the outrage continues

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Five years ago I wrote about the tendency on the web to tend toward constant doubt and outrage. Now, five years late, that trend continues, exacerbated by the platform monopolists who understand that outrage sells more advertising. I wrote that social media have created a worldwide Dunning-Kruger effect. Our collective self-perception of knowledge acquired through social media is greater than it actually is. And the outrage continues because we ignore our common humanity. We do.

I concluded that as we become more connected we should not be cutting out social media, instead we should be using them in smarter ways. Today we all have to work and live smarter, by connecting to our networks and communities. These are essential to ensure that we do not become drowned out by the noise of the Internet of Beefs.

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finds are back

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On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds. Please ignore last month’s post ;)

“The [Canadian] Charter [of Rights and Freedoms] is not just a law, it is an expression of Canada’s most basic and deepest values. ‘Notwithstanding’ the Charter means ‘I don’t share these values’. Any and every politician or government that proposes its use should face such an extreme backlash that no one would dare consider it.” —@DavidMitchell

“It is WILD that we now live in a time where my job as an astrophysics professor has gone from ‘learn cool things about space’ to ‘try to get someone to hold billionaires accountable for dropping shit on us from orbit'” —Prof Sam Lawler

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meta skills

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[Demis] Hassabis [CEO of Google’s DeepMind, Nobel Prize winner in Chemistry 2024] emphasized the need for “meta-skills,” such as understanding how to learn and optimizing one’s approach to new subjects, alongside traditional disciplines like math, science and humanities. —AP 2025-09-12

In the third bucket I discussed a conversation I had with a senior Human Resources executive at a large corporation in 2016. He noted that when it comes to managing people and their talents, there are three buckets. Two of these are easy to fill, while the third is the real challenge:

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plus de vendredi

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After many years of publishing my Fridays Finds, I have given up. Even Mastodon has made their user interface so opaque that after an hour I could not find the favourites I had marked for the last month. They were available on my phone app but I cannot be bothered trying to transfer each favourite from the phone to the desktop, where I usually write my posts. So it’s the end of an era. The first Fridays Find was posted in 2009 and there have been a total of 458, all in the archives.

Perhaps a listen to Who broke the Internet would be appropriate. I am writing much less here in public because I do not want my work scraped by the large language models that feed the likes of Chat GPT.

Here is a lovely photo shared on Mastodon to close this series.

Au revoir mes amis.

A thin ridge of dark, broken limestone is crowned by golden larches and deep‑green pines, where a small pale cabin sits near the edge catching low sunlight. Behind them rises an immense vertical wall of stratified rock, its slate‑blue surface etched with diagonal veins, folds, and fractures that create a dramatic, textured backdrop. The cliff face fills most of the frame, looming in cool shadow and emphasizing the scale contrast between the tiny treeline and the towering, glacially scoured mountainside.
“A reminder of how small we are next to the forces that shape the Earth”. — Tomasz Susuł

smarter and more empathetic

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On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

The Hater’s Guide To The AI Bubble

The Magnificent 7 stocks — NVIDIA, Microsoft, Alphabet (Google), Apple, Meta, Tesla and Amazon — make up around 35% of the value of the US stock market, and of that, NVIDIA’s market value makes up about 19% of the Magnificent 7. This dominance is also why ordinary people ought to be deeply concerned about the AI bubble. The Magnificent 7 is almost certainly a big part of their retirement plans, even if they’re not directly invested …

… In simpler terms, 35% of the US stock market is held up by five or six companies buying GPUs. If NVIDIA’s growth story stumbles, it will reverberate through the rest of the Magnificent 7, making them rely on their own AI trade stories.

And, as you will shortly find out, there is no AI trade, because generative AI is not making anybody any money.

“via Science Direct — Ceiling fans changed the particle trajectory downwards and reduced aggregated concentrations of particles in the breathing zone were reduced by 87%. Ceiling fans strongly affected the indoor airflow pattern and also showed a potential to reduce the exposure risk to horizontally directed coughs.” —@AugieRay

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it’s all just liking and sharing

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On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“Look, I know AI is controversial, but just for a moment, let’s set aside our preconceived notions, our biases, the environmental impact, the massive cost to train and run models, the labor exploitation, the intellectual property theft, the inaccuracies, the mania it causes in users, the destruction of search, the deskilling of professionals, the devaluation of creative work, job losses, and lack of economic value from enterprise implementations.

Wait, what were we talking about?”
—Max Leibman

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financial waterboarding

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On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“they called it trickle-down economics because ‘financial waterboarding’ didn’t poll well with focus groups” —JA Westenberg

“If you aren’t using AI, you run a very real risk of falling behind in the race to produce voluminous mediocrity while slowly forgetting how to do your own job.” —Max Leibman

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scalable stupidity

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On the last Friday of each month I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“The internet didn’t make us stupid. It made stupidity scalable.” —J.A. Westenberg

“Everything faded into mist. The past was erased, the erasure was forgotten, the lie became truth.” —George Orwell, ‘1984’

“you can give someone a fish and then teach them to fish. It’s a lot easier to learn how to fish when you’re not starving.” —ebel aurora

“Employers: Everyone must return to the office, because we work best when people collaborate face-to-face.
Also: We’re going to replace everyone with AI.”
—Jeff Johnson

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sensemaking through the slop

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The image below is one I have often used in explaining sensemaking with the PKM framework. It describes how we can use different types of filters to seek information and knowledge and then apply this by doing and creating, and then share, with added value, what we have learned. One emerging challenge today is that our algorithmic knowledge filters are becoming dominated by the output of generative pre-trained transformers based on large language models. And more and more, these are generating AI slop. Which means that machine filters, like our search engines, are no longer trusted sources of information.

As a result, we have to build better human filters — experts, and subject matter networks.

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working for capitalists

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The automation of human work is an ongoing objective of our capitalist systems. Our accounting practices amortize machines while listing people as costs, which keeps the power of labour down. The machines do not even have to be as good as a person, due to our bookkeeping systems that treat labour and capital differently. Labour is a cost while capital is an investment. Indeed, automation + capitalism = a perfect storm.

Recently, The Verge reported that the CEO of Shopify, an online commerce platform, told employees — ‘Before asking for more Headcount and resources, teams must demonstrate why they cannot get what they want done using AI.’ The underlying, completely misinformed assumption being that large language models and generative pre-trained transformers are as effective at thinking and working as humans.

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