Canada’s National AI Strategy: Is Canada Underweighting Intellectual Property Protection for SMEs?
Canada’s new National AI Strategy reflects an important shift in how the federal government understands artificial intelligence. AI is no longer framed primarily as a research or innovation issue. Instead, the Strategy presents it as strategic infrastructure tied to sovereignty, industrial competitiveness, productivity, and long-term economic resilience.
A central theme runs throughout the document: Canada has historically been successful at generating innovation but less successful at retaining commercialization outcomes and long-term value capture domestically. The Strategy repeatedly acknowledges that Canadian firms, talent, and intellectual property often scale abroad, leaving Canada with strong upstream innovation but weaker downstream ownership positions.
In many respects, the Strategy’s diagnosis is persuasive. Modern AI competition is no longer driven solely — or even primarily — by traditional patent portfolios. Increasingly, competitive advantage derives from combinations of proprietary datasets, compute access, deployment ecosystems, operational scale, tacit know-how, cloud infrastructure, and integration capability. The Strategy is therefore correct to emphasize sovereign compute, AI infrastructure, commercialization ecosystems, and adoption capacity as strategic concerns.
The more difficult question is whether the Strategy sufficiently addresses how Canadian SMEs will retain durable bargaining power and long-term value in that environment.
That issue matters because smaller firms do not compete under the same conditions as hyperscalers or dominant platform companies. Large AI firms can often rely on infrastructure ownership, ecosystem lock-in, market reach, and operational scale to sustain their positions. SMEs usually cannot.
As a result, intellectual property serves a different function for smaller firms than it does for dominant incumbents. For many SMEs, patents, copyright, licensing structures, trademarks, contractual protections, trade secrets, and data-governance frameworks are not simply legal assets. They are mechanisms for reducing dependency asymmetries, improving investment readiness, strengthening negotiation leverage, and protecting commercialization pathways. The deliberate adoption and practice of IP hygiene within SMEs remains a pillar of long-term growth and sustainability.
The Strategy arguably underemphasizes this practical reality.
A Shift in Focus Away from Traditional IP Protection
One of the most striking aspects of the Strategy is the limited attention dedicated to traditional forms of intellectual property protection. Patents in particular occupy a surprisingly modest place in the overall framework. Instead, the document consistently emphasizes openness, infrastructure, data mobilization, commercialization, interoperability, sovereign compute, open-source AI, and broad-based adoption.
This reflects an increasingly common policy perspective within the AI sector. Many policymakers and technologists now believe that long-term competitive advantage in artificial intelligence derives less from formal IP rights and more from operational ecosystems that are difficult to replicate. In the context of large frontier AI companies and hyperscalers, there is certainly some truth to this observation. Firms such as OpenAI, Anthropic, Google DeepMind, and Meta are not defended primarily through traditional patent portfolios (that said, while Meta Platforms and Alphabet are massive patent holders, OpenAI and Anthropic have healthy patent portfolios). Their advantages often stem from scale, access to data and compute, deep integration ecosystems, infrastructure ownership, and market reach.
But Canadian SMEs do not compete under those conditions.
This distinction is critically important because the role that intellectual property plays for SMEs is fundamentally different from the role it plays for dominant global platforms.
Why Formal IP Still Matters for SMEs
Unlike large multinational technology companies, Canadian SMEs generally lack massive compute resources, integrated cloud ecosystems, entrenched distribution channels, and dominant market positions. Although the Strategy aims to reduce some of these structural disadvantages over time, formal IP rights continue to serve functions that go far beyond simple exclusionary protection.
For many SMEs, patents help signal defensibility to investors, support valuation discussions, improve bargaining leverage in strategic partnerships, and reduce dependency asymmetries when dealing with much larger counterparties. Copyright, meanwhile, can play an increasingly important role in software, training datasets, interface design, outputs, and content governance. Trademarks help anchor trust and differentiation, particularly in enterprise and regulated markets where credibility matters deeply. Contractual IP frameworks and licensing structures frequently become essential tools for governing collaboration, deployment rights, commercialization pathways, and ownership allocation.
These protections are especially important in sectors where Canadian AI SMEs are actively emerging, including industrial automation, healthcare, energy systems, telecommunications, cybersecurity, advanced manufacturing, robotics, and defence-adjacent technologies. In many of these sectors, SMEs do not necessarily need to block the entire market. Instead, they need enough defensibility to negotiate effectively, attract capital, and avoid becoming economically subordinated to larger infrastructure players.
The Strategy, however, spends comparatively little time discussing these more practical SME realities.
The Risks of Over-Reliance on Trade Secrets
The Strategy appears to place substantial implicit faith in ecosystem-driven competitive protection and trade-secret-oriented value creation. While trade secrets can certainly be extremely valuable in AI, they are also operationally fragile, particularly for smaller organizations.
Maintaining secrecy protection in modern AI environments is difficult. SMEs frequently operate through collaborative development models involving cloud providers, universities, research institutes, external developers, integration partners, and investors. Employee mobility remains high, API-driven architectures can expose operational insights, and AI systems themselves create newer forms of vulnerability such as model extraction, reverse engineering, prompt leakage, and derivative fine-tuning.
Large hyperscalers can often sustain trade-secret-based protections precisely because they possess the operational scale and infrastructure control necessary to maintain them effectively. SMEs, by contrast, may struggle to preserve secrecy over long commercialization cycles involving partnerships, customer integration, procurement processes, and financing discussions.
An ecosystem that increasingly favours secrecy-based competitive protection may therefore unintentionally favour entrenched incumbents.
Open Source as Both Opportunity and Threat
The Strategy’s strong endorsement of open-source AI reflects another important policy tension. The government clearly views open-source ecosystems as mechanisms to promote accessibility, interoperability, competition, resilience, and reduced dependency on dominant technology providers.
There are legitimate reasons to support this approach. Open-source ecosystems can lower barriers to entry for SMEs, accelerate adoption, reduce vendor lock-in, encourage innovation, and create shared technical foundations that support broader participation across the economy.
At the same time, however, aggressive openness creates very real commercial risks for smaller firms. Without complementary forms of protection, Canadian innovators may inadvertently find themselves reproducing one of the country’s longstanding structural problems: local innovation combined with foreign monetization.
This concern is especially acute in AI because dominant multinational firms are uniquely positioned to absorb, scale, optimize, and commoditize openly available innovations. Large players possess the infrastructure, compute capacity, engineering resources, and distribution channels necessary to rapidly operationalize technologies developed elsewhere.
As a result, openness without broader strategic IP planning can sometimes weaken the long-term competitive position of smaller firms rather than strengthen it.
The Missing “Whole-of-IP” Framework
The deeper issue is not that the Strategy undervalues patents specifically. Rather, the document appears to underemphasize the importance of layered and integrated intellectual property strategies.
Modern AI commercialization rarely depends on a single form of protection. Increasingly, successful AI companies rely on carefully balanced combinations of patents, trade secrets, copyright, trademarks, contractual controls, licensing frameworks, data governance structures, standards participation, certification ecosystems, and selective open-source deployment.
A carefully calibrated “whole-of-IP” strategy recognizes that different forms of protection serve different strategic purposes. Patents may strengthen defensibility and attract investment. Trade secrets may protect tacit operational know-how. Copyright may govern datasets, software, and content assets. Trademarks may reinforce trust and market differentiation. Contracts may govern commercialization rights, collaboration structures, and deployment models. Selective openness may accelerate ecosystem adoption while preserving core economic advantages.
In practice, sophisticated AI commercialization rarely means “patent everything” or “open everything.” Instead, it requires disciplined balancing.
The Strategy understandably focuses on adoption, scale, infrastructure, and ecosystem development. However, without stronger emphasis on layered IP governance, there is a legitimate risk that Canadian SMEs could be encouraged toward commercialization models that ultimately weaken their long-term bargaining power internationally.
The Money Question: What Happens if Public Investment Creates Weakly Defensible Companies?
Another important tension sits beneath the Strategy’s repeated references to investment, commercialization, and SME support.
The federal government is proposing very significant public spending to accelerate AI adoption and scale Canadian AI companies. Across the document, the Strategy refers to major public investments tied to compute infrastructure, AI adoption, commercialization initiatives, mission-oriented programs, sovereign AI capabilities, venture financing, and SME support mechanisms. It also explicitly references leveraging existing programs such as Elevate IP and IP Assist to help Canadian companies commercialize intangible assets internationally.
At a high level, the logic is understandable. Canada wants to avoid repeating its historical commercialization problem in which promising domestic innovation is ultimately scaled, acquired, or monetized elsewhere. The Strategy therefore seeks to inject capital into the ecosystem in order to accelerate domestic growth, strengthen AI adoption, and support Canadian commercialization.
But this raises a difficult question:
What happens if substantial public investment is directed toward firms whose intellectual property positions are ultimately insufficiently defensible in global markets?
This is where the Strategy’s relative underemphasis on layered IP protection may eventually become problematic.
If Canadian SMEs are encouraged — either directly or indirectly — toward commercialization models built heavily around openness, interoperability, shared infrastructure, broad data access, and limited formal IP defensibility, the government risks funding companies that may struggle to retain long-term economic leverage once confronted with large global incumbents.
This risk becomes particularly acute in AI because dominant international players possess enormous advantages in:
- compute access;
- infrastructure ownership;
- distribution channels;
- customer ecosystems;
- capital reserves;
- and engineering scale.
In practice, intangible assets rarely become commercially valuable merely because they exist. To support financing, licensing, procurement, partnerships, and international commercialization, they often need to be sufficiently identified, governed, allocated, and formalized to function as durable business assets. Put differently, intangible assets often need to be strategically crystallized before they can be effectively commercialized.
Absent carefully structured intellectual property strategies, smaller Canadian firms may successfully innovate, raise public funds, develop technology, achieve early adoption, and still find themselves unable to defend margins, negotiate effectively, or maintain control over commercialization outcomes once larger actors enter the space.
In that scenario, Canada could unintentionally reproduce the very cycle the Strategy is attempting to prevent: public support helping generate innovation domestically, while long-term value capture ultimately migrates elsewhere.
The issue is not that openness, interoperability, or adoption are inherently problematic. In many respects, they are essential for AI ecosystem growth. However, openness without strategic defensibility can create structural asymmetries that disproportionately favour the largest market participants.
This is particularly important because AI commercialization often involves long investment horizons and substantial public support before profitability emerges. If SMEs lack durable ownership positions by the time global competition intensifies, Canadian taxpayers may effectively absorb portions of the commercialization risk while foreign firms capture disproportionate long-term economic value.
A stronger “whole-of-IP” framework may therefore not only be important for SMEs themselves, but also for protecting the effectiveness of Canada’s broader public investment strategy.
A carefully balanced approach combining patents, trade secrets, copyright, licensing control, branding, standards participation, contractual protections, and selective openness is more likely to produce firms capable of retaining long-term strategic leverage internationally. Without that balance, there is a legitimate risk that portions of Canada’s substantial AI investment strategy could indirectly subsidize future foreign value capture.
The Copyright Silence
Perhaps the most notable omission in the Strategy is its near-total silence regarding the rights of copyright holders whose works are used to train large language models and other generative AI systems.
The document speaks extensively about protecting Canadian innovation, supporting creators, safeguarding culture, commercializing intangible assets, and strengthening AI ecosystems. Yet it says almost nothing about training-data licensing, consent, remuneration, text-and-data mining, copyright exceptions, or transparency obligations relating to training corpora.
Given the global significance of these issues, the omission is difficult to ignore.
The government appears caught between two competing objectives. On one hand, the Strategy seeks to accelerate Canadian AI capability-building and support globally competitive domestic AI firms. On the other hand, the same document repeatedly emphasizes the need to protect Canadian creators, intellectual property, and cultural assets.
These goals may be on a collision course with each other.
Overly restrictive copyright frameworks could create serious barriers for Canadian AI entrants attempting to compete internationally. But excessively permissive approaches may also weaken creator rights and further erode Canada’s long-term ability to retain value from its own intellectual and cultural production.
For SMEs, the uncertainty surrounding training-data governance is not merely theoretical. Increasingly, it affects procurement discussions, investor diligence, customer indemnities, export readiness, licensing negotiations, and overall commercialization risk.
Conclusion
Canada’s National AI Strategy marks an important and overdue recognition that AI is fundamentally linked to economic sovereignty, industrial competitiveness, and long-term national resilience. The document correctly identifies many of the structural realities shaping modern AI competition, including the importance of compute infrastructure, commercialization capability, operational ecosystems, and strategic autonomy.
But for Canadian SMEs, the conversation cannot end with adoption and openness alone.
AI competitiveness is also about defensibility, ownership, bargaining power, appropriation risk, and durable value capture. Smaller firms operating in global markets often require carefully layered intellectual property strategies precisely because they lack the structural advantages available to dominant incumbents.
As Canada continues to build its AI ecosystem, policymakers will need to ensure that efforts to promote openness and rapid commercialization do not inadvertently weaken the ability of Canadian firms to retain and defend the value of what they create.
That balance may ultimately determine whether Canada becomes merely a strong adopter of AI technologies — or a durable owner of globally competitive AI businesses and intellectual property.