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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…

1. "Give me a prompt that does X"

   
   

   Rather than writing "Summarise this", write "Give me a prompt that summarises this". It delivers a much higher quality prompt.

2. "Critique your output"

   
   

   After I get an answer, I always use this prompt. It forces the LLM to rethink its answer.

3. "Summarise this doc and directly quote the final sentence"

   
   

   This reveals whether the LLM has actually scanned the whole doc (sometimes the LLM didn't bother to get to the end)

1. European Banks' AI Paradox Means Jobs Now, Cuts Later

The report opens with a counterintuitive idea: in the near term, AI is more likely to increase headcount at European banks than reduce it. That is because banks are still in the build-out phase. They need engineers, data scientists, governance specialists, and modernization teams to move from pilots to scaled deployments.

At the same time, Bloomberg Intelligence warns that the long-term promise of AI-driven cost savings is not guaranteed. The reported upside is large, but the road to capture it is hard. Banks that redesign workflows, data architecture, and legacy systems may benefit meaningfully. Those that merely layer AI on top of old infrastructure may spend heavily without achieving the hoped-for productivity gains.

2. Modernization Race to Separate Winners From Laggards

AI is not as a standalone tool, but as the latest test in a much longer modernization race. European banks…

AI is no longer a future-facing conversation for the financial sector. It is now a present-tense conversation. It is no longer viewed as a distant opportunity or a promising technology worth observing from afar, but as a strategic capability that is beginning to shape competitiveness, efficiency, and decision-making.

Still, the mood across the industry cannot be captured in a single word. There is enthusiasm, of course, because the potential of AI is enormous. But there is also prudence, because banking is a highly regulated, risk-intensive business built on critical processes where errors carry significant consequences.

On top of that, there is clear competitive pressure: no institution wants to fall behind in a technology that may redefine how financial services operate.

That balance between ambition and caution defines the current moment rather well. Many institutions have incorporated AI into their strategic narrative, yet only a limited number have achieved true industrial-scale…

Executive Summary

FMSB’s core message is that AI in trading is real, growing, and relevant, but still relatively early in its market-facing deployment. The report argues that the financial industry has long used quantitative models and machine learning, yet the newest generation of AI techniques is only beginning to be integrated into trading systems.

Rather than portraying AI as a revolutionary force that has already taken control of markets, the paper adopts a more grounded view: today’s AI is usually embedded in specific modules such as liquidity analysis, venue selection, pricing forecasts, or execution metrics, while humans remain firmly in charge of supervision and escalation.

A second major idea is that the risks of AI come less from the label “AI” itself and more from how broadly and how critically the model is used. A simple model supporting an input signal may be relatively low risk; an AI-driven system that…

🔗https://www.anthropic.com/research/labor-market-impacts

Measuring AI’s labor-market impact requires caution because earlier attempts to predict disruption from new technologies have often been less accurate than expected. The paper notes that past forecasts around offshoring, robot adoption, and even official occupational projections have produced mixed or limited predictive value.

Anthropic presents this study as an attempt to build a more practical framework for tracking AI’s labor effects early, before the evidence becomes obvious in headline employment data. The authors say their goal is not to claim that major labor disruption has already happened, but to create a measurement system that can be updated over time and may detect vulnerability before displacement is visible.

AI’s labor effects are unlikely to look like a sudden shock such as COVID, where the signal was so large that causal inference was relatively straightforward. Instead, AI may resemble slower-moving structural changes like the spread of the internet or the…