Industry and Technology Committee on May 28th, 2026

[The Vice-Chair, Conservative, Raquel Dancho]

Good morning, everyone.

Welcome to meeting number 40 of the House of Commons Standing Committee on Industry and Technology.

I would remind you that if you are using your earpiece and it's plugged in but not on your ear, please make sure that you place it in front of you on the coaster to protect the health and well-being of our interpreters.

Pursuant to Standing Order 108(2) and the motion adopted on September 22, 2025, the committee is resuming its study of the opportunities, risks and regulation of artificial intelligence in Canada's strategic industries.

We'd like to welcome a number of guests.

As an individual, Mr. Sean Mullin, senior fellow, Munk School of Global Affairs and Public Policy, the University of Toronto, is with us. Welcome, Mr. Mullin.

From the Society of Composers, Authors and Music Publishers of Canada, we have Ms. Jennifer Brown, chief executive officer. Welcome.

From UrbanLogiq we have Mr. Michael Lee, chief strategy officer. Welcome to the industry committee.

As you know, you will each have up to five minutes for your opening round, after which we will proceed with questions.

Mr. Mullin, I will turn it over to you for five minutes. Take it away.

[Sean Mullin, Senior Fellow, Munk School of Global Affairs and Public Policy, University of Toronto, As an Individual]

Thank you, Madam Chair and members of the committee, for the invitation to appear today.

My name is Sean Mullin. I am a senior fellow at the Munk School of Global Affairs and Public Policy at the University of Toronto, where I co-lead the AI competitiveness project with my colleague and co-author Jaxson Kahn. Together, we recently released a report entitled “Sovereign by Design: Strategic Options for Canadian AI Sovereignty”. A briefing note on this report has been submitted to members today.

The question before this committee sits at the centre of a difficult and strategic tension. Canada is a small, open economy and a middle power. Our prosperity and security depend on deep integration with global partners, in particular the United States. To date, that openness has largely been a source of strength, but with AI, integration also creates dependencies on foreign cloud infrastructure, advanced chips, foundation models and digital platforms. In this period of geopolitical rupture, those dependencies become riskier and can jeopardize Canadian sovereignty by creating channels for foreign leverage or coercion.

At the same time, failing to adopt AI is a sovereignty risk. A country that falls behind in AI adoption will become less productive, less competitive and more vulnerable. Therefore, the challenge is not to choose between adoption and sovereignty. It is to capture the benefits of AI while protecting Canadian data, jurisdiction, institutional capacity and economic autonomy.

Our report's central argument is that the goal of sovereign AI policy should be freedom from coercion, not isolation. For Canada, AI sovereignty cannot mean building an entirely domestic AI stack. No middle power can do that at frontier scale, but unmanaged dependency is also risky. It can leave Canada exposed to decisions made by foreign governments, foreign courts or foreign technology firms.

The policy objective therefore should be to structure Canada's dependencies in ways that preserve choice, reduce leverage and maintain the ability to act in the public interest. Viewed through this lens, AI sovereignty can be considered a spectrum, not a binary. Every step that reduces Canadian exposure to coercion or expands Canada's room to manoeuvre strengthens our sovereignty. This matters because it makes the policy challenge tractable. Canada does not need to solve every problem at once to make meaningful progress.

To identify where intervention matters most, our report maps the seven layers of the AI technology stack—from data to compute to the application layer—and assesses them against five dimensions of digital sovereignty: jurisdictional, operational, technological, societal and economic. The result is a vulnerability heat map that helps policy-makers see where the most serious risks are concentrated and sets priorities accordingly.

A key finding is that cloud infrastructure is Canada's most acute controllable vulnerability and, in our assessment, the highest priority area for federal action. Cloud is where policies like procurement reform, standards, Canadian ownership models, encryption controls, audit rights and pooled public sector demand can make a real difference.

Overall, we set out more than two dozen specific policy options across every layer of the stack. Our report does not present a single blueprint; rather, it presents a menu of strategic options that can be pursued at different speeds and in different combinations by different actors.

Canada's window for action also matters. As models continue to improve and AI adoption spreads across the economy, the infrastructure build-out over the next five to 10 years will dwarf today's capacity. Decisions made now about infrastructure, platforms, procurement and standards will harden into long-term commitments.

A final point is that interdependence with peer countries is not the same as dependency on a dominant power. Partnerships with like-minded middle powers can help Canada reduce vulnerability while maintaining shared access to advanced capabilities. Canada has meaningful strengths to build on: excellent researchers, abundant energy, a vibrant domestic tech sector, emerging sovereign infrastructure providers and democratic institutions worth protecting.

We believe that AI sovereignty is achievable for Canada, but it will require deliberate choices. The window for making these choices is now.

Thank you. I'd be pleased to elaborate on any specific recommendations in follow-up.

[The Vice-Chair, Conservative, Raquel Dancho]

Thank you, Mr. Mullin.

We'll go over to you, Ms. Brown.

[Jennifer Brown, Chief Executive Officer, Society of Composers, Authors and Music Publishers of Canada]

Good morning, Chair and members of the committee. My name is Jennifer Brown, and I am the CEO of SOCAN.

SOCAN represents songwriters, composers and music publishers. We collect licence fees for the public performance of virtually all musical works in Canada and for the reproduction of music. In turn, we distribute royalties to more than 200,000 members and to the millions of rights holders we represent through agreements with international societies. In 2025, SOCAN collected $587 million in licence fees.

Culture is an economic driver, contributing $65 billion to the Canadian economy in 2024. Canada is the third-largest exporter of music in the world, after the United Kingdom and the United States.

There is an opportunity for Canadian music to continue to thrive alongside AI. However, most AI companies are not paying for music. Instead, songwriters, composers and music publishers are currently powering advances in AI models without sharing in the economic benefits. Even worse, the resulting AI models can output vast numbers of complete songs in response to single prompts, and this AI-generated music can potentially replace the work of Canadian creators in the marketplace.

Major digital music services like Apple and Deezer report that more than one-third of the music uploaded to their services these days is fully AI-generated. That means they're being flooded with millions of AI-generated tracks every month. That poses an existential threat for our members.

A global study conducted in late 2024 estimates that under current conditions, up to 24% of music creators' revenues are at risk of disappearing because of AI by 2028. The possibility of any Canadian industry losing up to one-quarter of its revenues in the next two years should cause national alarm. However, that dire prediction is based on current market conditions.

We have the opportunity to change those conditions right now. The use of high-quality human-created music has been called essential to developing AI models, and we can recognize that value through fair compensation to creators and the companies that invest in them. We must focus on an approach that not only values human authorship but also grows the economic impact of Canada's cultural industries rather than shrinking them. Making clear the rejection of a text and data mining—or TDM—exception and establishing effective transparency obligations on training and outputs are essential conditions for the emergence of a functional licensing market.

Let me explain. First, Canada needs to take a strong stance against a text and data mining exception. AI companies are advocating for broad exceptions that would permit them to exploit copyright-protected works. The narrative that innovation and copyright cannot coexist is simply untrue. What is true is that stealing is not innovation. Earlier this year, SOCAN launched a nationwide letter-writing campaign against a TDM exception, obtaining support from almost 9,000 concerned Canadians and demonstrating consensus that the government must not legitimize the theft of creative works.

Internationally, both the Australian and the U.K. governments have publicly decided against implementing a TDM exception. Canada must take a similar approach and affirm that no new or modified copyright exceptions for text and data mining will be considered.

Second, Canada must establish effective transparency obligations on training and outputs. AI companies have simply helped themselves to music, scraping the Internet to train their systems. They must be required to disclose which copyright-protected works have been ingested and stored in their training datasets. It's also vital for AI companies to ensure that the outputs of their systems are labelled as “AI-generated”, clearly distinguishing them from the works of human creators. The labelling of AI outputs will allow the public to make informed choices about the type of music they consume.

SOCAN has been licensing new technologies for over 100 years. This is what we do. Every year, SOCAN licenses billions of individual uses of music by tens of thousands of businesses in Canada, and we collect more than half a billion dollars in royalties and distribute that money globally to millions of songwriters, composers and music publishers.

The challenges presented by AI are not unique. SOCAN is committed to making sure that music creators share in the value created both from the training of the AI systems and from the musical output they generate.

Thank you.

[The Vice-Chair, Conservative, Raquel Dancho]

Thank you very much, Ms. Brown.

We'll go over to Mr. Lee for five minutes.

[Michael Lee, Chief Strategy Officer, UrbanLogiq]

Good morning, Madam Chair and members of the committee. Thank you for the opportunity to appear.

My name is Michael Lee. I am the chief strategy officer of UrbanLogiq. UrbanLogiq is a company headquartered in Vancouver with Canadian ownership. We have deployed operational AI decision systems in municipalities, provincial and state ministries, emergency management agencies, and transportation and transit agencies across North America. I appear before this committee to share what a decade of operational deployments for governments by UrbanLogiq has taught us about the conditions under which AI generally improves government decisions and outcomes.

This committee has devoted significant time to the risks of frontier AI with autonomous agents. That scrutiny is certainly warranted, but it risks creating a second, quieter gap: treating all AI as equivalent and allowing legitimate concern about frontier systems to crowd out investment in the responsible use of AI, which is already delivering measurable public benefits. This use is machine and deep learning on structured datasets, with crash records, traffic counts, land use records and emergency incident histories, for example, in order to surface analysis for human decision-makers, who retain full authority over every consequential action.

Three things are true in every deployment by UrbanLogiq of solutions for government agencies.

First, the most valuable work is integrating the data governments already hold, not building AI models. The first question in any AI procurement should not be “What will it predict?”, but rather “What will it take to get our data ready?”

Second, successful deployments are those where staff can trace an insight into their inputs and where officials can defend a decision in a public meeting. Where deployments have stalled, the cause has rarely been the AI. More often, the government was not positioned to act, or citizens held fears that did not reflect what the system was doing. An audit trail is the mechanism through which AI becomes publicly sustainable and accountable.

Third, every deployment has demonstrated augmentation and results. For example, in B.C., land use review across federal, provincial, indigenous and municipal jurisdictions on one auditable platform compresses timelines from months to minutes. In Edmonton, planners move from static reports to dynamic scenario analyses. In San Jose, infrastructure investments are evaluated across traffic, equity, emissions and economic access before decisions are made, replacing months of consultant studies in minutes. In Minnesota, machine learning on incident data, building characteristics, weather and demographics has shifted the fire department from reactive response to predictive action.

These outcomes are not incidental. Decisions about land use, emergency response and infrastructure investments are decisions that define whether communities trust their governments. Accountability with a documented, traceable record is what makes them defensible.

These tools change what is possible. Our recommendations to this committee reflect what field experience has demonstrated as necessary in order to enable responsible, effective AI adoption in government.

Let me highlight four of them. One, invest in data integration before AI model investment. Two, mandatory auditability standards with immutable audit trails, model output lineage and explainability requirements should be procurement criteria. Three, require workforce plans with phased rollouts as a condition of government AI contracts. Four, mandate structural AI governance, including risk-calibrated controls, as a condition of government AI contracts.

The evidence is already in the field: a fire chief acting on risk data before a fire happens, a planner evaluating a decade of infrastructure decisions in an afternoon and a land administrator assessing approvals across jurisdictions in minutes rather than months. That is not what AI might do. It is what responsible AI is doing right now.

We ask this committee to give equal attention to the cost of moving too slowly. Canada needs a compliance framework that allows it to move confidently with AI that is proven, governed and ready, while maintaining appropriate caution around systems that are not. That distinction is the most important one the committee can draw.

Thank you.

[The Vice-Chair, Conservative, Raquel Dancho]

Thank you very much, Mr. Lee.

I'll now open the floor to questions, starting with Mr. Falk for six minutes.

[Ted Falk, Conservative, Provencher, MB]

Thank you, Madam Chair.

Thank you to all of our witnesses for coming to committee today and providing your testimony. I'm sure it's going to be a very interesting hour that we get to spend with you.

Mr. Mullin, I would like to begin with you. You mentioned the word “sovereignty” several times in your presentation, and you've talked about that in some of your writings. Can you explain a little further what you mean by sovereignty?

[Sean Mullin]

I'm happy to. Thank you for the question.

This was a key issue that we grappled with when my colleague and I set out to write this paper. Sovereignty is a very general concept. It has many different meanings in different contexts.

For the purposes of AI, we ended up looking through the literature on digital sovereignty, and we essentially landed on what we consider to be a workable definition for this space, which is that what we want to achieve with sovereignty here in Canada—at least our advice—is freedom from coercion. It's freedom from letting a foreign power or foreign country use vulnerabilities or dependencies in our AI technological stack against Canada.

We admit that that's maybe a bit different from the more formal, binary way of thinking about sovereignty under international law. When we applied our lens for where these sovereign vulnerabilities are, we applied five different dimensions, essentially, through the lens. Those are technological sovereignty, economic sovereignty, operational sovereignty, societal sovereignty and jurisdictional sovereignty.

I'm happy to elaborate more on each one of those five. They're all different lenses where we think Canada could be vulnerable. Indeed, any country could be vulnerable. We used those tests to essentially develop our recommendations in the report.

[Ted Falk, Conservative, Provencher, MB]

Would those be the layers that you talked about, or is that something different?

[Sean Mullin]

There are the layers of the tech stack, which are things like data, physical hardware, chips, cloud compute and foundation AI models, right down to the application layer. Those would be the layers. The second dimension of our analysis was the five dimensions of sovereignty. There's a heat map in our report that has different boxes, seven by five, and that's where we look to see where Canada's most vulnerable areas are and which areas are less vulnerable.

[Ted Falk, Conservative, Provencher, MB]

You talked about data centres or cloud storing. How critical is it for Canada to have its own data centres?

[Sean Mullin]

We think this is very important, but our advice is to approach it in a tiered way. It doesn't mean that every type of compute in Canada needs to be on a sovereign data centre. An approach we recommend is to have different tiers of sensitivity in terms of the operations. National security would be at the top. The second would be sensitive or personal data. The third would be general business data.

We have different recommendations. We think the most secure or the most sovereign should be at the top of that pyramid, where any type of interruption of national security or critical government services could be used to compromise Canada. That's where sovereignty matters the most. Then as you go down, our recommendation for, say, the general business community is that we should let market conditions dictate how sovereign a particular company should want to be. There are companies—

[Ted Falk, Conservative, Provencher, MB]

I would like to ask a few more questions, so I will cut you off at that point. You may have an opportunity to expand on that.

Ms. Brown, I'd like to ask you some questions.

I've thought a lot about the whole music industry and the space that you're operating in. It's interesting. Some time ago, somebody showed me a song that they had developed by giving it certain parameters and certain genres. It's actually quite amazing what AI can do.

How do we restrict AI from accessing copyrighted, protected material? Is that even possible?

[Jennifer Brown]

Unfortunately, we didn't get the opportunity to ask that question, quite honestly, because the AI companies just took the copyrighted materials. They did not ask for permission. They did not seek any consent from any body. They just used it for their training materials. Now we're doing a bit of catch-up.

For the most part, the creators themselves are not afraid of tech. They are innovators themselves. They want to use AI as a tool, but they also want basic respect. They're saying, “You took my life's work and used it to train your model. Now you're using it to output things for financial benefit and I'm not sharing in that.” Unfortunately—

[Ted Falk, Conservative, Provencher, MB]

AI is so good and so sophisticated. How do you know when they've pirated somebody's material?

[Jennifer Brown]

That is another good question, and that's why I'm asking for transparency. Not only did they steal something, but they didn't tell us what they took, and the labelling on the output.... We are actively looking at that now. There are things in which you can see a substantial copy in the output. We know that the works are in there.

There are different things. We believe that with the transparency of what went in and the labelling of what went out, there could be a chance to look at the attribution of what comes out of the platform.

[Ted Falk, Conservative, Provencher, MB]

Do you think AI models should have restrictions put on them as to where they access their data?

[Jennifer Brown]

Yes.

[The Vice-Chair, Conservative, Raquel Dancho]

Thank you very much.

We're going over to Mr. Ma for six minutes, please.

[Michael Ma, Liberal, Markham—Unionville, ON]

Thank you, Chair.

Welcome, witnesses. Thank you for being here today.

My first question is for Mr. Mullin.

You know that the employment effects remain uncertain but will likely be consequential in terms of worker displacement. I've worked in the technology industry for over 30 years, and it is often the case that new roles emerge to complement emerging technologies once they are mature.

Will AI follow similar models of impact on the workforce as previous tech revolutions, or is it different this time?

[Sean Mullin]

This is the million-dollar question out there in terms of the broader impact on the economy.

What I know from looking at the most recent emerging literature in this space—there are quite a few economists doing work on this—is that we've only started to see a little bit of an impact on labour markets, at least in the North American context. There's a little evidence right now that entry-level workers are potentially the ones being most impacted. This was from a study that came out of Stanford last year looking at the U.S. economy.

To look at your broader question, look at previous historical trends of technology. What always gets underestimated is what gets created by a new technology. I'm more of an optimist at heart when it comes to the capacity of humans to invent new demands and new ideas from technology. In the long run, I think we will find ways to employ everyone and to make sure that this technology is being utilized, but we need to be very careful about that transition and particularly people who might be disrupted by it happening too fast.

[Michael Ma, Liberal, Markham—Unionville, ON]

Certainly, in my early days in Korea, they talked about going to a paperless society, but here we are.

I have a similar question for Mr. Lee.

You've mentioned that AI has already achieved benefits. Do you believe that AI is expanding productivity and capacity?

[Michael Lee]

Yes. What we see with government agencies.... I'm also mindful of the workforce transition, because that is disruptive, certainly, for new technology adoption. The Government of Canada, for example, has significant data pools and sources. A lot of that is not on the Internet. It is the government's data.

To an extent, we're able to organize that data in better ways to make it more useful and to break down the data silos among government departments. When you're planning infrastructure or addressing indigenous communities' concerns or environmental permitting-type considerations, that data should be combined. That would provide government decision-makers at all levels a better opportunity to make the evaluation faster and not rely on large consultant reports.

Put the tools in the hands of the civil service. This takes a transition, certainly, and can be done responsibly through testing and validation. That will make people in government able to use these tools in a way that not only meets the complex challenges of climate change and the complex geopolitical challenges of getting projects built faster, but also serves vulnerable communities. In our discussions with government ministries, even here in Ottawa for the last number of months, we see many opportunities where this can make a difference.

[Michael Ma, Liberal, Markham—Unionville, ON]

Thank you, Mr. Lee.

I'm going back to Mr. Mullin.

In March 2026, you co-wrote a report entitled “Sovereign by Design: Strategic Options for Canadian AI Sovereignty”, alongside your colleague Jaxson Khan at the Munk School of Global Affairs and Public Policy. In the report, you argue in favour of the importance of AI sovereignty, especially in the context of the changing world order and trade threats by the United States.

What do you see as some of the largest vulnerabilities if Canada does not act now with regard to digital and AI sovereignty?