Garry Chan of VentureLab Explores Global AI from a Startup Angle

Earlier this month, Garry Chan represented Ontario’s hardtech and AI incubator ventureLAB at the Global Partnership on AI (GPAI) Summit in Belgrade, Serbia. The event brought together leaders from science, public policy, and industry to discuss the future of artificial intelligence. As an advocate for the early-stage startup ecosystem, Chan attended the summit to provide a unique perspective, representing the challenges and opportunities faced by emerging companies in the rapidly evolving AI sector.

Following the event, ventureLAB signed a memorandum of understanding with Serbia’s Science and Technology Parks, as well as the cities of Niš, Čačak, and Novi Sad. This agreement aims to foster collaboration between Canada and Serbia in hardware and software development, advancing both countries’ shared interests in AI innovation. This partnership highlights the growing global collaboration in AI, especially between emerging tech hubs like Serbia and established innovation centers like Ontario.

Chan’s presence at the GPAI Summit emphasized the importance of involving early-stage startups in discussions about the future of AI. He noted that while many founders excel at the “zero-to-one” stage of their businesses, the real challenge—and opportunity—lies in scaling from “one to one hundred.” As the Chief AI Advisor at ventureLAB, Chan brings a wealth of experience to the incubator. His career spans entrepreneurship, advising startups, facilitating commercialization, and scaling tech businesses globally. At ventureLAB, he focuses on how AI intersects with hardware innovation, helping startups commercialize and scale AI-driven technologies.

ventureLAB supports early-stage companies through its state-of-the-art AI Compute Lab, which provides startups with the tools necessary to test, optimize, and de-risk their solutions. The lab also offers real-world testing environments, where companies can refine their products and collaborate with industry leaders. By integrating expertise in semiconductors and hardware, ventureLAB helps startups bridge the gap between AI development and real-world application, with an eye on breakthroughs in critical sectors such as manufacturing, healthcare, and transportation.

Chan believes that early-stage startups are driving some of the most groundbreaking advancements in AI, though they often do so under immense constraints. Many of these companies lack the resources, infrastructure, and governance support needed to navigate the challenges of scaling AI technologies. During a conversation with BetaKit, Chan shared his thoughts on the crucial role that governance and ethical considerations play in the development of AI, particularly within early-stage companies.

One of the key takeaways from the GPAI Summit, according to Chan, was the need to integrate AI governance and ethics into early-stage companies from the outset. He pointed out that AI governance shouldn’t be seen as something abstract or distant; it should be something embedded in the DNA of a company from day one. By integrating ethical AI practices, safety measures, and governance frameworks into the startup’s operations early on, companies can avoid scrambling to implement them later as they scale. He compared this process to raising a child, where instilling good habits from the beginning ensures long-term success.

Chan also emphasized that while early-stage companies may be primarily focused on product development and financial sustainability, AI governance is just as important as financial management. He drew a parallel between managing a company’s cash flow—something that startups already understand and prioritize—and managing the ethical and regulatory aspects of AI. Just as financial governance is critical to survival, so too is data governance, privacy, protection, and security when it comes to AI. These considerations are essential for startups that plan to operate internationally, particularly in regions with stringent data protection regulations such as Europe and the US.

For early-stage companies, Chan explained, this means building robust data governance structures and AI infrastructure from the beginning. When a company starts with a single dataset from one customer in Canada, it may seem manageable. However, as the company grows and expands to global markets, it will need to ensure that its AI systems comply with international standards for data security and privacy. The ability to scale AI solutions in compliance with varying regulations is a critical part of early-stage companies’ long-term success.

To help address some of these challenges, ventureLAB has developed its Accelerate AI program. This initiative tackles three core challenges that early-stage AI companies face: productization, commercialization, and scaling. One key component of the program is the Deep Learning Training and Inference Program, which supports companies in productizing their solutions. This program brings together a deep bench of advisors, or executives-in-residence, who have decades of experience in both technical and business domains. While these advisors may not always have technical expertise, their business acumen is essential in helping startups navigate the complex process of scaling AI solutions and transitioning from concept to market-ready products.

At ventureLAB, the goal is to support early-stage companies not just in developing their products but in preparing them for the real-world challenges of commercialization and scaling. Through programs like Accelerate AI, ventureLAB aims to provide the infrastructure, mentorship, and resources that startups need to succeed, both within Canada and in the global marketplace.