AI Publications

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…