Canada’s AI Strategy: Did the Government’s AI Consultation Miss the Hard Trade-Offs?

The government recently released a “what we heard” report highlighting the public and expert response to its 30-day sprint AI consultation from last October. The consultation itself was launched on an accelerated timeline, framed as a rapid-input exercise to inform the development of a national AI strategy. It combined a broad public questionnaire with targeted expert input and was promoted as an opportunity to quickly capture both citizen concerns and sectoral expertise on issues ranging from AI adoption and commercialization to safety, governance and competitiveness.

The compressed consultation window and the scale of responses signalled that newly installed AI Minister Evan Solomon was eager to move fast on AI policy. However, it also raised early questions about how deeply such input could realistically be assessed and reflected in the final policy direction. With the release of the report, the effort increasingly appears to be a case of “consultation theatre”, with the government shaping the feedback to fit pre-determined policy objectives.

AI Summarizing AI: Efficiency or Oversimplification?

Described as the “largest public consultation in the history of ISED”, the report relies heavily on AI for its analysis as the government notes that it used “Cohere Command A, OpenAI GPT-5 nano, Anthropic Claude Haiku and Google Gemini Flash to read through the submissions and identify common themes.” Given that it received 64,600 responses to 26 questions, it says AI enabled it to shrink a process that would typically take many months into a matter of weeks.

In addition to the public consultation survey, Solomon formed a 28-person expert committee that provided the government with 32 different papers and reports. Those documents were similarly subject to AI analysis, with the “what we heard” report devoting several pages to the expert analysis and recommendations.

What the Expert Reports Actually Emphasize

Since the government used AI to summarize the expert reports, I thought I would do the same. I uploaded all 32 documents to both ChatGPT and Perplexity AI and asked for summaries of the major themes and areas of disagreement. While there are obviously many overlaps since these are summaries of the same documents, there are some notable differences that suggest the government has not provided the public with the full picture. Indeed, the direct advice from the experts that identifies challenging policy choices and their implications is consistently softened into a balancing discussion that creates an illusion of consensus that isn’t really there.

For example, the expert reports argue that Canada’s AI challenge is not about research excellence or talent creation, but rather execution. Whether commercialization of AI, AI adoption, infrastructure needs or scaling to globally competitive operations, the reports repeatedly emphasize Canada’s failure to move beyond world-leading research. The government summary isn’t nearly as frank.

Instead, it presents each of its previously established policy pillars as if they were parallel priorities with balanced objectives. The result is a policy approach in which everything matters and few trade-offs are acknowledged. The experts may have tried to sound the alarm on the risks to Canada if it fails to act, but that isn’t the message the government seemingly wants to communicate.

Speed as Strategy—and a Missing Self-Critique

The same is true for speed, which the expert reports frame as a strategic variable in which countries that move faster lead, while those that hesitate are left to regulate what others have built. The reports suggest the government bears some of the blame here, as some point to slow procurement, delayed funding decisions and regulatory approval process barriers. Once again, that isn’t in the government summary, where there is no acknowledgement that Canada’s slow pace may already be undermining its competitiveness, sovereignty and capacity to shape global AI norms.

Trust and Safety: Consensus or Contested Trade-Off?

Perhaps the most notable divergence comes from the issue of trust and safety. Just about everyone agrees that trust is essential for AI adoption, but the implementation of regulation draws different views. Some want to move quickly, while others warn that overly broad regulation will slow deployment, disadvantage domestic firms, and regulate technologies Canada does not control. Those disagreements largely disappear in the government’s report, where trust is presented as a settled consensus objective, rather than a contested policy domain with real trade-offs.

There are many other examples, including differing views on access to capital, digital sovereignty, regulatory sequencing and inclusion. Put 28 experts on a panel, and there are obviously going to be differing views. Indeed, that’s the point of gathering a diversity of perspectives. In theory, the government got what it asked for with expert reports that frequently point to uncomfortable questions. However, many disappear when hard choices are reframed as balanced policy choices.

The Real Test: Strategy, Trade-Offs and Political Will

What comes next will matter more than the consultation itself. The report is expected to feed directly into a forthcoming national AI strategy and related policy and funding announcements. That will be the real test: whether the government is prepared to surface the hard trade-offs identified by its own experts on speed, procurement, capital formation and regulatory sequencing, or whether it will continue to emphasize consensus language and balanced pillars.

If the strategy leans heavily toward trust and safety frameworks without matching urgency on commercialization, adoption and government-as-customer procurement reform, it risks reinforcing the very gaps about which the experts repeatedly warned.