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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…

🔗THE 2028 GLOBAL INTELLIGENCE CRISIS

Citrini Research’s “The 2028 Global Intelligence Crisis” is framed as a thought exercise written from the future (June 2028), not as a literal forecast. The authors are explicit about that. Their goal is to model a scenario that many AI bulls underweight: what if AI progress keeps exceeding expectations — and that very success becomes macroeconomically destabilizing? They position the essay as a left-tail risk map, not a doomer manifesto. 

The piece opens with a striking fictional macro snapshot: U.S. unemployment at 10.2%, the S&P down 38% from October 2026 highs, and markets already desensitized to bad labor prints. This future voice matters because the essay is written as a post-mortem, reconstructing how a “contained” AI disruption metastasized into a broad economic crisis in just two years. 

A central idea is the distinction between headline productivity and human economic participation. In the scenario,…

Fears of an AI-driven “jobpocalypse” are rising. Public figures warn that AI could hit labour markets like a tsunami, and the timing feels uncomfortable: unemployment is edging up in many advanced economies, entry-level roles are harder to find, and tech redundancies keep making headlines.

But the big claim — “AI is already destroying jobs at scale” — doesn’t hold up well under scrutiny. Labour markets did cool after ChatGPT’s release in November 2022, yet a slowdown that happens after a technology launch doesn’t prove the technology caused it.

Take the US, where AI investment has been most visible. Some observers argue that booming stock markets and falling job openings since around 2023 are proof that AI is boosting capital returns while squeezing workers. Zoom in, though, and the story shifts: job openings were already declining before ChatGPT arrived. A more straightforward explanation is macroeconomics. The Federal Reserve raised interest rates…

🔗https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity/

The last era of enterprise software produced giants by becoming systems of record: the place where data and workflows “officially live.” Think CRM, ERP, HRIS—tools that define what happened inside a business.

AI agents won’t replace those systems. They’ll sit on top as the new interface: you ask, they act, they coordinate. But for agents to work reliably, they need more than clean APIs and better governance. They need the missing layer that actually runs most companies day to day: decision traces.

The missing layer: decision traces

In real operations, the important moments rarely look like a tidy workflow. They look like:

• an exception approved “just this once”