. If you enjoyed this blog post, please take a minute to like, comment, subscribe and share. Thank you for reading!

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.

How We Built a Truly Global Powerhouse with 100 Million Users

Most people don’t realize just how global Wattpad’s business is. Here are a few fun facts:

• Only 25% of our 100 million users are from North America, while 25% come from LATAM, 25% from Europe, and 25% from Asia.

• Of the 50 languages on Wattpad, the most popular isn’t English—it’s Spanish. Other widely used languages include Bahasa Indonesia (10 million users) and Tagalog (6 million users), with millions more reading and writing in Italian, French, German, Portuguese, Vietnamese, and many others.

• Not only have our print books (yes, we’re a book publisher too) been New York Times bestsellers, but they’ve also hit #1 in multiple countries, including Germany and Colombia.

• #1 on Netflix globally and other streaming platforms? We’ve done that many times—including the Spanish smash hit A Través De Mi Ventana (Through My Window), which we co-produced with Netflix. Many #1-rated TV shows worldwide are based on Wattpad stories—and we co-produce them.

• #1 at the box office? We’ve achieved that in multiple countries as well.

How did we build this?

A lot of things made this happen, but I’ll highlight a few. It started on day one. Here’s a screenshot of our website when we launched in 2006.

Notice that we already supported many key languages worldwide. Why? Because only about 400 million people speak English as their first language—that’s less than 5% of the world’s population.

And we were right! The first language that took off wasn’t something we predicted—it was Vietnamese. We couldn’t have guessed that!

When the first Android phone came out (the T-Mobile G1), we were one of the first to support it. At that time, the iPhone was primarily a high-GDP country phenomenon, while low-GDP countries were dominated by $30 Android phones. When I travelled to these regions, I frequently brought back bags of inexpensive phones so our team could test and ensure our app worked on low-end devices. This allowed us to dominate globally.

When we raised growth capital, we didn’t just seek funding from Silicon Valley investors—we broadened our investor base to include backers from other countries. This helped us learn the nuances of international expansion while gaining support from investors who understood these markets.

When we launched subscriptions, we recognized that a one-size-fits-all model wouldn’t work. Some countries preferred à la carte purchases over all-you-can-read models. So, we introduced our own virtual currency, allowing users to buy content à la carte.

When we expanded into movies and TV shows, we didn’t just partner with Hollywood studios—we forged partnerships with entertainment companies across five continents. This ensured Wattpad story adaptations could be seen everywhere.

And the list goes on.

None of this happened automagically. It took years of conscious, deliberate effort. But once we built the foundation, expanding into new countries became incremental. There’s no free lunch, but it’s also not rocket science—it got easier and easier as we grew.

We built a truly global powerhouse with 100 million users.

If we could do it, you can too.

Choosing between the U.S. and international expansion is a false dichotomy—you can do both. As the world shifts toward intangible assets, building a global business is easier than ever.

Keep in mind that while the U.S. is the largest economy, it only accounts for approximately 26% of the world’s GDP. To create true optionality, not expanding globally—especially beyond the U.S.—is not an option.

Our experience in building a successful global business also allows us to help our portfolio companies scale internationally. We’ve been through the challenges of global expansion firsthand, and we actively share these insights to support the next generation of world-changing companies. Reach out to us if you want to be part of it!

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.

Only Optionality Can Make Canada Strong and Free

The tariffs are coming. We all know this isn’t really about fentanyl—only 19 kg of the U.S.’s supply comes from Canada, while close to 10,000 kg was seized at the U.S. border.

Even if we solved this tiny issue, Trump would find something else—maybe he’d complain that the snow in NYC is due to cold air from Canada and slap us with another tariff.

Trump’s playbook is simple: weaponize everything at his disposal to get what he wants.

He’s imposing tariffs on everything from us. We can debate whether to slap tariffs on orange juice or hair dryers in response, but that won’t materially change the outcome. How we react now is just noise—he holds all the leverage anyway. Canada will suffer in the short term, no matter what.

But we shouldn’t let a crisis go to waste. This is a golden opportunity to fix systemic issues that were previously near impossible to address—like interprovincial trade barriers. Yet even fixing that won’t solve the root problem.

Stepping back, the real issue is one of the first principles of leadership: Optionality.

Having alternatives always provides leverage. This principle applies broadly—not just to negotiations, but also to fundraising, supplier relationships, operations, company survival, M&A, and beyond—including leading a country.

Trump understands leverage better than most. This isn’t just about negotiation—even if we reach a deal this time, any agreement with him isn’t worth the paper it’s written on.

As a country, we are far too dependent on the U.S., and Trump knows it. Only by addressing our lack of optionality can we deal with him—and future U.S. presidents—on equal footing.

There is no quick fix. Only a new, decisive, visionary Prime Minister can guide Canada out of this mess.

The only way forward is to leverage what we do best—energy, natural resources, AI, and more—to create true optionality. As the world shifts toward intangible assets, ironically, our proximity to the U.S. is becoming less of a hindrance to diversification.

We must control our own destiny. We cannot allow any single country—U.S. or otherwise—to hold us hostage.

Only optionality can make Canada strong and free.

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.

Masterclass Series: Complete Redesign That Actually Works

Sonos replaced its CEO last week. The company faced significant backlash after launching a redesigned app earlier last year that was plagued by bugs, missing features, and connectivity issues, frustrating customers and tarnishing its reputation. This also led to layoffs, poor sales, and a significant drop in stock price.

While I usually don’t comment on companies I’m not involved with, as a long-time Sonos user, I was very frustrated that the alarm feature I had been relying on to wake me up in the morning for well over a decade disappeared overnight. There were other issues, too.

Throughout my career, I have worked on numerous redesign projects. A fiasco like this is totally avoidable. Today, I am sharing a couple of internal blog posts I wrote for my team (when I was Wattpad’s CEO) about this topic. Of course, these are just examples of the general framework I used. In practice, there are many specific details in each redesign that I helped guide the team through, as frameworks like this are like a hammer. Even the best hammer in the world is still just a hammer. The devil is in the details of how you use it.

These internal blog posts are just some of the hammers and drills in my toolbox that I use to help our portfolio CEOs navigate trade-offs and move fast without breaking things.

Happy reading through a sample of my collection of half a million words!

Note: These two posts have been mildly edited to improve readability.

Blog Post #1 – Subject: Feature Backward Compatibility

I have gone through major technology platform redesigns many times in my career. One problem that arises every single time is backward compatibility.

The reason is easy to understand: users can interact with complex products (such as Wattpad) in a million different ways. There is no way the engineering team could anticipate all the permutations.

There are two common ways to solve this problem. First, run an extensive beta program. This is what big companies like Apple and Microsoft do when they update their operating systems. This approach is also a great way to push some of the responsibility to their app developers. Even with virtually unlimited resources, crowdsourcing from app developers is still a far better approach. However, running an extensive beta program takes a lot of time and resources. Most companies can’t afford to do that.

The other approach is to roll out the changes progressively and incrementally. It is very tempting to make all the big changes at once, roll them out in one shot, and roll the dice. However, I am almost certain that it will backfire. Not only is it a frustrating experience for both users and engineers, but it also makes the project schedule much less predictable and, in most cases, causes the project to take much longer than anticipated.

Next year, when we focus on our redesign to reduce tech debt, don’t forget to set aside some time budget for these edge conditions that are so easily overlooked. Also, think about how we can roll out the changes more incrementally to minimize the negative impact on our users.

Blog Post #2 – Subject: The Reversibility and Consequentiality Framework

The other day, I spoke to the CEO of another consumer internet company. In terms of the scale of its user base, this company is much smaller than Wattpad, but we are still talking about millions of users here.

Like us, this company has been around for over a decade. Not surprisingly, technical debt has been an ongoing concern. A few years ago, the team decided to completely redesign its platform from the ground up. The redesign was a multi-year effort, and the team finally pulled back the curtain a year ago. While it is working fine now, this CEO told me that it took a few months before they fixed all the issues and reimplemented all the “missing” features because many of their users were using the product in “interesting” ways that the new version did not support.

These problems are fairly common when redesigning a new system from the ground up. In practice, it is simply impossible to take all the permutations into account, no matter how carefully you plan. However, if we mess things up, our user base is so large that it might negatively impact (or ruin!) 100 million people’s lives in the worst-case scenario.

On the flip side, over-planning could burn through a lot of unnecessary cycles.

One way or another, we should not let these challenges deter us from moving forward or even slow us down because there are many ways to mitigate potential problems. In principle, ensuring that the rollout is reversible and inconsequential is key.

The former is easy to understand: Can we roll back when things go wrong? Do we have a kill switch when updating our mobile apps? These are best practices that we have already been using.

However, at times, these best practices might not be possible. Can we reduce the consequentiality when rolling out? If the iOS app were completely redesigned, could we do it in smaller chunks, parallel-run the new and old versions at the same time, or try the new version on 0.1% of our users first? If not, could we roll out the new app in a small country first?

Again, our objective is not to avoid any problem at all costs. Our objective is to minimize (but not eliminate) the negative impact when things go wrong—not if things go wrong. Although Wattpad going dark for 100 million people for an extended period of time is not acceptable, in the spirit of speed, it is perfectly okay if we have ways to hit reverse or reduce the impact to only a small percentage of our users. These are not rocket science, but they do require a bit more thoughtfulness because our user base is so large that we can’t simply roll the dice.

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.

Welcoming Albert Chen as a Venture Partner at Two Small Fish Ventures

Today’s blog post is written by Eva and is a reblog of what was originally shared on the Two Small Fish Ventures website.

We are thrilled to announce that Albert Chen is joining Two Small Fish as a Venture Partner!

Albert brings a wealth of experience to our team. Like all our partners at TSF, Albert’s expertise spans the full spectrum—from technical innovation to product development to operational leadership in entrepreneurial startups. His impressive academic background further underscores his exceptional capabilities.

Albert earned his Ph.D. in BioMEMS, Acoustics, and Medical Engineering from the University of Waterloo. He also completed his undergraduate studies in Systems Design Engineering at Waterloo and participated in an international exchange program in Electrical Engineering at National Taiwan University.

Albert’s professional career is equally remarkable. Most notably, he served as the CTO of robotics and edge AI company Forcen. His diverse experience also includes roles at Metergy (smart energy), Excelitas (photonics), and North (smart glass, acquired by Google).

This is just a glimpse of Albert’s impressive journey. Follow him on LinkedIn to learn more about his background and accomplishments.

At Two Small Fish Ventures, we are committed to supporting bold founders shaping the future of technology through our experience. Albert’s extensive academic background, combined with his hands-on leadership and innovation experience, makes him an invaluable addition to our team.

Please join us in welcoming Albert to Two Small Fish!

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.

Two Small Fish Honoured to Be on the CVCA Top 50 List

Who are the top 50 VCs in Canada? Two Small Fish Ventures is one of them! At Two Small Fish Ventures, we are deeply honoured to be named among Canada’s top 50 venture capital firms in this year’s edition of The 50 — the annual guide produced by the Canadian Venture Capital & Private Equity Association (CVCA) and the Trade Commissioner Service (TCS).

This recognition is not just a badge for us; it’s a reflection of the thriving and globally respected Canadian venture ecosystem we are proud to be part of. We share this honour with an incredible group of firms that are shaping the future of technology, science, and innovation across the country and beyond.

If you are an entrepreneur, this list represents the Canadian VCs you should talk to — firms committed to partnering with visionary founders, pushing boundaries, and building category-defining companies.

We look forward to continuing to back the next generation of transformational founders and are grateful to the CVCA and TCS for this spotlight.

The Full List: Canada’s Top 50 VCs

Here’s the full list of the firms recognized this year (in alphabetical order):

1. Active Impact Investments

2. Amplify Capital

3. Amplitude Ventures

4. AQC Capital

5. BrandProject

6. Brilliant Phoenix

7. Conexus Venture Capital

8. CTI Life Sciences Fund

9. Diagram Ventures

10. Finchley Healthcare Ventures

11. First Ascent Ventures

12. Framework Venture Partners

13. Genesys Capital

14. Good News Ventures

15. Graphite Ventures

16. Greensoil PropTech Ventures

17. GreenSky Ventures

18. iGan Partners

19. Inovia Capital

20. INP Capital

21. InvestEco

22. Luge Capital

23. Lumira Ventures

24. MKB

25. McRock Capital

26. NGIF

27. Panache Ventures

28. Pelorus VC

29. Portage

30. Radical Ventures

31. Raven Indigenous Capital Partners

32. Real Ventures

33. Relay Ventures

34. Renewal Funds

35. Saltagen

36. Sandpiper Ventures

37. Sectoral Asset Management

38. Staircase Ventures

39. SVG Ventures | THRIVE

40. The51 Ventures

41. Two Small Fish Ventures

42. Vanedge Capital

43. Version One Ventures

44. Vistara Growth

45. White Star Capital

46. Whitecap Venture Partners

47. Yaletown Partners

48. Evok Innovations

49. Cycle Capital

50. Boreal Ventures

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.

Portfolio Highlight: ABR

The next frontier of AI lies at the edge — where data is generated. By moving AI toward the edge, we unlock real-time, efficient, and privacy-focused processing, opening the door to a wave of new opportunities. One of our most recent investments, Applied Brain Research (ABR), is leading this revolution by bringing “cloud-level” AI capabilities to edge devices.

Why is this important? Billions of power-constrained devices require substantial AI processing. Many of these devices operate offline (e.g., drones, medical devices, and industrial equipment), have access only to unreliable, slow, or high-latency networks (e.g., wearables and smart glasses), or must process data streams in real time (e.g., autonomous vehicles). Due to insufficient on-device capability, the only solution today is to send data to the cloud — a suboptimal or outright infeasible approach.

How does ABR solve this? ABR’s groundbreaking technology addresses these challenges by delivering “cloud-sized” high-performance AI on compact, ultra-low-power devices. This shift is transforming industries such as consumer electronics, healthcare, automotive, and a range of industrial applications, where latency, reliability, energy efficiency, and localized intelligence are essential.

What is ABR’s secret sauce? ABR’s unique approach is rooted in computational neuroscience. Co-founded by Dr. Chris Eliasmith, CTO and Head of the University of Waterloo’s Computational Neuroscience Research Group, ABR leverages a brain-inspired invention called the Legendre Memory Unit (LMU), which was invented by Dr. Eliasmith and his team of researchers. LMUs are provably optimal for compressing time-series data—like voice, video, sensor data, and bio-signals—enabling significant reductions in memory usage. Running the

LMU on ABR’s unique processor architecture has created a breakthrough that “kills three birds with one stone” by:

1. Increasing performance,

2. Reducing power consumption by up to 200x, and

3. Cutting costs by 10x.

This is further turbocharged by ABR’s AI toolchain, which enables customers to deploy solutions in weeks instead of months. Time is money, and ABR’s technology allows for advanced on-device functions—like natural language processing—without relying on the cloud. This unlocks entirely new use cases and possibilities.

At the helm of ABR is Kevin Conley, the CEO and a former CTO of SanDisk, alongside Dr. Chris Eliasmith. Together, they bring exceptionally strong leadership across both hardware and software domains—a rare but powerful combination that gives ABR a significant competitive advantage.

ABR’s vision aligns perfectly with our investment thesis and our belief that edge computing and software-hardware convergence represent the next frontier of opportunity in computing. We’re excited to see ABR power billions of devices in the years to come.

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.

Celebrating Professor Geoffrey Hinton’s Nobel Prize (and His Birthday)

In the past few days, Eva and I had the privilege of joining the University of Toronto delegation in Stockholm to celebrate University Professor Emeritus Geoffrey Hinton, the 2024 Nobel Laureate in Physics. The events, organized by the University, were a fitting tribute to Professor Hinton’s groundbreaking contributions to AI, a technology that will transform our world in the decades to come.

The celebration was a blend of thoughtful discussions, historic venues, and memorable moments. It all began with a birthday party for Professor Hinton, followed by a fireside chat, an inspiring dinner at the iconic Vasa Museum, and a panel exploring Canada’s leadership in AI at the Embassy of Canada to Sweden. Each event underscored not only Professor Hinton’s remarkable achievements but also the global impact of Canadian innovation in AI and technology more broadly.

Rather than recount every detail, I’ll let the pictures and their captions tell the story of this extraordinary week. It was an incredible opportunity for us to honour a visionary scientist.

Eating birthday cake with University of Toronto President Meric Gertler and Dean, Faculty of Arts & Science at University of Toronto Melanie Woodin.
This was the chip that was built for Professor Hinton in the late 80s for him to test his artificial neural network.
The chip was developed before sub-micron technology was widely available. Professor Hinton believes it might be 3-5 microns, but even he wasn’t 100% sure. Upon closer inspection, it appears there were six neurons on the grid.
Taking a picture with U of T President Meric Gertler and Chancellor Wes Hall and Professor Leah Cowen.
We are dining in front of the unique and well preserved warship Vasa from 1628!
Fireside chat with University Professor Emeritus Geoffrey Hinton, and Patchen Barss, science journalist, speaker and author.
Institute
Taking a picture with Untether AI co-founder Raymond Chik and Vector Institute CEO Tony Gaffney at the reception after the fireside chat.
An insightful panel moderated by Professor Leah Cowen. Panellists included Professor Eyal de Lara, Vector Institute CEO Tony Gaffney, Professor David Lie and Professor Amy Loutfi.
Ericsson’s Head of AI Jörgen GustafssonView Jörgen Gustafsson and Jason LaTorre, the Ambassador of Canada to Sweden.

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.

Naysayer

Important: Before continuing, read SRTX‘s CEO Katherine Homuth’s posts first (here and here).

Hi Katherine,

I read your blog posts about your current challenges and future plans. Originally, I planned to reply to you privately, but in the end, I decided to share my response as an open letter on my blog.

First of all, if you think I’m going to give you any advice, you’d be mistaken.

Why? Because I don’t think I’m qualified. I have never built a company that reinvented the textile industry. Unless I have “been there, done that,” you shouldn’t listen to me — even though TSF has been an investor in SRTX since the early days.

That said, there are numerous similarities between SRTX and Wattpad. One lesson I learned might be useful to you.

Both SRTX and Wattpad set out to reinvent industries that had remained largely unchanged for the past century. Wattpad raised over $100M USD and lost millions per year to build the business’s foundation — overcoming the chicken-and-egg problem — before we could monetize profitably. That had been our strategy from day one, and it was hard to explain on a spreadsheet. From the outside, it looks like it’s all wins. From the inside, we don’t know if the next leap forward will be our last.

Sound familiar? Like you mentioned, SRTX also raised well over $100M USD and lost millions of dollars to build the foundation of the business — overcoming the chicken-and-egg problem — before you could monetize profitably. That’s been your strategy from day one, and it was hard to explain on a spreadsheet. From the outside, it looks like it’s all wins. From the inside, you don’t know if the next leap forward will be your last.

When an entrepreneur is building a transformative company in an unconventional way, they will inevitably attract a lot of naysayers. These naysayers are usually missing key context (which is fine, as you don’t have to convince everyone), give unsolicited bad advice (which is uncool but typical of armchair coaches trying to look smart), and fail to recognize that there’s more than one way to build a massively successful company.

Here’s an example.

A few months after Wattpad was acquired, someone said to me:

“Congrats on your acquisition. But you could have been more capital-efficient. Compared to most B2B SaaS companies, your exit value relative to capital raised was not as high as it could be.”

WTF? He might as well have said McDonald’s generates more revenue than you.

Of course, we all know the proper benchmark is to compare Wattpad to other consumer companies like Snap, Twitter, or Facebook. In fact, Wattpad was massively more capital efficient — both on a per-user basis and an exit-value basis — than most other consumer companies at similar or larger scales. In some cases, we were ahead by an order of magnitude.

I wasn’t angry, upset, or offended by this ignorance, naivety, or arrogance because, over the years, I’ve had to deal with many naysayers — even after Wattpad’s successful exit.

Here’s the important lesson I learned:

Naysayers will always naysay.

They want to make themselves look smart.

They want to feel superior to you.

They don’t want to admit they were wrong, so they continue to naysay.

Most people don’t believe in moonshots because they can’t do what you do.

If you can use them to fire yourself up, that’s great. If not, don’t even spend a millisecond on them. Your time and energy are better spent focusing on finding a handful of new investors who believe in your vision, growing a fanbase that loves your product, and scaling your company. These are what you have been doing. The results will speak for themselves.

If anything, SRTX has de-risked so much over the years. Millions of people are already buying your unbreakable tights. It’s the best-selling tight in North America — unbreakable or not. A lot of capital has been raised, enabling your mega factory to become a reality. Major B2B partnerships have been formed to scale. New products, beyond tights, are soon to launch.

You’re very close to the top of Mount Everest. Who cares about those people who don’t dare to leave base camp?

Best,

Allen

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.

Contrarian Series: Best Exit Strategy? Not Having One

Note: One of the most common pieces of feedback we receive from entrepreneurs is that TSF partners don’t think, act, or speak like typical VCs. The Contrarian Series is meant to demystify this, so founders know more about us before pitching.

For Wattpad, it was exactly ten years between raising our first round of venture capital in 2011 and the company’s acquisition in 2021. Over that decade, we discussed countless topics in our board meetings.

But one topic we never discussed? Exit strategies.

I distinctly remember, a couple of years before the acquisition, I raised the question to a board member. “We’ve been venture-backed for almost ten years now. Should we start talking about exit…”

I couldn’t even finish the sentence. That board member cut me off:

“Allen, I just want you to build a great company.”

That moment stuck with me. Only after the acquisition did I fully appreciate the significance of those ten years as a venture-backed company without focusing on an exit.

Wattpad’s four largest investors—USV, Khosla Ventures, OMERS, and Tencent—enabled us to focus on building the business, not selling it. OMERS, as a pension fund, and Tencent, as a strategic investor, don’t operate under the typical 10-year fund cycle that drives many venture firms to push for exits. USV, with its consistent track record of generating world-class returns, had the trust of its LPs to prioritize long-term value over short-term outcomes. And Khosla Ventures? Well, no one can tell Vinod Khosla what to do, and he loves making big, long-term bets.

Their perspectives freed us to focus on building a great company rather than prematurely worrying about how to sell it.

In early 2020, a year before Wattpad was acquired for US$660M, we set an ambitious company objective: to become “Investment Ready.” This meant ensuring we could scale profitably and confidently project $100M+ in revenue with a minimum of 40% year-over-year growth. By the end of 2020, we wanted to be in a position to choose between preparing for an IPO (we even reserved our ticker symbol WTPD), raising growth capital to accelerate expansion, or scaling organically without any additional funding.

When an inbound acquisition offer came in mid-2020, this optionality proved invaluable. It allowed us to run a proper process with multiple interested parties. We were clear with potential acquirers: our preference was to remain independent. If the offer wasn’t higher than the value we could command through an IPO, we weren’t interested, and we would walk away. Because we had the fundamentals to back it up, no one doubted us.

This underscores an important point: the best way to generate a great outcome is to build an amazing business. Focus on creating value, and optionality will follow.

Any CEO who claims to have an exit strategy—especially in the early stages—is either naïve, disillusioned, or lying.

Here’s the reality: M&A is far less common than people think. The pool of serious potential acquirers often narrows to just a handful in the best-case scenarios. And even then, the stars have to align—you need the right timing, the right strategic fit, and the right price. It’s easier said than done.

Of course, that doesn’t mean I ignored the idea of acquisition entirely (and founders should consider M&A, but only under the right circumstances, and I will save it for another blog post). For instance, we built relationships with potential strategic acquirers and stayed aware of the landscape. But the time I spent on this was minimal. Even my leadership team occasionally asked why I never talked about M&A. The answer was simple: it wasn’t a priority.

Too many founders overthink their “exit strategy,” and it often backfires. Changing their product to appeal to a potential acquirer? Building one-sided partnerships in the hope they’ll buy the company? Hope is not a strategy.

The same goes for VCs. Some overthink their portfolio companies’ “exit strategy” because they worry about selling before the 10-year fund window closes. While this concern is valid, it doesn’t mean they should push their best portfolio companies to sell. There are many ways for VCs to liquidate their positions without forcing a sale. Ironically, the best way for a founder to help their investors exit is to focus on increasing enterprise value. Shares in a great company are always in demand.

For an early-stage startup, having an exit strategy is as absurd as asking an infant to decide which jobs they’ll apply to after university. The founders’ job is to nurture that infant—raise them into a great human being. The results will follow.

Build a great business, and everything else will fall into place. There’s an old saying: Great companies get bought, not sold. It couldn’t be more true.

P.S. Founders, if you have an exit strategy slide in your pitch deck, please remove it before pitching to us. TYSM!

P.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.

Celebrating a Legendary Educator

I was fortunate to not only learn from his textbook but also to be a student in his class. Few have the privilege of learning directly from a legend, and I consider myself incredibly lucky to have been in the right place at the right time—more than 30 years ago—to benefit from his lectures.

Who am I talking about? Professor Adel Sedra.

I wanted to take a moment to congratulate Professor Sedra on the recognition of his incredible legacy with the launch of a new permanent exhibit at the University of Toronto. His textbook, Microelectronic Circuits, co-authored with the late Professor Kenneth C. Smith, has been a cornerstone of engineering education for decades. To date, it has gone through eight editions (with Professor Tony Chan Carusone also part of the editorial team), sold more than a million copies, and been translated into nearly a dozen languages.

Here’s a fact I only recently discovered: it’s estimated that over three-quarters of electrical engineers in the world since 1982 have studied this book—yes, 75%!—widely known as “Sedra/Smith” after its authors.

“When they first sat down in 1982 to create the first draft, I don’t think either of the two co-authors fully realized that it would become the gold standard in the field,” said Christopher Yip, Dean of U of T Engineering.

As a professor, Professor Sedra was simply unparalleled in the field of microelectronics. His passion for teaching was evident, and his exams? They were tough—though I like to think I did alright! 😉

Watching this video gave me goosebumps.

As a 20-year-old at the time, I didn’t fully comprehend or appreciate that I was sitting in a classroom with a legendary professor, studying one of the earlier editions of what would become a truly iconic textbook.

Professor Sedra’s contributions to engineering education and his impact on generations of students are unmatched. This exhibit is a fitting tribute to a man who shaped how the world learns about microelectronics.

You can read more about this celebration of his legacy here: U of T Engineering News.

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.