Month: July 2025

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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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 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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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.

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.