Canada's AI Garden Needs Watering: Nicole Janssen

Canada must boost its support for AI innovation to prevent businesses from seeking opportunities elsewhere

EDMONTON, AB, Oct. 9, 2024 /PRNewswire/ - Canada is punching above its weight class in artificial intelligence (AI). Our researchers are doing world-class work and we are educating incredible talent, but our country is stumbling when it comes to turning those inputs into successful businesses. It's time for our policymakers to help build a thriving innovation ecosystem here—before all of our homegrown talent and intellectual property (IP) heads south.

I often use the analogy of building an AI garden where all the right conditions are there for vegetables (AI companies) of all different shapes and sizes to flourish. Although some vegetables may not be as abundant as anticipated in the end, it shouldn't be due to unfavorable growing conditions. Today, Canada's AI garden needs some serious tending. We have a strength in AI research and talent development that is far above what you would expect from a country of our size, and it's because our government was investing in AI three decades ago, long before it was a thing to invest in. While talent and research are key inputs to a successful ecosystem, in order to reap the benefits of those investments commercialization is essential.

Let's go through the key ingredients our garden needs, beyond talent and research.

AI Investment and Overcoming Funding Challenges

Without adequate funding, many promising startups will struggle to scale. Most Canadian investors are more comfortable with traditional opportunities, like real estate, and tend to shy away from AI because they don't feel knowledgeable enough about it. We've even found that most investment support services, such as lawyers and accountants, aren't familiar with terms common in technology businesses. This makes it incredibly challenging to find Canadian-based investors willing to take the leap into AI. Recent changes in tax policy, such as the capital gains tax announcement, further complicate the issue by creating an unwelcoming environment for both entrepreneurs and investors alike.

This is a meaty problem without a quick fix. Shifting Canadian investors' openness to technology will take time, but maintaining a competitive capital gains tax rate can help prevent them from looking outside Canada until they're ready to make that shift.

Building Client Trust to Drive Innovation Efforts

Without clients and partners, businesses can't survive. While companies can operate internationally, it's easier when they're located nearby. This is especially true with AI, where there is still considerable fear associated with the technology.

Canadian companies are very slow to innovate in comparison to the rest of the world. According to Statistics Canada, slower labor productivity growth since 2015 has been largely attributable to weak capital investment in innovation, which was pervasive across industries. In the second quarter of 2024 only six percent of companies reported using AI.  A country that falls behind in technology sees its productivity drop and will ultimately struggle to succeed in other areas. That's just a fact. The tech industry, comprising nearly 55,000 business establishments, delivers a direct economic impact estimated at $113.4 billion CAD, or approximately 5.5% of the overall Canadian economy—up from $104.6 billion CAD in the previous year. Given such a substantial contribution to our economy, it raises the question: why are we so slow to innovate?

The problem we're witnessing is the hostility toward startups, favoring big incumbents instead. We need new startups to fuel innovation, competition, and economic expansion. At the same time, we need to prevent large corporations from leveraging governmental power to stifle their emergence. Monopolies are the death of startups; startups force incumbents to continue to innovate, which is a good thing. To support this, the government can use procurement to become an early customer for new technologies. CCI released a powerful report that outlines just that.

As a Canadian AI business owner, when you struggle to find investors and clients in Canada, you have to ask yourself: Why am I here? That generates more and more of what so many Canadian entrepreneurs see as the goal—to build up their business and sell it to a bigger company in the U.S. This results in IP, talent, and future growth benefits all leaving Canada. That doesn't help us grow; we need more big AI companies. We need to double down on growing these companies in our own garden.

Favorable Regulations and the Need for Commercialization

It's great to allocate funding to AI, but there should be clear, achievable goals in place. Regulations must be feasible for "little tech" to comply with. For instance, AIDA, like the EU AI Act, has the potential to become so stringent that startups might find it impossible to participate in the AI ecosystem. To address these challenges, Canada's innovation landscape demands a collaborative effort from public institutions, private enterprises, academia, and policymakers.

We need to encourage commercialization and IP generation activities. Expenses related to filing a patent, or other practices to commercialize IP, should be eligible under Scientific Research and Experimental Development (SR&ED). The SR&ED focus on pure research and development does not accurately reflect the full life cycle of innovation, which includes a much more iterative and ongoing approach to product development, rather than an idealized notion of pure invention.

The surge in AI is a perfect example of this. Large language models (LLMs) existed long before the general public was aware of them. It was ChatGPT's interface that catalyzed their widespread adoption, driving innovation by making this technology more accessible.

A Thriving Ecosystem Attracts Global Attention

Thriving ecosystems are self-perpetuating. Successful AI companies result in more experienced AI talent, more investors, more mentorship, and more startups. People and businesses begin to relocate to be part of the successful ecosystem. If done right, early investments in key garden elements pay dividends for decades to come. With the right vision and strategy, Canada can turn early investments into long-term success, cultivating a thriving AI ecosystem that attracts talent, investors, and businesses for decades to come. This requires Canada to build a brand and a long term strategy for what it wants to be in this area though. Then we need to drop the humble for a while and start telling the world about all the great things we have here.

Within AI, we can position Canada as a global leader in responsible AI—a title we're well positioned for. The Canadian brand is one of responsibility—that we are trustworthy and that we are someone you can trust. It's time to leverage this strength to cultivate our AI garden. We want the world to see Canadian AI as a model of responsibility, attracting companies that wish to align with our brand and contribute to its growth rather than seeking opportunity elsewhere.

Conclusion

Being pro-business, pro-tech, and pro-entrepreneurship is a good place to start. Canadians can innovate all they want, but we're not being set up to win in Canada or on a global scale without action. Without strategic investments, supportive policies, and a commitment to nurturing homegrown talent, our potential for innovation will remain unrealized.

If we can't make our AI garden flourish and get all those right ingredients in there, we're not going to have a garden; we're going to have a bunch of weeds.

About AltaML

AltaML is a leading artificial intelligence (AI)-powered solutions developer. Working with organizations that want to use AI to leverage their data to develop solutions that drive tangible business results, AltaML empowers partners to create operational efficiency, reduce risks, and generate new sources of revenue. Through a deep understanding of organizational pain points and challenges, AltaML's solutions encompass the entire machine learning (ML) life cycle, from evaluating potential use cases and determining feasibility to piloting solutions, putting code into production, and ensuring model evolution.

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