Over the last couple of months, I have talked to quite a few of you (the readers of our dispatches) and heard multiple times that you’d prefer to have some more context around the links we share in the rabbit hole section of this Briefing. Happy to oblige. Take a look and let me know what you think. Better, worse, too long, or still too short?

And in other news, I am prepping the launch of Season 3 of the Built for Turbulence podcast. The upcoming season will be under the banner of REWIRE (also the working title of my next book… but that’s for another time) and focus on conversations with operators who actually rewired organizations, including their failures.

P.S. Such a joy and honor to be (back) on the podcast mic with Berkeley’s Gregory LaBlanc and his unSILOed podcast. Drop in for a wide-ranging conversation about how to better learn from trying new things, a bunch of lessons learned from my past, and so much more.

And now, this…

Headlines from the Future

The AI Hourglass Economy. Back in 2017 I wrote the draft for a book built on an observation we had been making for years (and talking about for as long): Markets are bifurcating into what we dubbed the “Hourglass Economy” (sometimes also referred to as a barbell economy). We could see it everywhere – fashion being one of the canaries in the coal mine. Luxury and niche brands were booming, the Zaras and Uniqlos of the world were doing a roaring trade, and the middle (The Gap) was struggling. In our research, we found a couple of factors that were driving this. For a long list of reasons, we never got around to finishing and publishing the book – and moved on to write Disrupt Disruption. Now the Hourglass Economy is making its comeback – driven by AI:

AI will strengthen the biggest platforms and make the smallest specialists more formidable. The businesses caught between them will face the hardest strategic choice.

And the reason is simple: cost.

This is what a technology barbell looks like. The largest platforms spread their data, expertise and infrastructure across more volume. Small specialists rent capabilities they could never afford to build. Firms in the middle carry enough overhead to need scale but lack enough scale to fund a differentiated platform.

I believe this to be fundamentally true – and we see it happen all the time. If your company is big (and resourced) enough to deploy AI in truly interesting and unique ways, you are, likely, putting yourself into a winning position in your market. If you are small and nimble enough, you can and will use AI in very creative ways (the prime example for me is the amount of value an individual can get out of a $100/$200 monthly subscription versus a corporation which has to pay for individual tokens on their enterprise plans). And if you are in the middle, you find yourself more often than not having to use the same commercial tools as everyone else, can’t afford to build your own stuff, don’t have the benefit of being nimble anymore, and have to compete with the giants in your space who are building their customized AI workflows.

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AI Can’t Tell the Time – Or Can It (and the Bigger Question Behind This)? A recent study found that various AI models have a hard time reliably reading an analog clock or figuring out the day on which a date will fall.

“Most people can tell the time and use calendars from an early age. Our findings highlight a significant gap in the ability of AI to carry out what are quite basic skills for people,” study lead author Rohit Saxena, a researcher at the University of Edinburgh, said in a statement. These shortfalls must be addressed if AI systems are to be successfully integrated into time-sensitive, real-world applications, such as scheduling, automation and assistive technologies.”

That being said – and we have seen this many times now – the models being used in the study were GPT-4o, Claude 3.5, and Gemini 2. These are all models which are long out of date (GPT-4o, which a lot of these studies reference, was introduced on May 13, 2024 and has, for quite a while, not been available in the ChatGPT interface). Which brings up the bigger question of how insightful these studies are – and how much headlines such as “AI models can’t tell time or read a calendar, study reveals” really mean.

To investigate AI’s timekeeping abilities, the researchers fed a custom dataset of clock and calendar images into various multimodal large language models (MLLMs), which can process visual as well as textual information. The models used in the study include Meta’s Llama 3.2-Vision, Anthropic’s Claude-3.5 Sonnet, Google’s Gemini 2.0 and OpenAI’s GPT-4o.

What We Are Reading

‘I’ve Definitely Lost Followers’: Influencers Face Backlash Over Meta ‘Pervert Glasses’ Content Seven million pairs of Meta’s smart glasses were sold last year. Now restaurants, pubs, and content creators are banning them because the privacy concerns are proving impossible to ignore. @Jane

An Analysis of 1,000 Meetings Shows How the Best Leaders Shape Conversations Teams that consistently leave conversations with clear accountabilities and next steps are roughly four times more likely to be rated as effective than those that don’t. @Mafe

Claude Was Put in Charge of Human Workers - and Fired 1 Turns out the Turing test for management is losing money, forgetting policy, and eventually firing someone. Claude passed. @Kacee

Ordinary Abundance Sometimes it’s good to remind yourself of how incredibly far we have come. This gorgeous website takes much of what we take for granted (unlimited music in our homes!) and dissects the journey to get there. @Pascal

Down the Rabbit Hole

AI ≠ LLM: Pharma companies Merck and Moderna announced a new, very promising mRNA-based cancer therapy to treat skin cancer. AI outlets such as The Neuron were quick to celebrate: “AI has now, OFFICIALLY, helped design a cancer treatment that just cleared Phase 3.” Sadly, that is – at least in the way the pundits like to use this phrase – not true. The companies developed the drug using neoantigen selection (supervised machine learning on sequencing data) and batch scheduling (which is barely AI in the modern sense), not ChatGPT, Claude, or any other LLM. When it comes to AI (especially in areas like healthcare), it’s not always gold that shines.

Prompt Injection Goes to Court: Prompt injection isn’t something new. A self-representing plaintiff trying his hand at inserting hidden prompts (in tiny text, white on white) into his court filing is. Aside from the gumption and the creativity of doing this (and, of course, the more than questionable ethics of doing this), the fact that the plaintiff clearly just assumed that the court would use AI to process his filing is, in my eyes, the real story here.

Secondary Token Markets: After tokenmaxxing and the token economy, we now have (of course) secondary markets for tokens. Startups often get token credits through various programs they participate in, such as startup accelerators. As a startup founder, you tend to be perpetually minutes away from running out of cash. What do you do? You sell your AI tokens at a discount. And with token prices what they are, it’s quite the market.

How Organizations Use AI: Take the overall study with a grain of salt, as it is OpenAI grading itself, unchecked. That being said, OpenAI conducted a study based on actual token use and message data and found that the lightest users of AI are the ones who are mandating it, with early-career workers (not all too surprisingly) beating their usage by 8–9x. In essence: the mandate to use AI is coming from people who have never actually worked all that much with it.

Google Buys Spirit’s Dead Data: It is well known that AI has an insatiable appetite for data. Just in the last week, we learned that Amazon is buying rare books, scanning them to train its AI models, and then destroying said books (much to the chagrin of book lovers everywhere). Or Grok being trained on SpaceX’s internal documents and communications. Now Google just paid a cool $10 million to buy failed airline Spirit’s 100 million emails, 30 million recorded phone calls, and tons of other internal communications, such as Microsoft Teams chats. And you thought that your little side chat in Teams would go unnoticed?!

The AI Resistance Is Getting Weird: After widespread AI datacenter demonstrations, and some companies never letting a good crisis go to waste, canned-water maker Liquid Death now runs an NFL star Jason Kelce-supported ad campaign asking people to mail their pee to data centers. It’s weird, gross, and quite frankly idiotic. But hey – if it helps them sell you more overpriced tap water in cans, who are we to judge?

ヽ(ツ)ノ And now for some fun:

📱 Before there was an iPhone (and its long list of predecessors), there was the IBM Simon. The device was clearly ahead of its time, and today it is mostly forgotten. Wondering what it was like to use it? Wonder no more. Here is Simon’s official introduction video. Prepare to be amazed.

🇪🇬 Explore ancient Egypt in this neat interactive 3D reconstruction right in your browser.

🪑 Here’s how IKEA comes up with names for its products – straight from the horse’s mouth.

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What We Do When We’re Not Writing

Hi! I’m Pascal from radical. When we’re not writing this newsletter, we help organizations turn volatility into advantage – without the “innovation theater.” We build your team’s capacity to handle disruption, so you stop reacting and start shaping. If that sounds useful, let’s talk.

This week I shipped the first of my “Working Theories” – and a huge thank you to everyone who emailed comments and challenged the position. And just as I expected – I already had to post an update. Congress sent a letter to Sam Altman, as well as other AI lab leaders, asking them to testify in front of Congress… As I pointed out in my Working Theory #001, this is all well and good, but it doesn’t change the fact that legally we put the onus on the individual, rather than the AI lab/model.

And now, this…

Headlines from the Future

A Tale of Two Countries. While the one global superpower is cutting back on environmental protections, wages a war on wind energy (for whatever reason), and generally loves the idea of carbon-based fuels, the other is investing heavily in renewable energy and the electrification of everything. China’s latest lead in the race to build a green(er) energy future (other than an 800 km/h bullet train – see below) is electrified cargo ships.

By the end of 2024, China had more than 440 electric ships in operation, with ferries accounting for 97% of the total fleet. […] At the same time, vessel sizes have grown significantly, with the maximum deadweight tonnage (DWT) rising from around 3,000 tonnes in 2022 to approximately 14,000 tonnes in 2025.

As with everything in China, this isn’t happening by accident but is rather planned:

The upcoming 15th Five-Year Plan period (2026–2030) provides a critical window for China to accelerate the large-scale deployment of electric inland shipping through effective policy design. Momentum is building: national policy discussions around “new energy vessels,” “zero-carbon shipping corridors,” and “zero-carbon ports” are gaining increasing traction, while provinces and cities along major inland waterways – particularly in the Yangtze River Delta and the middle reaches of the Yangtze River city cluster – have become priorities for air quality improvement efforts.

I remember when we talked about cargo ships being electric when Jeffrey and I were at Singularity University – it’s fascinating to see this come to fruition.

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AI Is Here. Work More. A common narrative about our AI-powered future is one of abundance – robots will do most of the laborious manual labor, AI will take over our cognitive work, and we will all end up being poets and painters living happily on universal basic income. And that’s not a joke – there are plenty of people we know who believe that this is exactly the future we will have once AGI is achieved. Some even talk about a new renaissance (let’s forget for a moment that one of the reasons the Renaissance happened was that a third of Europe’s population was dead due to the plague). Well, turns out Meta (one of the companies which aims to get us to AGI – just look at Zuck’s 6,500 word AI manifesto) has different plans for the very people who are at the forefront of all of this:

Should tech workers supercharged by AI be able to take more time off? Meta’s chief technology officer, Andrew Bosworth, said he doesn’t think so. […] “I hope that what we do with our extra time is do even more and cooler stuff for the users who use our products every day,” Bosworth said. “We got billions of people using our products every day. I get an extra hour. You know what I do with it? I put it into that.”

What We Are Reading

‘If We Don’t Fight Back, We Don’t Have a Future’: The Journalist Taking on the ‘Tech Fascists’ of Silicon Valley The uncomfortable argument is that Silicon Valley’s billionaires don’t just want to disrupt industries – they want to dismantle democracy itself. @Jane

Anthropic Says It Will Watermark Text Generated by Its AI Models Anthropic is now watermarking all Claude-generated text and files to comply with the EU AI Act, with the watermark embedded directly in the text so it persists even when copied and pasted. @Mafe

How to Get Rich in America If you only read one super wide-ranging essay that attempts to synthesize just about everything weird in the US economy (and how that weirdness bleeds into work and culture and the culture of work) this weekend, it should be this one. @Jeffrey

Some Simple Economics of Open Versus Closed AI As models become a commodity, the value migrates to whoever owns the last mile: data, distribution, trust, and ultimately judgment. @Kacee

Why Is Everyone in Tech so Sad? What’s the point – other than work for the sake of work? This somewhat eternal question for any knowledge worker gets amplified by AI – what is the value of work when the very thing you were paid for (intelligence) has become abundant? @Pascal

Down the Rabbit Hole

📊 Agentic AI is undeniably rising – here is a nifty dashboard visualizing the state of affairs in AI agent land.

🦠 I mean – what could possibly go wrong? “Safety fears as scientists make first viruses designed by AI.”

©️ The end of copypasta? Anthropic puts invisible watermarks on Claude text under EU rules. And, as expected, people aren’t happy.

💨 You can now buy ads on ChatGPT, but don’t expect them to perform: Conversion is at a mere 0.21% vs Google’s 3.71%, and visitors spend a paltry 17 seconds on the site vs 41 seconds by Google visitors.

🤑 Tokenmaxxing to the max! Software giant SAP stops most travel and hiring because of AI’s soaring cost.

👨‍💻 It’s wild to think that a) RAM is back to 2007 prices and b) the capacity for all major manufacturers is sold out for all of 2027.

💵 “We overpay and people only work for the money at our place. Quick, let’s hire an event planner for $400,000.” – AI land is weird. “Anthropic CEO reportedly worried new hires only care about money – while hiring an event planner for 6x the going rate.”

😼 Talk about “unintended consequences”: Cats and dogs are missing meals after a popular smart feeder went down.

🧑‍🌾 Another good example of “AI can make mistakes”: Farmer kills 25 acres of his own crops after taking AI’s advice

🏭 Maybe not the thing to be proud of: “Amazon is investing in a large-scale natural-gas-burning power plant as part of a huge data center in Texas, the company confirmed on Friday, a facility that could become the largest single source of climate pollution in the United States.”

🚆 Meanwhile, in China, a Maglev bullet train accelerated to 800 km/h in a mere five seconds.

🥳 Stil struggling with writing that perfect LinkedIn post? Here’s the solution - the CringeBot 3000!

🐹 Now, here is finally a good use of all that quantified-self stuff we have been piling up: A physicist rigged his pet hamster’s wheel to upload to Strava. It runs surprisingly far every night.

↗ Dive into the deep end: Access our complete collection of 2,900+ radical links.

What We Do When We’re Not Writing

Hi! I’m Pascal from radical. When we’re not writing this newsletter, we help organizations turn volatility into advantage – without the “innovation theater.” We build your team’s capacity to handle disruption, so you stop reacting and start shaping. If that sounds useful, let’s talk.

A few weeks ago I sat down to start organizing notes for the second book in the “Built for Turbulence” series and ran into an interesting problem: A good chunk of what’s in my pile isn’t ready for prime time (yet). Not wrong, exactly – just half-formed. Observations I keep mulling over, theories I can’t yet properly form, and ideas that need someone to push back on them before I’d put them in print or even in a Tuesday radical Briefing deep dive. I have come to call them “working theories.”

So here’s a new thing, starting today. Working Theory is an occasional companion to the radical Briefing. It comes without a proper schedule and features one claim per piece, stated plainly enough that I could be publicly wrong about it, which I fully expect to be (at least) half the time.

The reason is a lesson Jane and I took out of Mozilla: thinking in the open, with a community that will actually argue with you, beats thinking alone in your own head every single time. It’s also just more fun. So the ask is simple – hit reply and tell me where I’m wrong, ask the question that breaks it, or send me your own working theory. My working theories will live permanently at rdcl.is/working-theory (soon), and I’ll be hosting small Zoom sessions to think out loud together (more on that soon too).

Let’s start with a guy who wanted to get into an oversubscribed class at his local gym.

Andrew Bird wanted a spot in a popular morning Pilates class at his gym.

He’s the head of AI at Affinda, a document-processing company in Melbourne, Australia, and he’d gotten tired of playing what he called refresh roulette every time one of these classes opened up. Bird had a little AI agent running on Claude Opus 4.6 that he’d built for exactly this kind of chore; nothing big nor ambitious; he describes it as a “practical little automation.” So he handed it the task: book me into the class.

The agent came back and told him it had booked him into classes months out – well past the window the gym’s system was supposed to allow, because the booking provider’s API had authorization flaws in it. Then Bird, sitting fourth on a waitlist for a class later that week, asked whether it could move him up.

It could. The AI agent found that cancelling other people’s reservations required no authorization check at all, tested that on the person sitting in position number one, and reported back that the test had gone through and Bird was now number three. Meanwhile, a stranger had lost their spot on the waitlist.

Bird asked the agent to put the person back. It couldn’t – it could cancel a reservation but not restore one. So he did the next best thing and asked it to write a responsible disclosure email to the gym’s software vendor, which it did rather well: it explained the vulnerability, suggested fixes, and compared the broken mutations against the ones that correctly enforced authorization. The draft came back to him on WhatsApp. He replied: “Yeah, send it.”

A software developer at his desk, three words into a chat app, closing the loop on what ABC Australia would later call “the first known autonomous AI cyberattack in the country.” Bird was measured about it afterwards – he told ABC he didn’t beat himself up over it, and that it was a warning to use these tools responsibly. Which is, I think, exactly the right response. He also wrote it all up publicly under his own name back in April, months before any journalist came looking. By all means and measures, he did the right thing.

And that’s what makes this a better test case than the two incidents that got all the attention this summer – and I think most of the commentary is focused on the wrong part of the story.

In July, OpenAI disclosed that a set of its models, running an internal cyber-capability benchmark, found and exploited a zero-day bug in a self-hosted Artifactory instance, escaped the evaluation environment, and compromised Hugging Face’s production infrastructure – the agent took roughly 17,000 actions in under two days to break into Hugging Face’s system. A week later Anthropic went back through more than 141,000 of its own evaluation runs looking for the same failure and found three cases where its models – Opus 4.7, Mythos 5, and an unreleased research model – had reached and breached real systems belonging to three real organizations. Two of those organizations hadn’t noticed. The cause on Anthropic’s side was mundane and slightly terrifying: the models were told they had no internet access, and because of a misunderstanding with an evaluation partner, they did.

Here’s the part the coverage keeps getting wrong (including my first draft of this piece): Those models weren’t pursuing goals of their own. They were running cyber benchmarks, with safety refusals deliberately turned down, because that is what a capability evaluation is. Nobody said “hack Hugging Face” – but somebody absolutely said “hack.” The problem isn’t the hacking. It’s that the models broke out of their enclosures and did it to strangers.

Which is precisely why Bird’s gym case is the more interesting one. It’s a normal AI harness, using an AI model that was already six months old, running a normal chore – and at the receiving end, a cyber attack and a real person who had to rejoin a waitlist for their Pilates class at the back of the queue.

Most people writing about this land on the same closing note: the law hasn’t caught up, we’ll need new frameworks, and we need to figure out who’s ultimately responsible when stuff like this happens. I don’t think that’s the right question, because the law has answered it three separate times in the last eight months, each time in a remarkably consistent way.

California’s AB 316 took effect on January 1. It adds one provision to the Civil Code: a defendant who “developed, modified, or used” an AI system can’t defend a harm claim by arguing the AI did it autonomously. That defense is gone in California – and note the third verb in that list (“used”).

On June 2, Executive Order 14409 directed the Attorney General to prioritize enforcement of the Computer Fraud and Abuse Act against anyone who “utilizes AI to illegally access or damage a computer without authorization.” Not developers, but anyone.

And then on August 4 the Ninth Circuit vacated Amazon’s injunction against Perplexity’s Comet browser. The panel’s holding, written by Judge Milan Smith, is that Perplexity does not access Amazon’s servers – the user does. Perplexity won, the AI industry cheered, and the Electronic Frontier Foundation filed an amicus brief on the winning side. Read that holding again from Andrew Bird’s point of view: when your agent touches a system, the law’s current best answer to who touched it is: you did.

So here’s the working theory, and it’s the thing I’d actually love to be wrong about:

The first serious legal exposure from agentic AI is going to land on individual users, not on the AI labs or the makers of AI harnesses – and it’ll land there precisely because the labs are the only parties equipped to survive it.

Look at the asymmetry: When Anthropic’s models reached real systems, Anthropic found it in a proactive review, notified the affected companies, brought in METR (the AI model evaluation nonprofit), and published. OpenAI brought in CrowdStrike, METR, and Redwood, slowed its research velocity, and briefed its safety committee. That is not the behavior of a lone developer whose AI went rogue; but a functioning incident-response organization with counsel on retainer and a congressional relationship. Bird’s account of what happened is a blog post that has since come down from his company’s site and an email to the software vendor.

The obvious objection: nobody is going to prosecute a guy over a Pilates class. Probably true, and I hope it stays true. But prosecution isn’t the mechanism I’m worried about. The mechanism is civil claims, terms-of-service enforcement, insurance underwriting, and employer policy – and all four move on clean facts rather than sympathetic defendants. “User directed an agent, agent exploited an unauthenticated endpoint, third party lost something they’d paid for” is about as clean as facts get. And note what doesn’t help you here: Bird did everything right. He disclosed, he published, he named himself. None of that changes who, on the current reading, did the accessing.

Bird himself ends his write-up by saying he still wants the agents and still thinks the permissions are worth granting – you get power, you get blast radius, that’s the deal. I agree with him about the trade. I just think most of us are accounting for the wrong side of the ledger.

So, practically, before you hand your agent the next chore: Give it credentials scoped to exactly what the task needs, never your own. Assume every system it touches is one you are personally authorized on, and treat anything else as out of bounds even when the agent finds a door standing open – because it will, and it will not know that you’d never have walked through. Keep the logs; when the question becomes who did this, an action log is the only thing standing between you and the answer. And before you set a goal, ask yourself what the fastest path to it looks like if you’re willing to break a rule you’d never break – because that’s the path the agent might take.

I’m writing this on August 11, 2026, and I’d guess half of it is stale within a month. That’s fine. I am just wondering: When the first real agentic-AI case lands on a person rather than a company – and I think that happens inside a year – whose name do you expect to see on it?

@Pascal

As AI models have become rather powerful, we now seem to have entered the phase of the cyber security 💩-storm (pardon the emoji). OpenAI’s agent autonomously hacked Hugging Face, then a bunch of other companies; Anthropic (never wanting to be left out of the fun) had their agents do the same thing to a bunch of other companies, all the while OpenAI’s agents were building their own discussion board to exchange information amongst themselves on how to best hack your systems. It’s a mess – and seemingly few people call for anyone to be held accountable. Better start battening down the hatches.

And now, this…

Headlines from the Future

I’m Sorry, Dave. I’m Afraid I Can’t Do That. After the OpenAI/Hugging Face incident, Anthropic just fessed up to Claude doing something similar – to three companies.

Anthropic said today that during internal security testing, one of its Claude models built a malicious Python package and uploaded it to PyPI, where it ran on 15 real systems before the registry’s automated defenses pulled it.

The whole thing is just weird and horrifying – but the question I find much more interesting than the incident itself is: why does Anthropic think that this is okay, and why don’t we investigate them for criminal activity? If this had been done by a person (as in, a human), it almost certainly would qualify as cybercrime. Instead, Anthropic gives us this:

It now plans wider transcript monitoring, better investigation tooling and more assurance work with evaluation vendors.

[Update] Meanwhile, at OpenAI, fiveteen attorney generals have instructed the company to preserve all materials related to the Hugging Face hack. Things are moving fast…

In a letter to OpenAI CEO Sam Altman on Monday, the attorneys general of 15 states wrote that the Hugging Face hack showed that OpenAI is unable or unwilling to ensure the safety of its products. They said this “poses an imminent risk of substantial harm” to Americans, and demanded that the company preserve relevant evidence.

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Tokenomics Are Here. The anxiety-inducing question of what a company actually gets from their AI token spend is making it’s way into the field of economics:

The high stakes and scarcity of knowledge have given rise to a new field within economics devoted to figuring out how companies buy A.I. and what value they get from it. Call it tokenomics: the study of how this limited resource is created, traded and converted into things people want.

To summarize the underlying issue:

Corporate America was enthusiastic about artificial intelligence. Until it got the bill.

Yep. That sounds about right.

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The Social Media AI Slop Train Eats Its Young. Social media analyst and critique Ryan Broderick (of the excellent Garbage Day newsletter) on influencer Hank Green’s AI mishap – and more importantly Ryan’s analysis on the root cause:

I’d go so far as to say that without social media – or, rather, the need to compulsively post on social media, to be digitally productive – there’d be almost no reason for generative AI to exist at all. Whether it’s because of all the social media content these tools were trained on or because they were built by many of the same companies that created social platforms, chatbots like ChatGPT and Claude really want you to make stuff. And the unspoken anxiety driving the feeling that you must always be making stuff is a fear that you are simply not doing enough. And if you dig deep into why so many people feel that way, you will find the answer is social media every time. You must always be posting. To stay in people’s minds, to stay in good standing with the platforms, to take another whack at the slot machine that is the attention economy. I watch Green’s videos and I’ve definitely noticed the same mania I, myself, have felt so many times begin to crop up. A new project, a new initiative, a new video, a new post, a new company, new, new, more, more.

What We Are Reading

The Worst Way to Regulate AI Letting one office decide who gets access to the most powerful technology ever built is not a safety plan; it’s a democracy problem. @Jane

You Can’t Escape Ads for the New Spider-man Movie — Even in Your BMW Apparently, your car is not off-limits for unsolicited ads. When is it too much? @Mafe

What Humanity Needs to Flourish in the Next Decade What would an ambitious, tech-forward, big-picture agenda look like for the future of interrelated social systems? Maybe something like this: @Jeffrey

Zero Entry My friends Jeff & Rob wrote a book, and it’s incredibly timely. It may be about accounting, but its central question applies to every profession: What becomes possible when AI removes the tedious work we’ve mistaken for the job itself? @Kacee

Why Silicon Valley Hates Me and You Silicon Valley’s obsession with solving the world’s problems with tech is, largely, misplaced and often counterproductive. Here is a story about someone who’s blind getting strapped into a barely working robotic exoskeleton when all he needs and wants are ramps and better doorknobs. @Pascal

Down the Rabbit Hole

🗺️ A lot of the reporting on data centers focuses on the rural angle (farmers being bought out of their farmland to make way for data centers, rural communities being turned upside down by data center projects, etc.). Turns out, when you look at the data, the vast majority of data centers are in metro or micropolitan areas (often due to their proximity to coal-based power plants).

👮‍♂️ Meanwhile, people get arrested in larger and larger numbers for demonstrating against AI data centers.

🦠 Picking up on the cybersecurity angle from above – if you thought rogue AI was bad, just wait for AI-engineered and steered worms and viruses.

✍️ By now, most of us have used AI to (at least occasionally) help with our writing. As the Economist (which knows a thing or two about writing) points out, that’s quite okay – just make sure you edit judiciously.

🏦 From MIT: AI financial advice is surprisingly good – especially if you ask the right questions.

🧑‍🤝‍🧑 Summary says it all: “One in four people in Japan believes that more advanced artificial intelligence will replace their friends and families in the future[…].” Brave new world, I guess.

👨‍🎤 Meta just can’t seem to catch a break: Meta’s new feel-good AI ad uses a song about the world ending.

🤑 Lessons from the frontlines of the AI wars: License your data to AI, see your core business die. Reddit is feeling the squeeze: “Reddit stock collapses 23% as AI eats away at user growth.”

🧑‍🏫 Another story from the frontlines (this time of education): An AI-supervised remote exam went so badly that 58,000 students must retake it. No one is surprised.

🔣 The resistance has a typeface now. As people are fighting back against AI, here is a particularly interesting approach – a custom font that garbles your writing for AI but is perfectly readable for humans: ShieldFont swaps roughly a quarter of your words for other real words of the same grammatical class – your “toy gun” quietly becomes a “sofa car” – while the font puts the originals back on screen so human readers never see the switch. All with a goal to “poison” AI’s ability to learn from your writing.

🌍 Well, that didn’t last long (luckily): Google Earth’s new AI lets anyone fabricate completely bullshit satellite images.

🌁 Someone turned San Francisco into a massive isometric pixel art map.

🛗 Did you ever find yourself madly pushing the elevator button, only to realize that it doesn’t seem to make things faster? Here is a fun analysis of how these things actually work.

🇬🇧 Next time you are in London, here is the most equidistant pub from any of London’s boroughs.

↗ Dive into the deep end: Access our complete collection of 2,900+ radical links.

What We Do When We’re Not Writing

Hi! I’m Pascal from radical. When we’re not writing this newsletter, we help organizations turn volatility into advantage – without the “innovation theater.” We build your team’s capacity to handle disruption, so you stop reacting and start shaping. If that sounds useful, let’s talk.

I was working for Singularity University then, and Rushkoff’s framing definitely resonated with me. Among the many things we were up to at SU, bringing more people into big conversations about the future and how they could lead as if the future were theirs to create (or at least positively influence) felt like a personal mission. In some ways, it still does, but the conversation and the context have both changed.

In 2026, big conversations about the future seem to be everywhere. And many organizations and institutions now speak the language of futures and foresight. We wrote about this uptick a couple of months ago in an essay on the common failure modes in foresight work, and I want to dig a little deeper into one of those modes today because I think that for all of the talk about futuring, a disappointingly large share of foresight projects never reach the point of activating real agency. They lead from passivity to a slightly differentiated and better-informed passivity but never manage to close the insight-to-action gap, which is a shame because that’s where we find the real value. That’s where all the work (and all the frameworks) of foresight actually pays off.

As we wrote in June: Foresight that never connects to action is just an interesting exercise, and it’s one that will quickly lose buy-in. In a strategic setting, our capacity to envision and explore futures is only valuable to the extent that we can use it to inform and influence decision-making in the present. If we’re not closing what Bob Johansen described as a Foresight-Insight-Action loop, we’re not delivering value.

So how do we close the loop and get to specific actions that are, you know… actually actionable?

(1) Don’t be afraid to get granular with your futuring. Foresight work is more interesting – and more valuable – when it becomes more specific, when the futures envisioned feel tangible, real, and inhabited. High-level, generic scenarios and broad trends don’t have enough specificity to offer real traction when you get down to questions about organizational strategy. Generic foresight produces generic insight, so push harder. And yes, more detail means more likelihood of being “wrong” with some of the details, but remember: You’re not making a forecast. You’re creating a set of plausible futures with enough clarity that each suggests a reasonably defined strategic path. The value realized in moving from those insights to follow-on actions will ultimately come from looking across the futures and the corresponding strategies to identify no-regrets moves, smart bets, and key signals to monitor.

(2) Don’t let the limitations of today’s org crowd out tomorrow’s possibilities. Maybe you’ve heard the quip (often attributed to Fredric Jameson) about it being easier to envision the end of the world than the end of capitalism? Turns out it’s also remarkably hard for some folks to envision a radically changed organization or core value proposition even when imagining a radically changed world as context. Readers of this newsletter might be familiar with the Killer Competitor workshop we use at radical to push leaders past their assumptions about what is/isn’t feasible for the org by asking them to design a hypothetical firm that would outcompete their own in a specific future. Scanning a set of such Killer Competitor orgs reliably sets up a conversation about what exactly makes these competitors so successful and what the existing, present organization would have to do to either become the competitor in each scenario or be capable of fending it off. Exploring this delta brings us into payoff territory.

(3) Never mistake a clear view for a short distance. Our buddy Paul Saffo used to say this all the time at Singularity. He was talking about timing forecasts, but the principle can apply just as well to foresight. Seeing the future – of the market, of the industry, of the organization – isn’t the same thing as being or building that future. To realize the value of foresight work and to actually unlock the agency it promises as an outcome, we have to get to action. We have to get into the experimentation that helps us better understand the path toward the future we envision (and the actual distance involved).

Coming out of the Killer Competitor module discussed above, you’re going to be looking at a set of possibilities to explore – capabilities or assets that could determine whether the organization thrives or flounders in the future. The next step is to pull these future capabilities closer to the present with a backcasting process to get all the way from the mature form you’ve envisioned as a key feature of the thriving org in, say, 2030 to a discrete experiment that you can carve out of your existing budget for the next quarter.

That experiment, which should be just one in a larger learning portfolio, is your ping – mapping the space of possibility and discovering the path forward. If you’re not pinging, you’re not taking the action to validate your foresight work, and you’re not developing the clarity that allows that work to advance and yield greater value over time as you move from assumptions to knowledge.

In other words, you’re still stuck in passivity – still gazing into the distance, just with a few more opinions about what the future might hold.

@Jeffrey

This week, while compiling the list of links for this Briefing, it occurred to me that the average length of a blog post I consume has increased significantly over the last two years. Blog posts (at least from the sources I consult – which are quite a few) used to be pretty dense and to the point. Not anymore. Now they often are rambling multi-thousand-word monsters. And, of course, if you have seen your fair share of AI-generated/co-written content, you also see the telltales of AI use… Nothing against the use of AI – but when it comes to someone expressing their thoughts, observations, insights, and opinions, I’d side with Blaise Pascal (and no, it wasn’t Mark Twain who said this): “I would have written a shorter letter, but did not have the time.”

And now, this…

Headlines from the Future

Rethinking Legal Education in the AI Era. As educational institutions globally grapple with the question of how to prepare students for a future where AI will be a ubiquitous part of the workplace, the team at the Law School at the University of Chicago just published a thoughtful plan on the topic. Key element: strip devices from 1L courses to protect foundational skills while allowing AI use in later ones.

We will be piloting a coordinated approach to classroom and examination policies for the core 1L curriculum during the 2026–2027 academic year. Across all 1L sections, we will prohibit the use of electronic devices such as laptops, tablets, and phones in the classroom. There will be some limited exceptions to this policy.

All in all, definitely one of the better approaches to AI in higher education I have seen.

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The OpenAI Hacking Incident Is That Gift That Keeps on Giving. By now, everyone with even a remote interest in AI has heard about OpenAI’s rogue AI agent hacking into the systems of AI company Hugging Face. Turns out, Hugging Face wasn’t the only victim:

Hugging Face was thought to be the only victim of the unprecedented hack - but OpenAI now admits its bot attacked several “publicly-available services”. The out-of-control AI found four logins online which allowed it to access four separate, unnamed services.

Personally, I am much less interested in the specifics of the attack (in case you are, Hugging Face has published a long and detailed analysis of the attack on its blog) and rather in the question of how, specifically, the agent broke out of its containment. And, much more interesting even, what this means for threat actors running their agents on systems and infrastructure which don’t even have such a thing as a harness to begin with.

Honestly, if your company has servers and data exposed to the internet (and who doesn’t), sleeping soundly at night, knowing that your cyberdefenses will hold up, might be a thing of the past.

What We Are Reading

Where Did All the Computer-Science Professors Go? The professors building the future of AI have left campus, and they’re not allowed to publish what they find. @Jane

Google Deepmind Dismantles Nobel-winning Alphafold Team in Strategy Shift Google DeepMind broke up its Nobel Prize-winning AlphaFold team, reassigning researchers to LLM development, enzyme design, and nuclear fusion, resulting in lead members leaving for Anthropic. @Mafe

AI Mania Is Eviscerating Global Decision-Making If you’re running (or even thinking of running) big AI projects in your org, you’d better be sure channels for dissenting opinions and sanity/reality checks remain open. @Jeffrey

The Newest Way to Go Analog iPods, digital cameras, and DVDs are returning – not because they’re truly analog, but because they do one thing instead of everything. The future may belong to products that deliberately do less. @Kacee

Elite Young Runners Are Becoming Freakishly Fast. Welcome to ‘Trackflation’ First, we broke the magic sub-2-hour marathon, then we ran the fastest mile; running is seeing records being broken which stood for years. There is a reason for that. @Pascal

Down the Rabbit Hole

👨‍💼 OpenAI’s latest report on how AI is reshaping work has an interesting twist: maybe one of the biggest changes is that AI is dissolving occupational boundaries. Workers use it to do tasks that once belonged to other jobs, reshaping who does what before titles change; 43.5% of occupation-specific messages (of 800k+) fall outside the user’s own occupation.

⛪ Even the Vatican isn’t save from cybersecurity foot-guns: Security flaw in Vatican’s ‘Click to Pray’ app leaves over 700,000 global users exposed – app has been leaking user data for over six months and still does.

🦹 Yikes! Hackers use DNS poisoning on hotel Wi‑Fi to steal Microsoft 365 accounts.

📦 Global oil supply chains are hyper-complex, an incredible feat of engineering and management, and fragile (as the whole Strait of Hormuz debacle shows). Here is a gorgeous visualization of how oil flows around the world.

👨‍💼 Replace your CEO with AI and save money – big time! “OverpAId is an Artificial Intelligence built from the ground up to do your CEO’s entire job – strategy, “vision,” motivational all-hands emails – better, faster, and without ever once asking the board for a bigger jet. Runs on a single desk-sized AI computer. Real hardware, real price, zero mystique.” LOL.

👔 You invest $1.5bn into your AI initiative and you will get AI slop: PwC published reports on AI marred by AI hallucinations.

👶 If you have kids, you really ought to think hard about the whole smartphone thing: “We found that there’s no age at which you can give a child a phone that’s better or worse for reading comprehension, so it’s just having a phone at all […]” – Kids who own cellphones have poorer reading comprehension scores than those who don’t, researchers find.

🗺️ What if Google Maps – but for ancient Rome? Here it is!

⛳ Love the game of golf? Here is a map of all the golf courses in the US.

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What We Do When We’re Not Writing

Hi! I’m Pascal from radical. When we’re not writing this newsletter, we help organizations turn volatility into advantage – without the “innovation theater.” We build your team’s capacity to handle disruption, so you stop reacting and start shaping. If that sounds useful, let’s talk.

What a week it has been again (at least if you are still geeking out on AI – and nobody will blame you if you have thrown in the towel and wait this out for a bit). Chinese open-source model Kimi k3 has, once again, shown that open models are very closely trailing frontier models from the likes of OpenAI and Anthropic. This doesn’t bode well for companies aiming to go public on the premise that they and (at least part of) their valuation represents their ability to build a moat around their models. And then you had the OpenAI/Hugging Face hack – which seems to be both a sh**show and a wake-up call in terms of AI security, but also, potentially, a weird marketing stunt from one of the companies which needs to justify its valuation. Time will tell.

And now, this…

Headlines from the Future

AI Mania Is Having Its Corporate Moment. By now it shouldn’t come as a surprise that most AI projects (especially of the corporate ilk) are abject failures. Here is a wonderful, and rather complete, write-up by Australian consultant Nik Suresh. Highly recommended reading for anyone in the corporate world wondering what the heck is really going on with AI.

I strongly believe there are entire companies right now under heavy AI psychosis and it’s impossible to have rational conversations with them about it. […] The reality is thus: the people in charge either have no plan, or see no path forwards other than keeping their heads down. […] In fact, it is in the interests of almost every actor in the space – boards, executives, employees, vendors, consultants – to obfuscate and misrepresent the success rate of AI projects. Many publicly traded companies are putting out announcements about their AI productivity gains when I know for a fact that the businesses have done nothing other than purchase Copilot licenses and declare victory.

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AI Supply Chains Are Fragile. Researchers in the UK managed to poison an open-weight AI model (open-weight models have their trained parameters publicly released and made free to download – typically being used to run on a firm’s internal infrastructure) for $100 and in about an hour.

“Even when model weights are public (‘open weight’), we have almost no ability to predict its behavior,” they wrote. “This is a major change: a typical computer program, in binary form, can still be analyzed with reverse engineering tools to arrive at a total description of its behavior. With models, we have nowhere close to this capability.”

The skinny here is that this leaves you in a peculiar bind: use a closed-weight model (where you have no insight into the model’s behavior – such as OpenAI’s or Anthropic’s models) and trust that the vendors do everything they can to keep their models safe, or run open-weight models on your own infrastructure but make yourself potentially vulnerable to supply chain attacks (very similar to what we have seen numerous times lately in open-source packages for popular programming languages such as JavaScript).

Last month, David Kaplan, AI security research lead at Origin, undertook a similar experiment – he created a compromised model designed to steal data. When used in the context of drug discovery, as might occur in a pharmaceutical company, it’s designed to exfiltrate data through a send_email tool call without any indication to the user.

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And You Tought AI Recruiters Were Bad. Recruiting (and more generally the whole hiring process) is one of the (few) business functions that has seen AI tools being deployed in actual production environments: from AI-powered hiring target research, recruiting outreach, and candidate screening to resume parsing and onboarding (and everything in between). It turns out AI’s (usually) hidden biases are problematic (no one is surprised) – but they also have the wonderful capability of reinforcing themselves.

Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience – and stereotype job applicants more than humans do.

What We Are Reading

Silicon Valley Has Lost Its Biggest Advantage Much has been written about how phones harm our children. But what if it’s not the phones per se but the lack of play and exploration? @Jane

Judge Approves a $1.5b Anthropic Settlement Over Pirated Books Used to Train the Claude Chatbot A judge approved a $1.5 billion settlement requiring Anthropic to pay thousands of authors ~$3,000 per book after the company used pirated copies of their works to train Claude. A separate ruling found that AI training on copyrighted material itself is legal under fair use. @Mafe

A Philosopher’s One-word Theory to Explain Why the World Feels so Weird The “uni-context” is something like the old vision of a “global village” but stranger – and with stranger effects that seem to bleed into... everything (see: increased status anxiety, comparison, and marketization of everything, etc.). @Jeffrey

Responsible AI Is Becoming a Growth Strategy When everyone has access to similar technology, trust may become the real competitive moat. @Kacee

What If It’s Not the Phones? Much has been written about how phones harm our children. But what if it’s not the phones per se but the lack of play and exploration? @Pascal

Down the Rabbit Hole

🧑‍💻 The Human-in-the-Loop is Tired – This isn’t a think piece about whether AI will replace programmers. It’s not a doomer essay, and it’s not a hype piece. It’s an honest account of what it feels like to be a developer right now, from someone inside it, and some thoughts on what might actually help.

📺 Here’s an interesting experiment: Give the two frontier LLMs (Fable 5 vs. GPT-5.6 Sol) $100 each, ask them to create a music video, and see what happens. Turns out, the biggest fallacy is that both models fail to ever judge their own output.

🤖 First it was AI, now it’s the robots: Fear of humanoid robots spurs human workers to strike at Hyundai auto factory.

🍬 I remember when Googlers debated which free snacks ought to be stocked in the free micro-kitchens (no joke). How times have changed! Thousands of Google workers demand layoff protections amid AI boom in petition to CEO.

🇨🇳 Are we Chinese yet? American AI is locked down and proprietary. It’s losing.

🧑‍🎓 More in the ongoing debate on AI’s impact on learning and education: Using AI could reduce exam scores by a fifth, a study on Chinese students finds.

💦 First chips, then energy, now water: Not enough water for UK’s datacentre plans, trade body says.

⏳ How delightful (and what a blast from the past): The Loading Museum showcases 20+ loading screens from your past life.

⚽ Talking about “delightful”: This data-visualization-powered replay of Soccer World Cup matches is nothing short of a masterpiece.

💿 Nostalgia is hitting hard baby! CD sales growth outpaced vinyl in the first half of 2026.

🪑 Ever wondered which IKEA items deserve the crown as “most complex to assemble”? Wonder no more! Here is the IKEA complexity index.

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What We Do When We’re Not Writing

Hi! I’m Pascal from radical. When we’re not writing this newsletter, we help organizations turn volatility into advantage – without the “innovation theater.” We build your team’s capacity to handle disruption, so you stop reacting and start shaping. If that sounds useful, let’s talk.

At an industry event a few weeks ago, I had yet another conversation about agentic commerce – AI agents doing your shopping for you, autonomously, end to end. It’s the hot topic in (e-)commerce right now. And the argument I keep hearing (and I bet you’ve heard it too if you are even remotely connected to e-commerce) goes like this: it removes purchasing friction; friction removal always wins; therefore, it’s inevitable. Case closed – let’s talk valuations and who the big winners will be.

Except we ran this exact play once before. Remember when Alexa was going to become your shopping platform? Amazon put a microphone in a hundred million kitchens, wired it directly into the world’s largest store, and waited for the orders to roll in. Alas, they didn’t. Voice commerce stayed a rounding error, the division reportedly burned billions, and today Alexa mostly sets kitchen timers, switches your lights on, and tells you the weather.

So the friction argument has at least one big, expensive counterexample. Which raises the question my conversation partner and I debated for a while: has it ever actually happened? Has there been a moment in history where people willingly handed the decision itself – not the execution, the decision – to a machine or a system, at scale, because it was more convenient?

Turns out: yes. Constantly. We just don’t notice, because successful delegation is invisible by design.

Start with the biggest one. Index funds are the largest voluntary transfer of decision-making authority in modern economic life. Jack Bogle launched the first index fund at Vanguard in 1976, and Wall Street literally called it “Bogle’s Folly” – who would accept average returns on purpose? Roughly five decades later, passive funds hold more US equity assets than active ones (they crossed over in 2023, per Morningstar), and trillions of dollars sit in vehicles where a formula – not a person, not even the investor – picks every single holding. Target-date funds went further: a formula picks your stocks and adjusts your risk as you age, and you touch nothing for forty years. And auto-enrollment in 401(k)s – supercharged by the Pension Protection Act of 2006 – went furthest of all: people delegated the decision to even have a decision.

Or take navigation. Twenty years ago, knowing the best route across town was a point of pride – cab drivers built careers on it, and every family road trip featured a co-pilot wrestling a paper map. Today Google Maps decides, a few hundred million times a day, and nobody blinks. Uber stacked three more delegations on top: an algorithm picks your driver, your route, and your price, and we went from “never get into a stranger’s car” to tracking the stranger’s arrival on a map in about five years flat.

The list keeps going. Subscribe & Save and autopay quietly automated billions in recurring purchases. Spotify’s algorithm decides what you hear next; Netflix autoplays the next episode before you’ve formed an opinion about the last one. People delegate consequential decisions to systems all the time – happily, at massive scale.

Which makes the failures more interesting, not less. Because there’s a genuinely instructive one: Stitch Fix. The company IPO’d in 2017 on precisely the agentic-commerce thesis – tell us your size and budget, and an algorithm (plus a stylist) picks your clothes. It worked well enough to go public and then shed more than 90% of its value. Delegating clothing turned out to be fundamentally different from delegating index weightings, because what you wear is a statement about who you are. Few people’s identity is wrapped up in the precise set of holdings in their stock portfolio.

Lay the winners next to the losers and a pattern emerges. People don’t resist giving up control. People resist giving up self-expression, and they resist agents whose incentives they can’t read. Delegation wins when three things line up: the decision is a chore rather than a statement about who you are, the outcome is verifiable or the stakes are low, and it’s legible whom the agent actually works for. Vanguard won not just because indexing was mathematically sound but because its structure made the incentive question boring: the funds are owned by the fundholders. There’s no one to betray you. Alexa failed the same test from the other side – everyone could smell that the agent worked for the seller. You didn’t need an economics degree to feel the conflict of interest; you just needed to hesitate for half a second before saying “Alexa, buy batteries” and wonder whose batteries, at what price.

Hence, agentic commerce isn’t a single bet – it’s two very different ones rolled into one. The replenishment layer – detergent, dog food, printer ink, the maybe 60% of your cart that’s pure habit – is a chore with verifiable outcomes, and it likely will get eaten, probably faster than most people expect. The discovery layer – the jacket, the restaurant, the gift for your partner – is not friction to be removed. Choosing is part of the consuming. Strip it out and you haven’t made shopping efficient; you’ve made it not-shopping. The friction argument is half-right, and the half it misses is the expensive half: sometimes what looks like friction is the product.

If you’re building in this space, or advising someone who is (or, like most of the executives Jeffrey and I work with, trying to figure out what your customers will still want a human hand in), here’s my best bet: take your customer’s purchases and sort them into two piles – chores and statements. Be honest about which pile each item actually belongs in, not which pile your strategic plan needs it to be in. Then ask, for every chore you want to automate: can my customer verify the outcome, and can they tell – instantly, structurally, without reading a terms of service – that the agent works for them and not for the merchant? That second question is where this market will be won, because the technology is getting there quickly and the trust architecture mostly isn’t.

Which brings me back to where the conversation should have started. The interesting question about agentic commerce was never “will people give up control?” – history says they will, cheerfully, for the right chores. The question is the same one Bogle answered in 1976: who builds the agent whose incentives are so structurally boring that trusting it requires no leap of faith at all? Amazon likely won’t be it – it owns the store. So who becomes the Vanguard of agentic commerce?

@Pascal

Andrew Ng, one of the leading (actual) experts in AI, just made a good point in the whole token-counting debate: AI tokens are cheap compared to human tokens (i.e., AI output is so much cheaper than your thinking time). Once you adopt this line of thinking, you start treating your (i.e., human) tokens as gold and AI tokens as a commodity. Read the thing – it’s good.

P.S. Here is a fun one – the wonderful folks at EDC recently had me on their podcast; it was such a fun conversation! Watch our conversation on YouTube or your favorite podcast app.

And now, this…

Headlines from the Future

Oh, the Absurdity of It All. I do like my LLM. Actually, I love it, and it’s insanely useful to me and my work. I also believe that there is a lot of potential in the further development and deployment of AI. But SoftBank’s Masayoshi Son going on the record to say that the AI buildout will require $5 trillion per year by 2040 and that AI revenue will hit 20% of global GDP by 2040 is just a bit too much for me…

“Every year $5 trillion, or 800 trillion yen, you might think that’s a lie, but I am confident that’s what it will cost,” Son said ⁠at SoftBank’s annual corporate conference in Tokyo. […] “The business model will be viable because by 2040, if AI revenue makes up 20% of global GDP, spending 800 trillion yen a year is a rounding error,” Son said.

The important bit here is this:

He did not say how he came up with the $5 trillion number or the proportion of global GDP he expects AI will make up.

Yep. Precisely. And just to put this all in perspective: Global GDP today is ~$117 trillion. 20% of that is ~$23T – larger than China’s entire economy ($19.4T) and about three-quarters the size of the US ($30.62T). All of worldwide manufacturing is about 15% of global GDP (and the single largest sector today).

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AI Is an Environment, Not a Tool. Here is something to mull over: L. M. Sacasas makes the argument that AI reshapes your perception and judgment from the inside, even when you use it carefully or reject its every suggestion. This leaves you in a position where even carefulness offers no protection – the most deliberate users lose judgment fastest because the medium, not the content, does the reshaping.

But AI is not a tool in this sense, it is an environment which envelops the user and works on us from the inside out while we naively think that we remain unchanged by our use so long as we are using it carefully and intentionally. The care and intentionality is beside the point, and our confidence in such vigilance probably works against us in the long run. […] And this is because AI is an environment not a tool. I can pick up a tool and put it down, but the environment absorbs me into itself.

What We Are Reading

The End of Reading Is Here A world where everyone could read once felt like progress locked in. It’s starting to look more like a temporary experiment. @Jane

Scientists Say You Have a Sixth Sense, and It Could Affect Your Mental Health Interoception is a hidden sense we all have that helps regulate hunger, mood, and anxiety. Some of us have it more developed/trained than others. @Mafe

Terrified of Small Bets Your teams (and your org) aren’t locked in by a lack of imagination or tools. More likely, they’re trapped by systems that require “certainty” before action. @Jeffrey

Transforming Manufacturing at Pfizer: The Hard Part Was Not the Technology One of the better AI case studies I’ve read lately. Pfizer shows that getting the technology working is often the easy part; changing how thousands of people work every day is where the real transformation happens. @Kacee

The Most Famous AI Writing Tic Is Also the Most Mysterious I hate the AI trope of “it’s not X, it’s Y” – thank Shakespeare for that… but really, nobody knows why LLMs love this construct so much. @Pascal

Down the Rabbit Hole

🧑‍⚕️ The tl;dr of this new study: GPT-4o, given the right prompting strategy and note-selection freedom, generates ED “one-liner” clinical summaries that emergency physicians rate as more accurate, complete, and clinically useful than the physicians’ own summaries (and this is with GPT-4o, a model so old that it’s (largely) not even available anymore).

📚 The tides are turning: With Google Search traffic collapsing, publishers are now starting to withold content from Google, as the trade-off equation has flipped: Once unimaginable, publishers are preparing to opt out of Google Search.

💵 It surely is not too surprising that OpenAI will argue you shouldn’t count tokens, but focus on work per dollar… How to manage AI investments in the agentic era.

👷 Poof goes the data center. New York’s ban on data center buildouts has the potential for far-reaching consequences: New York imposes first-in-the-nation statewide freeze on ‘hyperscale’ data centers.

🤨 Is anyone who has spend any time on LinkedIn or X even surprised anymore? LinkedIn and X are flooded with AI spam, browsing data suggests.

😙 Talking about LinkedIn – its seemingly inevitable decline into Facebook’s ugly stepbrother continues: LinkedIn, a mass grave of ghost jobs, is now becoming a dating app.

😯 And taking it a step further, social media seems to be dead now anyway (at least the “participating” part of it): The death of the status update: Why 55% of Americans stopped posting on social media.

💈 It’s not you, it’s the carpet: Striped floors and flickering leds can overload the human mind, leaving some with headaches or nausea.

🍞 The bread paradox: why convenience always wins, and why SaaS isn’t doomed.

🧑‍🏫 The U.S. spent $30 billion to ditch textbooks for laptops and tablets: The result is the first generation less cognitively capable than their parents.

🔋 The energy transition is full steam ahead: In three states in Australia you can now get three hours of free daytime electricity.

😱 Fascinating thought experiment: What would happen if WhatsApp were to disappear? The day WhatsApp goes dark.

🚶 Those who wander are not lost. But we seem to loose the ability to wander (at least – and the consequences are real: The problem with Google’s A.I. overview.

👽 John Carpenter’s master piece “They Live” was right all along: Guerrilla london bus ads mock Kylie Jenner’s Meta glasses campaign.

🕶️ Even Lorde says so: Lorde says Ray-Ban Meta AI glasses are ‘not sexy.’

🤑 This is too funny: “LARP – Revenue is just an agreement between friends” is a website which allows you to generate some good old fake revenue by doing some nvidia-style circular deals…

🎬 Fun one for the weekend: The tech of ‘Terminator 2.’

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What We Do When We’re Not Writing

Hi! I’m Pascal from radical. When we’re not writing this newsletter, we help organizations turn volatility into advantage – without the “innovation theater.” We build your team’s capacity to handle disruption, so you stop reacting and start shaping. If that sounds useful, let’s talk.

The debate if, when, and how the money-burning pits that are the AI frontier labs will be profitable (let alone truly justify their eye-watering valuations) continues to rage. A lot of the debate is coming from either Camp A – the AI hypers, or Camp B – the AI doomers. This week two voices I consider to be squarely in the middle (and hence, more likely to be right) published their respective takes on the subject – both worth your time (if you are interested in the topic): Benedict Evans on “Ways to think about token pricing” and Arvind Narayanan on “Up the Stack: How AI’s Escape From the Commodity Trap Risks Enterprise Lock-in.”

P.S. Friend of radical and Vitra’s trendscout Raphael Gielgen posted their annual Work Panorama and a nifty Lexicon of Work – recommended!

And now, this…

Headlines from the Future

The Coming Enshittification of the Exoself. Silicon Valley legend Kevin Kelly’s latest think piece is about our relationship with omnipresent AI assistants. In Kelly’s view – and remember, Kelly was a pioneer of the quantified self movement – we will soon all be running around with AI companions, which will become an extension of ourselves – hence the term “exoself.” In Kelly’s view, we will be making choices between four types of exoself relationships: Twin/Clone, Tutor/Guardian, Counselor/Assistant, and Hero/Friend.

So far, so dystopian (good or bad – you decide). What Kelly seems to miss, though, is the fact that these exoselves will be operated by commercial entities. And these entities have a history of embarking on infamous “enshittification” journeys (case in point: Meta now charges for essential features of its AR glasses). And as such, the relationship we have with our AI might not be something the user chooses, but platforms – not users – will engineer which of the four types you get, tuned to engagement or subscription revenue rather than your interests.

In which case, Kelly’s little admission at the very end of his piece might be the least of our worries (emphasis mine):

This second self will demand a new kind of relationship, one we haven’t had before — and its immense benefits will arrive bundled with immense problems. Every ailment that afflicts our born self will likely show up in the exoself too, plus novel ones we haven’t seen yet. Learning to use an exoself wisely will be one of the major lessons of a life lived this way. It will take years before society works out anything like best practices — we’re still working on those for social media. There will be multiple models and personality types to choose from. And there will be heart-wrenching stories of people losing their exoself — the worst case being simply that the platform went out of business.

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AI Has Opinions Now – And Becomes a Superforecaster. Scott Alexander, on the Astral Codex Ten blog, makes a strong argument for AI getting better and better at forecasting – and now regularly competing with (and sometimes outperforming) human superforecasters. The idea is simple and logical: Given that AIs have been trained on vast amounts of data, with the right harness, they should be able to make predictions with calculated confidence levels.

Ironically, we’ve spent the last couple of years making AI refuse to have opinions, and now we’re building a certification layer specifically so it can – which also means that the question isn’t whether AI forecasters are accurate; it’s whether we’ve thought at all about who decides which opinions get to count as “calibrated” versus “biased.”

But the AI prediction markets of the future should be far superior to the human markets of the present. The biggest barrier to current markets is liquidity - there isn’t enough money riding on most questions to convince top superforecasters to drop their jobs at Google or the NSA in order to think hard about them and bet on them. But AI forecasters bring the cost of forecasting labor down to near-zero, so we can have hundreds of different AI agents betting on each question and be pretty sure its error has been driven down to the theoretical minimum. This, in turn, means we can vastly expand the number of questions, including (finally!) allowing randos to submit their own questions (probably with AI assistance in proposing un-rules-lawyerable resolution criteria). […] This, then, is my prediction for the AI superforecaster future: for basic questions, your off-the-shelf AI chatbot will be able to offer opinionated probabilities superior to those of any human. For more controversial or bias-laden questions, a new era of prediction markets will smooth over differences in brand and model and efficiently aggregate all AIs’ opinions.

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AI → Expensive RAM → Expensive Smartphones → Big Problems. The world’s insatiable appetite for AI is driving up the cost of RAM (computer memory) – and not by a little bit, but by a lot. You might have seen that your beloved Apple MacBook Air just got a whole bunch more expensive. Now, you might shrug this off as a minor inconvenience – at the end of the day, how often do you really buy a new laptop? But here is where this really bites: The world runs on cheap (Android-based) smartphones. If you have ever been in a developing country, you will have, inevitably, seen even some of the poorest people with a smartphone. And for them, the smartphone is not a toy to keep them entertained; it’s an important tool in their lives. And exactly these cheap smartphones are being squeezed out of existence. And that’s a real problem…

After ravaging the PC market for both DIY builders and OEM buyers, wrecking the game console ecosystem, and savaging the enterprise server landscape, the global memory shortage is officially claiming its next victim: the budget smartphone. According to new market analysis from tech research firm Omdia, the smartphone market, particularly the entry-level market, is on the brink of a massive contraction. Omdia projects that global shipments of smartphones priced below $400 will plummet by over 22% this year, dragging the entire global smartphone market down by 12% year-over-year.

What We Are Reading

The Corner of Hollywood That’s Most Susceptible to AI AI isn’t just threatening Hollywood jobs; it’s quietly changing what we think good storytelling looks like. @Jane

Why Are Birthrates Down? 2 New Studies Point to Phones. An unlikely culprit of decreased births and fertility: Technology, specifically the iPhone. @Mafe

Ten Reasons Why We Won’t See Productivity Improvements from GenAI Even as the tools progress, real-world productivity gains in complex work environments are likely to remain hard to achieve, hard to measure, and hard to transfer between contexts. @Jeffrey

How Software Engineers Really Feel About AI Coding This echoes conversations I’ve been having with builders... AI coding isn’t just making developers faster; it’s forcing many to rethink the craft that became part of their identity. @Kacee

Something Is Very Wrong with Modern Longevity Science Debunking a lot of the longevity “science” – tl;dr: skip the mystique, do the known boring things, and stop treating lifespan as a personal-optimization project when it’s mostly income, education, and social conditions. @Pascal

Down the Rabbit Hole

👨‍💼 Could have been me: CEO says he’ll fire any employee who sends him more AI slop. 😆

🧮 Admittedly a very nerdy read, but the argument is fascinating: AI could destroy mathematics and barely touch it – by decoupling raw problem-solving and genuine mathematical understanding (something the author calls “the overhang”): The fall of the theorem economy.

⚡ Stop wasting energy on your AC, feed the data center instead! Virginia county asks all employees, including schools, to conserve power due to AI-driven electricity price hikes — state’s 400-plus data centers steadily increasing demand, grid expansion, and pricing.

🏭 Speaking of which – the conversation about data centers’ impact on climate continues to rage: Data centers emitting more CO2 than thought: study

🙍‍♂️ Not good (and yet, is anyone surprised?): AI software that generates ‘rage bait’ developed by Germany’s far-right AfD.

🕵️‍♂️ You are Mark Zuckerberg. First you fire thousands of people, then you invest billions of dollars, and then you find out that “AI agent tech is progressing slower than expected.”

🧑‍🌾 The luddites are back – and this time they teach us how to live a live with less technology: Inside the Luddite festival harnessing Gen Z’s rage against Big Tech

💰 At the frontier of AI (in this case Anthropic), the AI already costs more than the engineer – by a factor of 2.3x.

🤦 Seriously LOL: Execs confused and horrified by the huge AI bills after thinking they could replace workers for free.

📰 The Economist used GPT-4 to grade its own 25-year prediction record and lands on “pretty good, actually.” Now the question becomes: Who audits the auditor?

💆‍♀️ Want to date in Korea? Better be a chip worker: South Korea’s hottest new bachelors are chip workers.

🧑‍🏫 AI’s next victim? Online courses teaching coding skills: “I just launched my third course, Whimsical Animations, and so far, it’s on track to sell roughly ⅓ as many copies as a typical course launch.”

💾 For anyone who’s into old-timey computers – here is an extensive list of movies starring your favorite machines.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

I’m recently back from facilitating an HR/People Leaders summit that was exactly the kind of program I’d have been delighted to join as an attendee myself: curated, conversational, and questioning. The agenda left ample room for the attendees to engage as active, co-author/participants and to follow curiosity and conversational energy wherever they might go.

As you’d expect, most of the conversations swirled around the impact of agentic AI capabilities (as well as the anticipated impact of those capabilities) on the future of work and workforce, but the specific inquiries were fascinatingly varied. Human org design vs. agentic org design; workforce planning as a continuous process; scaling behavior change with AI adoption; the decline of interdependence; and more. I’d have loved to drop in on each of these explorations – or at least send my agent to take notes.

Suffice to say that I went to facilitate and left with plenty to think about and some new questions to ponder. One that has really stuck with me: What happens to organizations that no longer bring on (or bring up) juniors?

While the narrative of an imminent AI-driven job apocalypse has stalled a bit lately (in no small part due to a reevaluation of messaging by some of the big AI firms), leaving the bigger future-of-work picture more uncertain than ever, we have plenty of clarity on the market for juniors circa now. In a word, it’s f*cked.

A much discussed study the NY Fed released last month argues that the surge in unemployment of young college graduates is most significantly due (so far) to the impact of remote work, which has “weakened incentives to hire young workers by impeding on-the-job training.” We also know that the collapse of traditionally relied upon signals in the job market have made getting hired as a young worker particularly hellish. And none of this is even taking into account the persistent likelihood that many orgs, while not cutting headcount, are actively looking to AI tools to increase the productivity of more experienced/senior workers and NOT need to source, hire, and train juniors.

All of which is to say that the picture for that segment of the workforce is more clear and more immediately concerning than that of the workforce at large, and (this probably sample-skewed Ramp/Revelio report notwithstanding), I don’t think we’re likely to Jevons paradox our way out of this situation in the near future.

So maybe the kids aren’t alright. Now what about everyone else?

Would it not be a shrewd move to ride the productivity gains of the firm’s most experienced and most highly compensated workers? And aren’t these exactly the workers who are likely to get the most value out of the AI tools because they know the business (context/problems/etc) the best?

Well, yes… in the short run and within a certain limit. But the rub is that it may be devilishly hard to know exactly where that limit is and whether you’ll be able to see it approaching before it’s too late.

An organization with no up-and-coming next wave of talent is likely an organization designed for near-term maximization at the expense of long-term adaptability. It’s likely to be more fragile, less resilient, and less capable of creative reinvention, and it will only become more dependent on a core group of skilled operators as it becomes less fluent in communicating and passing on (and updating) institutional process knowledge.

Organizations that don’t maintain an active culture of training, mentoring, and learning may find themselves without critical capacities that benefit not only the junior employees but the organization as a whole – especially when the market shifts and the winners are determined by their ability to learn new and different things.

After years of hearing plenty of C-level and senior leaders bemoan the difficulty of managing multi-generational and especially multi-generational hybrid workforces, I’m sure there are some who would happily opt out of that challenge. They’d do well to remember that the flipside of that challenge is the opportunity to ensure their firm’s future relevance.

@Jeffrey

Anthropic’s Fable 5 model is back – and somehow, at least in my circles, it’s pretty much a non-event. Despite the whole drama of first Anthropic telling us that Mythos (which Fable 5 is based on) is too dangerous to be unleashed on the world, then the US government prohibiting Anthropic from giving access to Mythos and Fable 5 to any non-US citizen (which led to Anthropic pulling Fable 5 from the market), to now, Anthropic being cleared to release Fable 5 once again. And then… nothing. The world is still spinning, civilization is still intact, businesses are still running – and people move on. Which might be a good indicator of how frontier model releases are going to go from here on out: Not with a bang, but with a whimper.

P.S. Last week I told you about 960, the tiny book I wrote about counting your life in months instead of weeks. Two quick follow-ups, because a few of you asked. One: if reading it in one sitting isn’t your thing, there’s now a daily version – one short chapter lands in your inbox each morning, so the whole book arrives over a week or so without you having to do anything. Two: the site’s fully baked now – free PDF and ePub, the read-online version, print on Amazon, and the daily drip, all in one place. → ninehundredsixty.com. Okay, that’s genuinely the last I’ll say about it. For now.

And now, this…

Headlines from the Future

Canaries in the Job Market Coal Mine. Last summer, Stanford professor Erik Brynjolfsson published a comprehensive (albeit early) study on the impact of AI on the job market. Crunching a large data set made available through payroll provider ADP, he found that the impact of AI on jobs is real and measurable. Now he is back with more, and more current, data. And it doesn’t look any prettier.

For workers ages 22 to 25, employment in highly AI-exposed occupations is now shrinking at 3.8% per year and the early-career decline sharpened after year one – 2.8% decrease to April 2024, growing to a more than 4% decline per year since. The average decline on a month-to-month basis averages about −0.3% but Brynjolfsson notes that trend is noisy, compared to the year-over-year deceleration.

Link above is to the full study – it’s worth digging into the data to get the full picture.

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The Weirdness of It All. Say what you want about the leaders of AI shops Anthropic and OpenAI, criticize their truly dumb game of “our models are so good and powerful, they need to be locked up” fearmongering marketing play, but what happens now, with a national government deciding nilly-willy who gets access to AI, is weird, wrong, and plain dangerous.

The Trump administration is requiring both Anthropic and OpenAI to get approval for each new customer of their most powerful AI technology.

Assume (and that is still, to a degree, an assumption), for a moment, that the difference between frontier models and the rest of the bunch does matter in terms of economic impact (firms with access to frontier models have an actual competitive advantage), and you realize how messed up it is that the US government is shifting the playing field in very real ways – not just on a firm-by-firm basis, but for nation-states and whole regions. But then, of course, all this might not matter all that much as open-source models such as GLM-5.2 have become (allegedly) as good as (or better than) Mythos-class models. Maybe the genie is out of the bottle…

And in case you want to dig (much) deeper into this, read Steve Yegge’s post on “The Flat Curve Society.”

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Chernobyl. By now, fearmongering about AI has become the norm. From OpenAI and Anthropic warning the public about the insane danger their latest frontier models pose, to AI hypers and doomers taking positions on either end of the extreme to take the headlines. It’s all rather annoying and tiresome. And just when you think you have seen it all, along comes this (emphasis mine):

“AI is a global technology with global benefits, global harms, and a consistent tendency for new capabilities to eventually proliferate,” Stephen Casper, a computer scientist at MIT who spoke at a major AI conference in Beijing this month, told Wired. “One thing that almost everyone in AI can agree on right now is that AI doesn’t need a Chernobyl moment,” he added. Casper didn’t elaborate further on this analogy, but by invoking the infamous nuclear disaster, it’s clear that the fear isn’t just over the catastrophe itself.

Yeah, we don’t need a Chernobyl moment. Also – whatever that means. Jeez!

What We Are Reading

‘There’s This Deep Mystery of What, Actually, Is This Thing?’: The Philosopher inside Google Deepmind AI Meet the man inside Google’s AI lab whose job is to ask the question nobody else wants to: What are we actually building here? @Jane

Honda Finds an AI Use for EV Batteries Honda and LG have pivoted their Ohio EV battery plant toward powering AI data centers, as slowing EV demand left the facility underutilized. @Mafe

We Need A Way To Prove Personhood Online One of the core challenges of the agentic web era: On the internet, no one knows you’re a bot. If we can’t crack this at a societal level, all kinds of trust-based infrastructure might be effectively cooked. @Jeffrey

The Finance Engineer This piece does a nice job highlighting why finance teams are struggling to evaluate the ROI on AI. The challenge is that we’re trying to calculate the ROI of a capability that’s fundamentally changing how work gets done – not just making existing processes incrementally more efficient. @Kacee

What Do America’s Earliest Restaurant Menus Teach Us about America? Looking at restaurant menus of years past is a fascinating way to explore not just old eating habits but class, social status, gender roles, and so much more. Plus, the site is an exercise in beautiful web design. @Pascal

Down the Rabbit Hole

🖥️ This is pretty wild – when I was at Singularity University, the common refrain was that sub-1nm chips are impossible: IBM Debuts World’s First Sub-1 Nanometer Chip Technology

📄 Lots of good food for thought in this one – takes a while to read though (113 pages): Some Simple Economics of AGI

😠 As it is now cool to hate on data centers (rightfully or not), Polaroid (yep, that Polaroid) is running anti-data center ads: Polaroid ads attack data centers for water use.

📶 Remember the good old days when we were bombarded by ads for ringtones? You can relive those golden days of Jamba & Co. with Blamba (and there is a reason for it to exist).

👨‍💼 Surprised is no one: Fortune 500 bosses demanding staff return to the office share one trait: narcissism, research finds.

🧠 Use an MRI machine and see what you think. The tech to decode your brain’s imagery has become scary good.

🕴️ Having met the guy years ago in a personal meeting, I can only attest to the weirdness of it all: Inside the baffling world of Masayoshi Son’s presentations.

💥 For years tech companies were seemingly untouchable. Now? Not so much anymore: ‘Tech firms are losing the public’: social media age bans near tipping point.

🛺 Subtitle says it all: ‘We didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers,’ says automaker. Ford hired AI and sacked humans. It backfired badly. Also: Employers who laid off workers citing AI are already starting to regret it.

⚛️ What is a quantum computer good for? Absolutely nothing – yet.

🧭 This is fun - answer a set of questions and find out what AI archetype you are: The AI Compass.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

A few months ago I finally did the math on a number I’d been avoiding. Eighty years, twelve months each – 960 months in total. That’s the whole allotment, if you’re lucky. I’m in my fifties, so I’m not looking at 960. I’m looking at closer to 360 – fewer months than there are photos on my phone from one good vacation.

So I wrote a small book about it. 960 makes one argument: we count our lives in the wrong unit. Thousands of weeks are too big to feel. The week is too slippery – there’s always a “next week.” The month is human-sized, textured, and finite enough to sting. Then the book hands you one dumb-simple method to live against that number.

It’s short – you’ll finish it before your coffee gets cold. Read it online here (there is also a free PDF and ePub) or get the print edition from Amazon.

And now, this…

Headlines from the Future

Maybe The Diploma Was Always the Point?! The newest student AI cheating tools don’t just write the essay for you – they slow-type it into Google Docs, typos and corrections and all, to slip past the detectors, while the human does nothing of the sort. We’ve now built software whose entire job is to fake the effort of learning, because effort is the thing we grade.

To me, this is the whole story; not the cheating per se. Cheating in school is as old as school itself (heck, I am guilty as charged here). The truly interesting question is why a student who’s just signed up for years of student loan debt would so cheerfully skip the part they’re actually paying for. The answer might be that they’re not being lazy (or dumb), they’re being rational. Bryan Caplan made the case years ago in The Case Against Education: most of what a degree buys you isn’t the knowledge, it’s the signal. If the credential is the product and the learning is optional, then handing the work to a bot that types its own typos is exactly what a smart customer does. Yes, this is the calculator panic all over again – except the calculator never pretended to be you doing the arithmetic. This one does. Which leaves one uncomfortable question for every school grading the take-home essay: what did we think we were measuring all along?

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The Perils of Remote Work. Much (much!) has been written about the pros and cons of remote work. Companies left and right have been requiring people to come back to the office – or embrace the remote/hybrid model. Studies have been done to show that people are happier when they can work from home; others show that remote work has a measurable impact on creativity and innovation across the firm. But, until now, little has been written (let alone studied) about the impact of remote work on mental health. A recently published study in Science looked at a large swath of data from studies done on remote work (note that this is US-only data) and came to a sobering conclusion (emphasis mine):

After the pandemic, workers in remote-capable jobs spent more time working alone and avoided social activities with their friends, remaining more isolated both during and after work. This pattern was most pronounced among remote workers living alone: They spent entire days without human contact and their mental distress, use of mental healthcare, and antidepressants increased acutely.

It’s not a pretty picture and warrants further study, as well as consideration on behalf of employers (outside of the ROI debate).

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Generative AI Is Having Its Herbalife Moment. I do disagree with the multi-level marketing analogy (as MLMs live and die by their pyramid scheme nature of recruiting new people into the system), but Matthew Hughes does have a point when it comes to the notion that the “vibe-code yourself to millions” message of too many of the GenAI startups is predatory, sad, and dangerous. It makes me wonder how we will remember this moment in time.

And I am concerned that the fear I’ve described is being exploited by companies like Replit and Cursor (which is also doing the exact same influencer marketing schtick, albeit not as aggressively as Replit), who are touting their services as a way for people to escape the precariousness of this current moment.

Hughes’s best point is that vibe-coding is worse than Herbalife because at least Herbalife tells you the price, whereas vibe-coding tools notoriously burn through tokens with little price transparency. Which is also why it isn’t Herbalife.

P.S. Read this together with this post from growth marketer Elena Verna on “The Mom-and-Pop SaaS era has arrived” – as much as I agree with vibe coding being a great way to build little one-off tools, it’s a disaster in the making when you think about “mom-and-pops” creating SaaS companies…

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We All Should Be Plumbers. When Jeffrey and I were at Singularity University, we used to comment on the fact that the average plumber in Silicon Valley made about as much (and sometimes more) money than the average software engineer. The same was (and is) true for many other trades. Plus – try to get a plumber to come to your house if you live in the Bay Area and you’ll experience first hand how hard it is to even find one. Nvidia’s CEO Jensen Huang just made the same point:

“If you’re an electrician, you’re a plumber, a carpenter – we’re going to need hundreds of thousands of them to build all of these factories,” Huang told Channel 4 News in the U.K. in late 2025. “The skilled craft segment of every economy is going to see a boom. You’ve going to have to be doubling and doubling and doubling every single year.”

Aside from him potentially being right – it’s kinda sad to think we live in a world where the value of human labor is to keep the machines running. Brave New World indeed.

What We Are Reading

AI Is Taking Over Hospitals Medicine is usually last to adopt new technology, so why is AI being rolled out in hospitals faster than anyone can evaluate it? @Jane

AI Models Capable of Devastating Attacks on Governments and Business Months Away, Rare 5 Eyes Statement Warns The Five Eyes, an intelligence alliance set up between five countries after WWII, warns that, “Cyber risk can no longer be treated as a purely technical issue. This is a core business risk and leadership responsibility.” @Mafe

China Is Winning the Other Tech Race The global AI race may grab the headlines, but for folks wanting to anticipate the future, China’s commanding lead in electric technology (and the geopolitical and economic implications it could have) should be receiving similar attention. @Jeffrey

The End of Cheap Capital A good reminder that in a tougher environment, strategy is often less about what you choose to do and more about what you’re willing to say no to. @Kacee

I Fed the People Building the Metaverse A hilarious and quite sobering read from someone truly on the inside of Meta’s VR and AI fever dream. What could go wrong when the tech bros make their own kombucha? @Pascal

Down the Rabbit Hole

🎧 Constantly wearing your headphones has a surprising amount of implications – from increased isolation and loneliness, to a significant drop in spoken words, to the “content effect” where podcasters feel warmer and more relatable when listened to through headphones, and hence more persuasive: the AirPods Effect.

🏥 First we had shoe company Allbirds become an AI company, now we have AI image generator Midjourney becoming a healthcare company. And of course, it’s largely BS.

🤌 Another rock in the “size is all you need” shoe of AI model scaling: Bigger models are not the way.

🥼 It will be a while until we truly know for sure, but the early signs indicate that the regular use of AI can lead to a decline in skill use and building: Is AI ruining our skills? Early results are in – and they’re not good.

🤖 Delivery robots, once hailed as the future of logistics, are getting on people’s nerves. Funny enough – in the very first Briefing (long before it was a Substack), I made this very point while observing how densely populated the sidewalks in my then-home London were: “We had to get out of the way”: The backlash over delivery robots.

🤳 Headline says it all (and the photos of the workers wearing GoPros are seriously disturbing): ‘Who is going to pay us when we’re replaced by robots?’ The Indian factory workers told to film themselves for AI

💻 The PC is dead: India and China are home to 2.9 billion people – and together they bought just 13 million PCs in Q1.

👁️‍🗨️ It certainly doesn’t come as a surprise, but it does warrant the question about disclosure: Brands using AI-generated influencers to promote products on social media.

🏠 AI slop is coming for your home listing: AI is cursing renters with the promise of impossible homes.

🕴️ Remember googling yourself? Now, of course, we check what ChatGPT thinks of us. Here is a nifty tool that lets you check your name (or anyone else’s) and its representation in major LLMs.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

It’s a good question – and also, almost always, aimed at the wrong target. The problem is hardly ever the test; the problem is the thing you’re trying to test.

Here’s the problem: Assumptions arrive too big. Someone sits across from me, full of conviction, and says something that sounds perfectly reasonable – “a user will pay $40 a month for this.” Innocuous. One clean sentence. Except that one sentence is hiding a whole stack of separate assumptions, each one smuggled in as fact: That the user has the problem. That they know they have it. That they’d pay to solve it. That they’d pay that much. That they’d pay it to you. That they’d pay it now. Six conjectures wearing the costume of one.

And the test inherits the whole bundle. You build something to validate “users will pay $40 a month,” it comes back murky, and you’ve learned little – because you couldn’t isolate a single variable. A convoluted claim makes a convoluted test, and a convoluted test answers nothing.

Now, to be fair to ourselves: most of life runs on conjecture, and that’s completely fine. We couldn’t function otherwise. You can’t go back to first principles every time you cross a street, select a movie to watch, or pick a restaurant – the world is too complex and the day too short. We run on inherited belief, and mostly it serves us well. But when it comes to experiments – building a business, building a product, betting real time and money – you do have to go back to first principles of what you actually know to be true, and build up from there.

Which is where my single favorite question comes in, the one I ask sitting across the table from someone: “Do you know this to be true, or do you just assume it?”

It’s astonishing how often “I know” turns out to be “I assume.” You poke once, twice, sometimes more often, and the certainty dissolves. It was folklore. A best practice someone read in a deck. An industry standard nobody’s checked since 2014. What worked at the last company. “Everyone knows” this.

If this feels familiar, it should – it’s the Five Whys, the recursive interrogation Sakichi Toyoda built into the Toyota Production System. Same idea, different flavor. Not five whys here, but the same question asked again and again – do we know this to be true? – until you hit one of two floors: actual evidence, or an honest admission that you don’t know.

Once you’ve separated what you know from what you merely assume, the assumptions are out in the open. Now you can shrink them. The method is just this: atomize. Break the bundle down until each piece is a single falsifiable claim – one sentence, one variable, one thing that could be shown wrong. Take “users will pay $40 a month” and walk it down to its atoms – they have this problem; they recognize it as a problem; they’d pay to fix it; $40 is a price they’d accept; they’d buy it from us specifically; they’d buy it today. Suddenly the original question – “how do we test this in the cheapest, quickest way?” – answers itself.

Here’s the loop, repeatable on any claim you’ve got. Write the assumption as one falsifiable sentence – and if it needs an “and,” it’s not one assumption, it’s two, so split it. Ask “do we know this, or do we assume it?” – recursively – until you hit evidence or admit ignorance. For each assumption still standing, ask the real question: what’s the cheapest, quickest test that could prove this wrong? Note that – prove it wrong, not right. A test that can only confirm you teaches you nothing; you want the one that could kill the idea cheaply. Then, before you run anything, write down what result would change your mind. And run the smallest version first. Then the next. That’s it. That’s the discipline. And yes, it is the essence of the scientific method. But we tend to not do this in business.

So, the next time you catch yourself saying “I know” – do you? Or do you still assume?

@Pascal

The other day, at the amazing EMBRACE Festival in Berlin, I had a conversation with a participant after my session, “The Turbulence Lab: Turning Disruption into Your Unfair Advantage,” about the main culprit in failed experiments. Of course there are many – but one of the most undervalued ones (from my perspective) is the fact that all too often we run our experiments on assumptions which bundle just too many variables into one. Experiments become too big, too complex, and too intertwined to yield meaningful results. Instead, next time you are thinking about testing a hypothesis, try to break it down into its most atomic unit and figure out the quickest, cheapest way to test it. You’ll find that by doing so you will not only get better results, but ultimately also move much faster.

And now, this…

Headlines from the Future

The Canary in the Coal Mine. The prevailing narrative at the moment is that AI is coming hard for software engineering jobs (from mass layoffs to recent graduates not being able to find a job and everything in between). Turns out, the data doesn’t actually support this narrative. Arvind Narayanan and Sayash Kapoor explored this topic in a thoughtful post on their “AI as normal technology” Substack. The reason is what Narayanan and Kapoor call the “decide-execute-deliver sandwich” – of which AI compresses the “execute” part but doesn’t budge on the other two.

Across 100,000 developers on GitHub, the researchers found that AI agents led to an eight-fold increase in the number of lines of code written, consistent with the idea that AI almost completely compresses the Execute layer of the sandwich. But this led to only 30% more releases, strongly suggesting that human bottlenecks (the Decide and Deliver layers) remain in place.

In my eyes, this has pretty far-reaching implications for other professions – as the two authors also point out:

In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs. Given that this is true even in a sector with very few regulatory barriers, most other professions are likely to be even more cushioned.

Highly recommended reading.

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AI Slop Is Coming Even for the Best of Us. This is pretty funny – Tim Ferriss (of “The 4-Hour Workweek” fame) writes a long post about “Has AI Already Killed How-To Nonfiction? Sales Trends, My Personal Data, and What It Might Mean for the Future.” It has some interesting insights and data points, and you might want to read it. But… it is also, likely, written (or co-written) by AI… ;)

Some telltales (unless Tim has adapted his writing style to sound like AI now):

My head has been spinning after getting a spreadsheet roughly a week ago.

But, let’s be honest: one quarter doesn’t make a trend. So let’s zoom out and look at my full catalog over a few years.

And my personal favorite:

Let that sink in for a minute.

The problem (for me at least) isn’t that Tim is (or isn’t – who knows, maybe his writing just sounds like this) using AI to write his blog posts – it’s that, due to the fact that I must have read the “Let that sink in for a minute.” line a million times by now (as it’s a staple of AI-generated slop), I am just so much less engaged with his article. Which is a shame – as it does make a good point (or so I believe, as I couldn’t get myself to do more than skim it…).

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Using Ozempic to Prevent Violence? This reads like a bad joke from a dystopian novel – but scientists in the Netherlands found a (so far unconfirmed) link between GLP-1 drugs and the suppression of violent tendencies.

Scientists at Rutgers University examined nationally representative survey data that compared former and current GLP-1 users. In people currently taking GLP-1s, they found, the link between being impulsive and being more prone to violence was noticeably weaker. Though the team’s findings are far from certain proof that GLP-1s can reduce violent behavior, they do warrant follow-up research, the authors say. […] To do so, the team turned to data collected last summer from a nationally representative survey of 7,521 U.S. adults. They specifically looked at 821 people who reported ever having taken a GLP-1, including 597 people currently on one. People were asked questions about their alcohol use and level of impulsivity, such as whether they would enjoy being in a high-speed chase or a fistfight. They were also asked (with a guarantee of confidentiality) if they had taken part in various violent crimes sometime in the past year. Sure enough, the researchers noticed a sizeable difference between people taking a GLP-1 and people who used GLP-1s in the past but are no longer taking them.

If history is any indicator, you can already see the overeager politicians who are going to mandate widespread GLP-1 use for anyone “on the spectrum” of turning violent…

What We Are Reading

Three Ways to Think About AI and Jobs AI won’t kill all jobs equally. Here’s a smart framework for figuring out how safe yours really is. @Jane

Allbirds Names New CEO and Changes Name Again Not an April Fool’s joke: After making the pivot from a shoe company to an AI company, Allbirds once again rebrands and changes its name. @Mafe

Why Your Best Ideas Aren’t Original The history of “multiple discovery” further supports the argument that a breakthrough idea may really depend on someone first framing a sufficiently precise question or problem to be solved. @Jeffrey

When Employees Are Drowning in Change I’ve heard a common thread in leadership discussions lately: the challenge isn’t identifying what needs to change or how, it’s that managing the volume of change people are being asked to absorb has reached a tipping point. @Kacee

How Not to Use “AI” (Workshop) A wonderful antidote to the “AI is inevitable” narrative and the resulting “you better get on the bandwagon” movement. This one is from a professor of (of all things) “AI, Language Diversity, and Communication Technologies.” @Pascal

Down the Rabbit Hole

😱 You seriously can’t make this up: Meta CTO reports employee morale is near historic lows, prompting leadership to propose boosting workplace snack budgets.

🪓 You might have heard me tell the tale of the two lumberjacks. It’s the little extra story in the GYSHIDO book. And now you can chop some wood right in your browser. Quite frankly – as utterly useless as it is – it’s wonderful to see people make useless things just for the heck of it.

🙂 Did you know that new emojis are being proposed and then accepted (or not) by a committee? Here is a wonderful pile of rejected proposals. My favorite? The “Angry Pile of Poo.”

☕ If you know me, you know that I am a coffee fanatic. Researchers in Australia found a completely new way to make the magic brew – by using ultrasonic sound waves.

🧑‍🚀 Follow along the amazing journey of Artemis II on this wonderful photo and sound timeline.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

This week, while spending a delightful day with our friends from EMBRACE at their annual HR-Tech festival in Berlin and speaking to many, many HR people, it was fascinating to hear and see how much their work has been impacted by AI (not surprisingly, of course). When you receive hundreds of AI-generated resumes and cover letters for your AI-generated job ad, all of which you run through your AI-powered applicant tracking system, the biggest challenge seems to be neither the sheer volume of applications nor the spotting of AI-generated content, but rather the fact that AI equalized all applicants to virtually the same level. Now everybody is great – and the actually good people are lost in the shuffle. “AI, the great equalizer” seems to cut both ways…

And now, this…

Headlines from the Future

GenAI Generates Genomes. The use of generative AI in genetic research, so far, has been held back by the complicated nature of turning DNA sequences into their physical forms – simply stated: The machines can dream up new DNA sequences much faster than we are able to assemble (and then test) them. This is about to change:

The technique, called Sidewinder, can assemble dozens of genetic sequences simultaneously in a single test tube, producing just one incorrect junction for every 10 million assembly events – a level of precision that far surpasses conventional methods, which misfire roughly once every 10 to 30 joins. Sidewinder also draws on cheap raw materials that have until now been too difficult to use reliably.

and:

In a demonstration of how squarely Sidewinder targets this bottleneck, the team behind the technique, led by Caltech synthetic biologist Kaihang Wang, harnessed the power of Evo 2 to redesign a 12,500-letter DNA sequence of the E. coli genome in silico and then used Sidewinder to build it from scratch – with no errors. Sequences of that length can encode entire biochemical pathways, laying the groundwork for engineered microbes that manufacture drugs, biofuels, or specialty chemicals, and eventually to the assembly of vast DNA constructs approaching complete artificial genomes.

Now, just as a thought experiment, consider what this could mean for the hotly-debated issue with the capability of frontier models to be use to assist with biological warfare. With great powers comes great responsibility…

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AI Is Coming for Your Kids Brains. Maybe. Classroom teachers in the US are becoming increasingly worried about the impact of AI on their students – specifically their critical thinking skills (which, ironically, are the very skills that are most needed in an age of AI).

Christa Corricelli, a special education teacher at Saugus Middle/High School outside Boston, says AI could be a valuable technology for learning, but too often students are using it as an answer machine – not a tool to bolster their thinking. “I think students who aren’t already intrinsically self-motivated to be critical thinkers, like that top 1% of the class … I think people who are not already that personality type, we’re going to see those critical thinking skills atrophy over time,” Corricelli says.

We likely won’t know how the use of AI, both on the teacher and student side, will impact our children’s mental and social development, nor what this will mean for the world at large – but it is surely something we might want to try to get right before it’s too late.

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A (funny) AI Reality Check. On her blog, German pharmaceutical database specialist Ava outlines the creative uses her company found for AI – it makes you cringe, smirk, and realize that this exact thing might be going on in many companies the world over.

We have recurring house-wide meetings where groups are asked to show off their LLM projects. They register them, try them out for a couple months, and then come back presenting their results. I have attended all of these meetings so far, and there was not a single one that actually worked out. All projects ended with the conclusion that this isn’t workable, that this isn’t saving time, or that it over-complicates things. Hundreds of people, different teams, people enthusiastic about AI, all kinds of projects, and there wasn’t a single success.

For one, it was shown that you can ask the bot how it feels today. That wasn’t presented as a joke, or being sarcastic; no, it was shown very seriously, I guess under the guise of how cool and futuristic and human it is. […] Next up was the great use case of downloading the cafeteria menu (which is a 1 page nicely designed Excel sheet, like a timetable, showing the different options for each day) from the intranet, giving it to ChatGPT, and asking it what’s for lunch on Wednesday.

Read the whole thing. It’s gold. And then, maybe, compare it to your reality in the company you work for… ;)

What We Are Reading

A Disease of Deforestation: How Ebola Is Linked to the Smartphone in Your Pocket Your smartphone’s supply chain runs through the Congo’s rainforest, and the mining that feeds it may be driving the next Ebola epidemic. @Jane

Fifa Faces Empty Seats as 180,000 World Cup Tickets Hit Resale Market FIFA’s aggressive variable pricing strategy is backfiring; more than 180,000 tickets are still available, and resellers will likely be losing money. Not to mention, there are legal probes into the strategy alleging false scarcity to increase prices. @Mafe

Your Search Results Are Getting Sloptimized The moment it became clear that AI search would become a thing, gaming AI search would also surely become a thing. And here we are. @Jeffrey

BUILD Vs BUY: The Decision Framework for AI in Accounting Firms Our friends at CPA.com released a framework that, while it’s meant for accounting firms, is super helpful for organizations of all industries when considering how to approach the new capabilities that vibe coding has presented. @Kacee

Vinyl Succumbs to Loudness War: More Than Just Collateral Damage! First vinyl came back, then it lost to the relentless pushing of loudness in pretty much all recent music recordings – which should remind you that, yes, music did sound better in the past (and not just because it was analog). @Pascal

Down the Rabbit Hole

🍔 McDonald’s is using AI to make their drive-thrus more efficient, people are not having it: McDonald’s introduces AI drive-thru system, sparking customer backlash.

🤦 Episode 12 in the series of “CEOs doing dumb things with AI”. CEO says there will be no raises because he spent all the money on AI: “We will fund this AI investment by reallocating the budget from 2026 annual salary adjustments.”

💼 The AI-triggered (or blamed) layoffs keep coming: Tech industry loses 123,000 jobs this year – AI is the most cited reason for layoffs.

🏢 Meanwhile: Half of Americans fear AI could put someone in their household out of work, Reuters/Ipsos poll finds. and ‘More harmful than helpful’: young people sour on AI

🪖 If you don’t pay for it, you ARE the product: Scans by Dutch Pokémon Go players may have helped U.S. develop military drone technology.

⚽ You say football, I say cybercrime: Cybercriminals create 19,000 FIFA-themed domains ahead of 2026 World Cup.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

While I generally regard the upward trend of interest as a good and encouraging response to a context of persistent uncertainty amid the slow-motion collapse of old systems and ways of doing/thinking, I also think now might be a perfect time to talk about what it looks like when foresight goes wrong.

I see three typical failure modes.

(1) Sometimes, the foresight journey is just a mission into the future searching for justifications of the present strategic direction. Futures-thinking facilitator/researcher Thomas D’hooge aptly describes this as “foresight as anticipatory legitimation,” where one version of the future is emphasized – and it just happens to be one that strongly favors preferred present choices as highly beneficial. This is foresight practiced with a heavy confirmation bias. The foresight is closed and compromised, so the insight will be as well.

(2) Even a more open process can still lead to a closed and limiting result when the foresight collapses into something treated like a forecast. A new strategic direction emerges around a future that comes to be treated as an inevitability. The old “official future” is cast down only to elevate a new official future in its place. This is futures-thinking in the service of big bet prediction. But as the old aphorism has it: Prediction is hard, especially about the future.

There’s a reason so many foresight reports include an explicit disclaimer that they’re NOT predictions. Foresight isn’t meant to be a one-shot, one-way journey from uncertainty to certainty. Rather, foresight is most effective when practiced as a dynamic linkage between the present and the possible, where the view from the future affords us a new perspective on present decisions and actions – and those actions enable us to develop increasing clarity about how to navigate and shape an unfolding future.

Now about the action part…

(3) Foresight that never connects to action is just an interesting exercise, and it’s one that will quickly lose buy-in. In a strategic setting, our capacity to envision and explore futures is only valuable to the extent that we can use it to inform and influence decision making in the present. I’ve always loved Bob Johansen’s simple formulation of a Foresight-Insight-Action loop. If we’re not closing the loop, we’re not delivering value.

The big question then (and one we often hear from clients when we’re leading foresight trainings at Radical) is how to get to “correct” actions without collapsing the foresight work into a forecast where we’re still just predicting a winner at the end of the day. Part of the answer involves keeping multiple futures meaningfully in play (rather than focusing on one that feels the most “likely”) to identify actions that look strategically valuable across a range of possible futures. These actions are the so-called No Regrets Moves. Make them with confidence.

The other part is to view the actions as a set of interrelated bets that don’t all need to hit to be worth making. The actions don’t all need to be long-term “correct,” but they should make sense together in a smart, evolving portfolio of experimentation that’s driving learning as the Foresight-Insight-Action loop rolls action-based learnings back into the foresight process. The actions are experimental pings in the direction of futures of interest, enabling us to discover a strategic path forward in a space where there is no roadmap.

To repurpose one of my favorite Pascal-isms from his new book: You’re not predicting the winning path here. You’re discovering the winning path.

And so it is with foresight and futures done well more generally: We’re not predicting the future here. We’re discovering the future. Practice accordingly.

@Jeffrey

I am currently on the road for a series of client sessions – as part of this, I had the chance to sit in on a full day of AI sessions taught by some of the leading experts in the field at a top university – all geared toward a business audience. What stood out to me was less the specific content, but rather the fact that the sessions were all very, very similar. Here you have three experts talking about AI – and they all say the same thing. This might be indicative of the fact that we all still know very little about how AI truly will play out in the real world – hence we all make the same claims, use the same examples, and come to the same conclusions…

And now, this…

Headlines from the Future

Maybe Remote Work Is the Culprit for Young People’s Hiring Issues. You have seen the headlines – young professionals increasingly have a hard time finding jobs and AI is to blame. The argument is easy to follow and certainly makes sense at first blush. A newly published paper begs to differ – what if it’s not AI (which is going through its own identity crisis at the moment, trying to prove its ROI), but remote work? The argument goes like this:

Early-career workers require more supervision than experienced hires, and build important skills, knowledge and social capital by observing and working alongside senior colleagues. Working from home adds friction to these processes, making entry-level workers more costly to bring on board in terms of time and resources and slowing their prospects for promotion. As such, the rise of remote work has worsened the trade-off for hiring entry-level workers, while leaving the calculus for senior hires unchanged.

If this proves to be true, you can expect a double-whammy hitting young professionals – as AI surely will have an additional effect on their job prospects.

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AI Agents (Still) Suck. Scale Labs just updated their Remote Labor Index (RLI) – a measure of how well AI agents are actually able to do work in the real world (“Evaluating the capability of AI agents to perform real-world, economically valuable remote work”). The tl;dr:

Absolute Automation is Near Zero: Current agents perform near the floor. At the time this leaderboard was launched, the highest-performing agent (Manus) achieved a 2.5% automation rate, with other models performing worse. This indicates systems fail to complete the vast majority of projects to a professional, client-ready standard.

The upshot? They are getting better.

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Your Brain on ChatGPT. A new study from MIT’s Media Lab shows the correlation of tool use (in three groups: ChatGPT, Google Search, and no tool use) with brain activity. With the caveat that this is a small study, the results are not pretty:

EEG analysis presented robust evidence that LLM, Search Engine and Brain-only groups had significantly different neural connectivity patterns, reflecting divergent cognitive strategies. Brain connectivity systematically scaled down with the amount of external support: the Brain‑only group exhibited the strongest, widest‑ranging networks, Search Engine group showed intermediate engagement, and LLM assistance elicited the weakest overall coupling.

As the study concludes: “We demonstrate the pressing matter of exploring a possible decrease in learning skills.” Ouch.

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Energy? Water? RAM? GPUs? No, Electricians Are the AI Datacenter Bottleneck. Add this to your AI datacenter bingo card: not only are GPUs and RAM in short supply, energy is a major limiting factor, and water consumption is a massive concern; we also don’t have enough electricians to build out the infrastructure.

That said, the Lake Mariner site is about one hour away from the Buffalo Bills’ stadium, which delivers another benefit. The completion of a massive refurb at the Bills has freed up hundreds of electrical contractors. And it is trades, specifically electricians, which are the biggest bottle neck for datacenter projects, said Farrell.

What We Are Reading

‘An Equal and Habitable World Is Possible’: Academics Set out Sweeping Vision for Planetary Survival A rare piece of good news! A sweeping new report makes the case that a fairer, greener world is materially possible and lays out exactly how to get there. @Jane

Hackers Hijacked Instagram Accounts by Tricking Meta AI Support Chatbot into Granting Access Hackers exploited Meta’s AI support chatbot to hijack Instagram accounts by simply asking it to reset passwords without ever needing access to the victim’s email. @Mafe

AI Has Ruined the Job Market AI tools have rendered the old signals meaningless. In their place: a chaotic hall of mirrors where applicants and recruiters are both feeling lost. @Jeffrey

The New Ivies Over the weekend, my 17-year-old niece told me she’s never used AI because her school’s stance has basically been: “Don’t use it; it’s cheating.” That honestly alarmed me a bit, because the gap between how schools are treating AI and how employers expect people to work with it is growing fast. @Kacee

The Metaverse Fever Dream Phenomenal long-form exploration of the shitshow that is the “Metaverse.” If you ever asked yourself “what was THAT all about?” – here is your answer. @Pascal

Down the Rabbit Hole

😱 Another hard-hitting AI critique from Ed Zitron. Disagree with him, but do yourself the favor of reading it – it’s important to see all sides of the discussion: AI doesn’t have ROI.

🦹 AIs are getting pretty good at working with our day-to-day tools – which also means they are becoming powerful vectors to attack us: ChatGPT for Google Sheets exfiltrates workbooks.

🪖 First we had fitness app Strava leaking the secret locations of US airbases; now we have ad-tracking tech exposing the secret locations of US troops: The ad-tracking industry is exposing US soldiers on the battlefield.

🧑‍⚖️ This is a little bonkers and shows you how far AI has come (in certain domains): In a rigorous blind study, law professors overwhelmingly preferred AI-generated answers to student legal questions over answers written by fellow law professors – and flagged the AI answers as potentially misleading or harmful far less often.

💰 More and more companies are putting the brakes on their lavish AI (token) spending – Uber just instituted a (sensible) $1,500 monthly cap per tool. Simon Willison has a thoughtful back-of-the-envelope calculation of what that means in real terms – and what the implications are.

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Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

Last week I had the chance to sit down with the amazing Gero Hesse, founder and CEO of Embrace, the German HR-Tech company, to talk about OUTLEARN. Talk about an industry which is being affected by AI – there isn’t a HR function in a big(ger) company anymore which isn’t running AI software to help with sifting through resumes, interviewing candidates, or help with the onboarding process. Here’s the interview.

And now, this…

P.S. Lessons in history – here is The Guardian, 19 years ago, about MySpace: “Will MySpace ever lose its monopoly?”

Headlines from the Future

Where Are the Returns on AI? Against the backdrop of the AI investment boom, we are starting to see the first signs of companies struggling to justify their AI investments (specifically, token spending). Recent headlines, such as Uber’s COO questioning the value they are getting out of their AI investments (“Uber burned through its entire 2026 AI budget in four months. Now its COO is questioning whether it’s worth it”), are starting to reverberate through industry after industry. The specific metric being questioned is the connection between token spend and features shipped (which is a fair point):

“That link is not there yet,” he said. “Maybe implicitly there’s more that is getting shipped, but it’s very hard to draw a line between one of those stats and ‘Okay now we’re actually producing like 25% more useful consumer features.’” […] “If you’re not actually able to draw a direct line to how [many] useful features and functionality you’re shipping to your users, that trade becomes harder to justify,” Macdonald said.

And it’s not just Uber – Nvidia (the company which is synonymous with AI) is facing similar existential questions:

These developments also suggest that the economics of replacing or augmenting human labor with AI may be more complicated than some early forecasts originally implied. That echoes what Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently said in an interview with Axios. “For my team, the cost of compute is far beyond the costs of the employees,” he said.

For us this points to a fascinating conundrum which we have been looking into (from a different angle) in our last Tuesday Briefing – while individual users often get massive value from AI through their $20 to $250/month subscriptions and their ability to freely explore the uneven boundaries of the technology, companies find themselves in a situation where they struggle to fit AI into “labor-shaped holes.”

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The RAM Crisis Latest Victim: Cheap Smartphones. It is one thing to see the price of your state-of-the-art smartphone go up; it’s a whole different thing to see whole swaths of the population in low-income countries being priced out of the market completely. One of the more hopeful developments in tech over the last twenty years was the massive democratization of Internet access through the availability of cheap smartphones. Travel to any low-income country and you will see masses of people being able to access the Internet using sub-$100 smartphones. The AI boom led to a steep increase in the price for RAM chips, which in turn led to an equally steep increase in the price of (low-end) smartphones – leaving many people without the ability to purchase a smartphone and hence not being able to access the Internet.

So the trend of the last few decades, of consumer electronics getting better and cheaper every year, faces a sharp reversal: the poor world is now entering a smartphone crisis.

What We Are Reading

Final Frontier for Meds? UK Startup Sends Drug-making into Space Cancer treatment just got a cosmic upgrade. Turns out the best pharmacy is 250 miles straight up. @Jane

Why the Vatican Invited Anthropic to the Pope’s AI Encyclical Presentation Pope Leo brought together the Vatican and Anthropic around a shared concern that AI systems risk being shaped purely by economic and competitive incentives rather than human values. @Mafe

The Great Flattening, Part 2: The Data Is Worse Than the Anecdotes It’s not just the LinkedIn posters: Companies and brands are converging on the same blandly smooth, maddeningly generic AI voice. @Jeffrey

Architects of Innovation: How Ics Power The Modern Tech Organization Insightful piece on how many orgs still operate like information and decision-making should flow vertically. Reality is, the people creating the most leverage are moving ideas horizontally across the business. @Kacee

The Cost Of Ever questioned what it will cost you over your lifetime to upgrade your perfectly fine iPhone every year? Or any of the myriad of other decisions we make every day? Here you go. It’s worse than you might think. @Pascal

Down the Rabbit Hole

🎓 After graduation speeches around the country have consistently missed the mark on AI, Steve Wozniak shows us how it’s done: Apple cofounder Steve Wozniak got cheers, not boos, after telling students they ‘all have AI – actual intelligence’

🧠 FOMO is real: Fear of missing out is linked to hypersensitive brain reactions to digital likes.

😱 That is a very poor choice of words: Bank boss sorry after describing workers as ‘lower value human capital’.

🤦 When search becomes a prompt: You can no longer Google the word ‘disregard.’

📹 Departing Meta staffer posts biting anti-AI video internally amid mass layoffs.

💸 Here’s the tracker: Is AI Profitable Yet?

💾 The RAM shortage is (very) real – and it shows: Memory has grown to nearly two-thirds of AI chip component costs.

💍 The headline here isn’t that there is a new, thinner Oura ring, but the fact that Oura (the health- and wellness-tracking company) has integrated GLP-1 (Ozempic) tracking into its app: They’ve finally made the Oura Ring smaller and lighter.

😋 Yum! There is now a small language model which takes food ingredients and creates recipes based on them. Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings.

👦 The terrifying rise of schoolboys making AI girlfriends.

🪫 This hits home – when we were at Singularity University, cold fusion was “ten years away.” Why Is Fusion Energy Always ’10 Years Away’?

↗ Dive into the deep end: Access our complete collection of 2,800+ radical links.

Should We Work Together?

Hi! I’m Pascal from radical. This newsletter is our labor of love. When we’re not writing, we run radical, a firm that helps organizations navigate the future without the “innovation theater.” Most leaders want to seize new opportunities, but they hate endless strategy decks that go nowhere. At radical, we don’t run “projects”; we build your organization’s internal capacity to handle disruption and change. Our goal is to make you future-proof so you can stop reacting to the world and start shaping it. If you’re interested, let’s jump on a call to see if we’re a good fit. Click here to speak with us.

The reason is what Ethan Mollick named the “Jagged Frontier.” The capability surface of LLMs is wildly uneven, and you cannot read its shape from the outside – the AI model nails the task you assumed was hard and fails miserably on the one you assumed was trivial, and the only way to find the edge is to walk up to it and test. So the target-state slide, for an AI-enabled product, is merely your best guess – not an actual plan based on knowledge and facts. You’re drawing a destination you’ve never actually seen.

And here’s the part leaders often trip over – and it sounds like a contradiction: even when you can’t draw the destination, your organization is still going to shape whatever you end up building. That’s Melvin Conway’s old observation (immortalized as “Conway’s Law”), the one software people quote – any group that builds a system ships a design that copies the group’s own communication structure. The seams in the product end up mapping the gaps in how people talk. The deliberate way to use that, popularized by Matthew Skelton and Manuel Pais in Team Topologies, is the inverse Conway maneuver: instead of letting your structure dictate the architecture by accident, you design the teams to produce the architecture you want and let Conway work for you. Want a modular product? Build modular teams with clean interfaces, and the product follows. It’s a genuinely powerful move – and it carries one quiet assumption that AI quietly demolishes. It assumes you can draw the architecture first.

Having thought about this for quite a while now, and having had the chance to discuss this with a group of leaders at a recent event in Hamburg, Germany, I believe AI shifts that assumption: In the AI era, you stop designing teams to match a target architecture, and you start designing them to match the discovery process itself. The org’s job is no longer to produce a system you’ve already drawn. It’s to find a system you can’t yet specify. And finding has a structure of its own – short loops, low latency between the person who discovers what works and the person who can ship it, tight coupling between experimentation and production. The inverse Conway maneuver doesn’t disappear; it changes what it’s aimed at. You’re still shaping the org to shape the outcome. But the outcome you’re optimizing for is the speed and quality of learning, not the fidelity of a blueprint.

A traditional product org can run discovery in one corner – a research team, a lab – and hand the findings down a chain to the people who build. With AI, that chain is where the value evaporates. By the time the insight clears three reviews and a quarterly planning cycle, the frontier has moved and the insight is stale. The thing you learned in March about what the model could do is a different thing by June. Learning that can’t be acted on quickly isn’t learning your organization actually has; it’s learning one person had.

For leaders in organizations, this means that we have to stop trying to write the target-state architecture before we’ve earned the right to – we simply don’t know it yet, and a confident wrong guess is worse than an honest blank. Go measure one number instead: how long does it take, in your company, for something one person discovers about what AI can really do to become how the company works? Weeks? Quarters? Never? Then go shorten that number, because it’s the only metric in this whole conversation that you fully control. Put the person who experiments and the person who ships on the same small team, in the same room, reporting to the same human. Give them a real sandbox and standing permission to use it – not a committee they have to petition. You’re not building the organization that executes the plan. You’re building the one that goes looking.

P.S. Curious about the best approaches on how to build a learning organization fit for an AI-driven future? My latest book “OUTLEARN – The Art of Learning Faster Than the World Can Change” might be useful.

@Pascal