As originally published in Digital Journal on April 16, 2026 by Dr. Andrew Forde.
Andrew Forde is KPMG Canada’s Head of AI Research and a partner in the firm’s Technology Strategy and Digital Transformation practice.
OpenAI CEO Sam Altman recently suggested that artificial intelligence will eventually be sold like electricity or water, metered by usage, delivered on demand, and paid for as a utility.
In that future, companies would purchase “tokens” of intelligence the way they pay their power bill: consuming what they need, when they need it.
It’s a compelling metaphor. It’s also a very dangerous one.
Canadian companies are racing to deploy AI pilots, copilots, and predictive systems, often through platforms built and controlled elsewhere. Strategies are being rewritten, budgets are shifting, and the competitive pressure is unmistakable.
In the rush to keep pace, many organizations are making a quiet tradeoff: gaining speed today by surrendering understanding tomorrow. The result is a growing reliance on systems that shape core business decisions, even as fewer leaders can explain how those systems work, or who ultimately controls them.
Transformative technologies do not create value simply by being consumed. Electricity powers a factory the same way for every competitor. Intelligence does not. It shapes pricing models, risk decisions, capital allocation, product design, and customer strategy. It is not a neutral input; it’s the logic by which organizations decide.
If intelligence becomes a metered utility controlled by a handful of providers, then decision making becomes capacity-constrained infrastructure. Access will depend on compute availability, pricing power, and platform terms. When supply tightens, prices rise. When models evolve, dependencies deepen.
Imagine a future where American tech companies turn off intelligence for Canadian firms whenever our governments collide.
Today’s risk for Canadian business leaders is not that they move too slowly on AI, it’s that they move superficially, deploying tools without building the internal understanding needed to govern them. If leaders respond with speed but not depth, they will hardwire dependency into their own enterprises. They will outsource core decision-making logic to foreign companies, allowing their proprietary data to strengthen ecosystems they do not control. And they may discover, too late, that they no longer understand the engine driving their own margins, risk models, or customer strategies.
We are already seeing SaaS companies change terms to restrict customers from freely using the data stored in their systems to train AI models. Organizations can put their data into the platforms, but they may not be able to use it to build intelligence outside of it.
This is precisely why, amid the urgency, a deeper question must be asked. Who truly understands the systems we’re betting our future on? Because deploying AI is not the same as understanding it, and in this moment, understanding is strategy. It determines where value collects, where risk concentrates, and who ultimately holds power.
This is why organizations that take AI seriously invest in building institutional depth to rigorously study how these systems work, how platform terms evolve, how models are governed, how data rights shift, and what it all means for enterprise control and competitiveness.
If AI is becoming embedded in the operating core of Canadian business, then independent, research-driven insight is not optional, it’s a prerequisite for control.
For business leaders, this is not about becoming technologists. It is about becoming more informed stewards of technological risk and opportunity.
When organisations approach AI from first principles (mathematics, systems theory, empirical validation), they gain something far more valuable than efficiency: they gain agency. In turn, their AI capability will become a strategic asset rather than a vendor-managed feature.
For Canada, the stakes are especially high.
Our researchers helped pioneer the scientific foundations of modern AI, yet scientific leadership does not automatically translate into economic leadership. Canada has one of the lowest levels of business-driven R&D investment in the G7, with the private sector accounting for roughly 47% of total R&D spending.
We produce breakthroughs, but too rarely turn them into lasting commercial advantage.
To capture this moment, we must move beyond only adopting tools built elsewhere. That means converting research strength into an applied enterprise capability, shaping, governing and scaling the systems that will define our economy. It requires tighter partnerships between universities, governments, and industry.
It means making real investments in scientific capability, not just software subscriptions.
Financial institutions manage risk in markets that move at machine speed. Health systems serve aging populations with constrained workforces and fragmented data. Energy companies make decades-long infrastructure decisions in an era of geopolitical instability.
Creating value and managing in these environments requires more than implementing emerging technologies once they are mature. It requires engaging with them while they are still evolving. And it requires leaders to move upstream: to invest in research literacy, to build internal technical depth, to collaborate more directly with the companies and labs developing frontier models, and to ensure their own data, talent, and governance structures are positioned to shape outcomes rather than simply absorb them.
In a market moving at this speed, surface-level adoption is easy. Serious understanding is harder, but it’s the difference between leading transformation and being shaped by it.
If businesses simply consume the systems shaping their future, they won’t be building that future, they will merely be living in it, renting the intelligence necessary to build it along the way.
And renting intelligence is a risky way to run a business.
