Canadian Startups Had A Tough Q3, And AI's Popularity Isn't Making A Big Difference
When we first read PitchBook's data on global venture investment in Q3, the first conclusion that my colleague Alex Wilhelm and I reached was that startups around the world wouldn't be mistaken to expect another quarter of declines.
As usual, the numbers are nuanced: European startups were faring slightly better than the rest of the world, seeing more venture capital deployed in the third quarter than in the first two quarters of the year. And, of course, the venture investor adage holds true: Good companies can always raise money.
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And it appears Canadian startups aren't going to escape the misfortunes of the rest of the world. Today, The Exchange is studying data from Tracxn that paints a fairly gloomy picture of the fundraising climate in the country. We also found a few AI-related nuggets and have notes from a Canadian VC: Eva Lau, co-founder of Two Small Fish Ventures (TSFV).
Funding to Canadian startups declined by 57% to $808 million in Q3 2023 from the previous quarter, per Tracxn's Geo Quarterly report. It's also 84% less than Canada's record fundraising quarter, Q2 2021, when startups collectively raised $5.12 billion.
No new Canadian unicorns were minted last quarter, either. The only company that raised at least $100 million, Toronto-based AI chip startup Tenstorrent, hit the $1 billion mark back in 2021.
That's not all: Canada saw fewer deals in the quarter, too, with only 71 startups raising money in Q3 2023 compared 102 in Q2 2023, and 146 back in Q3 2022.
From Silicon Valley To Toronto: Why AI Investments Are Heating Up
Image source: Getty Images
Written by Joey Frenette at The Motley Fool Canada
Amid the glorious tech rebound in the first half of the year, most of the attention was focused on the stars of mega-cap American tech. Indeed, Silicon Valley still seems to be the hotbed for all the most disruptive innovative trends, from generative artificial intelligence (AI) to the metaverse (or spatial computing, if you prefer) and more. Still, I believe Canada's tech scene doesn't get nearly as much respect as it deserves.
Undoubtedly, Shopify (TSX:SHOP) is one of Canada's fastest-rising stars. Though the Stock imploded during the brutal tech wreck of 2022, I still think it's a mistake to count the e-commerce firm out of the race, as it looks to embrace next-generative technologies, including generative AI.
Undoubtedly, it's not just Silicon Valley AI companies that can benefit from the rise of AI. Shopify and other Canadian tech firms also stand to win a great deal from its rise over the next 10 years and beyond.
In this piece, we'll look at two intriguing Canadian companies that I think could benefit a great deal from the so-called AI revolution (or the fourth industrial revolution, as some industry pundits refer to it).
ShopifyShopify briefly became Canada's largest company by market cap before it nosedived off a cliff, eventually falling down the rankings. The stock plunge in excess of 80% was horrifying if you overbought at the cliff. Still, I don't think you can classify Shopify as one of those companies to just forget about after its historic decline. Indeed, generative AI could play a major piece in the puzzle, as the company looks to invest heavily in its tech capabilities.
Mad Money host Jim Cramer recently stated that it may be better to wait for Shopify shares to pull back a bit before getting in. I think he's right on the money. Shopify stock may be a great company, but in this kind of environment, which is starting to become less hospitable to high-multiple, high-growth names, I think a 20-30% pullback is very much possible, even with the long-term jolt of AI factored in.
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You could argue SHOP stock has already fallen off, with shares off 20% from 52-week highs. Still, there's a lot of ground to give up if shares are to surrender the year's gains. All considered, Shopify stock is a must-buy stock that could make a big splash in the AI waters over the years.
Open TextOpen Text (TSX:OTEX), not to be confused with ChatGPT creator OpenAI, looks intriguing here. It has skin in the AI race, with the firm's move to sprinkle a bit of AI over its already impressive enterprise software portfolio. Indeed, the IT firm has been in recovery mode (like Shopify) of late. Though shares recently slipped, I still think investors should consider the name while it's treading water, as it's hard not to love the recent shift into AI.
Though Open Text may be lesser known versus Silicon Valley tech firms, I'd still not be afraid to be a net buyer at below $50 per share. Personally, I think the stock is intriguing right here, even in the face of prominent rate-related industry headwinds.
The post From Silicon Valley to Toronto: Why AI Investments Are Heating Up appeared first on The Motley Fool Canada.
Before you consider OpenText, you'll want to hear this.
Our market-beating analyst team just revealed what they believe are the 5 best stocks for investors to buy in October 2023... And OpenText wasn't on the list.
The online investing service they've run for nearly a decade, Motley Fool Stock Advisor Canada, is beating the TSX by 25 percentage points. And right now, they think there are 5 stocks that are better buys.
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Fool contributor Joey Frenette has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Shopify. The Motley Fool has a disclosure policy.
2023
"These Are Life-or-death Scenarios": Meet The Scientist Bringing AI To Toronto Hospitals
"These are life-or-death scenarios": Meet the scientist bringing AI to Toronto hospitals
Bo Wang, the new chief AI scientist at UHN—Canada's largest hospital network—on embracing tech, cutting down wait times and whether robots are coming for health care workers' jobs
Photo courtesy of University Health NetworkIn the near future, patients at Toronto's hospitals will be greeted by the stiff metallic smile of a scalpel-wielding robot. Okay, just kidding—but it's safe to say that the field of medicine will soon become a little more Jetsonesque. Case in point: the University Health Network (UHN), Canada's largest network of research hospitals, just appointed its very first chief AI scientist, Bo Wang, who has been tasked with overseeing the implementation of AI tools in hospitals across the city.
Both Wang and UHN have some experience in this field. Earlier this year, UHN launched its AI Hub, co-led by Wang, which aims to bring scientists and clinicians together to develop new AI technology. Wang's latest gig makes him the very first chief AI scientist in the country. Here, he breaks down what the job entails, why hospitals have been slow to adopt machine learning and why he doesn't expect physicians to be as resistant to the tech as, say, Hollywood screenwriters.
You have a PhD from Stanford. Is it safe to assume that you're a PhD-style doctor rather than an MD?Ha—correct, I'm not a medical doctor. My background is in computer science and computational biology.
Related: Meet the researcher investigating the future of AI in medicine
Is it unusual for someone who's not a medical doctor to be the chief of something at a hospital?Well, in this case the role is very new. But I do work with a lot of medical doctors, of course. I meet with them to discuss their burning questions about AI and assess how machine learning could help address challenges they're facing. Those sessions go both ways—they tell me what they care most about, and we tell them what's possible. Doctors are fantastic people. It's amazing that they manage to do any research given their busy schedules.
You must have fairly packed days as well, with the shiny new role. What does the chief AI scientist at UHN do?I have three main focuses. The first is research—developing AI models that can parse health-related data, publishing papers and working with other hospitals. The second is education. There's a lot of hype around AI. I want to demystify tools that can assist with note-taking and administrative tasks, and I want to help physicians know what's true and false about more long-term possibilities, such as AI that can assist with surgical procedures. The third pillar is what we call adoption. There are lots of research papers on AI out there. While there are a few AI tools being used in UHN hospitals, we're not seeing widespread adoption in Canada. As we know, our health care system is in crisis. If AI can help improve the system, it's important that we explore that.
Some hospitals in the US, like Mayo Clinic, are already using AI to help diagnose heart disease and strokes. Why do you think Canadian hospitals have been so hesitant?Well, the US government has invested a lot into basic research on AI in hospitals. Their investment is the highest in the world, which is why they're the leader in this field. In Canada, we've fallen behind in terms of our investments in new science. In addition to that, AI can digest huge amounts of data, but it's bulky and complicated. There can be a lack of trust from physicians because they don't fully understand how it works. Then there are ongoing legal issues, like ensuring that our infrastructure for storing patient data continues to abide by Canada's privacy laws, and of course ethical issues around the possibility of biases in the data we use to train AI.
Right, we know that AI tends to take on human biases. For example, an algorithm used to predict crime in the US disproportionally labelled Black defendants as "high risk."We've seen that with algorithms used for matching donor organs with potential patients. They tend to match more men than women because the data that was used to train them was mostly collected from men. These are life-or-death scenarios, so it's extremely important that we develop unbiased tools.
How do you troubleshoot for that sort of thing?First, you try to collect data in an unbiased way—making sure you have sufficient representation of different kinds of patients. Toronto hospitals have an advantage with that because it's such a diverse city. Then, we need to be testing these tools in more than one setting. At UHN, we try something out in one of our hospitals and then bring it into other hospitals or even reach out to colleagues in the US or Europe. That way, we can verify that it works well for different groups of people.
Related: Scenes from the AI revolution in university education
How do you imagine AI will change regular people's experiences in the health care system? I'm guessing fully robot-operated surgeries are still a ways out.Yes, but there are some real examples of how the patient experience is already changing. One is this app we co-developed called Medley. It's a regular mobile app—you can get it at the App Store—and it helps nurses at the Peter Munk Cardiac Centre remotely monitor patients with heart conditions. A patient inputs certain measurements at home, and the app uses AI to flag any abnormalities or changes. Then a nurse can call the patient to check in. Another example is a recent product we developed at UHN, which is a language model, sort of like ChatGPT. We connect it to a microphone, and it can listen to a conversation between a doctor and a patient. When they're done talking, the model can immediately summarize that conversation into clinical notes. That way, instead of spending the whole appointment glued to their computer, typing, doctors can actually look their patients in the eyes.
Could these kinds of changes mean that patients won't be stuck waiting in emergency rooms for 12 hours?That's the goal, yes.
In both of your earlier examples, physicians are still in the equation. Is it safe to say you're not working on an algorithm that could replace them entirely?Exactly. We're not making decisions for doctors; we're helping them make more-informed decisions while spending more time with their patients. AI is the perfect tool for precision medicine. For example, there is this concept called the "digital twin." We have all this data, which represents millions of past patients. So, when a new patient comes in, we can find their digital twin from the database—the person who is most similar to them based on, say, genomic profiling and medical history. We can learn from their twin's experience. If they tried certain drugs that failed, maybe we should avoid those drugs. If they benefited from a specific combination of treatments, maybe we should try similar treatments for this new patient.
Okay, fast forward ten years. How do you hope to see hospitals using AI?One thing I'm excited about is the potential for medically equipped AI training. We have a shortage of medical staff due to a lack of decent training programs. AI could be a part of the solution there. Language models could be used to simulate doctor-patient dialogue, which trainees could critique. Virtual reality could create 3-D models of tumours, which surgical residents could interact with. Also, we can develop AI to synthesize vast amounts of literature and help doctors make the most informed decisions. But I don't think current AI models are accurate enough for that yet.
In some industries, like the movie-making biz, there has been a lot of pushback against letting AI take on formerly human responsibilities. Should health care workers be worried for their jobs?I know that's a fear we hear a lot about, but I haven't seen it in health care. The president of the American Medical Association said, "It is clear to me that AI will never replace physicians—but physicians who use AI will replace those who don't." I think that's very accurate, because AI can greatly enhance a doctor's ability to manage their patient care and daily workflow.
You don't think there will be any Luddite hold-outs?There's a famous 2021 survey from the Peter Munk Cardiac Centre, which found that over half of physicians are suffering from burnout. Cumbersome tasks like note-taking are some of the driving factors behind that, and those are things we know AI could handle. Anything we can do to lower that percentage would only benefit our health care system.
This interview has been edited for length and clarity.
