Month: September 2025

$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.

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.

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.

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.

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.

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.

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!

P.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.