Canadian AI Ecosystem 2018

For a PDF version of this report, click here.

With last year’s Canadian AI Ecosystem report, we aimed to test our hypothesis that the Canadian AI Ecosystem is larger than it is often represented. Since then, we have already seen a 28% increase in the number of active AI-related startups in Canada. We want to continue to shed light on the burgeoning Canadian AI Ecosystem with an updated report that delves into a little more detail.

This report is the result of a broad collaboration. Great people from Consider Canada, Real Ventures, Invest Canada, and ISDE Canada, have provided us with a huge amount of data. We also aggregated data from Crunchbase, Angel.co, CBInsights, Tracxn, and Pitchbook. With the treasure trove of data we ended up with, it was only a question of doing some cleaning, organising it, some analysis work to identify the trends, and showing it to the world. Our database also goes to feed Canada.ai's directory where you can search Canada's AI startups.

We hope to show how the work of so many dedicated actors across the ecosystem has paid off, and we aim to give more visibility to the ecosystem as a whole.

A Few Missing Names

Some of the maps, research labs in particular, will be missing logos as we could not find a logo for some of the organizations. If you don’t see your logo please fill out .

A Growth Ecosystem

There were many interesting findings about the Canadian Ecosystem as a whole; the number of AI-related startups and enterprises is on the rise, as are deals and funding to back them. The talent pool is still going strong, but new challenges are arising.

Startups & Enterprise

From 2017 to 2018, there was a 28% increase in the number of active AI-related startups, with close to 650 active startups across all cluster cities.

The last few years have also seen a sharp increase in the number of large international players setting up their labs in Canadian cities. In particular, the main cluster cities (Montreal, Toronto, Vancouver) have seen pillar companies (e.g. Google, Uber, Facebook) setting up research groups, adding to already very active research communities.

The last year in particular has been notable in this regard. Our census of international actors for early 2017 gave us an estimate of around 20 large international actors, while this year we estimate the number to be closer to 50.

Investments

On average, the last 5 years (as of Q1 2018) saw a 49% increase in AI-related deals.

The nature of these deals is evolving. Venture/Angel-backed deals have dropped from 55% in 2013 to 36% today. Meanwhile, corporate actors have doubled their number of deals, and accelerators/incubators have tripled their number of deals.

Canadian investors sign most of these deals (62%), and international investors have kept a stable share of about 40% across the last five years.

Continued funding from local investors has kept powering the startup community and has built up credibility to the ecosystem, making it a prime target for international investors. This is evidenced by the multiple $100M+ deals that have happened in the last few years.

We can also see that the number of acquisitions is on the rise by an average of 50% in the last five years, and they are made mostly from international actors (Silicon Valley being where most of them originate from).

The trend toward continued acquisition and international investment tells us that startups are continuing to attract international attention, and thus that our thesis established last year is confirmed: the ecosystem is moving from being in an activation phase towards being in a globalization phase (or expansion for the bigger cluster cities).

Funding

When we compare the Canadian AI Ecosystem to similar ecosystems such as France, England and East Coast US (we’ve excluded Silicon Valley from this comparison because it is its own beast it terms of sheer scale), one of the biggest differentiators is the way in which the government supports the innovation needed to propel the ecosystem through continued funding.

This is why some of the most significant advances in ML research have come from Canadian universities, and why some of the best research labs in the world call Canada home.

With the latest announcement of $4B in science funding for the next several years, we’re sure to see a continued influx of quality research coming from AI research labs. This research breeds continuous innovation which is crucial to fuelling the emerging markets of the AI industry.

Talent

The continuous support to public research labs also sheds light on why the Canadian AI Ecosystem has one of the biggest talent pools in the world. When compared to other national ecosystems, Canada hosts the third largest number of AI experts.

We see that the growing number of accelerators and incubators associated directly with university campuses has had a strong impact in encouraging ambitious new talent to launch their startups close to home. Waterloo is a good example of a success story on this front, with 35% of their startups being born of those collaborations. Edmonton has followed a similar trajectory and has seen the most significant growth in the number of startups in the last year.

In other words, collaboration between universities (as talent and innovation generators) and startups/enterprises makes for stronger ecosystems that are better capable of resisting the talent pump of Silicon Valley.

Though this pipeline between academia and businesses has strengthened and the brain drain is consequently slowing, other challenges are increasingly putting pressure on the talent pool.

While the growing number of startups (and average number of employees) seems to be growing at a manageable rate, large international players coming into cluster cities are adding pressure by competing for that same talent, and ultimately keeping the talent gap open.

In Closing

All these observations, especially coupled with the boom in international actors setting up shop, indicate that the Canadian AI Ecosystem is succeeding at resisting the trap of being a “natural resource pool” for bigger ecosystems which was the biggest historical challenge of the ecosystem.

As AI-related startups in large cluster cities become more mature and stable, so too should the ecosystem’s ability to resist the historic brain drain. What is less certain is how the ecosystem will react to large actors applying a strong pressure on the talent pool locally, and this should be the most important challenge to face in the year ahead.

Thank you for reading!