Month: January 2026

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.