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- URL: www.lemonde.fr/blog/pike…
- Author: Ethan Mollick
- Category: rss
- URL: www.oneusefulthing.org/p/against…
- Author: Aravind
- Category: rss
- URL: newsletter.terrawatchspace.com/last-week…
🔗 Trump, national-capitalism at bay
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Let’s be clear: Trump’s national capitalism likes to flaunt its strength, but it is actually fragile and at bay. Europe has the means to confront it, provided it regains confidence in itself, forges new alliances and calmly analyzes the strengths and limitations of this ideological framework.
This is the first weakness of national capitalism: when powers reach a boiling point, they end up devouring each other. The second is that the dream of prosperity promised by national capitalism always ends up disappointing public expectations because it is, in reality, built on exacerbated social hierarchies and an ever-growing concentration of wealth.
When measured in terms of purchasing power parity, the reality is very different: the productivity gap with Europe disappears entirely.
The reality is that the US is on the verge of losing control of the world, and Trump’s rhetoric won’t change that.
In the face of Trumpism, Europe must, first and foremost, remain true to itself.
Europe must heed the calls from the Global South for economic, fiscal and climate justice.
🔗 Against "Brain Damage”
The final quote of “[o]ur fear of AI “damaging our brains” is actually a fear of our own laziness” has a lot of power, although it also oversees the mirrors of “nudges” that technology creates to act lazily.
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ways of using AI to help, rather than hurt, your mind.
If you outsource your thinking to the AI instead of doing the work yourself, then you will miss the opportunity to learn.
the harm happens even when students have good intentions.
we have increasing evidence that, when used with teacher guidance and good prompting based on sound pedagogical principles, AI can greatly improve learning outcomes.
Moving away from asking the AI to help you with homework to helping you learn as a tutor is a useful step.
find more in the Wharton Generative AI Lab prompt library.
while AI is more creative than most individuals, it lacks the diversity that comes from multiple perspectives.
The deeper risk is that AI can actually hurt your ability to think creatively by anchoring you to its suggestions. This happens in two ways.
the anchoring effect. Once you see AI’s ideas, it becomes much harder to think outside those boundaries.
Second, as the MIT study showed, people don’t feel as much ownership in AI generated ideas, meaning that you will disengage
how do you get AI’s benefits without the brain drain? The key is sequencing. Always generate your own ideas before turning to AI.
Every post I write, like this one, I do a full draft entirely without any AI
Only when it is done do I turn to a number of AI models and give it the completed post and ask it to act as a reader: Was this unclear at any point, and how, specifically could I clarify the text for a non-technical reader? And sometime like an editor: I don’t like how this section ends, can you give me 20 versions of endings that might fit better.
there is the option to have it help make us better. One interesting example is using AI as a facilitator.
If you want to keep the human part of your work: think first, write first, meet first.
Our fear of AI “damaging our brains” is actually a fear of our own laziness.
🔗 Last Week in Earth Observation: May 26, 2025
Aravind has a really interesting take on why the hyperscalers are moving into the weather model space.
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I think the real story here is not about weather accuracy, and it is definitely not about replacing ECMWF or NOAA in the future.
This is really about weather becoming part of the cloud infrastructure, about turning forecasting into a cloud-native service that’s deeply embedded within their compute ecosystems.
For sectors such as energy, insurance, agriculture, logistics and finance, weather is not just data, it is a key decision driver. If you can offer native, on-demand, customisable forecasts, users will start building their products and workflows around you: models, simulations, dashboards, alerts and triggers, aka a sticky service layer.
TL;DR: I think Google and Microsoft are trying to make weather foundational by turning it into a programmable infrastructure layer, that powers the horizontal layer of weather intelligence and climate services.