In Alberta, a new Memorandum of Understanding between the federal and Alberta governments—along with the big five oil sands giants—stated their joint commitment to the new West Coast Oil Pipeline and the Pathways Carbon Capture and Storage Project. The MOU outlines new steps forward on a bigger deal from last November between the feds and Alberta on energy development, and comes in the context of Alberta’s separation referendum on October 19. 

In BC, the new Canada-British Columbia Cooperative Prosperity Agreement details new federal support for resource development and infrastructure projects, in exchange for BC’s blessing on the new pipeline. BC also won some concessions around the pipeline route, and a promise for some revenues from the “economic upside” of the pipeline. 

Within a couple weeks, fire season erupted across the country, with wildfire smoke smothering eastern North America. In full climate change denial, President Trump and other U.S. politicians blamed Canada and threatened tariffs in retaliation. The cognitive dissonance between wildfires fuelled by a warming planet and the promotion of more fossil fuel megaprojects has been palpable in Canada as well. 

Even as fossil fuel corporations have record profits at their back, it is the federal government largely underwriting the expansion of oil and gas development, while removing barriers intended to protect the public interest and lower greenhouse gas emissions. Meanwhile, clean energy has landed as a viable, scalable alternative, suggesting Canada wants to become the wrong kind of energy superpower. 

New pipeline to BC coast

The new West Coast Oil Pipeline (WCOP) is technically still in its early development stages and a final investment decision will not be made until 2028 or 2029. According to the Alberta government’s submission to the federal Major Projects Office, the WCOP would cost between $35.2 and $43.7 billion, a cost that “compares favourably” to the Trans Mountain Pipeline Expansion (TMX). 

The TMX, of course, went wildly over budget—from $7.4 billion when the federal government took over the expansion project from Kinder Morgan in 2018 to more than $34 billion upon completion. The WCOP will also have substantial federal involvement, although ownership will be through “a new entity” that includes the Alberta Petroleum Marketing Commission and Pembina Pipeline Corporation.

A concession to BC is that the pipeline will not take a northern route and the federal North Coast tanker ban will remain in effect. Instead, the WCOP will largely follow the Trans Mountain pipeline system before terminating at Roberts Bank on the coastal outskirts of Metro Vancouver. This echoes the situation a decade ago when mass public opposition to Enbridge’s proposed Northern Gateway pipeline led the federal government, then under Justin Trudeau, to take it off the table in favour of the TMX.  

As a result of the cost over-runs, the tolls pipeline shippers pay have not reflected the full cost of building the TMX. Tom Gunton at Simon Fraser University estimates a subsidy to Canada’s oil industry between $9-19 billion based on comparing Trans Mountain’s interim tolls to what a private sector firm would charge to cover operating and capital costs. While Trans Mountain recently announced a settlement with the industry around the tolling structure, it’s unlikely the public subsidy will be fully eliminated. 

Trans Mountain will also be increasing pipeline capacity through a “mainline optimization project” that will enable an additional 300,000 barrels per day on its system. The WCOP would add one million barrels per day. In both cases, the federal government has promised BC a form of fiscal benefit or royalty, although the feds have not provided any further details.

The federal commitment to pipeline construction is also embodied in the new Major Projects Office itself. Inclusion of the WCOP in the major projects inventory would enable fast-tracking of permits and approvals. The MOU speaks to the federal government implementing a one year approval process for major projects, while Alberta feels it only needs 120 days. This runs the risk of running over Indigenous rights and circumventing environmental protection.

A carbon capture fig leaf

The federal government required that the new pipeline be accompanied by carbon capture and storage (CCS), a technology whose feasibility has yet to be demonstrated at scale or at reasonable cost. The Pathways CCS project would take carbon dioxide from 13 oil sands sites and transport them via pipeline to underground storage. At best, it’s a very expensive exercise, estimated at $20-30 billion, in branding Canadian oil as “low emissions” per barrel while simultaneously boosting overall production and emissions.

Oil and gas companies have already been using the Pathways project to promote a greener public image, though the companies have tried to put the onus on the federal government to pay for it. Through generous investment tax credits, the feds will be paying fifty cents on the dollar for capital investment in carbon capture equipment and 37.5 per cent on transportation and storage equipment. These tax credits are reduced to 25 per cent and 18.75 per cent respectively if used for enhanced oil recovery (EOR), a process of pumping carbon dioxide (CO₂) to repressurize older wells to extract more oil. 

Pathways would not be fully operational until 2035, at which point the MOU claims it would sequester a net 6 million tonnes (Mt) of CO₂ per year, which could increase to 16 Mt by 2045. The “net” refers to carbon sequestration after any EOR and is why the promised sequestration is so underwhelming. In context, Canada’s emissions from the oil sands were 89 Mt in 2023 and from the oil and gas industry as a whole, 208 Mt. 

Canada will see higher emissions from the oil and gas industry in line with increased pipeline capacity. It’s much worse in global terms, as exported oil and gas is counted where it is combusted, i.e. in another country’s inventory. For example, if the new combined 1.3 million barrels of pipeline capacity described above represented incremental production from the oil sands, this would translate into 185 Mt per year of CO₂ when combusted—the equivalent of one-quarter of all of Canada’s emissions economy-wide (694 Mt in 2023). 

Federal support is not limited to capital costs and the MOU commits Ottawa to “offer financing or support mechanisms that provide for operating cost support for carbon capture and storage projects”. Oil sands companies participating in Pathways will also get preferential treatment in Alberta’s industrial carbon pricing system (TIER) through a slower implementation of emissions pricing requirements. Agreement between the feds and Alberta earlier this year also weakened the targets for TIER, which is already insufficient with oil sands companies currently paying a mere nine cents per barrel on average as a “price on carbon.”

BC’s grand bargain

BC Premier David Eby heralded the July 2 Canada-BC Prosperity Agreement as “a big day for BC.” A new pipeline has been deemed the price of national unity in the age of Donald Trump’s trade war, so was there really any other choice? BC locked down federal financial support for key elements of the province’s resource-heavy Look West plan which it released last November (reviewed here). We can also count additional new federal spending in BC for housing and community infrastructure that was announced in June (including the controversial plan to bail out condo developers). 

The Prosperity Agreement is a major boost for liquefied natural gas and mining development in BC’s Northwest. BC Hydro has been planning a major electricity transmission line upgrade to power multiple new mining and liquefied natural gas (LNG) projects. The feds will contribute $3.9 billion toward this North Coast Transmission Line (NCTL)—out of a total price tag of $6 billion—although the federal money includes investment tax credits, lower-cost financing and First Nation equity support. The BC government has already exempted the NCTL from environmental assessment and review by the BC Utilities Commission.

BC intends for the NCTL to provide renewable electricity supply to future LNG facilities to reduce the gas they would otherwise burn to power their operations. This “clean LNG” would keep emissions within BC in check (albeit at the cost of rising electricity prices for other ratepayers), but the lion’s share of emissions would be outside of BC. LNG Canada, which has been in operation for a year, is a conventional facility burning gas, boosting BC’s annual emissions by more than 4 Mt per year (an increase of 7% for BC’s annual emissions). The facility has also had start-up challenges with excessive flaring and venting of “waste gas” into Kitimat’s air shed.

Also under construction in the Kitimat area is Cedar LNG, which has received $200 million from each of the federal and BC governments, which will use new electricity supply for its operations. Other LNG projects on the North coast are still awaiting a final investment decision, with LNG Canada Phase Two and Ksi Lisims LNG most likely to advance. Decisions have been held up by uncertainty about future LNG markets, and unless buyers are already lined up, few companies are going to approve capital investments in the tens of billions of dollars.

On the South coast of BC, just outside Squamish, Woodfibre LNG is also progressing through construction with operations likely to commence in 2028. While originally planned to be a smaller project, the federal Natural Resources Minister Tim Hodgson mused earlier this year that the plant’s capacity could be doubled or tripled. 

The Prosperity Agreement also pledged support for mining in BC, dubbed the Northwest Critical Mineral and Conservation Corridor. The main federal contribution is $500 million towards an underground expansion of the Red Chris copper mine. It’s not clear if this is financing, tax credits or cash, nor is it clear why the feds need to subsidize this mine when copper is increasingly in high demand. 

Mining is a dirty business and there’s no getting around tailings ponds and potential spills. The Mount Polley tailings dam collapse in 2014 released 25 billion tonnes of “toxic sludge” into the local watershed, including 134.1 tonnes of lead, 2.8 tonnes of cadmium and 2.1 tonnes of arsenic, making it the worst mining waste disaster in Canadian history. While the mine was back up running by 2015, charges were not filed against the company until late 2024 and the case is still before the courts. The BC government failed to implement promised regulatory reforms, and recently agreed to allow an increase in the height of the tailings dam (to an astonishing 77 metres) to extend the mine’s life another eight years.

While this looks like a win for mining and oil and gas companies, the fiscal benefits to governments are likely to be muted after we consider the vast subsidies at play. Employment benefits to workers and communities are largely on the construction side, as these are very capital intensive projects, with relatively few jobs once operational. 

As for conservation of “irreplaceable ecosystems”, the BC and federal governments merely commit to “undertake the development of a strategy.” 

The Prosperity Agreement will also have a large footprint in the Metro Vancouver area. Topping the list is a $10 billion federal expansion of the Roberts Bank shipping terminal to handle the new WCOP and other export commodities. The Port of Vancouver is also seeking to dredge Burrard Inlet to accommodate more capacity on oil tankers. The Tsleil-Waututh First Nation, who live on the inlet, are challenging this action in court. 

Potash exports were a sore point in 2025 when Saskatchewan’s Nutrien stated it would use Washington state ports due to lack of capacity at the Port of Vancouver. Federal officials have been scrambling to keep this activity within Canada. 

All of this ship traffic is bad news for endangered Southern resident killer whale populations.

The feds plan to spend about $50 million per year to mitigate impacts. Inevitably, additional oil and LNG tankers along with other increased shipping through the Salish Sea will be a big negative for marine life and will impact other industries like transportation and tourism.

Last but not least, the feds will also support up to $3 billion towards a new, higher-capacity Massey Tunnel under Fraser River to boost goods movement and alleviate congestion. Estimated costs have ballooned to $8.5 billion for the full project. The incoming NDP government in 2017 cancelled a planned bridge to replace the existing tunnel but progress has since been limited.

Wither climate action

All of this planned megadevelopment in the West might make sense in light of geopolitics in a world where carbon emissions did not matter. But the carbon implications are simply enormous and must be set against the real climate change the world is experiencing. Wildfires are again a top story but also floods had earlier raged Manitoba and Ottawa. In Europe, the death toll reached into the thousands due to unprecedented heat. 

Boosting oil and gas production and therefore global CO₂ emissions will cause damages into the future. Estimates of the “social cost of carbon”—the economic damages arising from greenhouse emissions—range from $50 to $250 per tonne of CO₂. Expanded oil and gas production in Western Canada could add as much as 200 MT of CO₂ per year to the atmosphere, or about $10 to $50 billion of damages every year. In light of the ongoing heat waves and wildfires, this is Bond villain territory.

The sad part is that both David Eby and Mark Carney get it on climate change. They do not deny the science and have been advocates for climate action in the past. Back when he was Bank of England governor in 2015, Carney made an important and influential speech on the mismatch between cause and effect: 

Climate change is the Tragedy of the Horizon. We don’t need an army of actuaries to tell us that the catastrophic impacts of climate change will be felt beyond the traditional horizons of most actors—imposing a cost on future generations that the current generation has no direct incentive to fix. That means beyond: the business cycle; the political cycle; and the horizon of technocratic authorities, like central banks, who are bound by their mandates.

Then, as now, Carney gave a great speech. But today’s speeches are more likely to trumpet Canada’s role as an energy superpower. Climate action has been deemed a loser at the polls. After patting ourselves on the back for our greenhouse gas emission targets and planning frameworks, Canada has dropped most of our climate policies. Contrast that with China, who never really made any public commitments, but then quietly took over clean energy technology supply chains.

But it’s much worse than not acting: Eby and Carney are championing a major expansion of the industry that is causing climate change, and propose to subsidize them even as they have profited so handsomely from recent supply shocks, whether from Iran or Ukraine. It doesn’t get more petro state than that. Apparently, the only thing that will stop Canada’s relentless march to produce and export ever more fossil fuels is if other countries stop buying. 

If anything, President Trump’s Iran war, and the vulnerability of supplies through the Strait of Hormuz, has also changed the calculus for Asian countries—upon whose purchases future Canadian oil and gas growth depends—to invest heavily in renewable technologies to become more self-reliant. Even before the Iran war, this overhang of uncertainty explains why no private sector proponent wants to build the pipeline given its massive price tag. 

Canada shifting into full-blown petro state mode is painful to watch for those of us who’ve been working on clean energy and climate policy for (in my case, 17!) years. How much of the future must we sacrifice for the sake of the present? The real lost opportunity is that this massive fiscal effort could alternatively transition Canada to a fully renewable, circular economy, build housing for all and make other investments that would raise our collective standard of living.

The post Fuel for the fires: Western Canada as an “energy superpower” appeared first on CCPA.

Ottawa is pursuing trade, investment, and security agreements that privilege corporate access and geopolitical leverage, even when doing so aligns Canada with governments implicated in repression, environmental harm, or grave humanitarian crises. The prime minister has described this approach as “principled and pragmatic,” as “values-based realism.”

The question Canadians should be asking is simple but uncomfortable: what values are we actually exporting? Consider Canada’s newly signed free trade and investment deals agreements with Ecuador and the United Arab Emirates (UAE).

UAE: Partnering with human rights abusers

Canada signed a Foreign Investment Promotion and Protection Agreement (FIPA) with the UAE in November 2025. This was complemented on July 24 with a rapidly negotiated Canada-UAE Comprehensive Economic Partnership Agreement (CEPA). Prime Minister Mark Carney framed these agreements as a gateway to “billions in bilateral investment,” particularly in critical minerals, energy, infrastructure, AI, and ports. 

The message was that Canada is positioning itself as open for business—and, more importantly, for investment, no matter where the money is coming from. But the UAE is hardly a neutral or benign partner. It has a documented record of suppressing dissent, restricting free expression, and exploiting migrant labour. It is also expanding fossil fuel production at a time when climate urgency demands the opposite.

More troubling still is the UAE’s role in the war in Sudan—one of the world’s worst humanitarian catastrophes. Since April 2023, Sudan has been devastated by mass displacement, famine, and widespread violence, much of it perpetrated by the paramilitary Rapid Support Forces (RSF). 

The RSF has been accused of ethnic cleansing and atrocities, particularly in West Darfur. Sudan brought proceedings against the UAE at the International Court of Justice in 2025, alleging that Emirati financial and logistical support had helped sustain the group’s campaign of violence (the proceedings were rejected on jurisdictional grounds). Investigative reporting has shown that the RSF controls gold-rich territory and that much of Sudan’s gold flows through UAE markets, providing a critical revenue stream that prolongs the conflict. 

Against this backdrop, Canada’s decision to deepen investment ties with the UAE raises serious questions about complicity and accountability. 

Ecuador: Attacking the constitution

The same can be said for Canada’s recent free trade agreement (FTA) with Ecuador, also signed last Friday.

The Trudeau government formally launched free trade talks with Ecuador in April 2024 (though exploratory discussions started in late 2022), while Ecuador’s democratic and human rights situation deteriorated sharply. The government has only become more authoritarian and heavy-handed since then.

Under current President Daniel Noboa, the government has relied extensively on militarization to suppress protests and dissent. Human Rights Watch and Amnesty International have documented extrajudicial killings, arbitrary arrests, ill-treatment of detainees, and enforced disappearances linked to sweeping states of emergency that suspend fundamental rights.

Ecuador’s government has used its security strategy to advance mining projects owned and operated by Canadian companies. In Cotopaxi province, communities opposing projects such as La Plata and Curipamba–El Domo have faced violent dispersals, criminalization, and state repression. At least 29 environmental and human rights defenders have been targeted with criminal proceedings, often resulting in convictions and harsh fines for peaceful protest that effectively recast community defenders as alleged “terrorists.” 

In Azuay province, Canadian mining company Dundee Precious Metals has brought criminal charges against community leaders following a peaceful cleanup “minga” (a traditional communal work gathering) in the páramo, a unique ecosystem in the highlands. These actions followed setbacks for the company, including the revocation of its environmental license and a complaint with the Ontario Securities Commission regarding undisclosed project risks. 

Public resistance has been widespread. Tens of thousands of Ecuadorians have mobilized to defend water, land, and Indigenous rights, particularly in Cuenca and across Azuay, where communities oppose mining in highland páramos that supply critical water sources. Human rights organizations have raised alarm over the state’s repression of social protest, including freezing activists’ bank accounts, and targeting civil society and Indigenous leaders. UN experts  have called on Ecuador to protect Indigenous rights, peaceful assembly, and due process—cornerstones of democratic governance.

Yet Canada’s trade strategy with Ecuador, far from showing “values-based realism,” will lock  in investor privileges precisely as democratic safeguards erode. Negotiated despite strong opposition from Indigenous nations, environmental defenders, and civil society groups, the FTA threatens to deepen socio-environmental conflict by strengthening corporate leverage while communities face repression and weakened rule of law.

Even more concerning, Canada’s agreement undermines the clearly expressed democratic will of Ecuadorians themselves in at least two ways.

First, the FTA contains an investor-state arbitration process that grants foreign investors the right to bypass Ecuadorian courts and sue the State before international tribunals—a mechanism Ecuador’s Constitution explicitly prohibits. 

That prohibition was the result of a long struggle. By 2008, investors had sued Ecuador more than a dozen times under various investment treaties, draining public finances and limiting regulatory space. 

The years that followed vindicated that caution. In 2012, a tribunal ordered Ecuador to pay U.S. oil company Occidental Petroleum roughly USD 2.3 billion, including interest, after the state terminated its concession for breach of contract. The award amounted to 59 per cent of Ecuador’s education budget and 135 per cent of its healthcare budget. These experiences led Ecuador to audit and terminate all its investment treaties by 2017.

Second, the agreement overrides a ban that Ecuadorians have since reaffirmed twice at the ballot box. Successive governments—from Lenín Moreno to Guillermo Lasso and now Daniel Noboa—have attempted to reintroduce international arbitration into Ecuadorian law. Voters have refused them each time.

In April 2024, 65 per cent voted against removing the constitutional ban. In November 2025, nearly 62 per cent rejected a proposal to draft a new constitution, widely seen as a backdoor attempt to reinstate international arbitration and roll back protections for Nature (“Pachamama”) and collective well-being (“Buen Vivir”). Extractive companies openly describe those protections as “obstacles to foreign investments.” Two referendums in less than two years, two decisive “no” votes.

The message could not be clearer. Yet Canada’s FTA cynically re-embeds precisely the system Ecuadorians have repeatedly rejected, allowing corporations to challenge public interest laws before private tribunals made up of trade lawyers moonlighting as arbitrators. In doing so, Canada risks aligning itself not with democratic sovereignty or environmental justice, but with predatory corporate interests—at a time when many countries around the world, including within the EU, are rethinking or abandoning these regimes altogether.

What “values?”

Taken together, these agreements with Ecuador and the UAE reveal a troubling trajectory. Rather than standing apart as a principled actor, Canada is deepening ties with states whose legal systems and human rights records are deeply flawed, prioritizing resource extraction, corporate access, and security cooperation with minimal or inadequate public debate. Where the Canadian state once spoke of people and the planet, it now answers to capital. 

Is Canada truly acting in defence of democracy, human rights, and the rule of law, or is it prioritizing short-term profit and geopolitical advantage? If the latter, how can the government possibly say its foreign policy is principled or “values-based?” When Canada consistently partners with repression, environmental destruction, and the erosion of sovereignty, Canadians must ask whose values policymakers are advancing, and at what cost.

The post Diversification against democracy in Canada’s latest global agreements appeared first on CCPA.

“Red tape” is the bogeyman of neoliberal economics, and cutting that red tape is a perennial call-to-arms for conservative politicians.

Over the past year, the federal government has attempted to get rid of pesky rules that “cause delays for major projects or economic growth,” leveraging such vehicles as the Major Projects Office and aptly-named Red Tape Reduction Office to accelerate deregulation. Reducing Canada’s regulatory “burden” will, allegedly, facilitate increased foreign investment and improve competitiveness for the Canadian economy by allowing private industry to flourish.

Putting aside the fact that “red tape” often exists for very good reasons—I, for one, would prefer that the federal government not be able to ignore environmental and human rights laws whenever it pleases—the appeal to competitiveness is equally suspect, and no more so than in the context of energy.

The federal government has lately taken to calling Canada an “energy superpower,” a phrase that was first popularized by Stephen Harper back in 2006. For the federal government then, as now, being an energy superpower means resting on the laurels of Canada’s existing clean energy infrastructure—most of which was built in the 1960s through 1990s—while aggressively promoting new public and private investment in the fossil fuel industry. For Harper, it was the oil sands. For Carney, it’s gas (and also the oil sands).

Crucially, this is not an economic vision that can be realized simply by cutting red tape. The Harper/Carney energy agenda requires the liberal application of what we might call black tape—the state subsidies, regulations and market facilitation that hold the fossil fuel industry together.

We see this at every stage of the supply chain today. On the upstream side, for example, the federal government is burning tremendous political capital to facilitate new oil and gas transportation infrastructure. On the downstream side, for example, the Privy Council recently admitted that one of the main arguments for accelerating Canada’s data centre build-out is that it creates a new market for Canadian gas producers.

These are not appeals to market forces. Indeed, if Canada’s energy sector was truly governed by market forces, we would be joining the rest of the world in going all in on solar, wind and batteries, which are cheaper, cleaner, faster to deploy and increasingly more reliable (as we will see below). Instead, we are artificially propping up industries, such as oil sands extraction, liquefied natural gas processing and nuclear power that would not otherwise be economically competitive. For example, expanding the nuclear sector in Ontario, as the federal government endorsed in its new nuclear strategy, could have a public price tag of $300 billion—three times the cost of producing the same amount of power using renewables.

Modern economies may be held together with tape, but it’s not all red tape. In Canada’s case, there is just as much black tape propping up whole sections of the economy.

And if our scissor fingers are itching to start cutting tape, that’s probably where we should start.

Storm surge: this month’s key reads

Battery breakthroughs are changing everything about the energy transition

Solar and wind are intermittent power sources. The sun doesn’t always shine and the wind doesn’t always blow, as fossil fuel backers like to remind us. And, for a long time, intermittency has been one of the key arguments against the adoption of solar and wind even where they are the cheapest sources of new power generation on paper.

There are two solutions to intermittency: stable baseload power (typically from hydro, nuclear or gas plants) and long-range power transmission. The wind may not always be blowing here, for example, but it is always blowing somewhere, so the bigger the grid the more you can moderate the effects of intermittency.

However, all of that is changing with the revolution in battery storage. You may already be aware that the cost of solar has fallen by 87 per cent since 2010 and the cost of wind has fallen by 55 per cent, but as a new report from the International Renewable Energy Agency, 24/7 renewables, highlights, the cost of batteries has also fallen by 93 per cent in the same period. Pairing solar and/or wind with on-site batteries at scale now can now provide “firm” (i.e. round-the-clock) power that is as reliable as a coal or gas plant for a fraction of the cost. A 100% clean grid is now the cheapest way to deliver reliable power around the globe, as Australia is already demonstrating.

In a new report, Power Play, the Canadian Climate Institute argues that Canada is in an especially good position to double down on solar and wind. Because we have so much hydro power delivering baseload, we can reap the benefits of cheap renewables while only requiring batteries to plug the gaps. Add in more long-range transmission and Canada can drive down costs even further while expanding generation—no nuclear or gas required.

Investing in renewable electricity is about as close as we get to a no-brainer in public policy. I agree with CCI’s recommendations to strengthen the Clean Electricity Regulations and back grid expansions with public money. The faster we expand clean power generation, the faster we can electrify (and decarbonize) the rest of the economy.

Research radar: the latest developments in work and climate

As Europe swelters, scientists prepare for worse. Temperatures in excess of 40 degrees Celsius are cooking France, the UK and other parts of Europe right now, which has already led to dozens of deaths. Things are likely to get worse, including in Canada, once El Niño fully takes hold in the coming months. The last El Niño in 2023-2024 led to the hottest year in recorded history. 2026 is likely to be worse.

Can the law save Canadian climate policy? A group of ENGOs and youth activists is suing the federal government for backsliding on climate policy, which is a violation of the government’s legislated commitments under the Net-Zero Act. Last year, I (reluctantly) called the legal system the last bastion of climate action, and we’ll see if that proves to be the case here. I’ll be following developments closely.

Canada-Alberta MOU will not, in fact, reduce emissions. It probably goes without saying, but, according to a new analysis from the Canadian Climate Institute, the pipeline-for-carbon pricing agreement negotiated between the federal and Alberta governments is unlikely to reduce overall greenhouse gas emissions compared to the previous policy trajectory. The good news, I suppose, is that the trajectory is not substantially worse than it was before, either. The bottom line is that this deal does not help climate action and, by locking in new fossil fuel infrastructure, will make it harder and more costly to transition later on.

The green buildings sector employs ten times as many workers as oil and gas. In Building Prosperity, the Canada Green Building Council quantifies the economic benefits of green construction, which refers to both retrofits and new builds with explicit energy efficiency and environmental goals. They peg the sector at half a million direct jobs, which is ten times more than direct jobs in oil and gas extraction. I’d argue it’s probably more like five times, but, even still, it’s an important reminder that the clean economy is already a larger employer than the fossil fuel industry and the gap is only widening. A new study from UK-based CBI Economics, The Race for Net Zero, similarly concludes that net-zero-aligned sectors employ more people at higher wages and with greater productivity than other sectors.

Alberta coal workers were left behind even as coal companies received big subsidies. The managed phase-out of coal-fired electricity generation is Canada’s greatest climate policy achievement. It was also a test run of the idea of a just transition for coal workers displaced by climate policies. How did it fare? In Workers Perspectives on Alberta’s Coal Workers Transition Program, the Labour Education Centre finds that the $40 million made available for workers was both insufficient and unjust in the context of a $1.1 billion support program for coal companies. The paper offers a variety of important recommendations, including more holistic transition programs co-developed with workers and their unions. Ian Hussey and I reached a similar conclusion in our 2019 analysis of the program.

Canadians are confused and complacent when it comes to climate change. Re.Climate’s 2026 update to their What Do Canadians Really Think About Climate Change? report finds that climate concern remains high but that climate action continues to fall as a public priority in this country. I’m especially worried about declining scientific literacy. For the first time since this report began, fewer than half of Canadians can correctly identify that climate change is primarily caused by human activities. We have a lot of work to do as climate communicators, but we’re also up against a disinformation machine with ludicrously deep pockets.

Climate change is driving up home insurance rates. A new report from Environmental Defence, Mounting Costs, finds that home insurance rates are rising by 12 per cent per year and that half the increase—equal to about $500 per year for the typical household—can be attributed to climate impacts. 

Fossil fuel financing is concentrating among the biggest banks. The 2026 edition of the Banking on Climate Chaos report reaches the interesting conclusion that global fossil fuel finance continues to increase even as many banks are scaling back their support for the fossil fuel industry. What that means is that the biggest, dirtiest banks are taking on a greater share of coal, oil and gas financing. All five of Canada’s big banks remain among the top 25 fossil fuel financiers globally, with RBC leading the charge.

Dark clouds: artificial intelligence on the horizon

Federal AI strategy wants Canadians to stop worrying and learn to love AI. The federal government released its long-delayed artificial intelligence strategy, AI for All, this month. It decries low levels of AI adoption in Canada, which the government blames on “low literacy and low trust.” Rachel Pettigrew and I had a lot to say about the government’s fevered commitment to AI adoption in spite of widespread concerns surrounding AI, so go check out our full analysis over on the CCPA blog. I also joined Paris Marx on the Tech Won’t Save Us podcast to break down the strategy in depth.

UK provides a better model for grappling with AI uncertainty. The Government of the UK released AI Scenarios 2030, which is a brilliant bit of forecasting and a model other governments should emulate. The report describes five distinct possibilities for AI impacts over the coming years, ranging from the air going out of the AI balloon (slow burn scenario) to radical social transformation (take-off scenario). The key takeaway here is that governments have an obligation to prepare for each of these scenarios, rather than banking on the best case scenario alone—which has been Canada’s approach to date.

One of the biggest risks of AI is one of the least discussed. To my chagrin, the federal AI strategy does not once acknowledge the risks to cognition and mental health associated with AI use. Yet, as a new article in the journal Nature explains, a growing body of evidence is finding precisely that AI is causing problematic deskilling in medicine, computer science and other fields. A separate study published this month by researchers at Stockholm University and the University of Hong Kong finds that AI use in secondary schools leads to faster homework completion times but a staggering 20 per cent drop in student exam scores. In my view, this is one of the most underappreciated long-term risks of the AI era, with widespread consequences that will be difficult to identify before it’s too late.

AI experts see catastrophic risks everywhere. The MIT AI Risk Initiative published a study, Prioritization of Risks from Artificial Intelligence, based on interviews with 272 international experts. It’s a rich and interesting attempt to quantify the many different risks associated with AI. The experts view AI misalignment (i.e., evil robots) and AI warfare (i.e. killer robots) as having the greatest potential downsides, but the report identifies potentially catastrophic risks in dozens of different domains, including power centralization, disinformation and environmental harm.

Generative AI systems are a human rights minefield. A new report from Amnesty International, Unlawful by design, documents the myriad human rights concerns associated with generative artificial intelligence, including privacy, discrimination and environmental impacts. The paper concludes with a long list of thoughtful recommendations, but it basically boils down to “regulate the damn thing.” It should be obvious, yet governments around the world are failing to do so.

The post Canada’s black tape economy appeared first on CCPA.

Uniquely, this exhibition was subjected to a sustained political campaign long before it even opened and any member of the public was able to see it. The Centre for Israel and Jewish Affairs (CIJA) began campaigning against it in November 2025, arguing that the museum had failed to consult the organizations it considered representative of Canada’s Jewish community. In May 2026, the Tel Aviv-based organization Shurat HaDin further escalated this campaign by threatening legal action and demanding that the museum pause the exhibition. Not least, Israel’s Ambassador to Canada wrote a letter to Prime Minister Carney demanding the exhibition be halted – an extraordinary attempt by a foreign diplomatic representative to influence the programming of a Canadian national museum.

Claims that “Jewish voices” were excluded should also be scrutinized. Jewish scholars participated in both the museum’s Palestinian Content Advisory Network (PCAN) and its broader consultations, while several Jewish organizations publicly supported the exhibition. No single organization can claim to represent all Jewish perspectives or demand a veto over how Palestinian history is presented. As with other exhibitions curated by the museum, PCAN also included people with lived experience as well as subject-matter experts, including co-author and PCAN member Yasmeen Abu-Laban. All this was in keeping with the museum’s standard practice of gathering expert input and centering the experiences of those suffering human rights abuses. Still, final choices and authority always remained with the museum, in keeping with curatorial independence.

The Canadian Museum for Human Rights, like all museums, should not be insulated from public debate. Indeed criticism from affected communities, scholars, visitors, and civil society organizations can help expose omissions, improve institutional practices, and contribute to the very reflection and dialogue that the CMHR was created to encourage. However, such concerted calls to pause or halt an exhibition before it has even opened are alarming. These calls cast a chill on both the free speech and creative independence of curators in future exhibitions at the Canadian Museum for Human Rights and other national institutions.

Worse still was the intervention of the Minister of Canadian Identity and Culture—the minister responsible for national museums—following the exhibition’s opening. 

After visiting the museum, Minister Marc Miller told the Canadian Press that the exhibition had made an “error in curation.” He identified various issues he felt should be “rectified” and specifically objected to the fact that Hamas, while mentioned briefly in the short text accompanying the exhibition, was not discussed as he would like. Yet the exhibition is focused on the Nakba, that is, the mass displacement of Palestinians from their homes during 1947 and 1948, events that took place roughly four decades before Hamas was founded.

At the same time, Miller acknowledged: “It isn’t up to me to speak to, or insert myself in, the curation of any particular exhibit.” The contradiction is difficult to avoid. The minister said he should not insert himself into an exhibition’s curation and then did exactly that—publicly identifying what he considered curatorial errors and calling for them to be corrected.

Miller’s words cannot be separated from his office. He was not invited into the public debate because of personal expertise in history, museum studies, or exhibition design. His comments became national news precisely because he was the minister responsible for the federal museum portfolio. When such a minister labels particular curatorial judgments erroneous and calls for “rectifying” the exhibition, the museum’s curatorial independence is on the line.

What the Museums Act establishes

The policy architecture surrounding Canada’s national museums helps explain why that matters.

The CMHR is not a branch of the Department of Canadian Heritage. It is a Crown corporation established under the federal Museums Act. The museum’s statutory purpose is “to explore the subject of human rights… in order to enhance the public’s understanding of human rights, to promote respect for others and to encourage reflection and dialogue.” The Act expressly authorizes the museum to undertake research, organize exhibitions, and communicate knowledge related to that mandate.

The legislation also establishes a division of responsibility. The museum’s Board of Trustees is responsible for fulfilling its statutory purpose and managing its “business, activities, and affairs.” The director serves as chief executive officer, controls and supervises the museum’s work and staff, and is responsible to the board, not to the minister. Museum employees are not members of the federal public service.

The CMHR’s own corporate plan reflects this arrangement. It describes the museum as a Crown corporation governed by a Board of Trustees, with the board accountable to Parliament through the minister. It also identifies the development of curatorial practice and accountable content choices as an internal institutional responsibility.

The distinction between oversight and curation is essential. The minister and Parliament may examine expenditures, appointments, compliance with legislation, and institutional governance. They do not decide which historical terminology curators should use, or which political context must appear in an exhibition. Miller did not merely raise a procedural question. He publicly identified content he considered deficient, stated what he believed the exhibition should say and seemingly demanded that it be revised.

His intervention was especially damaging because it validated, and rewarded, an organized campaign that had already sought to stop the exhibition from even opening. CIJA quickly presented Miller’s comments both as a direct result of its political pressure and as confirmation that its warnings had been justified.

An accountability question

Defending curatorial independence does not require treating museum professionals as infallible. Museums make mistakes. Their research and interpretive choices should be open to scrutiny. Boards must oversee institutional policies, finances, and strategic direction, and curators should be able to explain the evidence and processes behind their work.

The policy issue at hand is not whether the exhibition is beyond criticism or whether Canadians must agree with all of it. Indeed, some have felt the exhibition language was too restrained in detailing the violence leading to Palestinian dispossession and displacement, and Palestinian Canadians have been clear, from well before the Canadian Museum for Human Rights opened in 2014, that they wanted a permanent exhibition in the museum, rather than the mere two years this is scheduled to run. The issue is not about criticism, but about who has the authority to make and revise choices.

To our knowledge, there is no clear recent Canadian precedent for a federal minister publicly identifying the wording of a particular exhibition at a national museum as a curatorial error and calling for its correction. Canada has experienced many public controversies involving museums, but direct ministerial criticism of the specific language and interpretation used in a current national museum exhibition appears highly unusual.

That makes it important to establish the appropriate boundary now, rather than waiting for political involvement in museum content to become routine.

A warning from the Smithsonian

The Smithsonian Institution in the United States offers a warning about what can happen when the boundary between public oversight and historical interpretation begins to erode.

In March 2025, President Donald Trump issued an executive order directing Vice-President JD Vance, through Vance’s position on the Smithsonian’s Board of Regents, to seek the removal of what the administration called “improper ideology” from Smithsonian museums. The order also directed officials to pursue restrictions on federal funding for exhibitions considered inconsistent with the administration’s preferred presentation of American history.

The administration subsequently reviewed museum materials and publicly criticized the Smithsonian’s presentation of subjects including race, slavery, gender, and national history. In July 2026, Smithsonian Secretary Lonnie Bunch rejected a White House report’s characterization of the National Museum of American History as promoting a “radical” leftist ideology and “extreme political activism,” while the Organization of American Historians described the government’s approach as an effort to make historical presentation serve a political agenda.

More recently, the Trump administration further escalated its campaign against the Smithsonian by ordering that the National Parks Service (which controls the sidewalks outside Smithsonian museums) post signs outside the National Museum of American History redirecting visitors to what is deemed to be “accurate information regarding America’s history.” This move clearly denigrates the museum, its exhibitions, and curatorial independence.

Marc Miller’s comments are not equivalent in scale to the Trump administration’s systematic intervention. However, the lesson of the Smithsonian case is about what happens when policymakers weaken institutional independence. Political authorities may begin by condemning particular interpretations, proceed to requesting changes, and may eventually use appointments or funding to shape what museums are even permitted to say.

Clarifying the boundary

The federal government should respond to the CMHR controversy by strengthening, rather than testing, the arm’s-length model:

1. Minister Miller should clearly reaffirm that judgements about the historical framing and language of individual exhibitions belong to the museum’s established professional and governance processes.
2. The Department of Canadian Heritage should develop guidance distinguishing legitimate ministerial oversight from involvement in programming and curation. 
3. National museums should adopt explicit curatorial independence policies to better educate the public about the important role curators play and the value of freedom from censorship. These policies should also explain the respective responsibilities of trustees, senior management, and curatorial staff; the role played by advisors; the role of donors; as well as the procedures for addressing alleged factual errors.

These kinds of safeguards are not meant to protect museums from public criticism or debate, or the importance of maintaining public trust. But they would ensure that criticism is considered through transparent, evidence-based processes rather than according to the political influence and power of the person or organization making it.

Now that it is open, visitors should see Palestine Uprooted: Nakba Past and Present. Indeed, many visitors may learn about the history and culture of Palestinian Canadians for the first time; others may debate what is there and not in the exhibition based on pre-existing understanding. But museum content ought not be halted in advance under threat of legal retaliation, and it also should not be rewritten at the direction of a minister in light of Canada’s Museums Act. Arm’s-length independence means little unless it protects institutions when content is contested.

This article is part of The Omatsu Files, a running column written in memory of Rick Omatsu, who was born in 1939 in Vancouver, on the cusp of WWII, which would change his life—a life that was sadly marked by racism. As a Japanese Canadian, in 1942, Rick was sent to the B.C. interior. After the war, the family was forced by the government to leave the province. The family relocated to Hamilton, Ontario, where Rick remained until his passing at age 82. The Omatsu Files is a space dedicated to the voices of young researchers from equity-deserving backgrounds who are focusing on issues of equity, diversity, inclusion, and anti-racism.

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• Canada ranks near the bottom of OECD countries—28 out of 35—for its relatively low share of public health care spending compared to the private share of total health care spending
• Private health care spending grew nearly twice as fast as public spending in Canada, per person, from 2021 to 2023 (8.1 per cent vs. 4.3 per cent)
• Most private spending is on only a few things, with more than three-quarters of private health care spending (out-of-pocket and private insurance) going towards non-physician professionals, drugs, and long-term care
• Private spending has been growing especially fast in a handful of areas, with private spending on home and community care, non-physician professionals, and physicians growing fastest over the past three years (2021-23)
• Nova Scotia ($3,015), Alberta ($2,852), and Ontario ($2,852) had the highest per person private health care spending in the country
• Ontario ($5,425), New Brunswick ($5,685), and Alberta ($5,870) had the lowest per person provincial health care spending
• Saskatchewan and the Maritime provinces lead the country in private long-term care spending on a per person basis

Canada ranks near the bottom

Canada ranks near the bottom of OECD countries for share of public spending on health care

Canada ranks 28 out of 35 OECD countries for its lower share of public spending on health care, behind 27 other countries with universal health care systems. In the United Kingdom, New Zealand, and most of western Europe, public spending—rather than private out-of-pocket and insurance spending—comprises a greater share of total health care expenditure. In Germany, Sweden, Norway, and the United Kingdom more than 80 per cent of spending is public. In Canada, 71 per cent of spending was public and 29 per cent was private in 2022.

Private health spending growing fast

Private health care spending grew at almost twice the speed as public spending

Over the last three years of available data (2021-23), per person private health care spending outpaced public spending in Canada. At 8.1 per cent, private spending growth was almost double that of public spending growth of 4.3 per cent. Despite provincial (public) per person spending ($5,868) remaining greater than private spending ($2,602), the fact that private spending is growing faster than public spending is cause for concern.

Most private spending is on only a few things

Most private health care spending is on non-physician professionals, drugs, and long-term care

Seventy-seven per cent of private health care spending was on non-physician professionals (32 per cent), drugs (30 per cent), and long-term care (15 per cent) in Canada in 2023. 

This is not surprising considering these services are not universally publicly insured under the Canada Health Act and the federal government has abandoned national pharmacare. Private spending in these areas shows us the gaps in Canadian medicare that need to be addressed.

Across health care sectors, the average private per person spending was greatest in home and community care (17 per cent), on non-physician professionals (13 per cent), and on physicians (10 per cent) over the last three years.

Which provinces are doing worst?

Nova Scotia, Alberta, and Ontario have the highest private health care spending…

Nova Scotia ($3,015), Alberta ($2,852), and Ontario ($2,852) had the highest per person private health care spending in the country, and above the Canadian average of $2,602. 

…while Ontario, New Brunswick, and Alberta have the lowest provincial health care spending

Ontario ($5,425), New Brunswick ($5,685), and Alberta ($5,870) had the lowest per person provincial health care spending in 2023. The Canadian average for per person provincial health care spending was $5,868 in 2023.

Ontario and Alberta are among the provinces most rapidly advancing the privatization of public health care services, including surgeries, diagnostic imaging, and seniors’ care. If the public system is left to deteriorate by inadequate provincial funding (federal transfers are included here), this provides corporate profit-taking opportunities for private-pay health care services and publicly funded outsourcing.

What are provinces seeing private money spent on?

B.C., Alberta, and Ontario spend the most on private-pay non-physician professionals

B.C. ($1,024), Alberta ($975), and Ontario ($869) lead the country in per person private spending on non-physician professionals, including physiotherapists, dentists, and psychologists. Many of these health professionals are not part of the public health care system in most provinces, meaning that people must pay out-of-pocket or with private insurance to access these providers. 

We know that many of these health care providers are necessary for prevention and managing chronic diseases, yet the very low provincial spending on these professionals relative to private spending shows that these professionals are only accessible to those who can pay for them, despite their importance.

Quebec has the highest per person provincial spending on non-physician providers, which speaks to the superior public coverage for dental care, eye care, and some other allied health professional services.

Saskatchewan and the Maritimes lead the country in private long-term care spending

Private long-term care spending demonstrates that too many people pay out of pocket for this necessary health care. 

Saskatchewan ($631), Nova Scotia ($620), New Brunswick ($558), and P.E.I. ($513) lead the country in private per person long-term care spending—significantly higher than the Canadian average of $386 per person. 

Private long-term care spending comes from private-pay long-term care and residents in publicly funded long-term care paying for accommodation fees and other expenses that are not publicly covered. 

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On July 22, the Alberta government approved three Orders in Council that shed light on how the provincial government is building a private health insurance market and undermining equal access to health care services.

The three Orders in Council relate to the implementation of Bill 29 which the legislative assembly introduced in April 2026 as the legal framework for two-tier diagnostic testing without requiring a practitioner’s referral. 

This legislation formalizes queue-jumping for those who can pay out of pocket or with private insurance for self-referred testing in order to jump to the front of the line for treatment. It is also a move designed to feed the for-profit health care industry and create a market for private health insurance products.

The first Order in Council brings key provisions of Bill 29—the two-tier diagnostic testing legislation—into force effective July 31. 

The second amends the Alberta Health Care Insurance Regulation by creating a new category of “preventative health testing services” which are “not insured services” under the Alberta Health Care Insurance Act and to make it appear that the Alberta government is maintaining compliance with the Canada Health Act, even though it is not. The amendment also establishes that “all preventative health testing services…are extended health services” under a forthcoming Preventative Health Testing Services Benefit Regulation.

The third regulation maintains that certain procedures involved in providing these “preventative health testing services” remain restricted activities that must only be performed by authorized regulated health professionals. These include administering anesthetic gases for the purposes of anesthesia or sedation, radiopharmaceuticals, and diagnostic imaging contrast agents, and ionizing radiation (such as, x-rays).

In a press conference on July 27, the Alberta government announced that CT, MRI, ultrasound, x-ray, and CT services will initially be under the banner of these “preventative health testing services.” If cancer is found through these “preventative” scans, the government will reimburse for the private cost.  At the press conference, a radiologist spokesperson also revealed that the Alberta government has been already allowing private-pay medical imaging tests (that number in the “low tens of thousands” annually), which suggests that the Alberta government has already been in violation of the Canada Health Act.

What does it all mean?

These legislative amendments are part and parcel of establishing Alberta’s two-tier health care system with a private health insurance market for medically necessary health care services that are already covered under the public plan. 

The Alberta government is moving quickly to build a two-tier system

The Alberta government is working towards a September 2026 deadline to have its two-tier system up and running. In order to build this market, the Alberta government must give legal certainty to investors, for-profit facilities, insurance corporations, and physicians and health professionals intending to work in the private-pay tier. 

The implementation of private-pay diagnostic testing is part of the government’s overall plan for a two-tier system that will allow physicians and surgeons to work simultaneously in the private-pay market as well as the public system (referred to as “dual practice” under Bill 11).

A two-tier system requires private-pay diagnostic testing

The legislation and regulations are written in an open-ended manner to include any form of diagnostic testing, ranging from lab testing, medical imaging and any other diagnostic procedures performed in any health care settings. To start, the Alberta government is beginning with the most common medical imaging procedures.

A two-tier system demands that private-pay diagnostic testing be in place to enable queue-jumping. Individuals must generally have some form of diagnostic testing completed before jumping the queue for surgery or medical procedures.

Providing legal cover for private facilities, insurance corporations, and doctors engaging in two-tier medicine

Under the guise of “preventative health testing services,” these legislative amendments are intended to give legal cover to entities engaged in delivering or financing private-pay health care that provides preferential access, contrary to the Canada Health Act. 

The Alberta government is creating a new category of diagnostic testing that will expressly facilitate faster access based on ability to pay. If there are concerns identified from the “preventative” diagnostic testing, then those individuals will be able to jump to the front of the line for treatment. These practices are prohibited under the Canada Health Act, but the Alberta government wants to create new definitions to provide legal certainty provincially for private interests engaged in these unlawful activities.

For-profit interests have been using the language of “preventative” health services in order to blur the boundaries between medically necessary (publicly insured) services and uninsured services that are not medically required. Corporate interests increasingly use the language of  “preventative” health services in claiming that these services do not constitute unlawful patient fees and are therefore not against the Canada Health Act, even when these practices provide faster access to medically necessary care. 

In B.C., the provincial government sought a court injunction against Telus Health for a subscription-based health care program. In its petition filed in B.C. Supreme Court, the Medical Services Commission “investigated and determined that Telus Health is charging for or in relation to MSP covered medical services such that a reasonable person would consider that the purchase of Telus Health’s services would result in preferential treatment or priority access to those services.” Telus Health argued that “we do not charge for primary care services with our LifePlus service. Our fee is [for] preventative health uninsured services like dietitians, kinesiologists and wellness services.” 

However, the B.C. Medical Services Commission hired a private investigator to pose as a potential patient in order to determine if private payment was required to obtain medically necessary primary care covered under the public plan. This investigation revealed that in order to access a family physician one would need to pay the annual membership fee. 

The B.C. Medical Services Commission was also able to determine that the movement of family physicians into the private-pay tier reduced access to family medicine services within the publicly funded system. This finding challenges the longstanding argument that private-pay health care increases health care system capacity. Instead, the physician workforce shifts to the more lucrative private-pay market.

Telus Health and the B.C. government settled the dispute out of court with Telus voluntarily changing its program in order to come into compliance with provincial legislation banning preferential access to medically necessary health care.

Private-pay diagnostic testing could be a large segment of the new private health insurance market

The focus on private-pay diagnostic testing will be used by the Alberta government and the insurance industry to build the private health insurance market. Medical imaging, in particular, is already a burgeoning for-profit industry, with private facilities accepting both public and private payment. Telus Health has already been advertising $3,000 “total body MRIs”, which are not clinically recommended.

By using the language of “preventative health testing,” the Alberta government is creating the legal certainty for insurance corporations to sell products for “preventative” extended health services, even though these very diagnostic tests will be used as the main vehicle for preferential access to treatment.

Undermining primary health care and adding unnecessary testing

The Alberta government is opportunistically preying on Albertans’ valid concerns over long wait times by sanctioning private-pay diagnostic testing, which will ensure faster access for patients without a primary care provider or specialist. The move to self-referral for diagnostic testing undermines the foundational role of primary health care as the necessary front door to the health care system where primary care providers refer to medically necessary diagnostic testing. 

Even without self-referral for diagnostic testing, a 2017 report from Choosing Wisely Canada and the Canadian Institute for Health Information concluded that up to 30 per cent of imaging tests, procedures, and pharmaceutical therapies across eight priority areas are potentially unnecessary. Encouraging the growth of “preventative health testing services” through self-referral and private payment will dramatically increase unnecessary testing and draw limited health professionals out of the public system. 

Diagnostic testing and access to (cancer) treatment will depend on wealth

Even before Bill 11, Bill 29, and the introduction of an express diagnostic testing line, Alberta’s experiment with greater publicly funded, for-profit health care delivery resulted in longer public waits for nine out of 11 priority procedures, including all cancer surgeries tracked by the Canadian Institute for Health Information. 

The international evidence is clear. The movement of a limited pool of health care professionals, including medical imaging technologists and radiologists, into the private-pay market will increase wait times in the public system. Under these changes, access to diagnostic testing and medically necessary care—including cancer treatment—will depend on your wealth. 

Albertans who can afford expensive private health insurance premiums or pay thousands of dollars out of pocket, will receive their treatment faster. This is U.S.-style, two-tier health care plain and simple—and this recent move is about guaranteeing a market for private insurance companies and for-profit providers.

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Tit-for-tat retaliation is obviously not helpful to anyone, and the new tariffs reiterate to Canada how unreliable a trade partner the U.S. can be. A few weeks earlier, the U.S. stated it would not renew the Canada United States Mexico Agreement (CUSMA) agreement negotiated in Trump’s first term. Technically that agreement remains in force for another decade, but following the trade rules has never really been an American thing. Even before Trump, politics drove trade policy. 

The new tariffs are seeking to tighten the screws on Canada, in a bid to force concessions at the negotiating table for a new CUSMA deal. U.S. Trade Representative Jamieson Greer said the administration was looking to sign “interim deals” by the end of the year, while leaving some bigger issues to 2027. Trade protectionism has been a political winner for Trump and with the U.S. midterms on the horizon and the president on a losing streak, it is no surprise that he is once again threatening tariffs. 

A new kind of tariff 

The new tariff measures are a novel application of very old trade law, and stem from the Tariff Act of 1930. Section 338 enables the president to apply duties of up to 50 per cent to retaliate against countries “discriminating” against the United States. While the tariffs apply to only about $28 billion of Canadian exports (about five per cent of overall exports to the U.S.), this move may be testing the waters for future trade actions against other sectors of the Canadian economy or against other countries. 

For Trump, this new over-reach is desired because the first wave of tariffs in early 2025 were struck down by the U.S. Supreme Court in February 2026. These were known as the IEEPA tariffs, falling under the 1977 International Emergency Economic Powers Act, which grants the U.S. president authority to regulate economic transactions once a national emergency has been declared. 

It was under the IEEPA that the newly-inaugurated Trump administration claimed authority to impose tariffs on Canada and Mexico, based on a declared emergency related to purported imports of the opioid fentanyl and fears about immigration. Canada was later exempted from the IEEPA tariffs by virtue of the CUSMA. Trump‘s infamous “Liberation Day” tariffs on most countries of the world (and some bird-only islands) in April 2025 was also under the IEEPA. 

Rather than IEEPA, it is sectoral tariffs that have had the most impact on Canada to date. Section 232 of the Trade Expansion Act of 1962 allows the President to impose tariffs, quotas or other restrictions to imports when an investigation determines they threaten national security. Tariffs under Section 232 have adversely affected Canada’s steel and aluminum sectors, automobile supply chains, and some copper products. This includes layoffs at Algoma steel and Stellantis moving future production south (discussed here).

U.S. tariffs have also targeted Canadian softwood lumber exports through anti-dumping and countervailing duties. These are trade remedies that can be imposed when U.S. officials deem a foreign country to be selling at below-market prices to drive out competition (called dumping) or illegally subsidizing their industries. Allegations in softwood lumber are a longstanding issue for U.S. forestry companies and predate Trump. The trade war has prompted forestry mill closures in British Columbia by Canfor and others.

A closer look at the new tariffs

The section 338 tariffs are thus the fourth wave of trade shocks for Canada. Officially, this is retaliation against Canada‘s retaliation to earlier U.S. tariffs in 2025. It also suggests that Canada‘s retaliatory actions hit a nerve—and thus fulfilled their purpose of inflicting some pain in certain U.S. places and industries. 

The legal basis of the section 338 tariffs is the argument that Canada’s retaliatory measures discriminate only against the U.S., as opposed to another country. The three areas cited for violations are alcohol, dairy and automotive, but the US counter-measures include tariffs on a wide swath of Canadian exports. 

The alcohol tariffs are the most clear retaliation due to the removal of U.S. liquor from the shelves of most Canadian provinces. As a result, the Section 338 tariffs would be applied on almost all liquor heading south. 

In dairy, the U.S. claim is that Canada discriminates against American cheese relative to the European Union. But the tariffs are more of a harassment measure against Canada’s system of dairy supply management. This sector has been in the crosshairs for some time and is vulnerable to getting dismantled as Canada pursues a new trade deal. Currently, imports from the U.S. are tariff free up to a quota, after which they face a high tariff. 

In autos, Canada responded to 25 per cent sectoral tariffs—intended to bring Canadian-based manufacturing to relocate in the U.S.—with equivalent tariffs on U.S. imports. These were needed to level the playing field to maintain the auto industry in Canada. Nonetheless, the US is arguing that Canada‘s response justifies the additional tariffs placed on a wide-ranging 18-page list of goods. These include a number of agricultural commodities and forestry products to clothing/textiles, hockey sticks to essential oils. 

It’s tempting to try and parse the language of trade law and the specific details by sector, but the new tariffs are just Trump being Trump, using existing law in novel ways to expand the executive powers of the president. There will surely be a court challenge to these tariffs and possibly backlash in Congress. However, should the U.S. Supreme Court find these tariffs legal, they could confer upon the president new powers that could be used to apply leverage elsewhere. 

The list of what’s not covered by these new tariffs is also important: energy, potash, critical minerals, fish and anything already covered by the section 232 tariffs are exempt. If Canada was to seriously retaliate, this list would be a good start. 

Section 338 requires 30 days before any of these tariffs are implemented. That will give companies time to front-end the tariffs (as in early 2025) so there will likely be very minimal impacts on trade for the remainder of 2026. There is a high likelihood that once phones start ringing in Congress, there will be a move to exempt certain goods and/or lower the actual applied tariff, perhaps even abandon the whole thing. Who knows anymore?

The political context for the new tariffs is telling. President Trump has been on a losing streak with an ongoing war in Iran, low popularity at home amid rising costs overall and at the pump, with the U.S. midterm elections looming in the distance. Trump’s biggest political “wins” have come on trade and the application of tariffs to attack other countries, including long-time allies like Canada. Pushing the tariff button is one that Trump thinks will be a political winner for him.

The impact for U.S. consumers will be higher prices. Companies who import components and parts from Canada will face squeezed margins. The tariffs also perpetuate an aura of uncertainty about trade that undermines investment on both sides of the border.

Where to next?

Ultimately, the section 338 tariffs are part of the bigger play around CUSMA renegotiation. The United States chose on July 1 not to renew the agreement. By throwing his weight around, Trump hopes to pressure Canada into making concessions and signing an inferior deal. But is any commitment from Trump worth the paper it’s written on? At best, it’s a limited pass that could be revoked the next time the wind blows. Literally, in the case of wildfire smoke. Or any other whim of the president whose ego is so easily offended.

Thus far, Canada has made the right call by not feeling pressured to sign a bad deal, while buying time to expand other trade relationships and marshal massive public and private investment dollars into the country. And in spite of the tariff shock and disproportionate impacts in certain industries and places, the Canadian economy has held up much better than some thought possible in early 2025. 

Prime Minister Carney says that Canada-U.S. negotiations will intensify, and that he is seeking a comprehensive agreement that includes the key sectors like autos, forestry, steel and aluminum that have been subject to new tariffs because they have strong lobbies in Washington. What Canada is willing to concede in order to get that deal is not yet clear, and will be a critical test of Carney’s leadership.

The bigger question is inside our own border, what we trade and with whom, and what we can do for ourselves to boost living standards. In the short term, the easy path has been to loosen the reins on environmentally-destructive resource megaprojects. The challenge for Canada has always been how to use creative trade and industrial strategies to develop more sophisticated, higher value goods and services that the rest of the world wants. On this front, we have a lot more work to do.

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Canadians are worried about artificial intelligence (AI). Poll after poll finds that workers, students, artists, parents and other people who live and work in Canada view the proliferation of modern AI tools as a threat to jobs, cognition, democracy, human rights, the environment and more. Yet, to date, these concerns have been given short shrift in the national discourse surrounding AI, which has been dominated by the technology industry with the enthusiastic support of the federal government.

In its new national artificial intelligence strategy, the government acknowledges public skepticism of AI but concludes that “for Canada to thrive in the era of AI, Canadians need to trust in its promise.”¹ That promise includes assumptions of productivity gains, higher-quality services and geopolitical security. It is grounded in the belief, articulated largely without evidence, that AI will necessarily and inevitably reshape society for the better.

This report presents a critical alternative narrative that invites Canadians to think twice about the promise and inevitability of artificial intelligence. It is intended as a foundational resource for concerned citizens and civil society organizations in Canada, which have, to date, managed only a piecemeal response to the AI moment. To mount a more coordinated and effective response to AI in Canada—and to push back on the AI industry in particular—citizens require a better understanding of what AI is, what its implications are, and what should be done about it.

The development and adoption of AI entail a dizzying array of risks and harms, many of which this report addresses in detail, including:

• The unproven productivity benefits of AI tools, which are fueling unprecedented financial speculation;
• The erosion of job quality through workplace surveillance and algorithmic management, as well as the longer-term risks of large-scale job losses;
• The ballooning energy demands of AI data centres and their impacts on the environment and community well-being;
• The erosion of public health care through commercial AI systems;
• The stunting of cognitive development associated with AI use and its consequences for the education system and workforce development;
• The explosion of culture and knowledge production (i.e., “slop”) produced by AI systems that are trained on the works of real creators without consent or compensation;
• The rush to adopt AI in public services and public institutions without assurances of public control over those systems;
• The breakdown of information integrity and the rise of AI-fueled political machines, which threaten the democratic process itself;
• The disregard for human rights in AI applications spanning policing, immigration, warfare, hiring and other sensitive sectors; and
• The attack on Indigenous knowledge systems and data sovereignty by inherently colonial AI systems.

Drawing from these disparate concerns, the report identifies four key themes that encapsulate the current AI moment in Canada. Addressing AI at the personal, institutional and political level requires us to grapple with:

• The indiscriminate adoption of AI systems across all domains of society;
• The systematic devaluation of human thought, judgment and expression in favour of algorithmic output;
• The acceptance of real present harms in exchange for speculative future benefits; and
• The exacerbation—both incidental and deliberate—of existing structural inequalities.

Canada is not the only country grappling with these themes. This paper considers and compares the public policy responses of three key players in the field of AI governance: the United States, China and the European Union. Each is taking a drastically different regulatory approach, and each offers lessons for Canada. However, Canada is clearly lagging all three jurisdictions in terms of AI governance. While the release of a national AI strategy was a necessary first step, Canada still lacks a meaningful legislative or regulatory framework for AI.

It is not too late to put the public interest first in the AI era, but it will require a different approach. Based on the preceding analysis, this report presents six principles for forward-looking, public interest AI advocacy in Canada:

1. Distinguish between AI as technology, industry and ideology. The term “artificial intelligence” does not refer to anything in particular. By failing to define AI, we conflate the various technologies that fall under the AI umbrella; the multifarious researchers, developers and investors behind specific AI tools; and the foundational beliefs and political ideologies that tie them all together.

2. Recognize the value of original and diverse human thought. Humanity has inherent value, both practically and morally. We can and must make social, cultural and political choices about where AI usage is appropriate and where it infringes on our humanity in unacceptable ways.

3. Subject the AI industry to democratic oversight and control. Protecting the public interest in the AI era requires a comprehensive legislative and regulatory framework for AI governance that puts public well-being over private profit.

4. Apply the precautionary principle to AI harms. Some AI-driven harms are already here. Others loom on the horizon. In all cases, we must proceed more cautiously, which means slowing the development of AI systems and/or pausing their adoption until there is conclusive evidence supporting their safety and efficacy in a given context.

5. Prioritize augmentation over automation in workplace AI deployment. A pro-worker AI—one that supports workers rather than replacing or subjugating them—is possible, but only if workers have a say in how AI tools are deployed in their workplaces.

6. Develop AI systems that solve real problems in the public interest. Artificial intelligence, as a category of technology, has genuine potential for advancing the public interest. To realize those benefits will require not only stronger AI governance, but also a commitment to developing AI tools with greater participation from workers, governments and the broader public.

These six principles are intended, first and foremost, to serve as organizing principles for citizens and civil society organizations in Canada, but they also serve as policy principles for Canadian governments committed to the effective governance of AI systems and the AI industry.

According to the federal government, Canada must pursue the widespread adoption of artificial intelligence systems “with urgency.”² This report urges Canadians to think twice. To the extent that the rise of modern AI offers potential benefits, it also entails serious costs. To ensure the AI era serves the public interest, we must move slower, more carefully and with a clearer sense of the technological future we aspire to.

Introduction: What does artificial intelligence (AI) mean for Canada?

As AI tools proliferate in our workplaces, schools and homes, it is a question that is increasingly top of mind for concerned citizens. On the one hand, we are told by technology companies that the deep integration of artificial intelligence into private enterprise, public services and our daily lives will unlock profound benefits. From health care to education to business productivity and beyond, the AI industry promises to deliver broad and unprecedented economic and social welfare.³ In response, our governments are now racing to “drive the adoption of artificial intelligence across Canada’s economy and society” as quickly and as comprehensively as possible.⁴

On the other hand, the rapid rise of AI is driving public fear and mistrust. Many of the productivity benefits associated with AI are still speculative, but they are premised on ultimately automating entire domains of human labour. Moreover, the proliferation of unregulated commercial AI systems poses potentially serious risks to privacy, sovereignty, human rights, cognitive development, the democratic process and other vital public priorities.⁵ All the while, the financial and environmental bill associated with AI training and processing continues to grow to eye-watering heights.

Compounding the multifarious impacts of artificial intelligence today is a deep and fundamental uncertainty about the future. Even among AI experts and within the tech industry, there is significant disagreement about whether these systems work as advertised and how they will evolve over the coming years.⁶ How, and whether, consumers and institutions adopt AI adds another layer of uncertainty. The as-yet-unrealized potential for government regulation to re-shape the current AI moment is merely the cherry on top of a very shaky cake.

Nevertheless, for all the uncertainty, coming to terms with artificial intelligence is absolutely essential. Workers, students and other concerned citizens in Canada need to be equipped to confront this moment as pro-AI pressure from industry and governments mounts. There may be productive, pro-social applications of the various tools that fall under the AI banner, but we must approach AI development and adoption with a clearer sense of what problems these tools actually solve and what risks they entail for individuals and society alike.

This paper offers a crash course in artificial intelligence for concerned citizens in Canada. It begins with a high-level overview of the current AI moment, including the main technologies behind modern AI systems and the current shape of the AI industry. We then break down the current and potential implications of AI technologies across a wide variety of domains in the Canadian context, including labour, the environment, education, health care, culture and democratic institutions. We evaluate the regulatory approaches that have been taken so far in Canada and around the world to confront these implications, for better or worse, from which we identify best practices and notable gaps. Finally, drawing from the preceding analysis, we arrive at a set of six forward-looking principles for the responsible development and adoption of AI in Canada.

The goal of this paper is to establish a common conceptual framework for public engagement and advocacy on AI issues. To move forward collectively, we require a shared understanding of what artificial intelligence means, what principles should guide future AI development, and what role our governments and public institutions have to play. For concerned citizens and civil society groups, this paper offers a stable place to start in the midst of a deeply unstable moment. Although this paper does not make specific policy recommendations, the appendix outlines more than 60 policies that concerned citizens and organizations may consider in their advocacy efforts.

Context: Understanding the AI moment

Artificial intelligence refers generally to any computer system that takes data (input) and then produces either predictions or content (output) with some degree of autonomy from human oversight. In so doing, these systems perform tasks that would otherwise require human intelligence and decision-making.

In practice, this definition is extremely broad. Since the phrase “artificial intelligence” was first coined in the 1950s, it has been used to refer to a wide variety of different technologies, including everything from weather forecasting models to chess bots to spellchecking software. This imprecision is a key source of confusion today. When we hear the term “AI,” it is not always clear whether it refers to the general concept of (semi-)autonomous computing systems or to a particular technology that falls under the AI umbrella or to a class of consumer products being marketed by technology companies.

To make matters worse, “artificial intelligence” is itself a misleading phrase.⁷ Whereas “artificial” implies that these systems exist outside of human influence, they are fundamentally a product of human labour. These systems are designed by human engineers, trained on human data and refined by human users. Conversely, the term “intelligence” implies a human-like cognitive process, even though these systems—capable as they are of performing many human-like tasks—operate in a fundamentally different way than our own conscious brains. Researchers prefer to use terms like “deep learning” or “machine learning” to describe specific types of AI without the same connotations around intelligence.

Nevertheless, this paper uses the term “artificial intelligence” because it is broadly understood to refer to a particular set of technologies that have come to prominence in recent years with potentially transformative social and economic consequences. Where applicable, we use more precise language to refer to different categories of AI systems.

Categories of AI systems

Modern AI systems are sophisticated pieces of software that employ a handful of fundamental technical architectures.

Symbolic (or logic-based) AI involves a computer system that follows a set of formal rules, similar to traditional programming. Historically, these have also included so-called “expert systems” that draw inferences from a specific body of knowledge. For example, an AI system given the rules of chess can make deductions about optimal moves consistent with the rules of the game. The first few decades of AI development primarily used logic-based approaches and many AI systems today still do, especially in domains such as science or finance, where variables can be controlled and accuracy matters. However, these systems are often task-specific and thus struggle with messy real-world applications, such as interpreting or inferring natural language.

Machine learning (or pattern-based) AI involves the automated processing of vast amounts of data to identify patterns. For example, a machine learning system trained on works in the English language will come to recognize that a capital letter typically follows a period, even without specific instruction in the formal rules of English grammar. Machine learning has been the main area of focus for AI development for the past several decades and it is the basis for many modern AI applications, such as social media algorithms, facial recognition and chatbots. These systems can handle the messiness of the real world better than symbolic approaches, but machine learning algorithms are fundamentally illogical. These systems do not understand what they are predicting and are therefore prone to making errors. The capabilities of these systems are often described as emergent, which means they have strengths and weaknesses that cannot be predicted in advance.⁸

Deep learning (or multi-layer) AI is a subset of machine learning that attempts to replicate some of the complexity of the human brain. These systems use “neural networks” made up of layers of machine learning systems that can pass information between specialized nodes, allowing for more sophisticated pattern recognition than a simple machine learning system alone. While deep learning has a longer theoretical history, the modern boom in deep learning only began in 2017. Many applications of machine learning today, including advanced chatbots, are products of deep learning developed in the past decade.

Neurosymbolic AI attempts to combine the logic of symbolic systems with the flexibility of machine learning systems to mitigate the weaknesses of each. For example, coding assistants increasingly combine a (pattern-based) chatbot interface with a (logic-based) back-end code base for producing new code. Some experts view neurosymbolic systems as the “third wave” of AI after symbolic and machine learning systems, but, to date, they account for a minority of applications.⁹

The preceding AI architectures are the foundation for a wide variety of AI applications, many of which will be discussed throughout this paper. To understand the current AI moment, however, we need to specifically address one particular application: generative artificial intelligence.

Generative AI systems are a type of deep learning system that can create new multimedia content, such as text, images, audio or video, when prompted by a user. These systems do so by looking for patterns in their training data that align with the prompt and then extrapolating forward. For example, a generative AI image system trained on the works of Salvador Dali can be prompted to “create a painting in the style of Dali.” The output will be an image that reflects the training data—the colours, textures, compositions and so on that appear most often in Dali’s body of work. While the output is fundamentally an amalgam of its data sources, it is still an “original” image to the extent that it is not a direct reproduction of any particular Dali painting. The same principles are at work in deepfakes—the false reproduction of real peoples’ voices and appearance based on past recordings.

Large language models (LLMs) are a subset of generative AI systems that specifically work with text. LLMs are particularly important because they are the technology behind the chatbot-based AI systems, such as ChatGPT, Copilot, Claude and Gemini, that have driven widespread AI awareness and adoption since 2022. These models have proven to be increasingly capable of completing complex linguistic, creative and problem solving tasks previously assumed to be impossible for machines.

The aptitude that LLMs have for natural language creates an especially convincing illusion of consciousness that makes these systems easy to anthropomorphize, but, like all machine learning systems, LLMs are inherently illogical and, by any human definition, inherently unintelligent. They do not understand language or the real world to which that language refers—they merely predict the next word in a sentence in a sensible but fundamentally unintentional way. LLMs are prone to “confabulation” (also referred to as hallucination), which means they fabricate or misrepresent information and otherwise make basic factual or logical errors that a human never would. They struggle with reasoning, in part because they lack working memories.¹⁰ Generative AI systems like LLMs are also inherently backward-looking, which is to say they are limited by the data they were trained on and perform more poorly in novel circumstances and edge cases.

Yet, for all their limitations, LLMs and other generative AI systems have proven to be transformative. Even if they are not truly intelligent, their capacity for convincingly emulating human thought raises profound questions about the nature of human cognition and the value of human intellectual labour. As this paper shall discuss, the consequences are already widespread and compounding quickly.

As a final technical note, it is necessary to draw a distinction between prompt-based AI systems, such as chatbots, and agentic AI systems. A prompt-based system requires a user’s input at every step—you ask a question, and it provides an answer. Agentic systems, on the other hand, take a user’s general input but then make independent decisions about which specific steps are necessary to complete the task. An agentic system may be given control of a user’s email and calendar, for example, to proactively schedule meetings. Agentic systems are, at least in theory, considerably more powerful than prompt-based systems and are thus a growing focus for AI research and commercialization.

Dimensions of the AI industry

Similar to the concept of cyberspace—a virtual networked environment that exists outside the material world—artificial intelligence is often understood as an abstract or ephemeral phenomenon. When AI is discussed in news articles and policy reports, for example, it is often accompanied by images of robotic faces floating through abstract spaces, overlaid with math and geometry, rather than by photos of the human workers and data centres that build and sustain these systems.¹¹ However, AI today is very much a product of the real world, and understanding the AI industry and the physical infrastructure behind it is key to grappling with its implications.

The AI industry is made up of a complicated network of research institutions, technology companies, chip manufacturers, power utilities, financiers and other stakeholders. To the extent that it can be measured (independent of the rest of the technology industry), the AI industry was valued at an estimated US$400 billion in 2025.¹² Most of that value is being captured by U.S.-based big tech companies, such as Microsoft, Alphabet (Google) and OpenAI (ChatGPT), as well as the manufacturers of the computer chips that power AI data centres, such as Nvidia. While Canadian researchers are responsible for many key breakthroughs in the field of AI, Canadian firms have not commercialized AI to a similar extent. For example, Canada’s largest AI-focused firm Cohere was valued at just US$7 billion at the time of writing compared to OpenAI’s US$852 billion valuation. The value of OpenAI, like other AI companies, is largely speculative, based on potential future revenues and inflated by circular financing.¹³ Nevertheless, private investors are banking on the AI industry being worth as much as US$5 trillion within a decade—roughly equivalent to the entire economy of Germany or Japan.¹⁴

Modern AI systems work by processing enormous volumes of data, both during the development of new AI models (i.e., the “training” process) and for the operation of those models (i.e., the “inference” process). All of that processing occurs in dedicated data centres, which are large warehouses that contain thousands of specialized computer chips and consume large amounts of energy to process data quickly. The rapid expansion of AI services in the past few years has outstripped the growth of data centre capacity, leading to a gold rush of new chip manufacturing and data centre construction around the world. Total investment in new data centres in 2026 may reach half a trillion dollars.¹⁵ While the data centre boom has, to date, centred on the United States, it is increasingly spilling over into Canada and elsewhere as governments seek to attract investment in major infrastructure projects.

Even though AI is an increasingly capital-intensive industry, human labour plays a key role at every stage of development and operation. The data that AI models are trained on is collected from human outputs, including essentially all of the text and multimedia content ever posted on the internet (typically without the knowledge or consent of creators). Much of that data is then manually cleaned, sorted and tagged by precariously employed, low-wage human workers under poor working conditions, including prison labour.¹⁶ The AI models themselves are developed by human engineers based on the work of human researchers in academia and industry. The data centres that power the models are built and maintained by human workers. Finally, AI applications are tested and refined by human users. However, technology companies are increasingly trying to automate these steps. For example, some AI models are now being co-developed with AI coders and models are being trained on AI-generated “synthetic” data.

From a business perspective, the AI industry is taking the spend-now-earn-later approach that is common to Silicon Valley startups. Despite attracting unprecedented levels of private investment and attracting hundreds of millions of users, most AI services are losing billions of dollars per year as the costs of developing and operating AI models outstrip revenues.¹⁷ The dire need to raise those revenues explains, in part, the rapid integration of AI tools into many existing digital services accompanied by the aggressive marketing of AI companies. To justify their massive valuations, these companies are trying to show investors that their products are being used and will eventually turn a profit. The need for operational funding also explains the rush for private AI companies, such as OpenAI and Anthropic, to go public.

Assumptions driving the AI moment

As we shall discuss in the next section, measurable improvements in productivity due to artificial intelligence systems remain elusive. The gold rush of investment into the AI ecosystem is simply not justified by its real-world benefits to date. Instead, four key assumptions are driving the current AI moment.

First, AI companies (and many governments and employers) assume that AI will eventually deliver massive productivity gains. For example, Anthropic, which is the company behind the Claude chatbot, claims that AI adoption will double the rate of productivity growth in the U.S. over the next decade.¹⁸ While independent analyses are less optimistic, across a range of forecasts, the use of generative AI is expected to deliver labour productivity gains of, on average, about 17 per cent.¹⁹ These gains, if realized at scale, would indeed add trillions of dollars to the global economy over the coming decades, but they remain largely speculative.

Second, AI proponents argue that these systems are neutral and objective and therefore inherently superior to human judgment in many domains, independent of economic efficiency. For example, AI may be seen as less biased in areas such as hiring and the delivery of public services.²⁰ In practice, true neutrality is a myth, and AI systems are inherently biased based on their training data and engineering.²¹ Yet the allure of algorithmic objectivity remains a key argument for accelerated AI adoption.

Third, many AI proponents argue that the world is in a global AI race, and that the economies that develop and adopt AI the fastest will win this race.²² The AI race narrative is typically presented as a great power competition between the United States and China, which is a view that the U.S. government has adopted explicitly.²³ While the Chinese government has rejected the rhetoric of an AI race, its actual AI policy reflects a similar urgency to lead the world in AI development and adoption.²⁴ Other countries, including Canada, have adopted this framing, as have many businesses and public institutions that worry about being left behind by the technology.

In practice, it is unclear what “winning” or “losing” the AI race actually means. There is no automatic end point for technological development, nor is there a guaranteed commercial monopoly for any economy that first achieves particular AI capabilities. Indeed, the lack of a technological “moat” for generative AI systems means they are more vulnerable to competition than many previous disruptive technologies.

Fourth, and most fundamentally, the current AI moment is predicated on the assumption that technological innovation is inevitable and, moreover, that it necessarily leads to social progress. The myth of technological progress is not new, but with AI it has taken on a new life.²⁵ The framing of technological development as a moral good that all societies must embrace—or else be left behind—reproduces a colonial hierarchy in which the Global North sets the pace and direction of human progress and others are positioned as perpetually catching up.²⁶ This narrative is now fully centred on AI, and it is being perpetuated by a handful of mostly American corporations and a growing number of governments around the world. A fear of stifling innovation is commonly cited by AI proponents in industry and government as a reason to delay or weaken regulation of the sector, regardless of its harms.²⁷

As we turn toward an analysis of the real-world impacts of AI systems, we cannot lose sight of these pervasive assumptions, which are often invoked as justifications for the otherwise negative consequences of these technologies.

Implications: AI in the real world

Like electricity or the internet, many artificial intelligence systems are general purpose technologies, which means they have implications across all economic sectors and domains of human life. This grand scope presents a research challenge in itself as different domains have attracted different degrees of empirical attention. The labour and environmental consequences of AI, for example, are relatively better studied than the social, psychological and cultural implications.

To make matters worse, the novelty of many AI technologies means there are few longitudinal studies into their effects. In many contexts, it is simply too soon to determine, with confidence, what these systems are doing to the world. Future predictions are even more uncertain since there is no historical precedent to anchor them, and it is unclear how fast AI will improve, how businesses and consumers will respond, and what regulatory approaches governments will take in the years to come.

With these caveats in mind, this section provides a high-level overview of the current implications of modern AI systems across a wide variety of domains, in no particular order. It draws on emerging research and expert analysis to map the developing terrain while acknowledging that significant uncertainty remains and that there may be additional implications beyond those discussed here. The section concludes with an analysis of common themes across these domains.

Productivity and the economy

The most frequently cited argument for AI adoption is enhanced economic productivity. As noted above, increasing the rate of labour productivity growth by even one percentage point would add trillions of dollars to the global economy. In Canada specifically, research by the Vector Institute predicts that AI will add $300 billion to the Canadian economy over the next 10 years.²⁸

Although that figure appears significant, it amounts to less than one per cent of GDP, which is hardly transformative. To make matters worse, empirical studies of AI’s impact to date have not found that AI is delivering even these modest benefits. For example, a widely cited MIT study found that 95 per cent of enterprise adoption of AI systems is producing no measurable economic return.²⁹ Statistics Canada has similarly concluded that “there is no statistically significant direct association between AI adoption and productivity” among Canadian firms to date.³⁰

The fact that AI is not delivering measurable productivity benefits so far does not mean that it never will. As the technology improves and best practices are developed, businesses may yet realize those benefits. Nevertheless, fears of an AI-driven financial bubble loom over the industry.³¹ If AI-driven productivity fails to materialize at the scale and pace expected by investors, the financial crash of the AI industry would dwarf the dot-com bubble of the early 2000s. AI-connected firms account for a major share of stock indices, so a crash in the sector would have cascading effects across the economy, including for institutional investors, such as pension funds, and retail investors.

Labour and jobs

Despite popular fears of AI-driven job losses, the main impact of AI in the workplace to date has been a reorganization of how work is controlled, managed and valued rather than the broad displacement of workers. Statistics Canada reports that among firms using AI in 2025, 89 per cent saw no change in employment levels, while nearly half reported only small reductions in tasks.³² For their part, labour unions have expressed a greater immediate concern with the impacts of AI on job quality rather than on job quantity.³³

Underpinning these shifts is an expansion of workplace surveillance as AI systems now routinely collect data on worker location, pace, communications and physical behaviour. Employers are deploying AI to increase output and reduce labour costs, significantly impacting migrant workers and new Canadians, who are concentrated in sectors where workplace surveillance is more intensive, including warehousing and platform work.

“Algorithmic management” is the growing trend of human managerial tasks being handed off to AI systems. For example, in transportation and logistics, AI systems are being used to track and evaluate how workers drive while inward-facing cameras monitor drivers’ eyes to ensure they are watching the road at all times.³⁴ In office environments, AI can monitor keystrokes, screen activity and email content, transforming routine data into automated performance evaluations that may influence promotion, discipline or termination.³⁵ These systems can encode bias and discrimination based on race, gender or immigration status, while also reducing worker agency, displacing human management and suppressing union organizing.³⁶

Algorithmic tools also shape workers’ access to employment and earnings before they ever set foot in a workplace. In recruitment, AI screening systems are being used to filter candidates based on keyword matching rather than substantive qualifications, often bypassing strong applicants before any human review occurs.³⁷ In gig and platform work, AI compensation algorithms can identify which workers are likely to accept lower pay and adjust earning opportunities accordingly.³⁸ The threat of automation and a tightening job market weakens workers’ bargaining power, with those in AI-exposed sectors more likely to accept lower wages or worse conditions rather than risk replacement.

Nevertheless, over the long term, structural risks to the labour market remain a serious concern. Task level analyses suggest clerical work, data analysis, operations monitoring and writing carry the highest automation risk, while the trades, care work and roles requiring social perceptiveness show the lowest risk.³⁹ In Canada, three quarters of public sector jobs in business, finance and administration are highly exposed to automation of at least some tasks.⁴⁰ Exposure to AI task automation does not necessarily equate to job losses, and researchers disagree on what share of jobs is likely to be displaced by AI in the coming years.⁴¹ However, even if the total number of jobs stays the same, AI may still lead to a significant redistribution of work and workers, with widespread social consequences.⁴²

Because many at-risk occupations are disproportionately held by women, new Canadians and recent graduates, these shifts risk narrowing career pathways and advancement opportunities for those already facing barriers in the labour market. Statistics Canada data from early 2026 found that workers under 30 saw roughly half the employment growth of workers aged 30-49, with entry-level job postings declining at twice the rate of senior roles.⁴³ Although it is too early to determine to what extent AI is responsible for rising youth unemployment, it is almost certainly the case that junior roles, such as software developers, are being (or will be) automated before senior roles, such as software engineers. Young workers are thus the canary in the coal mine for AI labour market impacts.

Energy and environment

AI is driving one of the largest infrastructure expansions in modern history through the rapid construction of data centres. These facilities are highly energy intensive. In 2025, AI data centres consumed roughly two per cent of global electricity, a figure projected to more than double by 2030.⁴⁴ Although renewable generation capacity is expanding, data centre construction, often completed within two to three years, is consistently outpacing new clean energy development in North America.⁴⁵ As a result, much of the near-term electricity demand for AI infrastructure is being met by natural gas, coal and other carbon-intensive energy sources, in direct conflict with the necessity of industrial decarbonization to meet national and global climate targets.

A single hyperscale data centre can draw over 100 megawatts (MW) of power, equivalent to the annual electricity use of approximately 100,000 households.⁴⁶ In the U.S., communities in close proximity to data centres have experienced electricity price hikes of over 200 per cent.⁴⁷ Recent polling finds that two-thirds of Canadians expect AI data centres to drive up electricity prices here as well.⁴⁸ Rising consumer prices are due, in part, to utilities offering data centres subsidized industrial rates to attract investment while the infrastructure upgrades required to serve them, such as new transmission lines, substations and backup capacity, are financed through rate increases passed on to households. Data centres are rarely required to publicly disclose their energy consumption, making independent assessment of these costs difficult.

Proponents argue that AI could deliver environmental benefits through improved resource efficiency, climate modelling and precision agriculture.⁴⁹ However, like many other benefits of AI, these gains remain largely speculative to date. The AI applications most likely to deliver environmental benefits, such as targeted climate modelling tools, are narrow and task-specific, bearing little resemblance to the large generative AI systems driving the current infrastructure build-out.

Beyond energy, the AI supply chain includes critical mineral extraction, hardware manufacturing and the disposal of toxic electronic waste. This contributes to water contamination, biodiversity loss and greenhouse gas emissions.⁵⁰ Data centres also require large volumes of water for cooling their servers, with global consumption projected to rise from roughly 560 billion litres annually to more than 1.2 trillion litres by 2030.⁵¹ Data centres consume far less water than they do electricity, but many of these facilities are being built in water-stressed regions, which increases risks of water scarcity.⁵² As with electricity consumption, there is little transparency around data centre water use.

In general, the environmental costs of AI are disproportionately borne by rural, Indigenous and Global South communities, deepening existing patterns of environmental injustice.

Public health

Public health care systems face rising costs, staff shortages and aging populations. AI is being positioned as a response to these pressures. For example, machine learning systems can analyze patient histories, genetics and lifestyle factors to recommend individualized treatments and detect early complications.⁵³ At the population level, predictive tools can forecast disease outbreaks and identify at-risk groups early, enabling faster interventions and more efficient use of resources.⁵⁴ During the COVID-19 pandemic, for example, AI was used to analyze population data to identify optimal locations for vaccine sites.⁵⁵ Since 2022, DeepMind’s AlphaFold has predicted over 200 million protein structures, opening new pathways for treating cancer, Alzheimer’s and neglected tropical diseases. Its database is publicly available, giving researchers worldwide access to tools that previously required years of expensive laboratory work.⁵⁶ In diagnostics, AI is improving consistency in lung cancer screening, where early detection remains one of the most difficult clinical challenges.⁵⁷

These promising applications tend to share a common feature: they apply AI to well-defined, replicable tasks, such as pattern recognition in medical imaging or protein structure prediction, where consistency and scale are advantages. There is less evidence to support AI use in areas involving complex human judgment, contextual interpretation or emotional support, such as clinical settings or mental health care, even though users are increasingly turning to AI chatbots for health support.⁵⁸

In many health contexts, concerns about safety and equity are significant. Health data is among the most sensitive information people generate and health systems report hundreds of data breaches annually.⁵⁹ In many cases, that data is collected and sold to train AI systems without patients’ knowledge or consent.⁶⁰ The deployment of AI-enabled care depends on cost and the digital divide, risking a two-tier system where lower-income populations receive low-cost automated services as a substitute for higher-quality, human care (with or without AI assistance).⁶¹ Diagnostic systems trained on non-representative data can produce less accurate results for women, racialized communities and people in the Global South, embedding into clinical decision-making the same patterns of bias documented across AI systems more broadly.⁶²

AI tools deployed in clinical settings have also proven unreliable. For example, a 2025 audit procured by Ontario Health found that AI scribe systems fabricated clinical information in nearly half of cases, 60 per cent recorded incorrect medications, and 85 per cent missed key details about patients’ mental health.⁶³ The same concerns extend to consumers seeking medical advice from general-purpose commercial AI chatbots, which may make harmful recommendations leading to negative patient outcomes.⁶⁴ In mental health contexts specifically, the systems risk exploiting what researchers call the “Eliza effect”, which is the tendency for users to form emotional attachments to AI systems and overestimate their therapeutic capacity.⁶⁵

AI development remains concentrated among a small number of firms, placing critical health infrastructure under private control while commercial pressures accelerate deployment before risks are fully understood. To the extent that AI presents opportunities for Canada’s public health care system, the benefits will largely depend on how much oversight and control the public sector retains.

Education and cognitive development

Chronic underfunding and ballooning class sizes are straining public education in Canada. Governments and school boards are pressuring teachers to turn to AI tools to support assessment, lesson planning and differentiated instruction.⁶⁶ Meanwhile, students are turning to AI chatbots for homework, advice and emotional support, raising concerns about safety and dependence.⁶⁷ This pattern echoes longstanding critiques of social media use, where platforms designed to maximize engagement have been linked to addiction, anxiety and declining academic achievement. Ontario school boards have launched multi-billion-dollar lawsuits against major social media companies on exactly these grounds.⁶⁸

Emerging research suggests that reliance on generative AI tools may produce measurable cognitive harm. Preliminary findings from a MIT study found that students who regularly used ChatGPT showed reduced neural engagement, memory recall and ownership over their own thinking, even when the output scored well.⁶⁹ A related concern is sycophancy. Because many AI systems are designed to be agreeable, they confirm what users already believe rather than challenge it.⁷⁰ One study found that participants relying on AI were nearly five times less likely to reach correct conclusions than those who did not, while growing more confident in their wrong answers.⁷¹ Overreliance on AI risks stunting the cognitive development that education exists to support, including critical thinking, independent problem solving and the capacity to reach conclusions without assistance. When AI displaces mentorship, productive struggle and collective sense-making, it can strip learning of what makes it transformative.

According to the OECD, the core problem is not AI in education as such, but the dominance of off-the-shelf chatbots that were never designed for learning.⁷² Purpose-built tools co-created with teachers, students and communities could expand educational access for under-resourced students and reduce barriers for those with disabilities or learning in a second language.⁷³ However, the Brookings Institution global task force on AI in education found these conditions remain an exception, while commercial products face no binding obligations to demonstrate improved learning outcomes before entering classrooms.⁷⁴

OpenAI CEO Sam Altman has described a future in which intelligence is a utility that people purchase by the meter, signalling an intent to monopolize access to knowledge itself.⁷⁵ The long-term risks are substantial, not only for individual well-being but also for the workforce and for our very democracy, which depends on a citizenry that can think critically and independently.

Culture and knowledge production

Artists and cultural workers in Canada are among the first to feel the direct impacts of generative AI.⁷⁶ Across federal copyright consultations, union submissions and a 2024 National Summit on AI and Culture, Canadian creators have converged on three core demands: consent over how their work is used to train AI systems, credit when it is, and compensation when it generates value.⁷⁷

Major technology companies face active lawsuits over their use of copyright-protected material, while Canada’s copyright legislation has not kept pace. For example, in 2025, a coalition of Canadian media organizations, including the Toronto Star, Globe and Mail, Postmedia and CBC, commenced a copyright infringement action against OpenAI in Ontario, with the court confirming jurisdiction over a foreign AI company for the first time.⁷⁸ Because Canada still has no standalone copyright bill, creators are left to challenge infringement on a case-by-case basis.

The stakes extend beyond individual rights to the broader cultural ecosystem. Nearly one in four businesses in Canada’s information and cultural industries already uses generative AI, with adoption highest among the largest firms.⁷⁹ AI-driven algorithms threaten the discoverability of Francophone, Indigenous and independent Canadian content, deepening existing asymmetries between platform owners and creators.⁸⁰

At the knowledge level, AI generates outputs through pattern recognition rather than comprehension. The result is a proliferation of content that can appear authoritative while reproducing inaccuracies, bias, mediocrity and what some call “AI slop.”⁸¹ AI-generated material now comprises over half of new online content, making quality and credibility harder to locate amid a growing volume of AI-generated imitation.⁸²

Public services and public institutions

Canadian governments are rapidly integrating AI into the public service under the pretense of streamlining operations and modernizing service delivery. Delays in Employment Insurance payments, immigration processing, tax support and benefits delivery, for example, have material consequences for people’s lives, and slower public service delivery undermines public confidence. Processing backlogs are a legitimate problem that well-designed AI tools could help address.⁸³

However, there are also structural limits to what AI can do. The federal government’s own AI register documents over 400 instances of AI use across 42 agencies, with roughly one-in-five systems generating content that informs public decision-making.⁸⁴ Most of these systems were deployed without impact assessments, meaning their effects on service quality and accuracy remain largely unknown. Unlike human judgement, AI can only look backwards, bound by whatever patterns it was trained on and unable to adapt to circumstances it has never encountered.⁸⁵ Offloading core functions to AI risks hollowing out the expertise and institutional memory that an effective public administration requires over the long term.

Automated decision-making in public services is already causing demonstrable harm. The Canada Revenue Agency’s (CRA) AI chatbot “Charlie” cost taxpayers over $18 million and a 2025 report from the Auditor General found that it gave incorrect answers two-thirds of the time.⁸⁶ At Immigration, Refugees and Citizenship Canada (IRCC), the use of automation has coincided with a quadrupling of federal court cases since 2020, with lawyers documenting decisions that show no engagement with the evidence filed. In one case, IRCC acknowledged using AI to reject a permanent residence application based on job duties the algorithm had fabricated. In Quebec, an algorithmic overhaul of the social assistance system that eliminated assigned human agents is believed to have contributed to a patient’s death after a man received incorrect eligibility information without a caseworker who knew his file.⁸⁷

These technical limits are compounded by unequal access. AI-enabled service delivery assumes universal internet access and digital literacy, conditions that do not hold across the population. Rural, low-income and aging Canadians continue to rely on in-person and phone-based services.⁸⁸ Human judgment and contact are also needed for accountability, dignity and recourse. When these interactions are automated, there is a risk of displacing the relational core of the public service.⁸⁹

Underneath these concerns sits a more fundamental question of sovereignty. Digital sovereignty requires governments to account for where public data goes and who controls it. Forty per cent of federal AI systems are developed by external vendors, the majority American-owned,⁹⁰ meaning sensitive public data including health, immigration and tax information may be accessed through privately owned software governed by foreign jurisdictions. Canada’s AI Strategy for the Public Service deployed Microsoft Copilot for internal tasks,⁹¹ while the national AI strategy consultation used three U.S.-based LLMs to analyze over 64,000 public responses without clear disclosure on data handling.⁹²

Canada’s recent investments in sovereign AI infrastructure are nominally intended to address these concerns, but they will be insufficient if procurement continues to default to American-owned platforms while austerity erodes public sector capacity that institutions depend on to remain adaptive and legitimate. Canada’s AI Strategy for the Federal Public Service commits to responsible adoption but stops short of the procurement standards needed to ensure digital sovereignty in practice. The European Public Service Union has called for frameworks that require bidders to disclose ownership structures, mandate impact assessments before deployment, prevent public data from being repurposed or used to train AI systems without authorization, and ensure public authorities retain control over the systems they procure.⁹³ Canada’s procurement framework offers no such guarantees.

Democracy and governance

AI systems are contributing to new pressures on information integrity in ways that destabilize democratic participation. The problem begins with how information is curated. Algorithms owned by private companies can influence what millions of people know about an election, a policy or a geopolitical crisis—not based on accuracy or truth but on engagement.⁹⁴ These curated feeds narrow the range of perspectives people encounter and deepen political polarization.⁹⁵ Political campaigns exploit AI-driven micro-targeting techniques to deliver individualized messages calibrated for persuasion rather than addressing a shared public sphere.⁹⁶

Due to a 2024 British Columbia Supreme Court ruling, B.C. is the only province where provincial privacy law applies to federal political parties. Canadian parties elsewhere operate largely outside of privacy legislation, meaning there is limited insight into how parties collect voter data, use AI to target citizens or share that data with third-party technology firms. Exposure to AI-generated “deepfakes”—highly convincing fabrications of real people or real events—compounds the problem by making people more susceptible to believing misinformation or losing confidence in authentic evidence altogether.⁹⁷ Taken together, these conditions erode democratic life by producing a fragmented public, less able to engage in healthy debate, build shared values or hold power to account.

The governance of AI itself reflects a growing disconnect between governments and the public. Two thirds of Canadians express low trust in federal institutions,⁹⁸ 85 per cent want AI to be regulated,⁹⁹ and 79 per cent support taxing companies that replace workers with AI.¹⁰⁰ Yet policy-making in Canada is prioritizing competitiveness and alignment with industry interests, as we will discuss in the next section. Canada’s fall 2025 AI task force spent only a month studying the issue and it lacked adequate representation from civil society groups, labour unions and citizens.¹⁰¹ Canadians cannot meaningfully consent to technologies they have no say in shaping, and until democratic participation is built into how AI is governed, regulated and procured, decisions will continue to be made by those with the most to gain from them.

Human rights

AI has been used in ways that can support human rights and humanitarian response. For example, satellite imagery analysis enabled organizations such as Amnesty International to document attacks on civilian infrastructure and identify potential safe corridors in conflict zones.¹⁰² AI-powered translation tools and chatbots are being developed to help refugees navigate complex asylum systems.¹⁰³

However, a 2026 Amnesty International briefing concludes that the most widely used generative AI systems are fundamentally incompatible with international human rights law, finding that their reliance on unlawful web scraping constitutes mass invasion of privacy by design.¹⁰⁴ An investigation of OpenAI’s ChatGPT by the Privacy Commissioner of Canada similarly concluded that the company “did not respect Canadian privacy laws.”¹⁰⁵

Across justice, policing, immigration and warfare, government deployment of AI is transforming decision-making in ways that undermine core human rights protections. In policing, for example, facial recognition technologies and algorithmic risk assessments demonstrate documented racial bias and inaccuracy, contributing to wrongful arrests and expanded systemic discrimination.¹⁰⁶ Automated decision-making has been embedded in Canadian immigration and refugee processing for over a decade and predictive analytics systems influence who is flagged for secondary screening, delayed, or denied entry at borders, often with limited transparency and constrained avenues for appeal.¹⁰⁷

Globally, the stakes are most stark in warfare. For example, Israel is using AI-assisted targeting systems to generate strike lists, with human oversight reduced to just 20 seconds per decision, despite acknowledged error rates and authorization of significant civilian casualties.¹⁰⁸ This exemplifies moral outsourcing, where life-and-death decisions are delegated to systems devoid of conscience and incapable of ethical reasoning. Across domains, AI is accelerating harm, expanding the scale and speed at which violence and exclusion can be administered, while eroding due process and democratic oversight.

These examples are among the most acute, but human rights concerns arising from AI extend across employment, child welfare, fraud detection and any context where automated systems make or inform consequential decisions about people’s lives.

Indigenous data sovereignty

Dominant AI paradigms encode Western ways of knowing as universal standards—quantifiable, objective and algorithmically legible at the expense of knowledge systems that are relational, place-based, embodied and intergenerational.¹⁰⁹ Generative AI systems scrape and commodify knowledge at scale, assuming it can be abstracted from context, relationship or ownership. Absorbing Indigenous Knowledge Systems (IKS) into AI systems strips them of the ethical, spiritual and governance dimensions that give these systems life and meaning.¹¹⁰ Indigenous researchers call this “data colonialism”—the appropriation of human and ecological knowledge as a raw material, commodified for capitalist computation, replicating historical logics of dispossession and erasure into the digital realm.¹¹¹

In response, Indigenous data sovereignty movements are asserting the right to control how community knowledge is collected, used and governed. Rooted in Article 31 of the UN Declaration on the Rights of Indigenous Peoples, the “CARE Principles” for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility and Ethics) offer a critical alternative to data frameworks that prioritize unfettered open access and interoperability over Indigenous rights and collective interests.¹¹²

Emerging work in Indigenous algorithmics is moving beyond reductive data models toward systems designed around reciprocity and land-based ethics. Some communities are deploying AI on their own terms. For example, remote sensing enables the monitoring of illegal logging on Indigenous territories;¹¹³ the MaxEnt model helped the Heiltsuk Nation identify culturally significant plants;¹¹⁴ and Canada’s NRC Indigenous Languages Technology Project is supporting language revitalization through community-driven design.¹¹⁵

Grounded in Indigenous governance and knowledge systems, AI holds the potential to become a tool for self-determination and more just relations between humans, technologies and land. The challenge is not to integrate IKS into existing AI systems, but to dismantle the knowledge hierarchies those systems are built upon. Without that structural change, AI deepens digital colonialism.

Common themes

The rise of the modern artificial intelligence era has different implications in each of these domains, but there are clear through lines to our analysis. We identify four core themes that help answer the question of what AI means for Canada today.

The first theme is the indiscriminate adoption of AI systems across all domains of society. It may be the case that AI applications are ultimately of net benefit in some contexts, such as medical research, but not in others, such as primary health care. Determining where AI has value requires a degree of experimentation, but it also requires a sense of what problems these technologies are supposed to solve and an awareness of what kinds of solutions have been tried before. In many contexts today, AI is, instead, a solution in search of a problem. AI companies and governments promote AI adoption for its own sake rather than offering clear evidence for added value. As a consequence, both personal and institutional AI use is rising, but it is not necessarily delivering benefits for those users, institutions or the people they serve.

Indeed, the exceptionally high failure rate for AI pilots in the private and public sector is not necessarily an indictment of the underlying technology but, rather, of an unwarranted enthusiasm for AI adoption in contexts where it is not needed or may be actively harmful. Premature AI adoption at scale also introduces risks of its own, such as compounding security vulnerabilities, dependence on private, foreign technology and the deskilling of the workforce due to widespread cognitive offloading. These are avoidable problems if AI is developed more slowly and adopted with greater human oversight.

The second theme is the comprehensive devaluation of human thought, judgment and expression in favour of algorithmic output. The dehumanization and commodification of labour (physical and intellectual) is not new. It has proceeded apace since the industrial revolution. However, AI accelerates the trend to new extremes. No human domain is now beyond the scope of automation. Many sectors previously assumed to be “safe” from AI, such as creative and care work, are increasingly exposed.

Behind the application of AI systems to these domains is the fundamental assumption that the output of labour has value but the labour itself does not. For example, it does not matter whether a human doctor or an AI tool prescribes a medication to a patient, provided the prescription itself is passably similar. The same can be said of a piece of legislation, a business plan, a novel or a citizenship decision. This logic both dehumanizes labour and serves to depoliticize it. Human workers can organize, bargain and strike, which has historically forced employers and states to negotiate the terms under which work is conducted. When labour is automated, that leverage disappears with it and so does any clear accountability for the outputs of AI systems.

In practice, AI tools are still inferior to human expertise across many domains today. Moreover, the assumption that outputs matter but process does not fails to recognize that the labour itself is often the point. For example, writing an essay, a report or an email forces productive engagement with a topic, regardless of the quality of the final document. However, the gap between AI tools and human experts is narrowing, and as AI systems become increasingly capable of producing outputs that meet or exceed human standards, the value of intellectual labour becomes increasingly invisible and/or economically irrelevant. As a consequence, AI systems facilitate the centralization of power in the hands of employers and technology companies at the expense of workers and communities. The devaluation of human thought and the attendant concentration of power matters in both practical and moral terms, but, so far, it has not prominently featured in debates around AI.

The third theme is the tension between the speculative future benefits of AI systems and their real present harms. Where AI does offer theoretical benefits, as in business productivity, those gains are neither guaranteed nor immediate. In many cases, those gains hinge on continued technological development and the widespread social and commercial adoption of AI systems. Even in a best-case scenario, it may take many years for the AI-powered utopia promised by proponents to truly arrive.

Yet AI is already having material impacts on people and communities, and many of those impacts are negative. The erosion of the digital information environment due to AI is already a crisis, as is the use of unregulated and unmoderated AI tools in the education system and many workplaces. The proliferation of data centres powered by fossil fuels is actively undermining decarbonization efforts. Workplace surveillance and algorithmic management is accelerating. Policing and citizenship decisions are increasingly taken without human oversight.

To be truly socially beneficial, AI systems must meet high thresholds of capability and trustworthiness. To cause harm, they need only be accessible. Right now, AI systems are far more accessible than they are capable or trustworthy.

The fourth, and final, theme is the exacerbation of structural inequalities that are being driven by AI development and adoption. For example, to the extent that commercial AI use can or will improve productivity, the benefits will mainly accrue to employers at the expense of workers whose labour is automated, intensified or appropriated. AI in the public service, including in health care, shifts public resources away from public institutions and toward private firms. AI in sensitive social sectors, such as immigration, housing and policing, further dehumanizes and disempowers already marginalized groups and amplifies existing patterns of inequity across race, gender and class.

Ultimately, the biggest beneficiaries of widespread AI adoption are the private, mostly American tech companies that own and operate these systems. As they become more powerful, they are also able to exercise greater influence over regulators, users, workers and the rest of the private sector. The developers of AI tools have implied that the automation of labour (and the broader diminishment of human labour power) is a key goal of theirs, which means AI adoption is as much a political project as it is a technological project.

These are not the inevitable consequences of artificial intelligence itself. Instead, they reflect an AI industry that is produced by, and thus reinforces, a political and economic system of global capitalist exploitation. The difference between AI that surveils workers or empowers workers, for example, is political, not technical. What AI could do under different conditions of ownership, governance and democratic control remains an open question.

In the next section, we turn from implications to responses. We ask how governments around the world are responding to the AI moment and assess whether and how those policy measures are grappling with the themes identified above.

Policy: How governments are responding to AI

The three largest economies in the world—the U.S., China and the European Union—are all grappling with the AI moment in starkly different ways, which provides a useful natural experiment as well as offering diverse points of comparison for Canada. In this section, we assess each jurisdiction in turn, asking how they have responded to AI at the regulatory level and what each governance model reveals about that jurisdiction’s AI strategy. We recognize that the regulatory environment is evolving quickly, so the pictures presented here are more of a snapshot than a definitive analysis of each jurisdiction’s approach to AI governance.

We conclude with a summary of AI regulation in Canada and an assessment of how Canadian AI governance differs (or aligns) with public policy elsewhere.

United States

The trajectory of U.S. AI governance shifted sharply under Donald Trump’s 2025 AI Action Plan, rolling back the Biden administration’s emphasis on safe, secure and trustworthy AI. This shift is often framed as deregulation, but it is better understood as aggressive state intervention on behalf of the American tech industry.

A recent report from Data & Society describes this as the emergence of a “Big AI State” that is operationalized through three pillars. First, derisking data centre and energy development through executive orders that allow developers to bypass environmental review, to exploit federal lands, and to access public financing, limiting community input while socializing the costs of private AI investment. Second, the U.S. is exporting its AI tech stack as geopolitical infrastructure, offering full-service packages (compute, data systems, models and applications) through government-backed partnerships that condition market access on regulatory alignment with U.S. priorities. Third, the federal government is acquiring direct equity stakes in strategic firms throughout the AI supply chain, such as American rare earth mineral producers.¹¹⁶

The U.S. remains a fragmented regulatory environment, with states and municipalities actively advancing AI governance measures in the absence of federal guardrails. For example, over 350 bills related to AI and work were introduced in the 2025-26 legislative session alone, covering algorithmic management, automation disclosure and bias protections.¹¹⁷ The Trump administration responded with a December 2025 executive order aimed at preempting state AI regulation.¹¹⁸ Communities near proposed data centre sites are also organizing, with resistance expanding into litigation, calls for moratoria and electoral organizing.¹¹⁹

What is emerging is not an innovation economy but a technology infrastructure empire—over 5,000 data centres, more than ten times any other country, and $286 billion in private AI investment in 2025 alone.¹²⁰ The emphasis on rapid infrastructure buildout and market expansion reflects the broader pattern of indiscriminate AI adoption, where deployment is prioritized ahead of social value. The alignment between state policy and private capital accelerates the concentration of economic and political power, deepening the inequalities already associated with AI development. The AI Action Plan removes references to misinformation, equity and climate change from federal frameworks, purporting that AI should pursue “objective truth,” free of ideological bias.¹²¹ Yet framing bias mitigation as ideological overreach does not produce unbiased AI. Instead, it produces unaccountable AI. Rather than mitigating the risks of AI, the U.S. model is actively upscaling them. However, as resistance from elected officials, civil society and affected communities continues to mount, the foundations of the Big AI State are more contested than the elite consensus would suggest.

China

China’s strategy for AI is threefold: first, to compete with the U.S. for technological leadership; second, to reduce dependence on Western digital infrastructure; and, third, to extend state AI capacity across governance, services and security.¹²² Since the release of its New Generation AI Development Plan in 2017, China has backed AI-related technologies with large-scale investment and coordinated action across government, industry and research.¹²³ The release of DeepSeek-R1 in 2025, a generative AI model developed at a fraction of Western costs, established China as a legitimate competitor in frontier AI development.

Chinese firms often release models as “open weight,” meaning they can be downloaded and further developed. This accelerates global adoption of Chinese AI and challenges the U.S. strategy of controlling AI diffusion through export restrictions. China is embedding AI across society—from chatbots in local government services and education systems to factory robotics in industrial production—at a scale and pace unmatched elsewhere. The 2025 State Council Opinions on the “AI+” Initiative have set aggressive targets for deepening AI integration across energy, health, transportation and public services.¹²⁴

China regulates AI through a layered set of sector-specific rules oriented toward state priorities rather than individual rights. Measures include mandatory pre-deployment safety assessments, algorithmic registration requirements and content labelling rules requiring watermarks on AI-generated content.¹²⁵ AI systems are also prohibited from generating content that opposes the ruling party or the broader political system.¹²⁶ Enforcement is tightening through significant penalties and a revised cybersecurity law that brings AI directly under government oversight.¹²⁷ While these measures reflect strong state control, they also produce forms of protection that are often absent in less regulated markets. For example, China’s 2026 Interim Measures for Anthropomorphic AI prohibit virtual intimate relationships with minors, restrict content that could encourage harmful emotional dependence and require clear labelling so users know they are interacting with a machine.¹²⁸ UNICEF has recognized these provisions as a significant step forward for child protection in AI regulation.¹²⁹ In another notable case, a Hangzhou court ruled that AI replacement does not automatically justify terminating a labour contract, establishing that the costs of technological transformation cannot be shifted onto workers.¹³⁰

China is advocating for global AI governance through a proposed “World Artificial Intelligence Cooperation Organization.”¹³¹ Whereas the United States has abandoned human-centred multilateral coordination, China is moving to set the agenda. However, the tension in China’s approach is impossible to ignore. While simultaneously using AI to drive economic growth and technological leadership, China maintains tight control over information and data, and it operates the most extensive AI-enabled censorship and surveillance apparatus in the world. State coordination allows for more direct intervention in deployment and, in some cases, stronger legal protections for citizens. Yet it concentrates both governing authority and the informational advantages of AI within the state, subordinating human autonomy to state priorities and leaving little room for the public deliberation that democratic governance requires.

European Union

The European Union has pursued a dual strategy on AI: leading in rights-based regulation while simultaneously investing in rapid AI adoption and industrial capacity. These ambitions sometimes pull in opposite directions, and the consequences are visible in the architecture and rollout of the EU’s groundbreaking AI Act.

The EU has built a substantive policy framework for regulating the digital economy. The General Data Protection Regulation (GDPR) establishes strict rules for how personal data is collected, processed and shared.¹³² The Digital Markets Act and Digital Services Act extend this foundation, targeting platform monopolies and user rights, respectively, including recent investigations into cloud providers like Microsoft Azure and Amazon Web Services, whose infrastructure underpins much AI development.¹³³ Whether a handful of American companies can act as gatekeepers to Europe’s AI future is now an active regulatory question.

At the centre is the EU AI Act, the world’s first comprehensive AI law. It introduces a tiered, risk-based framework that bans certain applications of AI systems outright, such as social scoring systems that rate behaviour to determine access to services or opportunities.¹³⁴ It also imposes strict requirements on high-risk systems in employment, health care and public services, including mandatory audits, technical documentation, public registration and penalties reaching up to €35 million, or seven per cent of a company’s global turnover, for the most serious violations.¹³⁵ The act reflects a precautionary logic. Systems must demonstrate safety and accountability before entering the market.

Yet the AI Act contains structural exclusions that undermine its stated commitments. It allows a full exemption for military, defence and national security uses. In migrant contexts, for example, this enables the use of prohibited and high-risk tools like biometric identification, AI lie detectors and predictive scoring to be deployed without registration or public oversight.¹³⁶ Worker protections remain limited to notification and transparency, falling short of rights to appeal or refuse workplace AI deployment.¹³⁷ Environmental impacts and supply chains are also largely unaddressed.¹³⁸

The competitiveness pressure further constrains EU regulation. Through the Digital Omnibus, the council and parliament are negotiating compliance delays that critics argue create a window for risky systems to reach the market before oversight applies.¹³⁹ The same process is advancing proposals to narrow the GDPR’s definition of personal data, responding to industry arguments that existing privacy protections limit training data availability and disadvantage EU developers relative to less-regulated competitors. The EU has also actively pursued industrial policy through the AI Continent Action Plan, targeting €20 billion in annual AI investment, signalling that the AI race narrative is actively shaping its priorities.¹⁴⁰

Despite these limitations, the EU policy approach remains the most explicit attempt to govern AI in line with public interest principles. The EU exerts outsized global influence through the “Brussels Effect”, whereby global companies align their practices with EU legislation.¹⁴¹ When the GDPR passed, for example, major platforms restructured data privacy practices globally, rather than maintaining separate systems for EU users.¹⁴² The AI Act is expected to operate similarly, making regulatory authority the EU’s primary instrument of global AI influence. However, while the EU seeks to mitigate the harms of AI deployment, its parallel push for rapid adoption reproduces the same speculative logic identified earlier. As a result, while the EU is attempting to moderate some AI risks, it is not fundamentally altering the conditions producing those risks.

Canada

Canada entered the AI era as a research leader, with institutions that helped shape the technology now transforming the global economy.¹⁴³ The Canadian Institute for Advanced Research (CIFAR), which was first launched in 1982, has played a central role in convening researchers, industry and governments in service of AI development over the past four decades.¹⁴⁴ Canada was the first country to adopt a national AI strategy in 2017, which was administered by CIFAR, and a revised national AI strategy was released in 2026.¹⁴⁵ However, after years of false starts and unfulfilled promises, Canada still has no federal legislation in place to govern AI systems. Rather than develop a formal regulatory and governance structure for AI, the federal government has prioritized public-private partnerships with the AI industry. Surveys consistently rank Canadians among the most cautious and skeptical populace on AI adoption, but those concerns are not reflected in Canada’s approach to AI governance.¹⁴⁶

The failure to regulate is, in part, a structural problem. AI governance in Canada is mainly the purview of Innovation, Science and Economic Development Canada (ISED), which encompasses the Minister of Artificial Intelligence. The AI minister and ISED share a mandate to promote economic growth and to regulate the marketplace.¹⁴⁷ This dual mandate produces a predictable outcome, with policies that prioritize industry access over public oversight. For example, of 84 federal and provincial AI initiatives identified by academic researchers, 50 focus on promoting industry and innovation, compared to just 19 addressing social and workforce impacts.¹⁴⁸ The result is a regulatory landscape where standards about how AI shapes work, health and public life are either set by industry or are entirely absent.

The failed Artificial Intelligence and Data Act (AIDA) was emblematic of the government’s approach to AI regulation. The AIDA was drafted within ISED, with no public consultation before being tabled in 2022.¹⁴⁹ Of more than 300 stakeholder meetings held afterward, only nine included representation from civil society, who, along with researchers and labour unions, called for its withdrawal.¹⁵⁰ The AIDA was eventually abandoned when parliament dissolved in 2025.

The death of the AIDA and the election of a new federal government in spring 2025 created an opening for a different approach, but the new government has fallen into familiar patterns. In fall 2025, the government began consultations on a new AI strategy, which included both an expert task force, composed mainly of industry voices and industry-adjacent academics, and a public call for comments. The public portion of the consultation drew more than 11,000 participants—the largest in ISED’s history—signalling strong public demand for involvement.¹⁵¹ However, ISED privileged the views of its industry-dominated task force in reaching the conclusion that the government should prioritize innovation and growth of the AI industry rather than lead with democratic governance.¹⁵²

The pro-industry orientation of the consultation was reflected in the government’s new AI strategy, AI for All, which was released in June 2026. Of the plan’s six pillars, five are either directly or indirectly focused on scaling up AI development and increasing AI adoption across Canadian society. Among other goals, the strategy seeks to increase business adoption of AI from 12 per cent today to 60 per cent within a decade. The government also hopes to reach millions of students with “AI literacy” initiatives designed to encourage greater AI use. The strategy includes billions in subsidies for the AI industry, mainly focused on providing increased domestic compute capacity, although the sums remain relatively small compared to other public industrial spending.

The sixth pillar of the 2026 strategy is focused on AI safety. Among other commitments, the government promises to establish a “fundamental right to privacy,” to protect elections from misinformation, and to require the watermarking of AI-generated content. However, no details or timelines are provided. Moreover, not all of the AI-related concerns raised in the public consultations or by civil society experts are acknowledged or addressed in the strategy. For example, AI for All makes no mention of the environmental harms of AI infrastructure, of the risks to cognition and mental health of AI use, or of the theft of copyrighted works to train AI models.¹⁵³

Indeed, the new federal strategy is guilty of perpetuating all of the assumptions driving the AI moment that we discuss above. The strategy assumes AI will “unlock productivity gains” and “improve quality of work.” It speaks of the “urgency” of AI adoption. It also appeals to the myth of technological progress in calling for Canadians to “trust in [AI’s] promise.” The strategy provides little evidence to support any of these assertions. Even if the government delivers on its vague promise of safety regulation, the vision for AI described in this document is a far cry from a comprehensive, public interest governance framework.

The strategy’s aspirations of digital sovereignty are also difficult to reconcile with the government’s current practices. The federal government is deeply reliant on Microsoft and other American technology companies for computing systems and software, including for AI tools. Real sovereignty requires technical and legal safeguards, including audit rights, breach notification requirements, control over who can access Canadian data and the ability to refuse foreign government requests hardwired into enforceable contracts.¹⁵⁴ Microsoft’s own representatives have acknowledged under oath that they cannot guarantee data sovereignty to non-American customers, even when data is stored domestically.¹⁵⁵ By equating digital sovereignty with domestic data centre construction, the federal government is leaving the software layer—where data is created, processed and accessed—largely ungoverned.¹⁵⁶

Canada’s federal institutions have consistently chosen dependency and perceived economic competitiveness over accountability when the two have come into conflict. That pattern is unlikely to change while AI governance remains housed in a department whose mandate is to promote the industry it is meant to regulate. Canada is not without leverage. As a middle power, it has substantial procurement power, research capacity, existing legal frameworks and strong public demand for regulation. Yet governments have not yet used that leverage to advance a public interest approach to AI development and adoption.

In the absence of federal legislation, provinces have begun to fill in the gaps unevenly. For example, Manitoba has moved to address algorithmic price discrimination, Quebec requires transparency in workplace AI deployments, and Ontario requires disclosure around AI use in the hiring process. On the other hand, Alberta has exempted data centre proposals from provincial impact assessments, which has triggered legal action in the province.¹⁵⁷ For example, Sturgeon Lake Cree Nation issued a cease-and-desist to the provincial government over the Wonder Valley data centre project, stating they were never consulted and learned of the proposal through a press release, despite the project sitting on land shared under Treaty 8.¹⁵⁸ Unless and until the federal government provides legislative and regulatory clarity around AI issues, Canada’s AI governance landscape will remain a contested patchwork.

Each of the four jurisdictions examined in this section has taken a different approach to AI governance. The U.S. is prioritizing market power and tech imperialism, China is prioritizing centralized state oversight and coordination, and the EU is advancing comprehensive safeguards with carve-outs for industrial development. Canada’s AI governance approach, lacking a formal federal framework of its own, is largely shaped by these international dynamics rather than shaping them to reflect Canadian values.

Nevertheless, across all four jurisdictions, AI adoption is treated as an end in itself, with policies designed around industrial capacity and geopolitical competition rather than the needs of the people most affected by these systems. The public interest principles that should guide AI governance are largely absent from these approaches, which is precisely why articulating them matters.

Principles: Foundations for public interest AI advocacy

This paper sets out to establish a common conceptual framework for public interest advocacy around artificial intelligence issues in the Canadian context. For citizens, workers and civil society organizations to productively engage on AI issues, they require actionable principles that can serve as a foundation for specific recommendations and demands. Based on our analysis of the real-world implications of AI in Canada and informed by existing government responses to AI, we offer six core principles for consideration.

Note that these principles are deliberately high level. They are intended to be salient for citizens, civil society, employers and governments at all levels in Canada. We discuss next steps for specific policy recommendations in the concluding section to this report.

1. Distinguish between AI as technology, industry and ideology

The term “artificial intelligence” does not refer to any one phenomenon in particular. That ambiguity creates conceptual problems and practical risks for both AI advocacy and AI governance.

As a technology, AI refers to a category of computer systems that share a variety of underlying architectures. As an industry, AI refers to a network of researchers, developers, investors and other stakeholders that seek to build and/or bring AI technologies to market. As an ideology (or as a political project) AI refers to a belief in the inevitable transcendence of human capabilities (especially human labour) by automated computer systems.

These are overlapping conceptions, but they are distinct in important ways. When the Canadian federal government, for example, advances an “AI for all” agenda, it is unclear whether the ultimate goal is technological accessibility, industrial development or socio-economic transformation. That ambiguity may reflect a lack of clarity in the government’s own thinking, or it may be a deliberate obfuscation. For example, the federal government evokes the benefits of narrow, sector-specific AI applications (e.g., in agriculture and manufacturing) as justification for a society-wide project of generative AI adoption, including new data centres to power generative AI demand. This equivocation (i.e., bait-and-switch) is only possible because AI is not clearly defined in the strategy.

AI proponents must be clearer about whether they support AI as a technology (and which types of AI systems, specifically), AI as an industry, and/or AI as a matter of principle. For their part, AI critics must articulate whether their concerns stem from the AI technologies themselves, from the structure of the AI industry, or from AI as an ideological or political project.

2. Recognize the value of original and diverse human thought

To address AI as ideology, we must first recognize that original and diverse human thought has value in itself.

Many artificial intelligence proponents claim that by outsourcing your thinking, you reclaim your time. This devalues the process of human thinking by focusing on the outputs of intellectual labour alone. There are contexts in which the ends may justify the means, such as rote administrative tasks, but it is not a universal truth. As various commentators have remarked, we want technology to do our laundry so that we can spend more time creating art, but modern AI tools promise to create art so that we can spend more time doing the laundry.

A related consequence of a handful of AI tools being used by billions of people is that they are intellectually homogenizing. If ChatGPT or Claude or Gemini is viewed as “neutral”, then its voice and perspective—shaped by a small segment of mostly high-income, white, English-speaking, American men—becomes an unassailable default. The illusion of objectivity that these tools offer entrenches the epistemic hegemony of the tech industry and undermines the value of human diversity, including non-Western values and Indigenous knowledge systems.

There are practical reasons for believing in human diversity and originality—a raft of evidence across domains finds that incorporating multiple perspectives leads to better outcomes¹⁵⁹—but it is also a moral position. We can and must make social, cultural and political choices about where AI usage is appropriate and where it is not, and we cannot be afraid to reject AI systems outright where they infringe on our humanity in unacceptable ways.

3. Subject the AI industry to democratic oversight and control

To address AI as industry, we must establish a robust system of public interest AI governance.

Even if the hype surrounding the artificial intelligence industry today does not reflect the current capabilities of AI systems, there is no doubt that these systems are transformative and will be a destabilizing feature of societies and economies moving forward. Any technology this disruptive requires democratic oversight and control.

Oversight means an expectation of transparency from the AI industry. Among other issues, AI companies must be clear how data is collected to train an AI model, whose labour was used in that training and under what conditions, what assumptions the model has been programmed with, and what biases it exhibits in its outputs. It must also be clear how much energy and water these systems require to operate. All industry claims must be independently verifiable.

Democratic control means governing AI in the public interest with binding regulation. That begins by rejecting the primacy of private sector innovation, business investment and geopolitical competition. None of those priorities is more important than the safety and well-being of citizens. It also means ensuring democratic control over the data—both public and private—that is the lifeblood of the AI industry.

To govern effectively, the public sector must have the legislative authority and technical capacity to regulate the industry and to protect Canadians and their data. Preserving regulatory space is especially important given the changing nature of AI. Governments must be prepared to (re)regulate as new technologies, issues and evidence emerge.

4. Apply the precautionary principle to AI harms

To address AI as technology, we must stop AI systems where they are causing harm today and we must slow AI adoption where there is significant risk of harm in the future.

Some of the social and environmental harms that AI systems have created are already obvious and concrete, and those harms must be immediately prohibited. There is no defence for sexually exploitative AI deepfakes, for example. Nor should new physical infrastructure, such as data centres, be built in a manner that entrenches fossil fuel dependence in the face of an escalating climate crisis. Speculative future benefits do not excuse immediate material harms.

In many contexts, however, we cannot say with certainty what the long-term consequences of AI systems may be. For example, whether AI use by students will enhance learning or permanently weaken cognition—and under what circumstances—remains a vital open question. There is a real risk that future generations will lose the ability to perform complex cognitive and creative tasks unless they are deliberately shielded from AI use. Whether AI use in medicine will make doctors more accessible to marginalized groups or widen existing health inequities is similarly uncertain.

Until we have more evidence, especially in sensitive areas such as education, health care and social services, where the risks are significant and the consequences difficult to reverse, we should apply the precautionary principle. That means slowing or pausing the adoption of AI systems until there is conclusive evidence supporting their safety and efficacy.

5. Prioritize augmentation over automation in workplace AI deployment

To address AI as technology, we must also adopt a pro-worker orientation to AI deployment.

Productivity, in economic terms, is calculated simply as output divided by hours worked. Productivity can thus be increased either by increasing output with the same amount of labour or by producing the same amount of output with less labour input. In many cases, AI systems may be technically capable of doing both. Whether we augment workers with these systems to make them more productive, or whether we automate the tasks previously done by those workers, is a political choice.

When AI systems are deployed in workplaces, they should be oriented around the augmentation of the human workforce. However, realizing the productivity potential of an augmentation approach requires investment, training and negotiation, whereas automation promises a shortcut to easy productivity gains. To ensure employers do not default to the easy option when AI systems are deployed, workers must have power in the workplace. Collective bargaining is one of the greatest tools for ensuring workers exercise control over the introduction of AI systems in their workplaces. In non-unionized workplaces, stronger government regulation becomes essential for giving workers tools for managing and pushing back on AI deployments.

6. Develop AI systems that solve real problems in the public interest

Finally, to further address AI as industry, we must, to the extent possible, reorient the development of AI systems themselves toward the public interest.

In many contexts today, AI systems are a solution in search of a problem. AI-powered tools are marketed by the tech industry as productivity or well-being enhancing with little regard for the specific needs of users or of the unintended consequences of indiscriminate adoption. Governments are similarly guilty of pushing AI adoption based on the ideological assumption that AI is a net positive in all contexts—and with no acknowledgment that the adoption of AI systems may be counterproductive or create new problems.

Yet there are contexts in which AI systems offer solutions to real problems or where AI tools open new doors to human flourishing. AI systems in medical and scientific research can accelerate new discoveries, for example, while AI systems in the utilities sector can help the electricity grid operate more efficiently. To properly identify those contexts, and to ensure they deliver on their potential, we require greater involvement of workers and citizens in the development and deployment of new technologies. Better AI governance models will help, but that’s not the only path forward. Building new AI tools from the ground up, with public participation, can also ensure they better serve the public interest.

We should not be afraid to experiment with sector-specific AI applications co-developed with workers and the public sector, provided deployment is consistent with the principles of precaution and augmentation described above.

Conclusion: Toward an AI policy for Canada

The rapid rise of novel artificial intelligence tools is undermining the very value of human intellectual labour, all the while driving a gold rush of investment into a booming technology sector. Governments in Canada and around the world are all in on AI, which they view as central to the future of the economy. Indeed, the development and deployment of AI systems (and the accompanying physical infrastructure) is being sold as so vitally important for future well-being that it cannot afford to be slowed down by regulation or oversight, regardless of the concerning and increasingly visible harms associated with these technologies.

This paper has attempted to contextualize this tenuous situation and unpack its implications for concerned citizens in Canada. It considered the different types of AI technologies in question, the current shape of the AI industry and the assumptions driving the development and adoption of AI systems. It evaluated the measurable and potential future impacts of AI systems in the real world across a wide variety of domains. It also surveyed the various measures that governments in Canada and around the world have already taken to regulate artificial intelligence.

Based on this analysis, we arrived at six core principles for the responsible development of AI in Canada. We argue that governments, industry and civil society must:

1. Distinguish between AI as technology, industry and ideology,
2. Recognize the value of original and diverse human thought,
3. Subject the AI industry to democratic oversight and control,
4. Apply the precautionary principle to AI harms,
5. Prioritize augmentation over automation in workplace AI deployment, and
6. Develop AI systems that solve real problems in the public interest.

These are not specific policy recommendations aimed at any one government or institution, but there are clear policy implications. Above all else, artificial intelligence must be regulated through some combination of government legislation, workplace negotiation and broader social and cultural activism. The real and immediate harms that AI is causing are inexcusable when the only justification is the speculative promise of long-term benefits. There is also no guarantee that those benefits, if and when they arrive, will serve the broader public interest over private profits in the absence of a strong governance framework.

Specific policy ideas are included for reference and consideration in the appendix to this report. A common theme in the approaches identified in our research is that AI must serve people and not the other way around. Like any tool, whether AI systems ultimately benefit or harm the public is not inevitable. It is a choice that must be made thoughtfully, collectively and freely from the pressures of a self-interested technology industry.

Appendix: AI policy database

Table 1 summarizes specific AI-related policy recommendations that we encountered during our research. They are presented here as a resource in no particular order.

Acknowledgements

This report is the product of many months of engagement with researchers and activists across Canada. Thank you to everyone who participated in the CCPA’s AI Roundtable in November 2025 and the Council of Canadians’ Civil Society Summit on the AI Industry in May 2026, as well as the many people we met and learned from at various AI conferences and dialogues over the past year.

Thank you especially to Simon Enoch, Fenwick McKelvery, Sarah Ryan and Luke Stark for their invaluable comments on earlier drafts of this report. Any errors or omissions are the authors’ alone.

AI transparency statement

This report was researched, written, edited and designed entirely by humans without the use of artificial intelligence (AI) tools.

The post Think twice: A citizen’s guide to artificial intelligence in Canada appeared first on CCPA.

The fossil fuel industry sees data centres’ urgent and voracious appetite for energy as an untapped market that can lock in a new generation of fossil fuel infrastructure. Conversely, as the data centre industry’s insatiable appetite for energy has run up against grid constraints, it has looked to on-site fossil fuel generation. 

As data centre expansion has met growing public resistance, the data centre industry has replicated many of the same arguments and tactics of the fossil fuel industry to beat back critics. As both industries navigate the AI investment boom and the emerging challenges that have grown out of it, their interests have begun to align, creating a strange reciprocal relationship in concert with the AI arms race and the massive amounts of energy required to fuel it. While this relationship is most advanced in the United States, we are seeing early signals of a similar relationship developing in Canada as well. 

The need for speed

With the AI race increasingly premised on the ability to scale up training models and size of compute, companies and the policymakers that support them are assigning the highest priority to the speed of constructing data centres. Rather than wait in the queue to be added to existing electricity grids, data centre developers are demanding the right to develop their own on-site sources of generation “behind-the-meter” to hasten development. Despite early promises to opt for renewable power, the data centre industry has chosen speed over sustainability, with the vast majority of this on-site generation via natural gas, due to the speed with which it can be built and its perceived reliability. 

Indeed, the data centre construction boom is creating an equivalent natural gas boom across the United States. Estimates are that data centres alone have helped to nearly triple the demand for gas-fired power in the U.S. over the past two years. Build-out of gas infrastructure has followed demand, with new natural gas pipeline construction “looking at its biggest growth surge in nearly 20 years.”  

Recognizing that data centres represent a new and growing market for their products, the gas industry in particular has been keen to sell itself as the solution to data centre energy constraints. Statements like the one below from the gas industry advocacy group Natural Allies for a Clean Energy Future (NACE) spokesperson and former U.S. Representative Tim Ryan are emblematic of this industry talking point :

To meet demand and continue to lower emissions, we need a pragmatic energy mix since renewables alone cannot yet match the reliability and scale needed to power the data centres and homes alike. Natural gas is the best backstop because it can be dispatched instantly with plants able to turn on and off within minutes, allowing the grid to keep up when the sun isn’t shining or the wind isn’t blowing. 

Major oil companies are recognizing the potential of data centres as a new market, with both Exxon and Chevron announcing plans to provide gas-fired generation coupled with carbon capture technology to data centre customers. This also appears to be a development welcomed by the Canadian gas industry and the Government of Canada, which identified a key “public policy benefit” of data centre expansion as providing new markets for Canadian gas.

Unsurprisingly, the data centre industry also sees on-site gas generation as the quickest route to supplying its urgent and growing energy needs. The industry’s premier trade association in the United States, the Data Center Coalition (DCC)—whose members include big tech firms like Amazon Web Services, Microsoft, Meta, and Google as well as other major data centre developers and operators like Vantage, Equinix and Stack Infrastructure—has made common cause with the fossil fuel industry in lobbying for expedited permits and looser regulations for energy projects and infrastructure. Individual data centre developers have also lobbied (so far unsuccessfully)  for exemptions from the Clean Air Act in order to allow gas and diesel generators to emit beyond allowable limits. 

This synergy of political and economic interests have also resulted in shared membership in the new A.I. advocacy organization, the Artificial Intelligence Infrastructure Coalition (AIIC) led by former Senator Kyrsten Sinema. Launched in November 2025, the AIIC boasts a membership that includes oil and gas providers like Exxon and Duke Energy, tech companies like Google, Microsoft and Meta, as well as data centre developers like Digital Realty and QTS. 

The AIIC, as well as the DCC have been vocal backers of President Trump’s “A.I. Action Plan” which promotes expedited energy infrastructure and environmental deregulation to speed data centre construction and on-site generation. Similar lobbying efforts are also underway in Canada, with energy and power generation companies like Capital Power Corporation, Innio, TransAlta and Pembina Pipeline attempting to influence federal regulatory policy on energy policy and data centres. 

A Very Public Problem

The political organization of the data centre industry has grown in concert with emerging economic and political challenges to its continued expansion. According to a Public Citizen report, the number of data centre lobbyists in the U.S. has grown by 497 per cent in the last three years, from a mere 68 in 2022 to more than 400 in 2025. The industry has also created its own Political Action Committee (PAC) in Virginia, home to the country’s largest concentration of data centres. As the density of data centre development in the state has roiled public opposition to the industry’s continued expansion, the VA Digital Infrastructure PAC has made thousands of dollars worth of campaign contributions to state legislators in the hopes of staving off potentially restrictive legislation.

One of the biggest growing constraints to the industry’s continued expansion is public opinion. As data centre construction has exploded across the United States, so has public opposition. Facing an increasingly recalcitrant public, the data centre industry has come to the realization that public opinion is just as effective a constraint on their growth as energy access. Indeed, the industry has been consumed with reclaiming the narrative from opposition, which they argue is driven by misinformation.Steven Lim, Vice-President of marketing at NTT Global Data Centers acknowledges that “it’s time we get organized as an industry and tell our story instead of letting others control the narrative” .

The industry has responded to the backlash with high profile pronouncements aimed at addressing public concerns. Executives (alongside with President Trump) announced a voluntary “ratepayer protection pledge” to absorb more of the energy costs, stating that “the American people should not be footing the bill for the benefit of private companies.” Microsoft has gone even further with its ‘Community First AI Strategy’ which vows to pay higher electricity rates, commits to “replenish more of your water than we use,” promises to forgoes local government tax subsidies and  end the use of non-disclosure agreements (NDA) with local governments. 

While these pledges are voluntary and non-binding, they do signal that the industry is aware of how it is negatively perceived by the public.

The industry has also undertaken a series of advocacy and public relations campaigns. Meta spent $6.4 million on a series of advertisements highlighting the positive economic benefits of Meta data centres that have reversed the fortunes of rural communities. The DCC has been airing digital ads, distributing direct mailers and sending promotional text messages from 2024 to the present through “Virginia Connects,” a 501(c)4) not-for-profit organization formed by the DCC to “educate and engage with Virginians about the benefits that data centers provide statewide and in local communities.” The DCC also operates an informational and promotional website that emphasizes the necessity of data centres to the modern digital economy.

Recycled Talking Points

These advertisements and public relations materials recycle arguments that have long been staples of the fossil fuel industry. The first is the notion that data centres—and the data they store—are the foundation of modern conveniences and essential services like health information, instant communication, business logistics, transportation, emergency services and even national security. 

As Amazon puts it, “almost everything that happens online depends on data centres. Without them, the internet as we know it wouldn’t exist.” Such an argument would be very familiar to fossil fuel industry advocates. Conflating petroleum and petrochemicals with the luxuries of modernity and the convenience and abundance of the “American way of life” has been a long-standing argument of the fossil fuel industry. The implicit warning underlying such arguments is that to forgo fossil fuels (or data centres) is to forgo modernity. 

The second strategy dusted off from the oil industry playbook is what scholars of climate rhetoric call “technological optimism.” By holding out the promise of some future technological innovation that will ride to the rescue of the climate (like carbon capture), the oil industry has been able to scuttle the implementation of more aggressive climate action. The data centre industry adapts this by claiming that it is data centres themselves—and the AI models they train—that will ride to the rescue of the climate. Such arguments caution against excessive regulation for fear of stifling the innovation that will resolve these contradictions somewhere in the future. 

Convincing the community

Beyond general efforts to sway public opinion, convincing the communities where these facilities are actually being built has become in the words of OpenAI, “mission critical.”  As communities have increasingly resisted the data centre industry, it has increasingly drawn from the oil and gas industry’s community relations playbook. Indeed, the fossil fuel industry has encouraged the adoption of these practices with Mike Sommers, president of the American Petroleum Institute (API) warning that data centres are experiencing the same community backlash as hydraulic fracturing (better known as fracking) and need to combat it with the same tools. 

Community engagement enables firms not only to showcase their contributions to the community but also to proactively manage risk and respond to community concerns before they can catalyze into organized opposition. Company-sponsored information sessions during the early stages of a development, designed to identify key stakeholders as well as potential risks are becoming commonplace. Community investments by data centre developers in local infrastructure and essential services are usually widely promoted by firms. Similarly, many data centre employees are provided with paid time off in exchange for community volunteering – helping to promote and humanize the company. Examples abound of collaborations between data centre developers and local schools and colleges to encourage local workforce development (often for the data centre industry itself) as well as to demonstrate a long-term commitment to the community. While obviously not as advanced as in the U.S, we see similar community engagement efforts in Canada as developers fight to overcome entrenched community opposition. 

It is obviously much too early to make any assessments on whether the adoption of these practices will temper the suspicions and distrust of the data centre industry that so many communities harbour. Moreover, these suspicions are often exacerbated by the industry’s continued penchant for commercial secrecy. Lobbying efforts by industry to block state legislative efforts at greater transparency only further disillusion the public that senses a widening gulf between the industry’s words and its deeds. 

For many communities trying to deliberate over these projects, the lack of information from developers and from their own governments is perceived as profoundly undemocratic. However, if the data centre industry continues to lose public trust despite all these efforts, we may see the industry resort to more crude political influence tactics in order to weaken the responsiveness of politicians and regulators to public concerns. In fact, the industry has already become embroiled in local political corruption scandals and has shown no qualms using its wealth to back sympathetic politicians at the local and state level. The data centre industry’s growing collaboration with the most politically powerful industry in both the United States and Canada makes this possibility all the more concerning. 

The oil and gas industry really has no peer in the field of corporate propaganda and political influence. Learning at the knee of such a mentor makes it all the more important that the data centre industry be subject to robust democratic constraints and strictly regulated in the public interest. 

The post Strange bedfellows: How the tech industry learned to love fossil fuels appeared first on CCPA.

Observers on both the left and the right instantly pilloried the new partnership. Commentators on the right want market forces to do their work so that condo prices fall, while those on the left see justice in letting greedy developers eat dirt. Opposition leader Pierre Poilievre called for the House ethics committee to investigate this “condo bailout.” One wonders why the two governments didn’t wait until they had something more concrete to offer the public.

Subsequent remarks to the media clarified that this is primarily a BC government initiative to purchase condos that would go into a new rent-to-own program. Each of the BC and federal governments will put up $150 million in cash. Another $1.15 billion is notionally allocated for financing or loans though how this breaks down between BC and the feds is not clear. BC Premier David Eby wants to buy units below the cost of construction, while BC Housing minister Christine Boyle emphasized that the government intends to “negotiate a good price” by buying in bulk. That’s about all we know at this point. 

The Partnership on Condo Conversion is clearly a bailout—in the sense that it would help the balance sheets of distressed developers, put a floor on condo pricing and prevent losses in the banking system. On the other hand, Metro Vancouver is lacking genuinely affordable housing for ordinary working people and if there’s an opportunity to use the current moment to improve that situation, we should at least consider it. Obviously, paying $1 million for a vacant one-bedroom is a non-starter, but arguably, there is a price point where buying housing assets in bulk could make sense. The difficulty is the great divergence between home prices and incomes that has occurred over the past couple of decades. 

Let’s review the details on the inventory of unsold condos and crunch a few numbers based on what we know about incomes and affordability. While the BC government could thread the needle on this program, much depends on price, which units are acquired and for whom. A better option for the province would be to restore the similar amount of funding for non-profit housing that was abruptly cancelled in February’s BC budget.

Absorb this 

As of May 2026, CMHC reports that there were 5,849 “completed and unabsorbed apartments” (i.e. new empty condos) across BC, of which 4,376 units were in Metro Vancouver. Within Metro Vancouver the distribution of unsold condos is concentrated in four cities: Burnaby (1,179 units, 27 per cent of the total), Richmond (961 units, 22 per cent), Coquitlam (602 units, 14 per cent) and Vancouver (457 units, 10 per cent). Premier Eby also remarked that condos in the City of Vancouver were too expensive so the focus will be on the inner suburbs in Metro Vancouver. Politics may tempt the government to extend purchases to other parts of the province.

With a headline number of 2,200 units, the proposed BC-federal condo bailout thus represents about half of the unsold Metro Vancouver inventory. As the figure below shows, the number of unabsorbed units shot up dramatically over the past couple years to new all-time highs. In May 2024, there were only 1,262 “unabsorbed” units in Metro Vancouver. 

In light of the currently weak housing ownership market overall, this situation is going to continue. As developers complete new projects, their empty units add to the total number of unabsorbed. As of May, there were about 30,000 condo apartments under construction. The majority of these would have already been pre-sold but if 15 to 20 per cent remain unsold, then some 4,500 to 6,000 units could be added to the glut of new condos within a couple years. 

Thus far, developers with unsold units have been holding out from major price cuts, hoping for a housing market rebound that has yet to materialize. Interest rate cuts they anticipated in 2026, which would have boosted home sales, have been waylaid by the surge in inflation arising from the U.S. war on Iran. For developers, unsold inventory has the carrying costs of property taxes and interest on financing. At some point, they will have to lower prices so that empty units move. Policy makers are also concerned that new housing starts will plummet, affecting construction employment, although this has yet to happen as most new apartment projects are rental not condo projects.

According to the CMHC data, more than one-third (35 per cent) of the unabsorbed units across Metro Vancouver were listed above $1 million, rising to 58 per cent with asking prices above $800,000. Another third (34 per cent) are listed between $600,000 and $800,000, while only eight per cent are below $600,000. The data do not tell us the numbers of studios, one- and two or more bedroom units, their size, nor do we know the price per square foot. 

Afford this

The gap between current price points and anything remotely “affordable” is quite large. In 2024 Vancouver, median total income for individuals was $47,000. For economic families, median total income in 2024 was $135,400. Families, of course, necessarily need more space so smaller units are not going to be of interest.

A common threshold of affordability is for households to spend less than 30 per cent of gross (pre-tax) income on housing. The table shows some stylized scenarios of how different condo prices translate into associated monthly mortgage, strata fees and property taxes. Then it calculates the minimum income required, with housing costs set at 30 per cent of gross income. The bigger the discount the government can negotiate with developers, the further down the income ladder the program could reach.  

With a purported budget of $1.45 billion, buying 2,200 units would average almost $660,000 per unit. At that average price, the bailout smell of the program would be too hard to ignore. Much depends on how much a haircut developers are willing to take for a guaranteed price in the short term versus the costs and risks of holding out for a better price.

At heart, the proposed rent-to-own program is just another first-time homebuyer program. That is not necessarily a bad thing, but we have also seen many iterations of first time buyer programs from both the feds and BC governments. Over the past decade there have been a plethora of tax incentives and exemptions, savings accounts, shared equity mortgages, and most recently, a no GST for new builds, for first-time buyers. 

The core idea is the same: rather than cooling the housing market, these programs assist a narrow range of households to get into the home ownership market. In doing so, this inevitably props up real estate prices. 

To narrow down the field, the government would need to impose some criteria on who is eligible to buy these units, and terms about any future resale, so that these units themselves don’t become another speculative investment. The devil is in the details of any such program and careful design considerations would be needed.

Notably, two first-time buyer programs had similarly challenging financing and eligibility requirements as would the proposed rent-to-own program, and both have been shelved. The federal First-Time Home Buyer Incentive shared-equity mortgage, which began in 2019, offered to take on between five and 10 per cent of a mortgage, was ended in 2024. The 2017 BC HOME partnership program provided a provincial loan matching the buyer’s down payment up to a total of five per cent, and was wound down a couple years later. 

While the BC government could walk this knife edge, there’s good reason to be skeptical. Even if the government can step in as a middleman to buy supply in bulk, on the distribution side at best we have 2,200 lucky households that would be winners. All for a program that has enormous potential pitfalls structurally and politically. 

Just fund non-market housing 

The mess before us is largely the result of a failed investor-driven model of housing development, which relied on presales to investors and rising prices each year, juiced by a period of ultra low interest rates. In capitalist economies, euphoria often leads to over investment and a crash. This is BC’s turn for a correction. 

If anything, the BC government should lean into those market forces. Currently, unsold inventory is exempted from BC’s Speculation and VacancyTax, which was brought in seven years ago specifically to address a perceived problem of empty condos. Exemptions could expire after one year, for example, and the rate on domestic owners (0.5 per cent of assessed value) could be raised in line with the higher rate for foreign owners (two per cent). Others have noted that additional public resources for the legal system to accelerate foreclosures and court-ordered sales would also speed up price declines.

Some commentators have accused the left of shadenfreude in regards to the liquidity and solvency squeeze being felt by developers. In reality, housing advocates have been arguing for decades that federal and provincial governments need to get seriously back in the game of developing new non-market housing. This includes cooperatives and other non-profit rental housing, which was widely supported from the 1960s to early 1990s, and still comprises a lot of the existing affordable housing stock. 

The huge tragedy in all of this is that the newly announced condo bailout is the same size as the cut to non-profit housing made just a few months earlier in the BC Budget. BC had been slowly making progress in funding new non-market housing through its Community Housing Fund, which was created in 2018. Jill Atkey of the BC Non Profit Housing Association sums up the moment and comments: 

Last year, non-profit housing providers responded in good faith to calls for proposals under key provincial housing programs. 89 projects were submitted under the 2025 Community Housing Fund, totaling roughly 7,700 homes and at least $38 million in pre-development costs. Zero projects were awarded and the call was cancelled. The deferral of Indigenous Housing Fund projects that were awarded in 2024 adds significantly to that total. This is to mention nothing about years of lost momentum toward delivering desperately needed affordable homes.

The most sensible thing to do is to drop the condo bailout, and redirect those funds back into the non-market Community Housing Fund while letting the private condo market find its own equilibrium. If the BC government is to intervene in the condo mess, it should be refocused around increasing the supply of non-market housing rather than trying to midwife home ownership. Even clarifying who this intervention is intended to help would put some parameters on the price point, unit size and locations. 

Another possibility is expanding BC’s Rental Protection Fund, which was created a couple of years ago with $500 million for non-profits to acquire private market rentals. In this case, purchased condo units would go into a pool of non-market housing (whether managed by BC Housing or non-profits). This would make the most sense for buying entire buildings, where there’s integrated management and cleaning and so forth, rather than buying only the 20 to 30 per cent of units in a building that might be unsold. Again, much depends on what is available at what price, and purchased could be justified based on the future rental income.

Federal and BC officials should have known better than to drop this half-baked trial balloon. It was a rare political mis-step for Prime Minister Carney and I suspect it was just part of the bigger negotiation for BC to agree to a new bitumen pipeline from Alberta to the West Coast. At this point, it would probably be easiest politically to just walk away and drop the condo bailout, er, partnership. And come better prepared next time.

The post The Vancouver condo bailout props up a failed housing model  appeared first on CCPA.

Since the first modules were launched in 1998, the ISS has served as a permanently inhabited orbital laboratory, advancing scientific knowledge while demonstrating unprecedented international cooperation in space.

After nearly three decades of operation, however, structural aging, material degradation, escalating maintenance costs, and operational limitations have prompted the international partner agencies, including Canada, to plan its controlled deorbit during the early 2030s.

Current plans call for a controlled atmospheric re-entry that will direct the station’s remaining debris into the South Pacific Ocean near Point Nemo, the world’s largest designated spacecraft disposal area.

From an engineering perspective, this approach is understandable. It minimizes the risk of an uncontrolled re-entry over populated regions and is widely regarded as the safest available method for protecting human life and infrastructure. Consequently, discussions surrounding the ISS’s retirement have largely focused on orbital mechanics, engineering feasibility, and operational safety.

Yet this technical framing conceals a broader ethical question. Controlled deorbiting does not eliminate environmental risks; it redistributes them. Rather than removing the environmental consequences of disposing of a 400-tonne orbital structure, it transfers those consequences to one of Earth’s most remote marine environments.

Although Point Nemo is geographically isolated, it remains part of the global ocean—a shared ecological commons whose capacity to absorb repeated spacecraft disposal remains poorly understood. The ethical significance of the ISS deorbit therefore lies not simply in how the station is retired, but in what this decision reveals about humanity’s approach to managing technological waste across interconnected planetary environments.

This issue extends well beyond the fate of a single space station. The ISS is unlikely to be the last large orbital structure requiring retirement. Governments and private companies are already developing commercial space stations, orbital manufacturing facilities, data centres, and other long-term infrastructures in Earth orbit.

Decisions made today are likely to establish precedents for how future generations manage the end-of-life of increasingly large technological systems in space. What appears to be a single engineering decision may ultimately become the default model for disposing of orbital megastructures throughout the 21^st century.

The proposed disposal strategy exemplifies a process of moral distancing—the tendency to regard environmental harms as more acceptable when they occur in geographically remote locations that receive little political attention or public scrutiny.

Point Nemo has become the world’s spacecraft cemetery not because it lacks ecological value, but because its remoteness creates the perception that environmental consequences occurring there are ethically insignificant. Such reasoning reflects a broader pattern in environmental governance, whereby environmental burdens become politically acceptable once they are displaced beyond the immediate visibility of those who benefit from the activities producing them.

Viewed in this way, the retirement of the ISS is not merely an engineering challenge but a test of planetary governance. Geographic isolation should not be mistaken for ethical neutrality. The absence of nearby human populations neither diminishes the ecological significance of remote marine ecosystems nor relieves humanity of its responsibility to protect them.

Existing space governance frameworks emphasize operational safety and legal compliance but provide relatively little guidance regarding the environmentally responsible retirement of large orbital infrastructures. As a result, engineering efficiency and cost-effectiveness have often taken precedence over broader considerations of environmental stewardship, ecological justice, and intergenerational responsibility.

Rather than asking only whether controlled deorbiting complies with existing legal obligations or minimizes immediate risks, virtue ethics asks what this decision reveals about the character of a spacefaring civilization.

The virtue of prudence supports preventing an uncontrolled re-entry that could threaten human life. However, genuine prudence requires consideration of long-term ecological consequences, not merely the management of immediate operational hazards.

Likewise, the virtue of justice requires that environmental burdens be distributed fairly rather than shifted to remote ecosystems simply because they lack political visibility. A civilization committed to responsible stewardship should not equate remoteness with moral permissibility.

The ISS illustrates this expanding technological presence. Its final trajectory will link low Earth orbit, atmospheric re-entry, and the Pacific Ocean in a single technological process, demonstrating that modern environmental impacts increasingly transcend conventional ecological and political boundaries.

This interconnectedness exposes an important weakness in existing governance systems. International law generally regulates outer space, the atmosphere, and the oceans through separate legal regimes and institutions. Yet the retirement of the ISS shows that these domains are inseparable. A spacecraft constructed in orbit ultimately becomes an atmospheric and marine environmental issue. As technological systems become increasingly interconnected, governance must also evolve toward integrated models of planetary stewardship capable of evaluating cumulative impacts across multiple environmental domains.

Recognizing this ethical gap does not require rejecting controlled deorbiting outright. Rather, it requires acknowledging that alternatives deserve more serious consideration before ocean disposal becomes the default solution for future space infrastructure.

One possibility is on-orbit disassembly, in which astronauts or robotic systems dismantle large structures module by module. Individual components could then be safely deorbited, repurposed, or incorporated into future missions. Such an approach would reduce the concentration of debris entering the atmosphere while developing technologies that will be essential for servicing future commercial stations and deep-space habitats.

A second alternative is orbital recycling and manufacturing. Instead of treating the ISS as waste, its structural materials could become valuable feedstock for in-space construction, supporting an emerging circular space economy and reducing dependence on launches from Earth.

Other proposals include repurposing portions of the station for commercial research or education, preserving elements of the ISS as an orbital heritage site, or delaying deorbit until autonomous robotic servicing technologies become sufficiently advanced to enable safer dismantling and reuse. Although each option presents technical and economic challenges, collectively they demonstrate that ocean disposal is not the only conceivable end-of-life strategy.

Equally important are institutional alternatives. Before disposing of globally significant orbital infrastructure, the international community could require comprehensive environmental impact assessments, independent ethical review, transparent public consultation, and evaluation of multiple disposal scenarios.

The planned retirement of the ISS represents far more than the end of a remarkable engineering project. It is a defining moment in humanity’s evolving relationship with the environments it increasingly inhabits, transforms, and ultimately leaves behind.

The debate over Point Nemo is not simply about where the ISS should fall. It is about the kind of planetary governance humanity wishes to cultivate. If remote environments continue to serve as repositories for technological waste because they are politically invisible, moral distancing risks becoming institutionalized as a principle of environmental management. By contrast, adopting governance grounded in prudence, justice, stewardship, and intergenerational responsibility would establish a more sustainable ethical foundation for future space exploration. The retirement of the ISS should therefore be understood not as the conclusion of one historic mission, but as the beginning of a broader conversation about how a spacefaring civilization ought to manage its technological legacy across the shared environments of Earth and beyond.

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No need to worry, Minister Evan Solomon is telling Canadian workers. Canada’s AI for All policy assures us that the build-out of AI “will be powered by workers” and “over 250,000 new AI-relevant jobs will be created across the Canadian economy by 2031.” 

Almost no one believes that. In May, an Angus Reid survey of white collar, blue collar and retired Canadians, found that only three per cent believed that AI will significantly increase jobs. A 45 per cent plurality of those surveyed expected a significant loss of jobs. 

Not even the proponents of big data centres are claiming that the AI build out will result in significant new direct employment. The Bell AI Fabric proposal for an AI data centre in Sherwood, Saskatchewan, claims it will be the largest AI facility in Canada. But it isn’t trying to promote the project based on its underwhelming employment projections: 800 temporary construction jobs and 80 ongoing operational jobs, plus several hundred unspecified indirect jobs in the community. The somewhat smaller Telus AI proposal for two data centres in Vancouver, and an expansion of an existing facility in Kamloops, likewise touts more than 1,000 construction jobs and “hundreds” of permanent operating jobs. 

Of course, there is a fundamental reason for the dissonance between Solomon’s optimistic projections and everyone else. The purpose of AI technology is to increase productivity by aggregating knowledge, performing unprecedented large-scale data tasks, and greatly increasing labour productivity. That can mean a larger economy in the long run, but also less workers when AI is deployed in a workplace or industry.

Impacts on the labour market so far

There have not yet been large-scale job losses in Canada resulting from AI. In 2024, six per cent of Canadian firms that have implemented AI reduced employment as a consequence. By mid 2025, the number of AI adopting firms that expect job losses had jumped to 12 per cent. These are still relatively small numbers, but so too is the take-up of AI systems by Canadian businesses—less than 15 per cent of all businesses in 2025.

However, a KPMG survey of Canada’s biggest corporations reports a whopping 93 percent have begun investing in and implementing AI. They are just getting started, though; only 31 percent of those companies have fully embedded generative AI into their operations, and 98 percent have not yet had net returns from their AI investments. That will change as business moves to “agentic AI” that operates independently and drives “operational efficiency.”

At least one outlier report predicts much larger consequences. The Signal 49 report (formerly the Conference Board of Canada) estimated in January 2026 that AI automation could leave employment as much as 555,000 jobs below what it otherwise would have been by 2030, with a sustained recovery taking almost a decade before the productivity gains of AI eventually exceed projections without the AI build-out. To put that in perspective, Canada lost about 425,000 jobs in 2008-2009 after the financial crisis. A BNN Bloomberg story on the government’s response to the Signal report was not reassuring: “In a technical briefing with reporters …, government officials said they don’t contest or agree with the Conference Board of Canada report but will monitor the impacts of potential displacements.”

Signal 49 also projected that the impact of AI will be felt across the economy. Although the services and sales sector is the least AI exposed, it is the largest employment sector still affecting large numbers of workers. Other employment sectors like applied sciences, manufacturing, trades, transport, equipment operators, and agriculture are more AI exposed. Health care, education, business and finance land in the middle. The result is that the broad sweep of the Canadian working class will have a common struggle for job security and just transitions.

Is the Signal report overstated? Maybe. In his new book, The Reverse Centaur’s Guide to Life After AI, Canadian writer and pro-labour technology analyst Cory Doctorow situates the fight over AI in the workplace and over who controls its use. He argues against inevitable consequences. Don’t fight AI, he advises, “fight the boss.” 

Doctorow contends that AI companies and corporations deliberately exaggerate AI’s ability to displace workers to attract the large investments needed to fuel the AI build-out. The gargantuan investments by AI companies require continuous waves of new investments, and that means convincing Wall Street investors that AI will “replace vast swathes of the wage-earning human workforce.”

A growing consensus is that the largest economic threat that AI poses is the popping of the investment bubble currently driving the U.S. economy. “When the AI bubble pops, it will cause a recession and major job losses in the U.S.. Overheated capital spending on data centres and power plants, and excessive luxury consumption by those who’ve been made rich (on paper) by the speculative flight of the market, will quickly shift into reverse. That downturn will spill over into Canada,” Centre for Future Work Director Jim Stanford warns. 

Legislation and collective agreements

Regardless of the actual extent of AI worker displacement and net employment effects, AI has opened a new frontier for worker rights. The 2026 Canadian Labour Congress convention resolution on AI revealed the breadth of issues facing workers and their unions, and the extent of work to be done. The composite resolution, combining seven separate resolutions, called for establishing a CLC committee to review bargaining and arbitrations on AI, developing an AI policy to protect jobs, and addressing a litany of concerns ranging from training and transparency to fair compensation, surveillance and algorithmic management, human rights protections, regulatory oversight, and liability for technology companies. 

Unions are already having to put AI on the bargaining table. In telecommunications, despite collective agreement language to prevent layoffs resulting from technological change, Telus has cut 11,000 Canadian jobs and Bell about 8,000 since 2023. The telecoms will not attribute the job losses to technological change and instead point to changing customer service models and operational efficiencies. The telco unions know exactly what is going on. In the latest clerical agreement with Bell, Unifor negotiated a Joint Committee on Artificial Intelligence to review the introduction of AI in the workplace, and stronger technological change provisions. 

However the only practical response to a possible AI bomb dropping on Canadian workers in the short to medium term will be legislative. Canada’s three telecom unions—Unifor, USW and CUPE—told a parliamentary committee on AI regulation in June that a “comprehensive social dialogue” and a permanent sectoral working group to regulate the use of AI is needed.

In the case of the Canada Labour Code, reform is long overdue. Sections 51-55 of the code on technological change were written in 1973 and have not been substantially amended since. You can drive a fleet of Uber Teslas through the gaps that have arisen. The technological change provisions in Canadian labour law do not capture the scope of AI, and in any event merely oblige the employer to give notice, detail expected consequences and provide a rationale for the introduction of the new technology.

Some forms of AI in Canadian workplaces will meet even the very limited legal and collective agreement definitions of technological change. However, AI is more than a robot on an assembly line, or a new machine or accounting system that needs half as many workers. AI can potentially displace entire functions that employers deem unnecessary. The employer may claim that the new AI system is a new function never before carried out by the displaced workers. Not only workers, but whole departments and the managers they formerly reported to could be replaced. Of course these are all issues of bargaining and adjudication, but the scales of justice weigh heavily towards employers, and the absence of AI regulation or modern labour laws add many thumbs to the scale. 

For the majority of the working class which has no collective agreement coverage, and hence is protected only by employment standards legislation, there are no laws on technological change and only the beginnings of recognizing a new world of AI-driven work. 

Recent employment law changes in BC recognize a new category of AI driven “platform work” to ensure minimum wages and workers’ compensation coverage for platform workers, as well as pay transparency provisions to address algorithm-based pay. Ontario has enacted employment standards requiring disclosure if AI is used to screen, assess or select workers in hiring and employment matters, and its new Digital Platform Workers’ Rights Act requires minimum wage and pay transparency for platform workers. But the Ontario gig worker wage provisions are not real minimum wages, and the Ontario Act does not recognize platform workers as employees like the BC Act.

All these nascent regulations are new, incomplete and far from becoming national standards. At the federal level there are no worker rights laws or standards on AI. The AI for All strategy proposes no constraints or even monitoring of employment consequences, or any intention by the government to require disclosure, consultation, or negotiation regarding AI in the workplace. 

Those omissions are glaring when, only weeks before, the government’s consultation on the Canada Labour Code included the need for AI regulations as a question among a lengthy list of major issues from the right to strike to sectoral bargaining. However, all of that was compressed into a 30-day consultation, widely panned as insincere and a cover for the government’s intention to restrict strikes at ports, airlines and railroads.

The battles ahead

The AI invasion should be an urgent catalyst for labour to assemble an army to march on Ottawa and claim the influential role it deserves in AI regulation and labour law. It will need to be more than a polite lobby and come armed with extensive briefs and demands on behalf of all Canadian workers who are about to feel the invisible hand of a new and unregulated economic force.

But barring a startling political shift to slow and alter the AI trajectory, worker outcomes on AI are more likely to be settled piecemeal in multiple workplace, industrial and sectoral battlegrounds.

The seminal labour battle to date over AI is undoubtedly the lengthy and hard-fought strikes across the U.S. by the Writers Guild of America and the Screen Actors Guild in 2023. The WGA agreement that resulted makes AI a tool controlled by the worker—not a mandatory management technology. The agreement affirms that AI is not a writer and cannot do the work of a writer, nor can they force a writer to use AI. The SAG agreement requires consent and compensation for digital replications of actors, and consent for being photographed or scanned for that purpose. All performers, including background actors, must consent and be compensated for any digital replication and any future use of their replication, including after their death. 

The writers’ and actors’ strike underscored the power of solidarity to confront generational challenges before larger political and legislative norms are established. The terms of those agreements also tell us that AI is not just a new kind of technological change. The nature of work and the human integrity of workers are at issue.

It will take decades of collective bargaining and pivotal labour campaigns and strikes to determine the terms and conditions of AI in the Canadian world of work. This work begins with tech companies and employers far ahead. To catch up, labour must first imbue workers with a deep understanding of how AI affects their work and then forge a class-based bargaining strategy and political program for AI regulation and labour law reform.

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