Op-Ed: Canada tries to promote AI safety and equity at the UN
Paul Wallis
June 29, 2026
Paul Wallis
June 29, 2026
DIGITAL JOURNAL
Image:— © AFP
In the face of constant AI revelations and its ever-spreading economic effects, Canada is pushing for global action and consensus on AI rules, safety, and equity.
At the same time, a clear AI gap between wealthy and developing countries is widening. Canadian Ambassador to the United Nations David Lametti made the point succinctly to the Canadian press recently:
“The UN remains critically important, (it) remains perhaps the only institution in the world that can convene that kind of discussion on a more or less equal footing between Meta, Amazon Web, Microsoft, Apple and Google — and all of these other countries.”
The World Economic Forum (WEF) has come to more or less the same view from a different direction based on AI’s impact on global business and wider applications on trade. Loans and finance are particularly sensitive, and they’re already creating issues for developing countries. Exclusion from credit markets based on AI loan decisions alone creates capital problems that can stymie business growth and development.
The WEF estimates that “The global trade finance gap stands at $2.5 trillion, concentrated almost entirely in the emerging market corridors where transaction data is thinnest.”
This is where the “equity” issue espoused by Canada becomes critical. Such a huge shortfall in capital effectively shuts down trade almost entirely due to AI procedural behaviour in managing finance.
A tricky reality for the developing world
The UN’s own data supports this view with some added caveats. The many obvious opportunities for developing countries include significant burdens on their capital and raise many questions regarding even their capacity to develop.
According to the UNDP Regional Bureau for Asia and the Pacific (RBAP):
The infrastructure investment needed to advance AI is already a magnitude more than the current SDG financing gap. This is why widespread AI adoption must be treated as a central development objective.
The adoption and application realities are much more demanding than for the least developed countries. The UN is advocating AI as a critical development asset, but 1.2 billion people are being left behind.
Canada’s broad-brush UN approach to these problems may be the only realistic way of making the issues visible or even actionable. The vast mix of problems simply has no coherent profile. The world’s priorities are elsewhere. The slow drip of headlines for AI adoption in developing countries are a predictably chaotic and ineffectual sprinkle of anecdotes compared to the high-profile Big Tech news that drowns out all other considerations.
A polarized trade environment and China’s AI push into developing markets
The much bigger picture is a destructive form of technological polarization at the worst possible time. Geopolitics has created a series of minefields. Globalization is the world’s trade reality, and its reach is total throughout all economies.
Apart from Canada, the West isn’t providing much acknowledgment, much less leadership, for the rest of the world. China, however, was already promoting a “Digital Silk Road” earlier this year. According to the East Asia Forum, “This ambitious undertaking aims to link nearly 150 countries across Africa, Asia, Europe and beyond through a modern network of rails, roads and ports.”
The Chinese initiative is backed up by DeepSeek and its built-in business support networks. China is deploying AI and automation at an extraordinary speed.
The contrast between China and the US AI couldn’t be sharper, and it’s a grim dichotomy:
US trade policies have effectively created an obstacle course. These policies and the often-negative reactions to them now define global trade in the short term.
The Chinese open-source AI approach is the exact opposite of the dogmatic US proprietary stance. Open source is by far the easiest option for introducing AI.
Big capital investment in AI is a physical impossibility for many nations. The US may have priced itself out of some markets entirely.
Adopting a whole new class of technology and its support systems without even basic guidelines is equally impossible and unrealistic.
At ground level, Chinese AI and automation are being deployed across all industrial sectors, whereas US AI is far more high-end and exclusionary.
A US or China version of standardization of AI practices is therefore likely to be a major sticking point for the world as a whole. The two environments aren’t compatible.
Canada to the rescue? Maybe, and at least it’s a start.
By taking on the seemingly rather thankless task of addressing the realities of global AI adoption, Canada is lifting a very heavy load.
At the risk of euphemism, rules, fairness, and equity haven’t exactly been hot topics for Big Tech, Big Money, or anyone else. The US, in particular, has been actively trying to block AI regulation at the Federal level.
For global AI to work at all, standards are essential. Consider the possibilities of AI generating trade disputes and exacerbating the current situations. Equity and common practice are the basis of trade.
Failure to address these issues simply guarantees future problems, disputes, and dysfunction on a global scale. The world should be paying attention.
________________________________________________
Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
In the face of constant AI revelations and its ever-spreading economic effects, Canada is pushing for global action and consensus on AI rules, safety, and equity.
At the same time, a clear AI gap between wealthy and developing countries is widening. Canadian Ambassador to the United Nations David Lametti made the point succinctly to the Canadian press recently:
“The UN remains critically important, (it) remains perhaps the only institution in the world that can convene that kind of discussion on a more or less equal footing between Meta, Amazon Web, Microsoft, Apple and Google — and all of these other countries.”
The World Economic Forum (WEF) has come to more or less the same view from a different direction based on AI’s impact on global business and wider applications on trade. Loans and finance are particularly sensitive, and they’re already creating issues for developing countries. Exclusion from credit markets based on AI loan decisions alone creates capital problems that can stymie business growth and development.
The WEF estimates that “The global trade finance gap stands at $2.5 trillion, concentrated almost entirely in the emerging market corridors where transaction data is thinnest.”
This is where the “equity” issue espoused by Canada becomes critical. Such a huge shortfall in capital effectively shuts down trade almost entirely due to AI procedural behaviour in managing finance.
A tricky reality for the developing world
The UN’s own data supports this view with some added caveats. The many obvious opportunities for developing countries include significant burdens on their capital and raise many questions regarding even their capacity to develop.
According to the UNDP Regional Bureau for Asia and the Pacific (RBAP):
The infrastructure investment needed to advance AI is already a magnitude more than the current SDG financing gap. This is why widespread AI adoption must be treated as a central development objective.
The adoption and application realities are much more demanding than for the least developed countries. The UN is advocating AI as a critical development asset, but 1.2 billion people are being left behind.
Canada’s broad-brush UN approach to these problems may be the only realistic way of making the issues visible or even actionable. The vast mix of problems simply has no coherent profile. The world’s priorities are elsewhere. The slow drip of headlines for AI adoption in developing countries are a predictably chaotic and ineffectual sprinkle of anecdotes compared to the high-profile Big Tech news that drowns out all other considerations.
A polarized trade environment and China’s AI push into developing markets
The much bigger picture is a destructive form of technological polarization at the worst possible time. Geopolitics has created a series of minefields. Globalization is the world’s trade reality, and its reach is total throughout all economies.
Apart from Canada, the West isn’t providing much acknowledgment, much less leadership, for the rest of the world. China, however, was already promoting a “Digital Silk Road” earlier this year. According to the East Asia Forum, “This ambitious undertaking aims to link nearly 150 countries across Africa, Asia, Europe and beyond through a modern network of rails, roads and ports.”
The Chinese initiative is backed up by DeepSeek and its built-in business support networks. China is deploying AI and automation at an extraordinary speed.
The contrast between China and the US AI couldn’t be sharper, and it’s a grim dichotomy:
US trade policies have effectively created an obstacle course. These policies and the often-negative reactions to them now define global trade in the short term.
The Chinese open-source AI approach is the exact opposite of the dogmatic US proprietary stance. Open source is by far the easiest option for introducing AI.
Big capital investment in AI is a physical impossibility for many nations. The US may have priced itself out of some markets entirely.
Adopting a whole new class of technology and its support systems without even basic guidelines is equally impossible and unrealistic.
At ground level, Chinese AI and automation are being deployed across all industrial sectors, whereas US AI is far more high-end and exclusionary.
A US or China version of standardization of AI practices is therefore likely to be a major sticking point for the world as a whole. The two environments aren’t compatible.
Canada to the rescue? Maybe, and at least it’s a start.
By taking on the seemingly rather thankless task of addressing the realities of global AI adoption, Canada is lifting a very heavy load.
At the risk of euphemism, rules, fairness, and equity haven’t exactly been hot topics for Big Tech, Big Money, or anyone else. The US, in particular, has been actively trying to block AI regulation at the Federal level.
For global AI to work at all, standards are essential. Consider the possibilities of AI generating trade disputes and exacerbating the current situations. Equity and common practice are the basis of trade.
Failure to address these issues simply guarantees future problems, disputes, and dysfunction on a global scale. The world should be paying attention.
________________________________________________
Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
Canada’s AI buildout left out the water bill
Digital Journal Staff
July 2, 2026
Final Shots
Cooling can eat 30 to 45% of a data centre’s electricity load and millions of litres of water per megawatt, which puts efficiency in the operating budget, not the sustainability report.
Cooling and water use are now fair questions on any compute contract, and the answer is the kind of thing a board or CFO might reach before the technology leader does.
Digital Journal Staff
July 2, 2026
Photo by Getty Images on Unsplash
Canada wants a lot more AI compute on home soil, but it hasn’t decided how to account for the water that keeps it running.
The federal “AI for All” strategy, released June 4, projects the country will need 5.5 gigawatts of commercial compute over the next four years, and commits to 850 megawatts of domestic capacity by 2030. But there isn’t much detail on how water and land use will be measured or managed.
On power, Canada has a real edge. According to The Conversation, more than 83% of the grid runs on low-emission sources, which can cut a data centre’s operating emissions by up to 90%.
Water doesn’t get the same answer.
Wafr Technologies announced on July 2 that it raised $100 million toward a $300-million goal to build an AI research lab in Canada. This lab would be anchored on a proprietary cooling technology the company says cuts data centre water use by up to 95% and cooling power draw by up to 80%.
According to their press release, a typical data centre uses up to 10 million litres of water per megawatt annually, with cooling consuming 30 to 45% of total electricity load. As workloads grow, more the real cost and the compliance questions come from the data centre underneath, instead of the model itself.
So far, Wafr’s technology has been demonstrated in India and Dubai, but not yet at Canadian scale. The lab is planned, not built, and the raise is a third of the way there.
“Our vision is to build a globally recognized AI research lab in Canada and be a leader in how we can reduce the impact to water and energy,” says Bikram Singh, Wafr Technologies Co-founder and CEO.
When CIOs or CISOs sign a cloud or data centre contract, asking what the provider does on cooling and water is a fair question. It might even be one your board or CFO will get to before you do.
Canada wants a lot more AI compute on home soil, but it hasn’t decided how to account for the water that keeps it running.
The federal “AI for All” strategy, released June 4, projects the country will need 5.5 gigawatts of commercial compute over the next four years, and commits to 850 megawatts of domestic capacity by 2030. But there isn’t much detail on how water and land use will be measured or managed.
On power, Canada has a real edge. According to The Conversation, more than 83% of the grid runs on low-emission sources, which can cut a data centre’s operating emissions by up to 90%.
Water doesn’t get the same answer.
Wafr Technologies announced on July 2 that it raised $100 million toward a $300-million goal to build an AI research lab in Canada. This lab would be anchored on a proprietary cooling technology the company says cuts data centre water use by up to 95% and cooling power draw by up to 80%.
According to their press release, a typical data centre uses up to 10 million litres of water per megawatt annually, with cooling consuming 30 to 45% of total electricity load. As workloads grow, more the real cost and the compliance questions come from the data centre underneath, instead of the model itself.
So far, Wafr’s technology has been demonstrated in India and Dubai, but not yet at Canadian scale. The lab is planned, not built, and the raise is a third of the way there.
“Our vision is to build a globally recognized AI research lab in Canada and be a leader in how we can reduce the impact to water and energy,” says Bikram Singh, Wafr Technologies Co-founder and CEO.
When CIOs or CISOs sign a cloud or data centre contract, asking what the provider does on cooling and water is a fair question. It might even be one your board or CFO will get to before you do.
Final Shots
Canada’s AI strategy leans on the clean grid to handle emissions but leaves water use largely unaddressed, so the resource question falls to whoever runs the compute.
Cooling can eat 30 to 45% of a data centre’s electricity load and millions of litres of water per megawatt, which puts efficiency in the operating budget, not the sustainability report.
Cooling and water use are now fair questions on any compute contract, and the answer is the kind of thing a board or CFO might reach before the technology leader does.
Digital Journal Staff
June 30, 2026
Photo by Andrea Piacquadio on Pexels
A quarter of Canadian business leaders believe AI will have minimal impact on their organization over the next four years, according to BDO Canada’s AI Vision Report, released last week. That finding might be the most telling number in the report.
Anyone managing an enterprise technology stack has watched AI arrive through vendor updates they didn’t initiate.
Gartner projects that by 2028, a third of enterprise software applications will include agentic AI capabilities, up from less than 1% in 2024.
These features are being built into the platforms Canadian businesses already run, whether they asked for them or not. The leaders who think AI won’t touch them may already be running it.
The report, based on a BDO Canada survey of 520 Canadian business leaders who are members of the Angus Reid Forum, also found that 46% are experimenting with AI without achieving measurable ROI and only 18% have embedded AI into workflows and operations.
Digital Journal has reported the same pattern over the past two weeks. AI projects are clearing launch and missing ROI, and in some cases organizations are pulling live agents back into the sandbox after deployment.
“The next gap will not be between organizations using AI and not using AI. It will be between those redesigning work around AI and those funding disconnected pilots,” says Bill Syrros, national AI leader at BDO Canada.
The harder question is how many organizations know which side of that line they’re on. The report found only 18% have embedded AI into workflows and operations. The rest are either experimenting, holding back, or facing AI through the software they already use.
The BDO data found 46% of respondents can’t prove value from the AI they chose to adopt. Another 27% expect minimal impact, even as BDO says AI is becoming harder to separate from the enterprise software companies already use.
You can’t measure what you chose to buy if you can’t see what you’re already running.
Final shotsBDO Canada surveyed 520 business leaders and found 27% expect AI will have minimal impact on their organization over the next four years. Gartner projects a third of enterprise software will include agentic AI capabilities by 2028.
The survey found 46% of respondents experimenting with AI without measurable ROI, and only 18% have embedded AI into workflows and operations.
Every AI board update should include what is running, who owns it, and what changed because of it.
A quarter of Canadian business leaders believe AI will have minimal impact on their organization over the next four years, according to BDO Canada’s AI Vision Report, released last week. That finding might be the most telling number in the report.
Anyone managing an enterprise technology stack has watched AI arrive through vendor updates they didn’t initiate.
Gartner projects that by 2028, a third of enterprise software applications will include agentic AI capabilities, up from less than 1% in 2024.
These features are being built into the platforms Canadian businesses already run, whether they asked for them or not. The leaders who think AI won’t touch them may already be running it.
The report, based on a BDO Canada survey of 520 Canadian business leaders who are members of the Angus Reid Forum, also found that 46% are experimenting with AI without achieving measurable ROI and only 18% have embedded AI into workflows and operations.
Digital Journal has reported the same pattern over the past two weeks. AI projects are clearing launch and missing ROI, and in some cases organizations are pulling live agents back into the sandbox after deployment.
“The next gap will not be between organizations using AI and not using AI. It will be between those redesigning work around AI and those funding disconnected pilots,” says Bill Syrros, national AI leader at BDO Canada.
The harder question is how many organizations know which side of that line they’re on. The report found only 18% have embedded AI into workflows and operations. The rest are either experimenting, holding back, or facing AI through the software they already use.
The BDO data found 46% of respondents can’t prove value from the AI they chose to adopt. Another 27% expect minimal impact, even as BDO says AI is becoming harder to separate from the enterprise software companies already use.
You can’t measure what you chose to buy if you can’t see what you’re already running.
Final shotsBDO Canada surveyed 520 business leaders and found 27% expect AI will have minimal impact on their organization over the next four years. Gartner projects a third of enterprise software will include agentic AI capabilities by 2028.
The survey found 46% of respondents experimenting with AI without measurable ROI, and only 18% have embedded AI into workflows and operations.
Every AI board update should include what is running, who owns it, and what changed because of it.
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