Overcoming the Innovation-Commercialization Gap in AI

Key finding: Canada has the lowest amount of available AI compute among G7 countries, R&D intensity of just ~1.8% of GDP against an OECD average near 2.7%, and no Canadian-founded firms in the top portfolios of elite venture capitalists. The result is a "valuation and talent leakage" in which Canadian ideas and experts routinely generate economic value elsewhere.

Executive Summary

Canada faces a persistent challenge in translating its world-class research and early-stage innovations — especially in artificial intelligence (AI), advanced software, and algorithm development — into significant domestic economic output. Despite substantial public support through academic research funding, generous R&D tax credits (SR&ED), and a patchwork of federal/provincial grant programs, Canada's commercialization performance remains weak. The country boasts strong fundamentals in science and talent, but lags peers in business innovation and scale-up success, resulting in comparatively low contributions to GDP from emerging technologies.

Key diagnoses include fragmented and inefficient public innovation programs, a lack of commercialization infrastructure to bridge academia and industry, and private-sector shortcomings such as insufficient growth capital and a tendency for promising firms to be acquired or relocated abroad. Notably, no Canadian-founded tech firms appear in the top portfolios of elite venture capitalists (e.g. Andreessen Horowitz), and Canada has relatively few homegrown scale-ups reaching global leadership. The result is a "valuation and talent leakage" — Canadian ideas and experts often generate economic value elsewhere¹².

International comparisons underscore Canada's underperformance. The United States has forged an unparalleled innovation ecosystem, linking research to commercial and military applications at scale, supported by deep venture capital markets and mission-driven agencies like DARPA. The United Kingdom and Australia, while also striving to improve commercialization, have introduced targeted measures (from the UK's Catapult centres and new ARIA advanced research agency to Australia's $2.2B research translation agenda) that Canada can learn from. Canada's R&D intensity remains about 1.8% of GDP, below the OECD average ~2.7%³, and business R&D is especially weak at ~1.1%⁴. This contrasts with higher investments and more cohesive innovation strategies abroad, contributing to more frequent scale-up successes in those countries.

A critical analysis of the full AI/software innovation lifecycle reveals that Canada is lagging at multiple stages:

• Foundational Infrastructure. Canada lacks domestic capabilities in key enabling technologies like cutting-edge semiconductors, cloud computing infrastructure, and advanced communications networks at the scale of global leaders, leaving a gap in capacity for AI development.
• Core Research and Model Development. While Canadian researchers pioneered fundamental AI breakthroughs, most large-scale AI models and platforms are developed by foreign firms. Limited access to capital and compute resources means Canadian labs and startups struggle to lead in producing major AI systems.
• Application and Scale-up. Canadian industry has been slow to adopt AI at scale — nearly half of Canadian executives cite talent shortages and high costs as barriers to AI adoption⁵. Few Canadian startups grow into large AI product companies; many exit early via foreign acquisition, exporting the value of Canadian innovation.

This briefing recommends a comprehensive reform of Canada's innovation approach, aligning public policy and private incentives to finally bridge the gap between invention and industrial impact. Key evidence-backed recommendations include:

• Strategic Coordination. Develop a national innovation strategy with clear priorities (e.g. AI, quantum computing) and streamline the myriad of programs (NSERC, IRAP, grants) into a cohesive "single window" commercialization pipeline⁶. Empower the new Canada Innovation Corporation (CIC) to lead this mission with an outcome-driven mandate.
• Strengthening Commercialization Infrastructure. Establish public–private commercialization hubs and centres of excellence in critical tech domains (for example, AI, quantum), co-located with top research institutions, to provide expertise, mentorship, and facilities for prototyping and scaling new technologies⁷⁸. Expand initiatives that connect academia with industry (e.g. applied research networks, innovation clusters) to address the "valley of death" between lab and market.
• Incentivizing Scale-Up Capital. Reform financial incentives to unleash domestic growth capital. This includes tax breaks for venture investments in Canadian tech⁹, public co-investment programs to match private VC for later-stage rounds¹⁰, and leveraging pension funds and a possible sovereign venture fund to provide large-scale funding for high-growth firms. These steps aim to curb the reliance on foreign capital and prevent the premature sale of Canadian companies.
• Aligning Procurement and IP Policy. Implement innovation-friendly procurement practices requiring government and large incumbents to pilot and purchase Canadian tech solutions, giving startups vital reference customers. Tighten IP policies to ensure that publicly funded research leads to domestic IP generation; for example, limit SR&ED credits for multinational branch plants unless they invest in local IP commercialization².
• Talent Retention and Attraction. Double down on retaining top talent by offering competitive incentives for skilled AI and engineering professionals to stay in or relocate to Canada (building on existing tech immigration successes). Support training programs in entrepreneurship for researchers, and consider requirements (or incentives) for recipients of major scholarships or AI research chairs to engage in commercialization projects. Stem the "brain drain" so that Canadian expertise fuels domestic industry, not just Silicon Valley.

Taken together, these measures chart a path for Canada to become not just an innovator, but an innovation scale-up nation — converting its rich input of ideas into outputs of jobs, productivity, and prosperity. The following sections provide a detailed situational analysis and the rationale behind each recommendation, informed by data and international best practices.

Introduction: Canada's Innovation Paradox

Canada has long exhibited a striking innovation paradox: excellent research but weak commercialization¹¹. Multiple expert assessments have concluded that Canadian academic research is strong and well-regarded internationally, yet Canadian business innovation and the commercialization of that research are notably weak¹¹. This imbalance contributes to Canada's poor productivity growth and lagging economic gains from emerging technologies.

Despite being home to pioneering developments in AI and computing (such as early deep learning breakthroughs in Toronto and Montreal), Canada struggles to translate scientific advances (Technology Readiness Level 1–3) into market-ready products (TRL 7–9) and scalable companies. The traditional "linear model" of innovation — where academic discoveries seamlessly fuel industrial development — has not materialized in Canada¹². Instead, Canadian firms often don't capitalize on domestic research, due to structural and strategic factors in both the public and private sectors.

Several core issues define this challenge:

• Heavy Public R&D, Low Business R&D. Canada leans heavily on universities for R&D. Over a third of national R&D is performed in the higher-education sector (34%¹³), more than double the OECD average share¹⁴. By contrast, business R&D intensity is only ~1.1% of GDP, placing Canada near the bottom of G7 countries⁴. In 2023, Canada's total R&D spending was ~1.8% of GDP, well below peers (the OECD average ~2.7%)³. This imbalance means Canada's innovation engine is fueled by academic inquiry, but the private sector is not fully engaged in turning ideas into competitive products.
• Significant Public Support, Fragmented Strategy. The federal and provincial governments offer a plethora of grants, tax credits, and programs to spur innovation. For example, the federal Scientific Research & Experimental Development (SR&ED) tax credit program alone provides about $4.2 billion annually to over 20,000 claimants⁶. Additionally, 172 different federal programs under the Business Innovation and Growth Support umbrella delivered $5.9 billion to firms in 2022⁶. However, these efforts are fragmented across agencies (NSERC, NRC-IRAP, regional funds, etc.) and lack a unifying industrial strategy¹⁵. The result is overlapping mandates, complexity for innovators to navigate, and no clear accountability for achieving commercial outcomes.
• Underdeveloped Commercialization Pathways. There is a mismatch between research output and commercial uptake. Universities prioritize publication and discovery, while Canadian industry often remains risk-averse in adopting cutting-edge innovations. Tech transfer offices and incubators, though present, are not yielding high volumes of high-growth spin-offs relative to Canada's research strength. In many cases, promising IP from Canadian labs ends up licensed or sold abroad, or simply left on the shelf due to lack of venture support.
• Private-Sector Gaps. Canada's private sector has been slow to create global technology leaders in the AI/software space. There is scarce Canadian representation in top-tier global VC portfolios — for instance, Andreessen Horowitz (a16z), known for backing world-leading tech firms, had virtually no Canadian-founded companies in its major funds until very recently. (In 2025, one Toronto startup, OpenSesame, made headlines as the only Canadian company in a16z's accelerator cohort¹⁶, underscoring how rare such inclusion is.) Furthermore, Canada has few domestic unicorns relative to its size, and notable successes often relocate or get acquired by foreign companies early in their growth.

The consequences of these issues are evident in economic outcomes. Canada's GDP growth and productivity have been held back by weak innovation diffusion — firms in traditional sectors have not significantly boosted productivity through new technology adoption, and new tech sectors have not grown large enough domestically to move the needle. The country has "sustained prosperity despite weak innovation" in part by relying on other advantages (resource exports, a favourable exchange rate in the past)¹⁷, but this is not a sustainable path in a high-tech, high-productivity global economy. To secure future prosperity, Canada must foster an "aggressively innovative business sector,"¹⁸ especially in critical fields like AI where it has early advantages.

The remainder of this briefing diagnoses the underlying causes of Canada's commercialization shortfall in greater detail (across public and private spheres), draws comparisons with peer countries, and then outlines targeted recommendations to close the gap. The analysis pays particular attention to the AI/software sector lifecycle, given AI's central role in future growth and Canada's mix of strength (research talent) and weakness (scale-up and adoption) in this domain.

Public Sector Challenges: Academic Strength Meets Commercialization Weakness

1. Academic-Led Innovation Programs and Their Inefficiencies

The Canadian public sector heavily funds innovation through academia and collaborative R&D initiatives. While this has built a strong research base, several inefficiencies hinder the translation of research into commercial success:

• Mission vs. Mandate Misalignment. Agencies like the Natural Sciences and Engineering Research Council (NSERC) primarily support basic research and talent training. Canada's universities excel at publishing and fundamental discoveries — a strength evidenced by high-impact research output in fields like AI. However, until recently there was relatively less emphasis on applied R&D and commercialization. NSERC programs (e.g. Collaborative R&D grants, the new Alliance grants) encourage university-industry projects, but these are small steps toward application. Canada's innovation system lacked a dedicated mechanism to "pull" academic breakthroughs into the market. As a result, groundbreaking AI algorithms developed in Canadian labs often did not have a clear path to become products made in Canada. This gap has been identified in numerous studies, with one expert panel noting that Canada's research excellence hasn't translated to business innovation because most innovation is not linear — it requires business-driven pull and risk-taking which were missing¹².
• Fragmented Funding and Program Overlap. The innovation funding landscape in Canada is complex and siloed. Companies seeking support must navigate a maze of programs — from federal R&D grants (IRAP for small business, Strategic Innovation Fund for large projects, etc.), to tax credits (SR&ED), to provincial initiatives — each with its own criteria and bureaucracy. This fragmentation leads to duplication and administrative burden, potentially diluting impact. A recent analysis by Canada's U15 research universities highlighted that while billions are spent on business innovation programs, "the system is fragmented" and lacks alignment with an overarching industrial strategy⁶. For example, the Superclusters initiative (now re-branded as Global Innovation Clusters) injected funding into industry-led consortia, but without clear integration with other efforts or a long-term plan beyond 2025. Companies and researchers often struggle to piece together support from disparate sources, reducing efficiency and clarity of purpose.
• The SR&ED Paradox — Generosity without Focus. Canada's SR&ED tax incentive is one of the most generous R&D tax credit programs globally, offering refundable credits to firms performing R&D. It undoubtedly spurs R&D activity, but its impact on commercialization is questionable. SR&ED is firm-agnostic and outcome-agnostic — it rewards spending on R&D, not success in scaling or selling new products. Consequently, some firms treat SR&ED as a routine subsidy rather than a launchpad for innovation. Moreover, a large portion of SR&ED funding goes to foreign multinationals' Canadian branches², who perform R&D here (often to take advantage of tax credits) but may ultimately commercialize the resulting innovations abroad. This means Canadian taxpayers subsidize research that might not yield Canadian-owned IP or economic growth. Daniel Perry of the Council of Canadian Innovators noted that "a lot of the money provided by SR&ED goes to foreign multinationals with branch plants in Canada" even as Canadian entrepreneurs struggle for funding². In summary, the public incentives are not sufficiently targeted to drive Canadian commercialization outcomes — they are either too broad (like SR&ED) or too small-scale/project-based without scaling mechanisms.
• Weak Accountability for Commercial Outcomes. Academic and grant-based programs have historically measured success via research metrics (publications, patents filed, student training) rather than company growth or revenue generation. There has been little accountability for whether funded research leads to a new Canadian product or firm in 5–10 years. This has begun to change — for instance, the Networks of Centres of Excellence (NCE) program and newer innovation cluster programs require industry partnerships and some performance tracking. However, no single agency "owns" the mission of technology commercialization in Canada, which has led to diffuse responsibility. The recent creation of the Canada Innovation Corporation (CIC) is intended to address this by consolidating and focusing innovation support. The CIC, a new Crown corporation announced in 2023, explicitly aims to be "outcome-driven" and to prioritize research commercialization and IP development over pure science¹⁹²⁰. It will absorb NRC-IRAP and operate with private-sector leadership to bridge the gap from lab to market²¹²². If implemented effectively, CIC could correct some public-sector inefficiencies by reducing duplication and pushing for results. As academic Dan Breznitz observed, this is the first time government has "admitted we have a serious problem [with innovation]" and shifted focus to scaling in Canada rather than just funding more university research²⁰. The success of CIC will depend on strong execution, but its formation is a promising step to fix structural issues.

2. Commercialization Infrastructure and Support Systems

Even with adequate funding, innovations cannot scale without the right support infrastructure. Canada's innovation ecosystem has lacked some of the critical "in-between" institutions and incentives that help turn prototypes into products:

• Tech Transfer and IP Management. Canadian universities produce significant intellectual property (IP), but historically they have underperformed in patent commercialization and startup formation compared to U.S. peers. Part of this is due to scale (fewer large corporations to license technology), but part is systemic. Universities often have small tech transfer offices with limited resources; the incentive structures for professors lean toward publishing over patenting or company-building. In the U.S., by contrast, major research universities (Stanford, MIT, etc.) have cultivated cultures of entrepreneurship and have well-funded commercialization offices, sometimes backed by sizeable endowments or venture funds. Canada is catching up — initiatives like the Campus-Linked Accelerators in Ontario or UBC's entrepreneurship programs are positive — but many Canadian researchers still find it easier to join an established foreign company than to spin out a startup. The result is that Canadian IP frequently leaves the country. For example, elements of deep learning algorithms developed in Toronto were quickly absorbed into Google and other U.S. companies, rather than forming the basis of Canadian firms.
• "Valley of Death" Funding and Mentorship. There is a notorious gap between invention (often TRL 3–4) and commercialization (TRL 7+), often called the valley of death. Canada's support in this zone has been patchy. IRAP provides grants to help a company build a prototype, and organizations like Ontario's OCI or Sustainable Development Technology Canada (SDTC) help in specific sectors. However, there has not been a widespread equivalent of the U.S. SBIR (Small Business Innovation Research) program, which systematically funds early-stage tech companies through phases, including government procurement as a test bed. Moreover, large-scale mentorship or accelerator programs have mostly come from private sector or abroad (e.g. Creative Destruction Lab is a notable Canadian-born accelerator, but many startups also go through U.S. accelerators like Y Combinator). The result is that many entrepreneurs struggle to find not only money but also seasoned guidance to navigate the scaling process within Canada. In terms of infrastructure, the country could benefit from national commercialization hubs that bring together investors, industry experts, and researchers. The Australian Strategic Policy Institute suggests that universities often lack resources to commercialize on their own, and public–private hubs can fill this need⁷²³. Such hubs, if established in Canada (for example, around AI institutes in Toronto, Edmonton, Montreal), could provide shared facilities (for prototyping, testing) and business development support to nascent tech firms.
• Industrial Pull and Procurement. A critical component of innovation ecosystems is having sophisticated, early-adopter customers in the domestic market. In Canada, large industries (energy, mining, finance, etc.) have not consistently played this role for high-tech startups. Many Canadian corporates are conservative in purchasing from small domestic firms, preferring established global vendors. Additionally, government procurement at federal and provincial levels has seldom been leveraged to drive innovation adoption. Programs like Innovative Solutions Canada (a government challenge-based procurement for startups) are relatively small. The contrast is stark with the U.S., where defense and security procurement has historically been a huge driver of tech (e.g., the US government was the first client for semiconductor companies, the internet, etc.). Canada's public sector could do more to become a "first customer" for Canadian innovations. Without this, local startups often go overseas to find willing pilot customers, sometimes relocating in the process. Indeed, data indicates that many Canadian companies entering U.S. accelerators do not return to Canada²⁴, in part because they secure their first big clients or investors abroad, tying their future to that market. This represents a lost opportunity for Canadian commercialization at home.
• Brain Drain at the Commercialization Stage. Canada's strong academic programs attract and produce top talent (including many international students who study here). However, the career pathways for those highly skilled in STEM often lead out of the country. One reason is the relative scarcity of cutting-edge tech jobs in industry within Canada — many of the most attractive opportunities are with U.S. tech giants or fast-growing startups in Silicon Valley. This talent outflow includes not just researchers but also experienced executives who are needed to scale startups into large firms. The Research Money conference panel in 2025 highlighted that when Canada's scale-ups get acquired early (often at $50–100M revenue), we lose not only IP and talent but also "the venues required to train managers in how to run global companies."²⁵ If our managers never lead companies beyond mid-size, we don't build a domestic pool of experienced CEOs, CFOs, product managers, etc., who have taken a company from inception to IPO. This becomes a self-reinforcing problem — the next generation of startups has to look externally for that expertise. In short, Canada's innovation infrastructure is missing sufficient mechanisms to retain talent through the scale-up phase, whether via compelling local opportunities or incentive ties (such as requiring grant recipients to stay in Canada for a period, or offering fast-tracked visas to global experts to lead Canadian firms).

In summary, the public side of the equation in Canada has emphasized knowledge creation over commercialization for decades. The infrastructure to support entrepreneurship — from tech transfer improvements, to bridging funding, to strategic procurement — has not been as robust as needed to consistently produce world-leading companies from homegrown ideas. Reforms are underway (e.g., the new Canada Innovation Corporation and programs focusing on IP and scaling), but overcoming legacy inefficiencies will require sustained effort and cultural change in how public institutions view success in innovation (shifting from "papers published or grants disbursed" to "products launched and revenue generated").

Private Sector Challenges: The Missing Scale-up Culture

On the private sector side, Canada's innovation shortfall can be traced to challenges in venture capital, entrepreneurial scale-up mentality, and market dynamics that differ from more high-performing ecosystems:

1. Scarcity of Growth-Stage Capital and Investors

While early-stage startup funding in Canada has improved in recent years (with many seed and Series A funds now active), large late-stage financing is often lacking. Canadian venture capital firms tend to be smaller, and institutional investors (like pension funds and banks) historically have been cautious about investing in domestic tech ventures. Some key points:

• Domestic vs Foreign VC. Data shows that Canadian investors dominate the small deals (<$5M), but for large deals (>$50M), U.S. and foreign investors step in and often take over²⁶. As companies mature and need big infusions of capital to scale globally, they frequently have to court U.S. venture funds or corporates. Those foreign investors typically demand significant equity and sometimes relocation of the company headquarters to the U.S. for easier oversight or integration. This pattern leads to a situation where Canadian scale-ups become essentially U.S.-based as they approach IPO or high valuations — losing some Canadian economic benefits. For instance, several of Canada's promising AI startups, upon reaching a certain size, re-incorporated in Delaware or moved executives to Silicon Valley under pressure from U.S. venture capital. A recent example highlighted by BetaKit involves founders being urged to reincorporate in the U.S. as a condition of funding, something leading Canadian AI entrepreneurs have publicly cautioned against²⁷.
• Conservative Capital Culture. Despite ample capital in Canada (our pension funds and financial institutions manage huge asset pools), relatively little flows into domestic venture capital or tech projects. As CVCA and industry leaders have noted, Canadian capital often prefers "safe" assets like real estate over the perceived risks of tech startups²⁸. This is in part structural — there are disincentives or lack of incentives for Canadian institutional investors to take venture risk at home. The result is a form of market failure in venture financing: it's not that capital is absent, but it's not being mobilized for innovation at the needed scale. The outcomes include undercapitalized Canadian companies or dependence on foreign capital (with strings attached). This dynamic was also linked to "Dutch disease" by ASPI analysts — in resource-rich economies (Canada, Australia), high returns in commodities can divert investment away from tech during boom times¹⁰. With Canada's strong resource sector, when commodity prices are high, investors may find sufficient returns there, neglecting tech innovation.
• Consequences of Foreign-funded Growth. When the majority of later-stage funding comes from outside Canada, the ultimate returns on investment also flow out of Canada. A life-sciences industry analysis found that only ~10% of limited partners in Canadian VC funds were domestic²⁹ — meaning when Canadian biotech startups succeed or exit, most profits return to U.S. or global LPs, not to Canadian reinvestment. This hampers the recycling of gains into the next generation of Canadian funds and companies. Additionally, foreign VCs may have different exit priorities (often a faster sale or IPO) that can lead to "premature exits" for companies here. Professor David Wolfe noted that most Canadian tech firms get acquired when they reach modest scale, often at a 30% discount to comparable U.S. company valuations³⁰, because buyers know Canadian firms have fewer options to grow independently. Such early sales mean Canada frequently "undervalues its own scale-up firms" and loses the IP and talent to the acquiring foreign entity¹. This cycle makes it hard to ever develop a Canadian Google or Amazon — our promising firms are bought out before they can blossom into multi-billion-dollar giants.

2. Lack of Canadian "Anchor" Companies in Tech

Canada does not yet have a deep bench of domestic tech giants that can anchor the ecosystem (with notable exceptions like Shopify, and historically BlackBerry or Nortel, which either declined or were foreign-acquired). In the U.S., large technology companies (FAANG and others) not only contribute directly to GDP and innovation, but also act as acquirers, mentors, and customers for startups. In Canada, the absence of many large acquirers means entrepreneurs often look abroad for exit options, as discussed. It also means fewer experienced tech executives circulating in the ecosystem:

• Venture Portfolios and Global Perception. It was telling that until recently, none of the marquee investments of top Silicon Valley VCs were Canadian firms. This affects global perception — talented founders might feel they must move to the U.S. to be taken seriously or to access big-league investors. When Canadian startups do achieve high valuations, they sometimes shift domicile to tap U.S. public markets or larger pools of capital. As an example, Canadian-founded Element AI, once a flagship AI company, struggled to scale and was ultimately sold to a U.S. firm (ServiceNow) at a valuation that many saw as underwhelming for its promise. The lack of scale-ups reaching fruition domestically means fewer success stories to inspire and reinvest. Silicon Valley's culture benefits from a virtuous cycle where each generation of successful entrepreneurs becomes angel investors or mentors to the next. Canada is only beginning to get this cycle moving.
• Scale-Up Skills and Management. Building a company from startup to global leader requires specific skills — scaling operations, international marketing, navigating public markets, etc. Because so few Canadian companies reach that stage, there is a shortage of executives and advisors here who have "been there, done that." Wolfe's insight is salient: if we never allow tech managers to grow companies past $50M revenue, we never develop local expertise to get to $500M or $1B²⁵. This becomes a self-fulfilling prophecy; Canadian firms often hire U.S.-based CEOs or executives when trying to scale, which can sometimes pull the center of gravity out of Canada. Moreover, promising Canadian entrepreneurs might opt to join a big foreign tech firm for a stable, well-paid career instead of taking the risk to build the next Canadian giant, especially if they don't see peers doing it successfully at home. This risk aversion at the individual level is reinforced by a broader cultural risk aversion in Canadian business, often noted by commentators: our economy has been traditionally dominated by banks, resource companies, utilities — sectors that prize stability. The "particular Canadian attitude to business risk" noted in the CCA report³¹ manifests as caution in both investing and venturing, which is not ideally suited to fast-moving tech industries that require bold bets.
• Limited Presence in Emerging Deep Tech. In newer fields like quantum technologies or advanced biotechnology, Canada has strong research (e.g., Perimeter Institute for quantum, University of Waterloo's Institute for Quantum Computing) and a few startups (D-Wave, Xanadu in quantum). Yet, similar patterns emerge: D-Wave, for instance, while a pioneer in quantum computing hardware from Burnaby, struggled with funding and market adoption, allowing competitors abroad to catch up. Maintaining leadership in such fields is hard without a robust private sector push and significant capital. Comparatively, American and even some European companies have started to lead in quantum commercialization. This underlines that research leadership doesn't automatically yield industry leadership without an entire ecosystem in support.

3. Market Size and Global Integration

Canada's domestic market, though affluent, is mid-sized (~38 million people) and often closely integrated with the much larger U.S. market. This has dual effects: on one hand, integration allows Canadian companies relatively easy access to U.S. customers if they can compete; on the other, it means Canadian firms face U.S. competitors early and often, and sometimes it's easier to become part of a U.S. firm than to fight it. Additionally, in regulated sectors (like finance, healthcare), Canadian market idiosyncrasies (provincial regulations, dominance of a few large players) can make scaling a new tech solution nationally quite slow. Many startups find they must expand to the U.S. or globally fairly quickly to grow, which can stretch their resources and lead to relocation.

Canada also traditionally relies on international trade and investment — which is a strength — but in the innovation context, it has meant being a "branch plant" economy for tech. Major foreign tech companies (Google, Microsoft, Amazon, Meta, etc.) have Canadian R&D offices and hire Canadian talent, sometimes out-competing startups for the best engineers. While these R&D outposts contribute jobs, the IP and high-value economic gains typically accrue to the parent company abroad. Moreover, these giants can acquire emerging Canadian startups to integrate into their operations (Google's acquisition of Montreal's DeepMind team and Toronto's smart city Sidewalk Labs project are examples). Without deliberate strategies, Canada's role risks being primarily a consumer and junior partner in the technology economy rather than an originator of leading firms.

In summation, the private sector in Canada has yet to fully step into the opportunity presented by the country's innovative minds. Challenges in funding, risk culture, and market dynamics all contribute to a scenario where many Canadian innovations are refined and scaled elsewhere. The next section will contrast this situation with approaches in other countries to provide context and potential lessons.

International Comparisons: How Canada Stacks Up

To better understand Canada's underperformance in commercializing tech innovation, it is useful to examine how other advanced economies — the United States, the United Kingdom, and Australia in particular — manage the journey from lab to market. These comparisons highlight different policy choices and ecosystem features, some of which Canada could emulate or adapt.

United States: An Ecosystem for End-to-End Innovation

The U.S. is the unequivocal global leader in turning innovation into GDP and industry dominance. Several factors distinguish the American approach:

• Scale and Depth of Capital Markets. The U.S. boasts the deepest venture capital market in the world. In Silicon Valley and beyond, massive amounts of risk capital are available, and investors are experienced in nurturing companies from garage startups to multibillion-dollar enterprises. U.S. VC firms raised over $150B in some recent years, dwarfing Canadian totals. Crucially, there's strong availability of late-stage funding, allowing companies to stay private longer and grow bigger before exit. For example, companies like Uber or Airbnb raised dozens of rounds of financing, often hundreds of millions of dollars, something essentially unheard of for Canadian startups. This capital depth means U.S. firms can aggressively scale operations, talent, and marketing globally — achieving dominance and thus high economic impact (and job creation) domestically. It also means American startups don't have to sell early; they can pursue global leadership.
• Integration of Research, Defense, and Industry. The U.S. government directly drives commercialization in key tech areas via procurement and R&D programs. The Defense Advanced Research Projects Agency (DARPA) is a prime example — it funds high-risk, high-reward research with an eye to eventual military or commercial use, and it pulls teams from universities and companies together. This model produced foundational innovations (the internet, GPS, advanced AI applications) and essentially bridged TRLs by sustained funding and coordination. Additionally, the Small Business Innovation Research (SBIR) program ensures federal agencies set aside R&D funds for small companies to develop prototypes and then purchase those that work — providing both capital and a first market. There is nothing of comparable scale in Canada's federal toolkit. U.S. national research labs (e.g. in the Department of Energy system) also partner closely with industry, and universities in tech hubs pursue industry collaboration aggressively. The state of California is exemplary: the University of California system, combined with nearby national labs and private companies, creates a vibrant ecosystem "conducive to research commercialization." Top research institutions in the U.S. like Stanford or MIT actively foster startups (through policies like easy IP licensing and on-campus entrepreneurship support). This tight integration contrasts with Canada, where academia and industry historically operated in more separate spheres.
• Cultural Embrace of Entrepreneurship. American business culture celebrates entrepreneurship and tolerates failure in the pursuit of innovation. There is a strong narrative of the visionary founder, and successful entrepreneurs often become folk heroes who then invest in the next generation. This culture encourages top talent to start companies as a respected career path (versus joining an established firm). In Canada, this attitude has been more subdued, though it's improving among younger generations and with high-profile successes like Shopify's IPO that minted billionaire founders domestically. Additionally, the U.S. attracts global entrepreneurial talent — many immigrants go to the U.S. explicitly to build companies, contributing significantly to its innovation output (examples abound, from Google's Sergey Brin to Tesla's Elon Musk). Canada has started to leverage immigration for entrepreneurship, but the U.S. remains a strong magnet due to its market size and funding.
• Market Size and Home Advantage. The sheer size of the U.S. domestic market (over 10x Canada's population and an even larger share of global high-tech demand) gives its companies a home turf to grow on that is unrivaled. A startup that captures even 5% of the U.S. market in a tech niche often becomes a giant. With Buy American tendencies and natural local networks, U.S. firms often get first crack at U.S. customers — including government and Fortune 500 companies — which can propel early growth. American companies also benefit from network effects (for example, a social media platform launched in the U.S. has access to a huge base to achieve critical mass quickly). Canadian firms, in contrast, often must internationalize early, which is challenging and costly.

Outcome: The U.S. leads in commercial outputs — from the number of tech unicorns and IPOs to the share of GDP from ICT and knowledge industries. It also dominates in the critical enabling technologies: for instance, the U.S. currently leads high-performance computing and is among leaders in quantum computing research, partly due to heavy federal investment and strong university-industry linkages. The presence of companies like Google, Microsoft, NVIDIA, etc., means the U.S. not only generates ideas but also reaps huge economic rewards from them.

However, it's worth noting the U.S. model has its own gaps (notably a lot of basic research still relies on federal funding and university labs, and the U.S. also grapples with ensuring broad-based benefits of tech). But for the scope of this briefing, the U.S. provides a benchmark of a country that excels at scaling innovation.

United Kingdom: Pushing for Commercial Focus in a Strong Research Nation

The UK's situation in some ways mirrors Canada's — a strong academic sector and historically middling business innovation — but in the past decade the UK has taken deliberate steps to improve commercialization:

• Increased R&D Investment and Targets. The UK government recognized it underinvested in R&D and set a target to reach 2.4% of GDP in R&D by 2027. Recent data suggests the UK's GERD (gross R&D) has risen to around 2.7% of GDP³² (possibly due to statistical revisions), roughly at the OECD average. Public R&D funding has been boosted with a commitment of £22 billion annually by 2026. A significant portion of this increase is directed at innovation and applied research programs. For example, Innovate UK, the UK's innovation agency, provides grants and loans to companies developing new products, and runs competitions similar to SBIR. The UK also offers R&D tax credits, though it has adjusted them over time to encourage additionality and to crack down on abuse.
• Catapult Centres and Research Translation. The UK established a network of Catapult Centres — physical innovation hubs in areas like High-Value Manufacturing, Cell and Gene Therapy, Digital, Satellite Applications, etc. These centres are akin to Germany's Fraunhofer Institutes or the recommended commercialization hubs for Canada, serving as bridging institutions that provide technical facilities and expertise to help companies prototype and scale new technologies. They explicitly aim to address gaps in the innovation chain. The UK also recently launched the Advanced Research and Invention Agency (ARIA) in 2023, modeled loosely after DARPA, to fund high-risk, high-reward projects with agility. This shows a commitment to new institutional models for innovation beyond the traditional academic grant system.
• University-Industry Collaboration. While still an issue (the UK has had its own "European paradox" discussions), leading UK universities have become more entrepreneurial. Oxford and Cambridge have technology transfer arms (Oxford University Innovation, Cambridge Enterprise) that actively seed startups, some of which have grown significantly (e.g., Cambridge's chip design company ARM became a global leader in semiconductors). The UK has also seen success in fintech and AI spinoffs — notably DeepMind, started in London, which achieved such AI advances that Google acquired it for £400M in 2014. The acquisition underscores both a success (world-leading AI developed in UK) and the commercialization challenge (it took Google's resources to fully exploit it, and now its benefits accrue to Google/Alphabet, an American firm). The UK government has since worked on strategies to retain such benefits, including stronger scrutiny of foreign takeovers in sensitive tech and encouraging domestic funding for scale-ups (e.g., via the British Business Bank and Future Fund for startups).
• Access to Global Talent and Markets. Being in Europe (even post-Brexit, London remains very international) helped UK tech by attracting talent and by having easier access to EU markets. London became a fintech hub partly due to a welcoming regulatory environment and its position as a global financial center. The UK has been proactive with Tech Nation visas and other talent initiatives to bring in skilled workers — something Canada also does well with its Global Talent Stream visa. Additionally, UK entrepreneurs often think globally from the start, given the UK market alone is not huge (~67 million people).

Outcome: The UK's efforts have started to pay off in certain metrics. It leads Europe in the number of tech unicorns; London is second only to Silicon Valley and New York in fintech activity. The UK improved university-business collaboration ranking (though still behind the U.S.). Challenges remain (UK productivity growth has also been sluggish, and there are concerns about R&D dips recently³³), but the UK provides a case of a country actively engineering policy tools to turn research strength into industrial outcomes. For Canada, UK experiences with Catapults, targeted funds, and balancing foreign vs domestic interests in tech can be instructive — acknowledging that the UK too is learning to keep its "DeepMinds" from slipping away.

Australia: A Resource Economy Trying to Pivot to Tech

Australia, like Canada, has a small population, abundant natural resources, and historically lower R&D investment. It faces a similar imperative to diversify into high-tech, and has launched initiatives we can compare:

• R&D Spending and Business Focus. Australia's GERD is around 1.7% of GDP³⁴, very close to Canada's level. Business R&D in Australia is dominated by a few sectors and also saw a peak during a mining boom (where mining companies invested in some tech, then pulled back)³⁵. Recognizing a decline in business R&D, Australia has recently increased government support. The R&D Tax Incentive (their version of SR&ED) was retained but with modifications to encourage additional R&D and deter simply "extra claims." More interestingly, Australia created a A$15 billion National Reconstruction Fund (NRF) in 2023, of which a portion is allocated to support priority areas like renewable energy, medical products, and critical technologies including AI and quantum. This fund can take equity stakes, aiming to fill financing gaps in commercialization.
• University-Industry Links. Historically, Australia ranked very low in university-business collaboration — at one point only ~1.6% of Australian businesses collaborated with research institutions, the lowest in OECD³⁶. This is strikingly low and identified as a major weakness. In response, Australia launched a Research Translation and Commercialisation Agenda in 2022, a A$2.2B investment targeting "Australia's Economic Accelerator" program and "Trailblazer" university initiatives³⁷. These Trailblazer universities receive funding to focus on translating research in specific domains in partnership with industry (e.g., clean energy, defense tech). The government is trying to change academic incentives and provide funding at later TRLs to push inventions forward. It is essentially an attempt to break the silo and encourage a culture shift in academia to value commercialization.
• Success Stories and Role Models. Despite structural challenges, Australia has produced some notable tech successes that serve as encouragement. Atlassian, a software company (enterprise collaboration tools), was founded by two Australians in Sydney and grew to a NASDAQ-listed giant worth tens of billions without leaving Australia. Canva, a graphic design platform startup from Australia, reached a valuation over $40B as a private company and remains based in Australia. These examples show it is possible to build global-scale tech firms from a smaller country with the right product and global mindset. The Australian ecosystem, like Canada's, benefits when these companies stay — they become anchor employers and inspire others. Government policies, such as generous stock option schemes and support for startups, helped Atlassian and Canva, but importantly their founders chose to keep significant operations at home.
• Comparative Brain Drain. Australia, like Canada, has seen many talented graduates leave for Silicon Valley or elsewhere. But it also attracts talent regionally (from Asia). Both countries rely on immigration for tech skills. Australia is perhaps behind Canada in attracting AI talent globally, as Canada's early moves with the Pan-Canadian AI Strategy and immigration eased rules gave it an edge in AI researchers. However, Australia is catching up by funding more AI and quantum research centers, and leveraging its alliances (for example, under AUKUS, Australia invests in quantum and cyber with the US/UK). Another relevant aspect: Australia's foreign investment rules have sometimes blocked or scrutinized tech acquisitions by foreign entities to protect strategic capability — Canada has similar powers but has rarely used them for tech startups (more for resource sector). Using such powers judiciously might help a country retain key companies.

Outcome: Australia's innovation performance is still considered middling, but it demonstrates a resolve to not be left behind. It is injecting funds and creating new programs (often learning from other countries' models) to drive commercialization. The lesson for Canada is that peer countries with similar profiles are actively reforming their systems — standing still is not an option if we want to remain competitive. Also, the successes of Atlassian and Canva suggest that with determination and the right support, globally competitive companies can emerge outside the usual Silicon Valley sphere — a pertinent reminder that Canada, too, can cultivate its own champions in AI and software.

Summary of Comparisons

• The U.S. provides the archetype of a full-spectrum innovation ecosystem — massive capital, aggressive commercialization, and structural advantages — but its scale is unique.
• The U.K. has taken targeted steps to bridge gaps, like specialized innovation centres and policy tweaks, and is seeing some positive outcomes in startup growth and R&D focus.
• Australia shares many challenges with Canada and is currently in the process of bolstering its commercialization pipeline through significant funding and structural changes.

For Canada, these comparisons highlight a few key themes:

• Strategy and Coordination. Other nations are implementing coordinated strategies (AUKUS technology cooperation, UK's Industrial Strategy tied to R&D, etc.). Canada lacks a clearly articulated, long-term innovation-commercialization strategy, which is needed to align its efforts.
• Investment Levels. Investment matters — whether it's government R&D, tax incentives, or venture capital availability. Canada will likely need to boost both public and private R&D investment to stay competitive, as others are doing.
• Institutions. Innovative institutions (DARPA/ARIA-like agencies, translational research programs, commercialization hubs) have proven helpful. The creation of Canada's Innovation Corporation is a start; more may be needed (e.g., specialized national missions or translational programs in AI, quantum, etc.).
• Cultural and Market Factors. Encouraging a culture of risk-taking and addressing market barriers (like procurement and scale) is part of the solution — an area where lessons from the U.S. entrepreneurial culture and UK/Australia's recent policies can inform Canadian action.

With the context established, we now turn to a closer look at the technologies underpinning the AI industry — since AI is a focal point of Canada's innovation hopes — and assess Canada's position across the AI value chain.

Technology Landscape: Critical Technologies Underpinning AI (with Quantum Included)

The AI industry does not exist in isolation — it depends on a suite of critical technologies spanning hardware, software, and related fields. Understanding these underpinning technologies helps identify where Canada needs strength to capture value in AI. According to ASPI's Critical Technology Tracker (2023), the following key technology areas form the foundation of AI and advanced software industries (with quantum technologies included due to their rising importance):

Technology Area	Relevance to AI and Software Innovation
Advanced Communications (5G/6G & Optical Networks)	High-speed wireless (5G/6G) and fiber-optic communications are critical for connecting AI-powered devices, enabling IoT, and transmitting the massive data volumes that fuel AI systems. Low-latency, high-bandwidth networks allow real-time AI applications (e.g. autonomous vehicles, smart cities) to function38. China currently leads in advanced communications research38, raising the competitive bar.
High-Performance Computing & Semiconductors	AI development requires immense computing power. High-performance computing (HPC) systems and advanced integrated circuit design/fabrication (including specialized AI chips and accelerators) provide the processing muscle for training complex models39. The U.S. leads in HPC and chip design research39, thanks to its semiconductor industry. Canada has pockets of strength (e.g., research at universities, small startups in chip design), but no major domestic chip manufacturers — a vulnerability for self-sufficiency in AI infrastructure.
AI Algorithms and Machine Learning Techniques	This encompasses core AI algorithm research, machine learning (ML) frameworks, neural networks, and hardware accelerators for AI38. It is the heart of the AI industry: innovations here directly improve AI capabilities. ASPI data shows China leads in high-impact research on AI algorithms and hardware accelerators38, and also in machine learning subfields40. Canada's contribution to fundamental ML (Toronto, Montreal labs) is notable historically, but sustaining leadership requires continued R&D investment and translation of those algorithms into products.
Advanced Data Analytics	The ability to derive insights from large datasets underpins AI's value. Advanced data analytics overlaps with ML but includes broader techniques for big data processing, data mining, and statistical analysis at scale. This is crucial for industries adopting AI (e.g., analytics in healthcare or finance). China currently has a lead in advanced data analytics research, holding 13 of the top 20 institutions in this area41. Canada's strength in data science talent is an asset, but it needs platforms and companies that excel in turning data to insight domestically.
Natural Language Processing (NLP)	A specialized branch of AI focused on understanding and generating human language (including speech recognition). NLP is fundamental for applications like virtual assistants, translation services, and any AI that interacts with text or voice. The United States leads in NLP research impact42, with its tech companies at the forefront (e.g., OpenAI's GPT, Google's BERT came from U.S. initiatives). Canada has research groups in NLP and notable companies (e.g., Toronto's Cohere develops large language models), but must compete with heavy U.S. and Chinese investment in this area42.
Distributed Ledger Technologies (Blockchain)	Though not exclusively an AI technology, distributed ledgers (blockchain) provide data integrity, security, and decentralized infrastructure that can support AI systems (for example, securing data provenance for machine learning, or enabling decentralized AI marketplaces). ASPI includes it as a critical tech in computing43. China is leading in research on distributed ledger tech43. For Canada, a strong blockchain sector could complement AI by ensuring trust in data and transactions, especially important as AI becomes integrated in finance and supply chains.
Protective Cybersecurity Technologies	As AI is deployed widely, protecting AI systems and the data they use is vital. Cybersecurity tech — including automated threat detection (often AI-driven itself), encryption (including post-quantum cryptography), and network defense — underpins safe AI adoption. Canada's cyber research is decent (some universities and companies excel, and Australia and Canada both show up in top institution lists for cybersecurity research44). However, in terms of industry, Canada has few large cybersecurity firms. With AI raising stakes (e.g., adversarial attacks on ML, need for secure AI models), this area is increasingly intertwined with AI's success.
Quantum Computing and Quantum Technologies	Quantum computing promises to solve classes of problems exponentially faster, potentially revolutionizing AI by enabling new algorithms or significantly speeding up training. Quantum communications (e.g., quantum key distribution) and post-quantum cryptography intersect with AI on the security side — ensuring data and AI models remain secure against quantum adversaries. ASPI's tracker highlights four quantum tech sub-areas: computing, communications, post-quantum cryptography, and sensing. The U.S. currently leads in quantum computing research45, while China leads in quantum communication and sensing46. Canada has made quantum a priority — investing in institutes (Waterloo's Quantum Valley) and startups (D-Wave, Xanadu). It's critical that Canada include quantum in its AI strategy, since breakthroughs here could set the next frontier of computational advantage in AI.

Why this Matters: To convert AI innovation into GDP, a country needs competence (if not leadership) in as many of these underpinning technologies as possible. These areas are highly interrelated; for example, lacking advanced chip fabrication means reliance on foreign suppliers for AI hardware (a current issue globally), and lagging in communications infrastructure could slow adoption of data-heavy AI applications domestically (e.g., rural broadband limitations would stunt agri-tech AI). Countries like the U.S. and China dominate many of these foundational areas, which gives their AI industries a formidable base. For Canada, realistically, it cannot lead in all, but it should identify strategic niches to focus on:

• Leveraging our strength in AI algorithms and ML to maybe specialize in certain applications (e.g., reinforcement learning where Canadian researchers excel).
• Ensuring we stay at the table in quantum computing — an area where our research is strong and could translate to viable quantum software companies or specialized hardware startups with the right support.
• Cybersecurity and privacy could be a differentiator for Canadian AI (building AI that is secure and respects privacy, aligned with Canada's strong legal frameworks and international trust).
• Participating in global partnerships for hardware — for instance, collaborating with allies on semiconductor initiatives or securing access to latest chips (friend-shoring). The ASPI recommendations for allies include actions like technology visas and "friend-shoring" R&D among partners, which Canada can leverage to share strengths (our AI talent) in exchange for access to others' strengths.

In conclusion, the "grid" of critical tech underlying AI underscores that innovation policy can't just zoom in on AI algorithms in isolation; it must ensure the supporting ecosystem (from bandwidth to processors to secure systems) is in place. This wide-angle view informs the next section's analysis of where across the AI innovation lifecycle Canada is falling behind, and why.

The AI/Software Innovation Lifecycle: Gaps from Idea to Industry

Focusing specifically on AI and software — fields that are key to future economic growth — we can break down the innovation lifecycle into stages and evaluate Canada's performance at each:

A. Basic Research and Ideation (TRL 1–3) — Canada's Position: World-Class Research, Strong Talent

At the initial stage of new idea generation, Canada shines. Our universities and research institutes (Mila in Montreal, Vector Institute in Toronto, Amii in Edmonton, etc.) have produced seminal contributions in AI. Canadian researchers co-authored foundational papers in deep learning and neural networks, and Canada's AI research is highly cited globally. The Pan-Canadian AI Strategy launched in 2017 funded AI chairs and centers that helped attract top minds (including retaining pioneers like Yoshua Bengio and attracting others). This resulted in a vibrant research community. In terms of talent, Canada became a training ground for AI experts — many of whom completed PhDs or postdocs under luminaries here.

However, a critical issue emerges after ideas are born: who owns and develops them further? In Canada's case, while basic AI ideas thrived, the rights and further development often migrated. For instance, Geoffrey Hinton, after his breakthrough work on deep neural nets at U of T, was hired by Google — thereafter his work benefited Google's AI leadership. Similarly, Canadian labs tended to publish openly (which is good for science), but comparatively fewer patents and spin-off companies resulted at this stage. This is partly cultural: the academic emphasis was on openness and advancing knowledge, not on proprietary development. It can also be traced to funding — most Canadian AI research was publicly funded or with open publication mandates, not via defense contracts or corporate R&D which in other countries would lock up IP. Thus at TRL 1–3, Canada generates knowledge that feeds the global AI ecosystem, but already we see the seeds of the paradox: others may capitalize on that knowledge more effectively.

B. Applied R&D and Prototyping (TRL 4–6) — Canada's Position: Active Startups and Projects, But Limited Support to Cross the "Valley of Death"

This middle stage involves taking algorithms and concepts and building initial applications or prototypes — for example, turning a new machine learning technique into a software demo or pilot project for a specific use-case. Canada has many innovative startups and research-company collaborations operating in this range. For instance:

• Element AI (Montreal) was founded specifically to apply cutting-edge AI research to industry problems, essentially acting as an applied R&D factory. It developed some prototypes and solutions for clients, though struggled with product focus.
• CIFAR's AI Chairs program encouraged not just research but also some level of industry collaboration, planting seeds for applied projects.
• Various startups in fintech, health-tech, and other areas began creating AI-driven prototypes (e.g. MindBridge in Ottawa working on AI for auditing, BluWave-ai using AI for energy grid optimization).

The federal IRAP program and other grants support many companies at this stage to develop proofs of concept. Provincial innovation hubs (like MaRS in Toronto, or Innovate BC) provide mentorship and connections. Yet, many startups report that after initial prototype development, they face the infamous "valley of death" — where more funding and industry buy-in are needed to go further, but are hard to find.

Problems at this stage in Canada include:

• Difficulty securing larger Series A/B financing to hire teams and refine the product (as discussed, local VCs can be timid and U.S. VCs want relocation).
• Challenges in engaging potential customers for pilots — Canadian industries sometimes prefer to wait for proven solutions. For example, a Canadian AI startup might find Canadian banks or hospitals are reluctant to be first adopters, pushing the startup to seek U.S. pilot customers. Those who succeed abroad often then stay abroad.
• Lack of targeted government procurement to sustain pilots. The government could, in theory, be a lead customer (through Innovative Solutions Canada, etc.), but these programs are relatively small and not AI-focused enough to absorb the multitude of AI prototypes being built.

So at TRL 4–6, Canada sees a lot of promising experiments, but too few make it to robust scalable product. The exit of Element AI is illustrative: despite substantial public funding and hype, it never quite productized its innovations into a scalable platform before being acquired. This suggests a need for better commercialization guidance and more patient capital at this stage.

C. Deployment, Scaling, and Market Adoption (TRL 7–9) — Canada's Position: Slow Adoption, Early Exits, Few Scaled Companies

This final stage is where real economic value is captured — turning a working prototype into a fully deployable product or service, and scaling up production, sales, and integration into industry workflows. Here, Canada is markedly lagging:

• Few AI Unicorns/Scale-ups. By 2025, Canada's list of AI or advanced software "unicorns" (valuations >$1B) is very short. There is Coveo (Quebec City-based AI-powered search platform) which went public, GeoComply (fraud detection, Vancouver), ApplyBoard (education tech). But none approach the size of global peers. Shopify stands out as a tech unicorn (now a decacorn) but its domain is e-commerce, not AI specifically (though it uses AI internally). Constellation Software and OpenText are large Canadian software firms, but they grew in earlier eras and mainly through acquisitions. The absence of household-name AI product companies signals a failure to scale.
• Slow Industry Uptake. Surveys show Canadian businesses are aware of AI but lag in adoption. As referenced earlier, about 48% of Canadian executives cited lack of technical talent as a barrier to AI adoption, and high costs as another⁵. Many sectors have not moved beyond pilot projects. For example, Canada's resource industries (oil & gas, mining) have great potential for AI (predictive maintenance, exploration, etc.) but we haven't seen a wave of AI implementation there yet. Similarly, manufacturing SMEs in Canada aren't automating with AI as quickly as German or South Korean firms, for instance. This means domestic demand for AI products is not as robust as it could be, which in turn stymies local AI vendors.
• Talent Drain in Scaling Phase. Suppose a startup has a viable product; scaling it needs experienced talent in operations, marketing, and continuous R&D. Canada often loses out at this point because either the company relocates or hires many staff abroad, or the founders sell the company. The brain drain is not just at the PhD level, but also at the seasoned leadership level. Without enough mentors who have scaled companies, startups may stumble or opt out early (as noted, a systemic issue where we lack the managerial "venues" to practice scaling⁴⁷).
• Acquisitions by Foreign Firms. When a Canadian AI company shows promise, a typical outcome is acquisition by a larger U.S. tech company. This provides a return to investors and wealth to founders, which is positive on one hand. On the other hand, it often means the product is absorbed into a foreign company's suite, and any further job growth or IP development occurs under that foreign ownership. For example, Maluuba, a Montreal AI startup in NLP, was acquired by Microsoft; it became part of Microsoft Research. Great for Maluuba's team and validating Canada's NLP talent, but the products and profits became Microsoft's. We see similar stories in gaming (Edmonton's BioWare bought by Electronic Arts) and other software — success is too often measured by a sale, not by building a new Canadian anchor firm.

Collectively, these observations show Canada has a funnel issue — wide at the top (many ideas, startups) and very narrow at the bottom (few large-scale outcomes). We cultivate talent and startups, but other ecosystems reap the final gains. This lifecycle analysis reinforces the urgent need for interventions targeted at the later stages of innovation (scaling, adoption, retention). It's not enough to generate ideas; policies must help those ideas grow at home.

Recommendations: Closing the Gap — A Roadmap for Innovation & Commercialization Reform

This section outlines evidence-based recommendations to reform Canada's approach to innovation policy and better align public efforts with private sector success. The recommendations are structured around key themes identified in the analysis: strategy and coordination, funding and incentives, talent and culture, and infrastructure and partnerships. Each recommendation is aimed at enabling more Canadian innovations (especially in AI/software) to reach market and scale within Canada, thereby boosting GDP and long-term prosperity.

1. Formulate and Execute a National Innovation Strategy with Clear Commercialization Targets

Canada needs an overarching innovation strategy that unites academia, industry, and government goals, focusing on areas of strength and strategic importance. This strategy should:

• Identify Priority Technologies and Set Goals. Much like the ASPI Tracker's emphasis on critical technologies, Canada's strategy should prioritize domains (AI, quantum, clean tech, biotech, etc.) and set 5-10 year targets for each (e.g., number of Canadian firms in global top 10, or revenue targets from domestic tech sector). A national strategy provides direction so that funding isn't fragmented. Currently, the missing piece in Canada's support system is "a strategy for Canada's economy and an architecture to marshal collective strengths for industrial capability."⁴⁸ The innovation strategy should fill this void, ensuring that investments by NSERC, NRC, regional agencies, etc., align toward common outcomes.
• Coordinate and Consolidate Programs. The strategy should guide the new Canada Innovation Corporation (CIC) in rationalizing federal innovation programs. Redundant or siloed programs can be merged under CIC to provide companies a single touchpoint. With IRAP being absorbed into CIC²¹, this is an opportunity to integrate R&D grants with commercialization support in one organization that tracks firms' progress along the TRL chain. The CIC must also work closely with provincial programs to avoid duplication and confusion.
• Include Accountability Mechanisms. Establish metrics and public reporting for commercialization performance: e.g., increase in business R&D/GDP (aim to raise from 1.1% toward 2% over a decade), number of startups that scale past $100M valuation, reduction in time to exit, etc. By monitoring these, policymakers can adjust tactics. Making some funding contingent on commercialization attempts (without penalizing failures outright) could incentivize institutions to focus beyond research. For instance, university funding agencies could allocate a portion of grants to projects with industry partners or plans for translation. Departments should jointly own the strategy (Innovation, Science and Economic Development (ISED) and Finance, primarily) to ensure economic policy and innovation policy work hand in hand.

2. Boost Late-Stage Funding and Curb Premature Exits through Financial Incentives

To address the capital gap and early exit problem, the government should employ fiscal tools and direct investment strategies:

• Tax Incentives for Venture Investment. Encourage private investors to deploy capital in Canadian venture funds or startups by offering favourable tax treatment. This could include credits or deferrals for investments held for a certain period. As ASPI suggests, adjusting taxation to channel private money into venture is key⁹. For example, a labour-sponsored venture capital corporation (LSVCC) style credit could be modernized for tech, or capital gains tax on qualifying tech investments could be reduced. Such measures should be designed to reward patient capital that helps companies scale, not just quick flips.
• Public Co-Investment (Scale-Up Fund). Building on the Venture Capital Catalyst Initiative (VCCI) which put public money into VC funds, Canada can establish a dedicated Scale-Up Fund (perhaps via BDC or CIC) that matches late-stage investments in Canadian firms at, say, 30-40% of the round⁴⁹. This would reduce risk for large investors and entice bigger cheques. It also signals confidence in domestic winners. Countries like Israel had success in the 1990s with the Yozma program (government matching funds that kick-started their VC industry). Canada could similarly allocate a few billion dollars to co-invest in promising companies that have traction, ensuring they have the growth capital to remain independent longer.
• Preventing Premature Exits. While one cannot (and should not) ban acquisitions, policy can influence the calculus. One idea is a form of "IPO incentive": provide benefits to companies that go public in Canada (e.g., listing fee rebates, or government as an anchor order in IPOs via funds) to encourage scaling to IPO instead of private sale. Additionally, the government could expand the Investment Canada Act's scope to more carefully review foreign acquisitions of high-tech firms, not to block routinely but to delay or add conditions ensuring some R&D or jobs stay in Canada. If critical IP is at stake (as in certain quantum or AI defense-related tech), having the option to classify it as strategic can prevent know-how from being lost. At minimum, shining a light on the trend of early exits (via public data and perhaps moral suasion) could encourage founders to consider alternatives.
• Leverage Pension and Institutional Capital. Canada's massive pension funds (CPPIB, CDPQ, etc.) should be engaged to invest a portion of their assets in domestic innovation. They often cite fiduciary duty for going abroad, but some funds (e.g., CDPQ in Quebec) have taken proactive stances in tech investing locally. The federal government could set guidelines or targets for pension plans' Canadian innovation investments, or facilitate a fund-of-funds that pensions can participate in, managed by professional VCs, to direct more money to Canadian scale-ups. Since an overwhelming majority of VC LP money in Canada currently comes from abroad²⁹, increasing the Canadian share (even from 10% to 30–40%) would recycle more gains internally.

By strengthening the financing environment and reducing the urge/need to sell early, these steps aim to help more Canadian firms grow into the next Shopify or Atlassian rather than the next quick acquisition. When companies scale domestically, they create exponentially more jobs and economic value at home.

3. Reform SR&ED and R&D Support to Reward Commercial Outcomes and Canadian IP

The SR&ED tax credit and related R&D supports should be recalibrated to maximize their impact on commercialization:

• Target SR&ED to Smaller Canadian Firms. Consider capping SR&ED claims for large multinationals or adjusting the credit rate such that SMEs and Canadian-controlled companies get a higher benefit than foreign-controlled entities. If, as reports indicate, a large share of SR&ED goes to foreign companies' R&D centers², then ensuring more of that credit pool goes to Canadian firms could level the playing field. Alternatively, require that to receive SR&ED, a foreign firm must show how the R&D leads to some production or value in Canada (difficult to enforce, but some conditionality or incremental eligibility criteria could be added).
• Link a Portion of Credits/Grants to Commercial Metrics. For instance, an "innovation voucher" could be given as a bonus to SR&ED claimants that successfully patent and produce a new technology in Canada. Or NSERC's I2I (Idea to Innovation) grants that help commercialize academic research could be scaled up, with follow-on funding contingent on meeting milestones (prototypes, licenses, startup formation). The key is not to penalize pure research (which has its own merit), but to create additional rewards for those who pursue commercialization. Over time, this nudges the system culture towards seeing R&D through to application.
• Sovereign IP and Patent Strategy. Canada should create a sovereign patent fund or IP collective to help Canadian startups secure and retain core intellectual property. One challenge is many small firms don't patent due to cost or they sell IP early. A collective, possibly managed by CIC, could co-invest in patent portfolios of Canadian SMEs (providing expertise and funding) and ensure patents aren't all sold off to foreign entities. In critical tech areas, if a startup is being acquired, the government could consider retaining license rights to use the IP for national purposes (especially in defense or critical infrastructure) — somewhat analogous to how some countries have "golden share" or compulsory license provisions. This is delicate, but the principle is to retain benefits of IP developed with public support.
• Invest in Demand-Side Innovation (Pull Mechanisms). In addition to supporting R&D creators, support the adopters. For example, provide tax incentives or grants to companies in traditional sectors that integrate Canadian-made AI solutions into their operations. A manufacturing firm that partners with a Canadian AI startup to deploy a system could get a tax write-off for the cost. This creates local customers for local innovations, addressing the "no market at home" problem.

Refocusing R&D support in these ways will help transition Canada from a place that subsidizes research in general to one that invests in innovation with an eye on economic return. Other nations are restructuring their incentives similarly — for instance, ASPI recommends democracies "restructure taxation to divert private capital to venture and scale-up efforts," which aligns with the above.

4. Strengthen Commercialization Infrastructure: Hubs, Accelerators, and Procurement

Building on the need for structural support, Canada should create and expand mechanisms that directly facilitate going from lab to market:

• National Commercialization Hubs/Centres of Excellence. As suggested by ASPI and evidenced by UK Catapults, establishing physical or organizational hubs for key technologies can concentrate expertise and resources⁷²³. Canada could set up, for example, an AI Commercialization Hub adjacent to the Vector Institute or Mila, where startups get access to computing resources (perhaps a dedicated compute cluster for Canadian AI firms), business mentorship, and investor networks. Similarly, a Quantum Technology Center could be formed in Waterloo leveraging the existing quantum institutes, focusing on helping quantum startups prototype and find customers (governments could be early adopters for secure comms or quantum sensors). These hubs should be public-private partnerships, co-funded by government and industry, ensuring they are demand-driven. Universities would contribute space and research input, while companies provide market insight and later-stage R&D. By placing commercialization experts in universities (or vice versa), we break silos — researchers can get advice on how to spin off companies, and businesses can tap academic breakthroughs earlier.
• Accelerate and Expand Accelerators. Support programs like Creative Destruction Lab (CDL) or other incubators to enlarge their cohorts, especially outside major cities, to tap the full talent pool. Encourage sector-specific accelerators (for AI in healthcare, for AI in agriculture, etc.) possibly run in partnership with global players (e.g., the way Ontario had an Autodesk-funded AI accelerator in construction). Government can offer funding to these programs with the stipulation that they bring in international advisors and investors to Canada, rather than our startups having to go abroad for that exposure.
• Government as First Customer (Procurement Reform). Enact policies to use government procurement as a launchpad for Canadian tech:
  • Set aside a percentage of federal procurement for innovative Canadian companies (similar to the U.S. SBIR mandate). If say 5% of appropriate departmental budgets are earmarked for sourcing from Canadian SMEs with novel tech, that creates a guaranteed market segment.
  • Create a "test before you invest" program where government departments pilot new Canadian technologies and provide feedback and reference letters if successful. This de-risks the tech for other buyers.
  • Expand Innovative Solutions Canada and make it permanent with larger funding pools, focusing on pressing public sector problems that Canadian tech could solve (e.g., AI for border security, or for health data management).
  • At the provincial and municipal level, encourage similar innovation procurement — for example, smart city initiatives using Canadian AI startups for traffic or utilities management.

Evidence suggests leveraging procurement can significantly speed up commercialization. One panelist in 2025 noted fair access to markets is key — "who owns it, builds it and runs it really matters" in data and emerging tech⁵⁰, implying that if domestic firms don't get those opportunities, someone else will own our digital infrastructure.

• Defense and Security Innovation. Given AI and quantum's importance in defense (and the fact Canada is in Five Eyes and NATO), the Department of National Defence should expand programs like IDEaS (Innovation for Defence Excellence and Security) to contract more R&D from Canadian firms. Also, consider a Canadian DARPA-like entity, possibly as a special unit in CIC or DND, to fund high-risk projects and then work with companies to commercialize spinoffs. This not only yields security capabilities but can spawn civilian companies (as DARPA did with the internet, GPS, etc.). The recently announced NATO DIANA (Defense Innovation Accelerator for the North Atlantic) will have an office in Canada — we should maximize its integration with our startups for dual-use tech.

Overall, these infrastructure moves aim to create a supportive environment where innovators have the help they need at every step — whether it's facilities, expertise, or a first big contract.

5. Talent, Culture, and Immigration: Make Canada the Place to Launch and Grow

None of the above works without the right people and mindset. We must reinforce Canada's talent base and entrepreneurial culture:

• Retain Talent Through Opportunities. The best way to keep talent is to have exciting work and career growth at home. By implementing the recommendations to grow companies, many talented Canadians will stay because they can fulfill their ambitions here. In addition, specific retention tools could include:
  • Prestigious Fellowships or "moonshot" programs for top young innovators, where they get funding to start companies or lead major projects in Canada (similar to the Thiel Fellowship in the U.S., but to encourage staying in Canada).
  • Requiring that recipients of large scholarships (e.g., Vanier, or AI Chairs funding) spend a period (say 1-2 years) in Canada in industry after graduation or encouraging it through postdoc bridge programs with companies. Already, programs like Mitacs place grad students in businesses; expanding these integrates talent with industry early.
  • Promote success stories of those who stayed: building a narrative that Canadians can fulfill world-class dreams without moving. This is cultural — celebrating "Made in Canada" tech achievements in media and education, to inspire the next generation.
• Immigration for Innovation. Canada is actually a leader in using immigration to boost tech — the Global Talent Stream visa offers 2-week processing for skilled tech workers, and we actively recruit international students in STEM. Continue and enhance these:
  • Add a stream for founders and startup teams (some countries have startup visas, Canada does but could be more expansive). Make it easy for a talented team anywhere to choose Canada as their HQ by removing visa frictions and possibly providing startup grants or matching funds if they incorporate in Canada.
  • Reciprocal talent agreements with allies: As ASPI recommended new technology visas among allies⁵¹, Canada could partner with like-minded countries (UK, Australia, EU nations) to create a talent exchange or joint visa that makes it simple for experts to move to where projects are, including Canada. This could mitigate brain drain to a single locus (Silicon Valley) by creating multiple attractive hubs.
• Entrepreneurial Education and Culture. Encourage universities to value and teach entrepreneurship. Many Canadian universities now have innovation centers, but these should be more formally integrated. For example, allow professors to take sabbaticals to start companies without penalty, adjust tenure criteria to count patents or prototypes, and provide more entrepreneurial training for students (so that science/engineering grads know how to commercialize their ideas). The culture shift requires leadership: if university presidents and industry leaders champion commercialization as a noble pursuit (equal to pure research), attitudes will change. Government can facilitate by awarding innovation medals or prizes to researchers who successfully commercialize technology, raising their profile akin to how we celebrate publication excellence.
• Network Canadians Abroad Back to Canada. There is a large diaspora of Canadian tech talent in Silicon Valley, New York, London, etc. Initiatives to connect them back can pay dividends. For instance, maintain a directory or network (many informal ones exist) of Canadians abroad willing to mentor or invest in Canadian startups. Some might return if given the right role — the "boomerang" effect. The government can support events or programs (like the Canadian Tech Accelerators in the US that Global Affairs runs, but expanded to identify Canadian expats to involve them). These individuals can often be bridge-builders to global markets without the company leaving Canada.

6. Foster Global Partnerships While Protecting National Interests

Innovation is global, and Canada should leverage international collaboration to amplify its efforts:

• Allied Collaboration in Critical Tech. Join and actively contribute to partnerships like the Quad/AUKUS tech initiatives (even if not a member, Canada can engage through Five Eyes and G7) that facilitate sharing research and coordinating on tech standards and procurement. Working with allies can help pool resources — for example, co-develop AI or quantum solutions with the UK and Australia so each country isn't doing everything alone, but ensure Canadian firms are part of the supply chains. The ASPI report notes that democracies have a potential aggregate lead if they collaborate, and Canada should be part of that collective effort.
• Trade Agreements and IP. Use trade negotiations to open markets for Canadian tech and protect IP. Ensure new agreements have provisions that, say, prevent forced data localization or IP surrender in jurisdictions where Canadian companies might operate, so our firms can scale globally without compromising their assets.
• Attract Foreign R&D Investment Strategically. While we want to grow Canadian firms, attracting top-tier foreign R&D labs can also help (they train people, who may spin off startups). Canada should continue courting investments like Google's AI center in Montreal or Huawei's (now controversial) research here in 5G in earlier years, but negotiate to maximize local benefit (e.g., insist on some IP being registered in Canada or some collaboration with universities). If foreign multinationals benefit from Canadian incentives, ensure some knowledge spillovers.

7. Continuous Improvement: Evidence-Based Policy and Experimentation

Finally, the government should treat innovation policy itself as something to iterate on:

• Collect Data and Feedback. Set up an Innovation Observatory to track how companies progress, which programs they use, where they stumble (e.g., a longitudinal study of startups over 10 years). Use StatsCan and new data to monitor outcomes like IP retention, jobs created, etc. This evidence can show what's working or not — for example, if expanded procurement yields faster growth for firms, double down on it.
• Experiment Regionally or by Sector. Pilot new approaches on a small scale before national rollout. For instance, trial a large procurement program in one department (say, DND or Health) and evaluate results. Or have one province try an aggressive tax incentive and compare outcomes. Innovation in policy can mirror innovation in tech — try, measure, learn, adapt.
• Engage Stakeholders Continuously. Create formal channels for innovators to advise government — perhaps an annual Innovation Leaders conference or a standing council of startup CEOs and VCs that meets with the Ministers. Their ground-level insights will keep policy relevant to changing tech trends.

Conclusion

These recommendations, taken together, aim to overhaul Canada's innovation system from one heavy on research inputs to one stronger on commercialization outputs. The stakes are high: emerging technologies like AI will shape future economic and military power, and Canada cannot afford to remain a mere consumer or contributor of talent to others' success. By addressing both public and private sector shortcomings — inefficiencies in support programs, lack of scale-up capital, weak demand pull, cultural risk aversion — Canada can unlock the full value of the bright ideas born within its borders.

In implementing these changes, it's important to maintain what Canada does well (excellent science, openness, strong human capital) while correcting course on what it does not (scaling and capturing value). Other countries have shown it's possible to change trajectory with focused policy and partnerships. If Canada commits to bold action now — investing smartly in innovation and aligning efforts from campus labs to Bay Street boardrooms — it can build an economy where homegrown AI and software innovations routinely grow into world-leading companies, driving wealth and opportunity for Canadians. The window is open, but not indefinitely; the time to act is now, before the next wave of technology advancements once again sees Canadian ideas flourishing everywhere except Canada.

References

Footnotes

1. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩ ↩²

2. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩ ↩² ↩³ ↩⁴ ↩⁵

3. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩ ↩²

4. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩ ↩²

5. https://betakit.com/a16z-says-opensesame-to-canadian-agentic-ai-startup-for-its-speedrun-accelerator/ ↩ ↩²

6. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩ ↩² ↩³ ↩⁴

7. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩² ↩³

8. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

9. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩²

10. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩²

11. https://cca-reports.ca/wp-content/uploads/2018/10/paradoxlost_en.pdf ↩ ↩²

12. https://cca-reports.ca/wp-content/uploads/2018/10/paradoxlost_en.pdf ↩ ↩²

13. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩

14. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩

15. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩

16. https://betakit.com/a16z-says-opensesame-to-canadian-agentic-ai-startup-for-its-speedrun-accelerator/ ↩

17. https://cca-reports.ca/wp-content/uploads/2018/10/paradoxlost_en.pdf ↩

18. https://cca-reports.ca/wp-content/uploads/2018/10/paradoxlost_en.pdf ↩

19. https://betakit.com/newly-named-canada-innovation-corporation-will-see-budget-double-to-absorb-irap/ ↩

20. https://betakit.com/newly-named-canada-innovation-corporation-will-see-budget-double-to-absorb-irap/ ↩ ↩²

21. https://betakit.com/newly-named-canada-innovation-corporation-will-see-budget-double-to-absorb-irap/ ↩ ↩²

22. Newly-named Canada Innovation Corporation will see budget double to. https://betakit.com/newly-named-canada-innovation-corporation-will-see-budget-double-to-absorb-irap/ ↩

23. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩²

24. https://betakit.com/a16z-says-opensesame-to-canadian-agentic-ai-startup-for-its-speedrun-accelerator/ ↩

25. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩ ↩²

26. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩

27. a16z says OpenSesame to Canadian agentic AI startup for its speedrun. https://betakit.com/a16z-says-opensesame-to-canadian-agentic-ai-startup-for-its-speedrun-accelerator/ ↩

28. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩

29. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩ ↩²

30. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩

31. cca-reports.ca. https://cca-reports.ca/wp-content/uploads/2018/10/paradoxlost_en.pdf ↩

32. Bridging the innovation gap: the UK's R&D strengths and shortfalls. https://www.ciip.group.cam.ac.uk/reports-and-articles/bridging-the-innovation-gap/ ↩

33. UK R&D spending falls again -- why it matters for our future - NCUB. https://www.ncub.co.uk/insight/uk-rd-spending-falls-again-why-it-matters-for-our-future/ ↩

34. R&D and innovation in Australia: 2024 update. https://www.aph.gov.au/About_Parliament/Parliamentary_departments/Parliamentary_Library/Research/Research_Papers/2024-25/RandD_and_innovation_in_Australia_2024_update ↩

35. Research Note: Understanding Australia's declining R&D performance. https://www.aigroup.com.au/resourcecentre/research-economics/economics-intelligence/2023/understanding-australias-declining-rd-performance/ ↩

36. Driving Innovation -- Commercialisation and Translation - ACS. https://www.acs.org.au/campaign/2025-election-forum/driving-innovation.html ↩

37. Research Translation and Commercialisation Agenda. https://www.education.gov.au/research-translation-and-commercialisation-agenda ↩

38. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩² ↩³ ↩⁴

39. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩²

40. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

41. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

42. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩²

43. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩ ↩²

44. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

45. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

46. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

47. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩

48. [PDF] Talent, Research and Innovation in a Changing World (October. https://u15.ca/wp-content/uploads/2025/09/Canada-in-Transition-Talent-Research-and-Innovation-in-a-Changing-World-October-2025.pdf ↩

49. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩

50. https://researchmoneyinc.com/article/business-trying-to-scale-up-in-canada-face-multiple-challenges-with-no-silver-bullet-solutions ↩

51. ASPI's Critical Technology Tracker. https://drive.google.com/file/d/1RUbwTL-vgXmIfTHLTZkLPW2rfNuXfRvr ↩