AI Publications

Jake Moffatt needed to fly to Ontario for his grandmother's funeral. He went to the Air Canada website and asked the customer service virtual assistant about bereavement fares. The assistant told him he could buy a full-price ticket immediately and apply for a partial refund within 90 days after the flight. Moffatt bought the ticket, attended the funeral, and submitted his refund request. Air Canada rejected it.

The airline's actual policy was the exact opposite of what the assistant had explained. Bereavement fare requests had to be submitted before the flight. Air Canada admitted the assistant had provided misleading words. They pointed out that the assistant had included a hyperlink to the correct policy page. They refused to pay the refund.

Moffatt took Air Canada to the British Columbia Civil Resolution Tribunal. What followed was one of the most consequential legal arguments in the short history of corporate artificial intelligence.

Artificial intelligence has crossed the threshold from futuristic promise to enterprise reality. However, its mass adoption is revealing an invisible cost that many organizations failed to anticipate: the uncontrolled consumption of tokens. The narrative that AI is a cheap commodity software is colliding with the reality of usage-based billing.

When Uber integrated generative AI into its customer service and operational workflows, the initial budget projections seemed reasonable. Four months later, the company had spent millions on AI tokens, vastly exceeding their estimates. The Fortune 500 are experiencing the exact same pattern. These organizations are processing trillions of tokens annually, and 73 percent of them are currently exceeding their projected AI budgets. The problem is no longer training models. The problem is inference. The cost of running the models in production, driven entirely by token consumption, is rapidly becoming the critical factor determining the profitability of enterprise AI projects.

𝗪𝗵𝗮𝘁 𝗶𝘀…

Sometimes the best way to understand a new problem is to observe how we get it wrong when we try to solve it with old tools.

A few weeks ago, I was sitting with the management committee of an industrial company. They had spent three months trying to integrate artificial intelligence into their business processes. They had bought licences, run basic training sessions, and asked their teams to start using it. The verdict was unanimous and disappointing: the tool did not do what they needed.

"We asked it to cross the quarterly sales data with current inventory to predict stock-outs," the operations director told me, visibly frustrated. "It makes up numbers. It produces calculations that do not add up. We cannot trust it. And yet the marketing team says it writes their campaign emails perfectly in five seconds. It is a nice toy for drafting, but it does not work…

Ethan Mollick’s article argues that many companies are making a fundamental mistake in how they approach artificial intelligence. Instead of treating AI as a strange and transformative technology, they are trying to make it fit neatly into the same management logic used for ordinary enterprise software. In his view, this instinct to “normalize” AI may feel practical, but it strips away the very qualities that make the technology strategically important.

The article begins by pointing out the unusual nature of AI systems. A tool built to predict the next word in a sentence can also write code, generate business ideas, support decision-making, and even respond with a surprising level of emotional sensitivity. Because these capabilities do not fit traditional categories, organizations often respond by simplifying AI into something more familiar: another workflow tool, another efficiency system, another software rollout.

Mollick believes this “de-weirding” of AI is where the real problem…