Canada’s AI ecosystem is organized into a clear five-layer stack that separates where models are built, how they are run, and where value is ultimately captured. From infrastructure and data at the base, through development and middleware, to applications at the top, each layer performs a distinct role in moving AI from capability to deployment. Companies are active across all layers, but commercial outcomes are not created evenly. Infrastructure provides compute, data shapes model quality, development tools enable creation and optimisation, middleware coordinates production systems, and applications embed AI into everyday workflows. This structure signals a shift from experimentation to operational scale, where progress in the lower layers compounds into faster commercialisation at the top.

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Within the Top 100 AI startups, total funding is approximately $10.2B, distributed unevenly across the five layers of the AI stack. The Application Layer accounts for around $5.9B, followed by the Infrastructure Layer with approximately $2.5B and the AI Middleware Layer with $1.6B. The Data Layer has raised around $103M, while the AI/ML Development Layer accounts for approximately $90M, reflecting comparatively lower funding across the foundational layers of the AI stack.

Across the Application Layer, the largest funding rounds are concentrated in companies such as Waabi ($1.0B), StackAdapt ($537M), and Trulioo ($477M) which embed AI into established enterprise workflows such as marketing, identity, and compliance. These companies align with existing budget categories and operational systems, supporting repeatable deployment across large customer bases.

In the Infrastructure Layer, funding is concentrated among compute-focused companies such as Tenstorrent ($2.5B), Mimik ($14M), and Blumind ($14M). Investment at this layer is directed toward improving the throughput, latency, and efficiency of AI workloads, with these advances shaping the cost and performance of the applications and platforms built on top of the infrastructure. The AI Middleware Layer includes companies such as Cohere ($1.5B), Botpress ($40M), and Shakudo ($18M). Their products help coordinate models, data, and services within production environments, enabling interoperability across AI components and supporting deployment across a range of use cases.

Funding in the AI/ML Development Layer is led by Veeda AI ($90M), while the Data Layer has attracted $103M in funding toward platforms that manage and structure data for AI applications. Together, these layers provide foundational capabilities that support model development, data management, and deployment across the broader AI stack.

Taken together, the funding pattern shows a layered market where capital is concentrated at the application level, while the ability of these companies to scale and operate efficiently is supported by the infrastructure, middleware, development, and data layers beneath them. As AI moves further into production, the focus is increasingly shifting toward the cost, performance, and reliability of deployment rather than model capability alone. Companies are becoming more selective about the models and infrastructure they use, while demand for compute capacity, enterprise data integration, and secure deployment continues to grow. In Canada, this is increasing the strategic importance of the supporting layers of the AI stack, even as applications continue to attract the largest share of funding.