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’?

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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.

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

This last week I have had the privilege of speaking to a roomful of executives in the Dominican Republic about the ideas we laid out in both Disrupt Disruption as well as OUTLEARN. A week later, I came across Ben Evans’s latest deck on the state of AI (see below) – flip to slide 76 in that deck and you see a graph showing the number of employees working in the IT-BPM (business process management) industry in the Philippines. You could draw a similar slide for the Dominican Republic. And as much as Meta laying off 8,000 catches the headlines, I think the much bigger effect of AI will be on the folks working in places like the Philippines on behalf of global businesses…

And now, this…

Headlines from the Future

AI-related Job Losses? It’s Complicated. The seemingly (for good reason) never-ending debate about AI job losses got another entry with the release of the latest report from the US Bureau of Labor Statistics (BLS). The jobs you would expect to be at the highest risk of being replaced by AI are (at least looking at the raw data) being replaced: Customer service jobs dropped by 130,180 jobs in the latest report.

On Friday, in an ~annual data dump from BLS~, it emerged that a depression in these “artificial intelligence related occupations” really does appear to be happening. This category was down by 0.2% from May of 2024 to May of 2025, a tiny drop, but one made more notable by employment in general trending up 0.8% in the same time period.

Meanwhile, those affected by AI-related job losses are sometimes relegated to using their (human) skills to clean up the AI mess. Oh boy…

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AI Eats the World. The great Benedict Evans dropped his newest slide deck – this time on the state of affairs in AI. And when Ben talks, we listen. Skip the infrastructure cost slides and go straight to his insights on the commoditization of AI models (and hence why OpenAI & Co.’s moat might not be as deep as they make you believe), the challenge of AI for BPO-heavy countries like the Philippines, and the fun 1980s-era automation ads.

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Google eats AI. Or maybe it’s the other way around. After Google’s annual product fest, Google I/O, this week, the number of times AI was mentioned is hard to count. There is a humorous 52-second supercut of all the important announcements from the event (hint: it’s AI). But then there is also the beginning of the AI-fueled ad era (which, of course, had to come).

When researching a topic, consumers want to know exactly how a product suits their unique situation. In fact, 75% of people report making faster, more confident decisions using AI Mode in Search. That’s why we’re testing two new types of ads, built with Gemini, that offer relevant product details along with helpful guidance.

It will be interesting to see how this all plays out over the longer term – with a growing backlash against AI in general, it makes you wonder how enthralled people will be about ads being shoved into their AI-generated responses. And how much will you trust an AI-generated response, one which, to this day, is at risk of being hallucinated in the first place, to be accurate, when you know that there are commercial interests at play?

What We Are Reading

Elon Musk and the US Government Fought an AI Anti-discrimination Law. the Arguments Don’t Hold up. When the federal government joins a billionaire’s lawsuit to kill state AI protections, it sends every other state a clear message: don’t even try. @Jane

This Literary AI Scandal Changes Everything A literary prize is at the center of an AI scandal, but this time the accused authors aren’t owning up to anything, exposing a loophole that may be difficult to close in the future. @Mafe

How Agentic AI Supercharges Startups and Threatens Incumbents Feels like we’re underestimating how much advantage is shifting toward teams that can move, decide, and adapt quickly. @Kacee

Ask an Astronaut Ever wanted to ask an astronaut (an actual space-faring one) some questions? Such as: How do you shower in space? Here is a delightful opportunity to do so – and get some real answers. @Pascal

Down the Rabbit Hole

⏳ The ever-great Simon Willison on the state of AI within the last six months – so much change, so much progress: The last six months in LLMs in five minutes.

🖼️ Give people a real Monet, tell them that it’s AI-generated, and watch the comments flow… Yep, Monet is overrated. Apparently.

🙍‍♂️ The kids are not having it: The AI bots are coming and the young are booing, not applauding.

🤑 Ed Zitron’s latest take-down piece traces the money in AI – and it ain’t pretty: AI is too expensive.

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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.

After our recent plug for Kevin Kelly and his perspective on uncertainty (and the fact that even uncertainty is now uncertain). This week Kevin is back with a thoughtful (and thought provoking) piece on his experience with AI (“The Emergent Self Loop”) – it’s worth a read. Disagree with it (I do, at least in part), but ponder over it a bit. As someone once said: AI is not artificial but alien intelligence.

And now, this…

Headlines from the Future

First It Was AI RIFs, Now It’s AI LOCs. There can be no envy for CEOs trying to stay on top of the AI speed train these days. First they used AI to justify their layoffs – it just sounds so much better if you fire people due to “AI-related efficiency gains” (even better if you do so “anticipating” said gains). And now we have CEOs bragging about how much of the company’s code is AI-written. As if that means anything?! Both perspectives are navel-gazing at its finest…

Move over app downloads and EBITDA – the hot metric for CEOs is now AI productivity. In interviews and on quarterly earnings calls, CEOs are flaunting stats on how much code AI agents are generating. The trend began with AI companies like Anthropic, Meta, and Google, which have been grilled about their AI investments, and has continued with other companies eager to position themselves as AI-forward. From fintech to streaming, agentic AI adoption is the new status symbol among executives.

When will we see CEOs talking about how they are focusing on solving their customers’ problems again? It would make for a refreshing change…

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Turns out, the People Are Not so Hot on AI after All. A recent YouGov poll found that the average American is pretty pessimistic about the prospects of AI.

Most Americans (71%) feel that the pace of AI development is moving too fast. […] Most Americans are skeptical that everyone will benefit economically from AI. Nearly two-thirds (64%) of Americans say that it is slightly or very unlikely that AI will create economic gains that benefit everyone.

Not a good showing for a technology which is supposed to be the savior of humanity (or at least business). It makes you wonder how much of that perception is due to the hype and fearmongering by the fine folks who built AI. It surely can’t help if, for example, the CEO of Perplexity runs around and tells everyone that AI will replace them, right?

What We Are Reading

Brazil Caught up in Craze for Whistling-only Whatsapp Groups Hundreds of thousands of Brazilians are currently in WhatsApp groups where the only permitted communication is… whistling. @Jane

The State of Answer Engine Optimization Answer engine search is only going to continue to grow; AI-sourced site visitors have higher purchasing intent and a higher conversion rate than those arriving on websites from other channels. This report highlights the state of AEO and what companies are doing to get in the game. @Mafe

Data Makes the World Go ‘Round Fern Halper surfaces the disconnect happening in transformation right now: organizations layer AI on top of foundations that were already fragmented, disconnected, or poorly governed. @Kacee

Admit It, That Protein Shake Is Basically Soylent From Soylent Green to Soylent to your modern-day protein shake… I remember well the craze of “optimize your life by skipping food and going straight to the nutrients.” Heck, we fed this stuff to people at Singularity University’s executive program… @Pascal

Down the Rabbit Hole

🧑‍🔧 Futher to the point above on CEOs touting their AI-generated code generation and American’s increasingly not agreeing with AI, workers are engaging in all kinds of weird behavior: Meta employees launch protest against mouse-tracking tech at US offices. Amazon employees are inflating AI usage to top leaderboards and impress managers.

🏥 Maybe it is not the best idea to use LLMs for your healthcare needs?! Google’s healthcare AI made up a body part – what happens when doctors don’t notice? And: Medical AI transcriber for Ontario doctors ‘hallucinated,’ generated errors.

📝 I recently found myself in a Zoom meeting with four AI notetakers but no people – of course, I trolled the notetakers by talking gibberish. But there is a bigger issue here: They are making lawyers very nervous.

🦹‍♂️ AI models becoming eerily good at cybersecurity also means AI models becoming superbly good at hacking your systems. This is not a hypothetical anymore – it’s happening in the wild: ‘It’s here’: Google issues dire warning after catching hackers using AI to break into computers

🎓 Want to learn how AI actually works? Here is a fantastic course explaining the inner workings of LLMs using math an 11-year-old can understand.

🕵️ Wondering what your webbrowser knows about you? More than you might think… Scarily more.

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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.

They weren’t struggling to implement the agents. They were struggling to absorb the time.

In many cases, that newly available capacity didn’t translate into a rethinking of the work itself. Instead, it triggered a kind of organizational friction (yes, politics) because when time gets freed up at that scale, it begins to challenge deeply embedded assumptions about productivity, utilization, and value. In some firms, the instinct was to quietly refill the time with more of the same work. In others, it created internal tension because the system wasn’t designed to accommodate that level of slack. What surfaced wasn’t a technology gap, but a mindset and culture gap.

Contrast that with another firm I spoke with – similar agent deployed with similar time savings – where leadership refused to let the freed hours get reabsorbed into more returns. They made the trade explicit: every preparer owed a minimum of five hours a week to learning client advisory work, sitting in on CFO calls, and understanding what insights showed up in real conversations. Same efficiency gains from the AI implementation, but dramatically different approaches with their portfolio of time.

This is the broader shift that is just starting to come into focus. AI is not simply making work faster or more efficient – it is collapsing the time required to perform it. And that collapse is happening unevenly across functions, roles, and industries, which makes it harder to see as a single, coherent trend. But at the organizational level, the implication is consistent: the relationship between time and output is breaking down.

For decades, most operating models have been built on the assumption that more output requires more time applied to known processes. AI is disrupting that equation. When a meaningful portion of the day is no longer required for execution, the question is no longer how to optimize the work, but what the work should become. And this is where many organizations stall, because the default response is to treat freed-up time as excess capacity to be redeployed into the existing system. It feels rational, it preserves predictability, and it aligns with how performance has historically been measured. But it misses the larger opportunity.

The more useful way to think about this is through the lens of core and edge:

Core: the work that sustains the business as it exists today – repeatable, measurable, necessary.

Edge: the work that shapes what the business becomes next – exploratory, undefined, harder to quantify in the near term.

Historically, the core has consumed almost all the organizational oxygen, leaving the edge to a small subset operating on the periphery. What AI introduces is the chance to rebalance the equation – not by eliminating the core, but by collapsing the time it requires, and in the process opening up space to build for the future.

That space is where the real leverage sits, but it is not automatically captured. In fact, most organizations will default to reinvesting that time back into the core, driving incremental gains in efficiency or volume. There is nothing inherently wrong with that, but it is unlikely to create meaningful separation. The organizations that begin to differentiate will be the ones that deliberately redirect a portion of that freed time toward the edge – activities that expand capability, deepen customer understanding, rethink products/services, and build entirely new ways of creating value.

Of course, most organizations aren’t built for this. Performance systems, incentives, and management practices are all wired to optimize known processes. So when time is freed up, the system naturally pulls people back toward the core, because that is where success is defined and measured. This is why the friction shows up as “politics” or resistance.

The real gift of AI isn’t speed... it’s choice. Organizations are being given, perhaps for the first time at scale, the ability to decide what to do with time that was previously non-negotiable. That choice will shape not only how work gets done, but what kinds of capabilities are built and where differentiation emerges over time.

Most will respond by doing more work, quicker.

A smaller group will respond by doing different work altogether.

The gap between those two approaches is where the next wave of advantage will be created.

@Kacee

Silicon Valley legend Kevin Kelly recently wrote an excellent piece on “our certain uncertainties.” It is one of those rare pieces where I highlighted nearly every sentence. I highly recommend adding it to your weekend reading list – to whet your appetite, let me just give you a stitched-together quote:

So for the next 10-15 years we have perpetual, continuous, severe uncertainty. This is a burdensome weight because people hate uncertainty more than bad news. […] What we end up with is a poly-X, a multi-factored unknown, an uncertainty cascade, a pervasive lack of confidence about the future, in an era of ambiguity. […] The most effective response to this multi-layered persistent uncertainty is not to seek impossible stability, but to cultivate radical adaptability and radical optionality.

Read it. I wish I were as eloquent as Kevin – the ideas and concepts he shared are very much what we have been preaching for years as well (and I am sure will sound and look familiar).

And now, this…

Headlines from the Future

When Everyone Has AI and the Company Still Learns Nothing. We talked about a similar idea here on the Briefing before – we called it the “bifurcation of intelligence”: a world in which some companies deploy Copilot and call it a day, while others are rethinking their business models in an age of AI agents (and the rest of it). Robert Glaser digs deeper into this idea:

But the interesting AI work does not wait for the next community meeting. It appears inside a code review, a sales proposal, a research task, a product prototype, a production incident, a test strategy, a compliance question. Or when someone figures out that for a certain class of product components, they can set up something close to a dark factory: write the intent, let the agent run a very loose loop, apply enough backpressure to keep it on track, evaluate the outcome against strong scenarios, refine the intent, and repeatedly get high-quality results. By the time the story is cleaned up enough to become a best-practice slide, the important learning has often lost its teeth. What made it useful was the friction: the missing context, the test that failed, the weird API behavior, the moment where the agent sprawled into nonsense and someone had to pull it back.

And to stay in the theme of my new book OUTLEARN:

The next advantage is learning velocity. Who finds the real patterns faster? Who moves discoveries from individuals to teams to organizational capabilities? Who builds backpressure into agentic loops, so agents can’t sprawl? Who distributes useful agent capabilities without turning them into monolithic enterprise agents that fit nobody? Who finally uses agentic engineering to make agile real, instead of just slapping AI onto the old ceremonies?

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The Age-Gated Internet is Coming. In the (usually) well-meaning effort to keep minors from seeing stuff they shouldn’t, regulators around the world are pushing for age-gating the Internet. What started with sites which are obviously not for children, such as porn, is now being extended to social media and a bunch of other sites. There are a good number of reasons why this is a bad idea (and why it mostly doesn’t work anyway), but one of the more important ones is that it creates all kinds of privacy issues for all of us. Meta, not one to miss a beat, decided to take the bull by the horns:

Meta is unleashing AI that scans users’ bodies – from face shape to height – in an aggressive bid to root out underage accounts on Facebook and Instagram. The company announced Tuesday it was developing “advanced AI” that includes the use of visual analysis for detecting underage accounts. This new visual analysis technique will enable Meta’s AI to scan photos and videos for “visual clues” about a user’s age – including one’s height and bone structure.

Brave new world.

What We Are Reading

The Rise of Emotional Surveillance Burger King’s AI headset assistant is named Patty, and she’s judging whether you’re friendly enough. It is the scary future of work. @Jane

I’ve Covered Robots for Years. This One Is Different Instead of learning from videos of humans, these robots practice entirely in simulation, inventing their own solutions through trial and error, and they are scarily accurate. @Mafe

How to Remove the Wrong Kind of Friction (and Add the Right Kind) Plugging a very insightful, practical episode of a dear friend’s podcast here. Check out Lisa Kay Solomon’s conversation with Bob Sutton on friction as a design problem and its use and abuse in process, collaboration, and work. @Jeffrey

The Abundance Era We’ve spent the last decade overloading the skeleton (core systems) with things they were never meant to do. Advantage comes from treating the edge as tissue: something you can continuously rebuild, not protect. @Kacee

Appearing Productive in The Workplace An eloquent exploration of what happens when we remove “slowness” due to deliberate work from our outputs and instead focus on quantity as a measure of productivity. @Pascal

Down the Rabbit Hole

🤙 Action leads to reaction: “Telus uses ai to alter call-agent accents” – “AI ‘accent masking’ at overseas call centres sparks union backlash in Canada”

🚔 Turns out, self-driving cars commit traffic violations after all. And now, California gives them tickets.

👨‍💻 AI has the potential to make everyone a little smarter – but for the top 2% of workers, it might be a very different story: Ex-Meta manager says just 2% of engineers are winning the AI era.

🦹 If you are one of the people who have been writing in public (i.e., the Internet), you had better get used to the idea that your anonymity is gone: AI only needs 150 words to identify you. What does that mean for you?

🎬 How A.I. is transforming China’s entertainment industry.

🕴️ If in doubt, evoke Jevon’s Paradox and the world will be saved: Dario Amodei spent last year warning of an AI white-collar bloodbath. Now he’s changing the narrative.

📞 Nostalgic for your rotary phone of yesteryear? You can get it back! Here is the Rotary Un-Smartphone.

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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 quick personal update before we dive in: OUTLEARN launched on Tuesday and – I’ll be honest – the response has been beyond what I expected. The book hit #1 New Release in Amazon’s Strategy & Competition category on launch day and is currently sitting at #2 on the bestseller list in that same category.

But what’s even more awesome is your feedback. One early reader wrote that he bought three copies on launch day to hand out to colleagues at a startup they’re building together. Another described it as a “fully actionable page-turner” and said the reframing of postmortems as harvest meetings would – his words – “turbocharge how you extract practicable learnings from any project outcome.”

That’s exactly what I was hoping this book would do. Not sit on a shelf. Get used on a Monday morning.

If you haven’t grabbed a copy yet: Get it here. It’s 150 pages, every chapter ends with something deployable, and it’s cheaper than lunch. And if you’ve already read it – an honest Amazon review in these first two weeks genuinely makes the difference between a book that reaches people and one that disappears.

And now, on to our usual programming…

Here is an interesting argument: “AI doesn’t really ’think.’ Rather, it remembers how we thought together. And we’re about to stop giving it anything worth remembering.” This is from a provocative article by Bright Simons. I might not fully buy into all aspects of his argument, but his essay is very well worth reading (and pondering over). Let me leave you with just one more quote from the article: “The result is a world in which individual productivity rises while the collective pace of human thought starts to fall.”

Read the thing. And then, read this…

Headlines from the Future

Why (Conventional) Software Truly Is under Attack. Friend of radical Martin Alderson is back with a deep analysis of why many conventional software systems are under attack by AI – exemplified by the example of Figma, the once-darling Adobe-killer (and failed acquisition target of said company). Anthropic launched their new Design tool, automating 80% of what Figma does – as part of their Claude app, no design skills required.

But the structural point is harder to wriggle out of. Figma has ~2,000 employees. Anthropic has ~2,500 total and I doubt Claude Design took more than a handful to build. Figma now needs to out-execute a competitor whose inference is ~free to them, whose marginal cost to ship is roughly zero, and who employs fewer people on the competing product than Figma has on a single pod. That’s a very hard position to pivot out of. This feels like a preview of where SaaS economics are heading. The companies that built big orgs on the assumption of steady seat expansion are going to find themselves competing with products built by tiny teams inside the frontier labs. Figma just happens to be the first big public name where one of their primary inference suppliers has started competing against them

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Software Brain is Eating The World. If you do not read anything else this week, do yourself a favor and read this article by Nilay Patel on “Software Brain.” It’s a thoughtful piece about the disconnect between what the makers of AI think they are building, and what many of us experiences.

The entire human experience cannot be captured in a database. That’s the limit of software brain. That’s why people hate AI. It flattens them.

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Who Watches the Watchmen? This famous question, first posed by the Roman poet Juvenal in his Satires, dating back to the 1st century AD, is increasingly answered by: As long as you are feeding the machine, nobody… Meta, reportedly, is running surveillance software on work PCs of their employees:

Newswire Reuters reports that Meta management sent staff a memo informing them that they’ll soon run a new tool called “Model Capability Initiative” that will record their keystrokes, mouse movements, and even take occasional screenshots – all in the name of gathering data the social networking giant can use to build better AI models.

The staff doesn’t seem to be too happy about it: “Meta employees are up in arms over a mandatory program to train AI on their mouse movements and keystrokes.”

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4TB of Voice Samples Were Just Stolen from 40,000 AI Contractors. Voice cloning has become incredibly easy, good, and, in some cases, useful. Advanced AI models only need a few seconds of your voice to create a convincing clone – which also means that with those few seconds of your voice, I can create a convincing spoof of you. Which, in turn, means that when your voice data is stolen, you might be deep in the s#!%.

On April 4, 2026, the extortion group Lapsus$ posted Mercor on its leak site. The dump is reported at roughly four terabytes and bundles a payload that breach analysts have been warning about for two years: voice biometrics paired with the same person’s government-issued identity document. According to the leaked sample index, the archive covers more than 40,000 contractors who signed up to label data, record reading passages, and run through verification calls for AI training.

What We Are Reading

AI’s Next Frontier: People Skills AI models now beat humans on emotional intelligence tests, which only goes to prove that acing a test and understanding a feeling are completely different things. @Jane

Paypal Says AI Shopping Agents Are Creating an Invisible Storefront Economy Nearly all merchants are already seeing AI agent traffic, but fewer than 25% have the machine-readable catalogs, APIs, or the agent-compatible checkout systems needed to act in real time and convert on the spot. @Mafe

Scarce Assets: The Abundance-Driven Scarcity Supercycle Sticking with my theme from last week: Another angle on the idea (and value) of finding what remains scarce in markets where some things seem newly and wildly abundant. @Jeffrey

Chatgpt Traffic Analysis: Insights from 17 Months of Clickstream Data Most people assume AI search = more discovery, but this data suggests the opposite: that in fact distribution is compressing. If you’re not in the answer set, you’re effectively invisible. @Kacee

When the Internet Was a Place If you are fortunate enough to have experienced the Internet pre-Web 2.0, you know that it was a very, very different place. It’s high time to claim back some of those properties. @Pascal

Down the Rabbit Hole

🤔 Finally want to understand how LLMs actually work? Here is a wonderfully designed and easy-to-follow primer.

🤑 We mentioned it in the briefing last week. The era of heavily subsidized AI models might come to an end quicker than many of us thought or had hoped for. GitHub Copilot just announced that they will be moving solely to a usage-based billing model. Meanwhile, tokens get more expensive, and AI agents can now cost as much (and more) than human workers. No more free beer!

🏬 Someone set up a store and let an AI agent run it. Here is the story of how it’s going (hint: not great, but also not a complete disaster).

🧑‍🏫 A Catholic scholar argues that GenAI threatens authentic education by replacing the process of learning with the production of polished output, emphasizing the need for pedagogical redesign to restore the formation of thoughtful, responsible individuals: Repairing the ruins – Why AI can’t replace education.

💭 Your friendly public service announcement: A.I. should elevate your thinking, not replace it.

🕴️ Somehow I am genuinely surprised that this wasn’t the case before (and how this is news to begin with): Accenture to roll out Copilot to all 743,000 employees in boost for Microsoft

🙀 This story is making the rounds at the moment – an AI agent goes rogue, deletes a company’s entire production database, and then apologizes for it. The deeper cut is that it’s not just the AI agents fault, but the database system itself didn’t have any safeguards in place to prevent this from happening in the first place.

📰 The (sad) future of journalism – an OpenAI-linked news outlet appears to be entirely AI-generated. And the bigger picture: AI swarms could hijack democracy without anyone noticing.

🥳 XOXO, the Portland-based answer to SxSW, is no more. But its legacy lives on in the form of this wonderful (and wonderful-looking) XOXOFest website.

👻 Now we (finally) know: Spooky feelings in old houses may be caused by boiler sounds, study suggests.

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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.

In a now-famous study, participants played a virtual ball-tossing game while inside the scanner. At first, the other players passed the ball to you. You felt included. Then, abruptly, they stopped. They passed it only to each other, ignoring you completely. You were excluded. You were failing socially.

The brain scans revealed something harsh: the dorsal anterior cingulate cortex – the same region that activates when you’re punched in the stomach – lit up as though you’d been physically struck. Your brain doesn’t differentiate between the emotional pain of rejection and the physical pain of being hurt. It processes both as the same thing. Evolution shaped you this way. Throughout most of human history, social rejection meant expulsion from the tribe, which meant death. Your brain built a rapid alarm system to prevent it.

Which brings us to the most dangerous entity in your boardroom: not the skeptical CFO or the micromanager, but the prehistoric saboteur living inside your own skull – the amygdala.

Imagine – you’re in a quarterly review. The CFO asks a pointed question about your project’s burn rate. Your mouth goes dry. Your mind, which was sharp five seconds ago, suddenly feels foggy and slow. You know the numbers – you rehearsed the numbers – but they’ve vanished. You mumble something defensive. The meeting moves on. Later, in the elevator, the answer floods back, obvious and clear.

What happened? A war between your ears. In one corner: the prefrontal cortex, the CEO of your brain – logic, creativity, long-term planning all happen here. In the other corner: the amygdala, buried deep in your temporal lobes. Ancient. Fast. Unsophisticated but devastatingly effective. It doesn’t write poetry or design software – it keeps you alive by scanning for threats. And here’s the tragedy: these two systems operate like a seesaw. When one goes up, the other comes down.

The CFO’s pointed question? To your amygdala, it was indistinguishable from a predator in the tall grass. So it initiated a hostile takeover – flooding your bloodstream with cortisol, diverting energy to your legs and fists, and literally cutting blood flow to your prefrontal cortex. The lights in the CEO’s office went dark. You weren’t stupid in that meeting. You were chemically lobotomized.

This is the biological saboteur. And it’s running your company’s innovation efforts into the ground.

I want to throw something every time I walk into an innovation lab and see a poster that says “Fail Fast, Fail Forward.” It’s a beautiful sentiment. It is also biologically impossible in most organizations. You can’t just decide to be comfortable with failure any more than you can decide not to pull your hand back from a hot stove. If the environment triggers the threat response, the lizard wins. Every time.

You can buy all the bean bag chairs and install all the ping-pong tables you want, but if your culture punishes mistakes – even subtly – the amygdala wins every time. Simply stated: In most organizations, every idea that challenges the status quo is initially perceived by someone as a mistake. If your culture punishes mistakes, it is also punishing the early signals of innovation.

Think about the standard Monday morning status meeting. A project manager has to report that an initiative didn’t work. They’re nervous. The room goes quiet. You, the leader, say “It’s okay, we learned something” – but even those words betray you. “It’s okay” is a verdict disguised as comfort. It concedes that something wrong happened and positions you as the authority granting forgiveness. And forgiveness implies the possibility of its absence next time. If your tone is tight, if you sigh, if there’s a micro-expression of disappointment, the room detects it. Every brain in the room just received a signal: Error = Fear and Pain.

The result? People stop offering creative solutions (prefrontal cortex) and start offering defensive explanations (lizard). They stop saying “I wonder why that happened?” and start saying “Well, marketing didn’t give us the right assets.” All learning stops, and self-preservation begins.

So what do you do? You can’t wait another million years for evolution to update the firmware. You have to hack the software you have today.

Here’s one starting point: change the words you use. Words like “failure,” “mistake,” and “error” are loaded – they are threat triggers. When you ask “Why did this fail?”, you are practically begging the amygdala to wake up. Instead, try framing every outcome as a “first attempt in learning.” When you frame an outcome as a “failure,” you’re issuing a verdict. When you frame it as a “first attempt,” you’re describing a process. It implies iteration. It implies you’re not done yet. The lizard stays asleep. The CEO stays online.

And here’s the Monday morning test: at the start of your next crisis meeting, try calling out the biology explicitly. “Everyone’s lizard brain is freaking out right now. That’s normal. Let’s take two minutes to breathe so we can get our prefrontal cortexes back online.” It sounds silly. It works.

This is from Part II of my new book OUTLEARN: The Art of Learning Faster Than the World Can Change, which is live today on Amazon – paperback and ebook.

The book goes much deeper into the neuroscience, the linguistic hacks, and the math of why some failures lead to breakthroughs and others lead to bankruptcy. If this essay landed for you, grab a copy – and if you’re feeling generous, an honest Amazon review in the first week helps enormously.

@Pascal

Remember the glorious days when Uber and Lyft were heavily subsidized by their venture capital sugar daddies and you couldn’t get over how cheap it is to get a ride? Yeah, those days are gone (and much can be said about the market-distorting effects of the VC-fueled subsidies). Well, it increasingly looks like the sweet days of $20/month all-you-can-prompt AI plans are also coming to an end – pretty much all the major AI companies are tweaking their pricing strategies, making tokens for their latest frontier models much more expensive, and generally trying to dig themselves out of the “for every dollar we make, we lose five”-hole. It doesn’t come as a surprise – but it will be interesting to see what it does to market demand.

And now, this…

Headlines from the Future

Putting the AI Investment into Perspective. As the saying goes – a picture is worth a thousand words.

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Are the Costs of AI Agents Also Rising Exponentially? With AI models becoming more and more powerful, the cost of inference (at least for frontier models) is staying about the same (or increases) and the models consuming vastly more tokens for a given task. This being said, Toby Ord did a fascinating analysis of the cost of running AI agents as a function of “cost of labour” – and found that agents sometimes cost much more than human labour (“How is the ‘hourly’ cost of AI agents changing over time?”). In sum:

• This provides moderate evidence that:

• the costs to achieve the time horizons are growing exponentially,

• even the hourly costs are rising exponentially,

• the hourly costs for some models are now close to human costs.

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Cyber Security Is a Completely Different Game Now. If you have even half an ear to the ground when it comes to cybersecurity, you have heard stories about Anthropic’s newest model “Mythos” being held back as it is “too dangerous” – with the main fear being that it finds vulnerabilities in software with an unprecedented speed and accuracy. In fact, people are hacking all kinds of hard- and software using current state-of-the-art models such as GPT-5.4 or Opus for the last couple of months now. All of which turns cybersecurity into even more of a race between who can outspend whom, than it already is. In simple (AI economic) terms:

to harden a system we need to spend more tokens discovering exploits than attackers spend exploiting them [and] to harden a system you need to spend more tokens discovering exploits than attackers will spend exploiting them.

If you are running a system which has any public exposure surface (e.g. a website, an API, or an app), you better take this seriously. I wouldn’t be surprised if we will see tons of new exploits being executed in the next few months and years.

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DIY Sequence Your Whole Genome. We have been talking about an individual’s ability to sequence their own genome at home, using lab-grade but DIY equipment, for a while now (it was one of the predictions floating around in the ether in the heyday of Singularity University – it was always “just around the corner”). Now it has (finally) happened – alas, not for the faint of heart.

So this week I sequenced my genome entirely at home. Literally on my kitchen table.

What We Are Reading

A New Kind of Hybrid Car Is About to Hit America’s Streets EREVs are the exciting new hybrid technology everyone should know about. Your car runs on electric power but quietly refuels its own battery with gas, so you never have to worry about being stranded! @Jane

TikTok Makes Americans Want Chinese EVs They Can’t Have Chinese car brands are nearly absent from US roads due to tariffs and regulations, but are building American consumer desire through social media while playing a long-term strategy. @Mafe

What Will Be Scarce? An economist goes deep on a relatively optimistic scenario for the future of human labor, finding durable value in what he calls the “relational sector,” where the value of the service is likely to be increasingly linked to the human providing it. @Jeffrey

The End of One-Size-Fits-All Enterprise Software Pascal and I have been writing about this lately, we’re moving from standardized systems to outcome-driven architectures that can conform to the business. @Kacee

Our newsroom AI policy As companies (and in this case, newsrooms) around the world grapple with what it means to operate in an AI-enabled/driven world, it will become more and more important for organizations to establish (and publish) clear guidelines and disclosures on their use of AI – here is a good example from the Ars Technica newsroom. @Pascal

Down the Rabbit Hole

✊ “We believe in human beings.” Union leaders are escalating their anti-AI rhetoric, portraying the industry’s leaders as profit-hungry “oligarchs” eager to replace humans.

☀️ Shine (not drill), baby shine: IEA – Solar overtakes all energy sources in a major global first.

✍️ Hacking the system: A college instructor turns to typewriters to curb AI-written work and teach life lessons.

☕ A wonderful lesson in taking something that worked (ordering coffee through a carefully designed app) and making it worse by using AI: Ordering with the Starbucks ChatGPT app was a true coffee nightmare.

🧑‍⚖️ Let AI be the judge: Cooperative negotiation is a solvable problem (or so says this company).

🔐 Turns out – your cybersecurity does, in fact, withstand the (possibly coming) wave of quantum computer-powered attacks (despite the attention-grabbing headlines): Quantum computers are not a threat to 128-bit symmetric keys.

🎠 Ed Zitron, one of the most outspoken critics of AI, is back (and it’s worth reading – even if you don’t agree with him): Four Horsemen of the AIpocalypse.

↗ Dive into the deep end: Access our complete collection of 2,700+ 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, quite possibly, craziest story to emerge this week from the ever-nutty world of AI hype is, of course, the rebrand/relaunch of sneaker company Allbirds as an AI company – resulting in a $127 million increase in stock market value. I don’t even comment on how absurd all of this is. You know something is up when even the most die-hard AI-boosting publications start calling BS… Anyway – time for your weekly dose of news and analysis…

And now, this…

Headlines from the Future

Better Drug Side Effects Monitoring through Reddit? It shouldn’t come as a surprise that by harvesting the massive data trove that is Reddit, one can find drug side effects that are underreported in clinical trials. Reminds us of a pharma client of ours who mentioned that they consider Apple a massive threat to their business – as the company has a humongous amount of data on healthy people, whereas pharma companies typically only have data on sick people.

Using artificial intelligence to scan more than 400,000 Reddit posts, researchers from the University of Pennsylvania documented numerous reports of possible GLP-1 side effects that may be underrecognized in clinical trials – including menstrual changes, fatigue, and temperature sensitivities.

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Let’s Talk About AI’s Energy Footprint (Again). The linked article is a good and accessible summary of where we stand on AI’s energy footprint. The tl;dr is that AI’s current energy footprint is modest (comparable to streaming video). But demand is growing fast, reasoning models use 10–100x more energy than basic queries, and efficiency gains keep getting reinvested into more capability rather than saved. And what electricity powers the data centers is a much bigger question: Clean grid = net climate okay. Gas/coal grid = real problem.

Stop feeling guilty about prompts. Your Wh per query is not the lever that matters. You’ll do more climate good by eating one less steak, taking one fewer flight, or voting for better energy policy than by boycotting LLMs. What matters at the individual level is where you direct your attention. Demand the acceleration of the deployment of clean generation to meet data center demand; grid interconnections, nuclear licensing, transmission lines, and permitting reform are the bottleneck, not GPUs.

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The GenZ AI Tide is Turning. GenZ, supposedly the most AI-savvy generation entering the workforce right now, is not too thrilled about that whole AI thing.

Anger over AI is increasing among Gen Z at the same time excitement is fading. Nearly one-third of the survey’s respondents, 31%, said AI makes them feel angry, up 9 percentage points from last year. And just 22% said the technology makes them feel excited, down from 36% the prior year.

Reconcile this with the growing pressure on entry-level jobs, as well as overall job losses due to AI, and you have a storm brewing.

What We Are Reading

The Air Is Full of DNA - Here’s What Scientists Are Using It for Genetic breadcrumbs in the air reveal ecosystem secrets, spot sneaky invaders, and even track humans! @Jane

My Quest to Solve Bitcoin’s Great Mystery A detailed read about one man’s journey to find out who’s behind Satoshi Nakamoto. @Mafe

How To Future-Proof Your Career In The Age Of AI If cognitive flexibility, taste, and good judgment become critical differentiators in a world of abundant intelligence, does the most valuable background begin to look a lot like a classical interdisciplinary, liberal arts education? @Jeffrey

Will AI Start ‘Going Rogue’? the Chorus of Warnings Is Getting Louder When the people building the tech warn about loss of control, it may be a signal worth paying attention to. @Kacee

The Peril of Laziness Lost Here is an interesting argument from the world of software development: Laziness (in coding) leads us to more elegant, better-performing, and cleaner code. With AI coding tools, laziness suddenly has stopped being a virtue – if nothing else, AI happily keeps churning… And with laziness becoming a lost art, software will become worse. I’d venture to say that this is what is happening in every area AI touches. @Pascal

Down the Rabbit Hole

🍸 Rejoice! It is now legal to distill your own alcohol in the United States: US appeals court declares 158-year-old home distilling ban unconstitutional.

☠️ Nothing to see here. OpenAI backs bill that would limit liability for AI-enabled mass deaths or financial disasters.

🧑‍⚕️ Surprised is no one: Scientists invented a fake disease. AI told people it was real.

🎲 Ever wanted to increase your chances of beating your niece at Connect Four? Here’s the mathematically best way to do it: WeakC4, or distilling an emergent object.

🇪🇺 European tech sovereignty is a thing. It will be interesting to see how this plays out in long run. Latest point in case: France ditch Windows for Linux to cut reliance on US tech.

🔌 Have we reached the tipping point? Seven countries now generate 100% of their electricity from renewable energy.

✈️ Desperate times call for desperate measures. Great at gaming? US air traffic control wants you to apply.

🧑‍🎨 Life imitates art. This feels like it’s right out of an episode of Black Mirror: Mark Zuckerberg is reportedly building an AI clone to replace him in meetings.

✍️ You become what you write: Biased AI writing assistants shift users’ attitudes on societal issues.

📶 Data becomes a right. South Korea introduces universal basic mobile data access.

🤓 Nerd alert! Fascinating approach to improving AI’s coding abilities: Having a coding agent read a series of papers on the topic at hand before coding results in significant improvements in code quality.

📚 Lovely read: Stewart Brand on how progress happens.

🤔 More than half of Americans are ‘getting tired of hearing’ about AI, survey finds.

📈 From the MIT Tech Review: Want to understand the current state of AI? Check out these charts.

🪖 PSA: Wear your helmet! E-bike and scooter crashes are leading to more brain injuries.

↗ Dive into the deep end: Access our complete collection of 2,700+ 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.

I honestly don’t even know where to begin – so much stuff is happening in the world right now; it truly is a whirlwind. From your usual (over) dose of AI, to geopolitics – but also a plethora of wild, weird, and wonderful weak signals… Like the bike bell which cleverly defeats the noise-cancelling technology of a pedestrian’s earbuds. Or AI-singers capturing the top spots in the iTunes charts (now, remember – this is iTunes, the $0.99 a song download store, which makes that whole story even more bizarre). Dig into today’s Briefing – the results from this week’s web explorations will keep you busy.

P.S. In case you missed it – I built on Kacee’s excellent post in the last radical Briefing on “Vibe Coding Our Way to 70%” in a LinkedIn post.

And now, this…

Headlines from the Future

The AI Quiet Quitters. Shadow AI was the story for a while – workers sneaking ChatGPT past IT, doing in minutes what used to take hours, running an underground productivity movement from their personal accounts (or simply freeing up more time to watch TikTok). Management called it a governance problem. Workers called it getting the job done. It felt, in a strange way, like good news (just like the good old days when we all brought our personal Dropbox accounts to the workplace as we were sick and tired of 1980s SharePoint).

That era has quietly ended. A new global survey of 3,750 executives and employees across 14 countries finds that more than 54% of workers bypassed their company’s AI tools in the past 30 days and completed the work manually instead – and another 33% haven’t used AI at all. Eight in ten enterprise workers are avoiding the technology their employers are spending record sums to deploy. Shadow AI has become the AI no-show show.

Now the data tells a different story. The tool that workers once raced to adopt covertly has become, for a large and growing share of the workforce, the tool they’ve stopped using altogether. Not because it doesn’t work. Because they’re afraid of what happens when it works too well.

The piece also surfaces a huge trust gap: only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives – a 52-point chasm. Executives and employees are, as the report puts it, describing fundamentally different companies. The fear of obsolescence – FOBO, fear of becoming obsolete – has apparently crossed the threshold from anxiety into active avoidance. Which is, if you think about it, a perfectly rational response to a completely irrational situation.

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China Is Coming for You, Lil Miquela. If you know us, you know that we’ve been talking about virtual humans (and more specifically, virtual influencers) for a long time now. Our particular example was always Miquela Sousa, a virtual influencer created by the LA-based design agency Brud. Our particular fascination with Miquela and her brothers and sisters centers around the fact that she never ages, never gets sick, never has a bad hair day, travels anywhere, and works 24/7 without a break. Since we talked about her in 2017, she was joined by an ever-expanding family of virtual humans. Now China is closing in on them:

The Cyberspace Administration of ‌China’s proposed rules would require prominent “digital human” labels on all virtual human content and prohibit digital humans from providing “virtual intimate relationships” to those under 18, according to rules published for public comment until May 6.

and

“The governance of digital virtual humans is no longer merely an issue of industry norms; ⁠rather, it has become a strategic scientific problem that concerns the security of the cyberspace, public interests, and the high-quality development of the digital economy,” it added.

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Digital Transformation is (Finally) Dead. For twenty years, the world operated on a simple principle: buy standard software, don’t build. The logic made sense, as building was insanely expensive, risky, and slow. The result was highly standardized systems (well hello, SAP!) which we had to stretch well beyond what they were designed for, patch the gaps with middleware, hire consultants to integrate the integrators, and call the whole messy pile “transformation.”

This long piece by EY’s Colm Sparks-Austin makes the case that the economics have fundamentally flipped. AI and modern dev tools have made engineering capacity abundant. The constraint is no longer “can we build this?” It’s “do we know what to build and why?” Colm’s argument is sharp – treat the core (ERP, system of record) as the skeleton: rigid, compliance-bearing, changed rarely. And treat the edge – the customer-facing layer, the last mile – as tissue: built to regenerate when the market shifts.

Standardization is no longer a safety net. It is a ceiling.

The piece is long, but worth your time – especially if you work with or inside large enterprises still debating whether to “buy or build.” That debate is over.

What We Are Reading

Is AI Going to Turn Us All Into Middle Managers? Two of our favorite people, Johnathan and Melissa Nightingale, just gave one of the sharpest takes we’ve heard on AI, management, and the future of work. Go find their Galaxy Brain conversation. @Jane

Crypto Investment Scams Were the Most Costly Type of Fraud in the U.S. in 2025 Investment fraud, specifically crypto investment scams, accounted for 49% of all cyber-related complaints in 2025 to the FBI. @Mafe

AI Fatigue Is Real and Nobody Talks About It The real value is in sustainable output, and learning to work – sustainably – on new rhythms will be a significant piece of the AI transformation puzzle. @Jeffrey

When Silos Hinder Innovation – and When They Can Help Rethinking the innovation dogma… silos aren’t always the enemy; sometimes they can spark the best ideas. @Kacee

Are We Idiocracy Yet? Remember Mike Judge’s masterpiece, Idiocracy? If you have ever asked yourself how far the movie is from today’s reality – here is your answer. @Pascal

Down the Rabbit Hole

👨🏼‍💻 In the same vein as my comment on Kacee’s post from last week’s radical Briefing (see above), a leader at the global consulting firm EY wrote, “Why engineering replaces transformation as the engine of growth.” It’s worth a read.

🕵🏼‍♂️ The journalist who uncovered the Theranos scandal is behind the (maybe) next big unveil: Satoshi Nakamoto, the mysterious creator of Bitcoin, might have been found.

💻 Here’s a fun anecdote – as the world, once again, seems to be obsessed with LOC (lines of code) as a productivity metric, legendary software developer Bill Atkinson recalls delivering -2,000 lines of code to Apple.

🧑🏼‍🏫 Some things you just can’t make up: Students record their professors’ lecture, feed it into a speech-to-text AI, to then feed it into an LLM, to then ask/comment/respond to their teacher – in his tone and style (as the AI mimics the import).

🤥 Claude (the AI model) might be a little confused as to who said what: Claude mixes up who said what.

🎙️ The fake singers are coming – and they are coming for your top spots on the charts: iTunes takeover by fake AI singer “Eddie Dalton” – now occupies eleven spots on singles chart, number 3 on albums chart.

🤔 Take headlines like these with a huge grain of salt: “AI models will secretly scheme to protect other AI models from being shut down, researchers find.” Here is the study in question – and you shouldn’t be too surprised about the result, knowing that AI models are modelling their training data.

🔮 On the topic of predicting the future (when it comes to AI), here is the latest update from the folks at the AI Futures Project (yes, those were the folks who did the very optimistic/accelerated AI 2027 forecast).

🤷🏼 Ethan Mollick, the Wharton School professor who coined the term “jagged frontier” in his assessment of LLMs and their capabilities, makes the argument that the IT department is where AI goes to die.

🤳🏼 Take their phones away from them, and the kids will be fine! Well, not so fast… Australia’s teen social media ban is a flop. But there’s no joy in ‘I told you so’

🪫 The AI wars might be won over energy, not compute: Half of planned US data center builds have been delayed or canceled, growth limited by shortages of power infrastructure and parts from China – the AI build-out flips the breakers

👨🏼‍💼 Fire the people, save money, build AI data centers: Oracle’s 30,000 employee layoffs: Inside the $2.1 billion restructuring fueling a $156 billion AI data center bet.

⚡ Energy markets are turning very, very weird with the rise of renewables: Germany power prices turn deeply negative on renewables surge.

🚲 Signs of the times: A bicycle bell that outsmarts even smart headphones.

🪖 Talking about the future of warfare: Iran threatens “complete and utter annihilation” of OpenAI’s $30B Stargate AI data center in Abu Dhabi – regime posts video with satellite imagery of ChatGPT-maker’s premier 1GW data center

💼 Surprised is no one: Employers are using your personal data to figure out the lowest salary you’ll accept (but then, employees also write their resumes and cover letters using AI, cheat on tests using AI, etc.)

🧑🏼‍🚀 Just in time, as Artemis is doing its moon thing – calculate your cosmic distance from the day you were born.

🌘 Talking about the moon – this is as nerdy as it gets: The rebuilding of the Apollo guidance computer in glorious detail.

🌈 The Weather Channel goes full retro with their neat, new retrocast feature.

🚉 Can you identify each line on the London Underground by sound? Try it!

👨🏼‍🎨 The amazing art that went into the special effects for the Luc Besson movie The Fifth Element. Stunning.

↗ Dive into the deep end: Access our complete collection of 2,700+ 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.